Circuit theory predicts gene flow in plant and animal populations

TLDR

Maintaining connectivity for dispersal and gene flow is essential for conserving endangered species in fragmented landscapes, yet existing models cannot incorporate multiple pathways linking populations, making it difficult to identify habitats that promote connectivity. The study tests an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model, based on electrical circuit theory, simultaneously integrates all possible pathways connecting populations. The model vastly improves gene flow predictions, consistently outperforming conventional models when applied to threatened mammal and tree species, revealing that barriers were less important in structuring populations than previously thought, and providing the best-justified method to bridge landscape and genetic data with promise for ecology, evolution, and conservation planning.

Abstract

Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.