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| ===== Theory ===== | ===== Theory ===== | ||
| - | Ordination (from Latin //ordinatio//, putting things into order, or German //die Ordnung//, order) is a multivariate analysis, which searches for a continuous pattern in multivariate data, usually the data about species composition of community samples (sample × species matrix). We can imagine such multivariate data as samples located in multidimensional hyperspace, where each dimension is defined by an abundance of one species (example for a community of two samples with three species is on <imgref 3d_species_space>). The use of ordination in ecology was pioneered by English-born Australian botanist and ecologist [[wp>David_Goodall_(botanist)|David Goodall]], whose first paper using ordination (PCA) was published in 1954 ([[en:publications|Goodall 1954]]). | + | Ordination (from Latin //ordinatio//, putting things into order, or German //die Ordnung//, order) is a multivariate analysis, which searches for a continuous pattern in multivariate data, usually the data about species composition of community samples (sample × species matrix). We can imagine such multivariate data as samples located in multidimensional hyperspace, where each dimension is defined by an abundance of one species (example for a community of two samples with three species is on <imgref 3d_species_space>). The use of ordination in ecology was pioneered by English-born Australian botanist and ecologist [[wp>David_Goodall_(botanist)|David Goodall]], whose first paper using ordination (PCA) was published in 1954 ([[en:references|Goodall 1954]]). |
| The main assumption of ordination is that analyzed data are redundant, i.e. they contain more variables than is necessary to describe the information behind, and we can reduce the number of these variables (and dimensions) without loosing too much information. For example, in the case of species composition data, often some of the species are ecologically similar (e.g. species which prefer to grow in wet instead of dry habitat), meaning that the dataset contains several redundant variables (species) telling the same story. Or, to explain the redundancy in another way, from occurrence (or absence) of one species we can often predict occurrence (or absence) of several other species (e.g. if the sample includes species of wet habitats, we may expect that species preferring dry habitats will not be present, while other wet-loving species may occur). | The main assumption of ordination is that analyzed data are redundant, i.e. they contain more variables than is necessary to describe the information behind, and we can reduce the number of these variables (and dimensions) without loosing too much information. For example, in the case of species composition data, often some of the species are ecologically similar (e.g. species which prefer to grow in wet instead of dry habitat), meaning that the dataset contains several redundant variables (species) telling the same story. Or, to explain the redundancy in another way, from occurrence (or absence) of one species we can often predict occurrence (or absence) of several other species (e.g. if the sample includes species of wet habitats, we may expect that species preferring dry habitats will not be present, while other wet-loving species may occur). | ||
en/ordination.txt · Last modified: 2019/02/05 15:03 by David Zelený
