DOI: 10.5061/dryad.zgmsbcch5
title: Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

description: The ever-growing popularity of citizen science, as well as recent
 technological and digital developments, have allowed the collection of
 data on species’ distributions at an extraordinary rate. In order to take
 advantage of these data, information of varying quantity and quality needs
 to be integrated. Point process models have been proposed as an elegant
 way to achieve this for estimates of species distributions. These models
 can be fitted efficiently using Bayesian methods based on integrated
 nested Laplace approximations (INLA) with stochastic partial differential
 equations (SPDE). This approach uses an efficient way to model spatial
 autocorrelation using a Gaussian random field and a triangular mesh over
 the spatial domain. The mesh is constructed by user-defined variables, so
 effectively represents a free parameter in the model. However, there is a
 lack of understanding about how to set these mesh parameters, and their
 effect on model performance. Here, we assess how mesh parameters affect
 predictions and model fit to estimate the distribution of the serotine
 bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was
 fitted using five meshes of varying densities to a dataset comprising both
 structured observations from a national monitoring programme and
 opportunistic records. We demonstrate that mesh density impacted spatial
 predictions with a general loss of accuracy with increasing mesh
 coarseness. However, we also show that the finest mesh was unable to
 overcome spatial biases in the data. In addition, the magnitude of the
 covariate effects differed markedly between meshes. This confirms that
 mesh parameterisation is an important and delicate process with
 implications for model inference. We discuss how species distribution
 modellers might adapt their use of INLA in light of these findings.
description: We used two sources of
 <em>Eptesicus serotinus</em> data<em> </em>for our
 study, the first from the Field Survey which is part of UK’s Bat
 Conservation Trust’s (BCT) National Bat Monitoring Programme (NBMP). The
 Field Survey consists of a structured mobile acoustic survey where trained
 volunteers walk an approximately 3-kilometre-long transect within a
 randomly allocated 1-kilometre grid square. Counts of the number of bat
 passes are made at 12 points along the transect. For our study, we reduced
 the data to presences and absences (PA) per site (n = 666) because it is
 likely that the counts reflect bat activity (a combination of
 species abundance and time spent in the area)
 rather than true abundance due to their foraging behaviour. There is a
 risk of recording the same bat multiple times, which would add additional
 uncertainty to the analysis. The second dataset was from the National
 Biodiversity Network (NBN) Atlas which combines presence-only (PO) data
 from multiple sources. We excluded data that were not verified by expert
 verifiers, were from the NBMP Field Survey, and where coordinate
 uncertainty was more than 1 kilometre (remaining data n = 1374). For both
 datasets, data from 2005-2015 were used, which maximised the number of
 data points while assuming that the species’ range was stable over the
 chosen time period. We chose the
 following environmental covariates for analysis of the impact of mesh
 dimensions: mean annual temperature (°C)
 averaged across our study period; percentage cover arable land, broadleaf
 woodland, and improved grassland. These covariates were chosen because
 <em>E.
 serotinus</em> roosting sites are known to be
 associated with arable land, improved grassland, and broadleaf
 woodland.
 Their foraging sites are generally determined by the habitat available to
 them around their roosting site, and they are able to exploit a large
 variety of habitats for foraging. All covariate values
 were scaled and centred (mean = 0, SD = 1). Empirical variograms were
 calculated for all four covariates to explore the spatial autocorrelation
 in each.