Greenacre, MichaelUniversitat Pompeu Fabra. Departament d'Economia i Empresa2017-07-262017-07-262012-06-01Ecology, 2013, 94(2), 280-286http://hdl.handle.net/10230/19888Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report I demonstrate the advantages of the fuzzy coding of explanatory variables: first, nonlinear relationships can be diagnosed; second, more variance in the responses can be explained; and third, in the presence of categorical explanatory variables (for example, years, regions) the interpretation of the resulting triplot ordination is unified because all explanatory variables are measured at a categorical level.application/pdfengL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative CommonsFuzzy coding in constrained ordinations<resourceType xmlns="http://datacite.org/schema/kernel-3" resourceTypeGeneral="Other">info:eu-repo/semantics/workingPaper</resourceType><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">canonical correspondence analysis</subject><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">crisp coding</subject><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">dummy variables</subject><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">fuzzy coding</subject><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">redundancy analysis</subject><subject xmlns="http://datacite.org/schema/kernel-3" subjectScheme="keyword">Statistics, Econometrics and Quantitative Methods</subject><rights xmlns="http://datacite.org/schema/kernel-3">info:eu-repo/semantics/openAccess</rights>