Context-dependent trait covariances: how plasticity shapes behavioral syndromes
TL;DR: It is argued that separation from plasticity analyses represents a missed opportunity to integrate behavioral syndromes concepts, and through observations of multiple traits while manipulating environmental conditions, can quantify how the environment shapes behavioral correlations, thus quantifying how phenotypes are differentially constrained or integrated under different environmental conditions.
Abstract: The study of behavioral syndromes aims to understand among-individual correlations of behavior, yielding insights into the ecological factors and proximate constraints that shape behavior. In parallel, interest has been growing in behavioral plasticity, with results commonly showing that animals vary in their behavioral response to environmental change. These two phenomena are inextricably linked-behavioral syndromes describe cross-trait or cross-context correlations, while variation in behavioral plasticity describes variation in response to changing context. However, they are often discussed separately, with plasticity analyses typically considering a single trait (univariate) across environments, while behavioral trait correlations are studied as multiple traits (multivariate) under one environmental context. Here, we argue that such separation represents a missed opportunity to integrate these concepts. Through observations of multiple traits while manipulating environmental conditions, we can quantify how the environment shapes behavioral correlations, thus quantifying how phenotypes are differentially constrained or integrated under different environmental conditions. Two analytical options exist which enable us to evaluate the context dependence of behavioral syndromes-multivariate reaction norms and character state models. These models are largely two sides of the same coin, but through careful interpretation we can use either to shift our focus to test how the contextual environment shapes trait covariances.
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TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
9,847 citations
TL;DR: The authors identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies.
Abstract: Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.
57 citations
TL;DR: The authors identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies.
Abstract: Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.
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TL;DR: Gibert et al. as mentioned in this paper proposed an integrative framework based on how key environmental components influence the "building blocks" of ecoevolutionary responses to examine when plasticity aids or hinders adaptive evolution.
Abstract: Global biodiversity is jeopardised by unprecedented environmental change, the hallmark of the Anthropocene. To estimate the extinction risks of species, understanding how individuals and populations respond to changing environments is crucial.Adaptive evolution and phenotypic plasticity are two key mechanisms by which natural populations avoid extinction in the face of environmental change. However, the relative roles and interplay between the two are still unresolved.Whether plasticity hinders (H1) or facilitates (H2) adaptive evolution has been ardently researched, but without cross-study standardization of how changing environments impact whether (H1) or (H2) is more likely over time.We propose an integrative framework based on how key environmental components influence the ‘building blocks’ of ecoevolutionary responses to examine when plasticity aids or hinders adaptive evolution. We synthesise key microevolutionary and ecological processes regarding how natural populations respond to environmental change.Studies may benefit from this framework to deepen our understanding of how plasticity influences adaptive evolution by reframing H1 and H2 in the context of environmental change, and will thus increase our ability to forecast extinction risks in the Anthropocene. To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components – rate of change, variance, and temporal autocorrelation – affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world. To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components – rate of change, variance, and temporal autocorrelation – affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world. Understanding, quantifying, and predicting the ability of organisms to adapt to changing environments is at the core of ecoevolutionary research [1.Gibert P. et al.Phenotypic plasticity, global change, and the speed of adaptive evolution.Curr. Opin. Insect Sci. 2019; 35: 34-40Crossref PubMed Scopus (29) Google Scholar, 2.Vinton A.C. Vasseur D.A. Evolutionary tracking is determined by differential selection on demographic rates and density dependence.Ecol. Evol. 2020; 10: 5725-5736Crossref PubMed Scopus (2) Google Scholar, 3.Bell G. Gonzalez A. Evolutionary rescue can prevent extinction following environmental change.Ecol. Lett. 2009; 12: 942-948Crossref PubMed Scopus (351) Google Scholar, 4.Merilä J. Hendry A.P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence.Evol. Appl. 2014; 7: 1-14Crossref PubMed Scopus (704) Google Scholar]. In the face of unprecedented environmental change (see Glossary), natural populations, especially those with limited mobility/dispersal, can avoid extinction via phenotypic plasticity and/or adaptive evolution [4.Merilä J. Hendry A.P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence.Evol. Appl. 2014; 7: 1-14Crossref PubMed Scopus (704) Google Scholar]. However, our understanding of the interplay between adaptive evolution and plasticity in changing environments remains limited [1.Gibert P. et al.Phenotypic plasticity, global change, and the speed of adaptive evolution.Curr. Opin. Insect Sci. 2019; 35: 34-40Crossref PubMed Scopus (29) Google Scholar,5.Ancel L.W. Undermining the Baldwin expediting effect: does phenotypic plasticity accelerate evolution?.Theor. Popul. Biol. 2000; 58: 307-319Crossref PubMed Scopus (105) Google Scholar, 6.Ghalambor C.K. et al.Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature.Nature. 2015; 525: 372-375Crossref PubMed Scopus (338) Google Scholar, 7.van Gestel J. Weissing F.J. Is plasticity caused by single genes?.Nature. 2018; 555: E19-E20Crossref PubMed Scopus (17) Google Scholar, 8.Schmid M. et al.A tradeoff between robustness to environmental fluctuations and speed of evolution.Am. Nat. 2022; 200: E16-E35Crossref PubMed Scopus (1) Google Scholar]. This limitation is not trivial, for plasticity can itself evolve [9.Sommer R.J. Phenotypic plasticity: from theory and genetics to current and future challenges.Genetics. 2020; 215: 1-13Crossref PubMed Scopus (53) Google Scholar], be adaptive, or nonadaptive [10.Ghalambor C.K. et al.Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments.Funct. Ecol. 2007; 21: 394-407Crossref Scopus (1721) Google Scholar], and have differing effects on adaptive evolution [11.Levis N.A. Pfennig D.W. Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation.Proc. R. Soc. B Biol. Sci. 2019; 28620182754PubMed Google Scholar,12.Ashander J. et al.Predicting evolutionary rescue via evolving plasticity in stochastic environments.Proc. R. Soc. B Biol. Sci. 2016; 28320161690PubMed Google Scholar]. For decades, researchers have theorised whether plasticity facilitates or hinders adaptive evolution [9.Sommer R.J. Phenotypic plasticity: from theory and genetics to current and future challenges.Genetics. 2020; 215: 1-13Crossref PubMed Scopus (53) Google Scholar,13.Stearns S.C. The evolutionary significance of phenotypic plasticity.BioScience. 1989; 39: 436-445Crossref Google Scholar]; the evidence is contradictory, and general patterns have yet to emerge [5.Ancel L.W. Undermining the Baldwin expediting effect: does phenotypic plasticity accelerate evolution?.Theor. Popul. Biol. 2000; 58: 307-319Crossref PubMed Scopus (105) Google Scholar,10.Ghalambor C.K. et al.Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments.Funct. Ecol. 2007; 21: 394-407Crossref Scopus (1721) Google Scholar,11.Levis N.A. Pfennig D.W. Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation.Proc. R. Soc. B Biol. Sci. 2019; 28620182754PubMed Google Scholar,14.Gunderson A.R. Stillman J.H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming.Proc. R. Soc. B Biol. Sci. 2015; 28220150401PubMed Google Scholar,15.Johansson D. et al.Reciprocal transplants support a plasticity-first scenario during colonisation of a large hyposaline basin by a marine macro alga.BMC Ecol. 2017; 17: 14Crossref PubMed Scopus (13) Google Scholar]. The primary conflicting hypotheses for whether plasticity facilitates or hinders adaptive evolution are:•(H1) Plasticity weakens directional selection by masking genotypic variation (e.g., Bogert effect [16.Bogert C.M. Thermoregulation in reptiles, a factor in evolution.Evolution. 