Editing IPCC:AR6/WGII/Chapter-3 (section)

== 3.3 Linking Biological Responses to Climate-induced Drivers ==

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=== 3.3.1 Introduction ===

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This section assesses new evidence since AR5 ( [[#Pörtner--2014|Pörtner et al., 2014]] ) and SROCC ( [[#Bindoff--2019a|Bindoff et al., 2019a]] ) regarding biotic responses to multiple environmental drivers. It assesses differential sensitivities among life stages within individual organisms, changing responses across scales of biological organisation and the potential for evolutionary adaptation to climate change (e.g., [[#Przeslawski--2015|Przeslawski et al., 2015]] ; [[#Boyd--2018|Boyd et al., 2018]] ; [[#Reddin--2020|Reddin et al., 2020]] ), providing examples and identifying key gaps and uncertainties that limit our ability to project the ecological impact of multiple climate-induced drivers (Figure 3.8a). The assessment includes physiological responses to single environmental drivers and their underlying mechanisms ( [[#3.3.2|Section 3.3.2]] ), the characteristics of multiple drivers and organisms’ responses to them ( [[#3.3.3|Section 3.3.3]] ), short-term acclimation and longer-term evolutionary adaptation of populations ( [[#3.3.4|Section 3.3.4]] ), and it concludes with an assessment of progress in upscaling laboratory findings to ecosystems within ''in situ'' settings (Figure 3.8b; [[#3.3.5|Section 3.3.5]] ).

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[[File:8ba101b167539299b7ca66b9839b6246 IPCC_AR6_WGII_Figure_3_008.png]]

'''Figure 3.8 |''' '''The state of knowledge regarding ecological responses to environmental drivers in experimental settings.'''

'''(a)''' Schematic indicates where themes are discussed within [[#3.3|Section 3.3]] , and how they jointly inform policy. (Adapted from [[#Riebesell--2014|Riebesell and Gattuso, 2014]] ).

'''(b)''' The hierarchy of accumulating physiological knowledge (grey layers), from single (e.g., [[#Pörtner--2012|Pörtner et al., 2012]] ) to multiple drivers, and from simple outcomes (e.g., [[#Sciandra--2003|Sciandra et al., 2003]] ), interactions among drivers (e.g., [[#Crain--2008|Crain et al., 2008]] ) and identification of physiological roles of drivers (e.g., [[#Bach--2015|Bach et al., 2015]] ) to mechanistic understanding of drivers (e.g., [[#Thomas--2017|Thomas et al., 2017]] ). At present, the upper grey layer has been achieved, in full, for two drivers (e.g., temperature and nutrient concentrations), with validation of dual controls on phytoplankton growth rate ( [[#Thomas--2017|Thomas et al., 2017]] ). Hatched layers denote major advances since WGII AR5 [[IPCC:Wg2:Chapter:Chapter-6|Chapter 6]] ( [[#Pörtner--2014|Pörtner et al., 2014]] ). The green layer indicates the level of understanding potentially needed to project the response of marine life subjected to multiple drivers. Red horizontal arrows indicate the influence of confounding factors on our current understanding, including population genetics, fluctuating oceanic conditions or extreme events.

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=== 3.3.2 Responses to Single Drivers ===

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Anthropogenic CO 2 emissions trigger a suite of changes that alter ocean temperature, pH and CO 2 concentration, oxygen concentration and nutrient supply at global scales ( [[#3.2|Section 3.2]] ). The response pathways of these climate-induced drivers have been investigated primarily as single variables.

Temperature affects the movement and transport of molecules and, thereby, the rates of all biochemical reactions; thus, ongoing and projected warming ( [[#3.2.2.1|Section 3.2.2.1]] ) that remains below an organism’s physiological optimum will generally raise metabolic rates ( ''very high confidence'' ) ( [[#Pörtner--2014|Pörtner et al., 2014]] ). Beyond this optimum (T opt ; Figure 3.9), metabolism typically decreases sharply, finally reaching a critical threshold (T crit ) beyond which enzymes become thermally inactivated and cells undergo oxidative stress. Local and regional adaptation affect the heat tolerance thresholds of organisms. For example, organisms adapted to thermally stable environments (e.g., tropical, polar, deep sea) are often more sensitive to warming than those from thermally variable environments (e.g., estuaries) ( ''very high confidence'' ) ( [[#3.4|Section 3.4]] ; [[#Sunday--2019|Sunday et al., 2019]] ; [[#Collins--2020|Collins et al., 2020]] ). Heat tolerance also decreases with increasing organisational complexity ( [[#Storch--2014|Storch et al., 2014]] ; [[#Pörtner--2016|Pörtner and Gutt, 2016]] ) and is lower in eggs, embryos and spawning fish than for their larval stages or adults outside the spawning season ( ''high confidence'' ) ( [[#Dahlke--2020b|Dahlke et al., 2020b]] ). By altering physiological responses, projected changes in ocean warming ( [[#3.2.2.1|Section 3.2.2.1]] ) will modify growth, migration, distribution, competition, survival and reproduction ( ''very high confidence'' ) ( [[#Messmer--2017|Messmer et al., 2017]] ; [[#Dahlke--2018|Dahlke et al., 2018]] ; [[#Andrews--2019|Andrews et al., 2019]] ; [[#Pinsky--2019|Pinsky et al., 2019]] ; [[#Anton--2020|Anton et al., 2020]] ).

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[[File:f1d297be06dd24667b742d0d2b68b5f9 IPCC_AR6_WGII_Figure_3_009.png]]

'''Figure 3.9 |''' '''Organismal responses to single and multiple drivers.'''

'''(a)''' The generic temperature–response curve shows physiological process rates as a nonlinear function of a particular driver (e.g., temperature) with maximum rates (R max ) and temperature optima (T opt ). The driver range that keeps physiological rates above a certain threshold represents the organism’s range of phenotypic plasticity, while below that threshold, the critical temperature (T crit ), physiological performance is so low as to constitute stressful conditions.

'''(b)''' The response curve for one driver can depend on other drivers, here exemplified for temperature and pH in the central panel. This interaction causes rates as well as optima to change with pH (left) and temperature (right), indicated by the coloured lines. '''(c)''' Impacts of multiple drivers on processes can be additive (blue), synergistic (red) or antagonistic (green), that is, the cumulative effects of two (or more) drivers are equal to, larger than or smaller than the sum of their individual effects, respectively. Potential experimental outcomes affected by additive, synergistic and antagonistic interactions are shown for scenarios where drivers increase rates (left), decrease rates (centre) or cause opposite responses (right), showing how experimental outcomes can mask these mechanistic interactions. (For a quantitative analysis of effects of driver pairs on animals, see Figure 3.SM.2.) (Adapted from [[#Crain--2008|Crain et al., 2008]] and [[#Piggott--2015|Piggott et al., 2015]] ).

