Mechanistic models can reveal infection pathways from prevalence data: the mysterious case of polar bears <i>Ursus maritimus</i> and <i>Trichinella nativa</i>
Bibliographic record
Abstract
Parasites exhibit a diverse range of life history strategies. Transmission to a host is a key component of each life cycle but the difficulty of observing host–parasite contacts has often led to confusion surrounding transmission pathways. Given limited data on most host–parasite systems, flexible approaches are needed for disentangling the obscure transmission dynamics of these systems. Here, we develop a modelling framework for formally testing long‐standing hypotheses regarding how the parasitic nematode Trichinella nativa is maintained at high prevalences in polar bear populations. We evaluated transmission from marine prey, from scavenging terrestrial carrion, from cannibalism and from scavenging on dead infected bears as possible pathways, and assessed their respective importance by comparing model‐projected prevalences for each mechanism against observed total and age‐specific population prevalences in the Southern Beaufort Sea polar bear subpopulation. Cannibalism and the scavenging on conspecifics have previously been assumed to be critical transmission pathways, but despite data scarcity, our model exposes these mechanisms as ineffective across a wide range of plausible parameter values. Instead, our analyses suggest that transmission from the consumption of infected marine prey, and in particular seals, can explain observed prevalence levels by itself, with other transmission pathways likely playing varying small contributing roles. Furthermore, our model suggests that transmission declines with bear age, perhaps due to age‐dependent changes in diet or immunity. By formalising multiple transmission mechanisms in a unified, mathematical framework, we distilled several hypotheses to a likely main mode of T. nativa transmission to polar bears. The specifics of our model are tailored towards the T. nativa ‐polar bear system, but the approach is easily generalized; it provides a powerful, quantitative means for ecologists to explore competing hypothesis for parasite transmission and other difficult‐to‐observe animal interactions even in data‐poor systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".