An ecological approach to measuring the evolutionary consequences of gene flow from crops to wild or weedy relatives
Bibliographic record
Abstract
PREMISE OF THE STUDY: Agricultural practices routinely create opportunities for crops to hybridize with wild relatives, leading to crop gene introgression into wild genomes. Conservationists typically worry this introgression could lead to genetic homogenization of wild populations, over and above the central concern of transgene escape. Alternatively, viewing introgression as analogous to species invasion, we suggest that increased genetic diversity may likewise be an undesirable outcome. METHODS: Here, we compare the sensitivity of conventional population genetic metrics with species diversity indices as indicators of the impact of gene flow on genetic diversity. We illustrate this novel approach using multilocus genotype data (12 allozyme loci) from 10 wild (Beta vulgaris subsp. maritima) and eight putative crop-wild hybrid beet populations (B. vulgaris subsp. vulgaris × B. vulgaris subsp. maritima) scattered throughout Europe. RESULTS: Conventional population genetic metrics mostly failed to detect shifts in genetic composition of putative hybrid populations. By contrast, species diversity indices unambiguously revealed increased genetic diversity in putative hybrid populations. DISCUSSION: We encourage other workers to explore the utility of our more sensitive approach for risk assessment prior to the release of transgenic crops, with a view toward widespread adoption of our method in studies aimed at detecting allelic invasion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".