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.
The structure and function of natural ecosystems are shaped by complex interactions between biotic and abiotic processes acting at different spatial scales. In resource-limited environments, these interactions can give rise to self-organized, patterned landscapes (Rietkerk and Van de Koppel, 2008; Dyskin, 2007). Archetypal examples exist in arid and semi-arid 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 scale-free landscapes are thought to be considerably different. Regardless of their driving mechanisms, patterned landscapes create ecological heterogeneity and thus help maintain biological diversity (Kolasa and Rollo, 1991) and productivity, and increase system resiliency (van de Koppel and Rietkerk, 2004). Given their reliance on a critical resource (e.g., water, nutrients, or both), the presence of self-organized patterning also suggests that even subtle disturbances or environmental changes can lead to catastrophic shifts in ecosystem states (Kefi et al., 2007, 2011; Reitkerk et al., 2004). Therefore, understanding the mechanisms that govern the development and maintenance of landscape pattern is crucially important to conserving their ecological attributes, particularly as the exogenous drivers are disrupted by climate change and large-scale anthropogenic landscape modification.
The ridge–slough landscape in the Everglades (Florida, USA) is a patterned
landscape in which two distinct vegetation communities, ridges and sloughs,
comprise a self-organized landscape mosaic (Fig. 1a). Ridge patches occupy
higher soil elevations (
Adverse ecological impacts associated with reduced slough extent and connectivity (SCT, 2003) have led to pattern maintenance and restoration being focal points for restoration planning and assessment. A prerequisite for successful ecological restoration efforts is a clear understanding of pattern genesis mechanisms and the time frame for that process to occur. Despite multiple plausible mechanisms, these feedbacks remain poorly understood (Cohen et al., 2011) principally because of difficulties associated with observation and experimental manipulation at large enough spatial and temporal scales, a constraint that focuses attention on models of pattern genesis and degradation.
Several hypotheses have been proposed to explain the ridge–slough patterning and have produced models that are capable of generating elongated patches. In all cases, pattern evolution in modeled landscapes responds to water flow (and flow direction), but it remains unclear which flow attributes and/or processes govern the process. Moreover, it has been shown elsewhere that different combinations of processes may generate similar patterns (Eppinga et al., 2009), making inference of a single dominant mechanism challenging. Models of ridge–slough pattern genesis have invoked differential sediment transport (Larsen et al., 2011; Lago et al., 2010), nutrient redistribution (Ross et al., 2006), and biased subsurface flow induced by anisotropic hydraulic conductivity (Cheng et al., 2011) as driving mechanisms. Using a process-based numerical model, Ross et al. (2006) showed that differential evapotranspiration rates in higher-elevation soils may lead to the concentration of dissolved nutrients (particularly phosphorus), suggesting that this nutrient redistribution, alone or in combination with sediment and nutrient transport, may generate the ridge–slough pattern in the Everglades. According to Larsen and Harvey (2010, 2011) and Lago et al. (2010), ridge–slough-like flow-parallel patterns develop as the heterogeneous flow regimes caused by vegetation resistance and local elevation differences recursively dictate differential peat accretion, sediment transport, and erosion. Cheng et al. (2011) incorporated anisotropic hydraulic conductivity in a scale-dependent feedback (advection–diffusion) model to demonstrate the evolution of flow-parallel, elongated vegetation bands. Crucially, however, all of these models generate pattern by coupling local positive feedbacks with a distal negative feedback at some intermediate distance imposed either explicitly (Cheng et al., 2011) or implicitly (e.g., erosion and sediment transport by flow; Larsen and Harvey, 2010, 2011; Lago et al., 2010).
As we discuss below, although these models yield patches that are elongated in the direction of flow, the modeled landscapes lack several other critical pattern metrics unique to the conserved portion of the ridge–slough system. With the exception of Larsen and Harvey (2010, 2011), all other models generate patterns that are strikingly regular and with a characteristic separation distance (e.g., Cheng et al., 2011), a property that appears to be absent in the Everglades ridge–slough mosaic. Furthermore, our recent analyses (Casey et al., 2015) of the ridge–slough landscape have shown that the patches in this landscape are strongly scale-independent, suggesting that the negative feedback that stabilizes patch expansion in these landscapes is a global, rather than a distal, scale-dependent mechanism.
