Published September 26, 2024 | Version v1
Journal article Open

A large-scale in silico replication of ecological and evolutionary studies

  • 1. University of New South Wales
  • 2. Leiden University
  • 3. University of Chicago

Description

Despite the growing concerns about the replicability of ecological and evolutionary studies, no results exist from a field-wide replication project. We conduct a large-scale in silico replication project, leveraging cutting-edge statistical methodologies. Replicability is 30%–40% for studies with marginal statistical significance in the absence of selective reporting, whereas the replicability of studies presenting 'strong' evidence against the null hypothesis H0 is >70%. The former requires a sevenfold larger sample size to reach the latter's replicability. We call for a change in planning, conducting and publishing research towards a transparent, credible and replicable ecology and evolution.

Data availability

The data to reproduce the results of this study are available at https://github.com/Yefeng0920/replication_EcoEvo_git). The data are also available via Zenodo at https://doi.org/10.5281/zenodo.12748092 (ref. 12).

The code for reproducing the results of this study is available at https://github.com/Yefeng0920/replication_EcoEvo_git. The code is also available via Zenodo at https://doi.org/10.5281/zenodo.12748092 (ref. 12). We provide code implemented in R (v.4.0.1) and Julia (v.1.10.0). The reproducible R code in an interactive format (code chunks paired with results) also can be found in the Supplementary Information and at https://yefeng0920.github.io/replication_EcoEvo_git/.

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Large-scale-in-silico-replication-of-ecological-and-evolutionary-studies.pdf

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Additional details

Identifiers

DOI
10.1038/s41559-024-02530-5
Other
oai:uchicago.tind.io:13596

Funding

National Natural Science Foundation of China
32102597
Australian Research Council Discovery
DP210100812
Australian Research Council Discovery
DP230101248
University of New South Wales Library

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Statistics
Center(s) or Institute(s)
Data Science Institute