Altered seawater carbonate chemistry ( [[#3.2.3|Section 3.2.3.1]] ) affects specific processes to varying degrees. For example, higher CO 2 concentrations can increase photosynthesis and growth in some phytoplankton, macroalgal and seagrass species ( ''high confidence'' ) ( [[#Pörtner--2014|Pörtner et al., 2014]] ; [[#Seifert--2020|Seifert et al., 2020]] ; [[#Zimmerman--2021|Zimmerman, 2021]] ), while lower pH levels decrease calcification ( ''high confidence'' ) ( [[#Pörtner--2014|Pörtner et al., 2014]] ; [[#Falkenberg--2018|Falkenberg et al., 2018]] ; [[#Doney--2020|Doney et al., 2020]] ; [[#Fox--2020|Fox et al., 2020]] ; [[#Reddin--2020|Reddin et al., 2020]] ) or silicification ( ''low confidence'' ) ( [[#Petrou--2019|Petrou et al., 2019]] ). Organisms’ capacity to compensate for or resist acidification of internal fluids depends on their capacity for acid–base regulation, which differs due to organisms’ wide-ranging biological complexity and adaptive abilities ( ''low to medium confidence'' ) ( [[#Vargas--2017|Vargas et al., 2017]] ; [[#Melzner--2020|Melzner et al., 2020]] ). Detrimental impacts of acidification include decreased growth and survival, and altered development, especially in early life stages ( ''high confidence'' ) ( [[#Dahlke--2018|Dahlke et al., 2018]] ; [[#Onitsuka--2018|Onitsuka et al., 2018]] ; [[#Hancock--2020|Hancock et al., 2020]] ), along with lowered recruitment and altered behaviour in animals ( [[#Kroeker--2013a|Kroeker et al., 2013a]] ; [[#Wittmann--2013|Wittmann and Pörtner, 2013]] ; [[#Clements--2015|Clements and Hunt, 2015]] ; [[#Cattano--2018|Cattano et al., 2018]] ; [[#Esbaugh--2018|Esbaugh, 2018]] ; [[#Bednaršek--2019|Bednaršek et al., 2019]] ; [[#Reddin--2020|Reddin et al., 2020]] ). For finfish, laboratory studies of behavioural and sensory consequences of ocean acidification showed mixed results ( [[#Rossi--2018|Rossi et al., 2018]] ; [[#Nagelkerken--2019|Nagelkerken et al., 2019]] ; [[#Stiasny--2019|Stiasny et al., 2019]] ; [[#Velez--2019|Velez et al., 2019]] ; [[#Clark--2020|Clark et al., 2020]] ; [[#Munday--2020|Munday et al., 2020]] ). Calcifiers are generally more sensitive to acidification (e.g., for growth and survival) than non-calcifying groups ( ''high confidence'' ) ( [[#Kroeker--2013a|Kroeker et al., 2013a]] ; [[#Wittmann--2013|Wittmann and Pörtner, 2013]] ; [[#Clements--2015|Clements and Hunt, 2015]] ; [[#Cattano--2018|Cattano et al., 2018]] ; [[#Bednaršek--2019|Bednaršek et al., 2019]] ; [[#Reddin--2020|Reddin et al., 2020]] ; [[#Seifert--2020|Seifert et al., 2020]] ). For calcifying primary producers, including phytoplankton and coralline algae, ocean acidification has different, often opposing effects, for example, decreasing calcification while photosynthetic rates increase ( ''high confidence'' ) ( [[#Riebesell--2000|Riebesell et al., 2000]] ; [[#Van%20de%20Waal--2013|Van de Waal et al., 2013]] ; [[#Bach--2015|Bach et al., 2015]] ; [[#Cornwall--2017b|Cornwall et al., 2017b]] ; [[#Gafar--2019|Gafar et al., 2019]] ).

Oxygen concentrations affect aerobic and anaerobic processes, including energy metabolism and denitrification. Projected decreases in dissolved oxygen concentration ( [[#3.2.3|Section 3.2.3.2]] ) will thus impact organisms and their biogeography in ways dependent upon their oxygen requirements ( [[#Deutsch--2020|Deutsch et al., 2020]] ), which are highest for large, multicellular organisms ( [[#Pörtner--2014|Pörtner et al., 2014]] ). The upper ocean generally contains high dissolved-oxygen concentrations due to air–sea exchange and photosynthesis, but in subsurface waters, deoxygenation may impair aerobic organisms in multiple ways ( [[#Oschlies--2018|Oschlies et al., 2018]] ; [[#Galic--2019|Galic et al., 2019]] ; [[#Thomas--2019|Thomas et al., 2019]] ; [[#Sampaio--2021|Sampaio et al., 2021]] ). Many processes contribute to lowered oxygen levels: altered ventilation and stratification; microbial respiration enhanced by nearshore eutrophication; and less oxygen solubility in warmer waters. For example, deoxygenation in highly eutrophic estuarine and coastal marine ecosystems ( [[#3.4.2|Section 3.4.2]] ) can result from accelerated microbial activity, leading to acute organismal responses. Under hypoxia (oxygen concentrations ≤2 mg l –1 ; [[#Limburg--2020|Limburg et al., 2020]] ), physiological and ecological processes are impaired and communities undergo species migration, replacement and loss, transforming community composition ( ''very high confidence'' ) ( [[#Chu--2015|Chu and Tunnicliffe, 2015]] ; [[#Gobler--2016|Gobler and Baumann, 2016]] ; [[#Sampaio--2021|Sampaio et al., 2021]] ). Hypoxia can lead to expanding OMZs, which will favour specialised microbes and hypoxia-tolerant organisms ( ''medium confidence'' ) ( [[#Breitburg--2018|Breitburg et al., 2018]] ; [[#Ramírez-Flandes--2019|Ramírez-Flandes et al., 2019]] ). As respiration consumes oxygen and produces CO 2 , lowered oxygen levels are often interlinked with acidification in coastal and tropical habitats ( [[#Rosa--2013|Rosa et al., 2013]] ; [[#Gobler--2016|Gobler and Baumann, 2016]] ; [[#Feely--2018|Feely et al., 2018]] ) and is an example of a compound hazard (Sections 3.2.4.1, 3.4.2.4).

Increased density stratification and mixed-layer shallowing, caused by warming, freshening and sea ice decline, can alter light climate and nutrient availability within the surface mixed layer ( ''high confidence'' ) ( [[#3.2.2.3|Section 3.2.2.3]] ). As light and nutrient levels drive photosynthesis, changes in these drivers directly affect primary producers, often in different directions ( [[#Matsumoto--2014|Matsumoto et al., 2014]] ; [[#Deppeler--2017|Deppeler and Davidson, 2017]] ). Decreased upward nutrient supply is expected to decrease primary production in the low-latitude ocean ( ''medium confidence'' ) ( [[#3.4.4|Section 3.4.4.2.1]] ; [[#Moore--2018a|Moore et al., 2018a]] ; [[#Kwiatkowski--2019|Kwiatkowski et al., 2019]] ). Alternatively, higher mean underwater light levels resulting from changes in sea ice and/or mixed layer shallowing can increase primary production in high-latitude offshore regions, provided nutrient levels remain sufficiently high ( ''medium confidence'' ) ( [[#3.4.4|Section 3.4.4.2.1]] ; Cross-Chapter Paper 6; [[#Vancoppenolle--2013|Vancoppenolle et al., 2013]] ; [[#Deppeler--2017|Deppeler and Davidson, 2017]] ; [[#Tedesco--2019|Tedesco et al., 2019]] ; [[#Ardyna--2020|Ardyna and Arrigo, 2020]] ; [[#Lannuzel--2020|Lannuzel et al., 2020]] ). In some parts of the open Southern Ocean, where iron limitation largely controls primary productivity ( [[#Tagliabue--2017|Tagliabue et al., 2017]] ), changes in wind fields will deepen the summer mixed-layer depth ( [[#Panassa--2018|Panassa et al., 2018]] ), entrain more nutrients, and raise primary productivity in the future ( ''medium confidence'' ) (Cross-Chapter Paper 6; [[#Hauck--2015|Hauck et al., 2015]] ; [[#Leung--2015|Leung et al., 2015]] ; [[#Moore--2018a|Moore et al., 2018a]] ; [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ).

