Statistical inference methods for n‐dimensional hypervolumes: Applications to niches and functional diversity

Author:

Chen Daniel12,Laini Alex3ORCID,Blonder Benjamin Wong2ORCID

Affiliation:

1. Department of Statistics University of California at Los Angeles Los Angeles California USA

2. Department of Environmental Science, Policy, and Management University of California at Berkeley Berkeley California USA

3. Dipartimento di Scienze della Vita e Biologia dei Sistemi Università di Torino Torino Italy

Abstract

Abstract The size and shape of niche spaces or trait spaces are often analysed using hypervolumes estimated from data. The hypervolume R package has previously supported such analyses via descriptive but not inferential statistics. This gap has limited the use of hypothesis testing and confidence intervals when comparing or analysing hypervolumes. We introduce a new version of this R package that provides nonparametric methods for resampling, building confidence intervals and testing hypotheses. These new methods can be used to reduce the bias and variance of analyses, and well as provide statistical significance for hypervolume analysis. We illustrate usage on real datasets for the climate niche of tree species and the functional diversity of penguin species. We analyse the size and overlap of the respective niche or trait spaces. These statistical inference methods improve the interpretability and robustness of hypervolume analyses.

Publisher

Wiley

Reference21 articles.

1. Hypervolume concepts in niche- and trait-based ecology

2. Blonder B. dc165 Alexology Brown S. Mai J. G.‐S. &Olivroy. (2024).bblonder/hypervolume: 3.1.3(release) [computer software].Zenodo https://doi.org/10.5281/zenodo.10672454

3. Then-dimensional hypervolume

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