Fine-Scale Analysis Reveals Cryptic Patterns of Genetic Structure in Canada Geese
Bibliographic record
Abstract
In migratory birds, population-genetic structure is generally low, but philopatric species can have fine-scale patterns of differentiation. We investigated the population-genetic structure of the Canada Goose (Branta canadensis) across multiple spatial scales to determine whether genetic data support current delineations of subspecies and populations for management. We collected samples from two subspecies (B. c. interior and B. c.maxima) and four management populations across Ontario and Quebec. Using 7 microsatellites and 442 base pairs of mtDNA we found that genetic structure varied with scale. FST revealed low levels of genetic differentiation between subspecies and management populations, and individual-based clustering revealed no genetic differentiation. However, fine-scale spatial autocorrelation revealed significant levels of relatedness at distances <85 km. The lack of clear genetic structure may reflect recent human management. That our fine-scale analysis revealed significant genetic relationships suggests that genetic structure may increase and in time reflect that revealed by banding data. As our markers were unable to accurately distinguish between subspecies they will be of little use in estimating subspecific contribution to harvested stock. Alternative molecular markers under selective pressure may be more informative in assess targets for harvest.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".