Robustness to thermal variability differs along a latitudinal gradient in zooplankton communities
Bibliographic record
Abstract
Abstract Determining how thermal variability will affect the structure, stability, and function of ecological communities is becoming increasingly important as global warming is predicted to affect not only average temperatures but also increase the frequency of long runs of high temperatures. Latitudinal differences in the responses of ecological communities to changes in their thermal regimes have also been predicted based on adaptations over evolutionary time to different thermal environments. We conducted an experiment to determine whether variability in temperature leads to consistent changes in community structure, temporal dynamics, and ecosystem functioning in laboratory analogues of natural freshwater supralittoral rock pool communities inhabited by meiofauna and zooplankton collected from sub‐Arctic, temperate, and tropical regions. Thermal variability of +4 °C around mean temperature led to increased extinction frequency, decreases in consumer abundance, increases in temporal variability of consumer abundance, and shifts from predominately negative interactions observed under constant temperature to positive interactions in the temperate and tropical communities but not in the sub‐Arctic communities. That sub‐Arctic zooplankton communities may be more robust to thermal variability than temperate or tropical communities’ supports recent studies on macrophysiological adaptations of species along latitudinal gradients and suggests that increasing thermal variability may have the greatest effects on community structure and function in tropical and temperate regions.
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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 | 0.001 | 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".