A comparison of clustering methods for river benthic community analysis

Four commonly used clustering methods (UPGMA, Ward Linkage,Complete Linkage and TWINSPAN) were compared in their abilitytorecognise the structure of three river macroinvertebratesdatasetswhich were pre-determined based on habitat and biologicalcharacteristics or chemical water quality of sampling sites.DCA,NMDS and ANOSIM were applied to the same datasets to providefurther information about data structure, and nonparametrictestswere also undertaken on major chemical variables to justifythepredeterminations. The modified Rand Index was used to measuretheagreement between a particular solution and the pre-determinedclassification. The results showed that Ward Linkage performedbestwhen its use was broadened and used with the CY DissimilarityMeasure, followed by TWINSPAN and Complete Linkage with UPGMAbeingleast successful. There was evidence to suggest that theeffectiveness of some clustering methods (e.g. UPGMA) may varyatdifferent clustering levels, and simulation techniques whichhavebeen used to assess clustering methods could leave somepropertiesof clustering methods unexamined.