Refining and defining riverscape genetics: How rivers influence population genetic structure

The long history of reciprocal transplant studies testing the hypothesis of local adaptation has shown that populations are often adapted to their local environments. Yet many studies have not demonstrated local adaptation, suggesting that sometimes native populations are no better adapted than are genotypes from foreign environments. Local adaptation may also lead to trade-offs, in which adaptation to one environment comes at a cost of adaptation to another environment. I conducted a survey of published studies of local adaptation to quantify its frequency and magnitude and the costs associated with local adaptation. I also quantified the relationship between local adaptation and environmental differences and the relationship between local adaptation and phenotypic divergence. The overall frequency of local adaptation was 0.71, and the magnitude of the native population advantage in relative fitness was 45%. Divergence between home site environments was positively associated with the magnitude of local adaptation, but phenotypic divergence was not. I found a small negative correlation between a population's relative fitness in its native environment and its fitness in a foreign environment, indicating weak trade-offs associated with local adaptation. These results suggest that populations are often locally adapted but stochastic processes such as genetic drift may limit the efficacy of divergent selection.

Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .