The impact of overstory density on sapling height growth in the Missouri Ozarks: implications for interspecific differentiation during canopy recruitment
Bibliographic record
Abstract
Successful canopy recruitment is one of the most important components of sustainable forestry practices. For many desirable species in oak-dominated forests, insufficient sapling growth is a common limitation to successful recruitment. The objectives of this study were to (i) examine the impact of overstory density on sapling growth in the Missouri Ozarks, (ii) investigate the potential for overstory retention to promote compositional shifts via interspecific differences in sapling height growth, and (iii) compare the use of mean and near-maximum growth rates to quantify the impact of overstory density on sapling growth and height differentiation among species. We found that the periodic annual height increment of saplings decreased with increasing overstory density for all species groups in this study (red oaks (Quercus spp.), white oaks (Quercus spp.), hickories (Carya spp.), sassafras (Sassafras spp.), blackgum (Nyssa spp.), dogwood (Cornus spp.), red maple (Acer spp.), ashes (Fraxinus spp.), and elms (Ulmus spp.)). There was evidence of interspecific differentiation in growth rates during the sapling stage, and the observed differences were more pronounced at low overstory densities. Increasing overstory densities either reduced or eliminated the differences in growth among species. Although red oaks displayed the greatest maximum growth rates of all species under low overstory densities (<5 m2·ha−1), the growth advantage of red oaks was reduced with increasing overstory density. This may provide opportunities to shift species composition toward white oaks using partial harvesting regimes in the Missouri Ozarks. However, white oaks had little to no advantage in height growth over many competing species when overstory density exceeded about 10 m2·ha−1. This implies that the probability of recruitment under overstory densities greater than about 10 m2·ha−1is likely to decline for all oaks in the Missouri Ozarks. We found that using the 90th quantile of height growth rates to evaluate the impact of overstory density on sapling growth had two potential advantages over using the mean growth rate: (i) it provided better models of the limiting effects of overstory density on sapling height growth, and (ii) the focus was on the growth rates of stems that were most likely to recruit into the canopy.
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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 | 0.000 |
| 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) | 0.001 | 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".