Modeling coupled human-natural systems of pastoralism in East Africa

Abstract

Sustainability science studies the drivers of change that alter natural and human systems now and into the future. Systems with natural and human components (coupled systems) may be resilient or nonresilient to these changes. Our focus is on rangelands of East Africa, where drivers of change include rapid population growth, poverty, degraded rangelands, declining wildlife populations, land fragmentation, reduced mobility, and more frequent drought associated with climate change. Computational simulation is a widely used approach to understand coupled systems. Modeling allows us to simplify the representation of systems and to control system elements. Coupled systems applications often include one or more models simulating ecosystem responses, joined to an agent-based model representing individuals or households. With those applications, ecosystem attributes can affect human decision making, and decisions people make affect ecosystems. Internal drivers of change are incorporated into the processes and rules of the models. External drivers are assessed through scenario analyses. We applied a coupled systems approach to Samburu, Kenya, where residents raise cattle, goats, and sheep. We quantified the effects of immigration of herds during drought and changing wildlife populations on local household well-being. Immigration of outside animals caused a 0.5 decline in the number of animals per person and an increase in the amount of supplemental foods needed. Increasing wildlife populations caused declines in livestock numbers, as expected, but payments of the magnitude that may be expected did not offset losses. The coupled systems approach allows more direct inference of changes, and for changes in the ecosystem and human components to influence each other. Challenges include the complexity of the endeavor and the issues addressed, difficulties in model validation, the rates of change, and the political will that may override what scientists view as sustainable solutions. Modeling will improve as psychological research refines decision making rules, the temporal and spatial grain of the simulations increases, and the ability to model many thousands of agents expands.