Population dynamics of black spruce and white spruce near the arctic tree line in the southern Brooks Range, Alaska
Bibliographic record
Abstract
Black spruce (Picea mariana (Mill.) BSP) is the dominant species in interior Alaska but it is largely absent from the arctic tree line. To evaluate the importance of climate and fire as controls over the species distribution, we reconstructed stand history at three sites near its northern limit in Alaska, where it grows with white spruce (Picea glauca (Moench) Voss). We developed a matrix model to explore black spruce population dynamics and response to varying fire intervals. All sites burned in the early 1900s. High recruitment of black spruce occurred for <30 years following the fire, but most current black spruce recruitment is clonal and seed viability is low. White spruce recruitment has been consistently high since the fire, and the majority of seedlings in the stands are white spruce. Despite low recruitment, the matrix model suggests that black spruce populations are nearly stable, largely because of low adult mortality rates. Although black spruce recruitment is stimulated by fire, the model indicates that fire intervals <350 years would destabilize the population, primarily because of slow growth and low seed production. Population dynamics of black spruce at its northern limit in Alaska thus appear to reflect an interaction between fire, which determines the temporal pattern of tree recruitment, and climate, which limits tree growth and, presumably, viable seed production.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".