A multivariate approach to the determination of an indicator species pool for community-based bioassessment of marine water quality

Previous studies in Chinese coastal waters of the Yellow Sea have shown that periphytic ciliates are reliable indicators of marine water quality. However, traditional community-based bioassessments are time-consuming because they rely on the identification and enumeration of all species within the community. In order to improve bioassessment efficiency, step-best-matching analysis was used to identify which are the most reliable indicator species among periphytic marine ciliate communities. Based on indices of species richness, diversity and evenness, a subset of 48 species (out of a total of 141) was found to retain sufficient information for accurately predicting water quality, and was more strongly related to changes of environmental variables than the full species set. These results demonstrate that the step-best-matching analysis is a powerful approach for identifying an indicator species pool from a full species dataset of a community, and allows the development of time-efficient sampling protocols for community-based marine bioassessment.