PT  - JOURNAL ARTICLE
AU  - Virginia Morera-Pujol
AU  - Philip S. Mostert
AU  - Kilian Murphy
AU  - Tim Burkitt
AU  - Barry Coad
AU  - Barry J. McMahon
AU  - Maarten Nieuwenhuis
AU  - Kevin Morelle
AU  - Alastair Ward
AU  - Simone Ciuti
TI  - Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
AID  - 10.1101/2022.05.23.493051
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.23.493051
4099  - http://biorxiv.org/content/early/2022/05/23/2022.05.23.493051.short
4100  - http://biorxiv.org/content/early/2022/05/23/2022.05.23.493051.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.