Using n-dimensional hypervolumes for species distribution modelling: A response to Qiao et al. ()

Benjamin Blonder, Christine Lamanna, Cyrille Violle, Brian J. Enquist

Research output: Contribution to journalLetter

6 Citations (Scopus)

Abstract

Hypervolume approaches are used to quantify functional diversity and quantify environmental niches for species distribution modelling. Recently, Qiao et al. () criticized our geometrical kernel density estimation (KDE) method for measuring hypervolumes. They used a simulation analysis to argue that the method yields high error rates and makes biased estimates of fundamental niches. Here, we show that (a) KDE output depends in useful ways on dataset size and bias, (b) other species distribution modelling methods make equally stringent but different assumptions about dataset bias, (c) simulation results presented by Qiao et al. () were incorrect, with revised analyses showing performance comparable to other methods, and (d) hypervolume methods are more general than KDE and have other benefits for niche modelling. As a result, our KDE method remains a promising tool for species distribution modelling.

Original languageEnglish (US)
Pages (from-to)1071-1075
Number of pages5
JournalGlobal Ecology and Biogeography
Volume26
Issue number9
DOIs
StatePublished - Sep 1 2017
Externally publishedYes

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biogeography
niche
estimation method
modeling
niches
seeds
methodology
simulation
functional diversity
distribution
method

ASJC Scopus subject areas

  • Global and Planetary Change
  • Ecology, Evolution, Behavior and Systematics
  • Ecology

Cite this

Using n-dimensional hypervolumes for species distribution modelling : A response to Qiao et al. (). / Blonder, Benjamin; Lamanna, Christine; Violle, Cyrille; Enquist, Brian J.

In: Global Ecology and Biogeography, Vol. 26, No. 9, 01.09.2017, p. 1071-1075.

Research output: Contribution to journalLetter

Blonder, Benjamin ; Lamanna, Christine ; Violle, Cyrille ; Enquist, Brian J. / Using n-dimensional hypervolumes for species distribution modelling : A response to Qiao et al. (). In: Global Ecology and Biogeography. 2017 ; Vol. 26, No. 9. pp. 1071-1075.
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