HYDROECOLOGICAL MODELING AND DECISION SUPPORT SYSTEM FOR A PAYMENT FOR ECOSYSTEM SERVICES PROGRAM FOR RANCHLANDS OF SOUTH FLORIDA

Transcription

1 HYDROECOLOGICAL MODELING AND DECISION SUPPORT SYSTEM FOR A PAYMENT FOR ECOSYSTEM SERVICES PROGRAM FOR RANCHLANDS OF SOUTH FLORIDA By ANGELICA MARIE ENGEL A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA

2 2014 Angelica Marie Engel 2

3 To my loving parents-richard and Julie 3

4 ACKNOWLEDGMENTS First, I would like to acknowledge the United States Environmental Protection Agency for the funding provided to complete this research (EPA grant number R834567). I would like to acknowledge and thank my adviser, Dr. Sanjay Shukla. His passion for his craft as well as his support and encouragement for my ideas has helped me learn, grow as a student and researcher, and have enthusiasm for my work during my Master s research. I am very excited to continue to complete a Ph.D. with Dr. Shukla. I would also like to thank the team of EPA STAR (EPA grant number G08K10487) researchers from the University of Florida, the University of Central Florida, and the MacArthur Agro-ecology Research Center who collected and provided me with data and helped me through my research. Elizabeth Boughton Ph.D. was especially helpful in explaining the ecological data and collection. Gregory Hendricks Ph.D. and Chin-Lung Wu Ph.D. were instrumental in developing MIKE-SHE models. James Colee of UF/IFAS was an integral part of development and analysis of statistical methods. I would like to thank my parents and my little sister, Marissa. My mother, father, and sister have been a constant flow of love and encouragement. No matter the distance, they never failed to supply me with the support I needed. Lastly, I would like to acknowledge my boyfriend, Vagner Lomeu, for keeping me grounded, helping me smile through the work and long nights, and sharing his pride of my accomplishments and what I have done through continuing my education. 4

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS... 4 LIST OF TABLES... 8 LIST OF FIGURES ABSTRACT CHAPTER 1 INTRODUCTION OF PAYMENT FOR ECOSYSTEM SERVICES (PES) AND DECISION SUPPORT SYSTEMS (DSS) Florida Hydrologic History Ranchland in Florida Impacts to the Northern Everglades Ecosystems Ecosystem Services Payment for Ecosystem Services Hydroecological Models Trade-Offs Decision Support System Objectives PREDICTING HYDROECOLOGICAL WATER-RELATED SERVICES AND DIS- SERVICES IN A PAYMENT FOR ECOSYSTEM SERVICES (PES) PROJECT IN THE FLORIDA EVERGLADES Introduction Ecosystem Services Background and Need for Payment for Ecosystem Services Programs in Florida Ranchland as a Water Storage Service Provider Existing PES Programs and Their Expansion for Multiple PES Programs Trade-Offs in Payment for Ecosystem Services Programs Linking Hydrology to Ecology Hydrologic Models Ecological Models Plant hydroecological models Animal hydroecological models Research Need and Objective Objectives Process Overview Methodology Study Locations

6 Alderman Deloney Ranch Buck Island Ranch Pelaez and Sons Ranch Williamson Cattle Company Hydrologic Measurement Hydrological Variables Point measurements of water depth Wetland boundary Topography Wetland inundation area (IA) and volume (Vol) Hydroperiod (TI and TSI) Connectivity (DC and DSC) Ecological Measurements Hydrologic Modeling Scale Regression Analyses Median regression analyses Mean regression analyses Best Subsets Regression and Model Selection Uncertainty in Hydroecological Models Results Median-Based Hydroecological Models Median wetland vegetation Median forage vegetation Median weedy and exotic vegetation Median fish Median amphibian Median mosquito Median macroinvertebrate Models without Inundation Area Mean-Based Hydroecological Modeling Mean wetland vegetation Mean forage vegetation Mean weedy and exotic vegetation Mean fish Mean amphibian Mean mosquito Mean macroinvertebrates Mean Models without Inundation Area Water Storage Payment for Ecosystem Services Program Biodiversity Predictions with Median Models Biodiversity Predictions with Mean Models Uncertainty in Hydroecological Models Integrating Errors in Hydroecological and Hydrological Models Summary and Conclusion

7 3 DECISION SUPPORT SYSTEM FOR A MULTIPLE ECOSYSTEM SERVICE PAYMENT FOR ECOSYSTEM SERVICES (PES) PROGRAM Introduction Agro-Ecosystems as a Service Provider Florida History Payment for Ecosystem Services Programs in Florida Multiple Ecosystem Services Programs Biodiversity Ecosystem Services Dis-Services and Trade-Offs Decision Support Systems (DSS) Simplicity in Decision Support Systems Uncertainty in Decision Making Environmental Applications of Decision Support Systems Objectives Methods for Decision Support System Development Study Area and Background Hydroecological Modeling Hydrologic Modeling Development of the Northern Everglades Ranchland Decision Support Weighted Average Method (WAM) Discrete Compromise Programming Method (CP) Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) Alternatives Criteria Weighting Scenarios A Decision Support System for a Multiple Ecosystem Services Program Java-Based Stochastic Multicriteria Acceptability Analysis (JSMAA) Results Northern Everglades Ranchland Decision Support (NERDS) Analysis Buck Island Ranch Analyses Pelaez Analyses Integrating the Northern Everglades Ranchland Decision Support with JSMAA Buck Island Ranch JSMAA Results and Discussion Pelaez JSMAA Results and Discussion Summary and Conclusion SUMMARY AND CONCLUSION LIST OF REFERENCES BIOGRAPHICAL SKETCH

8 LIST OF TABLES Table page 2-1 The three vegetation groups for which hydroecological models were developed and the plants that were in each group Description of the measured and simulated hydrological variables used for developing the hydroecological models A sample of culvert riser board structures from watershed 35 of Buck Island Ranch Observed and estimated hydrologic data for four ranches Alderman, Buck Island Ranch, Pelaez, and Williamson Wetland-scale median hydroecological models developed by aggregating hydrological data and the median of non-zero ecological data for the four ranches Wetland-scale median hydroecological models developed by aggregating hydrological data and the median of non-zero ecological data for the four ranches without using inundation area as a predictor variable Wetland-scale mean hydroecological models developed by aggregating hydrological data and the mean of ecological data for the four ranches Wetland-scale hydroecological models developed by aggregating hydrological data and the mean of ecological data for the four ranches. The ecological data were not multiplied by inundation area Hydrologic variable estimations using outputs from MIKE-SHE/MIKE11 for eight water management scenarios for Buck Island Ranch Hydrologic variable estimations using outputs from MIKE-SHE/MIKE11 for seven water management scenarios for Pelaez Predictions of biodiversity services from hydroecological models developed using the median of ecological data for Buck Island Ranch for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure Predictions of biodiversity services from hydroecological models developed using the median of ecological data for Pelaez for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure

9 2-13 Predictions of biodiversity services from hydroecological models developed using the mean of ecological data for Buck Island Ranch for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure Predictions of biodiversity services from hydroecological models developed using the mean of ecological data for Pelaez for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure Back-transformed root mean square error (RMSE) values and percent errors for the median-based hydroecological models. The median-based models did not consider zero values Back-transformed root mean square error (RMSE) values and percent errors of mean hydroecological models The percent increase in error of biodiversity predictions when considering the error in estimations of hydrologic variables plus the error in the median hydroecological models Example importance weighting scenarios of criteria for three stakeholders (e.g. rancher, state agency, and conservation group) in a multiple ecosystem services program Example results for the seller stakeholder (e.g. rancher) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for the Buck Island Ranch Example results for the buyer stakeholder (e.g. state agency) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Buck Island Ranch Example results for an environmental stakeholder for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Buck Island Ranch Example results for a seller stakeholder (e.g. rancher) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez Example results for a buyer stakeholder (e.g. state agency) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez

10 3-7 Example results for an environmental stakeholder for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez Rank acceptability analysis from JSMAA for Buck Island Ranch which shows, as a percent of its total ranks, how often each alternative is in a given rank Central weights for water management scenarios (WMS) on Buck Island Ranch showing the weights for each criterion when each WMS is ranked first. The confidence factor is also shown Rank acceptability analysis from JSMAA for Pelaez Ranch which shows, as a percent of its total ranks, how often each alternative is in a given rank Central weights for water management scenarios (WMS) on Pelaez Ranch showing the weights for each criterion when each WMS is ranked first. The confidence factor is also shown

11 LIST OF FIGURES Figure page 2-1 A culvert with riser boards (CRB) structure installed in a wetland at the Palaez and Sons Ranch Flowchart of process for developing hydroecological models and integrating them with hydrologic models for decision analysis Northern Everglades basin map comprising of Lake Okeechobee, Caloosahatchee, and St. Lucie watersheds and the study ranch locations Alderman Deloney Ranch water management alternative boundary and groundwater locations and wetland ecological point sampling locations Maps of Buck Island Ranch with the water management alternative boundary of the project area, wetlands where ecological data were collected, and culvert locations Point sampling locations in wetlands on Buck Island Ranch Palaez Ranch wetlands with ecological sampling locations at wetland p1 and p Water management alternative boundary at the Williamson Cattle Co. ranch and groundwater sampling locations The opening screen of the Northern Everglades Ranchland Decision Support (NERDS) The Northern Everglades Ranchland Decision Support (NERDS) data entry page Example data entry page with data from Buck Island Ranch for eight water management scenarios (WMS) The Northern Everglades Ranchland Decision Support (NERDS) Multicriteria Decision Analysis (MCDA) Matrix page for example ranches Buck Island Ranch and Pelaez Ranch The Northern Everglades Ranchland Decision Support (NERDS) interface or Multicriteria Decision Analysis (MCDA) Dashboard page The Results Table page of the Northern Everglades Ranchland Decision Support (NERDS)

12 3-7 The JSMAA rank acceptability for Buck Island Ranch showing the percentage of times each alternative is ranked in each place of ranking Central weighting plot for Buck Island Ranch showing the weights for each criterion when each alternative is ranked first The JSMAA Rank acceptability for Pelaez showing the percentage of times each alternative is ranked in each place of ranking Central weighting plot for Pelaez showing the weights for each criterion when each alternative is ranked first

