Chapter 2: Terrestrial and Freshwater Ecosystems and Their Services

Coordinating Lead Authors: Camille Parmesan (France/USA/UK), Mike D. Morecroft (UK), Yongyut Trisurat (Thailand)

Lead Authors: Rita Adrian (Germany), Gusti Zakaria Anshari (Indonesia), Almut Arneth (Germany), Qingzhu Gao (China), Patrick Gonzalez (USA), Rebecca Harris (Australia), Jeff Price (UK), Nicola Stevens (South Africa), Gautam Hirak Talukdar (India)

Contributing Authors: Stavana E. Strutz (USA), David D Ackerly (USA), Elizabeth Anderson (USA), Philip Boyd (Australia), Jörn Birkmann (Germany), Vanessa Bremerich (Germany), Lluís Brotons (Spain), Polly Buotte (USA), Donovan Campbell (Jamaica), Edwin Castellanos (Guatemala), Yu-Yun Chen (Taiwan, Province of China), Guéladio Cissé (Mauritania/Switzerland/France), Sarah Cooley (USA), Annette Cowie (Australia), Meghnath Dhimal (Nepal), Sami Domisch (Germany), Simon Donner (Canada), Errol Douwes (South Africa), Luis E. Escobar (Guatemala/USA), Marta Rivera Ferre (Spain), Alexander Flecker (USA), Wendy Foden (South Africa), Rachael V. Gallagher (Australia), Aurora Gaxiola (Chile), Adugna Gemeda (Ethiopia), Michael Goulding (USA), Kerry-Anne Grey (South Africa), Elena López Gunn (Spain), Susan Harrison (USA), Jeff Hicke (USA), Nathalie Jeanne Marie Hilmi (Monaco/France), Gladys Barragan Jason (France), David A. Keith (Australia), Rachel Bezner Kerr (USA/Canada), Benjamin M. Kraemer (USA/Germany), Simone Langhans (Switzerland), Rodel Lasco (Philippines), Andrew Latimer (USA), Robert Lempert (USA), Salvador E. Lluch-Cota (Mexico), Julie Loisel (Canada, USA), Brendan Mackey (Australia), Paulina Martinetto (Argentina), Robert Matthews (UK), Timon McPhearson (USA), Mette Mauritzen (Norway), Guy Midgley (South Africa), Erin Mordecai (USA), Francisco Moreira (Portugal), Aditi Mukherji (India), Isla Myers-Smith (UK/USA), Gert-Jan Nabuurs (the Netherlands), Henry Neufeldt (Denmark/Germany), James Pearce-Higgins (UK), Gretta Pecl (Australia), Roque Pedace (Argentina), A. Townsend Peterson (USA), Dieter Piepenburg (Germany), Julio C. Postigo (Peru/USA), Juan Pulhin (Philippines), Marie-Fanny Racault (UK/France), Joacim Rocklöv (Germany/Sweden), Joeri Rogelj (UK/Belgium), Björn Rost (Germany), Marina Romanello (UK/Argentina/Italy), Angela Gallego-Sala (Spain/UK), Daniela Schmidt (UK/Germany), David Schoeman (Australia), Nathalie Seddon (UK), Jan C. Semenza (Italy), Michael C. Singer (France/UK), Pramod Kumar Singh (India), Jasper Slingsby (South Africa), Pete Smith (UK), Raman Sukumar (India), Maria Cristina Tirado (USA/Spain), Christopher Trisos (South Africa), Merritt Turetsky (USA), Beth Turner (Canada), Maarten van Aalst (the Netherlands), Kenneth Young (USA).

Review Editors: Carol Franco Billini (Dominican Republic/USA), Yakiv Didukh (Ukraine), Andreas Fischlin (Switzerland)

Chapter Scientists: Stavana E. Strutz (USA), Dalila Mezzi (France/Algeria)

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Figure 2.11

Figure Box 2.1.1

Figure Box 2.2.1

Figure Cross-Chapter Box EXTREMES.1

Figure Cross-Chapter Box EXTREMES.2

Figure Cross-Chapter Box ILLNESS.1

Figure Cross-Chapter Box NATURAL.1

Figure FAQ2.1.1

Figure FAQ2.2.1

Figure FAQ2.3.1

Figure FAQ2.4.1

Figure FAQ2.5.1

Figure FAQ2.6.1

This chapter should be cited as:

Parmesan, C., M.D. Morecroft, Y. Trisurat, R. Adrian, G.Z. Anshari, A. Arneth, Q. Gao, P. Gonzalez, R. Harris, J. Price, N. Stevens, and G.H. Talukdarr, 2022: Terrestrial and Freshwater Ecosystems and Their Services. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 197–377, doi:10.1017/9781009325844.004.

Executive Summary

Chapter 2, building on prior assessments 1 , provides a global assessment of the observed impacts and projected risks of climate change to terrestrial and freshwater ecosystems, including their component species and the services they provide to people. Where possible, differences among regions, taxonomic groups and ecosystem types are presented. Adaptation options to reduce risks to ecosystems and people are assessed.

Observed Impacts

Multiple lines of evidence, combined with the strong and consistent trends observed on every continent, make it very likely 2 that manyobserved changes in the ranges, phenology, physiology and morphology of terrestrial and freshwater species can be attributed to regional and global climate changes, particularly increases in the frequency and severity of extreme events (very high confidence3 ) {2.3.1;; 2.4.2; 2.4.5; Table 2.2; Table 2.3; Table SM2.1; Cross-Chapter Box EXTREMES in this chapter}. The most severe impacts are occurring in the most vulnerable species and ecosystems, characterised by inherent physiological, ecological or behavioural traits that limit their abilities to adapt, as well as those most exposed to climatic hazards (high confidence){;;; 2.4.5; 2.6.1; Cross-Chapter Box EXTREMES in this chapter}.

New studies since the IPCC 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR1.5) (with data for >12,000 species globally) show changes consistent with climate change. Where attribution was assessed (>4,000 species globally), approximately half of the species had shifted their ranges to higher latitudes or elevations and two-thirds of spring phenological events had advanced, driven by regional climate changes (very high confidence) . Shifts in species ranges are altering community make-up, with exotic species exhibiting a greater ability to adapt to climate change than natives, especially in more northern latitudes, potentially leading to new invasive species (medium confidence) {;}. New analyses demonstrate that prior reports underestimated impacts due to the complexity of biological responses to climate change (high confidence). {;;;; 2.4.5; Table 2.2; Table SM2.1; Table 2.3}

Responses of freshwater species are strongly related to changes in the physical environment (high confidence) {2.3.3;}. Global coverage of quantitative observations in freshwater ecosystems has increased since AR5. Water temperature has increased in rivers (up to 1°C per decade) and lakes (up to 0.45°C per decade) {; Figure 2.2}. The extent of ice cover has declined by 25% and duration by >2 weeks {; Figure 2.4}. Changes in flow have led to reduced connectivity in rivers (high confidence) {; Figure 2.3}. Indirect changes include alterations in river morphology, substrate composition, oxygen concentrations and thermal regime in lakes (very high confidence) {;}. Dissolved oxygen concentrations have typically declined and primary productivity has increased with warming. Warming and browning (increase in organic matter) have occurred in boreal freshwaters, with both positive and negative repercussions on water temperature profiles (lower vs. upper water) (high confidence) and primary productivity (medium confidence) as well as reduced water quality (high confidence) {; Figure 2.5}.

Climate change has increased wildlife diseases (high confidence). Experimental studies provide high confidence in the attribution of observed increased disease severity, outbreak frequency and the emergence of novel vectors and their diseases into new areas to recent trends in climate and extreme events. Many vector-borne diseases and those caused by ticks, helminth worms and the chytrid fungus (Batrachochytrium dendrobatidis, Bd) have shifted polewards and upwards and are emerging in new regions (high confidence). In the high Arctic and at high elevations in Nepal, there is high confidence that climate change has driven the expansion of vector-borne diseases (VBDs) that infect humans. {,,,,,,; Cross-Chapter Box ILLNESS in this chapter}

Forest insect pests have expanded northward, and the severity and extent of outbreaks have increased in northern North America and northern Eurasia due to warmer winters reducing insect mortality and longer growing seasons favouring more generations per year (high confidence) {;}.

Local population extinctions caused by climate change have been widespread among plants and animals, detected in 47% of 976 species examined and associated with increases in the hottest yearly temperatures (very high confidence) {}. Climate-driven population extinctions have been higher in tropical (55%) than in temperate (39%) regions, higher in freshwater (74%) than in marine (51%) or terrestrial (46%) habitats, and higher in animals (50%) than in plants (39%). Extreme heat waves have led to local fish dying out in lakes and mass mortality events in birds, bats, mammals and fish {,, Cross-Chapter Box EXTREMES in this chapter}. Intensification of droughts contributes to the disappearance of small or ephemeral ponds that often harbour rare and endemic species. {; Cross-Chapter Box EXTREMES in this chapter}

Global extinctions or near-extinctions have been linked to regional climate change in three documented cases{}. The cloud forest-restricted golden toad (Incilius periglenes) was extinct by 1990 in a nature preserve in Costa Rica following successive extreme droughts (medium confidence). The white sub-species of the lemuroid ringtail possum (Hemibelideus lemuroides) in Queensland, Australia, disappeared after heat waves in 2005 (high confidence): intensive censuses found only 2 individuals in 2009. The Bramble Cay melomys (BC melomys, Melomys rubicola) was not seen after 2009 and was declared extinct in 2016, with sea-level rise (SLR) and increased storm surge associated with climate change being the most probable drivers (high confidence). Additionally, the interaction of climate change and chytrid fungus (Bd) has driven many of the observed global declines in amphibian populations and the extinction of many species (high confidence) {}.

A growing number of studies have documented genetic evolution within populations in response to recent climate change (very high confidence). To date, genetic changes remain within the limits of known variation for species (high confidence). Controlled selection experiments and field observations indicate that evolution would not prevent a species becoming extinct if its climate space disappears globally (high confidence) . Climate hazards outside of those to which species have adapted are occurring on all continents (high confidence). More frequent and intense extreme events, superimposed on longer-term climate trends, have pushed sensitive species and ecosystems towards tipping points that are beyond the ecological and evolutionary capacity to adapt, causing abrupt and possibly irreversible changes (medium confidence). {2.3.1; 2.3.3;;; 2.6.1; Cross-Chapter Boxes ILLNESS and EXTREMES in this chapter}

Since AR5, biome shifts and structural changes within ecosystems have been detected at an increasing number of locations, consistent with climate change and increasing atmospheric CO2 (high confidence) . New studies are documenting the changes that were projected in prior IPCC reports have now been observed, including upward shifts in the forest/alpine tundra ecotone, northward shifts in the deciduous/boreal forest ecotones, increased woody vegetation in the sub-Arctic tundra and shifts in the thermal habitat in lakes (high confidence). A combination of changes in grazing, browsing, fire, climate and atmospheric CO2 is leading to observed woody encroachment into grasslands and savannah, consistent with projections from process-based models driven by precipitation, atmospheric CO2 and wildfires (high confidence) {2.4.3; Table 2.3; Table SM2.1; Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}. There is high agreement between the projected changes in earlier reports and the recent trends observed for areas of increased tree death in temperate and boreal forests and woody encroachment in savannas, grasslands and tundra {2.5.4; Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}. Observed changes impact the structure, functioning and resilience of ecosystems as well as ecosystem services, such as climate regulation (high confidence) {2.3; 2.4.2; 2.4.3; 2.4.4, 2.5.4, Figure 2.11, Table 2.5, Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}.

Regional increases in the area burned by wildfire (up to double natural levels), tree mortality of up to 20%, and biome shifts of up to 20 km latitudinally and 300 m up-slope have been attributed to anthropogenic climate change in tropical, temperate and boreal ecosystems around the world (high confidence), damaging key aspects of ecological integrity. This degrades the survival of vegetation, habitat for biodiversity, water supplies, carbon sequestration, and other key aspects of the integrity of ecosystems and their ability to provide services for people (high confidence). {;;;; Table 2.3; Table SM2.1}

Fire seasons have lengthened on one-quarter of vegetated areas since 1979 as a result of increasing temperature, aridity and drought (medium confidence). Field evidence shows that anthropogenic climate change increased area burned by wildfire above natural levels in western North America in the period 1984–2017: a doubling above natural for the western USA and 11 times higher than natural in one extreme year in British Columbia (high confidence) . In the Amazon, the Arctic, Australia and parts of Africa and Asia, burned area has increased, consistent with, although not formally attributed to, anthropogenic climate change. Wildfires generate up to one-third of ecosystem carbon emissions globally, a feedback that exacerbates climate change (high confidence). Deforestation, draining of peatlands, agricultural expansion or abandonment, fire suppression, and inter-decadal cycles such as the El Niño-Southern Oscillation (ENSO), can exert a stronger influence than climate change on increasing or decreasing wildfire in some regions {; Table 2.3; Table SM2.1; FAQ 2.3}. Increase in wildfire from the levels to which ecosystems are adapted degrades vegetation, habitat for biodiversity, water supplies and other key aspects of the integrity of ecosystems and their ability to provide services for people (high confidence). {,,,; Table 2.3; Table SM2.1}

Drought-induced tree mortality attributed to anthropogenic climate change has caused up to 20% loss of trees in the period 1945–2007 in three regions in Africa and North America (high confidence) . It has also potentially contributed to over 100 other cases of drought-induced tree mortality across Africa, Asia, Australia, Europe, and North and South America (high confidence). Field observations have documented post-mortality vegetation shifts (high confidence). Timber cutting, agricultural expansion, air pollution and other non-climate factors also contribute to tree death. Increases in forest insect pests driven by climate change have contributed to tree mortality and shifts in carbon dynamics in many temperate and boreal forest areas (very high confidence). The direction of changes in carbon balance and wildfires following insect outbreaks depends on the local forest insect communities (medium confidence). {; Table 2.3; Table SM2.1}

Terrestrial ecosystems currently remove more carbon from the atmosphere, 2.5–4.3 Gt yr-1, than they emit (+1.6 ± 0.7 Gt y-1), and so are currently a net sink of -1.9 ± 1.1 Gt y-1. Intact tropical rainforests, Arctic permafrost, peatlands and other healthy high-carbon ecosystems provide a vital global ecosystem service of preventing the release of stored carbon (high confidence) . Terrestrial ecosystems contain stocks of ~3500 GtC in vegetation, permafrost, and soils, three to five times the amount of carbon in unextracted fossil fuels (high confidence) and >4 times the carbon currently in the atmosphere (high confidence). Tropical forests and Arctic permafrost contain the highest ecosystem carbon stocks in aboveground vegetation and in soil, respectively, in the world (high confidence). Deforestation, draining, burning or drying of peatlands, and thawing of Arctic permafrost, due to climate change, has already shifted some areas of these ecosystems from carbon sinks to carbon sources (high confidence). {;;;}

Evidence indicates that climate change is affecting many species, ecosystems and ecological processes that provide ecosystem services connected to human health, livelihoods, and well-being (medium confidence). These services include climate regulation, water and food provisioning, pollination of crops, tourism and recreation. It is difficult to establish full end-to-end attribution from climatic changes to changes in a given ecosystem service and to identify the location and timing of impacts. The lack of attribution studies may delay specific adaptation planning, but there is evidence that protection and restoration of ecosystems builds resilience of service provision. {2.2; 2.3;; 2.4.4; 2.4.5; 2.5.3; 2.5.4; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.7; Cross-Chapter Boxes NATURAL, ILLNESS and EXTREMES in this chapter; Cross-Chapter Box COVID in Chapter 7; Cross-Chapter Box MOVING PLATE in Chapter 5; Box 5.3; section}

Projected Risks

Climate change increases risks to fundamental aspects of terrestrial and freshwater ecosystems, with the potential for species’ extinctions to reach 60% at 5°C global mean surface air temperature (GSAT) warming (high confidence), biome shifts (changes in the major vegetation form of an ecosystem) on 15% (at 2°C warming) to 35% (at 4°C warming) of global land (medium confidence), and increases in the area burned by wildfire of 35% (at 2°C warming) to 40% (at 4°C warming) of global land (medium confidence). {2.5.1; 2.5.2; 2.5.3; 2.5.4; Figure 2.6; Figure 2.7; Figure 2.8; Figure 2.9; Figure 2.11; Table 2.5; Table SM2.2; TableSM2.5; Cross-Chapter Box DEEP in Chapter 17; Cross-Chapter Paper 1}

Extinction of species is an irreversible impact of climate change, with increasing risk as global temperatures rise (very high confidence) . The median values for percentage of species at very high risk of extinction (categorized as “critically endangered” by IUCN Red List categories)(IUCN, 2001) are 9% at 1.5°C rise in GSAT, 10% at 2°C, 12% at 3.0°C, 13% at 4°C and 15% at 5°C (high confidence) , with the likely range of estimates having a maximum of 14% at 1.5°C and rising to a maximum of 48% at 5°C (Figure 2.7). Among the groups containing the largest numbers of species at a very high risk of extinction for mid-levels of warming (3.2°C) are: invertebrates (15%, and specifically pollinators at 12%), amphibians (11% overall, but salamanders are at 24%) and flowering plants (10%). All groups fare substantially better at lower warming of 2°C, with extinction projections reducing to <3% for all groups, except salamanders that reduced to 7% (medium confidence) (Figure 2.8a). Even the lowest estimates of species’ extinctions (median of 9% at 1.5°C rise GSAT’) are 1000 times the natural background rates. Projected species’ extinctions at future global warming levels are consistent with projections from AR4, but assessed for many more species with much greater geographic coverage and a broader range of climate models. {; Figure 2.6; Figure 2.7; Figure 2.8; Cross-Chapter Box DEEP in Chapter 17; Cross-Chapter Paper 1}

Species are the fundamental unit of ecosystems, and the increasing risk of local losses of species increases the risks of reduced ecosystem integrity, functioning and resilience with increasing warming (high confidence). As species become rare, their role in the functioning of the ecosystem diminishes (high confidence). Loss of species locally reduces the ability of an ecosystem to provide services and lowers its resilience to climate change (high confidence). At 1.58°C GSAT warming, >10% of species are projected to become endangered (median estimate, with “endangered” equating to a high risk of extinction, sensu IUCN), and at 2.07°C this rises to >20% of species, representing a high and very high risk of biodiversity loss, respectively (medium confidence) {2.5.4; Figure 2.8b, Figure 2.11; Table 2.5; Table SM2.5}. Biodiversity loss is projected for more regions with increasing warming, and will be worst in northern South America, southern Africa, most of Australia and at northern high latitudes (medium confidence) {; Figure 2.6}.

Climate change increases risks of biome shifts on up to 35% of global land at ≥4°C GSAT warming, that emission reductions could limit to <15% for <2°C warming (medium confidence). Under high-warming scenarios, models indicate shifts of extensive parts of the Amazon rainforest to drier and lower-biomass vegetation (medium confidence), poleward shifts of boreal forest into treeless tundra across the Arctic, and upslope shifts of montane forests into alpine grassland (high confidence). Area at high risk of biome shifts from changes in climate and land use combined can double or triple compared to climate change alone (medium confidence). Novel ecosystems, with no historical analogue, are expected to become increasingly common in the future (medium confidence). {2.3,, 2.5.2; 2.5.4, Figure 2.11; Table 2.5; Table SM2.4; Table SM2.5}

The risk of wildfire increases along with an increase in global temperatures (high confidence) . With 4°C GSAT warming by 2100, wildfire frequency is projected to have a net increase of ~30% (medium confidence) . Increased wildfire, combined with soil erosion due to deforestation, could degrade water supplies (medium confidence). For ecosystems with an historically low frequency of fires, a projected 4°C global temperature rise increases the risk of fires, with potential increases in tree mortality and the conversion of extensive parts of the Amazon rainforest to drier and lower-biomass vegetation (medium confidence). {;}

Continued climate change substantially increases the risk of carbon stored in the biosphere being released into the atmosphere due to increases in processes such as wildfire, tree mortality, insect pest outbreaks, peatland drying and permafrost thaw (high confidence). These phenomena exacerbate self-reinforcing feedbacks between emissions from high-carbon ecosystems (that currently store ~3000–4000 GtC) and increasing global temperatures. Complex interactions of climate change, land use change (LUC), carbon dioxide fluxes and vegetation changes, combined with insect outbreaks and other disturbances, will regulate the future carbon balance of the biosphere. These processes are incompletely represented in current earth system models (ESMs). The exact timing and magnitude of climate–biosphere feedbacks and potential tipping points of carbon loss are characterised by large uncertainty, but studies of feedbacks indicate that increased ecosystem carbon losses can cause large temperature increases in the future (medium confidence). (section 5.4, Figure 5.29 and Table 5.4 in (Canadell et al., 2021)), {;;;;;;; Figure 2.10; Figure 2.11; Table 2.4; Table 2.5; Table SM2.2 Table SM2.5}

Contributions of Adaptation Measures to Solutions

The resilience of biodiversity and ecosystem services to climate change can be increased by human adaptation actions including ecosystem protection and restoration (high confidence). Ecological theory and observations show that a wide range of actions can reduce risks to species and ecosystem integrity. This includes minimising additional stresses or disturbances; reducing fragmentation; increasing natural habitat extent, connectivity and heterogeneity; maintaining taxonomic, phylogenetic, and functional diversity and redundancy; and protecting small-scale refugia where micro-climate conditions can allow species to persist (high confidence). Adaptation also includes actions to aid the recovery of ecosystems following extreme events. Understanding the characteristics of vulnerable species can assist in early warning systems to minimise negative impacts and inform management intervention. {2.3; Figure 2.1;, 2.6.2, Table 2.6, 2.6.5, 2.6.7, 2.6.8}

There is new evidence that species can persist in refugia where conditions are locally cooler, when populations of the same species may be declining elsewhere (high confidence) {2.6.2}. Protecting refugia, for example, where soils remain wet during drought or fire risk is reduced, and in some cases creating cooler micro-climates, are promising adaptation measures {2.6.3; 2.6.5; Cross-Chapter Paper 1; CCP5.2.1 }. There is also new evidence that species can persist locally because of plasticity including changes in phenology or behavioural changes that move an individual into cooler micro-climates, and genetic adaptation may allow species to persist for longer than might be expected from local climatic changes (high confidence) {;, 2.6.1}. There is no evidence to indicate that these mechanisms will prevent global extinctions of rare, very localised species already near their climatic limits or species inhabiting climate/habitat zones that are disappearing (high confidence). {, 2.5.1,, 2.5.4, 2.6.1, 2.6.2, 2.6.5}

Since AR5, many adaptation plans and strategies have been developed to protect ecosystems and biodiversity, but there is limited evidence of the extent to which adaptation is taking place and virtually no evaluation of the effectiveness of adaptation measures in the scientific literature (medium confidence). This is an important evidence gap that needs to be addressed, to ensure a baseline is available against which to judge effectiveness and develop and refine adaptation in future. Many proposed adaptation measures have not been implemented (low confidence). {2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.8; 2.7}

Ecosystem restoration and resilience building cannot prevent all impacts of climate change, and adaptation planning needs to manage inevitable changes to species distributions, ecosystem structure and processes (veryhigh confidence). Actions to manage inevitable change include the local modification of micro-climate or hydrology, adjustment of site management plans and facilitating the dispersal of vulnerable species to new locations by increasing habitat connectivity and by active translocation of species. Adaptation can reduce risks but cannot prevent all damaging impacts so is not a substitute for reductions in greenhouse gas (GHG) emissions (high confidence). {2.2; 2.3; 2.3.1; 2.3.2; 2.4.5;;; 2.5.2;;; 2.5.4; 2.6.1; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.8; Cross-Chapter Box NATURAL in this chapter}

Ecosystem-based adaptation (EbA) can deliver climate change adaptation for people, with multiple additional benefits including those for biodiversity (high confidence). An increasing body of evidence demonstrates that climatic risks to people including floods, drought, fire and overheating, can be lowered by a range of EbA techniques in urban and rural areas (medium confidence). EbA forms part of a wider range of nature-based solutions (NbS); some have mitigation co-benefits, including the protection and restoration of forests and other high-carbon ecosystems as well as agro-ecological farming (AF) practices. However, EbA and other NbS are still not widely implemented. {2.2;; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6, 2.6.7; Table 2.7; Cross-Chapter Box NATURAL in this chapter; Cross-Chapter Paper 1}

To realise potential benefits and avoid harm, it is essential that EbA is deployed in the right places and with the right approaches for that area, with inclusive governance (high confidence) . Interdisciplinary scientific information and practical expertise, including Indigenous and local knowledge (IKLK), are essential to effectiveness (high confidence). There is a large risk of maladaptation where this does not happen (high confidence). {1.4.2; 2.2; 2.6; Table 2.7; Box 2.2; Figure Box 2.2.1; Cross-Chapter Box NATURAL in this chapter; Cross-Chapter Paper 1; 5.14.2}

EbA and other NbS are themselves vulnerable to climate change impacts (high confidence). They need to take account of climate change if they are to remain effective and they will be increasingly under threat at higher warming levels. NbS cannot be regarded as an alternative to, or a reason to delay, deep cuts in GHG emissions. (high confidence) {2.6.3, 2.6.5; 2.6.7; Cross-Chapter Box NATURAL in this chapter}

Climate Resilient Development

Protection and restoration of natural and semi-natural ecosystems are key adaptation measures in view of the clear evidence that damage and degradation of ecosystems exacerbates the impacts of climate change on biodiversity and people (high confidence). Ecosystem services that are under threat from a combination of climate change and other anthropogenic pressures include climate change mitigation, flood risk management, food provisioning and water supply (high confidence). Adaptation strategies that treat climate, biodiversity and human society as coupled systems will be most effective. {2.3; Figure 2.1; 2.5.4; 2.6.2; 2.6.3; 2.6.7; Cross-Chapter Boxes NATURAL and ILLNESS in this chapter}

A range of analyses have concluded that ~30–50% of Earth’s surface needs to be effectively conserved to maintain biodiversity and ecosystem services (high confidence). Climate change places additional stress on ecosystem integrity and functioning, adding urgency to taking action. Low-intensity sustainable management, including that performed by Indigenous Peoples, is an integral part of some protected areas, and can support effective adaptation and maintain ecosystem health. Food and fibre production in other areas will need to be efficient, sustainable and adapted to climate change to meet the needs of the human population. (high confidence) {Figure 2.1; 2.5.4; 2.6.2; 2.6.3; 2.6.7}

Natural ecosystems can provide the storage and sequestration of carbon at the same time as providing multiple other ecosystem services, including EbA (high confidence), but there are risks of maladaptation and environmental damage from some approaches to land-based mitigation (high confidence). Plantation, single-species forests in areas which would not naturally support forest, including savanna, natural grasslands and temperate peatlands, and replacing native tropical forests on peat soils, have destroyed local biodiversity and created a range of problems regarding water supply, food supply, fire risk and GHG emissions. Large-scale deployment of bioenergy, including bioenergy with carbon capture and storage (BECCS) through dedicated herbaceous or woody bioenergy crops and non-native production forests, can damage ecosystems directly or through increasing competition for land, with substantial risks to biodiversity. {2.6.3, 2.6.5, 2.6.6, 2.6.7; Box 2.2; Cross-Chapter Box NATURAL in this chapter; CCP7.3.2; Cross-Working Group Box BIOECONOMY in Chapter 5}

Terrestrial and aquatic ecosystems and species are often less degraded on land managed by Indigenous Peoples and local communities than on other land (medium confidence) . Involving indigenous and local institutions is a key element for developing successful adaptation strategies. IKLK includes a wide variety of resource-use practices and ecosystem stewardship strategies that conserve and enhance both wild and domestic biodiversity. {2.6.5; 2.6.7; Cross-Chapter Box NATURAL in this chapter; Chapter 15; Box 18.6; CCP2.4.1; CCP2.4.3; Box CCP7.1 }

Increases in the frequency and severity of extreme events, that WGI has attributed to human greenhouse gas emissions, are compressing the timeline available for natural systems to adapt and also impeding our ability to identify, develop and implement solutions (medium confidence). There is now an urgent need to build resilience and assist recovery following extreme events. This, combined with long-term changes in baseline conditions, means that implementing adaptation and mitigation measures cannot be delayed if these are to be fully effective. {2.3; Cross-Chapter Box EXTREMES in this chapter}

2.1 Introduction

2.1.1 Overview

We provide assessments of observed and projected impacts of climate change across species, biomes (vegetation types), ecosystems and ecosystem services, highlighting the processes that are emerging on a global scale. Where sufficient evidence exists, differences in biological responses across regions, taxonomic groups or types of ecosystems are presented, particularly when such differences provide meaningful insights into current or potential future autonomous or human-mediated adaptations. Human interventions that might build the resilience of ecosystems and minimise the negative impacts of climate change on biodiversity and ecosystem functioning are assessed. Such interventions include adaptation strategies and programmes to support biodiversity conservation and Ecosystem-based Adaptation (EbA). The assessments were done in the context of the Convention on Biological Diversity (CBD) and sustainable development goals (SDGs), whose contributions to climate resilient development (CRD) pathways are assessed. This chapter highlights both the successes and failures of adaptation attempts and considers potential synergies and conflicts with land-based climate change mitigation. Knowledge gaps and sources of uncertainty are included to encourage additional research.

The Working Group II Summary for Policymakers of the AR5 stated that ‘many terrestrial and freshwater species have shifted their geographic ranges, seasonal activities, migration patterns, abundances, and species interactions in response to ongoing climate change’ (IPCC, 2014d). Based on long-term observed changes across the regions, it was estimated that approximately 20–30% of plant and animal species are at risk of extinction when global mean temperatures rise 2–3°C above pre-industrial levels (Fischlin et al., 2007). In addition, the WGII AR5 Synthesis Report (IPCC, 2014e) broadly suggested that autonomous adaptation by ecosystems and wild species might occur, and proposed human-assisted adaptations to minimise negative climate change impacts.

Risk assessments for species, communities, key ecosystems and their services were based on the risk assessment framework introduced in the IPCC AR5 (IPCC, 2014b). Assessments of observed changes in biological systems emphasise detecting and attributing the impacts of climate change on ecological and evolutionary processes, particularly freshwater ecosystems, and ecosystem processes such as wildfires, that were superficially assessed in previous reports. Where appropriate, assessment of interactions between climate change and other human activities is provided.

Land use and land cover change (LULCC) as well as the unsustainable exploitation of resources in terrestrial and freshwater systems continue to be major factors contributing to the loss of natural ecosystems and biodiversity (high confidence). Fertiliser input, pollution of waterways, dam construction and the extraction of freshwater for irrigation put additional pressure on biodiversity and alter ecosystem function (Shin et al., 2019). Likewise, for biodiversity, invasive alien species have been identified as a major threat, especially in freshwater systems, on islands and in coastal regions (high confidence) (IPBES, 2018b; IPBES, 2018e; IPBES, 2018c; IPBES, 2018d; IPBES, 2019). Climate change and CO2 are expected to become increasingly important as drivers of change over the coming decades (Ciais et al., 2013; Settele et al., 2014; IPBES, 2019; IPCC, 2019c).

2.1.2 Points of Departure

Species diversity and ecosystem function influence each other reciprocally, while the latter forms the necessary basis for ecosystem services (Hooper et al., 2012; Mokany et al., 2016). Drivers of impacts on biodiversity, ecosystem function and ecosystem services have been assessed in reports by the IPCC, the Food and Agriculture Organization (FAO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) and the Global Environmental Outlook (Settele et al., 2014; FAO, 2018; IPBES, 2018b; IPBES, 2018e; IPBES, 2018c; IPBES, 2018d; IPBES, 2019; UNEP, 2019; Secretariat of the Convention on Biological Diversity, 2020). Most recently, the IPCC Special Report on Climate Change and Land (SRCCL) provided an assessment on land degradation and desertification, GHG emissions and food security in the context of global warming (IPCC, 2019c), and the IPBES–IPCC joint report on biodiversity and climate change provided a synthesis of the current understanding of the interactions, synergies and feedbacks between biodiversity and climate change (Pörtner et al., 2021). This chapter builds on and expands the results of these assessments.

Assessment of the impacts of climate change on freshwater systems has been limited in previous assessments, and inter-linkages between terrestrial and freshwater processes have not been fully explored (Settele et al., 2014; IPBES, 2019). Improved treatment of impacts on terrestrial and freshwater systems is critical, considering the revisions of international sustainability goals and targets, especially the conclusion that many of the proposed post-2020 targets of the CBD cannot be met due to climate change impacts (Arneth et al., 2020).

Previous reports highlighted the possibility of new ecosystem states stemming from shifts in thermal regimes, species composition, and energy and matter flows (Settele et al., 2014; Shin et al., 2019). Projecting such “tipping points” (see Glossary Appendix II) has been identified in previous reports as a challenge since monitoring programmes, field studies, and ecosystem and biodiversity modelling tools do not capture the underlying species–species and species–climate interactions sufficiently well to identify how biological interactions within and across trophic levels may amplify or dampen shifts in ecosystem states (Settele et al., 2014; Shin et al., 2019). Building on these previous analyses and the recent literature, Chapter 2 of AR6 provides new insights compared to those of previous assessments by (i) emphasising freshwater aspects and the interlinkages between freshwater and terrestrial systems, (ii) assessing more clearly the link between biodiversity and ecosystem functioning, (iii) assessing the impacts associated with climate change mitigation scenarios versus those of climate change including interactions with adaptation, and (iv) where possible, places findings in the context of the United Nations (UN) SDGs 2030 and services for human societies.

2.1.3 Guide to Attribution and Traceability of Uncertainty Assessments

For biological systems, we use the framework for detection and attribution outlined in AR5, in which biological changes observed are not attributed to global but rather to local or regional climate changes (Parmesan et al., 2013; Cramer et al., 2014). However, global distribution of regional responses is desirable to achieve generality, and data in prior reports were concentrated from the Northern Hemisphere. The critique of ‘global’ studies by (Feeley et al., 2017) argues that their naming is misleading, that most of them are far from global, and that a considerable geographic and taxonomic bias remains. This bias is diminishing, as regional data from the Southern Hemisphere is added and there is now representation from every continent.

Overall confidence in attributing biological changes to climate change can be increased in multiple ways (Parmesan et al., 2013), four of which we list here. First, confidence rises when the time span of biological records is long, such that decadal trends in climate can be compared with decadal trends in biological response, and long-term trends can be statistically distinguished from natural variability. Second, confidence can be increased by examining a large geographic area, which tends to diminish the effects of local confounding factors (Parmesan et al., 2013; Daskalova et al., 2021). Third, confidence is increased when there is experimental or empirical evidence of a mechanistic link between particular climate metrics and a biological response. Fourth, confidence is increased when particular fingerprints of climate change are documented that uniquely implicate climate change as the causal driver of the biological change (Parmesan and Yohe, 2003). These conditions constitute multiple lines of evidence, which, when they converge, can provide very high confidence that climate change is the causal driver of an observed change in a particular biological species or system (Parmesan et al., 2013).

Important factors that may confound or obscure effects of climate change are the presence of invasive species, changes in land use (LULCC) and, in freshwater systems, eutrophication (IPCC, 2019a). The temporal and spatial scale of studies also affects estimates of impacts. The most extreme published estimates of biological change tend to be derived from smaller areas and/or shorter time frames (Daskalova et al., 2021); a recent large global analysis of data for 12,415 species found that differences in study methodology accounted for most of the explained variance in reported range shifts (Lenoir et al., 2020). The importance of LULCC is frequently stressed, but there is a paucity of studies actually quantifying the relative effects of climate change and LULCC on species and communities. (Sirami et al., 2017) found only 13 such studies: four concluded that effects of LULCC overrode those of climate change, four found that the two drivers independently affected different species and five found that they acted in synergy.

2.2 Connections of Ecosystem Services to Climate Change

Ecosystems provide services essential for human survival and well-being. The Millennium Ecosystem Assessment defined ecosystem services as ‘the benefits people obtain from ecosystems’ including ‘provisioning services such as food and water; regulating services such as regulation of floods, drought, land degradation, and disease; supporting services such as soil formation and nutrient cycling; and cultural services such as recreational, spiritual, religious, and other nonmaterial benefits’ (Millennium Ecosystem Assessment, 2005).

The IPBES renamed the concept ‘nature’s contributions to people’ and broadened the definition to ‘the contributions, both positive and negative, of living nature (i.e., diversity of organisms, ecosystems, and their associated ecological and evolutionary processes) to the quality of life for people. Beneficial contributions from nature include such things as food provision, water purification, flood control, and artistic inspiration, whereas detrimental contributions include disease transmission and predation that damages people or their assets’ (IPBES, 2019). The concept was modified to include more social viewpoints and broaden the analyses beyond narrow economic stock-and-flow valuation approaches (Díaz et al., 2018). IPBES developed a classification of 18 categories of ecosystem services (see Table 2.1).

When anthropogenic climate change affects ecosystems, it can also affect ecosystem services for people. Climate change connects to ecosystem services by means of three links, i.e., climate change–species–ecosystems–ecosystem services. This chapter assesses these connections via all three links when end-to-end published scientific analyses are available for terrestrial and freshwater ecosystems. This type of robust evidence exists for some key ecosystem services (Section 2.5.3, 2.5.4), and is assessed in specific report sections: biodiversity habitat creation and maintenance (Sections 2.4, 2.5), regulation of detrimental organisms and biological processes (Sections,, 2.4.4, 2.5.3, 2.6.4, Cross-Chapter Box ILLNESS in this chapter), regulation of climate through ecosystem feedbacks in terms of carbon storage (Sections,,, and albedo (Section and the provision of freshwater from ecosystems to people (Section

For ecosystem services that do not have published scientific information to establish unambiguous links to climate change, the climate–species–ecosystem links are assessed. Global ecological assessments, including the Global Biodiversity Assessment (Heywood et al., 1995), the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005), and the IPBES Global Assessment Report (IPBES, 2019) have synthesised scientific information on the ecosystem–ecosystem services link, but a full assessment from climate change to ecosystem services is often impeded by limited quantitative studies that span this entire spectrum (see Mengist et al., 2020) for a review of this gap in montane regions).

IPCC and IPBES are collaborating to address gaps in the knowledge about the effects of climate change on ecosystem services (Pörtner et al., 2021). Table 2.1 provides a guide for finding information on climate change and individual ecosystem services in the AR6.

Table 2.1 | Connections of ecosystem services to climate change, indicating the 18 categories of nature’s contributions to people (IPBES, 2019), the most relevant sections in the AR6, and the level of evidence in this report for attribution to anthropogenic climate change of observed impacts on ecosystem services. The order of services in the table follows the order presented by IPBES and does not denote importance or priority. Connections denote observed impacts, future risks and adaptation. The order of connections follows the relevance or the order of sections. Numbers in parentheses refer to sections in this chapter.

Ecosystem service

Connections to climate change

Habitat creation and maintenance

Species extinctions (,, species range shifts (,, ecological changes in freshwater ecosystems (2.3.3,,,,,, 2.5.4,,, vegetation changes (2.4.3,,,,, 2.5.2,, biome shifts (, 2.5.4), wildfire (,, tree mortality (,

(robust evidence)

Pollination and dispersal of seeds and other propagules

Species extinctions (,, species range shifts (,, phenology changes (, See also Box 5.3.

(medium evidence)

Regulation of air quality

Wildfire (,, Chapter 7), tree mortality (,

(medium evidence)

Regulation of climate

Ecosystem carbon stocks, emissions, and removals (,, (Canadell et al., 2021), Amazon rainforest dieback (,,,,,, tundra permafrost thaw (,,, 2.5.4), biome shifts (2.4.3, 2.5.2,, wildfire (,, tree mortality (,, primary productivity changes (,

(robust evidence)

Regulation of ocean acidification

Ocean acidification (Canadell et al., 2021), changes in marine species distribution and abundance (Chapter 3)

(robust evidence)

Regulation of freshwater quantity, location and timing

Physical changes in freshwater systems (2.3.3), ecological changes in freshwater ecosystems (,,,,, tree mortality (,, freshwater supply from ecosystems (

(medium evidence)

Regulation of freshwater and coastal-water quality

Coastal ecosystem changes (Chapter 3), physical changes in freshwater systems (2.3.3), ecological changes in freshwater ecosystems (;,,,

(robust evidence)

Formation, protection and decontamination of soils and sediments

Agricultural ecosystem changes (Chapter 5), physical changes in freshwater systems (2.3.1), vegetation changes (2.4.3, 2.5.4), wildfire (,

(medium evidence)

Regulation of hazards and extreme events

Coastal ecosystem changes (Chapter 3), vegetation changes (2.4.3, 2.5.2), wildfire (,, Summary of hazards (2.3), Cross-Chapter Box EXTREMES in this chapter

(medium evidence)

Regulation of detrimental organisms and biological processes

Inter-species interactions (2.4.2), control of disease vectors (, 2.5.1, 2.6.4), insect-pest infestations (, Cross-Chapter Box ILLNESS in this chapter

(medium evidence)


Forestry plantation changes (Chapter 5), biomass changes in natural ecosystems (, bioeconomy (Cross-Working Group Box BIOECONOMY in Chapter 5), tree mortality (,

(limited evidence)

Food and feed

Agricultural ecosystem changes (Chapter 5), species extinctions (,, species range shifts (, nature-based services from natural ecosystems (Cross-Chapter Box NATURAL in this chapter), shifts in commercial food species (Cross-Chapter Box MOVING PLATE in Chapter 5)

(medium evidence)

Materials, companionship and labour

Forestry plantation changes (Chapter 5), species extinctions (,, species range shifts (, tree mortality (,

(limited evidence)

Medicinal, biochemical and genetic resources

Species extinctions (,, species range shifts (

(limited evidence)

Learning and inspiration

All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems

(limited evidence)

Physical and psychological experiences

All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems. See also, Chapter 15, CCP6.

(limited evidence)

Supporting identities

All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems. See also, Chapter 15, CCP6

(limited evidence)

Maintenance of options

All observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems, nature-based services from natural ecosystems (Cross-Chapter Box NATURAL in this chapter, Cross-Chapter Box DEEP in Chapter 17, Cross-Chapter Box MOVING PLATE in Chapter 5.

(limited evidence)

2.3 Hazards and Exposure

In AR6, Working Group I (IPCC, 2021a) describes changes in physical climate systems using the term ‘climatic impact-drivers’ (CIDs), which can have detrimental, beneficial or neutral effects on a system. In contrast, the literature on natural systems tends to focus on hazards, which include natural or human-induced physical events, impacts, or trends with the potential to cause negative effects on ecosystems and environmental resources. Hazards are affected by current and future changes in climate, including altered climate variability and extreme events (Ranasinghe et al., 2021). Hazards can occur suddenly (e.g., a heat wave or heavy rain event), or more slowly (e.g., land loss, degradation and erosion linked to multiple climate hazards compounding). Observed exposure and risks to protected areas are assessed in Section See also Cross-Chapter Box EXTREMES in this chapter.

Non-climatic hazards such as LUC, habitat fragmentation, pollution and invasive species have been the primary drivers of change in terrestrial and freshwater ecosystems in the past (high confidence) (Figure 2.1). These impacts have been extensively documented in reports by the IPBES (2021). However, while climate change has not been the predominant influence to date, its relative impact is increasing (IPCC SRCCL), with greater interactive effects of non-climate and climate hazards now occurring (Birk et al., 2020).

Figure 2.1 | Map of global land use change from 1982 to 2016. Based on satellite records of global tree canopy (TC), short vegetation (SV) and bare ground (BG) cover (from Song et al., 2018).

(a) Mean annual estimates of cover (% of pixel area at 0.05° resolution).

(b) Long-term change estimates (% of pixel area at 1.5° resolution), with pixels showing a statistically significant trend (n= 35 years, two-sided Mann–Kendall test, P < 0.05) in TC, SV or BG. The dominant changes are TC gain with SV loss; BG gain with SV loss; TC gain with BG loss; BG gain with TC loss; SV gain with BG loss; and SV gain with TC loss. Grey indicates areas with no significant change between 1982 and 2016.

2.3.1 Observed Changes to Hazards and Extreme Events

The major climate hazards at the global level are generally well understood (Ranasinghe et al., 2021) (WGI AR6 Interactive Atlas). Increased temperatures and changes to rainfall and runoff patterns; greater variability in temperature, rainfall, river flow and water levels; and rising sea levels and the increased frequency of extreme events means that greater areas of the world are being exposed to climate hazards outside of those to which they are adapted (high confidence) (Lange et al., 2020).

Extreme events are a natural and important part of many ecosystems, and many organisms have adapted to cope with long-term and short-term climate variability within the disturbance regime experienced during their evolutionary history (high confidence). However, climate changes, disturbance regime changes and the magnitude and frequency of extreme events such as floods, droughts, cyclones, heat waves and fire have increased in many regions (high confidence). These disturbances affect ecosystem functioning, biodiversity and ecosystem services (high confidence), but are, in general, poorly captured in impact models (Albrich et al., 2020b), although this should improve as higher-resolution climate models that better capture smaller-scale processes and extreme events become available (Seneviratne et al., 2021). Extreme events pose huge challenges for EbA (IPCC, 2012). Ecosystem functionality, on which such adaptation measures rely, may be altered or destroyed by extreme episodic events (Handmer et al., 2012; Lal et al., 2012; Pol et al., 2017).

There is high confidence that the combination of internal variability, superimposed on longer-term climate trends, is pushing ecosystems to tipping points, beyond which abrupt and possibly irreversible changes are occurring (Harris et al., 2018a; Jones et al., 2018; Hoffmann et al., 2019b; Prober et al., 2019; Berdugo et al., 2020; Bergstrom et al., 2021). Increases in the frequency and severity of heat waves, droughts and aridity, floods, fires and extreme storms have been observed in many regions (Seneviratne et al., 2012; Ummenhofer and Meehl, 2017), and these trends are projected to continue (high confidence) (Section, Cross-Chapter Box EXTREMES this Chapter) (Hoegh-Guldberg et al., 2018; Seneviratne et al., 2021).

