Modeling biocomplexity – actors, landscapes and alternative futures

Abstract

Increasingly, models (and modelers) are being asked to address the interactions between human influences, ecological processes, and landscape dynamics that impact many diverse aspects of managing complex coupled human and natural systems. These systems may be profoundly influenced by human decisions at multiple spatial and temporal scales, and the limitations of traditional process-level ecosystems modeling approaches for representing the richness of factors shaping landscape dynamics in these coupled systems has resulted in the need for new analysis approaches. Additionally, new tools in the areas of spatial data management and analysis, multicriteria decision-making, individual-based modeling, and complexity science have all begun to impact how we approach modeling these systems. The term "biocomplexity" has emerged a descriptor of the rich patterns of interactions and behaviors in human and natural systems, and the challenges of analyzing biocomplex behavior is resulting in a convergence of approaches leading to new ways of understanding these systems. Important questions related to system vulnerability and resilience, adaptation, feedback processing, cycling, nonlinearities and other complex behaviors are being addressed using models employing new representational approaches to analysis. An emerging application area is alternative futures analyses, the study of how complex coupled human/natural systems dynamically respond to varying management strategies and driving forces. This methodology is increasingly being used to inform decision makers about the implications of policy alternatives related to land and water management, expressed in terms related to human valuations of the landscape. Trajectories of change become important indicators of system sustainability, and models that can provide insight into factors controlling these trajectories are rapidly becoming essential tools for planning. The complexity inherent in these systems challenges the modeling community to provide tools that capture sufficiently the richness of human and ecosystem processes and interactions in ways that are computationally tractable and understandable. We examine one such tool, Evoland, which uses an actor-based approach to conduct alternative futures analyses in the Willamette Basin, Oregon. Actor-based approaches, spatially-explicit landscape representations, and complexity science are providing new ways to effectively model, and ultimately to understand, these systems.

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