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evs2014 [2017/10/10 01:19]
127.0.0.1 external edit
evs2014 [2018/12/29 07:20]
david
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 The problem is that one can get strong and significant relationship between mean Ellenberg indicator values and measured environmental variable even in case that species Ellenberg values are replaced by random numbers, and mean of such random numbers therefore have no ecological meaning. This paradox situation occurs in cases of environmental variables which have a strong effect on species composition. The reason is that weighted mean of species indicator values and environmental variable are not independent from each other. They are linked by changes in species composition:​ mean Ellenberg values are derived from species composition (by calculation),​ while environmental variables influence species composition. When relating mean Ellenberg values to environmental variables, it is important to separate the relationship between environmental variable and species composition (which is not of our interest in this case) from relationship between environmental variable and indicator values (which is what we are testing). The problem is that one can get strong and significant relationship between mean Ellenberg indicator values and measured environmental variable even in case that species Ellenberg values are replaced by random numbers, and mean of such random numbers therefore have no ecological meaning. This paradox situation occurs in cases of environmental variables which have a strong effect on species composition. The reason is that weighted mean of species indicator values and environmental variable are not independent from each other. They are linked by changes in species composition:​ mean Ellenberg values are derived from species composition (by calculation),​ while environmental variables influence species composition. When relating mean Ellenberg values to environmental variables, it is important to separate the relationship between environmental variable and species composition (which is not of our interest in this case) from relationship between environmental variable and indicator values (which is what we are testing).
  
-In a previous study, Zelený & Schaffers (2012) pointed out similar issue with testing the significance of relationship between mean Ellenberg values and scores from ordination diagrams or results of cluster analysis, which was explained by circularity of reasoning. Here, I will show that this issue is more general and includes also testing the relationship between mean Ellenberg values and environmental variables or experimental treatments. I will propose a way how to deal with this problem, illustrate it on examples of real vegetation data and demonstrate a simple software (http://bit.ly/ellenberg), which calculates the correct significance values for regression or correlation between mean Ellenberg values and other variables (environmental variables, ordination scores, cluster assignment etc.).+In a previous study, Zelený & Schaffers (2012) pointed out similar issue with testing the significance of relationship between mean Ellenberg values and scores from ordination diagrams or results of cluster analysis, which was explained by circularity of reasoning. Here, I will show that this issue is more general and includes also testing the relationship between mean Ellenberg values and environmental variables or experimental treatments. I will propose a way how to deal with this problem, illustrate it on examples of real vegetation data and demonstrate a simple software (https://davidzeleny.net/wiki/​doku.php/​eiv:​start), which calculates the correct significance values for regression or correlation between mean Ellenberg values and other variables (environmental variables, ordination scores, cluster assignment etc.).
  
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