The ambiguous role of partially protected marine protected areas in Australia: Results from a systematic literature review
Bibliographic record
Abstract
Marine protected areas (MPAs) are an important tool in helping to protect biodiversity in the oceans. Recent ratification of the Kunming-Montreal Global Biodiversity Framework (GBF) has ensured that globally we are committed to effectively protecting 30% of the world's oceans by 2030, in MPAs. In Australia there is considerable interest in the potential benefits that partially protected areas (PPAs) may provide. However, a consistent definition of a PPA is currently lacking, and urgently needed to conduct quantitative analyses of PPAs. We conducted a systematic literature review to understand the current knowledge surrounding PPAs and their potential benefits. We define a PPA, characterise PPA implementation in Australia, and present results for the outcomes of PPAs in terms of ecological, economic, and social indicators. Our review suggests that although 45% of Australia's marine environment is within MPAs, 61% of MPAs provide only partial protection. The Northern Territory (100%), New South Wales (81%), and Queensland (79.8%) have the highest percentage of MPAs that are partially protected, compared to Tasmania which has the smallest percentage of partially protected MPAs (13.12%). Tasmania also has the smallest percentage cover of MPAs (6.49% state waters). Most PPA management plans did not contain quantifiable Key Performance Indicators (KPIs) to be able to effectively monitor the progress of these PPAs against the stated outcomes. We find the benefits of PPAs to be ambiguous: PPAs generally provide benefits when compared to 'open' ocean, however this is not a consistent result. There are no PPAs that provide greater overall benefits when compared to fully protected MPAs. Only one state (South Australia) and the Commonwealth (Australian Marine Parks) are collecting publicly available baseline data to facilitate quantitative monitoring of PPAs. Contrary to fisheries management, there were no plans of action if the declared MPAs and PPAs failed to meet their declared objectives and goals. Some PPAs within Australia appear to be incompatible with conservation priorities according to the recent "MPA Guide" classification framework. This study highlights the need for clearer management rationale and plans for PPAs in Australia, as these comprise the majority of MPAs in Australia's Exclusive Economic Zone.
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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 | 0.022 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".