• Kshetrimayum Anand Singh

The technique of Principal Component Analysis (PCA) is generally applied to condense information contained in a large number of variables into a smaller set of new composite dimensions with minimum loss of information. Ecologists have long been using the technique of PCA to effectively reduce the dimensionality of large ecological datasets. The application of PCA to analyse the abundance of earthworm species in a mixed reserve forest area of Manipur has been carried out in this paper. Data on 12 species of earthworm collected during the 12 different months of the year from six replicates are used in the analysis. Necessary steps for the analysis are systematically highlighted and performed to reach to a valid conclusion. The result of the eigenanalysis using covariance matrix shows that the first and second components accounted for nearly 95% of the total variability in the original data so that the first two components will be enough to retain for further study. Graphical interpretations are also presented accordingly.

PCA, dimensionality, abundance, correlation matrix, eigen-analysis.

"Analyzing abundance of different earthworm species with respect to different months of the year in a reserve forest area of Manipur: Application of Principal Component Analysis.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.903-910, June 2019, Available :http://www.jetir.org/papers/JETIR1907C86.pdf

"Analyzing abundance of different earthworm species with respect to different months of the year in a reserve forest area of Manipur: Application of Principal Component Analysis.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp903-910, June 2019, Available at : http://www.jetir.org/papers/JETIR1907C86.pdf