Multiple anthropogenic stressors cause ecological surprises in boreal lakes
Bibliographic record
Abstract
Abstract The number of combinations of anthropogenic stressors affecting global change is increasing; however, few studies have empirically tested for their interactive effects on ecosystems. Most importantly, interactions among ecological stressors generate nonadditive effects that cannot be easily predicted based on single‐stressor studies. Here, we corroborate findings from an in situ mesocosm experiment with evidence from a whole‐ecosystem manipulation to demonstrate for the first time that interactions between climate and acidification determine their cumulative impact on the food‐web structure of coldwater lakes. Interactions among warming, drought, and acidification, rather than the sum of their individual effects, best explained significant changes in planktonic consumer and producer biomass over a 23‐year period. Further, these stressors interactively exerted significant synergistic and antagonistic effects on consumers and producers, respectively. The observed prevalence of long‐ and short‐term ecological surprises involving the cumulative impacts of multiple anthropogenic stressors highlights the high degree of uncertainty surrounding current forecasts of the consequences of global change.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".