13 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science HYDROECOLOGICAL MODELING AND DECISION SUPPORT SYSTEM FOR A PAYMENT FOR ECOSYSTEM SERVICES PROGRAM FOR RANCHLANDS OF SOUTH FLORIDA By Angelica Marie Engel December 2014 Chair: Sanjay Shukla Cochair: Gregory Kiker Major: Agricultural and Biological Engineering Services and dis-services for a payment for ecosystem services (PES) program were quantified by developing hydroecological models for ranchland wetlands. The PES program pays ranchers for providing water storage services on ranchlands of the Northern Everglades basin. The model predictions were integrated with a Decision Support System (DSS) to evaluate services, dis-services and resulting trade-offs. Hydroecological models were developed using ecological measurements from 15 wetlands at four ranches, to predict biodiversity services and dis-services resulting from increased water storage. The models predicted biodiversity responses to changes in wetland inundation characteristics. The biodiversity services models included wetland vegetation, fish, amphibian, and macroinvertebrate abundances. Models for dis-service predictions included forage loss, weedy-exotic vegetation, and mosquito abundance. Changes in hydrologic drivers of wetland ecology were predicted using a hydrologic model for a variety of spillage levels or water management scenarios (WMS) at the ranchland outlet. The DSS considered WMS implementation cost, water storage payment, and plant and animal services and dis-services. Application of the DSS at two 13

14 ranches showed that higher spillage levels provided higher storage and biodiversity services and some dis-services. Measurement and modeling errors along with uncertainties in weights were evaluated using a stochastic multicriteria acceptability analysis. The uncertainty analyses showed that rankings of WMS did not change significantly with and without the consideration of uncertainty. Therefore, the DSS was an effective and a robust tool to rank the WMS for a multiple PES program. Simplicity was fundamental in the development of hydrologic variables, hydroecological models, and the DSS. 14

15 CHAPTER 1 INTRODUCTION OF PAYMENT FOR ECOSYSTEM SERVICES (PES) AND DECISION SUPPORT SYSTEMS (DSS) Florida Hydrologic History The Northern Everglades (NE) region of Florida is home to some of the most famous ecosystems in the world. The list includes the Kissimmee River (KR), the Lake Okeechobee (LO), the St. Lucie and Caloosahatchee estuaries, and the Everglades National Park (ENP). The aforementioned ecosystems are home to many types of plants and animals as well as interesting hydrologic conditions. In the lower 48 states of the USA, Florida contains about 10% of the total wetland area, the landscape is very wet and easily flooded (EPA, 2013). South Florida is an attractive location to settle as well as develop agricultural practices because of the region s rich, organic soils, warm temperatures, and significant rainfall amounts. To accommodate for heavily inundated conditions and take advantage of the prime agricultural land, ditching and draining in the first half of the 20 th century bled the Floridian land and wetlands of water (Steinman and Rosen, 2000; Bohlen et al., 2009). The NE watershed totals about 1.4 million hectares (ha) and the water is managed through hundreds of flow control structures, pumps, gates, and many miles of canals and ditches (SFWMD et al., 2008; Bohlen et al., 2009). Ranchland in Florida Draining Florida s wet landscape through man-made structures and systems enables many types of agricultural production. Some of the agricultural practices in the NE region include orange, tomato, and sugarcane production, along with cow-calf ranching (SFWMD et al., 2008). Ranching will be the focus of this study. The cow-calf operations is the dominant landuse in the NE basin covering approximately 36% (0.5 million ha) of the basin area (Flaig and Reddy, 1995; SFWMD et al., 2008; Tweel and 15

16 Bohlen, 2008; Bohlen et al., 2009). The drained ranchland has enabled Florida to be ranked 12 th in the production of cattle in the United States (USDA-NASS, 2008). Impacts to the Northern Everglades Ecosystems Most of the ranching sites are north of LO. After a rainfall event, the water runs off of the nutrient rich pastures, into the ditches and canals, and flows quickly into LO. Extensive drainage reduced the watershed storage and disrupted the natural hydrology which has resulted in excessive flows to the KR-LO-Everglades ecosystem. Large, flashy inflows and increases in nutrient loading are damaging to LO, the estuaries, and ENP (Steinman and Rosen, 2000; Bohlen et al., 2009). Large influxes of nutrient, specifically the limiting nutrient phosphorus (P), have caused increased occurrences of algal blooms and eutrophication in LO (Steinman and Rosen, 2000). To protect the surrounding agricultural and residential land from a potential overflow or breach of LO and the Herbert Hoover Dike, large amounts of water are pumped from the lake to the St. Lucie and Caloosahatchee estuaries when lake water levels are too high (Steinman and Rosen, 2000; Bohlen et al., 2009). Large amounts of fresh water as well as high nutrient loads are damaging to estuarine systems (Steinman and Rosen, 2000; Bohlen et al., 2009; Yang et al., 2013). Unnatural flows and nutrient loads leaving LO and entering ENP have also had detrimental effects to ENP s ecological communities (Steinman and Rosen, 2000). Recognizing the need to protect the ecosystems of Florida, state agencies estimated that creating an additional one million acre-feet (1.2 billion m 3 ) of water storage in the NE basin could provide relief by returning flows entering LO to more manageable, more steady, and more natural levels (SFWMD et al., 2008). The South Florida Water Management District (SFWMD) has been instrumental in constructing 16

17 new reservoirs, stormwater treatment areas (STA), and wetland restoration projects to provide the new storage (SFWMD et al., 2008). Realizing that acquiring the needed storage will be difficult solely using public lands, private lands have been considered to provide part of the needed storage. Given the large percentage of private landuse in the region dedicated to ranching, ranchlands have been considered to provide part of the water storage need. Some ranch managers have begun adapting land management practices to provide the ecosystem service of water storage (Bohlen et al., 2009; Lynch and Shabman, 2011; Shabman and Lynch, 2013). Ecosystem Services Ecosystem services are defined as the many life-sustaining benefits that are provided by the ecosystems of nature (Millennium Ecosystem Assessment, 2005; Carpenter et al., 2006; EPA, 2012). These services are of vital importance to the life and well-being of humans (Nelson et al., 2009). Clean air, water purification, fertile soil for crop production, pollination, biodiversity, and coastal storm and flood protection are a few of the many well-known ecosystem services provided by the natural and agroecosystems of the Earth and of the NE watershed in Florida (Bingham, et al., 1995; Scarlett and Boyd, 2011; EPA, 2012). Water storage is an ecosystem service known to enhance or induce provision of other services. It is widely known that with increasing water availability, more ecologic activity occurs (Mitsch and Gosselink, 2007) and thus limited water storage capacity of the NE basin has adversely affected the biodiversity of the watershed. Therefore, increasing the water retention on the ranchlands in the NE watershed could provide more than one ecosystem service simultaneously: water storage and biodiversity (Bohlen et al., 2009). 17

18 Payment for Ecosystem Services Since flooded pastures may be a result of changing land management to increase water storage on ranchland, ranchers may require an incentive to provide part of the needed storage. In 2005, a multi-institution initiative began to develop a payment for ecosystem services (PES) program for water storage and treatment in NE basin. A PES program is a market-like program in which there is a buyer and a seller of ecosystem services (Shabman and Stephenson, 2007; Bohlen et al., 2009; Ribaudo et al., 2010; Gibbons et al., 2011; Shabman and Lynch 2013). A pilot program called Florida Ranchland Ecosystem Services Program (FRESP) was initiated to evaluate the applicability of a PES program in the NE basin. In the FRESP, state agencies (the buyer) paid the ranchers (the seller) for reducing surface flows from their ranch. Reducing surface flows relates to water storage (or detention); less surface flow leads to more water storage on the ranchland. Due to the success of the FRESP pilot program, with regards to both response from the ranchers and assumed provision of water storage services, the Northern Everglades Payment for Environmental Services (NE-PES) began in 2011 to pay ranchers for the ecosystem service of water storage. The NE-PES program recognizes that multiple ecosystem services can be derived from increasing the water storage on the ranches. For example, although water storage is the only service which is contracted for, contracted management may also be providing biodiversity services. If both water storage and biodiversity services are paid for, this would be ecosystem service stacking (Lentz et al., 2013, Robertson et al., 2014). Ecosystem service stacking refers to a single conservation site earning credits or getting paid for providing two or more overlapping ecosystem services (Robertson et al., 2014). Ecosystem service stacking is often promoted, but it can be easy for a 18

19 conservation site to violate the principle of additionality (double dipping) (Lentz et al., 2013, Robertson et al., 2014). Double dipping sometimes occurs when a manager of a conservation area sells one service (e.g. water storage) derived from specific land management adaptations, and then, the manager sells another service (e.g. biodiversity) derived from the same adaptations to a different agency or program (Lentz et al., 2013; Robertson et al., 2014). A rancher involved with NE-PES could be paid for providing biodiversity services if the rancher further improves the ranchland to specifically target increases in biodiversity. There would also have to be a buyer willing to pay for increased biodiversity services. If NE-PES could quantify biodiversity services, water storage payments could reflect which ranches provide water storage services along with biodiversity services: higher water storage payments to ranches providing greater biodiversity services. Ranches participating in FRESP and NE-PES were fitted with water management alternatives (WMA) which mainly use culverts with riser boards (CRB), to retain water on the ranches. The WMA is the boundary on the ranch which is providing water storage services and this boundary is defined by NE-PES. There are multiple water management scenarios (WMS) which could be implemented in the CRB structures. The different WMS are the different number of boards in the CRB structures. For example, a 1-Board WMS would have one riser board in the CRB; a 2-Board WMS would have two riser boards. The number of boards in a WMS could increase up to the maximum spillage level in the CRB. Increasing the elevation at which water spills out of the ranch causes damming behind the CRB structure increasing the water storage. 19

