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Examples

Our examples are selected to highlight different aspects of integrating social and ecological information, using examples from our work where applicable and a classic example in agent-based modeling that demonstrates several aspects of interest here.

Integrated Assessments with SAVANNA and DECUMA

The ecosystem modeling tool we have used the longest in integrated assessments is SAVANNA. Indeed, M. Coughenour of Colorado State University began developing SAVANNA while working on one of the first large-scale projects to consider humans and their environment in an integrated way, the South Turkana Ecosystem Project of the 1980s (Ellis et al. 1993; Little and Leslie 1999). In the late 1990s, the SAVANNA model was the integrative tool we used to bring together data collection and analysis efforts that included anthropological surveys, ecological field sampling, literature review, and spatial data (Galvin et al. 2006). SAVANNA is a spatially-explicit, processbased model of ecosystem change. Landscapes are divided into a series of square cells, with geographic data layers informing the model of cell attributes. The model represents plant functional groups, such as palatable grasses, dwarf unpalatable shrubs, or acacia trees. Plant functional groups compete for water, nutrients, light, and space, based on cell attributes such as soil type, weather data that includes temperature and precipitation, plus a suite of parameter files that describe or control plant growth and competition. At each time-step, plants may produce seed, germinate, grow, outcompete other functional groups and gain ground cover, or be outcompeted by other plants and die. Wildlife and livestock are represented in the model as populations that feed on the plants to gain energy, and expend energy through basal metabolism, movement, thermal regulation, plus reproduction including gestation and lactation. Net energy increase goes to weight gain, and an energy deficit to weight loss. Body weight is compared to an expected standard to yield a condition index that affects birth, death, and other vital rates. The model uses a weekly time-step, where the state of the system is simulated once each week, and produces spatial and temporal output once per month.

The use of a comprehensive model such as SAVANNA is time intensive. That said, the trade-off is its great flexibility in addressing scenarios. We used SAVANNA in scenario analyses to address 15 management options available to the conservators of Ngorongoro Conservation Area (Boone et al. 2002). We explored effects of drought, changes in livestock access and stocking, the expansion of cultivation and its effect on the system, changes in veterinary care, changes in water supplies, and human population growth. In general these scenarios were represented by making relatively simple changes to the files used by SAVANNA. For example, to represent changes in water supply that affect livestock and wildlife distribution, water sources were added or removed in a GIS and distance-to-water surfaces used in the model were recalculated. To represent improved veterinary practices, the survival of livestock was increased slightly. We have used the model to help extend the utility of climate forecasts to South African livestock owners (Boone et al. 2004), by itself and as linked to a mathematical linear programming model that provided measures of economic benefit to livestock owners (Thornton et al. 2004). In recent years we have used the integrated system to explore effects of fragmentation on livestock, wildlife, and people (e.g., Boone et al. 2005; Boone 2007; Hobbs et al. 2008).

Early in our work, P. Thornton of the International Livestock Research Institute, Nairobi, led an effort to extend our integrated modeling of areas to include the livestock owners and their households. He created the PHEWS model (Pastoral Household Economic Welfare Simulator) as a population-based representation of Maasai households, which is tightly joined to the SAVANNA model (Thornton et al. 2003, 2006). That model represented decision making by household owners using a series of ordered rules, applied to a modest number (9–24) of groups of households, such as poor, medium, and rich business owners with livestock. The flows of food energy and currency were tracked in the model.

The population-based nature of PHEWS prevented us from simulating individual households who own their own livestock herds. It follows that we could not have local ecosystem services influence the decision making of household owners – local conditions cannot be defined for populations of hundreds of households. We converted the PHEWS model into an agent-based representation called DECUMA (DECision-making Under Conditions of Uncertainty for Modeled Agents, and also the name of a Roman fate that influences the length of life). In DECUMA, individual households are represented as occurring at specific locations on earth, and they own specific livestock herds. When linked to SAVANNA, that allows us to have local ecosystem services influence the decision making of pastoral people, and to have their decisions influence ecosystem services. Boone et al. (2011) describes the DECUMA model and linkage with SAVANNA in detail.

