Putting the ‘landscape’ in landscape genetics

Abstract

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.

Effective population size is strongly correlated with breeding pond size in the endangered California tiger salamander, Ambystoma californiense

“…While most conservation genetics studies have focused on the importance of maintaining connectivity among populations, few have considered the ecological conditions necessary to maintain viable effective population sizes. Population connectivity is clearly important, both for sustaining genetic diversity and the complex source-sink dynamics that characterize many natural populations (Trenham et al 2001;Manel et al 2003;Storfer et al 2007;Spear et al 2005;Wang et al 2009). However, genetic diversity can also be protected by maintaining adequate effective population sizes Charlesworth and Willis 2009;Wang 2009a), and this may be particularly important as landscapes become fragmented and gene flow is interrupted.…”

“…While new methods in landscape genetics are increasingly sophisticated in their ability to assess the effects that the landscapes separating populations have on dispersal probability and the resulting population genetic structure (Manel et al 2003;Storfer et al 2007;Spear et al 2005;Giordano et al 2007;Wang 2010), few conservation and landscape genetics studies consider the effects that environmental variation has on intra-population variables such as demographic history and effective population size (Funk et al 1999;Driscoll 1999;Wang 2009a). For instance, in pond-breeding amphibians, substantial progress has been made in understanding the role of upland habitat in genetic structure and dispersal among populations (Trenham et al 2001;Wang et al 2009), but the effects of pond characteristics on population demography are relatively unknown.…”

The effect of segregation of flowering time on fine-scale spatial genetic structure in an alpine-snowbed herb Primula cuneifolia

“…Spatial and temporal patterns of gene flow with respect to landscape features have crucial effects on the spatial patterns of genetic variation (Sork et al, 1999;Manel et al, 2003;Storfer et al, 2007). In an alpine region, the snowmelt gradient is a critical landscape feature affecting genetic structure of plants (Hirao and Kudo, 2004).…”

Genetic structure in Red Junglefowl (Gallus gallus) populations: Strong spatial patterns in the wild ancestors of domestic chickens in a core distribution range

“…With IBD, MCMC models likely fail to explain spatially explicit genetic variation (Frantz, Cellina, Krier, Schley, & Burke, 2009; Schwartz & McKelvey, 2009). There are a few biological and technical procedures to accommodate the presence of IBD, including stratified sampling (as employed in this study) (Storfer et al., 2007) and correlogram analyses to determine whether or not IBD patterns exist. Beyond approximately 5 km, the correlograms did not detected significant IBD (Appendix S1B) and we observed consistent clustering results between our spatial and nonspatial models.…”