Editing IPCC:AR6/WGII/Chapter-2 (section)

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

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=== 2.4.1 Overview ===

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Global meta-analyses of terrestrial systems in AR3 and AR4 concentrated on long time frames (&gt;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, &gt;4000 species with long-term observational data had been studied in the context of climate change ( [[#Parmesan--2006|Parmesan, 2006]] ; [[#Parmesan--2015|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--2016|Scheffers et al., 2016]] ; [[#Wiens--2016|Wiens, 2016]] ; [[#Cohen--2018|Cohen et al., 2018]] ; [[#Feeley--2020|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%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ; [[#IPBES--2019|IPBES, 2019]] ; [[#Tickner--2020|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.

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=== 2.4.2 Observed Responses to Climate Change by Species and Communities (Freshwater and Terrestrial) ===

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==== 2.4.2.1 Observed Range Shifts Driven by Climate Change ====

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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--2009|Pöyry et al., 2009]] ; [[#Chen--2011|Chen et al., 2011]] ; [[#Grewe--2013|Grewe et al., 2013]] ; [[#Gibson-Reinemer--2015|Gibson-Reinemer and Rahel, 2015]] ; [[#MacLean--2017|MacLean and Beissinger, 2017]] ; [[#Pacifici--2017|Pacifici et al., 2017]] ; [[#Anderegg--2019|Anderegg et al., 2019]] ). Range shifts stem from local population extinctions along warm-range boundaries ( [[#Anderegg--2019|Anderegg et al., 2019]] ) as well as from the colonisation of new regions at cold-range boundaries ( [[#Ralston--2017|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 &lt;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--2015|Lenoir and Svenning, 2015]] ; [[#Rumpf--2019|Rumpf et al., 2019]] ; [[#Lenoir--2020|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--2003|Logan et al., 2003]] ). Milder winters and warmer growing seasons were likewise implicated in poleward expansions and increasing outbreaks of several forest pests ( [[#Weed--2013|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--2020|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--2019|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--2019|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--2019|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--2016b|Lynch et al., 2016b]] ), Europe ( [[#Comte--2013|Comte and Grenouillet, 2013]] ; [[#Gozlan--2019|Gozlan et al., 2019]] ) and Central Asia ( [[#Gozlan--2019|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--2012|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--2013|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--2010|Isaak et al., 2010]] ). Range contractions of the same species have been found in the Rocky Mountains watershed ( [[#Eby--2014|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--2015|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--2019|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--2017|Hotaling et al., 2017]] ). For freshwater invertebrates, no meaningful trends have been detected in geographic extent or population size for most species ( [[#Gozlan--2019|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--2007|Wiedner et al., 2007]] ), aided by a competitive advantage in eutrophic systems ( [[#Ekvall--2013|Ekvall et al., 2013]] ; [[#Urrutia-Cordero--2016|Urrutia-Cordero et al., 2016]] ).

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<span id="observed-local-population-and-global-species-extinctions-driven-by-climate-change"></span>
==== 2.4.2.2 Observed Local Population and Global Species’ Extinctions Driven by Climate Change ====

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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, 2.3.3.5, 2.4.2.6, Cross-Chapter Box EXTREMES in this chapter) ( [[#Parmesan--2013|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--2015|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--2020|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 T max increase of 0.456°C and 0.316°C versus a T mean increase of 0.153°C and 0.061°C for temperate ( ''n'' = 505 sites) and tropical ( ''n'' = 76 sites), respectively (P &lt; 0.001) ( [[#Román-Palacios--2020|Román-Palacios and Wiens, 2020]] ).

[[#Wiens--2016|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 &lt; 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|Wiens, 2016]] ).

Freshwater population extinctions are mainly due to habitat loss, the introduction of alien species, pollution, over-harvesting ( [[#Gozlan--2019|Gozlan et al., 2019]] ; [[#IPBES--2019|IPBES, 2019]] ) and climate change-induced epidemic diseases ( [[#Pounds--2006|Pounds et al., 2006]] )(see [[#2.4.2.7.1|Section 2.4.2.7.1]] ). Climate warming, particularly through the intensification and severity of droughts, contributes to the disappearance of small ponds which hold rare and endemic species ( [[#Bagella--2016|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 [[#2.3.3|Section 2.3.3.5]] ), when water temperature and oxygen concentrations surpass critical thresholds and threatening cold-water fish and amphibians ( [[#Thompson--2012|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--2013|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--2019|Cauvy-Fraunié and Dangles, 2019]] ), and the flatworm ''Crenobia alpina'' (Planariidae) has been reported as locally extinct in the Welsh Llyn Brianne river ( [[#Durance--2010|Durance and Ormerod, 2010]] ; [[#Larsen--2018|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 &gt;4–5 h over several days ( [[#Meade--2018|Meade et al., 2018]] ; [[#Turner--2020|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|Chandler, 2014]] ; [[#Weber--2021|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--1999|Pounds et al., 1999]] ; [[#Pounds--2006|Pounds et al., 2006]] ; [[#Puschendorf--2006|Puschendorf et al., 2006]] ; [[#Richards-Hrdlicka--2013|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 [[#2.4.2.7.1|Section 2.4.2.7.1]] 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 (&lt;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--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--2016|Gynther et al., 2016]] ; [[#Watson--2016|Watson, 2016]] ; [[#Woinarski--2016|Woinarski, 2016]] ; [[#Woinarski--2017|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--2007|Turner and Batianoff, 2007]] ; [[#Woinarski--2014|Woinarski et al., 2014]] ; [[#Gynther--2016|Gynther et al., 2016]] ; [[#Watson--2016|Watson, 2016]] ; [[#Woinarski--2017|Woinarski et al., 2017]] ).

In the IUCN Red List ( [[#IUCN--2019|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).''

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==== 2.4.2.3 Observed Changes in Community Composition Driven by Climate Change ====

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===== 2.4.2.3.1 Overall patterns of community change =====

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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--2017|Bowler et al., 2017]] ; [[#Hughes--2018|Hughes et al., 2018]] ; [[#Kuhn--2019|Kuhn and Gégout, 2019]] ; [[#Feeley--2020|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--2010|Forister et al., 2010]] ; [[#Steinbauer--2018|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--2022|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--2020|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--2013|Cahill et al., 2013]] ; [[#Ockendon--2014|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--2012|Devictor et al., 2012]] ) and lowland forest herbaceous plants in France ( [[#Bertrand--2011|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--2016|Adrian et al., 2016]] ; [[#Hossain--2016|Hossain et al., 2016]] ; [[#Short--2016|Short et al., 2016]] ; [[#Huisman--2018|Huisman et al., 2018]] ; [[#Gozlan--2019|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--2019|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--2016|Urban et al., 2016]] ).

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===== 2.4.2.3.2 Freshwater systems: mechanistic drivers and responses =====

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Physical changes in lakes (see [[#2.3.3|Section 2.3.3]] ) have affected primary production (see [[#2.4.4.5|Section 2.4.4.5.2]] ), algal-bloom formation and composition, zooplankton and fish size distribution, and species composition ( [[#Urrutia-Cordero--2017|Urrutia-Cordero et al., 2017]] ; [[#Gozlan--2019|Gozlan et al., 2019]] ; [[#Seltmann--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--2012|Jeppesen et al., 2012]] ; [[#Jeppesen--2014|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%20Senerpont%20Domis--2013|De Senerpont Domis et al., 2013]] ; [[#Jeppesen--2014|Jeppesen et al., 2014]] ; [[#Adrian--2016|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--2017|Yankova et al., 2017]] ) or overall primary productivity (see [[#2.4.4.5|Section 2.4.4.5.2]] ).

In a study of 1567 lakes across Europe and North America, [[#Kakouei--2021|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--2010|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--2016|Özkundakci et al., 2016]] ; [[#Hampton--2017|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%20Jr--2000|Lewis Jr, 2000]] ; [[#Van%20Bocxlaer--2012|Van Bocxlaer et al., 2012]] ). Many ancient tropical lakes (Malawi, Tanganyika, Victoria, Titicaca, Towuti and Matano) hold thousands of endemic animal species ( [[#Vadeboncoeur--2011|Vadeboncoeur et al., 2011]] ).

Observed effects of climate change on freshwater invertebrates are variable ( [[#Knouft--2017|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|Lencioni, 2018]] ; [[#Cauvy-Fraunié--2019|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--2013|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--2007|Daufresne et al., 2007]] ; [[#Chessman--2015|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--2019|O’Gorman et al., 2019]] ). Macrophytes benefit from rising water temperatures, but increased shading from increased phytoplankton biomass could offset this ( [[#Hossain--2016|Hossain et al., 2016]] ; [[#Short--2016|Short et al., 2016]] ; [[#Zhang--2017a|Zhang et al., 2017a]] ).

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===== 2.4.2.3.3 Emergence of novel communities and invasive species =====

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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--2021|Sentis et al., 2021]] ). Both simulated warming experiments ( [[#Zettlemoyer--2019|Zettlemoyer et al., 2019]] ) and long-term observations ( [[#Willis--2010|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--2020|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--2014|Dornelas et al., 2014]] ; [[#Evers--2018|Evers et al., 2018]] ; [[#Teixeira--2020|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--2012|Lurgi et al., 2012]] ; [[#Hobbs--2014|Hobbs et al., 2014]] ; [[#Heger--2019|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--2007|Williams and Jackson, 2007]] ). However, observations, experimental mesocosms ( [[#Bastazini--2021|Bastazini et al., 2021]] ), and theoretical models ( [[#Lurgi--2012|Lurgi et al., 2012]] ; [[#Sentis--2021|Sentis et al., 2021]] ) provide support that novel communities will continue to emerge with climate change ''(medium confidence)'' .

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<span id="observed-phenological-responses-to-climate-change"></span>
==== 2.4.2.4 Observed Phenological Responses to Climate Change ====

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Since AR5, the number of studies of changes in phenology (timing of biological events) has increased substantially, aided by advances in remote sensing ( [[#Piao--2019|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--2020|Menzel et al., 2020]] ). In the Tropics, by contrast, changes in precipitation have more strongly influenced animal phenology than have temperature changes ( [[#Cohen--2018|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--2019|Radchuk et al., 2019]] ).

Taxonomic groups have differed in their responses ( [[#Parmesan--2007|Parmesan, 2007]] ; [[#Thackeray--2010|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--2018|Keogan et al., 2018]] ). Newer reviews and analyses reveal differences in responses across continents and time intervals ( [[#Piao--2019|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--2019|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--2020|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--2019|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--2020|Menzel et al., 2020]] ). [[#Wang--2019|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--2020|Menzel et al., 2020]] ). In a long (67 years) European time series ( [[#Menzel--2020|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--2015|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 &lt;10–13 for ( [[#Parmesan--2003|Parmesan and Yohe, 2003]] ; [[#Root--2003|Root et al., 2003]] ; [[#Root--2005|Root et al., 2005]] ; [[#Poloczanska--2013|Poloczanska et al., 2013]] ) and &lt;0.001 for [[#Rosenzweig--2008|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--2013|Parmesan et al., 2013]] ; [[#Cramer--2014|Cramer et al., 2014]] ) from those that analysed all available data, including data from areas highly-impacted by non-climate drivers (e.g. LULCC).

{| class="wikitable"
|-
! '''Study'''

! '''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'''

|-
| colspan="7"| '''2.2a Observed phenological changes'''

|-
| ( [[#Parmesan--2003|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

| Global

| Yes

|-
| ( [[#Menzel--2006|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

| Europe

| Yes

|-
| ( [[#Parmesan--2007|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

| Global

| Yes

|-
| ( [[#Rosenzweig--2008|Rosenzweig et al., 2008]] )

| 55 studies (~100–200 species)

| T: 65%

M: 13%

F: 22%

| Various

| 90% of changes consistent with local/regional climate change

| Global

| Yes

|-
| ( [[#Thackeray--2010|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

| UK

| No

|-
| ( [[#While--2014|While and Uller, 2014]] )

| 59 populations, 17 studies

| T/F, 100% Amphibians

| Phenology

| 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

| Global

| No

|-
| ( [[#Gill--2015|Gill et al., 2015]] )

| 64 studies

| T: 100%

trees

| 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

| Global

| No

|-
| ( [[#Ficetola--2016|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

| Global

| No

|-
| ( [[#Halupka--2017|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

| Yes

|-
| ( [[#Kharouba--2018|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.

| Global

| No

|-
| ( [[#Cohen--2018|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.

| Europe

North America

Australia

Japan

| No

|-
| ( [[#Keogan--2018|Keogan et al., 2018]] )

| 145 populations, 209 time series

| T: Seabirds breeding sites

| Phenological trends

| No change in breeding dates between 1952 and 2015

| Global

| Yes

|-
| ( [[#Radchuk--2019|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

| No

|-
| ( [[#Piao--2019|Piao et al., 2019]] )

| Review

| 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

| Global

| No

|-
| ( [[#Menzel--2020|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 .

| Europe

| Yes

|-
| colspan="7"| '''2.2b. Observed Changes In Distributions, Abundances And Local Population Extinctions'''

|-
| ( [[#Parmesan--2003|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

| Global

| Yes

|-
| ( [[#Root--2003|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

| Global

| Yes

|-
| ( [[#Rosenzweig--2008|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

| Global

| Yes

|-
| ( [[#Pöyry--2009|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 (&lt;2.1 km)

| Finland

| Yes

|-
| ( [[#Tingley--2009|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

| Yes

|-
| ( [[#Chen--2011|Chen et al., 2011]] )

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

| T &gt;264

M &gt;10

F &gt;34

| Range shifts: elevation and latitude

| Mean upward elevation shift 11.0 m per decade

Poleward shift 16.9 km per decade

| Pseudo-global

| No

|-
| ( [[#Grewe--2013|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.

| Europe

| No

|-
| ( [[#Mason--2015|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

| UK

| Yes

|-
| ( [[#Gibson-Reinemer--2015|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)

| Global

| No

|-
| ( [[#Ficetola--2016|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

| Global

| No

|-
| ( [[#Scheffers--2016|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

| Global

| No

|-
| ( [[#Wiens--2016|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%

| Global

| Yes

|-
| ( [[#Bowler--2017|Bowler et al., 2017]] )

| 1167 populations, 22 communities

| T: 48%

M: 61%

F: 35%

| Abundance;

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

| Europe

| Yes

|-
| ( [[#Pacifici--2017|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--2017|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.

| Global

| No

|-
| ( [[#Ralston--2017|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

| No

|-
| ( [[#Rumpf--2019|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.

| Global

| No

|-
| ( [[#Freeman--2018|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.

| Global

| No

|-
| ( [[#Anderegg--2019|Anderegg et al., 2019]] )

| Meta-analysis

50 studies, &gt;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

| Yes;

drought not warming

|-
| ( [[#Román-Palacios--2020|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 &lt; 0.001, ''n'' = 505 sites) and smaller increases in mean temperatures (0.412°C vs. 1.231°C, P &lt; 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 &lt; 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)

| Global

| Yes

|}

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--2009|Adrian et al., 2009]] ; [[#De%20Senerpont%20Domis--2013|De Senerpont Domis et al., 2013]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Thackeray--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--2017|Dempson et al., 2017]] ).