An alternate explanation for ridge–slough patterning that includes a global negative feedback is the self-organizing-canal (SOC) hypothesis (Cohen et al., 2011; Heffernan et al., 2013). In this conceptualization, spatially anisotropic patterning emerges from global constraints on patch (both ridges and sloughs) expansion created by feedbacks between landscape geometry, water flow, and hydroperiod, which controls peat accretion (and therefore affects landscape geometry). In short, the SOC hypothesis proposes that elongated patches develop as the landscape incrementally adjusts its spatial geometry (ridge density, size, and shape) to optimize the discharge competence (i.e., ability to convey water; Heffernan et al., 2013; Kaplan et al., 2012; Cohen et al., 2011), and that this pattern may evolve without sediment or nutrient redistribution. Decrease in discharge competence primarily results from high ridge density but can be further intensified by reduced patch elongation (which lowers the probability of longitudinally connected sloughs). Both scenarios yield a global increase in hydroperiod for a given boundary flow (Kaplan et al., 2012). Increased hydroperiod, in turn, makes conditions more favorable for ridge-to-slough transitions, which decreases ridge density, lowering hydroperiod and ultimately tuning landscape pattern to hydrology.
The core assumption in the SOC hypothesis is that patch elongation occurs because changes in patch density affect discharge competence anisotropically. Specifically, expansion of ridge patches parallel to flow has a neutral effect on discharge competence (and thus hydroperiod), while patch expansion orthogonal to flow has a strong negative effect on discharge competence. Kaplan et al. (2012) demonstrated this feedback with a hydrodynamic model of surface water flow across randomly generated patterned landscapes of varying anisotropy, finding that hydroperiod decreased exponentially with increasing patch anisotropy. Using a patch-scale analytical model, Heffernan et al. (2013) demonstrated strong feedbacks between the soil elevation at any given location and an adjacent location perpendicular to flow, but no such feedback parallel to flow, lending support to the central mechanism of the SOC hypothesis. However, that analytical model was limited to two patches, where flow in one cell was directly controlled by flow in the only other cell. Further testing the SOC hypothesis requires evaluating the potential for this mechanism to generate anisotropic patterning at the landscape scale.
In this study we implemented the local and global feedbacks described in the SOC hypothesis (Cohen et al., 2011; Heffernan et al., 2013) in a stochastic cellular automata framework to model temporal evolution of the ridge–slough pattern. Transition probabilities between ridge and slough states were driven by hydroperiod (a global negative feedback) and local facilitation. Both isotropic and anisotropic neighborhood kernels were implemented, and simulations were performed under different combinations of local-facilitation strength and degree of anisotropy to investigate the role of each process in pattern development. Simulated ridge–slough landscapes were then compared to a suite of statistical and geostatistical characteristics that characterize the ridge–slough patterning observed in the best-conserved remnants of the Everglades.
We first expanded the hydrodynamic modeling procedure outlined by
Kaplan et al. (2012) to calculate hydroperiods for landscapes over a wide range of
ridge densities (%
Our cellular automata (CA) model consists of two states: ridges and sloughs. While the natural system contains variations in these states (e.g., wet prairie communities that can persist in short hydroperiod sloughs; Zweig and Kitchens, 2008), they are likely transitional states between sloughs and ridges and were not included. Tree islands were likewise neglected in the CA model. While tree islands are critically important to the Everglades landscape, they represent only approximately 3 % of the total landscape area, and their emergence and maintenance is thought to be controlled by different mechanisms than those explored here (Ross et al., 2006; Wetzel et al., 2009).
Schematic representation of steps in the cellular automata model
of ridge and slough pattern development. The upper central panel is a
third-order polynomial surface of hydroperiod (HP) vs. anisotropy (
System states in the ridge–slough landscape are differentiated by two primary characteristics: vegetation and soil elevation (Watts et al., 2010). Our model assumes that, when a cell transitions from one state to the other, vegetation and soil elevation attributes are updated immediately. The probabilities that govern transitions between states are dictated both by a global feedback based on hydroperiod and a local-facilitation effect of neighboring cells. A schematic of the model framework shows the recursive algorithm that generates landscape pattern (Fig. 2).
