The 2 Sigma Genus Concept in mammalogy: Lessons from Lasiurus
Bibliographic record
Abstract
Species concepts are well established and apply across diverse groups of organisms; however, there is no consensus on what defines higher taxonomic groups. The genus rank is important to taxonomists because it comprises part of the scientific name of an organism. A consistent and biologically meaningful method for determining generic status is needed for taxonomic stability and utility. Lasiurine bats are a group for which there is disagreement on how many genera to recognize. Some authors argue for splitting this group into three genera based on morphology, genetic divergence, and time of divergence; others argue that a single genus should be maintained. Here, we use lasiurines to explore generic-level taxonomy and how it is applied. Genetic divergence levels are compared among sister genera and within genera of vespertilionine bats using Cytochrome b (Cytb) sequences. We used Cytb because it is the most sequenced mitochondrial gene in mammals, but other genes might be more appropriate for a different taxon. Future methods will eventually use complete mitogenomes and genomes. We conclude that lasiurine bats are most appropriately divided into three genera to maintain taxonomic consistency within their subfamily. Since Linnaeus, the quarter millennium of progress in the science of mammalogy has provided a binomial nomenclatural basis from which can be extracted an acceptable range of genetic diversity upon which to establish generic level taxonomy. We offer a biologically meaningful operational definition of the genus, which we call the 2 Sigma Genus Concept, based on genetic divergence between a genus and its sister genus or lineage and compared to the divergence between sister pairs of established genera in the same higher taxonomic category. Our method is phylogenetic; sister genera are based on the best phylogeny for the higher taxonomic category. Genera must be monophyletic and differ from their closest relatives by not more than two standard deviations above or below the mean value of genetic distance for the larger taxonomic group in which they are contained. There should be genetic-based characters (e.g., morphology, protein structure, behavior) that are diagnostic for each genus. Our method is novel in that it uses the statistical distribution of sister divergences within a higher category to guide the allocation of generic status to monophyletic lineages. Within Vespertilioninae, the mean genetic distance between sister genera is 20.68% ± 3.89% (K2P) for the Cytb gene. Therefore, a proposed new genus should have >12.90% genetic distance to its sister genus. Genera that are > 2 standard deviations above the mean (>28.46%) are candidates to be recognized as a higher category such as tribe or subfamily. The sister-genus divergence for the monophyletic lineage that includes all lasiurine bats is 31.14% ± 1.64% which qualifies it as a higher category (i.e., tribe), and the sister-genus divergences of the three monophyletic lineages within Lasiurini (i.e., red, yellow and hoary bats) are 21.77% and 22.91% which qualifies them for genera. We show the applicability of the method beyond Vespertilioninae by providing a case study where we apply it in a distantly related subfamily of bats.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".