While the major climate hazards at the global level are generally well described with high confidence, there is less understanding about the importance of hazards on ecosystems when they are superimposed (Allen et al., 2010; Anderegg et al., 2015; Seidl et al., 2017; Dean et al., 2018), and the outcomes are difficult to quantify in future projections (Handmer et al., 2012). Simultaneous or sequential events (coincident or compounding events) can lead to an extreme event or impact, even if each event is not in themselves extreme (Denny et al., 2009; Hinojosa et al., 2019). For example, the compounding effects of SLR, extreme coastal high tide, storm surge, and river flow can substantially increase flooding hazard and impacts on freshwater systems (Moftakhari et al., 2017). On land, changing rainfall patterns and repeated heat waves may interact with biological factors such as altered plant growth and nutrient allocation under elevated CO2, affecting herbivore rates and insect outbreaks leading to the widespread dieback of some forests (e.g., in Australian eucalypt forests) (Gherlenda et al., 2016; Hoffmann et al., 2019a). Risk assessments typically only consider a single climate hazard with no changing variability, thereby potentially underestimating the actual risk (Milly et al., 2008; Sadegh et al., 2018; Zscheischler et al., 2018; Terzi et al., 2019; Stockwell et al., 2020).

Understanding impacts associated with the rapid rate of climate change is less developed and more uncertain than changes in mean climate. High climate velocity (Loarie et al., 2009) is expected to be associated with distribution shifts, incomplete range filling and species extinctions (high confidence) (Sandel et al., 2011; Burrows et al., 2014), although not all species are equally at risk from high velocity (see Sections, It is generally assumed that the more rapid the rate of change, the greater the impact on species and ecosystems, but responses are taxonomically and geographically variable (high confidence) (Kling et al., 2020).

For example, strong dispersers are less at risk, while species with low dispersal ability, small ranges and long lifespans (e.g., many plants, especially trees, many amphibians and some small mammals) are more at risk (IPCC, 2014b; Hamann et al., 2015) . This is likely to favour generalist and invasive species, altering species composition, ecosystem structure and function (Clavel et al., 2011; Büchi and Vuilleumier, 2014). The ability to track suitable climates is substantially reduced by habitat fragmentation and human modifications of the landscape such as dams on rivers and urbanisation (high confidence). Freshwater systems are particularly at risk of rapid warming, given their naturally fragmented distribution. Velocity of changes in surface temperature of inland standing waters globally was estimated as being 3.5 ± 2.3 km per decade from 1861 to 2005. From 2006 to 2099, this is projected to increase from 8.7 ± 5.5 km (representative concentration pathway, RCP2.6) to 57.0 ± 17.0 km (RCP8.5) per decade (Woolway and Maberly, 2020). Although the dispersal of the aerial adult stage of some aquatic insects can surpass these climate velocities, rates of change under mid- and high-emission scenarios (RCP4.5, RCP6.0, RCP8.5) are substantially higher than the known rates of the active dispersal of many species (Woolway and Maberly, 2020). Many species, both terrestrial and freshwater, are not expected to be able to disperse fast enough to track suitable climates under mid- and high-emission scenarios (medium confidence) (RCP4.5, RCP6.0, RCP8.5; (Brito-Morales et al., 2018).

2.3.2 Projected Impacts of Increases in Extreme Events

Understanding of the large-scale drivers and the local-to-regional feedback processes that lead to extreme events is still limited, and projections of extremes and coincident or compounding events remain uncertain (Prudhomme et al., 2014; Sillmann et al., 2017; Hao et al., 2018; Miralles et al., 2019). Extreme events are challenging to model because they are, by definition, rare, and often occur at spatial and temporal scales much finer than the resolution of climate models (Sillmann et al., 2017; Zscheischler et al., 2018). Additionally, the processes that cause extreme events often interact, as is the case for drought and heat events, and they are spatially and temporally dependent, for example, soil moisture and temperature (Vogel et al., 2017). Understanding feedbacks between land and atmosphere also remains limited. For example, positive feedbacks between soil and vegetation, or between evaporation, radiation and precipitation, are important in the preconditioning of extreme events such as heat waves and droughts, and can increase the severity and impact of such events (Miralles et al., 2019).

Despite recent improvements in observational studies and climate modelling (Santanello et al., 2015; Stegehuis et al., 2015; PaiMazumder and Done, 2016; Basara and Christian, 2018; Knelman et al., 2019), the potential to quantify or infer formal causal relationships between multiple drivers and/or hazards remains limited (Zscheischler and Seneviratne, 2017; Kleinman et al., 2019; Miralles et al., 2019; Yokohata et al., 2019; Harris et al., 2020). The mechanisms underlying the response are difficult to identify (e.g., responses to heat stress, drought and insects), effects vary among species and at different life stages, and an initial stress may influence the response to further stress (Nolet and Kneeshaw, 2018). Additionally, hazards such as drought are often exacerbated by societal, industrial and agricultural water demands, requiring more sophisticated modelling of the physical and human systems (Mehran et al., 2017; Wan et al., 2017). Observations of past compound events may not provide reliable guides as to how future events may evolve, because human activity and recent climate change continue to interact to influence both system functioning and a climate state not previously experienced (Seneviratne et al., 2021)

2.3.3 Biologically Important Physical Changes in Freshwater Systems

Physical changes are fundamental drivers of change at all levels of biological organisation, from individual species, to communities, whole ecosystems. The climate hazards specific to freshwater systems not documented elsewhere in AR6 are summarised here. Observed Change in Thermal Habitat and Oxygen Availability

Since AR5, evidence of changes in the temperature of lakes and rivers has continued to increase. Global warming rates for lake surface waters were estimated as 0.21°C–0.45°C per decade between 1970 and 2010, exceeding sea-surface temperature (SST) trends of 0.09°C per decade between 1980 and 2017 (robust evidence, high agreement ) (Figure 2.2; (Schneider and Hook, 2010; Kraemer et al., 2015; O’Reilly et al., 2015; Woolway et al., 2020b). Warming of lake surface water temperatures was variable within regions (O’Reilly et al., 2015) but more homogeneous than deep-water temperature changes (Pilla et al., 2020). Because temperature trends in lakes can vary vertically, horizontally and seasonally, complex changes have occurred in the amount of habitat available to aquatic organisms at particular depths and temperatures (Kraemer et al., 2021).

Figure 2.2 | Observed global trends in lake and river surface water temperature.

(a) Left panel: map of temperatures of lakes (1970–2010).

(b) Left panel: map of temperatures of rivers (1901–2010). Note that the trends of river water temperatures are not directly comparable within rivers or to lakes, since time periods are not consistent across river studies. Right panels (a) and (b) depict water temperature trends along a latitudinal gradient highlighting the above average warming rates in northern Polar Regions (polar amplification). Data sources for lakes: (O’Reilly et al., 2015; Carrea and Merchant, 2019; Woolway et al., 2020a; Woolway et al., 2020b). Data sources for rivers: (Webb and Walling, 1992; Langan et al., 2001; Daufresne et al., 2004; Moatar and Gailhard, 2006; Lammers et al., 2007; Patterson et al., 2007; Webb and Nobilis, 2007; Durance and Ormerod, 2009; Kaushal et al., 2010; Pekárová et al., 2011; Jurgelėnaitė et al., 2012; Markovic et al., 2013; Arora et al., 2016; Latkovska and Apsīte, 2016; Marszelewski and Pius, 2016; Jurgelėnaitė et al., 2017).

Changes in river water temperatures ranged from −1.21°C to +1.076°C per decade between 1901 and 2010 (medium evidence, medium agreement ) (Hari et al., 2006; Kaushal et al., 2010; Jurgelėnaitė et al., 2012; Li et al., 2012; Latkovska and Apsīte, 2016; Marszelewski and Pius, 2016). The more rapid increase in surface water temperature in lakes and rivers in regions with cold winters (O’Reilly et al., 2015) can, in part, be attributed to the amplified warming in polar and high-latitude regions (robust evidence, high agreement ) (Screen and Simmonds, 2010; Stuecker et al., 2018).

Shifts in thermal regime: Since AR5, the trend that lake waters mix less frequently continues (Butcher et al., 2015; Adrian et al., 2016; Richardson et al., 2017; Woolway et al., 2017). This results from greater warming of surface temperatures relative to deep-water temperatures, and the loss of ice during winter which prevents inverse thermal stratification in north temperate lakes (robust evidence, high agreement ) (Adrian et al., 2009; Winslow et al., 2015; Adrian et al., 2016; Schwefel et al., 2016; Richardson et al., 2017).

Oxygen availability: increased water temperature and reduced mixing cause a decrease in dissolved oxygen. In 400 lakes, dissolved oxygen in surface and deep waters declined by 4.1 and 16.8%, respectively, between 1980 and 2017 (Jane et al., 2021). The deepest water layers are expected to experience an increase in hypoxic conditions by >25% due to fewer complete mixing events, with strong repercussions for nutrient dynamics and the loss of thermal habitat (robust evidence, high agreement ) (Straile et al., 2010; Zhang et al., 2015; Schwefel et al., 2016). Observed Changes in Water Level

Depending on how the intensification of the global water cycle affects individual lake water budgets, the amount of water stored in specific lakes may increase, decrease or have no substantial cumulative effect (Notaro et al., 2015; Pekel et al., 2016; Rodell et al., 2018; Busker et al., 2019; Woolway et al., 2020b). The magnitude of hydrological changes that can be assuredly attributed to climate change remains uncertain (Hegerl et al., 2015; Gronewold and Rood, 2019; Kraemer et al., 2020). Attribution of water storage variation in lakes due to climate change is facilitated when such variations occur coherently across broad geographic regions and long time scales, preferably absent of other anthropogenic hydrological influences (Watras et al., 2014; Kraemer et al., 2020). There is increasing awareness that climate change contributes to the loss of small temporary ponds which cover a greater global area than lakes (Bagella et al., 2016).

Lakes fed by glacial melt water are growing in response to climate change and glacier retreat (robust evidence, high agreement ) (Shugar et al., 2020). Water storage increases on the Tibetan Plateau (Figure 2.3a) have been attributed to changes in glacier melt, permafrost thaw, precipitation and runoff, in part as a result of climate change (Huang et al., 2011; Meng et al., 2019; Wang et al., 2020a). High confidence in attribution of these trends to climate change is supported by long-term ground survey data and observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission (Ma et al., 2010; Rodell et al., 2018; Kraemer et al., 2020).

Figure 2.3 | Change in water extent in the Tibetan Plateau and annual mean global river flow.

(a) Changes in water storage on the Tibetan Plateau. Map of the Qinghai–Tibetan Plateau, Asia, showing the percent change in surface water extent from 1984 to 2019 based on LANDSAT imagery. Increases in surface water extent in this region are mainly caused by climate change-mediated increases in precipitation and glacial melt (Source: EC JRC/Google; (Pekel et al., 2016).

(b) Global map of the median trend in annual mean river flow derived from 7250 observatories around the world (in 1971–2010). Some regions are drying (northeast Brazil, southern Australia and the Mediterranean) and others are wetting (northern Europe), mainly caused by large-scale shifts in precipitation, changes in factors that influence evapotranspiration and alterations of the timing of snow accumulation and melt driven by rising temperatures (Source: (Gudmundsson et al., 2021).

In the Arctic, lake area has increased in regions with continuous permafrost, and decreased in regions where permafrost is thinner and discontinuous (robust evidence, high agreement ) (See Chapter 4) (Smith et al., 2005; Andresen and Lougheed, 2015; Nitze et al., 2018; Mekonnen et al., 2021). Observed Changes in Discharge

Analysis of river flows from 7250 observatories around the world covering the years 1971–2010 and identified spatially complex patterns, with reductions in northeastern Brazil, southern Australia and the Mediterranean, and increases in northern Europe (medium evidence, medium agreement ) (Gudmundsson et al., 2021). More than half of global rivers undergo periodic drying that reduces river connectivity (medium evidence, medium agreement ). Increased frequency and intensity of droughts may cause perennial rivers to become intermittent and intermittent rivers to disappear (medium evidence, medium agreement ), threatening freshwater fish in habitats already characterised by heat and droughts (Datry et al., 2016; Schneider et al., 2017; Jaric et al., 2019). In high-altitude/latitude streams, reduced glacier and snowpack extent, earlier snowmelt and altered precipitation patterns, attributed to climate change, have increased flow intermittency (Siebers et al., 2019; Gudmundsson et al., 2021). Patterns in flow regimes can be directly linked to a variety of processes shaping freshwater biodiversity, so any climate change-induced changes in flow regimes and river connectivity are expected to alter species composition as well as having societal impacts (See Chapter 3 in (IPCC, 2018b)) (Bunn and Arthington, 2002; Thomson et al., 2012; Chessman, 2015; Kakouei et al., 2018). Observed Loss of Ice

Studies since AR5 have confirmed ongoing and accelerating loss of lake and river ice in the Northern Hemisphere (robust evidence, high agreement ) (Figure 2.4). In recent decades, systems have been freezing later in winter and thawing earlier in spring, reducing ice duration by >2 weeks per year and leading to an increasing numbers of years with a loss of perennial ice cover, intermittent ice cover or even an absence of ice (Adrian et al., 2009; Kirillin et al., 2012; Paquette et al., 2015; Adrian et al., 2016; Park et al., 2016; Roberts et al., 2017; Sharma et al., 2019). The global extent of river ice declined by 25% between 1984 and 2018 (Yang et al., 2020). This trend has been more pronounced at higher latitudes, consistent with enhanced polar warming (large geographic coverage) (Du et al., 2017). Empirical long-term and remote-sensing data gathered in an increasingly large number of freshwater systems supports very high confidence in attributing these trends to climate change. For the decline of glaciers, snow and permafrost, see Chapter 4 (this report) and the Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019b).

Figure 2.4 | Global ice cover trends of lakes and rivers.

(a) Spatial distribution of current (light grey areas) and future (coloured areas) Northern Hemisphere lakes that may experience intermittent winter ice cover with climate warming. Projections were based on current conditions (1970–2010) and four established air temperature projections (Data source: (Sharma et al., 2019).

(b) Spatial distribution of projected change in Northern Hemisphere river ice duration under the RCP4.5 emission scenario by 2080–2100 relative to the period 2009–2029. White areas refer to rivers without ice cover in the period 2009–2029 (zero days). Reference period isolines indicate river ice duration in the period 2009–2029. Coloured areas depict loss of ice duration in days. Blue areas depict a projected increase in river ice duration. Grey land areas indicate a lack of Landsat-observable rivers (Data source: (Yang et al., 2020). Extreme Weather Events and Freshwater Systems

Since AR5, numerous drastic short-term responses have been observed in lakes and rivers, to both expected seasonal extreme events and unexpected supra-seasonal extremes extending over multiple seasons. Consequences for ecosystem functioning are not well understood (Bogan et al., 2015; Death et al., 2015; Stockwell et al., 2020). Increasing frequencies of severe floods and droughts attributed to climate change are major threats for river ecosystems (Peters et al., 2016; Alfieri et al., 2017). While extreme floods cause massive physical disturbance, moderate floods can have positive effects, providing woody debris that contributes to habitat complexity and diversity, flushing fine sediments, dissolving organic carbon and providing important food sources from terrestrial origins (Peters et al., 2016; Talbot et al., 2018). Droughts reduce river habitat diversity and connectivity, threatening aquatic species, especially in deserts and arid regions (Bogan et al., 2015; Death et al., 2015; Ledger and Milner, 2015; Jaric et al., 2019).

Rivers already under stress from human activities such as urban development and farming on floodplains are prone to reduced resilience to future extreme events (medium confidence) (Woodward et al., 2016; Talbot et al., 2018). Thus, the potential for floods to become catastrophic for ecosystem services is exacerbated by LULCC (Peters et al., 2016; Talbot et al., 2018). However, biota can recover rapidly from extreme flood events if river geomorphology is not greatly altered. If instream habitat is strongly affected, recovery, if it occurs, takes much longer, resulting in a decline of biodiversity (medium confidence) (Thorp et al., 2010; Death et al., 2015; Poff et al., 2018).

However, not all extreme events will have a biological impact, depending, in particular, on the timing, magnitude and frequency of events and the antecedent conditions (Bailey and van de Pol, 2016; Stockwell et al., 2020; Jennings et al., 2021; Thayne et al., 2021). For instance, an extreme wind event may have little impact on phytoplankton in a lake that was fully mixed prior to the event. Conversely, the effects of a storm on phytoplankton communities may compound when lakes have not yet recovered from a previous storm or if periods of drought alternate with periods of intense precipitation (limited evidence) (Leonard et al., 2014; Stockwell et al., 2020).

In summary, extreme events (heat waves, storms and loss of ice) affect lakes in terms of water temperature, water level, light, oxygen concentrations and nutrient dynamics, which, in turn, affect primary production, fish communities and GHG emissions (high confidence). These impacts are modified by levels of solar radiation, wind speed and precipitation (Woolway et al., 2020a). Droughts have a negative impact on water quality in streams and lakes by increasing water temperature, salinity, the frequency of algal blooms and contaminant concentrations, and reducing concentrations of nutrients and dissolved oxygen (medium confidence) (Peters et al., 2016; Alfieri et al., 2017; Woolway et al., 2020a). Understanding how these pressures subsequently cascade through freshwater ecosystems will be essential for future projections of their resistance and resilience towards extreme events (Leonard et al., 2014; Stockwell et al., 2020). See Table SM2.1 for specific examples of observed changes. Projected Changes in Physical Characteristics of Lakes and Rivers

Given the strength of relationship between past GSAT and warming trends at lake surfaces (Figure 2.2; Section and projected increases in heat waves, surface water temperatures are projected to continue to increase (Woolway et al., 2021). Mean May to October lake surface temperatures in 46,557 European lakes were projected to be 2.9°C, 4.5°C and 6.5°C warmer by 2081–2099 compared to the historic period (1981–1999) under RCP2.0, RCP6.0 and RCP8.5, respectively (Woolway et al., 2020a). Under RCP2.6, the average intensity of lake heat waves increases from 3.7°C to 4.0°C and the average duration from 7.7 to 27.0 days, relative to the historic period (1970–1999). For RCP8.5, warming increases to 5.4°C and duration increases dramatically to 95.5 days (medium confidence) (Woolway et al., 2021).

Worldwide alterations in lake mixing regimes in response to climate change are projected (Kirillin, 2010). Most prominently, monomictic lakes—undergoing one mixing event in most years—will become permanently stratified, while lakes that are currently dimictic—mixing twice per year—will become monomictic by 2080–2100 (medium confidence) (Woolway and Merchant, 2019). Nevertheless, predicting mixing behaviour remains an important challenge and attribution to climate change remains difficult (Schwefel et al., 2016; Bruce et al., 2018).

Under climate projections of 3.2°C warming, 4.6% of the ice-covered lakes in the Northern Hemisphere could switch to intermittent winter ice cover (Figure 2.4a; (Sharma et al., 2019). Unfrozen and warmer lakes lose more water to evaporation (Wang et al., 2018b). By 2100, global annual lake evaporation will increase by 16%, relative to 2006–2015, under RCP8.5 (Woolway et al., 2020b). Moreover, melting of ice decreases the ratio of sensible to latent heat flux, thus channelling more energy into evaporation (medium confidence) (Wang et al., 2018b). In the periods 2009–2029 and 2080–2100, average duration of river ice is projected to decline by 7.3 and 16.7 days under RCP4.5 and RCP8.5, respectively (Figure 2.4b; Yang et al., 2020).

Projections of lake water storage are limited by the absence of reliable, long-term, homogenous and spatially resolved hydrologic observations (Hegerl et al., 2015). This uncertainty is reflected in the widely divergent projections in response to future climate changes in individual lakes (Angel and Kunkel, 2010; MacKay and Seglenieks, 2012; Malsy et al., 2012; Notaro et al., 2015) . Selecting models that perform well when comparing hindcasted to observed past water storage variation often does little to reduce water storage projection uncertainty (Angel and Kunkel, 2010). This wide range of potential changes complicates lake management. For information on observed and projected changes in the global water cycle and hydrological regimes for streams, lakes, wetland, groundwater and their implications on water quality and societies, see Chapter 4, this report, and (Douville et al., 2021). For the role of weather and climate extremes on the global water cycle, see (Seneviratne et al., 2021).

In summary, with ongoing climate warming and an increase in the frequency and intensity of extreme events, observed increases in water temperature, losses of ice and shifts in thermal regime are projected to continue (high confidence).

Cross-Chapter Box EXTREMES | Ramifications of Climatic Extremes for Marine, Terrestrial, Freshwater and Polar Natural Systems

Authors: Rebecca Harris (Australia, Chapter 2, CCP3), Philip Boyd (Australia, Chapter 3), Rita Adrian (Germany, Chapter 2), Jörn Birkmann (Germany, Chapter 8), Sarah Cooley (USA, Chapter 3), Simon Donner (Canada, Chapter 3), Mette Mauritzen (Norway, Chapter 3), Guy Midgley (South Africa, Chapter 16); Camille Parmesan (France/USA/UK, Chapter 2), Dieter Piepenburg (Germany, Chapter 13, CCP6), Marie-Fanny Racault (UK/France, Chapter 3), Björn Rost (Germany, Chapter 3, CCP6), David Schoeman (Australia, Chapter 3), Stavana E. Strutz (USA/Chapter 2), Maarten van Aalst (the Netherlands, Chapter 16).


Increases in the frequency and magnitudes of extreme events, attributed to anthropogenic climate change by WGI (IPCC, 2021a), are now causing profound negative effects across all realms of the world (marine, terrestrial, freshwater and polar) (medium confidence) (Fox-Kemper et al., 2021; Seneviratne et al., 2021) (Sections 2.3.1, 2.3.2,,, Chapter 3, Chapters 9–12, this report). Changes to population abundance, species distributions, local extirpations, and global extinctions are leading to long-term, potentially irreversible shifts in the composition, structure and function of natural systems (medium confidence) (Frolicher and Laufkotter, 2018; Harris et al., 2018a; Maxwell et al., 2019; Smale et al., 2019). These effects have widespread ramifications for ecosystems and the services they provide—physical habitat, erosion control, carbon storage, nutrient cycling and water quality—with knock-on effects for tourism, fisheries, forestry and other natural resources (2.4.3, 2.4.4, 2.5.1, 2.5.2, 2.5.3, 2.5.4) (Kaushal et al., 2018; Heinze et al., 2021; Pörtner et al., 2021).

Increasingly, the magnitude of extreme events is exceeding the values projected for mean conditions for 2100, regardless of emissions scenario (Figure Cross-Chapter Box EXTREMES.1). This has collapsed the timeline that organisms and natural communities have to acclimate or adapt to climate change (medium confidence). Consequently, rather than having decades to identify, develop and adopt solutions, actions to build resilience and assist recovery following extreme events are required quickly if they are to be effective.

Figure Cross-Chapter Box EXTREMES.1 | A conceptual illustration of how extinction risk is affected by changes in the frequency, duration and magnitude of extreme weather or climate events (e. g,. drought, fire, flood and heat waves). Many organisms have adapted to cope with long- and short-term climate variability, but as the magnitude and frequency of extreme events increases, superimposed on the long-term climate trend, the threshold between survivable extreme weather events (yellow) and extremes that carry a high risk of causing population or species extinctions (red) is crossed more frequently. This can lead to local extinction events with insufficient time between to enable recovery, resulting in long-term, irreversible changes to the composition, structure and function of natural systems. When the extreme event occurs over a large area relative to the distribution of a species (e.g., a hurricane impacting an island which is the only place a given species occurs), a single extreme event can drive the global extinction of a species.

Recent extremes highlight the characteristics that enable natural systems to resist or recover from events, helping natural resource managers to develop solutions to improve the resilience of natural communities and identify the limits to adaptation (Bergstrom et al., 2021).

Marine Heat Waves

Consensus is emerging that anthropogenic climate change has significantly increased the likelihood of recent marine heat waves (MHWs) (medium confidence) (Oliver et al., 2018; Fox-Kemper et al., 2021). A widespread MHW occurred in the northeast Pacific in 2013–2015, with upper ocean temperature anomalies of up to 6.2°C relative to 2002–2012 (Gentemann et al., 2017). This event, termed the ‘Blob’, enhanced surface water stratification, decreasing nutrient supply, primary and community production and leading to widespread changes to open ocean and coastal ecosystems, with geographical shifts of key species across trophic levels, mass strandings of marine mammals, seabird mortalities and the closure of commercially important fisheries (Cavole et al., 2016; Piatt et al., 2020). The MHW reappeared in 2019 (‘Blob 2.0’) (Amaya et al., 2020), with similarly high temperature anomalies extending from Alaska to California, but the ecological effects of this event are expected to differ because the Blob originated in winter, and Blob2.0 intensified in summer (Amaya et al., 2020). Modelling suggests rapid shifts in the geographic distributions of important fish species in response to MHWs (Cheung and Frolicher, 2020), with projected decreased biomass and distributional shifts of fish at least four times faster and larger than the effects of decadal-scale mean changes throughout the 21st century under RCP8.5 (high confidence) (Cheung and Frolicher, 2020). MHWs can also dramatically increase CH4 emissions from oceans, a significant positive feedback to global warming (see also Chapter 3, this report) (Borges et al., 2019).

The Arctic region is warming more than twice as fast as the global mean, and polar organisms and ecosystems are likely to be particularly vulnerable to heat waves due to their specific thermal niches and physiological thresholds and also the lack of poleward ‘refugia’ (high confidence). The consequences of MHWs are exacerbated by concomitant sea ice melting and the freshening of surface waters, leading to secondary effects due to osmotic stress and failing pH homeostasis. Since sea ice-associated organisms are often critical components of polar food chains, cascading effects up to the top predators are expected. In 2015–2016, a MHW occurred in the Gulf of Alaska/Bering Sea (Walsh et al., 2018) which was unprecedented in terms of surface temperatures and ocean heat content, geographical extent, depth range and persistence, impacting the entire marine food web. Persistent warming favoured some phytoplankton species and triggered one of the largest algal blooms recorded in this region, with concomitant oyster farm closures due to uncommon paralytic shellfish-poisoning events (Walsh et al., 2018). There were also massive die-offs of common guillemots (Uria aalge) and puffins (Fratercula cirrhata) , attributed to starvation resulting from warming-induced effects on food supply (Jones et al., 2019). A 2017 survey found a 71% decline in the abundance of Pacific cod (Gadus macrocephalus) since 2015, likely due to an increase in metabolic demand and reduced prey supply during the MHWs (Barbeaux et al., 2020).

Terrestrial Heat Waves

Heat waves are now regularly occurring that exceed the physiological thresholds of some species, including birds and other small endotherms such as flying foxes (high confidence) (Sections, Heat waves in Australia, North America and southern Africa have caused mass mortality events due to lethal hyperthermia and dehydration (Saunders et al., 2011; Conradie et al., 2020; McKechnie et al., 2021), reducing fitness (du Plessis et al., 2012; Andrew et al., 2017; Sharpe et al., 2019; van de Ven et al., 2019; van de Ven et al., 2020), breeding success, and recruitment (Kennedy et al., 2013; Wiley and Ridley, 2016; Ratnayake et al., 2019) and affecting daily activity and geographic distributions (Albright et al., 2017). They also place enormous demands on wildlife management agencies and pose risks to human health (Welbergen et al., 2008).

Cross-Chapter Box EXTREMES

Recent mortality events affected 14 species of bird and fruit bats (Epomophorus wahlbergi) in South Africa when maximum air temperatures exceeded 43–45°C in 2020 (McKechnie et al., 2021). Passerine birds seem more vulnerable to lethal hyperthermia, due to the relative inefficiency of panting to lose heat (McKechnie et al., 2021) and also their small size, as heat tolerance generally increases with body mass (McKechnie et al., 2021). Several mass mortality events of flying foxes (Pteropus poliocephalus, P. alecto) have occurred in eastern Australia when maximum air temperatures exceeded 42° (Welbergen et al., 2008). Nineteen such events occurred between 1994 and 2008, compared to three events prior to 1994. In January 2002, maximum temperatures exceeded the 30-year average mean daily maximum by up to 16.5° and killed >3500 individuals (Welbergen et al., 2008). In 2014, an estimated 45,500 flying foxes died in a single day, when average maximum temperatures were ≥8°C above average (Bureau of Meteorology, 2014). Drought compounds the impacts, as mortality increases when water availability is low (Welbergen et al., 2008; Mo and Roache, 2020; McKechnie et al., 2021).

Antarctica encountered its first recorded heat wave in 2020. Record high temperatures occurred in East Antarctica (Robinson et al., 2020), with a maximum (9.2°) temperature ~7° above the mean maximum, and minimum temperatures > 0°. Record high temperatures (18.3°) were also recorded in West Antarctica (Robinson et al., 2020). It is too soon to know the impact on polar life, but such abrupt heating is expected to have wide-ranging effects on biota, from flash-flooding and dislodgement of plants, to excess meltwater supplying moisture to arid polar ecosystems ( Cross-Chapter Paper 6 Polar). Heat waves in Siberia in 2016, 2018 and 2020, with air temperature anomalies >6°, were associated with extensive wildfires, pest infestations and melting permafrost (Overland and Wang, 2021).

Freshwater Extremes

Heat waves, storms and floods affect the thermal regime and biogeochemical functioning of lakes and rivers (Woolway and Merchant, 2017; Vicente-Serrano et al., 2020). Extreme heat waves lead to abnormally high water temperatures (Till et al., 2019) and reduce the mixing of lakes (Woolway et al., 2021), causing a decrease in oxygen and deep-water oxygen renewal (Zhang et al., 2015). Ectotherms such as fish and invertebrates are particularly susceptible to such temperature and oxygen stress (Stoks et al., 2014). Their metabolic demands increase with rising temperature and a suitable habitat is eroded due to both high temperatures and lower oxygen concentrations in lakes and rivers. Till et al. (2019) attributed 502 fish kill events in the Wisconsin lakes (USA) to warmer summers in lakes that experienced abnormally high water temperatures. Such events are predicted to double by 2041–2059 and increase four-fold by 2081–2099 compared to historical levels (Till et al., 2019). This anticipated increase in die-offs may facilitate warm-water fish species displacing cool-water species (Hansen et al., 2017; Jennings et al., 2021). Floods mobilise nutrients and sediment, and aid dispersal of invasive species in rivers (Death et al., 2015), while drought extremes reduce river connectivity, threatening biodiversity in rivers (section (Tickner et al., 2020).

Cross-Chapter Box EXTREMES

Learnings from Recent Extremes

These examples show that the impact of an extreme event is a function of its characteristics and those of the exposed ecosystem. The timing, frequency, absolute magnitude and geographic extent of the extreme event, relative to antecedent conditions and the life cycle, resistance and resilience of the natural community, all determine the biological response (Figure Cross-Chapter Box Extremes.1) (Hillebrand et al., 2018; Gruber et al., 2021). The impact appears to be greater when extreme events occur more frequently, particularly when the interval between events is insufficient to allow recovery to previous population sizes (e.g., frequent fires and coral bleaching) or coincides with vulnerable life-cycle stages, even when populations are adapted to cope with such disturbances. Events occurring over large spatial areas reduce the potential for recolonisation from nearby populations (e.g., regional droughts causing widespread declines). Often the magnitude of extreme events exceeds historical levels, so organisms are less likely to be adapted to them, particularly when several extremes coincide (e.g., high water temperature and drought) (Duke et al., 2017). When hazards occur simultaneously (compound events), the impacts of extremes can be substantially aggravated, triggering a cascade of effects in ecosystems (Gruber et al., 2021).

Several characteristics of natural systems are associated with greater vulnerability to extreme events (Figure Cross-Chapter Box EXTREMES.2), knowledge of which can inform solutions to build resilience and aid recovery (Robinson et al., 2020). Resilience can be built prior to an event by minimising additional disturbances, such as water extraction from river systems, pollution of aquatic systems, fragmentation of land and LULCCs. Managing landscapes to reduce fragmentation and increase habitat extent, connectivity and heterogeneity, by increasing the number and extent of reserves, may provide local refugia from extreme events and enhance post-event recolonisation, but may be less effective for marine systems (Section 3.6). Maintaining taxonomic, phylogenetic and functional diversity is important, as more diverse systems may be more stable in the face of disturbances (Pimm, 1984; García-Palacios et al., 2018).

Figure Cross-Chapter Box EXTREMES.2 | Characteristics of natural systems that affect vulnerability and help identify solutions—both prior to and after extreme events—to build resistance, resilience and recovery

Several characteristics increase vulnerability: low or narrow thermal tolerance, high habitat specificity, low dispersal ability, long generation times, low competitive ability and life-cycle constraints that limit recovery or recolonisation. Populations living close to one or more limiting factors near range edges are also vulnerable (Arafeh-Dalmau et al., 2019). Understanding these characteristics can inform management intervention to aid recovery following an extreme event. For instance, knowledge of the flying fox’s physiological temperature threshold led to successful interventions, including misting populations to reduce mortality (Mo and Roache, 2020), and the development of a ‘heat stress forecaster’, an online tool which uses weather forecasts to identify roosts at risk of extreme heat events (Ratnayake et al., 2019). This early warning system increases the preparedness of wildlife management and conservation agencies, enabling efficient allocation of management resources towards the locations that are likely to be the most affected.

Monitoring following extreme events can help identify immediate impacts and the potential for cascading interactions, such as changes to competitive interactions following range shifts, impacts on freshwater ecosystems following wildfires and the spread of invasive species. Ongoing monitoring of recovery and effectiveness of management intervention is important, focussing on habitat-forming species (e.g., kelp, corals and dominant tree species) and keystone species (e.g., filter-feeders, macrophytes and top predators), as the loss of these species can lead to ecosystem tipping points, beyond which the system may not recover (Collins et al., 2019) (Sections 2.5.3;;; chapters 9–15, this report).

The acute impacts of extreme events, in addition to the chronic stress of changing mean conditions, are accelerating and amplifying the biological effects of climate change. This amplification is being observed globally and in all realms where life exists. Extreme events are compressing the timeline available for natural systems to adapt, and impeding our ability to identify, develop and adopt solutions. Recent events highlight the urgent need to mitigate global GHG emissions and identify solutions to halt accelerating impacts on natural systems (Díaz et al., 2020).

Cross-Chapter Box EXTREMES

2.4 Observed Impacts of Climate Change on Species, Communities, Biomes, Key Ecosystems and Their Services

2.4.1 Overview

Global meta-analyses of terrestrial systems in AR3 and AR4 concentrated on long time frames (>20 years) and findings from relatively undisturbed areas, where confidence in attributing observed changes to climate change is high. Recent global and regional meta-analyses (AR5 and later) have been broader, including data from degraded and disturbed areas and studies with shorter time frames (Tables 2.2a,b).

By the time of AR5, >4000 species with long-term observational data had been studied in the context of climate change (Parmesan, 2006; Parmesan and Hanley, 2015). Since then, thousands of new studies and additional species have been added, leading to higher confidence in climate change attribution (Table 2.2) (Scheffers et al., 2016; Wiens, 2016; Cohen et al., 2018; Feeley et al., 2020). Freshwater habitats have been under-represented in prior reports, but new long-term datasets, coupled with laboratory and field experiments, are improving our understanding. This assessment stresses observations from lakes and streams. As numbers of studies increase and data is increasingly extracted from areas with high LULCC, attribution is more difficult as habitat loss and fragmentation are known major drivers of changes in terrestrial and freshwater species (Ramsar Convention on Wetlands, 2018; IPBES, 2019; Tickner et al., 2020). Due to the overwhelming volume of literature, the assessments for chapter 2 concentrates on results from large continental or global-scale reviews and meta-analyses. Most of the assessment of studies conducted in individual countries can be found in Regional chapters, but this chapter does include studies across very large countries or political entities that occupy much of a continent (e.g., Canada, the USA, Australia or Europe), or studies that provide rare or uniquely-relevant information.

2.4.2 Observed Responses to Climate Change by Species and Communities (Freshwater and Terrestrial) Observed Range Shifts Driven by Climate Change

Poleward and upward range shifts were already attributable to climate warming with high confidence in AR5. Publication of observed range shifts in accord with climate change have accelerated since AR5 and strengthened attribution. Ongoing latitudinal and elevational range shifts driven by regional climate trends are now well-established globally across many groups of organisms, and attributable to climate change with very high confidence due to very high consistency across a now very large body of species and studies and an in-depth understanding of mechanisms underlying physiological and ecological responses to climate drivers (Table 2.2; Table 2.3, Table SM2.1) (Pöyry et al., 2009; Chen et al., 2011; Grewe et al., 2013; Gibson-Reinemer and Rahel, 2015; MacLean and Beissinger, 2017; Pacifici et al., 2017; Anderegg et al., 2019). Range shifts stem from local population extinctions along warm-range boundaries (Anderegg et al., 2019) as well as from the colonisation of new regions at cold-range boundaries (Ralston et al., 2017).

Many studies since AR4 have tended not to be designed as attribution studies, particularly recent large-scale, multi-species meta-analyses. That is to say, all the data available was included in such studies (from both undisturbed and highly degraded lands and including very short-term data sets of <20 years), with little attempt to design the studies to differentiate the effects of climate change from those of other potential confounding variables. These studies tended to find greater lag and a lower proportion of species changing in the directions expected from climate change, with the authors concluding that LULCC, particularly habitat loss and fragmentation, was impeding wild species from effectively tracking climate change (Lenoir and Svenning, 2015; Rumpf et al., 2019; Lenoir et al., 2020).

Attribution is strong for species and species-interactions for which there is a robust mechanistic understanding of the role of climate on biological processes (high confidence). Unprecedented outbreaks of spruce beetles occurring from Alaska to Utah in the 1990s were attributed to warm weather that, in Alaska, facilitated a halving of the insect’s life cycle from two years to one (Logan et al., 2003). Milder winters and warmer growing seasons were likewise implicated in poleward expansions and increasing outbreaks of several forest pests (Weed et al., 2013), leading to the current prediction that 41% of major insect pest species will further increase their damage as climate warms, and only 4% will reduce their impacts, while the rest will show mixed responses (Lehmann et al., 2020).

During their range shifts, forest pests remain climate-sensitive. For example, the distribution of the western spruce budworm is limited at its warm range edges by the adverse effects of mild winters on overwinter survival, and at its cool range by the ability to arrive at a cold-resistant stage before winter arrives (Régnière and Nealis, 2019). We might therefore expect tree mortality from insect outbreaks to be most severe at sites climatically less suitable for the plants, where plants would be under more stress. However, (Jaime et al., 2019), using separate species distribution models (SDMs) (MaxEnt) for the insects and plants, found that observed mortality of Scots pine from bark beetles was highest at sites that were most climatically suitable for the trees as well as for the insects. In a study of tree mortality in California, bark beetles selectively killed highly stressed fir trees, but killed pines according to their size irrespective of stress status (Stephenson et al., 2019).

Range shifts in a poleward and upward direction, following expected trajectories according to local and regional climate trends, are strongly occurring in freshwater fish populations in North America (Lynch et al., 2016b), Europe (Comte and Grenouillet, 2013; Gozlan et al., 2019) and Central Asia (Gozlan et al., 2019) (medium evidence, high agreement). Cold-water fish, such as coregonids and smelt have been negatively affected at the equatorial borders of their distributions (Jeppesen et al., 2012). Upward elevational range shifts in rivers and streams have been observed. Systematic shifts towards higher elevation and upstream were found for 32 stream-fish species in France following regional variation in climate change (Comte and Grenouillet, 2013). Bull trout (Salvelinus confluentus) in Idaho (USA), were estimated to have lost 11–20% (8–16% per decade) of the headwater stream lengths necessary for cold-water spawning and early juvenile rearing, with the largest losses occurring in the coldest habitats (Isaak et al., 2010). Range contractions of the same species have been found in the Rocky Mountains watershed (Eby et al., 2014). Likewise, the distribution of the stonefly Zapada glacier, endemic to the alpine streams of the Glacier National Park in Montana (USA), has been reduced over several decades by an upstream retreat to higher, cooler sites as water temperatures have increased and glacial masses have decreased (Giersch et al., 2015).

The melting of glaciers has led to a change in water discharge associated with community turnover in glacier-fed streams (Cauvy-Fraunié and Dangles, 2019). For instance, glacier-obligate macro-invertebrates have started disappearing when glacial cover drops below approximately 50% (robust evidence, high agreement ), reviewed in (Hotaling et al., 2017). For freshwater invertebrates, no meaningful trends have been detected in geographic extent or population size for most species (Gozlan et al., 2019).

An invasive freshwater cyanobacterium in lakes, Cylindrospermopsis raciborskii, originating from the tropics, has spread to temperate zones over the last few decades due to the climate change-induced earlier increase of water temperature in spring (Wiedner et al., 2007), aided by a competitive advantage in eutrophic systems (Ekvall et al., 2013; Urrutia-Cordero et al., 2016). Observed Local Population and Global Species’ Extinctions Driven by Climate Change

Disappearances of local populations within a species range are more frequent and better documented than whole species’ extinctions, and attribution to climate change is possible for sites with minimal confounding non-climatic stressors. Changes of temperature extremes are often more important to these local extinction rates than changes of mean annual temperature (high confidence) (see Sections 2.3.1, 2.3.2,,, Cross-Chapter Box EXTREMES in this chapter) (Parmesan et al., 2013). A global meta-analysis of 236 species of birds, mammals, amphibians, fish, invertebrates and plants across 132 independent studies found that changes in population abundances were strongly related to temperature variability globally, and significantly related to precipiation variability in lower latitudes (Pearce-Higgins et al., 2015). In a global study of 538 diverse plant and animal species, sites with local extinctions were associated with smaller changes of mean annual temperature but larger and faster changes of hottest yearly temperatures than sites where populations persisted (Román-Palacios and Wiens, 2020). Near warm range limits, 44% of species had suffered local extinctions. In both temperate and tropical regions, sites with local extinction had greater increases in maximum temperatures than those without: a Tmax increase of 0.456°C and 0.316°C versus a Tmean increase of 0.153°C and 0.061°C for temperate (n= 505 sites) and tropical (n= 76 sites), respectively (P < 0.001) (Román-Palacios and Wiens, 2020).

Wiens (2016) assumed that population extinctions were primarily driven by climate change when they occurred at elevational or latitudinal ‘warm edge’ range limits, and were at relatively undisturbed sites stated by the authors to be under increasing climatic stress. By this criterion, climate-caused local extinctions were widespread among plants and animals globally, detected in 47% of 976 species examined. The percentage of species suffering these extinctions was higher in the Tropics (55%) than in temperate habitats (39%), higher in freshwater (74%) than in marine (51%) or terrestrial (46%) habitats, and higher in animals (50%) than in plants (39%). The difference between plants and animals varied with latitude; in the temperate zone, a much higher proportion of animals than plants suffered range-limit extinctions (38.6% of 207 animal species vs. 8.6% of 105 plants, P < 0.0001) while at tropical sites, local extinction rates were (nonsignificantly) higher in plants (59% of 155 species) than in animals (52% of 349 species), the reverse of their temperate-zone relationship. Rates varied across animal groups from 35% in mammals, to 43% in birds, 56% in insects and 59% in fish (Wiens, 2016).

Freshwater population extinctions are mainly due to habitat loss, the introduction of alien species, pollution, over-harvesting (Gozlan et al., 2019; IPBES, 2019) and climate change-induced epidemic diseases (Pounds et al., 2006)(see Section Climate warming, particularly through the intensification and severity of droughts, contributes to the disappearance of small ponds which hold rare and endemic species (Bagella et al., 2016). Systematic data on the extent and biology of small ponds is, however, lacking on the global scale. Extreme heat waves can lead to large local fish kills in lakes (see Section, when water temperature and oxygen concentrations surpass critical thresholds and threatening cold-water fish and amphibians (Thompson et al., 2012). Evidence of a local extinction of some invertebrate species with a 1.4°C–1.7°C rise in mean annual stream winter temperature from 1981 to 2005 was reported in Abrahams et al. (2013). Population declines of specialist species in glacier-fed streams, such as the non-biting midge Diamesa davisi (Chironomidae), can be attributed to climate-change-driven glacier retreat (Cauvy-Fraunié and Dangles, 2019), and the flatworm Crenobia alpina (Planariidae) has been reported as locally extinct in the Welsh Llyn Brianne river (Durance and Ormerod, 2010; Larsen et al., 2018).

Many high montane possums in Australia have low physiological tolerance to heat waves, with death occuring due to heat-driven dehydration at temperatures exceeding 29°C–30°C for >4–5 h over several days (Meade et al., 2018; Turner, 2020). Major declines have been recorded for several species, population extinctions have occured at lower elevations since the early 2000s, and the white sub-species of the lemuroid ringtail possum (Hemibelideus lemuroides) in Queensland, Australia, disappeared after heat waves in 2005 (high confidence): intensive censuses found only 2 individuals in 2009 (Chandler, 2014; Weber et al., 2021).

Two terrestrial and freshwater species have become extinct in the wild, with climate change implicated as a key driver. The cloud forest-restricted golden toad (Incilius periglenes) was extinct by 1990 in a nature preserve in Costa Rica, driven by successive extreme droughts. This occurred in the absence of chytridiomycosis infection, caused by the fungal pathogen Bd, verified during field censuses of golden toad populations in the process of extinction as well as genetic analyses of museum specimens, although Bd was present in other frog species in the region (medium evidence, high agreement ) (Pounds et al., 1999; Pounds et al., 2006; Puschendorf et al., 2006; Richards-Hrdlicka, 2013). The interaction between expansion of chytrid fungus globally and local climate change is implicated in the extinction of a wide range of tropical amphibians (high confidence) (see Section Case Study 2 Chytrid fungus and climate change).

The BC melomys (Melomys rubicola), the only mammal endemic to the Great Barrier Reef, inhabited a small (5-hectare) low-lying (<3-m-high) cay in the Torres Strait Islands, Australia. Recorded as having a population size of several hundred in 1978, this mammal has not been seen since 2009 and was declared extinct in 2016 (Gynther et al., 2016). SLR and documented increases in storm surge and in tropical cyclones, driven by climate change, led to multiple inundations of the island in the 2000s. Between 1998 and 2014, herbacious vegetation, the food resource for the BC melomys, declined by 97% in area (from 2.2 down to 0.065 hectares), and from 11 plant species down to two (Gynther et al., 2016; Watson, 2016; Woinarski, 2016; Woinarski et al., 2017). The island was unihabited with few non-climatic threats, providing high confidence in the attribution of extinction of the BC melomys to climate change-driven increases in the frequency and duration of island inundation (Turner and Batianoff, 2007; Woinarski et al., 2014; Gynther et al., 2016; Watson, 2016; Woinarski et al., 2017).

