Accounting for female space sharing in St. Kilda Soay sheep (Ovis aries) results in little change in heritability estimates

Abstract

When estimating heritability in free-living populations, it is common practice to account for common environment effects, because of their potential to generate phenotypic covariance among relatives thereby biasing heritability estimates. In quantitative genetic studies of natural populations, however, philopatry, which results in relatives being clustered in space, is rarely accounted for. The two studies that have been carried out so far suggest absolute declines in heritability estimates of up to 43% when accounting for space sharing by relatives. However, due to methodological limitations these estimates may not be representative. We used data from the St. Kilda Soay sheep population to estimate heritabilities with and without accounting for space sharing for five traits for which there is evidence for additive genetic variance (birthweight, birth date, lamb August weight, and female post-mortem jaw and metacarpal length). We accounted for space sharing by related females by separately incorporating spatial autocorrelation, and a home range similarity matrix. Although these terms accounted for up to 18% of the variance in these traits, heritability estimates were only reduced by up to 7%. Our results suggest that the bias caused by not accounting for space sharing may be lower than previously thought. This suggests that philopatry does not inevitably lead to a large bias if space sharing by relatives is not accounted for. We hope our work stimulates researchers to model shared space when relatives in their study population share space, as doing so will enable us to better understand when bias may be of particular concern.

It takes two: Heritable male effects on reproductive timing but not clutch size in a wild bird population*

“…There are a number of ways this can be approached, each of which will be best suited to particular study systems, data structures, and research questions. In our case, we progressed from a typical approach to quantifying spatial heterogeneity (partitioning the study site into discrete areas that demarcate relatively homogeneous areas) to one that explicitly estimates variance attributable to spatial autocorrelation and has recently been applied in quantitative genetic studies of freeliving animal populations (Stopher et al 2012;Regan et al 2017). This latter approach relies on aggregating spatial locations of nestboxes within a grid, mimicking the 'block' design typically applied to agricultural and forest genetics trials (Dutkowski et al 2002).…”

“…For example, the shared natal environment of siblings contributes to the phenotypic resemblance of relatives and can upwardly bias additive genetic (co)variance estimates (Kruuk and Hadfield 2007;Hadfield et al 2013). Yet relatives can continue to share environments beyond independence through a combination of natal philopatry (Regan et al 2017) and local-scale environmental variation (Wilkin et al 2007a). If dispersal distances differ between the sexes, as is typical of the mammalian and avian populations used in quantitative genetic research of wild populations (Greenwood 1980), then joint consideration of male and female contributions to heritable variation may be subject to sex-dependent environmental bias.…”

“…In the case of birds, females generally disperse further from their natal origin (Greenwood 1980), such that environmentally driven spatial heterogeneity in the expression of the focal trait is expected to exert a greater bias on the estimation of male genetic effects. However, the phenotypic variance attributable to such positive spatial autocorrelation can itself be modeled (Costa e Silva et al 2001;Dutkowski et al 2002), as has been applied in quantitative genetic studies of free-living animal populations (Stopher et al 2012;Germain et al 2016;Regan et al 2017).…”

Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels

“…Additionally, while we predicted a negative covariance between neighbours due to competition for resources (especially during high-density conditions), this could be masked by positive spatial autocorrelation in resource availability within a study-area. This would generate a net signal of positive phenotypic covariance among-neighbours (Stopher et al 2012; Regan et al 2016; Thomson et al 2018). To control for this we fitted a term accounting for (non-socially determined) environmental heterogeneity in resource abundance.…”

Into the wild—WAMBAM goes to Canada

“…At a WAMBAM meeting in 2011 (see Table ), Katie Stopher (see Stopher et al., ) presented an analysis to disentangle common environment effects using spatial data, by including matrices describing common use of space in animal models, in an analogous way to how a matrix describing expected identity by decent is fundamental to the estimation of the additive genetic variance in these models. This approach has greatly taken hold, with, for instance, a detailed investigation of the potential for common environments to bias quantitative genetic inferences in Soay sheep by Charlotte Regan (see Regan et al., ), and with Isabel Winney and Caroline Thomson leading a round‐table discussion on the approach, and describing a forthcoming “how to…” paper to further facilitate use of the approach.…”