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1 BACKROUND Historically, models were limited in scale and scope due to a lack of computation power and topdown regional evaluations were limited to issues of national defense at Supercomputer Centers. With the ready accessibility and incorporation of spatial information, multivariate time series, object-oriented programming and the maturation of ecology into landscape and dynamic ecosystem simulation, regional assessments are now not just the norm, but a fundamental approach for managing, understanding and restoring ecosystems. The key is the incorporation of space as well as time into the analytical approaches of the past, at levels of resolution that are meaningful to the ecosystem management problems and questions we need to solve in today s more socio-economically astute environments (Sklar et al. 2005). The strength of the FCE regional, integrative modeling approach is that it can be used to: (1) quantitatively describe spatial landscape level phenomena; (2) predict rates of change as a function of long-term cumulative impacts; and (3) separate and distinguish drivers and stressors that operate at previously unknown spatial and temporal scales. There is, of course, a danger in making these models too big and complex where uncertainties cascade and multiply with time. We avoid this trap by building upon a relatively large suite of models, breaking the problems into explicit and manageable hypotheses, and looking beyond the FCE s database into those created by the South Florida Water Management District (SFWMD) and the US Army Corps of Engineers (USACOE) for the Comprehensive Everglades Restoration Plan (CERP) (Figure 1). 5$

2 !"#$%&'()$'(*+,-./-&0*$12.,-)$34536$ Figure 1. Summary of most of the FCE LTER models according to spatial and temporal domains, principal objectives and collaborative teams. Also shown is the domain of the South Florida Water Management Model (SFWMM), which is used as hydrologic boundary conditions for some of the FCE models. 3$

3 Water-Budget-Related Models To date, there have been four water-budget-related models efforts on Taylor and Shark River Sloughs (Zapata-Rios 2009, Michot et al. 2011, Saha et al. 2012, Spence 2011). As they are loosely related, they are all discussed in a single section. Zapata-Rios (2009) modeled the water budget for Taylor Slough from January 2008-July 2009 for the specific purpose of estimating the relative contribution of groundwater discharge. Similarly, Saha et al. (2012) modeled the water budget for Shark River Slough for calendar years In both studies, inputs, outputs, and change in storage were measured or calculated, yielding residual terms that included component errors and net groundwater exchange. Both found that net groundwater exchange, especially groundwater recharge, are important components of the water budget, though there is substantial inter- and intra-annual variation. Michot et al. (2011) developed a fine scale (<1 km 2 ) hydrodynamic model to evaluate flow and salinity in the lower Taylor Slough. A water-budget model was calibrated and validated using long-term hydrologic data and supplemented by hydrodynamic modeling using the MIKE FLOOD software platform to evaluate groundwater discharge and surface-water outflow. Model results suggest that groundwater discharge plays an important role in controlling surface-water salinity in the lower Taylor Slough. The model performance is satisfactory during the dry season when surface outflow is confined to the main channel. The model also provided guidance on the importance of off-channel overland flow, which flows into and out of the lower Taylor Slough as sheet flow during the wet season. Zapata-Rios (2009), Saha et al. (2012), and Michot et al. (2011) found that groundwater discharge plays an important role in controlling water availability and salinity in Taylor and Shark River Sloughs. Therefore, Spence (2011) developed a site-scale hydrodynamic model to calculate groundwater discharge at specific locations in Taylor and Shark River Sloughs. To model the density-dependent flow of groundwater, the SUTRA-MS model code was used (Hughes and Sanford 2004). SUTRA-MS models fluid mass, energy, and solute balance for variable-density, single-phase, saturated-unsaturated flow and multiple-species transport by using a combination of finite-element and implicit finite-difference solutions. This program was chosen for its ability to simulate energy flow in saturated, variable-density conditions caused by changes in heat as well as salinity. This model has been calibrated and validated using vertical arrays of temperature sensors collecting data every 15 minutes. To date, the model has only been calibrated, validated, and used for one-week time periods when hydraulic heads indicated that vertical fluxes would be greatest. Model results indicate that groundwater discharge may be a prominent source of water at one site in Taylor Slough but not at another site in Shark River Slough. The model is now being improved and expanded to other locations and time periods in both Taylor and Shark River Sloughs to improve the estimates of groundwater discharge over a larger area and over a longer period of time. HYMAN HYMAN is a finite-difference hydrology model that evaluates the relative significance of tides and rainfall deficits (i.e., rainfall evapotranspiration) on the seasonal patterns of soil saturation 7$

