HexSim: a modeling environment for ecology and conservation

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Abstract

<div class="section"> <a class="named-anchor" id="S1">  </a> <h5 class="section-title" id="d1447254e90">Context</h5> <p id="P1">Simulation models are increasingly used in both theoretical and applied studies to explore system responses to natural and anthropogenic forcing functions, develop defensible predictions of future conditions, challenge simplifying assumptions that facilitated past research, and to train students in scientific concepts and technology. Researcher’s increased use of simulation models has created a demand for new platforms that balance performance, utility, and flexibility. </p> </div><div class="section"> <a class="named-anchor" id="S2">  </a> <h5 class="section-title" id="d1447254e95">Objectives</h5> <p id="P2">We describe HexSim, a powerful new spatially-explicit, individual-based modeling framework that will have applications spanning diverse landscape settings, species, stressors, and disciplines (e.g. ecology, conservation, genetics, epidemiology). We begin with a model overview and follow-up with a discussion of key formative studies that influenced HexSim’s development. We then describe specific model applications of relevance to readers of Landscape Ecology. Our goal is to introduce readers to this new modeling platform, and to provide examples characterizing its novelty and utility. </p> </div><div class="section"> <a class="named-anchor" id="S3">  </a> <h5 class="section-title" id="d1447254e100">Conclusions</h5> <p id="P3">With this publication, we conclude a >10 year development effort, and assert that our HexSim model is mature, robust, extremely well tested, and ready for adoption by the research community. The HexSim model, documentation, worked examples, and other materials can be freely obtained from the website www.hexsim.net. </p> </div>

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Brad McRae (2006)

Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.

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Circuit theory predicts gene flow in plant and animal populations.

Brad McRae, Paul Beier (2007)

Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.