Landscape genetics and spatial pattern of phenotypic variation ofEristalis tenaxacross Europe

Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.

Microsatellite markers are routinely used to investigate the genetic structuring of natural populations. The knowledge of how genetic variation is partitioned among populations may have important implications not only in evolutionary biology and ecology, but also in conservation biology. Hence, reliable estimates of population differentiation are crucial to understand the connectivity among populations and represent important tools to develop conservation strategies. The estimation of differentiation is c from Wright's FST and/or Slatkin's RST, an FST -analogue assuming a stepwise mutation model. Both these statistics have their drawbacks. Furthermore, there is no clear consensus over their relative accuracy. In this review, we first discuss the consequences of different temporal and spatial sampling strategies on differentiation estimation. Then, we move to statistical problems directly associated with the estimation of population structuring itself, with particular emphasis on the effects of high mutation rates and mutation patterns of microsatellite loci. Finally, we discuss the biological interpretation of population structuring estimates.