1949; 3: 195-211Crossref PubMed Scopus (317) Google Scholar]), thus slowing the rate of genetic change [5.Ancel L.W. Undermining the Baldwin expediting effect: does phenotypic plasticity accelerate evolution?.Theor. Popul. Biol. 2000; 58: 307-319Crossref PubMed Scopus (105) Google Scholar,17.Wright S. Evolution in mendelian populations.Bull. Math. Biol. 1990; 52: 241-295Crossref PubMed Scopus (69) Google Scholar, 18.Anderson R.W. Learning and evolution: a quantitative genetics approach.J. Theor. Biol. 1995; 175: 89-101Crossref PubMed Scopus (64) Google Scholar, 19.Huey R.B. et al.Behavioral drive versus behavioral inertia in evolution: a null model approach.Am. Nat. 2003; 161: 357-366Crossref PubMed Scopus (516) Google Scholar].•(H2) Plasticity facilitates evolution by allowing the population to persist under environmental change long enough for genetic change to occur [20.Baldwin J.M. A new factor in evolution.Am. Nat. 1896; 30: 441-451Crossref Google Scholar, 21.Gibert J.-M. The flexible stem hypothesis: evidence from genetic data.Dev. Genes Evol. 2017; 227: 297-307Crossref PubMed Scopus (23) Google Scholar, 22.Levis N.A. Pfennig D.W. Evaluating ‘plasticity-first’ evolution in nature: key criteria and empirical approaches.Trends Ecol. Evol. 2016; 31: 563-574Abstract Full Text Full Text PDF PubMed Scopus (243) Google Scholar] (e.g., plasticity-first hypothesis [22.Levis N.A. Pfennig D.W. Evaluating ‘plasticity-first’ evolution in nature: key criteria and empirical approaches.Trends Ecol. Evol. 2016; 31: 563-574Abstract Full Text Full Text PDF PubMed Scopus (243) Google Scholar] or Baldwin effect [20.Baldwin J.M. A new factor in evolution.Am. Nat. 1896; 30: 441-451Crossref Google Scholar]). This debate remains unresolved. Despite cases where theoretical predictions agree with empirical findings [5.Ancel L.W. Undermining the Baldwin expediting effect: does phenotypic plasticity accelerate evolution?.Theor. Popul. Biol. 2000; 58: 307-319Crossref PubMed Scopus (105) Google Scholar,10.Ghalambor C.K. et al.Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments.Funct. Ecol. 2007; 21: 394-407Crossref Scopus (1721) Google Scholar,11.Levis N.A. Pfennig D.W. Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation.Proc. R. Soc. B Biol. Sci. 2019; 28620182754PubMed Google Scholar,14.Gunderson A.R. Stillman J.H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming.Proc. R. Soc. B Biol. Sci. 2015; 28220150401PubMed Google Scholar,15.Johansson D. et al.Reciprocal transplants support a plasticity-first scenario during colonisation of a large hyposaline basin by a marine macro alga.BMC Ecol. 2017; 17: 14Crossref PubMed Scopus (13) Google Scholar,23.Villellas J. et al.Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant.Ecol. Lett. 2021; 24: 2378-2393Crossref PubMed Scopus (7) Google Scholar], we lack a general framework to establish the context-dependence of plasticity's impact alongside climate change. Here, we introduce an environmentally explicit framework that allows for the development and testing of hypotheses regarding when and how plasticity interacts with evolution. We highlight three environmental change components: rate of mean change, environmental variability, and temporal autocorrelation. These environmental components distinctly impact evolutionary and ecological processes as mechanisms of population response [24.Vasseur D.A. et al.Increased temperature variation poses a greater risk to species than climate warming.Proc. R. Soc. B Biol. Sci. 2014; 28120132612Crossref Scopus (558) Google Scholar, 25.Ruokolainen L. et al.Ecological and evolutionary dynamics under coloured environmental variation.Trends Ecol. Evol. 2009; 24: 555-563Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar, 26.Pinek L. et al.Rate of environmental change across scales in ecology.Biol. Rev. 2020; 95: 1798-1811Crossref PubMed Scopus (15) Google Scholar] and are widely documented consequences of climate change [27.Meehl G.A. Tebaldi C. More intense, more frequent, and longer lasting heat waves in the 21st century.Science. 2004; 305: 994-997Crossref PubMed Scopus (2659) Google Scholar, 28.Rummukainen M. Changes in climate and weather extremes in the 21st century.WIREs Clim. Change. 