Data from: Correcting for missing and irregular data in home-range estimation

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datacite.RelatedIdentifierhttps://doi.org/10.1002/eap.1704
datacite.RelatedIdentifier.relatedIdentifierTypeDOI
datacite.RelatedIdentifier.relationTypeIsSupplementTo
dc.contributor.authorSetyawan, Edy
dc.contributor.authorSianipar, Abraham
dc.date.accessioned2018-03-02T14:32:10Z
dc.date.available2018-03-02T14:32:10Z
dc.date.issued2018-03-02
dc.date.submitted2018
dc.description.abstractHome-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large datasets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of homerange crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of datasets with which accurate space-use assessments can be made.
dc.identifier.doidoi:10.5441/001/1.3gj67c2k
dc.identifier.urihttps://datarepository.movebank.org/handle/10255/move.717
dc.language.isoeng
dc.relationEcological Applications
dc.relation.haspartdoi:10.5441/001/1.3gj67c2k/1
dc.relation.haspartdoi:10.5441/001/1.3gj67c2k/2
dc.relation.isreferencedbydoi:10.1002/eap.1704
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectManta birostris
dc.subjectanimal tracking
dc.subjectautocorrelation
dc.subjecthome range
dc.subjectIndonesia
dc.subjectirregular sampling
dc.subjectkernel density estimation
dc.subjectKomodo National Park
dc.subjectManta
dc.subjectManta alfredi
dc.subjectmarine tracking data
dc.subjectreef manta ray
dc.subjectsatellite telemetry
dc.subjectutilization distribution
dc.titleData from: Correcting for missing and irregular data in home-range estimation
dc.typeArticle
dspace.entity.typeData package
dwc.ScientificNameManta birostris
mdr.animal.count1
mdr.citation.BibTex
@misc{001/1_3gj67c2k,
  title = {Data from: Correcting for missing and irregular data in home-range estimation},
  author = {Setyawan, E and Sianipar, A},
  year = {2018},
  URL = {http://dx.doi.org/10.5441/001/1.3gj67c2k},
  doi = {doi:10.5441/001/1.3gj67c2k},
  publisher = {Movebank data repository}
}
mdr.citation.CSE
Setyawan E, Sianipar A. 2018. Data from: Correcting for missing and irregular data in home-range estimation. Movebank Data Repository. https://doi.org/10.5441/001/1.3gj67c2k
mdr.citation.RIS
TY  - DATA
ID  - doi:10.5441/001/1.3gj67c2k
T1  - Data from: Correcting for missing and irregular data in home-range estimation
AU  - Setyawan, Edy
AU  - Sianipar, Abraham
Y1  - 2018/03/02
KW  - Manta birostris
KW  - animal movement
KW  - animal tracking
KW  - autocorrelation
KW  - home range
KW  - Indonesia
KW  - irregular sampling
KW  - kernel density estimation
KW  - Komodo National Park
KW  - Manta
KW  - Manta alfredi
KW  - marine tracking data
KW  - reef manta ray
KW  - satellite telemetry
KW  - utilization distribution
KW  - Manta birostris
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.3gj67c2k
DO  - doi:10.5441/001/1.3gj67c2k
ER  -
mdr.journal.titleEcological Applications
mdr.location.count75
mdr.study.id396866882
relation.isAuthorOfDatapackagebe071604-fc34-4567-811c-69914a4390a7
relation.isAuthorOfDatapackagea198f909-5642-48a7-a8ea-ac32e29c67ce
relation.isAuthorOfDatapackage.latestForDiscoverybe071604-fc34-4567-811c-69914a4390a7
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sensor.nameGPS
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