What Is the Proper Methodto Delineate Home Range of anAnimal Using Today’s AdvancedGPS Telemetry Systems: The Initial Step

The formal concept of an animal’s home range, or derivations thereof, has been around for over half a century (Burt 1943). Within this time frame there have been countless published studies reporting home range estimators with no consensus for any single technique (Withey et al., 2001; Laver & Kelly 2008). Recent advances in global positioning system (GPS) technology for monitoring home range and movements of wildlife have resulted in locations that are numerous, more precise than very high frequency (VHF) systems, and often are autocorrelated in space and time. Along with these advances, researchers are challenged with understanding the proper methods to assess size of home range or migratory movements of various species. The most acceptable method of home-range analysis with uncorrelated locations, kernel-density estimation (KDE), has been lauded by some for use with GPS technology (Kie et al., 2010) while criticized by others for errors in proper bandwidth selection (Hemson et al., 2005) and violation of independence assumptions (Swihart & Slade 1985b). The issue of autocorrelation or independence in location data has been dissected repeatedly by users of KDE for decades (Swihart & Slade 1985a; Worton 1995, but see Fieberg 2007) and can be especially problematic with data collected with GPS technology. Recently, alternative methods were developed to address the issues with bandwidth selection for KDE and autocorrelated GPS data. Brownian bridge movement models (BBMM), which incorporate time between successive locations into the utilization distribution estimation, were recommended for use with serially correlated locations collected with GPS technology (Bullard 1999; Horne et al., 2007). The wrapped Cauchy distribution KDE was also introduced to incorporate a temporal dimension into the KDE (Keating & Cherry 2009). Improvements were developed in bandwidth selection for KDE

[1]  Stephen M. Krone,et al.  Analyzing animal movements using Brownian bridges. , 2007, Ecology.

[2]  Wayne M. Getz,et al.  A local nearest-neighbor convex-hull construction of home ranges and utilization distributions , 2004 .

[3]  Simon Benhamou,et al.  Dynamic Approach to Space and Habitat Use Based on Biased Random Bridges , 2011, PloS one.

[4]  Peter N. Laver,et al.  A Critical Review of Home Range Studies , 2008 .

[5]  Baoping Yan,et al.  Migration of Waterfowl in the East Asian Flyway and Spatial Relationship to HPAI H5N1 Outbreaks , 2010, Avian diseases.

[6]  M. C. Jones,et al.  A Brief Survey of Bandwidth Selection for Density Estimation , 1996 .

[7]  Michael A. Larson.  Appendix A – A Catalog of Software to Analyze Radiotelemetry Data , 2001 .

[8]  Simon Benhamou,et al.  Incorporating Movement Behavior and Barriers to Improve Kernel Home Range Space Use Estimates , 2010 .

[9]  R. Courtois,et al.  Effects of sampling effort based on GPS telemetry on home-range size estimations , 2002 .

[10]  B. R. Mitchell.  Comparison of Programs for Fixed Kernel Home Range Analysis , 2007 .

[11]  J. Takekawa,et al.  Implementing telemetry on new species in remote areas: recommendations from a large-scale satellite tracking study of African waterfowl , 2011 .

[12]  Robert C. Beason,et al.  Synchronous monitoring of vulture movements with satellite telemetry and avian radar , 2010 .

[13]  Wayne M. Getz,et al.  LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions , 2007, PloS one.

[14]  B. Worton.  Using Monte Carlo simulation to evaluate kernel-based home range estimators , 1995 .

[15]  W. D. Walter,et al.  Vulture Flight Behavior and Implications for Aircraft Safety , 2011 .

[16]  Tarn Duong,et al.  ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R , 2007 .

[17]  Joshua J. Millspaugh,et al.  Effects of sample size on kernel home range estimates , 1999 .

[18]  Trent L McDonald,et al.  Estimating habitat selection when GPS fix success is less than 100%. , 2009, Ecology.

[19]  Jon S. Horne,et al.  Identifying and prioritizing ungulate migration routes for landscape-level conservation. , 2009, Ecological applications : a publication of the Ecological Society of America.

