The goal of this research was to develop and test analytical tools needed to understand and predict the feedbacks among humans and aquatic species across complex landscapes. Our central organizing principle was that landscape patterns and changes in network structure and function are explained by energy and time optimizations of water flows, biota and humans. We focused on the road-river linkages because river ecosystems are vulnerable to anthropogenic perturbations that are related to road access. Typically, adding more complexity increases the variance and uncertainty of modeling predictions. Our main overarching hypothesis was that an integrated individual-based model would more accurately predict environmental effects than any single physical, biotic or social model by reducing unexplained variation. The mechanism generating this reduction in variance results from including cross-disciplinary connections and corresponding agent feedbacks to what otherwise would be missing in the individual disciplinary models. Our approach integrated multidisciplinary field studies into a unified individual-based model that addressed fundamental problems of biocomplexity to quantify and evaluate different dimensions of interacting human and natural systems.
We quantified individual usage and landscape responses to changes in the road network in four watersheds that vary in land use, road density, and access by people. We expected that simultaneous modeling of physical, hydrological and social networks within an individual/agent-based framework would result in reduced error variance relative to isolated subsystem models. Our team included a resource economist, GIS specialist, natural resource sociologist, hydrologist, geomorphologist, modeler and ecologists. The overall project design incorporated all disciplinary teams sampling river-road network intersections (nodes) at similar times to maximize our data synthesis. Our individual-based model resulted in a predictive model that allowed us to determine how humans chose visitation places as well as how the native aquatic biota were distributed across the landscape. Including multiple stressors and drivers did, indeed increase our explanatory power of the watershed physical and biotic processes.