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Much research in conservation biogeography is fundamentally dependent on obtaining reliable data on species distributions across space and time. Such data are now increasingly being generated using various types of public surveys. These data are often integrated with occupancy models to evaluate distributional patterns, range dynamics and conservation status of multiple species at broad spatio-temporal scales. Occupancy models have traditionally corrected for imperfect detection due to false negatives while implicitly assuming that false positives do not occur. However, public survey data are also prone to false-positive errors, which when unaccounted for can cause bias in occupancy estimates. We test whether false positives in a dataset collected from public surveys lead to overestimation of species site occupancy and whether estimators that simultaneously account for false-positive and false-negative errors improve occupancy estimates.
Western Ghats, India.
We fit occupancy models that simultaneously account for false positives and negatives to data collected from a large-scale key informant interview survey for 30 species of large vertebrates. We tested their performance against standard occupancy models that account only for false negatives.
Standard occupancy models that correct only for false negatives tended to overestimate species occupancy due to false-positive errors. Occupancy models that simultaneously accounted for false positives and negatives had greater support [lower Akaike's information criterion (AIC)] and, consistent with predictions, generated systematically lower occupancy estimates than standard models. Furthermore, accounting for false positives improved the accuracy of occupancy estimates despite the added complexity to the statistical estimator.
Integrating large-scale public surveys with occupancy modelling approaches is a powerful tool for informing conservation and management. However, in many if not most cases, it will be important to explicitly account for false positives to ensure the reliability of occupancy estimates obtained from public survey datasets such as key informant interviews, volunteer surveys, citizen science programmes, historical archives and acoustic surveys.