The information on the TCs was extracted from the International Best Track Archive for Climate Stewardship database, while the SST was obtained from the Centennial Time Scale dataset. In this study, a climatology analysis of the cyclonic activity in the North Atlantic (NATL) basin was performed to improve our understanding of how sea surface temperature (SST) and climate variability modes modulate tropical cyclones (TCs) activity. 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. 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. 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. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were 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. 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. Modern tracking devices allow for the collection of high‐volume animal tracking data at improved sampling rates over VHF radiotelemetry.
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