Mostert, P. S. | bioRxiv Science

bioRxiv2022

intSDM: A reproducible framework for integrated species distribution models

1O_LIThere has been an exponential increase in quantity and type of biodiversity data in recent years, including presence-absence, counts, and presence-only citizen science data. Species Distribution Models (SDMs) have typically been used in ecology to estimate current and future ranges of species, and are a common tool used when making conservation prioritisation decisions. However integration of these data in a model-based framework is needed to address many of the current large-scale threats to biodiversity. C_LIO_LICurrent SDM practice typically underutilizes the large amount of publicly available biodi-versity data and does not follow a set of standard best practices. Integrating different data types with open-source tools and reproducible workflows saves time, increases collaboration opportunities, and increases the power of data inference in SDMs. C_LIO_LIWe aim to address this issue by (1) proposing methods and (2) generating a reproducible workflow to integrate different available data types to increase the power of SDMs. We provide the R package intSDM, as well as guidance on how to accommodate users diverse needs and ecological questions with different data types available on the Global Biodiversity Information Facility (GBIF), the largest biodiversity data aggregator in the world. C_LIO_LIFinally, we provide a case study of the application of our proposed reproducible workflow by creating SDMs for vascular plants in Norway, integrating presence-only and presence-absence species occurrence data and climate data. C_LI

ecology↗

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↗