Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region Final Report to the Gulf of Mexico Foundation and the Habitat Conservation and Restoration Team of the Gulf of Mexico Alliance

D. Yoskowitz, C. Carollo, J. Beseres-Pollack, K. Welder, C. Santos, and J. Francis

June 2012 Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region

Table of Contents EXECUTIVE SUMMARY ...... 2 Introduction ...... 3 Sea Level Affecting Marshes Model 6 ...... 4 Ecological characterization of selected habitats...... 5 Fresh marsh ...... 5 Salt marsh ...... 8 Swamps ...... 9 Ecosystem services...... 10 Disturbance regulation or Hazard mitigation ...... 11 Recreation ...... 11 Food ...... 12 Aesthetics ...... 12 Nutrient cycling ...... 12 Soil retention ...... 12 Waste regulation ...... 13 Hydrological balance or Water regulation ...... 13 Habitat change under selected sea level rise scenarios ...... 13 Ecosystem services valuation...... 22 Methodology ...... 22 Results ...... 24 Heat map demonstrating differences in the demand for ecosystem services ...... 29 Limitations ...... 38 References ...... 39

Appendix A ...... 45

Suggested citation: Yoskowitz, D., C. Carollo, J. Beseres-Pollack, K. Welder, C. Santos, and J. Francis. (2012). Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region. Harte Research Institute. June. 75 pages.

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EXECUTIVE SUMMARY

The goal of this study was to identify the potential changes in ecosystem services values provided by wetland habitats in the Galveston Bay region.

Built exclusively upon the output produced during the Sea Level Affecting Marshes Model 6 (SLAMM 6) exercise for Galveston Bay, TX, the study shows that the selected habitats, fresh marsh, salt marsh, and swamp, present a steady decline in time under three sea level rise scenarios (A1B mean = 0.39m; A1Bmax = 0.69m; and 1.5m). Fresh marsh is the habitat that is projected to undergo the biggest changes under all sea level rise scenarios, with the loss of about 21% of its extent between 2009 and 2100 under the A1Bmax scenario. This trend is due to the fact that fresh marsh is displaced by salt marsh and other habitats and has insufficient time and/or space to migrate landward. The percentages of change for salt marsh and swamp are less prominent, but still significant with about 12% and 16% loss, respectively, between 2009 and 2100 under the A1Bmax sea level rise scenario. This trend is also shown in the values of ecosystem services provided by these habitats.

An ordinary least squares regression model was used to calculate the monetary value of selected ecosystem services provided by each chosen habitat in 2009 (initial condition) and in 2050 and 2100 under the A1B max (0.69m) sea level rise scenario. This method is called value transfer and specifically involves transferring meta-regression analysis functions. Meta-regression analysis combines estimates from multiple original studies and applies them to the policy site. This analysis shows that, under the A1Bmax sea level rise scenario, the value for the total area of fresh marsh including all ecosystem services (nutrient cycling, disturbance regulation, food, aesthetics, recreation, and water regulation) presents significant monetary losses, $87.7 million per year from 2009 to 2100. Looking at individual services for fresh marsh, disturbance regulation has major impact and shows a significant loss in total area values, $27.4 million from 2009 to 2100, with sea level rise. Under the same scenario, from initial condition to 2100, $13.7 million per year is lost in ecosystem services (disturbance regulation, recreation, food, aesthetics, nutrient cycling, and soil retention) provided by salt marsh. Looking at individual services, salt marsh follows the same pattern as fresh marsh; disturbance regulation shows the major economic impact with $4.3 million per year lost from 2009 to 2100, followed by recreation with $3.5 million per year lost from 2009 to 2100. Due to a lack of original studies valuing the ecosystem services provided by swamps, it was not possible to conduct a meta-regression analysis to calculate the change in ecosystem services values provided by this habitat; instead, point estimates using the average per hectare per year ecosystem services values were used. Under the sea level rise A1B max scenario, there is a cumulative economic loss of ecosystem services (recreation, waste regulation, disturbance regulation, food, and aesthetics) values provided by swamps of about $12 million, from current condition to 2100.

The ecosystem services value estimates provided here are only a small portion of what can be lost due to the decrease in habitat area and the loss of its associated services. These results may stress the importance of not only protecting built infrastructure, but also natural resources, such as habitat, from sea level rise.

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region

I. Introduction Sea level rise poses potential threats to not only manmade or built infrastructure but also to natural assets that provide numerous ecosystem services that impact human well-being. As relative sea level changes it is likely that the quality and distribution of our natural assets will change as well. For example, wetland losses or marked vegetation changes can result from accelerated sea level rise (Warren and Niering 1993, Craft et al. 2009) and tidal marshes are highly susceptible to sea level rise (Moorhead and Brinson 1995, Park et al. 1991). These bio-physical alterations will lead to changes in ecosystem services (ES). An understanding of these changes and their impacts is critical to managing these habitats effectively.

For the purpose of this study, the following definition of ecosystem services (ES) is used: ecosystem services are the contributions from Gulf of Mexico marine and coastal ecosystems that support, sustain, and enrich human life (Yoskowitz et al. 2010). In recent years, ES have attracted a substantial amount of discussion and research, but examples where ES concepts have been applied to real-world policies and decisions are rare. Yet, there is today a greater call for the incorporation of ES to inform the decision making process than at any other time. While there is a call to action there is also a great need to build the capacity to achieve these goals.

ES analysis is highly multidisciplinary, involving ecologists, physical scientists, modelers, economists, and social scientists. Large volumes of research and data, as well as input from communities of stakeholders, are required. Despite the difficulties, ES valuations can convey the full value of ecosystems in common units (monetary or otherwise) to decision-makers and help them understand the trade-offs involved in altering landscapes, whether for development, restoration, or other activities.

The project described in this report exclusively built upon the output produced during the Sea Level Affecting Marshes Model 6 (SLAMM 6) exercise for Galveston Bay, TX, carried out by Warren Pinnacle Consulting, Inc. (Section II) and sponsored by the Gulf of Mexico Alliance (GOMA) Habitat Conservation and Restoration Team (HCRT). The research question that drove the project: “as sea level rises, what is the potential change in ecosystem services values delivered by coastal habitats in the Galveston Bay region”? In addressing this question, the project team assessed the value of the prominent ES provided by marsh in the Galveston Bay complex and, at the same time, demonstrated the vulnerability of those services as a result of sea level rise. This directly supports the GOMA HCRT task to “monitor distribution and potential gain and loss of coastal habitats and measure the ecosystem services they provide” (GOMA 2009).

The project team, in consultation with regional expertise and based upon the list produced at the first Gulf of Mexico Ecosystem Services Workshop, held in Bay St. Louis, MS (Yoskowitz et al. 2010), linked the ecological function of marsh to the ES provided by this habitat. A description of the ecological functioning of selected habitats in Galveston Bay, TX, and how it links to the ES supplied is presented in Section III and IV.

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Changes in habitat extent were assessed given the modeling output of the SLAMM project; maps were produced and are included in Section V and Appendix. Also, changes in the value of the ES provided by marsh and swamps were calculated; the monetary valuation was conducted through value transfer using a meta-regression analysis described in Section VI.

A “heat map” was created to visually demonstrate the change in the value of services as sea level rise changes wetland habitat (Section VII). The heat map accounts for the intensity of the services and, therefore, is dependent upon the proximity of the habitat to the demand for the service. For example, the value of a specific habitat will be very different in relatively rural Chambers County, located in the East Galveston and Lower Trinity River SubBasins, than it would be in more urban Harris County, the majority of which is located in the Buffalo San-Jacinto River SubBasin. Last, study limitations are presented in section VIII.

II. Sea Level Affecting Marshes Model 6 According to Warren Pinnacle Consulting, Inc. (2011), the Sea Level Affecting Marshes Model 6 (SLAMM 6) looks at changes in tidal marsh and other habitat types as a response to sea level rise. This model includes the dominant processes involved in wetland conversion and shoreline modifications through long-term sea level rise. The main processes are inundation, erosion, saturation, and accretion and are described in the Warren Pinnacle Consulting, Inc. (2011) report as follows:  Inundation: The rise of water levels and the salt boundary are tracked by reducing elevations of each cell as sea levels rise, thus keeping mean tide level constant at zero. The effects on each cell are calculated based on the minimum elevation and slope of that cell.  Erosion: Erosion is triggered based on a threshold of maximum fetch and the proximity of the marsh to estuarine water or open ocean. When these conditions are met, horizontal erosion occurs at a rate based on site-specific parameters.  Saturation: Coastal swamps and fresh marshes can migrate onto adjacent uplands as a response of the water table to rising sea level close to the coast.  Accretion: Sea level rise is offset by sedimentation and vertical accretion using average or site- specific values for each wetland category. Accretion rates may be spatially variable within a given model domain or can be specified to respond to feedbacks such as frequency of flooding.  Salt Wedge Model: This part of the model is required because marsh-type is more highly correlated to salinity than elevation when fresh-water flow is significant. It also adds complexity to model development and this requires additional model specifications, including estuary geometry and freshwater flow projections.

Data sources used by Warren Pinnacle Consulting, Inc. to generate habitat maps are:  The digital elevation map derived from the 2007 Federal Emergency Management Agency LiDAR and the 2009 1/9 arc second National Elevation Data set (both provided by the Harte Research Institute);

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 The wetland data set derived from the U.S. Fish and Wildlife Services’ National Wetlands Inventory produced with a photo dated 2009 then converted to 30m cells for model calibration. The output of SLAMM 6 for Galveston Bay, TX, was made available to the project team, which consisted of habitat projection raster files for five sea level rise scenarios (A1Bmean, A1Bmax, 1m, 1.5m, and 2m) in ~25 year intervals (2025, 2050, 2075, and 2100). Initial condition was dated 2009. It was decided to utilize data from 2050 and 2100 under three sea level rise scenarios (A1Bmean = 0.39m; A1Bmax = 0.69m; and 1.5m) for the current project.

