Terrestrial DNA viromes are systematically more divergent from reference databases than marine ones, with polar ecosystems amplifying the contrast
Bibliographic record
Abstract
Abstract Viruses are the most abundant biological entities on Earth, yet most of their diversity remains unsampled and unevenly represented across ecosystems. To test whether viral novelty is structured more by biome or latitude, we analyzed 120 curated environmental DNA metagenomes spanning marine and terrestrial habitats across equatorial temperate, Arctic, and Antarctic regions. Using a unified metagenomic and phylogenomic workflow, we reconstructed viral protein families, linked environmental sequences to the IMGVR v4.1 reference collection, and quantified divergence using three complementary measures: within-clade patristic distance, the bridging edge to the nearest reference neighbor, and phylogenetic tree-shape statistics. Because distances within phylogenetic trees are not independent, all inference was performed at the sample level. Across all samples, reconstructed environmental viral sequences were substantially more divergent from current reference genomes than their database homologs. The dominant pattern was biome-driven: terrestrial viromes were consistently farther from IMGVR references than marine viromes in every latitudinal band, and this contrast was supported by both branch-length and topology-based metrics. Latitude modulated this baseline signal, with polar ecosystems, especially Antarctic terrestrial samples, occupying the most divergent end of the two-axis landscape. Tree imbalance statistics showed that terrestrial viral phylogenies were more deeply branching and more asymmetric than marine ones, providing independent support for the biome effect. In contrast, some previously reported polar contrasts weakened after correcting for pseudoreplication and using proper sample-level replication. These results argue that viral novelty is structured primarily by biome, with polar environments amplifying an already strong terrestrial–marine contrast. The findings also indicate that current reference databases remain disproportionately sparse for terrestrial, especially polar terrestrial, viromes. Expanding viral sampling in these environments will be essential for improving ecological inference, improving database coverage, and refining our understanding of global viral diversity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".