Why are ferns regularly over‐represented on state and provincial rare plant lists?
Bibliographic record
Abstract
ABSTRACT Several recent studies have suggested that rare species are not randomly distributed throughout plant taxa. This would appear to apply to North American ferns, which are frequently over‐represented on local lists of rare plant species. However, such lists often paint a skewed portrait of the true situation because of our tendency to recognize the rarity of well‐known and charismatic species while ignoring that of lesser‐known or less‐appreciated species. In order to verify if this over‐representation of ferns is a real and consistent trend throughout local floras in North America, we used data from what we consider to be the most complete and objective available database: NatureServe Explorer ( http://www.natureserve.org/explorer/ ). We compiled data on total vascular plant species, total fern species, as well as rare vascular plant species and rare fern species for each North American subnational (Canadian province or US state) flora. Rare species were defined as those belonging to one of NatureServe's ‘at risk’ categories. The null hypothesis that the contribution of rare ferns to total rare species did not differ from their contribution to the total vascular flora was assessed using χ2. Out of 64 subnational floras, we obtained significantly higher values than expected in 28 cases, and significantly lower in only one case. Similar trends hold true for individual fern families. These tendencies could be related to several factors of anthropogenic, biological, climatological, evolutionary, and geographical origin. However, we believe that the main reason is related to scale, namely the geopolitical units at which rarity is often studied. Our results illustrate one of the problems of a parochial approach to conservation, where the perceived rarity of an entire taxon is exaggerated because of the scale at which rarity is addressed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".