2012; 3: 115-129Crossref Scopus (108) Google Scholar, 29.Di Cecco G.J. Gouhier T.C. Increased spatial and temporal autocorrelation of temperature under climate change.Sci. Rep. 2018; 8: 14850Crossref PubMed Scopus (40) Google Scholar]. Consequently, there is an urgent need to integrate the effects of environmental change in a generalizable way. This will allow ecologists and evolutionary biologists to better contextualise, mechanistically understand, predict, and compare their findings. Moving optimum theory links environmental change to the resulting evolutionary responses according to changes in phenotypic traits. When a population is confronted with an environment that changes directionally, there is a critical rate of change that must be matched by change in the mean phenotype of the population. That is, the mean phenotype must remain close to the theoretical phenotypic optimum. In this context, a phenotypic lag between the mean phenotype and the optimum phenotype may emerge which, if too large, increases extinction risk [30.Kopp M. Matuszewski S. Rapid evolution of quantitative traits: theoretical perspectives.Evol. Appl. 2014; 7: 169-191Crossref PubMed Scopus (133) Google Scholar,31.Chevin L.-M. et al.Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field.Funct. Ecol. 2013; 27: 967-979Crossref Scopus (121) Google Scholar]. Evolutionary processes (e.g., selection, genetic variation) and ecological processes (e.g., life history, within-generation plasticity, and population dynamics) together influence how far a population can lag and persist. Thus, the contribution of plasticity to population persistence and adaptation is largely determined by this phenotypic lag. We argue that H1 and H2 are not mutually exclusive. Rather, plasticity may facilitate or hinder adaptive evolution depending on the properties of environmental change. To assess the impact of plasticity on adaptive evolution, we specify the links among the type of environmental change, plasticity, and adaptive evolution. Thus, we utilise theoretical and experimental work to:(i)Assess how three key components of environmental change (rate of mean change, variability, and temporal autocorrelation) each alter the evolutionary and ecological mechanisms behind phenotypic tracking of a moving optimum.(ii)Introduce a unified framework of testable hypotheses detailing how those three components of environmental change can influence the relative benefit of plasticity to adaptive evolution. To understand the role of plasticity in adaptive evolution, one needs to consider how different environmental components impact the mechanisms of evolutionary tracking in the absence of plasticity. For adaptive evolution to occur, natural selection must act on variation in a heritable trait. The genetic architecture of a trait under selection will, in part, determine the potential for adaptive evolution and ecoevolutionary dynamics [32.Yamamichi M. How does genetic architecture affect eco-evolutionary dynamics? A theoretical perspective.Philos. Trans. R. Soc. B Biol. Sci. 2022; 37720200504Crossref PubMed Scopus (2) Google Scholar]. Most traits that mediate population dynamics are determined by multiple genes, each of which typically has a small effect (quantitative traits) [32.Yamamichi M. How does genetic architecture affect eco-evolutionary dynamics? A theoretical perspective.Philos. Trans. R. Soc. B Biol. Sci. 2022; 37720200504Crossref PubMed Scopus (2) Google Scholar,33.Hill W.G. Understanding and using quantitative genetic variation.Philos. Trans. R. Soc. B Biol. Sci. 2010; 365: 73-85Crossref PubMed Scopus (193) Google Scholar]. One way to assess whether or not a quantitative trait may evolve is via the breeder’s equation, which equates the change in a trait to the selection differential times its narrow-sense heritability. Heritability is a function of both genetic variation [34.Young A.I. Solving the missing heritability problem.PLoS Genet. 2019; 15e1008222Crossref Scopus (102) Google Scholar,35.Zuk O. et al.The mystery of missing heritability: genetic interactions create phantom heritability.Proc. Natl. Acad. Sci. 2012; 109: 1193-1198Crossref PubMed Scopus (1036) Google Scholar] and the environment in which that variation is expressed [36.Boyer S. et al.Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment.PLoS Genet. 2021; 17e1009314Crossref Scopus (9) Google Scholar]. The contributions of environmental change/variation to phenotypic and genetic variation are often relegated to an error term that absorbs unmeasured uncertainties in quantitative genetic models ([37.Ørsted M. et al.Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait.Heredity. 