{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:40:59Z","timestamp":1786977659206,"version":"build-2736575974"},"reference-count":29,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T00:00:00Z","timestamp":1503360000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ecology"],"published-print":{"date-parts":[[2017,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The classical approach to ordination is to use variants of the Euclidean distance to measure differences between samples (e.g., sites in a community study) based on their observation vectors (e.g., abundance counts for a set of species). Examples include Euclidean distance on standardized or log\u2010transformed data, on which principal component analysis and redundancy analysis are based; chi\u2010square distance, on which (canonical) correspondence analysis is based; and Hellinger distance, using square roots of relative values in each multivariate vector. Advantages of the Euclidean approach include the neat decomposition of variance and the ordination's optimal biplot display. To extend this approach to any non\u2010Euclidean or nonmetric dissimilarity, a simple solution is proposed, consisting of the estimation of a weighted Euclidean distance that optimally approximates the dissimilarities. This preliminary step preserves the good properties of the classical approach while giving two additional benefits as by\u2010products. Firstly, the estimated species weights, quantifying each species\u2019 contribution to the dissimilarities, can be interpreted, and weights equal or close to zero can assist in variable selection. Secondly, the dimensionality remains that of the number of species, not the dimensionality inherent in the dissimilarities, which depends on the number of samples and can be considerably higher.<\/jats:p>","DOI":"10.1002\/ecy.1937","type":"journal-article","created":{"date-parts":[[2017,6,21]],"date-time":"2017-06-21T06:18:16Z","timestamp":1498025896000},"page":"2293-2300","source":"Crossref","is-referenced-by-count":12,"title":["Ordination with any dissimilarity measure: a weighted Euclidean solution"],"prefix":"10.1002","volume":"98","author":[{"given":"Michael","family":"Greenacre","sequence":"first","affiliation":[{"name":"Department of Economics and Business Universitat Pompeu Fabra &amp; Barcelona Graduate School of Economics Ramon Trias Fargas, 25\u201027 Barcelona 08005 Spain"},{"name":"Akvaplan\u2010niva FRAM, High North Research Centre for Climate and the Environment Troms\u00f8 9296 Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,8,22]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-842X.00285"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1890\/0012-9658(2003)084[0511:CAOPCA]2.0.CO;2"},{"key":"e_1_2_6_4_1","volume-title":"Modern multidimensional scaling","author":"Borg I.","year":"2005"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.2307\/1942268"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02294026"},{"key":"e_1_2_6_7_1","volume-title":"PRIMER v6: user manual\/tutorial","author":"Clarke K. 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