DOI: 10.5061/dryad.5mkkwh795
title: Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance

description: Using geospatial data of wildlife presence to predict a species
 distribution across a geographic area is among 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 access to larger amounts of 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 scarcer and
 more spatially restricted presence-absence data, with the lower quality,
 unstructured, but usually more extensive presence-only datasets.
 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. Here, we apply this innovative approach to fit ISDMs to
 empirical data, using presence-absence and presence-only data for the
 three prevalent 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
 estimates 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 checked the performance of the three species-specific
 models using two datasets, independent deer hunting returns and deer
 densities based on faecal pellet counts. Our work clearly demonstrates the
 applicability of spatially-explicit ISDMs to empirical data in a Bayesian
 context, providing a blueprint for managers to exploit unexplored and
 seemingly unusable data that can, when modelled with the proper tools,
 serve to inform management and conservation policies.
description: Presence absence (PA) data PA data for each
 species were obtained from Coillte based on surveys performed in a
 fraction of the 6,000 properties they manage (Table 1) by asking property
 managers (who visit the forests they manage on a regular basis) whether
 deer were present and, if so, what species. Properties range in size from
 less than one to around 2,900 ha, and to assign the PA value to a specific
 location, we calculated the centroid of each property using the function
 st_centroid() from the package sf in R (Pebesma 2018). The survey was
 mainly performed in 2010 and 2013, in addition to further data collected
 between 2014 and 2016. Some properties were surveyed only once in the
 period 2010–2016, but for those that were surveyed more than once, the
 value for that location was considered “absence” if deer had never been
 detected in the property in any of the surveys, and “presence” in all
 other cases. In addition to these surveys, Coillte commissioned density
 surveys based on faecal pellet sampling in a subset of their properties
 between the years 2007 and 2020. Any non-zero densities in these data were
 considered “presences”, and all zeros were considered “absences”. These
 data were also summarised across years when a property had been repeatedly
 sampled and counted as presence if deer had been detected in any of the
 sampling years. PA data for NI were obtained from a
 survey carried out by the British Deer Society in 2016. The survey divided
 the British territory into 100 km<sup>2</sup> grid cells, and
 deer presence or absence was assessed based on public contributions, which
 were then reviewed and collated by BDS experts. Since 100
 km<sup>2</sup> grid cells are quite large, we did not, as with
 the Coillte properties, calculate the centroid of each cell and assign the
 PA value of the cell to it. Instead, we randomly simulated positions
 within each cell and assigned the presence or absence value of the cell to
 each of them. We performed a sensitivity analysis to calculate an optimal
 number of positions that would capture the environmental variability
 within each cell, which was set to 5 random positions per grid cell. After
 processing, we obtained a total of 920 PA data across NI.
 2.2.2 Presence-only (PO) data PO data were
 collected from various sources, mainly (but not only) from citizen science
 initiatives. The National Biodiversity Data Centre (NBDC) is an Irish
 initiative that collates biodiversity data coming from different sources,
 from published studies to citizen contributions. From this repository, we
 obtained all contributions on the three species, a total of 1,430 records.
 To this, we added the 164 records of deer in Ireland downloaded from the
 iNaturalist site, another citizen-contributed database that collects the
 same type of data. From the resulting dataset, we (1) removed all
 observations with a spatial resolution lower than 1
 km<sup>2</sup>; (2) did a visual inspection of the data and
 comments and removed all observations that were obviously incorrect (i.e.
 at sea or that the comment specified it was a different species); (3)
 filtered out all the fallow deer reported in Dublin’s enclosed city park
 (Phoenix Park) since the population there was introduced and is
 artificially maintained and disconnected from the rest of populations in
 Ireland; and (4) filtered duplicate observations by retaining only one
 observation per user, location, and day. The Centre for Environmental Data
 and Recording (CEDaR) is a data repository for Northern Ireland (NI) that
 operates in the same way as the NBDC. They provided 872 records of deer in
 NI, coming from different survey, scientific, and citizen science
 initiatives, from which we removed all records provided with a spatial
 resolution lower than 1 km<sup>2</sup>. The location and
 species of 469 deer culled between 2019 and 2021 in NI were obtained from
 the British Agri-Food and Biosciences Institute. For the observations that
 did not have specific coordinates, we derived them from the location name
 or postcode if provided. As part of a nationally funded initiative to
 improve deer monitoring in Ireland (SMARTDEER), we developed a bespoke
 online tool to facilitate the reporting of deer observations by the
 general public and all relevant stakeholders e.g. hunters, farmers, or
 foresters. Observations were reported in 2021 and 2022 by clicking on a
 map to indicate a 1 km<sup>2</sup> area where deer have been
 observed. For each user and session, we calculated the area of the surface
 covered in squares, simulated a number of positions proportional to the
 size of the polygon, and distributed them within it to generate a number
 of exact positions equivalent to the area where the user had indicated an
 observation. In total, the SMARTDEER tool allowed us to collect 4,078
 presences across Ireland and NI. 2.3.2 Covariate
 selection Raster environmental covariates used in the
 models were obtained from the Copernicus Land Monitoring Service (©
 European Union, Copernicus Land Monitoring Service 2018, European
 Environment Agency EEA), whereas the vector layers (roads, paths) were
 obtained from the Open Street Map service (OpenStreetMap contributors,
 2017. Planet dump [Data file from January 2022]. https://planet.openstreetmap.org). Vector layers were transformed into distance layers (distance to roads, distance to paths) using the distance() function from the package raster, and into density layers (density of roads, paths) using the rasterize() function of the same package (Hijmans 2021). All raster layers were resampled to the lowest resolution available in the used covariates, resulting in a 1 km<sup>2</sup> resolution. A full description of the process of covariate selection (including screening for collinearity) can be found in the supplementary material. The covariates eventually used in the model were elevation (m), slope (degrees), tree cover (%), small woody feature density (%), distances to forest edge (m, positive distances indicate a location outside a forest, negative distances indicate a location within a forest), and human footprint index (Venter et al. 2016, 2018). All covariates were scaled by subtracting the mean and dividing by the standard deviation before entering the model (function scale() from the raster package).
description: Presence absence and presence only data is provided as a .csv
 file and can be opened in most spreadsheet softwares, either proprietary
 or open access. Covariate data is provided as as a grd
 file. Dowloading both the .grd and the .gri files, they can then be loaded
 in any geostatistical software such as ArcGIS (proprietary) or QGIS (open
 access) or in R (open access).