Long-term influence of glyphosate herbicide on demography and diversity of small mammal communities in coastal coniferous forest
Bibliographic record
Abstract
This study tested the hypothesis that (1) abundance and related demographic parameters of small mammal populations, and (2) species diversity of small mammal communities, would be adversely affected in herbicide-treated habitats at 9 and 11 years after treatment in coastal coniferous forest. Study areas were located in south-coastal British Columbia, Canada, in the Coastal Western Hemlock (CWH-dm) biogeoclimatic zone where small mammal populations were intensively sampled on paired control and treatment sites. Average density of deer mice (Peromyscus maniculatus) during summer periods (1981-1990) was lower in the treatment than control in the immediate post-treatment (PT) period (1982), with comparable numbers in 1983, 1985, and 1990. At the 11-year PT area, deer mouse numbers were similar on control and treatment sites in 1978, 1979, 1980, and 1990. Average density of Oregon voles (Microtus oregoni) was higher on treatment than control sites at 9 and 11 years after treatment. Townsend chipmunks (Eutamias townsendii) tended to be less abundant on the treatment than control site 9 years PT but were essentially absent from the 11-year PT study area. There was no difference in average density of shrew (Sorex spp.) populations between control and treatment sites at either study area. It is likely that post-harvest successional change has more of an impact on small mammal abundance than change induced by a herbicide treatment. Our results suggest that glyphosate herbicide did not adversely affect reproduction, survival, or growth of deer mice and Oregon voles in coastal forest a decade after application. Species richness and diversity of small mammal communities changed little over the decade following treatment. This study is the first investigation on the effects of forest herbicide use on demography and diversity of small mammal communities that extends to a decade
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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".