1. Species limits in butterflies (Lepidoptera: Nymphalidae): reconciling classical taxonomy with the multispecies coalescent
- Author
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Pável Matos-Maraví, Carla M. Penz, Niklas Wahlberg, and Alexandre Antonelli
- Subjects
0106 biological sciences ,0301 basic medicine ,0303 health sciences ,Species complex ,biology ,Phylogenetic tree ,Bayesian probability ,Haeterini ,biology.organism_classification ,010603 evolutionary biology ,01 natural sciences ,Nymphalidae ,Coalescent theory ,Satyrinae ,03 medical and health sciences ,Geography ,030104 developmental biology ,Evolutionary biology ,Insect Science ,Taxonomy (biology) ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology - Abstract
Species delimitation is at the core of biological sciences. During the last decade, molecular-based approaches have advanced the field by providing additional sources of evidence to classical, morphology-based taxonomy. However, taxonomy has not yet fully embraced molecular species delimitation beyond threshold-based, single-gene approaches, and taxonomic knowledge is not commonly integrated to multi-locus species delimitation models. Here we aim to bridge empirical data (taxonomic and genetic) with recently developed coalescent-based species delimitation approaches. We use the multispecies coalescent model as implemented in two Bayesian methods (DISSECT/STACEY and BP&P) to infer species hypotheses. In both cases, we account for phylogenetic uncertainty (by not using any guide tree) and taxonomic uncertainty (by measuring the impact of using or not a priori taxonomic assignment to specimens). We focus on an entire Neotropical tribe of butterflies, the Haeterini (Nymphalidae: Satyrinae). We contrast divergent taxonomic opinion—splitting, lumping and misclassifying species—in the light of different phenotypic classifications proposed to date. Our results provide a solid background for the recognition of 22 species. The synergistic approach presented here overcomes limitations in both traditional taxonomy (e.g. by recognizing cryptic species) and molecular-based methods (e.g. by recognizing structured populations, and not raise them to species). Our framework provides a step forward towards standardization and increasing reproducibility of species delimitations.
- Published
- 2019