LAREDO, C. (1), Nicolas, V. (2), Schaeffer, B. (3), Missoup, A. D. (4), Kennis, J. (5), Denys, C. (6)

(1) INRA, Paris, France
(2) MNHN, Paris, France
(3) INRA, Jouy-en-Josas, France
(4) Faculty of science, University of Douala, Douala, Cameroon
(5) University of Antwerp, Antwerp, Belgium
(6) MNHN, Paris, France

Poster in Barcoding Vertebrates other than Fish and Birds
Poster Location: B58

The Praomyini tribe is one of the most diverse and abundant groups of Old World rodents. Several species are known to be involved in crop damage and in epidemiology of several human or cattle diseases. Due to the existence of sibling species, their identification is often problematic. Thus, an easy fast and accurate species identification tool is needed for non-systematicians to correctly identify Praomyini species. The more widely mtDNA markers used to infer species boundaries in this tribe are the 16S and the Cytb genes. The CO1 gene proposed as the standard barcode has never been tested. So we evaluated its performance compared to those of 16S and Cytb genes.

According to several authors, barcoding results might be biased because intraspecific (resp. interspecific) variations are underestimated (resp. overestimated). To take this into account, 426 specimens representing 40 species (sampled across their geographical range) were sequenced for the three genes.The degree of intra-specific variability tends to be lower than the divergence between species, but no barcoding gap is detected. As in Austerlitz et al. (2009), we used supervised statistical classification methods such as k-Nearest-Neighbor or Random Forest to compare the performances of these three genes. The success rate of the statistical methods is excellent (up to 99% or 100%). The results obtained in species assignment show that the CO1 or the Cytb gene do better than the 16S gene. Moreover, we showed that very good results could also be obtained when shorter CO1 or Cytb DNA sequences were available. Finally, the Praomyini tribe is known to contain several cryptic species complexes. We observed that the supervised statistical methods were able to find them back. We completed this study by investigating species boundaries within complexes by means of unsupervised classification methods.

Keywords: Vertebrates, Disease Vectors/Pathogens/Parasites, Ecological Applications, Data Analysis, National and International Networks