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Correcting for missing and irregular data in home-range estimation.

C H Fleming^1,2,3, D Sheldon^4,5, W F Fagan²

¹Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Road, Front Royal, Virginia, 22630, USA.

Ecological Applications : a Publication of the Ecological Society of America

|February 17, 2018

Summary

This summary is machine-generated.

This study introduces an optimal weighting method to improve animal home-range estimates from tracking data. The novel approach corrects for irregular sampling, reducing errors and enhancing spatial resolution for accurate space-use assessments.

Keywords:

animal tracking data autocorrelation home range irregular sampling kernel density estimation marine tracking data utilization distribution

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Area of Science:

Ecology
Wildlife Biology
Spatial Ecology

Background:

Home-range estimation is crucial for understanding animal behavior and ecology.
Autocorrelation, irregular sampling, and small sample sizes complicate accurate home-range estimation.
Existing methods struggle with temporal biases inherent in animal tracking data.

Purpose of the Study:

To introduce a novel, optimal weighting method for autocorrelated animal tracking data.
To account for and correct temporal sampling bias, including irregular and missing data.
To improve the accuracy and spatial resolution of home-range estimates.

Main Methods:

Developed an optimal weighting method that downweights oversampled times and upweights undersampled times.
Created computationally efficient algorithms to handle large datasets.
Validated the method using simulated data and empirical examples (reef manta ray, Mongolian gazelle, African buffalo).

Main Results:

The optimal weighting method significantly reduces error in home-range estimates.
It increases the spatial resolution of home-range estimates, providing more detailed insights.
The method demonstrates effectiveness across various data types, including marine, duty-cycled, and low-crossing datasets.

Conclusions:

Optimal weighting is a robust solution for improving home-range estimation accuracy with biased tracking data.
The method is computationally efficient and broadly applicable to diverse animal tracking datasets.
This approach enhances the ability to conduct accurate space-use assessments in wildlife research.