From perplexing to predictive: are we ready to forecast insect disease susceptibility in a warming world?
Bibliographic record
Abstract
Insects are critical to our ecosystems, but we do not fully understand their future in our warming world. Rising temperatures are affecting insect physiology in myriad ways, including changes to their immune systems and the ability to fight infection. Whether predicted changes in temperature will contribute to insect mortality or success, and the role of disease in their future survival, remains unclear. Although heat can enhance immunity by activating the integrated defense system (e.g. via the production of protective molecules such as heat-shock proteins) and accelerating enzyme activity, heat can also compromise the immune system through energetic-resource trade-offs and damage. The responses to heat are highly variable among species. The reasons for this variability are poorly known, and we are lagging in our understanding of how and why the immune system responds to changes in temperature. In this Commentary, we highlight the variation in insect immune responses to heat and the likely underlying mechanisms. We suggest that we are currently limited in our ability to predict the effects of rising temperatures on insect immunity and disease susceptibility, largely owing to incomplete information, coupled with a lack of tools for data integration. Moreover, existing data are concentrated on a relatively small number of insect Orders. We provide suggestions for a path towards making more accurate predictions, which will require studies with realistic temperature exposures and housing design, and a greater understanding of both the thermal biology of the immune system and connections between immunity and the physiological responses to heat.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".