Development of Regional Individual Tree Static Equations for Managed Mixed Species Stands of the Acadian Region of North America
Bibliographic record
Abstract
The Forest Vegetation Simulator northeast variant (FVS-NE) is a commonly used growth and yield model for sustainable forest management in the Acadian Region of North America. This region encompasses three Atlantic Provinces of Canada (New Brunswick, Nova Scotia, and Prince Edward Island), the southern part of Quebec, and Maine in the USA. This geographical area consists of diverse vegetation types, naturally regenerated stands, and has a long history of forest management. Earlier studies have shown that FVS-NE produces biased predictions for permanent research plot data (e.g. Saunders et al., 2007). Consequently, the Cooperative Forest Research unit (CFRU) of the University of Maine has identified the need to reengineer the regional growth and yield model. In addition, there are extensive data available that has been collected by different sources such as US Forest Service Forest Inventory and Analysis (FIA), CFRU, and other USFS research installations. Besides, statistical techniques and computational abilities have vastly improved since the original FVS models were developed. Regional models to predict total height (H-D; Chapter 3) and height to crown base (HCB; Chapter 4) were constructed using an extensive database. Several candidate models were evaluated including the ones currently used by FVS. General nonlinear least squares (GNLS) and hierarchical nonlinear mixed effects (NLME) techniques were used for model fits and predictions. Different model selection criteria (MSC) were used to select the best among the candidate models. Coefficients of Determination (R ), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used as MSC for model fits, while mean absolute bias (MAB), mean bias (MB), root mean square error (RMSE), and percent error were used as MSC prediction statistics. Bootstrap technique was utilized to construct non-parametric confidence intervals (CI) of the MSC prediction. Models were evaluated at 5% significance level based on 95% CI of these criteria. Several individual- and stand-level allometric, competition, and site related covariates were evaluated. For the H-D models, the Chapman-Richards (C-R) model form was found to be superior to the FVS-NE model form for all MSC. For example, RMSE and MB were reduced by 67% and 99%, respectively, when FVS-NE was compared to the C-R models. Likewise, findings for the HCB model indicated that FVS-NE model was significantly biased for all species as the overall MB and RMSE were 0.1 lm (significant at 5%) andl.80m, respectively. A logistic equation with size (tree diameter at breast height (DBH), total height (HT), ratio of DBH to HT (DHR)) and competition (crown competition factor (CCF) and basal area larger than subject tree (BAL)) gave the best predictions for all of the species in this analysis. This model yielded an overall mean bias and RMSE of <0.01m (insignificant at 5%) and 1.59m, respectively, which represents a significant improvement in predictions compared to FVS-NE. In conclusion, the C-R and Richards models were the best among the tested models for H-D and HCB modeling, respectively. Among the various allometric, competition, and site related model covariates evaluated, DBH, CCF, BAL, and climatic site index (CSI) were the most effective in explaining variation in observed HT. Likewise, DBH, DHR, CCF and BAL were the best covariates for predicting HCB. Overall, this study has important implications for imputing missing HTs and HCBs, which is necessary for developing an effective growth and yield modeling system for the Acadian Region.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".