PCA – A Powerful Method for Analyze Ecological Niches
By F. Janžekovič, T. Novak. 2012
85 citations recorded by the source corpus.
Principal Component Analysis, PCA, is a multivariate statistical technique that uses orthogonal transformation to convert a set of correlated variables into a set of orthogonal, uncorrelated axes called principal components (James & McCulloch 1990; Robertson et al., 2001; Legendre & Legendre 1998; Gotelli & Ellison 2004). Ecologists are most frequently dealing with multivariate datasets. This is especially true in field ecology, and this is why PCA is an attractive and frequently used method of data ordination in ecology. PCA enables condensation of data on a multivariate phenomenon into its main, representative features by projection of the data into a two-dimensional presentation. The two created resource axes are independent, and although they reduce the number of dimensions–i.e. the original data complexity–they maintain much of the original relationship between the variables: i.e., information or explained variance (Litvak & Hansell 1990). This is helpful in focusing attention on the main characteristics of the phenomenon under study. It is convenient that, if the first few principal components (PCs) explain a high percentage of variance, environmental variables that are not correlated with the first few PCs can be disregarded in the analysis (Toepfer et al., 1998). In addition, applying PCA has become relatively userfriendly because of the numerous programs that assist in carrying out the computational procedure with ease (Doledec et al., 2000; Guisan & Zimmerman 2000; Robertson et al., 2001; Rissler & Apodaca 2007; Marmion et al., 2009).
Subfields: Ecological Modeling, Ecology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6792d1eda50e2ab089339eeb7e400c607c04ead8
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In Ecological Modeling
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Citations in the corpus
- The Multidimensional Stoichiometric Niche (2017, methodology): “PCA allows multivariate data compression into its main orthogonal features by displaying the data into a lower dimensional space (Janžekovi and Novak, 2012).”