2019; 122: 315-325Crossref PubMed Scopus (22) Google Scholar], but see [38.Wood C.W. Brodie III, E.D. Evolutionary response when selection and genetic variation covary across environments.Ecol. Lett. 2016; 19: 1189-1200Crossref PubMed Scopus (37) Google Scholar]). In the following sections, we discuss literature that addresses how rate of mean change, variability, and temporal autocorrelation in the environment each influence heritability, genetic variation, and selection. By considering the environmental impacts on these evolutionary mechanisms, we aim to understand the ability of genetic change to track a fitness optimum in changing environments. This understanding informs the importance of plastic responses in decreasing phenotypic lag. When the rate of environmental change is too slow, selection is weak and can be ineffective in part due to a small lag load [39.Burger R. Lynch M. Evolution and extinction in a changing environment: a quantitative-genetic analysis.Evolution. 1995; 49: 151-163Crossref PubMed Google Scholar,40.Guzella T.S. et al.Slower environmental change hinders adaptation from standing genetic variation.PLoS Genet. 2018; 14e1007731Crossref PubMed Scopus (9) Google Scholar]. As the rate of environmental change increases, selection strengthens, and the population can track the moving optimum with a consistent phenotypic lag [41.Lynch M. Lande R. Evolution and extinction in response to environmental change.in: Kareiva P.M. Biotic Interactions and Global Change. Sinauer Associates, 1993: 234-250Google Scholar]. In this range of environmental change, additive genetic variance and heritability can also increase [39.Burger R. Lynch M. Evolution and extinction in a changing environment: a quantitative-genetic analysis.Evolution. 1995; 49: 151-163Crossref PubMed Google Scholar,42.Bürger R. Evolution of genetic variability and the advantage of sex and recombination in changing environments.Genetics. 1999; 153: 1055-1069Crossref PubMed Google Scholar]. In this case, up to a certain intermediate rate of environmental change, genetic variation and evolutionary potential may be expected to increase. This can occur simply due to higher additive genetic variance and thus an increase in standing variation available to selection. However, phenotypic lag can become too large for the rate of selection to follow if the environment, and thus the trait optimum, changes too quickly [39.Burger R. Lynch M. Evolution and extinction in a changing environment: a quantitative-genetic analysis.Evolution. 1995; 49: 151-163Crossref PubMed Google Scholar,41.Lynch M. Lande R. Evolution and extinction in response to environmental change.in: Kareiva P.M. Biotic Interactions and Global Change. Sinauer Associates, 1993: 234-250Google Scholar,43.Gomulkiewicz R. Houle D. Demographic and genetic constraints on evolution.Am. Nat. 2009; 174: E218-E229Crossref PubMed Scopus (130) Google Scholar]. Here, the phenotypic lag increases, which can lead to decreased fitness and eventually local extinction [44.Lande R. Shannon S. The role of genetic variation in adaptation and population persistence in a changing environment.Evolution. 1996; 50: 434-437Crossref PubMed Google Scholar]. As such, the mean time to extinction in a natural population decreases as the rate of environmental change increases beyond the optimal rate [39.Burger R. Lynch M. Evolution and extinction in a changing environment: a quantitative-genetic analysis.Evolution. 1995; 49: 151-163Crossref PubMed Google Scholar]. Thus, the rate of environmental change in evolutionary experiments and theory is key to assess the potential benefit of plasticity on adaptive evolution. Moderate environmental variation can optimise selection, and ultimately evolutionary tracking [45.Abdul-Rahman F. et al.Fluctuating environments maintain genetic diversity through neutral fitness effects and balancing selection.Mol. Biol. Evol. 2021; 38: 4362-4375Crossref PubMed Scopus (5) Google Scholar,46.Bruijning M. et al.The evolution of variance control.Trends Ecol. Evol. 2020; 35: 22-33Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar] (but see [2.Vinton A.C. Vasseur D.A. Evolutionary tracking is determined by differential selection on demographic rates and density dependence.Ecol. Evol. 2020; 10: 5725-5736Crossref PubMed Scopus (2) Google Scholar]). In contrast, the ability of populations to evolutionarily track a shifting adaptive peak can increase with greater temporal autocorrelation [25.Ruokolainen L. et al.Ecological and evolutionary