The genetic basis of dispersal in a vertebrate metapopulation.

Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco-evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long-term individual-based ecological data set and high-density single-nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome-wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA 2 ), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA 2 across populations occupying ecologically relevant habitat types (farm vs. non-farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA 2 , and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco-evolutionary traits. The eco-evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations.

Dispersal in a house sparrow metapopulation: An integrative case study of genetic assignment calibrated with ecological data and pedigree information.

Dispersal has a crucial role determining ecoevolutionary dynamics through both gene flow and population size regulation. However, to study dispersal and its consequences, one must distinguish immigrants from residents. Dispersers can be identified using telemetry, capture-mark-recapture (CMR) methods, or genetic assignment methods. All of these methods have disadvantages, such as high costs and substantial field efforts needed for telemetry and CMR surveys, and adequate genetic distance required in genetic assignment. In this study, we used genome-wide 200K Single Nucleotide Polymorphism data and two different genetic assignment approaches (GSI_SIM, Bayesian framework; BONE, network-based estimation) to identify the dispersers in a house sparrow (Passer domesticus) metapopulation sampled over 16 years. Our results showed higher assignment accuracy with BONE. Hence, we proceeded to diagnose potential sources of errors in the assignment results from the BONE method due to variation in levels of interpopulation genetic differentiation, intrapopulation genetic variation and sample size. We show that assignment accuracy is high even at low levels of genetic differentiation and that it increases with the proportion of a population that has been sampled. Finally, we highlight that dispersal studies integrating both ecological and genetic data provide robust assessments of the dispersal patterns in natural populations.

Animal models with group-specific additive genetic variances: extending genetic group models.

BACKGROUND: The animal model is a key tool in quantitative genetics and has been used extensively to estimate fundamental parameters, such as additive genetic variance or heritability. An implicit assumption of animal models is that all founder individuals derive from a single population. This assumption is commonly violated, for instance in crossbred livestock or when a meta-population is split into genetically differentiated subpopulations. Ignoring that base populations are genetically heterogeneous and thus split into different 'genetic groups' may lead to biased parameter estimates, especially for additive genetic variance. To avoid such biases, genetic group animal models, which account for the presence of more than one genetic group, have been proposed. Unfortunately, the method to date is only computationally feasible when the breeding values of the groups are allowed to differ in their means, but not in their variances. RESULTS: We present an extension of the animal model that permits estimation of group-specific additive genetic variances. This is achieved by employing group-specific relatedness matrices for the breeding value components to different genetic groups. We derive these matrices by decomposing the full relatedness matrix via the generalized Cholesky decomposition, and by scaling the respective matrix components for each group. We propose a computationally convenient approximation for the matrix component that encodes for the Mendelian sampling variance, and show that this approximation is not critical. In addition, we explain why segregation variances are often negligible when analyzing the complex polygenic traits that are frequently the focus of evolutionary ecologists and animal breeders. Simulations and an example from an insular meta-population of house sparrows in Norway with three distinct genetic groups illustrate that the method is successful in estimating group-specific additive genetic variances, and that segregation variances are indeed negligible in the empirical example. CONCLUSIONS: Quantifying differences in additive genetic variance within and among populations is of major biological interest in ecology, evolution, and animal and plant breeding. The proposed method allows to estimate such differences for subpopulations that form a connected set of populations, and may thus also be useful to study temporal or spatial variation of additive genetic variances.