Analysis of Animal Space Use and Movements

By B. J. Kernohan, Robert A. Gitzen, Joshua J. Millspaugh. 2001

885 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/829ba9a10da5e617b6e3097ca78b1628e8f4c8b6

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In Small Animals

Citations in the corpus

Distribution of amphibians in terrestrial habitat surrounding wetlands (2007, background): “Bivariate kernel estimation is currently the preferred home range estimator in most situations (Seaman et al. 1999, Kernohan et al. 2001), but can produce biased estimates of space use when data are linear in nature (Blundell et al. 2001) and thus produces poor descriptions of amphibian habitat use…”
Effects of Point Pattern Shape on Home-Range Estimates (2008, methodology): “Home-range estimates are sensitive to the choice of bandwidth; bandwidths that are too large oversmooth the point pattern and overestimate home ranges, whereas those that are too small undersmooth and underestimate home ranges (Kernohan et al. 2001).”
Comparability of three analytical techniques to assess joint space use (2004, background): “However, MCP has important disadvantages, and currently kernel estimators might be the best “general-purpose” homerange estimators (Kernohan et al. 2001).”
Estimating Utilization Distributions with Kernel Versus Local Convex Hull Methods (2011, methodology): “Currently, most wildlife home range and space use studies estimate UDs by kernel density estimation (KDE) (Kernohan et al. 2001), but recently local convex hull construction (LCH) has been advocated as an alternative technique (Getz and Wilmers 2004, Getz et al. 2007, Huck et al. 2008).”
Noninvasive sampling and genetic variability, pack structure, and dynamics in an expanding wolf population (2014, methodology): “We determined their spatial distributions by 95% kernel analysis, choosing band width using the least-squares cross-validation method (Seaman et al. 1999; Kernohan et al. 2001), using the ADEHABITATHR package for R (Calenge 2006) and mapped them using ARCGIS 10.”
A Critical Review of Home Range Studies (2008, background): “Most of these results have been summarized by the reviews of Powell (2000) and Kernohan et al. (2001).”
Utilization Distribution Estimation Using Weighted Kernel Density Estimators (2007, background): “…practice to justify independence of observations based on knowledge of animal movements, for example, allowing sufficient time between observations so that the animal can relocate to any other point within its home range (Laird 1987, McNay et al. 1994, Swihart and Slade 1997, Kernohan et al. 2001).”
Spatial organization and food habits of jaguars (Panthera onca) in a floodplain forest (2007, background): “Several basic questions in ecology focus on understanding how animals are organized in space and time, and the factors affecting spatial arrangement and movement of individuals (Kernohan et al., 2001).”
Likelihood Cross-Validation Versus Least Squares Cross-Validation for Choosing the Smoothing Parameter in Kernel Home-Range Analysis (2006, methodology): “Home ranges are usually modeled from discrete observations, with the resulting estimate often described quantitatively as the utilization distribution (Kernohan et al. 2001).”
Social status determines behaviour and habitat usage in a temperate parrotfish: implications for marine reserve design (2008, methodology): “…of tracking, i.e. a continuous tracking of at least 24 h. Smoothing factor, a parameter selected by the user that is known to severely affect KUD calculations, was estimated by the least squares cross-validation method, as this is less affected by the number of observations (Kernohan et al. 2001).”
Analyzing animal movements using Brownian bridges. (2007, background): “Lastly, the ability to describe long-distance movements (e.g., migration routes) probabilistically represents a significant improvement over methods that simply connect locations using straight lines (e.g., Stokes et al. 1998, Saher and Schmiegelow 2004).”