Surviving the deluge: Examining the vulnerability of an estuarine mollusk assemblage to water changes associated to flooding conditions
Bibliographic record
Abstract
Estuaries face heightened vulnerability due to global changes, including frequent intensified flooding from high-intensity rainfall events, which induce sudden changes in environmental parameters. While estuarine organisms thrive in fluctuating conditions, their responses to intense and prolonged stressors may vary. Furthermore, the combined effects of stressors (e.g., decreased salinity and pH, increased turbidity) encountered during floods have rarely been studied on invertebrates under controlled conditions. Consequently, we evaluated the sensitivity of three dominant mollusk species (Littorina saxatilis, Macoma balthica, Mytilus spp.) to spring floods conditions. During 5 weeks, we exposed specimens to a 12-level gradient of exposure to periodic freshwater injections ranging from 0 to 9 days, under both control (salinity 0, pH 8.1, suspended sediment 0 mg L-1) and spring flood conditions (salinity 0, pH 5.7, suspended sediment 325 mg L-1). Mortality, growth and shell wear were measured after 2.5 and 5 weeks. For each species and sampling period, we used mixed linear models with two fixed factors: treatment (two levels, "control" or "spring floods") and hypoosmotic stress period (12 levels). Breakpoint analyses were used to identify sudden changes in each significant regression. Spring flood conditions amplified the negative impact of freshwater exposure on the mortality and induced greater shell wear of all species. Additionally, species-specific vulnerability levels were detected. Extreme events could therefore be key factors determining species distribution and functioning of estuarine ecosystems. Our study underscores the need to consider ecologically relevant combinations of environmental factors representing the complexity of natural conditions.
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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 | 0.000 |
| 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) | 0.000 | 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".