An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundanc

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Authors:	Ian Fiske, Richard Chandler
Title:	[download] (5882)unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance
Reference:	Vol. 43, Issue 10, Aug 2011Submitted 2010-05-06, Accepted 2011-07-29
Type:	Article
Abstract:	Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.
Paper:	[download] (5882)unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance (application/pdf, 470.6 KB)
Supplements:	[download] (473)unmarked_0.9-0.tar.gz: R source package (application/x-gzip, 700.4 KB)
	[download] (497)v43i10.R: R example code from the paper (application/octet-stream, 4.7 KB)

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关键词：Hierarchical Occurrence Wildlife Fitting package collection scientific techniques research address

unmarked: Models for Data from Unmarked Animals

This package fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.

Reference manual:	unmarked.pdf
Vignettes:	Capture-recapture Dynamic occupancy models Distance sampling analysis Species distributions Overview of unmarked