Autocorrelation-informed home range estimation: A review and practical guide

Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over very-high-frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Automated summary

Modern tracking devices allow for high-volume animal tracking data collection, but traditional statistical methods may underestimate or overestimate home range areas. The autocorrelated kernel density estimation (AKDE) family of estimators aims to address the complexities of modern movement data, improve statistical efficiency, and reduce biases.