Harnessing meta-analyses’ insights in ecology and evolution research

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Pietro Pollo, April Robin Martinig , Ayumi Mizuno , Kyle Morrison , Patrice Pottier , Lorenzo Ricolfi , Jess Tam, Coralie Williams, Yefeng Yang, Szymon Marian Drobniak , Malgorzata Lagisz, Shinichi Nakagawa 

Abstract

Meta-analyses are powerful tools to synthesise the literature in several fields of study, including ecology and evolution. However, it remains uncertain whether ecologists and evolutionary biologists fully comprehend meta-analyses’ findings or effectively apply them when citing these studies in their own research. Here, we first discuss key meta-analytical concepts and provide a guide to researchers in ecology and evolution on how to harness meta-analyses’ insights. For instance, we clarify the meaning of effect sizes and heterogeneity to improve understanding of meta-analyses’ quantitative findings. In addition, we analysed articles published in 2023 in ecology and evolution to investigate how frequently and in what context meta-analyses were cited. We found that approximately 21% of articles cited at least one meta-analysis, and that the relative number of citations of meta-analyses (0.04% of all citations analysed) was similar to the publication frequency of meta-analytical articles (0.06% of all articles). Most importantly, we found that while the direction of mean effect sizes from cited meta-analyses was often mentioned, the magnitude of effect sizes and the limitations of the data analysed were frequently overlooked. These findings underscore the need for improved citation practices of meta-analyses in ecological and evolutionary research, which our recommendations seek to promote.

DOI

https://doi.org/10.32942/X2PW5P

Subjects

Ecology and Evolutionary Biology

Keywords

Key words: impact factor, meta-regression, moderators, publication bias, scientific references., impact factor, meta-regression, moderators, publication bias, scientific references

Dates

Published: 2025-02-10 19:44

License

CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

Additional Metadata

Language:
English

Conflict of interest statement:
None

Data and Code Availability Statement:
All data and code used in this study are available at: https://pietropollo.github.io/meta_impact/.