Climate-induced drivers fluctuate on time scales ranging from diurnal to annual, with potential consequences for organismal responses (Figure 3.10), but these fluctuations are commonly not incorporated experimentally. Experiments that simulate natural fluctuations in drivers, especially beyond tidal or diel cycles, can result in more detrimental impacts than those based on quasi-constant conditions ( [[#Eriander--2015|Eriander et al., 2015]] ; [[#Sunday--2019|Sunday et al., 2019]] ), but can also ameliorate effects ( [[#Comeau--2014|Comeau et al., 2014]] ; [[#Laubenstein--2020|Laubenstein et al., 2020]] ; [[#Cabrerizo--2021|Cabrerizo et al., 2021]] ), confirming that the influence of environmental variability requires evaluation ( [[#Dowd--2015|Dowd et al., 2015]] ). Marine heatwaves exacerbate the impacts of rising mean temperatures, with major ecological consequences ( ''very high confidence'' ) ( [[#Frölicher--2018|Frölicher et al., 2018]] ; [[#IPCC--2018|IPCC, 2018]] ; [[#Arafeh-Dalmau--2020|Arafeh-Dalmau et al., 2020]] ; [[#Laufkötter--2020|Laufkötter et al., 2020]] ). Higher temperature variability decreased phytoplankton growth and calcification in ''Emiliania huxleyi'' relative to a stable warming regime ( [[#Wang--2019b|Wang et al., 2019b]] ). Diel fluctuations (i.e., over 24 h) in carbonate chemistry superimposed on current and future ''p'' CO 2 levels influenced diatom species differently, depending on their habitat ( [[#Li--2016|Li et al., 2016]] ). CO 2 fluctuations overlaid on changing mean values also altered phenotypic evolutionary outcomes of picoeukaryotic algae ( [[#Schaum--2016|Schaum et al., 2016]] ). In the bivalve ''Mytilus edulis'' , fluctuating pH regimes exerted higher metabolic costs ( [[#Mangan--2017|Mangan et al., 2017]] ), while salinity fluctuations might be more influential than pH fluctuations in other bivalves ( [[#Velez--2016|Velez et al., 2016]] ). The amplitude of diel and seasonal pH and CO 2 changes are projected to increase in the future due to lowered CO 2 seawater buffering capacity ( ''very high confidence'' ) ( [[#3.2.3|Section 3.2.3.1]] ; [[#Burger--2020|Burger et al., 2020]] ), which can impose additional stress on organisms.

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=== 3.3.3 Responses to Multiple Drivers ===

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Each organism encounters a unique combination of local and climate-induced drivers, which vary in space and time. The contribution of these drivers to an organism’s overall biological response, and thereby also potential risks for the organism, depends on the intensity and duration of its exposure to these drivers and associated sensitivities. Both geographic location (e.g., polar, tropical) and marine habitat (e.g., benthic, pelagic) strongly affect the combination of climate and non-climate drivers to which organisms are exposed. Non-climate drivers ( [[#3.1|Section 3.1]] ) can dominate outcomes or amplify vulnerability to climate-induced drivers, with mostly detrimental effects such as extirpation ( ''very high confidence'' ) ( [[#3.4|Section 3.4]] ; [[#Boyd--2018|Boyd et al., 2018]] ; [[#Gissi--2021|Gissi et al., 2021]] ), and unique feedbacks may exist between climate change and drivers like habitat loss or invasive species that further confound climate-change effects ( [[#Ortiz--2018|Ortiz et al., 2018]] ; [[#Wolff--2018|Wolff et al., 2018]] ; [[#Gissi--2021|Gissi et al., 2021]] ). Individual responses are further influenced by an organism’s behaviour, trophic level and life-history strategy (Figure 3.10; [[#Przeslawski--2015|Przeslawski et al., 2015]] ; [[#Boyd--2018|Boyd et al., 2018]] ). Evidence is increasing that some life-history stages are more sensitive to specific drivers than others ( [[#Dahlke--2020b|Dahlke et al., 2020b]] ). To identify the most influential drivers for an organism requires targeting key traits (e.g., calcification, reproduction). The trophic level of the organism must also be considered, because autotrophs directly depend on light and nutrients while invertebrates are often more sensitive to changes in oxygen or altered prey, but temperature plays a key role for both groups (Figure 3.10b).

Co-occurring environmental drivers often cause complex organismal responses ( ''high confidence'' ) ( [[#Pörtner--2014|Pörtner et al., 2014]] ). Individual drivers can have detrimental, neutral or beneficial effects, depending on the relationship between driver and physiological process ( [[#3.3.2|Section 3.3.2]] ; Figure 3.9a). Multiple drivers can have interactive effects, where the response to one driver alters the sensitivity to another, and outcomes cannot be deduced from individual drivers’ effects (Figure 3.9b). Impacts of multiple drivers can be additive, synergistic or antagonistic (Figure 3.9c; [[#Crain--2008|Crain et al., 2008]] ; [[#Piggott--2015|Piggott et al., 2015]] ; [[#Boyd--2018|Boyd et al., 2018]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ). Well-controlled laboratory studies on multiple-driver effects have revealed insights into the mode of action of individual drivers and their interdependence ( [[#Kroeker--2017|Kroeker et al., 2017]] ; [[#Gao--2019|Gao et al., 2019]] ; [[#Reddin--2020|Reddin et al., 2020]] ; [[#Seifert--2020|Seifert et al., 2020]] ; [[#Green--2021b|Green et al., 2021b]] ; [[#Sampaio--2021|Sampaio et al., 2021]] ). Understanding the outcomes of interactive drivers is important for robustly assessing risks to organisms under different climate-change scenarios.

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[[File:84e02cc957feafd19811c05647481180 IPCC_AR6_WGII_Figure_3_010.png]]

'''Figure 3.10 |''' '''The effect of environmental drivers differs depending upon organisms’ life history, and trophic strategy or habitat.'''

'''(a)''' pH variability differs for benthic invertebrates, such as sea urchins (in blue), and their pelagic larvae (in green); pH fluctuations over the annual cycle can be much larger in the water column (due to primary production) relative to the seafloor. Variability associated with behaviour and life stage strongly defines organisms’ niches and sensitivities to present and future conditions.

'''(b)''' Examples of organisms that are influenced by different suites of drivers that are set jointly by their habitat (e.g., benthic versus epipelagic settings) and trophic strategy (e.g., nutrients for phytoplankton, prey characteristics for grazers).

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==== 3.3.3.1 Effects of Multiple Drivers on Primary Producers ====

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Warming and rising CO 2 concentrations enhance growth and/or photosynthetic rates in many species of cyanobacteria, picoeukaryotes, coccolithophores, dinoflagellates and diatoms ( ''high confidence'' ) ( [[#Fu--2007|Fu et al., 2007]] ; [[#Sett--2014|Sett et al., 2014]] ; [[#Hoppe--2018a|Hoppe et al., 2018a]] ; [[#Wolf--2018|Wolf et al., 2018]] ; [[#Brandenburg--2019|Brandenburg et al., 2019]] ), and the optimum ''p'' CO 2 for growth and/or primary production shifts upward under warming ( ''medium confidence'' ) ( [[#Sett--2014|Sett et al., 2014]] ; [[#Hoppe--2018a|Hoppe et al., 2018a]] ). Warming and ocean acidification appear to jointly favour the proliferation and toxicity of harmful algal bloom (HAB) species ( ''limited evidence, high agreement'' ) ( [[#3.5.5.3|Section 3.5.5.3]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ; [[#Brandenburg--2019|Brandenburg et al., 2019]] ; [[#Griffith--2019a|Griffith et al., 2019a]] ; [[#Wells--2020|Wells et al., 2020]] ), but a 2021 analysis found no uniform global trend in HABs or their distribution over 1985–2018 once field data were adjusted for regional variations in monitoring effort ( [[#Hallegraeff--2021|Hallegraeff et al., 2021]] ). The predominantly detrimental impacts of ocean acidification on coccolithophores can partly be offset by warming ( [[#Seifert--2020|Seifert et al., 2020]] ) but also be exacerbated, depending on the magnitudes of drivers ( [[#D’Amario--2020|D’Amario et al., 2020]] ). For non-calcifying macroalgae, responses are highly species specific and often indicate synergistic interactions between warming and acidification ( [[#Kram--2016|Kram et al., 2016]] ; [[#Falkenberg--2018|Falkenberg et al., 2018]] ). Ocean acidification poses a large risk for coralline algae that is further amplified by warming ( ''medium confidence'' ) ( [[#3.4.2.2|Section 3.4.2.2]] ; [[#Cornwall--2019|Cornwall et al., 2019]] ). However, temperatures up to 5°C above ambient do not decrease calcification ( [[#Cornwall--2019|Cornwall et al., 2019]] ), and there is ''limited evidence'' that some species have the physiological capacity to resist acidification via pH upregulation at the calcification site ( [[#Cornwall--2017a|Cornwall et al., 2017a]] ). For seagrass, warming beyond a species’ thermal tolerance will limit growth and impact germination, but ocean acidification appears to increase thermal tolerance of some eelgrass species by increasing the photosynthesis-to-respiration ratio ( ''medium confidence'' ) ( [[#Egea--2018|Egea et al., 2018]] ; [[#Scalpone--2020|Scalpone et al., 2020]] ; [[#Zimmerman--2021|Zimmerman, 2021]] ).

