Abstract
Abstract. A century of hydrologic modification has altered the physical and biological drivers of landscape processes in the Everglades (Florida, USA). Restoring the ridge–slough patterned landscape, a dominant feature of the historical system, is a priority but requires an understanding of pattern genesis and degradation mechanisms. Physical experiments to evaluate alternative pattern formation mechanisms are limited by the long timescales of peat accumulation and loss, necessitating model-based comparisons, where support for a particular mechanism is based on model replication of extant patterning and trajectories of degradation. However, multiple mechanisms yield a central feature of ridge–slough patterning (patch elongation in the direction of historical flow), limiting the utility of that characteristic for discriminating among alternatives. Using data from vegetation maps, we investigated the statistical features of ridge–slough spatial patterning (ridge density, patch perimeter, elongation, patch size distributions, and spatial periodicity) to establish more rigorous criteria for evaluating model performance and to inform controls on pattern variation across the contemporary system. Mean water depth explained significant variation in ridge density, total perimeter, and length : width ratios, illustrating an important pattern response to existing hydrologic gradients. Two independent analyses (2-D periodograms and patch size distributions) provide strong evidence against regular patterning, with the landscape exhibiting neither a characteristic wavelength nor a characteristic patch size, both of which are expected under conditions that produce regular patterns. Rather, landscape properties suggest robust scale-free patterning, indicating genesis from the coupled effects of local facilitation and a global negative feedback operating uniformly at the landscape scale. Critically, this challenges widespread invocation of scale-dependent negative feedbacks for explaining ridge–slough pattern origins. These results help discern among genesis mechanisms and provide an improved statistical description of the landscape that can be used to compare among model outputs, as well as to assess the success of future restoration projects.
Highlights
The coupling of ecosystem processes operating at different scales can cause vegetation communities to form a wide variety of spatial patterns (Borgogno et al, 2009), ranging from highly regular striping, stippling, or maze-like patterns in woodland landscapes (Ludwig et al, 1999), tidal mud flats (Weerman et al, 2012), and boreal peatlands (Eppinga et al, 2010) to scale-free patterning in semi-arid landscapes (Kéfi et al, 2007; Scanlon et al, 2007)
We focus on Water Conservation Area 3 (WCA-3), located in the central Everglades, an area historically dominated by the ridge-slough landscape (Fig. 1), and where the best conserved patterning is found
Individual ridges exhibit numerous connections between adjacent elongated portions, with larger patches forming complex webs composed of multiple individual elements. This behavior is apparent in all sites, it appears to be density dependent, with most of the landscape spanned by one large patch in denser sites (e.g., 2, 5, 8, 9, 11–13, 23–28, 30–33)
Summary
The coupling of ecosystem processes operating at different scales can cause vegetation communities to form a wide variety of spatial patterns (Borgogno et al, 2009), ranging from highly regular striping, stippling, or maze-like patterns in woodland landscapes (Ludwig et al, 1999), tidal mud flats (Weerman et al, 2012), and boreal peatlands (Eppinga et al, 2010) to scale-free patterning in semi-arid landscapes (Kéfi et al, 2007; Scanlon et al, 2007). The mechanisms that produce these patterns are integral to understanding landscape origins and, for predicting appropriate remedies where patterns and underlying processes have been degraded and require restoration. The spatial arrangement of vegetation on the landscape has long been viewed as a manifestation of the dominant interactions and drivers (Hutchinson, 1957; Levin, 1992) and the scales at which they operate. By quantifying this spatial arrangement we can make process-based inferences about the underlying mechanisms (Gardner et al, 1987; Turner, 2005). Casey et al.: Hydrologic controls on spatial organization of the ridge–slough
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