Thinning Combined with Prescribed Burn Created Spatially Heterogeneous Overstory Structures in Contemporary Dry Forests: A Comparison Using LiDAR (2016) and Field Inventory (1934) Data
Bibliographic record
Abstract
Restoring current ponderosa pine (Pinus ponderosa Dougl. Ex P. and C. Laws)-dominated forests (also known as “dry forests”) to spatially resilient stand structures requires an adequate understanding of the overstory spatial variation of forests least impacted by Euro-American settlers (also known as “reference conditions”) and how much contemporary forests (2016) deviate from reference conditions. Because of increased tree density, dry forests are more spatially homogeneous in contemporary conditions compared to reference conditions, forests minimally impacted by Euro-American settlers. Little information is available that can be used by managers to accurately depict the spatial variation of reference conditions and the differences between reference and contemporary conditions. Especially, forest managers need this information as they are continuously designing management treatments to promote contemporary dry forest resiliency against fire, disease, and insects. To fill this knowledge gap, our study utilized field inventory data from reference conditions (1934) along with light detection and ranging and ground-truthing data from contemporary conditions (2016) associated with various research units of Blacks Mountain Experimental Forest, California, USA. Our results showed that in reference conditions, above-ground biomass—a component of overstory stand structure—was more spatially heterogeneous compared to contemporary forests. Based on semivariogram analyses, the 1934 conditions exhibited spatial variation at a spatial scale < 50 m and showed spatial autocorrelation at shorter ranges (150–200 m) compared to those observed in contemporary conditions (>250 m). In contemporary conditions, prescribed burn with high structural diversity treatment enhanced spatial heterogeneity as indicated by a greater number of peaks in the correlograms compared to the low structural diversity treatment. High structural diversity treatment units exhibited small patches of above-ground biomass at shorter ranges (~120 to 440 m) compared to the low structural diversity treatment units (~165 to 599 m). Understanding how spatial variation in contemporary conditions deviates from reference conditions and identifying specific management treatments that can be used to restore spatial variation observed in reference conditions will help managers to promote spatial variation in stand structure that has been resilient to wildfire, insects, and disease.
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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 | 0.001 | 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 | 0.001 | 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".