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To replicate, or not to replicate - that should not be a question.

David R Chalcraft1

  • 1Department of Biology, East Carolina University, Greenville, NC, 27858, USA.

Ecology Letters
|May 31, 2019
PubMed
Summary

Ecological models can be improved by increasing experimental factor levels, but this study shows that the simulations supporting this idea used a flawed prediction success metric. Replication remains crucial for reliable ecological predictions.

Keywords:
Ecological modelsexperimental designgeneral linear modelpolynomial contrastsprediction successreplicationstatisticstrend analysis

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Area of Science:

  • Ecology
  • Ecological modeling
  • Experimental design

Background:

  • Recent simulations proposed maximizing experimental factor levels over replication to enhance ecological model predictive ability.
  • This approach aimed to improve understanding of complex ecological systems.

Purpose of the Study:

  • To critically evaluate the methodology of recent simulations concerning experimental design in ecology.
  • To demonstrate the impact of a faulty prediction success metric on simulation outcomes.
  • To re-emphasize the fundamental importance of replication in ecological research.

Main Methods:

  • Analysis of simulation parameters and metrics used in prior ecological modeling studies.
  • Identification and critique of the 'prediction success' metric employed.
  • Theoretical reinforcement of the role of replication in statistical power and model validation.

Main Results:

  • The simulations suggesting a trade-off between factor levels and replication were based on an inappropriate metric for prediction success.
  • The faulty metric inflated the perceived benefits of sacrificing replication.
  • Correct application of prediction metrics highlights the continued necessity of replication.

Conclusions:

  • The previous recommendation to sacrifice replication for more experimental factor levels is unsubstantiated due to methodological flaws.
  • Replication is essential for robust ecological predictions and reliable model development.
  • Future research should prioritize sound statistical practices, including adequate replication, in experimental design.