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Range-weighted branch length difference (RWiBaLD), a new method for distinguishing meso-endemism from neo-endemism
Brent D Mishler1, Nunzio Knerr2, Joseph T Miller3
1Department of Integrative Biology, University and Jepson Herbaria, University of California, Berkeley, California, United States of America.
Plos One
|May 22, 2026
Summary
We introduce Range Weighted Branch Length Difference (RWiBaLD), a new method to analyze phylogenetic endemism (PE). RWiBaLD identifies specific evolutionary branches contributing to endemism, distinguishing between neo-, paleo-, and meso-endemism for better conservation insights.
Area of Science:
- Ecology
- Evolutionary Biology
- Phylogenetics
Background:
- Phylogenetic endemism (PE) is crucial for understanding biodiversity patterns.
- Current methods for studying PE often lack the granularity to pinpoint specific evolutionary contributions.
- A need exists to differentiate between various forms of endemism, including a missing category.
Purpose of the Study:
- To introduce a novel method, Range Weighted Branch Length Difference (RWiBaLD), for analyzing PE.
- To identify specific phylogenetic branches contributing to PE.
- To distinguish between neo-endemism, paleo-endemism, and introduce meso-endemism.
Main Methods:
- Developed RWiBaLD based on the difference between observed and comparison range-weighted phylogenetic trees.
- Applied RWiBaLD to the plant genus Acacia in Australia using existing spatial and phylogenetic data.
- Demonstrated RWiBaLD's utility in mapping PE onto phylogenies and dissecting landscape-level PE concentrations.
Main Results:
- RWiBaLD effectively identifies branches contributing most to PE.
- The method successfully distinguishes between neo-, paleo-, and the newly defined meso-endemism.
- Application to Acacia revealed detailed insights into PE composition and distribution.
Conclusions:
- RWiBaLD provides a powerful new tool for studying the evolutionary and ecological drivers of endemism.
- This method enables PE to be scored for phylogenetic comparative methods.
- RWiBaLD enhances conservation assessments by providing a nuanced understanding of endemism.
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