A novel framework for evaluating in-situ breeding management strategies in endangered populations.

Abstract

Conservation breeding management aims to reduce inbreeding and maximize the retention of genetic diversity in endangered populations. However, breeding management of wild populations is still rare, and there is a need for approaches that provide data-driven evidence of the likelihood of success of alternative in-situ strategies. Here, we provide an analytical framework that uses in-silico simulations to evaluate, for real wild populations, (1) the degree of population-level inbreeding avoidance, (2) the genetic quality of mating pairs, and (3) the potential genetic benefits of implementing two breeding management strategies. The proposed strategies aim to improve the genetic quality of breeding pairs by splitting detrimental pairs and allowing the members to re-pair in different ways. We apply the framework to the wild population of the Critically Endangered helmeted honeyeater by combining genomic data and field observations to estimate the inbreeding (i.e. pair-kinship) and genetic quality (i.e. Mate Suitability Index) of all mating pairs for seven consecutive breeding seasons. We found no evidence of population-level inbreeding avoidance and that ~91.6% of breeding pairs were detrimental to the genetic health of the population. Furthermore, the framework revealed that neither proposed management strategy would significantly improve the genetic quality or reduce inbreeding of the mating pairs in this population. Our results demonstrate the usefulness of our analytical framework for testing the efficacy of different in-situ breeding management strategies and for making evidence-based management decisions.