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September 2010 Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology
David I. Warton, Leah C. Shepherd
Ann. Appl. Stat. 4(3): 1383-1402 (September 2010). DOI: 10.1214/10-AOAS331


Presence-only data, point locations where a species has been recorded as being present, are often used in modeling the distribution of a species as a function of a set of explanatory variables—whether to map species occurrence, to understand its association with the environment, or to predict its response to environmental change. Currently, ecologists most commonly analyze presence-only data by adding randomly chosen “pseudo-absences” to the data such that it can be analyzed using logistic regression, an approach which has weaknesses in model specification, in interpretation, and in implementation. To address these issues, we propose Poisson point process modeling of the intensity of presences. We also derive a link between the proposed approach and logistic regression—specifically, we show that as the number of pseudo-absences increases (in a regular or uniform random arrangement), logistic regression slope parameters and their standard errors converge to those of the corresponding Poisson point process model. We discuss the practical implications of these results. In particular, point process modeling offers a framework for choice of the number and location of pseudo-absences, both of which are currently chosen by ad hoc and sometimes ineffective methods in ecology, a point which we illustrate by example.


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David I. Warton. Leah C. Shepherd. "Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology." Ann. Appl. Stat. 4 (3) 1383 - 1402, September 2010.


Published: September 2010
First available in Project Euclid: 18 October 2010

zbMATH: 1202.62171
MathSciNet: MR2758333
Digital Object Identifier: 10.1214/10-AOAS331

Keywords: Habitat modeling , occurrence data , pseudo-absences , quadrature points , species distribution modeling

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.4 • No. 3 • September 2010
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