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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Nondetection sampling bias in marked presence-only data.

Trevor J Hefley1, Andrew J Tyre2, David M Baasch3

  • 1Department of Statistics and School of Natural Resources, University of Nebraska-Lincoln 234 Hardin Hall, 3310 Holdrege Street, Lincoln, Nebraska, 68583.

Ecology and Evolution
|January 24, 2014
PubMed
Summary
This summary is machine-generated.

Species distribution models (SDMs) using presence-only data can be improved by accounting for nondetection and aggregation. New methods treating nondetection as missing data yield more reliable species distribution and abundance estimates.

Keywords:
Grus americanainhomogeneous Poisson point processmissing datanondetectionsampling biasspecies distribution modelwhooping crane

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Area of Science:

  • Ecology
  • Biostatistics
  • Computational Biology

Background:

  • Species distribution models (SDMs) analyze environmental influences on species abundance using presence-only records.
  • Standard SDMs may produce unreliable conclusions due to nondetection sampling bias and ignoring species aggregation behavior.
  • Nondetection sampling bias and aggregation are significant challenges in accurately modeling species distributions.

Purpose of the Study:

  • To develop and test a novel statistical model for species distribution modeling (SDM) that corrects for nondetection sampling bias and accounts for aggregation behavior.
  • To demonstrate that nondetection sampling bias can be effectively treated as missing data within SDMs.
  • To provide a robust method for analyzing opportunistic presence-only records.

Main Methods:

  • Developed a marked inhomogeneous Poisson point process model to simultaneously address nondetection and aggregation.
  • Treated nondetection sampling bias as missing data, utilizing well-established statistical corrective methods.
  • Employed an inhomogeneous Poisson point process for group abundance and a zero-truncated generalized linear model for group size, integrating them to model overall abundance.

Main Results:

  • The proposed methods performed well on simulated data when nondetection was accounted for.
  • Ignoring nondetection bias led to poor performance in the species distribution models.
  • The model successfully integrated group abundance and size to describe species distribution patterns.

Conclusions:

  • Researchers using presence-only data should consider and correct for nondetection sampling bias for more reliable species distribution models.
  • When detection probability data is available, applying methods that correct for nondetection is recommended.
  • The developed methods are broadly applicable to ecological studies using opportunistic presence-only records for various animal species.