<div id="2.4.2.5" class="h3-container"></div>

<span id="observed-complex-phenological-and-range-shift-responses"></span>
==== 2.4.2.5 Observed Complex Phenological and Range Shift Responses ====

<div id="h3-11-siblings" class="h3-siblings"></div>

Early meta-analyses tested the straightforward hypotheses that warming should shift timing earlier and ranges polewards. Once these trends had been established ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#Parmesan--2015|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--2012a|Cook et al., 2012a]] ; [[#Cook--2012b|Cook et al., 2012b]] ; [[#Legave--2015|Legave et al., 2015]] ). These turned out to be species requiring vernalisation (winter chilling) to speed their development in spring ( [[#Ettinger--2020|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--2012b|Cook et al., 2012b]] ).

Animal species can show vernalisation equivalent to that in plants ( [[#Stålhandske--2017|Stålhandske et al., 2017]] ). However, a semi-global meta-analysis of terrestrial animals failed to detect the delaying effects of warming winters ( [[#Cohen--2018|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--2018|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--2020|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--2015|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--2012|Tingley et al., 2012]] ).

Multiple responses can co-occur. [[#Hällfors--2021|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.

<div id="2.4.2.6" class="h3-container"></div>

<span id="observed-changes-to-physiology-and-morphology-driven-by-climate-change"></span>
==== 2.4.2.6 Observed Changes to Physiology and Morphology Driven by Climate Change ====

<div id="h3-12-siblings" class="h3-siblings"></div>

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--2012|Healy and Schulte, 2012]] ; [[#Gunderson--2015|Gunderson and Stillman, 2015]] ; [[#Deery--2021|Deery et al., 2021]] ), increased metabolic costs of living at elevated temperatures ( [[#Scheffers--2016|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--2008|Schwanz and Janzen, 2008]] ; [[#Schwanz--2016|Schwanz, 2016]] ; [[#Edmands--2021|Edmands, 2021]] ) and the feminisation of turtle populations ( [[#Telemeco--2009|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--2010|Mitchell and Janzen, 2010]] ; [[#Edmands--2021|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--2016|Refsnider and Janzen, 2016]] ; [[#Telemeco--2017|Telemeco et al., 2017]] ). Plasticity in heat tolerance (e.g., due to reversible acclimation or acclimatisation) can also potentially compensate for rising temperatures ( [[#Angilletta%20Jr--2009|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--2015|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--2015|Gunderson and Stillman, 2015]] ; [[#Sun--2021|Sun et al., 2021]] ). In many ectotherms, plasticity in thermal tolerance increases polewards, as thermal seasonality increases ( [[#Chown--2004|Chown et al., 2004]] ), contributing to higher vulnerability to warming in tropical organisms ( ''low confidence'' ) ( [[#Huey--2009|Huey et al., 2009]] ; [[#Campos--2021|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--2003|Angilletta et al., 2003]] ; [[#Stillman--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--2011|Bozinovic et al., 2011]] ; [[#Overgaard--2014|Overgaard et al., 2014]] ; [[#Magozzi--2015|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--2009|Galeotti et al., 2009]] ; [[#Karell--2011|Karell et al., 2011]] ). However, trends are not always linear or consistent across realms, taxonomic groups or geographic regions ( [[#Gotanda--2015|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--2016|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--2009|Daufresne et al., 2009]] ; [[#Winder--2009|Winder et al., 2009]] ; [[#Jeppesen--2010|Jeppesen et al., 2010]] ; [[#Crozier--2014|Crozier and Hutchings, 2014]] ; [[#Jeppesen--2014|Jeppesen et al., 2014]] ; [[#Farmer--2015|Farmer et al., 2015]] ; [[#Rasconi--2015|Rasconi et al., 2015]] ; [[#Woodward--2016|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--2020|Pomati et al., 2020]] Nutrients).

Evidence is weak for a consistent reduction in body size across taxonomic groups in terrestrial animals ( ''low confidence'' ) ( [[#Siepielski--2019|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--2014|Teplitsky and Millien, 2014]] ; [[#Gotanda--2015|Gotanda et al., 2015]] ; [[#Gardner--2019|Gardner et al., 2019]] ; [[#Hill--2021|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--2004|Yom-Tov and Yom-Tov, 2004]] ; [[#Gardner--2019|Gardner et al., 2019]] ; [[#Wilson--2019|Wilson et al., 2019]] ) or use of different body size measurements (linear vs. volumetric dimensions) ( [[#Salewski--2014|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--2014|Zeuss et al., 2014]] ; [[#MacLean--2016|MacLean et al., 2016]] ; [[#MacLean--2019a|MacLean et al., 2019a]] ), beetles ( [[#de%20Jong--1998|de Jong and Brakefield, 1998]] ; [[#Brakefield--2011|Brakefield and de Jong, 2011]] ; [[#Zvereva--2019|Zvereva et al., 2019]] ), dragonflies ( [[#Zeuss--2014|Zeuss et al., 2014]] ) and phasmids ( [[#Nosil--2018|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--2013|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--2007|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--2015|Kingsolver and Buckley, 2015]] ; [[#Stuart-Fox--2017|Stuart-Fox et al., 2017]] ; [[#Clusella-Trullas--2020|Clusella-Trullas and Nielsen, 2020]] ).

Interactions between morphological changes and changes in phenology may facilitate or constrain adaptation to climate change ( ''medium confidence'' ) ( [[#Hedrick--2021|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--2021|Zimova et al., 2021]] ). A study of 52 species of North American migratory birds, based on &gt;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--2020|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--2011|Bozinovic et al., 2011]] ; [[#Vázquez--2017|Vázquez et al., 2017]] ; [[#González-Tokman--2020|González-Tokman et al., 2020]] ). Many behavioural, morphological and physiological responses are highly species- and context-specific, making generalisations difficult ( [[#Bodensteiner--2021|Bodensteiner et al., 2021]] ). Recent advances in mechanistic understanding (from experiments), in process-based modelling (including micro-climates and developmental processes) ( [[#Carter--2021|Carter and Janzen, 2021]] ) and in the sophistication of niche models ( [[#Kearney--2009|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--2015|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--2021|Cooke et al., 2021]] ; [[#Ryan--2021|Ryan and Gunderson, 2021]] ).

<div id="2.4.2.7" class="h3-container"></div>

<span id="observed-impacts-of-climate-change-on-diseases-of-wildlife-and-associated-impacts-on-humans"></span>
==== 2.4.2.7 Observed Impacts of Climate Change on Diseases of Wildlife and Associated Impacts on Humans ====

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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--2001|Woolhouse et al., 2001]] ; [[#Woolhouse--2005|Woolhouse and Gowtage-Sequeria, 2005]] ; [[#McIntyre--2017|McIntyre et al., 2017]] ; [[#Salyer--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--2014|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--2014|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%20Polar%20Institute--2009|Norwegian Polar Institute, 2009]] ; [[#Tersago--2009|Tersago et al., 2009]] ; [[#Tabachnick--2010|Tabachnick, 2010]] ; [[#Paz--2015|Paz, 2015]] ; [[#Dewage--2019|Dewage et al., 2019]] ; [[#Deksne--2020|Deksne et al., 2020]] ; [[#Shocket--2020|Shocket et al., 2020]] ; [[#Couper--2021|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 [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] and Cross-Chapter Box ILLNESS in this chapter).

<div id="2.4.2.7.1" class="h4-container"></div>

<span id="direct-effects-of-climate-and-climate-change-on-reproduction-seasonality-the-length-of-the-growing-season-and-the-transmission-of-pathogens-vectors-and-hosts"></span>
===== 2.4.2.7.1 Direct effects of climate and climate change on reproduction, seasonality, the length of the growing season and the transmission of pathogens, vectors and hosts =====

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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--2013|Mordecai et al., 2013]] ; [[#Liu-Helmersson--2014|Liu-Helmersson et al., 2014]] ; [[#Moran--2014|Moran and Alexander, 2014]] ; [[#Bernstein--2015|Bernstein, 2015]] ; [[#Marcogliese--2016|Marcogliese, 2016]] ; [[#Ogden--2016|Ogden and Lindsay, 2016]] ; [[#Mordecai--2017|Mordecai et al., 2017]] ; [[#Short--2017|Short et al., 2017]] ; [[#Caminade--2019|Caminade et al., 2019]] ; [[#Cavicchioli--2019|Cavicchioli et al., 2019]] ; [[#Mordecai--2019|Mordecai et al., 2019]] ; [[#Liu--2020|Liu et al., 2020]] ; [[#Rocklöv--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--2018|MacFadden et al., 2018]] ; [[#Pärnänen--2019|Pärnänen et al., 2019]] ; [[#Shukla--2019|Shukla, 2019]] ; [[#McGough--2020|McGough et al., 2020]] ; [[#Rodriguez-Verdugo--2020|Rodriguez-Verdugo et al., 2020]] ).

There is increasing evidence of the role of extreme events in disease outbreaks ''(very high confidence)'' ( [[#Tjaden--2018|Tjaden et al., 2018]] ; [[#Bryson--2020|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--2010|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--2001|Anyamba et al., 2001]] ; [[#Marcheggiani--2010|Marcheggiani et al., 2010]] ; [[#Brown--2013|Brown and Murray, 2013]] ; [[#Paz--2015|Paz, 2015]] ; [[#Boyce--2016|Boyce et al., 2016]] ; [[#Wu--2016b|Wu et al., 2016b]] ; [[#Wilcox--2019|Wilcox et al., 2019]] ; [[#Nosrat--2021|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--2001|Anyamba et al., 2001]] ; [[#Zhou--2005|Zhou et al., 2005]] ; [[#Wu--2008|Wu et al., 2008]] ; [[#Brown--2013|Brown and Murray, 2013]] ; [[#Anyamba--2014|Anyamba et al., 2014]] ; [[#Boyce--2016|Boyce et al., 2016]] ). Conversely, flooding also increases flow rate and decreases parasite load and diversity in other aquatic wildlife ( [[#Hallett--2008|Hallett and Bartholomew, 2008]] ; [[#Bjork--2009|Bjork and Bartholomew, 2009]] ; [[#Marcogliese--2016|Marcogliese, 2016]] ; [[#Marcogliese--2016|Marcogliese et al., 2016]] ) and can reduce mosquito abundance by flushing them out of the system ( [[#Paaijmans--2007|Paaijmans et al., 2007]] ; [[#Paz--2015|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--2003|Chareonviriyaphap et al., 2003]] ; [[#Chretien--2007|Chretien et al., 2007]] ; [[#Padmanabha--2010|Padmanabha et al., 2010]] ; [[#Trewin--2013|Trewin et al., 2013]] ; [[#Paz--2015|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--2020|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--2014|Larsen and Mouritsen, 2014]] ; [[#Mouritsen--2018|Mouritsen et al., 2018]] ). Changes in temperature and precipitation, especially extreme events, can alter community structure ( [[#Larsen--2011|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--2012|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--2009|Laaksonen et al., 2009]] ; [[#Molnár--2013|Molnár et al., 2013]] ; [[#Waits--2018|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%20Polar%20Institute--2009|Norwegian Polar Institute, 2009]] ).

<div id="Case" class="h4-container"></div>

<span id="case-study-1-climate-change-impacts-on-pathogenic-helminths-in-europe"></span>
===== Case Study 1: Climate change impacts on pathogenic helminths in Europe =====

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Parasitic helminths can reduce growth and yield, kill livestock and infect humans and wildlife, leading to health, agricultural and economic losses ( [[#Fairweather--2011|Fairweather, 2011]] ; [[#Charlier--2016|Charlier et al., 2016]] ; [[#Charlier--2020|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%20Dijk--2008|van Dijk et al., 2008]] ; [[#van%20Dijk--2010|van Dijk et al., 2010]] ; [[#Fox--2011b|Fox et al., 2011b]] ; [[#Martínez-Valladares--2013|Martínez-Valladares et al., 2013]] ; [[#Charlier--2016|Charlier et al., 2016]] ; [[#Innocent--2017|Innocent et al., 2017]] ; [[#Mehmood--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--1995|Lee et al., 1995]] ; [[#Pritchard--2005|Pritchard et al., 2005]] ; [[#Poulin--2006|Poulin, 2006]] ; [[#van%20Dijk--2008|van Dijk et al., 2008]] ; [[#van%20Dijk--2010|van Dijk et al., 2010]] ; [[#Fairweather--2011|Fairweather, 2011]] ; [[#Fox--2011b|Fox et al., 2011b]] ; [[#Martínez-Valladares--2013|Martínez-Valladares et al., 2013]] ; [[#Bosco--2015|Bosco et al., 2015]] ; [[#Caminade--2015|Caminade et al., 2015]] ; [[#Caminade--2019|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'' ).

<span id="case-study-2-chytrid-fungus-and-climate-change"></span>
===== Case Study 2: Chytrid fungus and climate change =====

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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--2019|Scheele et al., 2019]] ; [[#Fisher--2020|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 &gt; 5200 m ( [[#Seimon--2017|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--2010|Rohr and Raffel, 2010]] ; [[#Puschendorf--2011|Puschendorf et al., 2011]] ; [[#Rowley--2013|Rowley and Alford, 2013]] ; [[#Raffel--2015|Raffel et al., 2015]] ; [[#Sauer--2018|Sauer et al., 2018]] ; [[#Cohen--2019a|Cohen et al., 2019a]] ; [[#Sauer--2020|Sauer et al., 2020]] ; [[#Turner--2021|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--2006|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--2014|Zumbado-Ulate et al., 2014]] ; [[#Sauer--2018|Sauer et al., 2018]] ; [[#Cohen--2019b|Cohen et al., 2019b]] ; [[#Neely--2020|Neely et al., 2020]] ; [[#Sauer--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--2006|Puschendorf et al., 2006]] ; [[#Puschendorf--2011|Puschendorf et al., 2011]] ; [[#Rowley--2013|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--2004|Retallick et al., 2004]] ; [[#Daskin--2011|Daskin et al., 2011]] ; [[#Puschendorf--2011|Puschendorf et al., 2011]] ; [[#Phillips--2013|Phillips and Puschendorf, 2013]] ; [[#Rowley--2013|Rowley and Alford, 2013]] ; [[#Zumbado-Ulate--2014|Zumbado-Ulate et al., 2014]] ; [[#Sapsford--2015|Sapsford et al., 2015]] ; [[#Voyles--2018|Voyles et al., 2018]] ; [[#Bradley--2019|Bradley et al., 2019]] ; [[#Fisher--2020|Fisher and Garner, 2020]] ; [[#McMillan--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--2014|Zumbado-Ulate et al., 2014]] ; [[#Cohen--2019b|Cohen et al., 2019b]] ; [[#Neely--2020|Neely et al., 2020]] ; [[#Turner--2021|Turner et al., 2021]] ).

