The role of management decisions in subspecies hybridization across wild turkey occupied range
Bibliographic record
Abstract
Abstract The expanded geographic range and recovery of wild turkey ( Meleagris gallopavo ) would not have been possible without management actions that included introducing and translocating individuals across North America. However, range‐wide genetic effects of management actions remain unknown despite the potential economic effect as hunters seek out clearly identifiable subspecies. We used DNA extracted from hunter‐collected feathers from 29 states and Ontario, Canada, to investigate genetic variation among wild turkey populations in their historic and introduced ranges. We compiled state‐level translocation and introduction data to investigate how different management actions were associated with the amount of admixture among subspecies for a subset of states. We found no difference in the level of admixture in contemporary populations of the wild turkey between their historic range and introduced range. However, the average admixture detected was affected by different restoration actions, including the number of subspecies introduced. Additionally, the number of unique states that wild turkeys were sourced from correlated with the amount of admixture detected but relocating those wild turkeys to many unique counties did not. Together, our results indicate that where wild turkeys were sourced from had a greater effect on admixture than where those individuals were released. Hybridization among subspecies of wild turkey, based on admixture levels, appears to be widespread and influenced by historical management actions that has contributed to the current genetic composition of local wild turkey populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".