Twenty-Five Years after Stand Thinning and Repeated Fertilization in Lodgepole Pine Forest: Implications for Tree Growth, Stand Structure, and Carbon Sequestration
Bibliographic record
Abstract
Silvicultural practices such as pre-commercial thinning (PCT) and repeated fertilization have been used successfully around the world to increase forest biomass for conventional wood products, biofuels, and carbon sequestration. Two complementary studies were designed to test the hypotheses (H) that large-scale PCT and PCT with repeated fertilization of young (13–17 years old) lodgepole pine (Pinus contorta var. latifolia) stands, at 25 years after the onset of treatments, would enhance (H1) productivity and structural features (diameter and height growth, crown volume and dimensions) of crop trees; (H2) merchantable volume of crop trees, (H3) abundance of understory conifer, herb and shrub layers, and (H4) carbon storage (sequestration) of all layers. Results are from two studies in south-central British Columbia, Canada. The PCT study had three densities: 500, 1000, and 2000 stems/ha, an unthinned, and old-growth stand replicated at three areas. The PCT-FERT study had four densities: 250, 500, 1000, and 2000 stems/ha with and without a repeated fertilization treatment, and an unthinned-unfertilized stand, replicated at two areas. Heavily thinned (≤1000 stems/ha) and fertilized stands generally had larger mean diameters and crown dimensions than lightly thinned or unfertilized stands, whereas mean heights of crop trees remained relatively unaffected, and hence partial support for H1. Despite differences in stand density (4- and 8-fold) of original crop trees, ingress of intermediate crop trees over the 25 years changed density dramatically. There was no support for H2, as mean merchantable volume of total crop trees was similar across stand densities owing to ingress, and was also similar in fertilized and unfertilized stands. In stands thinned to ≤500 trees/ha, there is an anticipated increase in crop tree density of 2.2 to 4.3 times over 25 years compared with the original post-thinning densities. Mean abundance of understory conifers was generally similar among stands owing to successional development towards canopy closure, whereas herbs and shrubs persisted only in canopy gaps in heavily thinned stands, thereby providing no support for H3. Mean carbon storage was similar across densities in both studies with ingress contributing a considerable amount to carbon sequestration at the lower densities. Fertilization increased (1.4 times) mean carbon storage significantly among total crop trees, total conifers, and the grand total of all layers, thereby providing partial support for H4. Thus, much of the potential above-ground carbon storage lost by thinning was restored in the heavily thinned stands at 25 years post-treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".