4 and soil salinity (Twilley and Chen 1998). The HYMAN model establishes an algorithm to calculate the daily water level, salt content and salinity as the combined effects of tidal exchange, rainfall, and runoff. Unlike the FORMAN and NUMAN models, which were calibrated using information from mangrove sites in the Everglades, HYMAN was developed using data from a basin mangrove forest located in the Rookery Bay National Estuarine Research Reserve in southwestern Florida near Naples. Model simulations for this site demonstrated that soil salinity at higher elevations in the intertidal zone, where tidal inundation frequency is reduced, is more sensitive to changes in rainfall deficit (Twilley and Chen 1998). The present version of HYMAN has been shown to be an accurate description of the hydrology of basin mangroves in lagoons with little upland input of fresh water, which is characteristic of southwestern Florida and throughout the Caribbean. The model was recently modified to take into consideration different topography and hydroperiod regimes along Shark River Slough. The model was able to simulate long-term observed pore water salinity regimes in areas that are strongly influenced by semidiurnal tides at 4 and 10 Km inland from the mouth of the Shark River estuary (Cheng-Feng et al. in preparation) (Figure 2). Figure 2. Simulated v. observed salinity porewater values in SRS-4 and SRS-6 in 2003, 2004, and Lattice-Boltzman Modeling The Lattice-Boltzmann (LB) Method is a technique for obtaining solutions to the Navier-Stokes (NS) equations for fluid flow (Chen and Doolen 1998). This occurs via the dynamics of a set of fictitious particles that propagate on a lattice grid according to a discrete Boltzmann equation. 8$

5 The method is particularly well suited for simulating low and medium Reynolds number flows in domains with complex boundaries (Sukop and Thorne 2006). The LB model involves applying the LB method to one- to one hundred km 2 scale flows in the ENP and surrounding areas. In order to simulate such large regions, flow is restricted to two-dimensions and occurs in a domain constructed from a satellite image. The flow is driven by time-dependent pressure boundary conditions using water level data acquired by USGS and National Park Service (USNPS) stations. Accurate flow fields are obtained by introducing a passive scalar, which is described by a modified version of the LB method, to simulate the movement of a tracer in the fluid. Simulation results may then be compared directly to tracer data that has been collected in several locations (Ho et al. 2009), and a 'best-fit' may be obtained by adjusting system parameters using the Parameter ESTimation package PEST (Doherty 2004). System parameters that may be adjusted include the pressure gradient amplitude, water viscosity, tracer diffusion coefficient, and rate of tracer loss to the environment. Also available for adjustment is a Dardis-McCloskey (1998) parameter that is used to allow water seepage into the 'solid' regions of the domain. Thus far, this model has been implemented in two different flow situations in the ENP and surrounding regions, namely low-speed flow in ridge-and-slough habitats and averaged flow in estuarine channels. While still under development, initial results of this technique show promise. Future developments may include use of bathymetric data to move from a pure 2D model to a shallow-water model, and the inclusion of Reynolds stress terms and similar in the NS equations, as well as coupling large-scale average flow to small scale turbulence. Ribbon Models Ribbon models are being used to conceptually link biogeochemistry in landscape units along transects in Taylor and Shark River Sloughs, employing a dynamic budget approach to simulating nutrient dynamics at a given location. These Ribbon models simulate water TP and ecosystem P stocks (e.g., vegetation, consumers, detritus, floc, and soil) and P fluxes (e.g., retention, transport, and cycling rates) for the landscape units sawgrass, wet prairie and moderately P-enriched sawgrass, and wet prairie habitats (Saunders et al. in review). The Ribbon models are not spatially explicit, physically distributed models; rather, they are unit-based models consisting of linked ordinary differential equations that represent stocks in and fluxes between each of the landscape units. These models have been parameterized with in situ measurements (from 2000 to present) of plant community composition, above- and belowground biomass and production, periphyton uptake, detritus decomposition, floc metabolism, nutrient mineralization/ immobilization, sedimentation, and water residence time. Applications of Ribbon models to date show the highest sensitivity of water TP and ecosystem P stocks and fluxes to exchanges of water TP and floc P between the landscape units, underscoring the importance of hydrological connectivity between landscape units in local-scale nutrient dynamics (Figure 3). These results highlight the need for further investigations of the biophysical mechanisms affecting water and detrital P dynamics in oligotrophic interior marshes, especially exchanges of P between landscape units both laterally and in the downstream direction (e.g., Childers 2006). 9$

