Adaptive self-organization of global swidden forests.

Abstract

Does swidden agriculture, a prototypical coupled human and natural system, exhibit a process of adaptive self-organization in which cultural practices balance environmental constraints through adaptive feedback? Here, we investigate whether quantitative signatures of adaptive self-organization can be detected in a dataset consisting of 18,000+ contiguous swidden patches in 18 remote sensing images of swidden mosaics from tropical and subtropical regions globally. We find that the distributions of patch sizes in 16 of 18 swidden areas exhibit power law patterns with scaling exponents ≈1, and correlation distances of ≈548 m. To account for these patterns, we develop a plausible ethnographically informed agent-based model of labor exchange, land use, and swidden site selection in which both sustainable and unsustainable resource uses can emerge out of interactions among individuals or households. By analyzing the model, we identify spatial synchronization of swidden sites as the driver of power law formation, while social norms of swidden labor can guide the system to an intermediate level of landscape disturbance. Both mechanisms are required to maintain harvests and ecosystem productivity at high levels. Our model advances theoretical understanding of the socioecological dynamics of swidden agriculture, and supports the hypothesis that adaptive self-organization may be a general characteristic of coupled human and natural systems.