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Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: January 11, 2020

Estimating resource selection with count data.

Ryan M Nielson1, Hall Sawyer

  • 1Western EcoSystems Technology, Inc. Laramie, Wyoming, 82070.

Ecology and Evolution
|August 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing animal locations using negative binomial regression to model habitat use intensity. This approach is more effective for large datasets and accounts for animal behavior variations.

Keywords:
Generalized linear modelPoisson regressionhabitat useoverdispersionpanel dataresource selection probability function

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Area of Science:

  • Wildlife ecology
  • Ecological modeling
  • Spatial statistics

Background:

  • Traditional resource selection functions (RSFs) use binary responses, ignoring habitat use intensity.
  • Advances in GPS technology generate large, correlated datasets, challenging traditional RSF methods.

Purpose of the Study:

  • To propose and demonstrate a novel approach for estimating resource selection functions (RSFs) by modeling habitat use intensity.
  • To adapt count-based regression, specifically negative binomial (NB) regression, for analyzing fine-scale animal location data.

Main Methods:

  • Utilized negative binomial (NB) regression to model the intensity of habitat use based on the relative frequency of animal locations.
  • Applied the NB RSF approach to GPS location data from 10 collared Rocky Mountain elk (Cervus elaphus).

Main Results:

  • The NB RSF approach effectively models habitat use intensity, accommodating large and temporally correlated GPS datasets.
  • Demonstrated the method's ability to handle among-animal variation and facilitate covariate interpretation.

Conclusions:

  • Negative binomial (NB) regression offers a conceptually simple and computationally efficient method for estimating RSFs.
  • The NB approach provides unbiased estimates of use intensity, even with correlated animal location data, making it suitable for modern ecological research.