6 Figure 3. Simulated v. observed ecosystem P accumulation, water TP, periphyton-p, and floc-p assuming isolated community dynamics (left two columns) and assuming exchanges of waterand floc-p between communities (right two columns). Symbols represent simulated mean annual values for a given year, and gray bars indicate minimum and maximum mean annual observed values over the 2000 to 2003 study period. Spatially Explicit Carbon Accumulation and Transport (SCAT) Model The Spatially explicit Carbon Accumulation and Transport (SCAT) model simulates accumulation and transport of organic matter in the sawgrass ridge and wet prairie slough habitats of Shark River Slough. Preliminary runs of the Shark River Slough SCAT model indicate that microtopographic differences between sawgrass ridges and wet prairie sloughs may arise based solely on differential rates of primary production and litter decomposition and can be reduced with drainage scenarios that allow for increased oxidation and subsequent subsidence of the sawgrass ridges. Additionally, using rainfall records, stage gage records, and the assumption that the construction of Tamiami Trail in the 1930s reduced water depths by 30 cm, the Shark River Slough SCAT model has been able to replicate downcore profiles and concentrations of fossil seeds showing greater water lily abundance in pre-drainage layers, followed by a recent invasion by sawgrass (Figure 4). :$

7 Figure 4. Simulated v. observed macrofossil profiles for a transect of soil cores in wet prairie/slough-sawgrass at SRS-2. NUMAN NUMAN simulates the vertical distribution of organic matter, N, and P concentrations, as well as rates of mineralization (Chen and Twilley 1999a). The NUMAN model adapted the SEMIDEC and CENTURY model approaches, with modifications to include some of the biogeochemical processes that are unique to coastal-forested wetlands, including litter production and wood decomposition. Litter fall can be the major input of organic matter to the soil surface. NUMAN has been used to forecast changes in soil organic matter and nutrients in riverine forests to test the relative importance of ecological and geophysical processes on the biogeochemistry of mangroves along the Shark River estuary. Model calibration and validation information was also obtained from the same sites informing the FORMAN model, although currently these models are not coupled. Comparisons of simulated and observed results demonstrated that landscape gradients of soil characteristics along the Shark River estuary could be adequately modeled by accounting for plant production, litter decomposition and export, and allochthonous input of mineral sediments. Model sensitivity analyses suggest that root production has a more significant effect on soil composition than litter fall. Similarly, model simulations indicate that the greatest change in organic matter, N, and P occurred from the soil surface to 5 cm depth (Chen and Twilley 1999a, Chen and Twilley 1999b). Current studies in belowground productivity and fine root and litter decomposition in other mangroves areas in the Everglades are providing new data for further calibration and incorporation of other mechanistic relationships to improve model applicability in other mangrove regions (e.g., Poret et al. 2007). PERIMOD PERIMOD is an empirical model that predicts periphyton quantity (e.g., cover, biomass, biovolume), quality (e.g., phosphorus, chlorophyll a, mineral content), and composition (e.g., percent weedy diatoms, edibility) from water quality and quantity input data (Gaiser et al. 2011). PERIMOD was developed from a comprehensive periphyton database for over 800 locations in ENP surveyed during the wet seasons from Periphyton attribute data were screened according to the stoplight criteria of Gaiser (2009) to remove unnaturally phosphorousenriched sites that would limit the ability of the model to describe natural changes in periphyton resulting from increased hydroperiods associated with hydrological restoration. This step does ;$

