Contrasting Patterns of Raccoon (<i>Procyon lotor</i>) Spatial Population Genomics Throughout a Rabies Management Area in Eastern North America
Bibliographic record
Abstract
ABSTRACT Wide‐ranging, generalist species provide both interesting and challenging opportunities for research questions focused on population structure. Their continuous distributions and ability to occupy diverse habitat types can obscure genetic signals of ancestry and geographic clustering. However, spatially informed population genetic approaches are notable for high‐resolution identification of geographic clusters that often elude more classical clustering models. The northern raccoon ( Procyon lotor ) is a broadly distributed species in North America, with populations in diverse habitats ranging from dense urban to rural landscapes. Wildlife management agencies have an interest in understanding raccoon ecology, given their propensity for human‐wildlife conflicts and zoonotic diseases. We combined samples from an extensive raccoon tissue repository with a RADcapture panel of 1000 microhaplotype loci to conduct spatial genetic analyses of raccoon populations in eastern North America. Our objective was to estimate patterns of genetic diversity on the landscape that may inform raccoon rabies management. Bayesian clustering analyses delineated multiple ancestry clusters that encompassed large areas across 22 US states and 2 Canadian provinces. We discovered a potential phylogeographic split between central and southern samples from those in the northeast region, which correlates with post‐Pleistocene recolonization detected in a multitude of species from the region. A finer scale structure was identified using spatially explicit analyses and demonstrated variable dispersal/gene flow patterns within specific regions. The Appalachian Mountain region restricted local connectivity among raccoons, while raccoon populations in central New York, the Ohio River Valley, southern Québec, and southern Alabama demonstrated high genetic connectivity. The results from this study highlight how raccoon ecology and historical biogeography can help contextualize contrasting hypotheses about the influence of landscape on raccoon movement patterns, which can inform management of zoonotic disease risks at regional scales.
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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) | 0.000 | 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".