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en:overview [2019/03/18 10:57]
David Zelený
en:overview [2019/03/18 11:13] (current)
David Zelený [Overview of analyses on this website]
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 The key unit in the analysis of community ecology data sets is **community sample** (plot, sample, sampling unit, relevé), representing presence/​absence or quantity (count, cover or biomass) of each species in each sample. The way how to handle such samples is via **[[similarity|ecological resemblance]]**,​ which can be quantified, e.g. by **compositional dissimilarity** between two such community samples. Compositional dissimilarity describes the imaginary distance between two community samples in the **multidimensional compositional space** - two samples with exactly the same species composition will occupy exactly the same spot in this space, and their distance will increase with increasing dissimilarity regarding species composition. Ecological resemblance and multidimensional compositional space are two main concepts which you need to understand before you turn into learning multivariate methods. Most ordination and classification methods are based on some of the compositional dissimilarity measures, although in some of them the measure itself is not explicitly mentioned. ​ The key unit in the analysis of community ecology data sets is **community sample** (plot, sample, sampling unit, relevé), representing presence/​absence or quantity (count, cover or biomass) of each species in each sample. The way how to handle such samples is via **[[similarity|ecological resemblance]]**,​ which can be quantified, e.g. by **compositional dissimilarity** between two such community samples. Compositional dissimilarity describes the imaginary distance between two community samples in the **multidimensional compositional space** - two samples with exactly the same species composition will occupy exactly the same spot in this space, and their distance will increase with increasing dissimilarity regarding species composition. Ecological resemblance and multidimensional compositional space are two main concepts which you need to understand before you turn into learning multivariate methods. Most ordination and classification methods are based on some of the compositional dissimilarity measures, although in some of them the measure itself is not explicitly mentioned. ​
  
-<​imgcaption ordi-vs-class|>​{{ :​obrazky:​ordination-vs-classification.png?​direct&​400|}}</​imgcaption>​ 
 **[[ordination|Ordination]]** is a way to make an order in the set of community samples and the way to reduce multidimensional information stored in community data into a few imaginable, interpretable and printable dimensions. There are many ordination methods, with different fields (botany, zoology, microbiology) preferring different ones. Ordinations are focused on finding interpretable trends in data, represented by changes in species composition with possible underlying changes in environmental gradients. We may use it either for a description of community pattern (which is usually the purpose of unconstrained = indirect ordination) or to explain and test changes in species composition by some (e.g. environmental,​ spatial, temporal) variables (constrained = direct ordination). **[[ordination|Ordination]]** is a way to make an order in the set of community samples and the way to reduce multidimensional information stored in community data into a few imaginable, interpretable and printable dimensions. There are many ordination methods, with different fields (botany, zoology, microbiology) preferring different ones. Ordinations are focused on finding interpretable trends in data, represented by changes in species composition with possible underlying changes in environmental gradients. We may use it either for a description of community pattern (which is usually the purpose of unconstrained = indirect ordination) or to explain and test changes in species composition by some (e.g. environmental,​ spatial, temporal) variables (constrained = direct ordination).
  
 **[[classification|Numerical classification]]** is conceptually a contrast of ordination (<imgref ordi-vs-class>​) - while ordination seeks the main gradients in the continuum of community samples, classification tries to separate this continuum into a finite number of groups (clusters), each containing more or less similar samples. ​ **[[classification|Numerical classification]]** is conceptually a contrast of ordination (<imgref ordi-vs-class>​) - while ordination seeks the main gradients in the continuum of community samples, classification tries to separate this continuum into a finite number of groups (clusters), each containing more or less similar samples. ​
  
-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).+<​imgcaption ordi-vs-class|Ordination ​(gradient analysis)(leftsearches for main gradients ​in species ​composition and (optionallyexplains them by environmental factorsNumerical classification ​(right) is cutting ​the continuum into homogeneous subsets ​of samples.>{{ :​obrazky:​ordination-vs-classification.png?​direct|}}</​imgcaption>​
  
-**[[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).+\\ 
 +**[[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 the sampled area (species-area curve) and sampling effort (bias due to undersampling).
  
  
en/overview.1552877828.txt.gz · Last modified: 2019/03/18 10:57 by David Zelený