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Modeling species occurrence dynamics with multiple states and imperfect detection

January 1, 2009

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture-recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics. ?? 2009 by the Ecological Society of America.

Citation Information

Publication Year 2009
Title Modeling species occurrence dynamics with multiple states and imperfect detection
DOI 10.1890/08-0141.1
Authors D.I. MacKenzie, J. D. Nichols, M.E. Seamans, R. J. Gutierrez
Publication Type Article
Publication Subtype Journal Article
Series Title Ecology
Series Number
Index ID 70035765
Record Source USGS Publications Warehouse
USGS Organization