Local facilitation of patch expansion was modeled based on the similarity of
neighboring cell states to the cell state under transition. That is, the
probability of a cell changing state is locally controlled by the
neighborhood of adjacent cells. Ecologically, these effects may arise due to
plant propagation (vegetative and reproductive), changes in primary
production at patch edges that change peat accretion rates, and potential
abiotic factors, such as nutrient and sediment transport mediated by flow
(Ross et al., 2006; Larsen et al., 2011). Local facilitation,
Note that Eq. (
The HP meta-model is the hydrologic foundation of the CA model (Fig. 2),
with HP variation creating the global negative feedback that drives changes
in ridge–slough configuration. Landscape patterns conducive to efficient
drainage (lower %
Transition probabilities between ridge and slough were modeled as the linear
combination of local effects (i.e., by surrounding neighbors) and global
effects (i.e., controlled by landscape HP), expressed as
The transition probability formulations (Eqs. 3 and 4) are identical to those used by Foti et al. (2012), with one key difference: these authors imposed the global negative feedback mechanism by directly setting a target vegetation density, whereas here a target hydroperiod is implemented. This formulation is based on observations of the temporal dynamics of change in the ridge–slough landscape, which suggest that ridge density can change quickly towards a landscape that is dominated by either ridge or slough based on hydroperiod (Nungesser, 2011). Furthermore, this construction allows for variable ridge density driven by HP, which, in turn, is controlled by the density and anisotropy of patches (Kaplan et al., 2012). Setting a target HP therefore explicitly considers bidirectional interactions between hydrology and landscape geometry, allowing for future modeling based on perturbations to hydrological forcing.
Simulations begin with a randomly generated, 3.5 km
The local feedback mechanism is dictated by the magnitudes of
Simulated ridge–slough landscapes from the CA model were compared, both qualitatively and quantitatively, to observed ridge and slough patterns in the best-conserved portion of the Everglades (referred hereafter as “reference landscapes”) (McVoy et al., 2011; Watts et al., 2010). Thirteen reference landscapes from a study of Everglades landscapes by Nungesser (2011) were augmented by eight additional reference landscapes presented by Casey et al. (2015). All pattern metrics were based on analyses of rasters created with a 10 m cell resolution from vector maps (Rutchey et al., 2005).
Comparisons between simulated and reference landscapes were based on seven
statistical and geostatistical characteristics: overall %
Patch density was calculated as ridge area divided by total domain area. Patch anisotropy was estimated as the ratio of the major (parallel to flow) and minor (orthogonal to flow) ranges of indicator semivariograms (Deutsch and Journel, 1998); the correlation length scales inferred from these spatial ranges were also of interest to ensure that the model predicted realistic patch geometry. Distributions of patch sizes, which follow power-law scaling in the reference landscapes, were evaluated for goodness of fit in comparison to the Pareto (power law) distribution using Monte Carlo tests (Clauset et al., 2009). The fractal dimension (PAFRAC), which measures patch shape complexity, was calculated from the fitted slope between patch area and perimeter. Finally, patch periodicity was evaluated using radial spectrum (r-spectrum) analysis, which extracts the spectral components of the landscape pattern as a function of possible wave numbers (i.e., spatial frequency divided by domain size). R-spectra, which are used to identify the characteristic wavelength and directional components of regular patterns, were obtained using two-dimensional Fourier transforms following methods outlined by Couteron and Lejeune (2001).
