RESEARCH ARTICLE Implications of incomplete networks on estimation of landscape genetic connectivity Ilona R. Naujokaitis-Lewis • Yessica Rico • John Lovell • Marie-Jose ´e Fortin • Melanie A. Murphy Received: 30 January 2012 / Accepted: 5 July 2012 / Published online: 19 July 2012 Ó Springer Science+Business Media B.V. 2012 Abstract Understanding processes and landscape fea- tures governing connectivity among individuals and pop- ulations 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 appli- cations 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 inter- actions 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. Keywords Network theory  Uncertainty  Network indices  Landscape genetics  Sampling issue Introduction In the face of global environmental changes, understanding the degree to which a landscape facilitates or impedes movement of organisms, and therefore their genes, is fundamental to ensuring species persistence and is known as landscape connectivity (Taylor et al. 1993). Network analyses are becoming a prominent approach to assess genetic connectivity among populations (Fortuna et al. 2009; Rozenfeld et al. 2008; Van Oppen et al. 2011). The two main structural elements of networks are nodes and edges. In a landscape genetics context, nodes can represent an individual unit (individual, group, population), while edges represent a relationship between nodes (connectivity, flow) quantified in terms of a genetic measurement. Instead of using direct observations of dispersal through mark- recapture-release studies, estimates of gene flow can be Electronic supplementary material The online version of this article (doi:10.1007/s10592-012-0385-3) contains supplementary material, which is available to authorized users. I. R. Naujokaitis-Lewis (&)  M.-J. Fortin Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada e-mail: ilona.naujokaitis.lewis@utoronto.ca Y. Rico Department of Ecology and Evolutionary Biology, University of Toronto, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada J. Lovell Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523-1177, USA M. A. Murphy Ecosystem Science and Management, University of Wyoming, 1000 E University Ave., Laramie, WY 82071, USA 123 Conserv Genet (2013) 14:287–298 DOI 10.1007/s10592-012-0385-3