20 Programs nationwide have been developed to encourage ecosystem service production, such cost sharing incentives for utilizing best management practices (BMPs) on agro-ecosystems. Although, programs like these encourage ecosystem service management, actual provision of ecosystem services from BMP programs are rarely documented and the programs don t quantify if services have been rendered (Wunder et al., 2008; Bohlen et al., 2009). The ranches participating in the FRESP and NE-PES programs were instrumented to monitor surface flows and nutrient loads to measure if targeted services were provided. The documented storage amounts from the FRESP and NE-PES programs are assumed to help dampen the timing and volume of water flowing from the ranchland to LO, the estuaries, and ENP (Shabman and Lynch, 2013). While water is a very important ecosystem service provided on agro-ecosystems, another important service that could be derived from water storage PES programs is biodiversity. If the assumption that water will be stored and rehydrate the wetlands of the ranchland is true, habitat will increase for many species of plants and animals (Bohlen et al., 2009; Lynch and Shabman, 2011). Hydroecological Models While water storage services can be quantified through monitoring, biodiversity is an important ecosystem service that is quite difficult to measure (Nelson et al., 2009). Through the development and application of hydroecological modeling, quantifications of ecological responses to changes in ranchland wetland hydrologic conditions can be achieved. Hydroecological models predict changes in ecology due to hydrological factors (Hannah et al., 2004). The development of hydroecological models requires hydrological and ecological data. Since many of the ranches that participate in the FRESP and NE-PES programs are fitted with hydrologic monitoring instrumentation, the 20

21 hydrologic data needed can be obtained from some of these ranches. Like the hydrological data, the ecological data can be collected from the wetlands on the ranches that are participating in the PES programs. Along with water storage services, integration of the hydrological and ecological data can enable quantification of biodiversity services. Trade-Offs Limiting interest only to the benefits of the ecosystem service of increased water storage means that one may not realize that ecosystem dis-services might arise from increased water storage. Trade-offs, therefore, need to be considered for stakeholders (Ruckelshaus et al., 2013). Some dis-services from the FRESP and NE-PES programs could include increases in exotic and weedy plants, decreases in cattle forage, and increases in mosquito populations (Stites et al., 2002; Costedoat, 2012). Trade-offs among ecosystem services are also a common occurrence. For example, increasing water storage could hinder the agricultural land s ability to provide food and fiber services (Viglizzo et al., 2012). There are also trade-offs that the stakeholders participating in the PES program will encounter. For example, each rancher who participates in the PES program will probably want to store as much water as possible without damaging too much pasture, but the buyer may not have the means to pay for unlimited amounts of storage. Therefore, the rancher(s) may have to settle for a lower payments or lower ecosystem services from the WMS. Multiple trade-off scenarios are apparent and will need to be analyzed. Decision Support System Multi-criteria decision analysis (MCDA) decision support system (DSS) tools can be used to evaluate the trade-offs mentioned. The MCDA is a systematic tool that helps 21

22 identify the best alternative when there are many different types of data and information available to stakeholders who value it differently (Kiker et al., 2005; Linhoss et al., 2013). The techniques used in MCDA can address decision making situations where trade-offs need to be considered and analyzed (Guitouni and Martel, 1997; Yoe, 2002; Kiker et al., 2005; Linhoss et al., 2013). Decision support systems and MCDA techniques require two components: alternative decisions and criteria present in the decision to be made (Yoe, 2002). Alternatives are different solutions to the same decision problem. Criteria are attributes present in each alternative and are used to judge different alternatives. There are many alternative decisions to address ecosystem service provisioning, and depending on the ecosystem service, there are many criteria set by various stakeholders that must be met by the alternatives. A DSS developed by Girard et al. (2005) utilizes MCDA techniques in a simple spreadsheet format to evaluate the best type of culvert pipe liner to use when repairing culverts. The PES programs can be evaluated in a similar manner and the MCDA DSS presented by Girard et al. can be used as a template in the development of a MCDA DSS for a PES program with a goal of enhancing water storage and biodiversity services. Objectives The goal of this study is to develop hydroecological models and DSS for predicting the ecological responses to the changes in wetland hydrology and evaluating the trade-offs. Objectives of the study are: 1) to estimate ecological services and disservices for a water storage PES program for the ranchlands of the NE basin by 1) using biological and hydrological measurements at ranchland wetlands to develop wetland-scale hydroecological models to predict ecological responses to changes in 22

23 hydrology of wetlands; and 2) use the hydroecological models in conjunction with the spatiotemporal predictions of wetland hydrology (e.g. inundation, storage, and hydroperiods) from a hydrological model to predict changes in biodiversity measures for different water management alternatives. The wetland-scale hydroecological models were developed using field-scale point measurements of ecological and hydrological measures; and 2) develop and implement a decision support system (DSS) to provide flexible and transparent trade-off evaluation of multiple ecosystem services and agricultural production functions at scales relevant to decisions by ranchers and regional decision makers. 23

24 CHAPTER 2 PREDICTING HYDROECOLOGICAL WATER-RELATED SERVICES AND DIS- SERVICES IN A PAYMENT FOR ECOSYSTEM SERVICES (PES) PROJECT IN THE FLORIDA EVERGLADES Ecosystem Services Introduction Ecosystem services are life-sustaining benefits that are provided by the ecosystems of the Earth (Millennium Ecosystem Assessment, 2005; Carpenter et al., 2006; EPA, 2012). Beginning in 2001, the Millennium Ecosystem Assessment was carried out to assess the changes in ecosystems and the consequences of those changes to human well-being. It established a basis for science to evaluate the needs for conservation and sustainable use of ecosystems. The Millennium Ecosystem Assessment identified four types of services essential to human life that are produced or provided by ecosystems. The four service types are: supporting, provisioning, regulating, and cultural services (Millennium Ecosystem Assessment, 2005). Supporting services are foundational services necessary for the production of all other ecosystem services. Some examples include nutrient cycling, soil formation, and primary productivity. Provisioning services include providing products like food, fiber and fresh water. Regulating services include results of the natural processes of the ecosystems and include services such as water, climate, and disease regulation. Cultural services are intangible benefits, like spiritual, educational, and cultural heritage values, which people obtain from ecosystems. The Millennium Ecosystem Assessment found that all ecosystem services have been negatively impacted by the growing human population. Strategies are therefore needed to restore and enhance ecosystems to better provide these essential ecosystem services. 24

25 Ecosystem services can be provided by both natural and man-made ecosystems. Agricultural detention areas and constructed wetlands are man-made ecosystems that have been shown to provide ecosystem services like water storage and treatment (Shukla et al., 2011; Vymazal, 2011). Some of the natural ecosystems that provide services include wetlands, salt marshes, rivers, forests, and grasslands (Porter et al., 2009). Another ecosystem, the managed agro-ecosystem, provides multiple ecosystem services such as food and fiber. Additionally, agro-ecosystems also provide ecosystem services which are not as obvious (Porter et al., 2009; Ribaudo et al., 2010) such as water storage, nutrient cycling, climate regulation, and biodiversity (Bohlen et al., 2009; Ribaudo et al., 2010). To encourage owners and managers of agro-ecosystems to increase the provision of ecosystem services, governmental and other agency programs have provided subsidies and cost sharing incentives for implementing Best Management Practices (BMP). However, actual provision of services from BMP programs are rarely documented and the programs seldom quantify if services have been rendered (Wunder et al., 2008; Bohlen et al., 2009). This begs the question as to whether or not the BMP projects actually provide the services intended. A different approach, a market-like plan, encourages the concept of payment for documented provision of ecosystem services where there is a buyer and a seller of the ecosystem service (Shabman and Stephenson, 2007; Bohlen et al., 2009; Ribaudo et al., 2010; Gibbons et al., 2011; Shabman and Lynch, 2013). This type of program is termed a payment for ecosystem service (PES) program, in which the buyer of the services could be the public or a governmental agency and the seller could be a farmer or a private land owner. The 25

26 Millennium Ecosystem Assessment (2005) identified the development of market-based incentives, such as PES programs, as a means to enhance ecosystem service provisioning. Background and Need for Payment for Ecosystem Services Programs in Florida In the Northern Everglades (NE) basin of south Florida, a need for enhancing ecosystem services was recognized due to the continued degradation of water quality of Lake Okeechobee (LO), the Everglades National Park (ENP), and the St. Lucie and Caloosahatchee estuaries. The ecological integrity of the aforementioned ecosystems started deteriorating in the 1940s when extensive networks of ditches and canals were constructed to drain the NE basin, a watershed characterized by a shallow water table and ubiquitous wetlands. The drainage network was constructed to support urbanization and agriculture. Extensive drainage led to reduced watershed storage capacity and disrupted the natural hydrology and resulted in excessive flows to the Kissimmee River and LO. Currently, water and nutrient fluxes from the NE watershed are exacerbated during the wet season (May to October) when influx of peak flows and nutrient loads result in excessive phosphorus (P) loads into the NE ecosystem. Excessive P can cause damaging effects like algal blooms and altered ecological function in LO, the estuaries, and the ENP (Steinman and Rosen, 2000) given that these ecosystems are P limited. During the wet season, water levels can become very high in LO. To protect LO s Herbert Hoover dike and surrounding residential and agricultural areas from flooding, water is released from the lake to the Caloosahatchee and St. Lucie rivers. Eventually the nutrient rich, fresh water reaches the estuaries which damages their fragile and unique ecology (Steinman and Rosen, 2000). Large amounts of fresh water and nutrient 26

27 loads cause low salinity levels and high concentrations of limiting nutrients such as P thereby adversely affecting the estuarine ecology (Steinman and Rosen, 2000; Bohlen et al., 2009; Yang et al., 2013). Estuaries are important ecosystems as they protect the coasts from high ocean surges and mix the ocean water with fresh water, which is an important characteristic for many types of fish, birds, and other species that inhabit these unique ecosystems (Mitsch and Gosselink, 2007; Day et al., 2012). Nutrient-rich water is also released from LO into the ENP. Considering that total maximum daily loads (TMDL) of P for LO are high relative to the P-limited environment in the ENP, releases of water from LO to the ENP have caused damaging effects to ENP s ecological form and function (EPA, 2001; Villa et al., 2014). Vast monocultures of cattail (Typha domingensis) have invaded large areas in the ENP, reducing biodiversity, and it is caused by high P-loading in the water entering the park (Surratt and Aumen, 2014). Reducing the volume and changing the timing of the inflow to LO by implementing a PES program to increase water regulating services north of the lake could improve the health of the LO, ENP, and the estuaries (Lynch and Shabman, 2011). Ranchland as a Water Storage Service Provider Agencies have estimated that 900,000 to 1,300,000 acre-feet (1 acre-foot = 1,233.5 m 3 ) of additional storage is needed to reduce the damaging flows entering LO, ENP, and the estuaries (SFWMD et al., 2008). Almost 1 million acre-feet of storage needs to be provided by public works projects of aboveground reservoirs, underground storage, and alternative dispersed water management programs on public lands (Bohlen et al., 2009). This leaves around 300,000 acre-feet of additional storage needed to protect the NE region. Considering that it will be difficult to acquire all of the needed storage solely on public lands, innovative programs are needed for achieving 27

