Fish spawning events stimulate trophic hotspots across freshwater food webs
Bibliographic record
Abstract
1.0 Abstract Mass reproductive events, where large numbers of organisms aggregate for synchronous reproduction, are often captivating and ecologically significant, boosting offspring survival and reducing time spent searching for mates. However, mass reproduction can also instigate cryptic but consequential responses across entire food webs. Reproductive materials are abundant and accessible resources that can attract mobile consumers from up to thousands of kilometers away. Yet, their consumption, especially in aquatic systems, is difficult to detect and rarely characterized. Here, we combine molecular techniques with acoustic telemetry, literature review, and extensive natural history observations to investigate the food web consequences of synchronized reproduction in freshwater fishes. First, we demonstrate that a common but underappreciated fish species, white sucker, creates a resource pulse used ubiquitously by consumers, from local invertebrates and fishes to mobile predatory fishes, birds, and terrestrial mammals. Spawning white sucker create trophic hotspots that attract consumers across trophic levels and ecosystems to feed on eggs, spawning adults, and aggregated egg predators. Then we show that egg provisioning and predation is widespread among north-temperate freshwater fish species, highlighting that resource pulses instigated by mass reproduction may play a critical but underappreciated role in freshwater and terrestrial ecosystems.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".