In the IUCN Red List (IUCN, 2019), 16.2% of terrestrial and freshwater species (n= 3,777 species) that are listed as endangered, critically endangered or extinct in the wild (n= 23,251 species) list climate change or severe weather as one of their threats.

In summary, local population extinctions caused by climate-change-driven increases in extreme weather and climate events have been widespread among plants and animals (very high confidence), and the first clear documentations of entire species driven extinct by recent climate change is emerging (medium confidence). Observed Changes in Community Composition Driven by Climate Change Overall patterns of community change

The most common type of community change takes the form of in situ decreases in cold-adapted species and increases in warm-adapted species (Bowler et al., 2017; Hughes et al., 2018; Kuhn and Gégout, 2019; Feeley et al., 2020). This process has lead to increases of species richness on mountaintops and decreased richness at adjacent lower elevations (medium evidence, high agreement ) (Forister et al., 2010; Steinbauer et al., 2018). While it is also expected from observed range shifts of individual species that species richness should increase along tropical/temperate ecotones and along temperate/boreal ecotones, to date this has not been well documented. Lewthwaite et al (Lewthwaite and Mooers, 2022) documented a small increase in local richness across Canada for 265 species of butterflies, but the stronger effect was an homogenization across the region, with generalist species generally expanding into new sites and leading to lower Beta-diversity (lower diversity among sites). In a study of 66 bumble bee species across North America and Europe, Soroye et al (Soroye et al., 2020) did not find the expected pattern, with most sites, regardless of latitude, declining in species richness, even when individual species benefited from warming or increased precipitation at some sites. Observed shifts in community composition have consequences for species’ interactions. Such indirect effects of climate change have been shown to often have greater impacts on species than the direct effects of climate itself, particularly for higher-level consumers (Cahill et al., 2013; Ockendon et al., 2014).

Analyses indicated that responses in range shifts and timing were lagging behind the changes expected from regional warming. This type of lag, where biological response is less than expected from known underlying physiology or general climatic limits, is called ‘climate debt’. Examples of climate debt, measured from community composition changes, come from birds and butterflies in Europe (Devictor et al., 2012) and lowland forest herbaceous plants in France (Bertrand et al., 2011). The French study found that larger debts occurred in communities with warmer baseline conditions and that some of the apparent debt stemmed from the ability of species to tolerate warming in situ, so no range shift was observed.

Prominent changes in freshwater community composition, such as increases in cyanobacteria and warm-tolerant zooplankton species, the loss of cold-water fish, gains in thermo-tolerant fish, macro-invertebrates, and floating macrophytes, are occurring (medium evidence, high agreement , medium confidence) (Adrian et al., 2016; Hossain et al., 2016; Short et al., 2016; Huisman et al., 2018; Gozlan et al., 2019). Geothermal streams have provided evidence about community structure and ecosystem function at high temperatures. A study of 14 such habitats reported simplified food web structures and shortened pathways of energy flux between consumers and resources (high confidence) (O’Gorman et al., 2019). Changes in the relative abundance of species, species composition and biodiversity due to warming trends, and non-climate-driven changes are to be expected in lakes and rivers globally. However, thus far, empirical evidence and mechanistic understanding to inform modelling is too limited to draw general conclusions about the nature of current and future climate change-driven changes within entire food webs on a global scale (Urban et al., 2016). Freshwater systems: mechanistic drivers and responses

Physical changes in lakes (see Section 2.3.3) have affected primary production (see Section, algal-bloom formation and composition, zooplankton and fish size distribution, and species composition (Urrutia-Cordero et al., 2017; Gozlan et al., 2019; Seltmann et al., 2019). Declines in the abundance of cold-stenothermal species, particularly the Arctic charr (Salvelinus alpinus), coregonids and smelt, and increases in eurythermal fish (e.g., the thermo-tolerant carp Cyprinus carpio, common bream, pike perch, roach and shad) have been observed in northern temperate lakes associated with warming trends (medium evidence, high agreement ) (Jeppesen et al., 2012; Jeppesen et al., 2014). These changes increase predation pressure on zooplankton and reduce grazing pressure on phytoplankton, which may result in higher phytoplankton biomass (De Senerpont Domis et al., 2013; Jeppesen et al., 2014; Adrian et al., 2016). Reduction in lake mixing lowers the concentration of nutrients in the epilimnion and may lead to higher silicon-to-phosphorous ratios that negatively affect diatom growth (Yankova et al., 2017) or overall primary productivity (see Section

In a study of 1567 lakes across Europe and North America, Kakouei (2021) identified climate change as the major driver of increases in phytoplankton biomass in remote areas with minimal LULCC. Greater temperature variability can be more important than long-term temperature trends as a driver of zooplankton biodiversity (Shurin et al., 2010). Reductions of winter severity attributed to anthropogenic climate change are increasing winter algal biomass, and motile and phototropic species, at the expense of mixotrophic species (Özkundakci et al., 2016; Hampton et al., 2017).

Tropical lakes are prone to loss of deep-water oxygen due to lake warming, with negative consequences for their fisheries and their biodiversity (Lewis Jr, 2000; Van Bocxlaer et al., 2012). Many ancient tropical lakes (Malawi, Tanganyika, Victoria, Titicaca, Towuti and Matano) hold thousands of endemic animal species (Vadeboncoeur et al., 2011).

Observed effects of climate change on freshwater invertebrates are variable (Knouft and Ficklin, 2017). In glacier-fed streams globally, climate change has caused community turnover and changes in abundances in terms of increased generalist and decreased specialist species (Lencioni, 2018; Cauvy-Fraunié and Dangles, 2019). In turn, dragonflies in flowing waters, monitored during the warming period from 1988 through 2006 in Europe, did not show consistent changes in their distribution (Grewe et al., 2013), reviewed in Knouft and Ficklin (2017). Long-term trends in the species composition and community structure of stream macro-invertebrates, specifically a general trend for decreases in species characteristic of cold, fast-flowing waters and increases of thermophilic species typical of stagnant or slow-moving waters, have been attributed to climate change (robust evidence, high agreement ) (Daufresne et al., 2007; Chessman, 2015). A study of 14 geothermal streams reported simplified food web structures and shortened pathways of energy flux between consumers and resources (O’Gorman et al., 2019). Macrophytes benefit from rising water temperatures, but increased shading from increased phytoplankton biomass could offset this (Hossain et al., 2016; Short et al., 2016; Zhang et al., 2017a). Emergence of novel communities and invasive species

As climate change is increasing the movements of species into new areas, there is concern about how exotic species are being impacted, either by becoming invasive or by already invasive species gaining even more advantage over native species. Modelling predicts that the effects of climate warming on food web structure and stability favour the success of invading species (Sentis et al., 2021). Both simulated warming experiments (Zettlemoyer et al., 2019) and long-term observations (Willis et al., 2010) have found phenologies of exotic species to respond more adaptively to warming than those of natives; in the long-term observations, the success of exotics was attributed to their greater phenological responsiveness. In an expert assessment of the future relative importance of different drivers of the impacts of biological invasions, climate change was named as the most important driver in polar regions, second-most important in temperate regions (after trade/transport), and third-most important in the tropics (after trade/transport and human demography/migration) (Essl et al., 2020).

However, not all exotic species become invasive. As novel climate conditions develop, novel communities made up of new combinations of species are emerging as populations and species adapt and shift their ranges differentially, not always with negative consequences (high confidence) (Dornelas et al., 2014; Evers et al., 2018; Teixeira and Fernandes, 2020). Novel communities differ in composition, structure, function and evolutionary trajectories, as the proportions of specialists and generalists, native, introduced and range-shifting species change and species interactions are altered, ultimately affecting ecosystem dynamics and functioning (Lurgi et al., 2012; Hobbs et al., 2014; Heger and van Andel, 2019). The exact nature of novel communities is difficult to predict because species-level uncertainties propagate at the community level due to ecological interactions (Williams and Jackson, 2007). However, observations, experimental mesocosms (Bastazini et al., 2021), and theoretical models (Lurgi et al., 2012; Sentis et al., 2021) provide support that novel communities will continue to emerge with climate change (medium confidence). Observed Phenological Responses to Climate Change

Since AR5, the number of studies of changes in phenology (timing of biological events) has increased substantially, aided by advances in remote sensing (Piao et al., 2019). Phenological studies have documented particularly consistent conclusions on responses of plants and animals to warming, including the advancement of spring events and the lengthening of growing seasons in temperate regions (via a combination of advancement of spring events and, to a lesser extent, the retardation of autumn events) (robust evidence, high agreement ) (Table 2.2, Table 2.3, Table SM2.1) (Menzel et al., 2020). In the Tropics, by contrast, changes in precipitation have more strongly influenced animal phenology than have temperature changes (Cohen et al., 2018). A meta-analysis compared observed phenological advances in birds with expectations due to warming local climates, and concluded that the observed advances fell short of what was expected and that a substantial phenological climate debt had been generated (Radchuk et al., 2019).

Taxonomic groups have differed in their responses (Parmesan, 2007; Thackeray et al., 2010), and a few have completely diverged from general trends. For example, seabirds continue to breed with their pre-climate-change phenologies (Keogan et al., 2018). Newer reviews and analyses reveal differences in responses across continents and time intervals (Piao et al., 2019). Mean advance in days per decade for plants was 5.5 in China and 3.0–4.2 in Europe, but only 0.9 in North America (Piao et al., 2019). Mean values for the retardation of autumn leaf fall, which can be more influenced by photoperiod and less by temperature than spring leaf-out, were 0.36 days per decade in Europe (Menzel et al., 2020), 2.6 days per decade in China and around 3 days per decade in the USA (medium evidence, high agreement ) (Piao et al., 2019).

The rapid rate of the advancement of spring events in the 1990s slowed down in the 2000s, and stalled or even reversed in some regions (Menzel et al., 2020). Wang et al. (2019) noted, from remote sensing, that during the ‘global warming hiatus’ from 1998 to 2012, there were no global trends in either spring green-up or autumn colouring. Annual crops, the timing of which is determined by farmers, were an exception. When natural systems were advancing fast prior to 1998, farmers advanced more slowly, but during the natural ‘hiatus’, farmed crops advanced faster than wild plants and cultivated trees (Menzel et al., 2020). In a long (67 years) European time series (Menzel et al., 2020), autumn leaf colouring showed delays attributed to winter and spring warming in 57% of observations (mean delay of 0.36 days per decade); spring and summer phenologies advanced in 89% of wild plants, despite decreased winter chilling, with around 60% of trends significant and ‘strongly attributable’ to winter and spring warming; and the growing season lengthened in 84% of cases (mean lengthening 0.26 days yr -1) (Table 2.2).

Table 2.2 | Global fingerprints of climate change impacts across wild species. (Updated from (Parmesan and Hanley, 2015). For each study for which data were made available, a response for an individual species or functional group was classified as (1) no response (i.e., no significant change in the measured trait over time), (2) if a significant change was found, the response was classified as either consistent or not consistent with expectations according to local or regional climate trends. Percentages are approximate and estimated for the studies as a whole. Individual analyses within the studies may differ. The specific metrics of climate change analysed for associations with biological change vary somewhat across studies, but most use changes in local or regional temperatures (e.g., mean monthly T or mean annual T), with some using precipitation metrics (e.g., total annual rainfall). For example, a consistent response would be poleward range shifts in areas that are warming. Probability (P) of getting the observed ratio of consistent-to-not consistent responses by chance was <10–13 for (Parmesan and Yohe, 2003; Root et al., 2003; Root et al., 2005; Poloczanska et al., 2013) and <0.001 for Rosenzweig et al. (2008). The last collumn distinguishes studies that were designed for attribution to climate change (e.g. by analysing only long-term data from relatively undisturbed habitats (see section 2.1.3 and 2.4.1)(Parmesan et al., 2013; Cramer et al., 2014) from those that analysed all available data, including data from areas highly-impacted by non-climate drivers (e.g. LULCC).


N: total numbers of species, functional groups or studies

Species in given system: Terrestrial (T) Marine (M) Freshwater (F)

Types of change

Changes documented

Geographical region

Study allows for attribution to climate change

2.2a Observed phenological changes

(Parmesan and Yohe, 2003)

677 species

T: 461 plants, 168 birds, 35 insects; T/F: 9 amphibians; F: 2 fish

Spring phenology

Overall: 9% delay; 27% no trend; 62% advance

Mean change 2.3 days per decade advance



(Menzel et al., 2006)

Agricultural crops, fruit trees, wild plants

100% T

Spring and autumn phenology

From 1971 to 2000, 48% responding as expected; spring advance 2.5 days per decade, mean autumn delay 0.2 days per decade, fruit ripening 2.4 days per decade advance; farming activities 0.4 days per decade advance



(Parmesan, 2007)

203 species

T, F

Spring phenology

Overall advance 2.8 days per decade

20 changes (delays), 153 advances, 8 no change; significantly more advance at higher latitudes



(Rosenzweig et al., 2008)

55 studies (~100–200 species)

T: 65%

M: 13%

F: 22%


90% of changes consistent with local/regional climate change



(Thackeray et al., 2010)

726 taxa

T: birds, moths, aphids, terrestrial plants; M and F: phytoplankton

Spring phenology

83.5% of ‘trends’ were advances; mean overall advance 3.9 days per decade; T plants 93% advancing, mean 5.8 days per decade; F plants 62% advancing, mean 2.3 days per decade; secondary consumers advanced less than primary consumers and producers



(While and Uller, 2014)

59 populations, 17 studies

T/F, 100% Amphibians


35% statistically significant change; mean advance 6.1 ± 1.65 days per decade; range 17.5 days delay to 41.9 days advance; 65% (n= 47 populations) found significant relationship between breeding phenology and temperature; higher latitudes advanced more



(Gill et al., 2015)

64 studies

T: 100%


Delay of autumn senescence

Delay averaged 0.33 days yr -1 and 1.20 days per degree Celsius warming; more delay at low latitudes across Northern Hemisphere; high-latitude species driven more by photoperiod than low-latitude species



(Ficetola and Maiorano, 2016)

66 studies of temperature effects;

15 of precipitation

T/F 100% amphibians

Phenology and abundances

Population dynamics driven by precipitation while breeding phenology driven by temperature



(Halupka and Halupka, 2017)

28 species multi-brooded, 27 species single-brooded, some species several populations

T 100% (birds)

Phenology: length of breeding season

Shows differences in sign of response between single and multi-broods and migrants vs. residents; Season extended by 4 days per decade for multi-brooded, shortened by 2 days per decade for single-brooded;

Multi: 26 species; 15 of 34 populations significantly extended, none significantly reduced

Northern Hemisphere


(Kharouba et al., 2018)

88 species in 54 pair-wise interactions

T: changes in relative phenologies of consumers and their resources

Asynchrony between consumers and resources has increased in some cases and decreased in others, with no significant trend; the prediction that asynchronies should be increasing in general is not supported.



(Cohen et al., 2018)

127 studies

T: 100% animals

Phenological trends

81% of 127 studies of animals show phenological change in direction of earlier spring; some studies were multi-species.

Mean advance since 1950: 2.88 days per decade.


North America




(Keogan et al., 2018)

145 populations, 209 time series

T: Seabirds breeding sites

Phenological trends

No change in breeding dates between 1952 and 2015



(Radchuk et al., 2019)

4835 studies, 1413 species

T: animals; T/F amphibians

Phenological trends

Greatest phenological advancements in amphibians, followed by insects and birds, in this order.

Global but most in Northern Hemisphere


(Piao et al., 2019)


T: Plants

Spring and autumn phenologies

Rate of advance slowing down across Northern Hemisphere and reversed in parts of western North America in response to regional cooling since 1980s



(Menzel et al., 2020)

53 species in Germany, 37 in Austria, 21 in Switzerland (includes overlaps)

T: Plants

Spring and autumn phenologies

Long time series: 1951–2018. Autumn leaf colouring: mean delay 0.36 days per decade; spring phenology (leaf-unfolding) mean advance 0.24 days per decade; summer phenology (fruit ripening) mean advance 0.26 days per decade. Growing season length mean increase 0.26 days yr -1 but farming season length decreased by 0.02 days yr -1.



2.2b. Observed Changes In Distributions, Abundances And Local Population Extinctions

(Parmesan and Yohe, 2003)

920 species

T: 85.2%

M: 13.5%

F: 1.3%

Distributions and abundances

50% of species (460/920) showed changes in distribution or abundances consistent with local or regional climate change



(Root et al., 2003)

926 species

T: 94%

M: 5.4%

F: 0.6%

Distributions and abundances

52% of species (483/926) showed changes in distribution or abundances consistent with local or regional climate change



(Rosenzweig et al., 2008)

18 studies

T: 65%

M: 13%

F: 22%

Distributions and abundances

90% of studies showed changes in distribution or abundances consistent with local or regional climate change



(Pöyry et al., 2009)

48 species

T: butterflies

Range shifts

From 1992 to 2004, 37 ranges shifted poleward, 9 shifted equatorially, 2 no change. Non-threatened species expanded poleward by 84.5 km, threatened species showed no significant change (<2.1 km)



(Tingley et al., 2009)

53 species

T: birds

Elevational range shifts

Resurvey (2003–2008) of historical elevational transects (1911–1929). 90.6% of species tracked their climate niche (temperature and/or precipitation) with regional climate change; Lower-elevation species (mean range centroid = 916 m) tracked only precipitation; high-elevation species (mean range centroid = 1944 m) tracked only temperature; species that tracked both temperature and precipitation had mid-elevation range centroids (1374–1841 m)

California, USA


(Chen et al., 2011)

24 taxonomic group × region combinations for latitude, 31 for elevation

T >264

M >10

F >34

Range shifts: elevation and latitude

Mean upward elevation shift 11.0 m per decade

Poleward shift 16.9 km per decade



(Grewe et al., 2013)

90 species

T/F Dragonflies

Shifts of northern range boundaries

48 poleward shifts; 26 equatorial; 16 no change from 1988 to 2006

Southern lentic (standing water) species expanded 116 km polewards; southern lotic (running water) and all northern species stayed stable.



(Mason et al., 2015)

21 animal groups, 1573 species

T: birds, Lepidoptera

T/F: Odonates

Range shifts in 3 time periods

Northward shifts 23 km per decade (1966–1975) and 18 km per decade (1986–1995), with significant differences among taxa in rates of change



(Gibson-Reinemer and Rahel, 2015)

13 studies, 273 species:

Plants, birds, mammals, marine inverterbrates

T and M

Range shifts in 2 or 3 areas for each species;

shift measured as change of limit or centroid

50% shifts of cold limits inconsistent with each other within species despite similar warming; species showing inconsistent shifts (including stable vs. directional or different directions) = 47% plants, 54% birds, 46% marine invertebrates, 60% mammals. Large difference in magnitude of range shifts when in same direction (mean difference 8.8 times)



(Ficetola and Maiorano, 2016)

66 studies of temperature effects; 15 studies of precipitation effects

T/F 100% (amphibians)

Phenology and abundances

Population dynamics driven by precipitation, breeding phenology driven by temperature



(Scheffers et al., 2016)

94 ecological processes

T, F, M

All possible types and levels of ecological change

82% of ecological processes affected by climate change



(Wiens, 2016)

976 species

T, F, M

Population extinction rates near warm latitudinal and elevational range limits

47% of species suffered climate-related local extinctions: fish 59%, insects 56%, birds 44%, plants 39%, amphibians 37%, mammals 35%



(Bowler et al., 2017)

1167 populations, 22 communities

T: 48%

M: 61%

F: 35%


population trends

T species with warm-temperature preference performed better than cool preferers; F and M species: no effect of temperature preference on performance; 47% of species with significant abundance changes: 61% M, 48% T, 35% F



(Pacifici et al., 2017)

873 mammals, 1272 birds

T: 100% (birds and mammals)

Multiple: range change, abundance, reproductive rate, survival, body mass

Estimated negative impacts (range contraction, reduced reproductive rates or other measures of fitness estimates) for IUCN-threatened species based on actual observed change in more common, related species; 47% threatened mammals and 23% birds negatively impacted by climate change in part of their ranges

Global for birds; mammals North America

Unclear (complex methods)

(MacLean and Beissinger, 2017)

21 studies

26 assemblages of taxonomically related species

T: Plants and animals

Range shifts in latitude and altitude related to species’ traits: dispersal, body size, habitat, diet specialization and historic range limit

High-latitude/altitude range boundaries shifted less than lower-latitude/altitude boundaries. Author explanation is that habitat limits were reached (e.g., mountain tops). Magnitudes of shifts positively related to dispersal traits and habitat breadth.



(Ralston et al., 2017)

46 species

T: Birds

Shifts in climate niche breadth, filling of climate space and overall abundance

Species increasing in abundance were also increasing breeding climate niche breadth and niche filling. Declining species were opposite: niche breadths narrowing and greater climate debt.

North America


(Rumpf et al., 2019)

1026 species

T: plants, invertebrates, vertebrates

Comparison of rates of range limit shift at leading and trailing elevational edges

No difference in mean rate of shift of leading and trailing edges; elevational range sizes not changing systematically. Greater lags in regions with faster warming.



(Freeman et al., 2018)

975 species, 32 elevational gradients

T: plants, endotherms, ectotherms

Comparison of rates of range limit shift at leading and trailing elevational edges

Mean change at warm limit 92 ± 455 m per degree Celsius; cool limit 131 ± 465 m per degree Celsius; (± SD, not significantly different from each other). Available area and range sizes decreased for mountaintop species.



(Anderegg et al., 2019)


50 studies, >100 species

T: 100% woody plants

Mortality at dry range edges

100 individual species + a community of 828 species

mortality at range edges due to drought was 33% greater than for core populations

Apparently global


drought not warming

(Román-Palacios and Wiens, 2020)

10 studies, 538 species, 581 sites

T: plants and animals

Analysis for drivers of population extinctions at warm range edges

44% of species had suffered local population extinctions near warm-range limits. In temperate regions, sites with local extinction had greater increases in maximum temperature than those without (0.456°C vs. 0.153°C, P < 0.001, n= 505 sites) and smaller increases in mean temperatures (0.412°C vs. 1.231°C, P < 0.001). In tropical regions, range edges with local extinction also had greater increases in maximum temperatures (0.316°C vs. 0.061°C, P < 0.001, n= 76), but changes in mean temperatures were similar between edges with and without extinctions (0.415°C vs. 0.406°C, P = 0.9)



Changes in freshwater systems are consistent with changes in terrestrial systems: earlier development of phytoplankton and zooplankton and earlier spawning by fish in spring as well as extension of the growing season are occurring (robust evidence, high agreement ) (Adrian et al., 2009; De Senerpont Domis et al., 2013; Adrian et al., 2016; Thackeray et al., 2016). Phenological changes in lakes have been related to rising water temperatures, reduced ice cover and prolonged thermal stratification (increasing evidence and agreement since AR5; very high confidence). Crozier and Hutchings (2014) reviewed the phenological changes in fish and documented that changes in the timing of migration and reproduction, age at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes in temperature. The median return time of Atlantic salmon to rivers in Newfoundland and Labrador advanced by 12–21 days over the past decades, associated with overall warmer conditions (Dempson et al., 2017). Observed Complex Phenological and Range Shift Responses

Early meta-analyses tested the straightforward hypotheses that warming should shift timing earlier and ranges polewards. Once these trends had been established (IPCC, 2014b; Parmesan and Hanley, 2015), exceptions to them became a focus of study. For example, in northern regions of the Northern Hemisphere, the spring flowering of some plants was delayed instead of being advanced as to be expected with warming (Cook et al., 2012a; Cook et al., 2012b; Legave et al., 2015). These turned out to be species requiring vernalisation (winter chilling) to speed their development in spring (Ettinger et al., 2020). For these plants, phenological changes result from the combined effects of advancement caused by spring warming and retardation caused by winter warming. Incorporating this level of complexity into analyses revealed that a greater proportion of species was responding to climate change than estimated according to the simple expectation that warming would always cause advancement (92% responding versus 72% from earlier analyses) (Cook et al., 2012b).

Animal species can show vernalisation equivalent to that in plants (Stålhandske et al., 2017). However, a semi-global meta-analysis of terrestrial animals failed to detect the delaying effects of warming winters (Cohen et al., 2018). The same animal-based meta-analysis contrasted phenological changes in temperate-zone animals, which are principally explained by changes in temperature, with those at lower latitudes, which tend to follow changes in precipitation (Cohen et al., 2018).

Vitasse et al. (2018), working with alpine trees, found that phenological delay with increasing elevation had declined from 34 days/1000 m in 1960 to 22 days/1000 m in 2016, greatly reducing the differences in timing between trees growing at different elevations. This reduction was greatest after warmer winters, suggesting that winter warming is a principal cause of the overall trend.

Lian et al. (2020) observed that earlier spring leaf-out in the Northern Hemisphere is causing increases in evapotranspiration that are not fully compensated by increased precipitation. The consequence is a greater soil moisture deficit in summer, expected to exacerbate impacts of heat waves as well as drought stress. In Arctic freshwater ecosystems, Heim et al. (2015) demonstrated the importance of seasonal cues for fish migration, which can be impacted by climate change due to reduced stream connectivity and fragmentation, earlier peak flows and increased evapotranspiration.

Precipitation has also been implicated in exceptions to the rule that ranges should be shifting to higher elevations. In dry climates, increases in precipitation accompanying climate warming can facilitate downslope range shifts (Tingley et al., 2012).

Multiple responses can co-occur. Hällfors et al. (2021), in a study of 289 Lepidoptera in Finland, found that, with warming, 45% had either shifted their range northward or advanced their flight season. The 15% of species that did both (shifting northward by 113.1 km and advancing their flight period by 2.7 days per decade, on average, over a 20-year period) had the largest population increases, and the 40% of species that showed no response had the largest population declines. Observed Changes to Physiology and Morphology Driven by Climate Change

Impacts on species physiology in terrestrial and freshwater systems have been observed, and attributed to climate change (medium confidence). These include changes in tolerance to high temperatures (Healy and Schulte, 2012; Gunderson and Stillman, 2015; Deery et al., 2021), increased metabolic costs of living at elevated temperatures (Scheffers et al., 2016) and shifts in sex ratios in species with temperature-dependent sex determination. For example, warmer temperatures have driven the masculinisation of lizard populations (Schwanz and Janzen, 2008; Schwanz, 2016; Edmands, 2021) and the feminisation of turtle populations (Telemeco et al., 2009). Skewed sex ratios can lead to mate shortages, reduced population growth, reduced adaptive potential and increased extinction risk, because genetic diversity decreases as fewer individuals mate and heterozygosity is lost (Mitchell and Janzen, 2010; Edmands, 2021).

Behavioural plasticity (flexibility) such as nest-site selection can provide a partial buffer from the effects of increasing temperature by placing the individual in a slightly cooler microclimate, but there are environmental and physical limits to this plasticity (medium confidence) (Refsnider and Janzen, 2016; Telemeco et al., 2017). Plasticity in heat tolerance (e.g., due to reversible acclimation or acclimatisation) can also potentially compensate for rising temperatures (Angilletta Jr, 2009), but ectotherms have relatively low acclimation in thermal tolerance and acclimation is expected to only slightly reduce the risk of overheating in even the most plastic taxa (low confidence) (Gunderson and Stillman, 2015).

Geographic variation in thermal tolerance plasticity is expected to influence the vulnerability and range shifts of species in response to climate change (Gunderson and Stillman, 2015; Sun et al., 2021). In many ectotherms, plasticity in thermal tolerance increases polewards, as thermal seasonality increases (Chown et al., 2004), contributing to higher vulnerability to warming in tropical organisms (low confidence) (Huey et al., 2009; Campos et al., 2021). Some species have evolved extreme upper thermal limits at the expense of plasticity, reflecting an evolutionary trade-off between these traits (Angilletta et al., 2003; Stillman, 2003). The most heat-tolerant species, such as those from extreme environments, may therefore be at a greater risk of warming because of an inability to physiologically adjust to thermal change (low confidence) (Bozinovic et al., 2011; Overgaard et al., 2014; Magozzi and Calosi, 2015).

Physiological changes have observable impacts on morphology, such as changes in body size (and length of appendages), and colour changes in butterflies, dragonflies and birds (medium confidence) (Galeotti et al., 2009; Karell et al., 2011). However, trends are not always linear or consistent across realms, taxonomic groups or geographic regions (Gotanda et al., 2015). Some morphological changes arise in response to environmental changes, rather than as the result of genetic adaptation or selection for an optimal body type. For example, dietary changes associated with climate change have led to changes in chipmunk skull morphology (Walsh et al., 2016).

Decreased body size has been suggested as a general response of species to climate change in freshwater species, given the temperature-related constraints of metabolism with larger size. Reduced body size in response to global warming has been documented for freshwater bacteria, plankton and fish, as well as a shift towards smaller species (Daufresne et al., 2009; Winder et al., 2009; Jeppesen et al., 2010; Crozier and Hutchings, 2014; Jeppesen et al., 2014; Farmer et al., 2015; Rasconi et al., 2015; Woodward et al., 2016). However, the lack of systematic empirical evidence in fresh waters, and confounding effects such as interactions between temperature, nutrient availability and predation, limit generalisations in attributing observed body size changes to climate change (low confidence) (Pomati et al., 2020 Nutrients).

Evidence is weak for a consistent reduction in body size across taxonomic groups in terrestrial animals (low confidence) (Siepielski et al., 2019). Decreased body size in warmer climates (as higher surface area-to-volume ratios maximise heat loss) is expected, based on biogeographic patterns such as Bergmann’s rule, but both increases and decreases have been documented in mammals, birds, lizards and invertebrates and were attributed to climate change (Teplitsky and Millien, 2014; Gotanda et al., 2015; Gardner et al., 2019; Hill et al., 2021). Contrasting patterns (increased body size) may be due to short-term modifications in selection pressures (e.g., changes to predation and competition), variation in life histories or as a result of interactions with climate variables other than temperature (e.g., changes to food availability along with rainfall changes) and other disturbances (Yom-Tov and Yom-Tov, 2004; Gardner et al., 2019; Wilson et al., 2019) or use of different body size measurements (linear vs. volumetric dimensions) (Salewski et al., 2014).

Several lines of evidence suggest the evolution of melanism in response to climate change (low confidence), with colour changes associated with thermoregulation being demonstrated in butterflies (Zeuss et al., 2014; MacLean et al., 2016; MacLean et al., 2019a), beetles (de Jong and Brakefield, 1998; Brakefield and de Jong, 2011; Zvereva et al., 2019), dragonflies (Zeuss et al., 2014) and phasmids (Nosil et al., 2018). Such changes may represent decreased phenotypic diversity and, potentially, genetic diversity (low confidence), but the consequences of climate change for the genetic structure and diversity of populations have not been widely assessed (Pauls et al., 2013). Simplistically, the thermal melanism hypothesis suggests that lighter (higher-reflectance) individuals should be fitter and therefore be selected for in a warmer climate (Clusella-Trullas et al., 2007). However, several biotic (e.g., thermoregulatory requirements, predator avoidance and signalling) and abiotic (e.g., UV, moisture and inter-annual variability) factors interact to influence changes in colour, making attribution to climate change across species and broad geographic regions difficult (Kingsolver and Buckley, 2015; Stuart-Fox et al., 2017; Clusella-Trullas and Nielsen, 2020).

Interactions between morphological changes and changes in phenology may facilitate or constrain adaptation to climate change (medium confidence) (Hedrick et al., 2021). For example, advancing phenology in migratory species may impose selection on morphological traits (e.g., wing length) to increase migration speed. If advancing spring phenology results in earlier breeding, this may offset the effect of rising temperatures in the breeding range and reduce the effect of increasing temperature on body size (Zimova et al., 2021). A study of 52 species of North American migratory birds, based on >70,000 specimens, showed that spring migration phenology has advanced over the past 40 years, concurrent with widespread shifts in morphology (reduced body size and increased wing length), perhaps to compensate for the increased metabolic cost of flight as body size decreases (Weeks et al., 2020).

A lack of understanding of physiological constraints and mechanisms remains a barrier to predicting many of the ecological effects of climate change (Bozinovic et al., 2011; Vázquez et al., 2017; González-Tokman et al., 2020). Many behavioural, morphological and physiological responses are highly species- and context-specific, making generalisations difficult (Bodensteiner et al., 2021). Recent advances in mechanistic understanding (from experiments), in process-based modelling (including micro-climates and developmental processes) (Carter and Janzen, 2021) and in the sophistication of niche models (Kearney et al., 2009) have improved projections, but comprehensive tests of geographic patterns and processes in thermal tolerance and plasticity are still lacking, with studies limited to a few phylogenetically restricted analyses showing mixed results (Gunderson and Stillman, 2015). Improved understanding of the mechanistic basis for observed geographic patterns in thermal tolerance and plasticity is needed to identify the physiological limits of species, the potential for adaptation and the presence of evolutionary trade-offs, which will strongly influence population declines, species range shifts, invasive interactions and the success of conservation interventions (Cooke et al., 2021; Ryan and Gunderson, 2021). Observed Impacts of Climate Change on Diseases of Wildlife and Associated Impacts on Humans

Assessment of changes in diseases of terrestrial and freshwater wild organisms was scarce in WGII AR4, AR5, IPCC SR1.5 and IPCC SRCCL. Further, most emerging infectious diseases (EIDs) are zoonoses, that is, they are transmissible between humans and animals, and are climate sensitive (Woolhouse et al., 2001; Woolhouse and Gowtage-Sequeria, 2005; McIntyre et al., 2017; Salyer et al., 2017). AR4 found weak-to-moderate evidence that disease vectors and their diseases had changed their distributions in concert with climate change, but attribution studies were lacking (Smith et al., 2014). In AR5, WGII AR5 Chapter 11, geographic expansion of a few VBDs to higher latitudes and elevations were detected and associated with regional climate trends, but the non-climatic drivers were not well assessed, leading to a medium confidence in attribution (Smith et al., 2014)). Here, we build on previous assessments by focussing on changes in the population dynamics and geographic distribution of diseases in wild animals as well as diseases in humans and domestic animals that are harboured, amplified and transmitted by wild animal reservoir hosts and vectors.

Increased disease incidence is correlated with regional climatic changes, as expected from a basic understanding of underlying biology and relationships between temperature, precipitation, and disease ecology (robust evidence, high agreement ) (Norwegian Polar Institute, 2009; Tersago et al., 2009; Tabachnick, 2010; Paz, 2015; Dewage et al., 2019; Deksne et al., 2020; Shocket et al., 2020; Couper et al., 2021). Whether increases in diseases in wild and domestic animals correspond to an increased risk of disease in nearby human populations is complicated by the potential buffering effects of the local medical system, access to health care and the socioeconomic status, education, behaviours and general health of the human population (see also Chapter 7 and Cross-Chapter Box ILLNESS in this chapter). Direct effects of climate and climate change on reproduction, seasonality, the length of the growing season and the transmission of pathogens, vectors and hosts

VBDs require arthropod vector hosts (e.g., insects or ticks), while other infectious diseases (e.g., fungi, bacteria and helminths) have free-living life stages and/or complex life cycles that require intermediate hosts (e.g., snails), all of which have temperature-driven rates of development and replication/reproduction (robust evidence, high agreement ) (Mordecai et al., 2013; Liu-Helmersson et al., 2014; Moran and Alexander, 2014; Bernstein, 2015; Marcogliese, 2016; Ogden and Lindsay, 2016; Mordecai et al., 2017; Short et al., 2017; Caminade et al., 2019; Cavicchioli et al., 2019; Mordecai et al., 2019; Liu et al., 2020; Rocklöv and Dubrow, 2020). Additionally, microbes such as bacteria thermally adapt to temperature changes through multiple mechanisms, indicating that warming will not reduce antibiotic resistance (MacFadden et al., 2018; Pärnänen et al., 2019; Shukla, 2019; McGough et al., 2020; Rodriguez-Verdugo et al., 2020).

There is increasing evidence of the role of extreme events in disease outbreaks (very high confidence) (Tjaden et al., 2018; Bryson et al., 2020). Heat waves have been associated with outbreaks of helminth pathogens, especially in sub-Arctic and Arctic areas. For example, a severe outbreak of microfilaremia, a VBD spread by mosquitoes and flies, plagued reindeer in northern Europe following extreme high temperatures (Laaksonen et al., 2010). More frequent and severe extreme events such as floods, droughts, heat waves and storms can either increase or decrease outbreaks, depending upon the region and disease (robust evidence, high agreement ) (Anyamba et al., 2001; Marcheggiani et al., 2010; Brown and Murray, 2013; Paz, 2015; Boyce et al., 2016; Wu et al., 2016b; Wilcox et al., 2019; Nosrat et al., 2021). Heavy precipitation events have been shown to increase some infectious diseases with aquatic life-cycle components such as mosquito-borne, helminth, and rodent-borne diseases (robust evidence, high agreement ) (Anyamba et al., 2001; Zhou et al., 2005; Wu et al., 2008; Brown and Murray, 2013; Anyamba et al., 2014; Boyce et al., 2016). Conversely, flooding also increases flow rate and decreases parasite load and diversity in other aquatic wildlife (Hallett and Bartholomew, 2008; Bjork and Bartholomew, 2009; Marcogliese, 2016; Marcogliese et al., 2016) and can reduce mosquito abundance by flushing them out of the system (Paaijmans et al., 2007; Paz, 2015).

Droughts reduce the aquatic habitat of some mosquito species while simultaneously increasing the availability of stagnant standing pools of water that are ideal breeding habitats for other species, such as dengue-vector Aedes mosquitoes (medium evidence, medium agreement ) (Chareonviriyaphap et al., 2003; Chretien et al., 2007; Padmanabha et al., 2010; Trewin et al., 2013; Paz, 2015). Extreme drought has been associated with an increase in bluetongue virus haemorrhagic disease in wildlife in eastern North America, although the mechanisms involved were not identified (Christensen et al., 2020). Heat waves in some regions, especially coastal regions, have increased parasitism and decreased host richness and abundance, leading to population crashes (Larsen and Mouritsen, 2014; Mouritsen et al., 2018). Changes in temperature and precipitation, especially extreme events, can alter community structure (Larsen et al., 2011) by increasing or decreasing parasites and their host organisms, and even altering host behaviour in ways that are advantageous to parasites (Macnab and Barber, 2012).

Climate change not only affects the occurrence of pathogens and their hosts in terms of geographic space but also impacts the temporal patterns of disease transmission. Warmer winters allow greater over-winter survival of arthropod vectors, which, coupled with lengthened transmission seasons, drive increases in vector population sizes, pathogen prevalence, and thus the proportion of vectors infected (robust evidence, high agreement ) (Laaksonen et al., 2009; Molnár et al., 2013; Waits et al., 2018). For example, a parasitic nematode lung worm (Umingmakstrongylus pallikuukensis) has shortened its larval development time by half (from two years to one year), which has increased infection rates in North American musk oxen (Norwegian Polar Institute, 2009).

Case Study 1: Climate change impacts on pathogenic helminths in Europe

Parasitic helminths can reduce growth and yield, kill livestock and infect humans and wildlife, leading to health, agricultural and economic losses (Fairweather, 2011; Charlier et al., 2016; Charlier, 2020). Attribution of increased incidence and risk of helminth disease to climate change is stronger than for other human diseases, thanks to long-term records and careful analysis of other anthropogenic drivers (e.g., LUC, agricultural/livestock intensification, and anti-helminthic intervention and resistance) (van Dijk et al., 2008; van Dijk et al., 2010; Fox et al., 2011b; Martínez-Valladares et al., 2013; Charlier et al., 2016; Innocent et al., 2017; Mehmood et al., 2017).

In Europe, evidence from laboratory studies, long-term surveillance, statistical analyses and modelling shows that multiple helminth pathogens and their host snails have extended their transmission windows and increased their survival, fecundity, growth and abundances (robust evidence, high agreement ). Furthermore, they have expanded or shifted their ranges poleward due to increases in temperature, precipitation and humidity (robust evidence, high agreement ) (Lee et al., 1995; Pritchard et al., 2005; Poulin, 2006; van Dijk et al., 2008; van Dijk et al., 2010; Fairweather, 2011; Fox et al., 2011b; Martínez-Valladares et al., 2013; Bosco et al., 2015; Caminade et al., 2015; Caminade et al., 2019). These documented changes in climate, hosts and pathogens have been linked to a higher incidence and more frequent outbreaks of disease in livestock across Europe (very high confidence).

Case Study 2: Chytrid fungus and climate change

Infection by the chytrid fungus, Bd (Batrachochytrium dendrobatidis), can cause chytridiomycosis in amphibians. Bd is widely distributed globally and has caused catastrophic disease in amphibians, associated with the decline of 501 species and extinction of a further 90 species, primarily in tropical regions of the Americas and Australia (Scheele et al., 2019; Fisher and Garner, 2020). Bd successfully travelled with high-elevation Andean frog species as they expanded their elevational ranges upward, driven by regional warming, to > 5200 m (Seimon et al., 2017).

New findings since AR5 from controlled laboratory experiments (manipulating temperature, humidity and water availability), intensive analyses of observed patterns of infection and disease in nature, and modelling studies have led to an emerging consensus that interactions between chytrids and amphibians are climate-sensitive, and that the interaction of climate change and Bd has driven many of the globally observed declines and extinctions of ~90 amphibian species (robust evidence, high agreement ) (Rohr and Raffel, 2010; Puschendorf et al., 2011; Rowley and Alford, 2013; Raffel et al., 2015; Sauer et al., 2018; Cohen et al., 2019a; Sauer et al., 2020; Turner et al., 2021).

The ‘thermal mismatch hypothesis’ posits that vulnerability to disease should be higher at warm temperatures in cool-adapted species and higher at cool temperatures in warmth-adapted species and is generally supported (Pounds et al., 2006). However, the most recent studies reveal more complex mechanisms underlying amphibian disease–climate change dynamics, including variation in thermal preferences among individuals in a single amphibian population (robust evidence, high agreement ) (Zumbado-Ulate et al., 2014; Sauer et al., 2018; Cohen et al., 2019b; Neely et al., 2020; Sauer et al., 2020).

Bd is not universally harmful; it has been recorded as endemic in frog populations that do not suffer disease, where it may be commensal rather than parasitic (Puschendorf et al., 2006; Puschendorf et al., 2011; Rowley and Alford, 2013). Projections of future impacts are difficult, as the virulence is variable across Bd populations and dependent upon the evolutionary and ecological history and evolutionary potential of both a local amphibian population and the endemic or invading Bd (robust evidence, high agreement ) (Retallick et al., 2004; Daskin et al., 2011; Puschendorf et al., 2011; Phillips and Puschendorf, 2013; Rowley and Alford, 2013; Zumbado-Ulate et al., 2014; Sapsford et al., 2015; Voyles et al., 2018; Bradley et al., 2019; Fisher and Garner, 2020; McMillan et al., 2020). Further, specific local habitats might serve as regional climate refugia from chytrid infection (e.g., hot and dry) (medium evidence, high agreement ) (Zumbado-Ulate et al., 2014; Cohen et al., 2019b; Neely et al., 2020; Turner et al., 2021). Changes in geographic distribution and connectivity patterns of pathogens

As species’ geographic ranges and migration patterns are modified by climate change (Section, Table 2.2), pathogens accompany them. Diverse vectors and associated parasites, pests and pathogens of plants and animals are being recorded at higher latitudes and elevations in conjunction with regional temperature increases and precipitation changes (robust evidence, high agreement ), although analysis of realised disease incidence often lacks the inclusion of non-climatic versus climate drivers, compromising attribution (Ollerenshaw and Rowlands, 1959; Purse et al., 2005; Laaksonen et al., 2010; van Dijk et al., 2010; Alonso et al., 2011; Genchi et al., 2011; Pinault and Hunter, 2011; Jaenson et al., 2012; Loiseau et al., 2012; Kweka et al., 2013; Medlock et al., 2013; Dhimal et al., 2014a; Dhimal et al., 2014b seasonal; Siraj et al., 2014; Khatchikian et al., 2015; Hotez, 2016a; Hotez, 2016b; Bett et al., 2017; Mallory and Boyce, 2017; Strutz, 2017; Booth, 2018; Dumic and Severnini, 2018; Carignan et al., 2019; Gorris et al., 2019; Le et al., 2019; Stensgaard et al., 2019b snails and; Brugueras et al., 2020; Gilbert, 2021).

At least six major VBDs affected by climate drivers have recently emerged in Nepal and are now considered endemic, with climate change implicated as a primary driver as LULCC has been assessed to have a minimal influence on these diseases (high confidence) (Table SM2.1). There is increasing evidence that climate warming has extended the elevational distribution of Anopheles, Culex and Aedes mosquito vectors above 2000 m in Nepal (limited evidence, high agreement ) (Dahal, 2008; Dhimal et al., 2014a; Dhimal et al., 2014b; Dhimal et al., 2015), with similar trends being recorded in neighbouring Himalayan regions (medium evidence, high agreement ) (Phuyal et al., 2020; Dhimal et al., 2021). Host animals in novel areas may be immunologically naive, and therefore more vulnerable to severe illness (Bradley et al., 2005; Hall et al., 2016).

Case Study 3: Arctic and sub-Arctic disease expansion and intensification

High Arctic regions have warmed by more than double the global average, >2°C in most areas (Sections, Figure 2.11, and Atlas in (IPCC, 2021a)). Experimental field ecology studies and computational models of Arctic and sub-Arctic regions indicate that milder winters have reduced the mortality of vectors and reservoir hosts and increased their habitat as forested taiga expands into previously treeless tundra (Table SM2.1) (Parkinson et al., 2014). Warmer temperatures and longer seasonal windows have allowed faster reproduction/replication, accelerated development and increased the number of generations per year of pathogens, vectors and some host animals, which, in turn, increases the populations of disease organisms and disease transmission (Sections, Higher numbers of ticks, mosquitoes, Culicoides biting midges, deer flies, horseflies and Simuliidae black flies, that transmit a variety of pathogens, are being documented in high-latitude regions and where they have been historically absent (robust evidence, high agreement ) (Waits et al., 2018; Caminade et al., 2019; Gilbert, 2021). In concert with these poleward shifts of hosts and vectors, pathogens, particularly tick-borne pathogens and helminth infections, have increased dramatically in incidence and severity from once-rare occurrences and have appeared in new regions (very high confidence) (Caminade et al., 2019; Gilbert, 2021).