28 additional storage on private lands. Representing about 0.5 million hectares (ha) of improved pasture (Bohlen et al., 2009), ranchland is the largest landuse in the area north of LO, and in the NE basin covering approximately 36% of the total area of the basin. Wetlands constitute approximately 15% of the area covered by ranchlands. Wetlands are known to provide water storage and flood protection services (Tweel and Bohlen, 2008). However, most of these ranchlands are drained using ditches which have reduced the storage capacity of the wetlands. Ranchlands are a viable option for supplying some of the remaining storage needed if their drainage can be regulated. Increasing water storage and reducing the surface outflow on ranches could help change the volume and timing of water flow from the ranchlands to LO, the estuaries, and the ENP. Reducing flow volume could also reduce nitrogen (N) and P loads to the LO and the ENP since much of the nutrients come from ranchlands (Steinman and Rosen, 2000; Shabman and Lynch, 2013). Earlier drainage can be partially reversed through the use of water control structures which can raise the surface water spillage level out of the wetlands. Given that many ranchers have already voluntarily implemented BMPs, they require other incentives to provide the needed storage, such as payment for water storage through a PES program. Existing PES Programs and Their Expansion for Multiple PES Programs A multi-institution initiative began in 2005 to develop a PES program for the provision of water storage and treatment in the NE basin to address the need for increased water storage and the potential use of ranchland as a provider. Following consultation with stakeholders (ranchers, environmental groups, and state and federal agencies), a pilot project (Florida Ranchland Environmental Services Project, FRESP) was initiated to evaluate the applicability of a PES program in the NE basin. 28

29 Participating ranchers were paid for providing the ecosystem services of water storage and nutrient retention on their ranchlands. The water storage service was defined as the reduction in surface flow from the ranch. The reduction in surface flow was achieved by increasing the spillage level of the outlet structures compared to the drained conditions. Due to the positive response to FRESP, as well as the presumed increase in water storage services provided as a result of the FRESP pilot program, a subsequent program was initiated. With approximately $7 million in funding from the South Florida Water Management District (SFWMD), the Northern Everglades Payment for Environmental Services (NE-PES) program began in The NE-PES paid ranchers for either the ecosystem service of water storage or nutrient removal from public waters. Ranchers that participated in these programs (FRESP and NE-PES) fitted their ranches with drainage control structures on a designated water management alternative (WMA) boundary. The WMA was defined by FRESP and NE-PES. The drainage control structures mainly included culverts with riser boards (CRB) to increase storage by raising the spillage level (Fig. 2-1). Multiple water management scenarios (WMS) could be implemented by adding or removing the riser boards from the CRB structures. With more boards in the CRB structures, it was assumed that the raised spillage level in the ditches would increase water storage on the ranches. While water storage and nutrient regulation are essential ecosystem services provided by agro-ecosystems, another important service that could be derived from rehydrating wetlands on ranchlands is enhanced biodiversity (Stites et al., 2002). Biodiversity is an ecosystem service not contracted for directly in the FRESP or NE- PES. Managing for water-related services on the ranchland results in the rehydration of 29

30 wetlands (Goswami and Shukla, 2014) supporting the assumption that biodiversity could be increased by increasing water storage (Stites et al., 2002; Lynch and Shabman, 2011). Currently, the ranchers are not paid directly for providing biodiversity services and are only paid for water storage or nutrient treatment services. However, the buyer in NE-PES (SFWMD) recognizes that other ecosystem services like biodiversity may be enhanced as a result of increased water storage. If it is found that biodiversity services can be linked to water storage services, additional payments for multiple biodiversity services achieved under the current water services contracts should not be made initially. If a rancher does not make changes to specifically enhance biodiversity services and is paid for providing biodiversity services on top of the contracted water storage services, the rancher would be double dipping (Lentz et al., 2014, Robertson et al., 2014). Double dipping is a violation of the assumption of additionality of ecosystem services. It sometimes occurs when a manager of a conservation area sells one service and then sells a second service derived from the same management adaptations to a different agency or program (Lentz et al., 2014; Robertson et al., 2014). If further adaptations were made to enhance the second service, then the manager could sell those services and not violate additionality. For the example of water storage and biodiversity services in south Florida, if additional management actions were taken that could further enhance biodiversity services for which there is a buyer, then payment for biodiversity services could be considered. NE-PES could also adapt payment strategies by linking quantifiable biodiversity services to increases in water storage. Water storage payments in NE-PES could reflect which ranches provide water storage services along with biodiversity 30

31 services: higher water storage payments to ranches providing greater biodiversity services. This would not be violating additionality assumptions. Instead, it would be ecosystem services bundling (Robertson et al., 2014). In ecosystem services bundling, it is recognized that there are distinct ecosystem services derived from a single conservation site, but they are sold as a single commodity to one buyer (Robertson et al., 2014). Although biodiversity is an important ecosystem service, it is difficult to quantify (Nelson et al., 2009) compared to water storage services. Although increased water storage is needed in the NE basin and enhanced biodiversity is a possible additional service gained through increased water availability, some dis-services may also be encountered when implementing water storage PES programs on the ranchland. Therefore, it is imperative that both services and dis-services be considered and tradeoffs be evaluated to address the overall benefit of PES programs. Trade-Offs in Payment for Ecosystem Services Programs FRESP and NE-PES and other similar programs have created awareness about the need of providing ecosystem services like water regulation and enhanced biodiversity. Increased storage of water and nutrients on ranchlands may also cause certain undesirable effects or dis-services (Viglizzo et al., 2012). Such dis-services could include increases in mosquito populations (Mutero et al., 2000; Ellis et al., 2006; Schafer et al., 2006) and invasive plants (Nelson et al., 2009). The combination of promoting services but also sustaining dis-services, gaining one thing but losing another, or wanting something but settling for another is known as trading-off (Yoe, 2002). There are also tradeoffs among ecosystem services which are evident when one 31

32 service is traded for another. For example, food and fiber production could be sacrificed for increased water storage (Viglizzo et al., 2012). Financial tradeoffs have to be considered as well. Increasing water storage under a PES program will likely result in the flooding of pasture used for grazing. This could result in the need for additional cattle feed (Bohlen et al., 2009). The rancher would need to be compensated for this financial loss as part of the PES program. For a more holistic evaluation of a PES program which addresses multiple services, services, disservices, and associated tradeoffs, need to be considered. The first step in such an evaluation is linking the water storage services with biodiversity services. Linking Hydrology to Ecology It is widely known that increasing water availability increases biological activity. Changes in wetland hydrology, particularly hydroperiod, can act as a limitation or catalyst to species richness (Mitsch and Gosselink, 2007; Raulings et al., 2010). The previously ditched and drained wetland ecosystems of southern Florida are examples of how hydrologic changes drive ecological changes as biodiversity has changed significantly in this region (Steinman and Rosen, 2000). Biological diversity has many definitions in the literature (Peet, 1974; Peet, 1975). The most common definition of diversity combines species richness (number of different species) with an evenness of how individuals are distributed (Peet, 1974; Peet, 1975). The evaluation of trade-offs among water storage and biodiversity services and dis-services requires the development of linkages between hydrology and ecology through hydroecologic modeling (Hannah et al., 2004). Development of hydroecological models which use changes in wetland water dynamics to predict changes in biodiversity can help 32

33 document biodiversity services in a multiple PES program of water storage services and biodiversity services. Hydrologic Models There are two components of hydroecological modeling, ecology and hydrology. The development of hydrologic models is different from the development of ecologic models. Stochastic and physically-based hydrological models are used in the prediction of water balance components including surface and groundwater flows and storages, and evapotranspiration. The data needs for hydrologic models vary but the most comprehensive models require surface water and groundwater elevations, topography, climactic conditions, soil type, drainage, vegetation, and other water inputs (e.g. irrigation), outputs (e.g. potential evapotranspiration), and boundary conditions. These variables can be measured relatively easily with well-established methods. Surface water or groundwater levels can be obtained either through pressure transducers for high frequency measurements or manually and these data can be used to determine variables such as water flows and inundation areas. High resolution weather data are easily available from a regional network of weather stations maintained by on-site, local, state, or federal agencies. Numerous models have been developed to predict hydrologic variables and one of the most widely used watershed hydrologic model is the Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998 and 2012). The SWAT model is a time continuous, semi-distributed, process-based river basin model (Neitsch et al., 2011; Arnold et al., 2012). The SWAT was developed to simulate the effects of alternative land management scenarios on water resources and non-point source pollution in large watersheds (Neitsch et al., 2011; Arnold et al., 2012). It has been used to simulate 33

34 sediment transport (Benaman et al., 2005), point and non-point source pollution (Narasimhan et al., 2010), and in many other applications (Gassman et al., 2007). It is also used throughout the NE watershed by the United States Department of Agriculture (USDA) for assessment of the Wetland Reserve Program for wetland restoration. The SWAT is an effective model; however, the model is limited in its ability to simulate water dynamics in the shallow water table environment of Florida since it does not have a detailed groundwater drainage component. Also, it is a lumped model which makes it difficult to represent the scattered wetlands on south Florida ranchlands where FRESP and NE-PES were implemented. South Florida has highly conductive sandy soils and shallow water table environment where the water table reaches the surface during the wet season (May-October). Such a highly interactive hydrologic system requires the use of an integrated surface water and groundwater model that can simulate the effects of drainage management options on surface and subsurface water levels and storages. Another spreadsheet based model, the Potential Water Retention Model (PWRM), was developed for SFWMD for the assessment of water storage under NE-PES; it has similar limitations to SWAT and has not been published or tested. In contrast the model, MIKE-SHE, developed by the Danish Hydraulic Institute (DHI, Helsingør, Denmark), is an integrated model capable of simulating the NE basin hydrology. The MIKE-SHE is a physically based model which simulates surface and subsurface water interactions (DHI, 2013). The MIKE-SHE/MIKE11 is used to simulate water flow, water level, and sediment transport in rivers, flood plains, canals, and other inland water bodies (DHI, 2013). The MIKE11 coupled with a water quality component called ECOLAB is used to simulated nutrient transformations. The MIKE11 is capable of 34

