The <i>n</i>‐dimensional hypervolume

  • Benjamin Blonder
    Department of Ecology and Evolutionary Biology University of Arizona 1041 E Lowell Street Tucson AZ 85721 USA
  • Christine Lamanna
    Rocky Mountain Biological Laboratory PO Box 619 Crested Butte CO 81224 USA
  • Cyrille Violle
    Centre d'Ecologie Fonctionnelle et Evolutive‐UMR 5175 CNRS Montpellier France
  • Brian J. Enquist
    Department of Ecology and Evolutionary Biology University of Arizona 1041 E Lowell Street Tucson AZ 85721 USA

書誌事項

公開日
2014-02-20
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1111/geb.12146
公開者
Wiley

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説明

<jats:title>Abstract</jats:title><jats:sec><jats:title>Aim</jats:title><jats:p>The Hutchinsonian hypervolume is the conceptual foundation for many lines of ecological and evolutionary inquiry, including functional morphology, comparative biology, community ecology and niche theory. However, extant methods to sample from hypervolumes or measure their geometry perform poorly on high‐dimensional or holey datasets.</jats:p></jats:sec><jats:sec><jats:title>Innovation</jats:title><jats:p>We first highlight the conceptual and computational issues that have prevented a more direct approach to measuring hypervolumes. Next, we present a new multivariate kernel density estimation method that resolves many of these problems in an arbitrary number of dimensions.</jats:p></jats:sec><jats:sec><jats:title>Main conclusions</jats:title><jats:p>We show that our method (implemented as the ‘hypervolume’ <jats:styled-content style="fixed-case">R</jats:styled-content> package) can match several extant methods for hypervolume geometry and species distribution modelling. Tools to quantify high‐dimensional ecological hypervolumes will enable a wide range of fundamental descriptive, inferential and comparative questions to be addressed.</jats:p></jats:sec>

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