Viral diversity and prevalence gradients in North American Pacific Coast grasslands
Bibliographic record
Abstract
Host-pathogen interactions may be governed by the number of pathogens coexisting within an individual host (i.e., coinfection) and among different hosts, although most sampling in natural systems focuses on the prevalence of single pathogens and/or single hosts. We measured the prevalence of four barley and cereal yellow dwarf viruses (B/CYDVs) in three grass species at 26 natural grasslands along a 2000-km latitudinal gradient in the western United States and Canada. B/CYDVs are aphid-vectored RNA viruses that cause one of the most prevalent of all plant diseases worldwide. Pathogen prevalence and coinfection were uncorrelated, suggesting that different forces likely drive them. Coinfection, the number of viruses in a single infected host (alpha diversity), did not differ among host species but increased roughly twofold across our latitudinal transect. This increase in coinfection corresponded with a decline in among-host pathogen turnover (beta diversity), suggesting that B/CYDVs in northern populations experience less transmission limitation than in southern populations. In contrast to pathogen diversity, pathogen prevalence was a function of host identity as well as biotic and abiotic environmental conditions. Prevalence declined with precipitation and increased with soil nitrate concentration, an important limiting nutrient for hosts and vectors of B/CYDVs. This work demonstrates the need for further studies of processes governing coinfection, and the utility of applying theory developed to explain diversity in communities of free-living organisms to pathogen systems.
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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 | 0.001 |
| 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 | 0.001 |
| 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".