35 representing hydraulic structures such as culvert and flashboard structures (Fig. 2-1), as well as pumps, gates etc. This feature of MIKE11 is helpful in accurately representing water management in south Florida. Given that MIKE-SHE/MIKE11 is a spatially explicit model, appropriate cell sizes can be used to represent ponded features (e.g. wetlands, ditches) of varying sizes allowing for an effective representation of ranchland hydrology. Coupling MIKE-SHE and MIKE11 allows for the simulation of the complex interactions among surface and subsurface water (Jaber and Shukla, 2012) enabling the user to quantify water retention, hydroperiod, and other hydrologic metrics at ranch sites. A study by Jaber and Shukla (2005) used MIKE-SHE/MIKE11 to simulate scenarios of water storage potential in an agricultural impoundment in south Florida. Jaber and Shukla (2005) predicted the effects of different alternatives which could be used to increase water storage on water storage capacity of the impoundment. It was suggested that the stored water could be used as an irrigation supply for citrus groves in the Caloosahatchee River basin. Through calibration and validation of the MIKE- SHE/MIKE11 model, Jaber and Shukla (2005) showed that the model can successfully simulate the hydrology of agricultural areas of south Florida. Using measurements or predictions from hydrologic models of surface and ground water levels, the hydrologic regime of a wetland can be characterized for different WMS (spillage levels) in the CRB structures. According to a site s hydrogeologic conditions, regional differences in topography and climate, and antecedent hydrologic conditions, the duration and frequency of inundation at a site varies (Skaggs et al., 1991; Winter, 1992; Brinson, 1993; Mausbach and Richardson, 1994). Using a graph that shows the height of the standing water in an area versus 35

36 time, the hydroperiod of a wetland can be predicted. All aspects of a wetland s water budget are represented by the hydroperiod (rainfall, evapotranspiration, inflow from neighboring areas, flooding, and net seepage of groundwater). Determining an average or representative hydroperiod for a site which has little to no hydrologic data is a challenge (Lewis et al., 1995). Water level data can be used in conjunction with high resolution topographic data (e.g. Light Detection and Ranging (LIDAR)) to quantify inundation areas and hydroperiods that are useful in developing hydroecological models. Ecological Models Ecological models are not as simple to develop as physically-based hydrological models. The biological world is more complex and chaotic than the physical world. Many more variables control and drive changes in biological communities than in hydrologic systems with measureable inputs and outputs of water. Data collection is but one aspect of ecological modeling that can be difficult. Plants are spatially fixed but most animal species can move about freely which makes it difficult to obtain accurate numbers. For example, it is difficult to obtain the population size of fish in a wetland because one cannot capture the entire population. Therefore, a representative sample has to represent the entire wetland. Such sampling has been conducted in many studies dealing with fish population dynamics (Han et al., 2007; Vanschoenwinkel, 2009; DeAnglis et al., 2010; Lee and Suen, 2011). Similar to the fish example, sampling can also be performed for other species. Ecological activity is also dependent on many more variables than hydrology; food availability, space, competition, and toxins are a few of the variables that affect the dynamics of an ecological community in addition to 36

37 hydrological variables. However, hydroecological models only focus on ecological relationships to hydrological variables. Plant hydroecological models Water regime is the main factor influencing plant composition and diversity in wetland ecosystems (Raulings et al., 2010). Water regime is a general term which describes the location, spatial extent, and timing of water in an ecosystem and can be described in terms of depth, hydroperiod, frequency, variability, and the rate and timing of wetting and drying cycles (Bunn et al., 1997; Raulings et al., 2010). A study by Raulings et al. (2010) demonstrated that plant species (obligate wetland or upland plants, facultative wetland, and facultative upland) richness and abundances depend on water depths and length of drying periods. Obligate wetland plants are those which only survive in saturated soil conditions and obligate upland plants are those which need dry conditions with no standing water. Facultative wetland and facultative upland plants are those which are often found in wetlands or uplands, respectively, but can survive in drier or wetter conditions, respectively. Raulings et al. (2010) also demonstrated that microtopography is important for many types of plants, especially facultative upland and upland plants. The study by Raulings et al. also stated that whole-of-wetland hydrologic regime description is insufficient in explaining variations among plant types and their abundances as the whole-of-wetland scale doesn t capture the micro-water regimes influencing the plants. However, wetland scale is the scale which is most relevant to the investigation of enhanced biodiversity services generated by a ranchland water storage PES program. To evaluate plant related services and dis-services as a result of increased water 37

38 storage on ranchlands (including wetlands), there is a need to develop models that can predict vegetation populations using hydrologic variables. Hydrologic factors that drive vegetation have been investigated and the studies usually focus on a specific spatial or temporal hydrologic variable and its effects on vegetation dynamics (Henszey et al., 2004; Loheide and Booth, 2010; Chui et al., 2011). The hydrologic variables could include groundwater levels, surface water levels, and duration of inundation (hydroperiod). Henszey et al. (2004) showed ground water level to be a good predictor for plant frequency (how often a plant of a certain species is present) in riparian grasslands of central Nebraska, USA. Loheide and Booth (2010) explained that plant species distribution and frequency can be described and predicted by stream bed morphology and hydrologic regime in the landscape surrounding streams in Nebraska, USA. Chui et al. (2011) demonstrated that a hypothetical groundwater draw-down due to landuse change and new construction downstream of the Nee Soon swamp forest in Singapore would decrease biomass of two types of vegetation in the swamp. The two plant types were termed flood resistant and less flood resistant plants. Modeling results from the same study (Chui et al., 2011) showed that the flood resistant plants would lose over 53% of their biomass and less flood resistant over 92% of their biomass due to an estimated average groundwater drawdown of 3.6 m from the new construction. Other studies have examined anthropogenic (man-made) changes and land management effects on the hydrologic regime and the resulting effects on plant distribution and species richness (Pollock et al., 1998; Bren and Sandell, 2004; Kleynhans et al., 2007; Foti et al., 2013). Aforementioned and other similar studies have 38

39 proven to be beneficial for understanding the drivers of vegetation form and function. However, there have been limited attempts to develop predictive models which combine spatial and temporal water dynamics of wetland systems into a single model for the prediction of plant species abundance. There are also few predictive models developed for the region of interest, the NE basin of south Florida. Predictive models for wetland vegetation at varying levels of water retention can quantify vegetative responses and can be related to impacts on biodiversity services and dis-services in the wetlands. As previously stated, a hydrologic model which integrates surface and subsurface hydrology interactions, like MIKE-SHE, can be used to predict multiple WMS of wetland inundation and hydroperiod. Water management scenarios of interest on the ranches would be different board heights of the CRB structures and how the board heights would change wetland hydrologic conditions. The spatially explicit surface and groundwater level simulations from MIKE-SHE can be used to calculate the hydrologic variables of the wetlands under the influence of the different WMS. The predictions of the hydrologic variables can then be used in the hydroecological models to predict vegetation increases or decreases due to increase or decrease in board height of the CRB structures. Animal hydroecological models Due to the diverse community of animals and insects which inhabit wetlands, there is also need to investigate the effects rehydration of ranchland wetlands through the implementation of water control structures on insect and animal populations. Fish, mosquitoes, amphibians, and invertebrates, to name a few, are important to wetland health and the health of other organisms (Minckley et al., 2011). Similar to vegetation 39

40 hydroecological models, studies to simulate animal and insect hydrology and ecology interactions have also been conducted. Fish are dependent on the ebbs and flows of water in a system, especially when there are seasonal wetting and drying patterns like in wetland systems. DeAngelis et al. (2009) investigated fish population dynamics in the wetlands of the Everglades and found that different species of fish utilize variables of the hydrologic cycle differently to navigate through the wetlands. Some fish species are carried with the current, some move in the direction of the flooding front, and others don t depend on hydrologic changes. Other investigations into the relationship between hydrology and fish ecology have been conducted to show how the natural and anthropogenic disturbances to the hydrologic cycle can affect fish species assemblage and spatial distribution (Han et al., 2007; Vanschoenwinkel et al., 2009; Lee and Suen, 2012). Mosquitoes are of interest because of their reputation for being a nuisance and because they are a food source for some animal species. Scientists in Australia developed a hydroecological model for mangrove wetlands and mosquito dynamics. The model helps to determine when treatment (pesticide) needs to be conducted based on the population size of the mosquitoes which increases due to rainfall or tidal influxes of water (Knight, 2011). Other studies determining drivers of mosquito activity found that micro-topography greatly affects the mosquito population (Ellis et al., 2006; Schafer et al., 2006). Therefore, the micro-topography of Florida s isolated wetlands, and not just changing hydrologic conditions, are likely to play a role in changes in mosquito populations. In a hydroecological mosquito model which only uses hydrologic variables to describe changes in mosquito populations in a wetland, volume could describe the 40

41 micro-topographic influences on mosquito populations. Micro-topography can be reflected in the volume of water in a wetland since volume is a function of area of inundation and depth of water in the wetland. Wetland systems are also home to many species of amphibians. There have been studies which simulate amphibian species richness and distribution based on the hydroperiod and wetland size (Snodgrass et al., 2000; Babbitt et al., 2003). Snodgrass et al. (2000) in an attempt to examine the relationships between amphibians, wetland size, and wetland hydroperiod found no relationship among wetland size and amphibian species richness. However, a significant linear relationship (R 2 = 0.54; p-value = 0.001) between wetland hydroperiod and amphibian species richness was observed (Snodgrass et al., 2000). Babbitt et al. (2003) investigated how amphibian communities respond to changes in wetland hydroperiod and predators and found that hydroperiod greatly affected amphibian assemblages and different species of amphibians were affected by the length of the hydroperiod differently. Overall, total amphibian richness and abundance was greatest at intermediate hydroperiods (nonpermanent wetland with hydroperiod greater than four months). The examples of hydroecological models above have shown that hydroecological models can predict changes in ecology as a result of changes in hydrology. However, they are likely to be unsuitable for a PES program like FRESP or NE-PES for a variety of reasons. Most of these examples do not address the species of concern; climate (subtropical Florida) or the flat topography of south Florida; nor do they provide easily useable predictive equations (or models) that can be used to quantify ecosystem services for decision making purposes. 41

