Circulating microbiome DNA in Southern Ocean seabirds: A novel tool for disease surveillance in polar ecosystems
Bibliographic record
Abstract
Marine ecosystems, particularly in polar regions, are undergoing rapid transformations due to climate change, influencing host-pathogen dynamics in wildlife populations. Seabirds, which form spatially structured social networks, serve as potential sentinels for pathogen surveillance, yet the composition and variability of their blood microbiome remain largely unexplored. The concept of a circulating blood microbiome is relatively new and debated as blood has traditionally been considered sterile. However, emerging evidence suggests that circulating microbial DNA (cmDNA) represents a transient microbial signature, potentially offering insights into host health, dysbiosis, and disease risks. In this study, we aimed to evaluate the feasibility and relevance of circulating microbial DNA (cmDNA) as a tool for pathogen surveillance in wild seabird populations. We identified inter-annual variability, sex-related, and age-related variability in blood microbiome composition, with core microbial signatures differing across sites and time periods. We also observed sex-biased microbial prevalence and age-related microbiome maturation, with dynamic shifts in diversity across chick developmental stages. Finally, we detected several potential pathogens, providing new insights into their distribution, prevalence, and potential implications for seabird health. These findings highlight the value of cmDNA analysis as an effective approach for wildlife disease surveillance and pathogen monitoring in polar ecosystems, contributing to broader efforts in marine conservation and biosecurity in the face of climate change-driven environmental shifts. • Circulating microbial DNA enables minimally invasive wildlife disease surveillance. • Core microbial signatures vary across sites, time periods, and host traits. • Microbial diversity shifts dynamically during chick development. • Potential pathogens detected may have implications for seabird health.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".