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en:overview [2019/03/18 11:00]
David Zelený
en:overview [2019/03/18 11:13] (current)
David Zelený [Overview of analyses on this website]
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 <​imgcaption ordi-vs-class|Ordination (gradient analysis)(left) searches for main gradients in species composition and (optionally) explains them by environmental factors. Numerical classification (right) is cutting the continuum into homogeneous subsets of samples.>​{{ :​obrazky:​ordination-vs-classification.png?​direct|}}</​imgcaption>​ <​imgcaption ordi-vs-class|Ordination (gradient analysis)(left) searches for main gradients in species composition and (optionally) explains them by environmental factors. Numerical classification (right) is cutting the continuum into homogeneous subsets of samples.>​{{ :​obrazky:​ordination-vs-classification.png?​direct|}}</​imgcaption>​
  
-Apart to //sample × species// matrix of species composition (**L** matrix), and optionally also //sample × environmental variable// matrix of environmental variables or other types of sample attributes (**R** matrix), in some cases we have also the third matrix, which contains species attributes like species traits or species indicator values (//species × traits matrix// or **Q** matrix). There are several methods ​of **[[traits|analysis of species attributes]]**, including three-matrix methods (like the //fourth corner// or RLQ analysis), or other ways of relating species and sample attributes (e.g. by calculating a community-weighted mean of species attributes for individual samples and relating them to environmental variables by regression).+\\ 
 +**[[traits|Analysis of species attributes]]** comes into question when, apart to //sample × species// matrix of species composition (**L** matrix), and //sample × environmental variable// matrix of environmental variables or other types of sample attributes (**R** matrix), in some cases we have also the third matrix, which contains species attributes like species traits or species indicator values (//species × traits matrix// or **Q** matrix). There are several methods ​how to relate ​species attributes ​to sample attributes (env. variables), ​e.g. by calculating a community-weighted mean of species attributes for individual samples and relating them to environmental variables by regression ​or correlation (community weighted mean approach), or the fourth corner, a three-matrix method (numerically closely related to CWM approach). Other options include ordination analyses like CWM-RDA or RLQ.
  
-**[[diversity_analysis|Diversity analysis]]** is in certain sense also analysis of species composition matrix, whose originally multidimensional information stored in samples × species matrix is reduced into one-dimensional variables (like numbers of species in samples - alpha diversity, differences in species composition among samples - beta diversity, or number of all species in the matrix - gamma diversity). But diversity is not only about numbers of species, but also about their relative abundances - we will briefly review also the concepts of true diversity, evenness and their representation by different diversity indices. Diversity is also influenced by sampled area (species-area curve) and sampling effort (bias due to undersampling).+**[[diversity_analysis|Diversity analysis]]** is in certain sense also analysis of species composition matrix, whose originally multidimensional information stored in samples × species matrix is reduced into one-dimensional variables (like numbers of species in samples - alpha diversity, differences in species composition among samples - beta diversity, or number of all species in the matrix - gamma diversity). But diversity is not only about numbers of species, but also about their relative abundances - we will briefly review also the concepts of true diversity, evenness and their representation by different diversity indices. Diversity is also influenced by the sampled area (species-area curve) and sampling effort (bias due to undersampling).
  
  
en/overview.1552878055.txt.gz · Last modified: 2019/03/18 11:00 by David Zelený