Our primary motivation in comparing model results to reference landscapes
for these metrics is to elucidate the nature of local interactions required
to create pattern that is statistically consistent with the best-conserved
portions of the extant Everglades (i.e., elongated, flow-parallel ridge
patches). We therefore applied a multi-criteria objective function to
quantify agreement between simulated and reference landscapes for the
pattern metrics listed above. Simulated landscapes received a score of 1
for each of the seven metrics that fell within the range of values observed
in reference landscapes, and the sum was used as an integrated measure of
pattern agreement (IMPA; maximum IMPA
Simulated landscapes for various
Hydrodynamic modeling of the discharge competence and landscape hydroperiod
suggested that %
Simulated landscapes for
The distribution of ridge areas in the simulated landscapes showed
significant support for power-law scaling following Clauset et al. (2009)
(i.e., we cannot reject the hypothesis that the distribution differs
significantly from power law at the 0.1 level). Power-law scaling was
consistent across model realizations, regardless of %
The perimeter–area scaling of patches showed that the modeled landscapes
were highly fractal (Fig. 5b), as evidenced by the linear relationship
between log (perimeter) and log (area) (slope
Mean values of statistical and geostatistical metrics in simulated
landscapes (symbols) relative to the ranges observed in reference landscapes
(shaded regions):
Summarizing the statistical and geostatistical properties of simulated
landscapes (Fig. 6, symbols), in comparison with values observed in
reference landscapes (shaded region), illustrates the relatively narrow
parameter space over which model outputs match the conserved (i.e.,
elongated, N–S oriented) patterning. Both %
A clear understanding of the processes underlying development of ecological patterns is integral to all ecosystem management and restoration. In the Everglades, venue for one of the largest and most ambitious ecosystem restoration efforts in history, the specific focus on landscape pattern as a restoration objective underscores the urgency of the process–pattern link. Identifying the suite of necessary and sufficient processes to create and maintain pattern will aid in prioritizing hydrologic restoration goals. Although multiple hypotheses exist for explaining the ridge–slough pattern, most of them attribute the development of these landscapes to one dominant process. The self-organizing-canal hypothesis (Cohen et al., 2011; Heffernan et al., 2013), on the other hand, ascribes pattern formation and maintenance to reciprocal feedbacks between landscape pattern and hydrology. Moreover, evidence of a strong feedback between pattern and hydroperiod (Kaplan et al., 2012) lends support for the SOC. Primacy of this mechanism vis-à-vis nutrient enrichment or sediment redistribution – and we note here that these mechanisms are not mutually exclusive – would imply markedly different water management objectives, specifically emphasizing flow volume sufficient to ensure appropriate hydroperiod vs. water level management or creation of episodic high velocity
The dominant spatial feature in the ridge–slough landscape is patch
orientation with flow. As a minimum criterion, models that fail to produce
flow-oriented elongation are clearly insufficient explanations for pattern
development. Our results suggest that the SOC mechanism can create
patterning consistent with the best-conserved ridge–slough landscape, but
only when local facilitation is directionally biased in the direction of
flow. This suggests the SOC alone is an insufficient mechanism. Previous
work comparing static landscapes (Kaplan et al., 2012) showed that anisotropy
exerted strong control on hydroperiod, but our model results suggest that in
a dynamic landscape changes in patch density (%
Recently, Heffernan et al. (2013) used an analytical model to explore the SOC, demonstrating that ridge and slough elevation divergence occurs spontaneously at some discharge levels and that the impact of a given cell on adjacent cells orthogonal to flow is far larger than parallel to flow. In short, pattern arises solely due to feedbacks between hydroperiod and discharge competence (i.e., capacity to convey water), which is controlled by the configuration orthogonal to flow. To reconcile these findings with our model results, we note that water flow in the Heffernan et al. (2013) model is limited to two flow paths, where occlusion of flow in one cell (e.g., due to peat accretion there) must, of necessity, force water through the other. In contrast, our model comprises a relatively large domain with hundreds to thousands of possible flow paths, weakening the influence of flow occlusion in any given cell on global hydroperiod. As a result, the role of anisotropy on discharge competence is diminished, and ridge density impacts on hydroperiod dominate.
We also note that the HP in this study is estimated assuming steady-state
flow conditions (as calculated over the 20-year period of record in Kaplan
et al., 2012) and does not represent the possible effects of temporal
fluctuations in flow that occur in the Everglades ecosystem. In order to
test whether a fluctuating hydrological regime would drive elongated ridge
formation under isotropic local facilitation, a variable hydrology scenario
was also implemented in the CA model based on reported variation in mean
flow into Lake Okeechobee over a 65–70-year cycle (Enfield et al., 2001).
