Range‐wide distribution of genetic diversity in the North American tree<i>Juglans cinerea</i>: a product of range shifts, not ecological marginality or recent population decline
Bibliographic record
Abstract
The spatial distribution of genetic diversity is a product of recent and historical ecological processes, as well as anthropogenic activities. A current challenge in population and conservation genetics is to disentangle the relative effects of these processes, as a first step in predicting population response to future environmental change. In this investigation, we compare the influence of contemporary population decline, contemporary ecological marginality and postglacial range shifts. Using classical model comparison procedures and Bayesian methods, we have identified postglacial range shift as the clear determinant of genetic diversity, differentiation and bottlenecks in 29 populations of butternut, Juglans cinerea L., a North American outcrossing forest tree. Although butternut has experienced dramatic 20th century decline because of an introduced fungal pathogen, our analysis indicates that recent population decline has had less genetic impact than postglacial recolonization history. Location within the range edge vs. the range core also failed to account for the observed patterns of diversity and differentiation. Our results suggest that the genetic impact of large-scale recent population losses in forest trees should be considered in the light of Pleistocene-era large-scale range shifts that may have had long-term genetic consequences. The data also suggest that the population dynamics and life history of wind-pollinated forest trees may provide a buffer against steep population declines of short duration, a result having important implications for habitat management efforts, ex situ conservation sampling and population viability analysis.
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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 | 0.000 | 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 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".