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Disturbance-driven changes in the variability of ecological patterns and processes.

Jennifer M Fraterrigo1, James A Rusak

  • 1Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011, USA. jmfrater@iastate.edu

Ecology Letters
|April 22, 2008
PubMed
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Ecological disturbance impacts system dynamics. Analyzing changes in ecological response variability offers a sensitive metric for understanding these effects, improving ecosystem predictability.

Area of Science:

  • Ecology
  • Ecological Dynamics
  • Environmental Science

Background:

  • Ecological disturbances are critical drivers of ecosystem dynamics.
  • Traditional analyses often use averaged responses, potentially masking important ecological information.
  • Ecological response variability is an underutilized but sensitive metric for disturbance impacts.

Purpose of the Study:

  • To present a conceptual model for predicting disturbance effects on ecological variability.
  • To integrate disturbance characteristics (extent, frequency, intensity) and recovery into variability predictions.
  • To provide a framework for understanding both immediate and time-lagged effects of disturbance.

Main Methods:

  • Developed a conceptual model to estimate qualitative changes in ecological variability.

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  • Compared model predictions with empirical data from diverse disturbance types and ecosystems.
  • Included a guide to methods for characterizing and detecting changes in variability.

    • Main Results:

    • The conceptual model effectively captures disturbance-driven changes in ecological variability.
    • Variability analysis reveals ecological differences often obscured by averaging.
    • Model performance was evaluated across various disturbance scenarios and ecosystems.

    Conclusions:

    • Variability is a sensitive indicator of ecological disturbance.
    • The conceptual model enhances understanding of ecosystem response to disturbance.
    • Considering variability improves the assessment of ecosystem behavior and predictability under disturbance.