Assessing penaeid shrimp diversity in the northwest of Peninsular Malaysia: an integrated framework in taxonomy and phylogeny

The taxonomic diversity of penaeid shrimps from the northwest coast of Peninsular Malaysia was investigated based on morphometric and molecular approaches. For geometric morphometrics (GM) approach, eighteen homologous landmarks were analyzed with principal component analysis (PCA) and canonical variate analysis (CVA) in Morpho J software. The morphological variations among species were attributed to body shape, rostrum, carapace head, and telson. The first four components accounted for 87.27% in the PCA, while for CVA, the first three components contributed to 78.47%. Although not absolute, there is a tendency for closely related species to cluster together. The CVA analyses clearly differentiated Metapenaeopsis stridulans , Megokris sedili , Metapenaeus brevicornis and Mierspenaeopsis sculptilis into discrete clusters, and highlighted the closeness of the groups; Mierspenaeopsis sculptilis - M. hardwickii , Penaeus merguiensis - P. semisulcatus, Metapenaeus affinis - M. dobsoni - M. ensis , and Penaeus monodon - P. pulchricaudatus. Molecular phylogeny among these species of penaeid shrimp were examined using mitochondrial cytochrome c oxidase subunit I (COI) gene sequences. Geometric morphometric analyses revealed shape overlap among the 12 shrimp species, yet significant differences were also detected. The morphometric and molecular multispecies analyses were largely in agreement. The phylogenetic signal was assessed by mapping the morphometric data onto three phylogenetic trees (Neighbour Joining-NJ, Maximum Likelihood-ML, and Bayesian Inference-BI) generated from the partial mitochondrial COI on the same 12 species. Results revealed non-significance (no phylogenetic signal) for NJ but significant phylogenetic signals (evolutionary significance) for ML and BI, suggesting that the shape difference among the penaeid shrimp species investigated was related to their evolutionary history. The NJ tree is prone to errors when dealing with deeper divergence times, whereas ML and BI trees are ideal for phylogeny tree reconstruction, which applies a model of sequence evolution on the data.