Implications of incomplete networks on estimation of landscape genetic connectivity
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Understanding processes and landscape features governing connectivity among individuals and populations is fundamental to many ecological, evolutionary, and conservation questions. Network analyses based on graph theory are emerging as a prominent approach to quantify patterns of connectivity with more recent applications in landscape genetics aimed at understanding the influence of landscape features on gene flow. Despite the strong conceptual framework of graph theory, the effect of incomplete networks resulting from missing nodes (i.e. populations) and their genetic connectivity network interactions on landscape genetic inferences remains unknown. We tested the violation of this assumption by subsampling from a known complete network of breeding ponds of the Columbia Spotted Frog (Rana luteiventris) in the Bighorn Crags (Idaho, USA). Variation in the proportion of missing nodes strongly influenced node-level centrality indices, whereas indices describing network-level properties were more robust. Overall incomplete networks combined with network algorithm types used to link nodes appears to be critical to the rank-order sensitivity of centrality indices and to the Mantel-based inferences made regarding the role of landscape features on gene flow. Our findings stress the importance of sampling effort and topological network structure as they both affect the estimation of genetic connectivity. Given that failing to account for uncertainty on network outcomes can lead to quantitatively different conclusions, we recommend the routine application of sensitivity analyses to network inputs and assumptions.
KeywordsNetwork theory Uncertainty Network indices Landscape genetics Sampling issue
This work was conducted as part of the Distributed Graduate Seminar (DGS) course on Landscape Genetics, supported in part by the National Center for Ecological Analysis and Synthesis, a Center funded by NSF (Grant #EF-0553768), the University of California, Santa Barbara, and the State of California. INL was supported by NSERC, YR by CONACYT, MJF by NSERC Discovery grant, and MAM by Colorado State University (W. C. Funk) and University of Wyoming. The authors thank Rodney Dyer for assistance with programming enquiries and the DGS Landscape Genetics group for valuable input.
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