Calcium interacts with temperature to influence <i>Daphnia</i> movement rates
Bibliographic record
Abstract
Predicting the ecological responses to climate change is particularly challenging, because organisms might be affected simultaneously by the synergistic effects of multiple environmental stressors. Global warming is often accompanied by declining calcium concentration in many freshwater ecosystems. Although there is growing evidence that these changes in water chemistry and thermal conditions can influence ecosystem dynamics, little information is currently available about how these synergistic environmental stressors could influence the behaviour of aquatic organisms. Here, we tested whether the combined effects of calcium and temperature affect movement parameters (average speed, mean turning frequency and mean-squared displacement) of the planktonic Daphnia magna , using a full factorial design and exposing Daphnia individuals to a range of realistic levels of temperature and calcium concentration. We found that movement increased with both temperature and calcium concentration, but temperature effects became considerably weaker when individuals were exposed to calcium levels close to survival limits documented for several Daphnia species, signalling a strong interaction effect. These results support the notion that changes in water chemistry might have as strong an effect as projected changes in temperature on movement rates of Daphnia , suggesting that even sublethal levels of calcium decline could have a considerable impact on the dynamics of freshwater ecosystems.
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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".