Integrative species delimitation reveals cryptic diversity in the southern Appalachian Antrodiaetus unicolor (Araneae: Antrodiaetidae) species complex

• Published in: Molecular ecology Vol. 29; no. 12; pp. 2269 - 2287
• Main Authors: Newton, Lacie G., Starrett, James, Hendrixson, Brent E., Derkarabetian, Shahan, Bond, Jason E.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.06.2020
• Subjects: Araneae; Biodiversity; Boundaries; Clustering; cohesion species; Cryptic species; Divergence; Genetic analysis; Genomes; Homology; integrative taxonomy; Learning algorithms; Machine learning; Mitochondria; Morphology; Niches; Population structure; Principal components analysis; RADseq; speciation; species delimitation; Species diversity; Sympatric populations
• ISSN: 0962-1083; 1365-294X
• DOI: 10.1111/mec.15483

Although species delimitation can be highly contentious, the development of reliable methods to accurately ascertain species boundaries is an imperative step in cataloguing and describing Earth's quickly disappearing biodiversity. Spider species delimitation remains largely based on morphological characters; however, many mygalomorph spider populations are morphologically indistinguishable from each other yet have considerable molecular divergence. The focus of our study, the Antrodiaetus unicolor species complex containing two sympatric species, exhibits this pattern of relative morphological stasis with considerable genetic divergence across its distribution. A past study using two molecular markers, COI and 28S, revealed that A. unicolor is paraphyletic with respect to A. microunicolor. To better investigate species boundaries in the complex, we implement the cohesion species concept and use multiple lines of evidence for testing genetic exchangeability and ecological interchangeability. Our integrative approach includes extensively sampling homologous loci across the genome using a RADseq approach (3RAD), assessing population structure across their geographic range using multiple genetic clustering analyses that include structure, principal components analysis and a recently developed unsupervised machine learning approach (Variational Autoencoder). We evaluate ecological similarity by using large‐scale ecological data for niche‐based distribution modelling. Based on our analyses, we conclude that this complex has at least one additional species as well as confirm species delimitations based on previous less comprehensive approaches. Our study demonstrates the efficacy of genomic‐scale data for recognizing cryptic species, suggesting that species delimitation with one data type, whether one mitochondrial gene or morphology, may underestimate true species diversity in morphologically homogenous taxa with low vagility.