Trade-offs between timber production, carbon stocking and habitat quality when managing woodlots for multiple ecosystem services
Bibliographic record
Abstract
SUMMARY Managing for multiple ecosystem services is a growing issue for forest managers. As trade-offs arise between conflicting management objectives, stakeholders must be informed of the possible outcomes of alternative choices in order to facilitate decision-making. We modelled stand dynamics under single-management and functional zoning multiple-management (TRIAD; i.e. three-zone) scenarios in different forest types typical of eastern North America with the Forest Vegetation Simulator (FVS). Timber production, carbon stocking and habitat quality ecosystem services were calculated with simulation outputs. Habitat quality was measured using a habitat suitability index that integrated stand structural indicators. A multi-criteria decision analysis (MCDA) was performed in order to rank scenarios. We show that the most intensive management yielded greater timber volumes but resulted in the weakest carbon and habitat quality scores. The TRIAD scenarios in sugar maple–beech stands offered the best compromise in services compared to single management. In shade-intolerant deciduous stands, there was a loss of timber production with TRIAD scenarios, but greater carbon stock and habitat quality were observed. Our study contrasts alternative management scenarios for ecosystem services in woodlots of different forest types. It confirms that multiple harvest systems better achieve multiple services. The coupling of simulation modelling with MCDA offers a simple and flexible method to help stakeholders and managers make sound decisions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".