Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

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 parameter..., We used two sources of Eptesicus serotinus data 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...,