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1 - Introduction and data types

Published online by Cambridge University Press:  05 May 2014

Petr Šmilauer
Affiliation:
University of South Bohemia, Czech Republic
Jan Lepš
Affiliation:
University of South Bohemia, Czech Republic
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Summary

Why ordination?

When you investigate the variation of plant or animal communities across a range of different environmental conditions, you typically find not only large differences in species composition of the studied communities, but also a certain consistency or predictability of this variation. For example, if you look at the variation of grassland vegetation in a landscape and describe the plant community composition using vegetation plots, then the individual plots can be usually ordered along one, two or three imaginary axes. The change in the vegetation composition is often small as you move your focus from one plot to those nearby on such a hypothetical axis.

This gradual change in the community composition can often be related to differing, but partially overlapping demands of individual species for environmental factors such as the average soil moisture, its fluctuations throughout the season, the ability of species to compete with other ones for the available nutrients and light, etc. If the axes along which you originally ordered the plots can be identified with a particular environmental factor (such as moisture or richness of soil nutrients), you can call them a soil moisture gradient, or a nutrient availability gradient. Occasionally, such gradients can be identified in a real landscape, e.g. as a spatial gradient along a slope from a riverbank, with gradually decreasing soil moisture. But more often you can identify such axes along which the plant or animal communities vary in a more or less smooth, predictable way, yet you cannot find them in nature as a visible spatial gradient and neither can you identify them uniquely with a particular measurable environmental factor. In such cases, we speak about gradients of species composition change.

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Publisher: Cambridge University Press
Print publication year: 2014

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  • Introduction and data types
  • Petr Šmilauer, University of South Bohemia, Czech Republic, Jan Lepš, University of South Bohemia, Czech Republic
  • Book: Multivariate Analysis of Ecological Data using CANOCO 5
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139627061.002
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  • Introduction and data types
  • Petr Šmilauer, University of South Bohemia, Czech Republic, Jan Lepš, University of South Bohemia, Czech Republic
  • Book: Multivariate Analysis of Ecological Data using CANOCO 5
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139627061.002
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Introduction and data types
  • Petr Šmilauer, University of South Bohemia, Czech Republic, Jan Lepš, University of South Bohemia, Czech Republic
  • Book: Multivariate Analysis of Ecological Data using CANOCO 5
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139627061.002
Available formats
×