Integrative population genetics and metagenomics reveals urbanization increases pathogen loads and decreases connectivity in a wild bee
Bibliographic record
Abstract
As urbanization continues to increase, it is expected that two-thirds of the human population will reside in cities by 2050. Urbanization fragments and degrades natural landscapes, threatening wildlife including economically important species such as bees. In this study, we employ whole genome sequencing to characterize the population genetics, metagenome and microbiome, and environmental stressors of a common wild bee, Ceratina calcarata. Population genomic analyses revealed the presence of low genetic diversity and elevated levels of inbreeding. Through analyses of isolation by distance, resistance, and environment across urban landscapes, we found that green spaces including shrubs and scrub were the most optimal pathways for bee dispersal, and conservation efforts should focus on preserving these land traits to maintain high connectivity across sites for wild bees. Metagenomic analyses revealed landscape sites exhibiting urban heat island effects, such as high temperatures and development but low precipitation and green space, had the highest taxa alpha diversity across all domains even when isolating for potential pathogens. Notably, the integration of population and metagenomic data showed that reduced connectivity in urban areas is not only correlated with lower relatedness among individuals but is also associated with increased pathogen diversity, exposing vulnerable urban bees to more pathogens. Overall, our combined population and metagenomic approach found significant environmental variation in bee microbiomes and nutritional resources even in the absence of genetic differentiation, as well as enabled the potential early detection of stressors to bee 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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".