Is pelagic top-down control in lakes augmented by benthic energy pathways?
Bibliographic record
Abstract
Modern food web studies are typically conducted from a trophic dynamic perspective that focuses on combined roles of top-down and bottom-up forces in regulating food web structure. Recognition of spatial food web subsidies in diverse ecosystems highlights the importance of energy flow as a foundation for understanding trophic dynamics. Here, we consider how different energy flow configurations might affect trophic dynamics in north-temperate lakes. A literature review revealed that littoral piscivores exert top-down control on prey fishes. In contrast, analysis of littoral predator diets indicated extensive omnivory and heavy reliance on zoobenthic prey. We explored this uncoupling between trophic dynamics (piscivores regulate prey fish) and energy flow (zoobenthos in piscivore diets) using a biomass dynamic model. This model compared top-down impacts of a piscivore on prey fishes under two scenarios: consumption of prey fish only and consumption of prey fish plus zoobenthos. The model predicted that elimination of zoobenthivory leads to a 50% reduction in piscivore standing stock and concomitant 2.5-fold increase in prey fish abundance (i.e., zoobenthivory plays a key role in mediating pelagic top-down control). These results highlight the role of benthicpelagic linkages in regulating trophic dynamics and underscore the value of whole-ecosystem approaches to the study of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".