Stand-level spatial dependence in an old-growth Jeffrey pine – mixed conifer forest, Sierra San Pedro Mártir, Mexico
Bibliographic record
Abstract
Descriptions of spatial patterns are important components of forest ecosystems, providing insights into functions and processes, yet basic spatial relationships between forest structures and fuels remain largely unexplored. We used standardized omnidirectional semivariance modeling to examine the spatial pattern of fuels and forest structure measured in a systematic nested plot grid covering 144 ha. Forest structure variables were spatially dependent at scales ranging from 62 to 572 m. Cross-variograms of fuels and forest structure showed both positive and negative correlations, ranging from 0.04 to 0.67. Notably, fine fuels were correlated positively and negatively with forest structure variables of white fir ( Abies concolor (Gord. & Glend.) Lindl. ex Hildebr.) and Jeffrey pine ( Pinus jeffreyi Balf.), respectively. Old-growth Jeffrey pine – mixed conifer forest within the study area exhibited both identifiable spatial correlations and high stand-level spatial heterogeneity, as demonstrated by the influence of outliers on the underlying spatial pattern. The spatial dependency of fuels with species-specific variables suggests that less common species may have a large influence in the characterization of forest attributes and that fuel classifications may be improved by accounting for the spatial distributions of overstory species. Spatial correlations have many applications to forest management, including the classification and mapping of forest structure, establishing guidelines for fuel treatments, and restoration of old-growth forest ecosystems.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".