Excess mortality of infected ectotherms induced by warming depends on pathogen kingdom and evolutionary history
Bibliographic record
Abstract
Climate change is causing extreme heating events and can lead to more infectious disease outbreaks, putting species persistence at risk. The extent to which warming temperatures and infection may together impair host health is unclear. Using a meta-analysis of >190 effect sizes representing 101 ectothermic animal host-pathogen systems, we demonstrate that warming significantly increased the mortality of hosts infected by bacterial pathogens. Pathogens that have been evolutionarily established within the host species showed higher virulence under warmer temperatures. Conversely, the effect of warming on novel infections-from pathogens without a shared evolutionary history with the host species-were more pronounced with larger differences between compared temperatures. We found that compared to established infections, novel infections were more deadly at lower/baseline temperatures. Moreover, we revealed that the virulence of fungal pathogens increased only when temperatures were shifted upwards towards the pathogen thermal optimum. The magnitude of all these significant effects was not impacted by host life-stage, immune complexity, pathogen inoculation methods, or exposure time. Overall, our findings reveal distinct patterns in changes of pathogen virulence during warming. We highlight the importance of pathogen taxa, thermal optima, and evolutionary history in determining the impact of global change on infection outcomes.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".