Scaling effects of temperature on parasitism from individuals to host–parasite systems
Bibliographic record
Abstract
ABSTRACT Parasitism is expected to change in a warmer future, but whether warming leads to substantial increases in parasitism remains unclear. Understanding how warming effects on parasitism in individual hosts (e.g., parasite load) translate to effects on population-level parasitism (e.g., prevalence, R 0 ) remains a major knowledge gap. We analyzed the temperature dependence of parasitism at both host and population levels in thirteen empirical vector-borne host–parasite systems and found a strong, significant positive correlation between the thermal optima of individual- and population-level parasitism. We also found a significant, positive correlation in eleven environmentally-transmitted parasite systems, though several of these systems exhibited thermal optima >5ºC apart between individual and population levels. Similarly, parasitism thermal optima were close to host performance thermal optima in vector-borne systems but not in environmentally-transmitted systems. We then adapted and simulated simple models for both transmission modes and found a similar pattern to the empirical systems: thermal optima in vector-borne systems were more strongly correlated across scales compared to environmentally-transmitted systems. Generally, our results suggest that information on the temperature-dependence, and specifically the thermal optimum, at either the individual- or population-level should provide a useful—though not quantitatively exact—baseline for predicting temperature dependence at the other level, especially in vector-borne parasite systems. Environmentally-transmitted parasitism may operate by a different set of rules, in which temperature-dependence is decoupled in some systems, requiring the need for trait-based studies of temperature-dependence at individual and population levels.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".