Anthropogenic pressures and life history predict trajectories of seagrass meadow extent at a global scale
Bibliographic record
Abstract
Seagrass meadows are threatened by multiple pressures, jeopardizing the many benefits they provide to humanity and biodiversity, including climate regulation and food provision through fisheries production. Conservation of seagrass requires identification of the main pressures contributing to loss and the regions most at risk of ongoing loss. Here, we model trajectories of seagrass change at the global scale and show they are related to multiple anthropogenic pressures but that trajectories vary widely with seagrass life-history strategies. Rapidly declining trajectories of seagrass meadow extent (>25% loss from 2000 to 2010) were most strongly associated with high pressures from destructive demersal fishing and poor water quality. Conversely, seagrass meadow extent was more likely to be increasing when these two pressures were low. Meadows dominated by seagrasses with persistent life-history strategies tended to have slowly changing or stable trajectories, while those with opportunistic species were more variable, with a higher probability of either rapidly declining or rapidly increasing. Global predictions of regions most at risk for decline show high-risk areas in Europe, North America, Japan, and southeast Asia, including places where comprehensive long-term monitoring data are lacking. Our results highlight where seagrass loss may be occurring unnoticed and where urgent conservation interventions are required to reverse loss and sustain their essential services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".