Home‐range analysis using radio‐tracking data–a review of problems and techniques particularly as applied to the study of mammals

TLDR

Studies of larger mammals have revealed persistent problems in home‑range analysis using radio‑tracking data. This paper provides a guide to help researchers design radio‑tracking studies, avoid common pitfalls, and address analytical challenges. The authors reviewed 93 papers, discussed pros and cons of common home‑range methods, and outlined how to determine minimum radio‑fixes, adjust inadequate sample sizes, and mitigate autocorrelation. The review found that most studies still lack sufficient data collection and sophisticated analytical techniques for accurate home‑range estimation.

Abstract

Abstract Ninety‐three papers on home‐range analysis using radio‐tracking data were reviewed; these papers were found in a literature search of 18 of the major journals likely to include such papers, published in the 5‐year period to the end of 1988. The review showed that even 25 years after the first radio‐tracking studies, in the majority of papers there was still insufficient attention given to accurate and sufficient data collection, and to using appropriate and sophisticated analytical techniques to assess home‐range size and configuration. This paper is designed to help people undertaking a radio‐tracking study to avoid some of the most common pitfalls. It is based on some of the problems we have experienced studying several species of larger mammals. We use our collective experience to produce a guide on how to plan a radio‐tracking study, to highlight some of the potential problems in designing the study and collecting the data, and to identify some of the difficulties that may be encountered during the analytical stages. The advantages and disadvantages of the most frequently used methods of home‐range analysis are discussed and methods for determining the minimum number of radio‐fixes and techniques for adjusting inadequate sample sizes are described, as are the problems that may be caused by autocorrelated data.