Thermal sensitivity of pelagic primary producers changes with nutrient supply ( ''high confidence'' ) ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Marañón--2018|Marañón et al., 2018]] ; [[#Fernández--2020|Fernández et al., 2020]] ). Phosphorus limitation lowers the temperature optimum for growth of phytoplankton, making these organisms more prone to heat stress ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Bestion--2018|Bestion et al., 2018]] ). This trend may hold for open-ocean phytoplankton, which are often iron-limited ( ''medium confidence'' ) ( [[#Boyd--2019|Boyd, 2019]] ). Such temperature-nutrient interactions might be especially relevant during summer MHWs ( [[#3.2.2.1|Section 3.2.2.1]] ; Cross-Chapter Box EXTREMES in Chapter 2; [[#IPCC--2018|IPCC, 2018]] ; [[#Holbrook--2019|Holbrook et al., 2019]] ; [[#DeCarlo--2020|DeCarlo et al., 2020]] ; [[#Hayashida--2020|Hayashida et al., 2020]] ), when primary producers are often nutrient-limited and near their thermal limits. Increasingly frequent and intense MHWs along with projected decreases in nutrient availability ( [[#3.2.3|Section 3.2.3.3]] ) may push some primary producers beyond tolerance thresholds. Temperature–nutrient interactions can also alter the photosynthesis-to-respiration ratio in phytoplankton ( [[#Marañón--2018|Marañón et al., 2018]] ). Overall, rising metabolic rates due to warming will be restricted to primary producers in high-nutrient regions ( ''medium confidence'' ) ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Marañón--2018|Marañón et al., 2018]] ). For zooxanthellae-containing corals, nutrient supply from upwelling or from runoff can increase coral susceptibility to bleaching during warm-season MHWs ( [[#DeCarlo--2020|DeCarlo et al., 2020]] ; [[#Wooldridge--2020|Wooldridge, 2020]] ).

The effects of ocean acidification on growth, metabolic rates or elemental composition of primary producers changes with nutrient availability and light conditions ( ''high confidence'' ) ( [[#Gao--2019|Gao et al., 2019]] ; [[#Seifert--2020|Seifert et al., 2020]] ). While interactions with nutrients are often additive in phytoplankton, diatoms revealed predominantly synergistic interactions ( [[#Seifert--2020|Seifert et al., 2020]] ). Growth or photosynthesis of some diatom and HAB species, for instance, are stimulated by ocean acidification only if nutrients are replete ( [[#Hoppe--2013|Hoppe et al., 2013]] ; [[#Boyd--2015b|Boyd et al., 2015b]] ; [[#Eberlein--2016|Eberlein et al., 2016]] ; [[#Griffith--2019a|Griffith et al., 2019a]] ). Interactions with light are more complex because relative effects of ocean acidification are larger under limiting irradiances, while saturating light levels decrease beneficial or detrimental effects on these processes ( [[#Kranz--2010|Kranz et al., 2010]] ; [[#Garcia--2011|Garcia et al., 2011]] ; [[#Rokitta--2012|Rokitta and Rost, 2012]] ; [[#Heiden--2016|Heiden et al., 2016]] ). For the coccolithophore ''Emiliania huxleyi'' , for example, the impacts of ocean acidification are less detrimental under high light availability, which could partly explain why this species is moving poleward ( [[#Winter--2014|Winter et al., 2014]] ; [[#Kondrik--2017|Kondrik et al., 2017]] ; [[#Neukermans--2018|Neukermans et al., 2018]] ), although acidification is more pronounced in polar waters ( [[#3.2.3|Section 3.2.3.1]] ; Cross-Chapter Paper 6). Under excess light, however, the detrimental impacts of ocean acidification are amplified for many species ( ''high confidence'' ) ( [[#Gao--2012|Gao et al., 2012]] ; [[#Li--2013|Li and Campbell, 2013]] ; [[#Zhang--2015|Zhang et al., 2015]] ; [[#Kottmeier--2016|Kottmeier et al., 2016]] ; [[#Gafar--2019|Gafar et al., 2019]] ). Lowered photo-physiological capacity to cope with high-light stress and avoid photodamage ( [[#Gao--2012|Gao et al., 2012]] ; [[#Li--2013|Li and Campbell, 2013]] ; [[#Hoppe--2015|Hoppe et al., 2015]] ; [[#Kvernvik--2020|Kvernvik et al., 2020]] ) is also consistent with observations that dynamic light regimes can become more stressful under ocean acidification ( [[#Jin--2013|Jin et al., 2013]] ; [[#Hoppe--2015|Hoppe et al., 2015]] ). Given the expected mixed-layer shallowing in some regions ( [[#3.2.2.3|Section 3.2.2.3]] ), the exposure to overall higher mean irradiances could shift the effects of acidification from beneficial to detrimental for some primary producers, depending on species and organismal traits ( ''medium confidence'' ) ( [[#Gao--2019|Gao et al., 2019]] ; [[#Seifert--2020|Seifert et al., 2020]] ).

Studies investigating two drivers provide most of the information on the wide range of interactive effects of drivers on phytoplankton ( [[#Gao--2019|Gao et al., 2019]] ; [[#Seifert--2020|Seifert et al., 2020]] ), although climate change alters several oceanic drivers concurrently ( [[#3.2|Section 3.2]] ). The few experimental studies that have addressed three or more drivers ( [[#Xu--2014|Xu et al., 2014]] ; [[#Boyd--2015b|Boyd et al., 2015b]] ; [[#Brennan--2015|Brennan and Collins, 2015]] ; [[#Brennan--2017|Brennan et al., 2017]] ; [[#Hoppe--2018b|Hoppe et al., 2018b]] ; [[#Moreno-Marín--2018|Moreno-Marín et al., 2018]] ) indicate that one or two drivers generally dominate the cumulative outcome, with others playing a subordinate role ( ''medium confidence'' ). In these studies, temperature had a disproportionately large influence, while other drivers differed in importance, depending on the type of primary producer, ecosystem characteristics and selected driver values.

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==== 3.3.3.2 Effects of Multiple Drivers on Animals ====

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When changing CO 2 concentrations affect marine ectotherms, they typically combine additively or synergistically with warming ( ''medium confidence'' ) (e.g., [[#Lefevre--2016|Lefevre, 2016]] ; [[#Reddin--2020|Reddin et al., 2020]] ; [[#Sampaio--2021|Sampaio et al., 2021]] ), and their cumulative effects can lead to detrimental, neutral or beneficial effects ( ''high confidence'' ) (Figure 3.9a; [[#Bennett--2017|Bennett et al., 2017]] ; [[#Büscher--2017|Büscher et al., 2017]] ; [[#Dahlke--2017|Dahlke et al., 2017]] ; [[#Foo--2017|Foo and Byrne, 2017]] ; [[#Johnson--2017b|Johnson et al., 2017b]] ; [[#Cominassi--2019|Cominassi et al., 2019]] ). Higher ocean CO 2 influences the thermal tolerance of species adapted to extreme but stable habitats in tropical and polar regions, more than that of thermally tolerant generalists ( ''high confidence'' ) ( [[#Byrne--2013|Byrne et al., 2013]] ; [[#Schiffer--2014|Schiffer et al., 2014]] ; [[#Flynn--2015|Flynn et al., 2015]] ; [[#Kunz--2016|Kunz et al., 2016]] ; [[#Pörtner--2017|Pörtner et al., 2017]] ; [[#Kunz--2018|Kunz et al., 2018]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ; but see [[#Ern--2017|Ern et al., 2017]] ), especially in early life stages ( [[#Dahlke--2020a|Dahlke et al., 2020a]] ). In thermal generalists from temperate and subtropical species, warming and ocean acidification generally have detrimental effects on growth and survival (e.g., [[#Gao--2020|Gao et al., 2020]] ), but warming can also alleviate the detrimental effects of ocean acidification by increasing metabolic rate and/or growth ( [[#Garzke--2020|Garzke et al., 2020]] ), provided that other conditions (e.g., thermal niche, food availability) are beneficial. For example, larval growth and survival of Australasian snapper ( ''Pagrus auratus'' ) appear to benefit from combined acidification and warming (but see [[#Watson--2018|Watson et al., 2018]] ; [[#McMahon--2020|McMahon et al., 2020]] ), introducing major uncertainties to population modelling ( [[#3.3.4|Section 3.3.4]] ; [[#Parsons--2020|Parsons et al., 2020]] ).

