DOI: 10.5061/dryad.79cnp5hx3
title: Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

description: The performance of species distribution models is known to be affected by
 the analysis grain and the positional error of species occurrences.
 Coarsening of the spatial analysis grain has been suggested to compensate
 for positional errors. Nevertheless, this way of dealing with positional
 errors has never been thoroughly tested. With increasing use of fine-scale
 environmental data in predictive models developed for conservation and
 climate change studies it is increasingly important to test this
 assumption. Species distribution models using fine-scale environmental
 data are more likely to be negatively affected by positional error as the
 inaccurate species occurrences might easier end up in unsuitable
 environment, which can result in inappropriate conservation actions. Here,
 we examine the trade-offs between positional error and analysis grain and
 provide recommendations for best practice. We generated virtual species
 using tree canopy height, topography wetness index, and altitude derived
 from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the
 positional error in the range of 5 m to 99 m and evaluated the effects of
 several spatial grains in the range of 5 m to 500 m. In total, we assessed
 49 combinations of positional accuracy and analysis grain. We used three
 common modelling techniques (MaxEnt, BRT and GLM) and four discrimination
 metrics to evaluate model performance (Sørensen index, overprediction and
 underprediction rate, AUC and TSS). We found that model performance
 decreased with increasing positional error in species occurrences and
 coarsening of the analysis grain. Most importantly, we showed that
 coarsening the analysis grain to compensate for positional error did not
 improve model performance. Our results reject coarsening of the analysis
 grain as a solution to address the negative effects of positional error on
 model performance. We recommend fitting models with the finest possible
 analysis grain (i.e., depending on data availablity) even when available
 species occurrences suffer from positional errors. If there are
 significant positional errors in species occurrence data, users are
 unlikely to benefit from making additional efforts to obtain higher
 resolution environmental data unless they also minimize the positional
 errors of species occurrences.
description: We used the <em>virtualspecies </em>package (ver.
 1.5) in  the statistical software R (R Core Team 2021) to generate virtual
 species. To begin, we defined the response of virtual species to the
 environmental gradient at a resolution of 5 x 5 m (i.e. the finest
 resolution at which environmental variables were available). We used a
 normal distribution with the following parameters: (i) mean canopy height
 of 9 m and standard deviation of 4 m; (ii) mean altitude of 846 m and
 standard deviation of 100 m; and (iii) mean TWI of 8 and standard
 deviation of 0.4 m. These parameters allowed us to simulate virtual
 species with a narrow niche breadth as it has been suggested that SDMs of
 such species are more prone to positional error. We then multiplied the
 responses to obtain an environmental suitability raster. We applied the
 probabilistic approach (logistic function with α = −0.05 and β = 0.3) to
 convert the environmental suitability raster into probabilities of
 occurrences that were subsequently used to sample binary presence-absence
 rasters. We developed both presence-only and presence-absence models (see
 below), using 99 presence sites and 200 absence sites (i.e. sample
 prevalence of 0.33), and a uniform random distribution for sampling
 species presences and absences.