Effects of alternative forest management on biomass and species diversity in the face of climate change in the northern Great Lakes region (USA)
Bibliographic record
Abstract
Northern Great Lakes forests represent an ecotone in the boreal–temperate transition zone and are expected to change dramatically with climate change. Managers are increasingly seeking adaptation strategies to manage these forests. We explored the efficacy of two alternative management scenarios compared with business-as-usual (BAU) management: expanding forest reserves meant to preserve forest identity and increase resistance, and modified silviculture meant to preserve forest function and increase adaptive capacity. Our study landscapes encompassed northeastern Minnesota and northern Lower Michigan, which are predicted to experience significant changes in a future climate and represent a gradient of latitude, forest type, and management. We used the LANDIS-II forest simulation model to simulate forest change under current climate, low emissions climate, and high emissions climate futures. Our results suggest that under a low emissions climate scenario, expanded reserves and modified silviculture strategies can be effective at increasing resistance by preserving forest composition, including legacy species (e.g., balsam fir (Abies balsamea (L.) Mill.)), and increasing adaptive capacity by maintaining or increasing aboveground biomass compared with BAU management. Under a high emissions climate scenario, the expanded reserve strategy was not effective at preserving legacy species; however, the modified silviculture strategy was effective at increasing aboveground biomass compared with BAU management. These results highlight alternative management options and limitations in the face of climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".