<div id="2.4.2.7.2" class="h4-container"></div>

<span id="changes-in-geographic-distribution-and-connectivity-patterns-of-pathogens"></span>
===== 2.4.2.7.2 Changes in geographic distribution and connectivity patterns of pathogens =====

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As species’ geographic ranges and migration patterns are modified by climate change ( [[#2.4.2.1|Section 2.4.2.1]] , 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--1959|Ollerenshaw and Rowlands, 1959]] ; [[#Purse--2005|Purse et al., 2005]] ; [[#Laaksonen--2010|Laaksonen et al., 2010]] ; [[#van%20Dijk--2010|van Dijk et al., 2010]] ; [[#Alonso--2011|Alonso et al., 2011]] ; [[#Genchi--2011|Genchi et al., 2011]] ; [[#Pinault--2011|Pinault and Hunter, 2011]] ; [[#Jaenson--2012|Jaenson et al., 2012]] ; [[#Loiseau--2012|Loiseau et al., 2012]] ; [[#Kweka--2013|Kweka et al., 2013]] ; [[#Medlock--2013|Medlock et al., 2013]] ; [[#Dhimal--2014a|Dhimal et al., 2014a]] ; [[#Dhimal--2014b|Dhimal et al., 2014b]] seasonal; [[#Siraj--2014|Siraj et al., 2014]] ; [[#Khatchikian--2015|Khatchikian et al., 2015]] ; [[#Hotez--2016a|Hotez, 2016a]] ; [[#Hotez--2016b|Hotez, 2016b]] ; [[#Bett--2017|Bett et al., 2017]] ; [[#Mallory--2017|Mallory and Boyce, 2017]] ; [[#Strutz--2017|Strutz, 2017]] ; [[#Booth--2018|Booth, 2018]] ; [[#Dumic--2018|Dumic and Severnini, 2018]] ; [[#Carignan--2019|Carignan et al., 2019]] ; [[#Gorris--2019|Gorris et al., 2019]] ; [[#Le--2019|Le et al., 2019]] ; [[#Stensgaard--2019b|Stensgaard et al., 2019b]] snails and; [[#Brugueras--2020|Brugueras et al., 2020]] ; [[#Gilbert--2021|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|Dahal, 2008]] ; [[#Dhimal--2014a|Dhimal et al., 2014a]] ; [[#Dhimal--2014b|Dhimal et al., 2014b]] ; [[#Dhimal--2015|Dhimal et al., 2015]] ), with similar trends being recorded in neighbouring Himalayan regions ( ''medium evidence'' , ''high agreement'' ) ( [[#Phuyal--2020|Phuyal et al., 2020]] ; [[#Dhimal--2021|Dhimal et al., 2021]] ). Host animals in novel areas may be immunologically naive, and therefore more vulnerable to severe illness ( [[#Bradley--2005|Bradley et al., 2005]] ; [[#Hall--2016|Hall et al., 2016]] ).

<div id="Case" class="h4-container"></div>

<span id="case-study-3-arctic-and-sub-arctic-disease-expansion-and-intensification"></span>
===== Case Study 3: Arctic and sub-Arctic disease expansion and intensification =====

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High Arctic regions have warmed by more than double the global average, &gt;2°C in most areas (Sections 2.3.1.1.2, Figure 2.11, and Atlas 11.2.1.2 in ( [[#IPCC--2021a|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--2014|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 2.4.2.4, 2.4.4.3.3). 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--2018|Waits et al., 2018]] ; [[#Caminade--2019|Caminade et al., 2019]] ; [[#Gilbert--2021|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--2019|Caminade et al., 2019]] ; [[#Gilbert--2021|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--2020|Huber et al., 2020]] ), tick-borne encephalitis and tularemia ( [[#Evander--2009|Evander and Ahlm, 2009]] ; [[#Parkinson--2014|Parkinson et al., 2014]] ; [[#Pauchard--2016|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--2019|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--2020|Hueffer et al., 2020]] ; [[#Ezhova--2021|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--2021|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--2018|Waits et al., 2018]] ; [[#Hueffer--2020|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--2017|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--2016|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--2020|Deksne et al., 2020]] ; [[#Stammler--2020|Stammler and Ivanova, 2020]] ).

<div id="2.4.2.7.3" class="h4-container"></div>

<span id="biodiversitydisease-links"></span>
===== 2.4.2.7.3 Biodiversity–disease links =====

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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--2015|Civitello et al., 2015]] ; [[#Young--2017b|Young et al., 2017b]] ; [[#Halliday--2020|Halliday et al., 2020]] ; [[#Rohr--2020|Rohr et al., 2020]] ; [[#Glidden--2021|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--2020|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 [[#2.4.2.1|Section 2.4.2.1]] , Chapter 8, Cross-Chapter Box MOVING PLATE in Chapter 5) ( [[#Patz--2004|Patz et al., 2004]] ; [[#Cleaveland--2007|Cleaveland et al., 2007]] ; [[#Karesh--2012|Karesh et al., 2012]] ; [[#Altizer--2013|Altizer et al., 2013]] ; [[#Allen--2017|Allen et al., 2017]] ; [[#Plowright--2017|Plowright et al., 2017]] ; [[#Faust--2018|Faust et al., 2018]] ; [[#Carlson--2020|Carlson et al., 2020]] ; [[#Gibb--2020|Gibb et al., 2020]] ; [[#Hockings--2020|Hockings et al., 2020]] ; [[#IPBES--2020|IPBES, 2020]] ; [[#Volpato--2020|Volpato et al., 2020]] ; [[#Glidden--2021|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--2004|Brashares et al., 2004]] ; [[#Leroy--2004|Leroy et al., 2004]] ; [[#Wolfe--2004|Wolfe et al., 2004]] ; [[#Rosen--2010|Rosen and Smith, 2010]] ; [[#Kurpiers--2016|Kurpiers et al., 2016]] ).

<div id="2.4.2.7.4" class="h4-container"></div>

<span id="implications-of-changes-in-diseases-in-wild-animals-for-humans"></span>
===== 2.4.2.7.4 Implications of changes in diseases in wild animals for humans =====

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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 5.5.1.3, 7.2.2.1, Cross-Chapter Box ILLNESS this Chapter) ( [[#Harvell--2002|Harvell et al., 2002]] ; [[#Norwegian%20Polar%20Institute--2009|Norwegian Polar Institute, 2009]] ; [[#Tersago--2009|Tersago et al., 2009]] ; [[#Tabachnick--2010|Tabachnick, 2010]] ; [[#Altizer--2013|Altizer et al., 2013]] ; [[#Garrett--2013|Garrett et al., 2013]] ; [[#Paz--2015|Paz, 2015]] ; [[#Wu--2016b|Wu et al., 2016b]] ; [[#Caminade--2019|Caminade et al., 2019]] ; [[#Dewage--2019|Dewage et al., 2019]] ; [[#Coates--2020|Coates and Norton, 2020]] ; [[#Deksne--2020|Deksne et al., 2020]] ; [[#Shocket--2020|Shocket et al., 2020]] ; [[#Couper--2021|Couper et al., 2021]] ; [[#Gilbert--2021|Gilbert, 2021]] ).

<div id="2.4.2.8" class="h3-container"></div>

<span id="observed-evolutionary-responses-to-climate-change"></span>
==== 2.4.2.8 Observed Evolutionary Responses to Climate Change ====

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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 2.4.2.3.1) ( [[#Devictor--2012|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--2018|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 {2.4.2.3.1} .

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|Singer, 2017]] ; [[#Socolar--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--2012|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--2014|Boutin and Lane, 2014]] ). Indeed, observed evolutionary impacts have been mainly documented in insects, especially at expanding range margins ( [[#Chuang--2016|Chuang and Peterson, 2016]] ) where evolutionary changes have increased dispersal ability ( [[#Thomas--2001|Thomas et al., 2001]] ) and decreased host specialisation ( [[#Bridle--2014|Bridle et al., 2014]] ; [[#Lancaster--2020|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--2001|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--2006|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|Singer, 2017]] ). Whether or not they can depends on the level of ‘niche conservatism’ operating at the species level ( [[#Lavergne--2010|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--2009|Bell and Gonzalez, 2009]] ; [[#Bell--2019|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--2019|Castaneda et al., 2019]] ; [[#Xue--2019|Xue et al., 2019]] ). Tests of thermal performance have been complicated, as both long-term acclimation and trans-generational effects occur ( [[#Sgro--2016|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--2011|Hoffmann and Sgro, 2011]] ; [[#MacLean--2019b|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--2015|Kristensen et al., 2015]] ). The hypothesis that heat-stress tolerance is evolutionarily constrained is further supported by experiments in which 22 ''Drosophila'' 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--2019b|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|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|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--2019|Leigh et al., 2019]] ). In [[#Leigh--2019|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--2010|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--2014|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'' ).

<div id="FAQ 2.1" class="h2-container"></div>

<span id="faq-2.1-will-species-become-extinct-with-climate-change-and-is-there-anything-we-can-do-to-prevent-this"></span>
=== FAQ 2.1 | Will species become extinct with climate change and is there anything we can do to prevent this? ===

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''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.

[[File:2485400c2cd49b3ce1052a00e87d9954 IPCC_AR6_WGII_Figure_2_FAQ_2.1.1.png]]

'''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).

<div id="FAQ 2.2" class="h2-container"></div>

<span id="faq-2.2-how-does-climate-change-increase-the-risk-of-diseases"></span>
=== FAQ 2.2 | How does climate change increase the risk of diseases? ===

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''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).

[[File:4cbc75e56b6cc39025c642db16106915 IPCC_AR6_WGII_Figure_2_FAQ_2.2.1.png]]

'''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.

<div id="2.4.3" class="h2-container"></div>

<span id="observed-changes-in-key-biomes-ecosystems-and-their-services"></span>
=== 2.4.3 Observed Changes in Key Biomes, Ecosystems and Their Services ===

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<div id="2.4.3.1" class="h3-container"></div>

<span id="detection-and-attribution-for-observed-biome-shifts"></span>
==== 2.4.3.1 Detection and Attribution for Observed Biome Shifts ====

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Attribution for biome (major vegetation form of an ecosystem) shifts is complex because of their extensive, sometimes continental, spatial scale ( [[#Whittaker--1975|Whittaker, 1975]] ; [[#Olson--2001|Olson et al., 2001]] ; [[#Woodward--2004|Woodward et al., 2004]] ). Therefore, non-climatic factors strongly influence biome spatial distributions ( [[#Ellis--2008|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--2010|Hegerl et al., 2010]] ; [[#Parmesan--2013|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--2012|Gonzalez et al., 2012]] ). However, drivers often have strong, significant interactions with one another, complicating quantitative assessment of the strength of individual drivers ( [[#Parmesan--2013|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|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--2005|Bond et al., 2005]] ). In these systems, their evolutionary history ( [[#Keeley--2011|Keeley et al., 2011]] ; [[#Strömberg--2011|Strömberg, 2011]] ; [[#Charles-Dominique--2016|Charles-Dominique et al., 2016]] ), distribution, structure and function have been shaped by climate and natural disturbances, such as fire and herbivory ( [[#Staver--2011|Staver et al., 2011]] ; [[#Lehmann--2014|Lehmann et al., 2014]] ; [[#Pausas--2015|Pausas, 2015]] ; [[#Bakker--2016|Bakker et al., 2016]] ; [[#Malhi--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--2013|Archibald et al., 2013]] ; [[#Malhi--2016|Malhi et al., 2016]] ; [[#Hempson--2017|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 2.4.3.2.1; 2.4.3.2.2; 2.4.3.5). 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).

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<span id="global-patterns-of-observed-biome-shifts-driven-by-climate-change"></span>
==== 2.4.3.2 Global Patterns of Observed Biome Shifts Driven by Climate Change ====

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<div id="2.4.3.2.1" class="h4-container"></div>

<span id="observed-biome-shifts-predominantly-driven-by-climate-change"></span>
===== 2.4.3.2.1 Observed biome shifts predominantly driven by climate change =====

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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--2010|Gonzalez et al., 2010]] ; [[#Settele--2014|Settele et al., 2014]] ). In these areas, temperature increased to 0.4°C–1.6°C above the pre-industrial period ( [[#Gonzalez--2010|Gonzalez et al., 2010]] ; [[#Settele--2014|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--2015|Morueta-Holme et al., 2015]] ; [[#Moret--2019|Moret et al., 2019]] ). Over 210 years, temperature increased by 1.7°C ( [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ) and the upper edge of alpine grassland shifted 100–450 m upslope ( [[#Moret--2019|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--2017|Sittaro et al., 2017]] ) and 20 km from 1970–2014 ( [[#Boisvert-Marsh--2019|Boisvert-Marsh et al., 2019]] ) and of temperate conifer into boreal conifer forest, 21 km from 1970–2015 ( [[#Boisvert-Marsh--2021|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--2021|Lu et al., 2021]] ) and at individual sites in Canada (54 m from 1900–2010) ( [[#Davis--2020|Davis et al., 2020]] ); China (300 m from 1910–2000) ( [[#Liang--2016|Liang et al., 2016]] ) (33 m from 1985–2014) ( [[#Du--2018|Du et al., 2018]] ); Nepal (50 m from 1860–2000) ( [[#Sigdel--2018|Sigdel et al., 2018]] ); Russia (150 m from 1954–2006) ( [[#Gatti--2019|Gatti et al., 2019]] ); and the USA (19 m from 1950–2016) ( [[#Smithers--2018|Smithers et al., 2018]] ) (38 m from 1953–2015) ( [[#Terskaia--2020|Terskaia et al., 2020]] ). Other upslope cases include shifts of temperate conifer forest in Canada ( [[#Jackson--2016|Jackson et al., 2016]] ) and the USA ( [[#Lubetkin--2017|Lubetkin et al., 2017]] ), temperate deciduous forest in Switzerland ( [[#Rigling--2013|Rigling et al., 2013]] ) and temperate shrubland in the USA ( [[#Donato--2016|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'' ).