As with ocean acidification, reduced oxygen availability further alters the influence of warming on metabolic rates ( ''high confidence'' ). Acidification and hypoxia can contribute to a decrease or shift in thermal tolerance, while the magnitude of this effect depends on the duration of exposure ( [[#Tripp-Valdez--2017|Tripp-Valdez et al., 2017]] ; [[#Cattano--2018|Cattano et al., 2018]] ; [[#Calderón-Liévanos--2019|Calderón-Liévanos et al., 2019]] ; [[#Schwieterman--2019|Schwieterman et al., 2019]] ). Warming and hypoxia are mostly positively correlated and tolerances to both phenomena are often linked after long-term acclimation (e.g., [[#Bouyoucos--2020|Bouyoucos et al., 2020]] ). Acute short-term heat shocks can impair hypoxia tolerance, for instance, in intertidal fish ( [[#McArley--2020|McArley et al., 2020]] ). This is relevant for shallow waters, specifically for MHWs ( [[#3.2.2.1|Section 3.2.2.1]] ; [[#Hobday--2016a|Hobday et al., 2016a]] ; [[#IPCC--2018|IPCC, 2018]] ; [[#Collins--2019a|Collins et al., 2019a]] ). Ocean acidification can increase hypoxia tolerance in some cases, possibly by downregulating activity ( [[#Faleiro--2015|Faleiro et al., 2015]] ) and/or changing blood oxygenation ( [[#Montgomery--2019|Montgomery et al., 2019]] ). Other studies, however, reported additive negative effects of acidification and warming on hypoxia tolerance ( [[#Schwieterman--2019|Schwieterman et al., 2019]] ; [[#Götze--2020|Götze et al., 2020]] ), in line with the oxygen- and capacity-limited thermal tolerance (OCLTT) hypothesis presented in AR5 ( [[#Pörtner--2014|Pörtner et al., 2014]] ): Warming causes increased metabolic rates and oxygen demand in ectotherms, which at some point exceed supply capacities (which also depend on environmental oxygen availability) and reduce aerobic scope. In consequence, expansion of OMZs and other regions where warming, hypoxia and acidification combine will further reduce habitat for many fish and invertebrates ( ''high confidence'' ) (Sections 3.4.3.2, 3.4.3.3).

Food availability modulates, and may be more influential than, other driver responses by affecting the energetic and nutritional status of animals ( [[#Cole--2016|Cole et al., 2016]] ; [[#Stiasny--2019|Stiasny et al., 2019]] ; [[#Cominassi--2020|Cominassi et al., 2020]] ). Laboratory studies conducted under an excess of food risk underestimating the ecological effects of climate-induced drivers, because increased feeding rates may help mitigate adverse effects ( [[#Nowicki--2012|Nowicki et al., 2012]] ; [[#Towle--2015|Towle et al., 2015]] ; [[#Cominassi--2020|Cominassi et al., 2020]] ). Lowered food availability from reduced open-ocean primary production (Sections 3.2.3.3, 3.4.4.2.1) will act as an additional driver, amplifying the detrimental effects of other drivers. However, warming and higher CO 2 availability may increase primary productivity in some coastal areas ( [[#3.4.4|Section 3.4.4.1]] ), ameliorating the adverse direct effects on animals (e.g., [[#Sswat--2018|Sswat et al., 2018]] ). Due to the few studies addressing food availability under multiple-driver scenarios ( [[#Thomsen--2013|Thomsen et al., 2013]] ; [[#Pistevos--2015|Pistevos et al., 2015]] ; [[#Towle--2015|Towle et al., 2015]] ; [[#Ramajo--2016|Ramajo et al., 2016]] ; [[#Brown--2018a|Brown et al., 2018a]] ; [[#Cominassi--2020|Cominassi et al., 2020]] ), there is ''medium confidence'' in its modulating effect on climate-induced driver responses.

Animal behaviour can be affected by ocean acidification, warming and hypoxia. While warming and hypoxia mostly induce avoidance behaviour, potentially leading to migration and habitat compression ( [[#3.4|Section 3.4]] ; [[#McCormick--2017|McCormick and Levin, 2017]] ; [[#Limburg--2020|Limburg et al., 2020]] ), the effects of acidification appear more complex. Some studies reported that acidification dominates behavioural effects ( [[#Schmidt--2017|Schmidt et al., 2017]] ), although outcomes vary with experimental design and duration of exposure ( ''low confidence, low agreement'' ) ( [[#Maximino--2010|Maximino and de Brito, 2010]] ; [[#Munday--2016|Munday et al., 2016]] ; [[#Laubenstein--2018|Laubenstein et al., 2018]] ; [[#Munday--2019|Munday et al., 2019]] ; [[#Sundin--2019|Sundin et al., 2019]] ; [[#Clark--2020|Clark et al., 2020]] ; [[#Munday--2020|Munday et al., 2020]] ; [[#Williamson--2021|Williamson et al., 2021]] ). Behaviour represents an integrated phenomenon that can be influenced both directly and indirectly by multiple drivers. For instance, increased ''p'' CO 2 can directly act on neuronal signalling pathways (e.g., Gamma-aminobutyric acid hypothesis; [[#Nilsson--2012|Nilsson et al., 2012]] ; [[#Thomas--2020|Thomas et al., 2020]] ) and influence learning ( [[#Chivers--2014|Chivers et al., 2014]] ), vision ( [[#Chung--2014|Chung et al., 2014]] ), and choice and escape behaviour ( [[#Watson--2014|Watson et al., 2014]] ; [[#Wang--2017b|Wang et al., 2017b]] ). There is further evidence that observed alterations in fish olfactory behaviour under ocean acidification may result from physiological and molecular changes of the olfactory epithelium, influencing olfactory receptors ( [[#Roggatz--2016|Roggatz et al., 2016]] ; [[#Porteus--2018|Porteus et al., 2018]] ; [[#Velez--2019|Velez et al., 2019]] ; [[#Mazurais--2020|Mazurais et al., 2020]] ). Temperature mainly drives metabolic processes and thus energetic requirements, which can indirectly influence behaviour, including increased risk-taking during feeding ( [[#Marangon--2020|Marangon et al., 2020]] ). Ocean warming also accelerates the biochemical reactions and metabolic processes that are primarily influenced by acidification. It is therefore difficult to generalise to what extent co-occurring ocean warming ameliorates or exacerbates effects of acidification on behaviour ( [[#Laubenstein--2019|Laubenstein et al., 2019]] ); outcomes depend upon species and life stage ( [[#Faleiro--2015|Faleiro et al., 2015]] ; [[#Chan--2016|Chan et al., 2016]] ; [[#Tills--2016|Tills et al., 2016]] ; [[#Wang--2018b|Wang et al., 2018b]] ; [[#Jarrold--2020|Jarrold et al., 2020]] ), interactions between species (e.g., [[#Paula--2019|Paula et al., 2019]] ) along with confounding factors including food availability and salinity ( ''medium confidence'' ) ( [[#Ferrari--2015|Ferrari et al., 2015]] ; [[#Pistevos--2015|Pistevos et al., 2015]] ; [[#Pimentel--2016|Pimentel et al., 2016]] ; [[#Pistevos--2017|Pistevos et al., 2017]] ; [[#Horwitz--2020|Horwitz et al., 2020]] ).