Zoonoses and VBDs that have been historically rare or never documented in the Arctic and sub-Arctic regions of Europe, Asia, and North America, such as anthrax, cryptosporidiosis, elaphostrongylosis, filariasis (Huber et al., 2020), tick-borne encephalitis and tularemia (Evander and Ahlm, 2009; Parkinson et al., 2014; Pauchard et al., 2016), are spreading poleward and increasing in incidence, associated with warming temperatures (robust evidence, high agreement , very high confidence) (Table SM2.1) (Omazic et al., 2019). Recent anthrax outbreaks and mass mortality events of humans and reindeer, respectively, have been linked to abnormally hot summer temperatures that caused the permafrost to melt and exposed diseased animal carcasses, releasing thawed, highly infectious Bacillus anthracis spores (medium evidence, medium agreement ) (Ezhova et al., 2019; Hueffer et al., 2020; Ezhova et al., 2021). Multiple contributing factors conspired over different timescales to compound a 2016 anthrax outbreak occurring on the Yamal peninsula: (i) rapid permafrost thawing for 5 years preceding the outbreak, (ii) thick snow cover the year before the outbreak insulated the warmed permafrost and kept it from re-freezing, and (iii) anthrax vaccination rates had decreased or ceased in the region (Ezhova et al., 2019; Ezhova et al., 2021). These precursors converged with an unusually dry and hot summer that: (i) melted permafrost, creating an anthrax exposure hazard; (ii) increased the vector insect population; and (iii) weakened the immune systems of reindeer, thereby increasing their susceptibility (Waits et al., 2018; Hueffer et al., 2020).

Warmer temperatures have increased blood-feeding insect harassment of reindeer with compounding consequences: (1) increased insect-bite rates lead to higher parasite loads, (2) time spent by reindeer in trying to escape biting flies reduces foraging while simultaneously increasing their energy expenditure, (3) the combination of (1) and (2) leads to poor body condition which subsequently leads to (4) reduced winter survival and fecundity (Mallory and Boyce, 2017). As temperatures warm and connectivity increases between the Arctic and the rest of the world, tourism, resource extraction and increased commercial transport will create additional risks of biological invasion by infectious agents and their hosts (Pauchard et al., 2016). These increases in introduction risk compounded with climate change have already begun to harm Indigenous Peoples dependent on hunting and herding livestock (horses and reindeer) that are suffering increased pathogen infection (high confidence) (Deksne et al., 2020; Stammler and Ivanova, 2020). Biodiversity–disease links

Anthropogenic impacts, such as disturbances caused by climate change, can reduce biodiversity via multiple mechanisms and increase the risk of human diseases (limited evidence, low agreement ), but more research is needed to understand the underlying mechanisms (Civitello et al., 2015; Young et al., 2017b; Halliday et al., 2020; Rohr et al., 2020; Glidden et al., 2021). Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18–72% higher) and abundance (21–144% higher) at sites under substantial human use (agricultural and urban land) compared with nearby undisturbed habitats (Gibb et al., 2020).

Exploitation of wildlife and degradation of natural habitats have increased opportunities for a ‘spill over’ of pathogens from wildlife to human populations and also the emergence of zoonotic disease epidemics and pandemics (robust evidence, high agreement ); animal and human migrations driven by climate change have added to this increased risk (medium evidence, medium agreement ) (see Section, Chapter 8, Cross-Chapter Box MOVING PLATE in Chapter 5) (Patz et al., 2004; Cleaveland et al., 2007; Karesh et al., 2012; Altizer et al., 2013; Allen et al., 2017; Plowright et al., 2017; Faust et al., 2018; Carlson et al., 2020; Gibb et al., 2020; Hockings et al., 2020; IPBES, 2020; Volpato et al., 2020; Glidden et al., 2021). Agricultural losses and subsequent food scarcity, increasing due to climate change, can also lead to an increase in the use of bushmeat, and thus increase the risk of diseases jumping from wild animals to humans (medium evidence, high agreement ) (Brashares et al., 2004; Leroy et al., 2004; Wolfe et al., 2004; Rosen and Smith, 2010; Kurpiers et al., 2016). Implications of changes in diseases in wild animals for humans

Changes in temperature, precipitation, humidity and extreme events have been associated with more frequent disease outbreaks, increases in disease incidence and severity, novel diseases and the emergence of vectors in new areas for wild animals, with a mechanistic understanding of the roles of these drivers from experimental studies providing high confidence for the role of climate change. However, attributing how this has impacted human infectious diseases remains difficult, and definitive attribution studies are lacking. The specific role of recent climate change is difficult to examine in isolation in most regions where human disease incidence has also been affected by LUC (particularly agricultural and urban expansion), changes in public health access and measures, socioeconomic changes, increased global movement of people and changes in vector and rodent control programs, supporting medium confidence in the role of climate change driving the observed changes in vector-borne and infectious human diseases globally. Exceptions are in areas noted above (the Arctic, sub-Arctic, and high-elevation regions), in which climate change fingerprints are strong and concurrent changes in non-climatic drivers are less pronounced than in other regions (high confidence for climate change attribution) (see Table SM2.1, Sections,, Cross-Chapter Box ILLNESS this Chapter) (Harvell et al., 2002; Norwegian Polar Institute, 2009; Tersago et al., 2009; Tabachnick, 2010; Altizer et al., 2013; Garrett et al., 2013; Paz, 2015; Wu et al., 2016b; Caminade et al., 2019; Dewage et al., 2019; Coates and Norton, 2020; Deksne et al., 2020; Shocket et al., 2020; Couper et al., 2021; Gilbert, 2021). Observed Evolutionary Responses to Climate Change

Previous sections document the tendency of species to retain their climate envelopes by some combination of range shift and phenological change (very high confidence). However, this tracking of climate change can be incomplete, causing species or populations to experience hotter conditions than those to which they are adapted, and thereby incur ‘climate debts’ (section (Devictor et al., 2012). The importance of population-level debt is illustrated by a study in which the estimated debt values were correlated with population dynamic trends in a North American migratory songbird, the yellow warbler, Setophaga petechia. Populations that were genetic outliers for their local climate space had larger population declines (greater debt) than those with genotypes closer to the average values for that particular climate space. Debt values were estimated from genomic analyses independent of the population trends, and were distributed across the species’ range in a mosaic, not simply concentrated at range margins, rendering the results robust to being confounded by broad-scale geographical trends (Bay et al., 2018). Soroye et al. (2020) found similar results for 66 species of bumble-bees across Europe and North America, with declines in abundances spread throughout species’ ranges, but being greatest where populations already near their climate limits were being pushed beyond their climatic tolerances with climate change {}.

In the absence of evolutionary constraints, climate debts can be cancelled by genetically based increases in thermal tolerance and the ability to perform in high ambient temperatures. In species already showing local adaptation to climate, populations currently living at relatively cool sites should be able to evolve to adopt the traits of populations currently at warmer sites as their local experience of climate changes (Singer, 2017; Socolar et al., 2017).

An increasing number of studies document evolutionary responses to climate change in populations not at their warm range limits (Franks and Hoffmann, 2012). Organisms with short generation times should have a higher capacity to genetically track climate change than species with long generation times, such as mammals (Boutin and Lane, 2014). Indeed, observed evolutionary impacts have been mainly documented in insects, especially at expanding range margins (Chuang and Peterson, 2016) where evolutionary changes have increased dispersal ability (Thomas et al., 2001) and decreased host specialisation (Bridle et al., 2014; Lancaster, 2020) (medium evidence, medium agreement).

Away from range margins, individual populations experiencing regional warming have evolved diverse traits related to climate adaptation. For example, pitcher-plant mosquitoes (Wyeomyia smithii) in Pacific Northwest America have evolved to wait for shorter days before initiating diapause. This adaptation to lengthening summers enables them to delay overwintering until later and add an extra generation each year (Bradshaw and Holzapfel, 2001). Among 26 populations of Drosophila subobscura studied on three continents, 22 experienced climate warming across two or more decades, and 21 of these 22 showed increasing frequency of the chromosome inversion characteristic of populations adapted to hot climates (robust evidence, high agreement) (Balanya et al., 2006).

However, for populations already at their warm range limits, their ability to track climate change in situ would require evolving to survive and reproduce outside their species’ historical climate envelope: abilities of wild species to do this is not supported by experimental or observational evidence (medium evidence, high agreement ) (Singer, 2017). Whether or not they can depends on the level of ‘niche conservatism’ operating at the species level (Lavergne et al., 2010). If a species whose range limits are determined by climate finds itself completely outside of its traditional climate envelope, extinction is expected in the absence of ‘evolutionary rescue’ (Bell and Gonzalez, 2009; Bell et al., 2019).

To investigate the evolutionary potential of a species to survive in a novel climate entirely outside its traditional climate envelope, experiments have been carried out on ectotherms testing thermal performance, thermal tolerance and their evolvabilities (Castaneda et al., 2019; Xue et al., 2019). Tests of thermal performance have been complicated, as both long-term acclimation and trans-generational effects occur (Sgro et al., 2016). However, the results to date have been consistent: despite widespread local adaptation to climate across species’ ranges, substantial constraints exist regarding the evolution of greater stress tolerance (e.g., high temperatures and drought) at warm range limits (medium evidence, high agreement ) (Hoffmann and Sgro, 2011; MacLean et al., 2019b).

For example, as temperature was experimentally increased, the amount of genetic variance in the fitness of Drosophila melanogaster decreased; in hot environments, flies had low evolvability (Kristensen et al., 2015). The hypothesis that heat-stress tolerance is evolutionarily constrained is further supported by experiments in which 22Drosophila spp. drawn from tropical and temperate climes were subjected to extremes of heat and cold. They differed, as expected, in cold tolerances, but not in heat tolerances nor in temperatures at which optimal performances were observed (MacLean et al., 2019b).

Plasticity (flexibility) in acclimating to thermal regimes helps organisms adapt to environmental change. The form and extent of plasticity can vary among populations experiencing different climates (Kelly, 2019) and generate phenotypic values outside the prior range for the species, but plasticity itself has not yet been observed to evolve in response to climate change (Kelly, 2019).

Relevant genetic changes in nature (e.g., affecting heat tolerance) have not yet been shown to alter the boundaries of existing genetic variation for any species. Further, a recent global analysis of 91 species found, on average, a 5.4–6.5% decline in genetic diversity within populations since the start of the Industrial Revolution, with much larger declines for island species (27.6–30.9% reductions) (Leigh et al., 2019). In Leigh et al. (2019), genetic declines were documented in both common and already endangered species of fish, mammals, birds, insects, amphibians and reptiles. These declines in genetic diversity, though not caused by climate change, decrease the abilities of wild species to adapt to climate change via evolutionary responses. Evolutionary rescue of entire species has not yet been observed in nature, nor is it expected, based on experimental and theoretical studies (medium evidence, high agreement ).

Hybridisation between closely related species has increased in recent decades as one species shifts its range boundaries and positions itself more closely to the other. Hybrids between polar bears and brown bears have been documented in northern Canada (Kelly et al., 2010). In North American rivers, hybridisation between invasive rainbow trout and native cutthroat trout has increased in frequency as the rainbow trout has expanded into warming waters (Muhlfeld et al., 2014). Whether climate-changed induced hybridisations can generate novel climate adaptations remains to be seen.

In summary, with our present knowledge, evolution is not expected to be sufficient to prevent the extinction of whole species if a species’ climate space disappears within the region they inhabit (high confidence).

FAQ 2.1 | Will species become extinct with climate change and is there anything we can do to prevent this?

Climate change is already posing major threats to biodiversity, and the most vulnerable plants and animals will probably go extinct. If climate change continues to worsen, it is expected to cause many more species to become extinct unless we take actions to improve the resilience of natural areas, through protection, connection and restoration. We can also help individual species that we care most about by reducing the stress that they are under from human activities, and even helping them move to new places as their climate space shifts and they need to shift to keep up.

Climate change has already caused some species to become extinct and is expected to drive more species to extinction. Extinction of species has always occurred in the history of our planet, but human activities are accelerating this process, such that the estimated 10% of species that humans have driven to extinction in the past 10,000 years is roughly 1,000 times the natural background rate. Recent research predicts that climate change would add to that, with estimates that about one-third of all plant and animal species are at high risk of extinction by 2070 if climate change continues at its current rate. Species can adapt to some extent to these rapidly changing climate patterns. We are seeing changes in behaviour, dispersal to new areas as the climate becomes more suitable, and genetic evolution. However, these changes are small, and adaptations are limited. Species that cannot adapt beyond their basic climate tolerances (their ability to survive extremes of temperature or rainfall) or successfully reproduce in a different climate environment from that in which they have evolved, will simply disappear. In the Arctic, for example, the sea ice is melting and, unless there are deep cuts in greenhouse-gas emissions, will probably disappear in summer within the century. This means that the animals that have evolved to live on sea ice—polar bears and some seals—will become extinct soon after the ice disappears.

Fortunately, there are some things we can do to help. We can take action to assist, protect and conserve natural ecosystems and prevent the loss of our planet’s endangered wildlife, such as:

‘Assisting’ the migration of species. This has many names, ‘assisted colonisation’, ‘assisted translocation’, ‘assisted migration’ and ‘assisted movement’. In effect, it is about helping endangered species to move to a new area with a good habitat for them to survive. ‘Passive’ assisted colonisation focusses on helping species move themselves, while the most ‘active’ form implies picking up individuals and transporting them to a new location. This is different from reintroductions that are already a normal part of conservation programs. Climate-driven translocations constitute moving plants or animals to an area where they have never lived historically, a new location that is now suitable for them due to climate change.

This active form of ‘assisted colonisation’ has been controversial, because exotic species can become invasive when they are moved between continents or oceans. For example, no one would advocate moving polar bears to Antarctica, as they would likely feast on native penguins, thus causing another conservation problem. However, moving species only a few hundred kilometers avoids most adverse outcomes, and this is often all that is needed to help a wild plant or animal cope with lower levels of climate change. In extreme cases, another type of assisted adaptation is to preserve species until we can stabilize then reverse climate change, and then reintroduce them to the wild. This might include moving them into zoos or into seed or frozen embryo banks.

Extending protected zones and their connectivity. The ability of species to move to new locations and track climate change are very limited, particularly when a habitat has been turned into a crop field or a city. To help species move between their natural habitats, we can increase the connectedness of protected areas, or simply create small patches or corridors of semi-wild nature within a largely agricultural or inhabited region that encourages wildlife to move through an area, and in which they are protected from hunting and poisons. These semi-wild protected areas can be very small, like the hedgerows between fields in England that provide both a habitat for many flowers, birds and insects and corridors to move between larger protected areas. Alternatively, it can just be an abandoned field that is now growing ‘weeds’ and with a ban on use of pesticides or herbicides, hunting or farming. For instance, in the USA, private landowners get a tax break by making their land a ‘wildlife conservation’ area by using no pesticide, not cutting weeds too often, putting up brush piles and bird boxes for nesting by mammals and birds, and providing a stable water source.

Assisting, protecting and conserving natural ecosystems would help enhance biodiversity overall as well as aiding already endangered species. Diverse plant and animal communities are more resilient to disturbances, including climate change. A healthy ecosystem also recovers more quickly from increases in extreme events, such as floods, droughts and heat waves, that are a part of human-driven climate change. Healthy ecosystems are critical to prevent species’ extinctions from climate change, but are also important for human health and well-being, providing clean, plentiful water, cleaning the air, providing recreation and holiday adventures, and making people feel happier, calmer and more content.

Figure FAQ2.1.1 | Possible actions to assist, protect and conserve natural ecosystems and prevent the loss of our planet’s endangered wildlife in the face of continued climate change. (Inspired by the Natural Alliance website© Chris Heward/GWCT).

FAQ 2.2 | How does climate change increase the risk of diseases?

Climate change is contributing to the spread of diseases in both wildlife and humans. Increased contact between wildlife and human populations increases disease risk, and climate change is altering where pathogens that cause diseases and the animals that carry them live. Disease risk can often be reduced by improving health care and sanitation systems, training the medical community to recognise and treat potential new diseases in their region, limiting human encroachment into natural areas, limiting wildlife trade and promoting sustainable and equitable socioeconomic development.

Diseases transmitted between humans and animals are called zoonoses. Zoonoses comprise nearly two-thirds of known human infectious diseases and the majority of newly emerging ones. COVID-19 is the most recent zoonosis and has killed millions of people globally while devastating economies. The risk posed by Emerging Infectious Diseases (EIDs) has increased because of: (1) the movement of wild animals and their parasites into new areas as a result of climate change, global trade and travel; (2) human intrusion in natural areas and the conversion of natural areas for agriculture, livestock, the extraction of industrial/raw materials and housing; (3) increased wildlife trade and consumption; (4) increased human mobility resulting from global trade, war/conflicts and migration, made faster and extending farther due to fossil fuel-powered travel; and (5) widespread antimicrobial use, which can promote antibiotic-resistant infections (Figure FAQ2.2.1).

Figure FAQ2.2.1 | How diseases move from the wild into human populations. Climate change may increase diseases in nature, but whether or not this leads to an increase in the risk of disease in humans depends upon a range of societal, infrastructural and medical buffers that form a shield protecting humans.

Climate change further increases risk by altering pathogen and host animal (1) geographic ranges and habitats; (2) survival, growth and development; (3) reproduction and replication; (4) transmission and exposure (5) behaviour; and (6) access to immunologically naïve animals and people who lack resistance to infection. This can lead to novel disease emergence in new places, more frequent and larger outbreaks, and longer or shifted seasons of transmission. Climate change is making it possible for many EIDs to colonise historically colder areas that are becoming warmer and wetter in temperate and polar regions and in the mountains. Vector-borne diseases (VBDs) are diseases spread by vectors such as mosquitoes, sand flies, kissing bugs and ticks. For example, ticks that carry the virus that causes tick-borne encephalitis have moved into the northern subarctic regions of Asia and Europe. Viruses like dengue, chikungunya and Japanese encephalitis are emerging in Nepal in hilly and mountainous areas. Novel outbreaks of Vibrio bacteria seafood poisoning are being traced to the the Baltic States and Alaska where they were never documented before. Many scientific studies show that the transmission of infectious disease and the number of individuals infected depends on rainfall and temperature; climate change often makes these conditions more favourable for disease transmission.

Climate change can also have complicated, compounding and contradictory effects on pathogens and vectors. Increased rainfall creates more habitat for mosquitoes that transmit diseases like malaria, but too much rain washes away the habitat. Decreased rainfall also increases disease risk when people without reliable access to water use containers to store water where mosquitoes, such as the vectors of dengue fever Aedes aegypti and A. albopictus, lay their eggs. Hotter temperatures also increase mosquito-bite rate, parasite development and viral replication! Certain species of snails are intermediate hosts for many helminth parasites that make humans, livestock and wild animals sick. When it gets hot, the snails can produce 2–3 times as many infective larvae; however, if it becomes too hot, many pathogens and their vectors cannot survive or reproduce.

Humans also contract zoonoses directly through their skin, mucus membranes and lungs, when eating or butchering animals or when they come into contact with pathogens that are shed into the air or passed in urine and faeces and contaminate water, food, clothing and other surfaces. Any activity that increases contact with wildlife, especially in high-biodiversity regions like the Tropics and subtropics, increases disease risk. Climate change-related disease emergence events are often rare but may become more frequent. Fortunately, there are ways to reduce risks and protect our health, as described below.

Habitat and biodiversity protection. Human encroachment into natural areas, due to expansion of agriculture and livestock, timber harvests, extraction of resources and urban development, has increased human contact with wild animals and creates more opportunities for disease spill-over (transmission from an animal to a new species, including humans). By conserving, protecting and restoring wild habitats, we can build healthier ecosystems that provide other services, such as clean air, clean and abundant water, recreation, spiritual value and well-being, as well as reduced disease spill-over. If humans must go into wild areas or hunt, they should take appropriate precautions such as wearing protective clothing, using insect repellant, performing body checks for vectors like ticks and washing their hands and clothing well.

Food resilience. Investing in sustainable agro-ecological farming will alleviate the pressure to hunt wild animals and reduce the conversion of more land to agriculture/livestock use. Stopping illegal animal trading and poaching and decreasing reliance on wild meats and products made from animal parts will reduce direct contact with potentially infected animals. This has the added benefit of increasing food security and nutrition, improving soil, reducing erosion, preserving biodiversity and mitigating climate change.

Disease prevention and response. The level of protection against infection is linked directly to the level of development and wealth of a country. Improved education, high-quality medical and veterinary systems, high food security, proper sanitation of water and waste, high-quality housing, disease surveillance and alarm systems dramatically reduce disease risk and improve health. Utilising a One Biosecurity or One Health framework further improves resilience. Sharing knowledge within communities, municipalities, regionally and between national health authorities globally is important to assessing, preventing and responding to outbreaks and pandemics more efficiently and economically.

Humans are facing many direct and indirect challenges because of climate change. The increase in EIDs is one of our greatest challenges, due to our ever-growing interactions with wildlife and climatic changes creating new disease transmission patterns. COVID-19 is a current crisis, and follows other recent EIDs: SARS, HIV/AIDS, H1N1 influenza, Ebola, Zika and West Nile fever. EIDs have accelerated in recent decades, making it clear that new societal and environmental approaches to wildlife interactions, climate change and health are urgently needed to protect our current and future well-being as a species.

2.4.3 Observed Changes in Key Biomes, Ecosystems and Their Services Detection and Attribution for Observed Biome Shifts

Attribution for biome (major vegetation form of an ecosystem) shifts is complex because of their extensive, sometimes continental, spatial scale (Whittaker, 1975; Olson et al., 2001; Woodward et al., 2004). Therefore, non-climatic factors strongly influence biome spatial distributions (Ellis and Ramankutty, 2008).

The most robust attribution studies use data from many species, individual locations with minimal confounding factors, particularly observed recent LULCC, and scale up by analysing multiple locations across a large zone between biomes, providing multiple lines of evidence (Hegerl et al., 2010; Parmesan et al., 2013). Multivariate statistical analyses aid attribution studies by allowing the assessment of relative weights among multiple factors, including variables related to climate change (Gonzalez et al., 2012). However, drivers often have strong, significant interactions with one another, complicating quantitative assessment of the strength of individual drivers (Parmesan et al., 2013). In these cases, manipulative experiments are critical in assessing attribution to the drivers of climate change.

Certain biomes exhibit a relatively stronger relationship to climate; for example, Arctic tundra generally has a distinct ecotone with boreal conifer forest (Whittaker, 1975). In these areas, attribution of biome shifts to climate change are relatively straightforward, if human LULCC is minimal. However, other biomes, such as many grassland systems, are not in equilibrium with climate (Bond et al., 2005). In these systems, their evolutionary history (Keeley et al., 2011; Strömberg, 2011; Charles-Dominique et al., 2016), distribution, structure and function have been shaped by climate and natural disturbances, such as fire and herbivory (Staver et al., 2011; Lehmann et al., 2014; Pausas, 2015; Bakker et al., 2016; Malhi et al., 2016). Disturbance variability is an inherent characteristic of grassland systems, and suitable ‘control’ conditions are seldom available in nature. Furthermore, due to the integral role of disturbance, these biomes have been widely affected by long-term and widespread shifts in grazing regimes, large-scale losses of mega-herbivores and fire suppression policies (Archibald et al., 2013; Malhi et al., 2016; Hempson et al., 2017). It is necessary to conduct climate change attribution on a case-by-case basis for grasslands; such assessments are complex as direct climate change impacts from either inherent variation within disturbance regimes or directional changes in background disturbances are difficult to separate (detailed in Sections;; Confidence in assessments is increased when the observed trends are supported by a mechanistic understanding of responses identified by physiological studies, manipulative field experiments, greenhouse studies and lab experiments (Table SM2.1). Global Patterns of Observed Biome Shifts Driven by Climate Change Observed biome shifts predominantly driven by climate change

AR5 and a meta-analysis found that vegetation at the biome level shifted poleward latitudinally and upward altitudinally due to anthropogenic climate change in at least 19 sites in boreal, temperate and tropical ecosystems from 1700 to 2007 (Gonzalez et al., 2010; Settele et al., 2014). In these areas, temperature increased to 0.4°C–1.6°C above the pre-industrial period (Gonzalez et al., 2010; Settele et al., 2014). Field research since the AR5 detected additional poleward and upslope biome shifts over periods of 24–210 years at numerous sites (described below), but were not directly attributed to anthropogenic climate change as the studies were not designed or conducted properly for full attribution assessment.

Many of the recently detected shifts are nevertheless consistent with climate change-induced temperature increases and observed in areas without agriculture, livestock grazing, timber harvesting and other anthropogenic land uses. For example, in the Andes Mountains in Ecuador, a biome shift was detected by comparing a survey by Alexander von Humboldt in 1802 to a re-survey in 2012, making this the longest time span in the world for this type of data (Morueta-Holme et al., 2015; Moret et al., 2019). Over 210 years, temperature increased by 1.7°C (Morueta-Holme et al., 2015) and the upper edge of alpine grassland shifted 100–450 m upslope (Moret et al., 2019).

Other biome shifts consistent with climate change and not substantially affected by local land use include: northward shifts in Canada of deciduous forest into boreal conifer forest, 5 km from 1970–2012 (Sittaro et al., 2017) and 20 km from 1970–2014 (Boisvert-Marsh et al., 2019) and of temperate conifer into boreal conifer forest, 21 km from 1970–2015 (Boisvert-Marsh and de Blois, 2021). Research detected upslope shifts of boreal and sub-alpine conifer forest into alpine grassland at 143 sites on four continents (41 m from 1901–2018) (Lu et al., 2021) and at individual sites in Canada (54 m from 1900–2010) (Davis et al., 2020); China (300 m from 1910–2000) (Liang et al., 2016) (33 m from 1985–2014) (Du et al., 2018); Nepal (50 m from 1860–2000) (Sigdel et al., 2018); Russia (150 m from 1954–2006) (Gatti et al., 2019); and the USA (19 m from 1950–2016) (Smithers et al., 2018) (38 m from 1953–2015) (Terskaia et al., 2020). Other upslope cases include shifts of temperate conifer forest in Canada (Jackson et al., 2016) and the USA (Lubetkin et al., 2017), temperate deciduous forest in Switzerland (Rigling et al., 2013) and temperate shrubland in the USA (Donato et al., 2016).

In summary, anthropogenic climate change caused latitudinal and elevational biome shifts in at least 19 sites in boreal, temperate and tropical ecosystems between 1700 and 2007, where temperature increased to 0.4°C–1.6°C above the pre-industrial period (robust evidence, high agreement ). Additional cases of 5–20 km northward and 20–300 m upslope biome shifts between 1860 and 2016, under a mean global temperature increase of approximately 0.9°C above the pre-industrial period, are consistent with climate change (medium evidence, high agreement ). Observed biome shifts from combined land use change and climate change

Research has detected biome shifts in areas where agriculture, fire use or suppression, livestock grazing, harvesting of timber and wood for fuel and other local land use substantially altered vegetation, in addition to changes in climatic factors and CO2 fertilisation. These studies were not designed or conducted in a manner to make climate change attribution possible, although many vegetation changes are consistent with climate change. For example, a global review of observed changes in tree lines found that, globally, two-thirds of tree lines have shifted upslope in elevation over the past 50 years or more, ((Hansson et al., 2021).

Upslope and poleward forest shifts have occurred where timber harvesting or livestock grazing has been abandoned, allowing the regeneration of trees at sites in Canada (Brice et al., 2019; Wang et al., 2020b), France (Feuillet et al., 2020), Italy (Vitali et al., 2017), Spain (Ameztegui et al., 2016) and the USA (Wang et al., 2020b) as well as in mountainous areas across Europe (Cudlin et al., 2017). Intentional use of fire drove an upslope forest shift in Peru (Bush et al., 2015) while mainly human-ignited fires drove the conversion of shrubland to grassland in a drought-affected area of the USA (Syphard et al., 2019b). In eastern Canada, timber harvesting and wildfire drove the conversion of mixed conifer–broadleaf forests to broadleaf-dominated forests (Brice et al., 2020; Wang et al., 2020b).

Shrub encroachment onto savanna has occurred at numerous sites, particularly across the Southern Hemisphere, mainly between 1992 and 2010 (Criado et al., 2020). Globally, overgrazing initiates shrub encroachment by reducing grasses more than woody plants, while fire exclusion maintains the shrub cover (D’Odorico et al., 2012; Caracciolo et al., 2016; Bestelmeyer et al., 2018). The magnitude of woody cover change in savannas is not correlated with mean annual temperature change (Criado et al., 2020); however, higher atmospheric CO2 increases shrub growth in savannas (Nackley et al., 2018; Manea and Leishman, 2019). A global remote-sensing analysis of biome changes from all causes, including agricultural and grazing expansion and deforestation, estimated that 14% of pixels changed between 1981 and 2012, although this approach can overestimate global changes, since it uses a new biome classification system which doubles the conventional biome classifications (Higgins et al., 2016). In addition to climate change, LULCC causes vegetation changes at the biome level (robust evidence, high agreement ). Observed Changes in Deserts and Arid Shrublands

Divergent responses to anthropogenic climate change are occurring within and across arid regions, depending on time period, location, detection methodology and vegetation type (see Cross-Chapter Paper 3). Emerging shifts in ecosystem structure, functioning and biodiversity are supported by evidence from modelled impacts of projected climate and CO2 levels. While observed responsiveness of arid vegetation productivity to rising atmospheric CO2 (Fensholt et al., 2012) may offset risks from reduced water availability (Fang et al., 2017), climate- and CO2-driven changes are key risks in arid regions, interacting with habitat degradation, wildfires and invasive species (Hurlbert et al., 2019).

Widespread vegetation greening, as projected in AR4, is occurring in arid shrublands (Zhang et al., 2019a; Maestre et al., 2021) as a result of increases in leaf area, woody cover and herbaceous production at desert–grassland interfaces (Gonsamo et al., 2021). Plant productivity in arid regions has increased (Fensholt et al., 2012) because of improved water-use efficiency associated with elevated CO2 (Norby and Zak, 2011; Donohue et al., 2013; Burrell et al., 2020; Gonsamo et al., 2021) (medium evidence, high agreement ), altered rainfall seasonality and amount (Rohde et al., 2019; Zhang et al., 2019a ) (robust evidence, high agreement ), increases in temperature (Ratajczak et al., 2014; Wilcox et al., 2018) (robust evidence, high agreement ) and heavy grazing (robust evidence, high agreement ), with the relative importance differing across locations (Donohue et al., 2013; Caracciolo et al., 2016; Archer et al., 2017; Hoffmann et al., 2019b; Rohde et al., 2019). Woody-plant encroachment into arid shrublands is occurring with high confidence in North America (Caracciolo et al., 2016; Archer et al., 2017) and southern Africa (du Toit and O’Connor, 2014; Ward et al., 2014; Masubelele et al., 2015a; Hoffman et al., 2019; Rohde et al., 2019), and with low confidence in central Asia (Li et al., 2015). In North America, sagebrush steppe changes have been attributed to increases in temperature and earlier snowpack melt (USGCRP, 2017; Mote et al., 2018; Snyder et al., 2019).

Non-native grasses are invading the sagebrush steppes (cold deserts) in North America (Chambers et al., 2014) attributed to warming (Bradley et al., 2016; Hufft and Zelikova, 2016). In the eastern semi-desert (Karoo) of South Africa, annual rainfall increases and a rainfall seasonality shift (du Toit and O’Connor, 2014) are increasing grassiness as arid grasslands expand into semi-desert shrublands (du Toit et al., 2015; Masubelele et al., 2015b; Masubelele et al., 2015a) causing fire in areas seldom burned historically (Coates et al., 2016).

Interactions of drought, warming and land management have caused vegetation mortality (see Section and reduced vegetation cover in shrublands, as projected by AR4 (Burrell et al., 2020). Increased heat and drought are causing the health and abundance of succulent species to decline (Musil et al., 2009; Schmiedel et al., 2012; Aragón-Gastélum et al., 2014; Koźmińska et al., 2019). Hot droughts, in particular, have been shown to reduce population resilience (Koźmińska et al., 2019). Observed Changes in Mediterranean-Type Ecosystems

Since AR5 (Settele et al. (2014), all five Mediterranean-type ecosystems (MTEs) of the world have experienced extreme droughts within the past decade, with South Africa and California reporting their worst on record (robust evidence, high agreement ) (Diffenbaugh et al., 2015; Williams et al., 2015a; Garreaud et al., 2017; Otto et al., 2018; Sousa et al., 2018). Climate change is causing these droughts to become more frequent and severe (medium evidence, medium agreement ) (AghaKouchak et al., 2014; Garreaud et al., 2017; Otto et al., 2018; Seneviratne et al., 2021).

MTEs show a range of direct responses to various forms of water deficit, but have also been affected by increasing fire activity linked to drought (Abatzoglou and Williams, 2016), and interactions between drought or extreme weather and fire affecting post-fire ecosystem recovery (Slingsby et al., 2017). Responses include shifts in functional composition (Acácio et al., 2017; Syphard et al., 2019a), decline of vegetation health (Hope et al., 2014; Asner et al., 2016a), decline or loss of characteristic species (White et al., 2016; Stephenson et al., 2019), shifts in composition towards more drought- or heat-adapted species and declining diversity (see also section (Slingsby et al., 2017.; Harrison et al., 2018).

Declines in plant health and increased mortality in MTEs associated with drought have been widely documented (robust evidence, high agreement ) (Section Remote-sensing studies show drought-associated mortality in post-fire vegetation regrowth in the Fynbos of South Africa (Slingsby et al., 2020b), reduced canopy health in forests within MTE zones of South Africa (Hope et al., 2014) and declines in canopy water content in the forests of California (Asner et al., 2016a). Several studies reported climate-associated responses of dominant or charismatic species. High mortality in the Clanwilliam cedar tree between 1931 and 2013 occurred at lower, hotter elevations in the Fynbos of South Africa (White et al., 2016). Drought reduced growth and increased mortality of the holm oak, Quercus ilex, on the Iberian Peninsula of Spain (Natalini et al. (2016). Portuguese shrublands experienced losses of many deciduous and evergreen oak species, and an increasing dominance of pyrophytic xeric trees (Acácio et al., 2017). The 2012–2015 drought in California caused high-canopy foliage dieback of the giant sequoia (Sequoiadendron giganteum) (Stephenson et al., 2019), increased the dominance of oaks relative to pines as a result of the increased water deficit, and led to large-scale tree mortality due to interactions of drought and insect pest outbreaks (McIntyre et al., 2015; Fettig et al., 2019).

Species distribution or community composition changes have contributed to declines in diversity and/or shifts towards more drought- or heat-adapted species (medium evidence, high agreement ). Two conifer species (Pinus longaeva and P. flexilis) shifted upslope 19 m from 1950 to 2016 in the Great Basin, USA, (Smithers et al., 2018). Reduced winter precipitation caused native annual forbs to recede, resulting in long-lasting and potentially unidirectional reductions in diversity in a Californian grassland (Harrison et al., 2018). More frequent extreme hot and dry weather between 1966 and 2010 caused a decline in diversity during the post-fire regeneration phase in the Fynbos of South Africa (Slingsby et al., 2017), resulting in shifts towards species with higher temperature preferences (Slingsby et al., 2017). In Italy, Del Vecchio et al. (2015) observed increases in plant cover and thermophilic species in coastal foredune habitats between 1989 and 2012.

In southern California, USA, areas of forest and woody shrublands are shifting to grasslands, driven by a combination of climate and land use factors such as increased drought, fire ignition frequency and increases in nitrogen deposition (robust evidence, high agreement ) (Jacobsen and Pratt, 2018; Park et al., 2018; Park and Jenerette, 2019; Syphard et al., 2019b).

The effects of climate change on heat, fuel and wildfire ignition limits show spatial and temporal variation globally (see Section, but there have been a number of observed impacts on MTEs (medium evidence, high agreement ). Climate change caused increases in fuel aridity and the area of land burned by wildfires across the western USA from 1985 to 2015 (Abatzoglou and Williams, 2016). Local and global climatic variability led to a 4-year decrease in the average fire return time in the Fynbos, South Africa, when comparing fires recorded in 1951–1975 and 1976–2000 (Wilson et al., 2010). In Chile, González et al. (2018) reported a significant increase in the number, size, duration and simultaneity of large fires during the 2010–2015 ‘megadrought’ when compared to the 1990–2009 baseline. Observed Changes in Savanna and Grasslands

Savannas consist of co-existing trees and grasses in tropical and temperate regions (Archibald et al., 2019). The global trend of woody encroachment reported in AR5 (Settele et al., 2014) is continuing (robust evidence, high agreement , very high confidence) (see Table SM2.1), with increases occurring in temperate savannas in North America (10–20% per decade) and tropical savannas in South America (8% per decade), Africa (2.4% per decade) and Australia (1% per decade) (O’Connor et al., 2014; Espírito-Santo et al., 2016; Skowno et al., 2017; Stevens et al., 2017; McNicol et al., 2018; Venter et al., 2018; Rosan et al., 2019). Additionally, the forest expansion into mesic savannas reported in AR5 (Settele et al., 2014) is continuing in Africa, South America and Southeast Asia (Marimon et al., 2014; Keenan et al., 2015; Baccini et al., 2017; Ondei et al., 2017; Stevens et al., 2017; Aleman et al., 2018; Rosan et al., 2019). Extreme high rainfall anomalies have also contributed to an increase in herbaceous and foliar production in the Sahel (Brandt et al., 2019; Zhang et al., 2019a).

New studies since AR5, using multiple study designs (experimental manipulations in lab and field, meta-analyses and modelling), attribute climate change increases in woody cover to elevated atmospheric CO2 (Donohue et al., 2013; Nackley et al., 2018; Quirk et al., 2019) and increased rainfall amount and intensity (robust evidence, high agreement ) (Venter et al., 2018; Xu et al., 2018b; Zhang et al., 2019a). Direct quantification of climate-change drivers is confounded with local LUC such as fire suppression (Archibald, 2016; Venter et al., 2018), heavy grazing (du Toit and O’Connor, 2014; Archer et al., 2017), removal of native browsers and, specifically, loss of mega-herbivores in Africa (medium evidence, medium agreement ) (Asner et al., 2016b; Daskin et al., 2016; Stevens et al., 2016; Davies et al., 2018). The relative importance of the climate- and non-climate-related causes of woody plants varies between regions, but there is general consensus that the impacts of climate change, specifically, increasing rainfall and rising CO2, are frequent and strong contributing factors of woody-cover increase (robust evidence, high agreement ).

Extensive woody-cover increases in non-forested biomes is reducing grazing potential (Smit and Prins, 2015) as well as changing the carbon stored per unit of land area (González-Roglich et al., 2014; Puttock et al., 2014; Pellegrini et al., 2016; Mureva et al., 2018) and the hydrological characteristics (Honda and Durigan, 2016; Schreiner-McGraw et al., 2020). Woody-cover encroachment also reduces biodiversity by threatening fauna and flora adapted to open ecosystems (Ratajczak et al., 2012; Smit and Prins, 2015; Pellegrini et al., 2016; Andersen and Steidl, 2019).

The global extent of grasslands is declining significantly because of climate change (medium confidence). In temperate and boreal zones, where about half of tree lines are shifting, they are overwhelmingly expanding poleward and upward, with an accompanying loss of montane and boreal grassland (robust evidence, high agreement ) whereas tropical tree lines have been generally stable (medium evidence, medium agreement ) (Harsch et al., 2009; Rehm and Feeley, 2015; Silva et al., 2016; Andela et al., 2017; Song et al., 2018; Aide et al., 2019; Gibson and Newman, 2019). The Eurasian steppes experienced a 1% increase in woody cover per decade since 2000 (Liu et al., 2021) and inner Mongolian grasslands in China experienced broad encroachment as well (Chen et al., 2015). Climatic drivers of woody expansion in temperature-limited grasslands, particularly alpine grasslands, are most frequently attributed to warming (robust evidence, high agreement , high confidence) (D’Odorico et al., 2012; Hagedorn et al., 2014), an increase in water and nutrient availability from thawing permafrost (medium evidence, high agreement ) (Zhou et al., 2015b; Silva et al., 2016) and rising CO2 (medium evidence, medium agreement ) (Frank et al., 2015; Aide et al., 2019). Interactions of LULCCs such as land abandonment, grazing management shifts and fire suppression with climate change are contributing factors (Liu et al., 2021)

Remote sensing shows overall increasing trends in both the annual maximum Normalized Difference Vegetation Index (NDVI) and annual mean NDVI in global grassland ecosystems between 1982 and 2011 (Gao et al., 2016). Multiple lines of evidence indicate that changes in grassland productivity are positively correlated with increases in mean annual precipitation (Hoover et al., 2014; Brookshire and Weaver, 2015; Gang et al., 2015; Gao et al., 2016; Wilcox et al., 2017; Wan et al., 2018). Increasing temperatures positively impact grassland production and biomass, especially in temperature-limited regions (Piao et al., 2014; Gao et al., 2016). However, it is expected that grasslands in hot areas will decrease production as temperatures increase (limited evidence, low agreement ) (Gang et al., 2015). Nevertheless, grassland responses to warming and drought are being ameliorated by increasing CO2 and associated improved water-use efficiency (Roy et al., 2016). For example, in a cool temperate grassland experiment, warming led to a longer growing season and elevated CO2 further extended growing by conserving water, which enabled most species to remain active longer (medium evidence, medium agreement ) (Reyes-Fox et al., 2014). Observed Changes in Tropical Forest

Overall declines of tropical forest cover (Kohl et al., 2015; Liu et al., 2015; Baccini et al., 2017; Harris et al., 2021), with declines more than triple the gains (Harris et al., 2021) have been driven primarily by deforestation and land conversion (robust evidence, high agreement ) (Lewis et al., 2015; Curtis et al., 2018; Assis et al., 2019). In opposition to this general trend, expansion of tropical forest cover into savannas and grasslands has occurred in Africa, South America and Australia (Marimon et al., 2014; Baccini et al., 2017; Ondei et al., 2017; Stevens et al., 2017; Aleman et al., 2018; Staver, 2018; Rosan et al., 2019).

Specific examples of climate change-driven range shifts of tropical deciduous forests upslope into alpine grasslands have been documented in the Americas (Chacón-Moreno et al., 2021; Jiménez-García et al., 2021) and Asia (Sigdel et al., 2018). However, tree line behaviours are diverse. A study in Nepal recorded that the tree line fomed by Abies spectabilis had been stable for more than a century, while the upper limit of large shrubs (Rhododendron campanulatum) had been advancing (Mainali et al., 2020). In both the Andes (Harsch et al., 2009) and Himalayas (Singh et al., 2021), most tree lines have been stable, leading (Rehm and Feeley, 2015) to postulate a ‘grass ceiling’ that has been difficult for trees to penetrate. The tree line shifts that have occurred are probably driven by interactions between changing land use (e.g., fire suppression) and climate changes such as increased rainfall, warming and elevated CO2 (via CO2 fertilisation or increases in water-use efficiency) (medium evidence, medium agreement ) (Cernusak et al., 2013; Huang et al., 2013; Van Der Sleen et al., 2015; Yang et al., 2016).

Increases in productivity of tropical forests (Gatti et al., 2014; Brienen et al., 2015; Baccini et al., 2017), Africa and southeast Asia (Qie et al., 2017) have been attributed to elevated CO2 (robust evidence, medium agreement ) (Ballantyne et al., 2012; Brienen et al., 2015; Sitch et al., 2015; Yang et al., 2016; Mitchard, 2018). The rates of these increases have been slowing down in the central Amazon (Brienen et al., 2015; de Meira Junior et al., 2020) and Southeast Asia (Qie et al., 2017). In contrast, the carbon sink (and hence the rate of biomass gain) in intact African forests was stable until 2010 and has only recently started to decline, indicating asynchronous carbon sink saturation in Amazonia and Africa, the difference being driven by rates of tree mortality (Hubau et al., 2020). At the global level, Hubau et al. (2020) argue that the carbon sink associated with intact tropical forests peaked in the 1990s and is now in decline.

Declines in productivity are most strongly associated with warming (Sullivan et al., 2020), reduced growth rates during droughts (Bennett et al., 2015; Bonai et al., 2016; Corlett, 2016), drought-related mortality (Brando et al., 2014; Zhou et al., 2014; Brienen et al., 2015; Corlett, 2016; McDowell et al., 2018), fire (Liu et al., 2017) and cloud-induced radiation limitation (robust evidence, high agreement ) (Deb Burman et al., 2020). Increases in the frequency and severity of droughts and shorter tree residence times due to increases in growth rates caused by elevated CO2 may be additional interactive factors increasing tree mortality (Malhi et al., 2014; Brienen et al., 2015). Vulnerability to drought varies between tree species and sizes, with large, older trees at the highest risk of mortality (McDowell et al., 2018; Meakem et al., 2018). Mortality risk also varies between forest types, with seasonal rainforests appearing to be the most vulnerable to drought (Corlett, 2016).

Lianas (long-stemmed woody vines) generally negatively impact trees, significantly reducing the growth of heavily infested trees (Reis et al., 2020). Lianas would benefit from climate change and disturbance (LingZi et al., 2014; Hodgkins et al., 2018). The extent of their suitable niche can increase (Taylor and Kumar, 2016), thereby decreasing forest biomass accumulation (robust evidence, high agreement ) (van der Heijden et al., 2013; Fauset et al., 2015; Estrada-Villegas et al., 2020).

