Impact Of Environmental Variation On Host Performance Differs With Pathogen Identity: Implications For Host-Pathogen Interactions In A Changing Climate
Bibliographic record
Abstract
Specialist and generalist pathogens may exert different costs on their hosts; thereby altering the way hosts cope with environmental variation. We examined how pathogen-challenge alters the environmental conditions that maximize host performance by simultaneously varying temperature and nutrition (protein to carbohydrate ratio; P:C) after exposure to two baculoviruses; one that is specific to the cabbage looper, Trichoplusia ni (TnSNPV) and another that has a broad host range (AcMNPV). Virus-challenged larvae performed better on more protein-biased diets, primarily due to higher survival, whereas unchallenged larvae performed best on a balanced diet. The environmental conditions that maximized host performance differed with virus identity because TnSNPV-challenge inflicted fitness costs (reduced pupal weight and prolonged development) whereas AcMNPV-challenge did not. The performance of TnSNPV-challenged larvae rose with increasing P:C across all temperatures, whereas temperature modulated the optimal P:C in AcMNPV-challenged larvae (slightly protein-biased at 16 °C to increasingly higher P:C as temperature increased). Increasing temperature reduced pupal size, but only at more balanced P:C ratios, indicating that nutrition moderates the temperature-size rule. Our findings highlight the complex environmental interactions that can alter host performance after exposure to pathogens, which could impact the role of entomopathogens as regulators of insect populations in a changing climate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".