<div id="2.4.3.2.2" class="h4-container"></div>

<span id="observed-biome-shifts-from-combined-land-use-change-and-climate-change"></span>
===== 2.4.3.2.2 Observed biome shifts from combined land use change and climate change =====

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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 CO 2 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--2021|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--2019|Brice et al., 2019]] ; [[#Wang--2020b|Wang et al., 2020b]] ), France ( [[#Feuillet--2020|Feuillet et al., 2020]] ), Italy ( [[#Vitali--2017|Vitali et al., 2017]] ), Spain ( [[#Ameztegui--2016|Ameztegui et al., 2016]] ) and the USA ( [[#Wang--2020b|Wang et al., 2020b]] ) as well as in mountainous areas across Europe ( [[#Cudlin--2017|Cudlin et al., 2017]] ). Intentional use of fire drove an upslope forest shift in Peru ( [[#Bush--2015|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--2019b|Syphard et al., 2019b]] ). In eastern Canada, timber harvesting and wildfire drove the conversion of mixed conifer–broadleaf forests to broadleaf-dominated forests ( [[#Brice--2020|Brice et al., 2020]] ; [[#Wang--2020b|Wang et al., 2020b]] ).

Shrub encroachment onto savanna has occurred at numerous sites, particularly across the Southern Hemisphere, mainly between 1992 and 2010 ( [[#Criado--2020|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--2012|D’Odorico et al., 2012]] ; [[#Caracciolo--2016|Caracciolo et al., 2016]] ; [[#Bestelmeyer--2018|Bestelmeyer et al., 2018]] ). The magnitude of woody cover change in savannas is not correlated with mean annual temperature change ( [[#Criado--2020|Criado et al., 2020]] ); however, higher atmospheric CO 2 increases shrub growth in savannas ( [[#Nackley--2018|Nackley et al., 2018]] ; [[#Manea--2019|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--2016|Higgins et al., 2016]] ). In addition to climate change, LULCC causes vegetation changes at the biome level ( ''robust evidence'' , ''high agreement'' ).

<div id="2.4.3.3" class="h3-container"></div>

<span id="observed-changes-in-deserts-and-arid-shrublands"></span>
==== 2.4.3.3 Observed Changes in Deserts and Arid Shrublands ====

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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 CO 2 levels. While observed responsiveness of arid vegetation productivity to rising atmospheric CO 2 ( [[#Fensholt--2012|Fensholt et al., 2012]] ) may offset risks from reduced water availability ( [[#Fang--2017|Fang et al., 2017]] ), climate- and CO 2 -driven changes are key risks in arid regions, interacting with habitat degradation, wildfires and invasive species ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ).

Widespread vegetation greening, as projected in AR4, is occurring in arid shrublands ( [[#Zhang--2019a|Zhang et al., 2019a]] ; [[#Maestre--2021|Maestre et al., 2021]] ) as a result of increases in leaf area, woody cover and herbaceous production at desert–grassland interfaces ( [[#Gonsamo--2021|Gonsamo et al., 2021]] ). Plant productivity in arid regions has increased ( [[#Fensholt--2012|Fensholt et al., 2012]] ) because of improved water-use efficiency associated with elevated CO 2 ( [[#Norby--2011|Norby and Zak, 2011]] ; [[#Donohue--2013|Donohue et al., 2013]] ; [[#Burrell--2020|Burrell et al., 2020]] ; [[#Gonsamo--2021|Gonsamo et al., 2021]] ) ( ''medium evidence'' , ''high agreement'' ), altered rainfall seasonality and amount ( [[#Rohde--2019|Rohde et al., 2019]] ; [[#Zhang--2019a|Zhang et al., 2019a]] ) ( ''robust evidence'' , ''high agreement'' ), increases in temperature ( [[#Ratajczak--2014|Ratajczak et al., 2014]] ; [[#Wilcox--2018|Wilcox et al., 2018]] ) ( ''robust evidence'' , ''high agreement'' ) and heavy grazing ( ''robust evidence'' , ''high agreement'' ), with the relative importance differing across locations ( [[#Donohue--2013|Donohue et al., 2013]] ; [[#Caracciolo--2016|Caracciolo et al., 2016]] ; [[#Archer--2017|Archer et al., 2017]] ; [[#Hoffmann--2019b|Hoffmann et al., 2019b]] ; [[#Rohde--2019|Rohde et al., 2019]] ). Woody-plant encroachment into arid shrublands is occurring with ''high confidence'' in North America ( [[#Caracciolo--2016|Caracciolo et al., 2016]] ; [[#Archer--2017|Archer et al., 2017]] ) and southern Africa ( [[#du%20Toit--2014|du Toit and O’Connor, 2014]] ; [[#Ward--2014|Ward et al., 2014]] ; [[#Masubelele--2015a|Masubelele et al., 2015a]] ; [[#Hoffman--2019|Hoffman et al., 2019]] ; [[#Rohde--2019|Rohde et al., 2019]] ), and with ''low confidence'' in central Asia ( [[#Li--2015|Li et al., 2015]] ). In North America, sagebrush steppe changes have been attributed to increases in temperature and earlier snowpack melt ( [[#USGCRP--2017|USGCRP, 2017]] ; [[#Mote--2018|Mote et al., 2018]] ; [[#Snyder--2019|Snyder et al., 2019]] ).

Non-native grasses are invading the sagebrush steppes (cold deserts) in North America ( [[#Chambers--2014|Chambers et al., 2014]] ) attributed to warming ( [[#Bradley--2016|Bradley et al., 2016]] ; [[#Hufft--2016|Hufft and Zelikova, 2016]] ). In the eastern semi-desert (Karoo) of South Africa, annual rainfall increases and a rainfall seasonality shift ( [[#du%20Toit--2014|du Toit and O’Connor, 2014]] ) are increasing grassiness as arid grasslands expand into semi-desert shrublands ( [[#du%20Toit--2015|du Toit et al., 2015]] ; [[#Masubelele--2015b|Masubelele et al., 2015b]] ; [[#Masubelele--2015a|Masubelele et al., 2015a]] ) causing fire in areas seldom burned historically ( [[#Coates--2016|Coates et al., 2016]] ).

Interactions of drought, warming and land management have caused vegetation mortality (see [[#2.4.4.3|Section 2.4.4.3]] ) and reduced vegetation cover in shrublands, as projected by AR4 ( [[#Burrell--2020|Burrell et al., 2020]] ). Increased heat and drought are causing the health and abundance of succulent species to decline ( [[#Musil--2009|Musil et al., 2009]] ; [[#Schmiedel--2012|Schmiedel et al., 2012]] ; [[#Aragón-Gastélum--2014|Aragón-Gastélum et al., 2014]] ; [[#Koźmińska--2019|Koźmińska et al., 2019]] ). Hot droughts, in particular, have been shown to reduce population resilience ( [[#Koźmińska--2019|Koźmińska et al., 2019]] ).

<div id="2.4.3.4" class="h3-container"></div>

<span id="observed-changes-in-mediterranean-type-ecosystems"></span>
==== 2.4.3.4 Observed Changes in Mediterranean-Type Ecosystems ====

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Since AR5 ( [[#Settele--2014|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--2015|Diffenbaugh et al., 2015]] ; [[#Williams--2015a|Williams et al., 2015a]] ; [[#Garreaud--2017|Garreaud et al., 2017]] ; [[#Otto--2018|Otto et al., 2018]] ; [[#Sousa--2018|Sousa et al., 2018]] ). Climate change is causing these droughts to become more frequent and severe ( ''medium evidence'' , ''medium agreement'' ) ( [[#AghaKouchak--2014|AghaKouchak et al., 2014]] ; [[#Garreaud--2017|Garreaud et al., 2017]] ; [[#Otto--2018|Otto et al., 2018]] ; [[#Seneviratne--2021|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--2016|Abatzoglou and Williams, 2016]] ), and interactions between drought or extreme weather and fire affecting post-fire ecosystem recovery ( [[#Slingsby--2017|Slingsby et al., 2017]] ). Responses include shifts in functional composition ( [[#Acácio--2017|Acácio et al., 2017]] ; [[#Syphard--2019a|Syphard et al., 2019a]] ), decline of vegetation health ( [[#Hope--2014|Hope et al., 2014]] ; [[#Asner--2016a|Asner et al., 2016a]] ), decline or loss of characteristic species ( [[#White--2016|White et al., 2016]] ; [[#Stephenson--2019|Stephenson et al., 2019]] ), shifts in composition towards more drought- or heat-adapted species and declining diversity (see also section 2.4.4.3) ( [[#Slingsby--2017|Slingsby et al., 2017]] .; [[#Harrison--2018|Harrison et al., 2018]] ).

Declines in plant health and increased mortality in MTEs associated with drought have been widely documented ( ''robust evidence'' , ''high agreement'' ) ( [[#2.4.4.3|Section 2.4.4.3]] ). Remote-sensing studies show drought-associated mortality in post-fire vegetation regrowth in the Fynbos of South Africa ( [[#Slingsby--2020b|Slingsby et al., 2020b]] ), reduced canopy health in forests within MTE zones of South Africa ( [[#Hope--2014|Hope et al., 2014]] ) and declines in canopy water content in the forests of California ( [[#Asner--2016a|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--2016|White et al., 2016]] ). Drought reduced growth and increased mortality of the holm oak, ''Quercus ilex'' , on the Iberian Peninsula of Spain ( [[#Natalini--2016|Natalini et al. (2016)]] . Portuguese shrublands experienced losses of many deciduous and evergreen oak species, and an increasing dominance of pyrophytic xeric trees ( [[#Acácio--2017|Acácio et al., 2017]] ). The 2012–2015 drought in California caused high-canopy foliage dieback of the giant sequoia ( ''Sequoiadendron giganteum'' ) ( [[#Stephenson--2019|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--2015|McIntyre et al., 2015]] ; [[#Fettig--2019|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--2018|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--2018|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--2017|Slingsby et al., 2017]] ), resulting in shifts towards species with higher temperature preferences ( [[#Slingsby--2017|Slingsby et al., 2017]] ). In Italy, [[#Del%20Vecchio--2015|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--2018|Jacobsen and Pratt, 2018]] ; [[#Park--2018|Park et al., 2018]] ; [[#Park--2019|Park and Jenerette, 2019]] ; [[#Syphard--2019b|Syphard et al., 2019b]] ).

The effects of climate change on heat, fuel and wildfire ignition limits show spatial and temporal variation globally (see [[#2.3|Section 2.3.6.1]] ), 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--2016|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--2010|Wilson et al., 2010]] ). In Chile, [[#González--2018|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.

<div id="2.4.3.5" class="h3-container"></div>

<span id="observed-changes-in-savanna-and-grasslands"></span>
==== 2.4.3.5 Observed Changes in Savanna and Grasslands ====

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Savannas consist of co-existing trees and grasses in tropical and temperate regions ( [[#Archibald--2019|Archibald et al., 2019]] ). The global trend of woody encroachment reported in AR5 ( [[#Settele--2014|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--2014|O’Connor et al., 2014]] ; [[#Espírito-Santo--2016|Espírito-Santo et al., 2016]] ; [[#Skowno--2017|Skowno et al., 2017]] ; [[#Stevens--2017|Stevens et al., 2017]] ; McNicol et al., 2018; [[#Venter--2018|Venter et al., 2018]] ; [[#Rosan--2019|Rosan et al., 2019]] ). Additionally, the forest expansion into mesic savannas reported in AR5 ( [[#Settele--2014|Settele et al., 2014]] ) is continuing in Africa, South America and Southeast Asia ( [[#Marimon--2014|Marimon et al., 2014]] ; [[#Keenan--2015|Keenan et al., 2015]] ; [[#Baccini--2017|Baccini et al., 2017]] ; [[#Ondei--2017|Ondei et al., 2017]] ; [[#Stevens--2017|Stevens et al., 2017]] ; Aleman et al., 2018; [[#Rosan--2019|Rosan et al., 2019]] ). Extreme high rainfall anomalies have also contributed to an increase in herbaceous and foliar production in the Sahel ( [[#Brandt--2019|Brandt et al., 2019]] ; [[#Zhang--2019a|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 CO 2 ( [[#Donohue--2013|Donohue et al., 2013]] ; [[#Nackley--2018|Nackley et al., 2018]] ; [[#Quirk--2019|Quirk et al., 2019]] ) and increased rainfall amount and intensity ( ''robust evidence'' , ''high agreement'' ) ( [[#Venter--2018|Venter et al., 2018]] ; [[#Xu--2018b|Xu et al., 2018b]] ; [[#Zhang--2019a|Zhang et al., 2019a]] ). Direct quantification of climate-change drivers is confounded with local LUC such as fire suppression ( [[#Archibald--2016|Archibald, 2016]] ; [[#Venter--2018|Venter et al., 2018]] ) '','' heavy grazing ( [[#du%20Toit--2014|du Toit and O’Connor, 2014]] ; [[#Archer--2017|Archer et al., 2017]] ), removal of native browsers and, specifically, loss of mega-herbivores in Africa ( ''medium evidence'' , ''medium agreement'' ) ( [[#Asner--2016b|Asner et al., 2016b]] ; [[#Daskin--2016|Daskin et al., 2016]] ; [[#Stevens--2016|Stevens et al., 2016]] ; [[#Davies--2018|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 CO 2 , 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--2015|Smit and Prins, 2015]] ) as well as changing the carbon stored per unit of land area ( [[#González-Roglich--2014|González-Roglich et al., 2014]] ; [[#Puttock--2014|Puttock et al., 2014]] ; [[#Pellegrini--2016|Pellegrini et al., 2016]] ; [[#Mureva--2018|Mureva et al., 2018]] ) and the hydrological characteristics ( [[#Honda--2016|Honda and Durigan, 2016]] ; [[#Schreiner-McGraw--2020|Schreiner-McGraw et al., 2020]] ). Woody-cover encroachment also reduces biodiversity by threatening fauna and flora adapted to open ecosystems ( [[#Ratajczak--2012|Ratajczak et al., 2012]] ; [[#Smit--2015|Smit and Prins, 2015]] ; [[#Pellegrini--2016|Pellegrini et al., 2016]] ; [[#Andersen--2019|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--2009|Harsch et al., 2009]] ; [[#Rehm--2015|Rehm and Feeley, 2015]] ; [[#Silva--2016|Silva et al., 2016]] ; [[#Andela--2017|Andela et al., 2017]] ; [[#Song--2018|Song et al., 2018]] ; [[#Aide--2019|Aide et al., 2019]] ; [[#Gibson--2019|Gibson and Newman, 2019]] ). The Eurasian steppes experienced a 1% increase in woody cover per decade since 2000 ( [[#Liu--2021|Liu et al., 2021]] ) and inner Mongolian grasslands in China experienced broad encroachment as well ( [[#Chen--2015|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--2012|D’Odorico et al., 2012]] ; [[#Hagedorn--2014|Hagedorn et al., 2014]] ), an increase in water and nutrient availability from thawing permafrost ( ''medium evidence'' , ''high agreement'' ) ( [[#Zhou--2015b|Zhou et al., 2015b]] ; [[#Silva--2016|Silva et al., 2016]] ) and rising CO 2 ( ''medium evidence'' , ''medium agreement'' ) ( [[#Frank--2015|Frank et al., 2015]] ; [[#Aide--2019|Aide et al., 2019]] ). Interactions of LULCCs such as land abandonment, grazing management shifts and fire suppression with climate change are contributing factors ( [[#Liu--2021|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--2016|Gao et al., 2016]] ). Multiple lines of evidence indicate that changes in grassland productivity are positively correlated with increases in mean annual precipitation ( [[#Hoover--2014|Hoover et al., 2014]] ; [[#Brookshire--2015|Brookshire and Weaver, 2015]] ; [[#Gang--2015|Gang et al., 2015]] ; [[#Gao--2016|Gao et al., 2016]] ; [[#Wilcox--2017|Wilcox et al., 2017]] ; [[#Wan--2018|Wan et al., 2018]] ). Increasing temperatures positively impact grassland production and biomass, especially in temperature-limited regions ( [[#Piao--2014|Piao et al., 2014]] ; [[#Gao--2016|Gao et al., 2016]] ). However, it is expected that grasslands in hot areas will decrease production as temperatures increase ( ''limited evidence'' , ''low agreement'' ) ( [[#Gang--2015|Gang et al., 2015]] ) ''.'' Nevertheless, grassland responses to warming and drought are being ameliorated by increasing CO 2 and associated improved water-use efficiency ( [[#Roy--2016|Roy et al., 2016]] ). For example, in a cool temperate grassland experiment, warming led to a longer growing season and elevated CO 2 further extended growing by conserving water, which enabled most species to remain active longer ( ''medium evidence'' , ''medium agreement'' ) ( [[#Reyes-Fox--2014|Reyes-Fox et al., 2014]] ).

