<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>J.D. Nichols</dc:contributor>
  <dc:contributor>J. Andrew Royle</dc:contributor>
  <dc:contributor>K. H. Pollock</dc:contributor>
  <dc:contributor>L.L. Bailey</dc:contributor>
  <dc:contributor>J.E. Hines</dc:contributor>
  <dc:creator>D.I. MacKenzie</dc:creator>
  <dc:date>2006</dc:date>
  <dc:description>This is the first book to examine the latest methods in analyzing presence/absence data surveys.  Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models.  It provides authoritative insights into the latest in estimation modeling; discusses multiple models which lay the groundwork for future study designs; addresses critical issues of imperfect detectibility and its effects on estimation; and explores the role of probability in estimating in detail.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:language>en</dc:language>
  <dc:publisher>Elsevier/Academic Press</dc:publisher>
  <dc:title>Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence</dc:title>
  <dc:type>book</dc:type>
</oai_dc:dc>