Native tree seedling growth and physiology responds to variable soil conditions of urban natural areas
Bibliographic record
Abstract
Soils in urban natural areas can be highly variable due to legacies of land use change that include excavation of existing soils and dumping of construction debris or other anthropogenic materials. As cities undertake large‐scale tree planting efforts to sustain and increase forest cover, understanding how urban soil quality influences native tree seedling survival and performance is important. In a greenhouse setting we examined growth and physiology of native silver maple ( Acer saccharinum ), black birch ( Betula lenta ), red oak ( Quercus rubra ), and Canadian serviceberry ( Amalanchier canadensis ) seedlings planted in soils collected from locations across New York, NY, U.S.A. The soils were collected from areas currently undergoing forest restoration, representing a range of soil nutrient quality and anthropogenic disturbance. We measured seedling survival, height growth, leaf chlorosis, and chlorophyll fluorescence for two growing seasons, after which seedlings were harvested to assess biomass allocation and foliar chemistry. Selected variables were standardized and combined to create a seedling stress index. Overall, seedlings performed best in the least disturbed urban soils and had the poorest performance in the more highly disturbed, nutrient‐poor urban soil types and a greenhouse mix. Species × soil type interactions on physiological responses indicate that tree species may not respond to urban soil conditions consistently. Consequently, matching native tree species to soil type could help optimize establishment and growth of urban forest restoration projects. Seedling stress scores from the first growing season were correlated with second year height growth for three of four species, illustrating their utility for managers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".