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

=== 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]] ).

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[[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]] ).

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