POISSON POINT PROCESS MODELS SOLVE THE PSEUDO-ABSENCE PROBLEM FOR PRESENCE-ONLY DATA IN ECOLOGY

• 成果类型: Article
• 署名作者: Warton, David I.; Shepherd, Leah C.
• 署名单位: University of New South Wales Sydney; University of New South Wales Sydney
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/10-AOAS331
• 发表日期: 2010
• 页码: 1383-1402
• 关键词: predicting species distributions

摘要

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.