TAXONOMIC LEVEL NEEDED TO DETECT SIGNIFICANT CHANGES IN POLYCHAETE COMMUNITIES OVER DIFFERENT MACROPHYTE ASSEMBLAGES ON ROCKY INTERTIDAL SHORES - Hadiyanto, - (Marine Research in Indonesia, 2017)
Environmental degradation has more significant impacts on rocky intertidal communities after global changes increase progressively. Thus, ecological monitoring should be conducted properly to analyse potential drivers and their impacts. However, most of the ecological monitoring in rocky intertidal shores is more interested in macroalgae. Polychaetes associated with macrophyte assemblages should be also involved in the monitoring because they are important in determining coastal health and productivity. A successful ecological monitoring should consider three factors: taxonomic level, statistical power, and sample size. In this study, those factors were analysed in the relationships between polychaetes and macrophytes. Four taxonomic levels of polychaetes (i.e. order, family, genus, species) were tested based on 25 samples collected from rocky intertidal shores of Gunung Kidul, Yogyakarta, Indonesia. Relationships between each of taxonomic richness of polychaetes and each of macrophytes variables (i.e. species richness, biomass, species composition) were analysed using a Generalised Linear Models fitted by Poisson Distribution and log link. The statistical power of those relationships and the sample size needed to obtain a strong statistical power (>0.8) were also recorded. Relationships between each of taxonomic composition of polychaetes and each of macrophyte variables were analysed using a distance-based Redundancy Analysis based on Bray-Curtis dissimilarity on log(x+1) transformed abundance data with 999 permutations. Results showed that family-based data analysis was sufficient to detect significant relationships between polychaetes and macrophytes. However, the statistical power of most relationships was relatively weak (<0.8). Hence, the family-based data analysis should select a 44-sample size to gain significant relationships with a strong statistical power.Â