dynamics under coloured environmental variation.Trends Ecol. Evol. 2009; 24: 555-563Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. Moreover, theoretical work predicts that positively autocorrelated environmental fluctuations can increase additive genetic variance and its ability to reduce genetic load. This increase in genetic variance allows the mean phenotype to track a changing environment more closely [25.Ruokolainen L. et al.Ecological and evolutionary dynamics under coloured environmental variation.Trends Ecol. Evol. 2009; 24: 555-563Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. Thus, evolutionary potential may be higher in temporally autocorrelated environments than in uncorrelated environments. The evolutionary effects of environmental variability and autocorrelation are often framed in terms of increasing frequencies of novel and unfavourable environments [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar]. Greater environmental variability and lower temporal autocorrelation expose individuals to environments that are novel and often unfavourable, and their impact on evolutionary response is mixed depending on other factors at play [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar]. In addition, a direct consequence of higher variability and higher autocorrelation is that populations spend less time in temporal refugia [29.Di Cecco G.J. Gouhier T.C. Increased spatial and temporal autocorrelation of temperature under climate change.Sci. Rep. 2018; 8: 14850Crossref PubMed Scopus (40) Google Scholar], which reduces fitness. Conversely, theoretical and empirical research have shown that exposure to unfavourable environments can also lead to increased additive genetic variance, thereby increasing the evolutionary potential of a trait [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar]. This increase in additive genetic variance can occur when selection is ineffective at removing mutations that are maladaptive only in rare environments [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar,48.Kawecki T.J. et al.Mutational collapse of fitness in marginal habitats and the evolution of ecological specialisation.J. Evol. Biol. 1997; 10: 407-429Crossref Scopus (115) Google Scholar]. Moreover, novel environments can reveal cryptic, or previously unexpressed genetic variation [12.Ashander J. et al.Predicting evolutionary rescue via evolving plasticity in stochastic environments.Proc. R. Soc. B Biol. Sci. 2016; 28320161690PubMed Google Scholar]. Thus, exposure to novel, and unfavourable environments could increase genetic variation and therefore heritability. Determining the magnitude and frequency that genetic variance increases in response to environmental novelty and harshness is non-trivial, as the opposite effect can also occur [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar,49.Wilson A.J. et al.Environmental coupling of selection and heritability limits evolution.PLoS Biol. 2006; 4e216Crossref PubMed Scopus (186) Google Scholar, 50.Gaitán-Espitia J.D. et al.Geographical gradients in selection can reveal genetic constraints for evolutionary responses to ocean acidification.Biol. Lett. 2017; 1320160784Crossref Scopus (14) Google Scholar, 51.Gebhardt-Henrich S.G. Van Noordwijk A.J. Nestling growth in the Great Tit I. Heritability estimates under different environmental conditions.J. Evol. Biol. 1991; 4: 341-362Crossref Scopus (122) Google Scholar]. The effect of environmental novelty and harshness depends on the system-specific evolutionary history, and interaction between environmental and genetic effect [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar]. For example, both environmental novelty and harshness can decrease additive genetic variance if an unfavourable condition prevents individuals from expressing the underlying genetically determined benefits from a trait [51.Gebhardt-Henrich S.G. Van Noordwijk A.J. Nestling growth in the Great Tit I. Heritability estimates under different environmental conditions.J. Evol. Biol. 1991; 4: 341-362Crossref Scopus (122) Google Scholar]. In such cases, selection could favour the regulation of gene expression such that alleles are not expressed in an unfavourable environment. This lack of expression in unfavourable environments may occur through decreasing the heritability of traits underpinned by associated alleles [47.Hoffmann A.A. Merilä J. Heritable variation and evolution under favourable and unfavourable