42 The hydroecological studies (vegetation and animals) usually utilize a specific aspect of hydrology; a spatial variable (depth, topography, connectivity) or a temporal variable (hydroperiod, seasonal rainfall, and drought). The outputs of the models are in descriptive form and usually use a scale: low to high elevation (Henszey et al., 2004), near or far from the stream bank (Loheide and Booth, 2010), or anthropogenic changes and habitat loss will cause a shift in the vegetation (Foti et al., 2013). If biodiversity is to be quantified in a water storage PES program the focus of hydroecological modeling has to combine spatial and temporal variables in simpler models for quantifying biodiversity services. Given that hydrological factors, such as depth and hydroperiod, have been shown to be important for the growth and production of many biological populations, these factors need to be quantified either through measurement or modeling. For a water storage PES program, multiple water management scenarios (WMS) need to be evaluated to select the best WMS that provides the maximum additional storage and biodiversity related services and minimizes dis-services. Because measurement is resource intensive, measurement-based quantification of hydrologic variables such as hydroperiod and depths is not economically feasible. Therefore, there is need to predict the important hydrological variables under a variety of WMS using hydrologic models. Research Need and Objective Current implementation of the ranch-scale WMA in NE-PES has suggested that water storage ecosystem services can be provided through the PES program (Shabman and Lynch, 2013). While water storage services can be provided by increasing the spillage levels in NE-PES, it is not proven that biodiversity services are also provided. 42

43 To evaluate biodiversity related services, ecological measurements are needed for the participating NE-PES ranches. Once completed, correlations between hydrological and ecological data will need to be evaluated. If significant, the biodiversity services can be predicted as a function of hydrological variables using hydroecological models. If developed, the hydroecological models will allow for predictions of biodiversity services for different WMS (spillage levels). As a reminder, WMA is the water management alternative boundary on the ranch and were defined in FRESP and NE-PES, and the WMS are the different spillage levels which can be implemented in the CRB structures. If the biodiversity services for water storage PES programs like NE-PES can be provided, it will allow for development of multiple ecosystem services program. In such a program, services other than water storage can also be used as criteria to select and determine the payment. Hydroecological models are needed to estimate the current and potential provision of biodiversity services under the current NE-PES WMA as well as other available WMS. Since it is not feasible to collect hydrologic data for all WMS, hydrologic modeling is needed to quantify changes in hydrologic drivers of wetland ecology for different WMS. Objectives The goal of this study was to estimate ecological services and dis-services for a water storage PES program for the ranchlands of the NE basin. Specific objectives were to: 1) use biological and hydrological measurements at ranchland wetlands to develop wetland-scale hydroecological models to predict ecological responses to changes in hydrology of wetlands; and 2) use the hydroecological models in conjunction with the spatiotemporal predictions of wetland hydrology (e.g. inundation, storage, and 43

44 hydroperiods) from a hydrological model for predicting changes in biodiversity measures for different WMS. Process Overview The steps for evaluating the water and biodiversity ecosystem services, disservices, and trade-offs are shown in Fig At the ranch WMA scale, the scale at which services are rendered and paid for, the first step is to determine if the raised spillage levels at the ranch scale results in a change in hydrologic variables that describe the hydropattern of wetlands (e.g. water level, inundation area and time). If yes, the next step would be to quantify biodiversity ecosystem services or dis-services through the use of hydroecological models. This quantification depends on whether statistically significant predictive relationships can be developed to predict wetland ecology measures using hydropattern-related variables. Once developed, the hydroecological models are used to quantify services and dis-services for all WMS. Finally, all WMS with their services, dis-services, and trade-offs are evaluated in a decision support system (DSS) for selecting the best WMS. If a WMS does not make any difference in wetland hydrology then no biodiversity services would be delivered. Alternative scenarios (raised spillage levels) are evaluated to test if there is a change in hydrologic variables. If it is found that other WMS do change the hydrologic variables, then, the process to determine the optimum trade-off scenario can begin again. Methodology The first step in quantifying ecological responses to changes in hydrologic conditions and enabling the evaluation of biodiversity services in a water storage PES program is to measure hydrological and ecological variables. Included in this study are six measures of biodiversity service and dis-service responses to hydrological changes. 44

45 Biodiversity services include wetland plants, fish, amphibians, and macroinvertebrates. Stressors include invasive plants and mosquitoes. Forage or upland plant production is included because of its importance to ranchers, and if forage production decreases due to flooding of pastures, this would be a dis-service. If, however, forage production increases, this would be considered a service. Several ranch vegetation types had to be grouped due to the great richness of species found on the ranches. Three groups of vegetation were measured: wetland vegetation, forage vegetation, and weedy and exotic vegetation. Of these, abundance of each type was considered. Abundance is an important measure of ecological activity, and is the frequency with which a species occurs in a sample. For example, if there are 100 individuals in a sample of fish from a wetland, the abundance would be 100. For the prediction of vegetation cover, correlations among hydrological variables and the vegetation groups were developed to test for correlation. A hydroecological model was then developed for each of the vegetation groups based on the hydrological variables with significant correlation to the vegetation groups. Wetland plants are important for identifying wetland ecosystem health, integrity, and value for conservation (Pearman et al., 2006; Reiss, 2006; Pinilla, 2010). Specific plants in the wetland vegetation group are presented in Table 2-1. Since ranches are the providers of the ecosystem services, modeling forage vegetation response to increases in hydrology was important as the cattle use the forage, specifically Bahia grass (Paspalum notatum), as a food source. Should too much forage be damaged by flooding from raised spillage, the ranchers would need to 45

46 provide supplemental feed. Forage species included in the forage vegetation model are listed in table 2-1. The third group of vegetation modeled was weedy and exotic vegetation. These are important as invasion of exotic plants on forage grasses is undesirable. Ranchers spend significant resources to improve pastures to enable enough yield of forage to sustain cattle production. Plants included in the weedy and exotic vegetation group are listed in Table 2-1. All plant groups were identified by plant ecologists from the University of Central Florida (UCF) and Archbold Biological Station (ABS). Fish were considered as they are an important food supply for migratory wading birds that frequent the wetlands on the ranchland (DeAngelis et al., 2009; Minckley et al., 2011). They are also important predators for mosquitoes, insects, and other small fish that can affect the population of prey species. Frogs are also indicators of wetland health and conservancy (Snodgrass et al., 2000; Babbitt et al., 2003). Mosquitoes are known to be disease carrying vectors and are nuisances (Vanschoenwinkel et al., 2009) and are an important food source for fish, frogs, and other animals in the wetlands. Increasing occurrences of inundated conditions could increase populations of mosquitoes (Mutero et al., 2000; Ellis et al., 2006; Schafer et al., 2006), therefore they can be modeled using inundation characteristics. Macroinvertebrates were considered and modeled as they have been shown to provide important food sources for migratory birds (Horvath et al., 2012) and for residential bird, mammal, and herptile species on ranchlands (Steinman et al., 2003). Study Locations Data for this study were collected from four ranches located in the NE basin (Fig. 2-3) containing wetlands that had been ditched and drained for use as pasture for cattle. 46

47 Point-scale hydrological and ecological data were collected for Alderman-Deloney Ranch (Alderman), Buck Island Ranch (BIR), Williamson Cattle Co. (Williamson), and Pelaez and Sons Ranch (Pelaez). These four sites were selected because of willing landowners, a range of hydrological conditions, and the extent of hydrological data available. Of these ranches, Alderman, BIR, and Williamson participated in the FRESP where CRB structures were installed to raise the spillage levels. Pelaez was part of a research study (Goswami and Shukla, 2014; Shukla et al., 2014) where the long-term effects of water retention, similar to those implemented in the FRESP, were investigated. Alderman Deloney Ranch The Alderman Deloney Ranch (Alderman) (Fig. 2-4) is located in the northeast area of Okeechobee County in the SFWMD C-25 drainage basin. The soils in this region are sandy and mostly classified as flatwood soils which dominate the Florida landscape and the four ranches studied. Flatwood regions are typically characterized by nearly level topography, shallow water table, and poor drainage. Alderman is owned by the Alderman family and was used for grazing as unimproved pasture until the early 1960s when it was planted with tomatoes for two years. An intense ditch network exists on the ranch and was constructed for irrigation purposes when the ranch was used for vegetable farming. Alderman has two areas of shallow depressional freshwater marshes on the property. For over 40 years Alderman has been used for grazing as improved pasture. The drainage network at Alderman has lowered the natural water table, decreased the hydroperiod of the two large wetlands, and reduced the depth of flooding. Two CRB structures were installed as part of FRESP at the outfall ditch of each wetland located on the property to raise the spillage level to increase storage. 47

48 Wetlands ap1, ap2, and ap3 were the wetlands from which ecological data were collected (Boughton et al., 2011). Buck Island Ranch Buck Island Ranch (BIR) (Fig. 2-5 and 2-6) is located in Highlands County in the Harney Pond Canal (C-41) watershed. Most of the BIR pastures used to be central Florida dry prairie with embedded seasonal wetlands throughout. These wetlands were part of a slightly more elevated, drier section of land known as Buck Island. The broad section of wet prairie and marshes that is now BIR flowed like a small river of grass between Lake Istokpoga and Lake Okeechobee. The BIR property has been used for agricultural purposes since the 1900s. Up until the 1950s most of the property had been used for native range but part of it has been used for farming vegetables. Establishment of Bahia grass on approximately half of the ranch, including the areas used in this study, improved the pastures. The pastures are interlaid by small isolated wetlands, with depths ranging from about 12 (30 cm) to 36 inches (91 cm). In the 1940s and continuing to the 1970s, the wetlands were ditched and drained. Currently, BIR is leased to the MacArthur Agro-ecology Research Center and the pastures are used for grazing for cow-calf operations. As part of FRESP, CRB structures were installed as well as repairs were made to damaged structures. Management strategies of the CRB structures were adopted for the 2,800 acres of drained improved pastures to increase water storage on the ranchland (Boughton et al., 2011). Pelaez and Sons Ranch Pelaez and Sons Ranch (Pelaez) (Fig. 2-7) is a cow-calf ranch located 13 km northwest of LO in subtropical central south Florida. Of the four wetlands, two were selected for the water retention study that started in 2005 (Goswami and Shukla, 2014). 48

