Population genetic structure of raccoons (Procyon lotor) inhabiting a highly fragmented landscape
Bibliographic record
Abstract
The raccoon ( Procyon lotor (L., 1758)), a generalist mesocarnivore, has thrived in anthropogenically fragmented landscapes. In this study we utilized 13 microsatellites to analyze the population genetic structure in raccoons occupying an agriculturally fragmented habitat. Our analyses revealed low, but significant, levels of genetic structure (FST = 0.022, P < 0.001) among 645 raccoons inhabiting 29 spatially distinct habitat patches (neighborhoods). We found no evidence for the presence of either isolation by distance or barriers. However, spatial autocorrelation revealed evidence for social structure at the raccoon-neighborhood scale. About 30% of the variance in levels of social structure among raccoon neighborhoods was explained by effects of habitat-patch size and the percentage of land cover represented by forest around the individual habitat patch. We found strong evidence of sex-biased dispersal using direct (parentage analysis) and indirect (relatedness and assignment indices) genetic estimators. However, our analysis revealed that among dispersing raccoons there was no difference in dispersal distance between sexes and that dispersal events are randomly distributed with respect to distance. Our study provides evidence that demographic and behavioral processes affecting raccoon spatial organization may be most critical at very fine scales of biological organization, such as within individual habitat patches.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".