Effects on understory biomass and forage 8–10 years after precommercial thinning of Sitka spruce – western hemlock stands in southeast Alaska
Bibliographic record
Abstract
In southeast Alaska, United States, multiple-use forest management objectives include both timber production and wildlife habitat. Following stand-replacing disturbances such as clear-cutting, Sitka spruce (Picea sitchensis (Bong.) Carrière) and western hemlock (Tsuga heterophylla (Raf.) Sarg.) naturally regenerate and competitively dominate resources, excluding understory biomass and biodiversity. Thinning may mitigate the effects of canopy closure and permit understory development, but evidence of the effect on understories 8–10 years after thinning is lacking. We report results 4–5 and 8–10 years after thinning experiments on the Tongass National Forest to demonstrate the effects of precommercial thinning (thinned versus control), stand age (15–25, 25–35, and 35–50 years), and weather on understory dynamics and Sitka black-tailed deer (Odocoileus hemionus sitkensis Merriam, 1898) forage availability. Stand density negatively affected understory biomass, whereas temperature and precipitation positively interacted to increase biomass. Thinning had an enduring effect on understories, with biomass at least twice as great in thinned versus unthinned stands through year 10. We identified compositional differences from thinning as stand age class increased. Deer forage responded similarly to biomass, but thinning-induced differences faded with increased winter snowfall scenarios, especially in older stands. This study aids the understanding of stand overstory and understory development following silvicultural treatments in the coastal temperate rain forest of Alaska and suggests management implications and applications for balancing objectives throughout the forest type.
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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".