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Formalized synthesis opportunities for ecology: systematic reviews and meta‐analyses
Authors:Christopher J. Lortie
Affiliation:Dept of Biology, York Univ., , Toronto, Ontario, M3J 1P3 Canada
Abstract:Narrative reviews are dead. Long live systematic reviews (and meta‐analyses). Synthesis in many forms is now a driving force in ecology. Advances in open data for ecology and new tools provide vastly improved capacity for novel, emergent knowledge synthesis in our discipline. Systematic reviews and meta‐analyses are two formal synthesis opportunities for ecologists that are now accepted as traditional publications, but the scope of validated syntheses will continue to expand. To date, systematic reviews are rarely used whilst the rate of meta‐analyses published in ecological journals is increasing exponentially. Systematic reviews provide an overview of the literature landscape for a topic, and meta‐analyses examine the strength of evidence integrated across different studies. Effective synthesis benefits from both approaches, but better data reporting and additional advances in the culture of sharing data, code, analytics, workflows, methods and also ideas will further energize these efforts. At this junction, synthetic efforts that include systematic reviews and meta‐analyses should continue as stand‐alone publications. This is a necessary step in the evolution of synthesis in our discipline. Nonetheless, they are still evolving tools, and meta‐analyses in particular are simply an extended set of statistical tests. Admittedly, understanding the statistics and assumptions influence how we conduct synthesis much as statistical choices often shape experimental design, i.e. ANOVA versus regression‐based experiments, but statistics do not make the paper. Current steps – primary research articles need to more effectively report evidence, sharing scientific products should expand, systematic reviews should be used to identify research gaps/delineate literature landscapes, and meta‐analyses should be used to examine evidence patterns to further predictive ecology.
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