conditions.Trends Ecol. Evol. 1999; 14: 96-101Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar]. Here, heritability could decrease when additive genetic effects determine a trait such as body size. If unfavourable conditions decrease growth rate, this decrease can lead to a reduction in the additive genetic variance. In turn, depending on the mechanisms at play, evolutionary tracking may be either facilitated or hindered in environments with an increasing rate of change, variation, and/or autocorrelation. Whether or not a population is likely to successfully track a moving environmental optimum will in part determine the necessity of plasticity to help bridge this gap. Thus, the impact of the environmental change variables on evolutionary tracking should be considered when addressing H1-2. The importance of phenotypic plasticity in adaptive evolution depends on changes in population size, which influences the likelihood of local extinctions [52.O’Grady J.J. et al.What are the best correlates of predicted extinction risk?.Biol. Conserv. 2004; 118: 513-520Crossref Scopus (195) Google Scholar,53.Clements C.F. et al.Early warning signals of recovery in complex systems.Nat. Commun. 2019; 10: 1681Crossref PubMed Scopus (29) Google Scholar]. Such impacts of population size depend on life history strategies; for example, long-lived species can persist longer at small population sizes than short-lived species, whose populations can collapse quickly [54.Wright J. et al.Life-history evolution under fluctuating density-dependent selection and the adaptive alignment of pace-of-life syndromes.Biol. Rev. 2019; 94: 230-247Crossref Scopus (51) Google Scholar,55.Reynolds J. Life histories and extinction risk.in: Blackburn T.M. Gaston K.J. Macroecology. Blackwell Publishing, 2003: 195-217Google Scholar]. Furthermore, these strategies can determine the rate of trait evolution [8.Schmid M. et al.A tradeoff between robustness to environmental fluctuations and speed of evolution.Am. Nat. 2022; 200: E16-E35Crossref PubMed Scopus (1) Google Scholar]. It is therefore vital to consider the impact of different environmental components on population dynamics and life history to understand the impact of the type of environmental change on the interplay between plasticity and adaptive evolution. Higher rates of mean environmental change typically lead to decreases in population size [56.Spooner F.E.B. et al.Rapid warming is associated with population decline among terrestrial birds and mammals globally.Glob. Change Biol. 2018; 24: 4521-4531Crossref PubMed Scopus (80) Google Scholar]. This finding suggests that local extinction will increase as the rate of climate warm
17 citations
TL;DR: In this article, the authors broadly reviewed literature on individual variation in behavior and physiology, and located 39 datasets with sufficient repeated measures to evaluate individual differences in residual variance, and analyzed these datasets using methods that permit direct comparisons of parameters across studies.
Abstract: Behavioral and physiological ecologists have long been interested in explaining the causes and consequences of trait variation, with a focus on individual differences in mean values. However, the majority of phenotypic variation typically occurs within individuals, rather than among individuals (as indicated by average repeatability being less than 0.5). Recent studies have further shown that individuals can also differ in the magnitude of variation that is unexplained by individual variation or environmental factors (i.e., residual variation). The significance of residual variation, or why individuals differ, is largely unexplained, but is important from evolutionary, methodological, and statistical perspectives. Here, we broadly reviewed literature on individual variation in behavior and physiology, and located 39 datasets with sufficient repeated measures to evaluate individual differences in residual variance. We then analyzed these datasets using methods that permit direct comparisons of parameters across studies. This revealed substantial and widespread individual differences in residual variance. The magnitude of individual variation appeared larger in behavioral traits than in physiological traits, and heterogeneity was greater in more controlled situations. We discuss potential ecological and evolutionary implications of individual differences in residual variance and suggest productive future research directions.