<div id="2.4.3.6" class="h3-container"></div>

<span id="observed-changes-in-tropical-forest"></span>
==== 2.4.3.6 Observed Changes in Tropical Forest ====

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Overall declines of tropical forest cover ( [[#Kohl--2015|Kohl et al., 2015]] ; [[#Liu--2015|Liu et al., 2015]] ; [[#Baccini--2017|Baccini et al., 2017]] ; [[#Harris--2021|Harris et al., 2021]] ), with declines more than triple the gains ( [[#Harris--2021|Harris et al., 2021]] ) have been driven primarily by deforestation and land conversion ( ''robust evidence'' , ''high agreement'' ) ( [[#Lewis--2015|Lewis et al., 2015]] ; [[#Curtis--2018|Curtis et al., 2018]] ; [[#Assis--2019|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--2014|Marimon et al., 2014]] ; [[#Baccini--2017|Baccini et al., 2017]] ; [[#Ondei--2017|Ondei et al., 2017]] ; [[#Stevens--2017|Stevens et al., 2017]] ; Aleman et al., 2018; [[#Staver--2018|Staver, 2018]] ; [[#Rosan--2019|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--2021|Chacón-Moreno et al., 2021]] ; [[#Jiménez-García--2021|Jiménez-García et al., 2021]] ) and Asia ( [[#Sigdel--2018|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--2020|Mainali et al., 2020]] ). In both the Andes ( [[#Harsch--2009|Harsch et al., 2009]] ) and Himalayas ( [[#Singh--2021|Singh et al., 2021]] ), most tree lines have been stable, leading ( [[#Rehm--2015|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 CO 2 (via CO 2 fertilisation or increases in water-use efficiency) ( ''medium evidence'' , ''medium agreement'' ) ( [[#Cernusak--2013|Cernusak et al., 2013]] ; [[#Huang--2013|Huang et al., 2013]] ; [[#Van%20Der%20Sleen--2015|Van Der Sleen et al., 2015]] ; [[#Yang--2016|Yang et al., 2016]] ).

Increases in productivity of tropical forests ( [[#Gatti--2014|Gatti et al., 2014]] ; [[#Brienen--2015|Brienen et al., 2015]] ; [[#Baccini--2017|Baccini et al., 2017]] ), Africa and southeast Asia ( [[#Qie--2017|Qie et al., 2017]] ) have been attributed to elevated CO 2 ( ''robust evidence'' , ''medium agreement'' ) ( [[#Ballantyne--2012|Ballantyne et al., 2012]] ; [[#Brienen--2015|Brienen et al., 2015]] ; [[#Sitch--2015|Sitch et al., 2015]] ; [[#Yang--2016|Yang et al., 2016]] ; [[#Mitchard--2018|Mitchard, 2018]] ). The rates of these increases have been slowing down in the central Amazon ( [[#Brienen--2015|Brienen et al., 2015]] ; [[#de%20Meira%20Junior--2020|de Meira Junior et al., 2020]] ) and Southeast Asia ( [[#Qie--2017|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--2020|Hubau et al., 2020]] ). At the global level, [[#Hubau--2020|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--2020|Sullivan et al., 2020]] ), reduced growth rates during droughts ( [[#Bennett--2015|Bennett et al., 2015]] ; [[#Bonai--2016|Bonai et al., 2016]] ; [[#Corlett--2016|Corlett, 2016]] ), drought-related mortality ( [[#Brando--2014|Brando et al., 2014]] ; [[#Zhou--2014|Zhou et al., 2014]] ; [[#Brienen--2015|Brienen et al., 2015]] ; [[#Corlett--2016|Corlett, 2016]] ; [[#McDowell--2018|McDowell et al., 2018]] ), fire ( [[#Liu--2017|Liu et al., 2017]] ) and cloud-induced radiation limitation ( ''robust evidence'' , ''high agreement'' ) ( [[#Deb%20Burman--2020|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 CO 2 may be additional interactive factors increasing tree mortality ( [[#Malhi--2014|Malhi et al., 2014]] ; [[#Brienen--2015|Brienen et al., 2015]] ). Vulnerability to drought varies between tree species and sizes, with large, older trees at the highest risk of mortality ( [[#McDowell--2018|McDowell et al., 2018]] ; [[#Meakem--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|Corlett, 2016]] ).

Lianas (long-stemmed woody vines) generally negatively impact trees, significantly reducing the growth of heavily infested trees ( [[#Reis--2020|Reis et al., 2020]] ). Lianas would benefit from climate change and disturbance ( [[#LingZi--2014|LingZi et al., 2014]] ; [[#Hodgkins--2018|Hodgkins et al., 2018]] ). The extent of their suitable niche can increase ( [[#Taylor--2016|Taylor and Kumar, 2016]] ), thereby decreasing forest biomass accumulation ( ''robust evidence'' , ''high agreement'' ) ( [[#van%20der%20Heijden--2013|van der Heijden et al., 2013]] ; [[#Fauset--2015|Fauset et al., 2015]] ; [[#Estrada-Villegas--2020|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--2014|Malhi et al., 2014]] ; [[#Deb--2018|Deb et al., 2018]] ) and intensifying fire risk and potential dieback ( [[#Lapola--2018|Lapola et al., 2018]] ; [[#Marengo--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|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--2014|Malhi et al., 2014]] ; [[#Lewis--2015|Lewis et al., 2015]] ). Warming and drought increase the invasion of grasses into forest edges and increase fire risk ( ''robust evidence'' , ''high agreement'' ) ( [[#Brando--2014|Brando et al., 2014]] ; [[#Balch--2015|Balch et al., 2015]] ; [[#Lewis--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--2014|Brando et al., 2014]] , 2020; [[#Balch--2015|Balch et al., 2015]] ; [[#Lewis--2015|Lewis et al., 2015]] ).

<div id="2.4.3.7" class="h3-container"></div>

<span id="observed-changes-in-boreal-and-temperate-forests"></span>
==== 2.4.3.7 Observed Changes in Boreal and Temperate Forests ====

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The AR5 found increased tree mortality, wildfire and plant phenology changes in boreal and temperate forests ( [[#Settele--2014|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 ( [[#2.4.3.2.1|Section 2.4.3.2.1]] ); range shifts of plants ( [[#2.4.2.1|Section 2.4.2.1]] ); earlier blooming and leafing of plants ( [[#2.4.2.4|Section 2.4.2.4]] ); poleward shifts in tree-feeding insects ( [[#2.4.2.1|Section 2.4.2.1]] ); increases in insect pest outbreaks ( [[#2.4.4.3.3|Section 2.4.4.3.3]] ); increases in the area burned by wildfire in western North America ( [[#2.4.4.2.1|Section 2.4.4.2.1]] ); increased drought-induced tree mortality in western North America ( [[#2.4.4.3.1|Section 2.4.4.3.1]] ); and thawing of the permafrost that underlies extensive areas of boreal forest ( [[#2.4.3.9|Section 2.4.3.9]] )( [[#2.3|Section 2.3.2.5]] in ( [[#Gulev--2021|Gulev et al., 2021]] )). Atmospheric CO 2 from anthropogenic sources has also increased net primary productivity (NPP) ( [[#2.4.4.5.1|Section 2.4.4.5.1]] ). 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 ( [[#2.4.4.2.3|Section 2.4.4.2.3]] ), long-lasting smouldering below-ground fires in Canada and the USA ( [[#Scholten--2021|Scholten et al., 2021]] ), tree mortality in Europe ( [[#2.4.4.3.3|Section 2.4.4.3.3]] ) and post-fire shifts of boreal conifer to deciduous broadleaf tree species in Alaska ( [[#Mack--2021|Mack et al., 2021]] ). From 1930 to 1960, boreal forest growth became limited more by precipitation than temperature in the Northern Hemisphere ( [[#Babst--2019|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 ( [[#2.4.3.2.2|Section 2.4.3.2.2]] ), changes in wildfire in Europe affected by fire suppression, fire prevention and agricultural abandonment ( [[#2.4.4.2.3|Section 2.4.4.2.3]] ), and forest species composition changes in Scotland due to nitrogen deposition from air pollution ( [[#Hester--2019|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--2018|Song et al., 2018]] ) and in Canada between 1984 and 2015 ( [[#Guindon--2018|Guindon et al., 2018]] ), but forest plantations and regrowth are probable drivers ( [[#Song--2018|Song et al., 2018]] ).

<div id="2.4.3.8" class="h3-container"></div>

<span id="observed-changes-in-peatlands"></span>
==== 2.4.3.8 Observed Changes in Peatlands ====

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Globally, peatland ecosystems store approximately 25% (600 ± 100 GtC) of the world’s soil organic carbon ( [[#Yu--2010|Yu et al., 2010]] ; [[#Page--2011|Page et al., 2011]] ; [[#Hugelius--2020|Hugelius et al., 2020]] ) and 10% of the world’s freshwater resources ( [[#Joosten--2002|Joosten and Clarke, 2002]] ), despite only occupying 3% of the global land area ( [[#Xu--2018a|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|IPCC, 2007]] ), while SR1.5 briefly mentioned the combined effects of changes in climate and land use on peatlands ( [[#IPCC--2018b|IPCC, 2018b]] ). New evidence confirms that climate change, including extreme weather events (e.g., droughts; [[IPCC:Wg2:Chapter:Chapter-8#8.3.1|Section 8.3.1.6]] ), permafrost degradation ( [[#2.3|Section 2.3.2.5]] ), SLR ( [[#2.3.3.3|Section 2.3.3.3]] ) and fire ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.3.2|Section 5.4.3.2]] ) ( [[#Henman--2008|Henman and Poulter, 2008]] ; [[#Kirwan--2012|Kirwan and Mudd, 2012]] ; [[#Turetsky--2015|Turetsky et al., 2015]] ; [[#Page--2016|Page and Hooijer, 2016]] ; [[#Swindles--2019|Swindles et al., 2019]] ; [[#Hoyt--2020|Hoyt et al., 2020]] ; [[#Hugelius--2020|Hugelius et al., 2020]] ; [[#Jovani-Sancho--2021|Jovani-Sancho et al., 2021]] ; [[#Veraverbeke--2021|Veraverbeke et al., 2021]] ), superimposed on anthropogenic disturbances (e.g., draining for agriculture or mining; [[IPCC:Wg2:Chapter:Chapter-5#5.2.1|Section 5.2.1.1]] ), has led to rapid losses of peatland carbon across the world ( ''robust evidence'' , ''high agreement'' ) ( [[#Page--2011|Page et al., 2011]] ; [[#Leifeld--2019|Leifeld et al., 2019]] ; [[#Hoyt--2020|Hoyt et al., 2020]] ; [[#Turetsky--2020|Turetsky et al., 2020]] ; [[#Loisel--2021|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--2014|Bonn et al., 2014]] ; [[#Martin-Ortega--2014|Martin-Ortega et al., 2014]] ; [[#Tiemeyer--2017|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--2005|Lafleur et al., 2005]] ; [[#Dommain--2011|Dommain et al., 2011]] ; [[#Lund--2012|Lund et al., 2012]] ; [[#Cobb--2017|Cobb et al., 2017]] ; [[#Evans--2021|Evans et al., 2021]] ; [[#Novita--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 CO 2 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--2002|Page et al., 2002]] ) and the 2015 fire season (380 TgC yr -1 ) ( [[#Field--2016|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--2015|Cole et al., 2015]] ; [[#Mezbahuddin--2015|Mezbahuddin et al., 2015]] ; [[#Fanin--2017|Fanin and van der Werf, 2017]] ; [[#Taufik--2017|Taufik et al., 2017]] ; [[#Cole--2019|Cole et al., 2019]] ). Low soil moisture contributes to increased fire propagation ( [[IPCC:Wg2:Chapter:Chapter-12#12.4|Section 12.4.2.2]] ) ( [[#Dadap--2019|Dadap et al., 2019]] ; [[#Canadell--2021|Canadell et al., 2021]] ), causing long-lasting fires responsible for smoke and haze pollution ( ''robust evidence'' , ''high agreement'' ) ( [[#Ballhorn--2009|Ballhorn et al., 2009]] ; [[#Page--2009|Page et al., 2009]] ; [[#Gaveau--2014|Gaveau et al., 2014]] ; [[#Huijnen--2016|Huijnen et al., 2016]] ; [[#Page--2016|Page and Hooijer, 2016]] ; [[#Hu--2018|Hu et al., 2018]] ; [[#Vadrevu--2019|Vadrevu et al., 2019]] ; [[#Niwa--2021|Niwa et al., 2021]] ). Increases in fires and smoke lead to habitat loss and negatively impact regional faunal populations ( ''limited evidence'' , ''high agreement'' ) ( [[#Neoh--2015|Neoh et al., 2015]] ; [[#Erb--2018b|Erb et al., 2018b]] ; [[#Thornton--2018|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--2013|Roucoux et al., 2013]] ; [[#Gallego-Sala--2018|Gallego-Sala et al., 2018]] ; [[#Wang--2018a|Wang et al., 2018a]] ; [[#Dargie--2019|Dargie et al., 2019]] ; [[#Ribeiro--2021|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'' ) ( [[#2.3.1|Section 2.3.1.3.4]] ) ( [[#Helbig--2020|Helbig et al., 2020]] ; [[#Zhang--2020|Zhang et al., 2020]] ). In Europe, increasing mean annual temperatures in the Baltic, Scandinavia, and continental Europe ( [[IPCC:Wg2:Chapter:Chapter-12#12.4|Section 12.4.5.1]] ) have led to widespread lowering of peatland water tables at intact sites ( [[#Swindles--2019|Swindles et al., 2019]] ), desiccation and die-off of sphagnum moss ( [[#Bragazza--2008|Bragazza, 2008]] ; [[#Lees--2019|Lees et al., 2019]] ) and increased intensity and frequency of fires, resulting in a rapid carbon loss ( [[#Davies--2013|Davies et al., 2013]] ; [[#Veraverbeke--2021|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--2011|Cai and Yu, 2011]] ; [[#Shiller--2014|Shiller et al., 2014]] ; [[#Ott--2016|Ott and Chimner, 2016]] ).

