Data from: Landscape effects on the contemporary genetic structure of Ruffed Grouse (Bonasa umbellus) populations
Bibliographic record
Abstract
The amount of dispersal that occurs among populations can be limited by landscape heterogeneity, which is often due to both natural processes and anthropogenic activity leading to habitat loss or fragmentation. Understanding how populations are structured and mapping existing dispersal corridors among populations is imperative to both determining contemporary forces mediating population connectivity, and informing proper management of species with fragmented populations. Furthermore, the contemporary processes mediating gene flow across heterogeneous landscapes on a large scale are understudied, particularly with respect to widespread species. This study focuses on a widespread game bird, the Ruffed Grouse (Bonasa umbellus), for which we analyzed samples from the western extent of the range. Using three types of genetic markers, we uncovered multiple factors acting in concert that are responsible for mediating contemporary population connectivity in this species. Multiple genetically distinct groups were detected; microsatellite markers revealed six groups, and a mitochondrial marker revealed four. Many populations of Ruffed Grouse are genetically isolated, likely by macrogeographic barriers. Furthermore, the addition of landscape genetic methods not only corroborated genetic structure results, but also uncovered compelling evidence that dispersal resistance created by areas of unsuitable habitat is the most important factor mediating population connectivity among the sampled populations. This research has important implications for both our study species and other inhabitants of the early successional forest habitat preferred by Ruffed Grouse. Moreover, it adds to a growing body of evidence that isolation by resistance is more prevalent in shaping population structure of widespread species than previously thought.
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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 | 0.001 | 0.002 |
| 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) | 0.010 | 0.002 |
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".