Data from: Effects of harvesting of increasing intensities on genetic diversity and population structure of white spruce
Bibliographic record
Abstract
Forest harvesting of increasing intensities is expected to have intensifying impacts on the genetic diversity and population structure of postharvest naturally regenerated stands by affecting the magnitude of evolutionary processes, such as genetic drift, gene flow, mating system, and selection. We have tested this hypothesis for the first time by employing widely distributed boreal white spruce (Picea glauca) as a model and controlled, replicated experimental harvesting and regeneration experiment at the EMEND project site (http://www.emendproject.org). We used two approaches. First, genetic diversity and population structure of postharvest natural regeneration after five harvesting treatments (green tree retention of 75%, 50%, 20%, and 10%, and clearcut) were assessed and compared with those of the unharvested control (pristine preharvest old-growth) in two replicates each of conifer-dominated (CD) and mixed-wood (MW) forest, using 10 (six EST (expressed sequence tag) and four genomic) microsatellite markers. Second, genetic diversity and population structure of preharvest old-growth were compared with those of postharvest natural regeneration after five harvesting treatments in the same treatment blocks in one replicate each of CD and MW forests. Contrary to our expectations, genetic diversity, inbreeding levels, and population genetic structure were similar between unharvested control or preharvest old-growth and postharvest natural regeneration after five harvesting treatments, with clearcut showing no negative genetic impacts. The potential effects of genetic drift and inbreeding resulting from harvesting bottlenecks were counterbalanced by predominantly outcrossing mating system and high gene flow from the residual and/or surrounding white spruce. CD and MW forests responded similarly to harvesting of increasing intensities. Simulated data for 10, 50, and 100 microsatellite markers showed the same results as obtained empirically from 10 microsatellite markers. Similar patterns of genetic diversity and population structure were observed for EST and genomic microsatellites. In conclusion, harvesting of increasing intensities did not show any significant negative impact on genetic diversity, population structure, and evolutionary potential of white spruce in CD and MW forests. Our first of its kind of study addresses the broad central forest management question how forest harvesting and regeneration practices can best maintain genetic biodiversity and ecosystem integrity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".