DECUMA has been applied in Kajiado District, southwest Kenya, and applications are ongoing in Samburu, Kenya, as well as Mali and Tibet. The Malian application provides an example of the usefulness of making tools used in integration portable. In that work, led by N. Hanan of South Dakota State University, we are exploring changes in the hydrology of lakes, the roles that pastoral people have had in those changes, and the benefits to them. A hydrological model (SWAT; Gassman et al. 2007) is being linked to the ecosystem model called ACE (African Carbon Exchange), which in turn is being linked to DECUMA. By programmatically isolating the materials DECUMA requires from an ecosystem model (see Boone et al. 2011 for details), we can relatively easily link the model with any ecosystem simulation tool that can provide the needed information (e.g., forage availability and forage acquired by animals).

Our ongoing analyses in Samburu, Kenya demonstrates this kind of integrated modeling. C. Lesorogol of Washington University, St. Louis, Missouri has gathered in-depth anthropological data for two study sites in southwest Samburu District, Mbaringon and Siambu. The sites differ in ecological settings, with Mbaringon at lower elevation and with less rainfall, for example. But the main difference of anthropological interest is that Siambu is subdivided, and Mbaringon remains communal lands (although somewhat fragmented). In the 1970s residents within some districts in Kenya began to subdivide into individually owned parcels. In Siambu, the land was divided into 240 small individually owned parcels. We are also investigating changes in Samburu norms, where the sense of reciprocity and sharing is less important in young peoples' lives.

Our integrated assessments are driven by both theoretical questions and by questions put forth by stakeholders (Reid et al. 2009). The eight scenarios (numbered below) we are addressing in Samburu reflect this, and highlight the flexibility of using comprehensive simulation tools such as SAVANNA and DECUMA. Central to our work are questions of subdivision and its effects. We are simulating sedentarizing people and their animals on individually owned parcels, and the effects of that on livelihoods (1). Another scenario asks about the influence of commercial cropping in Siambu and fence building in Mbaringon, and the effects of loss of access has on livestock (2). These types of scenarios are represented in the modeling system by altering spatial surfaces or agent behaviors so as to prevent animals from leaving home parcels or from using areas that are inaccessible. We describe a diversity of scenarios to demonstrate the utility of integrated modeling but discuss one (number 8) in more detail here.

Both Siambu and Mbaringon are grazing refuges for herders outside those areas. When drought conditions hold in other areas of Samburu, herders move their animals into these areas. In a scenario, we are adding additional livestock to each area, and summarizing effects on the resident animals (3). Plains zebras (Equus quagga) and occasionally Grevy's zebras (Equus grevyi) are joined by various antelopes in the Mbaringon study site. We will vary the numbers of wildlife by a factor of four in scenarios, with and without tourism benefits to local people, to judge effects on livestock numbers and household livelihoods (4). Livestock sales are increased in simulations, above the observed number of sales that is typical (5). This is an

example of a scenario that Samburu residents asked us to include, given their interest in intensifying their livestock management. A program is in place now, led by

C. Lesorogol, to introduce enhanced and highly productive goat breeds into Siambu. In a scenario, attributes of goats are modified to represent mixed herds of local and enhanced breeds, and effects on livelihoods judged (6). Residents asked us to explore the implications of improved crop yields on livelihoods (7). This scenario addresses a variety of management options, streamlined so as to be amenable to simulation with our tools. For example, land owners struggle to decide whether increased production from high-yield seed stocks outweigh their increased costs, whether to invest in chemical fertilizers (very few do in the region), investing in water projects for irrigation, and the usefulness versus costs of drought tolerant seeds.

The last scenario (8) compares the costs and benefits of increasing or decreasing veterinary care for livestock. Residents seek to balance the money they spend on veterinary care with the benefits they receive through improved livestock health and survival. We used an application of SAVANNA and DECUMA to Mbaringon to address this scenario. These preliminary results report outcomes from three simulations (baseline, increased livestock survival by 3 %, decreased by 3 %); in practice we do 20 or more simulations of each type to yield error estimates. Such a seemingly small change in survival for large herbivores can have dramatic impacts on population dynamics. In this example, Fig. 9.2 (top) shows about a 1 tropical livestock unit (TLUs) increase for each adult equivalent (AE). These metrics are methods of standardizing livestock of different species (e.g., a cattle is 1 TLU, and a sheep or goat is 0.1 TLU) and humans of different ages and sexes (e.g., an adult male is 1 AE, and a child 6–12 years old is 0.85 AE) (Boone et al. 2011). Figure 9.2 (bottom) demonstrates one of the many linkages within the SAVANNA-DECUMA integrated system. The average proportion of households' diets composed of supplemental food decreases when more funds are spent on veterinary care. This must be weighed against the costs of the improved care.

 
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