Rigorous evidence identification is essential for systematic reviews and meta‐analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments. This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed, and Web of Science. A novel, query‐based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall, and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analyzed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system. We call for database owners to recognize the requirements of evidence synthesis and for academic journals to reassess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.

Google Scholar (GS), a commonly used web-based academic search engine, catalogues between 2 and 100 million records of both academic and grey literature (articles not formally published by commercial academic publishers). Google Scholar collates results from across the internet and is free to use. As a result it has received considerable attention as a method for searching for literature, particularly in searches for grey literature, as required by systematic reviews. The reliance on GS as a standalone resource has been greatly debated, however, and its efficacy in grey literature searching has not yet been investigated. Using systematic review case studies from environmental science, we investigated the utility of GS in systematic reviews and in searches for grey literature. Our findings show that GS results contain moderate amounts of grey literature, with the majority found on average at page 80. We also found that, when searched for specifically, the majority of literature identified using Web of Science was also found using GS. However, our findings showed moderate/poor overlap in results when similar search strings were used in Web of Science and GS (10–67%), and that GS missed some important literature in five of six case studies. Furthermore, a general GS search failed to find any grey literature from a case study that involved manual searching of organisations’ websites. If used in systematic reviews for grey literature, we recommend that searches of article titles focus on the first 200 to 300 results. We conclude that whilst Google Scholar can find much grey literature and specific, known studies, it should not be used alone for systematic review searches. Rather, it forms a powerful addition to other traditional search methods. In addition, we advocate the use of tools to transparently document and catalogue GS search results to maintain high levels of transparency and the ability to be updated, critical to systematic reviews.

Reliable synthesis of the various rapidly expanding bodies of evidence is vital for the process of evidence-informed decision-making in environmental policy, practice and research. With the rise of evidence-base medicine and increasing numbers of published systematic reviews, criteria for assessing the quality of reporting have been developed. First QUOROM (Lancet 354:1896–1900, 1999 ) and then PRISMA (Ann Intern Med 151:264, 2009 ) were developed as reporting guidelines and standards to ensure medical meta-analyses and systematic reviews are reported to a high level of detail. PRISMA is now widely used by a range of journals as a pre-submission checklist. However, due to its development for systematic reviews in healthcare, PRISMA has limited applicability for reviews in conservation and environmental management. We highlight 12 key problems with the application of PRISMA to this field, including an overemphasis on meta-analysis and no consideration for other synthesis methods. We introduce ROSES (RepOrting standards for Systematic Evidence Syntheses), a pro forma and flow diagram designed specifically for systematic reviews and systematic maps in the field of conservation and environmental management. We describe how ROSES solves the problems with PRISMA. We outline the key benefits of our approach to designing ROSES, in particular the level of detail and inclusion of rich guidance statements. We also introduce the extraction of meta-data that describe key aspects of the conduct of the review. Collated together, this summary record can help to facilitate rapid review and appraisal of the conduct of a systematic review or map, potentially speeding up the peer-review process. We present the results of initial road testing of ROSES with systematic review experts, and propose a plan for future development of ROSES.

Systematic mapping was developed in social sciences in response to a lack of empirical data when answering questions using systematic review methods, and a need for a method to describe the literature across a broad subject of interest. Systematic mapping does not attempt to answer a specific question as do systematic reviews, but instead collates, describes and catalogues available evidence (e.g. primary, secondary, theoretical, economic) relating to a topic or question of interest. The included studies can be used to identify evidence for policy-relevant questions, knowledge gaps (to help direct future primary research) and knowledge clusters (sub-sets of evidence that may be suitable for secondary research, for example systematic review). Evidence synthesis in environmental sciences faces similar challenges to those found in social sciences. Here we describe the translation of systematic mapping methodology from social sciences for use in environmental sciences. We provide the first process-based methodology for systematic maps, describing the stages involved: establishing the review team and engaging stakeholders; setting the scope and question; setting inclusion criteria for studies; scoping stage; protocol development and publication; searching for evidence; screening evidence; coding; production of a systematic map database; critical appraisal (optional); describing and visualising the findings; report production and supporting information. We discuss the similarities and differences in methodology between systematic review and systematic mapping and provide guidance for those choosing which type of synthesis is most suitable for their requirements. Furthermore, we discuss the merits and uses of systematic mapping and make recommendations for improving this evolving methodology in environmental sciences.

Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify relevant records for a review by making use of explicit mentions of one article within another. Citation chasing is a popular supplementary search method because it helps to build on the work of primary research and review authors. It does so by identifying potentially relevant studies that might otherwise not be retrieved by other search methods; for example, because they did not use the review authors' search terms in the specified combinations in their titles, abstracts, or keywords. Here, we briefly provide an overview of citation chasing as a method for systematic reviews. Furthermore, given the challenges and high resource requirements associated with citation chasing, the limited application of citation chasing in otherwise rigorous systematic reviews, and the potential benefit of identifying terminologically disconnected but semantically linked research studies, we have developed and describe a free and open source tool that allows for rapid forward and backward citation chasing. We introducecitationchaser, an R package and Shiny app for conducting forward and backward citation chasing from a starting set of articles. We describe the sources of data, the backend code functionality, and the user interface provided in the Shiny app.