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

=== 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.

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'''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]] ).

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'''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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