DISSERTATION RESEARCH: Integrative Taxonomy and Species Delimitation of Hakes (Merluccius, Merlucciidae)

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How the distinction between species is recognized is becoming increasingly objective and cross-disciplinary. Despite the computational challenges of large datasets, new methods allow genetic and non-genetic data to be analyzed together. These methods offer great potential for genome-wide sampling of variation at different evolutionary scales and an opportunity to understand taxonomic groups with complex evolutionary histories. The 16 currently recognized species of hake, a commercially valuable group of fishes, are often difficult to identify because they are very similar morphologically and there is a high level of within-species variation. This confusion has increased in recent years because there has been continuous splitting and lumping of species based on limited sampling, and limited data on morphological and genetic variability. Some hake species are of conservation concern, with at least one population of the North Pacific Hake being listed under the Endangered Species Act. The poor understanding of the taxonomy of these fishes has led to inaccuracies in fisheries data, making management and conservation difficult. This research seeks to shed new light on the taxonomy of hakes by applying the new approaches of using both genetic and anatomical information together. The possibility of genome-wide biases, such as rate heterogeneity, underscores the need for corroboration from independent data such as morphology. In contrast, morphological data contains a mixture of informative and misleading traits that are able to delimit some taxa but unable to retrieve others. This study will empirically test the applicability of new integrative methods that combine genome-wide single nucleotide polymorphisms (SNP) and morphological data in a Bayesian framework for delimiting hake species. A two-step approach to species delimitation will be used: a species 'discovery' step (without a priori species hypotheses) to assign individuals to putative groups, followed by a 'validation' step, which tests the robustness of the groups. This study will result in a better understanding of the evolution and taxonomy of a group of commercially valuable fishes, and these results can be directly applied to fisheries management.