SLAMM 6 produced habitat projection maps and incorporated some habitat migration rules, including preventing habitat (as well as water) migration adjacent to diked areas (not subject to sea level rise below 2m/century). However, it permitted habitat migration onto developed areas throughout the region. Therefore, for the purpose of this project, the original projection maps were modified in ESRI ArcGIS 9.3 so that only beach and water could “migrate” onto developed land. The developed land layer used in the SLAMM 6 run was derived from the National Land Cover Database “percent impervious coverage” 2006. The Spatial Analyst raster processing tools within the ESRI ArcGIS 9.3 was used in a series of steps to identify habitats that were projected to migrate onto developed land: habitats were reclassified as “developed land” in future scenarios, with the exception of open ocean, estuarine water, ocean beach, and estuarine beach. These habitats were permitted to migrate over developed land in future scenarios because migration of beach and water along the shoreline with elimination of developed land does occur. Developed land adjustments were performed for the selected three sea level rise scenarios in years 2050 and 2100.

Among all of the SLAMM 6 habitats (Fig. 1), the ones selected for analysis in this project have been renamed fresh marsh, salt marsh, and swamp. Fresh marsh comprises SLAMM 6 Inland fresh marsh and Tidal fresh marsh; salt marsh comprises SLAMM 6 Irregularly flooded marsh and Regularly flooded marsh; swamp comprises SLAMM 6 Swamp, Tidal swamp, and Cypress swamp. This grouping was done to allow performing a value transfer analysis through meta-regression utilizing the original studies available through the Gulf of Mexico Ecosystem Services Valuation database (GecoServ.org). These sources are limited in number and do not allow for fine differentiation within each group, for example, between regularly and irregularly flooded marshes.

III. Ecological characterization of selected habitats Fresh marsh Tidal freshwater marshes occur along the upper portions of estuaries, above the oligohaline zone. Although dominated by the tides, tidal freshwater marshes are located just upstream of saline waters and are not salty. They are unique habitats characterized by both terrestrial-riverine and oceanic- estuarine influence (Yozzo and Diaz 1999). By definition, their average annual salinity is below 0.5 ppt (Odum 1988). Tidal freshwater marshes are distributed worldwide – however, the largest occur in association with major river systems. In comparison to tidal salt marsh systems, relatively little is known about the ecological significance or functioning of these systems.

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Non-tidal freshwater marshes are nonforested wetlands dominated by rushes, grasses, sedges, and other emergent plants (Weller 1994). Water levels are generally shallow, from a few inches to a few feet, with the majority of water originating from surface and groundwater sources. In-land freshwater marshes contain high levels of nutrients and are highly productive ecosystems (Brinson and Lugo 1981). They are defined by ocean-derived salinities lower than 0.05‰ (Odum 1988) and are broken into three categories: Lacustrine, Palustrine, and Riverine. Lacustrine marshes are those that lack trees, shrubs and persistent emergents, much like Riverine marshes, but differ in that they are located in either topographic depressions or dammed river channels. Palustrine marshes are defined by similar topographic structure, with an areal extent less than 8 hectares (ha) and the presence of trees, shrubs, and persistent emergent vegetation (U.S. FWS, 1992). These freshwater habitats have suffered severe degradation in some regions due to a number of stressors related to human development (Noss et al. 1994). Like other marsh ecosystems, freshwater marshes are important for their role in water filtration and reduction of flood impacts on surrounding areas.

Food source The primary energy sources in freshwater marshes come from a combination of marsh macrophytes, benthic microalgae, phytoplankton, and terrestrial organic matter (Odum et al. 1973). Direct gazing of marsh plants tends to be important, due to their relatively high palatability and the presence of leaf- grazing and seed-eating birds and mammals (Mitsch and Gosselink 1986). In comparison to salt marshes, where the dominant consumers are crabs, amphipods, and shrimp (Odum et al. 1982), in freshwater marshes, larval and adult insects play a key role along with a few crustacean species (Odum et al. 1984). Common prey available to foraging nekton include diptera and ephemeroptera insect larvae, oligochaetes (Tubificidae, Naidae, Enchytraeidae), and amphipod and isopod crustaceans; microcrustaceans such as ostracodes, copepods and cladocerans are particularly important prey for post-larval and juvenile fish species (Yozzo and Diaz 1999).

In seasonally flooded non-tidal freshwater marshes, insects typically dominate the macroinvertebrate community (Batzer and Wissinger 1996). Epiphytic invertebrates also colonize the surfaces of submerged and floating aquatic plants, forming a key food resource for juvenile fishes and macrocrustaceans (Yozzo and Diaz 1999).

Habitat Tidal freshwater marshes are used by numerous estuarine and freshwater species (McIvor and Odum 1986). In contrast to tidal salt marshes, which are commonly dominated by a single plant species (e.g. Spartina alterniflora), tidal freshwater marshes have high species diversity and richness, and are characterized by plants restricted to freshwater or low salinities. Tidal freshwater marshes commonly include mixed communities of grasses, rushes, sedges, shrubs and herbaceous plants. Submerged and floating aquatic vegetation is also a common feature, with principal species including Eurasian water milfoil (Myriophyllum spicatum), tapegrass (Vallisineria americana), water stargrass (Zosterella dubia), pondweeds (Potamogeton spp.), waterweeds (Elodea spp.) water chestnut (Trapa natans), and duckweeds (Lemna spp.) (Yozzo and Diaz 1999).

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region

Fishes utilize different parts of tidal freshwater marsh habitats for different purposes. Upper reaches of tidal freshwater marshes have been shown to support greater densities of fishes than marshes further downstream (Rozas and Odum 1987a). Submerged aquatic vegetation of tidal freshwater marsh creeks was found to be important as habitat for forage fishes, and was utilized to a lesser extent as nursery habitat by recreational species (Rozas and Odum 1987b). Intertidal rivulets are important corridors for fishes moving between tidal creeks and the flooded marsh surface (Rozas et al. 1988). Densely vegetated marsh surfaces adjacent to depositional creek banks are utilized by larger numbers of small fishes and shrimps during flood tide than those adjacent to erosional banks, with lower predator encounters and higher food availability the likely process driving this pattern (McIvor and Odum 1988).

Non-tidal freshwater marsh in the Gulf of Mexico region is generally characterized by Eleocharis spp., Panicum hemitomon and Sagittaria falcata (Chabreck and Condrey 1979). These ecosystems provide important feeding and resting habitat for wintering bird populations, due in part to the abundance of food resources (Winslow 2001). Alligators are also well documented residents of freshwater marsh habitats, playing a role as a large predator on birds and mammals (Weller 1994). Because of their unique habitat value, non-tidal freshwater marshes also provide important recreational opportunities, including boating (i.e. kayaking), fishing, hiking, and hunting.

Nutrient cycles Tidal freshwater marshes may support higher net primary production rates than salt marshes. Vascular plants in salt water invest more energy than freshwater plants to exclude or remove salts, which could otherwise be used for growth (Mendelssohn and Burdick 1987). Tidal freshwater marsh plants demonstrate relatively high decomposition rates due in part to their relatively high amounts of nitrogen and low amounts of cellulose. As a result, detritus formed in tidal freshwater marsh habitats has relatively high nutritive quality, and is preferentially selected by detritus consumers (Smock and Harlowe 1983). Similar to salt marsh habitats, tidal freshwater marshes tend to have a net uptake of nitrite, nitrate, and phosphate in the spring (at the start of and during the growing season) and a net export of reduced nitrogen and phosphorus in the fall and winter due to dead and decomposing plant material (Odum 1988). Under the proper conditions, freshwater tidal marshes could remove substantial amounts of nitrogen (e.g. to assimilate domestic sewage) (Simpson et al. 1983).

Freshwater inflow serves as an important nutrient source to non-tidal freshwater marshes (DeLaune et al. 1986). In freshwater marshes with standing water year-round, nutrients tend to be sequestered in organic detrital debris, where anaerobic conditions slow decomposition (Weller 1994). Non-tidal freshwater marshes ecosystems may exhibit nitrogen limitation, suggesting that they can improve water quality of surrounding water bodies by removing nutrients from surface flows. Experiments have documented that water passing through freshwater marshes is less enriched in nutrients than the source waters (Kadlec 1979).

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Salt marsh Salt marshes are a common feature of temperate estuaries throughout the world. Besides being one of the most productive plant communities in the world, salt marshes are important elements of estuarine ecosystems that: 1) provide a food source to numerous estuarine and coastal consumers, 2) serve as habitat for large numbers of juvenile and adult organisms, and 3) play an important role in estuarine chemical cycles (Day et al. 1989). For several hours around high tide when areas of the salt marsh are inundated, they provide expanded habitat and an important feeding opportunity and reduction in predation risk to aquatic organisms (Hettler 1989). Salt marshes also function as important foraging and breeding habitat for a variety of bird species (e.g. Benoit and Askins 2002, Shriver et al. 2004)

Food source Salt marshes and their associated benthic and epifaunal flora contribute to a detritus-based food web (Boesch and Turner 1984) as well as being directly utilized by fishes and invertebrates for feeding and reproduction (Kneib and Stiven 1978, Peterson and Turner 1994). The use of salt marsh detritus in food webs usually occurs in close proximity to the salt marsh (Deegan et al. 2002). For example, numerous macrobenthic and meiofauna species, which are essential prey of many ecologically and economically important fishery species, are known to consume organic detritus (Tenore et al. 1982). Salt marsh support of offshore fisheries is more likely via direct export of juvenile fish and through migrations of nekton species (Deegan et al. 2002). Young-of-the-year summer flounder captured leaving salt marsh creeks on ebb tides displayed significantly greater stomach fullness than fish entering the creeks on flood tides, suggesting they take advantage of high concentrations of prey available in salt marsh creeks (Rountree and Able 1992).

