Post provided by ELIZA GRAMES

The number of studies published every year in ecology and evolutionary biology has increased rapidly over the past few decades. Each new study contributes more to what we know about a topic, adding nuance and complexity that helps improve our understanding of the natural world. To make sense of this wealth of evidence and get closer to a complete picture of the world, researchers are increasingly turning to systematic review methods as a way to synthesise this information.

What is a Systematic Review?

Systematic reviews, first developed in public health fields, take an experimental design approach to reviewing the literature. They treat the search for primary studies as a transparent and reproducible data gathering process. The rigorous methods used in systematic reviews make them a trusted form of evidence synthesis. Researchers use them to summarise the state of knowledge on a topic and make policy and practice recommendations.

Systematic reviews are rigorous and time-intensive, though. Their adoption in ecology and evolutionary biology has been hampered by the resources and time it takes to conduct them. Because these fields don’t have standardised terminology – such as the Medical Subject Heading (MeESH) system used in medicine – that can be used to search for studies, this problem is exacerbated. But the proper implementation of systematic review methods is critically important to advancing our understanding of ecology and evolutionary biology. Using these methods properly will help researchers make evidence-based policy recommendations for conservation and other management applications.

How Can We Make Systematic Reviews Easier?

To reduce the burden of conducting a systematic review, we developed an efficient, partially automated method of generating search strategies. Our method reduces the amount of time required for gathering evidence for a review, without sacrificing comprehensiveness. It relies on keyword co-occurrences in a body of related literature to identify important phrases and synonyms for the keywords initially chosen.

To make the method accessible and promote transparency in how search strategies are developed, we implemented it in the R package litsearchr. We tested our method and found that it reduces the amount of time required to develop a search strategy by 90%! Not only that, it also performs as well as or better than published search strategies developed with conventional methods..

litsearchr is only one piece of a larger, interdisciplinary effort to make systematic reviews more feasible and responsive to pressing policy needs. It’s integrated with the metaverse: an ongoing, collaborative project led by Martin Westgate.

metaverse aims to link a suite of R packages that help users conduct systematic review. Ultimately, the goal of the metaverse project is to provide tools for each step of the process, from developing a search strategy, all the way through to conducting a meta-analysis and visualising results. We hope that by lowering the barriers to developing a comprehensive electronic search strategy, litsearchr (and other automated approaches to evidence synthesis) will advance our understanding of ecology and evolutionary biology by facilitating rigorous synthesis methods.

To find out more about litsearchr, read our Methods in Ecology and Evolution article ‘An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks’