# High-resolution analysis of red deer (Cervus elaphus) management units in a Central European region of high human population density reveals severe effects on genetic diversity and differentiation

## TL;DR

This study shows that high human population density in Germany severely impacts red deer genetic diversity and connectivity, with some areas being highly isolated.

## Contribution

The study provides high-resolution genetic data on red deer management units in a densely populated region, enabling targeted wildlife management.

## Key findings

- 30% of the AMUs were highly isolated with effective population sizes (Ne) < 100.
- 47.5% of the AMUs showed clear connectivity and could be grouped into 4 larger regions.
- Isolated areas had low genetic diversity and high homozygosity.

## Abstract

The threat of isolation to red deer (Cervus elaphus) has been described in numerous European studies. The consequences range from reduced genetic diversity and increased inbreeding to inbreeding depression. It has been shown that the underlying factors cannot be generalised, but vary greatly in their effects depending on local conditions. The aim of this study was to analyse in detail the genetics of red deer in a large German federal state with a population density of 532 inhabitants per km2 and 23.8% settlement and traffic area, in order to generate data for future management of the region. 1199 individual samples of red deer were collected in all 20 Administrative Management Units (AMUs) and compared with existing results from the neighbouring state of Hesse (19 AMUs). All 2490 individuals from both states were clustered using Bayesian methods and connectivity between neighbouring AMUs was quantified. Overall, 30% of the AMUs were found to be highly isolated, mostly with effective population sizes (Ne) < 100. In contrast, 47.5% of the AMUs still had clear connectivity, allowing them to be merged into 4 larger red deer regions. For the small isolated areas, low genetic diversity was found in units with high homozygosity and low Ne. With high sampling density and identical methodology, detailed information on AMUs can be obtained and the degree of vulnerability of individual AMUs as part of the overall population can specifically be validated. Such data can help improve future wildlife management.