49 Two CRB structures were installed near the outlet of the main ditch that drained these two wetlands. An extensive monitoring network including weather, hydrologic (surface and ground water levels), and water quality variables were installed in The monitoring started in June 2005 and ended in May One wetland is a deep depressional wetland and the other is a shallow depressional wetland. Both wetlands receive surface and ground water flows from the upland improved pastures containing Bahia grass. In the 1900s the wetlands were ditched and drained to support vegetable operations. After vegetable operations ended, the ranch was converted to improved pasture for cow-calf operations. Similar to other sites, the CRB structure was used to increase the spillage level and store water in the wetlands (Shukla et al., 2014). Williamson Cattle Company The Williamson Cattle Company (Williamson) (Fig. 2-8) is located in Okeechobee County and drains within the Taylor Creek watershed within the NE basin. One large palustrine emergent freshwater wetland was converted to improved pasture 40 years ago and has been maintained as improved pasture since. Lateral ditches drain the part of the ranch that mostly contains the wetland. The WMA on the ranch is an estimated 900 acres in total. There is a single outflow point at the northwest boundary of the wetland. A new CRB structure was installed as part of FRESP with the aim of increased storage and reduced flows (Boughton et al., 2011). Hydrologic Measurement At all four study sites, hydrologic measurements comprising of surface water flows at the ranch outflow, depth of water in the wetlands, and groundwater levels were conducted from May 2010 to February 2012 (Table 2-2). These data were collected for three ranches and were obtained from the FRESP program while for Pelaez ranch they 49

50 were obtained from an UF/IFAS (University of Florida/Institute of Food and Agricultural Sciences) study (Shukla et al., 2014). Point-scale measurements of water depth were taken on the ranches in the wetlands at the time of ecological sampling (Bohlen et al., 2014). Groundwater data were also recorded at 15-minute intervals from May 2010 to February Topographic data at the sites were obtained from a combination of topographic survey (Williamson and Alderman) and high resolution LIDAR (Light Detection and Ranging) (BIR and Pelaez). The integration of point-scale and topographic data allowed for the calculation of spatial and temporal hydrologic variables for wetland scale water availability and service analysis. Hydrological Variables Many hydrological variables affect wetland ecology and can be used to develop the hydroecological models. To enable an ease of use of the hydroecological models by many stakeholders, measures of hydrology which could be relatively easily measured or estimated were identified for their use in the hydroecological model development. Point measurements of water depth The first and most basic hydrologic variable is the depth (d) of water in the wetland. This was measured using a metric ruler at each point on days when ecological sampling measures were made from May 2010 to February 2012 (Table 2-4). The locations of the points were recorded using a Trimble GeoXT GPS (Trimble, Westminster, CO, USA). Average wetland depth for a measurement date was calculated for each wetland by averaging the water depth measurements taken at the sampling locations on a sampling date 50

51 Wetland boundary During the ecological sampling, the wetland perimeters were mapped using the Trimble GeoXT GPS by walking along the wetland edge and tracking the path. The perimeter of the water was considered the wetland edge and is the wetland boundary. The wetland edge was not walked each time ecological measurements were taken since the wetland was not completely full of water on each sampling day. The wetland perimeters data from the GPS were converted to shapefiles which were used within ArcGIS v.10.1 (ESRI, Redlands, CA, USA) for deriving hydrological variables. Topography Accurate DEMs (digital elevation models) were developed for the BIR and Pelaez ranch sites using previously collected LIDAR (Light Detection and Ranging) data (Guzha and Shukla, 2011; Shukla et al., 2011). For the BIR this was collected and processed by NCALM (National Center for Airborne Laser Mapping) at the UF with an estimated vertical accuracy of cm in April 2006 and provided by the ABS. For the Pelaez site, LIDAR was also collected and processed by NCALM in May 2008 and provided by the Water Program of SWFREC (Southwest Florida Research and Education Center), UF/IFAS. The DEMs for the other two sites, Alderman and Williamson, were developed from data collected during a ground survey using a Trimble S6 Total Station (Trimble, Westminster, CO, USA) in July 2013 conducted by the Biology Department of UCF, Orlando, FL. Wetland inundation area (IA) and volume (Vol) Point-scale measurement and the DEMs were combined using ArcGIS to estimate inundation area (IA) (Table 2-4). Inundation area refers to the wetland area with standing water. Inundation areas were estimated for each wetland on all four 51

52 ranches for the dates of ecological sampling using ArcGIS v Percent inundation area (%IA) is the ponded area of the wetland divided by the total wetland area and represents the area of the defined wetland which is covered by water. Percent inundation area can be larger than 100% because the area of inundation may be larger than the defined wetland boundary. Wetland volume was determined using the average of measured depths and the DEM within the ArcGIS for each sampling date. Hydroperiod (TI and TSI) In this study, hydroperiod is defined as the number of consecutive days when at least one part of the wetland had 15 cm (6 inches) or more water (Table 2-4). To determine hydroperiod, daily water depth in the wetlands was used. Daily measurements of water levels at the monitoring wells (Fig. 2-4, 2-5, 2-7, 2-8) located within or in the vicinity of each wetland were used and compared to the lowest elevation in the wetland to determine the hydroperiod. For each sampling date, the number of previous consecutive wet (at least 15 cm water depth) days was summed and was termed time inundated (TI). Another water regime descriptor, the time since inundated (TSI) was also calculated as the length of time in days since the wetland last had at least 15 cm of water depth (Table 2-4). For each sampling date, TSI was calculated by summing the previous consecutive days dry (less than 15 cm water depth). Connectivity (DC and DSC) Two more temporal variables of water regimes, days the wetland was connected to the drainage network (DC) and days since the wetland was last connected to the drainage network (DSC) were also estimated for development of hydroecological models (Table 2-4). These variables were calculated in a similar manner to TI and TSI with the additional data of surface water level in the nearest ditch. The wetland was 52

53 considered connected if the water level in the ditch was the same as the wetland water level. For each sampling date, the number of previous days connected (DC) were summed. If on the sampling date the wetland was not connected to the drainage network, the number of previous days not connected were summed and termed days since connected (DSC). There are many other variables that could affect the plant and animal abundance (e.g. precipitation, temperature, etc.) but were not considered. Rather, the focus in this study was on inundation area, percent inundation area, volume, hydroperiod, and connectivity because these variables have been shown to exert major influences on ecological shifts (Snodgrass et al., 2000; DeAngelis et al., 2009; Caldwell et al., 2011). Ecological Measurements Ecological measurements were taken by the EPA STAR team (Bohlen et al., 2014) and provided for use in this thesis. Wetland ecological species data were collected over a two-year period (2010 to 2012) at the four ranches. Point-scale measurements were taken under the conditions of raised spillage levels, referred to here as water-managed conditions. Ecological data were not collected for the baseline conditions. Ecological measurements were made during the wet season (May 15 to October 15). At each wetland, stratified random sampling was adopted for vegetation, fish, amphibians, macroinvertebrates, and mosquitoes (Fig. 2-4, 2-6, 2-7, and 2-8). To obtain representative samples at each wetland, samples were taken from the shallow, mid-depth, and deep zones of each wetland (Bohlen et al., 2014). A three-member sampling team collected the ecological data. One person collected vegetation, another collected vertebrate, and the last collected invertebrate data. The person collecting plant 53

54 data would set out first and the others were a few meters on either side (no more than 5 m away) to minimize disturbance to animals while sampling. To measure the vegetation, a one meter square (1 m 2 ) circular frame was placed on the vegetation and the percent cover was counted for each plant type (weedy and exotics, wetland, and forage). The point measurements represent a percent cover of plant per m 2 at a point in the wetland. Not all vegetation species were considered because of the high plant species richness. Based on criteria developed by the EPA STAR ecologists, only known indicator plant species for wetlands, plant stressors to biodiversity, and plant species that would cause loss of forage were considered. Plant data collected were used to calculate species richness, abundance, and percent cover (refer to Table 2-1 for plant species). Invertebrates and mosquitoes were sampled using a one meter (1 m) sweep of a D-frame Aquatic Dip Net among vegetation not disturbed by other ecological sampling. The specimens collected were transferred into containers, preserved in 70% isopropyl alcohol, and transported to the UCF Biology Department laboratories for identification and determination of the number of species and individuals under the supervision of Drs. John Fauth and David Jenkins (Bohlen et al., 2014). Fish, amphibian, and other macroinvertebrate data were collected using a drop box with a cross sectional area of 0.25 m 2. In the drop box, a column of water was isolated in which a dip net was used to collect all fish, amphibians, and other macroinvertebrates. Sweeps with a tightly fitted dip net were conducted until two consecutive sweeps were empty up to a maximum of 20 sweeps. The number of species and individuals of all fishes, amphibians, macroinvertebrates, and mosquitoes collected was recorded for each sampling day to 54

55 represent number of fish, amphibian, etc. per m 2 at a point in the wetland. All plant and animal species data were entered into an MS Access database (Microsoft, Santa Rosa, CA, USA), and extensive quality assurance/quality control (QA/QC) was conducted by the ecologists from UCF and ABS. The resulting data were provided for developing hydroecological models. Hydrologic Modeling Biodiversity related variables were measured only under one spillage level condition (above baseline). Therefore, to evaluate the biodiversity and water storage effects under different spillage levels, including the baseline (ditch bottom), results from two separate modeling studies at BIR (Hendricks et al., 2014) and Pelaez (Wu, 2014) were used. A MIKE-SHE modeling study was conducted by SWFREC Water Program (Sanjay Shukla, Chin-Lung Wu, Gregory Hendricks, and Alphonce Guzha) for BIR (Hendricks et al., 2014) and Pelaez (Wu, 2014) and results were provided for the use in this thesis. Williamson and Alderman were not modeled using MIKE-SHE/MIKE11, therefore biodiversity service quantification was limited to BIR and Pelaez. Hydroecological model development did not depend on the outputs from MIKE-SHE. The outputs from MIKE-SHE were used to develop hydrologic variables for WMS conditions which couldn t be measured. Once a MIKE-SHE model is developed for Alderman or Williamson ranches, those MIKE-SHE outputs could be used to evaluate changes in biodiversity services on those ranches. The different WMS (different spillage levels) included the baseline, incremental addition of riser boards above the baseline to the top of the CRB structure. Baseline is the bottom of the ditch or canal with no water control structure impeding water flow out of the ranch. Example CRB structures and associated spillage levels are presented in 55