Climate change continues to degrade forests by reducing resilience to pests and diseases, increasing species invasion, facilitating pathogen spread (Malhi et al., 2014; Deb et al., 2018) and intensifying fire risk and potential dieback (Lapola et al., 2018; Marengo et al., 2018). Drought, temperature increases and forest fragmentation interact to increase the prevalence of fires in tropical forests (robust evidence, high agreement ). Warming increases water stress in trees (Corlett, 2016) and, together with forest fragmentation, dramatically increases the desiccation of forest canopies—resulting in deforestation that then leads to even hotter and drier regional climates (Malhi et al., 2014; Lewis et al., 2015). Warming and drought increase the invasion of grasses into forest edges and increase fire risk (robust evidence, high agreement ) (Brando et al., 2014; Balch et al., 2015; Lewis et al., 2015). Droughts and fires additively increase mortality and, consequently, reduce canopy cover and above-ground biomass (Cross-Chapter Paper 7) (Brando et al., 2014, 2020; Balch et al., 2015; Lewis et al., 2015). Observed Changes in Boreal and Temperate Forests

The AR5 found increased tree mortality, wildfire and plant phenology changes in boreal and temperate forests (Settele et al., 2014). Expanding on these conclusions, this assessment, using analyses of causal factors, attributes the following observed changes in boreal and temperate forests in the 20th and 21st centuries to anthropogenic climate change: upslope and poleward biome shifts at sites in Asia, Europe and North America (Section; range shifts of plants (Section; earlier blooming and leafing of plants (Section; poleward shifts in tree-feeding insects (Section; increases in insect pest outbreaks (Section; increases in the area burned by wildfire in western North America (Section; increased drought-induced tree mortality in western North America (Section; and thawing of the permafrost that underlies extensive areas of boreal forest (Section in (Gulev et al., 2021)). Atmospheric CO2 from anthropogenic sources has also increased net primary productivity (NPP) (Section In summary, anthropogenic climate change has caused substantial changes in temperate and boreal forest ecosystems, including biome shifts and increases in wildfire, insect pest outbreaks and tree mortality, at a global mean surface temperature (GMST) increase of 0.9°C above the pre-industrial period (robust evidence, high agreement ).

Other changes detected in boreal forests and consistent with, but not formally attributed to, climate change, include increased wildfire in Siberia (Section, long-lasting smouldering below-ground fires in Canada and the USA (Scholten et al., 2021), tree mortality in Europe (Section and post-fire shifts of boreal conifer to deciduous broadleaf tree species in Alaska (Mack et al., 2021). From 1930 to 1960, boreal forest growth became limited more by precipitation than temperature in the Northern Hemisphere (Babst et al., 2019).

For some vegetation, changes in land use and management have exerted more influence than climate change. These include upslope and poleward forest shifts in Europe following the abandonment of timber harvesting or livestock grazing (Section, changes in wildfire in Europe affected by fire suppression, fire prevention and agricultural abandonment (Section, and forest species composition changes in Scotland due to nitrogen deposition from air pollution (Hester et al., 2019). Remote sensing suggests that the area of temperate and boreal forests increased in Asia and Europe between 1982 and 2016 (Song et al., 2018) and in Canada between 1984 and 2015 (Guindon et al., 2018), but forest plantations and regrowth are probable drivers (Song et al., 2018). Observed Changes in Peatlands

Globally, peatland ecosystems store approximately 25% (600 ± 100 GtC) of the world’s soil organic carbon (Yu et al., 2010; Page et al., 2011; Hugelius et al., 2020) and 10% of the world’s freshwater resources (Joosten and Clarke, 2002), despite only occupying 3% of the global land area (Xu et al., 2018a). The long-term role of northern peatlands in the carbon cycle was mentioned for the first time in IPCC AR4 (IPCC, 2007), while SR1.5 briefly mentioned the combined effects of changes in climate and land use on peatlands (IPCC, 2018b). New evidence confirms that climate change, including extreme weather events (e.g., droughts; Section, permafrost degradation (Section, SLR (Section and fire (Section (Henman and Poulter, 2008; Kirwan and Mudd, 2012; Turetsky et al., 2015; Page and Hooijer, 2016; Swindles et al., 2019; Hoyt et al., 2020; Hugelius et al., 2020; Jovani-Sancho et al., 2021; Veraverbeke et al., 2021), superimposed on anthropogenic disturbances (e.g., draining for agriculture or mining; Section, has led to rapid losses of peatland carbon across the world (robust evidence, high agreement ) (Page et al., 2011; Leifeld et al., 2019; Hoyt et al., 2020; Turetsky et al., 2020; Loisel et al., 2021). Other essential peatland ecosystem services, such as water storage and biodiversity, are also being lost worldwide (robust evidence, high agreement ) (Bonn et al., 2014; Martin-Ortega et al., 2014; Tiemeyer et al., 2017).

The switch from carbon sink to carbon source in peatlands globally is mainly attributable to changes in the depth of the water table, regardless of management or status (robust evidence, high agreement ) (Lafleur et al., 2005; Dommain et al., 2011; Lund et al., 2012; Cobb et al., 2017; Evans et al., 2021; Novita et al., 2021). Across the temperate and tropical biomes, extensive drainage and deforestation have caused widespread water table draw-downs and/or peat subsidence, as well as high CO2 emissions (medium evidence, high agreement ). Climate change is compounding these impacts (medium evidence, medium agreement ). For example, in Indonesia, the highest emissions from drained tropical peatlands were reported in the extremely dry year of the 1997 El Niño (810–2570 TgC yr -1) (Page et al., 2002) and the 2015 fire season (380 TgC yr -1) (Field et al., 2016). These prolonged dry seasons have also led to tree die-offs and fires, which are relatively new phenomena at these latitudes (medium evidence, high agreement ) (Cole et al., 2015; Mezbahuddin et al., 2015; Fanin and van der Werf, 2017; Taufik et al., 2017; Cole et al., 2019). Low soil moisture contributes to increased fire propagation (Section (Dadap et al., 2019; Canadell et al., 2021), causing long-lasting fires responsible for smoke and haze pollution (robust evidence, high agreement ) (Ballhorn et al., 2009; Page et al., 2009; Gaveau et al., 2014; Huijnen et al., 2016; Page and Hooijer, 2016; Hu et al., 2018; Vadrevu et al., 2019; Niwa et al., 2021). Increases in fires and smoke lead to habitat loss and negatively impact regional faunal populations (limited evidence, high agreement ) (Neoh et al., 2015; Erb et al., 2018b; Thornton et al., 2018).

In large, lowland tropical peatland basins that are less impacted by anthropogenic activities (i.e., the Amazon and Congo river basins), the direct impact of climate change is that of a decreased carbon sink (limited evidence, medium agreement ) (Roucoux et al., 2013; Gallego-Sala et al., 2018; Wang et al., 2018a; Dargie et al., 2019; Ribeiro et al., 2021). As for the temperate and boreal regions, climatic drying also tends to promote peat oxidation and carbon loss to the atmosphere (medium evidence, medium agreement ) (Section (Helbig et al., 2020; Zhang et al., 2020). In Europe, increasing mean annual temperatures in the Baltic, Scandinavia, and continental Europe (Section have led to widespread lowering of peatland water tables at intact sites (Swindles et al., 2019), desiccation and die-off of sphagnum moss (Bragazza, 2008; Lees et al., 2019) and increased intensity and frequency of fires, resulting in a rapid carbon loss (Davies et al., 2013; Veraverbeke et al., 2021). Nevertheless, longer growing seasons and warmer, wetter climates have increased carbon accumulation and promoted thick deposits regionally, as reported for some North American sites (limited evidence, medium agreement ) (Cai and Yu, 2011; Shiller et al., 2014; Ott and Chimner, 2016).

In high-latitude peatlands, the net effect of climate change on the permafrost peatland carbon sink capacity remains uncertain (Abbott et al., 2016; McGuire et al., 2018b; Laamrani et al., 2020; Loisel et al., 2021; Sim et al., 2021; Väliranta et al., 2021). Increasing air temperatures have been linked to permafrost degradation and altered hydrological regimes (; Figure 2.4a;; Box 5.1), which have led to rapid changes in plant communities and bio-geochemical cycling (robust evidence, high agreement ) (Liljedahl et al., 2016; Swindles et al., 2016; Voigt et al., 2017; Zhang et al., 2017b; Voigt et al., 2020; Sim et al., 2021). In many instances, permafrost degradation triggers thermokarst land subsidence associated with local wetting (robust evidence, high agreement ) (Jones et al., 2013; Borge et al., 2017; Olvmo et al., 2020; Olefeldt et al., 2021). Permafrost thaw in peatland-rich landscapes can also cause local drying through increased hydrological connectivity and runoff (Connon et al., 2014). In the first decades following thaw, increases in methane, CO2 and nitrous oxide emissions have been recorded from peatland sites, depending on surface moisture conditions (Schuur et al., 2009; O’Donnell et al., 2012; Elberling et al., 2013; Matveev et al., 2016; Euskirchen et al., 2020; Hugelius et al., 2020). Conversely, some evidence suggests increased peat accumulation after thaw (Jones et al., 2013; Estop-Aragonés et al., 2018; Väliranta et al., 2021). There is also a need to consider the impact of wildfire on permafrost thaw, due to its effect on soil temperature regime (Gibson et al., 2018), as fire intensity and frequency have increased across the boreal and Arctic biomes (limited evidence, high agreement ) (Kasischke et al., 2010; Scholten et al., 2021).

The CO2 emissions from degrading peatlands is contributing to climate change in a positive feedback loop (robust evidence, high agreement). At mid-latitudes, widespread anthropogenic disturbance led to large historical GHG emissions and current legacy emissions of 0.15 PgC yr -1 between 1990 and 2000 (limited evidence, high agreement ) (Maljanen et al., 2010; Tiemeyer et al., 2016; Drexler et al., 2018; Qiu et al., 2021). About 80 million hectares of peatland have been converted to agriculture, equivalent to 72 PgC emissions in 850–2010 CE (Leifeld et al., 2019; Qiu et al., 2021). In Southeast Asia (SEA), an estimated 20–25 Mha of peatlands have been converted to agriculture with carbon currently being lost at a rate of ~155 ± 30 MtC yr −1 (Miettinen et al., 2016; Leifeld et al., 2019; Hoyt et al., 2020). Extensive deforestation and drainage have caused widespread peat subsidence and large CO2 emissions at a current average of ~10 ± 2 tonnes ha -1 yr -1, excluding fires (Hoyt et al., 2020), with values estimated from point subsidence measurements being as high as 30–90 tonnes CO2 ha −1 yr −1 locally (robust evidence, high agreement ) (Wösten et al., 1997; Matysek et al., 2018; Swails et al., 2018; Evans et al., 2019; Conchedda and Tubiello, 2020; Anshari et al., 2021). On average, at the global scale, increases in GHG emissions from peatlands have primarily come from the compounded effects of LUC, drought and fire, with additional emissions from some thawing-permafrost peatlands (robust evidence, high agreement ). Observed Changes in Polar Tundra

Warming at high latitudes, documented in both AR4 and AR5, is leading to earlier snow and sea ice melt and longer growing seasons (IPCC, 2021a) which are continuing to alter tundra plant communities (medium evidence, high agreement ) (Post et al., 2009; Gauthier et al., 2013). Woody encroachment and increases in vegetation productivity, observed in both AR4 and AR5, are widespread and continuing. Both experiments and monitoring indicate that climate warming is causing increases in shrub, grass and sedge abundance, density, frequency, and height, with decreases in mosses and/or lichens (robust evidence, high agreement ) (Myers-Smith et al., 2011; Bjorkman et al., 2018; Bjorkman et al., 2019). Shrub growth is climate-sensitive and is greater in years with warmer growing seasons (Myers-Smith et al., 2015 ). Plant species that prefer warmer conditions are increasing (Elmendorf et al., 2015; Bjorkman et al., 2018), plant cover is increasing and bare ground is decreasing in long-term monitoring plots (Bjorkman et al., 2019; Myers-Smith et al., 2019). Animals such as moose, beavers and songbirds may already be responding to these vegetation changes by expanding their ranges northward or upslope into shrub tundra (Boelman et al., 2015; Tape et al., 2016a; Tape et al., 2016b; Tape et al., 2018).

In addition to direct warming, indirect effects of climate change, first found in AR4 and AR5, continue, such as thawed permafrost, altered hydrology and enhanced nutrient cycling, and these processes are causing pronounced vegetation changes (medium evidence, medium agreement ) (Schuur et al., 2009; Natali et al., 2012). Soil moisture status influences temperature sensitivity of plant growth and canopy heights (Myers-Smith et al., 2015 ; Ackerman et al., 2017; Bjorkman et al., 2018). In tundra ecosystems, permafrost thawing can decouple below-ground plant growth dynamics from above-ground dynamics, with below-ground root growth continuing until soils re-freeze in autumn (Cross-Chapter Paper 6) (Iversen et al., 2015; Blume-Werry et al., 2016; Radville et al., 2016).

2.4.4 Observed Changes in Ecosystem Processes and Services Observed Browning of Rivers and Lakes

In boreal coniferous areas, there has been an increase in the transporting of terrestrial-derived dissolved organic carbon (DOC) into rivers and lakes, which has caused increased opacity and a shift toward a brown colour (browning). There was little assessment of this in AR5. This process is driven by climate change, and stems from hydrological intensification, greening of the Northern Hemisphere and degradation of carbon sinks in peatlands (robust evidence, high agreement ) (Solomon et al., 2015; Catalán et al., 2016; de Wit et al., 2016; Finstad et al., 2016; Creed et al., 2018; Hayden et al., 2019). These factors enhance terrestrial productivity, alter vegetation communities and affect the hydrological control of the production and transport of DOC (Weyhenmeyer et al., 2016). Non-climate-related drivers of browning are: declining atmospheric sulphur deposition, forestry practices and LULCCs (see Table SM2.1 for detail).

Browning creates a positive feedback to climate by absorbing photosynthetically active radiation, which accelerates upper water (epilimnetic) warming (Solomon et al., 2015). Browning of lakes leads to shallower and more stable thermoclines, and thus overall deep water cooling (Solomon et al., 2015; Williamson et al., 2015), and can provoke a transition of the seasonal mixing regime from a mixed lake (polymictic) to one that is seasonally stratified (Kirillin and Shatwell, 2016).

The ecological responses of browning are a concomitant effect of climate change and nutrient status. Results from long-term, large-scale lake experiments have been variable, showing both strong synergistic effects (Urrutia-Cordero et al., 2016) and no significant effects of browning on plankton community food webs (Rasconi et al., 2015). Browning has driven a shift from auto- to heterotrophic/mixotrophic-based production (Urrutia-Cordero et al., 2017) and supports heterotrophic metabolism of the bacterial community (Zwart et al., 2016). Browning may also accelerate primary production through the input of nutrients associated with dissolved organic matter (DOM) in nutrient-poor lakes and increase cyanobacteria, which cope better with low light intensities (Huisman et al., 2018) and toxin levels (Urrutia-Cordero et al., 2016). However, the synergistic impacts of browning and climate change on aquatic communities depends on regional precipitation patterns (Weyhenmeyer et al., 2016), watershed type (de Wit et al., 2016) and the length of the food chain (Hansson et al., 2013). Quantitative attribution of browning to climate change remains difficult (medium evidence, medium agreement ).

In summary, new studies since AR5 have explicitly estimated the effects of warming and browning on freshwaters in boreal areas, with complex positive and negative repercussions on water temperature profiles (lower vs. upper water) (high confidence) and primary production (medium confidence).

Figure 2.5 | Large-scale observed changes in freshwater ecosystems attributed to climate change over more than four decades. For description and references, see Sections 2.3.3, 2.4.2 and Observed Changes in Wildfire Detection and attribution of observed changes in wildfire

Wildfire is a natural and essential component of many forest and other terrestrial ecosystems. Excessive wildfire, however, can kill people, cause respiratory disease, destroy houses, emit carbon dioxide and damage ecosystem integrity (see Sections and Anthropogenic climate change increases wildfire by exacerbating its three principal driving factors: heat, fuel and ignition (Moritz et al., 2012; Jolly et al., 2015). Non-climatic factors also contribute to wildfires—in tropical areas, fires are set intentionally to clear forest for agricultural fields and livestock pastures (Bowman et al., 2020). Urban areas and roads create ignition hazards. Governments in many temperate-zone countries implement policies to suppress fires, even natural ones, producing unnatural accumulations of fuel in the form of coarse woody debris and high densities of small trees (Ruffault and Mouillot, 2015; Hessburg et al., 2016; Andela et al., 2017; Balch et al., 2017; Lasslop and Kloster, 2017; Aragao et al., 2018; Kelley et al., 2019). Globally, 4.2 million km 2 of land per year burned on average from 2002 to 2016 (Giglio et al., 2018), with the highest fire frequencies in the Amazon rainforest, deciduous forests and savannas in Africa and deciduous forests in northern Australia (Earl and Simmonds, 2018; Andela et al., 2019).

Since the AR5 and the IPCC Special Report on Land, published research has detected increases in the area burned by wildfire, analysed relative contributions of climate and non-climate factors and attributed burned area increases above natural (recent historical) levels to anthropogenic climate change in one part of the world, western North America (robust evidence, high agreement) (Abatzoglou and Williams, 2016; Partain et al., 2016; Kirchmeier-Young et al., 2019; Mansuy et al., 2019; Bowman et al., 2020). Across the western USA, increases in vegetation aridity due to higher temperatures from anthropogenic climate change doubled burned area from 1984 to 2015 over what would have burned due to non-climate factors including unnatural fuel accumulation from fire suppression, with the burned area attributed to climate change accounting for 49% (32–76%, 95% confidence interval) of cumulative burned area (Abatzoglou and Williams, 2016). Anthropogenic climate change doubled the severity of a southwest North American drought from 2000 to 2020 that has reduced soil moisture to its lowest levels since the 1500s (Williams et al., 2020), driving half of the increase in burned area (Abatzoglou and Williams, 2016; Holden et al., 2018; Williams et al., 2019). In British Columbia, Canada, the increased maximum temperatures due to anthropogenic climate change increased burned area in 2017 to its highest extent in the 1950–2017 record, seven to eleven times the area that would have burned without climate change (Kirchmeier-Young et al., 2019). In Alaska, USA, the high maximum temperatures and extremely low relative humidity due to anthropogenic climate change accounted for 33–60% of the probability of wildfire in 2015, when the area burned was the second highest in the 1940–2015 record (Partain et al., 2016). In protected areas of Canada and the USA, climate factors (temperature, precipitation, relative humidity and evapotranspiration) accounted for 60% of burned area from local human and natural ignitions from 1984 to 2014, outweighing local human factors (population density, roads and built area) (Mansuy et al., 2019).

In summary, field evidence shows that anthropogenic climate change has increased the area burned by wildfire above natural levels across western North America in the period 1984–2017, at GMST increases of 0.6°C–0.9°C, increasing burned area up to 11 times in one extreme year and doubling it (over natural levels) in a 32-year period (high confidence). Observed changes in wildfire globally

Regarding global terrestrial area as a whole, wildfire trends vary depending on the time period of analysis. From 1900 to 2000, global average fire frequency, based on field data, increased 0.4% but the change was not statistically significant (Gonzalez et al., 2010). Fire frequency increased on one-third of global land, mainly from burning for agricultural clearing in Africa, Asia and South America, slightly less than the area of fire frequency decrease, mainly from fire suppression across Australia, North America and Russia (Gonzalez et al., 2010). Analyses of the Global Fire Emissions Database document shows that, from 1996 to 2015, global burned area decreased at a rate of −0.7% yr -1 (Forkel et al., 2019) but the change was not statistically significant (Giglio et al., 2013). From 1998 to 2015, global burned area decreased at a rate of −1.4 ± 0.5% yr -1 (Andela et al., 2017). The area of fire increases was one-third of the area of decreases, due to reduced vegetation cover from agricultural expansion and intensification (Andela et al., 2017) and from increased precipitation (Forkel et al., 2019). Furthermore, much of the decreasing trend derives from two years: 1998 with a high burned area and 2013 with low burned area (Forkel et al., 2019). Wildfire does not show a clear long-term trend for the world as a whole because of increases and decreases in different regions (medium evidence, medium agreement ).

Where the global average burned area has decreased in the past two decades, higher correlations of rates of change in burning to human population density, cropland area and livestock density than to precipitation indicate that agricultural expansion and intensification were the main causes (Andela et al., 2017). The global decrease of fire frequency from 2000 to 2010 is correlated with increasing human population density (Knorr et al., 2014). The fire-reducing effect of reduced vegetation cover following expansion of agriculture and livestock herding can counteract the fire-increasing effect of the increased heat and drying associated with climate change (Lasslop and Kloster, 2017; Arora and Melton, 2018; Forkel et al., 2019). The reduced burning needed after the initial clearing for agricultural expansion drives much of the decline in fires in the Tropics (Andela et al., 2017; Earl and Simmonds, 2018; Forkel et al., 2019). The human influence on fire ignition can be seen through the decrease documented on holy days (Sundays and Fridays) and traditional religious days of rest (Earl et al., 2015). Overall, human land use exerts an influence on wildfire trends for global terrestrial area as a whole that can be stronger than climate change (medium confidence). Observed changes in wildfire in individual regions with complex attribution

While burned area has increased in parts of Asia, Australia, Europe and South America, published research has not yet attributed the increases to anthropogenic climate change (medium evidence, high agreement ).

In the Amazon, deforestation for agricultural expansion and the degradation of forests adjacent to deforested areas cause wildfire in moist humid tropical forests not adapted to fire (robust evidence, high agreement ) (Fonseca et al., 2017; van Marle et al., 2017; da Silva et al., 2018 ; da Silva et al., 2021; dos Reis et al., 2021; Libonati et al., 2021). Roads facilitate deforestation, fragmenting the rainforest and increasing the dryness and flammability of vegetation (Alencar et al., 2015). Extreme droughts that occur during warm phases of the ENSO and the Atlantic Multi-Decadal Oscillation combine with the degradation of vegetation to cause extreme fire events (robust evidence, high agreement ) (Fonseca et al., 2017; Aragao et al., 2018; da Silva et al., 2018 ; Burton et al., 2020; dos Reis et al., 2021; Libonati et al., 2021). In the State of Roraima, Brazil, distance to roads and infrastructure that enable deforestation and ENSO were the factors most explaining fire occurrence in the extreme 2015–2016 fire season (Fonseca et al., 2017). From 1973 to 2014, burned area increased in the Amazon, coinciding with increased deforestation (van Marle et al., 2017). In the State of Acre, Brazil, burned area increased 36-fold from 1984 to 2016, with 43% burned in agricultural and livestock settlement areas (da Silva et al., 2018 ). In the extreme fire year 2019, 85% of the area burned in the Amazon occurred in areas deforested in 2018 (Cardil et al., 2020). Even though relatively higher moisture in 2019 led to burning below the 2002–2019 average across most of South America, burning in areas of recent deforestation in the Amazon were above the 2002–2019 average, indicating that deforestation, not meteorological conditions, triggered the 2019 fires (Kelley et al., 2021; Libonati et al., 2021). Furthermore, from 1981 to 2018, deforestation in the Amazon reduced moisture inputs to the lower atmosphere, increasing drought and fire in a self-reinforcing feedback (Xu et al., 2020). In the Amazon, deforestation exerts an influence on wildfire that can be stronger than climate change (robust evidence, high agreement ).

In Australia, burned area increased significantly between the periods 1950–2002 and 2003–2020 in the southeast state of Victoria, with the area burned in the 2019–2020 bushfires being the highest on record (Lindenmayer and Taylor, 2020). In addition to the deaths of dozens of people and the destruction of thousands of houses, the 2019–2020 bushfires burned almost half of the area protected for conservation in Victoria, two-thirds of the forests allocated for timber harvesting (Lindenmayer and Taylor, 2020), wildlife and extensive areas of habitat for threatened plant and animal species (Geary et al., 2021). Generally, past timber harvesting did not lead to more severe fire canopy damage (Bowman et al., 2021b). Across southeastern Australia, the fraction of vegetated area that burned increased significantly in eight of the 32 bioregions from 1975 to 2009, but decreased significantly in three bioregions (Bradstock et al., 2014). Increases in four bioregions were correlated to increasing temperature and decreasing precipitation. Decreases in burned area occurred despite increased temperature and decreased precipitation. Analyses of climate across Australia from 1950 to 2017 (Dowdy, 2018; Harris and Lucas, 2019) and during periods with extensive fires in 2017 in eastern Australia (Hope et al., 2019), in 2018 in northeastern Australia (Lewis et al., 2020), and in period 2019–2020 in southeastern Australia (Abram et al., 2021; van Oldenborgh et al., 2021) indicate that temperature and drought extremes due to the ENSO, Southern Annular Mode and other natural inter-decadal cycles drive inter-annual variability of fire weather. While the effects of inter-decadal climate cycles on fire are superimposed on long-term climate change, the relative importance of anthropogenic climate change in explaining changes in burned area in Australia remains unquantified (medium evidence, high agreement ).

In Africa, the rate of change of burned area on the continent as a whole ranged from a non-statistically significant −0.45% yr -1 in the period 2002–2016 (Zubkova et al., 2019) to a significant −1.9% yr -1 in the period 2001–2016 (Wei et al., 2020). These decreases coincided with areas of agricultural expansion or areas where drought reduced fuel loads (Zubkova et al., 2019; Wei et al., 2020). It is possible, however, that the 500-m spatial resolution of Modis remote-sensing fire data underestimates the area burned in Africa by half, by missing small fires (Ramo et al., 2021). In the Serengeti-Mara savanna of east Africa, burned area showed no significant change from 2001 to 2014, although an increase in domestic livestock would tend to reduce the grass cover that fuels savanna fires (Probert et al., 2019).

In Mediterranean Europe, the area burned in the region as a whole decreased from 1985 to 2011 (Turco et al., 2016), although the burned area for Spain did not show a significant long-term increase from 1968 to 2010 (Moreno et al., 2014) whereas that for Portugal in 2017 was the highest in the period 1980–2017 (Turco et al., 2019). Increased summer maximum temperature and decreased soil moisture explained most of the burned area observed, suggesting a contribution of climate change, but fire suppression, fire prevention, agricultural abandonment and reforestation as well as the reduction in forest area exerted even stronger influences on burned area than the climate across Mediterranean Europe (robust evidence, high agreement ) (Moreno et al., 2014; Turco et al., 2017; Viedma et al., 2018; Turco et al., 2019).

In the Arctic tundra and boreal forest, where wildfire has naturally been infrequent, burned area showed statistically significant increases of ~50% yr -1 across Siberia, Russia, from 1996 to 2015 (Ponomarev et al., 2016) and 2% yr -1 across Canada from 1959 to 2015 (Hanes et al., 2019). Wildfire burned ~6% of the area of four extensive Arctic permafrost regions in Alaska, USA, eastern Canada and Siberia from 1999 to 2014 (Nitze et al., 2018). In boreal forest in the Northwest Territories, Canada and Alaska, USA, the area burned by wildfire increased at a statistically significant rate of 6.8% yr -1 in the period 1975–2015, (Veraverbeke et al., 2017), with smouldering below-ground fires that lasted through the winter covering ~1% of burned area in the period 2002–2016 (Scholten et al., 2021). While burned area was correlated with temperature and reduced precipitation in Siberia (Ponomarev et al., 2016; Masrur et al., 2018) and correlated with lightning, temperature and precipitation in the Northwest Territories and Alaska (Veraverbeke et al., 2017), no attribution analyses have examined relative influences of climate and non-climate factors.

In Indonesia, deforestation and draining of peat swamp forests dries out the peat, providing substantial fuel for fires (Page and Hooijer, 2016). Extreme fire years in Indonesia, including 1997, 2006 and 2015, coincided with extreme heat and aridity during the warm phase of the ENSO (Field et al., 2016). Fire-resistant forest in 2019 covered only 3% of peatlands and 4.5% of non-peatlands on Sumatra and Kalimantan (Nikonovas et al., 2020).

In Chile, the area burned in the summer of 2016–2017 was 14 times the mean for the period 1985–2016 and the highest on record (Bowman et al., 2019). While this extreme fire year coincided with the highest daily mean maximum temperature in the period 1979–2017 (Bowman et al., 2019) in central Chile (the area of highest fire activity), burned area from 1976 to 2013 showed the highest correlation with the precipitation cycles of the ENSO and the temperature cycles of the Antarctic Oscillation (Urrutia-Jalabert et al., 2018).

Overall, burned area has increased in the Amazon, Arctic, Australia and parts of Africa and Asia, consistent with, but not formally attributed to anthropogenic climate change (medium evidence, high agreement ). Deforestation, peat draining, agricultural expansion or abandonment, fire suppression and inter-decadal cycles such as the ENSO exert a stronger influence than climate change on wildfire trends in numerous regions outside of North America (high confidence). Observed changes in fire seasons globally

The IPCC AR6 WGI assessed fire weather (Ranasinghe et al., 2021), while this chapter assesses the impacts of changes in fire weather: burned area and fire frequency. The global increases in temperature from anthropogenic climate change have increased aridity and drought, lengthening the fire weather season (the annual period with a heat and aridity index greater than half of its annual range) on one-quarter of global vegetated area and increasing the average fire season length by one-fifth from 1979 to 2013 (Jolly et al., 2015). Climate change has contributed to increases in the fire weather season or the probability of fire weather conditions in the Amazon (Jolly et al., 2015), Australia (Dowdy, 2018; Abram et al., 2021; van Oldenborgh et al., 2021), Canada (Hanes et al., 2019), central Asia (Jolly et al., 2015), East Africa (Jolly et al., 2015) and North America (Jain et al., 2017; Williams et al., 2019; Goss et al., 2020). In forest areas, the burned area correlates with fuel aridity, a function of temperature; in non-forest areas, the burned area correlates with high precipitation in the previous year, which can produce high grass fuel loads (Abatzoglou et al., 2018). Fire use in agriculture and raising livestock or other factors have generated a second fire season on approximately one-quarter of global land where fire is present, despite sub-optimal fire weather in the second fire season (Benali et al., 2017). In summary, anthropogenic climate change, through a 0.9°C surface temperature increase since the pre-industrial period, has lengthened or increased the frequency of periods with heat and aridity that favour wildfire on up to one-quarter of vegetated area since 1979 (robust evidence, high agreement ). Observed changes in post-fire vegetation

Globally, fire has contributed to biome shifts (Section and tree mortality (Sections, attributed to anthropogenic climate change. Research since the AR5 has also found vegetation changes from wildfire due to climate change. Through increased temperature and aridity, anthropogenic climate change has driven post-fire changes in plant regeneration and species composition in South Africa (Slingsby et al., 2017), and tree regeneration in the western USA (Davis et al., 2019b). In the fynbos vegetation of the Cape Floristic Region, South Africa, post-fire heat and drought and the legacy effects of exotic plant species reduced the regeneration of native plant species, decreasing species richness by 12% from 1966 to 2010 and shifting the average temperature tolerance of species communities upward by 0.5°C (Slingsby et al., 2017). In burned areas across the western USA, the increasing heat and aridity of anthropogenic climate change from 1979 to 2015 pushed low-elevation ponderosa pine (Pinus ponderosa) and Douglas fir (Pseudotsuga menziesii) forests across critical thresholds of heat and aridity that reduced the post-fire tree regeneration by half (Davis et al., 2019b). In the southwestern USA, where anthropogenic climate change has caused drought (Williams et al., 2019) and increased wildfire (Abatzoglou and Williams, 2016), high-severity fires have converted some forest patches to shrublands (Barton and Poulos, 2018). Field evidence shows that anthropogenic climate change and wildfire, together, altered vegetation species composition in the southwestern USA and Cape floristic region, South Africa, reducing post-fire natural regeneration and species richness of tree and other plant species, between 1966 and 2015, at GMST increases of 0.3°C–0.9°C (medium evidence, high agreement ). Observed Changes in Tree Mortality Observed tree mortality globally

Anthropogenic climate change can cause tree mortality directly via increased aridity or drought (Section or indirectly through wildfire (Section and insect pests (Section Catastrophic failure of the plant hydraulic system, in which a lack of water causes the xylem to lose hydraulic conductance, is the principal mechanism of drought-induced tree death (Anderegg et al., 2016; Adams et al., 2017; Anderegg et al., 2018; Choat et al., 2018; Menezes-Silva et al., 2019; Brodribb et al., 2020).

Up through the AR5 (Settele et al., 2014), detection and attribution analyses had found that anthropogenic climate change, with global temperature increases of 0.3°C–0.9°C above the pre-industrial period and the increases in aridity exceeding the effects of local non-climate change factors, caused three cases of drought-induced tree mortality of up to 20% in the period 1945–2007 in western North America (van Mantgem et al., 2009), the African Sahel (Gonzalez et al., 2012) and North Africa (le Polain de Waroux and Lambin, 2012). Increased wildfire and pest infestations, driven by climate change, also contributed to North American tree mortality (van Mantgem et al., 2009). In addition, a meta-analysis of published cases found that drought consistent with, but not formally attributed to, climate change had caused tree mortality at 88 sites in boreal, temperate and tropical ecosystems (Allen et al., 2010), with 49 additional cases found by the AR5 (Settele et al., 2014).

Since the AR5 (Settele et al., 2014), global meta-analyses found at least 15 (Allen et al., 2015) and 25 (Hartmann et al., 2018) additional sites, respectively, of drought-induced tree mortality around the world. These and other global analyses found more rapid mortality than previously (Allen et al., 2015), rising background mortality (Allen et al., 2015), mortality increasing with drought severity (Greenwood et al., 2017), mortality of tropical trees increasing with temperature (Locosselli et al., 2020), mortality increasing with tree size for many species (Bennett et al., 2015), mortality predominantly at the dry edge of species ranges (Anderegg et al., 2019) and three-quarters of drought-induced mortality cases leading to a change in the dominant species (Batllori et al., 2020). Multiple non-climate factors contribute to tree mortality, including timber cutting, livestock grazing and air pollution (Martinez-Vilalta and Lloret, 2016). Globally, tropical dry forests lost, from all causes, 95,000 km 2, 8% of their total area, from 1982 to 2016, the most extensive area of mortality of any biome (Song et al., 2018).

In summary, anthropogenic climate change caused drought-induced tree mortality of up to 20% in the period 1945–2007 in western North America, the African Sahel and North Africa, via global temperature increases of 0.3°C–0.9°C above the pre-industrial period and increases in aridity, and it contributed to over 100 other cases of drought-induced tree mortality in Africa, Asia, Australia, Europe and North and South America (high confidence). Field observations document accelerating mortality rates, rising background mortality and post-mortality vegetation shifts (high confidence). Water stress, leading to plant hydraulic failure, is the principal mechanism of drought-induced tree mortality. Timber cutting, agricultural expansion, air pollution and other non-climate factors also contribute to tree death. Observed tree mortality in tropical ecosystems

In the Brazilian Amazon, deforestation to clear agricultural land comprises the principal cause of tree mortality, reducing forest cover by an average of 13,900 km 2 yr -1 from 1988 to 2020 (Assis et al., 2019). In addition, in a set of 310 Amazon field plots, an annual average temperature increase of 1.2°C from 1950 to 2018 (Marengo et al., 2018) contributed to tree mortality of ~40% from 1983 to 2011 (Brienen et al., 2015). In another set of plots, mortality among newly recruited trees of mesic genera increased and drought-tolerant genera became more abundant from 1985 to 2015 (Esquivel-Muelbert et al., 2019). In other plots, tree mortality did not show a statistically significant change from 1965 to 2016, but rose abruptly in severe drought years, mainly during warm phases of the ENSO (Aleixo et al., 2019). Nearly half the area of the Amazon has experienced extremely dry conditions during ENSO warm phases; this can cause extensive wildfire (Section Wildfires can increase tree mortality rates by >600% above rates in non-burned areas, with the higher mortality persisting for up to a decade after a fire (Silva et al., 2018; Berenguer et al., 2021). Climate change has contributed to tree mortality in the Amazon rainforest (medium evidence, medium agreement ).

In the African Sahel, field research has continued to detect tree mortality, ranging from 20 to 90% in the period 1965–2018 (Kusserow, 2017; Trichon et al., 2018; Dendoncker et al., 2020), and declines in tree biodiversity, with up to 80% local losses of tree species in the period 1970–2014 (Hanke et al., 2016; Kusserow, 2017; Ibrahim et al., 2018; Dendoncker et al., 2020), consistent with, but not formally attributed to, climate change. In Algeria, mortality of the Atlas cedar (Cedrus atlantica) increased from 1980 to 2006, coinciding with a ~1°C spring temperature increase, but non-climate factors were not examined (Navarro-Cerrillo et al., 2019). Across southern Africa, nine of the 13 oldest known (1100–2500 years old) baobab trees (Adansonia digitata) have died since 2005, although the causes are unknown (Patrut et al., 2018). In South Africa, savanna trees experienced an order of magnitude increase in mortality, related, but not formally attributed to, decreased rainfall (Case et al., 2019). In Tunisia, insect infestations related, but not formally attributed to, hotter temperatures led to mortality of cork oaks (Quercus suber) (Bellahirech et al., 2019). Observed tree mortality in boreal and temperate ecosystems

The most extensive research into tree mortality since the AR5 has been in the western USA, where anthropogenic climate change accounted for half the magnitude of a drought in the period 2000–2020 that has been the most severe since the 1500s, (Williams et al., 2020) and for one-tenth to one-quarter of the magnitude of the 2012–2014 period of th e severe drought in California that lasted from 2012 to 2016 (Williams et al., 2015a). Across the western USA, anthropogenic climate change doubled tree mortality between 1955 and 2007 (van Mantgem et al., 2009). Lodgepole pine (Pinus contorta) mortality increased 700% from 2000 to 2013 (Anderegg et al., 2015) and piñon pine (P. edulis) experienced >50% mortality from 2002 to 2014 (Redmond et al., 2018). In montane conifer forest in California, anthropogenic climate change has increased tree mortality by one-quarter (Goulden and Bales, 2019). One-quarter of the trees died in some areas, with mortality rates of ponderosa pine (P. ponderosa) and sugar pine (P. lambertiana) increasing to up to 700% of pre-drought rates (Stephenson et al., 2019; Stovall et al., 2019). Substantial field evidence shows that anthropogenic climate change has caused extensive tree mortality in North America (robust evidence, high agreement ).

In western North America, increased infestations of bark beetles and other tree-feeding insects that benefit from higher winter temperatures (section in (IPCC, 2021a)) and longer growing seasons (section in (IPCC, 2021a)) have killed drought-stressed trees (Section (Anderegg et al., 2015; Kolb et al., 2016; Lloret and Kitzberger, 2018; Redmond et al., 2018; Stephens et al., 2018; Fettig et al., 2019; Restaino et al., 2019; Stephenson et al., 2019). Increasing temperatures have allowed bark beetles to move further north and to higher elevations, survive through the winter at sites where they would previously have died and reproduce more often (Raffa et al., 2008; Bentz et al., 2010; Jewett et al., 2011; Macfarlane et al., 2013; Raffa et al., 2013; Hart et al., 2017; Stephenson et al., 2019; Teshome et al., 2020; Koontz et al., 2021). Under warmer conditions, some insects that were previously innocuous have become important agents of tree mortality (Stephenson et al., 2019; Trugman et al., 2021). Field observations show mixed effects of bark beetle-induced tree mortality on subsequent fire-caused tree mortality (Andrus et al., 2016; Meigs et al., 2016; Candau et al., 2018; Lucash et al., 2018; Talucci and Krawchuk, 2019; Wayman and Safford, 2021). From 1997 to 2018, ~5% of the forest area in the western USA died from bark beetle infestations (Hicke et al., 2020). Under most circumstances, trees that have been weakened by drought are more vulnerable to being killed by bark beetles (Anderegg et al., 2015; Kolb et al., 2016; Lloret and Kitzberger, 2018; Redmond et al., 2018; Stephens et al., 2018; Fettig et al., 2019; Restaino et al., 2019; Stephenson et al., 2019; Koontz et al., 2021). In summary, climate change has contributed to bark beetle infestations that have caused much of the tree mortality in North America (robust evidence, high agreement ) (see also Section

Across Europe, rates of tree mortality in field inventories from 2000 to 2012 were highest in Spain, Bulgaria, Sweden and Finland, positively correlated to maximum winter temperature and inversely correlated to spring precipitation (Neumann et al., 2017). Tree mortality in Austria, the Czech Republic, Germany, Poland, Slovakia and Switzerland doubled from 1984 to 2016, correlated with intensified logging and increased temperatures (Senf et al., 2018). Drought-related tree mortality rates from 1987 to 2016 were highest in the Ukraine, Moldova, southern France and Spain (Senf et al., 2020). Climate contributed to tree mortality across Europe from 1958 to 2001 (Seidl et al., 2011). In addition, insect infestations related to higher temperatures (Okland et al., 2019) have caused the extensive mortality of Norway spruce (Picea abies) across nine European countries (Marini et al., 2017; Mezei et al., 2017). Across the Mediterranean Basin, a combination of drought, wildfire, pest infestations and livestock grazing (Peñuelas and Sardans, 2021) has driven tree mortality. In summary, climate change has contributed to tree mortality in Europe (high confidence) (see also Section Tree mortality and fauna

A global meta-analysis of 59 studies encompassing 631 cases of animal abundance changes in areas of tree mortality over the past 7–59 years, primarly in North America and Australia, with a few sites in other regions (e.g. Europe). Overall, in areas with documented high tree mortality, bird abundances increased (n=186 bird species), there was no significant trend for mammals (n=33 species), a slight trend towards declines in invertebrates (n=28 species), and insufficient information to categorize the responses of reptiles (n=20 species). However, within groups, significant differences appeared. Mammals that use trees as refugia showed declines with tree mortality (high confidence) , but flying mammals (e.g. bats) increased (medium confidence). Ground-nesting, ground-foraging, tree-hole nesting and bark-foraging birds increased most, but nectar-feeding and foliage-gleaning birds declined (high confidence). Within invertebrates, declines were strongest in ground-foraging predators and detritivores (medium confidence) (Fleming et al., 2021). Observed Terrestrial Ecosystem Carbon Observed terrestrial ecosystem carbon globally

Terrestrial ecosystems contain carbon stocks: 450 GtC (range 380–540 GtC) in vegetation, 1700 ± 250 GtC in soils that are not permanently frozen and 1400 ± 200 GtC in permafrost (Hugelius et al., 2014; Batjes, 2016; Jackson et al., 2017; Strauss et al., 2017; Erb et al., 2018a; Xu et al., 2021a). Ecosystem carbon stocks, totalling 3000–4000 GtC (from the lowest and highest estimates above), substantially exceed the ~900 GtC carbon in unextracted fossil fuels (see(Canadell et al., 2021)).

Deforestation, draining of peatlands and the expansion of agricultural fields, livestock pastures and human settlements and other LULCCs emitted carbon at a rate of 1.6 ± 0.7 Gt yr -1 from 2010 to 2019, (Friedlingstein et al., 2020), of which wildfires and peat burning emitted 0.4 ± 0.2 Gt yr -1 from 1997 to 2016 (van der Werf et al., 2017). Anthropogenic climate change has caused some of these emissions through increases in wildfire (Section and tree mortality (Section, but the fraction of the total remains unquantified. LUC produced ~15% of global anthropogenic emissions, from fossil fuels and land (Friedlingstein et al., 2020). Terrestrial ecosystems removed carbon from the atmosphere through plant growth at a rate of -3.4 ± 0.9 Gt yr -1 from 2010 to 2019 (Friedlingstein et al., 2020).

Tropical deforestation and the draining and burning of peatlands produce almost all of the carbon emissions from LUC (Houghton and Nassikas, 2017; Friedlingstein et al., 2020), while forest growth accounts for two-thirds of ecosystem carbon removals from the atmosphere (Pugh et al., 2019b). Global terrestrial ecosystems comprised a net sink of -1.9 ± 1.1 Gt yr -1 from 2010 to 2019 (Friedlingstein et al., 2020), mainly due to growth in forests (Harris et al., 2021; Xu et al., 2021a), mitigating ~31% of global emissions from the burning of fossil fuels and LUC (Friedlingstein et al., 2020).

In summary, terrestrial ecosystems contain 3000–4000 GtC in vegetation, permafrost and soils, three to five times the amount of carbon in unextracted fossil fuels and 4.4 times the carbon currently in the atmosphere (robust evidence, high agreement ). Tropical deforestation, the draining and burning of peatlands and other LULCCs emit 0.9–2.3 GtC yr -1, ~15% of the global emissions from fossil fuels and ecosystems (robust evidence, high agreement ). Terrestrial ecosystems currently remove more carbon from the atmosphere (-3.4±0.9 Gt yr -1) than they emit (+1.6±0.7 Gt yr -1), a net sink of -1.9±1.1 Gt yr -1 (Friedlingstein et al., 2020) . Thus, tropical rainforests, Arctic permafrost and other ecosystems provide the global ecosystem service of naturally preventing carbon from contributing to climate change (high confidence). Observed stocks in high-carbon terrestrial ecosystems

The ecosystem that attains the highest above-ground carbon density in the world is the coast redwood (Sequoia sempervirens) forest in California, USA, with 2600 ± 100 tonnes ha -1 carbon (Van Pelt et al., 2016). The ecosystem with the second highest documented carbon density in the world is the mountain ash (Eucalyptus regnans) forest in Victoria, Australia, with ~1900 tonnes ha -1 (Keith et al., 2009). In the Tropics, tropical evergreen broadleaf forests (rainforests) in the Amazon, the Congo and Indonesia attain the highest carbon densities, reaching a maximum of 230 tonnes ha -1 in the Amazon (Mitchard et al., 2014) and the Congo (Xu et al., 2017). Temperature increases reduce the tropical rainforest above-ground carbon density 9.1 tonnes ha -1 per degree Celsius, through reduced growth and increased tree mortality (Sullivan et al., 2020).

Tropical forests contain the largest vegetation carbon stocks in the world, with 180–250 GtC above and below ground (Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016). The Amazon contains a stock of 45–60 GtC (Baccini et al., 2012; Mitchard et al., 2014; Englund et al., 2017).

Ecosystems with high soil carbon densities include the peat bogs in Ireland with up to 3000 tonnes ha -1 (Tomlinson, 2005), the Cuvette Centrale swamp forest peatlands in Congo with an average of ~2200 tonnes ha -1 (Dargie et al., 2017), the Arctic tundra with an average of ~900 tonnes ha -1 (Tarnocai et al., 2009) and the mangrove peatlands in Kalimantan, Indonesia, with an average of 850 ± 320 tonnes ha -1 (Murdiyarso et al., 2015). Arctic permafrost contains 1400 ± 200 GtC to a depth of 3 m, the largest soil carbon stock in the world (Hugelius et al., 2014). Globally, peatlands contain 470–620 GtC (Page et al., 2011; Hodgkins et al., 2018), of which boreal and temperate peatlands contain 415 ± 150 GtC (Hugelius et al., 2020) and tropical peatlands contain 80–350 GtC (Page et al., 2011; Dargie et al., 2017; Gumbricht et al., 2017; Ribeiro et al., 2021). Other analyses increase the upper estimates for boreal and temperate peatlands to 800–1200 GtC (Nichols and Peteet, 2019; Mishra et al., 2021b).