Habitat Both transient and resident communities of fishes and invertebrates utilize salt marshes as habitat. Vegetated salt marsh appears to have a higher nursery value than nonvegetated marsh (Minello et al. 1999). Blue crabs, Callinectes sapidus—which historically support one of the largest commercial fisheries in the Gulf of Mexico (Rees 1963)—utilize salt marshes more than unvegetated areas as nursery habitat (Thomas et al. 1990). Examination of a Galveston Bay salt marsh indicated that significantly higher densities of brown shrimp Penaeus aztecus were located in intertidal Spartina alterniflora habitat compared to subtidal nonvegetated habitats (Zimmerman et al. 1984). Fishes also derive habitat benefits from salt marshes: regularly-flooded Spartina marsh was used by greater than 50% of the fishes reported from adjacent estuarine open water habitat in a North Carolina estuary (Hettler 1989). Intertidal Spartina habitat appears to be the principal nursery habitat for the mummichog species Fundulus heteroclitus (Linnaeus) and F. luciae (Baird) (Kneib 2003).

The physical structure of salt marsh vegetation can provide a refuge for juvenile estuarine fauna (Nelson 1979, Heck and Thoman 1981). Experimental studies reported that the simulated vegetative structure of Spartina alterniflora reduced predation rates of pinfish (Lagodon rhomboides) and Atlantic croaker (Micropogonias undulatus) on juvenile brown shrimp Farfantepenaeus aztecus, but did not affect predation rates of red drum (Sciaenops ocellatus) and speckled trout (Cynoscion nebulosus) (Minello and Zimmerman 2003). Similarly, in simulated laboratory experiments, sparse and dense assemblages of

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region plants allowed increased survival of infaunal bivalves from blue crab (C. sapidus) predation compared with control treatments (Blundon and Kennedy 2003).

Nutrient cycles Salt marshes are very productive systems with abundant dissolved nutrients. In some estuarine systems, the annual export of dissolved nutrients from salt marshes to coastal waters can account for over 40% of the dissolved nitrogen in near shore waters (Valiela 1995). Spartina alterniflora dominated communities contribute up to 80% of the total carbon production of Atlantic and Gulf of Mexico estuarine systems (Turner 1976). Detritus production and nutrient processing may contribute to the enrichment and regulation of estuarine food webs (Boesch and Turner 1984). Nutrient concentrations in marsh sediments are much higher than estuarine waters that periodically flood the marsh surface, indicating their role as a large source of nutrients to estuarine systems (Chalmers 1979). Besides salt marsh sediments and particulate detritus, evidence also indicates that nutrients released from aboveground plant tissues may be an underappreciated yet significant carbon and nitrogen source for salt marsh food webs (Turner 1993).

Swamps Swamps are forested tidal wetlands that are generally hydrologically connected to an adjacent pond, lake, or river. Swamp forests near the Gulf of Mexico are dominated by baldcypress (Taxodium distichum) and water tupelo (Nyssa aquatica) (Williams et al. 2010). Swamps are generally shallow and maintain standing water for at least part of the year. Sediments, nutrients, and floodwaters from rivers and uplands are retained in these lowland systems and slowly released to downstream habitats (Wharton 1970). Hydroperiod is a major driver of species distribution: the longer the inundation period, the fewer the number of species that can tolerate the increasingly stressful conditions (Duever 1984). For cypress swamps in particular, fluctuating water level is critical for long-term survival because seedlings can only germinate on dry land (Mitsch and Ewel 1979). Fires are another defining feature of swamp systems and are important in reducing the buildup of organic matter and releasing nutrients back into the soils (Ewel 1990).

Response to climate and human changes Sea-level rise can be expected to have significant effects on freshwater tidal swamps, with predicted transitions to mangrove forests near the mouths of rivers and extension of floodplain swamps inland (Williams et al. 2010). Swamps may also convert to open water if invaded by salt, a process which has already been documented in Louisiana (Wicker et al. 1980). In addition, the combination of flooding and salinity stress may reduce survival and growth more than either stress alone (Allen et al. 1996). Levees and other water control structures that reduce flooding risk also have altered sediment and fresh water dynamics, with salt water intruding farther inland and destroying cypress swamps (Titus 1986). Increasing vegetative productivity of swamp ecosystems is particularly important in areas such as Louisiana where coastal subsidence results in a relative sea-level rise that is even greater than eustatic sea-level rise (Conner and Day, 1988). While cypress swamp forests can withstand semi-continuous flooding, and already established trees can survive for many years under continuously flooded conditions, the seedlings require a dry period for germination (Conner et al. 1989). Therefore,

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Habitat Swamps host a wide variety of wildlife. Abundant prey species such as mosquitofish (Gambusia holbrooki) and sailfin mollies (Poecilia latipinna) attract predatory animals such as long-legged wading birds (Frederick and McGehee 1994). The Great Cypress Swamp provides habitat for almost 50 species of birds that are utilizing the habitat during migrations as well as for foraging and breeding (Bird Observations 2012). Great egrets were observed to concentrate in the most biologically productive forested wetland habitats in the San Francisco Bay area (Kelly et al. 1993). Similarly, significant increases in foraging rates and reproductive success was observed at foraging sites with low dissolved oxygen levels (due to high nutrient loading), perhaps due to increased vulnerability of prey in these areas (Hafner et al. 1992). The vertical dimension of cypress vegetation provides both additional habitat and food for the higher trophic levels. Vertical vegetation provides increased opportunities for herbivory by terrestrial fauna such as mammals, birds and terrestrial invertebrates (Heymans et al. 2002).

Nutrient cycles The nutrient cycles of swamps are unique among other forested ecosystems. Regular flooding leads to changes in the solubility and chemical behavior of important soil nutrients as well as the rates of nutrient export, storage, and processing (Megonigal and Day 1988). Productivity is strongly tied to hydrology and nutrient regime. Swamps with periodic floods of short duration are generally thought to be more productive due to subsidies of nutrients and water than those that are continuously inundated or drained (Mitsch and Ewel, 1979). However, these benefits of flooding may be diminished by physiological stresses of anaerobic soils and drought (Megonigal et al. 1997). Aboveground net primary production in swamps of the southeastern U.S. is estimated to vary from 200-2000 g C m-2 yr-1 (Megonigal et al. 1997). Forested floodplains are generally more productive than still-water systems because of nutrient inputs from freshwater sources other than precipitation (Megonigal and Day 1988). Sediment organic matter is an important nutrient source for microorganisms, with large contributions from fine roots of forested wetland plants. Detrital accumulation also accounts for a large part (in some cases > 50%) of the total organic matter in swamp ecosystems, particularly those with large accumulations of peat (Megonigal and Day 1988). Nutrient retention is related to loading rate, with higher retention at low loading rates (Day et al. 2006). Tidal forested freshwater wetlands can also be used for assimilation of treated municipal wastewater, with numerous studies demonstrating that natural and constructed systems can be effective tertiary processors (Day et al. 2006).

IV. Ecosystem Services Wetlands, including marshes and swamps, are lands in between terrestrial and aquatic systems where the water table is frequently at or near the surface or the land is covered by shallow water (Cowardin et al. 1979). They are one of the most productive ecosystems (Barbier et al. 1997, Natural Resources Conservation Service 2008, New World Encyclopedia, 2009) providing a series of benefits to people such

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region as clean water, recreational opportunities, harvestable fish, and protection against storms. These benefits are called ecosystem services, that is, the contributions from the environment that support, sustain, and enrich human life (Yoskowitz et al. 2010, Zedler and Elliot 2006). According to the outputs of the first Gulf of Mexico Ecosystem Services Workshop (Yoskowitz et al. 2010), the most important ES for swamps and marshes are disturbance regulation, recreation, aesthetics, nutrient cycling, soil retention, waste regulation, and hydrological balance or water regulation. These services are discussed below.

Disturbance regulation or Hazard mitigation Disturbance regulation is the role of marshes and swamps in restraining extreme events such as floods, droughts, or hurricanes (de Groot et al. 2009). Protection against floods and storms, and recovery from droughts are instances of ecosystem responses to environmental variability controlled mostly by vegetation structure. For example, marshes and swamps protect humans from flood damages due to their water-storage capacity; they act as buffers by collecting floodwaters, slow their courses, and reduce their peak water levels (Zedler and Elliot 2006). Consequently, these habitats reduce flood- danger and damage to infrastructure. Disturbance regulation is often considered one of the most valuable ES (Costanza et al. 1997). Galveston is an area prone to be hit by hurricanes; therefore, ecosystems that can reduce the damages of such extreme storms can be really important. As sea level rises, the risk for flooding increases and marshes and swamps become crucial factors in dampening those risks.

Recreation Marshes and swamps provide opportunities for tourism and recreational activities such as fishing, birding, and hunting. In the ES valuation literature, recreation is one the most frequently valued services (HRI 2012; Plantier-Santos et al. 2012) probably due to the fact that: (1) stakeholders value this service highly, (2) it is considered easier to measure, and/or (3) its relation with human welfare is easier to establish.

Wildlife-related recreational activities play a significant role in the Texas economy. In 2006, there were approximately 5.5 million people in Texas who participated in wildlife-associated recreational activities (including fishing, hunting, and wildlife watching), spending over $9 billion in wildlife-associated expenditures. Not surprisingly, Texas was the state with the highest wildlife-associated expenditures, followed by California with nearly $8 billion. Of all the expenditures, $6 billion were for fishing and hunting alone. In fact, Texas is the state with more hunters (a total of 1.1 million of residents and non- residents) and the second with more anglers (2.5 million of residents and non-residents) in the nation (U.S. Department of the Interior et al. 2006). These numbers show how a large portion of recreational expenditures depends upon healthy ecosystems. For this reason, it is in the stakeholders’ best interest to protect the well-being and functionality of these habitats not only from human stressors, such as pollution, but also from climate stressors, such as sea level rise.

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Food Food production is a portion of primary production that can be extractable as food. In the case of marshes and swamps, the presence of edible plants and animals, like fish and crustaceans, makes these habitats indirect provider of food for humans. This service is one of the most frequently valued services in the ES valuation literature (HRI 2012, Plantier-Santos et al. 2012), showing the importance stakeholders place in the ability of ecosystems to provide food for humans.

Aesthetics Aesthetics is the appreciation of natural scenery, other than through recreation activities (de Groot et al. 2009). For marshes and swamps, the aesthetic quality of the ecosystem would be based on elements such as structural diversity, quality of the water, “greenness”, and tranquility. An example of how people appreciate a certain habitat is by looking at the number of houses that border that habitat or the amount of users of scenic routes. A way of valuing this service is by using hedonic price, a method that analyzes variations in house prices that reflect the home owner’s willingness to pay to live close to natural areas (HRI 2012). Galveston Bay is a good example of this; despite higher house prices, insurance costs, and probability of being hit by a hurricane, people still want to own a house close to natural environments.

