Evaluating the effectiveness of Marine Conservation Strategies: challenges for sustainable ocean management
Bibliographic record
Abstract
In recent decades, the conservation of marine ecosystems has garnered global attention due to the substantial evidence of ocean degradation and biodiversity loss. Despite significant advances in marine science, a comprehensive understanding and adequate protection of marine environments still need to be improved. Marine ecosystems face cumulative anthropogenic pressures, both locally and globally, including overfishing, pollution, and climate change, which diminish biodiversity and degrade ecosystem services essential for food security, carbon storage, and climate regulation. As a response, Marine Protected Areas (MPAs) have emerged as a primary conservation tool under frameworks such as the Convention on Biological Diversity (CBD) and the recent High Seas Treaty. However, the effectiveness of Marine Conservation Strategies (MCSs) is debated due to several factors. These include remote locations, unclear zoning, and poor delimitation (14%), lack of regulations or contradictory regulations (8%), lack of stakeholder involvement (7%), global warming or warming waters (7%), and weak governance or coordination (6%). These issues are the main drivers of inefficacy, often leading to these strategies being labeled as "paper parks" which are protected areas in name only.This study provides a systematic literature review (n=225, among 1772 articles) of the effectiveness of Marine Conservation Strategies (MCSs) in addressing the challenges posed by these drivers of ecosystem and biodiversity loss, considering that we currently have different spatial management tools to conserve marine ecosystems. We examine the diverse conservation strategies, comparing their strengths, limitations, and interactions within marine spatial planning frameworks. By synthesizing existing literature and identifying research gaps, this review aims to support the development of adaptive and dynamic conservation tools that align with the Kunming-Montreal Global Biodiversity Framework and the Biodiversity Beyond National Jurisdiction (BBNJ) agreement. Our findings underscore the necessity for multifaceted, resilient conservation strategies, considering the temporal and spatial scales with repercussions in the functional scale, that can adapt to the evolving natural dynamism of marine ecosystems, ensuring long-term sustainability and resilience.
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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 | 0.074 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".