Tropical forests and Arctic permafrost contain the highest ecosystem carbon stocks in above-ground vegetation and soil, respectively, in the world (robust evidence, high agreement ). These ecosystems form natural sinks that prevent the emission to the atmosphere of 1400–1800 GtC that would otherwise increase the magnitude of climate change (high confidence). Biodiversity and observed terrestrial ecosystem carbon

High biodiversity and ecosystem carbon generally occur together, with rainforests in the Amazon, Congo and Indonesia containing the largest above-ground vegetation carbon stocks (Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016) and the highest vascular plant species richness (Kreft and Jetz, 2007) in the world. Above-ground ecosystem carbon and animal species richness show high correlation but also high spatial variability (Strassburg et al., 2010). Above-ground carbon is correlated to genus richness globally (Cavanaugh et al., 2014), but to species richness only in local areas (Poorter et al., 2015; Sullivan et al., 2017). Species richness generally increases vegetation productivity in the humid tropics while tree abundance increases productivity in drier conditions (Madrigal-Gonzalez et al., 2020). Across the Amazon, ~1% of tree species contain 50% of the above-ground carbon, due to abundance and maximum height (Fauset et al., 2015). Above-ground carbon in tropical forests shows positive correlations to vertebrate species richness (P values not reported) (Deere et al., 2018; Di Marco et al., 2018). In logged and burned tropical forest in Brazil, species richness of plants, birds and beetles increased with carbon density up to ~100 tonnes ha -1 (Ferreira et al., 2018).

National parks and other protected areas which, in June 2021, covered 15.7% of global terrestrial area (UNEP-WCMC et al., 2021) contain ~90 GtC in vegetation and ~150 GtC in soil (one-fifth and one-tenth, respectively, of global stocks) and remove carbon from the atmosphere at a rate of ~0.5 Gt yr -1 (one-sixth of global removals) (Melillo et al., 2016). The most strictly protected areas contain carbon at higher densities, but illegal deforestation and fires in some protected areas emit 38 ± 17 Mt yr -1 globally (Collins and Mitchard, 2017). In the Amazon, protected areas store more than half of the above-ground vegetation carbon stocks of the region, but account for only one-tenth of net emissions (Walker et al., 2020). Conservation of high biodiversity areas, particularly in protected areas, protects ecosystem carbon, prevents emissions to the atmosphere and reduces the magnitude of climate change (high confidence). Observed emissions and removals from high-carbon terrestrial ecosystems

Most global deforestation is occurring in tropical forests (Pan et al., 2011; Liu et al., 2015; Houghton and Nassikas, 2017; Erb et al., 2018a; Li et al., 2018; Harris et al., 2021), primarily as a result of clearing for agricultural land (Hong et al., 2021), causing primary tropical forest to comprise a net source of carbon from 2001 to 2019: emissions to the atmosphere 0.6 GtC yr -1, removals from the atmosphere -0.5 GtC yr -1 and net 0.1 GtC yr -1 (Harris et al., 2021). While wildfires emitted an average of 0.4 ± 0.2 GtC yr -1 from 1997 to 2016 (van der Werf et al., 2017), individual fire seasons can emit the same magnitude, such as the 0.4 GtC from the Amazon fires of 2007 (Aragao et al., 2018), the 0.5 GtC from the Amazon fires of 2015–2016 (Berenguer et al., 2021) and the 0.2 Gt from the Australia fires of 2019–2020 (Shiraishi and Hirata, 2021). Wildfires thus account for up to one-third of annual average ecosystem carbon emissions, while major fire seasons can emit up to two-thirds of global ecosystem carbon (medium evidence, medium agreement ).

Primary boreal and temperate forests also comprised net sources in the period 2001–2019; however, when including all tree age classes, boreal, temperate and tropical forests were net sinks (boreal -1.6 ± 1.1 Gt yr -1, temperate -3.6 ± 48 Gt yr -1), as growth exceeded permanent forest cover losses (Harris et al., 2021), with boreal and temperate forests being much stronger sinks (Pan et al., 2011; Liu et al., 2015; Houghton and Nassikas, 2017). Estimates of carbon removals from remote sensing may provide more accurate estimates of boreal forest carbon balances than ESMs which overestimate regrowth after timber harvesting and other disturbance (Wang et al., 2021a). Mortality of the boreal forest in British Columbia from mountain pine beetle infestations converted 374,000 km 2 from a net carbon sink to a net carbon source (Kurz et al., 2008). Modelling suggests that a potential increase in water-use efficiency and regrowth could offset the losses in part of the forest mortality area (Giles-Hansen et al., 2021).

The Amazon as a whole was a net carbon emitter in the period 2003–2008 (Exbrayat and Williams, 2015; Yang et al., 2018b), primarily due to the expansion of agricultural and livestock areas, which caused over two-thirds of deforestation from 1990 to 2005 (De Sy et al., 2015; De Sy et al., 2019). Four sites in the Amazon also showed net carbon emissions in the period 2010–2018, from deforestation and fire (Gatti et al., 2021). In the Amazon, deforestation emitted 0.17 ± 0.05 GtC yr -1 from 2001 to 2015 (Silva Junior et al., 2020) while fires emitted 0.12 ± 0.14 GtC yr -1 from 2003 to 2015 (Aragao et al., 2018). An analysis of the Amazon carbon loss from deforestation and degradation estimated a loss of 0.5 Gt yr -1 in the period 2010 -2019, with degradation accounting for three-quarters (Qin et al., 2021). Intact old-growth Amazon rainforest has been a net carbon sink from 2000 to 2010 (-0.45 Gt yr -1, min. 0.31, max. 0.57) (Hubau et al., 2020) but may have become a net carbon source in 2010–2019 (0.67 Gt, for the entire period, uncertainty not reported) (Qin et al., 2021). These factors combined—recent impacts of climate change on undisturbed forest, coupled with deforestation and agricultural expansion, along with associated intentional burning—have caused Amazon rainforest to become an overall net carbon emitter (medium confidence).

In Indonesia and Malaysia, draining and burning of peat swamp forests for oil palm plantations emitted 60–260 MtC yr -1 from 1990 to 2015, converting peatlands in that period from a carbon sink to a carbon source (Miettinen et al., 2017; Wijedasa et al., 2018; Cooper et al., 2020). Deforestation of mangrove forests caused 10–30% of deforestation emissions in Indonesia from 1980 to 2005 (Donato et al., 2011; Murdiyarso et al., 2015), even though mangroves comprised only 3% of Indonesia primary forest area in 2000 (Margono et al., 2014; Murdiyarso et al., 2015).

In North America, wildfire emitted 0.1 ± 0.02 GtC yr -1 from in the period 1990–2012, but regrowth was slightly greater, producing a net sink (Chen et al., 2017). In California, USA, two-thirds of the 70 MtC emitted from natural ecosystems in 2001–2010 came from the 6% of the area that burned (Gonzalez et al., 2015). Anthropogenic climate change caused up to half of the burned area (Section

In the Arctic, anthropogenic climate change has thawed permafrost (Guo et al., 2020), leading to emissions of 1.7 ± 0.8 GtC yr -1 in winter in the period 2003–2017 (Natali et al., 2019). Wildfires in the Arctic tundra in Alaska from ~1930 to 2010 caused up to a depth of 0.5 m of permafrost thaw (Brown et al., 2015), exposing peatland carbon (Brown et al., 2015; Gibson et al., 2018) including soil carbon deposits up to 1600 years old (Walker et al., 2019).

Tropical deforestation, the draining and burning of peatlands and the thawing of Arctic permafrost due to climate change have caused these ecosystems to emit more carbon to the atmosphere than they naturally remove through vegetation growth (high confidence). Observed Changes in Primary Productivity Observed changes in terrestrial primary productivity

The difference between photosynthesis by plants (gross primary productivity, GPP) and plant energy use through respiration is the net growth of plants (NPP), which removes CO2 from the atmosphere and mitigates emissions from deforestation and other LUCs (Section Global terrestrial NPP has exceeded emissions due to land use since the early 2000s, making terrestrial ecosystems a net carbon sink (Friedlingstein et al., 2020).

Global terrestrial NPP increased by 6% from 1982 to 1999 through increased temperature and increased solar radiation in the Amazon from decreased cloud cover (Nemani et al., 2003), and then decreased 1% from 2000 to 2009, because of extensive droughts in the Southern Hemisphere (Zhao and Running, 2010). From 1999 to 2015, increased aridity caused extensive declines in the NDVI globally, particularly semiarid ecosystems (Huang et al., 2016), indicating widespread decreases in NPP (Yuan et al., 2019).

Global terrestrial GPP increased 2% from 1951 to 2010 and continued increasing at least until 2016, with increased atmospheric CO2 showing a greater influence than natural factors (Li et al., 2017; Fernandez-Martinez et al., 2019; Liu et al., 2019a; Cai and Prentice, 2020; Melnikova and Sasai, 2020). Global forest area increased 7% from 1982 to 2016, mainly from forest plantations and regrowth in boreal and temperate forests in Asia and Europe (Song et al., 2018); regrowth in secondary forests >20 years old, mainly in boreal, temperate and subtropical regions, generated a net removal of 7.7 Gt yr -1CO2 from the atmosphere from 2001 to 2019 (Harris et al., 2021). Vegetation growth that exceeds the modelled CO2 fertilisation, gaps in field data and incomplete knowledge of plant mortality and soil carbon responses introduce uncertainties into quantifying the magnitude of CO2 fertilisation (Walker et al., 2021). A combination of CO2 fertilisation of global vegetation and secondary forest regrowth has increased global vegetation productivity (medium evidence, medium agreement ).

The relative increase in GPP per unit of increased atmospheric CO2 declined from 1982 to 2015, indicating a weakening of any CO2 fertilisation effect (Wang et al., 2020c). Increased growth from CO2 fertilisation has begun to shorten the lifespan of trees due to a trade-off between growth rate and longevity, based on analyses of tree rings of 110 species around the world (Brienen et al., 2020). Furthermore, water availability controls the magnitude of NPP (Beer et al., 2010; Jung et al., 2017; Yu et al., 2017), including water from precipitation (Beer et al., 2010), soil moisture (Stocker et al., 2019), groundwater storage (Humphrey et al., 2018; Madani et al., 2020a) and atmospheric vapour (Novick et al., 2016; Madani et al., 2020b). Drought stress reduced NPP across tropical forests from 2000 to 2015 (Zhang et al., 2019b) and GPP in the tropics from 1982 to 2016 (Madani et al., 2020b). Drought stress has also reduced GPP in some semiarid and arid lands (Huang et al., 2016; Liu et al., 2019a). In addition, nitrogen and phosphorus constrain CO2 fertilisation (Terrer et al., 2019), although phosphorus limitation of tropical tree growth is species-specific (Alvarez-Clare et al., 2013; Thompson et al., 2019). NPP has decreased during some time periods and in some regions where drought stress has exerted a greater influence than increased atmospheric CO2 (medium evidence, high agreement ). Observed changes in freshwater ecosystem productivity

Temperature affects primary productivity by moderating phytoplankton growth rates, ice cover, thermal stratification and the length of growing seasons (Rühland et al., 2015; Richardson et al., 2018). Global warming has reinforced eutrophication, especially cyanobacteria blooms (Wagner and Adrian, 2009; Kosten et al., 2012; O’Neil et al., 2012; De Senerpont Domis et al., 2013; Adrian et al., 2016; Visser et al., 2016; Huisman et al., 2018) (very high confidence). Conversely , warming can reduce cyanobacteria in hypertrophic lakes (Richardson et al., 2019). Freshwater cyanobacteria may benefit directly from elevated CO2 concentrations (Visser et al., 2016; Ji et al., 2017; Huisman et al., 2018; Richardson et al., 2019).

Macrophyte growth in freshwaters is likely to increase with rising water temperatures, atmospheric CO2 and precipitation (robust evidence, high agreement ) (Dhir, 2015; Hossain et al., 2016; Short et al., 2016; Reitsema et al., 2018). Nonetheless, primary productivity in rivers is variable and unpredictable (Bernhardt et al., 2018) because seasonal variations in temperature and light are uncorrelated, frequent high-flow events reduce biomass of autotrophs and droughts can strand and desiccate autotrophs.

In large, nutrient-poor lakes, warming-induced prolonged thermal stratification can reduce primary production (medium confidence) (Kraemer et al., 2017). Warming may reduce phytoplankton concentrations when temperature-induced increases in consumption of phytoplankton outpace increases in phytoplankton production (De Senerpont Domis et al., 2013). These decreases in productivity may be under-recognised responses to climate change.

Summary: There is robust evidence of an increase in primary production along with warming trends. However, increases or declines of algae cannot entirely be attributed to climate change; they are lake-specific and modulated through weather conditions, lake morphology, salinity, land use and restoration and biotic interactions (medium confidence) (O’Beirne et al., 2017; Velthuis et al., 2017; Rusak et al., 2018; Ho et al., 2019).

FAQ 2.3 | Is climate change increasing wildfire?

In the Amazon, Australia, North America, Siberia and other regions, wildfires are burning wider areas than in the past. Analyses show that human-caused climate change has driven the increases in burned area in the forests of western North America. Elsewhere, deforestation, fire suppression, agricultural burning and short-term cycles like El Niño can exert a stronger influence than climate change. Many forests and grasslands naturally require fire for ecosystem health but excessive wildfire can kill people, destroy homes and damage ecosystems.

Figure FAQ2.3.1 | (a) Springs Fire, May 2, 2013, Thousand Oaks, California, USA (photo by Michael Robinson Chávez, Los Angeles Times). (b) Cumulative area burned by wildfire in the western USA, with (orange) and without (yellow) the increased heat and aridity of climate change.

Wildfire is a natural and essential part of many forest, woodland and grassland ecosystems, killing pests, releasing plant seeds to sprout, thinning out small trees and serving other functions essential for ecosystem health. Excessive wildfire, however, can kill people with the smoke causing breathing illnesses, destroy homes (Figure FAQ2.3.1a) and damage ecosystems.

Human-caused climate change increases wildfire by intensifying its principal driving factor, heat. The heat of climate change dries out vegetation and accelerates burning. Non-climate factors also cause wildfires. Agricultural companies, small-scale farmers and livestock herders in many tropical areas cut down forests and intentionally set fires to clear fields and pastures. Cities, towns and roads increase the number of fires that people ignite. Governments in many countries suppress fires, even natural ones, producing unnatural accumulations of fuel in the form of coarse woody debris and dense stands of small trees. The fuel accumulations cause particularly severe fires that burn upwards into tree crowns.

Evidence shows that human-caused climate change has driven increases in the area burned by wildfire in the forests of western North America. Across this region, the higher temperatures of human-caused climate change doubled burned area from 1984 to 2015, compared with what would have burned without climate change (Figure FAQ2.3.1b). The additional area burned, 4.9 million hectares, is greater than the land area of Switzerland. Human-caused climate change drove a drought from 2000 to 2020 that has been the most severe since the 1500s, severely increasing the aridity of vegetation. In British Columbia, Canada, the higher maximum temperatures of human-caused climate change increased burned area in 2017 to its widest extent in the 1950–2017 record, seven to eleven times the area that would have burned without climate change. Moreover, in national parks and other protected areas of Canada and the USA, most of the area burned from 1984 to 2014 can be attributed to climate factors (temperature, rainfall and aridity) and these outweigh local human factors (population density, roads and urban area).

In other regions, wildfires are also burning wider areas and occurring more often. This is consistent with climate change, but analyses have not yet shown if climate change is more important than other factors. In the Amazon, deforestation by companies, farmers and herders who cut down and intentionally burn rainforests to expand agricultural fields and pastures causes wildfires even in relatively moister years. Drought exacerbates these fires. In Australia, much of the southeastern part of the continent has experienced extreme wildfire years, but analyses suggest that El Niño, a heat phenomenon that cycles up and down periodically, is more important than long-term climate change. In Indonesia, intentional burning of rainforests for oil palm plantations and El Niño seem to be more important than long-term climate change. In Mediterranean Europe, fire suppression seems to have prevented any increasing trend in burned area but the suppression and abandonment of agricultural lands have allowed fuel to build up in some areas and contribute to major fires in years of extreme heat. In Canada and Siberia, wildfires are now burning more often in permafrost areas where fire was rare, but analyses are lacking regarding the relative influence of climate change. For the world as a whole, satellite data indicate that the vast amount of land converted from forest to farmland in the period 1998–2015 actually decreased the total burned area. Nevertheless, the evidence from the forests of western North America shows that human-caused climate change has, at least on one continent, clearly driven increases in wildfire.

2.4.5 Conclusions on Observed Impacts

The consistency of patterns of biological change with expectations from regional or global warming processes, coupled with an understanding of underlying processes and the coherence of these patterns at both regional and global scales, all form multiple lines of evidence (Parmesan et al., 2013) that it is very likely that the observed range shifts and phenological changes in individual species can be attributed to regional and global climate changes (very high confidence) (Section 2.4.2, Table 2.2; Table 2.3; Table SM2.1) (Parmesan et al., 2013).

Global and regional meta-analyses of diverse systems, habitats and taxonomic groupings document that approximately half of all species with long-term records have shifted their ranges poleward and/or upward in elevation and ~2/3 have advanced their timing of spring events (phenology) (very high confidence) (Section 2.4.2, Table 2.2) (Parmesan and Hanley, 2015; Parmesan, 2019). Changes in abundance tend to match predictions from climate warming, with warm-adapted species significantly outperforming cold-adapted species in warming habitats (Feeley et al., 2020) and the composition of local communities becoming more ‘thermophilised’, that is, experiencing an ‘increase in relative abundance of heat-loving or heat-tolerant species’ (high confidence) (Section (Cline et al., 2013; Feeley et al., 2020).

New studies since AR5, with more sophisticated analyses designed to capture complex responses, indicate that past estimates of the proportion of species impacted by recent climate change were underestimates due to unspoken assumptions that local or regional warming should lead solely to poleward/upward range shifts and advancements of spring timing (high confidence) (Duffy et al., 2019). More complex analyses have documented cases of winter warming driving delayed spring timing of northern temperate species due to chilling requirements, and increased precipitation driving species’ range shifts downslope in elevation, and eastward and westward in arid regions (high confidence). Further new studies have shown that phenological changes have, in some cases, successfully compensated for local climate change and reduced the extent of range shifts (medium confidence). The limited number of studies of this type make it difficult to estimate the generality of these effects globally (Section, Table 2.2).

Responses in freshwater species are consistent with responses in terrestrial species, including poleward and upward range shifts, earlier timing of spring plankton development, earlier spawning by fish and the extension of the growing season (high confidence). Observed changes in freshwater species are strongly related to anthropogenic climate change-driven changes in the physical environment (e.g., increased water temperature, reduced ice cover, reduced mixing in lakes, loss of oxygen and reduced river connectivity) (high confidence). While evidence is robust for an increase in primary production in nutrient rich lakes along with warming trends (high confidence), increasing or declining algal formations are lake-specific and are modulated through variability in weather conditions, lake morphology, changes in salinity, stoichiometry, land use and restoration measures and food web interactions. In boreal coniferous forest, there has been an increase in terrestrial-derived DOM transported into rivers and lakes as a consequence of climate change (which has induced increases in runoff and greening of the Northern Hemisphere) as well as from changes in forestry practices. This has caused waters to become brown, resulting in an acceleration of upper-water warming and an overall cooling of deep water (high confidence). Browning may accelerate primary production through the input of nutrients associated with DOM in nutrient-poor lakes and increases the growth of cyanobacteria, which cope better with low light intensity (medium confidence) (Sections,,,

Field research since the AR5 has detected biome shifts at numerous sites, poleward and upslope, that are consistent with increased temperatures and altered precipitation patterns driven by climate change, and support prior studies that attributed such shifts to anthropogenic climate change (high confidence). New studies help fill previous geographic and habitat gaps, for example, documenting upward shifts in the forest/alpine tundra ecotone in the Andes, Tibet and Nepal, and northward shifts in the deciduous/boreal forest ecotones in Canada. Globally, woody encroachment into open areas (grasslands, arid regions and tundra) is likely being driven by climate change and increased CO2, in concert with changes in grazing and fire regimes (medium confidence) (Section 2.4.3).

Climate change has driven, or is contributing to, increased tree mortality directly through increased aridity and droughts and indirectly through increased wildfires and insect pests in many locations (high confidence). Analyses of causal factors have attributed increasing tree mortality at sites in Africa and North America to anthropogenic climate change, and field evidence has detected tree mortality due to drought, wildfires and insect pests in temperate and tropical forests around the world (high confidence). Water stress, leading to plant hydraulic failure, is a principal mechanism of drought-induced tree mortality, along with the indirect effects of climate change mediated by community interactions (high confidence) (Section

Terrestrial ecosystems sequester and store globally critical stocks of carbon, but these stocks are at risk from deforestation and climate change (high confidence). Tropical deforestation and the draining and burning of peatlands produce almost all of the carbon emissions from LULCC. In the Arctic, increased temperatures have thawed permafrost at numerous sites, dried some areas and increased fires, causing net emissions of carbon from soils (high confidence) (Sections,

Globally, increases in temperature, aridity and drought have increased the length of fire seasons and doubled the potentially burnable land area (medium confidence). Increases in the area burned have been attributed to anthropogenic climate change in North America (high confidence). In parts of Africa, Asia, Australia and South America, the area burned has also increased, consistent with anthropogenic climate change. Deforestation, peat-burning, agricultural expansion or abandonment, fire suppression and inter-decadal cycles strongly influence fire occurrence. The areas with the greatest increases in the length of the fire season include the Amazon, western North America, western Asia and East Africa (Section

The changes in biodiversity and ecosystem health that we have observed, and project will continue, pose a risk of declines in human health and well-being (e.g., tourism, recreation, food, livelihoods and quality of life) (medium confidence). Clear attribution of these impacts is often not possible, but inferences can be made by comparison of the observed changes in biodiversity/ecosystem health and the known services from these particular ecosystems.

2.5 Projected Impacts and Risk for Species, Communities, Biomes, Key Ecosystems and Their Services

Under the risk assessment framework that was introduced in AR5 (IPCC, 2014b), risk means the probability of harmful consequences resulting from climate change. It results from the interaction of vulnerability, exposure and hazard and can be represented as the probability of occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur (see Chapter 1, this report). The framework defines vulnerability as a pre-existing condition, incorporating the extent to which species or ecosystems are susceptible to, or unable to cope with, the adverse effects of climate change. Vulnerable species have limited adaptive capacity, stemming from physiological and behavioural constraints, limited dispersal abilities and restricted resource requirements or capacities for distributional and genetic changes (Foden et al., 2013; Cizauskas et al., 2017; Foden et al., 2019). Traits that render entire ecosystems vulnerable are harder to define, but it is clear that vulnerabilities are high in the coldest habitats, in those with limited geographic ranges such as low-lying islands and in specialised, restricted habitats such as serpentine outcrops in California (Anacker and Harrison, 2012) and dry meadows in Fennoscandia and Tibet (Yang et al., 2018a). Ecosystem vulnerability can depend critically on the fates of plants that function as ‘foundation species’, providing community biomass above and below the ground, structuring habitat for fauna and providing ecosystem services such as erosion control (Camac et al., 2021).

2.5.1 Projected Changes at Species and Community Levels Assessment of Models and Sources of Uncertainties

Methods for projecting the impacts of climate change on biodiversity can be classified into three types: (1) statistical models such as SDMs (Elith and Leathwick, 2009); (2) mechanistic or process-based models (Chuine and Régnière, 2017) and (3) trait-based models (Pacifici et al., 2015). It is only recently that models have been developed looking at lower levels of warming like 1.5°C (Hoegh-Guldberg et al., 2018; Warren et al., 2018).

SDMs or niche-based models assess potential geographic areas of suitable climate for the species in current conditions and then project them into future conditions (Trisurat, 2018; Vieira et al., 2018). There are limitations in all models and it is critical that modellers understand the assumptions, proper parameterization and limitations of each model technique, including differences between climate models, emission scenarios or RCPs and baselines (Araujo et al., 2019). Several systems automate the development of SDMs, including R-packages (Beaumont et al., 2016; Hallgren et al., 2016), and other model types (Foden et al., 2019) and aid in the use of climate model data (Suggitt et al., 2017), including allowing for connectivity constraints (Peterson et al., 2013). Buisson et al. (2010) found most variation in model outputs stems from differences in design, followed by general circulation models (GCMs).

Mechanistic approaches, also known as process-based models, project the responses of species to climate changes by explicitly incorporating known biological processes, thresholds and interactions (Morin and Thuiller, 2009; Maino et al., 2016). Mechanistic models are able to accommodate a broad range of mechanisms of climate change impacts and include species-specific characteristics such as dispersal distances, longevity, fecundity, genetic evolution and phenotypic plasticity. However, sufficient knowledge is available for only a few well-studied species. Species’ traits have been used to more broadly estimate potential climate change impacts (Foden et al., 2013; Cizauskas et al., 2017).

Most models are on a large scale (20–50 km), and so cannot capture micro-climatic refugia generated by diversities of slope aspect, elevation or shade (Suggitt et al., 2015; Suggitt et al., 2018). In analysing records of 430 climate-threatened and range-declining species in England, (Suggett et al., 2015; Suggitt et al., 2018) showed that topographic diversity reduced population declines most strongly in areas experiencing the most local warming and in the species most sensitive to warming. Under these circumstances, topographic diversity reduced the risk of population extinction by 22% for plants and 9% for insects.

None of the modelling techniques are predictions of the future, they are rather projections of possible futures. To date, only a few studies have validated model performance against observations, but the studies that have been conducted do generally validate models using either SDMs or process-based models (Johnston et al., 2013; Fordham et al., 2018). SDMs should be considered as hypotheses of what a future world might look like if the climate projections came to pass. Suggestions have been made on how to start bringing more biotic interactions into SDMs (Early and Keith, 2019), but limited basic ecological understanding of interactions, along with limits on computation and funding, constrains how far and how fast these modelling techniques can advance.

Table 2.3 | Assessing uncertainty in detection and attribution of observed changes in terrestrial and freshwater species and ecosystems to climate change. The lines of evidence used to support given uncertainty statements, including confidence statements, of the attribution of key conclusions on observed biological changes to climate change and increased atmospheric CO2. Icons represent lines of evidence. This is a summary table that is fully detailed in Table SM2.1. Risk Assessment and Non-Modelling Approaches

In order to add realism and reliability to risk assessments at the species and community levels, non-modelling approaches, based on known biological traits or processes as well as on expert opinion (Camac et al., 2021), are used to temper model outputs with ground-based validation. Trait-based assessment approaches use species’ biological characteristics as predictors of sensitivity, adaptive capacity and extinction risk due to climate change. Climate exposure can be estimated using GIS-based modelling, statistical programs or expert judgment (Chin et al., 2010). These trait-based approaches are widely applied to predict responses of biodiversity to climate change because they do not require modelling expertise or detailed distibutional data (Pacifici et al., 2015; Willis et al., 2015). Most of these methods have not been independently validated and do not allow direct comparison of vulnerability and risk across taxonomic groups.

Some studies have combined two or three approaches for the assessment of climate change risk to biodiversity, in order to capture the advantages of each and avoid their limitations. Warren et al. (2013) used combinations of SDMs and trait-based approaches to estimate the proportions of species losing their climatically suitable ranges under the various future scenarios of climate and dispersal rate. Similarly, spatial projections of exposure to climate change were combined with traits to assess the vulnerability of sub-Saharan amphibians (Garcia et al., 2014). Laurance et al. (2012) combined 31 functional groups of species and 21 potential drivers of environmental change, in order to assess both the ecological integrity and threats to protected tropical areas on a global scale. Keith et al. (2014) used a combination of three approaches (SDMs–trait–mechanistic) to determine how long before extinction a species would become eligible for listing as threatened, based on the IUCN Red List criteria. Risk of Species’ Extinctions Overview

This assessment of current findings is of studies across a range of taxa and modelling techniques. Extinction risk estimates whether or not a particular species may be at risk of extinction over the coming decades if climatic trends continue, and usually does not take into account other human-induced stressors (e.g., invasive species or pollution). It is not a prediction that a species will definitely become extinct because, even when complete loss of a species’ range is projected, the scale of the model cannot estimate persistence in very small-scale micro-climatic refugia (that can be on the order of metres in size) (Suggitt et al., 2015; Suggitt et al., 2018). Individuals and populations can survive after the conditions for successful reproduction are gone, leading to a lagged decline, called ‘extinction debt’ (see section (Alexander et al., 2018). Therefore, range loss is an established criterion for assessing endangerment status and risk of extinction. As a species range becomes smaller and occupied habitats become more isolated, the likelihood of a single stochastic event causing extinction increases. It is this combination of projected loss of climatically suitable space and additional stressors (especially LULCC of critical habitat) that is expected to drive future extinctions.

The IUCN Red List Criteria (IUCN, 2019) classifies a species as ‘critically endangered’ if it has suffered a range loss of ≥80%, with a resulting likelihood of extinction of >50% in the near term (10–100 yrs, depending upon generation length). A species is classified as ‘endangered’ if it has suffered a range loss of ≥50%, with a resulting likelihood of extinction of >20% in the near term (10–100 years). In this assessment, a species that is projected to become classified as ‘endangered’ is deemed to be at ‘high risk’ of extinction, and becoming classified as ‘critically endangered’ is deemed at ‘very high risk’ of extinction. Projections for freshwater biodiversity

Because risk to freshwater species has been limited in past reports, this section provides details of freshwater risk. Lakes, rivers and freshwater wetlands cover approximately 7.7–9.1% of global land surface area; (Lehner et al., 2008; Fluet-Chouinard et al., 2015; Allen and Pavelsky, 2018) and hold 9.5% of the Earth’s described animals (Balian et al., 2008), with climate change indicated as a threat to 50–75% of fish (Xenopoulos et al., 2005; Darwall and Freyhof, 2015). Climate change is cited as a primary factor in species’ extinction risk due to changes in water temperatures, stream flow, loss of cold water habitat, increased variability of precipitation and increased disease risk from warming temperatures (robust evidence, high agreement , high confidence) (Knouft and Ficklin, 2017; Pletterbauer et al., 2018; Jaric et al., 2019; Reid et al., 2019) adding to the stress of overexploitation and LULCC (Craig et al., 2017; IPBES, 2019).

Increased frequency of stream drying events, reducing hydrologic connectivity and limiting access of native fishes to spawning habitats is projected for RCP8.5 in Colorado, USA (medium evidence, medium agreement ) (Jaeger et al., 2014). Cold-water habitats and associated obligate species are particularly vulnerable, and losses in these habitats have been both documented and projected, for example, in salmonids (Santiago et al., 2016; Fullerton et al., 2017; Merriam et al., 2017). River networks are projected to lose connections to cold tributary refugia, that are important thermal refuges for cold water species (robust evidence, high agreement ) (Isaak et al., 2016) during low flows (Merriam et al., 2017).

Community turnovers are expected in freshwaters as cold-adapted species lose and warm-adapted species gain climatically suitable habitat (Domisch et al., 2011; Domisch et al., 2013; Shah et al., 2014). While a number of warm-adapted species may experience range expansions, the majority of species are predicted to lose climatically suitable areas by, on average, 38–44%, depending on the emission scenario (A2a and B2a) (medium evidence) (Domisch et al., 2013).

Molluscs are projected to be the most at-risk group, given their limited dispersal capability (Woodward et al., 2010). Mediterranean freshwater fish are especially susceptible to climate change due to increasing flood and drought events and the risk of surpassing critical temperature thresholds (Santiago et al., 2016; Jaric et al., 2019). In southern Europe, aquatic insects (Ephemeroptera, Plecoptera and Trichoptera) are endangered by climate change (Conti et al., 2014). European protected areas are not expected to be sufficient under warming to provide habitat for the majority of rare molluscs and fish (Markovic et al., 2014). Observed trends agree with model projections in direction, but magnitude remains uncertain (medium evidence, medium agreement , medium confidence) (see Figure 2.8 for extinction risk globally for dragonflies, amphibians and turtles).

Regional threats from climate change have been reported for 40% of amphibians in China, (Wu, 2020), 33% of European freshwater fish species (Janssen et al., 2016) and 56–69% of odonates in Australia, (Bush et al., 2014b). Assessment of site-specific extirpation for 88 aquatic insect taxa projected that climate change-induced hydrological alteration would result in a 30–40% loss of taxa in warmer, drier ecoregions and a 10–20% loss in cooler, wetter ecoregions (medium evidence, medium agreement ) (Pyne and Poff, 2017). In Africa’s Albertine Rift, 51% (n= 551) of fish are expected to be impacted by climate change, with 5.5% at a high risk due to their sensitivity and poor adaptative capability (medium evidence, high agreement ) (Carr et al., 2013).

The GLOBIO-Aquatic model (Janse et al., 2015 a) links models for demography, economy, LUCs, climate change, nutrient emissions, a global hydrological model and a global map of water bodies. It projects that changes in both water quality (eutrophication) and quantity (flow) will generate negative relations in freshwater ecosystems between the persistence of species originally present in each community and a constellation of stressors, including harmful algal blooms. Under a 4°C rise by 2050, mean abundance of species is projected to decline by 70% in running water and by 80% in standing water (medium evidence, high agreement , medium confidence) (Janse et al., 2015 a ). Global projections of extinction risk

In previous reports, risk assessed from the literature was generally based on estimates of overall range contractions with climate change. In AR4, extinction risk was carefully quantified: ‘There is medium confidence that approximately 20–30% of species assessed so far are likely to be at increased risk of extinction if increases in global average warming exceed 1.5–2.5°C (relative to mean temperatures from 1980–1999). As global average temperature increase exceeds about 3.5°C, model projections suggest significant extinctions (40–70% of species assessed) around the globe.’ These estimates approximately correspond to 50–80% reductions in range size (depending upon study), that this assessment equates with a ‘high’ and ‘very high’ extinction risk, respectively (IPCC, 2007). AR5 stated: ‘a large fraction of terrestrial and freshwater species face increased extinction risk under projected climate change during and beyond the 21st century, especially as climate change interacts with other pressures (high confidence)’ (Field et al., 2014). A series of multi-species and global analyses have been published since AR5, using both statistical models and trait-based approaches.

In this chapter, risk to species, with implications for ecosystems, is assessed using three different approaches. First is an assessment of the geographic distributions of local species’ losses at different levels of GSAT warming, termed ‘local biodiversity loss’, measured as the proportion of species within a given location becoming classified as “endangered” or worse (sensu IUCN), and so at highrisk of local population losses (local population extinctions) (Figure 2.6). This measure provides the best estimates of which sites are at most risk of losing substantial numbers of species locally, leading to degradation of that ecosystem’s ability to function.

Figure 2.6 | Biodiversity loss for different areas at increasing levels of climate change. The higher the percentage of species projected to lose suitable climate in a given area, the higher the risk to ecosystem integrity, functioning and resilience to climate change. Warming levels are based on global levels (GSAT) above pre-industrial temperatures. Colour shading represents proportion of species for which the climate is projected to become sufficiently unsuitable that the species becomes locally ‘endangered’ and at high risk of local extinction within a given pixel across their current distributions at a given GSAT warming level, based on underlying data (Warren et al., 2018) (modelled n= 119,813 species globally, with no dispersal, averaged over 21 CMIP5 climate models). Areas shaded in deep orange and red represent a significant risk of biodiversity loss (areas where climates become sufficiently unsuitable that it renders >50% and >75% of species at high ris k of becoming locally extinct, respectively). The maps of species richness remaining have been overlaid with a landcover layer (2015) from the European Space Agency (ESA) Climate Change Initiative. This landcover layer leaves habitats classified by the ESA as natural as transparent. Areas with a landcover identified as agriculture are 5% transparent, such that the potential species richness remaining if the land had not been converted for agriculture shows as pale shading of the legend colours (very pale yellow to very pale red). These paler areas represent biodiversity loss due to habitat destruction, but with a potential to be restored, with yellow shading having the potential for restoration to greater species richness than orange or red shading.

Second is assessment of the proportions of species becoming endangered globally (not just locally), so at highrisk of global extinction of the species, termed ‘global biodiversity loss’ (Figure 2.8b). This metric (losing > 50% of suitable climate space across the species’ entire range) also serves to estimate a species’ becoming sufficiently rare that the species no longer fully contributes to ecosystem functioning, a state that often occurs decades before complete extinction (death of the last individual). The proportions of species becoming at highrisk of global extinction is the foundation for the burning embers diagram on global biodiversity loss in Table 2.5 and Figure 2.11.

Third is an assessment of risk of the proportions of species becoming at very highrisk of extinction globally at different levels of GSAT warming, measured using the IUCN criteria for ‘critically endangered’, and termed ‘species’ extinction risk’ (Figure 2.7 and Figure 2.8a). This measure is closest to assessing the complete loss of a species in the wild and can be used to compare to past (palaeo) extinction rates. These three approaches provide complementary information of the overall risks to individual species, to biodiversity at the community scale, and to ecosystem integrity and functioning at different levels of warming.

Figure 2.7 | Global assessment of species’ extinction risks under different levels of warming. Graph shows a synthesis of climate-driven models of individual species projected to become at very highrisk of extinction globally (i.e, becoming “critically endandered” sensu IUCN by losing >80% of their suitable climate space or through estimates of extinction risk from process-based models). The relationship between modelled projections of extinction (expressed as a proportion of species at a risk of extinction assessed in individual studies) and GSAT increase above the pre-industrial average. Data (global sample size n= 178 modelled estimates) were taken from a number of sources, including digitization of data points in Figure 2 in the synthetic analysis of (Urban, 2015) (note: unweighted for sample size) n= 126; Table 4.1 of AR4 WGII Chapter 2 (Fischlin et al., 2007), n= 40; (Hannah et al., 2020)n= 6; and (Warren et al., 2018) n= 6. The quantile regression (which is robust to the non-normal distribution of the response variable, and less sensitive to data outliers) was chosen as a descriptive statistic to fit quantile estimates for levels relevant to informing likely (between the 0.17 and 0.83 quantiles, shaded in orange) and very likely ranges (between the 0.05 and 0.95 quantiles, shaded in green) relating extinction risk to GSAT increase (quantile regression implemented using the Barrodale and Roberts algorithm in XLSTAT). The roughly equivalent estimate of this risk as expressed in AR4 (Fischlin et al., 2007) is indicated by the dotted block indicating the medium confidence statement ‘Approximately 20–30% of plant and animal species assessed so far (in an unbiased sample) are likely to be at increasingly high risk of extinction as global mean temperatures exceed a warming of 2–3°C above pre-industrial levels (medium confidence). ’ This box is open on the right side because AR4 estimates stipulated temperatures at or exceeding the given levels. Thick dark horizontal bars show the median values of percent of species at very high risk of extinction at 1.5°C, 2°C, 3°C, 4°C and 5°C, indicating that half of the data points lie above the bar and half below for a given level of global warming.

Risk of local biodiversity loss, estimated as the proportion of species in a given area projected to become endangered (sensu IUCN), and therefore at highrisk of extinction, is projected to affect a greater number of regions experiencing increasing warming. About one-third of land area risks more than 50% of species becoming “endangered” by 4.0° GSAT warming (Figure 2.6). That is, the deep orange and red areas in Figure 2.6 are those areas for which >50% of species currently inhabiting those ecosystems are projected to lose >50% of their climatically suitable habitat. Species’ losses are projected to be worst in northern South America, southern Africa, most of Australia and at northern high latitudes (medium confidence) (Figure 2.6).

For risk of global biodiversity loss, at 1.58°C global warming (median estimate), >10% of species are projected to become “endangered”, and so at highrisk of extinction (sensu IUCN). At 2.07°C (median) >20% of species are projected to become endangered. Ten-twenty percent losses represent high and very highrisk of biodiversity losses, respectively, substantial enough to reduce ecosystem integrity and functioning (medium confidence) (Figure 2.8b) (see Section 2.5.4; Figure 2.11; Table 2.5, Table SM2.5).

Risk of global biodiversity loss differs among taxonomic groups. The percent of species projected at high risk of extinction was 49% for all insects, 44% for all plants and 26% for all vertebrates at ~3°C global rise in temperature (Figure 2.8b) (Warren et al., 2018). These estimates dropped considerably at lower levels of warming, down to 18%, 16% and 8% at 2°C; and 6%, 8% and 4% at 1.5°C (Figure 2.8b) (Warren et al., 2018), so not entirely dissimilar to the numbers in AR4 (Figure 2.7).

Figure 2.8 | Percent of species of different groups classified as being at risk of extinction.

(a) Species groups listed projected to be at a very highrisk of extinction, corresponding to the IUCN Red List criteria for a species classified as ‘critically endangered’ (v3.1) by losing >80% of its climatically suitable range area.

(b) Species groups listed projected to be at a highrisk of extinction, corresponding to the IUCN Red List criteria for a species classified as ‘endangered’ (v3.1) by losing >50% of its climatically suitable range area. For (a) and (b), values were calculated from the underlying data in (Warren et al., 2018). Values for each temperature are the mean values across 21 CMIP5 models. The grey band represents the high end of extinction risk from the 10th percentile of the climate models to show the maximum range of values, while the low end (90th percentile, 1.5°C) is not shown as it is too small to appear on the plots. Taxa marked with * represent potential benefits from adaptation, specifically dispersal at realistic rates (Warren et al., 2018); those with no * have dispersal rates that are essentially not detected in the spatial resolution of the models (20 km). See (Warren et al., 2018) for caveats and more details. Sample size for each group is as follows: 1) fungi (16187 species); 2) all plants (72399 species), broken down into sub-groups of plants: flowering plants (52310 species), timber species (1328 species), grasses (3389 species) and pines (340 species); 3) all invertebrates (33,949 species), broken down into sub-groups of invertebrates: annelid worms (155 species), flies (4809 species), beetles (7630 species), moths (6910 species), true bugs (1728 species), spiders (2212 species), all pollinators (1755 species), butterflies (1684 species), ants/bees/wasps (5914 species), dragonflies (599 species); 4) Chordates (12642 species), broken down into major groups: 4i) all amphibians (1055 species), broken down into sub-groups of amphibians: frogs (887 species) and salamanders (163 species); 4ii) reptiles (1850 species), snakes (1741 species) and turtles (94 species); 4iii) all mammals (1769 species), broken down into sub-groups of mammals: ungulates (80 species), bats (500 species), carnivores (107 species), 4iv) all birds (7968 species), broken down into sub-groups of birds: passeriforme birds (4744 species), and non-passeriforme birds (3224 species).

‘Species’ extinction risk’, estimated as at very highrisk of extinction globally, i.e. becoming “critically endangered” (sensu IUCN) is shown in Figures 2.7 across 178 studies and in Figure 2.8a split by taxonomic group. The percentage of species at very highrisk of extinction (median estimates and maximum likely range) will be 9% (max. 14%) at 1.5°C, 10% (max. 18%) at 2°C, 12% (max. 29%) at 3.0°C, 13% (max. 39%) at 4°C and 15% (max. 48%) at 5°C (Figure 2.7). Maximum estimates of species at very highrisk of extinction reach 60% within the 95% quartiles, ie the very likely range, for 5°C GSAT warming. Among the groups containing the largest numbers of species at a very high risk of extinction for mid-levels of projected warming (3.2°C rise in GSAT) are: invertebrates (15%), specifically pollinators (12%), amphibians (11%, but 24% for salamanders) and flowering plants (10%) (Figure 2.8a). All groups fare substantially better at 2°C, with extinction projections reducing to <3% for all groups, except salamanders at 7% (medium confidence) (Figure 2.8a).

Figure 2.8 also shows the benefits of dispersal in reducing extinction risk in birds, mammals, butterflies, moths and dragonflies (depicted with an asterix). While dispersal may benefit individual species, it poses additional risks to communities and ecosystems that species are moving into, as interactions between species are changed or eliminated.

Projected species extinctions at future global warming levels are in accord with projections from AR4, assessed on much larger numbers of species with much greater geographic coverage and a broader range of climate models. (Figure 2.7; Figure 2.8a). Even the lowest estimates of species extinction (median of 9% at 1.5°C warming, Figure 2.7) are 1000 times the natural background rates (De Vos et al., 2015).

Using data from geological timescales, (Song et al., 2021) predicted that a warming of 5.2°C above pre-industrial levels would result in a mass extinction comparable to that of the five mass extinctions over the past 540 Myr, on the order of 70–85% of species becoming extinct, in the absence of non-climatic stressor. (Mathes et al., 2021) found evidence in the geological record that short-term rapid warming, on top of long-term warming trends, increases extinction risk by up to 40% over that expected from the long-term trend alone, with a biodiversity ‘memory’ of up to 60 Myr, indicating an additonal risk of multi-decadal overshoot.

Most of the large-scale studies that have been performed are for losses based on climate alone (Figures 2.6, 2.7, 2.8). However, climate is rarely the only stressor affecting species survival. Habitat loss is currently the largest driver of range loss and extinction risk for most species (IUCN, 2019). Communities in different regions are becoming more similar to each other as species tolerant of human activities prosper and spread, with many rare and endemic species already having been driven to extinction, primarily by LULCC (Pimm et al., 2006). Thus, it will likely be the interaction of climate change and habitat conversion (often driven by climate change) that will ultimately determine the risk and ability of many species to survive over the next century. Changing Risks of Diseases

Multiple studies predict increases in disease incidence or geographic and phenological changes of pathogens, vectors and reservoir host species due to climate change with or without other non-climatic variables (González et al., 2010; Moo-Llanes et al., 2013; Roy-Dufresne et al., 2013; Liu-Helmersson et al., 2014; Laporta et al., 2015; Ryan et al., 2015; Haydock et al., 2016; Hoover and Barker, 2016; Prist et al., 2017; Blum and Hotez, 2018; Dumic and Severnini, 2018; Hundessa et al., 2018; Ryan et al., 2019; Ryan et al., 2021). However, models predicting changes in infectious disease risk are complex and sometimes produce conflicting results and lack consensus (Caminade et al., 2014; Giesen et al., 2020). For example, malaria is projected to increase in some regions of Africa, Asia and South America by the end of the 21st century if public health interventions are not sufficient, but is also forecasted to decrease in some higher-risk areas (Cross-Chapter Box Illness in this chapter) (Peterson, 2009; Caminade et al., 2014; Ryan et al., 2015; Khormi and Kumar, 2016; Leedale et al., 2016; Murdock et al., 2016; Endo and Eltahir, 2020; Mordecai et al., 2020).