Nutrient cycling Nutrients are known to be essential for the growth of organisms (Lavelle and Berhe 2005). The ability of an ecosystem to store, process, and acquire nutrients, such as nitrogen and phosphorus, is called nutrient cycling. Nutrients balance is directly related to aspects important to humans, such as water quality and clarity, food production, and the presence of fish. Contrarily, alterations to nutrient cycling resulting in nutrient surplus, cause eutrophication of soils and water bodies; alterations to nutrient cycling resulting in nutrient deficit, cause soil exhaustion and loss of fertility (Lavelle and Berhe 2005). Unsustainable agricultural practices such as soil fertilization release excessive levels of nutrients in aquatic systems leading to eutrophication. An example of eutrophication is the increase of phytoplankton in water bodies, which can result in the depletion of oxygen in the water, i.e. hypoxia, and consequently in the reduction of fish populations and degradation of water quality (Lavelle and Berhe 2005). Healthy ecosystems are dependent upon efficient cycling and availability of nutrients (Lavelle & Berhe 2005) and both swamps and marshes are important players in cycling nutrients.

Soil retention Coastal erosion is a serious hazard not only for people living near the coast, but also for organisms living along the coasts in bays, estuaries, and shallow water (Stewart 2009). Marshes play an important role in controlling coastal erosion by preventing soil loss by wind and runoff and avoiding buildup of silt; this service is called soil retention (Farber et al. 2006). Marsh vegetation is crucial in retaining the soil and consequently it is frequently used as a shoreline erosion control measure (Broome et al. 1992). This service is directly linked to human well-being since it influences elements such as water quality, water clarity, fisheries, and, as a result, recreational opportunities. Even if it is considered to be very important, this service is still not frequently valued in the ES valuation literature (HRI 2012).

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region

Waste regulation Waste regulation or treatment is the role of marshes and swamps in removing and breaking down non- nutrient compounds and materials (Farber et al. 2006). Organic wastes are frequently introduced into coastal and marine ecosystems; both marshes and swamps can help filter and decompose those materials (Millennium Ecosystem Assessment 2005). An indicator of this service is the maximum amount of chemicals that can be recycled or halted on a sustainable basis by ecosystems (de Groot et al. 2009). Marshes are often used as natural water treatment systems, making it more beneficial to invest in the ecological integrity of these habitats rather than let them degrade and build water treatment facilities to replace its services. New York City is a good example of this. The City’s nine million residents rely on a series of reservoirs in the Catskill and Delaware watershed, in upstate New York, as their drinking water source. However, in 1989, increased housing development and pollution started to pose a threat to the watershed’s water quality. Additional deterioration in the water quality would require New York City to build a filtration system to ensure that drinking water would meet federal standards. However, this requirement could be waived if natural conditions could provide safe water (US EPA, 1996). The City faced two choices: build a water filtration system or protect its watershed to ensure water quality. The filtration systems would cost between $6 billion to $8 billion, plus $300 million in annual maintenance costs; watershed protection would cost approximately $1 billion to $1.5 billion. The City decided that investing in the watershed and restoring the ecosystems responsible for water purification was less- costly and a more sustainable option (National Research Council of the National Academies 2004). This shows that (1) investing in ecosystem conservation to maintain services can be effective and practical, and (2) waste regulation is directly linked to features that are essential to humans, such as clean drinking water.

Hydrological balance or Water regulation Water regulation is the role swamps and marshes have in water infiltration and gradual release of water. It concerns the flow of water across the planet surface and the cadence of drought-flood cycle (de Groot et al. 2009, Farber et al. 2006). An indicator of this service is the water retention capacity in soils or at the surface (de Groot et al. 2009). Changes in land cover and, particularly modifications that alter the water storage potential, such as the conversion of marshes to croplands, can strongly influence the capacity of water regulation in marshes and swamps (Millennium Ecosystem Assessment 2005).

V. Habitat change under selected sea level rise scenarios The first task undertaken by the project team was to analyze changes in fresh marsh, salt marsh, and swamp under the three selected sea level rise scenarios (A1Bmean = 0.39m; A1Bmax = 0.69m; and 1.5m) in 2050 and 2100 compared to the initial condition (Fig. 1). Habitat maps were created and are presented in this section for the A1Bmax (0.69m) sea level rise scenario only; maps for the remaining two scenarios can be found in Appendix.

The distribution of fresh marsh, salt marsh, and swamp in 2050 and 2100 is displayed in Figure A1 and A2 (in Appendix), respectively. These maps show a general decline of the three habitats due to sea level rise. However, the changes undergone by each habitat are difficult to detect in these maps. Therefore,

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Harte Research Institute new maps were created for each habitat to better display habitat present and lost (compared to initial condition) in 2050 and 2100 (Figs. 2 – 7). Moreover, the extent of fresh marsh, salt marsh, and swamp was calculated for the initial condition, and for 2050 and 2100 under the three sea level rise scenarios (Table 1).

Table 1. Extent of fresh marsh, salt marsh, and swamp (in hectares) for 2009 (initial condition), and 2050 and 2100 under the three selected sea level rise scenarios

2009 2050 2100 0.39 m 0.69m 1.5m 0.39 m 0.69m 1.5m Fresh Marsh 53,248 52,812 50,255 43,491 48,926 42,302 29,969 Salt Marsh 34,317 34,041 32,281 30,873 32,132 30,366 26,753 Swamp 11,507 11,763 11,368 10,314 10,569 9,630 8,477

Both the maps and the table show a steady decline in time of the three habitats under the three sea level rise scenarios. Fresh marsh is the habitat that is projected to undergo the biggest changes under all sea level rise scenarios. Specifically, in 2050, compared to the initial condition, fresh marsh shows loss of 0.82%, 5.62%, and 18.32% under the A1Bmean, A1Bmax, and 1.5m sea level rise scenarios, respectively; and in 2100 loss of 8.12%, 20.56%, and 43.71% under the A1Bmean, A1Bmax, and 1.5m sea level rise scenarios, respectively. This trend is due to the fact that fresh marsh is displaced by salt marsh and other habitats and has insufficient time and/or space to migrate landward. The percentages of change for salt water marsh in 2050 under the three sea level rise scenarios (0.80%, 5.93%, and 10.04%) are very similar to those of fresh water marsh; however, the projected losses of salt water marsh in 2100 (6.37%, 11.51%, and 22.04%) are less than for fresh water marsh. Last, in 2050 swamp shows a 2.22% gain under the A1Bmean scenario and a loss of 1.21% and 10.37% under the A1Bmax and 1.5m scenarios, respectively; in 2100, swamp is projected to lose 8.15%, 16.31%, and 26.33% of its extent under the A1Bmean, A1Bmax, and 1.5m sea level rise scenarios, respectively.

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Figure 1. Galveston Bay study area, displayed with SLAMM 6 land classes (initial condition, 2009) (Source: National Wetlands Inventory, 2009 and Warren Pinnacle Consulting, 2011)

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Figure 2. Fresh marsh lost (compared to initial condition) and present in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 3. Fresh marsh lost (compared to initial condition) and present in 2100 under the A1Bmax (0.69m) sea level rise scenario

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Figure 4. Salt marsh lost (compared to initial condition) and present in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 5. Salt marsh lost (compared to initial condition) and present in 2100 under the A1Bmax (0.69m) sea level rise scenario

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Figure 6. Swamp lost (compared to initial condition) and present in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 7. Swamp lost (compared to initial condition) and present in 2100 under the A1Bmax (0.69m) sea level rise scenario

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VI. Ecosystem services valuation In this study, the project team chose to use meta-regression analysis as the value transfer approach to value the ecosystem services provided by marsh and swamp in Galveston Bay. Value Transfer (VT), also referred to as benefit transfer, is a common practice in economics involving the use of existing data in a different setting other than that for which it was collected (Champ et al. 2003, Desvousges et al. 1998, Loomis 1992). The goal of VT is to estimate the benefits of one study area by adapting an estimate or benefit from another study. The original site is usually called the study site and the location for which information is needed is called the policy site (Champ et al. 2003, Markandya et al. 2008, Woodward and Wui 2001). There are two ways of conducting VT: one involves transferring final economic values and the other involves transferring functions.

For swamps, due to a lack of available studies, it was not feasible to use meta-regression analysis as VT method. Instead, final economic values were directly transferred. The project team used, for each service, the average estimates of available swamp studies provided by GecoServ (GecoServ.org).

For marshes, transferring functions was deemed to be more appropriate because the functions can be adapted to the conditions of the policy site and are believed to perform better than transferring final values alone (Champ et al. 2003, EPA 2000, Liu and Stern 2008, Nelson and Kennedy 2008). Function transfers can be demand functions or meta-regression analysis functions. Meta-regression analysis combines estimates from multiple studies and applies them to the policy site. It allows researchers to statistically explain differences among studies due to the valuation methods used, characteristics of the surrounding population, survey type, etc. It is also able to predict the influence these variables may have on the final outcome (Liu and Stern 2008, Nelson and Kennedy 2008).

The goal of the economic valuation was to assign a monetary value to the ES provided by Galveston Bay marshes and swamps to highlight not only their economic importance, but also to show the loss of ES values as sea level rises and habitat is lost. Most ES lack a market where their value is stated and compared to alternative uses. This oftentimes leads to ES not being included in the decision-making process and here lies the need to value ES monetarily.

Methodology The successful application of meta-regression analysis requires the use of many original studies that are applicable to GoM habitats. The Harte Research Institute developed an ecosystem services valuation database (www.GecoServ.org), which gathers studies valuing ES worldwide (Plantier Santos et al. 2011). The studies used for this analysis were exclusively primary studies, based on original research that value ES provided by both freshwater and saltwater wetlands. A total of 52 studies providing 114 estimates of ES values were included in the analysis. Recreation, food, and disturbance regulation were the services with the largest number of estimates (17, 17, and 16 respectively), while water regulation and soil retention had only one estimate.

