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Mol Ecol Resour. 2018 May;18(3):580-589. doi: 10.1111/1755-0998.12765. Epub 2018 Feb 24.

A model-based solution for observational errors in laboratory studies.

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Marine Mammal Laboratory, Alaska Fisheries Science Center, National Oceanic and Atmospheric Administration, Seattle, WA, USA.
Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA.
Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA.


Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false-negative and false-positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false-negative errors and more recently have even been used to address false-positive errors, too. Current modelling options assume false-positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false-positive test result. We present an extension to the occupancy modelling framework that allows false-positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model-based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.


eDNA ; false negative; false positive; imperfect detection; occupancy; sensitivity; specificity

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