In high-latitude peatlands, the net effect of climate change on the permafrost peatland carbon sink capacity remains uncertain ( [[#Abbott--2016|Abbott et al., 2016]] ; [[#McGuire--2018b|McGuire et al., 2018b]] ; [[#Laamrani--2020|Laamrani et al., 2020]] ; [[#Loisel--2021|Loisel et al., 2021]] ; [[#Sim--2021|Sim et al., 2021]] ; [[#Väliranta--2021|Väliranta et al., 2021]] ). Increasing air temperatures have been linked to permafrost degradation and altered hydrological regimes (2.3.3.2; Figure 2.4a; 2.4.3.9; Box 5.1), which have led to rapid changes in plant communities and bio-geochemical cycling ( ''robust evidence'' , ''high agreement'' ) ( [[#Liljedahl--2016|Liljedahl et al., 2016]] ; [[#Swindles--2016|Swindles et al., 2016]] ; [[#Voigt--2017|Voigt et al., 2017]] ; [[#Zhang--2017b|Zhang et al., 2017b]] ; [[#Voigt--2020|Voigt et al., 2020]] ; [[#Sim--2021|Sim et al., 2021]] ). In many instances, permafrost degradation triggers thermokarst land subsidence associated with local wetting ( ''robust evidence'' , ''high agreement'' ) ( [[#Jones--2013|Jones et al., 2013]] ; [[#Borge--2017|Borge et al., 2017]] ; [[#Olvmo--2020|Olvmo et al., 2020]] ; [[#Olefeldt--2021|Olefeldt et al., 2021]] ). Permafrost thaw in peatland-rich landscapes can also cause local drying through increased hydrological connectivity and runoff ( [[#Connon--2014|Connon et al., 2014]] ). In the first decades following thaw, increases in methane, CO 2 and nitrous oxide emissions have been recorded from peatland sites, depending on surface moisture conditions ( [[#Schuur--2009|Schuur et al., 2009]] ; [[#O’Donnell--2012|O’Donnell et al., 2012]] ; [[#Elberling--2013|Elberling et al., 2013]] ; [[#Matveev--2016|Matveev et al., 2016]] ; [[#Euskirchen--2020|Euskirchen et al., 2020]] ; [[#Hugelius--2020|Hugelius et al., 2020]] ). Conversely, some evidence suggests increased peat accumulation after thaw ( [[#Jones--2013|Jones et al., 2013]] ; [[#Estop-Aragonés--2018|Estop-Aragonés et al., 2018]] ; [[#Väliranta--2021|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--2018|Gibson et al., 2018]] ), as fire intensity and frequency have increased across the boreal and Arctic biomes ( ''limited evidence'' , ''high agreement'' ) ( [[#Kasischke--2010|Kasischke et al., 2010]] ; [[#Scholten--2021|Scholten et al., 2021]] ).

The CO 2 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--2010|Maljanen et al., 2010]] ; [[#Tiemeyer--2016|Tiemeyer et al., 2016]] ; [[#Drexler--2018|Drexler et al., 2018]] ; [[#Qiu--2021|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--2019|Leifeld et al., 2019]] ; [[#Qiu--2021|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--2016|Miettinen et al., 2016]] ; [[#Leifeld--2019|Leifeld et al., 2019]] ; [[#Hoyt--2020|Hoyt et al., 2020]] ). Extensive deforestation and drainage have caused widespread peat subsidence and large CO 2 emissions at a current average of ~10 ± 2 tonnes ha -1 yr -1 , excluding fires ( [[#Hoyt--2020|Hoyt et al., 2020]] ), with values estimated from point subsidence measurements being as high as 30–90 tonnes CO 2 ha −1 yr −1 locally ( ''robust evidence'' , ''high agreement'' ) ( [[#Wösten--1997|Wösten et al., 1997]] ; [[#Matysek--2018|Matysek et al., 2018]] ; [[#Swails--2018|Swails et al., 2018]] ; [[#Evans--2019|Evans et al., 2019]] ; [[#Conchedda--2020|Conchedda and Tubiello, 2020]] ; [[#Anshari--2021|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'' ).

<div id="2.4.3.9" class="h3-container"></div>

<span id="observed-changes-in-polar-tundra"></span>
==== 2.4.3.9 Observed Changes in Polar Tundra ====

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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|IPCC, 2021a]] ) which are continuing to alter tundra plant communities ( ''medium evidence'' , ''high agreement'' ) ( [[#Post--2009|Post et al., 2009]] ; [[#Gauthier--2013|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--2011|Myers-Smith et al., 2011]] ; [[#Bjorkman--2018|Bjorkman et al., 2018]] ; [[#Bjorkman--2019|Bjorkman et al., 2019]] ) ''.'' Shrub growth is climate-sensitive and is greater in years with warmer growing seasons ( [[#Myers-Smith--2015|Myers-]] [[#Smith--2015|Smith et al., 2015]] ). Plant species that prefer warmer conditions are increasing ( [[#Elmendorf--2015|Elmendorf et al., 2015]] ; [[#Bjorkman--2018|Bjorkman et al., 2018]] ), plant cover is increasing and bare ground is decreasing in long-term monitoring plots ( [[#Bjorkman--2019|Bjorkman et al., 2019]] ; [[#Myers-Smith--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--2015|Boelman et al., 2015]] ; [[#Tape--2016a|Tape et al., 2016a]] ; [[#Tape--2016b|Tape et al., 2016b]] ; [[#Tape--2018|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--2009|Schuur et al., 2009]] ; [[#Natali--2012|Natali et al., 2012]] ). Soil moisture status influences temperature sensitivity of plant growth and canopy heights ( [[#Myers-Smith--2015|Myers-]] [[#Smith--2015|Smith et al., 2015]] ; [[#Ackerman--2017|Ackerman et al., 2017]] ; [[#Bjorkman--2018|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--2015|Iversen et al., 2015]] ; [[#Blume-Werry--2016|Blume-Werry et al., 2016]] ; [[#Radville--2016|Radville et al., 2016]] ).

<div id="2.4.4" class="h2-container"></div>

<span id="observed-changes-in-ecosystem-processes-and-services"></span>
=== 2.4.4 Observed Changes in Ecosystem Processes and Services ===

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<div id="2.4.4.1" class="h3-container"></div>

<span id="observed-browning-of-rivers-and-lakes"></span>
==== 2.4.4.1 Observed Browning of Rivers and Lakes ====

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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--2015|Solomon et al., 2015]] ; [[#Catalán--2016|Catalán et al., 2016]] ; [[#de%20Wit--2016|de Wit et al., 2016]] ; [[#Finstad--2016|Finstad et al., 2016]] ; [[#Creed--2018|Creed et al., 2018]] ; [[#Hayden--2019|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--2016|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--2015|Solomon et al., 2015]] ). Browning of lakes leads to shallower and more stable thermoclines, and thus overall deep water cooling ( [[#Solomon--2015|Solomon et al., 2015]] ; [[#Williamson--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--2016|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--2016|Urrutia-Cordero et al., 2016]] ) and no significant effects of browning on plankton community food webs ( [[#Rasconi--2015|Rasconi et al., 2015]] ). Browning has driven a shift from auto- to heterotrophic/mixotrophic-based production ( [[#Urrutia-Cordero--2017|Urrutia-Cordero et al., 2017]] ) and supports heterotrophic metabolism of the bacterial community ( [[#Zwart--2016|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--2018|Huisman et al., 2018]] ) and toxin levels ( [[#Urrutia-Cordero--2016|Urrutia-Cordero et al., 2016]] ). However, the synergistic impacts of browning and climate change on aquatic communities depends on regional precipitation patterns ( [[#Weyhenmeyer--2016|Weyhenmeyer et al., 2016]] ), watershed type ( [[#de%20Wit--2016|de Wit et al., 2016]] ) and the length of the food chain ( [[#Hansson--2013|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'' ).

<div id="_idContainer027" class="Figure"></div>

[[File:91f7befa6456321ae09212fa9420be41 IPCC_AR6_WGII_Figure_2_005.png]]

'''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 2.5.3.6.2.

<div id="2.4.4.2" class="h3-container"></div>

<span id="observed-changes-in-wildfire"></span>
==== 2.4.4.2 Observed Changes in Wildfire ====

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<div id="2.4.4.2.1" class="h4-container"></div>

<span id="detection-and-attribution-of-observed-changes-in-wildfire"></span>
===== 2.4.4.2.1 Detection and attribution of observed changes in wildfire =====

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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 2.4.4.2 and 2.4.4.4). Anthropogenic climate change increases wildfire by exacerbating its three principal driving factors: heat, fuel and ignition ( [[#Moritz--2012|Moritz et al., 2012]] ; [[#Jolly--2015|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--2020|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--2015|Ruffault and Mouillot, 2015]] ; [[#Hessburg--2016|Hessburg et al., 2016]] ; [[#Andela--2017|Andela et al., 2017]] ; [[#Balch--2017|Balch et al., 2017]] ; [[#Lasslop--2017|Lasslop and Kloster, 2017]] ; [[#Aragao--2018|Aragao et al., 2018]] ; [[#Kelley--2019|Kelley et al., 2019]] ). Globally, 4.2 million km 2 of land per year burned on average from 2002 to 2016 ( [[#Giglio--2018|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--2018|Earl and Simmonds, 2018]] ; [[#Andela--2019|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--2016|Abatzoglou and Williams, 2016]] ; [[#Partain--2016|Partain et al., 2016]] ; [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ; [[#Mansuy--2019|Mansuy et al., 2019]] ; [[#Bowman--2020|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--2016|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--2020|Williams et al., 2020]] ), driving half of the increase in burned area ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ; [[#Holden--2018|Holden et al., 2018]] ; [[#Williams--2019|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--2019|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--2016|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--2019|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'' ).

<div id="2.4.4.2.2" class="h4-container"></div>

<span id="observed-changes-in-wildfire-globally"></span>
===== 2.4.4.2.2 Observed changes in wildfire globally =====

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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--2010|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--2010|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--2019|Forkel et al., 2019]] ) but the change was not statistically significant ( [[#Giglio--2013|Giglio et al., 2013]] ). From 1998 to 2015, global burned area decreased at a rate of −1.4 ± 0.5% yr -1 ( [[#Andela--2017|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--2017|Andela et al., 2017]] ) and from increased precipitation ( [[#Forkel--2019|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--2019|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--2017|Andela et al., 2017]] ). The global decrease of fire frequency from 2000 to 2010 is correlated with increasing human population density ( [[#Knorr--2014|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--2017|Lasslop and Kloster, 2017]] ; [[#Arora--2018|Arora and Melton, 2018]] ; [[#Forkel--2019|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--2017|Andela et al., 2017]] ; [[#Earl--2018|Earl and Simmonds, 2018]] ; [[#Forkel--2019|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--2015|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'' ).

<div id="2.4.4.2.3" class="h4-container"></div>

<span id="observed-changes-in-wildfire-in-individual-regions-with-complex-attribution"></span>
===== 2.4.4.2.3 Observed changes in wildfire in individual regions with complex attribution =====

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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--2017|Fonseca et al., 2017]] ; [[#van%20Marle--2017|van Marle et al., 2017]] ; [[#da%20Silva--2018|da]] [[#Silva--2018|Silva et al., 2018]] ; [[#da%20Silva--2021|da Silva et al., 2021]] ; [[#dos%20Reis--2021|dos Reis et al., 2021]] ; [[#Libonati--2021|Libonati et al., 2021]] ). Roads facilitate deforestation, fragmenting the rainforest and increasing the dryness and flammability of vegetation ( [[#Alencar--2015|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--2017|Fonseca et al., 2017]] ; [[#Aragao--2018|Aragao et al., 2018]] ; [[#da%20Silva--2018|da]] [[#Silva--2018|Silva et al., 2018]] ; [[#Burton--2020|Burton et al., 2020]] ; [[#dos%20Reis--2021|dos Reis et al., 2021]] ; [[#Libonati--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--2017|Fonseca et al., 2017]] ). From 1973 to 2014, burned area increased in the Amazon, coinciding with increased deforestation ( [[#van%20Marle--2017|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%20Silva--2018|da]] [[#Silva--2018|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--2020|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--2021|Kelley et al., 2021]] ; [[#Libonati--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--2020|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--2020|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--2020|Lindenmayer and Taylor, 2020]] ), wildlife and extensive areas of habitat for threatened plant and animal species ( [[#Geary--2021|Geary et al., 2021]] ). Generally, past timber harvesting did not lead to more severe fire canopy damage ( [[#Bowman--2021b|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--2014|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|Dowdy, 2018]] ; [[#Harris--2019|Harris and Lucas, 2019]] ) and during periods with extensive fires in 2017 in eastern Australia ( [[#Hope--2019|Hope et al., 2019]] ), in 2018 in northeastern Australia ( [[#Lewis--2020|Lewis et al., 2020]] ), and in period 2019–2020 in southeastern Australia ( [[#Abram--2021|Abram et al., 2021]] ; [[#van%20Oldenborgh--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--2019|Zubkova et al., 2019]] ) to a significant −1.9% yr -1 in the period 2001–2016 ( [[#Wei--2020|Wei et al., 2020]] ). These decreases coincided with areas of agricultural expansion or areas where drought reduced fuel loads ( [[#Zubkova--2019|Zubkova et al., 2019]] ; [[#Wei--2020|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--2021|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--2019|Probert et al., 2019]] ).

