DNA barcoding aids in generating a preliminary checklist of the lichens and allied fungi of Calvert Island, British Columbia: Results from the 2018 Hakai Terrestrial BioBlitz
Bibliographic record
Abstract
Bioblitzes are a tool for the rapid appraisal of biodiversity and are particularly useful in remote and understudied regions and for understudied taxa. Lichens are an example of an often overlooked group, despite being widespread in virtually all terrestrial ecosystems and having many important ecological functions. We report the lichens and allied fungi collected during the 2018 terrestrial bioblitz conducted on Calvert Island on the Central Coast of British Columbia, Canada. We identified 449 specimens belonging to 189 species in 85 genera, increasing the total number of species known from Calvert Island to 194, and generated Internal Transcribed Spacer (ITS) sequences for 215 specimens from 121 species. Bryoria furcellata , Chaenothecopsis lecanactidis and C. nigripunctata were collected for the first time in British Columbia. We also found Pseudocyphellaria rainierensis , which is listed as Special Concern on the federal Species at Risk Act, and other rarely reported species in British Columbia including Opegrapha sphaerophoricola , Protomicarea limosa , Raesaenenia huuskonenii and Sarea difformis . We demonstrate that DNA barcoding improves the scope and accuracy of expert-led bioblitzes by facilitating the detection of cryptic species and allowing for consistent identification of chemically and morphologically overlapping taxa. Despite the spatial and temporal limitations of our study, the results highlight the value of intact forest ecosystems on the Central Coast of British Columbia for lichen biodiversity, education and conservation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".