Climate warming restructures an aquatic food web over 28 years
Bibliographic record
Abstract
Climate warming can restructure lake food webs if trophic levels differ in their thermal responses, but evidence for these changes and their underlying mechanisms remain scarce in nature. Here we document how warming lake temperatures by up to 2°C, rather than changes in trophic state or fishing effort, have restructured the pelagic food web of a large European lake (Lake Maggiore, Italy). Our approach exploited abundance and biomass data collected weekly to yearly across five trophic levels from 1981 to 2008. Temperature generally had stronger effects on taxa than changes in fish predation or trophic state mediated through primary productivity. Consequently, we found that, as the lake warmed, the food web shifted in numerical abundance towards predators occupying middle trophic positions. Of these taxa, the spiny water flea (Bythotrephes longimanus) most prospered. Bythotrephes strongly limited abundances of the keystone grazer Daphnia, strengthening top-down structuring of the food web. Warmer temperatures partly restructured the food web by advancing peak Bythotrephes densities by approximately 60 days and extending periods of positive population growth by three times. Nonetheless, our results suggested that advances in the timing and size of peak Bythotrephes densities could not outpace changes in the timing and size of peak densities in their Daphnia prey. Our results provide rare evidence from nature as to how long-term warming can favour higher trophic levels, with the potential to strengthen top-down control 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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".