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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
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Modified home range kernel density estimators that take environmental interactions into account.

Guillaume Péron1

  • 1Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558, F-69622 Villeurbanne, France.

Movement Ecology
|May 30, 2019
PubMed
Summary

A new semi-parametric method refines animal home range estimation by incorporating movement behaviors. This approach creates more realistic home range shapes than previous methods, improving ecological analyses.

Keywords:
AKDEMovement ecologyPoint process patternResource selectionSemiparametricStep selection functionTemporal autocorrelation

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Area of Science:

  • Movement Ecology
  • Spatial Ecology
  • Wildlife Biology

Background:

  • Kernel density estimation (KDE) is widely used in movement ecology to determine animal locations.
  • Traditional KDE is sensitive to temporal autocorrelation, leading to alternatives like autocorrelated kernel density estimation (AKDE).
  • Existing methods can produce overly smoothed home ranges that don't reflect environmental barriers.

Purpose of the Study:

  • To introduce a semi-parametric variant of AKDE for more realistic home range delineation.
  • To integrate animal movement mechanisms into bandwidth optimization and base kernels.
  • To accommodate landscape features like land cover and linear permeability.

Main Methods:

  • Developed a semi-parametric autocorrelated kernel density estimation (AKDE) approach.
  • Incorporated movement mechanisms, including land cover selection and feature permeability, into the model.
  • Utilized a step selection framework for implementation.

Main Results:

  • The new method produced distinct home range contours for plains zebra compared to previous methods.
  • Demonstrated the influence of a railway, woodland avoidance, and grassland preference on home range estimation.
  • Highlighted significant differences in home range shapes based on environmental factors.

Conclusions:

  • The semi-parametric AKDE balances the realism of parametric models with the applicability of non-parametric methods.
  • This approach offers a more biologically realistic representation of animal home ranges.
  • Future work will focus on improving extrapolations by understanding movement mechanisms and resource use.