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A novel quantitative framework for riverscape genetics.

Shannon L White1,2, Ephraim M Hanks3, Tyler Wagner4

  • 1Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, Pennsylvania, 16802, USA.

Ecological Applications : a Publication of the Ecological Society of America
|April 28, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new framework and model for riverscape genetics to better understand how river environments affect gene flow. This approach improves the analysis of genetic connectivity in river ecosystems for conservation efforts.

Keywords:
bidirectional geneflowbrook troutmigration rateriverscape geneticssimultaneous autoregressivespatially structured ecological network

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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.