Lichen biodiversity and conservation status in the Copeland Forest Resources Management Area: a lichen-rich second-growth forest in southern Ontario
Bibliographic record
Abstract
Southern Ontario is the most densely populated region in Canada. As a result, ubanization, industrialization, and agriculture are extensive. Few ecosystems in the region have been unaltered, and second-growth forests now dominate the remaining natural landscape. To better understand the lichen diversity in these second-growth forests, we inventoried 24 distinct vegetation communites in the Copeland Forest Resources Management Area (1780 ha) located between Barrie and Orillia in September and October 2011, recording 154 species in 79 genera. One species, Lecidea sarcogynoides, was collected for the first time in Canada and is reported for the first time in North America; one additional species, Micarea micrococca, was collected for the first time in Ontario and is reported for the first time in Canada; three species that have previously been collected in Ontario — Bellemerea cinereorufescens, Phlyctis speirea, and Xanthoparmelia angustiphylla — are reported for the first time in the province; and Candelariella lutella was collected and is reported for the second time in Ontario and the third time in Canada. In addition, six species with a provincial status rank of S1 (critically imperilled) or S2 (imperilled) were located: Arthonia byssacea, Arthonia ruana, Chaenothecopsis pusiola, Cresponea chloroconia, Pachyphiale fagicola, and Placynthiella uliginosa. Our results show that second-growth forests can be important refugia for lichen diversity. The majority of the lichen diversity within the Copeland Forest was contained in a small number of sites (6 of 24). This suggests that management strategies should integrate lichen diversity by targeting species-rich areas. We found that sites with a high variation in: canopy closure, tree species, tree age, moisture, and the presence of snags had the highest lichen diversity. Forest managers in southern Ontario can use our results to identify species-rich areas on their properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".