n‐dimensional hypervolume

• Published in: Global ecology and biogeography Vol. 23; no. 5; pp. 595 - 609
• Main Authors: Blonder, Benjamin, Lamanna, Christine, Violle, Cyrille, Enquist, Brian J
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2014; John Wiley & Sons Ltd; Blackwell; Wiley Subscription Services, Inc
• Subjects: anatomy and morphology; Animal and plant ecology; Animal, plant and microbial ecology; Biogeography; Biological and medical sciences; community ecology; data collection; Datasets; Density; Density estimation; Ecological genetics; Ecological modeling; Ecological niches; Environmental niche modelling; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; Geometry; hole; Hutchinson; hypervolume; kernel density estimation; MACROECOLOGICAL METHODS; Methods and techniques (sampling, tagging, trapping, modelling...); morphospace; niche; niche overlap; Phenotypic traits; species distribution modelling; Synecology; Density estimation; Hole; kernel density estimation; niche overlap; Biogeography; Ecology; Overlap; Modeling; morphospace; hypervolume; Hutchinson; Spatial distribution; Geographic distribution; Ecological niche; niche; Environmental niche modelling; species distribution modelling; Distribution range
• ISSN: 1466-822X; 1466-8238; 1466-822X
• DOI: 10.1111/geb.12146

AIM: 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. INNOVATION: 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. MAIN CONCLUSIONS: We show that our method (implemented as the ‘hypervolume’ R 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.