Fifteen-year microbiome survey of endangered killer whales ( <i>Orcinus orca</i> ) reveals declining diversity and population differences
Bibliographic record
Abstract
ABSTRACT The endangered Southern Resident killer whale ( Orcinus orca ) (SRKW) population is burdened by multiple anthropogenic stressors with limited non-invasive approaches for health surveillance. The gut microbiome is interconnected with host physiology and can be characterized using remotely collected fecal samples, creating unique opportunities to integrate environmental and individualized host-associated features of health. In this study, we used fecal samples collected over a 15-year period (2005-2019) from 77% of the living, wild SRKW population (56 individuals, 1-17 time points per individual), as well as fecal samples from the conspecific Northern and Alaska Resident killer whale populations, to characterize distal gut microbiota structure and genomic composition. SRKW microbiotas were individualized and distinct from those of the nearby Northern and Alaska Resident populations, both of which exhibit consistently higher fecundity and survivorship. During the study period, SRKW fecal microbiota species richness declined, despite stability over periods of time less than or equal to 1 year. Several potential bacterial pathogens, such as Fusobacterium spp., achieved dominance in the fecal microbiotas of SRKW individuals sampled within 6 months of death. These findings demonstrate the feasibility and value of harnessing non-invasively collected fecal samples and microbiome profiles for longitudinal killer whale health surveillance.
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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 | 0.000 | 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 | 0.000 |
| 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".