Bridging conservation and policy: evaluating national targets to reduce mangrove loss under the Kunming–Montreal biodiversity framework
Bibliographic record
Abstract
Abstract The Kunming–Montreal Global Biodiversity Framework (GBF) aims to halt biodiversity loss by 2030, with Targets 1 and 3 focusing on reducing forest loss and expanding protected areas. Mangroves, as biodiversity hotspots offering crucial ecosystem services, have seen some conservation gains, yet key drivers of high-value mangrove loss remain unaddressed in intergovernmental policy frameworks. It is the first global assessment linking GBF Targets 1 & 3 to mangrove loss drivers and ecosystem assessment. We apply an interdisciplinary approach—combining global-scale geospatial analysis of mangrove loss trajectories between 2000 and 2016 and ecosystem value distribution, and thematic policy analysis. We classify all 120 countries where mangroves are present by their short- and long-term mangrove loss management strategies and evaluate the inclusion of relevant actions under Targets 1 and 3 of National Biodiversity Strategies and Action Plans (NBSAPs). Between 2000 and 2016, 78% of mangrove loss occurred in areas rich in biodiversity, biomass, and coastal protection, mostly outside protected zones. Of 120 mangrove-holding countries, 30 (25%) experienced significant loss. Among them, 11 have the potential to implement short-term mitigation by expanding or managing protected areas, though only 5 included these strategies in national targets. Four countries referenced broader measures like indigenous rights and the prioritisation of ecosystem service hotspots. Only Cameroon, Colombia, Gabon, Panama, and Tanzania are positioned to address major loss drivers within the GBF timeline. This paper is the first global assessment of GBF-aligned national targets to mitigate mangrove loss, contributing to SDGs 14 and 15. We show that mangrove loss cannot be halted by 2030 under the current level of national targets. Policy amendments at national scales can include short-term (area-based protection) and long-term strategies (restoration, rehabilitation and ecosystem-based approaches) to halt mangrove loss.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".