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Mathematics 2019, 7(3), 245; https://doi.org/10.3390/math7030245

The Use of the Evenness of Eigenvalues of Similarity Matrices to Test for Predictivity of Ecosystem Classifications

Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
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Received: 6 December 2018 / Revised: 26 February 2019 / Accepted: 7 March 2019 / Published: 9 March 2019
(This article belongs to the Special Issue New Paradigms and Trends in Quantitative Ecology)
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Abstract

The use of the evenness (E(λ)) of the eigenvalues of similarity matrices corresponding to different hierarchical levels of ecosystem classifications, is suggested to test correlation (or predictivity) between biological communities and environmental factors as one alternative of analysis of variance (parametric or non-parametric). The advantage over traditional methods is the fact that similarity matrices can be obtained from any kind of data (mixed and missing data) by indices such as those of Goodall and Gower. The significance of E(λ) is calculated by permutation techniques. One example of application of E(λ) is given by a data set describing plant community types (beech forests of the Italian peninsula). View Full-Text
Keywords: correlation; eigenanalysis; evenness; Occam’s razor correlation; eigenanalysis; evenness; Occam’s razor
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Feoli, E.; Ganis, P. The Use of the Evenness of Eigenvalues of Similarity Matrices to Test for Predictivity of Ecosystem Classifications. Mathematics 2019, 7, 245.

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