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

Citation
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
Abstract
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).
Keywords
Syncerus caffer,animal movement,animal tracking,autocorrelated kernel density estimation,home range,Kruger National Park,movement modeling,Syncerus caffer,utilization distribution
Taxa
Taxon
Syncerus caffer
African Buffalo, African buffalo, Cape buffalo
Sensors
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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}
}
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  - 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
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.j900f88t
DO  - doi:10.5441/001/1.j900f88t
ER  - 
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