What evidence exists on the performance of nature-based solutions interventions for coastal protection in biogenic, shallow ecosystems? A systematic map protocol.

Background: Anthropogenic pressures and climate change threaten the capacity of ecosystems to deliver a variety of services, including protecting coastal communities from hazards like flooding and erosion. Human interventions aim to buffer against or overcome these threats by providing physical protection for existing coastal infrastructure and communities, along with added ecological, social, or economic co-benefits. These interventions are a type of nature-based solution (NBS), broadly defined as actions working with nature to address societal challenges while also providing benefits for human well-being, biodiversity, and resilience. Despite the increasing popularity of NBS for coastal protection, sometimes in lieu of traditional hardened shorelines (e.g., oyster reefs instead of bulkheads), gaps remain in our understanding of whether common NBS interventions for coastal protection perform as intended. To help fill these knowledge gaps, we aim to identify, collate, and map the evidence base surrounding the performance of active NBS interventions related to coastal protection across a suite of ecological, physical, social, and economic outcomes in salt marsh, seagrass, kelp, mangrove, shellfish reef, and coral reef systems. The resulting evidence base will highlight the current knowledge on NBS performance and inform future uses of NBS meant for coastal protection.

Methods: Searches for primary literature on performance of NBS for coastal protection in shallow, biogenic ecosystems will be conducted using a predefined list of indexing platforms, bibliographic databases, open discovery citation indexes, and organizational databases and websites, as well as an online search engine and novel literature discovery tool. All searches will be conducted in English and will be restricted to literature published from 1980 to present. Resulting literature will be screened against set inclusion criteria (i.e., population, intervention, outcome, study type) at the level of title and abstract followed by full text. Screening will be facilitated by a web-based active learning tool that incorporates user feedback via machine learning to prioritize articles for review. Metadata will be extracted from articles that meet inclusion criteria and summarized in a narrative report detailing the distribution and abundance of evidence surrounding NBS performance, including evidence clusters, evidence gaps, and the precision and sensitivity of the search strategy.