School of BioSciences - Research Publications
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ItemDesigning occupancy surveys and interpreting non-detection when observations are imperfect(WILEY, 2012-04)Abstract Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non‐detections, and (2) the number of sequential non‐detections necessary to assert, with a pre‐specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species’ occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non‐detections necessary to assert absence with a pre‐specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non‐detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling.
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ItemPopulationViabilityAnalysis(Wiley, 2012-08-31)Abstract Population viability analysis (PVA) assesses risks of population decline of species, and how those risks can be managed. Typically, stochastic population models are used to characterize the deterministic and stochastic components that govern changes in population size. Sources of stochasticity include demographic stochasticity due to the chance birth and death of individuals, stochasticity in the environment that is common to all individuals, chance variation in genetic composition, and spatial structure. These factors can interact to create extinction vortices that drive population decline. Predicted risks of population decline from PVA models are usually uncertain due to imprecise parameter estimates, imperfect knowledge about factors that influence dynamics, and uncertainty about future conditions. Despite these sources of uncertainty, changes in risks due to management can be predicted more reliably. Using these changes in risk, PVA can help determine efficient management strategies for reducing high contemporary rates of extinction.