Global typologies of coastal wetland status to inform conservation and management
Bibliographic record
Abstract
Global-scale conservation initiatives and policy instruments rely on ecosystem indicators to track progress towards targets and objectives. A deeper understanding of indicator interrelationships would benefit these efforts and help characterize ecosystem status. We study interrelationships among 34 indicators for mangroves, saltmarsh, and seagrass ecosystems, and develop data-driven, spatially explicit typologies of coastal wetland status at a global scale. After accounting for environmental covariates and gap-filling missing data, we obtained two levels of clustering at 5 and 18 typologies, providing outputs at different scales for different end users. We generated 2,845 cells (1° (lat) × 1° (long)) globally, of which 29.7% were characterized by high land- and marine-based impacts and a high proportion of threatened species, 13.5% by high climate-based impacts, and 9.6% were refuges with lower impacts, high fish density and a low proportion of threatened species. We identify instances where specific actions could have positive outcomes for coastal wetlands across regions facing similar issues. For example, land- and marine-based threats to coastal wetlands were associated with ecological structure and function indicators, suggesting that reducing these threats may reduce habitat degradation and threats to species persistence. However, several interdimensional relationships might be affected by temporal or spatial mismatches in data. Weak relationships mean that global biodiversity maps that categorize areas by single indicators (such as threats or trends in habitat size) may not be representative of changes in other indicators (e.g., ecosystem function). By simplifying the complex global mosaic of coastal wetland status and identifying regions with similar issues that could benefit from knowledge exchange across national boundaries, we help set the scene for globally and regionally coordinated conservation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".