On the utility of taxonomy to reflect biodiversity: the example of Lasiurini (Chiroptera: Vespertilionidae)
Bibliographic record
Abstract
The taxonomic history of bats of the tribe Lasiurini (Chiroptera: Vespertilionidae) has undergone significant changes over time. Authors at different times have recognized various numbers of genera and subgenera within the tribe. The most recent proposed change to generic level taxonomy (that there should be three genera recognized instead of a single genus) has been debated in the literature. We reviewed papers that commented on the recent changes to lasiurine generic taxonomy, as well as those that have adopted the new taxonomy and the ones that have not. We also reviewed the relevant taxonomic literature from 1942 to the present that shows the fluid taxonomic history of these bats. The literature review shows that the recently proposed taxonomic change recognizing the three groups of lasiurine bats as distinct genera is the only taxonomy that differentiates the tribe from the genera. Examination of times to most recent common ancestor (TMRCA) of 24 vespertilionid genera shows Lasiurus, if it comprises all Lasiurini, to be an outlier. Here, we support the recognition of three genera and explain how this arrangement best reflects the evolutionary history and biodiversity of the tribe by bringing the three distinct lineages in line with other vespertilionid genera with respect to divergence times and genetic distances. Considering the Lasiurini to comprise a single genus, Lasiurus, that genus has the greatest TMRCA of all vespertilionid genera analyzed, comparable only to the genus Kerivoula of the monotypic subfamily Kerivoulinae. However, recognizing the three deeply diverged lasiurine lineages (red bats, yellow bats, and hoary bats) as genera brings their TMRCAs in line with other genera and approximates the mean TMRCA of the 24 genera analyzed. Opponents of Baird et al.’s taxonomy argued that these three lineages should be considered as subgenera to avoid changing scientific names for purpose of nomenclatural stability and ease of conducting a literature search and because the three deep lineages are all monophyletic. That argument ignores the biological reality that these lineages are morphologically distinct, and that they are genetically as distinct from one another as other genera of vespertilionid bats; there is ample precedent in the mammalian literature to use values of TMRCA as a metric to maintain consistency of higher taxonomic categories such as genus. We encourage other mammalogists to utilize taxonomy to its maximum descriptive potential, while taking into account phylogenetic relationships of the taxa of interest.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".