A National Multi-Scale Assessment of Regeneration Deficit as an Indicator of Potential Risk of Forest Genetic Variation Loss
Bibliographic record
Abstract
Genetic diversity is essential because it provides a basis for adaptation and resilience to environmental stress and change. The fundamental importance of genetic variation is recognized by its inclusion in the Montréal Process sustainability criteria and indicators for temperate and boreal forests. The indicator that focuses on forest species at risk of losing genetic variation, however, has been difficult to address in a systematic fashion. We combined two broad-scale datasets to inform this indicator for the United States: (1) tree species occurrence data from the national Forest Inventory and Analysis (FIA) plot network and (2) climatically and edaphically defined provisional seed zones, which are proxies for among-population adaptive variation. Specifically, we calculated the estimated proportion of small trees (seedlings and saplings) relative to all trees for each species and within seed zone sub-populations, with the assumption that insufficient regeneration could lead to the loss of genetic variation. The threshold between sustainable and unsustainable proportions of small trees reflected the expectation of age–class balance at the landscape scale. We found that 46 of 280 U.S. forest tree species (16.4%) may be at risk of losing genetic variation. California and the Southeast encompassed the most at-risk species. Additionally, 39 species were potentially at risk within at least half of the seed zones in which they occurred. Seed zones in California and the Southwest had the highest proportions of tree species that may be at risk. The results could help focus conservation and management activities to prevent the loss of adaptive genetic variation within tree 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".