Framing MPAs: primary research publications presenting attitudes towards Marine Protected Areas in Australia (2000 - 2022)
This dataset identifies the publications presenting primary attitudinal research on Marine Protected Areas (MPAs) conducted between 2000 - 2022. Peer-reviewed scientific literature and government, consultant, and industry reports which explicitly asked for an attitudinal response to MPAs were identified through Web of Science, Google Scholar, and Google databases using a step-by-step review protocol developed in accordance with published systematic and scoping literature review methods. To explore these publications, 12 review categories were defined to identify the agencies producing primary attitudinal research on MPAs and the population of interest from which the studies sampled. This dataset demonstrates that Australian government agencies are the largest contributing parties to attitudinal research on MPAs in Australia. Direct and proximate user groups, namely fishers and local communities, represent the most significant populations of interest from which these studies sample.
Steps to reproduce
The contextual scope of the paper was defined to include all research publications with a focus on collecting and reporting primary attitudinal data on Marine Protected Areas (MPAs) in an Australian context after the EPBC Act came into force on the 16th of July 2000. To be included in our review, publications had to satisfy all of the following defined selection criteria: 1) Does the publication explicitly document primary attitudinal research on MPAs? 2) Was the data collection conducted after the EPBC Act 1999 came into force? 3) Is the primary realm of the research Australian oceanic marine environments? 4) Is the publication available to be read in-full online? Step-by-step Review Procedure: Step 1. Search: To identify published academic literature, the Web of Science database was searched using the following search string: (“marine environment*” OR “marine protected area*” OR “MPA*” OR “marine park*” OR “marine sanctuar*” OR “marine reserve*” OR “ocean*") AND (“public” OR “communit*” OR “stakeholder*” OR “user*”) AND (“attitud*” OR “perception*” OR “behaviour*” OR “value*” OR “knowledge” OR “understanding” OR “awareness” OR “connection*” OR “opinion*” OR “social license” OR “human dimension” OR "acceptability") OR (“survey” OR “interview” OR “review” OR "questionnaire" OR “submission”) AND (“Australia”). To identify government and industry literature, Google and Google Scholar were searched using the following search string (slightly adjusted from academic literature search string due to Google’s search enquiry 32 word limit): (“marine” OR “marine protected area” OR “marine park” OR “marine sanctuary” OR “marine reserve” OR “ocean”) AND (“public” OR “community” OR “stakeholder” OR “user”) AND (“attitude” OR “perception” OR “behaviour” OR “value” OR “knowledge” OR “understanding” OR “awareness” OR “connection” OR “opinion” OR “social license” OR “human dimension”) AND (“survey” OR “interview” OR “review” OR “submission”). Step 2. Initial Screening: A title-based scan of the candidate academic articles and first 100 results of both the Google and Google Scholar searches identified in step 1. Was conducted to assess broad relevance. Step 3. Data Review: The selection criteria (identified above) were applied to each article remaining from step 2. based on a reading of the abstract and/or a full text scan Step 4. Citation Searching & Cross-Pollination: citation searching through relevant databases and authors’ professional networks to identify other publications meeting selection criteria. Step 5. Review Category Development: review categories were developed based on study scope and aim. Step 6. Dataset Cleaning: remove duplicate articles based on temporal selection where multiple publication use identical datasets. Step 7. Article Review: Data based on the review categories defined in step 5. were collected based on a full-text reading of the publication and inserted into the corresponding review categories in the attached dataset.