Determining Occurrence Dynamics when False Positives Occur: Estimating the Range Dynamics of Wolves from Public Survey DataReport as inadecuate

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Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters extinction and colonization, focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection false negatives and misclassification false positives when estimating occurrence parameters for gray wolves in northern Montana from 2007–2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state’s annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.

Author: David A. W. Miller , James D. Nichols, Justin A. Gude, Lindsey N. Rich, Kevin M. Podruzny, James E. Hines, Michael S. Mitchell



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