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Modeling false positive detections in species occurrence data under different study designs.

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    False positive detections in species occupancy data can cause significant bias. This study introduces a general framework and three sampling designs to model false positives, improving occupancy estimation accuracy.

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    Area of Science:

    • Ecology
    • Conservation Biology
    • Statistical Modeling

    Background:

    • False positive detections in presence-absence data can severely bias species occupancy pattern estimation.
    • Existing occupancy models may not fully account for observational errors like false positives, especially across diverse sampling designs.

    Purpose of the Study:

    • To develop a general framework for modeling false positive detections in occupancy studies.
    • To extend existing modeling approaches to accommodate a broader range of sampling designs.
    • To provide guidance on selecting appropriate designs and modeling strategies for occupancy research.

    Main Methods:

    • Identified three common sampling designs incorporating ambiguous and known-truth data.
    • Developed models differing in the hierarchical level of known-truth data incorporation (site vs. observation level).
    • Provided likelihoods and R/BUGS code for model implementation.

    Main Results:

    • The proposed framework and models effectively account for false positives across different sampling designs.
    • Demonstrated how incorporating known-truth data at various model levels impacts occupancy estimates.
    • Established clear terminology and practical guidance for researchers.

    Conclusions:

    • Accurate modeling of false positives is crucial for reliable species occupancy estimation.
    • The presented framework offers flexibility and improved accuracy for diverse ecological study designs.
    • Researchers can use the provided tools and guidance to mitigate bias from observational errors.