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Estimating thresholds in occupancy when species detection is imperfect. 2011

Jay E Jones, and Andrew J Kroll, and Jack Giovanini, and Steven D Duke, and Matthew G Betts
Weyerhaeuser Company, WTC 1B20, P.O. Box 9777, Federal Way, Washington 98063-9777, USA. jay.jones@weyerhaeuser.com

Identification of thresholds (state changes over a narrow range of values) is of basic and applied ecological interest. However, current methods of estimating thresholds in occupancy ignore variation in the observation process and may lead to erroneous conclusions about ecological relationships or to the development of inappropriate conservation targets. We present a model to estimate a threshold in occupancy while accounting for imperfect species detection. The threshold relationship is described by a break-point (threshold) and the change in slope (threshold effect). Imperfect species detection is incorporated by jointly modeling species occurrence and species detection. We used WinBUGS to evaluate the model through simulation and to fit the model to avian occurrence data for three species from 212 sites with two replicate surveys in 2007-2008. To determine if accounting for imperfect detection changed the inference about thresholds in avian occupancy in relation to habitat structure, we compared our model to results from a commonly used threshold model (segmented logistic regression). We fit this model in both frequentist and Bayesian modes of inference. Results of the simulation study showed that 95% posterior intervals contained the true value of the parameter in approximately 95% of the simulations. As expected, the simulations indicated more precise threshold and parameter estimates as sample size increased. In the empirical study, we found evidence for threshold relationships for four species by covariate combinations when ignoring species detection. However, when we included variation from the observation process, threshold relationships were not supported in three of those four cases (95% posterior intervals included 0). In general, confidence intervals for the threshold effect were larger when we accounted for species nondetection than when we ignored nondetection. This model can be extended to investigate abundance thresholds as a function of ecological and anthropogenic factors, as well as multispecies hierarchical models.

UI MeSH Term Description Entries
D011156 Population Density Number of individuals in a population relative to space. Overpopulation,Population Size,Underpopulation,Densities, Population,Density, Population,Population Densities,Population Sizes
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D004463 Ecology The branch of science concerned with the interrelationship of organisms and their ENVIRONMENT, especially as manifested by natural cycles and rhythms, community development and structure, interactions between different kinds of organisms, geographic distributions, and population alterations. (Webster's, 3d ed) Bionomics,Ecologies
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D001717 Birds Warm-blooded VERTEBRATES possessing FEATHERS and belonging to the class Aves. Aves,Bird
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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