Hierarchical models of animal abundance and occurrence

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 11; no. 3; pp. 249 - 263
• Main Authors: Royle, J.A, Dorazio, R.M
• Format: Journal Article
• Language: English
• Published: Washington, DC American Statistical Association and the International Biometric Society 01.09.2006; American Statistical Association; International Biometric Society
• Subjects: Agronomy. Soil science and plant productions; animal ecology; Animals; Bayesian theory; Biological and medical sciences; Biometrics, statistics, experimental designs, modeling, agricultural computer applications; computer software; Ecological modeling; equations; Estimators; Fish; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; Inference; mathematical models; Metapopulation ecology; Modeling; Multilevel models; Parametric models; population density; population dynamics; Spatial models; statistical analysis; Bayes estimation; Animal model; Site occupancy; Point-count sampling; Removal sampling; Empirical Bayes; Capture recapture method; Capture-recapture; Observer; Multiple observer sampling; Sampling; Bayesian analysis; Occurrence; Ecological abundance
• ISSN: 1085-7117; 1537-2693
• DOI: 10.1198/108571106X129153

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.