RESEARCH ARTICLE A new, multi-scaled graph visualization approach: an example within the playa wetland network of the Great Plains Nancy E. McIntyre • Richard E. Strauss Received: 15 July 2012 / Accepted: 19 February 2013 Ó Springer Science+Business Media Dordrecht 2013 Abstract We employed a sliding-window approach at multiple scales (window sizes and dispersal dis- tances) to calculate seven standard graph-theoretical metrics within a subset of a large, freshwater wetland network. In contrast to most graph analyses, which quantify connectivity at a single (global) scale or at a patch-level scale, a multi-scaled, sliding-window approach provides an assessment that bridges these two approaches to examine patch clusters. As a case study we focused on a subset of a habitat patch network in a *20,000 km 2 area encompassing 2,782 playa wetlands in the panhandle of Texas. Playas are seasonal wetlands of the southern Great Plains of North America that form a network of regional habitat resources for wildlife. The large size of this network meant that global metrics failed to capture localized properties, so we used contour mapping to visualize continuous surfaces as functions of playa density, linkage density, and other topological traits at differ- ent window sizes and dispersal distances. This tech- nique revealed spatial patterns in the components (i.e., the network properties of regions of the landscape at a given dispersal scale), with the spatial scale of habitat clustering varying with the size of the sliding window and dispersal distance. Using a tool familiar to landscape ecology (sliding-window methodology) in a novel way (to examine ecological networks at multiple scales), our approach provides a way to represent ecologically determined local-scale graph properties and illustrates how a multi-scaled approach is useful in examining habitat connectivity to inves- tigate graph properties. Keywords Contour mapping  Graph theory  Network  Sliding window Introduction In our increasingly fragmented world, natural resource conservation hinges on development of rigorous yet rapid methods to assess connectivity (that is, how landscape patterns can facilitate or impede ecological flows), identify key habitat patches, and quantify system vulnerability to future fragmentation events. Graph (network) approaches to these objectives have experienced a recent surge in popularity (Bunn et al. 2000; Fall et al. 2007; Urban et al. 2009; Dale and Fortin 2010; Rayfield et al. 2011) because they provide a framework for testing hypotheses about the influ- ences of landscape structure on dispersal, gene flow, or extinction risk (Urban and Keitt 2001; Minor and Urban 2008). Unlike traditional indices of landscape fragmentation, which can be difficult to link to demographic processes, a network approach is rela- tively easy to implement and interpret and can serve as a proxy for more ‘‘data-hungry’’ spatially explicit N. E. McIntyre (&)  R. E. Strauss Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409-3131, USA e-mail: nancy.mcintyre@ttu.edu 123 Landscape Ecol DOI 10.1007/s10980-013-9862-4