Floods connect tropical river-floodplain food webs but shrink fish community isotopic trophic niches
Bibliographic record
Abstract
Lateral connectivity between the floodplain and river channel is hypothesised to expand fish community isotopic trophic niches and increase overlap of river-floodplain food webs. To evaluate how flood magnitude influences trophic dynamics in a large tropical free-flowing river (Roper River, Australia), we measured community isotopic niches in low and high magnitude flood years in wetland and river habitats. Contrary to our hypothesis, isotopic niche area of the river fish community contracted following the high magnitude flood year, when compared to a low magnitude flood year. Wetland fishes maintained more similar niche space regardless of flood conditions, but showed the same pattern of contracting niche area following a large magnitude flood. Niche overlap was lowest for river low magnitude flood and wetland high magnitude flood and highest for river and wetland high magnitude flood. Our results suggest that river-floodplain fish communities exploit a wider isotopic range of food sources during low flood years, including enriched sources of potentially marine origin, while sharing abundant less diverse resources in high magnitude flood years. As water resources in tropical rivers are developed, our findings highlight the importance of conserving multiple dimensions of connectivity in riverine landscapes to maintain intact river-floodplain food webs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".