Assessing the factors influencing natural regeneration patterns in the diverse, multi‐cohort, and managed forests of Maine,<scp>USA</scp>
Bibliographic record
Abstract
Abstract Questions What are the primary biotic and abiotic factors driving composition and abundance of naturally regenerated tree seedlings across forest landscapes of Maine? Do seedling species richness (SR) and density (SD) decrease with improved growing conditions (climate and soil), but increase with increased diversity of overstorey composition and structure? Does partial harvesting disproportionately favour relative dominance of shade‐intolerant hardwoods (PIHD) over shade‐tolerant softwoods (PTSD)? Location Forest landscapes across the diverse eco‐regions and forest types of Maine,USA. Methods This study usedUSDAForest Service Forest Inventory Analysis permanent plots (n = 10 842), measured every 5 yr since 1999. The best models for each response variable (SR,SD,PIHDandPTSD) were developed based onAICand biological interpretability, while considering 35 potential explanatory variables incorporating climate, soil, site productivity, overstorey structure and composition, and past harvesting. Results Mean annual temperature was the most important abiotic factor, whereas overstorey tree size diversity was the most important biotic factor forSRandSD. Both mean annual temperature and overstorey tree size diversity had a curvilinear relationship withSRandSD. Average overstorey shade tolerance and percentage tolerant softwood basal area in the overstorey were the top predictor variables ofPIHDandPTSD,respectively. Partial harvesting favouredPIHDbut notPTSD. Conclusions This is one of the first studies to comprehensively evaluate a number of factors influencing naturally established tree seedlings at a broad landscape scale in the Northern Forest region of the easternUSAand Canada. Despite limitations associated with relatively small plot size, large seedling size class and lack of direct measurements of light, water and nutrients, this study documents the influence of these factors amid high variability associated with patterns of natural regeneration. The curvilinear relationship between mean annual temperature withSRandSDsupports the argument that species richness and abundance usually have unimodal relationships with productivity indicators, whereas the curvilinear relationship between overstorey tree size diversity andSRandSDsuggest that moderate overstorey diversity incorporates multiple species as well as higher seedling individuals.
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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 | 0.000 |
| 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) | 0.001 | 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".