McMahon, B. J. | Search bioRxiv

bioRxiv2023

Inbreeding or outbreeding depression? How to manage an endangered and locally adapted population of red grouse Lagopus scotica

The balance between the inbreeding and outbreeding continuum is a fundamental debate within conservation genomics. To inform this debate we use genome wide data to show that inbreeding is higher in Irish red grouse (Lagopus scotica) and that this smaller and isolated population exhibits divergence from the British. Outlier analysis identified divergence in several genomic regions linked to genes coding for ecologically important traits such as phenotypic differences in plumage colour and sequence divergence among coding regions in the melanin pathway. We also find differences among immune genes and loci involved in food intake and energy control. The two populations thus appear locally adapted. Divergence between the source and the target population when used for conservation aided restocking may have negative consequences if locally adapted alleles are swamped and/or maladapted genotypes are introduced, leading to outbreeding depression. While avoiding inbreeding by maintaining large populations is important, our study illustrates that conservationists and managers should also consider population divergence and local adaptation. We argue against translocations among Ireland and Britain as a conservation strategy. The results of our study demonstrate the value of focusing on local populations and habitat management when aiming to enhance the conservation status of specific species.

genomics↗

bioRxiv2022

Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance.

The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

ecology↗