Interacting effects of local and global stressors on mussel beds and ecosystem functioning
Bibliographic record
Abstract
Biogenic habitats, such as mussel beds, provide various functions in their associated ecosystems. However, these habitat-forming species are exposed to cumulative impacts as the number and diversity of anthropogenic stressors increase, particularly in estuarine ecosystems. Experiments designed to test the effect of single and multiple interacting stressors on mussel beds and associated biotic components are rare (i.e. in situ experiments are uncommon, as they usually occur in laboratory settings). We conducted a field experiment in the St. Lawrence estuary (Québec, Canada) to address this gap. We transplanted blue mussels (Mytilus spp.) to mimic mussel beds and exposed them to increased nutrient concentrations and thermal stress at three intervals (6.5, 10.5 and 15 weeks) during May through September. For each transplant, we evaluated epizoic microalgal biomass (as pigment biomass), microbial activity and oxygen uptake, and mortality levels and energy content in the tissues of Mytilus spp. following three exposure times. No effects were found for chlorophyll a biomass, microbial activity and oxygen uptake, and mortality in mussels. In contrast, we found thermal stress and nutrient input interacted to create antagonistic and synergistic effects on energy content in Mytilus spp. at different exposure times and exerted additive effects over time on phaeopigments and the ratio of chlorophyll a/phaeopigments. Our work highlights the importance of combining multiple biological components (i.e. multiple biological responses measured at different scales of biological complexity) and different experimental approaches to capture the complexity behind stressor interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".