While malaria risk is predicted to decrease in some lowland tropical areas as temperatures become too hot for vector or parasite development, other warm-adapted diseases, like dengue and Zika, transmitted by A. aegypti, are predicted to increase (Cross-Chapter Box Illness in this chapter, chapter 7) (Ryan et al., 2019; Ryan et al., 2021). In more temperate regions, arboviruses and other VBDs with wider thermal breadths, such as West Nile fever, Ross River fever and Lyme disease, are predicted to increase with climate warming (Ogden et al., 2008; Leighton et al., 2012; Shocket et al., 2018; Shocket et al., 2020; Couper et al., 2021). Drought can exacerbate these effects of temperature (Paull et al., 2017).

A global analysis of 7346 wildlife populations and 2021 host–parasite combinations found that organisms adapted to cool and mild climates are likely to experience increased risks of outbreaks along with climate warming, while warm-adapted organisms may experience a lower disease risk, providing further support for predictions that climate change will increase the transmission of infectious diseases at higher latitudes across a taxonomically diverse array of pathogens (robust evidence, high agreement ) (Cohen et al., 2020). A study examining the future risk of arboviruses (chikungunya, dengue, yellow fever and Zika viruses) spread by A. aegypti and A. albopictus projected increased disease risk due to interactions of multiple variables, including increased human connectivity, urbanisation and climate change (Kraemer et al., 2019), although vector species’ ranges will broaden only slightly (Campbell et al., 2015).

In sum, climate change is expected to expand and redistribute the burden of vector-borne and other environmentally transmitted diseases of wild animals, domesticated animals and humans, by shifting many regions toward the thermal optima of VBD transmission for multiple parasites, thereby increasing risk of transmission, while pushing temperatures above optimal and towards upper thermal limits for other vectors and pathogens, thus decreasing their transmission (high confidence) (see also chapter 7) (Mordecai et al., 2019; Mordecai et al., 2020). These effects are mediated by other human impacts such as LUC, mobility, socioeconomic conditions and vector and pathogen control measures (Parham et al., 2015; Tjaden et al., 2018).

2.5.2 Projected Changes at Level of Biomes and Whole Ecosystems Global Overview, Assessment of Ecosystem-Level Models and Sources of Uncertainties

Shifts in terrestrial biome and changes in ecosystem processes in response to climate change are most frequently projected with dynamic global vegetation models (DGVMs) or land-surface models that form part of ESMs, which use gridded climate variables, atmospheric CO2 concentration and information on soil properties as input variables. Since AR5, most DGVMs have been upgraded to capture carbon–nitrogen cycle interactions (e.g., (Le Quéré et al., 2018), many also include a representation of wildfire and fire–vegetation interactions (Rabin et al., 2017) and a small number now also account for land management (e.g., wood removal from forests and crop fertilisation harvest of irrigation (Arneth et al., 2017). Other forms of disturbance, such as tree mortality, in response to, for example, episodic weather extremes or insect pest outbreaks, are relatively poorly represented or not at all, although they demonstrably impact calculated carbon cycling (Pugh et al., 2019a). Simulated biome shifts are generally in agreement in projecting broad patterns on a global scale but vary greatly regarding the simulated trends in historical and future carbon uptake or losses, both regionally and globally (Chang et al., 2017; Canadell et al., 2021).

Similar to other models, models to project large-scale changes in vegetation and ecosystem processes have to deal with structural uncertainty (associated with the choice and the representation of processes in models), input-data uncertainty (associated with variability in initial conditions and parameter values) and error propagation (associated with coupling models) (Rounsevell et al., 2019). The IPBES methodological assessment report on scenarios and models of biodiversity and ecosystem services provides a comprehensive overview over the relevant issues (Ferrier et al., 2016).

In order to assess performance, most models have been individually evaluated against a range of observations. Moreover, in the annual updates of the global carbon budgets, a model has to meet a small set of basic criteria to have its output included (Le Quéré et al., 2018). More systematic benchmarking approaches have also been proposed that utilise a range of different datasets (Kelley et al., 2013; Chang et al., 2017) to assess multiple simulated processes. These methods, in principle, facilitate assigning quality scores to models based on their overall performance (Kelley et al., 2013). So far, this scoring does not yet allow a clear quality ranking of models, since individual DGVMs tend to score well for some variables and badly for others. A recent comparison of global fire–vegetation model outputs was also able to clearly identify outliers when using a formalised benchmarking and scoring approach (Hantson et al., 2020). However, benchmarking does not address sources of uncertainty and it would be advisable to perform ‘perturbed-physics’ experiments, in which multiple model parameters are varied in parallel more frequently as a means to test parameter-value uncertainty (Wramneby et al., 2008; Booth et al., 2012; Lienert and Joos, 2018).

Species diversity impacts ecosystem functioning and hence ecosystem services (Hooper et al., 2012; Mokany et al., 2016). So far, however, integrated modelling of ecosystem processes and biodiversity across multiple trophic levels and food webs is in its infancy (Harfoot et al., 2014). Whether or not the enhanced integration of state, function and functional diversity across multiple trophic levels in models will markedly alter projections of how ecosystems respond to climate change thus remains an open research question.

Beyond dynamic simulation of biome shifts and carbon cycling, which are important aspects of climate regulation, DGVMs can also provide information on a number of variables closely linked to other ecosystem services such as water availability, air quality or food provisioning (Krause et al., 2017; Rabin et al., 2020). However, they are not intended to provide a comprehensive assessment of ecosystem services. For these, other approaches applied but, to date, these are mostly applied on regional scales and are only weakly dynamic (Ferrier et al., 2016). Projected Changes Globally at the Biome Level

Climate change and the associated change in atmospheric CO2 levels already exacerbate other human-caused impacts on the structure and composition of land and freshwater ecosystems, such as LULCC, nitrogen deposition and pollution. The relative importance of these drivers for ecosystems over the coming decades will likely differ between biomes, but climate change and atmospheric CO2 will be pervasive unless there is a rapid lowering of fossil-fuel emissions and warming (high confidence) (Pereira et al., 2010; Warren, 2011; Ostberg et al., 2013; Davies-Barnard et al., 2015; Pecl et al., 2017; Ostberg et al., 2018). Global vegetation and ESMs agree on climate change-driven shifts of biome boundaries of potentially hundreds of kilometres over this century, combined with several substantial alterations that take place within biomes (e.g., changes in phenology, canopy structure and functional diversity, etc.). Large discrepancies exist between models and between scenarios regarding the region and the speed of change (Gonzalez et al., 2010; Pereira et al., 2010; Pecl et al., 2017), but robust understanding is emerging in that the degree of impact increases in high-emission and high-warming scenarios (high confidence) (Figure 2.9).

Figure 2.9 | Projected fraction of global terrestrial area that could experience a biome shift by 2100. Shifts due to climate change (filled symbols) or a combination of climate change and LUC (outline symbols), from publications in Supplementary Table SM2. 3 (projected vulnerabilities and risks of ecosystems to biome shifts). Filled circles (Bergengren et al., 2011), filled squares (Alo and Wang, 2008), filled diamonds (Gonzalez et al., 2010), filled triangle pointing up (Scholze et al., 2006), filled triangle pointing down (Sitch et al., 2008), filled triangle on its side (Li et al., 2018), filled cross (Warszawski et al., 2013), outlined circle (Ostberg et al., 2018)and outlined diamond (Eigenbrod et al., 2015).

Substantial changes in vegetation structure and ecosystem processes are already happening (see Section 2.4). Many of these observations have already been projected to take place as early as at least IPCC AR3 (Rosenzweig et al., 2007), and can they now be increasingly tested for their robustness with observational evidence. These multiple changes in response to warming (and changes in precipitation and increasing atmospheric CO2 levels that go hand-in-hand with warming) are further expected for already relatively small additional temperature increases. In particular, in cold (boreal and tundra) regions, as well as in dry regions (high confidence), alterations of 2–47% of the areal extent of terrestrial ecosystems in scenarios of <2°C warming above pre-industrial levels have been projected, increasing drastically with higher-warming scenarios (Warren, 2011; Wårlind et al., 2014). More recent work, applying also probabilistic methods, confirm the risk of drastic changes in vegetation cover (e.g., forest to non-forest or vice versa) at the end of the 21st century even for approximately 2°C warming scenarios, especially in tundra, and also in tropical forest and savannah regions, with more subtle changes (within a given biome type) likely to occur in all regions (Ostberg et al., 2013; Ostberg et al., 2018). Model studies have found 5–20% of terrestrial ecosystems affected by warming of around 2°C–3°C, increasing to above one-third at a warming of 4°C–5°C (Ostberg et al., 2013; Warszawski et al., 2013).

In general, vegetation types are projected to be moving into their ‘neighbouring’ climates, depending on whether temperature or precipitation is expected to be the predominant factor and how vegetation interacts with the increasing CO2 levels in the atmosphere (Wårlind et al., 2014; Scheiter et al., 2015; Schimel et al., 2015; Huntzinger et al., 2017). For instance, boreal or temperate forest vegetation is simulated to migrate polewards, closed tropical (moist) forest is expected to transition towards dry tropical forest types, while climate-driven degradation might expand arid vegetation cover (Sections– However, ‘novel ecosystems’, that is, communities with no current or historical equivalent because of the novel combinations of abiotic conditions under climate change, are expected to be increasingly common in the future (medium confidence), although the regions where these novel ecosystems might emerge are still disputed (Reu et al., 2014; Radeloff et al., 2015; Ordonez et al., 2016). The possibility of these novel ecosystems and the communities that live within them are a challenge for current modelling of ecosystem shifts, and new approaches to conservation will be required that are designed to adapt to rapid changes in species composition and the ensuing challenges. Risk to Arid Regions

Shifts in arid system structure and functioning that have been observed to date (Section are projected to continue (medium confidence). These include widespread woody plant encroachment, notably in savanna systems in Africa, Australia and South America, and are attributed to interactions of LULCC, climate change and CO2 fertilisation effects (Fensholt et al., 2012; Fang et al., 2017; Stevens et al., 2017). Arid Mongolian steppe grassland did not respond to experimentally elevated CO2 (Song et al., 2019). Woody encroachment is projected to continue or not reverse in North American drylands (Caracciolo et al., 2016) and southern African arid ecosystems (Moncrieff et al., 2014b). Dryland woody encroachment may increase carbon stocks, depending on emissions scenario (Martens et al., 2021), but reduce soil water and biodiversity of grassland-dependent species diversity (Archer et al., 2017). Warm season (C4) grass expansion into arid shrublands risks sudden ecosystem transformation due to introduced wildfire (Bradley et al., 2016), a risk anticipated for grass-invaded desert ecosystems of Australia and the southwestern USA (Horn and St. Clair, 2017). Novel fire regimes in grassy shrublands have enhanced grass cover locally in the southern African Nama-Karoo (du Toit et al., 2015).

Range retractions are projected for endemic plants in southern Africa (Young et al., 2016) and dry woodlands in Morocco (Alba-Sánchez et al., 2015). Increasing thermal stress is projected to increase woody plant mortality in the Sonoran Desert ecosystems (Munson et al., 2016) and facilitate perennial grass replacement by xeric shrubs in the southwestern USA (Bestelmeyer et al., 2018). Ecological effects may occur rapidly when extreme events compound long-term trends (Hoover et al., 2015), but evolve more slowly as opportunity costs accumulate due to warming (Cross-Chapter Paper 3) (Cunningham et al., 2021). Risk to Mediterranean-Type Ecosystems (MTEs)

The regions containing MTEs all show high confidence in projected increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes, and medium confidence in increasing ecological drought due to increased evapotranspiration (in all regions) and reduced rainfall (excluding California, USA, where model agreement is low) (see WGI Chapter 11). Projections also show a robust increase in the intensity and frequency of heavy precipitation in the event of ≥2°C warming for MTEs in South Africa, the Mediterranean Basin and California, USA, but are less clear for Australia and Chile (Seneviratne et al., 2021).

MTEs are characterised by the distinctive seasonal timing of precipitation and temperature, and the disruption of this regime is likely to be critical for their maintenance. Unfortunately, projections of changes in rainfall seasonality have received less attention and are far more uncertain than many other aspects of climate change (Pascale et al., 2016; Breinl et al., 2020), thus limiting our ability to predict the ecological consequences of climate change in MTEs. Responses to experimental manipulation of rainfall seasonality show the potential for shifts in plant functional composition and diversity loss, but results vary with soil type (van Blerk et al., 2021).

Unfortunately, global- and regional-scale dynamic vegetation models show a poor performance for large areas of MTEs, because they do not characterise shrub and Crassulacean acid metabolism (CAM)-photosynthetic plant functional types well (Moncrieff et al., 2015). Furthermore, the grain of these models is too coarse for quantifying impacts to many vegetation formations which are patchy or of limited extent (e.g., small stands of trees). There is high confidence that observations of high mortality in trees and other growth forms, reduced reproductive and recruitment success, range shifts, community shifts towards more thermophilic species and type conversions are set to continue, due to either direct climate impacts through drought and other extreme weather events or to their interaction with factors like fire and pathogens (Sections,;;;;,,, 2.5.4).

Fire is a key driver across most MTEs due to summer-dry conditions. Climate projections for the MTEs translate into high confidence that periods of low fuel moisture will become more severe and prolonged, and that episodes of extreme fire weather will become more frequent and severe (see (Douville et al., 2021; Seneviratne et al., 2021)). This will lead to the birth of novel fire regimes in MTEs, characterised by an increase in the probability of greater burned area and extreme wildfire events (e.g., megafires), with associated loss of human life and property, long-term impacts on ecosystems and acceleration of the possible loss of resilience and capacity to recover (Abatzoglou and Williams, 2016; González et al., 2018; Boer et al., 2020; Moreira et al., 2020; Nolan et al., 2020; Duane et al., 2021; Gallagher et al., 2021).

Fire is virtually certain to have additional impacts through compound events (see Section 11.8 in (Seneviratne et al., 2021)). Extreme post-fire weather is extremely likely to continue to impact diversity (Slingsby et al., 2017), retard vegetation regrowth (Slingsby et al., 2020a) and accelerate vegetation shifts (Batllori et al., 2019). Any increases in the intensity and frequency of heavy precipitation are highly likely to compromise soil stability in recently burnt areas (Morán-Ordóñez et al., 2020). The impacts of fire often depend on interactions with non-climatic factors such as habitat fragmentation (Slingsby et al., 2020b) and management (Steel et al., 2015) or the spread of flammable exotic plantation forestry and invasive species (Kraaij et al., 2018; McWethy et al., 2018). Managing these factors provides opportunities for adaptation and mitigation (Moreira et al., 2020). (See sections and

Human adaptation and mitigation responses to climate change may create additional threats to MTEs. MTEs have dry summers by definition, posing a challenge for the year-round supply of water to growing human populations and agriculture. With recent major droughts in all MTEs (Section, there is increasing reliance on groundwater for the bulk of the water supply (Kaiser and Macleod, 2018). The majority of groundwater systems have exceeded or are rapidly approaching their environmental flow limits (de Graaf et al., 2019), threatening human populations and ecosystems that depend on these systems for their persistence through unfavourable climatic conditions (McLaughlin et al., 2017). Similarly, much of the MTEs are open shrublands and grasslands and proposed extensive tree-planting to sequester atmospheric CO2 could result in a loss of biodiversity and threaten water security (Doblas-Miranda et al., 2017; Bond et al., 2019). Risk to Grasslands and Savannas

Worldwide, woody cover is increasing in savannas (Buitenwerf et al., 2012; Donohue et al., 2013; Stevens et al., 2017), as a result of interactions of elevated CO2 and altered fire and herbivory impacts, some of which stems from LULCC (high confidence) (see Section; Cross-Chapter Paper 3.2) (Venter et al., 2018; Wu et al., 2021). In some regions, altered climate may also contribute (Cross-Chapter Paper 3.2). Elevated CO2 benefits plants with C3 photosynthesis (often woody plants), more than C4 species (Moncrieff et al., 2014a; Scheiter et al., 2015; Knorr et al., 2016a). Increases in woody vegetation in grassy ecosystems could provide some carbon increase (medium confidence) (Zhou et al., 2017; Mureva et al., 2018), but is expected to decrease biodiversity (Smit and Prins, 2015; Abreu et al., 2017; Andersen and Steidl, 2019) and water availability (Honda and Durigan, 2016; Stafford et al., 2017) and alter ecosystem services like grazing and wood provision (high confidence) (Anadón et al., 2014b).

The relative importance of climate, disturbance (e.g., fire/herbivory) and plant feedbacks in shaping present and future savanna distribution varies between continents (Lehmann et al., 2014), which makes projections of changing the biome extent challenging (Moncrieff et al., 2016). It has been shown that simulation studies that do not account for CO2 interactions but only consider climate change impacts do not realistically capture the future distribution of savannas (high confidence) (Higgins and Scheiter, 2012; Moncrieff et al., 2016; Scheiter et al., 2020). Due to the continued strong effect of CO2 on tree and shrub-to-grass ratios in future, models suggest a loss of savanna extent and conversion into closed canopy forest/thicket and an expansion of savanna-type vegetation into arid grasslands (Wårlind et al., 2014; Moncrieff et al., 2016). In arid savannas and their interface to grasslands, survival of woody vegetation (which may be stimulated to grow by increasing CO2) will depend on their capacity to survive potentially more severe and frequent droughts (Sankaran and Staver, 2019). Across a range of models, for RCP4.5 future climate change and CO2 concentrations, savanna expanse declines by around 50% (converting to closed canopy systems) by 2070 in Africa and South America, 25% in Asia and with small changes in Australia (Moncrieff et al., 2016; Kumar et al., 2021). Future fire-spread is expected to be reduced with increased woody dominance (Scheiter et al., 2015; Knorr et al., 2016b; Scheiter et al., 2020), feeding back to further increase tree-to-grass ratios (high confidence).

Like the tropical forest biome, savannas are at a high risk, given the projected climate changes in combination with LULCC (see Cross-Chapter Paper 3). About 50% of the Brazilian Cerrado has been converted to agricultural land and pastures (Lehman and Parr, 2016), and African savannas have been proposed to follow a similar tropical agricultural revolution pathway to enhance agronomic prosperity (Ryan et al., 2016). In fact, indirect climate change impacts arising from mitigation efforts may be particularly perilous to savannas; extensive tree-planting to restore ecosystems and remove CO2 from the atmosphere, as pledged, for example, under the African Forest Restoration Initiative, could lead to carbon losses and the loss of biodiversity as well as damage the water balance if trees are planted on what was naturally grassland or savanna (Box 2.2; FAQ 2.6) (Bond et al., 2019). Risk to Tropical Forests

Key factors affecting the future distribution of tropical humid and dry forests are amounts and seasonalities of precipitation, increased temperatures, prolonged droughts and droughted-moderated fires (robust evidence, high agreement ) (Bonai et al., 2016; Corlett, 2016; Lyra et al., 2017; Anderson et al., 2018; da Silva et al., 2018 ; Fontes et al., 2018; O’Connell et al., 2018; Aguirre-Gutiérrez et al., 2019; Bartlett et al., 2019; Brando et al., 2019; Stan and Sanchez-Azofeifa, 2019). The probability of severe drought is projected to quadruple in natural areas in Brazil with >2°C warming (Barbosa and Lakshmi Kumar, 2016; Marengo et al., 2020). Most multi-model studies assuming rapid economic growth/business-as-usual scenarios (A2, A1B and RCP8.5) show an increase in future woody biomass and areas of woody cover towards the end of the 21st century in temperate regions (Boit et al., 2016; Nabuurs et al., 2017) and tropical forests in East Africa (Ross et al., 2021) but a decrease in the remaining tropical regions (Anadón et al., 2014a; Boit et al., 2016; Lyra et al., 2017; Nabuurs et al., 2017; Maia et al., 2020). Terrestrial species are predicted to shift to cooler temperatures and higher elevations (Pecl et al., 2017). Tropical species are more susceptible to climate warming than temperate species (Rehm and Feeley, 2016; Sentinella et al., 2020). This susceptibility will be exacerbated by road-building increasing the ease of access into forests (Brinck et al., 2017; Taubert et al., 2018; Bovendorp et al., 2019; Senior et al., 2019). Furthermore, most tropical cloud forest species are unable to invade grasslands and this will increase the risk of extinctions in tropical cloud forests (Rehm and Feeley, 2015).

SLR as the result of climate change is likely to influence mangroves in all regions, with greater impact on North and Central America, Asia, Australia and East Africa than on West Africa and South America (robust evidence, high agreement ) (Alongi, 2015; Ward et al., 2016). On a small scale, mangroves are potentially moving landward (Di Nitto et al., 2014), while on a large scale they will continue to expand poleward (Alongi, 2015).

Most simulations predict a significant geographical shift of transition areas between tropical forests and savanna in the tropical and subtropical Americas and Himalayas (Anadón et al., 2014a; Rashid et al., 2015). Forest dieback, as postulated for the Amazon region, does not occur in the majority of simulations (Malhi et al., 2009; Poulter et al., 2010; Rammig et al., 2010; Higgins and Scheiter, 2012; Huntingford et al., 2013; Davies-Barnard et al., 2015; Sakschewski et al., 2016; Wu et al., 2016a). Model projections of future biodiversity in tropical forests are rare. Arguably, species are most vulnerable to climate change effects at higher altitudes or at the dry end of tropical forest occurrence (medium evidence, medium agreement ) (Krupnick, 2013; Nobre et al., 2016; Trisurat, 2018). Tropical lowlands are expected to lose plant species as temperatures rise above species’ heat tolerance, but could also generate novel communities of heat-tolerant species (robust evidence, high agreement ) (Colwell et al., 2008; Trisurat et al., 2009; Trisurat et al., 2011; Krupnick, 2013; Zomer et al., 2014a; Zomer et al., 2014b; Sullivan et al., 2020; Pomoim et al., 2021).

Statistical models that correlate data on species abundance with information on human pressures, such as LUCs (Srichaichana et al., 2019), population density (Leclère et al., 2020) and hunting (Mockrin et al., 2011), found in tropical and subtropical forests that birds, invertebrates, mammals and reptiles show a decline in their probability of presence with declining forest cover, particularly pronounced in forest specialists or species with narrow ranges (Newbold et al., 2014). Different soil fauna groups showed different responses in abundance and diversity to climate change conditions (Coyle et al., 2017; Facey et al., 2017) but these responses can impact decomposition rates and biogeochemical cycles (medium evidence, low agreement ).

Invasive plant species are predicted to expand upward by 500–1,500 m in the western Himalayas (Thapa et al., 2018), and by 6–35% yr -1 from the current extent in South America (robust evidence, high agreement ) (Bhattarai and Cronin, 2014). Global assessment (Wang et al., 2017) also revealed that ecoregions of high-elevation tropical forests and subtropical coniferous forests have a high risk of invasive plant expansion in low-CO2- emission scenarios, with negative impacts on ecosystem functioning and local livelihoods (Shrestha et al., 2019).

The impact of unsustainable land use on tropical forests continues in all regions (see Cross-Chapter Paper 7). Projected climate changes will not only impact biodiversity but also the livelihoods of affected people (robust evidence, high agreement ). Increased drought drives crop failures that cause local communities to expand their agricultural area by further clearing native forests (Desbureaux and Damania, 2018). Climate change is projected to enlarge the area of suitability for booming tree crops such as oil palm, acacia, Eucalyptus and rubber (Koninck et al., 2011; Cramb et al., 2015; Nath, 2016; Hurni et al., 2017; Li et al., 2017; Varkkey et al., 2018). An increase of 8% in the area of rubber plantations in Yunnan Province, China, between 2002–2010 to higher altitudes due to decreased environmental limits, has potentially increased pressure on the remaining biodiversity both within and outside of protected areas (Zomer et al., 2014a). As a consequence, the suitable area for mammals is projected to be reduced by 47.7% (RCP2.6) and 67.7% (RCP8.5) by 2070, with large variability depending on the different species (Cross-Chapter Paper 7) (Brodie, 2016). To minimize these potential threats, the Yunnan provincial government has identified suitable areas for the establishment of national parks, including the Asian Elephant National Park since 2006. And the government of China developed a national park system in 2013 across the country. Risks to Boreal and Temperate Forests

As in the Arctic, warming substantially exceeding the global average has already been observed in the northern parts of the temperate and boreal forest zone (Gauthier et al., 2015), and is projected to continue (see Cross-Chapter Paper 6 and (Lee et al., 2021)). As a consequence, boreal tree species are expected to move northwards (or in mountainous regions, upwards) into regions dominated by tundra, unless constrained by edaphic features, and temperate species are projected to grow in regions currently occupied by southern boreal forest (high confidence). In both biomes, deciduous trees are simulated to grow increasingly in regions currently dominated by conifers (Wårlind et al., 2014; Boulanger et al., 2017). These simulation results have been supported by observational examples. In eastern Siberia, fire disturbance of larch-dominated forest was followed by recovery to birch-dominated forest (Stuenzi and Schaepman-Strub, 2020). In Alberta, lodgepole pine (Pinus contorta) lost its dominant status after attacks by mountain pine beetles (Dendroctonus ponderosae) caused the canopy to switch to non-pine conifers and broadleaf trees (Axelson et al., 2018). In contrast to the examples above, some boreal forests have proven resilient to disturbances including recent unprecedented insect outbreaks (Campbell et al., 2019a; Prendin et al., 2020).

Reforestation, either natural or anthropogenic, leads to summer cooling and winter warming of the ground, while forest thinning or removal by fire has reverse effects, deepening the upper layer that is free of permafrost (Stuenzi et al., 2021a). Interactions between permafrost and vegetation are important. For example, trees in the east Siberian taiga obtained water mostly from rain in wet summers and permafrost melt water in dry summers (Sugimoto et al., 2002), suggesting that these forests will be particularly vulnerable to the combination of drought with the retraction of permafrost further underground due to climate warming. Risk to Peatland Systems

The overall effect of climate change on the extent of northern peatlands is still debated (limited evidence, low agreement ). It is expected that climate change will drive the expansion of high-latitude peatlands poleward of their present distribution due to warming, permafrost degradation and glacier retreat, which could provide new land and conditions favourable for peat development (limited evidence, medium agreement ) (Zhang et al., 2017b), as seen during the last de-glacial warming (robust evidence, high agreement ) (MacDonald et al., 2006; Jones and Yu, 2010; Ratcliffe et al., 2018). Peatland area loss (shrinking) near the southern limit of their current distribution or in areas where the climate becomes unsuitable is also expected (medium evidence, medium agreement ) (Section (Finkelstein and Cowling, 2011 Gallego-Sala and Prentice, 2013; Schneider et al., 2016; Müller and Joos, 2020) (Müller and Joos, 2021), but they could persist if moisture is maintained via their capacity to self-regulate. In western Canada, a study suggests that peatlands may persist until 2100, even though the climate will be less suitable (Schneider et al., 2016). Simulations suggest that climate change-driven increases in temperature and atmospheric CO2 could drive reductions in the northern peatland area up to 18% (SSP1–2.6), 41% (SSP2–4.5) and 61% (SSP5–8.5) by 2300 (Müller and Joos, 2020). This is in contrast with the findings of northern peatland persistence and expansion under RCP2.6 and RCP6.0 scenarios in 1861–2099 by another modelling study (Qiu et al., 2020). In the Tropics, the only available study suggests peatland area will increase until 2300, mainly due to increases in precipitation and the CO2 fertilisation effect (Müller and Joos, 2020; Müller and Joos, 2021).

The combination of changes in climate and land use represents a substantial risk to peatland carbon stocks, but full assessment is impeded because peatlands are yet to be included in ESMs (limited evidence, high agreement ) (Loisel et al., 2021). It is expected that the carbon balance of peatlands globally will switch from sink to source in the near future (2020–2100), mainly because tropical peatland emissions, together with those from climate change-driven permafrost thaw, will likely surpass the carbon gain expected from climate change-driven enhanced plant productivity in northern high latitudes (Gallego-Sala et al., 2018; Chaudhary et al., 2020; Turetsky et al., 2020; Loisel et al., 2021) which are mainly caused by groundwater drawdown (robust evidence, medium agreement ) (Hirano et al., 2014; Brouns et al., 2015; Cobb et al., 2017; Itoh et al., 2017; Evans et al., 2021). The overall northern peatland carbon sink has been simulated to persist for at least 300 years under RCP2.6, but not under RCP8.5 (Qiu et al., 2020).

Increases in the extent, severity and duration of fires are expected in all peatland regions in the future due to temperature increases (Section, changes in precipitation patterns (Section and increases in ignition sources (e.g., lightning) (Section, with associated rapid carbon losses to the atmosphere (medium evidence, high agreement ) (Dadap et al., 2019; Chen et al., 2021a; Nelson et al., 2021). For example, drought has been linked to fires in Southeast Asian peatlands (Field et al., 2009) and there are predicted decreases in mean summer precipitation (10–30%) for high and low RCPs, particularly over the Indonesian region, by the mid and late 21st century (Section (Tangang et al., 2020; Taufik et al., 2020). During wet years, the fire probability in Indonesian peatlands also significantly increases (by 15–40%) when temperatures in July to October surpass 0.5°C anomalies compared to the 1995–2015 baseline (Fernandes et al., 2017). Overall, current evidence suggests that peat carbon losses via fire have the potential to be equal to, or greater than, losses due to human peatland drainage and disturbance (limited evidence, high agreement ) (Turetsky et al., 2015).

Regarding permafrost peatlands, studies differ, with some projecting a net loss and others a net gain of carbon (medium evidence, low agreement ) (Estop-Aragonés et al., 2018; Hugelius et al., 2020; Loisel et al., 2021; Väliranta et al., 2021). In some permafrost peatlands, prolonged and warmer growing seasons due to climate change (Section, along with increases in nitrogen deposition since 1850 (Lamarque et al., 2013), are promoting plant primary productivity. Other studies indicate that increased nitrogen-mediated sequestration could be exceeded by increased decomposition due to climate change-driven warming and fire (medium evidence, low agreement ) (Natali et al., 2012; Vonk et al., 2015; Keuper et al., 2017; Burd et al., 2018; Estop-Aragonés et al., 2018; Gallego-Sala et al., 2018; Serikova et al., 2018; Wild et al., 2019; Chaudhary et al., 2020; Hugelius et al., 2020).

Any climate change- or human-driven degradation of peatlands will also entail losses in water storage (limited evidence, high agreement ) (Wooster et al., 2012; Hirano et al., 2015; Cole et al., 2019; Taufik et al., 2019) and biodiversity (Harrison, 2013; Lampela et al., 2017; Renou-Wilson et al., 2019). The environmental archive contained in peat that preserves records of vegetation, hydrology, climate change, pollution and/or human disturbances is also being lost as peatlands degrade (Kasischke and Turetsky, 2006; MacDonald et al., 2006; Turunen, 2008; Field et al., 2009; Flannigan et al., 2009; Jones and Yu, 2010; Kasischke et al., 2010; Peterson et al., 2010; Finkelstein and Cowling, 2011; Rooney et al., 2012; Gallego-Sala and Colin Prentice, 2013; Lamarque et al., 2013; Hirano et al., 2014; Brouns et al., 2015; Turetsky et al., 2015; Miettinen et al., 2016; Schneider et al., 2016; Cobb et al., 2017; Fernandes et al., 2017; Itoh et al., 2017; Gallego-Sala et al., 2018; Greiser and Joosten, 2018; Ratcliffe et al., 2018; Dadap et al., 2019; Leifeld et al., 2019; Chaudhary et al., 2020; Hoyt et al., 2020; Müller and Joos, 2020; Qiu et al., 2020; Tangang et al., 2020; Taufik et al., 2020; Turetsky et al., 2020; Chen et al., 2021a; Evans et al., 2021; Loisel et al., 2021; Nelson et al., 2021; Qiu et al., 2021). Risks to Polar Tundra Ecosystems

For boreal–tundra systems, AR5 projected the transformation of species composition, land cover and permafrost extent, decreasing albedo and increasing GHG emissions (medium confidence). SR1.5 classified tundra and boreal forests as particularly vulnerable to degradation and encroachment by woody shrubs (high confidence). The SROCC projected climate-related changes to arctic hydrology, wildfires and abrupt thaw (high confidence) and the broad disappearance of arctic near-surface permafrost this century, with important consequences for global climate (very high confidence). Chapter 2 of AR6 has focused on new key findings about observed and projected changes in tundra vegetation and related hydrology, with implications for feedbacks to the climate system.

Due to the rapid warming at high northern latitudes, the Arctic tundra is one of the terrestrial biomes where climate change impacts are already clearly visible (Settele et al., 2014; Uboni et al., 2016). Climate models project that warming of the Arctic is likely to continue at more than double the global rate. Compared to the period 1995–2014, mean annual surface air temperatures in the Arctic tundra are projected to increase by 7.9°C–10°C by the end of the century in scenarios of high GHG emissions (RCP7.0 and RCP8.5). In scenarios of low GHG emissions (RCP1.9 and RCP2.6), the projected increase is 2.6°C–3.2°C (Lee et al., 2021). The Arctic is also projected to have amongst the largest increases in precipitation globally, but with high uncertainty. In contrast to climate change, LUC is projected to be very low in Arctic tundra systems (van Asselen and Verburg, 2013).

Models of vegetation response to climate project acceleration in the coming decades of observed increases in shrub dominance and boreal forest encroachment that have been driven by recent warming (Settele et al., 2014), leading to a shrinking of the area of tundra globally (medium confidence) (Mod and Luoto, 2016; Gang et al., 2017). Simulating changes in tundra vegetation is complicated by permafrost dynamics (e.g., the formation of thaw ponds and draining of existing ponds), changes in precipitation and low nutrient availability (which may promote the abundance of graminoids) (van der Kolk et al., 2016). Changes in vegetation, when combined with warming and increased precipitation effects on soil thawing and carbon cycling, are projected to modify GHG emissions and have biophysical feedbacks to regional and global climate. High uncertainty in modelled carbon cycle changes arises from differences between the vegetation models (Nishina et al., 2015; Ito et al., 2016). In addition, climate change is expected to strongly interact with other factors, such as fire, to further increase uncertainty in projections of tundra ecosystem function (Jiang et al., 2017). Committed Impacts of Climate Change on Terrestrial Ecosystems and Implications of Overshoot

Projections point to potentially large changes of canopy structure and composition within and across the terrestrial biomes in response to climate change and changes in atmospheric CO2. These changes will contribute to altered ecosystem carbon uptake and losses, biophysical climate feedbacks (Sections 2.3.2; 2.4.4;;,, Figure 2.10, Table 2.4) and multiple other ecosystem services (Sections 2.5.3, 2.5.4) as well impacts on biodiversity (Sections 2.4.2, 2.4.3, 2.4.4, 2.4.5,,, 2.5.2, Figure Box 2.1.1, Table Box 2.1.1, Table SM2.4). Until now, most studies project changes over next decades until the end of this century.

However, there is an increasing body of literature that has found continued, longer-term responses of ecosystems to climate change, so-called ‘committed changes’, that arise from lags that exist in many systems. Many processes in ecosystems take more than a few decades to quasi-equilibrate to environmental changes. Therefore, the trends of changing vegetation cover identified in simulations of transient warming continue to show up in simulations that hold climate change at low levels of warming (medium confidence) (Boulton et al., 2017; Pugh et al., 2018; Scheiter et al., 2020). Such changes, which could tip ecosystems into an alternative state, could also be triggered by a ‘warming overshoot’ if global warming were to exceed a certain threshold, even if mean temperatures afterwards decline again (Albrich et al., 2020a).

For instance, even if warming achieved by 2100 remained constant after 2100, such committed responses continue to occur. These include: (1) continued Amazon forest loss (Boulton et al., 2017), consistent with results in Pugh et al. (2018) that found continued tropical forest cover loss across a range of models and simulation setups, and (2) across Africa, an increased shift towards woody C3 vegetation was found in equilibrium state, the overall response depending on the atmospheric CO2 concentration (Scheiter et al., 2020). In Pugh et al. (2018), the opposite was found for boreal forest cover, which showed a strong committed increase. The committed changes in vegetation composition correspond to large committed changes in terrestrial carbon uptake and losses (Boulton et al., 2017; Pugh et al., 2018; Scheiter et al., 2020), and would plausibly also appear in other ecosystem functioning and services. These studies point to the importance of having not only a multi-decadal but also a multi-century perspective when exploring the impacts of political decisions on climate change mitigation taken now. Even if climate-warming targets are met, published evidence so far suggests that fundamental changes in some ecosystems are likely as these correspond to well-understood ecosystem physiological responses that trigger long-term changes in composition.

Box 2.1 | Assessing Past Projections of Ecosystem Change against Observations

To assess future climate change impacts on ecosystems, we use models to project their future distribution. Comparing the trends in the observed changes against the projections can help assess the strength of the model projections. In this box, we compare observed trends of changes in ecosystem structure to projections highlighted in previous IPCC reports, specifically AR3 (IPCC, 2001), AR4 (Fischlin et al., 2007) and AR5 (Settele et al., 2014). We use this to assess how well the projections are matching up with observed changes. The map represents studies documenting observed changes in common plant functional groups (e.g., trees, grasses and shrubs). Studies documenting changes in plant functional groups were collated from published papers in natural and semi-natural areas. Studies were included if climate change or interactions between climate change and land use showed a causal link to the observed change (Table SM2.4). Studies were excluded if the changes were only from landscape/land use transformation (e.g., deforestation). In each paper, we recorded the geographical location and type of functional change, and noted the causes. Observed changes are plotted onto a biome map derived from the WWF ecoregions database (Olson et al., 2001). Trends in changing plant functional types are good indicators of potential biome shifts and are used to assess how observations match up with projections.

Figure Box 2.1.1 | Observed changes in the distribution of plant functional types that are caused by climate change or a combination of land use and climate change. Shifts in plant functional types are indicative of shift in biome function and structure. Based upon studies listed in Table SM2.4 and section 2.4.

Table Box 2.1.1 | Comparison of projections on biome change from AR3, AR4 and AR5 (IPCC, 2001; Fischlin et al., 2007; Settele et al., 2014), with observed changes in ecosystems as assessed in this report (see Section 2.4, Figure Box 2.1.1, Table SM2.4). Observed changes marked in bold show good agreement with past projections; those in red show mismatch with observations and projections.





Observed trends 1990–2021


Increased disturbance by fire and warming will cause a loss of unique habitats

Loss of 65% of area due to warming. Increased fire frequencies will favour resprouting plants. An increase in grass dominance. Forest expansion within MTEs due to elevated CO2.

Range contractions of all species

Increase in water deficit and fire activity (Sections, a decline in diversity; tree mortality (Fig. Box 2.1.1) with resprouting trees worst affected.

Increasing dominance of grasses (often alien). Increasing dominance of deciduous over evergreen species (Fig. Box 2.1.1).


Tree and shrub encroachment into tundra

Increased woody plant growth due to longer and warmer growing seasons and shrub tundra replacing dwarf tundra

Poleward expansion of tundra into polar desert and encroachment of coniferous trees into tundra

Continued woody expansion in tundra regions with reduced surface albedo due to less snow and more woody cover

Increase in woody shrub cover in tundra and expansion of boreal forest into tundra (Fig. Box 2.1.1,

Boreal forest

Reduced productivity due to weather-related disturbances (e.g., increased fire risk).

Deciduous broadleaf tree encroachment into boreal forest.

Extensive boreal tree spread into tundra.

Boreal forest dieback within boreal zone and contraction of boreal forest at southern ecotone with continental grasslands

Expansion into tundra and upslope treeline advance (Section and Fig Box 2.1.1).

Increased mortality due to drought, fire, beetle infestations (Sections,,

Tropical forest

Increasing CO2 concentration would increase NPP

Increases in forest productivity and biomass through increased CO2 with localised decreases in the Amazon. Shift in forest species composition.

Expansion of forest area into mesic savanna.

Shift in the climate envelope of moist tropical forests but forests are less likely to undergo major retractions or expansions than suggested in AR4

Expansion of tropical forest into savannas in Africa, Asia, South America (Section, Fig. Box 2.1.1).

Forest biomass increases (though slowing) (Section

Forest degradation from drought, warming, fire and shorter residence time of trees (Section

Shift in species composition towards species with more aridity-adapted traits (Section

Temperate forest

Forest decline and increased mortality

Increase in tree mortality from drought-related declines.

A general increase of deciduous vegetation at the expense of evergreen vegetation is predicted at all latitudes.

Map indicates a shift towards deciduous species in western North America (Fig. Box 2.1.1).

Tree death due to interactions of drought, pest outbreaks and fire (,,

Grasslands and savannas

Increasing CO2 concentration will increase NPP

Increased tree dominance in savannas and grasslands (from elevated CO2), with C3 plants benefitting more than C4 plants

Rising CO2 will increase the likelihood of woodier states (but the transition will vary in different environments)

Greening and encroachment across tropical and temperate savannas in Africa, Asia, Australia and America (Section

Expansion of trees into grasslands and advancement of tree lines.

Signs of increased C4 grass productivity in drought conditions. Increased C3 grass productivity (Section

Desert/arid shrublands

An increase in desert vegetation productivity was projected in southern Africa, the Sahel, central Australia, the Arabian Peninsula and parts of central Asia due to a positive impact of rising atmospheric CO2

Greening (increased leaf area index [LAI] and woody cover) and increased herbaceous production are occurring at desert–grassland interfaces (Cross-Chapter Paper 3)

Assessment: There is high agreement between observations and projections of tree death in temperate and boreal forests, with current projections (AR6) indicating this trend will continue (Sections,, 2.5.4). Forest death is most widely recorded in central Europe and western North America (Fig. Box 2.1.1). There is also very high agreement between observations and projections of woody encroachment in savannas, grasslands and tundra, with projections also indicating that this trend is likely to continue (Sections,,,, 2.5.4). Observations of desert-greening show good agreement with earlier projections. Patterns of desertification are also occurring, although the geographical match between projections and observations shows moderate agreement, likely due to the strong role of land use in this process. Projections of tropical forest expansion into mesic savannas and boreal forest expansion into tundra also show agreement with the observations.

Projections of the future of Mediterranean shrublands, deserts, xeric shrublands and temperate grassy systems are limited, making assessment of this relationship less clear. It is also unclear, due to limited observations, how widespread a shift there is from deciduous forest species to evergreen forest species. Some observations suggest this is occurring, but it is not clear how widespread this change is and if the geographical pattern is as projected.

2.5.3 Risk Assessment of Ecosystems and Related Services Risks in Protected Areas

National parks and other protected areas which, in June 2021, covered 15.7% of the global terrestrial area (UNEP-WCMC et al., 2021), conserve greater biodiversity than adjacent unprotected areas (Gray et al., 2016), and protect one-fifth of global vegetation carbon stocks and one-tenth of global soil carbon stocks (Section This section assesses climate change specifically in protected areas. Even though it is included in a part of the chapter on projected risks, it includes both observed exposure and projected risks to gather the information on protected areas into one place. Observed exposure of protected areas

In 2009, deforestation, agricultural expansion, overgrazing and urbanisation exposed one-third of the global protected area (6 million km 2) to intense human pressure, a 6% increase from 1993 (Venter et al., 2016; Jones et al., 2018). The exposure to observed climate change has not yet been quantified for protected areas globally, but research has analysed the spatial patterns and magnitudes of observed changes for the 360,000 km 2 system of US national parks (Gonzalez et al., 2018) including the first national park in the world. From 1895 to 2010, mean annual temperature of the US national park area increased at a rate of 1°C ± 0.2°C per century, double the rate of the whole USA, and precipitation decreased in 12% of the national park area, compared with 4% for the whole USA, due to a high fraction of US national park area being in the Arctic, at high elevations, and in the arid southwestern USA (Gonzalez et al., 2018). In addition, analyses of weather-station measurements in and near six South African national parks found that the maximum temperature increased at a rate of 0.024°C ± 0.003°C yr -1 from 1960 to 2010 (Van Wilgen et al., 2016). While a substantial fraction of global protected area has been exposed to observed changes in human land cover, the global exposure to observed climate change is unquantified. Projected risks in protected areas

Under a climate change scenario of ~3.5°C temperature increase by 2070, current climate could disappear from individual protected areas that comprise half the global protected area, and novel climates (climate conditions that are currently not present in an individual protected area) could emerge in half the global protected area (Hoffmann et al., 2019b). A lower-emissions scenario of ~1.5°C could reduce the disappearance of current climate conditions to 40% and the exposure to novel climates to 41% (Hoffmann et al., 2019b). Models project the highest exposure to novel climates in subtropical projected areas (Hoffmann and Beierkuhnlein, 2020). Projected disappearance of current climate conditions in protected areas is most extensive in Africa, Oceania, and North and South America (Elsen et al., 2020).

Projections indicate greater exposure of tropical rainforests, shrublands and grasslands, temperate conifer forests and grasslands, and tundra to novel climates (Hoffmann et al., 2019b; Elsen et al., 2020). A climate change scenario of ~3.5°C temperature increase by 2100 could expose 32% of the protected area in humid tropical forests (1.6 million km 2 in 2000) to climate that would be novel to humid tropical-forest protected areas; by 2050, the climate currently present in humid tropical-forest protected areas could disappear from 0.6 million km 2 (12% of the current total area) (Tabor et al., 2018). High rates of deforestation and climate change combined could expose 2% of the humid tropical-forest protected area (Tabor et al., 2018). Regional analyses under RCP8.5 also project the substantial disappearance of the current climate in protected areas in Bolivia, Chile and Peru (Fuentes-Castillo et al., 2020), Canada, Mexico and the USA (Batllori et al., 2017; Holsinger et al., 2019), China (Zomer et al., 2015), Europe (Nila et al., 2019) and Indonesia (Scriven et al., 2015). Projected climate change could expose an extensive part of the global protected area to disappearing and novel climate conditions (high confidence) (Cross-Chapter Paper 1).

