Environmental stressors, complex interactions and marine benthic communities’ responses
Bibliographic record
Abstract
The increasing number and diversity of anthropogenic stressors in marine habitats have multiple negative impacts on biological systems, biodiversity and ecosystem functions. Methods to assess cumulative effects include experimental manipulations, which may identify non-linear responses (i.e. synergies, antagonisms). However, experiments designed to test these ideas are uncommon, generally focusing on single biological responses. We conducted a manipulative experiment to investigate the isolated and combined effects of warming (+ 6 °C), salinity variation (freshwater pulses or presses), and nutrient enrichment (natural or enriched) following one and three month's exposure, on responses measured at multiple levels of biological complexity in a simple bivalve assemblage. More specifically, we determined effects on bivalve mortality, growth, shell mineralization, and energy content, as well as microphytobenthos biomass. Salinity variation and nutrient enrichment, individually and combined, caused strong impacts on some of the measured variables and their effect varied through time. In contrast, warming had no effect. Our work highlights the prevalence of antagonistic interactions, the importance of examining effects of single and multiple stressors through time, and of considering multiple responses to understand 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 | 0.000 | 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 | 0.001 |
| 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".