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

Abstract

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.

Robust inference on large‐scale species habitat use with interview data: The status of jaguars outside protected areas in Central America

“…Ultimately, we demonstrated that failure to account for false‐positive detections may lead to substantial overestimation of habitat use, which can be troublesome if the target species is of conservation concern (Miller et al., ). By using untrained citizen accounts of species occurrence, false positives are likely to occur due to misplaced geographical accounts of animal sightings, errors in recall of the timing of detections, species misidentification or people willingly providing a false account.…”

Accounting for false‐positive acoustic detections of bats using occupancy models

“…Here, we have demonstrated an alternative approach that seeks to model false positives, rather than eliminate or ignore them (Royle & Link ; Miller et al . , ). Using a false‐positive occupancy model, we estimated false‐positive rates in acoustic presence/absence surveys for bats and illustrated the effect of false positives on occupancy parameters.…”

“…Fortunately, recently developed models enable unbiased estimation of probability of presence even when misidentifications occur (Royle & Link ; Miller et al . , ). Such models are attractive because they obviate the need for error‐free species identification.…”

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

“…Our dynamic false-positive detection model is based on the Bayesian formulation of the multiple detection method of Miller et al (2013). However, unlike the Miller model, we made use of informative priors, rather than secondary datasets, to resolve identifiability issues.…”

“…Models to allow for the presence of false-positive observations were first developed by Royle and Link (2006) within the context of single-season occupancy models (MacKenzie et al, 2002). Typically this involves jointly analyzing the dataset of interest alongside a second, independent dataset at which a subset of sites are monitored using secondary detection methods in which the probability of false-positive observations is considered impossible (Chambert, Miller, & Nichols, 2015;Miller et al, 2011Miller et al, , 2013. Royle and Link (2006) addressed this issue by forcing a constraint upon the model that the false-positive error rate must be lower than the true detection rate.…”