While hypoxia can dominate multiple-driver responses locally ( [[#Sampaio--2021|Sampaio et al., 2021]] ), warming is the fundamental physiological driver for most marine ectotherms, globally, as it directly affects their entire biochemistry and energy metabolism. Other influential drivers include ocean acidification, salinity ( ''high confidence'' ) ( [[#Lefevre--2016|Lefevre, 2016]] ; [[#Whiteley--2018|Whiteley et al., 2018]] ; [[#Reddin--2020|Reddin et al., 2020]] ) or food availability/quality ( ''medium confidence'' ) ( [[#Nagelkerken--2016|Nagelkerken and Munday, 2016]] ; [[#Gao--2020|Gao et al., 2020]] ). Fluctuating and decreasing salinity may aggravate the detrimental effects of warming and elevated CO 2 , because dilution with freshwater lowers acid–base buffering capacity, resulting in lower pH and calcium carbonate saturation state ( [[#Dickinson--2012|Dickinson et al., 2012]] ; [[#Shrivastava--2019|Shrivastava et al., 2019]] ; [[#Melzner--2020|Melzner et al., 2020]] ).

<div id="3.3.4" class="h2-container"></div>

<span id="acclimation-and-evolutionary-adaptation"></span>
=== 3.3.4 Acclimation and Evolutionary Adaptation ===

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Climate change is and will continue to be a major driver of natural selection, causing important changes in fitness-related (e.g., growth, reproduction, survival) and functional (e.g., body/cell size, morphology, physiology) traits, and in the genetic diversity of natural populations ( ''medium confidence'' ) ( [[#Pauls--2013|Pauls et al., 2013]] ; [[#Merilä--2014|Merilä and Hendry, 2014]] ). Climate-change impacts will continue to be exacerbated by interactions with non-climate drivers such as habitat fragmentation or loss, pollution or resource overexploitation, which limit the adaptive potential of populations to future conditions ( [[#Trathan--2015|Trathan et al., 2015]] ; [[#Gaitán-Espitia--2021|Gaitán-Espitia and Hobday, 2021]] ). However the ultimate responses to complex change are conditioned by the rate and magnitude of environmental change, organisms’ capacity for acclimation, the degree of local adaptation of natural populations and populations’ potential for adaptive evolution (Figure 3.11; [[#Pespeni--2013|Pespeni et al., 2013]] ; [[#Calosi--2017|Calosi et al., 2017]] ; [[#Vargas--2017|Vargas et al., 2017]] ). These controlling factors are mainly determined by local environmental conditions encountered by populations across their geographic distribution ( [[#Boyd--2016|Boyd et al., 2016]] ). In highly fluctuating environments (e.g., upwelling regions, coastal zones), multiple drivers can change and interact across temporal and spatial scales, generating geographic mosaics of tolerances and sensitivities to environmental and climate change in marine organisms ( ''medium confidence'' ) ( [[#Pespeni--2013|Pespeni et al., 2013]] ; [[#Boyd--2016|Boyd et al., 2016]] ; [[#Vargas--2017|Vargas et al., 2017]] ; [[#Li--2018a|Li et al., 2018a]] ). A further challenge for marine life lies in its ability to cope with extreme events such as MHWs (Cross-Chapter Box EXTREMES in Chapter 2). The interplay between the abruptness, intensity, duration, magnitude and reoccurrence of extreme events may alter or prevent evolutionary responses (e.g., adaptation) to climate change and the potential for acclimation to extreme conditions such as MHWs ( [[#Cheung--2020|Cheung and Frölicher, 2020]] ; [[#Coleman--2020a|Coleman et al., 2020a]] ; [[#Gurgel--2020|Gurgel et al., 2020]] ; [[#Gruber--2021|Gruber et al., 2021]] ).

<div id="_idContainer030" class="Figure"></div>

[[File:b3648869bb89095feb3c1b8a5647826d IPCC_AR6_WGII_Figure_3_011.png]]

'''Figure 3.11 |''' '''Micro-evolutionary dynamics in response to environmental change.''' Simplified conceptual framework shows two main eco-evolutionary trajectories for natural populations over time (vertical axis from top to bottom). If environmental stress is low, rapid responses (within a generation) through plastic phenotypic adjustments and selection (across generations) sustain fitness, enhancing maintenance of viable populations across generations. In contrast, if environmental stress is high, ongoing phenotypic plasticity and acclimation may be insufficient to buffer the negative effects, exacerbating the loss of fitness (change of colour to orange/yellow/red). Ultimately, very high stress conditions accelerate population decline, enhancing the risk of species extinction.

Some studies have documented higher phenotypic plasticity and tolerance to ocean warming and acidification in marine invertebrates ( [[#Dam--2013|Dam, 2013]] ; [[#Kelly--2013|Kelly et al., 2013]] ; [[#Pespeni--2013|Pespeni et al., 2013]] ; [[#Gaitán-Espitia--2017a|Gaitán-Espitia et al., 2017a]] ; [[#Vargas--2017|Vargas et al., 2017]] ; [[#Li--2018a|Li et al., 2018a]] ), seaweeds ( [[#Noisette--2013|Noisette et al., 2013]] ; [[#Padilla-Gamiño--2016|Padilla-Gamiño et al., 2016]] ; [[#Machado%20Monteiro--2019|Machado Monteiro et al., 2019]] ) and fish ( ''medium confidence'' ) ( [[#Sandoval-Castillo--2020|Sandoval-Castillo et al., 2020]] ; [[#Enbody--2021|Enbody et al., 2021]] ) living in coastal zones characterised by strong temporal fluctuations in temperature, pH, ''p'' CO 2 , light and nutrients. For these populations, strong directional selection with intense and highly fluctuating conditions may have favoured local adaptation and increased tolerance to environmental stress ( ''low confidence, low evidence'' ) ( [[#Hong--2015|Hong and Shurin, 2015]] ; [[#Gaitán-Espitia--2017b|Gaitán-Espitia et al., 2017b]] ; [[#Li--2018a|Li et al., 2018a]] ).

Other mechanisms acting within and across generations can influence selection and inter-population tolerances to environmental and climate-induced drivers. For instance, transgenerational effects and/or developmental acclimation, both ‘carry-over effects’ (where the early-life environment affects the expression of traits in later life stages or generations), can influence within- and cross-generational changes in the tolerances of marine organisms ( ''medium confidence'' ) to ocean warming ( [[#Balogh--2020|Balogh and Byrne, 2020]] ) and acidification ( [[#Parker--2012|Parker et al., 2012]] ). Over longer time scales, increasing tolerance to these drivers may be mediated by mechanisms such as transgenerational plasticity ( [[#Murray--2014|Murray et al., 2014]] ), leading to locally adapted genotypes as seen in bivalves ( [[#Thomsen--2017|Thomsen et al., 2017]] ), annelids ( [[#Rodríguez-Romero--2016|Rodríguez-Romero et al., 2016]] ; [[#Thibault--2020|Thibault et al., 2020]] ), corals ( [[#Putnam--2020|Putnam et al., 2020]] ) and coralline algae ( [[#Cornwall--2020|Cornwall et al., 2020]] ). However, transgenerational plasticity is species specific ( [[#Byrne--2020|Byrne et al., 2020]] ; [[#Thibault--2020|Thibault et al., 2020]] ) and, depending on the rate and magnitude of environmental change, it may either be insufficient for evolutionary rescue ( [[#Morgan--2020|Morgan et al., 2020]] ) or could induce maladaptive responses (i.e., reduced fitness) in marine organisms exposed to multiple drivers ( ''medium confidence, low evidence'' ) (Figure 3.11; [[#Griffith--2017|Griffith and Gobler, 2017]] ; [[#Parker--2017|Parker et al., 2017]] ; [[#Byrne--2020|Byrne et al., 2020]] ).

