Detrended correspondence analysis

About: Detrended correspondence analysis is a research topic. Over the lifetime, 1257 publications have been published within this topic receiving 60183 citations. The topic is also known as: DCA.

Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis

Abstract: A new multivariate analysis technique, developed to relate community composition to known variation in the environment, is described. The technique is an extension of correspondence analysis (reciprocal averaging), a popular ordination technique that extracts continuous axes of variation from species occurrence or abundance data. Such ordination axes are typically interpreted with the help of external knowledge and data on environmental variables; this two—step approach (ordination followed by environmental gradient identification) is termed indirect gradient analysis. In the new technique, called canonical correspondence analysis, ordination axes are chosen in the light of known environmental variables by imposing the extra restriction that the axes be linear combinations of environmental variables. In this way community variation can be directly related to environmental variation. The environmental variables may be quantitative or nominal. As many axes can be extracted as there are environmental variables. The method of detrending can be incorporated in the technique to remove arch effects. (Detrended) canonical correspondence analysis is an efficient ordination technique when species have bell—shaped response curves or surfaces with respect to environmental gradients, and is therefore more appropriate for analyzing data on community composition and environmental variables than canonical correlation analysis. The new technique leads to an ordination diagram in which points represent species and sites, and vectors represent environmental variables. Such a diagram shows the patterns of variation in community composition that can be explained best by the environmental variables and also visualizes approximately the "centers" of the species distributions along each of the environmental variables. Such diagrams effectively summarized relationships between community and environment for data sets on hunting spiders, dyke vegetation, and algae along a pollution gradient.

Detrended correspondence analysis: an improved ordination technique

Abstract: Studies by ourselves and others (Swan 1970, Austin & Noy-Meir 1972, Beals 1973, Hill 1973, 1974, Austin 1976a, b, Fasham 1977, Gauch Whittaker & Wentwarth 1977, Noy-Meir & Whittaker 1977, Orloci 1978, Gauch, Whittaker & Singer 1979) have found faults with all ordination techniques currently in use, at least when applied to ecological data specifying the occurrences of species in community samples. These faults certainly do not make existing techniques useless; but they mean that results must be interpreted with caution. Even with the best techniques, the underlying structure of the data is often poorly expressed.

Data Analysis in Community and Landscape Ecology

Abstract: Ecological data has several special properties: the presence or absence of species on a semi-quantitative abundance scale; non-linear relationships between species and environmental factors; and high inter-correlations among species and among environmental variables. The analysis of such data is important to the interpretation of relationships within plant and animal communities and with their environments. In this corrected version of Data Analysis in Community and Landscape Ecology, without using complex mathematics, the contributors demonstrate the methods that have proven most useful, with examples, exercises and case-studies. Chapters explain in an elementary way powerful data analysis techniques such as logic regression, canonical correspondence analysis, and kriging.

A Theory of Gradient Analysis

Abstract: Publisher Summary This chapter concerns data analysis techniques that assist the interpretation of community composition in terms of species' responses to environmental gradients in the broadest sense. All species occur in a characteristic, limited range of habitats; and within their range, they tend to be most abundant around their particular environmental optimum. The composition of biotic communities thus changes along environmental gradients. Direct gradient analysis is a regression problem—fitting curves or surfaces to the relation between each species' abundance, probability of occurrence, and one or more environmental variables. Ecologists have independently developed a variety of alternative techniques. Many of these techniques are essentially heuristic, and have a less secure theoretical basis. This chapter presents a theory of gradient analysis, in which the heuristic techniques are integrated with regression, calibration, ordination and constrained ordination as distinct, well-defined statistical problems. The various techniques used for each type of problem are classified in families according to their implicit response model and the method used to estimate parameters of the model. Three such families are considered. The treatment shown here unites such apparently disparate data analysis techniques as linear regression, principal components analysis, redundancy analysis, Gaussian ordination, weighted averaging, reciprocal averaging, detrended correspondence analysis, and canonical correspondence analysis in a single theoretical framework.