LAUSR

Cryptic species – genetically distinct species that are morphologically difficult to distinguish – present challenges to systematists. Operationally, cryptic species are very difficult to identify and sole use of genetic data or morphological data can fail to recognize evolutionarily isolated lineages. We use morphometric data to test species boundaries hypothesized with genetic data in the North Caribbean bark anole (Anolis distichus), a suspected species complex. We use univariate and multivariate analyses to test if candidate species based on genetic data can be accurately diagnosed. We also test alternative species delimitation scenarios with a model fitting approach that evaluates normal mixture models capable of identifying morphological clusters. Our analyses reject the hypothesis that the candidate species are diagnosable. Neither uni- nor multivariate morphometric data distinguish candidate species. The best-supported model included two morphological clusters; however, these clusters were uneven and did not align with a plausible species divergence scenario. After removing two related traits driving this result, only one cluster was supported. Despite substantial differentiation revealed by genetic data, we recover no new evidence to delimit species and refrain from taxonomic revision. This study highlights the importance of considering other types of data along with molecular data when delimiting species.