A novel quantitative framework for riverscape genetics

Area of Science:

• Ecology
• Genetics
• Conservation Biology

Background:

• Riverscape genetics requires methods accounting for stream network structures and bidirectional gene flow.
• Existing quantitative methods have limitations in analyzing spatial autocorrelation and gene flow in riverine systems.

Purpose of the Study:

• To present a general framework and novel bidirectional geneflow in riverscapes (BGR) model for riverscape genetic studies.
• To improve the analysis of genetic connectivity and the influence of environmental covariates on gene flow in riverine ecosystems.

Main Methods:

• Developed a framework involving pairwise genetic distance estimation and a spatially structured ecological network (SSEN).
• Introduced the BGR model using isolation-by-resistance, simultaneous autoregressive models, and generalized Wishart distribution.
• Applied the framework to analyze brook trout populations in Pennsylvania, comparing BGR with MRM and STRUCTURE.

Main Results:

• The BGR model demonstrated greater power in detecting covariate effects on gene flow compared to MRM and STRUCTURE.
• Identified specific riverscape covariates influencing genetic connectivity in brook trout populations.
• The BGR model effectively handles partial barriers and uncommon variables in riverscape analyses.

Conclusions:

• The proposed BGR framework offers a more informative and robust approach for riverscape genetics research.
• This method enhances the assessment of population connectivity and identification of conservation threats in riverine species.
• The flexible framework provides intuitive results for evaluating management scenarios across diverse riverine networks and taxa.