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After the study selection, descriptive information was coded; this included the dependent variable (2008 U.S. dollars per hectare per year) and several independent or explanatory variables (Table 2). These variables were chosen because they describe the characteristics of the primary study (study characteristics), the characteristics of the ecosystem (wetland characteristics), and the population surrounding the habitat (socio-economic characteristics). Also, similar variables were used in previous meta-regression studies (Brander et al. 2006, Brouwer et al. 1999, Ghermandi et al. 2008 Rosenberger and Loomis 2003, Shrestha and Loomis 2001) and are thought to influence ES values (dependent variable).

Table 2. Explanatory Variables included in the meta-regression analysis Group Variables Descriptive/Independent Type of Variable used Variables Study characteristics Valuation Method Group of 7 dummies

(XS ) Year of the Study Interval (1=1970, 2=1971,…,38=2007) Wetland Type of Wetland Binary/dummy (1= coastal, 0=

characteristics (Xw ) non-coastal) Ecosystem Services Valued Group of 16 dummies

Area (ha) Continuous (log scale) Region of the Study Continuous index (1 to 4) Socio-economic Population Density Ratio (population per square

characteristics (XE) mile) (log scale) Income Per capita Continuous (log scale) Female (%) Ratio Bachelor’s Degree Ratio

A classical ordinary least squares regression model was used and is described below:

ln (yi) = a + bs Xs + bw Xw + bE XE + ui

where ln (yi) is the natural logarithm of the dependent variable (2008 U.S.$ per hectare per year); i is an index for all 114 observations; a is the constant term; bs , bw and bE are the coefficients of the explanatory variables; Xs are study characteristics; Xw are the wetland characteristics; XE the socio-economic characteristics; and u is the margin of error.

Table 3. Explanatory power of the meta-regression model Model R R Adjusted R Std. Error of the Durbin- Square Square Estimate Watson 1 .789 .622 .486 .7830955 1.855

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The explanatory power of this model is shown in Table 3. These values are higher than previous meta-regression studies such as Brouwer et al. (1999) (R2 = 0.38), Rosenberg and Loomis (2003) (R2 = 0.27), Ghermandi et al. (2008) (R2= 0.48 and adjusted R2 = 0.43), Brander et al. (2006) (R2 = 0.55 and adjusted R2 =0.45), and Shrestha and Loomis (2001) (adjusted R2 = 0.26). The adjusted R2 for this study is also slightly higher than the mean value of 0.479 for 130 meta-analyses found in Nelson and Kennedy (2008). The Durbin-Watson test shows if there are serial correlations between errors: values less than 1 or greater than 3 are a cause for concern. For this model this is not a cause of concern.

Once the meta-regression was performed, coefficients were adjusted to the policy site to account for socio-demographic characteristics; habitat gains and losses from SLAMM 6 estimates (years 2050 and 2100 using the A1Bmax sea level rise scenario) were included to show the impact of sea level rise on ES values. Here lies the uniqueness of meta-regression analysis: functions can be transferred from study sites to policy sites by adjusting the explanatory variables to the characteristics of the latter. In this manner, previous research is used to value unstudied sites with a more robust technique. Our assumptions for this study are: 1) The economic conditions (number of businesses, per capita GDP, etc…) in 2050 and 2100 are the same as today (2008); 2) The demographic conditions (population, income, etc…) are the same in 2050 and 2100 as they are today; and 3) Demand for ecosystem services stays the same. Essentially, all current conditions will be the same in 2050 and 2100 except for the changing habitats.

Results The model described above was used to calculate the monetary value of marsh and swamp ES, such as disturbance regulation, recreation, aesthetics, food, nutrient cycling, soil erosion, and water regulation. For saltwater and freshwater marsh, the services chosen were the top six listed in the proceeding of first Gulf-wide ES Workshop (Yoskowitz, 2010). For swamps, the services chosen were based on available ES literature. The monetary values were calculated for each habitat in 2009 (initial condition) and 2050 and 2100 under the A1Bmax (0.69m) sea level rise scenario. To account for changes in ES value due to sea level rise, all descriptive variables were kept unchanged except area (ha); this is because the analysis focused on the change in habitat extent and it was not feasible to estimate the socio-economic characteristics of the population

(XE) in 2050 and 2100. Results are presented in Tables 4a, 4b, and 4c.

Overall, sea level rise is the cause for the loss of 16,775 ha of freshwater marsh, saltwater marsh, and swamp habitat combined, from 2009 to 2100 (under the A1Bmax sea level rise scenario); of those lost hectares, 10,946 ha are lost freshwater marshes. This results in the cumulative loss of over $113 million in ES provided by these three habitats during the same period, given the SLAMM 6 estimates.

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Fresh Marsh Of the three habitats analyzed under the A1Bmax scenario, freshwater marsh shows the largest area losses due to sea level rise (21% lost from initial condition to 2100). As the marsh decreases in size, each hectare becomes scarcer and, consequently, there is a marginal increase in per hectare values (Table 4). However, the value for the total area including all ES indicates significant monetary losses (Table 4): from the initial condition to 2050, the total monetary loss of ES is $23.6 million per year; from 2050 to 2100, the loss is $64.1 million per year; and from the initial condition to 2100, the total loss is $87.7 million per year. Looking at individual services, disturbance regulation has the major economic impact; it shows a marginal increase in per hectare values from 2009 to 2050 and 2100, but a significant loss in total area values with sea level rise ($27.4 million from 2009 to 2100) (Table 4a). These calculated values are considered conservative since only six out of 24 existing ES were taken into consideration. However, monetary values are important because they can illustrate the importance of ES, which oftentimes is not included in the decision-making process.

Salt Marsh Like with freshwater marshes, saltwater marshes decrease in size as sea level rises. From initial condition to year 2050, 2,036 hectares (ha) of marshes are projected to be lost; from 2050 to 2100, 1,915 ha. From initial condition to 2100, 3,951 ha of saltwater marshes are projected to be lost (12%) under the A1Bmax scenario. As the total area of salt marsh decreases, so does the total value of the ES provided by this habitat. From initial condition to 2050, the monetary loss of ES values is about $7 million per year; from 2050 to 2100, additional $6.68 million per year is lost; and from initial condition to 2100, $13.7 million per year is lost in ES provision. On a per hectare basis, as salt marsh extent decreases and habitat becomes scarcer, ES values per ha increase, but only slightly. Looking at individual services, salt marsh follows the same pattern as fresh marsh; disturbance regulation shows the major economic impact with $4.3 million per year lost from 2009 to 2100 under the A1Bmax scenario, followed by recreation with $3.5 million per year lost from 2009 to 2100 under that same scenario (Table 4b).

In short, as the extent of salt marshes decreases, the ES value per hectare increases marginally, and the total ES values decrease significantly. The total ES value lost is considered conservative, since only six ES were taken into consideration for this analysis. It is important, however, to provide a monetary value to highlight the importance of ES to humans and society.

Swamp Due to a lack of original studies valuing the ES provided by swamps, it was not possible to conduct a meta-regression analysis to calculate the change in ES values provided by this habitat; instead, point estimates using the average per hectare per year ES values were used. As a consequence, the estimates provided in Table 4c only take into consideration changes in per year values, rather than per hectare per year values as well. Under the sea level rise A1Bmax scenario, there is a cumulative economic loss of ES provided by swamps of about $12 million, from current condition to 2100. Half of this loss is due to just one ES, waste regulation; this is due to the fact

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Harte Research Institute that this service has the highest per ha value in 2009 ($3,349.54). Since a point estimate transfer was used, the change in per hectare values, as habitat is lost, could not be calculated. Like with the other two habitats, the total economic loss is considered conservative since only five ES were taken into account.

In summary, as habitat decreases, per hectare values (when feasible to calculate them) increase marginally and total area values decrease significantly. This is not inconsistent with economic theory. We assume that demand for ES does not change, so when the habitats that provide those services decrease then the ES become scarcer and their marginal value increases (Lynn, 1991). In the meta-regression analysis, area had a negative coefficient, that is, a negative effect on the dependent variable. Meaning, the larger the area, the smaller the ES value per hectare; vice versa, the smaller the areal extent, the higher the ES value per hectare. The economic loss caused by the area changes in the three habitats is significant. Yet, as noted before, it can be considered conservative since it only accounts for five or six ES per habitat, rather than the 24 existing services. Additionally, the values do not take into consideration important factors such as the quality of the services provided or the fragmentation of the habitat as sea level rises and human pressure increases. As time passes, the quality of some ES may degrade while others increase due to habitat fragmentation and human stressors, such as urban development and pollution. The degradation of ecosystem services quality affects their monetary value and could be an important factor in determining economic values if there were a way to address it. The ES value estimates provided here are only a small portion of what can be lost due to the decrease in habitat area and the loss of its associated ES. This illustrates the importance of not only protecting built infrastructure, but also natural resources, such as marine habitat, from sea level rise.

