Personality predicts ectoparasite abundance in an asocial sciurid
Bibliographic record
Abstract
Abstract Parasitism is a consequence of complex interactions between host, parasite, and their shared environment, and host behavior can influence parasite risk. Animal personality (i.e., consistent behavioral differences that are repeatable across time and context) can influence parasitism with more explorative individuals typically hosting greater parasite loads. Host “sociality” is known to impact parasite risk with more social individuals typically at higher risk of acquiring or transmitting parasites, but other behaviors could also be important. We quantified personality in least chipmunks (Tamias minimus), including repeatability of behavioral traits, and determined whether these personality traits affected ectoparasite prevalence and abundance. We measured personality using standardized hole‐board tests and quantified ectoparasitism of 39 least chipmunks over 2 years at a site in southeastern Manitoba, Canada. We found that activity and exploration were repeatable within the context of the hole‐board test for least chipmunks, which suggests that these traits reflect personality. More exploratory individuals hosted a greater abundance of ectoparasites compared to less exploratory individuals. Our results are consistent with past studies implicating personality as a factor in host–parasite dynamics and suggest that exploration may be an important behavioral correlate of parasite acquisition.
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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 | 0.000 | 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".