In Mediterranean Europe, the area burned in the region as a whole decreased from 1985 to 2011 ( [[#Turco--2016|Turco et al., 2016]] ), although the burned area for Spain did not show a significant long-term increase from 1968 to 2010 ( [[#Moreno--2014|Moreno et al., 2014]] ) whereas that for Portugal in 2017 was the highest in the period 1980–2017 ( [[#Turco--2019|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--2014|Moreno et al., 2014]] ; [[#Turco--2017|Turco et al., 2017]] ; [[#Viedma--2018|Viedma et al., 2018]] ; [[#Turco--2019|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--2016|Ponomarev et al., 2016]] ) and 2% yr -1 across Canada from 1959 to 2015 ( [[#Hanes--2019|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--2018|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--2017|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--2021|Scholten et al., 2021]] ). While burned area was correlated with temperature and reduced precipitation in Siberia ( [[#Ponomarev--2016|Ponomarev et al., 2016]] ; [[#Masrur--2018|Masrur et al., 2018]] ) and correlated with lightning, temperature and precipitation in the Northwest Territories and Alaska ( [[#Veraverbeke--2017|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--2016|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--2016|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--2020|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--2019|Bowman et al., 2019]] ). While this extreme fire year coincided with the highest daily mean maximum temperature in the period 1979–2017 ( [[#Bowman--2019|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--2018|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'' ).

<div id="2.4.4.2.4" class="h4-container"></div>

<span id="observed-changes-in-fire-seasons-globally"></span>
===== 2.4.4.2.4 Observed changes in fire seasons globally =====

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The IPCC AR6 WGI assessed fire weather ( [[#Ranasinghe--2021|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--2015|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--2015|Jolly et al., 2015]] ), Australia ( [[#Dowdy--2018|Dowdy, 2018]] ; [[#Abram--2021|Abram et al., 2021]] ; [[#van%20Oldenborgh--2021|van Oldenborgh et al., 2021]] ), Canada ( [[#Hanes--2019|Hanes et al., 2019]] ), central Asia ( [[#Jolly--2015|Jolly et al., 2015]] ), East Africa ( [[#Jolly--2015|Jolly et al., 2015]] ) and North America ( [[#Jain--2017|Jain et al., 2017]] ; [[#Williams--2019|Williams et al., 2019]] ; [[#Goss--2020|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--2018|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--2017|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'' ).

<div id="2.4.4.2.5" class="h4-container"></div>

<span id="observed-changes-in-post-fire-vegetation"></span>
===== 2.4.4.2.5 Observed changes in post-fire vegetation =====

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Globally, fire has contributed to biome shifts ( [[#2.4.3.2|Section 2.4.3.2]] ) and tree mortality (Sections 2.4.4.2, 2.4.4.3) 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--2017|Slingsby et al., 2017]] ), and tree regeneration in the western USA ( [[#Davis--2019b|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--2017|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--2019b|Davis et al., 2019b]] ). In the southwestern USA, where anthropogenic climate change has caused drought ( [[#Williams--2019|Williams et al., 2019]] ) and increased wildfire ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ), high-severity fires have converted some forest patches to shrublands ( [[#Barton--2018|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'' ).

<div id="2.4.4.3" class="h3-container"></div>

<span id="observed-changes-in-tree-mortality"></span>
==== 2.4.4.3 Observed Changes in Tree Mortality ====

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<div id="2.4.4.3.1" class="h4-container"></div>

<span id="observed-tree-mortality-globally"></span>
===== 2.4.4.3.1 Observed tree mortality globally =====

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Anthropogenic climate change can cause tree mortality directly via increased aridity or drought ( [[#2.4.4.3.3|Section 2.4.4.3.3]] ) or indirectly through wildfire ( [[#2.4.4.2.1|Section 2.4.4.2.1]] ) and insect pests ( [[#2.4.4.3.3|Section 2.4.4.3.3]] ). 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--2016|Anderegg et al., 2016]] ; [[#Adams--2017|Adams et al., 2017]] ; [[#Anderegg--2018|Anderegg et al., 2018]] ; [[#Choat--2018|Choat et al., 2018]] ; [[#Menezes-Silva--2019|Menezes-Silva et al., 2019]] ; [[#Brodribb--2020|Brodribb et al., 2020]] ).

Up through the AR5 ( [[#Settele--2014|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%20Mantgem--2009|van Mantgem et al., 2009]] ), the African Sahel ( [[#Gonzalez--2012|Gonzalez et al., 2012]] ) and North Africa ( [[#le%20Polain%20de%20Waroux--2012|le Polain de Waroux and Lambin, 2012]] ). Increased wildfire and pest infestations, driven by climate change, also contributed to North American tree mortality ( [[#van%20Mantgem--2009|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--2010|Allen et al., 2010]] ), with 49 additional cases found by the AR5 ( [[#Settele--2014|Settele et al., 2014]] ).

Since the AR5 ( [[#Settele--2014|Settele et al., 2014]] ), global meta-analyses found at least 15 ( [[#Allen--2015|Allen et al., 2015]] ) and 25 ( [[#Hartmann--2018|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--2015|Allen et al., 2015]] ), rising background mortality ( [[#Allen--2015|Allen et al., 2015]] ), mortality increasing with drought severity ( [[#Greenwood--2017|Greenwood et al., 2017]] ), mortality of tropical trees increasing with temperature ( [[#Locosselli--2020|Locosselli et al., 2020]] ), mortality increasing with tree size for many species ( [[#Bennett--2015|Bennett et al., 2015]] ), mortality predominantly at the dry edge of species ranges ( [[#Anderegg--2019|Anderegg et al., 2019]] ) and three-quarters of drought-induced mortality cases leading to a change in the dominant species ( [[#Batllori--2020|Batllori et al., 2020]] ). Multiple non-climate factors contribute to tree mortality, including timber cutting, livestock grazing and air pollution ( [[#Martinez-Vilalta--2016|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--2018|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.

<div id="2.4.4.3.2" class="h4-container"></div>

<span id="observed-tree-mortality-in-tropical-ecosystems"></span>
===== 2.4.4.3.2 Observed tree mortality in tropical ecosystems =====

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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--2019|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--2018|Marengo et al., 2018]] ) contributed to tree mortality of ~40% from 1983 to 2011 ( [[#Brienen--2015|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--2019|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--2019|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 ( [[#2.4.4.2.3|Section 2.4.4.2.3]] ). Wildfires can increase tree mortality rates by &gt;600% above rates in non-burned areas, with the higher mortality persisting for up to a decade after a fire ( [[#Silva--2018|Silva et al., 2018]] ; [[#Berenguer--2021|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|Kusserow, 2017]] ; [[#Trichon--2018|Trichon et al., 2018]] ; [[#Dendoncker--2020|Dendoncker et al., 2020]] ), and declines in tree biodiversity, with up to 80% local losses of tree species in the period 1970–2014 ( [[#Hanke--2016|Hanke et al., 2016]] ; [[#Kusserow--2017|Kusserow, 2017]] ; [[#Ibrahim--2018|Ibrahim et al., 2018]] ; [[#Dendoncker--2020|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--2019|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--2018|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--2019|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--2019|Bellahirech et al., 2019]] ).

<div id="2.4.4.3.3" class="h4-container"></div>

<span id="observed-tree-mortality-in-boreal-and-temperate-ecosystems"></span>
===== 2.4.4.3.3 Observed tree mortality in boreal and temperate ecosystems =====

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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--2020|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--2015a|Williams et al., 2015a]] ). Across the western USA, anthropogenic climate change doubled tree mortality between 1955 and 2007 ( [[#van%20Mantgem--2009|van Mantgem et al., 2009]] ). Lodgepole pine ( ''Pinus contorta'' ) mortality increased 700% from 2000 to 2013 ( [[#Anderegg--2015|Anderegg et al., 2015]] ) and piñon pine ( ''P. edulis'' ) experienced &gt;50% mortality from 2002 to 2014 ( [[#Redmond--2018|Redmond et al., 2018]] ). In montane conifer forest in California, anthropogenic climate change has increased tree mortality by one-quarter ( [[#Goulden--2019|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--2019|Stephenson et al., 2019]] ; [[#Stovall--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 3.3.1.1 in ( [[#IPCC--2021a|IPCC, 2021a]] )) and longer growing seasons (section 2.3.4.3.1 in ( [[#IPCC--2021a|IPCC, 2021a]] )) have killed drought-stressed trees ( [[#2.4.2.1|Section 2.4.2.1]] ) ( [[#Anderegg--2015|Anderegg et al., 2015]] ; [[#Kolb--2016|Kolb et al., 2016]] ; [[#Lloret--2018|Lloret and Kitzberger, 2018]] ; [[#Redmond--2018|Redmond et al., 2018]] ; [[#Stephens--2018|Stephens et al., 2018]] ; [[#Fettig--2019|Fettig et al., 2019]] ; [[#Restaino--2019|Restaino et al., 2019]] ; [[#Stephenson--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--2008|Raffa et al., 2008]] ; [[#Bentz--2010|Bentz et al., 2010]] ; [[#Jewett--2011|Jewett et al., 2011]] ; [[#Macfarlane--2013|Macfarlane et al., 2013]] ; [[#Raffa--2013|Raffa et al., 2013]] ; [[#Hart--2017|Hart et al., 2017]] ; [[#Stephenson--2019|Stephenson et al., 2019]] ; [[#Teshome--2020|Teshome et al., 2020]] ; [[#Koontz--2021|Koontz et al., 2021]] ). Under warmer conditions, some insects that were previously innocuous have become important agents of tree mortality ( [[#Stephenson--2019|Stephenson et al., 2019]] ; [[#Trugman--2021|Trugman et al., 2021]] ). Field observations show mixed effects of bark beetle-induced tree mortality on subsequent fire-caused tree mortality ( [[#Andrus--2016|Andrus et al., 2016]] ; [[#Meigs--2016|Meigs et al., 2016]] ; [[#Candau--2018|Candau et al., 2018]] ; [[#Lucash--2018|Lucash et al., 2018]] ; [[#Talucci--2019|Talucci and Krawchuk, 2019]] ; [[#Wayman--2021|Wayman and Safford, 2021]] ). From 1997 to 2018, ~5% of the forest area in the western USA died from bark beetle infestations ( [[#Hicke--2020|Hicke et al., 2020]] ). Under most circumstances, trees that have been weakened by drought are more vulnerable to being killed by bark beetles ( [[#Anderegg--2015|Anderegg et al., 2015]] ; [[#Kolb--2016|Kolb et al., 2016]] ; [[#Lloret--2018|Lloret and Kitzberger, 2018]] ; [[#Redmond--2018|Redmond et al., 2018]] ; [[#Stephens--2018|Stephens et al., 2018]] ; [[#Fettig--2019|Fettig et al., 2019]] ; [[#Restaino--2019|Restaino et al., 2019]] ; [[#Stephenson--2019|Stephenson et al., 2019]] ; [[#Koontz--2021|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 [[#2.4.2.1|Section 2.4.2.1]] ).

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--2017|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--2018|Senf et al., 2018]] ). Drought-related tree mortality rates from 1987 to 2016 were highest in the Ukraine, Moldova, southern France and Spain ( [[#Senf--2020|Senf et al., 2020]] ). Climate contributed to tree mortality across Europe from 1958 to 2001 ( [[#Seidl--2011|Seidl et al., 2011]] ). In addition, insect infestations related to higher temperatures ( [[#Okland--2019|Okland et al., 2019]] ) have caused the extensive mortality of Norway spruce ( ''Picea abies'' ) across nine European countries ( [[#Marini--2017|Marini et al., 2017]] ; [[#Mezei--2017|Mezei et al., 2017]] ). Across the Mediterranean Basin, a combination of drought, wildfire, pest infestations and livestock grazing ( [[#Peñuelas--2021|Peñuelas and Sardans, 2021]] ) has driven tree mortality. In summary, climate change has contributed to tree mortality in Europe ( ''high confidence'' ) (see also [[#2.4.2.1|Section 2.4.2.1]] ).

<div id="2.4.4.3.4" class="h4-container"></div>

<span id="tree-mortality-and-fauna"></span>
===== 2.4.4.3.4 Tree mortality and fauna =====

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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--2021|Fleming et al., 2021]] ).

<div id="2.4.4.4" class="h3-container"></div>

<span id="observed-terrestrial-ecosystem-carbon"></span>
==== 2.4.4.4 Observed Terrestrial Ecosystem Carbon ====

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<div id="2.4.4.4.1" class="h4-container"></div>

<span id="observed-terrestrial-ecosystem-carbon-globally"></span>
===== 2.4.4.4.1 Observed terrestrial ecosystem carbon globally =====

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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--2014|Hugelius et al., 2014]] ; [[#Batjes--2016|Batjes, 2016]] ; [[#Jackson--2017|Jackson et al., 2017]] ; [[#Strauss--2017|Strauss et al., 2017]] ; [[#Erb--2018a|Erb et al., 2018a]] ; [[#Xu--2021a|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--2021|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--2020|Friedlingstein et al., 2020]] ), of which wildfires and peat burning emitted 0.4 ± 0.2 Gt yr -1 from 1997 to 2016 ( [[#van%20der%20Werf--2017|van der Werf et al., 2017]] ). Anthropogenic climate change has caused some of these emissions through increases in wildfire ( [[#2.4.4.2.1|Section 2.4.4.2.1]] ) and tree mortality ( [[#2.4.4.3.1|Section 2.4.4.3.1]] ), but the fraction of the total remains unquantified. LUC produced ~15% of global anthropogenic emissions, from fossil fuels and land ( [[#Friedlingstein--2020|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--2020|Friedlingstein et al., 2020]] ).

