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SER: An R package to characterize environmental regimes.

Naicheng Wu1, Kun Guo2, Yi Zou3

  • 1Department of Geography and Spatial Information Techniques Ningbo University Ningbo China.

Ecology and Evolution
|March 15, 2023
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Summary

Environmental regimes, the historical dynamics of environmental conditions, improve ecological predictions. The SER R package helps estimate these regimes for better environment-community relationship insights.

Keywords:
R packageSERenvironmental legacyenvironmental variableshistorical legacyregime

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Area of Science:

  • Ecology
  • Environmental Science
  • Data Science

Background:

  • Environmental conditions fluctuate over time, influencing ecological communities.
  • Traditional studies often overlook historical environmental dynamics (environmental regimes), focusing solely on contemporary data.
  • Understanding these regimes is crucial for accurately modeling environment-community relationships.

Purpose of the Study:

  • To introduce SER, an R package designed for estimating environmental regimes.
  • To demonstrate the utility of SER across various environmental and biotic variables.
  • To highlight the impact of incorporating environmental regimes into ecological analyses.

Main Methods:

  • Development of the SER R package for calculating environmental regimes.
  • Application of SER to diverse environmental variables (e.g., nutrient concentration, light, dissolved oxygen).
  • Analysis of temporal beta-diversity components with and without environmental regime data.

Main Results:

  • The SER package facilitates the estimation of environmental regimes over user-defined time scales (days, months, years).
  • Inclusion of environmental regimes significantly increased the explained variation in temporal beta-diversity.
  • Environmental regimes provide a more comprehensive understanding of ecological dynamics.

Conclusions:

  • Environmental regimes are essential for advancing the study of environment-community interactions.
  • The SER package offers a practical tool for ecologists and other scientists to incorporate historical environmental data.
  • The framework has potential applications beyond ecology, including social sciences and epidemiology.