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Table 4a. Value of ES provided by fresh marsh in Galveston Bay under the A1Bmax sea level rise scenario (0.69m)

Fresh Initial condition Initial condition 2050 2050 Loss of Value 2100 2100 Loss of Value Loss of Value Marsh (US$ 2008 /per (Total Area) (US$ 2008 /per ha (Total Area) from initial (US$ 2008 /per (Total Area) from 2050 to from initial ha/per year) /per year) condition to ha /per year) 2100 condition to 2050 2100 Nutrient Cycling $1,662 $88,519,456 $1,681 $84,514,368 -$4,005,088 $1,741 $73,629,039 -$10,885,329 -$14,890,418 Disturbance $3,058 $162,832,442 $3,094 $155,465,041 -$7,367,401 $3,202 $135,441,367 -$20,023,674 -$27,391,075 Regulation Food $330 $17,566,790 $334 $16,771,976 -$794,814 $345 $14,611,769 -$2,160,206 -$2,955,021 Aesthetics $2,206 $117,470,291 $2,232 $112,155,314 -$5,314,977 $2,310 $97,709,871 -$14,445,443 -$19,760,421 Recreation $2,518 $134,101,645 $2,548 $128,034,178 -$6,067,468 $2,637 $111,543,560 -$16e,490,618 -$22,558,086 Water $21 $1,098,361 $21 $1,048,665 -$49,696 $22 $913,599 -$135,067 -$184,762 Regulation Total $9,795 $521,588,986 $9,909.25 $497,989,542 -$23,599,444 $10,256 $433,849,204 -$64,140,338 -$87,739,782

Table 4b. Value of ES provided by salt marsh in Galveston Bay under the A1Bmax sea level rise scenario (0.69m)

Salt Marsh Initial condition Initial condition 2050 2050 Loss of Value 2100 2100 Loss of Value Loss of Value (US$ 2008 /per (Total Area) (US$ 2008 /per (Total Area) from initial (US$ 2008 /per (Total Area) from 2050 to from initial ha/per year) ha /per year) condition to ha /per year) 2100 condition to 2050 2100 Disturbance $1,338 $45,915,906 $1,354 $43,722,381 -$2,193,525 $1,371 $41,633,872 -$2,088,509 -$4,282,033 Regulation Recreation $1,102 $37,814,323 $1,115 $36,007,832 -$1,806,491 $1,129 $34,287,828 -$1,720,004 -$3,526,495 Food $144 $4,953,528 $146 $4,716,885 -$236,643 $148 $4,491,571 -$225,314 -$461,957 Aesthetics $965 $33,124,571 $977 $31,542,122 -$1,582,449 $989 $30,035,434 -$1,506,688 -$3,089,137 Nutrient Cycling $727 $24,960,941 $736 $23,768,491 -$1,192,451 $745 $22,633,129 -$1,135,361 -$2,327,812 Soil Retention $0.69 $23,810 $0.70 $22,673 -$1,137 $0.71 $21,590 -$1,083 -$2,220 Total $4,278 $146,793,079 $4,330 $139,780,383 -$7,012,696 $4,383 $133,103,424 -$6,676,959 -$13,689,654

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Table 4c. Value of ES provided by swamp in Galveston Bay under the A1Bmax sea level rise scenario (0.69m)

Swamps Initial condition Initial condition 2050 Loss of Value 2100 Loss of Value Loss of Value (US$ 2008 /per (Total Area) (Total Area) from initial (Total Area) from 2050 to from initial ha/per year) condition to 2100 condition to 2050 2100 Recreation $328.67 $3,782,005.69 $3,736,307.41 -$45,698.28 $3,165,092.10 -$571,215.31 -$616,913.59 Waste $3,349.54 $38,543,156.78 $38,077,436.74 -$465,720.04 $32,256,070.20 -$5,821,366.54 -$6,287,086.58 Regulation Disturbance $528.08 $6,076,559.03 $6,003,135.48 -$73,423.55 $5,085,362.25 -$917,773.23 -$991,196.78 Regulation Food $145.22 $1,671,075.31 $1,650,883.57 -$20,191.74 $1,398,492.68 -$252,390.90 -$272,582.63 Aesthetics $2,015.00 $23,186,605.00 $22,906,439.40 -$280,165.60 $19,404,450.00 -$3,501,989.40 -$3,782,155.00 Total $6,366.51 $73,259,401.80 $72,374,202.60 -$885,199.20 $61,309,467.23 -$11,064,735.37 -$11,949,934.58

*Note: This scenario is considered conservative since only five ES are taken into consideration and factors such as the quality of the services provided, which can be degraded over time, are not considered.

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VII. Heat map demonstrating differences in the demand for ecosystem services A heat map was created to visually demonstrate the change in the value of services as sea level rise changes wetland habitat. The heat map accounts for the intensity of the services and, therefore, is dependent upon the proximity of the habitat to the demand for the service. For example, the value of a specific habitat will be very different in relatively rural Chambers County, located in the East Galveston and Lower Trinity River SubBasins, than it would be in more urban Harris County, the majority of which is located in the Buffalo San-Jacinto River SubBasin. The heat map was built upon the Human Footprint Index developed for the Texas coastal water basins at the Harte Research Institute in 2009 (Fig. 8). The Human Footprint Index is a spatial measurement of the relative human influence in land use/land covers (Sanderson et al. 2002). The index represents the sum of human activities and infrastructure; it expresses it as a continuum of the influence across a specific area (basin), revealing major patterns (Brenner et al. 2009). It was assessed using spatial indicators of human population and infrastructure that have the most immediate impact on wildlife and habitats and their capacity to benefit humans (Brenner et al. 2009).

Figure 8. Human Footprint Index for the study area (Source: Brenner et al. 2009, modified)

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Raw data from the Human Footprint Index project were used to calculate median indices for the river SubBasins in the study area. Although median values had been assigned to river basins in the original Human Footprint Index project, new calculations were performed to assign median values to the SubBasins outlined in the most recently updated Texas watershed boundary map (Fig. 9), maintained by the Texas Natural Resources Information System and the U.S. Geological Survey (2010). The dataset is comprised of 6 levels of "nested" hydrologic units (HUCs):  HUC 2 (Region)  HUC 4 (SubRegion)  HUC 6 (Basin)  HUC 8 (SubBasin)  HUC 10 (Watershed)  HUC 12 (SubWatershed). HUC 8 was selected as unit to represent the Human Footprint Index and heat map.

Figure 9. Median value of the Human Footprint Index per river SubBasin in the study area

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Assessment of Changing Ecosystem Services Provided by Marsh Habitat in the Galveston Bay Region

The median human footprint index value calculated for each river SubBasin was utilized as a weight and assigned to the studied habitats to produce heat maps (Figs. 10-15) in 2050 and 2100. Results show that the higher values are assigned to habitats within SubBasins with higher population densities and infrastructure. For those habitats located in sub-basins with intense footprints, the services associated with those habitats will have greater value due to the proximity of where they are demanded. ES by definition are demand driven. For example, a marsh with no built infrastructure behind it will not be providing the ES of disturbance regulation.

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Figure 10. Heat map for fresh marsh in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 11. Heat map for fresh marsh in 2100 under the A1Bmax (0.69m) sea level rise scenario

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Figure 12. Heat map for salt marsh in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 13. Heat map for salt marsh in 2100 under the A1Bmax (0.69m) sea level rise scenario

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Figure 14. Heat map for swamp in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure 15. Heat map for swamp in 2100 under the A1Bmax (0.69m) sea level rise scenario

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VIII. Limitations One of the limitations of this project is the use of value transfer instead of a primary valuation method. Conducting a primary study is believed to lead to more accurate results, but due to funding constraints, the most feasible alternative for this study has been to apply value transfer as the ES valuation method. Additionally, the more studies are added to the regression, the more robust the results are found to be. Therefore, a second limitation is the lack of valuation studies that could be included when conducting the meta-regression analysis to value ecosystem services provided by marsh.

Several assumptions were made throughout the study that could have influenced the final results. In value transfer, it is assumed that every ecosystem hectare is worth the same amount of money; however, each hectare’s quality and ability to provide services can vary significantly. Due to unavailable data, it was not possible to capture differences in habitat quality, which would influence the monetary value of the services provided. Moreover, under the studied sea level rise scenarios, unknown were the values for socio-economic characteristics of each time period (2050 and 2100). Ideally, values for all the descriptive variables of the model would be available. In this case the only known value were area (ha), output of SLAMM6, which measured changes in the extent of the habitat due to sea level rise for year 2050 and 2100. Therefore, the descriptive variables were kept unchanged because it was not feasible to estimate the socio-economic characteristics of the population ( ) in 2050 and 2100

In summary, the major limitations of this study include the inability to capture differences in the quality of the habitat (including fragmentation and degradation) and of the services provided, and to estimate socio-economic characteristics over time.

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IX. References Allen JA, Pezeshki SR, Chambers JL (1996) Interaction of flooding and salinity stress on baldcypress (Taxodium distichum). Tree physiology 16: 307-313 Barbier E, Acreman M, and Knowler IB (1997) Economic valuation of wetlands. A guide for policy makers and planners. RAMSAR Convention Bureau Department of Environmental Economics and Environmental Management. University of York, Institute of Hydrology. IUCN- The World Conservation Union Batzer DP, Wissinger SA (1996) Ecology of insect communities in nontidal wetlands. Annu Rev Entomol 41: 75-100 Benoit LK, Askins RA (2002) Relationship between habitat area and the distribution of tidal marsh birds. The Wilson Bulletin 114:314-323 Bird Observations (2012) eBird. Ithaca, New York. Available at http://www.ebird.org. Retrieved March 24, 2012 Blundon JA, Kennedy VS (2003) Refuges for infaunal bivalves from blue crab, Callinectes sapidus (Rathbun), predation in Chesapeake Bay. J Exp Mar Biol Ecol 65:67-81 Boesch DF, Turner RE (1984) Dependence of fishery species on salt marshes: the role of food and refuge. Estuaries 7:460-468 Brander LM, Florax RJGM and Vermaat JE (2006) The empirics of wetland valuation: a comprehensive summary and a meta-Analysis of the literature. Environmental & Resource Economics, 33(2), 223– 250 Brenner J, McKenzie A, Yoskowitz D (2009) Human footprint index – Texas coastal water basins. Poster presented at TBEM’09, UTMSI, Port Aransas, TX Brinson MM, Lugo AE (1981) Primary productivity, decomposition and consumer activity in freshwater wetlands. Ann Rev Ecol Syst 12: 123-61 Broome SW, Rogers Jr SM, and Seneca ED (1992) Shoreline Erosion Control Using Marsh Vegetation and Low-Cost Structures. Retrieved from http://ccrm.vims.edu/livingshorelines/documents/HowTo/NC%20Planting%20Tidal%20Marsh.pdf Brouwer R, Langford IH, Bateman IJ and Turner RK (1999) A meta-analysis of wetland contingent valuation studies. Regional Environmental Change, 1(1), 47–57 Chabreck RH and Condrey RE (1979) Common vascular plants of the Louisiana marsh. Sea Grant Publication No. LSU-T-79-003. Center for Wetland Resources, Louisiana State University, Baton Rouge, Louisiana Chalmers AG (1979) The effects of fertilization on nitrogen distribution in a Spartina alterniflora salt marsh. Est Coast Mar Sci 8:327-337 Champ PA, Boyle KJ and Brown TC (2003) A primer on nonmarket valuation. Dordrecht, the Netherlands: Kluwer Academic Publishers Conner WH, Day JW (1988) Rising water levels in coastal Louisiana: implications for two forested wetland areas in Louisiana. J. Coastal Res. 4(4): 589–596 Conner WH, Day JW, Bergeron JC (1989). A Use Attainability Analysis of Wetlands for Receiving Treated Municipal and Small Industry Wastewater: A Feasibility Study Using Baseline Data From Thibodaux, LA. Center for Wetland Resources, Louisiana State University, Baton Rouge, LA, p. 78 Costanza R, d’ Arge R, de Groot R, Farber S, Grasso M, Hannon B, Limburg K, et al (1997) The value of the world’s ecosystem services and natural capital. Nature, 387(15), 253–260 Cowardin L M, Carter V, Golet F C, and LaRoe ET (1979) Classification of wetlands and deepwater habitats of the United States. US Department of the Interior, Fish and Wildlife Service Craft C, Clough J, Ejman J, Joye S, Park R, Penning S, Guo H, and Machmullet M (2009) Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services. Frontiers in Ecology and the Environment 7:73-78

