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

==== 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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