Endure your parasites: Sleepy Lizard (<i>Tiliqua</i> <i>rugosa</i>) movement is not affected by their ectoparasites
Bibliographic record
Abstract
Movement is often used to indicate host vigour, as it has various ecological and evolutionary implications, and has been shown to be affected by parasites. We investigate the relationship between tick load and movement in the Australian Sleepy Lizard (Tiliqua rugosa (Gray, 1825)) using high resolution GPS tracking. This allowed us to track individuals across the entire activity season. We hypothesized that tick load negatively affects host movement (mean distance moved per day). We used a multivariate statistical model informed by the ecology and biology of the host and parasite, their host–parasite relationship, and known host movement patterns. This allowed us to quantify the effects of ticks on lizard movement above and beyond effects of other factors such as time in the activity season, lizard body condition, and stress. We did not find any support for our hypothesis. Instead, our results provide evidence that lizard movement is strongly driven by internal state (sex and body condition independent of tick load) and by external factors (environmental conditions). We suggest that the Sleepy Lizard has largely adapted to natural levels of tick infection in this system. Our results conform to host–parasite arms race theory, which predicts varying impacts of parasites on hosts in natural systems.
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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) | 0.002 | 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".