Data from: History matters: contemporary versus historic population structure of bobcats in the New England region, USA
Bibliographic record
Abstract
Habitat fragmentation and genetic bottlenecks can have substantial impacts on the health and management of wildlife species by lowering diversity and subdividing populations. Population genetic comparisons across time periods can help elucidate temporal changes in populations and the processes responsible for the changes. Bobcats (Lynx rufus) are wide-ranging carnivores and are currently increasing in abundance across an expanding range. Bobcat populations in New England have fluctuated in the past century in response to changes in their prey base, harvest pressure, and landscape development. We genotyped contemporary (2010–2017) and historic (1952–1964) bobcats from New England and Quebec, Canada at a suite of microsatellite loci and tested for differences in diversity, effective population size, and gene flow. Over 20 generations separated the sampling periods, and the intervening years were marked by drastic changes in land use and species management regimes. We found a general decrease in genetic diversity and differing population genetic structure through time. Effective population size decreased at the end of the historic period, coincident with a spike in harvest, but rebounded to greater numbers in the contemporary period. Our results suggest that bobcat populations in the region are robust, but development and range dynamics may play a significant role in population structure. Our study also highlights the benefits of a historical perspective in interpreting contemporary population genetic data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".