Cargando…

LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions

Parametric 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) nonparame...

Descripción completa

Detalles Bibliográficos
Autores principales: Getz, Wayne M., Fortmann-Roe, Scott, Cross, Paul C., Lyons, Andrew J., Ryan, Sadie J., Wilmers, Christopher C.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797616/
https://www.ncbi.nlm.nih.gov/pubmed/17299587
http://dx.doi.org/10.1371/journal.pone.0000207
_version_ 1782132312525242368
author Getz, Wayne M.
Fortmann-Roe, Scott
Cross, Paul C.
Lyons, Andrew J.
Ryan, Sadie J.
Wilmers, Christopher C.
author_facet Getz, Wayne M.
Fortmann-Roe, Scott
Cross, Paul C.
Lyons, Andrew J.
Ryan, Sadie J.
Wilmers, Christopher C.
author_sort Getz, Wayne M.
collection PubMed
description Parametric 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).
format Text
id pubmed-1797616
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-17976162007-02-28 LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions Getz, Wayne M. Fortmann-Roe, Scott Cross, Paul C. Lyons, Andrew J. Ryan, Sadie J. Wilmers, Christopher C. PLoS One Research Article Parametric 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). Public Library of Science 2007-02-14 /pmc/articles/PMC1797616/ /pubmed/17299587 http://dx.doi.org/10.1371/journal.pone.0000207 Text en Getz et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Getz, Wayne M.
Fortmann-Roe, Scott
Cross, Paul C.
Lyons, Andrew J.
Ryan, Sadie J.
Wilmers, Christopher C.
LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title_full LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title_fullStr LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title_full_unstemmed LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title_short LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions
title_sort locoh: nonparameteric kernel methods for constructing home ranges and utilization distributions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797616/
https://www.ncbi.nlm.nih.gov/pubmed/17299587
http://dx.doi.org/10.1371/journal.pone.0000207
work_keys_str_mv AT getzwaynem locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions
AT fortmannroescott locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions
AT crosspaulc locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions
AT lyonsandrewj locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions
AT ryansadiej locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions
AT wilmerschristopherc locohnonparameterickernelmethodsforconstructinghomerangesandutilizationdistributions