A network approach for identifying and delimiting biogeographical regions

Area of Science:

• Ecology
• Biogeography
• Network Theory
• Conservation Biology

Background:

• Biogeographical regions are crucial for understanding ecological and evolutionary processes.
• Current species turnover methods for mapping biodiversity are susceptible to sampling biases.
• Accurate delimitation of biogeographical regions is essential for effective conservation strategies.

Purpose of the Study:

• To develop a novel method for objective identification and delimitation of biogeographical regions.
• To overcome limitations of existing similarity-based algorithms, particularly in transition zones.
• To provide a robust framework for mapping global biodiversity patterns.

Main Methods:

• Applied a community detection approach from network theory.
• Incorporated complex, higher-order species presence-absence patterns.
• Tested the method on global amphibian, US vascular plant, and hypothetical transition zone datasets.

Main Results:

• The network-based method successfully identified biogeographical regions, outperforming traditional approaches.
• The approach effectively handled biotic transition zones, a common challenge in spatial ecology.
• Demonstrated robust performance across diverse datasets, including global and regional species distributions.

Conclusions:

• The proposed community detection method offers an objective and data-driven approach to delineate biogeographical regions.
• This technique enhances the accuracy of biodiversity pattern analysis and regionalization.
• Provides a valuable tool for global biogeographical assessments and conservation planning.