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Related Experiment Video

Updated: Mar 6, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Testing for density dependence allowing for weather effects.

Peter Rothery1, Ian Newton1, Lois Dale1

  • 1Institute of Terrestrial Ecology, Monks Wood, Abbots Ripton, Huntingdon, Cambridgeshire, PE17 2LS, UK Fax: +44 (0) 1487 773 487, , , , , , GB.

Oecologia
|March 18, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test for population density dependence that accounts for weather variations. The method provides stronger evidence of density dependence in ecological time-series data compared to existing tests.

Keywords:
Autoregressive modelButterfly and songbird populationsKey words Density dependenceStatistical powerWeather effects

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Area of Science:

  • Ecology
  • Population Dynamics
  • Statistical Modeling

Background:

  • Understanding population density dependence is crucial for ecological research.
  • Traditional methods may not fully account for environmental factors like weather.
  • Incorporating weather effects can improve the accuracy of detecting density dependence.

Purpose of the Study:

  • To develop and validate a novel statistical test for density dependence in ecological time-series data.
  • To assess the impact of weather covariates on the detection of density dependence.
  • To compare the performance of the new test against existing methods.

Main Methods:

  • A discrete-time autoregressive model was employed to analyze changes in population density.
  • Weather effects were incorporated as a covariate within the model.
  • Parametric bootstrapping was utilized to determine the null distribution of the test statistic.
  • Computer simulations were conducted to evaluate statistical power.

Main Results:

  • The proposed test demonstrated increased statistical power when weather effects were included.
  • Application to butterfly and songbird data revealed stronger evidence of density dependence.
  • The new method outperformed two standard tests in detecting density dependence.

Conclusions:

  • The developed statistical test offers a more robust approach to identifying density dependence in ecological populations.
  • Accounting for weather effects significantly enhances the ability to detect density-dependent processes.
  • This method has broad applicability for analyzing population time-series data in ecology.