Recruitment, abundance, and predation on the blue mussel (<i>Mytilus edulis</i>) on northeastern estuarine rocky shores
Bibliographic record
Abstract
We report on patterns of abundance, recruitment, and predation on the blue mussel (Mytilus edulis) in three human‐dominated estuaries in the northeastern United States. Through replicate field experiments and observational studies at multiple sites nested within each of the three estuaries, we investigated the relative influences of local and regional scale variation in select bottom‐up and top‐down factors on blue mussel populations on wave‐protected rocky shores. The most striking result was the decoupling between adult abundance and recruitment: mussel recruitment rates were highest in the most northern estuary, Casco Bay, while adult abundances were highest in the most southern estuary, Long Island Sound. We detected evidence of top‐down forcing on adult abundance by consumers in the two more southern estuaries, Narragansett Bay and Long Island Sound, but not in Casco Bay. Finally, we observed some indications of bottom‐up forcing on mussel abundance and recruitment at the within‐estuary scale, but these signals were not consistent among estuaries or across the responses measured (e.g., adult abundances and recruitment rates). Our results support previous work demonstrating the importance of both top‐down and bottom‐up influences on rocky shore populations, and also highlight how future research—particularly integrating studies of the different ontogenetic stages of mussels—could further advance understanding of biological population dynamics in this and other systems.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".