Monitoring the Small and Slimy — Protected Areas Should Be Monitoring Native and Non-Native Slugs (Mollusca: Gastropoda)
Bibliographic record
Abstract
Although slugs (Mollusca: Gastropoda) are known to be important generalist herbivores, fungivores, and detrivores in a variety of ecosystems, little is known about their abundance and diversity in protected areas. Likewise, the presence of non-native slug species and their impacts on invaded ecosystems have also not been well documented. In this study, the abundance and diversity of native and non-native slugs was investigated in a sensitive protected area comprised of a recently burned black spruce (Picea mariana) - lichen (Cladonia) woodland in Terra Nova National Park, Newfoundland, Canada. To estimate the diversity and abundance of slugs, pitfall traps were established in areas of high-burn intensity, including sites within and at the edge of the burn, low-burn intensity, and a non-burned reference. Of the nine slug taxa known from Newfoundland, five were captured within burned sites; of those five taxa, only one, Deroceras laeve, is native. Almost 90% of captures were of non-native taxa; dominant slug taxa were the introduced Arion subfuscus aggregate (agg.) and A. hortensis agg. The majority of captures occurred at the edge of the burn, and least in the high-intensity open sites. Given that non-native species can dominate the slug fauna in naturally disturbed areas, it is recommended that monitoring for these non-native invasive species and their impact on native vegetation be implemented within protected areas. The invasive nature of non-native slugs and their pivotal role in influencing bio-diversity and plant regeneration suggests that these invertebrates are key elements within a monitoring framework of protected areas.
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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 | 0.000 |
| 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) | 0.001 | 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".