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Eumerus is one of the most diverse genera of hoverfly worldwide. Species delimitation within genus is considered to be difficult due to: (a) lack of an efficient key; (b) non-defined taxonomical status of a large number of species; and (c) blurred nomenclature. Here, we present the first molecular study to delimit species of the genus by using a fragment of the mitochondrial cytochrome-c oxidase subunit I gene (COI) gene. We assessed 75 specimens assigned to 28 taxa originating from two biogeographic zones: 22 from the western Palaearctic and six from the Afrotropical region. Two datasets were generated based on different sequence lengths to explore the significance of availability of more polymorphic sites for species delimitation; dataset A with a total length of 647 bp and dataset B with 746 bp. Various tree inference approaches and Poisson tree processes models were applied to evaluate the putative 'taxonomical' vs. 'molecular' taxa clusters. All analyses resulted in high taxonomic resolution and clear species delimitation for both the dataset lengths. Furthermore, we revealed a high number of mitochondrial haplotypes and high intraspecific variability. We report two major monophyletic clades, and seven 'molecular' groups of taxa formed, which are congruent with morphology-based taxonomy. Our results support the use of the mitochondrial COI gene in species diagnosis of Eumerus.

Understanding tumor progression and metastatic potential are important in cancer biology. Metastasis is the migration and colonization of clones in secondary tissues. Here, we posit that clone migration events between tumors resemble the dispersal of individuals between distinct geographic regions. This similarity makes Bayesian biogeographic analysis suitable for inferring cancer cell migration paths. We evaluated the accuracy of a Bayesian biogeography method (BBM) in inferring metastatic patterns and compared it with the accuracy of a parsimony-based approach (metastatic and clonal history integrative analysis, MACHINA) that has been specifically developed to infer clone migration patterns among tumors. We used computer-simulated datasets in which simple to complex migration patterns were modeled. BBM and MACHINA were effective in reliably reconstructing simple migration patterns from primary tumors to metastases. However, both of them exhibited a limited ability to accurately infer complex migration paths that involve the migration of clones from one metastatic tumor to another and from metastasis to the primary tumor. Therefore, advanced computational methods are still needed for the biologically realistic tracing of migration paths and to assess the relative preponderance of different types of seeding and reseeding events during cancer progression in patients.

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This study provides an overview of the Eumerus minotaurus taxon group, diagnosing a new species, E. anatolicus Grković, Vujić and Radenković sp. n. (Muğla, Turkey), and unraveling three cryptic species within E. minotaurus: E. karyates Chroni, Grković and Vujić sp. n. (Peloponnese, Greece), E. minotaurus Claussen and Lucas, 1988 (Crete and Karpathos, Greece) and E. phaeacus Chroni, Grković and Vujić sp. n. (Corfu and Mt Olympus, Greece; Mt Rumija, Montenegro). We applied an integrative taxonomic approach based on molecular, morphological and wing geometric morphometric data to corroborate and delimit cryptic species within the complex. In addition, we discuss the latent biogeographic patterns and speciation processes leading to configuration of the E. minotaurus group based on palaeogeographic evolution of the Aegean. Mitochondrial phylogeographic analysis suggested that speciation within the E. minotaurus group is attributable to formation of the mid-Aegean Trench and Messinian Salinity Crisis, and was integrated at the Pleistocene. We show that more accurate estimates of divergence times may be based on geological events rather than the standard arthropod mtDNA substitution rate.

Summary Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes. In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. Availability and implementation PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.

Dispersal routes of metastatic cells are not medically detected or even visible. A molecular evolutionary analysis of tumor variation provides a way to retrospectively infer metastatic migration histories and answer questions such as whether the majority of metastases are seeded from clones within primary tumors or seeded from clones within pre-existing metastases, as well as whether the evolution of metastases is generally consistent with any proposed models. We seek answers to these fundamental questions through a systematic patient-centric retrospective analysis that maps the dynamic evolutionary history of tumor cell migrations in many cancers. We analyzed tumor genetic heterogeneity in 51 cancer patients and found that most metastatic migration histories were best described by a hybrid of models of metastatic tumor evolution. Synthesizing across metastatic migration histories, we found new tumor seedings arising from clones of pre-existing metastases as often as they arose from clones from primary tumors. There were also many clone exchanges between the source and recipient tumors. Therefore, a molecular phylogenetic analysis of tumor variation provides a retrospective glimpse into general patterns of metastatic migration histories in cancer patients.