Applications of graph theory to landscape genetics.

Colin J. Garroway,Jeff Bowman,Denis Carr,Paul J. Wilson

doi:10.1111/j.1752-4571.2008.00047.x

Colin J. Garroway, Jeff Bowman + Show 2 more

Open Access

https://doi.org/10.1111/j.1752-4571.2008.00047.x

Copy DOI

Abstract

We investigated the relationships among landscape quality, gene flow, and population genetic structure of fishers (Martes pennanti) in ON, Canada. We used graph theory as an analytical framework considering each landscape as a network node. The 34 nodes were connected by 93 edges. Network structure was characterized by a higher level of clustering than expected by chance, a short mean path length connecting all pairs of nodes, and a resiliency to the loss of highly connected nodes. This suggests that alleles can be efficiently spread through the system and that extirpations and conservative harvest are not likely to affect their spread. Two measures of node centrality were negatively related to both the proportion of immigrants in a node and node snow depth. This suggests that central nodes are producers of emigrants, contain high-quality habitat (i.e., deep snow can make locomotion energetically costly) and that fishers were migrating from high to low quality habitat. A method of community detection on networks delineated five genetic clusters of nodes suggesting cryptic population structure. Our analyses showed that network models can provide system-level insight into the process of gene flow with implications for understanding how landscape alterations might affect population fitness and evolutionary potential.

Highlights

Key goals of the emerging field of landscape genetics are to gain an understanding of how processes such as migration, genetic drift, and the distribution and connectivity of populations affect genetic structure (Manel et al 2003; Storfer et al 2007)
There are three well-described classes of networks that may be of general interest to landscape geneticists: small-world, scale-free, and random. These classes are of interest because they each imply characteristic dynamic features that can be interpreted in the context of dispersal, gene flow, resilience to extirpations, and genetic structure (Table 2)
Pairwise distances among node centroids were written as a genetic distance matrix with the off diagonal values representing network edges, which were weighted as the statistical distance between nodes

Summary

Introduction

Key goals of the emerging field of landscape genetics are to gain an understanding of how processes such as migration, genetic drift, and the distribution and connectivity of populations affect genetic structure (Manel et al 2003; Storfer et al 2007). There are three well-described classes of networks that may be of general interest to landscape geneticists: small-world, scale-free, and random (described in Table 2; Barabasi and Albert 1999; Erdos and Renyi 1959; Watts and Strogatz 1998) These classes are of interest because they each imply characteristic dynamic features that can be interpreted in the context of dispersal, gene flow, resilience to extirpations, and genetic structure (Table 2). Previous research has demonstrated that fishers are territorial and relatively philopatric, exhibiting short dispersal distances for a carnivore of their size (Arthur et al 1993; Kyle et al 2001; Koen et al 2007) We hypothesized that this would lead to a highly clustered network of genetic connectivity, with either small-world or scale-free properties. We assessed the ability of a network clustering technique, modularity optimization, to identify population genetic structure

Methods

Results

Discussion

Literature cited