Meta‐ecosystem engineering: Nutrient fluxes reveal intraspecific and interspecific feedbacks in fragmented mussel beds
Bibliographic record
Abstract
Ecologists still have to elucidate the complex feedback interactions operating among biodiversity and ecosystem processes in engineered systems. To address this, a field experiment was conducted to mimic natural mussel bed meta-ecosystems (Mytilus spp.) of the lower St. Lawrence Estuary (Quebec, Canada) and partition the effects of their biotic and abiotic properties and spatial structure on ecosystem processes and community dynamics of associated macro-invertebrates. We found positive intraspecific feedbacks between mussels and their recruits, and negative interspecific feedbacks between mussels and their associated ecosystem. These feedbacks were associated with mussel bed ecosystem processes (fluxes of ammonium and oxygen). In addition, we showed that proximity between mussel patches increased within-patch nutrient fluxes. Our study revealed the potential for meta-ecosystem engineering to drive feedback interactions between community and ecosystem functioning in marine fragmented systems. It also shows the relevance of meta-ecosystem theories as a conceptual framework to elucidate biotic and abiotic processes controlling ecosystem and community structure. Such framework could contribute to ecosystem-based management of spatially structured systems such as reserve networks and fragmented ecosystems.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".