Impact of fire and harvest on forest ecosystem services in a species‐rich area in the southern Appalachians
Bibliographic record
Abstract
Abstract To mitigate and adapt to climate change, forest carbon sequestration and diversity of the ecosystem must be included in forest management planning, while satisfying the demand for wood products. The future provisions of ecosystem services under six realistic management scenarios were assessed to achieve that goal. These services were carbon sequestration, types and quantities of roundwood harvested, and different indicators of forest health—biomass of major species, species diversity, and variation of tree age. A spatially explicit forest succession model was combined with statistical analyses to conduct the assessment at the level of both the whole forest landscape and different ecological zones (ecozones) within. An important aspect of this study was to explore the effects of the biophysical heterogeneity of different ecological zones on the outcomes of different management scenarios. The study area was located in an area of the southern Appalachian Mountains in North Carolina with high tree diversity and active forest management activities. Along with a range of management practices, such richness in diversity allowed us to examine the complexity of the interaction between management activities and species competition. The results showed that fire suppression had a greater effect on increasing biomass carbon sequestration than any management scenario that involves harvest and replanting afterward, but at the expense of other indicators of forest health. The effect of fire on species composition was the largest in the xeric parts of the study area. Based on the study results, it was proposed that a low harvest intensity with a mix of fire and fire suppression across the landscape would best balance the need for roundwood products, biomass carbon sequestration, and desirable species composition. This study also demonstrated that the combination of a spatially explicit forest succession model and statistical analyses could be used to provide a robust and quantifiable projection of ecosystem service provisions and possible trade‐offs under different management scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".