Browsing by Author "Lele, Subhash R."

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    Dealing with detection error in site occupancy surveys: what can we do with a single survey?
    (2012) Lele, Subhash R.; Moreno, Monica; Bayne, Erin
    AIM: Site occupancy probabilities of target species are commonly used in various ecological studies, e.g. to monitor current status and trends in biodiversity. Detection error introduces bias in the estimators of site occupancy. Existing methods for estimating occupancy probability in the presence of detection error use replicate surveys. These methods assume population closure, i.e. the site occupancy status remains constant across surveys, and independence between surveys. We present an approach for estimating site occupancy probability in the presence of detection error that requires only a single survey and does not require assumption of population closure or independence. In place of the closure assumption, this method requires covariates that affect detection and occupancy. METHODS: Penalized maximum-likelihood method was used to estimate the parameters. Estimability of the parameters was checked using data cloning. Parametric boostrapping method was used for computing confidence intervals. IMPORTANT FINDINGS: The single-survey approach facilitates analysis of historical datasets where replicate surveys are unavailable, situations where replicate surveys are expensive to conduct and when the assumptions of closure or independence are not met. This method saves significant amounts of time, energy and money in ecological surveys without sacrificing statistical validity. Further, we show that occupancy and habitat suitability are not synonymous and suggest a method to estimate habitat suitability using single-survey data.
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    Improved estimation of site occupancy using penalized likelihood
    (2010) Moreno, Monica; Lele, Subhash R.
    When detection or occupancy probability is small or when the number of sites and number of visits per site is small, maximum likelihood estimators (MLE) of site occupancy parameters have large biases, are numerically unstable, and the corresponding confidence intervals have smaller than nominal coverage. We propose an alternative method of estimation, based on penalized likelihood. This method is numerically stable, the estimators have smaller mean square error than the MLE, and associated confidence intervals have close to nominal coverage.