In genetic clustering, individuals are divided into groups based on their genetic fingerprints. Individuals that are genetically similar are assigned to the same group (or "cluster"), regardless of where they were captured or found. These clusters can be examined in combination with spatial data to deduce information about the movement of animals among populations. This can be used, for example, to investigate whether a particular road presents an obstacle to animals.
Example: blocked wildlife corridor
When wild animals move back and forth between Jura and Central Switzerland, they use the Suret wildlife corridor in the canton of Aargau. This is a bottleneck in the route linking Black Forest, Jura and Central Switzerland, but its permeability is impaired by roads, railway lines and the river Aare.
The Suret wildlife corridor (between the two red lines) in the canton of Aargau. The corridor is intersected by several potential barriers, which are labelled and marked with black lines. From top to bottom: the river Aare, in part dammed, the four-track SBB railway line between Bern and Zurich (unfenced), the T5 cantonal motorway and the A1 national motorway.
In a genetic study of roe deer (Capreolus capreolus), WSL researchers identified which landscape elements in the wildlife corridor presented the largest obstacles. A total of 176 deer from the region were genetically examined and grouped into genetic clusters.
The clustering shows that the two motorways separate the roe deer habitats much more strongly than river Aare. By contrast, the unfenced SBB railway line is permeable and does not currently form a barrier. As a result of this situation, the roe deer populations are isolated to a greater or lesser extent and are genetically different where their habitats are fragmented. Large-scale defragmentation measures are planned to make the wildlife corridor more permeable and the WSL study is helpful in the design of new wildlife under- or overpasses. The study will be repeated in a few years’ time to investigate whether the defragmentation measures implemented have achieved the desired effect and have connected the regional deer populations.