Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology

Presence-only data, point locations where a species has beenrecorded as being present, are often used in modeling the distribu-tion of a species as a function of a set of explanatory variables—whether to map species occurrence, to understand its associationwith the environment, or to predict its response to environmentalchange. Currently, ecologists most commonly analyze presence-onlydata by adding randomly chosen “pseudo-absences” to the data suchthat it can be analyzed using logistic regression, an approach whichhas weaknesses in model specification, in interpretation, and in imple-mentation. To address these issues, we propose Poisson point processmodeling of the intensity of presences. We also derive a link betweenthe proposed approach and logistic regression—specifically, we showthat as the number of pseudo-absences increases (in a regular or uni-form random arrangement), logistic regression slope parameters andtheir standard errors converge to those of the corresponding Poissonpoint process model. We discuss the practical implications of theseresults. In particular, point process modeling offers a framework forchoice of the number and location of pseudo-absences, both of whichare currently chosen by ad hoc and sometimes ineffective methods inecology, a point which we illustrate by example.