Models of species distributions are increasingly being used to address a variety of problems in ecology and conservation biology. In many applications, perfect detectability of species, given presence, is assumed. While this problem has been acknowledged and addressed through the development of occupancy models, we still know little regarding whether or not addressing the potential for imperfect detection improves the predictive performance of species distribution models in nature. Here, we ask if explicitly accounting for imperfect detection improves predictive performance of species distribution models relative to approaches that assume perfect detection. We contrast logistic regression models of species occurrence that do not correct for detectability to hierarchical occupancy models that explicitly estimate and adjust for detectability, and maximum entropy models that circumvent the detectability problem by using data from known presence locations only. We use a large-scale, long-term monitoring database across western Montana and northern Idaho to contrast these models for nine landbird species that cover a broad spectrum in detectability.
Results/Conclusions
Overall, occupancy models were similar to or better than other approaches in terms of predictive accuracy, as measured by the Area Under the ROC Curve (AUC) and Kappa, with maximum entropy tending to provide the lowest predictive accuracy. Models varied in the types of errors associated with predictions, such that some model approaches may be preferred over others in certain situations. As expected, predictive performance varied across a gradient in species detectability, with logistic regression providing lower relative performance for less detectable species and Maxent providing lower performance for highly detectable species. Occupancy models showed no strong relationship with detection probability and any source of predictive error, suggesting this approach can perform as well for highly detectable species as for difficult to detect species.
