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Generalized model-based solutions to false-positive error in species detection/nondetection data.

John D J Clare1, Philip A Townsend1, Benjamin Zuckerberg1

  • 1Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, Wisconsin, 53706, USA.

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|November 15, 2020
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Summary
This summary is machine-generated.

Ecological models often misinterpret data due to false positives. This study presents a generalized model to correct these errors, improving estimates for species abundance and arrival times.

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abundanceimperfect detectionmisclassificationmonitoringphenologyprecision/recallspecies distribution model

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Area of Science:

  • Ecology
  • Ecological Modeling
  • Wildlife Biology

Background:

  • Detection/nondetection data are crucial for ecological studies but are prone to errors like false positives.
  • Existing models primarily address false positives in occupancy estimation, leaving other ecological parameters unaddressed.
  • False-positive errors are common across various sampling methods in ecological research.

Purpose of the Study:

  • To introduce a generalized statistical framework for accounting for false-positive errors in ecological detection/nondetection data.
  • To demonstrate that previous models for false-positive error are specific cases of this generalized structure.
  • To assess the impact of false-positive errors on ecological parameter estimation and the effectiveness of proposed solutions.

Main Methods:

  • Developed a generalized statistical model to incorporate false-positive error correction.
  • Conducted simulations to evaluate bias in abundance and migratory arrival time estimators when ignoring false positives.
  • Applied site-confirmation and observation-confirmation designs within the modeling framework.
  • Validated the model using empirical data on gray fox abundance.

Main Results:

  • Ignoring false-positive error led to severe bias (20-70%) in abundance and migratory arrival time estimates, even with low false-positive rates (5-10%).
  • Bias was exacerbated when false positives occurred in unlikely true detection locations or times.
  • Models incorporating false-positive error correction, particularly using confirmation designs, substantially reduced bias.
  • Empirical data on gray fox abundance confirmed that false positives can distort spatial predictions.

Conclusions:

  • False-positive errors significantly bias ecological estimates beyond occupancy, impacting abundance, phenology, and spatial predictions.
  • The generalized model provides a flexible and effective approach to mitigate false-positive bias across diverse ecological models.
  • Model-based solutions for false-positive error are generalizable and crucial for accurate ecological inference and decision-making.