The Gut Microbial Community Structure of the North American River Otter (<i>Lontra canadensis</i>) in the Alberta Oil Sands Region in Canada: Relationship with Local Environmental Variables and Metal Body Burden
Bibliographic record
Abstract
Abstract The Alberta Oil Sands Region in Canada is home to one of the largest oil bitumen deposits in the world. The North American river otter (Lontra canadensis) is a top predator with a small home range and is sensitive to disturbances; it has been designated as a sentinel species for the potential impacts of the natural resource exploitation on freshwater ecosystems in the Alberta Oil Sands Region. With an increasing interest in noninvasive biomarkers, recent studies suggest that gut microbiota can be used as a potential biomarker of early biological effects on aquatic wildlife. The goal of the present study was to determine the river otter gut microbial structure related to environmental variables characterizing mining activities and metal body burden. We obtained 18 trapped animals from and surrounding the surface mineable area of the Alberta Oil Sands Region. The gut microbial community structure was characterized using high-throughput sequencing of 16S rRNA gene amplicon analyses. Trace metal concentrations in the liver were measured by inductively coupled plasma–mass spectrometry. Our study revealed that the gut bacteria of river otters in the Alberta Oil Sands Region clustered in 4 groups dominated by Peptostreptococcaceae, Carnobacteriaceae, Enterobacteriaceae, Clostridiaceae, and Nostocaceae. We show that arsenic, barium, rubidium, liver-body weight ratio, and δ15N were associated with each cluster. When comparing affected versus less affected sites, we show that river otter gut bacterial community and structure are significantly related to trophic level of the river otter but not to Alberta Oil Sands Region mining activities. Our study reveals that the gut bacterial dynamics can provide insights into the diet and habitat use of river otters but that more work is needed to use it as a pollution biomarker. Environ Toxicol Chem 2020;39:2516–2526. © 2020 SETAC
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".