Impacts of Climate Warming and Nitrogen Deposition on Alpine Plankton in Lake and Pond Habitats: an In Vitro Experiment
Bibliographic record
Abstract
The combined effects of multiple ecological stressors determine the net impact of global change on environmentally sensitive alpine and polar environments. For example, climate warming and nitrogen deposition both increasingly affect ecosystems at high elevations. We hypothesized that the net impact of warming and nitrogen on alpine plankton differs between consumers and producers because of the greater environmental sensitivity of higher trophic levels. Also, we expected that habitat conditions would mediate the responses of plankton to these two stressors as sediments function as ecological buffers against environmental change. These hypotheses were tested in a growth chamber by applying temperature (8 vs. 15°C) and nitrogen (200 vs. 1000 µg N L−1) treatments to a planktonic alpine community in the presence and absence of sediments obtained from Pipit Lake, Banff National Park, Alberta. A significant nitrogen-temperature interaction affected phytoplankton abundance because the positive effect of fertilization depended on warming. Warming also amplified the effect of nitrogen on herbivores while suppressing the fecundity of omnivores. The presence of sediments suppressed the positive effect of warming on herbivores, but stimulated omnivores. The observed prevalence of non-additive effects highlights the strong potential for global change causing future ecological surprises in alpine environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".