Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Wintle, Brendan A.; Walshe, Terry; Parris, Kirsten M.; McCarthy, Michael A.

doi:10.1111/j.1472-4642.2011.00874.x

Cited by 71 publications

(76 citation statements)

References 35 publications

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“…The probability of obtaining 3 nondetections if the site is truly occupied is only 3.6% (i.e., (1 − 0.66) 3 ; McArdle, 1990, Wintle, Walshe, Parris, & McCarthy, 2012, thus the model is unlikely to conclude that sites 2 or 3 are occupied. The probability of obtaining 3 nondetections if the site is truly occupied is only 3.6% (i.e., (1 − 0.66) 3 ; McArdle, 1990, Wintle, Walshe, Parris, & McCarthy, 2012, thus the model is unlikely to conclude that sites 2 or 3 are occupied.…”

Section: Discussionmentioning

confidence: 99%

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

Cruickshank

Bühler²,

Schmidt

2019

Conservat Sci and Prac

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Data collected by volunteers are an important source of information used in species management decisions, yet concerns are often raised over the quality of such data.Two major forms of error exist in occupancy datasets; failing to observe a species when present (imperfect detection-also known as false negatives), and falsely reporting a species as present (false-positive errors). Estimating these rates allows us to quantify volunteer data quality, and may prevent the inference of erroneous trends. We use a new parameterization of a dynamic occupancy model to estimate and adjust for false-negative and false-positive errors, producing accurate estimates of occupancy. We validated this model using simulations and applied it to 12 species datasets collected from a 15-year, large-scale volunteer amphibian monitoring program. False-positive rates were low for most, but not all, species, and accounting for these errors led to quantitative differences in occupancy, although trends remained consistent even when these effects were ignored. We present a model that represents an intuitive way of quantifying the quality of volunteer monitoring datasets, and which can produce unbiased estimates of occupancy despite the presence of multiple types of observation error. Importantly, this allows the quality of volunteer monitoring data to be assessed without relying on comparisons with expert data.

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Section: Discussionmentioning

confidence: 99%

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

Cruickshank

Bühler²,

Schmidt

2019

Conservat Sci and Prac

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“…; Wintle et al . ). For example, wildlife management agencies must assess the risk to endangered bats from proposed development projects, such as wind energy projects (U.S.…”

Section: Methodsmentioning

confidence: 97%

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

Clement

Rodhouse

Ormsbee

et al. 2014

Journal of Applied Ecology

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Summary1. Acoustic surveys have become a common survey method for bats and other vocal taxa. Previous work shows that bat echolocation may be misidentified, but common analytic methods, such as occupancy models, assume that misidentifications do not occur. Unless rare, such misidentifications could lead to incorrect inferences with significant management implications. 2. We fit a false-positive occupancy model to data from paired bat detector and mist-net surveys to estimate probability of presence when survey data may include false positives. We compared estimated occupancy and detection rates to those obtained from a standard occupancy model. We also derived a formula to estimate the probability that bats were present at a site given its detection history. As an example, we analysed survey data for little brown bats Myotis lucifugus from 135 sites in Washington and Oregon, USA. 3. We estimated that at an unoccupied site, acoustic surveys had a 14% chance per night of producing spurious M. lucifugus detections. Estimated detection rates were higher and occupancy rates were lower under the false-positive model, relative to a standard occupancy model. Un-modelled false positives also affected inferences about occupancy at individual sites. For example, probability of occupancy at individual sites with acoustic detections but no captures ranged from 2% to 100% under the false-positive occupancy model, but was always 100% under a standard occupancy model. 4. Synthesis and applications. Our results suggest that false positives sufficient to affect inferences may be common in acoustic surveys for bats. We demonstrate an approach that can estimate occupancy, regardless of the false-positive rate, when acoustic surveys are paired with capture surveys. Applications of this approach include monitoring the spread of WhiteNose Syndrome, estimating the impact of climate change and informing conservation listing decisions. We calculate a site-specific probability of occupancy, conditional on survey results, which could inform local permitting decisions, such as for wind energy projects. More generally, the magnitude of false positives suggests that false-positive occupancy models can improve accuracy in research and monitoring of bats and provide wildlife managers with more reliable information.

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“…A number of studies have shown how likelihood and Bayesian methods can be used to calculate the probability that a location is negative given that a non-detection has been observed and use this to inform estimates of population abundance (Tyre et al 2003, Mackenzie and Royle 2005, Guillera-Arroita et al 2010, Wintle et al 2012. Hughes et al (2002), for example, show how for plant pathogens the number of samples required to ensure a FIG.…”

Section: Discussionmentioning

confidence: 99%

A generic risk‐based surveying method for invading plant pathogens

Parnell

Gottwald

Riley

et al. 2014

Ecological Applications

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Abstract. Invasive plant pathogens are increasing with international trade and travel, with damaging environmental and economic consequences. Recent examples include tree diseases such as sudden oak death in the Western United States and ash dieback in Europe. To control an invading pathogen it is crucial that newly infected sites are quickly detected so that measures can be implemented to control the epidemic. However, since sampling resources are often limited, not all locations can be inspected and locations must be prioritized for surveying. Existing approaches to achieve this are often species specific and rely on detailed data collection and parameterization, which is difficult, especially when new arrivals are unanticipated. Consequently regulatory sampling responses are often ad hoc and developed without due consideration of epidemiology, leading to the suboptimal deployment of expensive sampling resources. We introduce a flexible risk-based sampling method that is pathogen generic and enables available information to be utilized to develop epidemiologically informed sampling programs for virtually any biologically relevant plant pathogen. By targeting risk we aim to inform sampling schemes that identify high-impact locations that can be subsequently treated in order to reduce inoculum in the landscape. This ''damage limitation'' is often the initial management objective following the first discovery of a new invader. Risk at each location is determined by the product of the basic reproductive number (R 0 ), as a measure of local epidemic size, and the probability of infection. We illustrate how the risk estimates can be used to prioritize a survey by weighting a random sample so that the highest-risk locations have the highest probability of selection. We demonstrate and test the method using a high-quality spatially and temporally resolved data set on Huanglongbing disease (HLB) in Florida, USA. We show that even when available epidemiological information is relatively minimal, the method has strong predictive value and can result in highly effective targeted surveying plans.

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Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Cited by 71 publications

References 35 publications

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

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

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

A generic risk‐based surveying method for invading plant pathogens

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