8 not preclude the ability to use model output to assess water quality deterioration. A parallel database was constructed that included potential driving or predictor variables (e.g., water depth, hydroperiod, soil depth, plant cover). Periphyton attribute and predictor datasets were joined through partial regression models to determine the best fit equations for quantity, quality and compositional metrics, which were validated using a test dataset. The equations can receive manipulated inputs, including output from the Everglades Landscape Model (ELM) which includes all of the PERIMOD predictors. The output from PERIMOD can then be evaluated in the same way that current system status is assessed (i.e., through the stoplight system, using the same criteria) and can be fed into other models that rely on periphyton attributes as inputs. For instance, periphyton dry mass and organic content predictions could feed back into ELM to improve predictions of soil accretion throughout the system, while predictions of edibility could be used in food web models dependent on food source abundance and quality. These mechanistic food web models are being explored through coordinated trophic dynamic modeling (Sargeant et al. 2010, 2011). The model is currently being applied to three key classes of scenarios: (1) assess ecosystem response to restoration, (2) predict future ecosystem states under alternative restoration scenarios, and (3) provide output for other ecosystem response or food web models. FORMAN FORMAN is an individual-based gap model (Chen and Twilley 1998) with a code based on the JABOWA and FORET models. Within the model, a forest stand is assumed as a composite of many gaps, which do not interact with each other. FORMAN simulates mangrove forest development by integrating demographic processes with soil nutrient availability and salt stress. The gaps described in FORMAN are equal-sized (500 m 2 ), corresponding to the area covered by single, large, dominant trees in natural forests (Chen and Twilley 1998). The specific location of a tree within a gap is not considered in FORMAN and light competition is represented by stratified, averaged leaf layers. Also, nutrient availability and salinity are assumed to be homogeneous within a gap. The model demonstrates that nutrient availability, initial conditions of forest structure following disturbance, and species-specific recruitment rates act in combination to control the regeneration of mangrove forests along the Shark River estuary (Chen and Twilley 1999a, Chen and Twilley 1999b). Further modifications of the FORMAN model are needed to include mechanisms of seedling dispersal and the site-specific establishment rates of saplings in mangrove forests (Berger et al. 2008). The model has been used in other geographical regions where mangrove regeneration and forest structure has been forecasted as a result of hydrological rehabilitation following mangrove forest large-scale mortality (Twilley et al. 1998, Simard et al. 2008). MANHAM/MANBUTHAM Modeling efforts are underway to predict the impact of SLR on coastal plant communities in the southern Everglades (Sternberg et al. 2007, Teh et al. 2008, Saha et al. 2012, Jiang et al. 2012). Coastal plant communities are comprised of mangroves, buttonwoods, and coastal hardwood hammocks in declining order of salinity tolerance. MANHAM hypothesized that mangrove and coastal hardwood hammock communities maintain conditions to favor themselves by regulating stomatal conductance and therefore transpiration. With increasing salinization, as happens in the dry season, as well as over a longer term trajectory under SLR, hardwood hammocks lower their <$

9 transpiration and water uptake, thereby preventing increased salinity in their rhizosphere, whereas mangroves freely transpire, thereby increasing salinity in the root zone (Kozlowski 1997, Munns 2002). MANBUTHAM extends the MANHAM model to include buttonwood forests. MANBUTHAM is calibrated using transpiration values under varying salinity levels from greenhouse experiments and field measurements (Saha et al. 2012, Saha and Sadle 2012) and with groundwater salinity measurements. Preliminary runs of MANBUTHAM suggest that SLR will cause buttonwood communities to be rapidly replaced by mangrove communities and that hardwood hammock communities would initially be unchanged but would eventually be replaced by mangrove communities. Greater Everglades Fish (GEFISH) Model The Greater Everglades Fish Model (GEFISH) is a two-dimensional, spatially explicit model for the lower trophic level food web dynamics and fish functional groups that are supported by the lower trophic level food web dynamics. The model domain is scaled to cover a typical area and topography of the Everglades landscape, such as Taylor Slough. GEFISH contains a food web structure with the following levels: primary producer (periphyton), detritus, small invertebrate detritivores, three small fish functional groups, crayfish, and a piscivore, all modeled as state variables. GEFISH also contains a submodel for varying the limiting nutrient, i.e., P, but that submodel is not currently in use. Hydrology is represented by rising and falling water levels through the year, following typical empirical patterns. In this food web structure, the fish are assumed to move seasonally, with a fraction being allowed to move up the gradient during floods and down the gradient during falling water. Of particular interest is estimating the spatial and temporal distributions of small fish biomass that is stranded by declining water level during the wading bird breeding season and accessible to the wading birds. The model is currently being applied to three key classes of scenarios: (1) The lower elevation end of a marsh landscape is bordered by a canal containing piscivorous fish, where the carrying capacity for the piscivorous fish can be varied, but the piscivorous fish are not able to move far into the marsh; (2) a new piscivorous fish will be introduced that can move from the canal into the marsh area to relatively shallow areas of the landscape; (3) spreader swales will be introduced in the model that will form permanent refuges for smaller fish, but also will increase the potential habitat for piscivorous fish. Within each of these classes of scenarios, the effects of numerous variations of seasonal water patterns are being studied. Seagrass Ecosystem Assessment and Community Organization Model (SEACOM) A mechanistic simulation model of seagrass-water column interactions for Florida Bay has the goal of understanding effects of hydrologic and salinity restoration via managed freshwater discharge on submersed aquatic vegetation (SAV) and phytoplankton communities (Madden et al. 2007, Madden in press). The SEACOM, calibrated for nine basins in the bay, describes biological and nutrient dynamics and is parameterized from experimental data in the field, mesocosms, bioassays, and monitoring. Recent updates include incorporation of the growth dynamics and recruitment of Ruppia, a low-salinity species, and calibration for two mangrove ponds of Taylor River, an important step in linking SEACOM with other FCE models and to address FCE II hypotheses on coastal ecotone dynamics. A preliminary but fully functional phytoplankton module has been integrated into the core SAV model. Other improvements =$

