Study area
The study area was situated in the North of the Canton of Bern, in an area known as Oberaargau. The main towns in the area are Langenthal and Herzogenbuchsee. The river Aare flows through the area in a West to East direction. The landscape is dominated by intensive agricultural land interwoven with patches of forest and smaller settlements (Fig. 2). There are varying densities of ECAs throughout the area. Previous studies have shown that some grasshopper species occur in higher densities in ECAs, while others occur in equally high densities on intensive agricultural land and ECAs (Albrecht et al. 2010).
Furthermore, a large part of our study belongs to the Smaragd-area Oberaargau, which is one of the areas in the Swiss-wide species protection project Smaragd (www.smaragdoberaargau.ch). In this area, habitat maintenance and connectivity measures are being implemented for various endangered focal species. For instance, through special mowing regimes agricultural drainage ditches and small streams are upgraded for the endangered damselfly Coenagrion mercuriale (www.smaragdoberaargau.ch/index.
php?option=com_content&view=article&id=104&Itemid=99). The valley basins in our study area used to be characterized by seasonally flooded hay meadows (German: Wässermatten). This practice was aban-doned at the beginning of the 20th century, but recently hay meadows are being flooded again to restore their former unique flora and fauna (Boschi et al. 2003). A hygrophilic species that thrives particularly well on these floodplains is the large marsh grasshopper Stethophyma grossum.
Fig. 2. Typical landscape in the Oberaargau study area. Intensive agricultural land is interspersed with forest patches and residential areas. In the background one can see the Jura mountains. Photo: M.J. van Strien.
Datasets
We chose the Southern Damselfly Coenagrion mercuriale as focal species for ditch habitats. The breed-ing habitat of this species in the Oberaargau are slow-flowbreed-ing, calcareous, summer-warm streams and ditches (Sternberg et al. 1999). However, it is unknown whether the ditches currently inhabited by C. mer-curiale are functionally connected. A previous study (Hepenstrick 2008) has located and characterised all existing populations in the study area. Thus, a full sampling was possible for this species. We performed both a mark-release-resight study and a genetic analysis. In May and June 2009 we marked 455 individu-als at three different ditches (Fig. 3). We then determined longevity, resight-rate and maximal movement distance. After the mating season in July 2009, we also sampled 450 mid-legs of C. mercuriale individuals from 19 stream sections (Fig. 4) for genetic analysis with microsatellites (Watts et al. 2004).
The larger marsh grasshopper Stethophyma grossum can be found in wetland areas throughout the Oberaargau (Fig. 5). Development of eggs and larvae depends on high moisture content of the top soil (Marzelli 1995; Koschuh 2004). The breeding habitat of this species is, therefore, characterized by the availability of moist areas. In August and September 2010, we checked all streams, ditches, valley bot-toms and other moist areas in the Oberaargau for populations of S. grossum (Fig. 4). We sampled 963 individuals from 53 locations. At each location we took tissue samples from up to 30 individuals. These individuals were genotyped with microsatellites that we developed for this study making use of next gen-eration sequencing techniques (Keller et al. in press).
Fig. 3. Two male Coenagrion mercuriale. Markings were applied to wings in order to recognise individuals in the mark-release-resight study. Photo: D. Keller.
Fig. 4. Overview of sampling designs of several landscape genetic studies investigating insect dispersal in the Oberaargau in 2009 and 2010.
Previous studies in Switzerland have observed an increase of Orthopteran species on newly established ECAs, but less so on surrounding intensive agricultural land (Schneider and Walter 2001). Therefore, we chose several grassland grasshopper species to determine if their reproductive habitat (i.e. ECAs) was also their main migration habitat. Albrecht et al. (2010) classified grasshopper species based on their affinity to ECAs. Stenotopic species predominantly occurred in ECAs, disperser species occurred in high densities in ECAs and their abundance decreases with distance to the ECA, and ubiquist species occurred in equal quantities in and outside ECAs. We selected two disperser (Chorthippus biguttulus and Gomphocerippus rufus) and one ubiquist (Chorthippus albomarginatus) as study species. No a priori population location information was available for these species in our study area. Therefore, we randomly selected 200 sample points of which half were located in ECAs and half on intensive agricultural land (Fig. 4). We checked each location for the presence of these species and sampled up to ten individuals per species and location. Since no microsatellite markers were available for the three study species, genetic analysis was performed using amplified fragment length polymorphisms (AFLP).
