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Hill-Chao numbers allow decomposing gamma multifunctionality into alpha and beta components.

Anne Chao¹, Chun-Huo Chiu², Kai-Hsiang Hu¹

¹Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan.

Ecology Letters

|December 11, 2023

Summary

Biodiversity-ecosystem functioning (BEF) research now includes landscape-level impacts. A new statistical framework using Hill-Chao numbers decomposes multifunctionality, revealing new insights at beta and gamma scales.

Keywords:

BETA-FOR project Hill numbers alpha multifunctionality beta multifunctionality diversity decomposition ecosystem functions gamma multifunctionality species diversity

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Area of Science:

Ecology
Biodiversity Science
Ecosystem Functioning

Background:

Biodiversity-ecosystem functioning (BEF) research confirms biodiversity's positive impact on ecosystem functions at local scales.
Landscape homogenization in the Anthropocene raises concerns about biodiversity loss at beta (across ecosystems) and gamma (regional) scales impacting ecosystem functioning.

Purpose of the Study:

To propose a novel statistical framework for decomposing multifunctionality at gamma scales into alpha and beta components.
To enable the assessment of BEF relationships at landscape scales, integrating local and regional perspectives.
To allow weighting of individual ecosystem functions within the analysis.

Main Methods:

Developed a statistical framework based on Hill-Chao numbers.
Applied the framework to decompose multifunctionality at gamma scales into alpha and beta components.
Incorporated the ability to weight individual ecosystem functions.

Main Results:

The novel framework successfully decomposes multifunctionality at gamma scales into alpha and beta components.
New biodiversity-ecosystem functioning (BEF) insights were discovered for beta and gamma scales.
The approach connects local- and landscape-scale BEF assessments.

Conclusions:

The proposed Hill-Chao number-based framework is a critical, previously missing tool for BEF research.
This approach is applicable across diverse ecosystems, bridging experimental and natural settings.
It advances our understanding of how biodiversity loss at multiple scales impacts ecosystem functioning.