Quantitative gradient of subsidies reveals a threshold in community‐level trophic cascades
Bibliographic record
Abstract
Evidence varies on how subsidies affect trophic cascades within recipient food webs. This could be due to complex nonlinearities being masked by single-level manipulations (presence/absence) of subsidies in past studies. We predicted that trophic cascade strength would increase nonlinearly across a gradient of subsidies. We set out to reveal these complex, nonlinear relationships through manipulating a quantitative gradient of detrital subsidies to lake benthic food webs along with the presence/absence of trout. Contrary to our prediction, we found that trophic cascades only occurred at low subsidy levels, disappearing as subsidies increased. This threshold in trophic cascade strength may be due to an increase in intermediate predators in the absence of top predators, as well as changes in the proportion of armored vs. un-armored primary consumers. Future studies on the effect of subsidies on trophic cascade strength need to incorporate naturally occurring gradients to reveal the complex direct and indirect interactions within food webs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".