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

<div id="2.4.4.4.2" class="h4-container"></div>

<span id="observed-stocks-in-high-carbon-terrestrial-ecosystems"></span>
===== 2.4.4.4.2 Observed stocks in high-carbon terrestrial ecosystems =====

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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%20Pelt--2016|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--2009|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--2014|Mitchard et al., 2014]] ) and the Congo ( [[#Xu--2017|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--2020|Sullivan et al., 2020]] ).

Tropical forests contain the largest vegetation carbon stocks in the world, with 180–250 GtC above and below ground ( [[#Saatchi--2011|Saatchi et al., 2011]] ; [[#Baccini--2012|Baccini et al., 2012]] ; [[#Avitabile--2016|Avitabile et al., 2016]] ). The Amazon contains a stock of 45–60 GtC ( [[#Baccini--2012|Baccini et al., 2012]] ; [[#Mitchard--2014|Mitchard et al., 2014]] ; [[#Englund--2017|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|Tomlinson, 2005]] ), the Cuvette Centrale swamp forest peatlands in Congo with an average of ~2200 tonnes ha -1 ( [[#Dargie--2017|Dargie et al., 2017]] ), the Arctic tundra with an average of ~900 tonnes ha -1 ( [[#Tarnocai--2009|Tarnocai et al., 2009]] ) and the mangrove peatlands in Kalimantan, Indonesia, with an average of 850 ± 320 tonnes ha -1 ( [[#Murdiyarso--2015|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--2014|Hugelius et al., 2014]] ). Globally, peatlands contain 470–620 GtC ( [[#Page--2011|Page et al., 2011]] ; [[#Hodgkins--2018|Hodgkins et al., 2018]] ), of which boreal and temperate peatlands contain 415 ± 150 GtC ( [[#Hugelius--2020|Hugelius et al., 2020]] ) and tropical peatlands contain 80–350 GtC ( [[#Page--2011|Page et al., 2011]] ; [[#Dargie--2017|Dargie et al., 2017]] ; [[#Gumbricht--2017|Gumbricht et al., 2017]] ; [[#Ribeiro--2021|Ribeiro et al., 2021]] ). Other analyses increase the upper estimates for boreal and temperate peatlands to 800–1200 GtC ( [[#Nichols--2019|Nichols and Peteet, 2019]] ; [[#Mishra--2021b|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'' ).

<div id="2.4.4.4.3" class="h4-container"></div>

<span id="biodiversity-and-observed-terrestrial-ecosystem-carbon"></span>
===== 2.4.4.4.3 Biodiversity and observed terrestrial ecosystem carbon =====

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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--2011|Saatchi et al., 2011]] ; [[#Baccini--2012|Baccini et al., 2012]] ; [[#Avitabile--2016|Avitabile et al., 2016]] ) and the highest vascular plant species richness ( [[#Kreft--2007|Kreft and Jetz, 2007]] ) in the world. Above-ground ecosystem carbon and animal species richness show high correlation but also high spatial variability ( [[#Strassburg--2010|Strassburg et al., 2010]] ). Above-ground carbon is correlated to genus richness globally ( [[#Cavanaugh--2014|Cavanaugh et al., 2014]] ), but to species richness only in local areas ( [[#Poorter--2015|Poorter et al., 2015]] ; [[#Sullivan--2017|Sullivan et al., 2017]] ). Species richness generally increases vegetation productivity in the humid tropics while tree abundance increases productivity in drier conditions ( [[#Madrigal-Gonzalez--2020|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--2015|Fauset et al., 2015]] ). Above-ground carbon in tropical forests shows positive correlations to vertebrate species richness (P values not reported) ( [[#Deere--2018|Deere et al., 2018]] ; [[#Di%20Marco--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--2018|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--2016|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--2017|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--2020|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'' ).

<div id="2.4.4.4.4" class="h4-container"></div>

<span id="observed-emissions-and-removals-from-high-carbon-terrestrial-ecosystems"></span>
===== 2.4.4.4.4 Observed emissions and removals from high-carbon terrestrial ecosystems =====

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Most global deforestation is occurring in tropical forests ( [[#Pan--2011|Pan et al., 2011]] ; [[#Liu--2015|Liu et al., 2015]] ; [[#Houghton--2017|Houghton and Nassikas, 2017]] ; [[#Erb--2018a|Erb et al., 2018a]] ; [[#Li--2018|Li et al., 2018]] ; [[#Harris--2021|Harris et al., 2021]] ), primarily as a result of clearing for agricultural land ( [[#Hong--2021|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--2021|Harris et al., 2021]] ). While wildfires emitted an average of 0.4 ± 0.2 GtC yr -1 from 1997 to 2016 ( [[#van%20der%20Werf--2017|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--2018|Aragao et al., 2018]] ), the 0.5 GtC from the Amazon fires of 2015–2016 ( [[#Berenguer--2021|Berenguer et al., 2021]] ) and the 0.2 Gt from the Australia fires of 2019–2020 ( [[#Shiraishi--2021|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--2021|Harris et al., 2021]] ), with boreal and temperate forests being much stronger sinks ( [[#Pan--2011|Pan et al., 2011]] ; [[#Liu--2015|Liu et al., 2015]] ; [[#Houghton--2017|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--2021a|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--2008|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--2021|Giles-Hansen et al., 2021]] ).

The Amazon as a whole was a net carbon emitter in the period 2003–2008 ( [[#Exbrayat--2015|Exbrayat and Williams, 2015]] ; [[#Yang--2018b|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%20Sy--2015|De Sy et al., 2015]] ; [[#De%20Sy--2019|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--2021|Gatti et al., 2021]] ). In the Amazon, deforestation emitted 0.17 ± 0.05 GtC yr -1 from 2001 to 2015 ( [[#Silva%20Junior--2020|Silva Junior et al., 2020]] ) while fires emitted 0.12 ± 0.14 GtC yr -1 from 2003 to 2015 ( [[#Aragao--2018|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--2021|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--2020|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--2021|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--2017|Miettinen et al., 2017]] ; [[#Wijedasa--2018|Wijedasa et al., 2018]] ; [[#Cooper--2020|Cooper et al., 2020]] ). Deforestation of mangrove forests caused 10–30% of deforestation emissions in Indonesia from 1980 to 2005 ( [[#Donato--2011|Donato et al., 2011]] ; [[#Murdiyarso--2015|Murdiyarso et al., 2015]] ), even though mangroves comprised only 3% of Indonesia primary forest area in 2000 ( [[#Margono--2014|Margono et al., 2014]] ; [[#Murdiyarso--2015|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--2017|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--2015|Gonzalez et al., 2015]] ). Anthropogenic climate change caused up to half of the burned area ( [[#2.4.4.2.1|Section 2.4.4.2.1]] ).

In the Arctic, anthropogenic climate change has thawed permafrost ( [[#Guo--2020|Guo et al., 2020]] ), leading to emissions of 1.7 ± 0.8 GtC yr -1 in winter in the period 2003–2017 ( [[#Natali--2019|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--2015|Brown et al., 2015]] ), exposing peatland carbon ( [[#Brown--2015|Brown et al., 2015]] ; [[#Gibson--2018|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'' ).

<div id="2.4.4.5" class="h3-container"></div>

<span id="observed-changes-in-primary-productivity"></span>
==== 2.4.4.5 Observed Changes in Primary Productivity ====

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<div id="2.4.4.5.1" class="h4-container"></div>

<span id="observed-changes-in-terrestrial-primary-productivity"></span>
===== 2.4.4.5.1 Observed changes in terrestrial primary productivity =====

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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 CO 2 from the atmosphere and mitigates emissions from deforestation and other LUCs ( [[#2.4.4.4|Section 2.4.4.4]] ). Global terrestrial NPP has exceeded emissions due to land use since the early 2000s, making terrestrial ecosystems a net carbon sink ( [[#Friedlingstein--2020|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--2003|Nemani et al., 2003]] ), and then decreased 1% from 2000 to 2009, because of extensive droughts in the Southern Hemisphere ( [[#Zhao--2010|Zhao and Running, 2010]] ). From 1999 to 2015, increased aridity caused extensive declines in the NDVI globally, particularly semiarid ecosystems ( [[#Huang--2016|Huang et al., 2016]] ), indicating widespread decreases in NPP ( [[#Yuan--2019|Yuan et al., 2019]] ).

Global terrestrial GPP increased 2% from 1951 to 2010 and continued increasing at least until 2016, with increased atmospheric CO 2 showing a greater influence than natural factors ( [[#Li--2017|Li et al., 2017]] ; [[#Fernandez-Martinez--2019|Fernandez-Martinez et al., 2019]] ; [[#Liu--2019a|Liu et al., 2019a]] ; [[#Cai--2020|Cai and Prentice, 2020]] ; [[#Melnikova--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--2018|Song et al., 2018]] ); regrowth in secondary forests &gt;20 years old, mainly in boreal, temperate and subtropical regions, generated a net removal of 7.7 Gt yr -1 CO 2 from the atmosphere from 2001 to 2019 ( [[#Harris--2021|Harris et al., 2021]] ). Vegetation growth that exceeds the modelled CO 2 fertilisation, gaps in field data and incomplete knowledge of plant mortality and soil carbon responses introduce uncertainties into quantifying the magnitude of CO 2 fertilisation ( [[#Walker--2021|Walker et al., 2021]] ). A combination of CO 2 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 CO 2 declined from 1982 to 2015, indicating a weakening of any CO 2 fertilisation effect ( [[#Wang--2020c|Wang et al., 2020c]] ). Increased growth from CO 2 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--2020|Brienen et al., 2020]] ). Furthermore, water availability controls the magnitude of NPP ( [[#Beer--2010|Beer et al., 2010]] ; [[#Jung--2017|Jung et al., 2017]] ; [[#Yu--2017|Yu et al., 2017]] ), including water from precipitation ( [[#Beer--2010|Beer et al., 2010]] ), soil moisture ( [[#Stocker--2019|Stocker et al., 2019]] ), groundwater storage ( [[#Humphrey--2018|Humphrey et al., 2018]] ; [[#Madani--2020a|Madani et al., 2020a]] ) and atmospheric vapour ( [[#Novick--2016|Novick et al., 2016]] ; [[#Madani--2020b|Madani et al., 2020b]] ). Drought stress reduced NPP across tropical forests from 2000 to 2015 ( [[#Zhang--2019b|Zhang et al., 2019b]] ) and GPP in the tropics from 1982 to 2016 ( [[#Madani--2020b|Madani et al., 2020b]] ). Drought stress has also reduced GPP in some semiarid and arid lands ( [[#Huang--2016|Huang et al., 2016]] ; [[#Liu--2019a|Liu et al., 2019a]] ). In addition, nitrogen and phosphorus constrain CO 2 fertilisation ( [[#Terrer--2019|Terrer et al., 2019]] ), although phosphorus limitation of tropical tree growth is species-specific ( [[#Alvarez-Clare--2013|Alvarez-Clare et al., 2013]] ; [[#Thompson--2019|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 CO 2 ( ''medium evidence'' , ''high agreement'' ).

<div id="2.4.4.5.2 " class="h4-container"></div>

<span id="observed-changes-in-freshwater-ecosystem-productivity"></span>
===== 2.4.4.5.2 Observed changes in freshwater ecosystem productivity =====

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Temperature affects primary productivity by moderating phytoplankton growth rates, ice cover, thermal stratification and the length of growing seasons ( [[#Rühland--2015|Rühland et al., 2015]] ; [[#Richardson--2018|Richardson et al., 2018]] ). Global warming has reinforced eutrophication, especially cyanobacteria blooms ( [[#Wagner--2009|Wagner and Adrian, 2009]] ; [[#Kosten--2012|Kosten et al., 2012]] ; [[#O’Neil--2012|O’Neil et al., 2012]] ; [[#De%20Senerpont%20Domis--2013|De Senerpont Domis et al., 2013]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Visser--2016|Visser et al., 2016]] ; [[#Huisman--2018|Huisman et al., 2018]] ) ( ''very high confidence'' ) ''.'' Conversely '','' warming can reduce cyanobacteria in hypertrophic lakes ( [[#Richardson--2019|Richardson et al., 2019]] ). Freshwater cyanobacteria may benefit directly from elevated CO 2 concentrations ( [[#Visser--2016|Visser et al., 2016]] ; [[#Ji--2017|Ji et al., 2017]] ; [[#Huisman--2018|Huisman et al., 2018]] ; [[#Richardson--2019|Richardson et al., 2019]] ).

Macrophyte growth in freshwaters is likely to increase with rising water temperatures, atmospheric CO 2 and precipitation ( ''robust evidence'' , ''high agreement'' ) ( [[#Dhir--2015|Dhir, 2015]] ; [[#Hossain--2016|Hossain et al., 2016]] ; [[#Short--2016|Short et al., 2016]] ; [[#Reitsema--2018|Reitsema et al., 2018]] ). Nonetheless, primary productivity in rivers is variable and unpredictable ( [[#Bernhardt--2018|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--2017|Kraemer et al., 2017]] ). Warming may reduce phytoplankton concentrations when temperature-induced increases in consumption of phytoplankton outpace increases in phytoplankton production ( [[#De%20Senerpont%20Domis--2013|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--2017|O’Beirne et al., 2017]] ; [[#Velthuis--2017|Velthuis et al., 2017]] ; [[#Rusak--2018|Rusak et al., 2018]] ; [[#Ho--2019|Ho et al., 2019]] ).

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=== FAQ 2.3 | Is climate change increasing wildfire? ===

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''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.''

[[File:747b19ab02cc1ee7a537f308cb1ba94c IPCC_AR6_WGII_Figure_2_FAQ_2.3.1.png]]

'''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.

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=== 2.4.5 Conclusions on Observed Impacts ===

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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--2013|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'' ) ( [[#2.4.2|Section 2.4.2]] , Table 2.2; Table 2.3; Table SM2.1) ( [[#Parmesan--2013|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)'' ( [[#2.4.2|Section 2.4.2]] , Table 2.2) ( [[#Parmesan--2015|Parmesan and Hanley, 2015]] ; [[#Parmesan--2019|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--2020|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)'' ( [[#2.4.2.3|Section 2.4.2.3]] ) ( [[#Cline--2013|Cline et al., 2013]] ; [[#Feeley--2020|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--2019|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 ( [[#2.4.2.5|Section 2.4.2.5]] , 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 2.4.2.1, 2.4.2.2, 2.4.2.3, 2.4.2.4).

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 CO 2 , in concert with changes in grazing and fire regimes ( ''medium confidence'' ) ( [[#2.4.3|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'' ) ( [[#2.4.4.3|Section 2.4.4.3]] ).

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 2.4.4.4, 2.5.3.4).

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 ( [[#2.4.4.2|Section 2.4.4.2]] ).

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.

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