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Summary and Conclusions
Models by their nature are incomplete representations of the realities they seek to describe. Some are caricatures of reality, some seek to emulate real-world higher level patterns, and some seek quantitative agreement with patterns through space and time (Axtell and Epstein 1994). The correctness and utility of a model should be considered in the context of its purpose. A purpose becoming more common is to represent ecological and social systems, and the linkages in between. In general, ecological modeling is more advanced than modeling social systems – there are many opportunities for advancing knowledge and methods in social simulation. Agent-based modeling has been useful in representing human decision making. The method is highly flexible, and able to incorporate individual variation and path dependencies. If-then structures and the parameters used in them become hypotheses that may be tested, in direct analogy to field experiments. Linked ecosystem and agent-based models allow changes in ecosystem services to be reflected in the behaviors that people exhibit. In turn, the behaviors that people make can alter the services an ecosystem provides (Bonabeau 2002). For example, changes in forage availability may be simulated in an ecosystem model, which influences the ways in which people distribute their livestock, which in turn affects forage availability in later periods. In an example we demonstrate an integrated model of two areas in Samburu, Kenya. That is an example where we seek to be in quantitative
Fig. 9.3 The NetLogo interface for a simulation exploring the usefulness of dry season versus wet season dispersal for Maasai pastoralists and their animals
Fig. 9.4 The number of cattle herds that may be supported on a stylized landscape with different numbers of randomly distributed water wells, and either a pattern of wet season dispersal during grazing (solid line) or dry season dispersal (dotted line). Standard error bars are shown
agreement with the observed system. It also demonstrates the flexibility (and challenge) that comes with using comprehensive tools. An example from the literature depicts agents learning and adapting, and the means in which agents may form networks where information is shared. Lastly, we demonstrate a stylized simulation of livestock dispersal patterns in grazing lands of Kenya.
Our emphasis on agent-based modeling should not suggest that its use is the only way to integrate across the natural and social sciences. As always, the questions to be addressed dictate the approach and tools to be used. Some other useful pathways have been cited here, such as participatory mapping and other participatory methods, which allow for the inclusion of indigenous knowledge in research. Other more mainstream means of modeling may be used in integration, such as empirical methods or systems modeling approaches. Spatial analyses using geographic information systems help bridge social and natural sciences, for example by providing geographic context to household survey results (e.g., Boone et al. 2000). Remote sensing allows effects of human activities to be placed in the context of broad spatial scales, with sampling through space and time and without undo expense. Land use change quantified using remotely sensed data is now a well-developed field. Planning and the use of scenario analyses allow interdisciplinary teams to integrate aspects of their work. For example, questions about changes in social systems may be framed by scenarios regarding changing climate or changes in the services that an ecosystem provides.
Though tools and pathways of integration are increasing, the ability to integrate across the sciences it is not without challenges. There remain issues of scale, including mismatched social-organizational scales, such as comparing administrative boundaries with landscape scales in which ecological flows such as water, wildlife and soil nutrients occur. There are scales of drivers and impacts that go between the local, regional, national, and to the global scale, such as climate change, land use and other policies, market influences and others that are difficult and sometimes intractable. There are also challenges associated with human populations that are important but sometimes difficult to include in integration efforts and include information on equity, gender, justice, class, ethnicity, power and history. These are important because they determine winner and losers of social ecological inquiry and they are central to realizing change in practice on the ground.
There are factors that help to integrate social sciences and natural resources. These include new theories and methods. Theories of political ecology (cf Robbins 2012), resilience and social-ecological systems (cf Folke 2006), and common property (cf Ostrom 2002) as examples can help us ask multi-scale questions, incorporate socially and culturally structured relations into the research (such as gendered decision-making roles or ethnicity/class and degraded or resource poor landscapes) and iteratively linked human decisions to environmental outcomes and vice versa. This inclusion of complexity calls for mixed methods; we are no longer tied to mainstream disciplinary methods but rather a set of mixed methods may be used to answer the problems at hand. These include Photovoice, videography, qualitative unstructured and semi-structured interviews, focus groups, workshops, participatory modeling, formal surveys and social network analysis. By coupling these methods with the ecological and geographical methods and tools mentioned above, including agent based modeling, we can continue to develop solutions to timely and important societal and environmental problems.
Acknowledgements Our thanks to our many colleagues who have participated in the research examples we have cited. Support for preparing this chapter was provided by US National Science Foundation grants DEB-1010465, BCS-0822752, and DEB-0919383.
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