SPECIES IN THE FECES: DNA METABARCODING TO DETECT POTENTIAL GASTROPOD HOSTS OF PARELAPHOSTRONGYLUS TENUIS CONSUMED BY MOOSE (ALCES ALCES)
Bibliographic record
Abstract
Our understanding of wildlife multihost pathogen transmission systems is often incomplete due to the difficulty of observing contact between hosts. Understanding these interactions can be critical for preventing disease-induced wildlife declines. The proliferation of high-throughput sequencing technologies provides new opportunities to better explore these cryptic interactions. Parelaphostrongylus tenuis, a multihost parasite, is a leading cause of death in some moose (Alces alces) populations threatened by local extinction in the midwestern and northeastern US and southeastern Canada. Moose contract P. tenuis by consuming infected gastropod intermediate hosts, but little is known about which gastropod species moose consume. To gain more insight, we used a genetic metabarcoding approach on 258 georeferenced and temporally stratified moose fecal samples collected May-October 2017-20 from a declining population in the north-central US. We detected moose consumption of three species of gastropods across five positive samples. Two of these (Punctum minutissimum and Helisoma sp.) have been minimally investigated for the ability to host P. tenuis, while one (Zonitoides arboreus) is a well-documented host. Moose consumption of gastropods documented herein occurred in June and September. Our findings prove that moose consume gastropod species known to become infected by P. tenuis and demonstrate that fecal metabarcoding can provide novel insight on interactions between hosts of a multispecies pathogen transmission system. After determining and improving test sensitivity, these methods may also be extended to document important interactions in other multihost disease 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 | 0.002 | 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".