Nondetection sampling bias in marked presence-only data
Article first published online: 2 DEC 2013
© 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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Ecology and Evolution
Volume 3, Issue 16, pages 5225–5236, December 2013
How to Cite
Ecology and Evolution 2013; 3(16): 5225–5236
- Issue published online: 22 DEC 2013
- Article first published online: 2 DEC 2013
- Manuscript Accepted: 23 OCT 2013
- Manuscript Revised: 21 OCT 2013
- Manuscript Received: 20 AUG 2013
- Platte River Recovery Implementation Program
- National Science Foundation Integrative Graduate Education and Research Traineeship. Grant Number: NSF-DGE-0903469
- Grus americana ;
- inhomogeneous Poisson point process;
- missing data;
- sampling bias;
- species distribution model;
- whooping crane
- Species distribution models (SDM) are tools used to determine environmental features that influence the geographic distribution of species' abundance and have been used to analyze presence-only records. Analysis of presence-only records may require correction for nondetection sampling bias to yield reliable conclusions. In addition, individuals of some species of animals may be highly aggregated and standard SDMs ignore environmental features that may influence aggregation behavior.
- We contend that nondetection sampling bias can be treated as missing data. Statistical theory and corrective methods are well developed for missing data, but have been ignored in the literature on SDMs. We developed a marked inhomogeneous Poisson point process model that accounted for nondetection and aggregation behavior in animals and tested our methods on simulated data.
- Correcting for nondetection sampling bias requires estimates of the probability of detection which must be obtained from auxiliary data, as presence-only data do not contain information about the detection mechanism. Weighted likelihood methods can be used to correct for nondetection if estimates of the probability of detection are available. We used an inhomogeneous Poisson point process model to model group abundance, a zero-truncated generalized linear model to model group size, and combined these two models to describe the distribution of abundance. Our methods performed well on simulated data when nondetection was accounted for and poorly when detection was ignored.
- We recommend researchers consider the effects of nondetection sampling bias when modeling species distributions using presence-only data. If information about the detection process is available, we recommend researchers explore the effects of nondetection and, when warranted, correct the bias using our methods. We developed our methods to analyze opportunistic presence-only records of whooping cranes (Grus americana), but expect that our methods will be useful to ecologists analyzing opportunistic presence-only records of other species of animals.