Undermining the foundation: a brief overview of the effects of a widespread invader on coastal ecosystem engineers
Bibliographic record
Abstract
By creating habitats or influencing the immediate physical environment, ecosystem engineers shape the diversity, function and services provided by ecosystems. Thus, the disruption of these species is relevant given their broad influence on native communities and ecosystems. As such, we review the effects (positive, negative, or neutral) of a widespread invasive species, the European green crab ( Carcinus maenas ) on key coastal ecosystem engineers. We examined the literature and focused on 53 published studies to assess reported impacts on well-known macrophytes, mussels, oysters and clams. Despite the wide range of response variables measured and reported, green crab effects were overwhelmingly negative. These effects were mediated by direct (through consumption and sediment burrowing) or indirect mechanisms (through seed consumption, alteration of habitat quality or effects on related species), and were often context dependent. These conclusions are limited by ongoing green crab expansions where possible impacts have not been yet documented, and by cases of neutral or minor impacts that remain unpublished. Green crab effects often result in disruption rather than the loss of local ecosystem engineers, but they clearly add to the ongoing effects of other global stressors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".