Assessing individual patterns of<i><scp>E</scp>chinococcus multilocularis</i>infection in urban coyotes: non‐invasive genetic sampling as an epidemiological tool
Bibliographic record
Abstract
Summary In epidemiological studies of wildlife parasites, faecal genotyping has been introduced to prevent bias in estimates of parasite prevalence from faecal samples collected in the field. Such an approach could be particularly relevant in the study of Echinococcus multilocularis transmission in urban settings, where estimates of prevalence and patterns of infection in wild canid hosts are key parameters used in zoonotic risk assessment and management. However, no previous study has evaluated the reliability of E. multilocularis faecal prevalence, and individual patterns of infection in definitive hosts remain poorly understood. We evaluated faecal genotyping as an epidemiological tool, using E. multilocularis in urban coyotes Canis latrans as our study system. Combining parasitological analysis and multilocus individual genotyping of coyote faeces, we compared faecal parasite prevalence with the prevalence obtained from genotyped faecal samples. Furthermore, we assessed patterns of individual infection, such as re‐infection rates and phenology of parasite egg excretion. Of 425 faeces collected in five urban sites, we genotyped 142 samples (33·4%) corresponding to 60 unique individual coyotes. Number of genotyped samples per coyote ranged between 1 and 10 (mean = 2·3). Genotypes were obtained at 4–6 microsatellite loci and had a mean reliability of 0·9975. Faecal prevalence of E. multilocularis in genotyped coyotes was 25·0%, and similar to results previously obtained from non‐genotyped faeces. Faecal genotyping allowed estimating a re‐infection rate of individual coyotes of 57·1% and to observe temporal patterns of parasite infection that were not detected using non‐genotyped faeces. Synthesis and applications . If compared to independent data obtained through coyote post‐mortem examination, our results suggest that reliable estimates of overall parasite prevalence in definitive host populations can be efficiently obtained through well‐designed field collection and traditional faecal parasitological analysis. However, faecal genotyping allows assessing the dynamics of individual infections, which could otherwise only be estimated by using invasive techniques. Combining faecal genotyping with parasitology has a great potential in assessing zoonotic risk transmission in urban areas, as well as advancing the field of wildlife ecology, disease ecology and conservation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".