Abstract
Abstract. Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing-canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.
Highlights
The structure and function of natural ecosystems are shaped by complex interactions between biotic and abiotic processes acting at different spatial scales
Hydrodynamic modeling of the discharge competence and landscape hydroperiod suggested that %R exerts dominant control on the landscape hydroperiod, while anisotropy is of secondary importance
Our results suggest that only a subset of simulated landscapes meet the multiple conditions observed in reference landscapes, while power-law scaling, patch complexity, and aperiodic patterning were evident in all simulations, regardless of parameterization
Summary
The structure and function of natural ecosystems are shaped by complex interactions between biotic and abiotic processes acting at different spatial scales. Archetypal examples exist in arid and semiarid ecosystems (Foti and Ramirez, 2013; Saco et al, 2007; Scanlon et al, 2007; Klausmeier, 1999; Mabbutt and Fanning, 1987) and peatlands (Prance and Schaller, 1982; Eppinga et al, 2009; Larsen and Harvey, 2011) These patterns range from regular mosaics with characteristic length scales to scale-free patterns exhibiting heavy-tailed patch size distributions (von Hardenberg et al, 2010). While both types of patterns typically signify the resource-limited nature of their respective environments, the primary biotic and/or abiotic processes that dictate the evolution of regular and scalefree landscapes are thought to be considerably different Regardless of their driving mechanisms, patterned landscapes create ecological heterogeneity and help maintain biological diversity (Kolasa and Rollo, 1991) and productivity, and increase system resiliency (van de Koppel and Rietkerk, 2004). Understanding the mechanisms that govern the development and maintenance of landscape pattern is crucially important
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