OpenMEE: Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology

By Byron C. Wallace, M. Lajeunesse, George Dietz, Issa J. Dahabreh, Thomas A. Trikalinos, Christopher H. Schmid, J. Gurevitch. Methods in Ecology and Evolution, 2017

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Meta‐analysis and meta‐regression are statistical methods for synthesizing and modelling the results of different studies, and are critical research synthesis tools in ecology and evolutionary biology (E&E). However, many E&E researchers carry out meta‐analyses using software that is limited in its statistical functionality and is not easily updatable. It is likely that these software limitations have slowed the uptake of new methods in E&E and limited the scope and quality of inferences from research syntheses. We developed OpenMEE: Open Meta‐analyst for Ecology and Evolution to address the need for advanced, easy‐to‐use software for meta‐analysis and meta‐regression. OpenMEE has a cross‐platform, easy‐to‐use graphical user interface (GUI) that gives E&E researchers access to the diverse and advanced statistical functionalities offered in R, without requiring knowledge of R programming. OpenMEE offers a suite of advanced meta‐analysis and meta‐regression methods for synthesizing continuous and categorical data, including meta‐regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation. OpenMEE also supports data importing and exporting, exploratory data analysis, graphing of data, and summary table generation. As intuitive, open‐source, free software for advanced methods in meta‐analysis, OpenMEE meets the current and pressing needs of the E&E community for teaching meta‐analysis and conducting high‐quality syntheses. Because OpenMEE's statistical components are written in R, new methods and packages can be rapidly incorporated into the software. To fully realize the potential of OpenMEE, we encourage community development with an aim to advance the capabilities of meta‐analyses in E&E.

Subfields: Artificial Intelligence, Geometry and Topology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/cbc0efb749f535dfad718cf3878706ed3013217a

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