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Integrated Modeling

Ecologists have developed in-depth knowledge about many elements in systems, although much remains to be learned. Prior to the 1980s, a majority of experiments on species interactions were on plots of 1 m2 or less (Kareiva and Andersen 1988). New pathways of exploration and enabling technologies fostered a new type of ecological research exploring spatial scale and macroecology (Gaston and Blackburn 2000; Schneider 2001). But the pace of integrating and synthesizing information has been slow (Carpenter et al. 2009). In the 1960s to 1980s social science borrowed ecological terms and analyses such as energy flow studies, adaptation studies and the ecosystem concept and several important studies emerged (e.g., Vayda and McCay 1975; Thomas 1976). But increasing complexity was brought into these studies of human-environment interactions including landscape history (Crumley 1994), policy and power (e.g., Brosius 1997; Escobar 1998) and cultural meanings (Peet and Watts 1996; Berkes 1999). These were aided by new tools such as geographic information systems (GIS) and participatory mapping. New conceptual models that included micro-cultural processes like perception and macro-societal processes such as globalization at various scales were recognized as important elements of research in human-environment interactions (e.g., Liverman et al. 1998).

Subfields in ecology and social science disciplines are once again rapidly developing in large part due to advances in tools such as remote sensing, GIS and modeling. Other current impetuses are a growing human population, increasing stressors on landscapes from local to global scales, and a demand by the public that science address real-world, practical problems likely to have societal impacts. Sustainability science has emerged to address complex problems at the intersection of ecological and social science, with contributions from engineering, atmospheric, and medical sciences. Sustainability science goes beyond traditional hypothesis testing, and instead addresses real-world problems that “blend[s] theory and analysis with political awareness and policy concerns” (Galvin et al. 2006:159). Transdisciplinary teams of ecologists, anthropologists, and others come together to address questions of resilience, adaptive capacities that includes issues of inequality, class, gender and justice, and the sustainability of social-ecological systems (e.g., Folke et al. 2002; Berkes et al. 2003; Leach et al. 2012).

At the core of sustainability science endeavors to understand coupled systems are often computer models that are linked together in an integrated way. In general, the goal of this integration is to have the services ecosystems provide (MEA 2005) influence the behavior and conditions of people and societies, and in turn, to have human decisions and behaviors influence ecosystem services. Different models simulate different components of a coupled system, and many blueprints are used. For example, a hydrology model may be used to represent river flows, an ecosystem model simulates forest growth and carbon sequestration, and an agent-based model (see Sect. 9.4) may represent timber harvesters (see Sect. 9.5 for examples from our work). Often these are well-established models that have been used in discovery for years. New is the effort to link these models together to create an integrated system that includes both humans and the environment. Team members think deeply about their own fields and the simulation tools that each uses, and consider the points of connection between fields. In the example, primary connections may include the harvest of timber, economic benefits from harvest, and increased water runoff from harvested hillsides. The team identifies secondary and tertiary connections as well, perhaps including temperature changes in streams or changes in microclimates (Beschta et al. 1987), and decides what is to be included in connections between models, and excluded. The models are then linked either loosely or tightly (Galvin et al. 2006), a continuum of connectedness depending on the models being used and the questions being asked. Methods used in linking models and other considerations are beyond our scope here [see An and López-Carr (2011) and other entries in that special issue for an introduction]. The goal is to create an integrated set of computer simulation models that support assessment of future conditions under different decision pathways. Generally, even with quite simple constituent models, coupled systems models provide sufficient so-called levers and other controls to address a variety of scenarios.

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