[20]  Lee A. Vierling,et al.  Effects of habitat on GPS collar performance: using data screening to reduce location error , 2007 .

[21]  David W. Macdonald,et al.  Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home- range analyses with least-squares cross-validation , 2005 .

[22]  R. Swihart,et al.  Estimating Utilization Distributions with Kernel Versus Local Convex Hull Methods , 2011 .

[23]  J. A. Schaefer,et al.  Caribou movement as a correlated random walk , 2000, Oecologia.

[24]  John Fieberg,et al.  Kernel density estimators of home range: smoothing and the autocorrelation red herring. , 2007, Ecology.

[25]  D. Winkler,et al.  Movement ecology of migration in turkey vultures , 2008, Proceedings of the National Academy of Sciences.

[26]  John M. Marzluff,et al.  Effects of Tagging and Location Error in Wildlife Radiotelemetry Studies , 2001 .

[27]  Stanley M Tomkiewicz,et al.  Global positioning system and associated technologies in animal behaviour and ecological research , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[28]  Jason Matthiopoulos.  Model-supervised kernel smoothing for the estimation of spatial usage , 2003 .

[29]  Robert A. Gitzen,et al.  Analysis of Animal Space Use and Movements , 2001 .

[30]  Francesca Cagnacci,et al.  The home-range concept: are traditional estimators still relevant with modern telemetry technology? , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  R. Powell,et al.  An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis , 1996 .

[32]  Paul R. Moorcroft,et al.  Home range analysis using a mechanistic home range model , 1999 .

[33]  B. Worton.  Kernel methods for estimating the utilization distribution in home-range studies , 1989 .

[34]  Young K. Truong,et al.  On bandwidth choice for density estimation with dependent data , 1995 .

[35]  Clément Calenge,et al.  The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals , 2006 .

[36]  Jean-Michel Gaillard,et al.  Roe deer Capreolus capreolus home-range sizes estimated from VHF and GPS data , 2008 .

[37]  Norman A. Slade,et al.  Influence of sampling interval on estimates of home-range size , 1985 .

[38]  A. Rodgers,et al.  Differences in home-range size computed in commonly used software programs , 2001 .

[39]  S. Amstrup,et al.  Using satellite radiotelemetry data to delineate and manage wildlife populations , 2004 .

[40]  M. Hazelton,et al.  Plug-in bandwidth matrices for bivariate kernel density estimation , 2003 .

[41]  Sandro Lovari,et al.  Effects of sampling regime on the mean and variance of home range size estimates. , 2006, The Journal of animal ecology.

[42]  Steve Cherry,et al.  Modeling utilization distributions in space and time. , 2009, Ecology.

[43]  E. Debevec,et al.  LINEAR HOME RANGES: EFFECTS OF SMOOTHING, SAMPLE SIZE, AND AUTOCORRELATION ON KERNEL ESTIMATES , 2001 .

[44]  W. H. Burt.  Territoriality and Home Range Concepts as Applied to Mammals , 1943 .

[45]  Robert A. Gitzen,et al.  Bandwidth Selection for Fixed-Kernel Analysis of Animal Utilization Distributions , 2006 .

[46]  M. Hebblewhite,et al.  Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[47]  P. Forer,et al.  Performance Characteristics of Small Global-Positioning-System Tracking Collars , 2010 .

[48]  W. D. Walter,et al.  An Integrated Vehicle-Mounted Telemetry System for VHF Telemetry Applications , 2008 .

[49]  B. Manly,et al.  Resource selection by animals: statistical design and analysis for field studies. , 1994 .

[50]  Norman A. Slade,et al.  Testing For Independence of Observations in Animal Movements , 1985 .

[51]  C. Loader.  Bandwidth selection: classical or plug-in? , 1999 .

[52]  Christopher O. Kochanny,et al.  GPS radiotelemetry error and bias in mountainous terrain , 2002 .

[53]  Michael D. Johnson,et al.  An Advanced, Low-Cost, GPS-Based Animal Tracking System , 2006 .