**Part 5**

**Animal Telemetry**

246 Modern Telemetry

[18] Kim J., Rahmat-Samii, Y. (2004). Implanted antennas inside a human body simulations,

[19] Soontornpipit, P., Furse, C.M., Chung, Y.C., (2004). Design of Implantable Microstrip

*Microwave Theory and Techniques*, vol. 52, no. 8, pp. 1944-1951, August 2004 [20] Soontornpipit, P., Furse, C.M., Chung, Y.C., (2004). Miniaturized Biocompatible

[21] Lee, C.M., Yo, T.C., Luo, C.H., Tu, C.H., Juang, Y.Z., (2007). Compact Broadband

[22] Bradley P.D. (2006). An Ultra Low Power High Performance Medical Implant

*Biomedical Circuits and Systems Conference*, pp. 158-161, November 2006 [23] Evaluating Compliance with FCC Guidelines for Human Exposure to Radiofrequency

[24] CST Microwave Studio, Computer System Technology, GmbH, Darmstadt, Germany [25] Chan Wai Po, F., De Foucauld, E., Delavaud, C., Ciais, P., Kerhervé, E., (2008). A Vector

[26] Yan, Y., Perreault, D.J. (2006). Analysis and Design of High Efficiency Matching

[27] Chan Wai Po, F., de Foucauld, E., Morche, D., Vincent, P., Kerhervé, E. (2011). A Novel

[28] Ludwig, R., Bogdanov, G., (2000). RF Circuit Design: Theory and Applications, 2nd

[29] Chan Wai Po, F., de Foucauld, E., Vincent, P., Hameau, F., Morche, D., Delavaud, C., dal

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edition Upper Saddle River, NJ: Prentice-Hall, 2000

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Supp. C

September 2006

November 2011

May 2009

designs and characterizations, *IEEE Transactions on Microwave Theory and* 

Antenna for Communication with Medical Implants, *IEEE Transactions on* 

Microstrip Antenna Using Genetic Algorithm, *IEEE Transactions on Antennas and* 

Stacked Implantable Antenna for Biotelemetry with Medical Devices, *Electronic* 

Communication System (MICS) transceiver for Implantable Devices, *IEEE* 

Electromagnetic Fields, Office Eng. Tech., FCC, Washington DC, FCC OET Bull. 65,

Automatic Matching Network Designed for Wireless Medical Telemetry, *IEEE* 

Networks, *IEEE Transactions on Power Electronics*, vol. 21, no. 5, pp. 1484-1491,

Method for Synthesizing an Automatic Matching Network and Its Control Unit, *IEEE Transactions on Circuits and Systems I: Regular paper*, vol. 58, Issue 11,

Molin, R., Pons, P., Pierquin, R., Kerhervé, E. (2009). A Fast and Accurate Automatic Matching Network Designed for Ultra Low Power Medical Applications, *IEEE International Symposium on Circuits and Systems*, pp. 673-676,

**12** 

*USA* 

**What Is the Proper Method** 

W. David Walter1, Justin W. Fischer1,

*1USDA, APHIS, Wildlife Services,* 

*& Graduate Degree Program in Ecology,* 

*Colorado State University, Fort Collins, Colorado,* 

**to Delineate Home Range of an** 

**Animal Using Today's Advanced** 

Sharon Baruch-Mordo2 and Kurt C. VerCauteren1

*National Wildlife Research Center, Fort Collins, Colorado, 2Department of Fish, Wildlife, and Conservation Biology,* 

 **GPS 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

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. Introduction** 

collected with GPS technology.