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Day JW, Hall CAS, Kemp WM, Yáñez-Arancibia A (1989) Estuarine Ecology, Vol. John Wiley & Sons, New York Day JW, Whestphal A, Pratt R, Hyfield E, Rybczyk J, Kemp GP, Day JN, Marx B (2006). Effects of long-term municipal effluent discharge on the nutrient dynamics, productivity, and benthic community structure of a tidal freshwater forested wetland in Louisiana. Ecological Engineering 27:242-257 Deegan LA, Hughes JE, Rountree RA (2002) Salt marsh ecosystem support of marine transient species. In: Weinstein MP, Kreeger DA (eds) Concepts and Controversies in Tidal Marsh Ecology. Kluwer Academic Publishers, Dordrecht, The Netherlands, p 333-365 de Groot RS, Alkemade R, Braat L, Hein L and Willemen L (2009) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity, In Press, Corrected Proof DeLaune RD, Smith CJ, Sarafyan MN (1986) Nitrogen cycling in a freshwater marsh of Panicum hemitomon on the deltaic plain of the Mississippi River. J Ecology 74(1): 249-256 Desvousges WH, Johnson FR and Banzhaf HS (1998) Environmental policy analysis with limited information: Principles and applications of the transfer method. Cheltenham, UK and Northampton, MA: Edward Elgar Duever M J (1984). Environmental factors controlling plant communities of the Big Cypress Swamp. In Environments of South Florida: Past and Present II. Gleason PJ (Ed.) Miami Geological Society, Miami, FL pp. 127-137 EPA (2000) Guidelines for preparing economic analyses p: 1–227. Environmental Protection Agency. Washington, D.C., USA Ewel KC (1990) Swamps. In Ecosystems of Florida. RL Meyers and JJ Ewel (Eds.). University of Central Florida Press, Orlando, pp 281-323 Farber S, Costanza R, Childers DL, Erickson J, Gross K, Grove M, Hopkinson CS, et al (2006) Linking Ecology and Economics for Ecosystem Management. BioScience, 56(2), 121–133 Frederick PC, McGehee SM (1994) Wading bird use of wastewater treatment wetlands in Central Florida, USA. Coastal waterbirds 17(1): 50-59 Ghermandi A, van den Bergh J, Brander LM, De Groot HL, Nunes P, et al (2008) The economic value of wetland conservation and creation: A meta-analysis. Fondazione Eni Enrico Mattei Working Papers, 238 Gulf of Mexico Alliance (GOMA) (2009) Governors’ action plan II for healthy and resilient coasts. 35pp Hafner H, Dugan PJ, Kersten M, Pineau O, Wallace JP (1992). Flock feeding and food intake in Little Egrets (Egretta garzetta) and their effects on food provisioning and reproductive success. Ibis 135:25-32 Heck KL, Thoman TA (1981) Experiments on predator-prey interactions in vegetated aquatic habitats. J Exp Mar Biol Ecol 53:125-134 Hettler WF (1989) Nekton use of regularly flooded saltmarsh cordgrass habitat in North Carolina, USA. Mar Ecol Prog Ser 56:111-118 Heymans JJ, Ulanowicz RE, Bondavalli C (2002) Network analysis of the South Florida Everglades graminoid marshes and comparison with nearby cypress ecosystems. Ecological Modeling 149: 5-23. HRI (2012) Valuation Database. GecoServ-Gulf of Mexico Ecosystem Services Valuation Database. Retrieved March 2, 2012, from http://www.gecoserv.org/ Kadlec JA (1979) Nitrogen and phosphorus in inland freshwater wetlands. T.A. Bookhour (Ed.), Waterfowl and Wetlands — An Integrated Review, La Crosse Printing, La Crosse, Wisconsin (1979), pp. 17–41 Kelly JP, Pratt HM, Greene PL (1993) The distribution, reproductive success, and habitat characteristics of heron and egret breeding colonies in the San Francisco Bay area. Colonial Waterbids 16:18-27

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Kneib RT (1984) Patterns in the utilization of the intertidal salt marsh by larvae and juveniles of Fundulus heteroclitus (Linnaeus) and Fundulus luciae (Baird). J Exp Mar Biol Ecol 83:41-51 Kneib RT (2003) Bioenergetic and landscape considerations for scaling expectations of nekton production from intertidal marshes. Mar Ecol Prog Ser 264:279-296 Kneib RT, Stiven AE (1978) Growth, reproduction, and feeding of Fundulus heteroclitus (L.) on a North Carolina salt marsh. J Exp Mar Biol Ecol 31:121-140 Lavelle P and Berhe AA (2005) Nutrient Cycling. Ecosystems and human well-being: current state and trends: findings of the Condition and Trends Working Group of the Millennium Ecosystem Assessment, 1, 331 Liu S and Stern DI (2008) A meta-analysis of contingent valuation studies in coastal and near-shore marine ecosystems. Munich Personal RePEc Archive (MPRA) Paper, 11720, 1–39 Loomis JB (1992) The Evolution of a More Rigorous Approach to Benefit Transfer: Benefit Function Transfer. Water Resources Research, 28(3): 701–705 Lynn M (1991) Scarcity effects on value: A quantitative review of the commodity theory literature. Psychology and Marketing, 8(1), 43–57 Markandya A, Nunes P, Brauer I, Brink P, Kuik O, and Rayment M (2008) The Economics of Ecosystems and Biodiversity- Phase 1 (Scoping) Economic Analysis and Synthesis (p. 142). Venice, Italy: Final report for the European Commission McIvor CC, Odum WE (1986) The flume net: a quantitative method for sampling fishes and macrocrustaceans on tidal marsh surfaces. Estuaries 9: 219-224 McIvor CC, Odum WE (1988) Food, predation risk, and microhabitat selection in a marsh fish assemblage. Ecology 69(5); 1341-1351 Megonigal JP, Day FP (1988) Organic matter dynamics in four seasonally flooded forest communities of the Dismal Swamp. Amer J Bot 75(9): 1334-1343 Megonigal JP, Conner WH, Kroeger S, Sharitz RR (1997) Aboveground production in southeastern floodplain forests: a test of the subsidy-stress hypothesis. Ecology 78(2): 370-384 Mendelssohn IA, Burdick DM (1987) The relationship of soil parameters and root metabolism to primary production in periodically inundated soils. In Ecology and Management of Wetlands, London, Croom Helm.(Ed.) DD Hook et al. pp. 398-428 Mendelssohn IA, Burdick DM (1987) The relationship of soil parameters and root metabolism to primary production in periodically inundated soils. In Ecology and Management of Wetlands, London, Croom Helm. (Ed.) DD Hook et al. pp. 398-428 Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: Synthesis (p. 155). Washington, D.C., USA: Island Press Minello TJ, Able KW, Weinstein MP, Hays CG (2003) Salt marshes as nurseries for nekton: testing hypotheses on density, growth and survival through meta-analysis. Mar Ecol Prog Ser 246:39-59 Minello TJ, Zimmerman RJ (2003) Fish predation on juvenile brown shrimp, Penaeus aztecus Ives: The effect of simulated Spartina structure on predation rates. J Exp Mar Biol Ecol 72:211-231 Mitsch WJ, Ewel KC (1979) Comparative biomass and growth of cypress in Florida wetlands. American Midland Naturalist 101(2): 417-426 Mitsch WJ, Gosselink JG (1986) Wetlands. New York: Van Nostrand Reinhold. 539 pp Moorhead KK, and Brinson MM (1995) Response of wetlands to rising sea level in the lower Coastal Plain of North Carolina. Ecological Applications 5:261-271 Natural Resources Conservation Service (2008) Wetlands Contend for Most Productive Wildlife Habitat. United States Department of Agriculture. Natural Resources Conservation Service. Governmental. National Research Council of the National Academies (2004) Valuing Ecosystem Services: Toward better environmental decision-making. Washington, D.C., U.S.A.: National Academy Press

