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Functional R code is rare in species distribution and abundance papers
Kenneth F Kellner1, Jeffrey W Doser2,3, Jerrold L Belant1
1Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, USA.
Ecology
|November 20, 2024
Summary
Analytic reproducibility in ecology is low, with only 28% of studies sharing data and code, and 7% providing functional code. Improving these practices is crucial for scientific credibility.
Area of Science:
- Ecology
- Computational Biology
- Scientific Methodology
Background:
- Analytic reproducibility is vital for scientific credibility in ecological research.
- The current extent of analytic reproducibility in ecological literature is not well understood.
- Hierarchical models are frequently used for species distribution and abundance estimation in ecology.
Purpose of the Study:
- To assess the level of analytic reproducibility in ecological research using hierarchical models.
- To determine the availability and functionality of data and code in published ecological studies.
- To identify key areas for improvement in ecological research practices.
Main Methods:
- A survey of 497 papers published between 2018-2022 in 9 ecology journals.
- Focus on papers utilizing hierarchical models for species distribution and abundance.
- Evaluation of two components: data/code availability and code functionality.
Main Results:
- Only 28% of surveyed papers provided accessible data and code.
- A mere 7% of papers had code that executed without errors.
- Analytic reproducibility is currently the exception, not the norm, in ecology.
Conclusions:
- Ecological research often falls short of analytic reproducibility standards.
- Recommendations for authors include testing code in clean environments and simplifying code.
- Suggestions for journals involve validating data/code repositories and elevating their review status.
Keywords:
abundanceanalytic reproducibilitycomputational reproducibilitydistributionhierarchical modelsMore Related Videos
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