Webinar on Using Unmarked: An R Package for Fitting Hierarchical Models of Species Abundance and Occurrence

Register for the webinar now at: https://www1.gotomeeting.com/register/422553153
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For more information, please contact Richard Chandler (rchandler@usgs.gov)

Instructors: Andy Royle and Richard Chandler; USGS Patuxent Wildlife Research Center
Date: 25-26 Jan (Wed/Thur) 2012, 1:30 PM EST – 4:00 PM EST (US Eastern Standard Time)
Venue: On-line
Cost: Free
Prerequisites: A working knowledge of modern regression methods (GLMs, mixed models) and the R programming language is required.

Modeling spatial and temporal variation in abundance and occurrence lies at the core of ecology and its applications such as conservation, wildlife management and monitoring science. Many sampling protocols have been devised for obtaining information about species abundance and occurrence when observations are subject to imperfect detection of individuals or species. Examples include occurrence sampling, repeated counts, removal models, double observer models, and distance sampling. Inference about such data is conveniently based on hierarchical models, which include a model of the underlying state variable (e.g., presence or absence at a site), and a model of the conditional detection process (e.g., probability of detection given presence). The hierarchical modeling framework is also convenient for modeling the state and observation processes using spatial and temporal covariates.

The new R package unmarked (Fiske and Chandler 2011) contains functions to analyze hierarchical models using likelihood and classical frequentist methods. It includes some classes of models which are not available using any other software package. For example, hierarchical distance sampling models and distance sampling models for open populations. This course introduces key hierarchical models used in the analysis of abundance and species occurrence. We provide an overview of the design and basic functionality of unmarked and provide detailed examples of a number of specific functions including:
- site-occupancy models (MacKenzie et al. 2002, 2003)
- binomial and multinomial N-mixture (Royle 2004a,b; Dorazio et al. 2005)
- hierarchical distance sampling models (Royle et al. 2004)
- dynamic models of distribution (MacKenzie et al. 2003) and of abundance (Chandler et al. 2011; Dail & Madsen 2011)

Workshop Outline
* Introduction to hierarchical models and unmarked.
* Overview of unmarked functionality
* Formatting data for unmarked
* Occupancy models
* Modeling abundance with N-mixture models
* Modeling abundance with multinomial mixture models
* Hierarchical distance-sampling models
* Open population N-mixture models: The Dail-Madsen model.
* Open population distance-sampling models
* Open population occupancy models (modeling colonization and extinction)

References

Chandler, R.B., J.A. Royle and D.I. King. 2011. Inference about density and temporary emigration in unmarked populations. Ecology 92:1429-1435.

Dail, D. and L. Madsen. 2011. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics, 67:577-587.

Dorazio, R.M., H.L. Jelks and F. Jordan. 2005. Improving Removal-Based Estimates of Abundance by Sampling a Population of Spatially Distinct Subpopulations. Biometrics 61:1093-1101.

Fiske, I. and R.B. Chandler. 2011. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software 43:1-23.

MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle and C.A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248-2255.

MacKenzie, D.I., J.D. Nichols, J.E. Hines, M.G. Knutson and A.B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200-2207.
Royle, J.A. 2004a. N-Mixture Models for estimating population size from spatially replicated counts. Biometrics, 60(1):108-115.

Royle, J.A. 2004b. Generalized estimators of avian abundance from count survey data. Animal Biodiversity and Conservation, 27:375-386.

Royle, J.A., D.K. Dawson, and S. Bates. 2004. Modeling abundance effects in distance sampling. Ecology, 85(6):1591-1597.

Royle, J.A. and R.M. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations, and Communities. Academic Press, San Diego, CA. xviii, 444