Genetic signature of immigrants and their effect on genetic diversity in the recently established Scandinavian wolf population

Abstract

Transboundary connectivity is a key component when conserving and managing animal species that require large areas to maintain viable population sizes. Wolves Canis lupus recolonized the Scandinavian Peninsula in the early 1980s. The population is geographically isolated and relies on immigration to not lose genetic diversity and to maintain long term viability. In this study we address (1) to what extent the genetic diversity among Scandinavian wolves has recovered during 30 years since its foundation in relation to the source populations in Finland and Russia, (2) if immigration has occurred from both Finland and Russia, two countries with very different wolf management and legislative obligations to ensure long term viability of wolves, and (3) if immigrants can be assumed to be unrelated. Using 26 microsatellite loci we found that although the genetic diversity increased among Scandinavian wolves (n = 143), it has not reached the same levels found in Finland (n = 25) or in Russia (n = 19). Low genetic differentiation between Finnish and Russian wolves, complicated our ability to determine the origin of immigrant wolves (n = 20) with respect to nationality. Nevertheless, based on differences in allelic richness and private allelic richness between the two countries, results supported the occurrence of immigration from both countries. A priori assumptions that immigrants are unrelated is non-advisable, since 5.8% of the pair-wise analyzed immigrants were closely related. To maintain long term viability of wolves in Northern Europe, this study highlights the potential and need for management actions that facilitate transboundary dispersal.

Wolf-dog hybrids arise: new insight into the eastern fringe of the Northern European wolf population

“…< 0.05) less than the expected He = 0.746, which could presume the predominance of inbreeding. This is proved by the obtained coefficient Fis = 0.131, which increased compared to the indexes previously noted for Karelia in 1995-2000 Fis = 0.094 (Aspi et al 2009) and in 1997-2000 Fis = 0.063 (Åkesson et al 2022). In Finland the inbreeding coefficient was also slightly lower in 2007-2009 Fis = 0.108 (Jansson et al 2012).…”

“…At different periods, attempts were made to assess the level of differentiation of these populations using current sets of autosomal STR. Thus, Aspi et al, (2009) using 10, and Jansson et al (2014) 17 microsatellite markers indicate that the Finnish and Karelian populations are differentiated, whereas Åkesson et al (2022) using a set of 26 autosomal STRs did not reveal significant inter-population subdivision.…”

“…Thus, J. Aspi et al (2009) using 10, and E. Jansson et al (2014) using 17 microsatellite markers found that the Finnish and Karelian populations were differentiated, whereas M. Åkesson et al (2022) using a set of 26 autosomal STRs did not reveal significant differentiation between the populations.…”

Demographic history shapes North American gray wolf genomic diversity and informs species' conservation

“…One limitation is that our genetic focus does not explore the fitness effects of such trends; however, such metrics are often central in conservation strategies. Although we currently do not report on fitness‐related consequences, evaluations of such have been conducted on highly bottlenecked and inbred populations like Isle Royale and Scandinavia (Åkesson et al., 2022; Hagenblad et al., 2009; Robinson et al., 2019). The wolves of the northern Rocky Mountains currently have an increased mortality rate due to relaxed regulation.…”

Connecting research and practice to enhance the evolutionary potential of species under climate change

“…Although their broad‐scale use is likely to be limited due to cost, these costs are rapidly decreasing, and the tools can be applied to many types of questions. Examples of use include determining inbreeding rates when inbreeding depression is suspected (e.g., Townsend et al, 2009), assessing effectiveness of genetic restoration efforts (e.g., Weeks et al, 2017), identifying genetic erosion (e.g., Thompson et al, 2019), detecting hybridization (e.g., Garroway et al, 2010), identifying local adaptations (e.g., Peláez et al, 2020), monitoring of genetic diversity (e.g., Hollingsworth et al, 2020), assessing carnivore population size to avoid wildlife‐related conflicts (e.g., Åkesson et al, 2022), evaluating ecosystem resilience (e.g., Wernberg et al, 2018), or acquiring baseline information about genetic diversity for conservation‐planning purposes (e.g., Lorenzana et al, 2020). Moreover, molecular tools can be combined with spatial data to identify factors that are governing genetic structure and connectivity (i.e., landscape genetics) and help practitioners identify which populations are most critical to maintaining or restoring gene flow across a network or metapopulation (Castillo et al, 2016), although care must be taken to ensure appropriate sampling and analyses (Hoffmann, Miller, & Weeks, 2021).…”