Abstract
Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build “animal models” to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.
Highlights
Additive genetic variance (VA) and heritability (h2, the ratio of genetic to phenotypic variance) are fundamental parameters in our understanding of the evolutionary potential and dynamics of traits in nature (Lande 1982; Houle 1992)
We investigated the effects of incorporating spatial autocorrelation (SAC) and home range overlap on the estimation of quantitative genetic parameters in four female traits: spring home range size (SHR), rut home range size (RHR), offspring birth weight (BW), and lifetime breeding success (LBS)
Our analyses show that evolutionary biologists and ecologists working in natural systems should consider modeling fine-scale spatial processes if they want to fully understand the environmental drivers of phenotypic variation and accurately estimate quantitative genetic parameters
Summary
Additive genetic variance (VA) and heritability (h2, the ratio of genetic to phenotypic variance) are fundamental parameters in our understanding of the evolutionary potential and dynamics of traits in nature (Lande 1982; Houle 1992). Failing to properly account for such shared environmental effects is known to bias estimates of parameters derived from “animal models” (Kruuk and Hadfield 2007), and it has become common practice to account for certain kinds of shared environmental effects (e.g., parental identity, nest, group, or region of study area) by incorporating these into models as fixed or random effects (e.g., Kruuk et al 2001; MacColl and Hatchwell 2003; Charmantier et al 2004; Wilson et al 2005; Kruuk and Hadfield 2007) Beyond these shared environment effects, social structure and natal philopatry—both of which are ubiquitous in wild vertebrates—are likely to result in spatial associations among relatives throughout individuals’ lives. To our knowledge, such an approach has yet to be applied to test the importance or nature of SAC underlying phenotypic variation, or its effects on parameter estimates from “animal models,” in any wild vertebrate system
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