Landscape genetic methods
Genetic clustering analysis combined with kriging interpolation and the overlay technique
Bayesian genetic clustering methods group individuals into clusters based on genetic data. Some methods exclude (e.g. STRUCTURE; Pritchard et al. 2000) and others include spatial information (TESS; Chen et al. 2007).
For each individual, the assignment probability to each cluster is calculated, which can afterwards be interpolated over the entire study area using kriging. The resulting interpolated grids can then be used to detect landscape barriers to gene flow by overlaying land cover maps (i.e. the overlay approach; Storfer et al. 2010).
Fig. 5. Mating pair of the larger marsh grasshopper Stethophyma grossum in the Oberaargau region. Photo: D. Keller.
Corridor/transect analysis
Corridor (transect) analysis assesses landscape elements in straight-line corridors of a certain width between all pairs of populations (e.g. Emaresi et al. 2011). By regressing a distance matrix composed of genetic differentiation (pairwise FST) against distance matrices of landscape elements using multiple regression analysis on distance matrices (Lichstein 2007), landscape elements that enhance or hinder gene flow can be identified.
Least cost transect analysis (LCTA)
To quantify the landscape between two populations, landscape geneticists often use resistance-to-move-ment surfaces (Adriaensen et al. 2003). However, parameterization of resistance surfaces is usually a subjective process. In contrast, transect-based approaches, as used above, might oversimplify dispersal patterns by assuming rectilinear migration between populations. In order to overcome these shortcom-ings, we developed an approach that combines these two techniques: least‐cost transect analysis (LCTA). In a first step, binary resistance surfaces representing migration habitat and matrix are created.
Each selected landscape element is in turn regarded as preferred migration habitat, regardless of its hypothesized inhibitive or facilitative nature. In a second step, least-cost paths are calculated between all population pairs from these resistance surfaces. This path is subsequently buffered to create a transect of a certain width. The effective distance and the proportion of each landscape element is calculated in the transect to form a set of landscape predictor variables per resistance surface. As in the above corridor/
transect approach, we then use regression analyses to determine the most likely migration habitat and those landscape elements in the transects that enhance or inhibit gene flow.
Simulation study
In the simulation program, populations were randomly placed in a habitat-matrix landscape. The propor-tion of habitat and the level of habitat fragmentapropor-tion can be varied for each landscape. Probability of mi-gration between populations is derived from a mimi-gration distribution (Ibrahim et al. 1996). This distribution can be the result of a random walk (i.e. a normal distribution) or the result of many sedentary individuals and several long-distance migrators per population (i.e. a leptokurtic distribution). Each generation, in-dividuals either remain in their source population or migrate to other populations. Over a certain number of generations the genetic differentiation between populations is assessed. We can then determine if the input settings can be retrieved from a landscape genetic analysis of the data.
Network topology
Network analysis has recently been introduced for assessing connectivity in ecology and conservation biology (Urban et al. 2009; Urban and Keitt, 2001). A network consists of a set of nodes, which are con-nected by edges. In ecology, habitats or populations can be defined as nodes and potential migration represents edges (Galpern et al. 2011). Edges are usually represented by geographic distances and often, nodes are only connected if their intermediate distance is below a certain distance threshold, which is the maximum migration distance of a species. Network topology can identify well-connected communi-ties of populations as well as “weak”, i.e. hardly connecting edges. By creating weighted networks, where edges are given weights according to genetic distances, for instance pairwise genetic differentiation (FST), connectivity can further be analysed.