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

=== 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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