Tackling rarity and sample bias with large-scale biodiversity monitoring: a case study examining the status, distribution and ecology of the lichen <i>Cladonia rei</i> in Alberta, Canada
Bibliographic record
Abstract
Abstract Species conservation depends on accurate data, but for many lichens existing collections are geographically biased and contain many taxonomic errors. It is unclear whether ‘non-expert’, systematic monitoring schemes can address these sources of error, particularly for taxonomically challenging lichens (e.g. species requiring chemistry for accurate identification). In this case study we use the Alberta Biodiversity Monitoring Institute (ABMI), a large-scale, systematic, multi-taxon monitoring programme, to better understand the ecology and distribution of a putative rare species, Cladonia rei. Collections of C. rei from Alberta dating from 1947 suggested the species was broadly distributed but rare, with seven accessioned specimens. We used comparative morphology, thin-layer chromatography and habitat modelling to compare historical records against more recent material from ABMI surveys. Contrary to the historical collections, ABMI samples suggest C. rei is almost entirely limited to the dry mixed grassland, northern fescue grassland and aspen parkland natural regions, and that within these ecosystems it is relatively common. The typical ecotype exhibited included a persistent primary thallus, podetia with a persistent basal cortex, and secondary squamules; typically they lacked cups, well-developed apothecia and fumarprotocetraric acid, and ramifications were sparse. Cladonia rei was consistently found in pastures and undisturbed grasslands that hosted relatively rich communities of epigeic lichens, thus it does not appear to act as a pioneer in Alberta or to commonly occupy the anthropogenic niches documented elsewhere. In summary, large-scale, systematic, non-targeted monitoring employing novices redressed issues of sample bias through almost 300 C. rei collections, simultaneously improving the ecological understanding of a putative rare species.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".