Exact Bayesian inference for animal movement in continuous time

Blackwell, Paul G.; Niu, Mu; Lambert, Mark S.; LaPoint, Scott

Exact Bayesian inference for animal movement in continuous time

Dateien

Blackwell_0-418386.pdfGröße: 677.39 KBDownloads: 211

Datum

2016

Autor:innen

Blackwell, Paul G.

Niu, Mu

Lambert, Mark S.

LaPoint, Scott

Open Access-Veröffentlichung

Open Access Hybrid

Sammlungen

Biologie

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Methods in Ecology and Evolution. 2016, 7(2), pp. 184-195. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.12460

Zusammenfassung

It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems.
We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values.
We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time.
Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.

Fachgebiet (DDC)

570 Biowissenschaften, Biologie

Zitieren

ISO 690

BLACKWELL, Paul G., Mu NIU, Mark S. LAMBERT, Scott LAPOINT, 2016. Exact Bayesian inference for animal movement in continuous time. In: Methods in Ecology and Evolution. 2016, 7(2), pp. 184-195. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.12460

BibTex

@article{Blackwell2016-02Exact-40024,
  year={2016},
  doi={10.1111/2041-210X.12460},
  title={Exact Bayesian inference for animal movement in continuous time},
  number={2},
  volume={7},
  issn={2041-2096},
  journal={Methods in Ecology and Evolution},
  pages={184--195},
  author={Blackwell, Paul G. and Niu, Mu and Lambert, Mark S. and LaPoint, Scott}
}

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/123456789/40024">
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Niu, Mu</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-09-08T09:19:12Z</dcterms:available>
    <dcterms:title>Exact Bayesian inference for animal movement in continuous time</dcterms:title>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-09-08T09:19:12Z</dc:date>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/40024"/>
    <dc:contributor>Lambert, Mark S.</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:contributor>LaPoint, Scott</dc:contributor>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/40024/1/Blackwell_0-418386.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Blackwell, Paul G.</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/40024/1/Blackwell_0-418386.pdf"/>
    <dc:contributor>Blackwell, Paul G.</dc:contributor>
    <dc:creator>LaPoint, Scott</dc:creator>
    <dcterms:abstract xml:lang="eng">It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems.&lt;br /&gt;We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values.&lt;br /&gt;We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time.&lt;br /&gt;Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.</dcterms:abstract>
    <dcterms:issued>2016-02</dcterms:issued>
    <dc:language>eng</dc:language>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:contributor>Niu, Mu</dc:contributor>
    <dc:creator>Lambert, Mark S.</dc:creator>
  </rdf:Description>
</rdf:RDF>