Evaluation of alternative management strategies for maintenance of genetic variation in wildlife populations

Abstract

Wildlife management strategies are often designed around a population's demographic goals, but such strategies also can inadvertently impact genetic variation. For species like bison Bison bison, where management includes the regular removal of individuals to maintain restricted population sizes on constrained landscapes, management actions can be tailored to address genetic diversity retention in addition to simply maintaining a target population size. In this study, we provide an assessment of alternative culling strategies for maintenance of genetic variation in managed wildlife populations. Our primary goal was to compare the long-term retention of genetic variation and accumulation of inbreeding among three types of culling strategies, including one that considered genetic variation directly by measuring variation at a suite of variable loci [mean allele frequency (MAF) strategy], one that used genome-wide measures of variation [mean kinship (MK) strategy] and one that relied solely on demographic information (sex and age; RANDOM). To achieve this goal, we built an individual-based model, parameterized in accordance with bison biology, to project levels of genetic variation and inbreeding over time under each of the three management strategies. Our results suggest wildlife management strategies that incorporate goals for retaining genetic variation (MAF and MK strategies) are better suited to preserving the evolutionary potential of wildlife populations than those that focus solely on a target size and demographic stability (RANDOM). In particular, the MK culling strategy performed the best at maximizing the retention of genome-wide variation. These results extend previous work demonstrating the utility of pedigree-based mate selection strategies in captive population management, and show that such strategies maximize the retention of genome-wide variation under culling practices as well. These models will aid in the long-term management of bison, and can be adapted to other managed wildlife species.

Challenges and science-based implications for modern management and conservation of European ungulate populations

“…There is an urgent need for research to quantify these risks and identify solutions, as well as to develop policies and regulations to manage these emerging threats. Genotyping methods could be implemented as valuable tools to monitor genetic changes in ungulate populations, maintenance of their original genetic structure and to perform more precise censuses, particularly in the case of rare or endangered species (Giglio et al 2016). …”

Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation

“…Even in cases where the evidence for genetic adaptation is strong, management interventions should strive to conserve adaptive variation without eroding genomewide variation (Giglio, Ivy, Jones, & Latch, 2016; Haig, Ballou, & Derrickson, 1990; Spielman, Brook, & Frankham, 2004). Conversely, management actions designed to preserve genomewide variation may either involve risks of disrupting local adaptation to nonclimatic factors (e.g., biotic interactions, soils) if local adaptation is not well understood, or could result in outbreeding depression if individuals from long‐diverged populations are mixed and interbreed (see Frankham et al., 2011 for guidance on when this might occur).…”

swinger: a user‐friendly computer program to establish captive breeding groups that minimize relatedness without pedigree information

“…There is a possible overestimation of genetic diversity in the offspring when using the same markers for estimating relatedness in the breeders. However, reducing average relatedness in breeding groups has been theoretically and empirically demonstrated as an effective method to retain genetic diversity in captive breeding programmes without pedigree information (Sonesson 2001;Allendorf et al 2010;Ivy & Lacy 2012;Giglio et al 2016). Moreover, the use of genomic data (ddRAD, GBS, etc.)…”