An accessible, data-driven approach for robust regional calibration of the Forest Vegetation Simulator for improved stand structure and carbon density modeling
Bibliographic record
Abstract
Growth and yield models are an essential tool for predicting the long-term response of forests to management and disturbance. Model evaluation and calibration are challenging, however, given data limitations for observing stand structural changes with age across heterogeneous forest landscapes. Here we present an approach for calibrating the lodgepole pine (LP) forest model in the Central Rockies variant of the Forest Vegetation Simulator (FVS-CR) using Forest Inventory and Analysis (FIA) plot data from the US Forest Service. Previous evaluation showed the FVS-CR LP model is generally successful in reproducing known patterns of stand dynamics. However, the default model settings tend to result in unrealistic stand successional behavior and over-estimate the density, basal area, and especially carbon density of mature lodgepole pine forest stands as compared to expectations. Here we develop a generalized model calibration procedure based on simulating bare-ground re-growth of a single mixed lodgepole stand and comparing to a forest growth chronosequence constructed from FIA plot measurements in Colorado and Wyoming. We set parameters such as basal area increment and maximum tree size to match corresponding characteristics observed directly in the FIA plot data. The remaining ‘free’ parameters (notably small tree height increment and tree mortality parameters) were then tuned for best fit against the FIA chronosequence in terms of five different stand characteristics: live and dead basal area, trees per hectare, quadratic mean diameter, and average height. Improving model fit against all five stand characteristics simultaneously required substantial increases to mortality-related parameters, particularly for the shade-tolerant species present in mixed stands. These parameters had to be adjusted empirically rather than based on literature values, suggesting some underlying model structural challenges. However, the resulting recalibrated FVS-CR LP model achieves much better representation of expected lodgepole stand structure and successional behavior, and can more credibly be used to evaluate carbon storage outcomes for different forest management choices in the region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".