The importance of taxonomy for determining species distribution: a case study using the disjunct lichen <i>Brodoa oroarctica</i>
Bibliographic record
Abstract
Species-focused conservation requires a thorough understanding of species’ distributions. Delineating a species’ distribution requires taxonomic knowledge and adequate occurrence data. For plants and fungi, herbaria represent a valuable source of large-scale occurrence data. Advances in digital technology mean that data from many herbarium collections worldwide are now easily accessible. However, species concepts can change over time requiring herbarium records to be re-examined and databases updated, which does not always occur synchronously across all collections. Therefore, non-critical use of these data can promote inaccuracies in understanding species distributions. Taxonomic revisions are common in understudied organisms, such as lichens. Here, we illustrate how changing taxonomy and non-critical acceptance of online data affects our understanding of disjunct distributions, using the lichen Brodoa oroarctica (Krog) Goward as an example. Defining the distribution of the arctic lichen B. oroarctica is confounded by changing taxonomy and uncertainty of herbarium records that pre-date taxonomic revisions. We review the distribution of this species in the literature and in aggregate occurrence databases, and verify herbarium specimens that represent disjunct occurrences in eastern North America to present an updated account of its distribution and frequency in eastern North America. We show that knowledge of changing species taxonomy is essential to depicting accurate species distributions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".