Contrasting occupancy models with presence-only models: Does accounting for detection lead to better predictions?

Species distribution models are popular statistical tools for inferring potential distribution range of species across space and time and are extensively used in conservation planning. Models based on presence-only data (e.g., MaxEnt) are widely used; however, these models assume perfect species detectability. Occupancy modelling is considered a better modelling technique since it accounts for species detectability. Presence-only models are relatively simpler, requiring only presence locations, while occupancy models are data hungry models requiring detection/non-detection data from multiple visits to the survey sites. We utilized data from the Kerala Bird Atlas (India) and modelled current distribution for 109 species using MaxEnt and occupancy approaches. MaxEnt performed well even with less occurrences, while occupancy model failed for species with fewer than 40 records. In terms of evaluation metrics, AUC and Root Mean Square Error, both models performed relatively better for species with low occurrences than those with high occurrences (generalist species). The comparison metrics (Relative-rank scores, Root Mean Square Error, Hellinger distance and Expectation of Shared Presences) were significantly correlated with the number of occurrences; MaxEnt and occupancy based SDMs for widespread species had more concordance than SDMs of narrowly distributed species. There was some discordance between algorithms with regards to diversity hotspot. Selection of best combination of variables and correction for overprediction can help improve performances of both models and improve consistency between them. Given the data hungry nature of occupancy models and marginal difference with the MaxEnt models; it appears that latter is better suited for predicting the distribution of rare species and studies dealing with cumulative data from multiple species.