Assessing the multiple benefits of partially protected marine protected areas in Australia: A systematic review protocol
Bibliographic record
Abstract
BACKGROUND: There is global pressure to protect more of the world's oceans, primarily to protect biodiversity, and to fulfill the "30 by 30" goal set by the International Union for the Conservation of Nature (IUCN) that has recently been ratified under the Kunming-Montreal Global Biodiversity Framework at the fifteenth Conference of Parties (COP-15). Fully protected marine protected areas (MPAs) provide the highest level of protection for biodiversity from destructive or extractive practices and may limit access to the area itself. Fully protected MPAs (also commonly referred to as 'no-take MPAs') ban all fishing activities, thereby removing the realisation of direct economic and social benefits from resource extraction within these areas. However, fully protected MPAs can still act as source of productivity to surrounding areas, while also providing an important scientific reference role for off-reserve management thereby providing indirect economic and social outcomes, as well as biodiversity benefits. Sustainable marine resource management strives to achieve 'triple-bottom line' benefits, where economic, social, and biodiversity benefits are maximised in managed areas of the ocean. Implementing 'partially protected' areas (PPAs) in areas of high biodiversity value (i.e., inshore, productive areas of the ocean) that allow for some extractive activities, may allow us to supplement fully MPAs to meet IUCN conservation goals, while maximising social and economic benefits. However, our current understanding lacks explicit quantitative assessments of whether and how PPAs can benefit (or otherwise) biodiversity, while also providing economic and social benefits. This study provides a method to systematically review the scientific and legislative literature to understand how PPAs may contribute to conserving biodiversity while also providing social and economic benefits to Australia. METHODS AND EXPECTED OUTPUTS: The implementation of partially protected areas (PPAs) requires careful consideration of many potentially competing factors, and an understanding of the types of partial protection already in place in a region. We have developed a systematic literature review protocol focussing on the primary research question: "What is the current state of partially protected area (PPA) implementation across Australian marine areas?". The aim of the review is to provide marine resource managers with a comprehensive overview of PPAs in Australia, including associated goals and stated management strategies to achieve these goals, and a methodological approach that may be utilised globally. The review protocol was designed by the research team for a Fisheries Resource and Development Corporation (FRDC) strategic research grant and will seek input from a project steering committee for the project on aggregation of the initial results. The steering committee is made up of stakeholders from a wide range of backgrounds and interests, covering marine conservation, fisheries management, Indigenous values, and academic research in Australia. Multiple academic databases, alongside Australian Federal, State, and Territory legislation and related policies will be reviewed using Boolean keyword search strings for both academic databases and relevant grey literature. Results from eligible documents will be compiled and insights from the review collated to provide information on the status of PPA implementation in Australia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".