However, the simulations driven by cyclically varying hydrology coupled with
isotropic local facilitation did not drive ridge elongation in the resulting
landscapes (i.e.,
As noted above, the model developed here does create compelling ridge–slough patterning given anisotropic local-facilitation effects. The plausibility of multiple model mechanisms, including those presented here, for creating flow-oriented elongation suggests that additional landscape characteristics are necessary for evaluating model performance. The additional proposed pattern metrics (ridge density, anisotropy, autocorrelation range, patch size distribution, fractal dimension, and periodicity) provide a more nuanced and comprehensive basis on which to compare model outputs to real landscapes. This approach is similar to Larsen and Harvey (2010) wherein multi-metric comparisons between modeled and real landscapes were made, but includes new potentially relevant pattern metrics. While it was beyond the scope of the current work to compare the multiple existing models of the ridge–slough landscape, we note that our model outputs agree reasonably well with observations in the best-conserved ridge–slough landscapes for all of the proposed metrics.
Among the most important differences between our model and others for the
ridge–slough pattern is invocation of an inhibitory feedback that operates
globally rather than at a characteristic spatial scale. Constraints to patch
expansion are induced at the entire domain scale, and not over local and/or
intermediate scales, as is the case in Ross et al. (2006), Lago et al. (2010),
Cheng et al. (2011) and Larsen and Harvey (2011). The principal
reason for invoking a global rather than intermediate feedback is the
inherent difference between focusing on hydroperiod/water depths, which are
reasonably uniform over large areas, and flow velocity or solute
redistribution, which are more spatially heterogeneous. Global feedbacks
have been widely invoked to understand and simulate vegetation patterning
(e.g., Scanlon et al., 2007; Foti et al., 2012), and induce three pattern
features that merit particular attention: power-law scaling of patch areas,
high fractal dimension (i.e., highly crenulated patch edges), and the
absence of a characteristic pattern wavelength (which would be expected in
regular patterning). Our simulations closely matched observed power-law
scaling of patch areas (including the scaling parameter,
Pattern geometry – including flow-oriented elongation, which is the sentinel
feature of this landscape – is strongly controlled by the local-facilitation
function in our model. The remaining three metrics (%
Power-law scaling of patch sizes has been associated with vegetation self-organization in many landscapes (e.g., Scanlon et al., 2007; Kefi et al., 2007, 2011) but has only recently been evaluated for the Everglades (Foti et al., 2012) and specifically for ridge–slough patterned landscape (Casey et al., 2015). Most studies of the ridge–slough landscape have emphasized the perceived regular nature of the pattern, including invocation of a pattern wavelength of ca. 150 m (SCT, 2001; Larsen et al., 2007; Watts et al., 2010). Notably, power-law scaling of patch area is incompatible with regular patterning because the basis of such patterning is the presence of distal negative feedbacks that truncate patch expansion at some particular spatial range (van de Koppel and Crain, 2007). It is therefore critically important that our simulated landscapes, wherein inhibitory feedbacks are global and not scale-dependent, exhibit this power-law scaling behavior (Fig. 5a). It is also notable that the landscapes follow such scaling regardless of ridge density or local-facilitation parameters, which is also observed in all reference landscapes with various patch densities (Casey et al., 2015). These results are consistent with the concept of robust criticality in ecological systems, where local spatial interactions lead to power-law clustering of patches well below the percolation threshold (Kefi et al., 2011; Vandermeer et al., 2008). While the generality of power-law scaling in both simulated and real landscapes limits this metrics utility as a model diagnostic, it lends strong support for the primacy of scale-free processes underlying ridge–slough pattern formation.
Interestingly, patch complexity in the real ridge–slough landscapes revealed a non-fractal nature (nonlinear perimeter–area scaling; Casey et al., 2015). Since the simulated landscapes show a highly linear perimeter–area scaling and hence highly fractal patterns, this highlights one of the attributes of the ridge–slough landscapes that our model is not able to entirely reproduce. In contrast, Foti et al. (2012) recently reported that sawgrass patches, the dominant vegetation of the ridge–slough landscape in the Everglades, were fractals. However, their landscapes were analyzed at significantly coarser scale (40 m pixel) as opposed to 10 m pixel as in this study, which is likely to miss the finer scale crenulations in patch edges that increase the complexity.
