Fine-scale population structure and sex-biased dispersal in bobcats (Lynx rufus) from southern Illinois
Bibliographic record
Abstract
In mammal populations, the spatial and genetic structure can be affected by dispersal, philopatry, and relatedness. Bobcats ( Lynx rufus (Schreber, 1777)) are thought to exhibit typical mammalian dispersal behaviour where males disperse and females are philopatric, potentially leading to higher relatedness among females compared with males. We used 10 microsatellite loci to examine population structure and sex-biased dispersal in 146 bobcats sampled in southern Illinois during 1993–2001 using population genetic descriptive statistics, a Bayesian clustering algorithm, relatedness (rxy), and autocorrelation analyses. A randomization test demonstrated that female dyads had significantly higher rxy values with respect to randomly selected dyads (rxy = 0.093 ± 0.222, P = 0.012) and spatial autocorrelation analyses determined that females in close proximity (<5 km) had a high probability of being related (P = 0.001). Conversely, rxy values for males were not different from the null distribution (rxy = 0.019 ± 0.122, P = 0.3158) and no significant relationships were found with spatial autocorrelation analysis. Additionally, it was demonstrated that bobcats in southern Illinois approximated a panmictic population with no obvious barriers to gene flow. The pattern of relatedness observed in this study confirmed that females were philopatric and males dispersed, corroborating existing observational data for this species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".