The 2 Sigma Genus Concept in mammalogy: Lessons from Lasiurus
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,016 |
| Communication savante | 0,003 | 0,010 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».