Impact of non‐pathogenic bacteria on insect disease resistance: importance of ecological context
Bibliographic record
Abstract
1. The aerial surface of plants is a habitat for large and diverse microbial communities; termed the phyllosphere. These microbes are unavoidably consumed by herbivores, and while the entomopathogens are well studied, the impact of non‐pathogenic bacteria on herbivore life history is less clear. 2. Previous work has suggested that consumption of non‐entomopathogenic bacteria induces a costly immune response that might decrease the risk of infection. However, we hypothesised that insect herbivores should be selective in how they respond to commonly encountered non‐pathogenic bacteria on their host plants to avoid unnecessary and costly immune responses. 3. An ecologically realistic scenario was used in which we fed cabbage looper, Trichoplusia ni Hübner, larvae on cabbage or cucumber leaves treated with the common non‐entomopathogenic phyllosphere bacteria, Pseudomonas fluorescens and P. syringae . Their constitutive immunity and resistance to a pathogenic bacterium ( Bacillus thuringiensis ; Bt ) and a baculovirus ( T. ni single nucleopolyhedrovirus) were then examined. 4. While feeding on bacteria‐treated leaves reduced the growth rate and condition of T. ni , there was no effect on immunity (haemolymph antibacterial and phenoloxidase activities and haemocyte numbers). Phyllosphere bacteria weakly affected the resistance of T. ni to Bt but the direction of this effect was concentration dependent; resistance to the virus was unaffected. Host plant had an impact, with cucumber‐fed larvae being more susceptible to Bt . 5. The lack of evidence for a costly immune response to non‐entomopathogenic bacteria suggests that T. ni are probably adapted to consuming common phyllosphere bacteria, and highlights the importance of the evolutionary history of participants in multi‐trophic interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".