Changes to the understory vegetation community of the Acadian Forest a decade after herbicide use
Bibliographic record
Abstract
Abstract Herbicides are commonly used in forestry to enhance conifer tree growth by reducing the abundance of competitive, undesired, early successional species. Reducing the abundance of understory species could also lead to changes in community composition that need to be documented to aid the understanding of any downstream ecosystem changes. We examined the effects of glyphosate-based herbicides on the abundance, diversity, and community composition of the understory vegetation community of forests located in the temperate-boreal transition zone. We sampled 37 blocks in two ecoregions of the Acadian Forest in eastern Canada that were harvested over the last 15 years. Species richness, Shannon’s diversity, or evenness did not differ among blocks with different herbicide history. However, community composition differed between the non-herbicide and herbicide blocks in both ecoregions. Overall, 26.5% of the plant community variation was explained by the factors herbicide use (10.6%), Biomass Growth Index/site quality (8.6%), time since harvest (3.6%), and ecoregion (1.7%). We found 16 indicator species that differentiated the non-herbicide (9 species) and herbicide blocks (7 species). Indicator species for non-herbicide blocks included two blueberry species, three shrubs (two flowering), and two ferns, whereas indicator species for herbicide blocks were largely perennial forbs. Together, our results indicate that herbicide use does not alter species richness but does reduce shrub abundance, a change that persists throughout the 10 years post-herbicide application captured in our study. The reduced shrub layer likely leads to other changes in the plant community. Herbicide use is associated with subtle changes to the understory plant community, and these changes are missed when only alpha diversity is used to examine the effects of herbicides use on community composition.
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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) | 0.000 | 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".