We are analyzing https://link.springer.com/article/10.1007/s004420100716.

Title:
Ecologically meaningful transformations for ordination of species data | Oecologia
Description:
This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Science
Education
Careers

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We find it hard to spot revenue streams.

Some websites aren't about earning revenue; they're built to connect communities or raise awareness. There are numerous motivations behind creating websites. This might be one of them. Link.springer.com might be plotting its profit, but the way they're doing it isn't detectable yet.

Keywords {🔍}

analysis, data, article, species, correspondence, access, privacy, cookies, content, information, publish, search, ordination, log, journal, research, oecologia, transformations, legendre, gallagher, biplots, distance, rda, canonical, variables, open, discover, springer, function, optional, personal, parties, policy, find, track, ecologically, meaningful, published, october, cite, pierre, eugene, explore, constrained, euclidean, principalcomponent, pca, redundancy, cca, community,

Topics {✒️}

principal-component analysis canonical correspondence analysis month download article/chapter multivariate data analysis ecologically meaningful transformations redundancy analysis correspondence analysis chi-square distance [preserved related subjects article oecologia aims privacy choices/manage cookies full article pdf european economic area k-means partitioning check access université de montréal succursale centre-ville instant access conditions privacy policy usage analysis multifactorial analysis constrained ordination article legendre community composition data draw biplots displaying species data published species data tables accepting optional cookies squares loss function obtain species biplots journal finder publish article log analysis article cite personal data privacy policy usa eugene books a community data species data optional cookies information manage preferences data protection subscription content similar content gallagher euclidean distance [ euclidean distance essential cookies

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Ecologically meaningful transformations for ordination of species data
         description:This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.
         datePublished:2001-10-01T00:00:00Z
         dateModified:2001-10-01T00:00:00Z
         pageStart:271
         pageEnd:280
         sameAs:https://doi.org/10.1007/s004420100716
         keywords:
            Biplot diagram
            Canonical correspondence analysis
            Correspondence analysis
            Principal-component analysis
            Redundancy analysis
            Ecology
            Plant Sciences
            Hydrology/Water Resources
         image:
         isPartOf:
            name:Oecologia
            issn:
               1432-1939
               0029-8549
            volumeNumber:129
            type:
               Periodical
               PublicationVolume
         publisher:
            name:Springer Berlin Heidelberg
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Pierre Legendre
               affiliation:
                     name:Université de Montréal
                     address:
                        name:Département de sciences biologiques, Université de Montréal, Montréal, Canada
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Eugene D. Gallagher
               affiliation:
                     name:University of Massachusetts at Boston
                     address:
                        name:Department of Environmental, Coastal & Ocean Sciences, University of Massachusetts at Boston, Boston, USA
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:
         hasPart:
            isAccessibleForFree:
            cssSelector:.main-content
            type:WebPageElement
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Ecologically meaningful transformations for ordination of species data
      description:This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.
      datePublished:2001-10-01T00:00:00Z
      dateModified:2001-10-01T00:00:00Z
      pageStart:271
      pageEnd:280
      sameAs:https://doi.org/10.1007/s004420100716
      keywords:
         Biplot diagram
         Canonical correspondence analysis
         Correspondence analysis
         Principal-component analysis
         Redundancy analysis
         Ecology
         Plant Sciences
         Hydrology/Water Resources
      image:
      isPartOf:
         name:Oecologia
         issn:
            1432-1939
            0029-8549
         volumeNumber:129
         type:
            Periodical
            PublicationVolume
      publisher:
         name:Springer Berlin Heidelberg
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Pierre Legendre
            affiliation:
                  name:Université de Montréal
                  address:
                     name:Département de sciences biologiques, Université de Montréal, Montréal, Canada
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Eugene D. Gallagher
            affiliation:
                  name:University of Massachusetts at Boston
                  address:
                     name:Department of Environmental, Coastal & Ocean Sciences, University of Massachusetts at Boston, Boston, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:
      hasPart:
         isAccessibleForFree:
         cssSelector:.main-content
         type:WebPageElement
["Periodical","PublicationVolume"]:
      name:Oecologia
      issn:
         1432-1939
         0029-8549
      volumeNumber:129
Organization:
      name:Springer Berlin Heidelberg
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:Université de Montréal
      address:
         name:Département de sciences biologiques, Université de Montréal, Montréal, Canada
         type:PostalAddress
      name:University of Massachusetts at Boston
      address:
         name:Department of Environmental, Coastal & Ocean Sciences, University of Massachusetts at Boston, Boston, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Pierre Legendre
      affiliation:
            name:Université de Montréal
            address:
               name:Département de sciences biologiques, Université de Montréal, Montréal, Canada
               type:PostalAddress
            type:Organization
      name:Eugene D. Gallagher
      affiliation:
            name:University of Massachusetts at Boston
            address:
               name:Department of Environmental, Coastal & Ocean Sciences, University of Massachusetts at Boston, Boston, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Département de sciences biologiques, Université de Montréal, Montréal, Canada
      name:Department of Environmental, Coastal & Ocean Sciences, University of Massachusetts at Boston, Boston, USA
WebPageElement:
      isAccessibleForFree:
      cssSelector:.main-content

External Links {🔗}(27)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.26s.

LINK . SPRINGER . COM {}