The ridge–slough landscape is often described as exhibiting a repeating geostatistical pattern with a wavelength of 50–400 m in the direction orthogonal to flow (Larsen and Harvey, 2010; Lago et al., 2010; Cheng et al., 2010; Cohen et al., 2011). Spatial periodicity in patterned ecosystems has been attributed to the interplay between positive and negative feedbacks acting at different spatial scales. Short-range facilitation causes vegetation aggregation in dense clusters, but patch expansion is inhibited by some intermediate-range negative force acting at a specific distance. In this way, vegetation self-organizes into a periodic configuration (Rietkerk and Van de Koppel, 2008; von Hardenberg et al., 2010). Accordingly, the models presented by Ross et al. (2006) and Cheng et al. (2010) generate highly uniform, elongated patches that possess a clear periodicity. Surprisingly, however, the observed ridge and slough landscape appears to lack periodic spatial structure (Fig. 5c; Casey et al., 2015), suggesting there is no characteristic wavelength to the landscape. Even more surprising is that this aperiodic behavior is retained across a wide gradient of hydrologic modification. It is therefore notable that the model presented here lacks periodic spatial structure. The lack of landscape periodicity argues strongly against invocation of intermediate-range negative feedbacks. The observed pattern is more consistent with a global negative feedback that inhibits patch expansion across the entire landscape.
We note that the spatial scale (i.e., spatial resolution and extent) can
strongly influence various landscape pattern metrics (e.g., Wu et al., 2002;
Levin, 1992) we have used in this study. Geostatistical methods (e.g.,
semivariogram) are inherently affected by cell size (Lausch et al., 2013;
Atkinson and Tate, 2000; Atkinson, 1993), while cellular automata models are
also influenced by cell and neighborhood size (Pan et al., 2010; Ménard
and Marceau, 2005; Chen and Mynett, 2003). Our modeling results and
interpretations are based on 10 m grid size. While the minimum mapping unit
(MMU) varies from 20 to 50 m (Nungesser, 2011; Rutchey et al., 2005),
smaller features (
Mechanisms of local facilitation in patterned landscapes are generally
attributable to more than one biotic/abiotic factor, which can be difficult
to measure or determine at landscape scales (Cohen et al., 2011). Our model
yields novel insights about the role of a generalized local-facilitation
process and its spatial extent in emergent ridge–slough patterns (e.g.,
local-facilitation effects confined to 40 m parallel to flow and even less
perpendicular to flow). However, while the model creates compelling pattern
based on the combination of global inhibition and anisotropic local
facilitation, the mechanisms that induce anisotropic facilitation remain
unclear. Several potential mechanisms exist. Flow may enable directional
seed dispersal (i.e., hydrochory; Nilsson et al., 2010) particularly for
sawgrass. Crucially, however, despite prolific seed production, most
sawgrass reproduction is vegetative (Miao et al., 1998). Local anisotropic
facilitation may also arise from sediment entrainment and deposition (Larsen
et al., 2007), though we note that the invoked flow velocity effects to date
have focused on inhibitory feedbacks (i.e., constraints on patch expansion),
not local-facilitation effects. However, if deposition occurs preferentially
downstream (e.g., at the tails of ridges) rather than at ridge edges, the
cumulative effect would be anisotropic facilitation. Another mechanism
posits lower phosphorus uptake efficiency in ridges than in sloughs, leading
to longer uptake lengths (sensu Newbold et al., 1981), and thus further
downstream transport of available
While determining the mechanism that controls local-facilitation effects is clearly critical for successfully protecting and restoring landscape pattern, our work suggests that processes driving ridge–slough pattern development and maintenance may be represented by a generalized local-facilitation function and a global inhibitory feedback, potentially signifying a unifying explanation of ridge–slough pattern development. The model results presented herein provide the first test of ridge–slough simulations against a suite of expanded landscape-scale statistical and geostatistical properties, several of which strongly support inference of a dominant role for global feedbacks between pattern and hydroperiod in structuring this sentinel landscape.
Support for this work was provided by the Army Corps of Engineers through the Monitoring and Assessment Plan (MAP) Restoration, Coordination, and Verification (RECOVER) program of the Comprehensive Everglades Restoration Plan. J. W. Jawitz was supported by the Florida Agricultural Experiment Station. Edited by: N. Ursino