Warming-induced excess deaths of infected animals depend on pathogen kingdom and evolutionary history
Bibliographic record
Abstract
Abstract Climate change is causing extreme heating events. Simultaneously, climate change and human activities are leading to more prolonged and intense infectious disease outbreaks. The extent to which warming and infection may together impact host species persistence is, however, unclear. Using a meta-analysis of >190 effect sizes representing 101 ectothermic animal host-pathogen systems, we provide broad evidence that experimentally increased temperatures drove higher pathogen virulence, specifically pathogen-induced host mortality. Such pattern was mainly driven by excess host death caused by bacterial infections combined with warming, particularly if the pathogenic bacteria were naturally established within the host species, though novel infections without known host-pathogen evolutionary history were more lethal at lower temperatures. Importantly, larger temperature increases were associated with more host deaths hinting at the escalating threat for animal species as the world continues to warm. We found that the virulence of fungal pathogens increased only when temperatures were shifted upwards towards their thermal optimum. The magnitude of these effects was not impacted by host life-stage, immune complexity, or variable experimental protocols. Overall, our findings reveal distinct patterns of pathogen virulence change under warmer temperatures, suggesting that the impact of global warming on infectious disease outcomes would depend on pathogen traits (taxonomic kingdom, thermal tolerance) and host-pathogen evolutionary history. Author Summary Human-induced climate warming is one of the biggest challenges in our times. Simultaneously, climate change is associated with more intense infectious disease outbreaks, suggesting that temperature rises also influence disease dynamics. Growing numbers of studies have investigated the effect of warming on disease severity (or pathogen virulence) in different animal host-pathogen systems. However, individual studies did not always agree with each other, and how increased temperature and pathogen infection together impact animal survival remains unclear. Here, we resolved this uncertainty by conducting a meta-analysis of >190 effect sizes representing 101 animal host-pathogen systems. We provided broad evidence that, higher temperatures caused more deaths of infected animals, particularly for animals with bacterial infections under warmer conditions. We found that larger temperature rises were associated with more animal deaths, suggesting the increased threat for host species as the world continues to warm. We also found that pathogenic fungi were more sensitive to heat than bacterial pathogens, and temperature changes the virulence of fungal pathogens in relation to their thermal optimum.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".