ITS-2 rDNA Nemabiome Metabarcoding in two Isolated Populations of Wild Roe Deer (Capreolus Capreolus) Reveals Variation in Gastrointestinal Nematode Community Composition Between Regions and Among Age Classes
Bibliographic record
Abstract
Abstract Background Gastrointestinal nematodes are ubiquitous for both domestic and wild ungulates which have varying consequences to health and fitness. They exist as complex communities of multiple co-infecting species and we have a very limited understanding of how these communities vary in different hosts, regions and circumstances or on how this affects their impacts. Methods We have undertaken ITS-2 rDNA nemabiome metabarcoding on populations of nematode larvae isolated from fecal samples of roe deer of different sex and age classes in the two isolated populations of Chizé and Trois Fontaines in France not co-grazing with any domestic ungulate species. Results We identified 100 Amplified Sequence Variants (ASVs) that assigned to 14 gastrointestinal nematode taxa overall either at genus (38%), or species (62%) level. These taxa were dominated by parasites classically found in cervids - e.g., Ostertagia leptospicularis , Spiculopteragia spp. Higher parasite species diversity was present in the Trois Fontaine than in the Chize populations including the presence of some species more typically seen in domestic livestock, ( Haemonchus contortus , Bunostomum trigonocephalum , Cooperia punctata , Teladorsagia circumcincta ). No differences in parasite species diversity or community composition was seen in the samples collected from three zones of differing habitat quality within the Chize region. Young roe deer hosted the highest diversity of gastrointestinal nematodes, with more pronounced effects of age apparent in Trois Fontaines. The effect of host age differed between gastrointestinal nematode species, e.g. there was little effect on Ostertagia leptospicularis but a high effect on Trichostrongylus spp. No effect of host sex was detected in either site. Conclusions The presence of some livestock parasite species in the Trois Fontaine roe deer popualtions was surprising given the isolation of this population away from grazing domestic livestock. However, it should be noted that a small group of sheep were resident in the region, albeit separately fenced, which raises some interesting questions regarding the introduction of non-native parasite species into wildlife populations. Overall, our results suggest complex interactions between hosts, environments and parasites and illustrate the power of the nemabiome metabarcoding approach to elucidate the composition of gastrointestinal nematode communities in wildlife.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".