Evidence maps and evidence gaps: evidence review mapping as a method for collating and appraising evidence reviews to inform research and policy

Abstract

Evidence reviews are a key mechanism for incorporating extensive, complex and specialised evidence into policy and practice, and in guiding future research. However, evidence reviews vary in scope and methodological rigour, creating several risks for decision-makers: decisions may be informed by less reliable reviews; apparently conflicting interpretations of evidence may obfuscate decisions; and low quality reviews may create the perception that a topic has been adequately addressed, deterring new syntheses (cryptic evidence gaps). We present a new approach, evidence review mapping, designed to produce a visual representation and critical assessment of the review landscape for a particular environmental topic or question. By systematically selecting and describing the scope and rigour of each review, this helps guide non-specialists to the most relevant and methodologically reliable reviews. The map can also direct future research through the identification of evidence gaps (whether cryptic or otherwise) and redundancy (multiple reviews on similar questions). We consider evidence review mapping a complementary approach to systematic reviews and systematic maps of primary literature and an important tool for facilitating evidence-based decisionmaking and research efficiency.

Research Weaving: Visualizing the Future of Research Synthesis

“…Systematic mapping (sometimes called 'evidence mapping') is a method derived from systematic reviews, with the goal of classifying the types of research on a broad topic [9][10][11][12][13][14]. Systematic mapping is still a nascent methodology, with the first systematic maps appearing only in the last decade [9,10].…”

A comment on computational biology and connecting the dots.

“…Understanding, observing, ideating, and testing can be expeditions in natural history, experimentation, or computation via models and simulations. Big ideas that make connections and help solve mysteries can come from computation (Nussinov 2015). Gaps in data (O'Leary et al 2017), true versus false zeros (Martin et al 2005), and the 'residuals' of exploratory data analyses can all ignite new ideas.…”

“…Big ideas that make connections and help solve mysteries can come from computation (Nussinov 2015). Gaps in data (O'Leary et al 2017), true versus false zeros (Martin et al 2005), and the 'residuals' of exploratory data analyses can all ignite new ideas. At times, seeing the forest from the trees involves counting all the trees, crunching the numbers, and discovering the forest.…”

“…Computational biology is the perfect intersection or eye of the storm for changes in contemporary ecology and evolution. Computational biology is quantitative and at times atomic (Nussinov 2013) because it can illuminate broad patterns for biologists from very fine scales (Leonard 2007;Rosse et al 1998). Contemporary computation biology has become more integrated into the broader community of biological research (Nussinov 2015) through big data, models, and other computational tools that examine or support the examination of biological processes.…”

“…Computational biology is quantitative and at times atomic (Nussinov 2013) because it can illuminate broad patterns for biologists from very fine scales (Leonard 2007;Rosse et al 1998). Contemporary computation biology has become more integrated into the broader community of biological research (Nussinov 2015) through big data, models, and other computational tools that examine or support the examination of biological processes. All biology is likely computational biology to some extent 'because it is the questions you address that matter, not the tools you use' (Markowetz 2017), and thus, in ecology and evolution, we are unified and overlap in our shared focus on the dynamics and interactions within natural systems.…”