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Difficulties in benchmarking ecological null models: an assessment of current methods
Chai Molina1,2, Lewi Stone3,4
1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, 08544, USA.
Ecology
|December 14, 2019
Summary
Ecologists often use null models to study species interactions, but current benchmarking methods for selecting hypothesis tests are flawed. These popular approaches for analyzing ecological communities are not a reliable guide for choosing appropriate statistical tests.
Area of Science:
- Ecology
- Biogeography
- Community Ecology
Background:
- Species interactions structure ecological communities, a key area of study in ecology and biogeography.
- Null model approaches are widely used for hypothesis testing in community ecology.
- The selection of null models and statistics allows for numerous hypothesis tests to detect community-wide processes.
Purpose of the Study:
- Critique popular benchmarking methods for selecting hypothesis tests in community ecology.
- Address shortcomings in using artificial data sets to assess the performance of ecological hypothesis tests.
- Clarify terminological errors regarding Type I and Type II errors in ecological null model approaches.
Main Methods:
- Analysis of existing literature on null model approaches in community ecology.
- Identification of conceptual and methodological flaws in benchmarking hypothesis tests.
- Discussion of the implications of these flaws for ecological research.
Main Results:
- Popular benchmarking methods for selecting null hypothesis tests are fundamentally flawed.
- The use of artificial data sets to benchmark ecological tests is not a sound approach.
- Misapplication of Type I and Type II error concepts undermines current methods.
Conclusions:
- Existing benchmarking methods do not provide a reliable basis for selecting null hypothesis tests in community ecology.
- There is no simple method for benchmarking null hypothesis tests effectively.
- These identified issues persist in current ecological research, affecting the development of new models and statistics.
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