Sickness behaviour and the effect of sex, age, and immune status on individual behavioural variation in Tenebrio molitor
Bibliographic record
Abstract
Sick animals generally behave differently than healthy individuals by, for example, being less active and exploratory. How an individual responds to illness is also likely to be mediated by the individual's age because age will dictate the individual's ability to fight a challenge. To date, empirical research on sickness behaviour has focused on the population-level average effect of ill health on behaviour. No study has examined how sickness affects individual behavioural variation, which can affect not only survival and reproductive success but also disease transmission via interactions with conspecifics. In this study, we use a repeated measures design to experimentally test the hypothesis that an immune challenge will induce sickness behaviour in yellow mealworm beetles (Tenebrio molitor) and that the effect on behavioural expression will be dose- and age-dependent. We test the prediction that an immune challenge will reduce beetle activity and exploration at the population level as well as modify variation in behavioural expression among individuals with individuals receiving a stronger challenge expressing more sickness behaviour. Although we found little evidence that T. molitor experiences sickness behaviour, we did find that older beetles were more active than younger ones. There was very little evidence that age, sex, and immune status affect behavioural variation among and within individuals but the phenotypic correlation between activity and exploration is driven by a correlation within individuals. Observed effects within individuals are likely driven by a significant effect of test sequence; behavioural expression significantly decreased in the second of the repeated tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".