Size matters: Host body mass overshadows climate change in parasite prevalence among semi-arid ground squirrels (Xerus inauris)
Bibliographic record
Abstract
Climate change is a phenomenon in which global temperatures are rising, and animals respond by undergoing thermal stress, which may be linked to increased susceptibility to ectoparasites. Environmental temperature greatly influences ectoparasites as they rely on external heat sources to regulate their body temperature. This study investigated the effects of increasing maximum and minimum temperatures on ectoparasites abundance (number of parasites on an individual) and prevalence (number of infected individuals) of adult female African Cape ground squirrels (Xerus inauris). This study also investigated the effects of host body mass and host body condition on ectoparasite abundance and prevalence. We did not find a relationship between rising temperatures and ectoparasite loads (abundance and prevalence), nor did we find a relationship between body condition and ectoparasite loads. We speculate that the thermoregulatory behaviours of Cape ground squirrels mitigate ectoparasite loads. We did not find evidence for a relationship between host body mass and abundance; however, we found a significant negative relationship between host body mass and parasite prevalence. Our results show that a higher proportion of individuals are infected with fleas compared to lice. It remains uncertain whether the inverse relationship between host body mass and prevalence is due to increased skin strength or other traits in larger individuals. The different life history traits of the parasites could explain why fleas are more prevalent than lice. Fleas, being facultative parasites, can avoid the grooming behaviour of hosts. In contrast, lice, being obligate parasites, are bound to remain on their host, thus, are more vulnerable to being removed. As climate change persists, the host-parasite relationship between Cape ground squirrels and ectoparasites may be worth revisiting.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".