Nondetection sampling bias in marked presence‐only data

Nondetection sampling bias in marked presence‐only data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecology and Evolution Wiley

Nondetection sampling bias in marked presence‐only data

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Publisher
Wiley
Copyright
© 2013 Published by John Wiley & Sons Ltd.
ISSN
2045-7758
eISSN
20457758
DOI
10.1002/ece3.887
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Ecology and EvolutionWiley

Published: Dec 1, 2013

References

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