Mean daily temperatures can predict the thermal limits of malaria transmission better than rate summation
Bibliographic record
Abstract
Abstract Temperature shapes the distribution, seasonality, and magnitude of mosquito-borne disease outbreaks. Mechanistic models predicting transmission often use mosquito and pathogen thermal responses from constant temperature experiments. However, mosquitoes live in fluctuating environments. Rate summation (nonlinear averaging) is a common approach to infer performance in fluctuating environments, but its accuracy is rarely validated. We measured three mosquito traits that impact transmission (bite rate, survival, fecundity) in a malaria mosquito ( Anopheles stephensi ) across temperature gradients with three diurnal temperature ranges (0, 9 and 12°C). We compared thermal suitability models with temperature-trait relationships observed under constant temperatures, fluctuating temperatures, and those predicted by rate summation. We mapped results across An. stephenesi ’s native Asian and invasive African ranges. We found: 1) daily temperature fluctuation significantly altered trait thermal responses; 2) rate summation partially captured decreases in performance near thermal optima, but also incorrectly predicted increases near thermal limits; and 3) while thermal suitability characterized across constant temperatures did not perfectly capture suitability in fluctuating environments, it was more accurate for estimating and mapping thermal limits than predictions from rate summation. Our study provides insight into methods for predicting mosquito-borne disease risk and emphasizes the need to improve understanding of organismal performance under fluctuating conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".