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A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling
by
Escobar, Luis E.
, Qiao, Huijie
, Saupe, Erin E.
, Ji, Liqiang
, Soberón, Jorge
in
Biogeography
/ CORRESPONDENCE
/ data collection
/ Ecological niches
/ Ecological space
/ Ecology
/ Hutchinsonian hypervolumes
/ minimum volume ellipsoid
/ multivariate kernel density estimation
/ New records
/ niche
/ Niches
/ Species
/ Virtual environments
/ virtual species
2017
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A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling
by
Escobar, Luis E.
, Qiao, Huijie
, Saupe, Erin E.
, Ji, Liqiang
, Soberón, Jorge
in
Biogeography
/ CORRESPONDENCE
/ data collection
/ Ecological niches
/ Ecological space
/ Ecology
/ Hutchinsonian hypervolumes
/ minimum volume ellipsoid
/ multivariate kernel density estimation
/ New records
/ niche
/ Niches
/ Species
/ Virtual environments
/ virtual species
2017
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Do you wish to request the book?
A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling
by
Escobar, Luis E.
, Qiao, Huijie
, Saupe, Erin E.
, Ji, Liqiang
, Soberón, Jorge
in
Biogeography
/ CORRESPONDENCE
/ data collection
/ Ecological niches
/ Ecological space
/ Ecology
/ Hutchinsonian hypervolumes
/ minimum volume ellipsoid
/ multivariate kernel density estimation
/ New records
/ niche
/ Niches
/ Species
/ Virtual environments
/ virtual species
2017
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A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling
Journal Article
A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling
2017
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Overview
Blonder et al. (2014, Global Ecology and Biogeography, 23, 595–609) introduced a new multivariate kernel density estimation (KDE) method to infer Hutchinsonian hypervolumes in the modelling of ecological niches. The authors argued that their KDE method matches or outperforms several methods for estimating hypervolume geometries and for conducting species distribution modelling. Further clarification, however, is appropriate with respect to the assumptions and limitations of KDE as a method for species distribution modelling. Using virtual species and controlled environmental scenarios, we show that KDE both under- and overestimates niche volumes depending on the dimensionality of the dataset and the number of occurrence records considered. We suggest that KDE may be a viable approach when dealing with large sample sizes, limited sampling bias and only a few environmental dimensions.
Publisher
John Wiley & Sons Ltd,Wiley Subscription Services, Inc
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