Stand spatial structure outcomes of forest adaptation treatments in northern hardwood forests in North America
Bibliographic record
Abstract
Spatial arrangement of trees is determined by a complex suite of factors, including disturbance history, competition, and resource availability. These spatial patterns drive adaptive capacity by influencing arrangement of growing space, neighborhood competitive relationships, and disturbance response, with irregular patterns supporting higher adaptive capacity. While spatial structure in relation to disturbance and climate change resilience has been studied in dry conifer forests and old-growth temperate forests, it has never been explored in the context of climate adaptive management in mesic, second-growth forests. To address this gap, we analyzed tree spatial patterns in second-growth northern hardwood forests under four different climate adaptation management approaches: no action; resistance or resilience to impacts of climate change; and transition to future-adapted forest types. We used spatial point statistics approaches to describe how patterns differed among the four treatments. We found that the treatments focused on future adaptation led to patterns with variable tree spacing and clumping, while those focused on perpetuating current conditions resulted in less pattern variation. This indicates that adaptation strategies that include uneven-aged regeneration methods that restore and maintain tree spatial patterns historically generated by gap dynamics can be successful in altering resource availability patterns and adaptation space in forest stands.
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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 | 0.001 |
| 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) | 0.001 | 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".