10 include linkages with the SICS (Southern Inland and Coastal Systems) model for water and salinity boundary conditions, EFDC (Environmental Fluid Dynamics Code) for internal circulation, and the FATHOM model for coarse-scale water transport and salinity determination. SEACOM is being used to simulate the phytoplankton bloom that occurred in the eastern bay from The model demonstrates that a single pulse of phosphorus, similar in magnitude, timing and duration to that observed in late summer 2005, is sufficient to sustain phytoplankton blooms for months to years from internal recycling. SEACOM is also evaluating system responses to pulsed nutrient inputs such as from tropical storm runoff and to climate change scenarios of increased temperature and water levels. SEACOM scenarios of changing nutrient cycling rates and basin residence times reveal ecosystem thresholds or!tipping points" in which longer water residence times, efficient nutrient retention, and pulsed nutrient inputs can push the ecosystem toward algal dominance over SAV-dominance. Slope, Land Use, Excluded Areas, Urban Areas, Transportion, and Hillshade (SLEUTH) Model SLEUTH is a cellular automata (CA) land use change model that employs Monte Carlo simulation, originally developed by Clarke and Gaydos (1997). Universal by design, it has already been applied in over 60 metropolitan regions of the world, developing a community of scholars who not only perform research with SLEUTH but also on SLEUTH (e,g., a recent Association of American Geographers Conference had a session devoted solely to SLEUTH), increasing its accuracy and extending its capabilities. It is also the only spatially explicit urban growth model currently used in FCE research. Since SLEUTH calibrates and forecasts urban growth and other land uses that are a direct result of human decision making, this makes SLEUTH the sole FCE model to forecast human behavior. Though we do not suggest SLEUTH should remain the sole social science model used at FCE, its current singularity gives it a pivotal role in current and upcoming integrative research and model coupling. SLEUTH has already been successfully calibrated and run in the Redland area, an area offering a gradient of protected, agricultural, and urbanized lands, of Miami Dade County (Onsted and Roy Chowdhury, under revision), but FCE researchers also have significant experience employing it elsewhere as well as innovating novel uses for it (Onsted and Osherenko 2008; Wu et al. 2009; Onsted 2010; Onsted and Clarke 2011; Onsted and Clarke 2012). Comprehensive Heuristic Model (CHM) A Comprehensive Heuristic Model (CHM) is has been developed and is constantly being revised to link the many individual conceptual models currently guiding independent research activities (Figure 5). The overall goal of the CHM is to (1) convey the integrated nature of the existing data, modeling, and synthesis; (2) highlight gaps in existing data, modeling, and synthesis; and (3) guide future research activities and generate future research hypotheses. The CHM really represents the soft coupling of concepts, conceptually representing the complex physical, chemical, biological, and anthropogenic drivers and interactions in the greater South Florida socioecosystem. The model domain is partitioned into two subsystems, the ecophysical system comprising the Everglades, Florida Bay, and the coastal Gulf of Mexico, and the anthropogenic system, comprising the built system, especially Miami-Dade County, and the 54$

