Twelve-year responses of planted and naturally regenerating conifers to variable-retention harvest in the Pacific Northwest, USA
Bibliographic record
Abstract
We studied patterns of conifer regeneration over 12 years as part of a regional-scale experiment in variable-retention harvest in the Pacific Northwest, the DEMO Study. We compared survival and height growth of planted conifers and density and seral composition of natural regeneration among treatments with differing retention levels (15% versus 40%) and patterns (dispersed versus aggregated) replicated across a range of latitudes and forest zones. We also assessed plot-scale relationships of natural regeneration with overstory density and basal area, competing vegetation, and slash accumulations. Early (1- to 2-year) survival of planted seedlings was greater in dispersed treatments (Pinus monticola Douglas ex D. Don, Abies spp.) or unaffected by retention level or pattern (Pseudotsuga menziesii (Mirb.) Franco). Later (5- to 12-year) survival did not differ (all species), but growth was distinctly reduced in dispersed treatments and (or) at higher levels of retention. Density of natural regeneration was 1.5–2.5 times greater in dispersed treatments than in the cleared areas of aggregated treatments. Low-level dispersed retention promoted Pseudotsuga, the early-seral dominant, presumably by enhancing seed rain within a relatively high-light environment. Dispersed retention favored late-seral conifers. The ability to manipulate retention pattern and level to influence regeneration density and composition provides managers with flexibility in developing structurally complex and compositionally diverse forests.
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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) | 0.001 | 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".