Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape

Abstract

Red deer (Cervus elaphus) throughout central Europe are influenced by different anthropogenic activities including habitat fragmentation, selective hunting and translocations. This has substantial impacts on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, anthropogenic infrastructures) potentially has led to low levels of genetic diversity. Mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. Based on these findings, we suggest that connectivity among existing management units should be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.

The evolutionary consequences of human–wildlife conflict in cities

“…Heightened road densities in urban environments present a salient environmental challenge that can restrict successful colonization of viable urban habitats. Though taxa from multiple clades are affected, mortality risks are especially high for large vertebrates within cities (Edelhoff et al., 2020; Honda et al., 2018; Johnson et al., 2020) and at the urban–wildland interface (Proctor et al., 2020; St. Clair et al., 2019; Wynn‐Grant et al., 2018), where human‐modified attributes of the landscape and speed limits increase (Neumann et al., 2012). All these factors contribute to the reduced occupancy and population abundances of larger fauna in urban systems.…”

Human-driven genetic differentiation in a managed red deer population

“…In absolute terms however, the data of the Hessian population show rather favourable heterozygosity and lower Fis values in comparison with other national (Poetsch et al 2001;Kuehn et al 2003;Zachos et al 2007;Edelhoff et al 2020) and international studies (Hmwe et al 2006a, b;Nussey et al 2007;Nielsen et al 2008;Sanchez-Fernandez et al 2008;Zsolnai et al 2009). The lowest heterozygosity and the highest F values are typically found in small islet populations and populations with longer history of isolation and low population sizes (Hmwe et al 2006a, b;Hajji et al 2008;Zachos and Hartl 2011;Zachos et al 2016;Edelhoff et al 2020). However, it must be taken into account that both measures are decisively influenced by the markers used, their number, and the sample size (Reiner et al 2019).…”

“…However, Jost's D also achieves a maximum of 0.24 for an area comparison (KF to OD), which realistically no longer permits any genetic exchange due to the intervening traffic and settlement areas (Rhine-Main metropole, several fenced motorways). A comparison with Jost's D values from Schleswig-Holstein (Edelhoff et al 2020) showed 87% higher values for the Hessian areas. Despite the lack of absolute comparability, this at least indicates an even greater differentiation in Hesse than in the clearly isolated areas of Schleswig-Holstein that are marked by inbreeding depressions (Zachos et al 2007).…”

“…Limited gene flow increases genetic drift and with it the risk to loose genetic diversity (Wang and Schreiber 2001;Hartl et al 2003;Perez-Espona et al 2008;Frantz et al 2012;Kropil et al 2015;Edelhoff et al 2020). In the long-term, this could result in increasing degrees of inbreeding (Frankham 2008;Stopher et al 2012;Mukesh et al 2013) and inbreeding depressions (Slate et al 2002;Walling et al 2011).…”

“…The most likely K-value determined by STRUCTURE harvester was only recognised as a crude measure to describe the level of genetic structure patterns. To identify underlying nested clusters, hierarchical STRUCTURE analysis was performed (Pritchard and Wen 2003;Edelhoff et al 2020) by using the clusters of previous runs as input and setting the 'LOCPRIOR' as sampling location. Subsequent analyses were done only on the clusters, identified in the previous run.…”