Acclimation to environmental pressures and climate change via phenotypic plasticity ( [[#3.3.3|Section 3.3.3]] ; [[#Collins--2020|Collins et al., 2020]] ) enables species to undergo niche shifts such that their present-day climatic niche is altered to incorporate new or shifted conditions ( [[#Fox--2019|Fox et al., 2019]] ). Although plasticity provides an adaptive mechanism, it is ''unlikely'' to provide a long-term solution for species undergoing sustained directional environmental change (e.g., global warming) ( ''medium confidence'' ) ( [[#Fox--2019|Fox et al., 2019]] ; [[#Gaitán-Espitia--2021|Gaitán-Espitia and Hobday, 2021]] ). Beyond the limits for plastic responses (Figure 3.9; [[#DeWitt--1998|DeWitt et al., 1998]] ; [[#Valladares--2007|Valladares et al., 2007]] ), genetic adjustments are required to persist in a changing world (Figure 3.11; [[#Fox--2019|Fox et al., 2019]] ). The ability of species and populations to undergo these adjustments (i.e., adaptive evolution) depends on extrinsic factors including the rate and magnitude of environmental change (important determinants of the strength and form of selection; [[#Hoffmann--2011|Hoffmann and Sgrò, 2011]] ; [[#Munday--2013|Munday et al., 2013]] ), along with intrinsic factors such as generation times and standing genetic variation ( [[#Mitchell-Olds--2007|Mitchell-Olds et al., 2007]] ; [[#Lohbeck--2012|Lohbeck et al., 2012]] ). Accurately assessing the degree of acclimation and/or adaptation across space and time is difficult and constrains studying adaptive evolution in natural populations. There is a major gap in climate-change biology related to the study of evolutionary responses in complex and long-lived multicellular organisms. Insights on organismal acclimation, adaptation and evolution rely on studies of small, short-lived marine organisms, such as phytoplankton, which divide rapidly and contain high genetic variation in large populations. ( [[#Schaum--2016|Schaum et al., 2016]] ; [[#Cavicchioli--2019|Cavicchioli et al., 2019]] ; [[#Collins--2020|Collins et al., 2020]] ).

Experimental evolution suggests that microbial populations can rapidly adapt (i.e., over 1–2 years) to environmental changes mimicking projected effects of climate change ( ''medium confidence'' ). Phytoplankton adaptive mechanisms include intraspecific strain sorting and genetic changes ( [[#Bach--2018|Bach et al., 2018]] ; [[#Hoppe--2018b|Hoppe et al., 2018b]] ; [[#Wolf--2019|Wolf et al., 2019]] ). The evolutionary responses of microbes are conditioned by the number and characteristics of interacting drivers ( ''low confidence'' ) ( [[#Brennan--2017|Brennan et al., 2017]] ). For example, in a high-salinity adapted strain of the phytoplankton ''Chlamydomonas reinhardtii'' , the selection intensity and the adaptation rate increased with the number of environmental drivers, accelerating the adaptive evolutionary response ( [[#Brennan--2017|Brennan et al., 2017]] ). For this and other phytoplankton species, a few dominant drivers explain most of the phenotypic and evolutionary changes observed ( [[#Boyd--2015a|Boyd et al., 2015a]] ; [[#Brennan--2015|Brennan and Collins, 2015]] ; [[#Brennan--2017|Brennan et al., 2017]] ).

Adaptation can be impeded, delayed or constrained in eukaryotic microbial populations as a result of reduced genetic diversity and/or the presence of functional and evolutionary trade-offs ( [[#Aranguren-Gassis--2019|Aranguren-Gassis et al., 2019]] ; [[#Lindberg--2020|Lindberg and Collins, 2020]] ; [[#Walworth--2020|Walworth et al., 2020]] ). In the marine diatom ''Chaetoceros simplex'' , a functional trade-off between high-temperature tolerance and increased nitrogen requirements underlies inhibited thermal adaptation under nitrogen-limited conditions ( ''low confidence'' ) ( [[#Aranguren-Gassis--2019|Aranguren-Gassis et al., 2019]] ). When selection is strong due to unfavourable environmental conditions, microbial populations can encounter functional and evolutionary trade-offs evidenced by reducing growth rates while increasing tolerance and metabolism of reactive oxygen species ( [[#Lindberg--2020|Lindberg and Collins, 2020]] ). Other trade-offs can be observed in offspring quality and number ( [[#Lindberg--2020|Lindberg and Collins, 2020]] ). These findings contribute towards a mechanistic framework describing the range of evolutionary strategies in response to multiple drivers ( [[#Collins--2020|Collins et al., 2020]] ), but other hazards, such as extreme events (e.g., MHWs), still need to be included because their characteristics may alter the potential for adaptation of species and populations to climate change ( [[#Gruber--2021|Gruber et al., 2021]] ).

<div id="3.3.5" class="h2-container"></div>

<span id="ecological-response-to-multiple-drivers"></span>
=== 3.3.5 Ecological Response to Multiple Drivers ===

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Assessing ecological responses to multiple climate-induced drivers requires a combination of approaches, including laboratory- and field-based experiments, field observations (e.g., natural gradients, climate analogues), study of paleo-analogues and the development of mechanistic and empirical models ( [[#Clapham--2019|Clapham, 2019]] ; [[#Gissi--2021|Gissi et al., 2021]] ). Experimental studies of food-web responses are often limited to an individual driver, although recent manipulations have used a matrix of &gt;1000-l mesocosms to explore ecological responses to both warming and acidification (see Box 3.1; [[#Nagelkerken--2020|Nagelkerken et al., 2020]] ). Hence, complementary approaches are needed to indirectly explore the mechanisms underlying ecosystem responses to global climate change ( [[#Parmesan--2013|Parmesan et al., 2013]] ). Observations from time series longer than modes of natural variability (i.e., decades) are essential for revealing and attributing ecological responses to climate change (e.g., [[#3.4|Section 3.4]] ; [[#Barton--2015b|Barton et al., 2015b]] ; [[#Brun--2019|Brun et al., 2019]] ). Also, paleorecords provide insights into the influence of multiple drivers on marine biota (Cross-Chapter Box PALEO in Chapter 1; [[#Reddin--2020|Reddin et al., 2020]] ). Specifically, associations between vulnerabilities and traits of marine ectotherms in laboratory experiments correspond with organismal responses to ancient hyperthermal events ( ''medium confidence'' ) ( [[#Reddin--2020|Reddin et al., 2020]] ). This corroboration suggests that responses to multiple drivers inferred from the fossil record can help provide insights into the future status of functional groups, and hence food webs, under rapid climate change.

Multi-species and integrated end-to-end ecosystem models are powerful tools to explore and project outcomes to the often-interacting cumulative effects of climate change and other anthropogenic drivers ( [[#3.1|Section 3.1]] ; [[#Kaplan--2016|Kaplan and Marshall, 2016]] ; [[#Koenigstein--2016|Koenigstein et al., 2016]] ; [[#Peck--2018|Peck and Pinnegar, 2018]] ; [[#Tittensor--2018|Tittensor et al., 2018]] ; [[#Gissi--2021|Gissi et al., 2021]] ). These models can integrate some aspects of the knowledge accrued from manipulation experiments, paleo- and contemporary observations, help test the relative importance of specific drivers and driver combinations, and identify synergistic or antagonistic responses ( [[#Koenigstein--2016|Koenigstein et al., 2016]] ; [[#Payne--2016|Payne et al., 2016]] ; [[#Skogen--2018|Skogen et al., 2018]] ; [[#Tittensor--2018|Tittensor et al., 2018]] ). As these models are associated with wide-ranging uncertainties (SM3.2.2; [[#Payne--2016|Payne et al., 2016]] ; [[#Trolle--2019|Trolle et al., 2019]] ; [[#Heneghan--2021|Heneghan et al., 2021]] ), they cannot be expected to accurately project the trajectories of complex marine ecosystems under climate change; hence, they are most useful for assessing overall trends and in particular for providing a plausible envelope of trajectories across a range of assumptions ( [[#Fulton--2018|Fulton et al., 2018]] ; [[#Peck--2018|]] [[#Peck--2018|Peck et al., 2018]] ; [[#Tittensor--2018|Tittensor et al., 2018]] ). On a global scale, ecosystem models project a −5.7 ± 4.1% ( ''very likely range'' ) to −15.5 ± 8.5% decline in marine animal biomass with warming under SSP1-2.6 and SSP5-8.5, respectively, by 2080–2099 relative to 1995–2014, albeit with significant regional variation in both trends and uncertainties ( ''medium confidence'' ) ( [[#3.4.3.4|Section 3.4.3.4]] ; [[#Tittensor--2021|Tittensor et al., 2021]] ). Biological interactions may exacerbate or buffer the projected impacts. For instance, trophic amplification (strengthening of responses to climate-induced drivers at higher trophic levels) may result from combined direct and indirect food-web-mediated effects ( ''medium confidence'' ) ( [[#3.4.3.4|Section 3.4.3.4]] ; [[#Lotze--2019|Lotze et al., 2019]] ). Alternatively, compensatory species interactions can dampen strong impacts on species from ocean acidification, resulting in weaker responses at functional-group or community level than at species level ( ''medium confidence'' ) ( [[#Marshall--2017|Marshall et al., 2017]] ; [[#Hoppe--2018b|Hoppe et al., 2018b]] ; [[#Olsen--2018|Olsen et al., 2018]] ; [[#Gissi--2021|Gissi et al., 2021]] ). Globally, the projected reduction of biomass due to climate-induced drivers is relatively unaffected by fishing pressure, indicating additive responses of fisheries and climate change ( ''low confidence'' ) ( [[#Lotze--2019|Lotze et al., 2019]] ). Regionally, projected interactions of climate-induced drivers, fisheries and other regional non-climate drivers can be both synergistic and antagonistic, varying across regions, functional groups and species, and can cause nonlinear dynamics with counterintuitive outcomes, underlining the importance of adaptations and associated trade-offs ( ''high confidence'' ) (Sections 3.5.3, 3.6.3.1.2, 4.5, 4.6; [[#Weijerman--2015|Weijerman et al., 2015]] ; [[#Fulton--2018|Fulton et al., 2018]] ; [[#Hansen--2019|Hansen et al., 2019]] ; [[#Trolle--2019|Trolle et al., 2019]] ; [[#Zeng--2019|Zeng et al., 2019]] ; [[#Holsman--2020|Holsman et al., 2020]] ; [[#Pethybridge--2020|Pethybridge et al., 2020]] ; [[#Gissi--2021|Gissi et al., 2021]] ).

