This paper presents a support vector data description (SVDD)-based method for finding new benthic species from microscopic images and its application to taxonomy position estimation. First, the proposed method generates hyperspheres that represent taxonomic species taxa of known species and enables automatic species classification. Furthermore, weight estimation of visual features based on multiple kernel learning (MKL) is used in this approach to realize automatic weighting of categorical traits that are traditionally determined by taxonomists. Next, based on the traditional taxonomic classification scheme, the proposed method merges the hyperspheres of similar species and generates new hyperspheres that represent ultra-species taxa in higher hierarchies. Then, from the obtained results, a new decision tree, whose nodes are hyperspheres of species taxa and ultra-species taxa, is constructed. By using this decision tree, new benthic species can be found from target samples, and their taxonomic positions can also be estimated.