Inhomogeneous Poisson point process for species distribution modelling: relative performance of methods accounting for sampling bias and imperfect detection

Species distribution models (SDMs) have become tools of great importance in ecology, as advanced knowledge of suitable species habitat is required for the process of global biodiversity conservation. Presence-only data are the more abundant and readily available data widely used in SDM applications. These data should be treated as a thinned Poisson process to account for detection errors related to sampling bias and imperfect detection that arise in them. Failure to do so could be detrimental to SDM’s predictions. This study assesses the effects of the species abundance, the variation in detection probability, and the number of sites visited in planned surveys on the performance of SDMs accounting for detection errors using simulated data. The results show that the accuracy and precision of estimates differ depending on models and species abundance. Their main difference lies in their ability to estimate ꞵ0, the model intercept. The lower the species abundance, the higher the bias and variance of β̂ 0. Furthermore, the lower the detection probability, the higher the bias and variance of β̂0. However, ꞵ1, the slope parameter, is estimated with almost high accuracy and precision for all models. This study demonstrates the low efficiency of accounting for sampling bias and imperfect detection based on presence-only data alone. Analysing presence-only data in conjunction with point-count outperformed the other approaches, whatever the species abundance, as long as the detection probability is at least 0.25 with average values of detectability covariates. The acceptable accuracy and precision, the minimum number of sites to consider vary depending on species abundance. At least 200 sites are required for the rare species, whereas 50 sites can suffice for the abundant species. Since collecting high-quality data are very expensive, this study emphasizes the need to promote initiatives such as citizen science programs that aim to collect species occurrence data with as little bias as possible.