A new experimental approach to investigate grazing on epiphytic lichens by gastropods in a controlled laboratory environment
Bibliographic record
Abstract
Abstract The grazing impact on epiphytic lichens by a non-native gastropod species is documented and quantified for the first time in the province of Newfoundland and Labrador using a unique combination of a multiple-choice feeding design with lichen transplant techniques under controlled laboratory conditions. The feeding experiment included three arboreal lichen species sewn onto a mesh and attached to red maple sticks inside a terrarium where four Cepaea nemoralis snails were introduced and allowed to graze over a five-day period. The three lichen species used in the feeding trials included the green-algal Platismatia glauca , the cephalolichen Lobaria pulmonaria , and the cyanolichen Lobarina scrobiculata. The trials also included two sets of L. pulmonaria from different regions of the island of Newfoundland. The levels of grazing and preference by the snails was very high. Herbivory was high for all lichen species but was highest for Platismatia glauca , for which 70% of thalli were consumed after only 24 hours. Our results show that C. nemoralis is probably affecting the distribution and abundance of epiphytic lichens in the forests of Newfoundland and Labrador. Furthermore, our observations of intense herbivory on reproductive structures challenges the universal application of the optimal defence theory in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".