The ecological dynamics and consequences of phytoplasma in a spring ephemeral
Bibliographic record
Abstract
Abstract Ecologists have long sought to link within-population dynamics to patterns of species occurrence and persistence across the range. Theoretical models suggest pathogens have important implications for plant populations and perhaps even limit plant distributions, though more empirical evidence is needed, especially in wild plant populations. In the field, we characterized the infection dynamics of phytoplasma, a vectored and sterilizing bacterial pathogen, in wild populations of white trillium. We identified the highest molecular matches for the local bacterial strain and putative vectors. We then leveraged iNaturalist data to reveal patterns and potential drivers of phytoplasma occurrence across the range of white trillium. Within populations, we found that phytoplasma symptoms were more likely in low host density plots, where insects occupied a higher proportion of trillium, indicative of an encounter-dilution effect. Leafhoppers in the genus Empoasca were positive for phytoplasma, suggesting they are a candidate vector of disease between trilliums at our field site. The closest match to our local phytoplasma was related to ‘ Candidatus Phytoplasma pruni’, ribosomal group 16SrIII-F. Phytoplasma symptoms were widely distributed across trillium’s range, even at range edges, and probability of symptoms was significantly increased by proximity to cropland. We studied the impacts of a sterilizing, vector-transmitted pathogen on host plant populations and patterns of infection across host range. Our results suggest sap-feeding insects do not occupy all hosts at high host densities, thereby reducing transmission and leading to higher likelihood of finding infection at low host densities, and a higher proportion of host plants infected at low host densities. At a range-wide scale, we found evidence that the pathogen was able to persist across both the core and edge of host ranges. Thus, negative-density dependent sterilizing pathogens may challenge host populations across the range.
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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 | 0.000 |
| 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".