Social Behaviours and Networks of Vervet Monkeys Are Influenced by Gastrointestinal Parasites
Bibliographic record
Abstract
Substantial research has shown that while some parasite infections can be fatal to hosts, most infections are sub-clinical and non-lethal. Such sub-clinical infections can nonetheless have negative consequences for the long-term fitness of the host such as reducing juvenile growth and the host's ability to compete for food and mates. With such effects, infected individuals are expected to exhibit behavioural changes. Here we use a parasite removal experiment to quantify how gastrointestinal parasite infections affect the behaviour of vervet monkeys (Chlorocebus aethiops) at Lake Nabugabo, Uganda. Behavioural profiles and the structure of nearest neighbour relationships varied significantly. As predicted, after deworming the duration of the resting events decreased, which is consistent with the idea that parasite infections are energetically costly. In contrast to what was predicted, we could not reject the null hypothesis and we observed no change in either the frequency or duration of grooming, but we found that the duration of travel events increased. A network analysis revealed that after deworming, individuals tended to have more nearest neighbours and hence probably more frequent interactions, with this effect being particularly marked for juveniles. The heightened response by juveniles may indicate that they are avoiding infected individuals more than other age classes because it is too costly to move energy away from growth. We consider that populations with high parasite burden may have difficulties developing social networks and behaviours that could have cascading effects that impact the population in general.
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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 | 0.000 | 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".