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