Quantifying the causal pathways contributing to natural selection

Abstract

The consequences of natural selection can be understood from a purely statistical perspective. In contrast, an explicitly causal approach is required to understand why trait values covary with fitness. In particular, key evolutionary constructs, such as sexual selection, fecundity selection, and so on, are best understood as selection via particular fitness components. To formalize and operationalize these concepts, we must disentangle the various causal pathways contributing to selection. Such decompositions are currently only known for linear models, where they are sometimes referred to as "Wright's rules." Here, we provide a general framework, based on path analysis, for partitioning selection among its contributing causal pathways. We show how the extended selection gradient-which represents selection arising from a trait's causal effects on fitness-can be decomposed into path-specific selection gradients, which correspond to distinct causal mechanisms of selection. This framework allows for nonlinear effects and nonadditive interactions among variables, which may be estimated using standard statistical methods (e.g., generalized linear [mixed] models or generalized additive models). We thus provide a generalization of Wright's path rules that accommodates the nonlinear and nonadditive mechanisms by which natural selection commonly arises.

The evolutionary ecology of fatty‐acid variation: Implications for consumer adaptation and diversification

“…The metabolic traits (i.e., traits related to fatty acid synthesis as well as fatty acid retention and oxidation) we summarise in Table 1 are also embedded within a hierarchy of other potentially fitness‐relevant consumer traits (Table 2). Natural selection acts upon the heritable intraspecific metabolic traits in the context of other subordinate and emergent functional traits in the hierarchy (Figure 5; Henshaw et al, 2020; Laughlin et al, 2020). Where there is a heritable basis for metabolic traits, there is the potential for adaptive evolution of consumer metabolism in response to natural selection.…”

On the evolution of trophic position

“…(b) However, foraging entirely on animals might come with a fitness cost if they are less abundant than plants. This might create a trade-off between foraging on abundant resources (usually plants) and food quality (usually animals), creating selection gradients with an optimal trophic position for individuals with intermediate levels of animal prey in their diet A prevailing challenge in evolutionary ecology is to determine the ecological mechanisms underpinning trait evolution (Henshaw et al, 2020;MacColl, 2011;Wade & Kalisz, 1990), and the same challenge holds for understanding the evolution of trophic position by natural selection. In a study of Gasterosteus aculeatus populations, Bolnick and Araújo (2011) found covariation among trophic position, foraging traits (gill raker morphology), and individual growth rate (a proxy for fitness).…”

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

“…However, hierarchical G2P maps with partial knowledge of intermediate processes offer promise for predicting long-term response to selection, given their success in improved short-term predictions of non-stationary effects of alleles. An obstacle in the practical applications of such hierarchical G2P modeling approaches is non-identifiability, also referred to as equifinality or the many-to-one property (Lamsal et al, 2018;Barghi et al, 2020;Henshaw et al, 2020;Kruijer et al, 2020;Tsutsumi-Morita et al, 2021). Effects can be non-identifiable due to unmeasured confounders that generate correlated errors between effects, which results in multiple, equally likely hierarchical G2P maps for experimental data sets.…”

“…At the same time, predictions can be extracted from each level of the hierarchical G2P map, allowing the decomposition of individual performance into additive genetic, total genetic, and phenotypic merit. Decomposition of path-specific values in hierarchical G2P maps has been demonstrated in evolutionary and quantitative genetics (Lande and Arnold, 1983;Gianola and Sorensen, 2004;Valente et al, 2010Valente et al, , 2013Henshaw et al, 2020;Janeiro et al, 2020;Pegolo et al, 2020). Therefore, the ability to exploit different sources of improved crop performance under a single prediction framework could improve crop improvement pipelines' accuracy and flexibility to navigate performance landscapes for current and future environments (Messina et al, 2011(Messina et al, , 2020Technow et al, 2020).…”