Predicting basal area increment in a spatially explicit, individual tree model: a test of competition measures with black spruce
Bibliographic record
Abstract
A current trend in the development of forest stand models is to use spatially explicit, individual-tree information to simulate forest dynamics with increased accuracy. By adding spatial information, such as tree coordinates, crown shape, and size, it is hypothesized that the computation of the model's driving function is improved over traditional competition indices, especially when simulating multistoried stands. In this paper, we want to test whether computationally demanding competition indices outperform traditional indices in predicting mean basal area increment. The study was undertaken in old, uneven-aged black spruce (Picea mariana (Mill.) BSP) stands in northeastern Quebec, Canada. The predictability of individual tree growth rates was related to crown dimensions and other stand and tree variables measured in the field. Data were collected from 90 trees coming from stands of varying site quality (range 9.616.5 m height at 50 years, age taken at 1 m) and age (range 66257 years). Hegyis's distance-dependent competition index was found to be the most strongly correlated competition measure (r = 0.57) with mean basal area growth of the last 20 years. This value, 12% higher than the value obtained from the best distance-independent competition index (r = 0.45), clearly shows that precision gains can be achieved when estimating basal area increment with spatial indices in black spruce stands. Using indices computed from virtual hemispherical images did not prove superior to simpler distance-dependent indices based on their individual correlations with basal area increment. When included in a basal area increment model for the last 20 years of growth, however, the gains in precision were comparable to Hegyi's competition index. This indicates that indices derived from a hemispherical approach have some value in spatially explicit forest simulations models but that further tests using younger stands are needed to confirm this result in black spruce stands.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| 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".