Analysis of habitat use based on radio-tagged animals presents difficulties inadequately addressed by current methods. Areas of concern are sampling level, data pooling across individuals, non-independence of habitat proportions, differential habitat use by groups of animals, and arbitrary definition of habitat availability. We advocate proportional habitat use by individual animals as a basis for analysis. Hypothesis testing of such nonstandard multivariate data is done by compositional analysis, which encom- passes all MANOVA/MANCOVA-type linear models. The applications to habitat use range from testing for age class effects or seasonal differences, to examining relationships with food abundance or home range size. We take as an example the comparison of habitat use and availability. The concepts are explained and demonstrated on two data sets, illustrating different methods of treating missing values. We compare utilized with available habitats in two stages, examining home range selection within the overall study area first, then habitat use within the home range. At each stage, assuming that use differs from random, habitats can be ranked according to relative use, and significant between-rank differences located. Compositional analysis is also suited to the analysis of time budgets or diets.

Compositional Analysis of Habitat Use From Animal Radio-Tracking Data

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/ home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably >50), and report sample sizes in published results.

Effects of sample size on kernel home range estimates

1. Although the home range is a fundamental ecological concept, there is considerable debate over how it is best measured. There is a substantial literature concerning the precision and accuracy of all commonly used home range estimation methods; however, there has been considerably less work concerning how estimates vary with sampling regime, and how this affects statistical inferences. 2. We propose a new procedure, based on a variance components analysis using generalized mixed effects models to examine how estimates vary with sampling regime. 3. To demonstrate the method we analyse data from one study of 32 individually marked roe deer and another study of 21 individually marked kestrels. We subsampled these data to simulate increasingly less intense sampling regimes, and compared the performance of two kernel density estimation (KDE) methods, of the minimum convex polygon (MCP) and of the bivariate ellipse methods. 4. Variation between individuals and study areas contributed most to the total variance in home range size. Contrary to recent concerns over reliability, both KDE methods were remarkably efficient, robust and unbiased: 10 fixes per month, if collected over a standardized number of days, were sufficient for accurate estimates of home range size. However, the commonly used 95% isopleth should be avoided; we recommend using isopleths between 90 and 50%. 5. Using the same number of fixes does not guarantee unbiased home range estimates: statistical inferences differ with the number of days sampled, even if using KDE methods. 6. The MCP method was highly inefficient and results were subject to considerable and unpredictable biases. The bivariate ellipse was not the most reliable method at low sample sizes. 7. We conclude that effort should be directed at marking more individuals monitored over long periods at the expense of the sampling rate per individual. Statistical results are reliable only if the whole sampling regime is standardized. We derive practical guidelines for field studies and data analysis.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-2656.2006.01164.x

Effects of sampling regime on the mean and variance of home range size estimates

Kernel density estimators have been used to estimate home range size but little is known of their statistical properties. I applied kernel-based estimators of home range size, calculated from 95% probability contours of nonparametric density estimators, to computer-simulated radiolocation data. Four hypothetical models of home range suggested by Boulanger and White (1990) were used to evaluate bias and precision of these estimators in estimating known home range sizes. Kernel methods compared well with the best methods that are available for home range size estimation provided the appropriate level of smoothing was selected. I used brush rabbit (Sylvilagus bachmani) telemetry data to illustrate how Monte Carlo methods may also be used to assess estimator performance from field radiolocation data. A kernel estimator is preferred to a harmonic mean estimator in this example because it is less biased (i.e., the harmonic mean method has an inherent problem).

http://www.jstor.org/stable/pdfplus/3801959.pdf

Using Monte Carlo simulation to evaluate kernel-based home range estimators

1. Destructive subsampling or restrictive sampling are often standard procedures to obtain independence of spatial observations in home range analyses. We examined whether home range estimators based upon kernel densities require serial independence of observations, by using a Monte Carlo simulation, antler flies and snapping turtles as models.



2. Home range size, time partitioning and total straight line distances travelled were tested to determine if subsampling improved kernel performance and estimation of home range parameters.



3. The accuracy and precision of home range estimates from the simulated data set improved at shorter time intervals despite the increase in autocorrelation among the observations.



4. Subsampling did not reduce autocorrelation among locational observations of snapping turtles or antler flies, and home range size, time partitioning and total distance travelled were better represented by autocorrelated observations.



5. We found that kernel densities do not require serial independence of observations when estimating home range, and we recommend that researchers maximize the number of observations using constant time intervals to increase the accuracy and precision of their estimates.

Eliminating autocorrelation reduces biological relevance of home range estimates

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.

T. Woollard

Papers

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