11/30/2022 0 Comments Uji inferensi pdf![]() ![]() ![]() In datasets with high sample heterogeneity, NMS analyses with Sørensen and Jaccard distance were the most sensitive for recovery of complex gradients. AMMI, T-RF- centered PCA, and DCA were the most robust methods in terms of producing ordinations that consistently reached a consensus with other methods. The comparison of statistical methods typically yielded similar empirical results. Larger variation due to T × E indicated larger differences in microbial communities between environments/ treatments and thus demonstrated the utility of ANOVA to provide an objective assessment of community dissimilarity. ![]() ![]() For the 10 datasets examined in this study, ANOVA revealed that the variation from Environment main effects was always small, variation from T-RFs main effects was large, and variation from T-RF × Environment (T × E) interactions was intermediate. Our objectives were i) to determine the distribution of variation in T-RFLP datasets using analysis of variance (ANOVA), ii) to determine the more robust and informative multivariate ordination methods for analyzing T-RFLP data, and iii) to compare the methods based on theoretical considerations. In this study, we empirically tested and theoretically compared ten diverse T-RFLP datasets derived from soil microbial communities using the more common ordination methods in the literature: principal component analysis (PCA), nonmetric multidimensional scaling (NMS) with Sørensen, Jaccard and Euclidean distance measures, correspondence analysis (CA), detrended correspondence analysis (DCA) and a technique new to T-RFLP data analysis, the Additive Main Effects and Multiplicative Interaction (AMMI) model. The analysis of T-RFLP data has developed considerably over the last decade, but there remains a lack of consensus about which statistical analyses offer the best means for finding trends in these data. ![]()
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