Literature DB >> 32888209

Quantifying the causal pathways contributing to natural selection.

Jonathan M Henshaw1,2, Michael B Morrissey3, Adam G Jones2.   

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.
© 2020 The Authors. Evolution published by Wiley Periodicals LLC on behalf of The Society for the Study of Evolution.

Keywords:  Causal derivative; causality; path analysis; structural equation modeling (SEM)

Mesh:

Year:  2020        PMID: 32888209     DOI: 10.1111/evo.14091

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  4 in total

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3.  Social competition as a driver of phenotype-environment correlations: implications for ecology and evolution.

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

Authors:  Owen M Powell; Kai P Voss-Fels; David R Jordan; Graeme Hammer; Mark Cooper
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  4 in total

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