Effects of Bacillus thuringiensis subsp. kurstaki application on non-target nocturnal macromoth biodiversity in the eastern boreal forest, Canada
Bibliographic record
Abstract
Lepidoptera, including butterflies and moths, play vital roles as herbivores, pollinators, and food sources, but also include species considered forest pests. The impact of Bacillus thuringiensis subs. kurstaki (Btk), a widely used bio-insecticide for controlling forest pests like the spruce budworm, on non-target lepidoptera in Canada remains uncertain. To address this, I established a replicated field study to evaluate the effects of Btk on non-target nocturnal macromoth communities in the eastern boreal forest of western Newfoundland, Canada. Over two years, I sampled moths across four groups: north treatment, north control, south treatment, and south control. My analysis focused on species diversity, abundance, and composition. Results showed no significant differences in total abundance or species composition between treatment and control groups. In 2022, control sites had significantly higher Hill numbers for Shannon and Simpson diversity compared to treatment sites. In 2021, differences in Hill numbers were only observed between north controls and treatments. These findings indicate that after multiple years of treatment, there can be shifts in the relative abundance of certain species, but without significant changes in species richness, total abundance, or composition between control and treatment groups. These results suggest that Btk can lead to stand-level shifts in relative abundance but does not substantially alter community structure during the early stages of treatment. The responses of species are idiosyncratic, likely influenced by differences in phenology and voltinism. Monitoring the impacts of Btk on non-target lepidoptera is crucial to effectively manage forest pests while minimizing unintended consequences for non-target species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".