Hydrological control of a floodplain subsidy to littoral riverine fish
Bibliographic record
Abstract
Ecological subsidies have been hypothesized to be key factors affecting the dynamics of the recipient ecosystems. We assessed the impact of the flood regime on the assimilation of a trophic subsidy produced in the floodplain of a large fluvial lake, Lake Saint-Pierre (Quebec, Canada) on five invertivore littoral fish species (yellow perch (Perca flavescens), pumpkinseed (Lepomis gibbosus), bridle shiner (Notropis bifrenatus), golden shiner (Notemigonus crysoleucas), and banded killifish (Fundulus diaphanus)). We hypothesized that the incorporation of floodplain-derived carbon into fish biomass and individual fitness would increase in years with higher magnitude and longer duration floods. Studying four consecutive years with contrasting spring-flood events, we used a stable isotope mixing model to estimate floodplain contribution, and linear mixed models to analyze the impact of hydrology on floodplain contribution and fish body condition. Floodplain and main river’s invertebrates exhibited statistically distinct isotopic ratios (t test: p value < 0.001). Floodplain contribution and body condition were positively correlated with flooding intensity and duration for all species. Body condition was positively correlated with the assimilation of floodplain-subsidy for golden shiner and banded killifish. Our study identified flooding intensity as a main driver influencing the assimilation of floodplain-derived carbon by littoral fish and their fitness in a large river ecosystem.
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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) | 0.001 | 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".