Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys

Ferguson, Paige F.B.; Conroy, Michael J.; Hepinstall‐Cymerman, Jeffrey

doi:10.1111/2041-210x.12442

Cited by 31 publications

(49 citation statements)

References 27 publications

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“…Additional complications could be considered. For example, the assumption that detections classified as certain are never false positives might be violated (Ferguson, Conroy & Hepinstall‐Cymerman ). One could investigate the effect of this violation on optimal design or the effect of more conservative or liberal protocols for classifying detections as certain on the accuracy of model estimates (Miller et al .…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, Miller et al . () proposed that surveyors might use two survey methods, one of which generates uncertain detections, while the other only generates certain detections (see Ferguson, Conroy & Hepinstall‐Cymerman for a Bayesian formulation). For example, an acoustic survey might yield uncertain detections (false positives possible) while a capture survey might generate only certain detections (no false positives).…”

Section: Introductionmentioning

confidence: 99%

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Designing occupancy studies when false‐positive detections occur

Clement

2016

Methods Ecol Evol

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Summary Recently, estimators have been developed to estimate occupancy probabilities when false‐positive detections occur during presence–absence surveys. Some of these estimators combine different types of survey data to improve estimates of occupancy. With these estimators, there is a trade‐off between the number of sample units surveyed, and the number and type of surveys at each sample unit. Guidance on efficient design of studies when false positives occur is unavailable. For a range of scenarios, I identified survey designs that minimized the mean square error of the estimate of occupancy. I considered an approach that uses one survey method and two observation states and an approach that uses two survey methods. For each approach, I used numerical methods to identify optimal survey designs when model assumptions were met and parameter values were correctly anticipated, when parameter values were not correctly anticipated and when the assumption of no unmodelled detection heterogeneity was violated. Under the approach with two observation states, false‐positive detections increased the number of recommended surveys, relative to standard occupancy models. If parameter values could not be anticipated, pessimism about detection probabilities avoided poor designs. Detection heterogeneity could require more or fewer repeat surveys, depending on parameter values. If model assumptions were met, the approach with two survey methods was inefficient. However, with poor anticipation of parameter values, with detection heterogeneity or with removal sampling schemes, combining two survey methods could improve estimates of occupancy. Ignoring false positives can yield biased parameter estimates, yet false positives greatly complicate the design of occupancy studies. Specific guidance for major types of false‐positive occupancy models, and for two assumption violations common in field data, can conserve survey resources. This guidance can be used to design efficient monitoring programmes and studies of species occurrence, species distribution or habitat selection, when false positives occur during surveys.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing occupancy studies when false‐positive detections occur

Clement

2016

Methods Ecol Evol

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“…In conclusion, previous studies have shown, both empirically and with simulations, that even when including some level of certain detections, heterogeneity in detection and false occupancy rates can induce bias in estimates of habitat occupancy (Ferguson et al. , Miller et al. , Banner et al.…”

Section: Discussionmentioning

confidence: 73%

“…However, even when false‐positive detection is included in occupancy models, heterogeneity in true and false detection rates can induce bias in estimates of habitat occupancy (Ferguson et al. , Miller et al. , Banner et al.…”

Section: Discussionmentioning

confidence: 99%

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Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

et al. 2019

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Habitat occupancy models, designed to deal with non‐detection of a target species in occupied sites, have been expanded to allow for false‐positive detections when species are mistakenly detected in unoccupied sites. When a subset of the data are unambiguous detections, such occupancy models can produce reliable results. However, if not properly accounted for, heterogeneity in the rate of false‐positive detection between sites may bias estimates of habitat occupancy. We studied habitat occupancy of the southeastern beach mouse (Peromyscus polionotus niveiventris), a species along Florida's Atlantic coast that is threatened due to the reduction of its core range by over 79% since 1950. Southeastern beach mice were detected based on their footprints in track tubes, but because of co‐occurrence of the cotton mouse (Peromyscus gossypinus) with overlapping footprint size, we had to allow for possible false‐positive detections. Differences in the relative abundance of these two species between habitats may have resulted in heterogeneity in the false‐positive rate. By combining uncertain (track tube) and certain (live capture) detection methods, we were able to use covariates to account for heterogeneity in false‐positive detection rates between habitats. Southeastern beach mouse habitat occupancy was higher in coastal dune and strand habitat than in coastal scrub or interior scrub habitats. The rate of false‐positive detections was much higher in coastal scrub habitat than in coastal dune and strand habitat or interior scrub habitat, reinforcing the need for methods that accommodate heterogeneity in the rate of false positives to reduce bias in estimates of habitat occupancy. Using simulations, we show that heterogeneity in the false‐positive detection rate leads to bias in habitat occupancy estimates unless properly accounted for. We also discuss how differences in relative abundance between the target species and a confusing species can directly impact the heterogeneity of false‐positive detections.

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Detection criteria and post‐field sample processing influence results and cost efficiency of occupancy‐based monitoring

Lonsinger

Knight

Waits

2021

Ecological Applications

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Optimization of occupancy‐based monitoring has focused on balancing the number of sites and surveys to minimize field efforts and costs. When survey techniques require post‐field processing of samples to confirm species detections, there may be opportunities to further improve efficiency. We used scat‐based noninvasive genetic sampling for kit foxes (Vulpes macrotis) in Utah, USA, as a model system to assess post‐field data processing strategies, evaluate the impacts of these strategies on estimates of occupancy and associations between parameters and predictors, and identify the most cost‐effective approach. We identified scats with three criteria that varied in costs and reliability: (1) field‐based identification (expert opinion), (2) statistical‐based morphological identification, and (3) genetic‐based identification (mitochondrial DNA). We also considered four novel post‐field sample processing strategies that integrated statistical and genetic identifications to reduce costly genetic procedures, including (4) a combined statistical‐genetic identification, (5) a genetic removal design, (6) a within‐survey conditional‐replicate design, and (7) a single‐genetic‐replicate with false‐positive modeling design. We considered results based on genetic identification as the best approximation of truth and used this to evaluate the performance of alternatives. Field‐based and statistical‐based criteria prone to misidentification produced estimates of occupancy that were biased high (˜1.8 and 2.1 times higher than estimates without misidentifications, respectively). These criteria failed to recover associations between parameters and predictors consistent with genetic identification. The genetic removal design performed poorly, with limited detections leading to estimates that were biased high with poor precision and patterns inconsistent with genetic identification. Both statistical‐genetic identification and the conditional‐replicate design produced occupancy estimates comparable to genetic identification, while recovering the same model structure and associations at cost reductions of 67% and 74%, respectively. The false‐positive design had the lowest cost (88% reduction) and recovered patterns consistent with genetic identification but had occupancy estimates that were ˜32% lower than estimated occupancy based on genetic identification. Our results demonstrate that careful consideration of detection criteria and post‐field data processing can reduce costs without significantly altering resulting inferences. Combined with earlier guidance on sampling designs for occupancy modeling, these findings can aid managers in optimizing occupancy‐based monitoring.

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Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys

Cited by 31 publications

References 27 publications

Designing occupancy studies when false‐positive detections occur

Designing occupancy studies when false‐positive detections occur

Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

Detection criteria and post‐field sample processing influence results and cost efficiency of occupancy‐based monitoring

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