Distinguishing Microsite and Competition Processes in Tree Growth Dynamics: An A Priori Spatial Modeling Approach
Bibliographic record
Abstract
Spatially oriented studies have examined the role of competition on plant populations and communities but not the combined effects of microsite heterogeneity and competition. The aim of this study was threefold: first, to apply and test a common geostatistical tool (semivariograms) to disentangle competition and microsite effects; second, to assess the results of this methodology against a generalized early stand development model for tree populations; and third, to examine the role and timing of microsite and competition processes in early population stages. We mapped and measured annual relative growth rates of trees in three different-aged ponderosa pine stands in Patagonia, Chile. We tested the relative support of five a priori semivariogram-based hypotheses and showed that through stand development, many sites followed our expected sequence of semivariogram models. These translated to initial spatially random growth followed by microsite-dominated, mixed microsite and competition, and finally pure competition effects on growth. Our approach will have many and diverse applications wherever processes differ in the type of spatial pattern they exhibit as well as in spatial scale. We emphasize that this methodology works best when there is strong a priori support for the hypotheses being tested but the timing, strength, and occurrence of processes are not known.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".