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

=== 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]] ).

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