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Testing the productive-space hypothesis: rational and power.

David M Post1

  • 1Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520-8105, USA. david.post@yale.edu

Oecologia
|July 20, 2007
PubMed
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The productive-space hypothesis explains food-chain length using ecosystem size and resources. Evaluating two testing methods reveals the single gradient approach has high error rates, while the dual gradient approach has low power for resource availability. Further testing is needed.

Area of Science:

  • Community ecology
  • Ecological modeling
  • Food web dynamics

Background:

  • Food-chain length variation is a key ecological challenge.
  • The productive-space hypothesis links food-chain length to ecosystem size and resource availability.
  • Two methods, single and dual gradient approaches, exist for testing this hypothesis.

Purpose of the Study:

  • To evaluate the efficacy of single and dual gradient approaches for testing the productive-space hypothesis.
  • To estimate Type 1 and Type 2 error rates for each method using simulated data.
  • To assess the power of the dual gradient approach in a real-world dataset.

Main Methods:

  • Simulated datasets were used to estimate Type 1 and Type 2 error rates for single and dual gradient models.

Related Experiment Videos

  • Models tested relationships between food-chain length, ecosystem size, and resource availability.
  • Retrospective power analysis was conducted on the Post et al. (2000) dataset.

    • Main Results:

    • The single gradient model showed high power but a high Type 1 error rate, frequently supporting the hypothesis erroneously.
    • The dual gradient model had a low Type 1 error rate but low power to detect resource availability effects.
    • The Post et al. dataset had sufficient power to reject the hypothesis in north temperate lakes.

    Conclusions:

    • The single gradient approach is unreliable due to high Type 1 errors.
    • The dual gradient approach is more reliable but may lack power for certain variables.
    • The productive-space hypothesis requires broader testing across diverse ecosystems to assess its generality.