Spatial variation in reference conditions: historical tree density and pattern on a Pinus ponderosa landscape
Bibliographic record
Abstract
The reference conditions of historical tree density and pattern underpin ecological restoration and management of Pinus ponderosa Douglas ex Lawson & C.Lawson forests in western North America, yet the potential spatial variation in these variables across the landscape remains unclear. We reconstructed historical (1880) tree density and spatial pattern on 1 ha plots at 53 sites within a 110 000 ha P. ponderosa landscape in northern Arizona, compared these variables among US Forest Service ecosystem classification units, and modeled spatial variation with environmental variables. Mean tree density differed 19-fold among nine ecosystem types, and regression trees using four soil or climatic variables explained 62%–74% of the variation in density. Although density was more sensitive to environmental variation than was pattern, we did not find the clumped pattern widely described for P. ponderosa forests to be universal across ecosystems. Results suggest that (i) multivariate combinations of soil and climatic properties influenced historical forest structure, (ii) as much variation exists in reference conditions within the study landscape as between P. ponderosa regions, (iii) ecosystem classification is a useful framework for quantifying spatial variation in reference conditions, and (iv) determining spatial variation in reference conditions can assist resource managers in prioritizing areas for management and in developing ecosystem-specific management strategies within landscapes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".