# Analysis of community ecology data in R

David Zelený

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en:rda_cca_examples

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 en:rda_cca_examples [2019/02/26 23:34]David Zelený en:rda_cca_examples [2019/03/07 21:16]David Zelený Both sides previous revision Previous revision 2019/03/07 21:16 David Zelený 2019/02/26 23:34 David Zelený 2019/02/06 11:29 David Zelený 2019/02/06 11:27 David Zelený [Example 1: Vltava river valley dataset (RDA)] 2019/01/26 18:43 David Zelený [RDA, tb-RDA & CCA (constrained ordination)] 2019/01/26 18:37 David Zelený 2019/01/23 08:13 David Zelený [Example 1: Vltava river valley dataset] 2019/01/23 08:09 David Zelený created 2019/03/07 21:16 David Zelený 2019/02/26 23:34 David Zelený 2019/02/06 11:29 David Zelený 2019/02/06 11:27 David Zelený [Example 1: Vltava river valley dataset (RDA)] 2019/01/26 18:43 David Zelený [RDA, tb-RDA & CCA (constrained ordination)] 2019/01/26 18:37 David Zelený 2019/01/23 08:13 David Zelený [Example 1: Vltava river valley dataset] 2019/01/23 08:09 David Zelený created Line 54: Line 54: The two variables explain 8.9% of the variance (the row ''​Constrained''​ and column ''​Proportion''​ in the table above, can be calculated also as the sum of eigenvalues for the constrained axes divided by total variance (inertia): (0.04023+0.02227) /​0.70476=0.08869. The first constrained axis (RDA1) explains 0.04023/​0.70476=5.7% of the variance, while the second (RDA2) explains 0.02227/​0.70476=3.2%. Note that the first unconstrained axis (PC1) represents 0.07321/​0.70476=10.4% of total variance, which is more than both explanatory variables together; the first two unconstrained explain (0.07321+0.04857)/​0.70476=17.3%. This means that the dataset may be structured by some strong environmental variable(s) different from pH and soil depth (we will check this below). The two variables explain 8.9% of the variance (the row ''​Constrained''​ and column ''​Proportion''​ in the table above, can be calculated also as the sum of eigenvalues for the constrained axes divided by total variance (inertia): (0.04023+0.02227) /​0.70476=0.08869. The first constrained axis (RDA1) explains 0.04023/​0.70476=5.7% of the variance, while the second (RDA2) explains 0.02227/​0.70476=3.2%. Note that the first unconstrained axis (PC1) represents 0.07321/​0.70476=10.4% of total variance, which is more than both explanatory variables together; the first two unconstrained explain (0.07321+0.04857)/​0.70476=17.3%. This means that the dataset may be structured by some strong environmental variable(s) different from pH and soil depth (we will check this below). + + The relationship between the variation represented by individual (constrained and unconstrained) ordination axes can be displayed using the barplot on eigenvalues:​ + + constrained_eig <- tbRDA\$CCA\$eig/​tbRDA\$CA\$tot.chi*100 + unconstrained_eig <- tbRDA\$CA\$eig/​tbRDA\$CA\$tot.chi*100 + barplot (c(constrained_eig,​ unconstrained_eig),​ col = c(rep ('​red',​ length (constrained_eig)),​ rep ('​black',​ length (unconstrained_eig))),​ las = 2, ylab = '% variation'​) + ​ + + (note that all information about the eigenvalues and total inertia is in the object calculated by ''​vegan'''​s ordination function (''​rda''​ in this case, stored in the list ''​tbRDA''​),​ you just need to search a bit inside to find it - consider using the function ''​str''​ to check the structure of tbRDA first). + + {{:​obrazky:​tb_rda_vltava_barplot_eig.png?​direct|}} + Let's see the ordination diagram: Let's see the ordination diagram: 