Daniela Keller

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least‐cost paths or straight‐line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least‐cost paths represent a single migration route. A transect‐based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least‐cost transect analysis (LCTA). Habitat‐matrix RSs were used to create least‐cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least‐cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.

How to make landscape genetics beneficial for conservation management

Many landscape genetic studies promise results that can be applied in conservation management. However, only few landscape genetic studies have been used by practitioners. Here, we identified scientific topics in landscape genetics that need to be addressed before results can more successfully be applied in conservation management. For each topic, weaknesses of common practice in landscape genetic analysis are described by presenting examples from current studies and further recommendations for improvements are outlined. First, we suggest matching the extent of the study area with those of conservation management units and the study species’ dispersal potential when designing landscape genetic studies. Second, the quality of the underlying statistical models should be optimised, and models should include variables that are useful for management implementation. Third, to further improve the applicability of landscape genetic studies, thresholds for landscape effects on gene flow should be identified. Fourth, landscape genetic models could be used for the development of conservation planning tools, which ideally also incorporate the above described thresholds. Fifth and as discussed in earlier studies, the use of multiple species and replication at the landscape scale is recommended. Although it appears that only few landscape genetic studies have been applied in practical management until now, examples presented in this article show that landscape genetic methods can provide important information to formulate concrete management implications. Thus, addressing the above-mentioned scientific topics in landscape genetic studies would enhance the benefits of their results for practitioners.

Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow

For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (< 3 km), we calculated several measures of landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

Development of microsatellite markers for the wetland grasshopper Stethophyma grossum

Stethophyma grossum is a threatened Eurosiberian grasshopper species. Since it is bound to wetlands, S. grossum is often used as indicator for extensive wet meadows. To study its movement capability and dispersal habitat in landscape genetic analyses, we developed ten polymorphic microsatellite markers, making use of next generation sequencing. Markers were tested on 75 individuals collected in five populations from Switzerland. We found four to 18 alleles per locus. Observed and expected heterozygosities varied between 0.215–0.893 and 0.397–0.831, respectively. One marker seems to be sex-chromosome X-linked and one showed high null allele frequencies, a phenomenon generally detected in microsatellite studies on grasshoppers.

Characterization of microsatellite loci in Leucorrhinia caudalis, a rare dragonfly endangered throughout Europe

Leucorrhinia caudalis is a rare dragonfly, threatened throughout Europe. It only survived in a single population in Switzerland in the 1980s. However, it recently spread and colonized new ponds. In order to be able to study contemporary migration in this species, eight new microsatellite markers were developed and tested on 24 individuals from six Swiss ponds. We detected three to eleven alleles per polymorphic locus and found observed and expected heterozygosities of 0.250 to 0.875 and 0.215 to 0.840, respectively.