11 connections between humans and the natural environment. The CHM highlights the interdependent nature of the various components of the socioecosystem as well as the mechanisms and pathways by which the various components of the socioecosystem are affected by changes in water and land use and climate, especially SLR. For instance, sea level rise can flood coastal freshwater-dependent plant communities and associated fauna, cause saltwater intrusion into coastal aquifers, and increase the frequency and severity of flooding in coastal urban areas. Freshwater flow reductions, especially in the dry season, can amplify these effects; this indicates that there are water-use management options available that could be used to mitigate the effects of sea level rise. The CHM is organized into three tiers. Tier 1 represents the general linkages between the drivers and the ecophysical and anthropogenic subsystems. Drivers include presses (e.g., climate cycles and change) and pulses (e.g., floods, droughts, and hurricanes) (Collins et al. 2011). These drivers directly and indirectly affect the ecophysical and anthropogenic subsystems, most notably through the controlling hydrology which is essential to the support of both subsystems. The ecophysical subsystem is represented by the National Parks and Preserves (e.g., Everglades and Biscayne National Parks), comprising physical, chemical, and biological attributes and processes (e.g., hydrology and trophic dynamics). The anthropogenic subsystem is represented by human settlement (e.g., Miami-Dade County and the agricultural areas), comprising the products of decision-making (e.g., land and water use) and altered physical, chemical, and biological attributes and processes (e.g., heat-island effect and exotic flora and fauna). The two subsystems interact and feedback upon one another. Tier 2 of the CHM includes two modules, one each focused on the ecophysical and anthropogenic subsystems. The generalized attributes and processes within the ecophysical subsystem are broadly divided into freshwater, ecotone, and estuarine and marine zones. These components interact with each other through the exchange of materials (e.g., the flux of water and nutrients) and the modulation of drivers (e.g., the dampening of saltwater intrusion). The generalized attributes and processes within the anthropogenic subsystem include climatemodulation (e.g., heat-island effect); land-use change and associated resource demand and waste; water demand, cycling and quality; lifestyles and resource consumption; and education and health. Both are still portrayed as being driven by the regional and global press and pulse drivers and as interacting with one another. Tier 3 of the CHM includes all of the individual conceptual models that have already been otherwise developed in the individual modeling efforts described above. For example, Price et al. (2006) proposed a conceptual model describing surface-water and groundwater interactions, including groundwater discharge, at the terrestrial-marine interface. This conceptual model can be expressed as a nested individual conceptual model within the Tier 2 ecophysical subsystem. These nested conceptual models can then be used to integrate information across disciplines. For example, Childers (2006) synthesized field results, showing that primary productivity in Shark River Slough is highest towards the coast, while Saha et al. (2012) developed a water-budget model that resonates with related water-budget and hydrodynamic modeling (e.g., Zapata-Rios 2009, Michot et al. 2011, Spence 2011), showing that groundwater discharge is an important component of the water budget in the coastal Everglades. The union, then, of this Tier 3 conceptual model and this additional information leads to the generation of a new research 55$

12 hypothesis, namely that the high primary productivity in the coastal Everglades is the result of phosphorous-rich groundwater discharge. Figure 5. Tiers 1-3 of the CHM. Tier 1 shows the press and pulse drivers controlling hydrology and the ecophysical and anthropogenic subsystems, which also interact with one another; Tier 2 shows greater detail in the ecophysical and anthropogenic subsystems; and Tier 3 shows how individual conceptual models are nested within Tier 2 and how these nested models can incorporate other information and be used to to generate new hypotheses. 53$

13 MANGAL One of the central objectives of the FCE LTER is to understand the changing community-scale dynamics of the coastal Everglades under different restoration and/or climate-change scenarios. To do so requires the ability to forecast how mangrove structure and function will respond to changes in physical and chemical hydrology; topography, including relative elevation under sea level rise; and the flows of geophysical energies. There are local, regional, and global drivers that control regulators (e.g., salinity, sulfides), resources (e.g., nutrients, light), and hydroperiods (e.g., frequency and duration of inundation) that collectively serve as stressors that result in diverse patterns of mangrove properties across a variety of environmental settings. Independent, discipline-specific models have been developed to study each of these processes in the coastal Everglades. However, only collectively can they be used to better understand the changing community-scale dynamics of the coastal Everglades under different restoration and/or climatechange scenarios. The MANGAL modeling effort is under development to do just this (Twilley and Rivera-Monroy 2005). The name MANGAL comes from Macnae (1968), who referred to the mangrove ecosystem, i.e., the interaction of mangrove trees with other biotic communities and abiotic environments, as "mangal." MANGAL uses a salinity box model, such as SALSA, which is based on a similar model by Miller and McPherson (1991), or a hydrodynamic model, such as the hydrodynamic model developed by Michot et al. (2011), to model salinities under specified restoration and/or climate-change scenarios. These results then serve as input data to a meta-model composed of soft-coupled individual, discipline-specific models for mangrove hydrology (HYMAN; Twilley and Chen 1998), nutrient biogeochemistry (NUMAN; Chen and Twilley 1998b), and forest dynamics (FORMAN; Chen and Twilley 1998a), which interact and feedback on one another to collectively model community-scale mangrove interactions (Figure 6). Restoration and/or climate-change scenarios represent varying inputs of freshwater and saltwater to the SALSA model or other hydrodynamic models. These then provide surface-water salinities for specific locations, which are then used along with other variables as input data to the HYMAN model to estimate soil porewater salinities in the mangrove forest. This is a critical step, because a major limitation in the analysis of restoration and/or climate-change scenarios is the conversion of surface-water salinities in channels to soil porewater salinities in adjacent wetlands. The soil porewater salinities generated by HYMAN are then used along with other variables as input data to the FORMAN model to estimate changes in mangrove forest structure, including changes in the products of net primary production. The products of net primary production generated by FORMAN are then used along with other variables as input data to the NUMAN model to estimate nutrient concentrations and organic matter accumulation, the results of which then feed back into the FORMAN and HYMAN models, respectively. Overall, MANGAL provides insight into community-scale responses of mangroves to specific restoration and/or climate-change scenarios, allowing the testing of causal mechanisms of system restoration and degradation. MANGAL can also assist in selecting performance measures for monitoring programs that evaluate project effectiveness. 57$

