Data and R scripts for: Effects of hunting on genetic diversity, inbreeding and dispersal in Finnish black grouse (Lyrurus tetrix)

While intensive hunting activities, such as commercial fishing and trophy hunting, can have profound influences on natural populations, less intensive recreational hunting can also have more subtle effects on animal behaviour, habitat use and movement, with implications for population persistence. Lekking species such as the black grouse (Lyrurus tetrix) may be especially prone to hunting as leks are temporally and spatially predictable, making them easy targets. Furthermore, inbreeding in black grouse is mainly avoided through female-biased dispersal, so any disruptions to dispersal caused by hunting could lead to changes in gene flow, leading to an increasing risk of inbreeding. We therefore investigated the impact of hunting on genetic diversity, inbreeding and dispersal on a metapopulation of black grouse in Central Finland. We genotyped 1,065 adult males and 813 adult females from twelve lekking sites (six hunted, six unhunted) and 200 unrelated chicks from seven sites (two hunted, five unhunted) at up to thirteen microsatellite loci. Our initial confirmatory analysis of sex-specific fine-scale population structure revealed little genetic structure in the metapopulation. Levels of inbreeding did not differ significantly between hunted and unhunted sites in both adults and chicks. However, immigration rates into hunted sites were significantly higher among adults compared to immigration into unhunted sites. We conclude that the influx of migrants into hunted sites may compensate for the loss of harvested individuals, thereby increasing gene flow and mitigating inbreeding. Given the absence of any obvious potential barriers to gene flow in Central Finland, a spatially heterogeneous matrix of hunted and unhunted regions may be crucial to ensure sustainable harvests into the future.This repository contains the raw data and R scripts executed to produce the manuscript results and its figures. The full workflow including other softwares as well as intermediate data files can be found at https://github.com/rshuhuachen/blackgrouse-hunting. To execute the workflow, execute the R scripts in order from 1 - 7 in R or RStudio. Raw genotypes are uploaded as .stru files, formatted to be analysed with STRUCTURE software, as well as .csv files where one row represents one individual. To open the raw data files, any text editor will suffice, and to execute the workflow, R is required with the necessary packages installed. Please refer to the README file for full details on the scripts and raw data files.Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: 454606304Funding provided by: Academy of FinlandCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341Award Number: 7211271We extracted genomic DNA from whole blood using a BioSprint 15 DNA Blood Kit (Qiagen, Ref. 940017) and a Kingfisher magnetic particle processor. Individual male black grouse were genotyped at 12 autosomal microsatellite loci (BG6, BG15, BG16, BG18, BG19, BG20 (Piertney & Höglund, 2001); TTT1, TTD2, TTD3 (Caizergues et al., 2001); TUD6, TUT3, TUT4, (Segelbacher et al., 2000). The adults were additionally genotyped at TTT2 (Caizergues et al., 2001) bringing the total number of microsatellites genotyped in adults and chicks to 13 and 12 respectively. Microsatellite genotyping was performed following the protocol described in (Lebigre et al., 2007)