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Book•
01 Jan 1981
TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Abstract: Part 1 Genetic constitution of a population: Hardy-Weinberg equilibrium. Part 2 Changes in gene frequency: migration mutation. Part 3 Small populations - changes in gene frequency under simplified conditions. Part 4 Small populations - less simplified conditions. Part 5 Small populations - pedigreed populations and close inbreeding. Part 6 Continuous variation. Part 7 Values and means. Part 8 Variance. Part 9 Resemblance between relatives. Part 10 Heritability. Part 11 Selection - the response and its prediction. Part 12 Selection - the results of experiments. Part 13 Selection - information from relatives. Part 14 Inbreeding and crossbreeding - changes of mean value. Part 15 Inbreeding and crossbreeding - changes of variance. Part 16 Inbreeding and crossbreeding - applications. Part 17 Scale. Part 18 Threshold characters. Part 19 Correlated characters. Part 20 Metric characters under natural selection.
20,288 citations
Journal Article•
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
9,847 citations
"Context-dependent trait covariances..." refers background in this paper
...Through these estimates, we can calculate cross-environmental correlations in a trait (Roff 1997; Falconer 1981; Brommer 2013) and the changes in covariances between traits (Brommer and Class 2015)....
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...In the case of continuous environmental predictors, a trait can instead be modeled as a function of the environment—that is, the more familiar reaction norm approach (Falconer 1981; Roff 1997), which has been the dominant analytical tool of behavioral ecologists (Dingemanse et al....
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TL;DR: Measures of directional and stabilizing selection on each of a set of phenotypically correlated characters are derived, retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection.
Abstract: Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et
4,990 citations
TL;DR: The costs and limits of phenotypic plasticity are thought to have important ecological and evolutionary consequences, yet they are not as well understood as the benefits of plasticity.
Abstract: The costs and limits of phenotypic plasticity are thought to have important ecological and evolutionary consequences, yet they are not as well understood as the benefits of plasticity. At least nine ideas exist regarding how plasticity may be costly or limited, but these have rarely been discussed together. The most commonly discussed cost is that of maintaining the sensory and regulatory machinery needed for plasticity, which may require energy and material expenses. A frequently considered limit to the benefit of plasticity is that the environmental cues guiding plastic development can be unreliable. Such costs and limits have recently been included in theoretical models and, perhaps more importantly, relevant empirical studies now have emerged. Despite the current interest in costs and limits of plasticity, several lines of reasoning suggest that they might be difficult to demonstrate.
2,109 citations
TL;DR: These models utilize the statistical relationship which exists between genotype‐environment interaction and genetic correlation to describe evolution of the mean phenotype under soft and hard selection in coarse‐grained environments.
Abstract: Studies of spatial variation in the environment have primarily focused on how genetic variation can be maintained. Many one-locus genetic models have addressed this issue, but, for several reasons, these models are not directly applicable to quantitative (polygenic) traits. One reason is that for continuously varying characters, the evolution of the mean phenotype expressed in different environments (the norm of reaction) is also of interest. Our quantitative genetic models describe the evolution of phenotypic response to the environment, also known as phenotypic plasticity (Gause, 1947), and illustrate how the norm of reaction (Schmalhausen, 1949) can be shaped by selection. These models utilize the statistical relationship which exists between genotype-environment interaction and genetic correlation to describe evolution of the mean phenotype under soft and hard selection in coarse-grained environments. Just as genetic correlations among characters within a single environment can constrain the response to simultaneous selection, so can a genetic correlation between states of a character which are expressed in two environments. Unless the genetic correlation across environments is ± 1, polygenic variation is exhausted, or there is a cost to plasticity, panmictic populations under a bivariate fitness function will eventually attain the optimum mean phenotype for a given character in each environment. However, very high positive or negative correlations can substantially slow the rate of evolution and may produce temporary maladaptation in one environment before the optimum joint phenotype is finally attained. Evolutionary trajectories under hard and soft selection can differ: in hard selection, the environments with the highest initial mean fitness contribute most individuals to the mating pool. In both hard and soft selection, evolution toward the optimum in a rare environment is much slower than it is in a common one. A subdivided population model reveals that migration restriction can facilitate local adaptation. However, unless there is no migration or one of the special cases discussed for panmictic populations holds, no geographical variation in the norm of reaction will be maintained at equilibrium. Implications of these results for the interpretation of spatial patterns of phenotypic variation in natural populations are discussed.
2,019 citations