Given the limitations of individual ecological models discussed above, model intercomparisons, such as the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP; [[#Tittensor--2018|Tittensor et al., 2018]] ) show promise in increasing the robustness of projected ecological outcomes ( [[#Tittensor--2018|Tittensor et al., 2018]] ). Model ensembles include a greater number of relevant processes and functional groups than any single model and thus capture a wider range of plausible responses. Among the global Fish-MIP models, there is ''high'' (temperate and tropical areas) to ''medium agreement'' (coastal and polar regions) on the direction of change, but ''medium'' (temperate and tropical regions) to ''low agreement'' (coastal and polar regions) on magnitude of change ( [[#Lotze--2019|Lotze et al., 2019]] ; [[#Heneghan--2021|Heneghan et al., 2021]] ). Although model outputs are validated relative to observations to assess model skills ( [[#Payne--2016|Payne et al., 2016]] ; [[#Tittensor--2018|Tittensor et al., 2018]] ), the Fish-MIP models under-represent some sources of uncertainty, as they often do not include parameter uncertainties and do not usually include impacts of ocean acidification, oxygen loss or evolutionary responses because there remains high uncertainty regarding the influences of these processes across functional groups. Ensemble model investigations like Fish-MIP have also identified gaps in our mechanistic understanding of ecosystems and their responses to anthropogenic forcing, leading to model improvement and more rigorous benchmarking. These investigations could inspire future targeted observational and experimental research to test the validity of model assumptions ( [[#Payne--2016|Payne et al., 2016]] ; [[#Lotze--2019|Lotze et al., 2019]] ; [[#Heneghan--2021|Heneghan et al., 2021]] ). The state of the art in such experimental research is presented in Box 3.1.

<div id="box-3.1" class="h2-container box-container"></div>

'''Box 3.1 | Challenges for Multiple-Driver Research in Ecology and Evolution'''
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The majority of the examples in [[#3.3|Section 3.3]] are from studies mimicking projected conditions in the year 2100 that report the responses of an individual species or strain to multiple drivers. This powerful generic experimental approach has largely been restricted to single species because it is logistically complex to conduct experiments that straddle multiple trophic levels, and that also include more than two drivers (see Figure Box 3.1.1b); the need for multiple replicates, drivers and treatment levels greatly increase the work required ( [[#Parmesan--2013|Parmesan et al., 2013]] ; [[#Boyd--2018|Boyd et al., 2018]] ). It is challenging to apply this experimental approach to communities or ecosystems (see Figure Box 3.1.1). To date, most research on community or ecosystem response to climate-induced drivers has been in large-volume (&gt;10,000 l) mesocosms ( [[#Riebesell--2014|Riebesell and Gattuso, 2014]] ), or at natural analogues such as CO 2 seeps, in which only one driver (ocean acidification) is altered (see (4) in Figure Box 3.1.1). Only very recently have two drivers been incorporated into climate-change manipulation studies examining responses of primary producers to secondary consumers (see (5) in Figure Box 3.1.1a; [[#Nagelkerken--2020|Nagelkerken et al., 2020]] ). Therefore, ‘natural experiments’ from the geological past ( [[#Reddin--2020|Reddin et al., 2020]] ) provide insights into how food webs and their constituents respond to complex change involving multiple drivers. Contemporary observations are occasionally long enough (&gt;50 years) to capture community responses to complex climate change. For example, [[#Brun--2019|Brun et al. (2019)]] reported a shift in zooplankton community structure in the North Atlantic (1960–2014), with major biogeochemical ramifications.

Conducting sufficiently long manipulation experiments to study the effect of adaptation on organisms is equally difficult (see Figure Box 3.1.1b), with much research restricted to multi-year studies of the microevolution of fast-growing (more than one division per day) phytoplankton species responding to single drivers ( [[#Lohbeck--2012|Lohbeck et al., 2012]] ; [[#Schaum--2016|Schaum et al., 2016]] ). In a few experimental evolution studies (see (7) in Figure Box 3.1.1a; [[#Brennan--2017|Brennan et al., 2017]] ), multiple drivers have been used, but none have used communities or ecosystems (see Figure Box 3.1.1b). Nevertheless, the fossil record provides ''limited evidence'' of adaptations to less rapid (relative to present day) climate change ( [[#Jackson--2018|Jackson et al., 2018]] ). Despite the need to explore ecological or biogeochemical responses to projected future ocean conditions, logistical challenges require that assessments of climate-change impacts at scales larger than mesocosms use large-scale, long-term ''in situ'' observational studies (as documented in [[#3.4|Section 3.4]] ).

[[File:8eafd3902628b260b0e8dd3410612e60 IPCC_AR6_WGII_Figure_3_Box_3_1_1.png]]

'''Figure Box 3.1.1 |''' '''Knowledge gaps between current scientific understanding and that needed to inform policy.''' The conceptual space relating driver number, (Driver axis), ecological organisation (Space axis) and evolutionary acclimation state (Time axis), modified from [[#Riebesell--2014|Riebesell and Gattuso (2014)]] .

'''(a)''' Spheres indicate suites of studies that illustrate the progress of research, including multiple drivers: (1) one species and one driver ( [[#Hutchins--2013|Hutchins et al., 2013]] ); (2) one species and multiple drivers (five; [[#Boyd--2015a|Boyd et al., 2015a]] ). Ecology: (1) one driver, one species; (3) one driver, planktonic community ( [[#Moustaka-Gouni--2016|Moustaka-Gouni et al., 2016]] ); (4) one driver (high-CO 2 seep) and (benthic) ecosystem ( [[#Fabricius--2014|Fabricius et al., 2014]] ); (5) two drivers and nearshore ecosystem ( [[#Nagelkerken--2020|Nagelkerken et al., 2020]] ). Evolution: (1) acclimated organism and one driver; (6) adapted organisms and one driver ( [[#Listmann--2016|Listmann et al., 2016]] ); (7) adapted organism and multiple drivers ( [[#Brennan--2017|Brennan et al., 2017]] ).

'''(b)''' Trends in research trajectories since 2000 from a survey of 171 studies ( [[#Boyd--2018|Boyd et al., 2018]] ). Note the dominance of multiple-driver experiments at the species level (lower left cluster); the focus on acclimation (red triangle) rather than adaptation (blue dot); and the focus of investigation on three or fewer drivers. (Redrawn from [[#Boyd--2018|Boyd et al., 2018]] ).

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