The effect of positional error on fine scale species distribution models increases for specialist species

Abstract

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models' performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

“…The effect of imprecise records on niche breadth depends on how well precise records sample geographic and niche space (Moudrý & Šímová 2012). Generally, species that have few records and/or narrower niche breadth will be affected more by the inclusion of imprecise records (Tulowiecki et al 2015;Collins et al 2017;Velásquez-Tibatá et al 2016; but see Gábor et al 2020). This was indeed true in our case.…”

“…To date, the general consensus has been to discard imprecise records because their use can inflate estimates of niche breadth and reduce the apparent accuracy of ecological niche models. This widespread aversion to using spatially imprecise records may be due in part to the fact that nearly all studies evaluating the effects of coordinate imprecision do so by adding spatial error to erstwhile precise records (e.g., Graham et al 2008;Fernandez et al 2009;Osborne & Leitão 2009;Gueta & Carmel 2016;Mitchell et al 2016;Hefley et al 2017;Soultan & Safi 2017;Tulowiecki et al 2015;Gábor et al 2020). Although this approach keeps sample sizes constant between treatments with or without spatially imprecise records, it is not reflective of real-world situations where assessors usually start with a mix of relatively precisely-and impreciselygeolocated records but must decide how to delineate the two groups and whether or not to discard the imprecise ones.…”

“…Importantly, decisions regarding the spatial precision of occurrence points create a trade-off between utilizing a sufficient number of records to represent the full geographic and environmental distribution of a species, and ensuring a high-fidelity match between occurrences and environmental data. On the one hand, the use of imprecise records can inflate niche breadth (Feeley & Silman 2010;Collins et al 2017) and reduce the predictive accuracy and inferential power of ecological niche models (Graham et al 2008;Fernandez et al 2009;Tulowiecki et al 2015;Osborne & Leitão 2009;Gábor et al 2020;Mitchell et al 2016;Cheng et al 2021). These issues arise because environmental values to which imprecise records should be matched remains uncertain.…”

Evaluating the Suitability of Multi-Scale Terrain Attribute Calculation Approaches for Seabed Mapping Applications

“…The disparity between positional accuracy of sonar data and ground-truth samples can be problematic when using terrain attributes to characterize the benthic environment, and may introduce error that negatively impacts downstream habitat maps (G abor et al 2020;Strong 2020). While the positional accuracy of sonar soundings is constrained primarily by the positioning, synchronization, and motion compensation systems of the acquisition platform, positional uncertainty of ground-truth data can be influenced by additional sources such as equipment drift from the vessel.…”

Testing the ability of species distribution models to infer variable importance

“…How do other tests of importance compare to the ones evaluated here? How does data type (presence/ background versus presence/absence versus abundance) affect inference (Gábor et al 2020)? Answering these questions will require expanding beyond the reductionist approach used in this work.…”