Predation risk increases immune response in a larval dragonfly (<i>Leucorrhinia intacta</i>)
Bibliographic record
Abstract
Predators often negatively affect prey performance through indirect, non-consumptive effects. We investigated the potential relationship between predator-induced stress and prey immune response. To test this, we administered a synthetic immune challenge into dragonfly larvae (Leucorrhinia intacta) and assessed a key immune response (level of encapsulation) in the presence and absence of a caged predator (Anax junius) at two temperatures (22 degrees C and 26 degrees C). We hypothesized that immune response would be lowered when predators were present due to lowered allocation of resources to immune function and leading to reduced encapsulation of the synthetic immune challenge. Contrary to our expectations, larvae exposed to caged predators had encapsulated monofilaments significantly more than larvae not exposed to caged predators. Levels of encapsulation did not differ across temperatures, nor interact with predator exposure. Our results suggest that the previously observed increase in mortality of L. intacta exposed to caged predators is not driven by immune suppression. In situations of increased predation risk, the exposure to predator cues may induce higher levels of melanin production, which could lead to physiological damage and high energetic costs. However, the costs and risks of increased allocations to immune responses and interactions with predation stress remain unknown.
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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 | 0.000 | 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".