Publikation:

Estimating encounter location distributions from animal tracking data

Lade...
Vorschaubild

Dateien

Noonan_2-z8u15nld1xjg1.pdf
Noonan_2-z8u15nld1xjg1.pdfGröße: 2.21 MBDownloads: 177

Datum

2021

Autor:innen

Noonan, Michael J.
Martinez‐Garcia, Ricardo
Kays, Roland
Hirsch, Ben T.
Caillaud, Damien
Payne, Eric
Sih, Andrew
Sinn, David L.
et al.

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Methods in Ecology and Evolution. Wiley. 2021, 12(7), pp. 1158-1173. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.13597

Zusammenfassung

  1. Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied.

    2. Here, we bridge this gap by introducing a method for describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open-source software and demonstrate the broad ecological relevance of this distribution.

    3. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins Cebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location-specific encounter probability.

    4. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

animal movement, Cebus capucinus, contact, home range, interactions, Tiliqua rugosa

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690NOONAN, Michael J., Ricardo MARTINEZ‐GARCIA, Grace DAVIS, Margaret C. CROFOOT, Roland KAYS, Ben T. HIRSCH, Damien CAILLAUD, Eric PAYNE, Andrew SIH, David L. SINN, 2021. Estimating encounter location distributions from animal tracking data. In: Methods in Ecology and Evolution. Wiley. 2021, 12(7), pp. 1158-1173. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.13597
BibTex
@article{Noonan2021-07Estim-53828,
  year={2021},
  doi={10.1111/2041-210X.13597},
  title={Estimating encounter location distributions from animal tracking data},
  number={7},
  volume={12},
  issn={2041-2096},
  journal={Methods in Ecology and Evolution},
  pages={1158--1173},
  author={Noonan, Michael J. and Martinez‐Garcia, Ricardo and Davis, Grace and Crofoot, Margaret C. and Kays, Roland and Hirsch, Ben T. and Caillaud, Damien and Payne, Eric and Sih, Andrew and Sinn, David L.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/53828">
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53828/1/Noonan_2-z8u15nld1xjg1.pdf"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:contributor>Hirsch, Ben T.</dc:contributor>
    <dc:contributor>Payne, Eric</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53828/1/Noonan_2-z8u15nld1xjg1.pdf"/>
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc/4.0/"/>
    <dcterms:abstract xml:lang="eng">1. Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied.&lt;br /&gt;&lt;br /&gt;2. Here, we bridge this gap by introducing a method for describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open-source software and demonstrate the broad ecological relevance of this distribution.&lt;br /&gt;&lt;br /&gt;3. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins Cebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location-specific encounter probability.&lt;br /&gt;&lt;br /&gt;4. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package.</dcterms:abstract>
    <dc:rights>Attribution-NonCommercial 4.0 International</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-05-31T09:40:17Z</dcterms:available>
    <dc:contributor>Crofoot, Margaret C.</dc:contributor>
    <dc:contributor>Martinez‐Garcia, Ricardo</dc:contributor>
    <dc:creator>Davis, Grace</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53828"/>
    <dc:creator>Martinez‐Garcia, Ricardo</dc:creator>
    <dcterms:title>Estimating encounter location distributions from animal tracking data</dcterms:title>
    <dc:creator>Noonan, Michael J.</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Sih, Andrew</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2021-07</dcterms:issued>
    <dc:contributor>Sinn, David L.</dc:contributor>
    <dc:creator>Payne, Eric</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-05-31T09:40:17Z</dc:date>
    <dc:contributor>Caillaud, Damien</dc:contributor>
    <dc:contributor>Noonan, Michael J.</dc:contributor>
    <dc:creator>Crofoot, Margaret C.</dc:creator>
    <dc:creator>Caillaud, Damien</dc:creator>
    <dc:contributor>Davis, Grace</dc:contributor>
    <dc:creator>Sinn, David L.</dc:creator>
    <dc:creator>Hirsch, Ben T.</dc:creator>
    <dc:creator>Sih, Andrew</dc:creator>
    <dc:contributor>Kays, Roland</dc:contributor>
    <dc:creator>Kays, Roland</dc:creator>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Diese Publikation teilen