Multiple species drive flexible lake food webs with warming
Bibliographic record
Abstract
Abstract Climate change is rewiring the food webs that determine the fate of diverse ecosystems. Mobile generalist consumers are responding to climate change by rapidly shifting their behaviour and foraging, driving food webs to flex. Although these responsive generalists form a key stabilizing module in food web structure, the extent to which they are present throughout whole food webs is largely unknown. Here, we show that multiple species comprising key trophic roles drive flexible lake food webs with warming. By examining lakes that span a 7°C air temperature gradient, we found significant reductions in nearshore derived carbon and nearshore habitat use with increased temperature in three of four fish species. We also found evidence that the response of lake trout to increased temperatures may reduce their biomass and cascade to release their preferred prey, the pelagic forage fish cisco. Our results suggest that climate warming will shift lake food webs toward increased reliance on offshore habitats and resources. We argue that species across trophic levels broadly couple lake macrohabitats, suggesting that potentially stabilizing responsive consumers are present throughout food webs. However, climate change appears to limit their ability to responsively forage, critically undermining a repeated stabilizing mechanism in 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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".