Assessment of drivers of spatial genetic variation of a ground-dwelling bird species and its implications for conservation

Abstract

In modern wildlife ecology, spatial population genetic methods are becoming increasingly applied. Especially for animal species in fragmented landscapes, preservation of gene flow becomes a high priority target in order to restore genetic diversity and prevent local extinction. Within Central Europe, the Alps represent the core distribution area of the black grouse, Lyrurus tetrix. At its easternmost Alpine range, events of subpopulation extinction have already been documented in the past decades. Molecular data combined with spatial analyses can help to assess landscape effects on genetic variation and therefore can be informative for conservation management. Here, we addressed whether the genetic pattern of the easternmost Alpine black grouse metapopulation system is driven by isolation by distance or isolation by resistance. Correlative ecological niche modeling was used to assess geographic distances and landscape resistances. We then applied regression-based approaches combined with population genetic analyses based on microsatellite data to disentangle effects of isolation by distance and isolation by resistance among individuals and subpopulations. Although population genetic analyses revealed overall low levels of genetic differentiation, the ecological niche modeling showed subpopulations to be clearly delimited by habitat structures. Spatial genetic variation could be attributed to effects of isolation by distance among individuals and isolation by resistance among subpopulations, yet unknown effects might factor in. The easternmost subpopulation was the most differentiated, and at the same time, immigration was not detected; hence, its long-term survival might be threatened. Our study provides valuable insights into the spatial genetic variation of this small-scale metapopulation system of Alpine black grouse.

Keywords: Lyrurus tetrix; conservation genetics; ecological niche modeling; isolation by distance; isolation by resistance; maximum likelihood population effects (MLPE) models.

FIGURE 1

Results of population genetic analyses, ecological niche modeling, and landscape genetic approaches on 195 Styrian black grouse individuals. (a) Digital elevation model of the study area Styria, with all 195 individuals, classified in 10 subpopulations (black outline, 5‐km buffer around presence points, identified by Sittenthaler et al., 2018) and four clusters (green‐, yellow‐, orange‐, and gray‐colored areas of suitable habitat, as identified in this study). Least‐cost‐paths by Linkage Mapper 1.1 were classified into five quantiles of effective resistances calculated by Circuitscape 4.0. The inset shows the area of the Alps (dark gray) provided by the European Environment Agency and the location of our study area (black square). (b) Ecological niche model by MaxEnt 3.4.1, representing the resistance surface

FIGURE 2

Principal component analysis with four retained PCs of the 195 Styrian black grouse genotypes. PC1 (x axis; 3.9% explained variance) versus PC2 (y axis; 3.6% explained variance) (top) and PC1 (x axis, 3.9%) versus PC3 (y axis, 3.5%) (bottom). Different colors indicate the assignment of subpopulations to four clusters

FIGURE 3

Spatial genetic structure of the 195 Styrian black grouse samples as found by memgene 1.0.1 (Galpern et al., 2014). Circles of similar size and color indicate individuals with similar scores (large black and large white circles describe opposite extremes). The first memgene variable explains 28% of the spatial genetic variation and the second and third variable 19% and 15%, respectively. Colored polygons indicate the assignment of subpopulations to the four clusters. Axes in UTM WGS84