Age-related patterns of forest complexity and carbon storage in pine and aspen–birch ecosystems of northern Minnesota, USA
Bibliographic record
Abstract
Forest managers are seeking strategies to create stands that can adapt to new climatic conditions and simultaneously help mitigate increases in atmospheric CO 2 . Adaptation strategies often focus on enhancing resilience by maximizing forest complexity in terms of species composition and size structure, while mitigation involves sustaining carbon storage and sequestration. Altered stand age is a fundamental consequence of forest management, and stand age is a powerful predictor of ecosystem structure and function in even-aged stands. However, the relationship between stand age and either complexity or carbon storage and sequestration, especially trade-offs between the two, are not well characterized. We quantified these relationships in clearcut-origin, unmanaged pine and aspen chronosequences ranging from <10 to >130 years in northern Minnesota. Complexity generally increased with age, although compositional complexity changed more over time in aspen forests and structural complexity changed more over time in pine stands. Although individual carbon pools displayed various relationships with stand age, total carbon storage increased with age, whereas carbon sequestration, inferred from changes in storage, decreased sharply with age. These results illustrate the carbon and complexity consequences of varying forest harvest rotation length to favor younger or older forests and provide insight into trade-offs between these potentially conflicting management objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".