Historical range of variability in live and dead wood biomass: a regional-scale simulation study
Bibliographic record
Abstract
The historical range of variability (HRV) in landscape structure and composition created by natural disturbance can serve as a general guide for evaluating ecological conditions of managed landscapes. HRV approaches to evaluating landscapes have been based on age-classes or developmental stages, which may obscure variation in live and dead stand structure. Developing the HRV of stand structural characteristics would improve the ecological resolution of this coarse-filter approach to ecosystem assessment. We investigated HRV in live and dead wood biomass in the regional landscape of the Oregon Coast Range by integrating stand-level biomass models and a spatially explicit fire simulation model. We simulated historical landscapes of the region for 1000 years under pre-Euro-American settlement fire regimes and calculated biomass as a function of disturbance history. The simulation showed that live and dead wood biomass historically varied widely in time and space. The majority of the forests historically contained 500–700 Mg·ha–1 (50–70 kg·m–2) of live wood and 50–200 Mg·ha–1 (5–20 kg·m–2) of dead wood. The current distributions are more concentrated in much smaller amounts for both biomass types. Although restoring the HRV of forest structure is not necessarily a management goal for most landowners and managing agencies, departure from the reference condition can provide relative measure to evaluate habitat conditions for managers seeking to use forest structure as a means to maintain or restore ecosystem and species diversity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".