A statistical thermodynamic model of the organizational order of vegetation

The complex pattern of vegetation is the macroscopic manifestation of biological diversity and the ecological order in space and time. How is this overwhelmingly diverse, yet wonderfully ordered spatial pattern formed, and how does it evolve? To answer these questions, most traditional approaches have been empirical and inductive, based primarily on vegetation classification and gradient analysis. In contrast, here we develop a statistical thermodynamic model of the organizational order of vegetation (OOV) that can be used to derive vegetation patterns at large scales. We define OOV as a thermodynamic measure of the degree of structural and functional self-organization of natural vegetation in a given environment, which is related to the complexity and stability of ecosystems. The model unites OOV, ecosystem entropy, actual annual evapotranspiration, and mean annual temperature in the same thermodynamic framework. We use the model to calculate OOV values for major world biomes and derive their global pattern. The vegetation gradients predicted by the model contain the major ecoclines empirically identified by Communities and Ecosystems (1975) 385. In addition, the thermodynamic model predicts more gradients of ecosystems at the global scale, which provides theoretical insights into large-scale vegetation pattern analysis. This paper demonstrates that, as we deal with the complexity, diversity, and heterogeneity of ecological systems, statistical and non-equilibrium thermodynamics may serve as both a theoretical framework and a practical modeling approach to integrate patterns and processes across scales.