A systematic mapping protocol for understanding knowledge exchange in forest science.
When making decisions about forest and environmental management, managers and policymakers often rely upon scientific knowledge. There is a well-documented 'knowledge-integration gap' where often the production of knowledge and its use are not aligned. Though there are several theoretical frameworks that conceptualize how knowledge is exchanged between producers of scientific knowledge and users of that information, there has been little attention to documenting knowledge exchange practices and their effectiveness, especially about forests. In the systematic map, we will examine the peer-reviewed academic and grey literature to document and classify the knowledge exchange techniques suggested and adopted by knowledge producers and users in the forest sciences globally. Characterizing this knowledge exchange landscape will provide new information about which techniques are used and their frequency, if there is evidence of effectiveness for particular techniques, and recommendations for best practices. This map will also show whether approaches to knowledge exchange differ between sectors (e.g. academia, government). We will create a systematic literature map as defined by the Collaboration for Environmental Evidence to capture case studies of, or theories about, knowledge exchange related to forest science. The search of peer-reviewed academic and grey literature will be conducted in English and French in two academic databases (BASE and Scopus) and one specialist database (Research Gate). Candidate search strings will be evaluated against a test list of documents to determine strings with maximum sensitivity and specificity. Eligibility criteria will be applied to items at two screening stages: (1) title and abstract and (2) full-text. All screening decisions will be recorded in a database with 15% of full-text screening decisions validated. Items retained for inclusion will have data extracted according to a standardized strategy. Each reviewer conducting data extraction will have at least three of their extractions validated.