Species distribution models: Administrative boundary centroid occurrences require careful interpretation

Describing and understanding species distributions and the factors driving them is fundamental to ecology and biogeography. Species distribution models (SDMs) allow one to investigate objectives of identifying ecologically important factors to the distribution, estimating species-environment responses, predicting the probability of species occurrence, and predicting species presence or absence. Mosquito occurrence records used in SDMs are often imprecise and represented as a centroid of a geopolitical/administrative boundary. Using a virtual species, we investigated the effect of centroids on SDMs and determined which methodology was best suited to provide accurate and applicable conclusions for each of the objectives. We compared 12 distinct algorithms, four levels of pseudo-absences, and three predictor sets to determine the optimal SDM methodology for each objective. The ability of methodology considerations to account for the effects of centroids varied for each objective. Ecolog-ically important predictors were misidentified but could be best approximated by generalized additive models with 10,000 pseudo-absences. Response curves only captured the expected positive or negative trends. Centroids limited SDMs' ability to differentiate expected probabilities, resulting in overprediction of high probability areas. Response curves and occurrence probabilities were best estimated by generalized boosting regression models. Species presence was largely over-estimated within southern regions, but underpredicted in northern regions, and was best estimated by weighted mean ensembles. Overall, generalized boosting regression methods and (weighted) mean ensembles provided the most reliable conclusions across all four objectives. Further, the most reliable conclusions were consistently observed with equal pseudo-absences when considered with the removal of low-contributing predictors, except for predictor identification.