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Data analysis in community and landscape ecology / edited by R.H.G. Jongman, C.J.F. ter Braak & O.F.R. van Tongeren.

Contributor(s): Material type: TextTextPublisher: Cambridge : Cambridge University Press, 1995Description: 1 online resource (xxi, 299 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511525575 (ebook)
Other title:
  • Data Analysis in Community & Landscape Ecology
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 574.5/0285 20
LOC classification:
  • QH541.2 .D365 1995
Online resources: Summary: 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.
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Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.

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