UNDERSTORY VEGETATION AND ENVIRONMENT RESPONSES TO TILLAGE, FOREST HARVESTING, AND CONIFER PLANTATION DEVELOPMENT
Bibliographic record
Abstract
Increasing demand to conserve biodiversity in managed forests necessitates better understanding of the impacts of forestry practices on the species‐rich herbaceous layer. We compared composition and diversity of understory vegetation, environmental features, and stand structure of forests in New Brunswick, Canada, under four different management scenarios, including: young (24–66 yr) naturally regenerated forests and conifer plantations (19–64 yr) established after clear‐cutting, mature (77–100 yr) naturally regenerated forests originating from natural disturbance (insect defoliation), and old‐field plantations (31–77 yr) established on abandoned agricultural fields. The objectives were to determine how plant composition, forest structure, and microenvironment differed among stand types and which environmental or structural features were related to understory species. Detrended correspondence analysis (DCA) and multiresponse permutation procedure (MRPP) indicated that stand types differed in species composition and environments. Plantations were significantly lower in density of snags, deciduous canopy cover, and leaf substrate, and higher in coniferous canopy cover and needle, twig, and moss substrates than the natural stands. Old‐field plantations had less cover of pits and mounds than all other stand types. Mature natural stands contained the greatest amount of coarse woody debris (CWD) in all decay classes and snags >14 cm diameter, and the lowest density of trees >5 cm diameter and moss cover. Canonical correspondence analysis (CCA) showed that species in both natural stand types are associated with decayed CWD, deciduous cover, and leaf substrate. Species in cutover plantations were associated with coniferous cover and needle substrate. Species composition in old‐field plantations was distinct, with the lowest species richness and diversity of all stand types. We present a conceptual model illustrating the initial direct effects of previous land use and harvesting or postharvest treatments and the subsequent indirect effects associated with plantation or natural stand development on environmental features and understory vegetation. We recommend extending plantation harvest cycles to facilitate reestablishment and expansion of plant populations and maintaining diverse tree canopy composition, coarse woody debris, and pit and mound topography to sustain critical habitat for vascular understory plant species in managed forests.
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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 | 0.001 | 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".