Recent multilevel demographic and compositional shifts in North Carolina Piedmont forests
Bibliographic record
Abstract
Forest ecosystems in the eastern United States are undergoing significant compositional and demographic shift. To understand these changes, we used Forest Inventory & Analysis data (2003 - 2021) to examine landscape-scale trends in the North Carolina Piedmont, focusing on forest type groups, taxonomic family, and species. We assessed metrics such as annual net primary productivity, relative density, and biodiversity, aiming to determine: i) Which forest group types are changing most, ii) Whether these changes extend to lower taxonomic units, and iii) How stability has shifted over time. Our findings reveal an increasing dominance of Pinus species, particularly naturally regenerated loblolly pine (Pinus taeda), accompanied by sweetgum (Liquidambar styraciflua). This shift corresponds to rising prevalence of pine and oak-pine forest type groups. Notably, while red maple (Acer rubrum) consistently had high seedling densities, its recruitment lagged behind species like sweetgum and yellow-poplar (Liriodendron tulipifera), defying broader regional trends. These results highlight a clear progression from species-level changes to broader taxonomic families and forest types, emphasizing a shift toward pine in the region. The study underscores the importance of multi-level analyses for capturing ecological trends and advancing understanding of forest dynamics in changing 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".