Generalized site occupancy models allowing for false positive and false negative errors.
ABSTRACT Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.
SourceAvailable from: Krithi Karanth[Show abstract] [Hide abstract]
ABSTRACT: Aim Information on patterns and determinants of spatial distributions remains poorly available for many widespread species of conservation importance. The sloth bear Melursus ursinus in the Indian subcontinent exemplifies this requirement. We aimed at assessing (1) distribution patterns of sloth bears at two spatial scales, (2) ecological and anthropogenic factors that determine bear occupancy. Location We estimated sloth bear habitat occupancy at a nationwide scale across India and at the landscape scale (38, 540 km 2) in the Western Ghats of
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ABSTRACT: AimInformation on patterns and determinants of spatial distributions remains poorly available for many widespread species of conservation importance. The sloth bear Melursus ursinus in the Indian subcontinent exemplifies this requirement. We aimed at assessing (1) distribution patterns of sloth bears at two spatial scales, (2) ecological and anthropogenic factors that determine bear occupancy.LocationWe estimated sloth bear habitat occupancy at a nationwide scale across India and at the landscape scale (38, 540 km2) in the Western Ghats of Karnataka, India.Methods We used a grid-based occupancy approach to determine sloth bear distribution patterns. At the nationwide scale, we used data from questionnaire surveys of field experts (grid-cell size ~2818 km2; 1326 cells). At the landscape scale, we conducted field surveys of bear signs (grid-cell size = 188 km2; 205 cells). Detection/non-detection data from both surveys were analyzed using occupancy modelling methods that account for imperfect detection. We examined the influence of scale-specific ecological and social covariates on patterns of occupancy.ResultsNationwide, sloth bears occupied an estimated 67% of plausible bear habitat in contrast to 46% derived from methods that disregard detectability. Bear distribution was positively influenced by deciduous forests, scrub and barren areas, regions with high human densities and cultural tolerance. At the landscape scale, bears occupied 61% of the area versus 54% estimated from methods ignoring detectability. Occupancy probabilities increased with forest cover and topographic heterogeneity, whereas annual precipitation and human disturbance showed negative effects.Main conclusionsOur study underlines the need to integrate human-modified areas with existing conservation landscapes. Given its widespread nature, functional role, conservation status and relatively benign interactions with humans, we propose recognizing sloth bear as an umbrella species for securing unprotected habitats in India. Protection of widespread species like the sloth bear in other landscapes may complement current conservation strategies for large mammalian communities.Diversity and Distributions 05/2015; DOI:10.1111/ddi.12335 · 5.47 Impact Factor
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ABSTRACT: Data quality is a common source of concern for large-scale citizen science projects like eBird. In the case of eBird, a major cause of poor quality data is the misiden-tification of bird species by inexperienced contributors. A proactive approach for improving data quality is to discover commonly misidentified bird species and to teach inexperienced birders the differences between these species. To accomplish this goal, we develop a latent variable graphical model that can identify groups of bird species that are often confused for each other by eBird participants. Our model is a multi-species extension of the classic occupancy-detection model in the ecology literature. This multi-species extension requires a structure learning step as well as a computationally expensive parameter learning stage which we make efficient through a variational approximation. We show that our model can not only discover groups of misidentified species, but by including these misidentifications in the model, it can also achieve more accurate predictions of both species occupancy and detection.Proceedings of the 28th Conference on Artificial Intelligence (AAAI), Quebec City, Quebec, Canada; 07/2014