Long‐term declines and recovery of meadow area across the world’s seagrass bioregions
Bibliographic record
Abstract
Abstract As human impacts increase in coastal regions, there is concern that critical habitats that provide the foundation of entire ecosystems are in decline. Seagrass meadows face growing threats such as poor water quality and coastal development. To determine the status of seagrass meadows over time, we reconstructed time series of meadow area from 175 studies that surveyed 547 sites around the world. We found an overall trajectory of decline in all seven bioregions with a global net loss of 5602 km 2 (19.1% of surveyed meadow area) occurring since 1880. Declines have typically been non‐linear, with rapid and historical losses observed in several bioregions. The greatest net losses of area occurred in four bioregions (Tropical Atlantic, Temperate North Atlantic East, Temperate Southern Oceans and Tropical Indo‐Pacific), with declining trends being the slowest and most consistent in the latter two bioregions. In some bioregions, trends have recently stabilised or reversed. Losses, however, still outweigh gains. Despite consistent global declines, meadows show high variability in trajectories, within and across bioregions, highlighting the importance of local context. Studies identified 12 different drivers of meadow area change, with coastal development and water quality as the most commonly cited. Overall, however, attributions were primarily descriptive and only 10% of studies used inferential attributions. Although ours is the most comprehensive dataset to date, it still represents only one‐tenth of known global seagrass extent, with conspicuous historical and geographic biases in sampling. It therefore remains unclear whether the bioregional patterns of change documented here reflect changes in the world's unmonitored seagrass meadows. The variability in seagrass meadow trajectories, and the attribution of change to numerous drivers, suggest we urgently need to improve understanding of the causes of seagrass meadow loss if we are to improve local‐scale management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".