Predicting evolution over multiple generations in deteriorating environments using evolutionarily explicit Integral Projection Models

Abstract

To predict how populations will be impacted by human-induced environmental change, it is necessary to understand how their numerical dynamics will be altered (Chevin et al., 2010;Coulson et al., 2011). One way to do this is to ask how human-induced biotic and abiotic environmental change will affect the survival and reproductive rates that determine temporal variation in population growth and fitness (Tuljapurkar, 2013;Tuljapurkar & Caswell, 2012). These rates are functions of (i) ecosystem, community and population level processes and (ii) individual attributes such as age, sex and phenotypic trait values (Ellner et al., 2016). The phenotypic traits that contribute to determining survival and reproductive rates are, by definition, fitness-related traits under selection (Lande, 1982).The functions that link phenotypic trait values to survival and recruitment are termed fitness functions. Any human-induced biotic

Life history mediates the trade‐offs among different components of demographic resilience

“…This plasticity in plant life histories (Salguero‐Gómez, Jones, Archer et al, 2016) could explain the lack of phylogenetic signal in their demographic resilience components. Incorporating environmental stochasticity and genetic data would also allow exploring the role of plasticity in the evolution of demographic resilience in further detail (Coulson et al, 2021). However, it also must be noted that plants and vertebrates have rather distinct evolutionary histories (Graham et al, 2000; Streelman & Danley, 2003), making their comparisons somewhat challenging to interpret.…”

Building integral projection models with nonindependent vital rates

“…Temporal correlations among vital rates (e.g., a good year is good for each of growth, survival and reproduction) are captured naturally when year‐specific transition kernels are estimated or correlated random effects are estimated (Childs et al, 2004; Hindle et al, 2018; Metcalf et al, 2015). Correlations in individual heterogeneity among multiple traits have been considered for life‐history tradeoffs and eco‐evolutionary IPMs (Coulson et al, 2021; Kentie et al, 2020). However, there remains a need for systematic formulation and comparison of multiple kinds of correlated vital rates.…”

A novel analytical framework to quantify co‐gradient and countergradient variation

“…Spatial Cov GE may play an important role with respect to the ecological impacts of climate change (Arietta & Skelly, 2021; Gienapp et al, 2008; Merilä, 2012). Theoretical studies exploring the role of adaptation and plasticity in population responses to climate change typically model temporally fluctuating environments without including a spatial component (Ashander et al, 2016; Chevin & Hoffmann, 2017; Chevin et al, 2010; Coulson et al, 2017, 2021; Lande, 2009; Scheiner et al, 2017, 2019), and thus do not capture the potential influence of spatial Cov GE . Some of these studies modeled intrinsic properties of the genotype‐phenotype map as covariance in pleiotropic effects on multiple traits (Coulson et al, 2017, 2021; Via et al, 1995), but how genetic (co)variance influences the pattern of spatial Cov GE in a metapopulation, and thus metapopulation's responses to climate change, has not been rigorously addressed by theory and remains an important area of future research.…”

“…Panels A and B are filtered to show the power to detect moderate effect sizes (between 0. without including a spatial component (Ashander et al, 2016;Chevin & Hoffmann, 2017;Chevin et al, 2010;Coulson et al, 2017Coulson et al, , 2021Lande, 2009;Scheiner et al, 2017Scheiner et al, , 2019, and thus do not capture the potential influence of spatial Cov GE . Some of these studies modeled intrinsic properties of the genotype-phenotype map as covariance in pleiotropic effects on multiple traits (Coulson et al, 2017(Coulson et al, , 2021Via et al, 1995), but how genetic (co)variance influences the pattern of spatial Cov GE in a metapopulation, and thus metapopulation's responses to climate change, has not been rigorously addressed by theory and remains an important area of future research. The metric provided here can now be combined with more traditional metrics (reviewed in Table S3) to address this gap.…”