Continued climate change increases the risks to individual species and vegetation types in protected areas. Under a climate change scenario of 4°C temperature increase by 2100, the suitable climate for two species of baobab trees (Adansonia perrieri and A. suarezensis) in Madagascar could shift entirely out of the protected areas network (Vieilledent et al., 2013). Other species and vegetation types at risk from the partial disappearance of suitable climate in protected areas include Atlantic Forest amphibians in Brazil (Lemes et al., 2014), birds in Finland (Virkkala et al., 2013), birds and trees in Canada and Mexico (Stralberg et al., 2020), bog woodlands in Germany (Steinacker et al., 2019), butterflies and mammals in Egypt (Leach et al., 2013) and tropical dry forests in Mexico (Prieto-Torres et al., 2016). Projected disappearance of suitable climate conditions in protected areas increase risks to the survival of species and vegetation types of conservation concern in tropical, temperate and boreal ecosystems (high confidence) (Cross-Chapter Paper 1).

Protected rivers, lakes and other freshwater protected areas require inter-catchment connectivity to maintain species and population movements (Bush et al., 2014a; Hermoso et al., 2016; Thieme et al., 2016), but dams and other barriers interrupt connectivity (Grill et al., 2019). Climate change could also reduce freshwater connectivity (Section Globally, over two-thirds of river reaches (by length) lack protected areas in their upstream catchments and nine-tenths of river reaches (by length) do not achieve full, integrated protection (Abell et al., 2017).

Terrestrial and freshwater protected areas can also serve as climate change refugia, that is, locations where suitable conditions may persist for the species into the future (e.g., Section In Canada, Mexico and the USA, only a fraction of the protected area is located in potential climate change refugia under a 4°C temperature increase, estimated at 4% (Michalak et al., 2018) to 7% (Batllori et al., 2017). Potential refugia from biome shifts due to climate change under temperature increases of 1.8°C–3.4°C cover <1% of the area of US national parks (Gonzalez et al., 2010), a fraction that diminishes to near zero when climate change is combined with habitat fragmentation due to LUC (Eigenbrod et al., 2015). Protected areas in boreal ecosystems could serve as refugia for species shifting north in Canada (Berteaux et al., 2018) and Finland (Lehikoinen et al., 2019). Invasive species, habitat loss and other disturbances in protected areas could be lower than in unprotected areas across Europe (Gallardo et al., 2017), specifically in Spain (Regos et al., 2016), and also in Sri Lanka (Kariyawasam et al., 2020). Protected areas conserve refugia from climate change under a temperature increase of 4°C, which is important for biodiversity conservation but is limited to <10% of the current protected area (medium confidence). Risks to Ecosystems and Services from Wildfire Future projections of wildfire globally

Continued climate change under high-emission scenarios that increase global temperature ~4°C by 2100 could increase global burned area by 50% (Knorr et al., 2016b) to 70% (Kloster and Lasslop, 2017) and global mean fire frequency by ~30% (Gonzalez et al., 2010), with increases on one-third (Gonzalez et al., 2010) to two-thirds (Moritz et al., 2012) and decreases on one-fifth (Gonzalez et al., 2010; Moritz et al., 2012) of land globally. Lower emissions that would limit the global temperature increase to <2°C would reduce projected increases of global burned area to 30% (Lange et al., 2020) to 35% (Kloster and Lasslop, 2017) and projected increases of fire frequency to ~20% (Gonzalez et al., 2010; Huang et al., 2015). Continued climate change could further lengthen fire weather seasons (Ranasinghe et al., 2021). Models combining projected climate change with potential agricultural expansion project decreases in total burned area (Huang et al., 2015; Knorr et al., 2016b; Park et al., 2021). The area of projected increases in burned area and fire frequency due solely to continued climate change is higher for the world as a whole than the area of projected decreases (medium evidence, medium agreement ).

Increased wildfire due to continued climate change increases risks of tree mortality (Sections,,, biome shifts (Section and carbon emissions (Sections, Wildfire and biome shifts under a projected climate change of 4°C above the pre-industrial period, combined with international trade and transport, cause high risks from invasive species across one-sixth of the global area including extensive high-biodiversity regions (Early et al., 2016).

Wildfire risks to people include death and destruction of their homes, respiratory illnesses from smoke (Ford et al., 2018; Machado-Silva et al., 2020), post-fire flooding from areas exposed by vegetation loss and degraded water quality due to increased sediment flow (Dahm et al., 2015) and the chemical precursors of carcinogenic trihalomethanes when water is later chlorinated for drinking (Section (Uzun et al., 2020). Under RCP8.5 and shared socioeconomic pathway SSP3 (high population growth, slow urbanisation), the number of people living in fire-prone areas could increase by three-quarters to 720 million in 2100, in a projected global population of 12.4 billion people (Knorr et al., 2016b). Lower emissions under RCP4.5 could reduce the number of people at risk by 70 million. In these projections, human population growth increases human exposure to wildfires more than increases in burned area (Knorr et al., 2016b). A global temperature increase <2°C could increase global population exposure to wildfire by ~30% (Lange et al., 2020). Increased wildfire under continued climate change increases the probability of human exposure to fire and risks to public health (medium evidence, high agreement ). Future projections of wildfire in high-risk areas

Regions identified by multiple global analyses as being at a high risk of increased burned area, fire frequency and fire weather include: the Amazon (Gonzalez et al., 2010; Huang et al., 2015; Knorr et al., 2016b; Burton et al., 2018; Abatzoglou et al., 2019), Mediterranean Europe (Gonzalez et al., 2010; Burton et al., 2018; Abatzoglou et al., 2019), the Arctic tundra (Moritz et al., 2012; Flannigan et al., 2013), Western Australia (Gonzalez et al., 2010; Burton et al., 2018; Abatzoglou et al., 2019) and the western USA (Gonzalez et al., 2010; Moritz et al., 2012; Knorr et al., 2016b). Higher-resolution spatial projections indicate high risks of increased wildfire in the Amazon, Australia, boreal ecosystems, Mediterranean Europe and the USA with climate change (medium evidence, medium agreement ).

In the Amazon, climate change under RCP8.5, combined with high deforestation, could double the area of high fire probability (Fonseca et al., 2019), double the burned area by 2050 (Brando et al., 2020), increase the burned area by 400–2800% by 2100 (Le Page et al., 2017) and increase fire intensity by 90% (De Faria et al., 2017). Lower GHG emissions (RCP4.5) and reduced deforestation could reduce the risk of fires to a one-fifth increase in the area of high fire probability (Fonseca et al., 2019) and a 100–500% increase in burned area by 2100 (Le Page et al., 2017). Moreover, increased fire, deforestation and drought, acting via vegetation–atmosphere feedbacks, increase the risk of extensive forest dieback and potential biome shifts of up to half of the Amazon rainforest to grassland, a tipping point that could release an amount of carbon that would substantially increase global emissions (Oyama and Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008; Nepstad et al., 2008; Malhi et al., 2009; Settele et al., 2014; Lyra et al., 2016; Zemp et al., 2017a; Zemp et al., 2017b; Brando et al., 2020). Continued climate change, combined with deforestation, increases risks of wildfire and extensive forest dieback in the Amazon rainforest (robust evidence, high agreement ).

In Australia, climate change under RCP8.5 increases the risk of pyro-convective fire by 20–40 days in rangelands of Western Australia, South Australia and the Northern Territory (Dowdy et al., 2019). Pyro-convective fire conditions could reach more frequently into the more populated areas of New South Wales, particularly at the start of the austral summer (Di Virgilio et al., 2019). GCMs do not agree, however, on the areas of projected fire increase in New South Wales (Clarke and Evans, 2019). Increases in heat and potential increases in wildfire threaten the existence of temperature montane rainforest in Tasmania, Australia (Mariani et al., 2019).

In Mediterranean Europe, climate change of 3°C of warming could double or triple the burned area whereas keeping the temperature increase to 1.5°C could limit the increase in burned area to 40–50% (Turco et al., 2018). Under RCP8.5, the frequency of heat-induced fire weather could increase by 30% (Ruffault et al., 2020). Severe fire followed by drought could cause biome shifts of forest to non-forest (Batllori et al., 2019) and tree mortality >50% (Dupire et al., 2019).

In the Arctic tundra, boreal forests and northern peatlands, including permafrost areas, climate change under the scenario of a 4°C temperature increase could triple the burned area in Canada (Boulanger et al., 2014), double the number of fires in Finland (Lehtonen et al., 2016), increase the lightning-driven burned area by 30–250% (Veraverbeke et al., 2017; Chen et al., 2021a), push half of the area of tundra and boreal forest in Alaska above the burning threshold temperature and double the burned area in Alaska (Young et al., 2017a). Thawing of Arctic permafrost due to a projected temperature of 4°C and the resultant wildfires could release 11–200 GtC which could substantially exacerbate climate change (Section

In the USA, climate change under RCP8.5 could increase the burned area by 60–80% by 2049 (Buotte et al., 2019) and the number of fires with an area >50 km 2 by 300–400% by 2070 (Barbero et al., 2015). In montane forests, climate change under RCP8.5 increases the risk of fire-facilitated conversion of ~7% of forest to non-forest by 2050 (Parks et al., 2019). In California, climate change under a scenario of a 4°C temperature increase could double fire frequency in some areas (Mann et al., 2016), but emission reductions that limit the temperature increase to ~2°C could keep this from increasing (Westerling et al., 2011). Carbon dioxide fertilisation and increased temperature under climate change could increase invasive grasses and wildfire in desert ecosystems of the southwestern USA where wildfire has historically been absent or infrequent, and increase the mortality of the sparse tree cover (Horn and St. Clair, 2017; Klinger and Brooks, 2017; Syphard et al., 2017; Moloney et al., 2019; Sweet et al., 2019).

In summary, under a high-emission scenario that increases global temperature 4°C by 2100, climate change could increase the global burned area by 50–70% and the global mean fire frequency by ~30%, with increases on one- to two-thirds and decreases on one-fifth of global land (medium confidence). Lower emissions that would limit the global temperature increase to <2°C would reduce projected increases of burned area to ~35% and projected increases of fire frequency to ~20% (medium confidence). Increased wildfire, combined with erosion due to deforestation, could degrade water supplies (high confidence). For ecosystems with an historically low fire frequency, a projected 4°C rise in global temperature increases risks of fire, contributing to potential tree mortality and conversion of over half the Amazon rainforest to grassland and thawing of the Arctic permafrost that could release 11–200 GtC that could substantially exacerbate climate change (medium confidence). Risks to Ecosystems and Services from Tree Mortality

Under continued climate change, increased temperature, aridity, drought, wildfire (Section and insect infestations (Section will tend to increase tree mortality across many parts of the world (McDowell et al., 2020). Loss of boreal and temperate forest to fire, wind and bark beetles could cause more negative than positive effects for most ecosystem services, including carbon storage to regulate climate change (Sections,,,, water supply for people (Section, timber production and other forest products (Chapter 5) and protection from hazards (Thom and Seidl, 2016). In addition, deforestation in tropical and temperate forests can increase local temperatures by 0.3°C–2°C (Hesslerová et al., 2018; Lejeune et al., 2018; Zeppetello et al., 2020) and this effect can extend up to 50 km (Cohn et al., 2019).

In Amazon rainforests, the relatively lower buffering capacity for plant moisture during drought increases the risk of tree mortality and, combined with increased heat from climate change and fire from deforestation, the possibility of a tipping point of extensive forest dieback and a biome shift to grassland (Oyama and Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008; Nepstad et al., 2008; Malhi et al., 2009; Salazar and Nobre, 2010; Settele et al., 2014; Lyra et al., 2016; Zemp et al., 2017b; Brando et al., 2020). This could occur at a 4°C–5°C temperature increase above that of the pre-industrial period (Salazar and Nobre, 2010). Under RCP8.5, half the Amazon tropical evergreen forest could turn into grassland through drought-induced tree mortality and wildfire, but lower emissions (RCP4.5) could limit this loss to ~5% (Lyra et al., 2016). The decline in precipitation due to reduced evapotranspiration inputs after forest loss could cause additional Amazon forest loss of one-quarter to one-third (Zemp et al., 2017a). Similarly, in Guinean tropical deciduous forest in Africa, climate change under RCP8.5 could increase mortality 700% by 2100 or 400% under lower emissions (RCP4.5; (Claeys et al., 2019). These projections indicate risks of climate change-induced tree mortality reducing tropical forest areas in Africa and South America by up to half under a 4°C increase above the pre-industrial period, but a lower projection of a 2°C increase could limit the projected increases in tree mortality (robust evidence, high agreement ).

Temperate and boreal forests possess greater diversity of physiological traits related to plant hydraulics, so they are more buffered against drought than tropical forests (Anderegg et al., 2018). Nevertheless, in temperate forests, drought-induced tree mortality under RCP8.5 could cause the loss of half the Northern Hemisphere conifer forest area by 2100 (McDowell et al., 2016). In the western USA, under RCP8.5, one-tenth of forest area is highly vulnerable to drought-induced mortality by 2050 (Buotte et al., 2019). In California, increased evapotranspiration in Sierra Nevada conifer forests increases the potential fraction of the area at risk of tree mortality by 15–20% per degree Celsius (Goulden and Bales, 2019). In Alaska, fire-induced tree mortality from climate change under RCP8.5 could reduce the extent of spruce forest (Picea sp.) by 8–44% by 2100 (Pastick et al., 2017). Under RCP8.5, tree mortality from drought, wildfire and bark beetles could reduce the timber productivity of boreal forests in Canada by 2100 below the current levels (Boucher et al., 2018; Chaste et al., 2019; Brecka et al., 2020). In Tasmania, projected increases in wildfire (Fox-Hughes et al., 2014) increase the risk of mortality of mesic vegetation (Harris et al., 2018b) and threaten the disappearance of the long-lived endemic pencil pine (Athrotaxis cupressoides) (Holz et al., 2015; Worth et al., 2016) and temperate montane rainforest (Mariani et al., 2019). These projections indicate risks of climate change-induced tree mortality reducing some temperate forest areas by half under emissions scenarios of 2.5°C–4°C above the pre-industrial period (medium evidence, high agreement ). Risk to Terrestrial-Ecosystem Carbon Stocks

Globally, increasing atmospheric CO2 enhances the terrestrial sink but temperature increases constrain it, reflecting the biological process understanding highlighted in previous IPCC reports (high confidence). Analyses of atmospheric inversion model output and spatial climate data indicate a sensitivity of net ecosystem productivity to CO2 fertilisation of 3.1 ± 0.1 Gt to 8.1 ± 0.3 Gt per 100 ppm CO2 (~1°C increase) and a sensitivity to temperature of -0.5 ± 0.2 Gt to -1.1 ± 0.1 Gt per degree Celsius (Fernandez-Martinez et al., 2019). The future of the global land carbon sink (Section nevertheless remains highly uncertain because (i) of regionally complex interactions of climate change and changes in atmospheric CO2 with vegetation, soil and aquatic processes, (ii) episodic events such as heat waves or droughts (and related impacts through mortality, wildfire or insects, pests and diseases) (Section, are so far only incompletely captured in carbon cycle models, (iii) the legacy effects from historic LUC and environmental changes are incompletely captured but likely to decline in future and (iv) lateral carbon transport processes such as the export of inland waters and erosion are incompletely understood and modelled (Pugh et al., 2019a; Friedlingstein et al., 2020; Krause et al., 2020; Canadell et al., 2021).

Enhanced carbon losses from terrestrial systems further limit the available carbon budget for global warming staying below 1.5°C (Rogelj et al., 2018). Analyses of satellite remote sensing and ground-based observations have indicated that, between 1982 and 2015, the CO2 fertilisation effect has already declined, implying a negative climate system feedback (Wang et al., 2020c). Peatlands, permafrost regions and tropical ecosystems are particularly vulnerable due to their large carbon stocks, in combination with over-proportional warming, increases in heat waves and droughts and/or a complex interplay of climate change and increasing atmospheric CO2 (Sections,,

Model projections suggest a reduction of permafrost extent and potentially large carbon losses for all warming scenarios (Canadell et al., 2021). Already a mean temperature increase of 2°C could reduce the total permafrost area extent by about 5–20% by 2100 (Comyn-Platt et al., 2018; Yokohata et al., 2020). Associated CO2 losses in the order of 15 Gt up to nearly 70 Gt by 2100 have been projected across a number of modelling studies (Schneider von Deimling et al., 2015; Comyn-Platt et al., 2018; Yokohata et al., 2020). Limiting the global temperature increase to 1.5°C versus 2°C could reduce projected permafrost CO2 losses by 2100 by 24.2 Gt (median, calculated for a 3-m depth) (Comyn-Platt et al., 2018). Losses are possibly underestimated in the studies that consider only the upper permafrost layers. Likewise, the actual committed carbon loss may well be larger (e.g., eventually a loss of approx. 40% of today’s permafrost area extent if climate is stabilised at 2°C above pre-industrial levels) due to the long time scale of warming in deep permafrost layers (Chadburn et al., 2017). It is not known at which level of global warming an abrupt permafrost collapse (estimated to enhance CO2 emissions by 40% in 2300 in a high-emissions scenario) compared to gradual thaw (Turetsky et al., 2020) would have to be considered an important additional risk. Large uncertainties arise also from interactions with changes in surface hydrology and/or northward migrating woody vegetation as climate warms, which could dampen or even reverse projected net carbon losses in some regions (McGuire et al., 2018a; Mekonnen et al., 2018; Pugh et al., 2018). Overall, there is low confidence on how carbon–permafrost interactions will affect future carbon cycle and climate, although net carbon losses and thus positive (amplifying) feedbacks are likely (Sections, (Shukla et al., 2019). See also WGI AR6 (Canadell et al., 2021) for a discussion on impacts of higher-emission and warming scenarios.

Peatland carbon is estimated as about 550–1000 Gt in northern latitudes (many of these peatlands would be found in permafrost regions) (Turetsky et al., 2015; Nichols and Peteet, 2019) and >100 Gt in tropical regions (Turetsky et al., 2015; Dargie et al., 2017). For both northern mid- and high-latitude and tropical peatlands, a shift from contemporary CO2 sinks to sources were simulated in high-warming scenarios (Wang et al., 2018a; Qiu et al., 2020). Due to the lack of large-scale modelling studies, there is low confidence for climate change impacts on peat carbon uptake and emissions. The largest risk to tropical peatlands is expected to arise from drainage and conversion to forestry or agriculture, which would outpace the impacts of climate change (Page and Baird, 2016; Leifeld et al., 2019; Cooper et al., 2020). The magnitude of possible carbon losses is uncertain, however, and depends strongly on socioeconomic scenarios (Sections,;,

For tropical and subtropical regions, the interplay of atmospheric CO2 with precipitation and temperature becomes of particular importance for future carbon uptake, since in warm and dry environments, elevated CO2 fosters plants with C3 photosynthesis and enhances their water-use efficiency relative to C4 species (Moncrieff et al., 2014a; Midgley and Bond, 2015; Knorr et al., 2016a). As a consequence, enhanced woody cover is expected to occur in the future, especially in mesic savannas, while in xeric savannas an increase in woody cover would occur in regions with enhanced precipitation (Criado et al., 2020). Even though semiarid regions have dominated the global trend in land CO2 uptake in recent decades (Ahlström et al., 2015), so far, most studies that investigated future climate change impacts on savanna ecosystems have concentrated on changes in the extent of land area affected ( rather than on carbon cycling, with medium confidence for increasing woody cover:grass ratios (Moncrieff et al., 2014a; Midgley and Bond, 2015; Moncrieff et al., 2016; Criado et al., 2020). Increases in woody vegetation in what is now grass-dominated would possibly come with a carbon benefit, for instance, it was found that a broad range of future climate and CO2 changes would enhance vegetation carbon storage on Australian savannas (Scheiter et al., 2015). Results from a number of field experiments indicate, however, that impacts on total ecosystem carbon storage may be smaller due to a loss in below-ground carbon (Coetsee et al., 2013; Wigley et al., 2020). Nunez et al. (2021) critique existing incentives to promote the invasion of non-native trees into treeless areas as a means of carbon sequestration, raising doubts about the effects on fire, albedo, biodiversity and water yield (see Box 2.2).

Substantial climate change-driven impacts on tropical tree cover and vegetation type are projected in all studies, irrespective of whether or not the degree amounts to a forest “dieback” (Sections,,, (Davies-Barnard et al., 2015; Wu et al., 2016a; Zemp et al., 2017a; Canadell et al., 2021) . Accordingly, models also suggest a continuation of tropical forests acting as carbon sinks (Huntingford et al., 2013; Mercado et al., 2018). A recent study combining field plot data with statistical models (Hubau et al., 2020) indicates that, in the Amazonian and possibly also in the African forest, the carbon sink in above-ground biomass already declined in the three decades up to 2015. This trend is distinct in the Amazon whereas data from Africa suggests a possible decline after 2010. The authors estimate the vegetation carbon sink in 2030–2040 to decline to zero±0.205 PgC yr -1 in the Amazon and to 0.26±0.215 PgC yr -1 in Africa (a loss of 14% compared to the present). Their results suggest that, over time, CO2 fertilisation is outweighed by the impacts of higher temperatures and drought that enhance tree mortality and diminish growth. The degree of thermal resilience of tropical forests is still uncertain, however (Sullivan et al., 2020).

The lack of simulation studies that seek to quantify all important interacting factors (CO2, drought and fire) for future carbon cycling in savannas and tropical forests and the apparent disagreement between trends projected in models compared to data-driven estimates result in low confidence regarding the direction or magnitude of carbon flux and pool-size changes. Similar to tropical peatlands, given projected human population growth and socioeconomic changes, the continued conversion of forests and savannas into agricultural or pasture systems very likely poses a significant risk of rapid carbon loss which will amplify the climate change-induced risks substantially (high confidence) (, (Aragao et al., 2014; Searchinger et al., 2015; Aleman et al., 2016; Nobre et al., 2016).

The impacts of climate-induced altered animal composition and trophic cascades on land-ecosystem carbon cycling globally are as yet unquantified (Schmitz et al., 2018), even though climate change is expected to lead to shifts in consumer–resource interactions that also contribute to losses of top predators or top herbivores (Sections,, 2.5.4; (Lurgi et al., 2012; Damien and Tougeron, 2019). Cascading trophic effects triggered by top predators or the largest herbivores propagate through food webs and reverberate through to the functioning of whole ecosystems, notably altering productivity, carbon and nutrient turnover and net carbon storage (medium confidence) (Wilmers and Schmitz, 2016; Sobral et al., 2017; Stoner et al., 2018). Across different field experiments, the ecosystem consequences of the presence or absence of herbivores and carnivores have been found to be quantitatively as large as the effects of other environmental change drivers such as warming, enhanced CO2, fire and variable nitrogen deposition (medium confidence) (Hooper et al., 2012; Smith et al., 2015). Some local and regional modelling experiments have begun to explore animal impacts on vegetation dynamics and carbon and nutrient cycling (Pachzelt et al., 2015; Dangal et al., 2017; Berzaghi et al., 2019). Turnover rate is the chief factor that determines future land-ecosystem carbon dynamics and hence carbon–climate feedbacks (Friend et al., 2014). To improve projections, it is imperative to better quantify the broader role of carnivores, grazers and browsers and the way these interact in global studies of how ecosystems respond to climate change. Feedbacks between Ecosystems and Climate

The possibility of feedbacks and interactions between climate drivers and biological systems or ecological processes was identified as a significant emerging issue in AR5, and has since also been highlighted in the SRCCL and the SR1.5. It is virtually certain that land cover changes affect regional and global climate through changes to albedo, evapotranspiration and roughness (very high confidence) (Perugini et al., 2017). There is growing evidence that biosphere-related climate processes are being affected by climate change in combination with disturbance and LULCC (high confidence) (Jia et al., 2019). It is virtually certain that land surface change caused by disturbances such as forest fires, hurricanes, phenological changes, insect outbreaks and deforestation affect carbon, water and energy exchanges, thereby influencing weather and climate (very high confidence) (Table 2.4; Figure 2.10) (Bright et al., 2013; Brovkin et al., 2013; Naudts et al., 2016; Prăvălie, 2018).

Figure 2.10 | Terrestrial ecosystem feedbacks, which affect the Earth’s climate system dynamics. Perturbations and implications for climate system dynamics (warming/cooling) are shown for the three global forest biomes (adapted from Figure 5 in (Prăvălie, 2018). The strength of the mechanism is estimated in general terms, based on the magnitude of carbon storage and evaporative cooling processes that characterise each forest biome (Bonan, 2008). Carbon storage includes forest biomass, without accounting for carbon dynamics in soil, peat and underlying permafrost deposits. Implications of bio-geochemical shifts were only estimated in relation to the intensification of the carbon cycle and increase in biomass at high latitudes, assuming nitrogen availability for the stoichiometric demands of forest vegetation.

Feedbacks can be positive or negative (i.e., amplify or dampen the original forcing), vary spatially and seasonally, and act over large geographic areas and long time periods (more than decades), making them difficult to observe and quantify directly (Schimel et al., 2015; Canadell et al., 2021). Due to the positive impacts of CO2 on vegetation growth and ecosystem carbon storage (high confidenc e) (Sections, (Canadell et al., 2021), the associated climate feedback is negative (i.e., increased removal of atmospheric CO2 and dampened warming, compared to an absence of the feedback). By contrast, projected global losses of carbon in warmer climates (Canadell et al., 2021) imply a positive climate feedback. WGI (Canadell et al., 2021) assesses an overall increase in land carbon uptake through the 21st century. However, the overall strength of the carbon cycle–climate feedback remains very uncertain. One of the underlying reasons may be complex interactions with ecosystem water balance and nitrogen and phosphorous availability, which are poorly constrained by observational evidence and incompletely captured in ESMs (Section (Huntzinger et al., 2017; Canadell et al., 2021).

Land ecosystems contribute substantially to global emissions of nitrous oxide and methane . As with CO2, these emissions respond both directly and indirectly to atmospheric CO2 concentration and climate change, and this gives rise to potential additional bio-geochemical feedbacks in the climate system. A large part of these emissions stem from land and water management, such as fertilizer application, rice production, aquaculture or animal husbandry (Jia et al., 2019). However, nearly 60% of total nitrous oxide emissions (in 2007–2016) has been estimated to stem from natural ecosystems, especially in the Tropics (Tian et al., 2019; Canadell et al., 2021), while freshwater wetlands and peatlands are estimated to contribute between 83% (top-down estimates) and 40% (bottom-up estimates) of total natural CH4 (and 31 and 20% of total methane emissions, respectively) for the period 2008–2017 (Canadell et al., 2021). Median CH4 emissions from northern-latitude wetlands in 2100 were estimated to be 12.1 and 13.5 PgC in emission scenarios leading to 1.5°C and 2°C warming, respectively (Comyn-Platt et al., 2018). Likewise, global warming has been attributed to soil N2O emission increases since the pre-industrial period of 0.8 (0.3–1.3) TgN yr -1 (Tian et al., 2020). Overall, climate feedbacks from future altered land ecosystem emissions of CH4 or N2O are uncertain, but are expected to be small (Canadell et al., 2021).

Changes in regional biodiversity are integral parts of ecosystem–climate feedback loops, including and beyond carbon cycle processes (Figure 2.10; Table 2.4). For instance, the impacts of climate-induced altered animal composition and trophic cascades on ecosystem carbon turnover (see Sections, could be a substantive contribution to carbon–climate feedbacks (low confidence). Additional surface–atmosphere feedbacks that arise from changes in vegetation cover and subsequently altered albedo, evapotranspiration or roughness (often summarised as biophysical feedbacks) can be regionally relevant and could amplify or dampen vegetation cover changes (Jia et al., 2019).

Climate-induced shifts towards forests in what is currently tundra would be expected to reduce regional albedo especially in spring, but also during parts of winter when trees are snow-free (whereas tundra vegetation would be covered in snow), which amplifies warming regionally (high confidence) (Perugini et al., 2017; Jia et al., 2019). Trees would also enhance momentum absorption compared to low tundra vegetation, thus impacting surface–atmosphere mixing of latent and sensible heat fluxes (Jia et al., 2019). Boreal forests insulate and stabilize permafrost and reduce fluctuations of ground temperature: the amplitude of variation of ground surface temperatures was 28°C at a forested site, compared to 60°C in nearby grassland (Section (Bonan, 1989; Stuenzi et al., 2021a; Stuenzi et al., 2021b). Likewise, a shift in moist tropical forests towards vegetation with drought-tolerant traits could possibly reduce evapotranspiration, increase albedo, alter heat transfer at the surface and lead to a negative feedback to precipitation (Section (Jia et al., 2019). In savannas, restoration of woody vegetation has been shown to enhance cloud formation and precipitation in response to enhanced transpiration and turbulent mixing, leading to a positive feedback on woody cover (Syktus and McAlpine, 2016). While this has not yet been systematically explored, similar feedbacks might also emerge from a CO2-induced woody cover increase in savannas (low confidence) (Section

Since biophysical feedbacks can contribute to both surface temperature warming or cooling, analyses so far suggest that, on a global scale, the net impact on climate change is small (Perugini et al., 2017; Jia et al., 2019), unless these feedbacks also accelerate vegetation mortality and lead to substantive carbon losses (Zemp et al., 2017a; Lemordant and Gentine, 2019). More than one-third of the Earth’s land surface has at least 50% of its evapotranspiration regulated by vegetation, and in some regions between 40 and >80% of the land’s evaporated water is returned to land as precipitation. Locally, both directly human-mediated and climate change-mediated changes in vegetation cover can therefore notably affect annual average freshwater availability to human societies, especially if negative feedbacks amplify the reduction of vegetation cover, evapotranspiration and precipitation (medium confidence) (Keys et al., 2016; Keys and Wang-Erlandsson, 2018).

Since AR5, freshwater ecosystems (lakes, reservoirs, rivers and ponds) have been increasingly recognised as important sources of GHG emissions (CO2, CH4 and N2O) into the atmosphere. Key mechanisms which contribute to rising GHG emissions from freshwater ecosystems are the temperature imbalance between photosynthesis and respiration (respiration increases more than photosynthesis with rising temperature), CO2 and CH4 emissions from exposed sediments during droughts, increased transport of matter from land to water, changes in water retention time in rivers and lakes and the effects of temperature on lake stratification and anoxia that favour CH4 emissions.

DelSontro et al. (2018) assembled the largest global data set to date on emission rates from lakes of CO2, CH4 and N2O and found that they co-vary with lake size and trophic state. They estimated that moderate global increases in eutrophication of lakes could translate to 5–40% increases in the GHG effect in the atmosphere. Moreover, they estimated that GHG emissions from lakes and impoundments in past decades accounted for 1.25–2.30 PgCO2 yr -1 (DelSontro et al., 2018), thus around 20% of global CO2 emissions from the burning of fossil fuels (9.4 PgCO2 yr -1) (Friedlingstein et al., 2020).

Global warming will strongly enhance freshwater CH4 emissions through a disproportionate increase in ebullition (gas flux) by 6–20% per 1°C increase in water temperature (Aben et al., 2017). It can be expected that ongoing eutrophication enhanced by climate change-related increases in the release of sediment nutrients and the loading of organic carbon and nutrients from catchments will enhance CH4 ebullition on a global scale (Aben et al., 2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; Beaulieu et al., 2019; Sanches et al., 2019). The strongest increase in ebullition is expected in shallow waters where sediment temperatures are strongly related to atmospheric temperature (Aben et al., 2017). Given that small ponds and shallow lakes are the most abundant freshwater ecosystems globally, these may become hot spots of CH4 ebullition in the future (Aben et al., 2017). On average, CH4, CO2 and N2O account for 75, 23 and 2% of the total CO2-equivalent emissions, respectively, in lakes (DelSontro et al., 2018).

Furthermore, the exposure of lake and river sediments during droughts activates the decomposition of buried organic carbon. In dry river beds, mineralisation of buried organic matter is likely to increase with climate change as anoxic sediments are oxygenated downwards during drying, along with pulses of microbial activity following re-wetting of desiccated sediment. Conservative estimates indicate that adding emissions from exposed sediments of dry inland waters across diverse ecosystem types and climate zones to current global estimates of CO2 emissions could result in a 6% (~0.12 PgC yr −1) increase of total inland water CO2 emission rates covering streams and rivers (334 mmol m -2 day -1), lakes and reservoirs (320 mmol m -2 day -1) and small ponds (148 mmol m -2 day -1) (Marcé et al., 2019; Keller et al., 2020).

Overall, uncertainty as to the quantity of carbon fluxes within freshwater ecosystems and between terrestrial and freshwater systems, and subsequent emissions to the atmosphere remains very high (Raymond et al., 2013; Catalán et al., 2016; Stanley et al., 2016; Evans et al., 2017; Drake et al., 2018; Seekell et al., 2018; Sanches et al., 2019; Bodmer et al., 2020; Keller et al., 2020; Canadell et al., 2021) (see Table SM2.1.). Projections of carbon fluxes are, for example, challenged by the complex interaction between rising water temperatures, loss of ice, changes in hydrology, ecosystem productivity, increased extreme events and variation in terrestrial-matter transport. While we are still short of empirical data, particularly in the Tropics (DelSontro et al., 2018), improvements in sensor technology (Eugster et al., 2011; Gonzalez-Valencia et al., 2014; Maeck et al., 2014; Delwiche et al., 2015) and the use of statistically robust survey designs (Beaulieu et al., 2016; Wik et al., 2016) have improved the accuracy of measurements of GHG emissions in freshwater ecosystems. Global networks such as the Global Lakes Ecological Observatory Network (GLEON) increasingly allow a global view of carbon fluxes, thereby improving estimates of the contribution of freshwater ecosystems to global GHG emissions to the atmosphere.

In summary for freshwater systems, Drake et al. (2018) aggregated contemporary estimates of CO2 and CH4 emissions from freshwater ecosystems with global estimates made by Raymond et al. (2013), and arrived at an estimate of 3.9 PgC yr -1. Rivers and streams accounted for 85% and lakes and reservoirs for 15% of the emissions (Raymond et al., 2013). This trend will continue under scenarios of nutrient loading to inland waters over the next century where increased CH4 emission of inland water has an atmospheric impact of 1.7–2.6 PgC/CO2-eq y −1, which is equivalent to 18–33% of annual CO2 emissions from burning fossil fuels (medium evidence, medium agreement ) (Beaulieu et al., 2019). For comparison, annual uptake of CO2 in land ecosystems is estimated as 3.4 (± 0.9) PgC yr −1 (Friedlingstein et al., 2020). The freshwater numbers combine CO2 and CH4 and are thus not directly comparable. However, they are indicative of the importance of better accounting for freshwater systems in global carbon budgets.

Table 2.4 | Terrestrial and freshwater ecosystem feedbacks which affect the Earth’s climate system dynamics, according to (Prăvălie, 2018).


Implications for warming/feedback mechanism

The Earth’s climate system dynamics

Phenological changes


Increased primary productivity and plant growth with CO2 fertilisation (Mao et al., 2016; Wang et al., 2018a); increasing growing season length (Peñuelas et al., 2009; Barichivich et al., 2013); reduced diurnal temperature range through evapotranspiration (mid latitudes) and albedo (high latitudes) caused by vegetation greening (Jeong et al., 2011); increased CO2 storage in biomass (cooling) (Keenan et al., 2014); reduced albedo in snow-covered regions as canopies become taller and darker (warming); increased evapotranspiration, a key component of the global water cycle and energy balance which influences global rainfall, temperature and atmospheric motion (Zeng et al., 2017)

Insect outbreaks


Reduced carbon uptake and storage (warming); increased surface albedo (cooling) (Landry et al., 2016); increased CO2 emissions (warming); decreased LAI and gross primary productivity (Ghimire et al., 2015), leading to reduced evapotranspiration and increased land surface temperature (Bright et al., 2013)

Range shifts

(sections,,,, 2.4.3)

Reduced albedo in snow-covered regions as trees expand polewards (warming) (Chae et al., 2015); enhanced permafrost thawing; expansion of insect outbreak range, increasing forest impact (Pureswaran et al., 2018); biome-dependent changes in albedo and evapotranspiration regimes (Naudts et al., 2016); reduction in snow and ice albedo in freshwater due to loss of ice (warming) (Lang et al., 2018)

Die-off and large-scale mortality events


Decreased GPP; decline in carbon storage (warming); increased CO2 emissions; increased solar radiation, reduced soil moisture and higher surface runoff; albedo effects (Lewis et al., 2011; Prăvălie, 2018)



Reduced carbon storage (warming) (Pugh et al., 2019a); increase in (regional) surface air temperature due to reduced evaporation (less cooling); increased albedo in high-latitude systems (regional radiative cooling) (Loranty et al., 2014); increased air temperature and diurnal temperature variation (Alkama and Cescatti, 2016), locally and globally (Winckler et al., 2019); reduced precipitation (Perugini et al., 2017); decreased biogenic volatile organic compounds (BVOC) and aerosol emissions (warming through direct and indirect aerosol effects; cooling associated with reduction in atmospheric methane (Jia et al., 2019)

Forest degradation


Reduced carbon storage (warming) (de Paula et al., 2015; Bustamante et al., 2016; de Andrade et al., 2017; Mitchard, 2018)


Carbon losses because biomass is less developed at forest edges (Pütz et al., 2014; Chaplin-Kramer et al., 2015; Haddad et al., 2015)

Air pollution

Decreased plant productivity, transpiration and carbon sequestration in forests with lower biomass due to ozone toxicity (Sitch et al., 2007; Ainsworth et al., 2012); increased (regional) productivity due to increase in diffuse solar radiation caused by terrestrial aerosols (Xie et al., 2021)

Declining populations of megafauna

Changes to physical and chemical properties of organic matter, soils and sediments influence carbon uptake and storage (Schmitz et al., 2018); increased or decreased carbon storage biomass and carbon storage, with differences across biomes determined by floristic structure and animal size (Bello et al., 2015; Osuri et al., 2016; Peres et al., 2016; He et al., 2017; Berzaghi et al., 2018; Schmitz et al., 2018; He et al., 2019)



Increased carbon and aerosol emissions (van der Werf et al., 2017); surface warming (Liu et al., 2019b); albedo effect dependent on ecosystem and species-level traits (Rogers et al., 2015; Chen et al., 2018a) (initial albedo decreases post-fire; increased albedo where snow exposure is increased by canopy removal and species composition changes during recovery); black carbon deposition on snow and sea ice (short-term) (Randerson et al., 2006); indirect increases in carbon emissions due to soil erosion (Caon et al., 2014)

Changes in forest composition (sections,,,

Reduced carbon storage due to the decline of biomass (warming) (McIntyre et al., 2015)

Woody encroachment in non-forested ecosystems

(sections,,,,,, Box 2.1)

Reduced production, increased water use, reduced albedo and altered land–atmosphere feedbacks; increased carbon storage in woody savannas (Zhou et al., 2017; Mureva et al., 2018); uncertain feedbacks to the carbon cycle (some suggest an increase, others a decrease)

NPP shifts


Reduced albedo following high-latitude expansion of trees caused by photosynthetic enhancement of growth (cooling); increased photosynthesis and net ecosystem production (NEP) (Fernandez-Martinez et al., 2019); increased NPP in nutrient-limited ecosystems due to increased nitrogen deposition from agriculture and combustion (Du and de Vries, 2018; Schulte-Uebbing and de Vries, 2018); nutrient-limited lakes are likely to become less productive, while nutrient-rich lakes are likely to become more productive due to warming-induced prolongation of stable stratification (Adrian et al., 2016; Kraemer et al., 2017)

Bio-geochemical shifts

Decline in carbon storage due to nitrogen limitation in nutrient-limited systems (warming) (Reich et al., 2014; Wieder et al., 2015); increased carbon storage on land (Peñuelas et al., 2013) and in lakes (Heathcote et al., 2015; Mendonça et al., 2017); increase in CO2 and CH4 emissions from freshwater ecosystems due to increased eutrophication (DelSontro et al., 2018), the imbalance between losses and gains of CO2 by photosynthesis and respiration (the metabolic theory of ecology), enhanced emissions from exposed river and lake sediments during droughts and re-wetting (Marcé et al., 2019; Keller et al., 2020), enhanced CH4 ebullition of seasonally hypoxic lakes (Aben et al., 2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; Beaulieu et al., 2019; Sanches et al., 2019) and increased transfer of organic carbon from land to water (particularly in permafrost areas) (Wauthy et al., 2018) Risks to Freshwater Ecosystem Services: Drinking Water, Fisheries and Hydropower

AR5 named water supply and biodiversity as freshwater ecosystem services vulnerable to climate change. We discuss the risks to these and to additional services identified by model projections based both on climate-change scenarios (Schröter et al., 2005; Boithias et al., 2014; Huang et al., 2019; Jorda-Capdevila et al., 2019) and on the Common International Classification of Ecosystem Services (high confidence) (CICES, 2018). The effects of floods, droughts, permafrost and glacier-melting on global changes in water quality, particularly with respect to contamination with pollutants, are described in Section 4.2.6. Risks to the quantity and quality of drinking water

Forests and other vegetated ecosystems assist the production of drinkable water by facilitating the infiltration of rainfall and snowfall into the ground, where water either moves through the saturated soil zone to supply streams and other surface waters or infiltrates further to recharge groundwater aquifers (Ellison et al., 2012; Bonnesoeur et al., 2019). Globally, 4 billion people depend on forested watersheds for drinking water (Mekonnen and Hoekstra, 2016). Chapter 4 assesses the physical science of water supply, including precipitation, runoff and hydrology as well as the social aspects of human water use. This section assesses the ecological aspects of risks to freshwater supplies for people.

Diminished vegetation cover following wildfires (Section and tree mortality (Section can reduce long-term water infiltration, increase soil erosion and flash floods and release sediment that degrades drinking water quality. Widlfires increase impacts of extreme precipitation events due to climate change, which contribute to increased surface runoff and hence increased risks of land erosion, landslides and flooding (Ebel et al., 2012; Robinne et al., 2020). Under current conditions, nearly half the global land area is at a moderate-to-high risk of water scarcity due to wildfires (Robinne et al., 2018; Robinne et al., 2020). From 1984 to 2014, wildfires in the western USA affected 6–11% of stream and river length (Ball et al., 2021). Under a high-emissions scenario of a 3.5°C temperature increase, post-fire erosion across the western USA could double sedimentation and degrade drinking water quality in one-third of watersheds by 2050 (Sankey et al., 2017). In Brazil, post-fire vegetation loss tends to increase runoff, reduce infiltration and reduce groundwater recharge and flow of springs (Rodrigues et al., 2019). Runoff from wildfires can contain DOC precursors for the formation of carcinogenic trihalomethanes during chlorination of water for drinking (Uzun et al., 2020) as well as chromium, mercury, selenium and other toxic trace metals (Burton et al., 2016; Burton et al., 2019).

Net effects of deforestation and afforestation on runoff and water supply depend on local factors, leading to conflicting evidence of effects of land cover change (Ellison et al., 2012; Chen et al., 2021b), but combinations of climate change and deforestation are projected to reduce water flows (Olivares et al., 2019). In southern Thailand, the combination of the conversion of forest to rubber plantations and a one-third increase in rainfall could increase erosion and sediment load by 15% (Trisurat et al., 2016). In the watershed that supplies São Paulo, Brazil, afforestation could increase water quantity and quality (Ferreira et al., 2019). In most regions with dry or Mediterranean subtropical climates, projected climate change can reduce surface water and groundwater resources (Doell et al., 2015). In northeast Spain, reduced precipitation and vegetation cover under the high-emissions scenario of a 3.5°C temperature increase could reduce drinking water supplies by half by 2100 (Bangash et al., 2013).

Changes in algal biomass development and the spread of cyanobacteria blooms, related to global warming, resemble those triggered by eutrophication with the well-known negative effects on the services lakes provide, particularly for drinking water provision and recreation (robust evidence, high agreement , high confidence) (Carvalho et al., 2013; Adrian et al., 2016; Gozlan et al., 2019).

Based on a 10% increase in precipitation, (de Wit et al., 2016) estimated an increased mobilisation of organic carbon from soils to freshwaters of at least 30%, demonstrating the importance of climate wetting for the carbon cycle. Browning negatively affects the taste of drinking water and this may be difficult to address (Kothawala et al., 2015; Kritzberg et al., 2020). It also often reduces attractiveness for recreational purposes, especially swimming (Arthington and Hadwen, 2003; Keeler et al., 2015). Based on a worst-case climate scenario until 2030, (Weyhenmeyer et al., 2016) projected an increase in the browning of lakes and rivers in boreal Sweden by a factor of 1.3. The chemical character of DOM, as modified by climate change (Kellerman et al., 2014), determines its amenability to removal by water treatment (Ritson et al., 2014). Therefore, in order to provide safe and acceptable drinking water, more advanced, more expensive and more energy/resource-intensive technical solutions may be required (Matilainen et al., 2010).

In summary, climate change increases risks to the integrity of watersheds and the provision of safe, acceptable freshwater to people (medium evidence, medium agreement ). Risks to freshwater fisheries and biodiversity

Climate change will increase water temperatures and decrease dissolved oxygen levels (Section 2.3.1), impacting freshwater fisheries which form an important ecosystem service (Vári et al., 2022). People living in the vicinity of cold lakes will be affected by projected losses of ice. In a worst-case scenario (an air temperatures increase of 8°C), 230,400 lakes and 656 million people in 50 countries will be impacted (Reid et al., 2019; Sharma et al., 2019). Winter ice-fishing (Orru et al., 2014), transportation via ice roads (Prowse et al., 2011) and cultural activities (Magnuson and Lathrop, 2014) are ecosystem services at stake from the ongoing loss of lake ice.

Eutrophication of central European lakes has wiped out a significant proportion of the endemic fish fauna (Vonlanthen et al., 2012), so climate-induced further eutrophication is expected to represent an additional threat to fish fauna and commercial fisheries (Ficke et al., 2007). Given that the ecological consequences of lake warming may be especially strong in the Tropics (Section, ecosystem services may be most affected there. Tropical lakes support important fisheries (Lynch et al., 2016a; McIntyre et al., 2016) that provide a critical source of nutrition to adjacent human populations. These lakes are especially prone to the loss of deep-water oxygen due to warming, with adverse consequences for the productivity of fisheries and for biodiversity (medium evidence, medium agreement ) (Lewis Jr, 2000; Van Bocxlaer et al., 2012).

Tropical lakes tend to be hotspots of freshwater biodiversity (Vadeboncoeur et al., 2011;