Urban environments harbor greater oomycete and <i>Phytophthora</i> diversity, creating a bridgehead for potential new pathogens to natural ecosystems
Bibliographic record
Abstract
Abstract Anthropogenic activities contribute to changes in the range and distribution of species. Globalization is resulting in human‐mediated dispersal that is causing a breakdown in normal biogeographic barriers. But the impact of anthropogenic activities on plant pathogen communities is still poorly understood. We conducted an eDNA metabarcoding study to compare communities of oomycetes, a group of eukaryotic microorganisms that comprises important crop and tree pathogens, in urban, natural, and interface environments. Oomycete diversity and abundance were highest in human impacted urban environments and lowest in natural environments, while the interface environments were intermediate. Urban environments had the highest proportion of sites where species of the plant pathogenic genus Phytophthora were found, as well as the largest number of unknown or undescribed Phytophthora species. The taxa overlap between urban and interface environments was one order of magnitude larger than the overlap between urban and natural environments. Our analyses show that urban/natural interface areas likely act as a bridge for invasion into natural environments. This could impact both the natural biota and natural ecosystem processes. Our study serves as a warning that some Phytophthora species introduced from nurseries or spread by human movement could pose a threat to natural ecosystems. Shifting patterns in oomycete communities could interfere with natural ecosystem processes and result in increases in disease and ecosystem declines.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".