Abundance and arboreal tendencies of slugs in forested wetlands of southwestern Nova Scotia, Canada
Bibliographic record
Abstract
Non-native slugs, such as Arion, are becoming a concern for land managers in Nova Scotia, Canada, particularly in forested wetlands. They appear to have a highly diverse diet and may pose a particular risk to native slug species and to rare or at-risk lichens. We provide novel information on the distribution, abundance, arboreal tendencies, and seasonality of slugs in forested wetlands across southwestern Nova Scotia. We collected a total of 402 slugs representing seven species including two native species, Pale Mantleslug (Pallifera dorsalis) and Meadow Slug (Deroceras laeve), and five non-native Arion taxa. The three most abundantly caught taxa were Northern Dusky Arion (Arion fuscus), D. laeve, and Western Dusky Slug (Arion subfuscus). Arion fuscus and D. laeve were collected on the forest floor and on lichen-bearing trees, while A. subfuscus was collected only on the ground. All three taxa showed differences in collectability between July and September and low arboreal tendencies. We highlight that further studies are needed to better understand the biology and ecology of this largely neglected invertebrate group that seems to be dominated by non-native Arion species in the study region. Such information is crucial for conservationists and forest managers untangling the question of how non-native slugs affect native slug taxa and other groups including at-risk lichens.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".