From coast to coast to coast: ecology and management of seagrass ecosystems across Canada
Bibliographic record
Abstract
Seagrass meadows are among the most productive and diverse marine ecosystems, providing essential structure, functions, and services. They are also among the most impacted by human activities and in urgent need of better management and protection. In Canada, eelgrass ( Zostera marina) meadows are found along the Atlantic, Pacific, and Arctic coasts, and thus occur across a wide range of biogeographic conditions. Here, we synthesize knowledge of eelgrass ecosystems across Canada’s coasts, highlighting commonalities and differences in environmental conditions, plant, habitat, and community structure, as well as current trends and human impacts. Across regions, eelgrass life history, phenology, and general species assemblages are similar. However, distinct regional differences occur in environmental conditions, particularly with water temperature and nutrient availability. There is considerable variation in the types and strengths of human activities among regions. The impacts of coastal development are prevalent in all regions, while other impacts are of concern for specific regions, e.g., nutrient loading in the Atlantic and impacts from the logging industry in the Pacific. In addition, climate change represents a growing threat to eelgrass meadows. We review current management and conservation efforts and discuss the implications of observed differences from coast to coast to coast.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".