November 27, 2020 Dataset Closed Access

Calabrese, Justin M.; Fleming, Christen; Noonan, Michael J.; Dong, Xianghui

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  <identifier identifierType="DOI">10.14278/rodare.613</identifier>
  <creators>
    <creator>
      <creatorName>Calabrese, Justin M.</creatorName>
      <givenName>Justin M.</givenName>
      <familyName>Calabrese</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0575-6408</nameIdentifier>
      <affiliation>CASUS</affiliation>
    </creator>
    <creator>
      <creatorName>Fleming, Christen</creatorName>
      <givenName>Christen</givenName>
      <familyName>Fleming</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9356-6518</nameIdentifier>
      <affiliation>Smithsonian Conservation Biology Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Noonan, Michael J.</creatorName>
      <givenName>Michael J.</givenName>
      <familyName>Noonan</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4512-0535</nameIdentifier>
      <affiliation>University of British Columbia</affiliation>
    </creator>
    <creator>
      <creatorName>Dong, Xianghui</creatorName>
      <givenName>Xianghui</givenName>
      <familyName>Dong</familyName>
      <affiliation>University of Maryland</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Research Data for: ctmmweb: A graphical user interface for autocorrelation-informed home range estimation</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>AKDE</subject>
    <subject>animal movement</subject>
    <subject>autocorrelation</subject>
    <subject>ctmm</subject>
    <subject>telemetry</subject>
    <subject>tracking data</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-11-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/613</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsReferencedBy">10.1101/2020.05.11.087932</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-31776</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-31774</relatedIdentifier>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/casus</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/ecfunded</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/hzdr</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
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  <version>1.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/closedAccess">Closed Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Estimating animal home ranges is a primary purpose of collecting tracking data. All conventional home range estimators in widespread usage, including minimum convex polygons and kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. This incongruence between estimator assumptions and empirical reality leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) resolves this conflict by modeling the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm, and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb&amp;rsquo;s capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100000001</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/NSF//1458748/">1458748</awardNumber>
      <awardTitle>ABI Innovation: Advanced mathematical, statistical, and software tools to unlock the potential of animal tracking data</awardTitle>
    </fundingReference>
  </fundingReferences>
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