Accelerating regrowth of temperate‐maritime forests due to environmental change
Bibliographic record
Abstract
Abstract To understand how environmental changes have influenced forest productivity, stemwood biomass ( B ) dynamics were analyzed at 1267 permanent inventory plots, covering a combined 209 ha area of unmanaged temperate‐maritime forest in southwest British Columbia, Canada. Net stemwood production (Δ B ) was derived from periodic remeasurements of B collected over a 40‐year measurement period (1959–1998) in stands ranging from 20 to 150 years old. Comparison between the integrated age response of net stemwood production, Δ B ( A ), and the age response of stemwood biomass, B ( A ), suggested a 58 ± 11% increase in Δ B between the first 40 years of the chronosequence period (1859–1898) and the measurement period. To estimate extrinsic forcing on Δ B , several different candidate models were developed to remove variation explained by intrinsic factors. All models exhibited temporal bias, with positive trends in (observed minus predicted) residual Δ B ranging between of 0.40 and 0.64% yr −1 . Applying the same methods to stemwood growth ( G ) indicated residual increases ranging from 0.43 and 0.67% yr −1 . Higher trend estimates corresponded with models that included site index ( SI ) as a predictor, which may reflect exaggeration of the age‐decline in SI tables. Choosing a model that excluded SI , suggested that Δ B increased by 0.40 ± 0.18% yr −1 , while G increased by 0.43 ± 0.12% yr −1 over the measurement period. Residual G was significantly correlated with atmospheric carbon dioxide ( CO 2 ), temperature ( T ), and climate moisture index ( CMI ). However, models driven with climate and CO 2 , alone, could not simultaneously explain long‐term and measurement‐period trends without additional representation of indirect effects, perhaps reflecting compound interest on direct physiological responses to environmental change. Evidence of accelerating forest regrowth highlights the value of permanent inventories to detect and understand systematic changes in forest productivity caused by environmental change.
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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".