The n‐dimensional hypervolume

By B. Blonder, C. Lamanna, C. Violle, Brian J. Enquist. 2014

642 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/be8cd4a140e6509b9aaef90af50814c102dbcdb3

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Ants as indicators of environmental change and ecosystem processes (2017, methodology): “The raw functional richness of ant ssemblages also decreased with elevation, which we confirmed y using a Hutchinsonian hypervolume approach (data not shown; tors 83 (2017) 527–537 533 Blonder et al., 2014).”
Decomposing the causes for niche differentiation between species using hypervolumes (2018, methodology): “Incorporating Different Data Types in Hypervolume Estimation A limitation to the current hypervolume algorithms, is that they require datasets with continuous variables (Blonder et al., 2014; Blonder, 2018).”
Bringing Elton and Grinnell Together: a quantitative framework to represent the biogeography of ecological interaction networks (2016, methodology): “Despite its intuitive interpretation and easy translation into species distribution models (Boulangeat et al., 2012; Blonder et al., 2014), the concept has been frequently criticized (Hardin, 1960; Peters, 1991; Silvertown, 2004), and several attempts have been made to expand and improve it…”
Functional diversity metrics using kernel density n-dimensional hypervolumes (2020, methodology): “Full details on these approaches have been discussed elsewhere (Blonder et al., 2014, 2018; Blonder, 2016).”
Functional richness: Overview of indices and underlying concepts (2018, methodology): “The N-hypervolume index is computed from this truncated distribution pattern (see Blonder et al. 2014 for more details on the mathematical properties) and the extension of this method to multiple dimensions).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “Metrics of niche breadth can range from volumetric measurements of ndimensional hypervolumes (e.g., Blonder et al. 2014), to reduced principal component axes of occupied regions of environmental space (e.g., Saupe et al. 2015), to counts of unique habitat types presumed to be occupied (e.g.,…”
Habitat filtering determines the functional niche occupancy of plant communities worldwide (2018, positive): “In the present study, each hypervolume (functional volume of a species) was constructed using a quantile threshold of 0.05, 1,000 Monte Carlo samples per data point, and a fixed kernel bandwidth of 0.5 SD (Blonder et al., 2014; Lamanna et al., 2014).”
Traits Without Borders: Integrating Functional Diversity Across Scales. (2016, methodology): “Considering niches as uniform features, as in the convex hull volume and hypervolume methods [47,48], can result into biased estimates of overlap (overestimates in the examples in Figure IC).”