14 Figure 6. Schematic of the MANGAL model, a meta-model composed of soft-coupled individual, discipline-specific models that collectively model community-scale mangrove interactions in the coastal Everglades. Figure adapted from Twilley and Rivera-Monroy et al. (2005). Everglades Landscape Model (ELM) The Everglades Landscape Model (ELM) is a regional-scale, integrated ecological assessment tool designed to help us understand Everglades hydro-ecological dynamics, and to predict the relative responses of the landscape to alternate water management scenarios. In simulating changes to habitat distributions, the ELM dynamically integrates hydrology, water quality, soils, periphyton, and vegetation. It is a spatially distributed model with a regular, square grid that has been applied at resolutions ranging from 1 ha to 1 km 2, in domains ranging from approximately 100 to 10,000 km 2, depending on the research objective. Since its initial development in the 1990's, the model has been refined through a number of versions, with the Open Source code, data, and extensive documentation (latest version 2.8.4, Fitz and Paudel 2011) available at That documentation includes statistical and graphical assessments of the calibration and validation of multiple Performance Measure output variables, with an earlier version reviewed for CERP applications by an expert panel (Mitsch et al. 2007) and the CERP IMC. An ongoing use of the ELM is to explore hypotheses related to better understanding the hydroecological dynamics of the Everglades, including extrapolation of field scale research to larger spatial and temporal domains - such as ENP where FCE research is focused. For example, in a 433 km 2 northern Everglades impoundment, Fitz and Sklar (1999) used a 0.25 km 2 resolution 58$

15 application to show how water flows influenced P distributions in the surface waters and soils over decadal time scales, leading to altered biomass and succession of macrophyte (e.g., sawgrass and cattail) and periphyton communities along a eutrophication gradient. In turn, the changes in vegetation and land surface elevation via peat accretion/oxidation had direct feedbacks to hydrology that altered overland flows and transpiration losses. The importance of these interactions among physical, chemical, and biological processes was further highlighted in century scale ELM simulations in a 100 ha domain at ~1.5 ha resolution in the central Everglades. Much of the historical (e.g., 200 years before present) Everglades was comprised of an anisotropically-patterned habitat of elevated sawgrass ridges interspersed with lower elevation, deeper water sloughs. In the past century, much of that patterned habitat has been lost due to management-induced changes in nutrients and water levels and flows, altering the hydro-ecological drivers that had developed and maintained the system (reviewed by Larsen et al. 2011). Under synthetic (but realistic) hydrologic inputs in the ELM simulation, differential macrophyte and periphyton turnover rates led to bimodal peat accumulation rates which maintained elevation differences between ridges and sloughs and, thus, the landscape pattern (Figure 7), supporting some of the conclusions from field studies by Watts et al. (2010). Another research-oriented application of ELM involves its use as boundary conditions to other ecological models. Using output from a regional (i.e., ~10,000 km 2 at 0.25 km 2 ) resolution ELM application, we conducted model experiments to evaluate the response of Florida Bay seagrass communities to increases in managed inflows to the Everglades, with concomitant increases in P loads into the Everglades, which are possibly exported to Florida Bay. Increased Everglades water and P inflows led to cascading downstream P dynamics, with some increases in P mass outflows from the boundary of the ELM along Florida Bay. Using 10 year simulations of the SEACOM seagrass community model (Madden et al. 2007), interactions between phytoplankton and seagrass communities exhibited a range of sensitivity to the altered P loads, depending on the location of the estuarine basin relative to inflows, the magnitude of flow changes and P loads, and to physical characteristics of the basin such as depth and turnover rate. An important use of this ELM simulation framework is to evaluate the relative ecological benefits of alternative management scenarios in the greater Everglades region. Because of the significant ecosystem degradation due to anthropogenic loads of P into the Everglades (reviewed by McCormick et al. 2011), constructed wetlands (Stormwater Treatment Areas, or STAs) have been built along the northern Everglades boundaries, to use the process of biological filtering to remove excess P from the surface waters that flow into the Everglades (Pietro et al. 2009). To compare future Everglades ecosystem responses to water management scenarios with and without STAs, Fitz et al. (2004) used a 1 km 2 resolution application of the regional ELM. Under the future scenario that included the significant reductions in Everglades P loads via STAs, the desirable periphyton and macrophyte communities were maintained, with ecologically significant decreases in ecosystem P accumulation rates compared to the other simulation scenario without STAs (and thus significantly increased P loads). In the central Everglades, a CERP project will ultimately "decompartmentalize" the multiple impoundments that have significantly altered the water flows and depths compared to those of the historical Everglades. This CERP Decomp Project 59$

