From clear lakes to murky waters – tracing the functional response of high‐latitude lake communities to concurrent ‘greening’ and ‘browning’
Bibliographic record
Abstract
Abstract Climate change and the intensification of land use practices are causing widespread eutrophication of subarctic lakes. The implications of this rapid change for lake ecosystem function remain poorly understood. To assess how freshwater communities respond to such profound changes in their habitat and resource availability, we conducted a space‐for‐time analysis of food‐web structure in 30 lakes situated across a temperature‐productivity gradient equivalent to the predicted future climate of subarctic Europe (temperature +3°C, precipitation +30% and nutrient +45 μg L −1 total phosphorus). Along this gradient, we observed an increase in the assimilation of pelagic‐derived carbon from 25 to 75% throughout primary, secondary and tertiary consumers. This shift was overwhelmingly driven by the consumption of pelagic detritus by benthic primary consumers and was not accompanied by increased pelagic foraging by higher trophic level consumers. Our data also revealed a convergence of the carbon isotope ratios of pelagic and benthic food web endmembers in the warmest, most productive lakes indicating that the incorporation of terrestrial derived carbon into aquatic food webs increases as land use intensifies. These results, reflecting changes along a gradient characteristic of the predicted future environment throughout the subarctic, indicate that climate and land use driven eutrophication and browning are radically altering the function and fuelling of aquatic food webs in this biome.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".