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Nelson WG (1979) Experimental studies of selective predation on amphipods: Consequences for amphipod distribution and abundance. J Exp Mar Biol Ecol 38:225-245 Nelson J and Kennedy PE (2008) The Use (and Abuse) of Meta-Analysis in Environmental and Natural Resource Economics: An Assessment. Pennsylvania: Department of Economics, Pennsylvania State University New World Encyclopedia (2009) Wetland. New World Encyclopedia. Retrieved April 27, 2011, from http://www.newworldencyclopedia.org/entry/Wetland Noss RF, LaRoe ET, Scott JM (1994) Endangered ecosystems of the United States: a preliminary assessment of loss and degradation. Washington, D.C. U.S. Dept. of the Interior, National Biological Service, Publications Unit, U.S. Fish and Wildlife Service. 58 pp Odum WE (1988) Comparative ecology of tidal freshwater and salt marshes. Ann Rev Ecol System 19: 147-176 Odum WE, Zieman JC, Heald EJ (1973) The importance of vascular plant detritus to estuaries. In Coastal Marsh and Estuary Management, ed. R. H. Chabreck, pp. 91-114. Baton Rouge: La. State Univ. Press Odum WE, McIvor CC, Smith TJ (1982) The ecology of the mangroves of south Florida: a community profile. US Fish and Wild. Serv. FWSIOBS-811 24. 144 pp Odum WE, Smith TJ, Hoover JK, McIvor CC (1984) The ecology of tidal freshwater marshes of the United States east coast: a community profile. US Fish Wild. Serv. FWSIOBS-83117. 177 pp Park RA, Trehan MS, Mausel PW, and Howe RC (1989) The Effects of Sea Level Rise on U.S. Coastal Wetlands. Pages 1-1 to 1-55. in J. B. Smith and D. A. Tirpak, eds. The Potential Effects of Global Climate Change on the United States, Appendix B - Sea Level Rise. U.S. Environmental Protection Agency, Washington, D.C. Peterson GW, Turner RE (1994) The value of salt marsh edge vs. interior as a habitat for fish and decapod crustaceans in a Louisiana tidal marsh. Estuaries 17:235-262 Plantier-Santos C, Carollo C, and Yoskowitz DW (2012) Gulf of Mexico Ecosystem Service Valuation Database (GecoServ): Gathering ecosystem services valuation studies to promote their inclusion in the decision-making process. Marine Policy, 36(1), 214–217 Rees GH (1963) Progress on blue crab research in the south Atlantic. Proc Gulf Carib Fish Inst 15:110-115 Rountree RA, Able KW (1992) Foraging habits, growth, and temporal patterns of salt marsh creek habitat use by young-of-year summer flounder in New Jersey. Trans Am Fish Soc 121:765-776 Rosenberger RS and Loomis JB (2003) Benefit transfer. In P. A. Champ, K. J. Boyle, & T. C. Brown (Eds.), A Primer on Nonmarket Valuation (The Economics of Non-Market Goods and Resources) (pp. 445– 482). The Netherlands: Kluwer Academic Publishers Rozas LP, McIvor CC, Odum WE (1988) Intertidal rivulets and creekbanks: corridors between tidal creeks and marshes. Mar Ecol Prog Ser 47: 303-307 Rozas LP, Odum WE (1987a) Use of tidal freshwater marshes by fishes and macrofaunal crustaceans along a marsh stream-order gradient. Estuaries 10(1): 36-43 Rozas LP, Odum WE (1987b) Fish and macrocrustacean use of submerged plant beds in tidal freshwater marsh creeks. Mar Ecol Prog Ser 38: 101-108 Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, Woolmer G (2002) The human footprint and the last of the wild. BioScience 52(10): 891-904 Shrestha RK and Loomis JB (2001) Testing a meta-analysis model for benefit transfer in international outdoor recreation. Ecological Economics, 39(1), 67–83 Shriver WG, P HT, Gibbs JP, Vickery PD (2004) Landscape context influences salt marsh bird diversity and area requirements in New England. Biological Conservation 119:545-553 Simpson RL, Good RE, Walker R, Frasco BR (1983) The role of Delaware River freshwater tidal wetlands in the retention of nutrients and heavy metals. Journal of Environmental Quality 12:41-48

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Smock LA, Harlowe KL (1983) Utilization and processing of freshwater wetland macrophytes by the detritivore Asellus forbesi. Ecology 64:1556-65 Stewart R (2009) Coastal Erosion. Our Ocean Planet. Oceanography in the 21st century- an online textbook. Department of Oceanography, Texas A&M University. Retrieved from http://oceanworld.tamu.edu/resources/oceanography-book/coastalerosion.htm Tenore KR, Cammen LM, Findlay SEG, Phillips N (1982) Perspectives of research on detritus: do factors controlling the availability of detritus to macroconsumers depend on its source? J Mar Res 40:473- 490 Thomas JL, Zimmerman RJ, Minello TJ (1990) Abundance patterns of juvenile blue crabs (Callinectes sapidus) in nursery habitats of two Texas bays. Bull Mar Sci 46:115-125 Titus JG (1986) Greenhouse Effect, Sea Level Rise, and Coastal Zone Management. Coastal Zone Management Journal 14:3-35 Turner RE (1976) Geographic variations in salt marsh macrophyte production: a review. Contrib Mar Sci 20:47-68 Turner RE (1993) Carbon, nitrogen, and phosphorus leaching rates from Spartina alterniflora salt marshes. Mar Ecol Prog Ser 92:135-140 U.S. Department of the Interior, Fish and Wildlife Service (FWS) (1992) Classification of Wetlands and Deepwater Habitats of the United States. Fish and Wildlife Service – Biological Services Program U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census Bureau (2006) 2006 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. 2006 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation U.S. EPA (1996) Watershed Progress: New York City Watershed Agreement. U.S. Environmental Protection Agency. Governmental. Retrieved August 30, 2011, from http://water.epa.gov/type/watersheds/nycityfi.cfm Valiela I (1995) Marine Ecological Processes, Vol 2. Springer-Verlag, New York Warren Pinnacle Consulting, Inc. (2011) Application of the Sea-Level Affecting Marshes Model (SLAMM 6) to Galveston Bay. Report prepared for the Nature Conservancy. 79pp. Warren RS and Niering WA (1993) Vegetation change on a Northeast tidal marsh: interaction of sea-level rise and marsh accretion. Ecology 74:96-103 Weller MW (1994) Freshwater Marshes: ecology and management. University of Minnesota Press. 154 pp Wharton C (1970) Southern River Swamp: A multiple-use environment. Atlanta: Bureau of Business and Economic Research. Georgia State University Wicker K, DeRouen M, O'Connor D, Roberts E, and Watson J (1980) Environmental Characterization of Terrebonne Parish: 1955-7978. Baton Rouge: Coastal Environments, Inc. Williams K, Pinzon ZS, Stumpf RP, Raabe EA (2010). Sea-level Rise and Coastal Forests on the Gulf of Mexico. Open File Report 99-441. US Dept of Interior, US Geological Survey. Winslow CJ (2001) Estimation of waterfowl food abundance in coastal freshwater marshes of Louisiana and Texas. Master’s Thesis. Louisiana State University. 59 pp Woodward RT and Wui YS (2001) The economic value of wetland services: a meta-analysis. Ecological Economics, 37(2), 257–270 Yoskowitz D, Santos C, Allee B, Carollo C, Henderson J, Jordan S, and Ritchie J (2010) Proceedings of the Gulf of Mexico Ecosystem Services Workshop (p. 16). Bay St. Louis, Mississippi: Harte Research Institute for Gulf of Mexico Studies. Texas A&M University-Corpus Christi Yozzo DJ, Diaz RJ (1999) Tidal freshwater wetlands: invertebrate diversity ecology, and functional significance. In Batzer DP, Rader RB, Wissingger SA (Eds.), Invertebrates in Freshwater Wetlands of North America: Ecology and Management. Wiley. pp 889-918

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Zedler JB, and Elliot K (2006) Why are wetlands so valuable? Retrieved from http://botany.wisc.edu/zedler/images/Leaflet_10.pdf Zimmerman RJ, Minello TJ, Zamora G (1984) Selection of vegetated habitat by brown shrimp, Penaeus aztecus, in a Galveston Bay salt marsh. Fish Bull 82:325-336

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Appendix A

Figure A1. Distribution of fresh marsh, salt marsh, and swamp in 2050 under the A1Bmax (0.69m) sea level rise scenario

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Figure A2. Distribution of fresh marsh, salt marsh, and swamp in 2100 under the A1Bmax (0.69m) sea level rise scenario

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Figure A3. Distribution of fresh marsh, salt marsh, and swamp in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A4. Distribution of fresh marsh, salt marsh, and swamp in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A5. Fresh marsh lost (compared to initial condition) and present in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A6. Fresh marsh lost (compared to initial condition) and present in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A7. Salt marsh lost (compared to initial condition) and present in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A8. Salt marsh lost (compared to initial condition) and present in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A9. Swamp lost (compared to initial condition) and present in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A10. Swamp lost (compared to initial condition) and present in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A11. Heat map for fresh marsh in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A12. Heat map for fresh marsh in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A13. Heat map for salt marsh in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A14. Heat map for salt marsh in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A15. Heat map for swamp in 2050 under the A1Bmean (0.39m) sea level rise scenario

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Figure A16. Heat map for swamp in 2100 under the A1Bmean (0.39m) sea level rise scenario

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Figure A17. Distribution of fresh marsh, salt marsh, and swamp in 2050 under the 1.5m sea level rise scenario

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Figure A18. Distribution of fresh marsh, salt marsh, and swamp in 2100 under the 1.5m sea level rise scenario

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Figure A19. Fresh marsh lost (compared to initial condition) and present in 2050 under the 1.5m sea level rise scenario

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Figure A20. Fresh marsh lost (compared to initial condition) and present in 2100 under the 1.5m sea level rise scenario

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Figure A21. Salt marsh lost (compared to initial condition) and present in 2050 under the 1.5m sea level rise scenario

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Figure A22. Salt marsh lost (compared to initial condition) and present in 2100 under the 1.5m sea level rise scenario

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Figure A23. Swamp lost (compared to initial condition) and present in 2050 under the 1.5m sea level rise scenario

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Figure A24. Swamp lost (compared to initial condition) and present in 2050 under the 1.5m sea level rise scenario

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Figure A25. Heat map for fresh marsh in 2050 under the 1.5m sea level rise scenario

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Figure A26. Heat map for fresh marsh in 2100 under the 1.5m sea level rise scenario

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Figure A27. Heat map for salt marsh in 2050 under the 1.5m sea level rise scenario

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Figure A28. Heat map for salt marsh in 2100 under the 1.5m sea level rise scenario

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Figure A29. Heat map for swamp in 2050 under the 1.5m sea level rise scenario

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Figure A30. Heat map for swamp in 2100 under the 1.5m sea level rise scenario

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