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Related Experiment Video

Updated: May 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Reconciling resource utilization and resource selection functions.

Mevin B Hooten¹, Ephraim M Hanks, Devin S Johnson

¹U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Departments of Fish, Wildlife & Conservation Biology and Statistics, Colorado State University, Fort Collins, CO, USA.

The Journal of Animal Ecology

|April 12, 2013

Summary

This summary is machine-generated.

Resource utilization functions (RUFs) can approximate resource selection functions (RSFs) for animal space use studies. Modifications to RUFs are needed for accurate inference, especially when accounting for location error in telemetry data.

Keywords:

kernel density estimation space use spatial statistics utilization distribution

Related Experiment Videos

Last Updated: May 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Ecology
Wildlife Biology
Spatial Analysis

Background:

Utilization distributions (UDs) are commonly used in animal space use studies due to their computational simplicity.
Resource utilization functions (RUFs) are often applied using UDs as response variables in regressions with spatial covariates.

Purpose of the Study:

To investigate the relationship between RUFs and resource selection functions (RSFs).
To identify potential biases in estimating resource selection coefficients using RUFs.
To provide guidance on appropriate methods for resource selection analysis based on data characteristics.

Main Methods:

Heuristic and simulation-based exploration of RUF and RSF relationships.
Investigation of bias sources in RUF analyses, including spatial covariance modeling.
Application of methods to real telemetry data.

Main Results:

RUFs can approximate RSFs and offer insights into resource selection under specific conditions and modifications.
Spatial covariance modeling in RUF analyses can introduce bias.
RSF methods are generally preferred for point process telemetry data, while modified RUFs may reduce bias with location error.

Conclusions:

RUFs offer a viable, though approximate, approach to resource selection analysis.
Methodological choices in analyzing animal space use data significantly impact inference.
Understanding data properties, such as location error, is crucial for selecting the most appropriate analytical method.