Planting Native Herbaceous Species During Land Reclamation: 3-Year Growth Response to Soil Type and Competing Vegetation
Bibliographic record
Abstract
In forest land reclamation, revegetation efforts often focus on restoring tree composition, while the recovery of the understory vegetation community is typically left to natural regeneration. This regeneration relies mainly on wind-dispersed seeds, ingress from adjacent intact forests, or seed emergence from stockpiled surface soils. We examined the growth and survival of nursery-propagated, field-planted native herbaceous forbs on a reclaimed industrial site where topsoil placement depth was varied to manipulate soil nutrient availability and levels of competing vegetation. A pre-emergent herbicide was applied to half of the standard topsoil plots to assess the impact of ruderal vegetation competition. We addressed the following two questions: (1) How does placed topsoil depth affect the growth and survival of native forbs? We hypothesized that deeper topsoil (higher nutrient availability) would enhance growth but reduce survival due to increased competition. (2) Does competing ruderal vegetation negatively affect survival and/or growth? We hypothesized that competition would reduce growth in all species, but that Canada goldenrod (Solidago canadensis L.) would show greater resilience due to its pioneering nature. The results showed that S. canadensis exhibited consistently high growth and survival across all topsoil treatments, confirming its competitive advantage. Showy aster (Eurybia conspicua (Lindl.) G.L.Nesom) survival remained high during no-topsoil and shallow-topsoil treatments, with reductions under standard-depth topsoil linked to increased competition. Spreading dogbane (Apocynum androsaemifolium L.) survival varied but tended to be higher in no-topsoil and shallow-topsoil conditions. These findings suggest that certain native forbs can thrive across a range of soil conditions, and that Canada goldenrod, in particular, is a strong candidate for revegetation programs where competition from ruderal vegetation is a concern.
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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 | 0.001 |
| 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".