Data from: Nonparameteric kernel methods for constructing home ranges and utilization distributions

Simple item page
datacite.RelatedIdentifierhttps://doi.org/10.1371/journal.pone.0000207
datacite.RelatedIdentifier.relatedIdentifierTypeDOI
datacite.RelatedIdentifier.relationTypeIsSupplementTo
dc.contributor.authorCross, Paul C.
dc.contributor.authorBowers, Justin A.
dc.contributor.authorHay, Craig T.
dc.contributor.authorWolhuter, Julie
dc.contributor.authorBuss, Peter
dc.contributor.authorHofmeyr, Markus
dc.contributor.authordu Toit, Johan T.
dc.contributor.authorGetz, Wayne M.
dc.date.accessioned2016-11-14T19:24:41Z
dc.date.available2016-11-14T19:24:41Z
dc.date.issued2016-11-14
dc.date.submitted2016
dc.description.abstractParametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: “fixed sphere-of-influence,” or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an “adaptive sphere-of-influence,” or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original “fixed-number-of-points,” or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).
dc.identifier.doidoi:10.5441/001/1.j900f88t
dc.identifier.urihttps://datarepository.movebank.org/handle/10255/move.609
dc.language.isoeng
dc.relationPLOS ONE
dc.relation.haspartdoi:10.5441/001/1.j900f88t/1
dc.relation.haspartdoi:10.5441/001/1.j900f88t/2
dc.relation.isreferencedbydoi:10.1371/journal.pone.0000207
dc.relation.isreferencedbydoi:10.1111/2041-210X.12559
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectSyncerus caffer
dc.subjectanimal movement
dc.subjectanimal tracking
dc.subjectautocorrelated kernel density estimation
dc.subjecthome range
dc.subjectKruger National Park
dc.subjectmovement modeling
dc.subjectSyncerus caffer
dc.subjectutilization distribution
dc.titleData from: Nonparameteric kernel methods for constructing home ranges and utilization distributions
dc.typeArticle
dspace.entity.typeData package
dwc.ScientificNameSyncerus caffer
mdr.animal.count6
mdr.citation.BibTex
@misc{001/1_j900f88t,
  title = {Data from: Nonparameteric kernel methods for constructing home ranges and utilization distributions},
  author = {Cross, PC and Bowers, JA and Hay, CT and Wolhuter, J and Buss, P and Hofmeyr, M and du, Toit, JT and Getz, WM},
  year = {2016},
  URL = {http://dx.doi.org/10.5441/001/1.j900f88t},
  doi = {doi:10.5441/001/1.j900f88t},
  publisher = {Movebank data repository}
}
mdr.citation.CSE
Cross PC, Bowers JA, Hay CT, Wolhuter J, Buss P, Hofmeyr M, du Toit JT, Getz WM. 2016. Data from: Nonparameteric kernel methods for constructing home ranges and utilization distributions. Movebank Data Repository. https://doi.org/10.5441/001/1.j900f88t
mdr.citation.RIS
TY  - DATA
ID  - doi:10.5441/001/1.j900f88t
T1  - Data from: Nonparameteric kernel methods for constructing home ranges and utilization distributions
AU  - Cross, Paul C.
AU  - Bowers, Justin A.
AU  - Hay, Craig T.
AU  - Wolhuter, Julie
AU  - Buss, Peter
AU  - Hofmeyr, Markus
AU  - du Toit, Johan T.
AU  - Getz, Wayne M.
Y1  - 2016/11/14
KW  - Syncerus caffer
KW  - African buffalo
KW  - animal movement
KW  - animal tracking
KW  - autocorrelated kernel density estimation
KW  - home range
KW  - Kruger National Park
KW  - movement modeling
KW  - Syncerus caffer
KW  - utilization distribution
KW  - Syncerus caffer
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.j900f88t
DO  - doi:10.5441/001/1.j900f88t
ER  -
mdr.journal.titlePLOS ONE
mdr.location.count42653
mdr.study.id1764627
relation.isAuthorOfDatapackageafc890dc-f82f-46ad-a54d-e3af558ad964
relation.isAuthorOfDatapackage1e9f966e-6f1c-423a-bc9b-a39cb214f880
relation.isAuthorOfDatapackage3c895577-b732-48c1-a43e-ded7976bca81
relation.isAuthorOfDatapackagec4aba5ee-5734-49ce-a5c7-80a8a7ddfbc4
relation.isAuthorOfDatapackage85e070e9-43f5-4557-82bb-47569157f58f
relation.isAuthorOfDatapackageaedd0012-9d3e-47e7-bed2-7ce834e1ecfc
relation.isAuthorOfDatapackage0e59255d-43bc-44af-a0fc-01897d4368a7
relation.isAuthorOfDatapackage51bea94b-a89c-4d87-8a0b-7f01ab9d8368
relation.isAuthorOfDatapackage.latestForDiscoveryafc890dc-f82f-46ad-a54d-e3af558ad964
relation.isSensorOfDatapackage32573e6b-4e7b-4144-b181-0288c3682347
relation.isSensorOfDatapackage.latestForDiscovery32573e6b-4e7b-4144-b181-0288c3682347
relation.isTaxonOfDatapackage0e8f8956-e56c-4659-a3be-1ebd626258a4
relation.isTaxonOfDatapackage.latestForDiscovery0e8f8956-e56c-4659-a3be-1ebd626258a4
sensor.nameGPS
Files
Original bundle
Now showing 1 - 3 of 3
Thumbnail Image
Name:
Kruger African Buffalo, GPS tracking, South Africa-reference-data.csv
Size:
1.94 KB
Format:
Unknown data format
Description:
dataset-file
Download
Thumbnail Image
Name:
README.txt
Size:
10.78 KB
Format:
Plain Text
Description:
dc_readme
Download
Thumbnail Image
Name:
Kruger African Buffalo, GPS tracking, South Africa.csv
Size:
3.84 MB
Format:
Unknown data format
Description:
dataset-file
Download
Collections
Data packages