Spatial scale affects landscape genetic analysis of a wetland grasshopper

Abstract

Most landscape genetic studies assess the impact of landscape elements on species' dispersal and gene flow. Many of these studies perform their analysis on all possible population pairs in a study area and do not explicitly consider the effects of spatial scale and population network topology on their results. Here, we examined the effects of spatial scale and population network topology on the outcome of a landscape genetic analysis. Additionally, we tested whether the relevant spatial scale of landscape genetic analysis could be defined by population network topology or by isolation-by-distance (IBD) patterns. A data set of the wetland grasshopper Stethophyma grossum, collected in a fragmented agricultural landscape, was used to analyse population network topology, IBD patterns and dispersal habitats, using least-cost transect analysis. Landscape genetic analyses neglecting spatial scale and population network topology resulted in models with low fits, with which a most likely dispersal habitat could not be identified. In contrast, analyses considering spatial scale and population network topology resulted in high model fits by restricting landscape genetic analysis to smaller scales (0-3 km) and neighbouring populations, as represented by a Gabriel graph. These models also successfully identified a likely dispersal habitat of S. grossum. The above results suggest that spatial scale and potentially population network topology should be more explicitly considered in future landscape genetic analyses.

Isolation-by-distance in landscapes: considerations for landscape genetics

“…For our analyses, an unbiased estimator of genetic differentiation was not necessary, as we have maintained a fixed number of demes and a fixed deme size throughout our simulations. Furthermore, pairwise estimates of such estimates of historical gene flow have shown to be highly correlated (Van Strien et al, 2012;Keller et al, 2013). We therefore assume that the differences in calculation of both estimates had a negligible effect.…”

“…An advantage of using correlograms for assessing IBD is that it could enhance the comparability of results from empirical landscape genetic studies with those from population genetic theory and simulations. However, empirical (Keller et al, 2013) and simulated ( Figure 6) F STdistance plots show that scatter of pairwise F ST values is generally high and increases with interdeme distance. In addition, the number of sampled demes is generally low in landscape genetic studies (mean ¼ 11; Jenkins et al, 2010).…”

“…For landscape genetics, it is thus important to differentiate between these two types of limitations. Indeed, in a landscape genetic analysis on a grasshopper species Keller et al (2013) found that, compared with considering all deme pairs, a much higher model fit and a better distinguishability of the most likely migration habitat was obtained when only deme pairs separated up to 3 km were considered. This distance threshold corresponded to the distance at which the F STdistance plot flattened out in a case-IV IBD pattern (Keller et al, 2013), but it also corresponded to the estimated maximum migration distance of this grasshopper (van Strien et al, 2014).…”

“…Indeed, in a landscape genetic analysis on a grasshopper species Keller et al (2013) found that, compared with considering all deme pairs, a much higher model fit and a better distinguishability of the most likely migration habitat was obtained when only deme pairs separated up to 3 km were considered. This distance threshold corresponded to the distance at which the F STdistance plot flattened out in a case-IV IBD pattern (Keller et al, 2013), but it also corresponded to the estimated maximum migration distance of this grasshopper (van Strien et al, 2014). Theoretical population genetic studies have shown that the distance at which an F ST -distance plot flattens out can be larger than the maximum migration distance in equilibrium (that is, asymptotic case-I F STdistance relationship) and non-equilibrium situations (Slatkin, 1993).…”

“…However, to highlight dispersal barriers due to landscape elements, several authors have recently argued that landscape genetic analyses should be restricted to only those pairs of demes between which direct gene flow is possible (Angelone et al, 2011;Keller et al, 2013). As most landscape genetic studies make use of historical gene flow measures (for example, F ST ; Jenkins et al, 2010;Storfer et al, 2010), gene flow is not only measured between deme pairs that are within each other's maximum migration distance, but also between demes beyond this distance that experience indirect gene flow (over several generations) via intermediate demes.…”