Decades of eelgrass meadow dynamics across the northeast Pacific support seascape-scale conservation
Bibliographic record
Abstract
Abstract Eelgrass meadows provide vital nearshore habitats and ecosystem services, but they have declined from human stressors and conservation efforts are now widespread. Dynamic ecosystems like eelgrass meadows naturally rearrange as disturbance and recruitment unfold across seascapes. However, some decisions that protect eelgrass only consider extant meadows, thus ignoring the potential for change. Here, we report decades of eelgrass dynamics observed across the northeast Pacific. Our observations support conservation expanded to the seascape scale, which includes potentially inhabitable areas along with extant meadows. We found that total seascape meadow area changed over time, and changes within seascapes were often asynchronous. Some meadows rearranged across seascapes over multiple kilometres and decades. Also, some seascapes compartmentalized meadow collapse, which enabled later recovery, or supported local recruitment that substantially increased total meadow area. These observations were consistent with hierarchical patch dynamics, which promote ecosystem persistence over larger space and time scales. Thus, to enable the dynamics that underpin eelgrass persistence, it is necessary to keep many eelgrass habitat options open across seascapes, rather than protect only extant meadows. Given that dynamic, hierarchical ecosystems are common along marine shorelines, this approach may be effective for both nearshore ecosystems in general and for eelgrass in particular.
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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".