Tools to understand seasonality in health: quantification of microbe loads and analyses of compositional ecoimmunological data reveal complex patterns in Mojave Desert Tortoise (<i>Gopherus agassizii</i>) populations
Bibliographic record
Abstract
Using data from six wild Mojave Desert Tortoise (Gopherus agassizii (Cooper, 1861)) populations, we quantified seasonal differences in immune system measurements and microbial load in the respiratory tract, pertinent to this species’ susceptibility to upper respiratory tract disease. We quantified bacteria-killing activity of blood plasma and differential leukocyte counts to detect trends in temporal variation in immune function. We used centered log-ratio (clr) transformations of leukocyte counts and stress that such transformations are necessary for compositional data. We tested animals for the potential pathogen Pasteurella testudinis Snipes and Biberstein, 1982 with a newly created quantitative polymerase chain reaction (qPCR) assay, as well as for the known respiratory pathogens Mycoplasma agassizii Brown et al., 2001 and Mycoplasma testudineum Brown et al., 2004. We found very little disease and suggest that P. testudinis is a prevalent, commensal microbe in these Mojave Desert Tortoise populations, and its quantification may be a tool to study natural fluctuations in microbe levels in Mojave Desert Tortoise respiratory tracts. Our analyses showed that both the potential for inflammatory responses and microbe levels are highest in the spring for healthy Mojave Desert Tortoises, when lymphocyte levels are lowest. The genetic and statistical tools that we used are easily applicable to other wildlife systems and provide the necessary data to quantify species-wide trends in health and test hypotheses pertinent to host–microbe dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".