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A new selection criterion for statistical home range estimation.

A Baíllo1, J E Chacón2

  • 1Departamento de Matemáticas, Universidad Autónoma de Madrid, Madrid, Spain.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

Choosing the best animal home range estimator is now possible with a new ranking method. This penalized overestimation ratio balances estimator accuracy and data fit for ecological studies.

Keywords:
Nonparametricpenalizationset estimationutilization distribution

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Area of Science:

  • Ecology
  • Statistics
  • Wildlife Biology

Background:

  • Animal home range estimation defines an animal's typical geographic area.
  • Current ecological literature offers various home range estimators.
  • Selecting the optimal estimator for a given dataset remains an open challenge.

Purpose of the Study:

  • To introduce a novel numerical index for ranking home range estimators.
  • To provide a method for selecting the 'best' home range from multiple estimations.
  • To optimize tuning parameters for nonparametric home range models.

Main Methods:

  • Development of the penalized overestimation ratio (POR) index.
  • The POR balances estimator overestimation with shape adjustment to data.
  • Application to Mongolian wolf tracking data and simulation studies.

Main Results:

  • The POR provides a quantitative method to rank home range estimators.
  • The method is applicable to real-world data and any estimator type.
  • Optimization of the POR aids in selecting parameters for nonparametric estimators.

Conclusions:

  • The penalized overestimation ratio offers a robust solution for choosing among home range estimators.
  • This ranking procedure enhances the reliability of ecological spatial analyses.
  • The method facilitates more accurate wildlife movement and habitat use studies.