16 ( is focused on restoring the timing and quantity of water flows within and out of Water Conservation Area 3, with the primary restoration goal being to achieve a homogenous overland sheet flow of water, a process that is hypothesized to restore the original (or at least maintain existing) ridge and slough habitats. However, such restoration is constrained by P eutrophication: if increases in water volumes are associated with excessive P concentrations, the ecosystem will degrade due to excessive P loading to the ecosystem. The 0.25 km 2 regional ELM application was used in model experiments that were analogous to the basic scenarios that were anticipated to be developed in the future by CERP Decomp Project team members. We developed those scenario experiments to compare a baseline run compared to two alternative restoration scenarios, evaluating the relative benefits of hydrologic restoration among runs, while also explicitly considering any potential eutrophication constraint (Fitz et al. 2011). A Multi-Criteria Decision Analysis indicated that the alternative with more homogenous sheet flow was preferred over the base and other alternative, despite evidence of some localized eutrophication risk. More recently, a CERP government agency team evaluated alternative restoration scenarios for Phase 1 of the CERP Decomp Project. The SFWMM (v6.0) and RSMGL (v2.3.1) were used to evaluate hydrologic benefits associated with seven future restoration alternatives, relative to a future baseline run; the ELM (v2.8.4) was applied to evaluate the magnitude of any P eutrophication constraints associated with all of the scenarios relative to the baseline run. ELM output Performance Measures included the spatial distributions and magnitudes of: (1) water column P concentration, (2) soil P concentration, (3) soil P accumulation rate, and (4) soil peat accretion rate. To understand or better interpret some of the formal Performance Measures, the agency team used additional Supplementary Performance Indicators, including spatial distributions and magnitudes of water depth, overland flow velocity, water column chloride (flow tracer) concentration, and soil porewater P concentration. In detailed evaluations of the RSMGL and ELM Performance Measure outputs, the agency team selected a restoration scenario that had the maximum hydrologic benefits compared to the baseline, and no evidence of eutrophication risks. This latter evaluation (CERP Decomp Water Quality Team 2011) was best summarized by the P accumulation rate Performance Measure (example in Figure 8), which was determined to be the most sensitive and accurate metric of P eutrophication. 5:$

17 Figure 7. Pattern of differing magnitudes of peat accretion rate during the century-long simulation. Figure on left shows the initial condition map of land elevation, where approximately five tree islands are evident by the associated black/near-black patches. Note the discontinuity shown in the color scale for accretion rate, associated with the bimodal distribution of D-EE(,(0+($ Figure 8. P accumulation rate Performance Measure output, restricted to the study area of interest for Decomp PIR 1, comparing the future baseline to one of the seven future scenarios (named Alternative A). The left and right maps show the spatial distribution and magnitude of the P accumulation rate over the 36 year simulation period. The middle map shows the difference in P accumulation rate between Alternative A and the future baseline. Differences less than the targeted difference-threshold (±10 mg P m -2 yr -1 ) are bounded by grey contours, indicating relatively small differences that are of relatively minor ecological significance and/or are less than the probable bounds of the model accuracy. 5;$