Spatial Genetic Analyses Reveal Cryptic Population Structure and Migration Patterns in a Continuously Harvested Grey Wolf (Canis lupus) Population in North-Eastern Europe

Abstract

Spatial genetics is a relatively new field in wildlife and conservation biology that is becoming an essential tool for unravelling the complexities of animal population processes, and for designing effective strategies for conservation and management. Conceptual and methodological developments in this field are therefore critical. Here we present two novel methodological approaches that further the analytical possibilities of STRUCTURE and DResD. Using these approaches we analyse structure and migrations in a grey wolf ( Canis lupus ) population in north-eastern Europe. We genotyped 16 microsatellite loci in 166 individuals sampled from the wolf population in Estonia and Latvia that has been under strong and continuous hunting pressure for decades. Our analysis demonstrated that this relatively small wolf population is represented by four genetic groups. We also used a novel methodological approach that uses linear interpolation to statistically test the spatial separation of genetic groups. The new method, which is capable of using program STRUCTURE output, can be applied widely in population genetics to reveal both core areas and areas of low significance for genetic groups. We also used a recently developed spatially explicit individual-based method DResD, and applied it for the first time to microsatellite data, revealing a migration corridor and barriers, and several contact zones.

Multi‐level patterns in population genetics: Variogram series detects a hidden isolation‐by‐distance‐dominated structure of Scandinavian brown bears Ursus arctos

“…The structure analysis detected four clusters, but even with a separate analysis testing for IBD, it is challenging to assess the reliability of this result (Frantz, Cellina, Krier, Schley, & Burke, ; Schwartz & McKelvey ). The addition of the recent cluster placement approach for processing structure results (Hindrikson et al., ) added valuable information, indicating asymmetrical dispersal and gene flow. However, also this analysis showed comparably large and strong cluster cores with little overlap, making it hard to evaluate the strength of the structuring.…”

“…We performed ten independent runs for each K , with a burn‐in period of 100,000 Markov–Chain–Monte–Carlo (MCMC) iterations and a subsequent sampling of 1,000,000 MCMC iterations. Individuals’ posterior probabilities (cluster membership values q ) were evaluated by sample placements to determine cluster core areas and areas with low occupancy probability (Hindrikson et al., ). First a raster was placed over the study area, followed by estimating the probability of each raster cell belonging to each cluster, by calculating the inverse distant weighted average ( w = 1/dist.)…”

“…As structure alone is not spatially explicit, we paired it with another recently published approach by Hindrikson et al. (), which evaluates the placement of population clustering using the posterior probability values (Hindrikson et al., ; Plumer et al., ).…”

Combining phylogenetic and demographic inferences to assess the origin of the genetic diversity in an isolated wolf population

“…Samples from WBALK, WCARP and WBALT were not used in the simulations because these populations are still in connection one another and also with unsampled wolf populations in eastern Europe [51,52]. …”

Decades of population genetic research reveal the need for harmonization of molecular markers: the grey wolf Canis lupus as a case study

“…, Hindrikson et al. ). In addition, occasional hybridization with dogs has been reported throughout Europe (Q7; e.g.…”