Data from: Integration of molecular, ecological, morphological and endosymbiont data for species delimitation within the Pnigalio soemius complex (Hymenoptera: Eulophidae)

Integrative taxonomy is a recently developed approach that uses multiple lines of evidence such as molecular, morphological, ecological and geographical data to test species limits, and it stands as one of the most promising approaches to species delimitation in taxonomically difficult groups. The Pnigalio soemius complex (Hymenoptera: Eulophidae) represents an interesting taxonomical and ecological study case, as it is characterized by a lack of informative morphological characters, deep mitochondrial divergence, and is susceptible to infection by parthenogenesis-inducing Rickettsia. We tested the effectiveness of an integrative taxonomy approach in delimiting species within the P. soemius complex. We analysed two molecular markers (COI and ITS2) using different methods, performed multivariate analysis on morphometric data and exploited ecological data such as host–plant system associations, geographical separation, and the prevalence, type and effects of endosymbiont infection. The challenge of resolving different levels of resolution in the data was met by setting up a formal procedure of data integration within and between conflicting independent lines of evidence. An iterative corroboration process of multiple sources of data eventually indicated the existence of several cryptic species that can be treated as stable taxonomic hypotheses. Furthermore, the integrative approach confirmed a trend towards host specificity within the presumed polyphagous P. soemius and suggested that Rickettsia could have played a major role in the reproductive isolation and genetic diversification of at least two species.COI_alignmentFasta file containing aligned COI sequencesITS2_alignmentFasta file containing ITS2 sequences aligned by using the MAFFT algorithm G-INS-iIndividual by individual infection statusPDF file containing Individual by individual infection status.individual.infection.status.pd