Upcoming Webinar Jan. 25-26 1:30-4:30pm: Using unmarked- An R package for fitting hierarchical models of species abundance and occurrence

Andy Royle and Richard Chandler will be offering a webinar on using unmarked: An R package for fitting hierarchical models of species abundance and occurrence 25-26 Jan (Wed/Thur) 2012, 1:30 PM EST – 4:00 PM EST. Please see the description below. The webinar is open to all who are interested without charge. For more information, please contact Richard Chandler (rchandler@usgs.gov). Please forward this message to others who may be interested. Register Now at: https://www1.gotomeeting.com/register/422553153

Once registered you will receive an email confirming your registration with information you need to join the Webinar. I will be retiring on December 31, and many of my web resources will not continue to be available. USGS plans to continue some webinar courses, and I will be offering some courses after I retire on R at http://paulrstat.com/. I will take the liberty of adding you to my emailing list for information and courses about the R system for statistical computing and graphics. 


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 (Washington DC and New York).  To find the time where you are go to http://worldtimezone.net/time-us12.html and click on Call Planner.
Venue:        on-line
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)

A working knowledge of modern regression methods (GLMs, mixed models) and the R programming language is required.

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)