Comparing long-term projected outcomes of adaptive silvicultural approaches aimed at climate change in red pine forests of northern Minnesota, USA
Bibliographic record
Abstract
The Adaptive Silviculture for Climate Change (ASCC) project was developed to test ecosystem-specific adaptation approaches. The first ASCC trial was installed on the Cutfoot Experimental Forest (CEF) in northern Minnesota, USA, in 2014. Three adaptation treatments (resistance, resilience, and transition), along with a no action control, were tested and compared using Forest Vegetation Simulator to determine their relative success. We compared mean annual increment (MAI) and mortality and determined how well each treatment achieved its species composition and stand structure targets. MAI was highest in the no action (3.77 ± 0.43 m3·ha–1·year–1) and lowest in the transition (1.72 ± 0.16 m3·ha–1·year–1). However, MAI for the transition treatment continually increased over time, which extended culmination age. The no action control had the highest mortality with 38.76 (±1.32) trees·ha–1 per 10-year timestep, while the resistance and transition treatments had the lowest levels at 9.36 (±0.49) and 4.19 (±0.35) trees·ha–1, respectively. Our findings highlight the relative success of the transition, which had lower mortality, greater structural diversity, and a future-climate-adapted species composition. The results from this study provide important context for adaptive silviculture aimed at climate change and offers an example of potential outcomes of these forest adaptation options.
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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 | 0.001 | 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 | 0.000 | 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".