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Summary1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data. 2. We introduce the R package 'abc' that implements several ABC algorithms for performing parameter estimation and model selection. In particular, the recently developed nonlinear heteroscedastic regression methods for ABC are implemented. The 'abc' package also includes a cross-validation tool for measuring the accuracy of ABC estimates and to calculate the misclassification probabilities when performing model selection. The main functions are accompanied by appropriate summary and plotting tools. 3. R is already widely used in bioinformatics and several fields of biology. The R package 'abc' will make the ABC algorithms available to a large number of R users. 'abc' is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/ abc/index.html.

Knowledge of relatedness between pairs of individuals plays an important role in many research areas including evolutionary biology, quantitative genetics, and conservation. Pairwise relatedness estimation methods based on genetic data from highly variable molecular markers are now used extensively as a substitute for pedigrees. Although the sampling variance of the estimators has been intensively studied for the most common simple genetic relationships, such as unrelated, half-and full-sib, or parent-offspring, little attention has been paid to the average performance of the estimators, by which we mean the performance across all pairs of individuals in a sample. Here we apply two measures to quantify the average performance: first, misclassification rates between pairs of genetic relationships and, second, the proportion of variance explained in the pairwise relatedness estimates by the true population relatedness composition (i.e., the frequencies of different relationships in the population). Using simulated data derived from exceptionally good quality marker and pedigree data from five long-term projects of natural populations, we demonstrate that the average performance depends mainly on the population relatedness composition and may be improved by the marker data quality only within the limits of the population relatedness composition. Our five examples of vertebrate breeding systems suggest that due to the remarkably low variance in relatedness across the population, marker-based estimates may often have low power to address research questions of interest. INFERRING relatedness among pairs of individuals plays a central role in our understanding of many areas of genetics and population biology. For example, the extent of relatedness between individuals is important in the study of social evolution (e.g., Hamilton 1964;Cheverud 1985) and studies incorporating measures of relatedness have influenced our understanding of the mechanism of kin selection in natural populations (e.g., Choe and Crespi 1997). In quantitative genetics the estimation of genetic variance components, allowing estimation of heritability and genetic correlation, requires pairs of individuals with known relatedness (Lynch and Walsh 1998). In conservation biology, knowledge of relatedness is essential in captive management, where the goal is to preserve the genetic variation of the wild population from which the founders were drawn (e.g., Lacy 1994). Relatedness estimates are also used when testing hypotheses about inbreeding avoidance (e.g., Reusch et al. 2001;Richardson et al. 2004) and isolation by distance (e.g., Matocq and Lacey 2004).Relatedness has traditionally been estimated from pedigrees. Given an outbred source population and good recording, laboratory or managed populations can instantly provide pedigree information. However, many relevant ecological and evolutionary questions can only be addressed in free-living populations with the help of molecular marker data (Kruuk 2004). When relatedness estimation can be simp...

Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a 'genomic' quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation.

Understanding local adaptation in forest trees is currently a key research and societal priority. Geographically and ecologically marginal populations provide ideal case studies, because environmental stress along with reduced gene flow can facilitate the establishment of locally adapted populations. We sampled European silver fir (Abies alba Mill.) trees in the French Mediterranean Alps, along the margin of its distribution range, from pairs of high- and low-elevation plots on four different mountains situated along a 170-km east-west transect. The analysis of 267 SNP loci from 175 candidate genes suggested a neutral pattern of east-west isolation by distance among mountain sites. F(ST) outlier tests revealed 16 SNPs that showed patterns of divergent selection. Plot climate was characterized using both in situ measurements and gridded data that revealed marked differences between and within mountains with different trends depending on the season. Association between allelic frequencies and bioclimatic variables revealed eight genes that contained candidate SNPs, of which two were also detected using F(ST) outlier methods. All SNPs were associated with winter drought, and one of them showed strong evidence of selection with respect to elevation. Q(ST)-F(ST) tests for fitness-related traits measured in a common garden suggested adaptive divergence for the date of bud flush and for growth rate. Overall, our results suggest a complex adaptive picture for A. alba in the southern French Alps where, during the east-to-west Holocene recolonization, locally advantageous genetic variants established at both the landscape and local scales.

Recognition that evolution operates on the same timescale as ecological processes has motivated growing interest in eco-evolutionary dynamics. Nonetheless, generating sufficient data to test predictions about eco-evolutionary dynamics has proved challenging, particularly in natural contexts. Here we argue that genomic data can be integrated into the study of eco-evolutionary dynamics in ways that deepen our understanding of the interplay between ecology and evolution. Specifically, we outline five major questions in the study of eco-evolutionary dynamics for which genomic data may provide answers. Although genomic data alone will not be sufficient to resolve these challenges, integrating genomic data can provide a more mechanistic understanding of the causes of phenotypic change, help elucidate the mechanisms driving eco-evolutionary dynamics, and lead to more accurate evolutionary predictions of eco-evolutionary dynamics in nature.

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