Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator.

Area of Science:

• Ecology
• Wildlife Biology
• Conservation Science

Background:

• Estimating animal home ranges is crucial for wildlife management and conservation.
• Kernel density estimation (KDE) is a common method, but it assumes independent data.
• Animal tracking data is inherently autocorrelated, violating the KDE assumption.

Purpose of the Study:

• To address the underestimation of home ranges caused by autocorrelation in tracking data.
• To develop a new method, autocorrelated kernel density estimation (AKDE), suitable for autocorrelated movement data.

Main Methods:

• Derived an autocorrelated KDE (AKDE) method from first principles.
• Applied AKDE to Mongolian gazelle relocation data.
• Utilized simulations based on observed movement processes to validate AKDE.

Main Results:

• Conventional KDE grossly underestimates home ranges when using autocorrelated data.
• KDE performance degrades with improved data quality due to increased autocorrelation.
• AKDE provides more accurate home range estimates compared to conventional KDE.

Conclusions:

• AKDE is a statistically sound and effective method for analyzing autocorrelated animal movement data.
• Accurate home range estimation using AKDE will significantly benefit wildlife conservation and management.
• The study highlights the limitations of traditional KDE for modern tracking datasets.