Interactive effects of higher temperature and dissolved organic carbon on planktonic communities in fishless mountain lakes
Bibliographic record
Abstract
Summary We tested the hypothesis that higher temperature and dissolved organic carbon (DOC) concentration increase dissolved and particulate carbon (C) relative to phosphorus (P), thereby reducing algal food quality for P‐limited cladocerans while not affecting N‐limited copepods. Also, we expected alpine zooplankton to respond more strongly than those from warmer montane lakes to increased water temperature. Plankton from two alpine lakes and two montane lakes were incubated in vitro for 30 days at 10 or 17 °C and with ambient or +80% DOC, which was achieved by concentrating humic substances from each lake via reverse osmosis. Dissolved organic carbon amendments and warming significantly increase particulate C : P under montane, but not alpine conditions. While higher water temperature and DOC separately reduced phytoplankton abundance, together they increased phytoplankton by stimulating uptake of P. Warming stimulated only Daphnia while suppressing the abundance of the calanoid copepod Hesperodiaptomus when they originated from the three coldest lakes. Particulate C : P was positively correlated with Daphnia abundance and negatively correlated with Hesperodiaptomus, probably due to greater P‐retention by Daphnia. Our findings highlight the importance of interactions between the ecological effects of higher temperature and increased inputs of terrestrial organic matter to forecasts of the net impact of global warming on mountain lakes. Such predictions may be confounded if they are derived solely from the expected sum of single effects by each climatic factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".