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<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>Jamie Thornton</dc:contributor>
  <dc:contributor>Vickie M. Backus</dc:contributor>
  <dc:contributor>Matthew G. Hohmann</dc:contributor>
  <dc:contributor>Erik A. Lehnhoff</dc:contributor>
  <dc:contributor>Bruce D. Maxwell</dc:contributor>
  <dc:contributor>Kurt Michels</dc:contributor>
  <dc:contributor>Lisa Rew</dc:contributor>
  <dc:creator>Kathryn M. Irvine</dc:creator>
  <dc:date>2013</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species&amp;ndash;environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species,&amp;nbsp;&lt;/span&gt;&lt;i&gt;Bromus inermis&lt;/i&gt;&lt;span&gt;. The census of&amp;nbsp;&lt;/span&gt;&lt;i&gt;B. inermis&lt;/i&gt;&lt;span&gt;&amp;nbsp;provides a good example of a species distribution that is both sparsely (1.9&amp;nbsp;&lt;/span&gt;&lt;i&gt;%&lt;/i&gt;&lt;span&gt;&amp;nbsp;prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey&amp;nbsp;&lt;/span&gt;&lt;i&gt;B. inermis&lt;/i&gt;&lt;span&gt;. However, inferences about the relationships between&amp;nbsp;&lt;/span&gt;&lt;i&gt;B. inermis&lt;/i&gt;&lt;span&gt;&amp;nbsp;presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1002/env.2223</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>International Environmetrics Society</dc:publisher>
  <dc:title>A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of &lt;i&gt;Bromus inermis&lt;/i&gt;</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>