56 Table 2-3. Locations of these CRB structures in Table 2-3 within the BIR are shown in Fig The MIKE-SHE/MIKE11 modeling completed for BIR evaluated eight WMS. The WMS were termed Baseline, 1-Board, 2-Board, FRESP, 3-Board, 4-Board, 5- Board, and 6-Boards or higher. Baseline was the ditch without any water management structure. The WMS termed x-board indicates that there were x-number of boards in the CRB structures. The FRESP WMS was the WMS used in the FRESP program. For the FRESP WMS, the number of boards varied in each CRB structure. The MIKE- SHE/MIKE11 modeling completed for Pelaez evaluated seven WMS, and they were termed Baseline, 1-Board, 2-Board, 3-Board, 4-Board, 5-Board, and 6-Board. Since Pelaez did not participate in FRESP, FRESP was not a WMS evaluated at Pelaez. Scale Measurement and modeling of different services and dis-services occurred at different scales in this study. The scales include point scale (measurement), wetland scale (hydroecologic modeling), and ranch or WMA scale (quantification of water storage through MIKE-SHE models and biodiversity). Ecologic and wetland water stage data were collected at the point scale. Hydroecologic models were developed for biodiversity predictions at the wetland scale. In this study, the watershed is an area of the ranch in which the water is separated from other water by elevation, canals, or ditches and flows into separate wetlands, ditches, and canals. A ranch can have multiple watersheds. The ranch or the WMA scale is the scale at which water storage services are provided and paid for. The WMA boundary was defined in the FRESP or NE-PES programs. Because the ecologic and wetland depth data were collected at the point scale and the hydroecologic models were developed at the wetland scale, the original point- 56

57 scale data had to be translated to represent wetland scale data. The median could be used to translate the data from point-scale to wetland scale. Rousseeuw (1991), Whitmee and Orme (2013), and Schaefer et al. (2014) used the median to represent scaled-up ecological data. However, due to the nature of ecological data, it is common not to find any specimens to record (value of zero). The ecological data from the ranchland wetlands contained many zeroes affecting the statistical ability to develop predictive relationships between ecological and the hydrological variables. Therefore, in an initial approach, the zeroes were removed from the data sets for all ecological communities then using the median to scale from the point to the wetland scale. Although this improved the predictive relationships, it inflated predictions and increased the chance of getting a higher coefficient of determination (R 2 ) value, and the predictive relationships developed only apply when these species are present. Therefore, in addition to the median approach, the mean of the daily point scale data (zeroes included) was also used to develop hydroecological models. While this approach resulted in lower R 2 values in the hydroecological models, the relationships developed were more consistent with the strength of relationships known from hydrologic drivers of ecology in the literature and with the alternative statistical approaches used by the ecologists in the EPA STAR team. The data were pooled to develop general hydroecological models. This means that the models could be used to predict ecological responses at all four of the ranches. The EPA STAR team did find evidence in some of their models to implicate the need for individual ranch models (one model for each BIR, Pelaez, Alderman, and Williamson) 57

58 (Bohlen et al., 2014). However, the general models are needed to predict biodiversity services across ranches involved in the water storage PES program. The median and mean values of the point measurements on the sampling date were multiplied by the IA on the same date to estimate species abundance for each plant and animal population for a measurement date. To scale the wetland biodiversity services to the ranch WMA scale, the biodiversity services at all wetlands were summed to approximate ranch WMA scale biodiversity services. Regression Analyses Regressions analyses were conducted to develop and evaluate hydroecological models. While there are complexities in the different scales (e.g. point-scale measurements of ecological data and the need for wetland-scale estimation of biodiversity services), there is a need to develop simple relationships to quantify ecosystem services (Henzsey et al., 2004; Ruckelshaus et al., 2013). Several recent studies such as Nelson et al. (2010), Rucklshaus et al. (2013), Petz et al. (2014), and Laurans and Mermet (2014) all discuss the usability, and actual utilization of ecosystem service evaluation tools and how simplicity is a key component in delivering models which can be understood and used by the stakeholders. Therefore, the approach taken in this study was to work towards the development of simple statistical models for use in a decision support system (DSS). The DSS would evaluate biodiversity services and water storage services for multiple WMS. Simple multivariate linear regressions were used to simulate interactions between hydrology and ecology. One model was developed for each of: wetland vegetation, forage vegetation, weedy and exotic vegetation, fish abundance, amphibian abundance, mosquito abundance, and macroinvertebrate abundance. 58

59 To develop a multivariate linear hydroecological model, several assumptions had to be met. First was the assumption of normality in the ecological data. For the median regression analysis, the data were log-transformed and tested for normality using the Anderson-Darling test. The transformed median data met the normality assumption. Similarly, the mean data were natural log-transformed to achieve a normal distribution and were tested for normality. Second, the mean data were assumed to have a constant variance, this assumption was tested through residual plots, and they were found to be of constant variance. Finally, the data were assumed to be independent among ranches and measurement dates. Independence was checked through autocorrelation testing. Median regression analyses The hydroecological model development which used the median to scale from the point to wetland scale only refers to situations when the species were present (Phillips et al., 2006). The regression analyses which used the median to scale from the point to wetland scale refers to the relationship between hydrology and plant and animal species when these species are present. Using only data with presence, the median was used to scale the point measurements to the wetland-scale for plant and animal species for a sampling day (Rousseeuw, 1991; Whitmee and Orme, 2013; Schaefer et al., 2014). The median was multiplied by IA to represent wetland scale abundance. A table for each plant and animal population (fish, amphibian, mosquito, macroinvertebrates, forage vegetation, wetland vegetation, and weedy and exotic vegetation) was created and had a median count, an abundance (median multiplied by IA), and a measurement or estimation of each hydrologic variable for all measurement dates. 59

60 Mean regression analyses A second set of models was developed using the mean of the daily point samples instead of the median to scale from point to wetland scale. All of the zero measurements were included to calculate the mean. For the mean analyses, two sets of regressions were developed for each plant and animal group. The first set of regressions tested the response of abundance (mean multiplied by IA) for each plant and animal group (Table 2-7). The second set of regressions tested the response of the mean of the sampling date without multiplying by IA for each plant and animal group (Table 2-8). Like the median data, each sampling date had a mean count, a total abundance for each plant and animal group (mean multiplied by IA), and the same hydrologic variables that were used in the median analyses. All of the assumptions of multivariate linear modeling were also met for the mean-based analyses through the Anderson-Darling test, auto correlation test, and review of residual plots. The reason linear models were chosen to represent the interactions between hydrology and ecology was to generate results that can be used to develop a userfriendly decision support system (DSS) tool. Eventually the hydroecological models are to be incorporated into a simple DSS. The DSS will allow stakeholders to evaluate the services, dis-services, and trade-offs of water storage and biodiversity ecosystem services for multiple WMS on ranchland. Therefore, simple measures of hydroecology relationships need to be used so that a rancher, policy maker, or other stakeholder can easily apply the hydroecological models. Best Subsets Regression and Model Selection After the assumptions of a linear model were met, the first step in the model development was to use a best subset regression technique. Mallows Cp values were 60

61 obtained from the best subsets regression and the model with the best Mallows Cp was chosen. The best Cp value is the one that is less than or equal to the number of predictors (Ott and Longnecker, 2001). Mallows Cp value aims at minimizing error and biased estimation by not including more predictive variables than are needed in a multivariate linear model (Mallows, 1973; Shibata, 1981; Baraud, 2000; Birgé and Massart, 2007). After a model was selected from the subsets of models, a general linear multivariate regression equation was developed using the best subset of hydrologic predictors. The models are general models, meaning the data from all four ranches were pooled to develop the model. These models are not site-specific. Residual plots were made to demonstrate whether or not the assumption of a linear model is justified. ANOVA tables were analyzed for each hydroecological model to ensure that the hydrologic predictor variables were significant (P 0.05). Coefficient of determination (R 2 ) and root mean square errors (RMSE) for the regressions were reported (Tables 2-4, 2-5, 2-6, and 2-7). Through the use of the hydroecological models, quantification of the abundance of the biodiversity services at various levels of water storage could be achieved. Uncertainty in Hydroecological Models As is true of any model, uncertainty and error are inherent and this is true of the hydroecological models developed. Due to the many complexities of the data collected as well as the need for trade-offs in payments for ecosystem services, uncertainty and error need to be evaluated. These uncertainties need to be communicated to the stakeholders for a transparent PES program. Errors from data collection, hydrologic variables estimations, and hydroecological model development all result in errors in the predictions of biodiversity services. Solely from the hydroecological model predictions, 61

62 there can be significant errors associated with the biodiversity services predictions. The root mean square error (RMSE) for each model was calculated using observed median data and the predicted median from the models using the following: RMSE = ± 1 n (P i O i ) 2 n i=1 Where n is the number of data points, P is the predicted values from the hydroecological models, and O is the observed data from field measurement. The predicted RMSE values from the hydroecological regressions could not simply be backtransformed to represent normal-scale errors in estimations of biodiversity services. Back-transformation of predicted values rather than the RMSE values can be compared to observed median or mean measurements. Therefore, by back-transforming all of the data predicted in the hydroecological models and reducing the abundance back to point-scale data, the RMSE for the hydroecological models can be calculated. Backtransformation of the predicted data was completed by raising e to the predicted model values. This results in normal-scale (not log-scale) data, which can then be reduced to point-scale data by dividing the back-transformed data by the IA. These data were then used in the RMSE equation to estimate the errors in predictions of biodiversity services. Since there is error in the predictions from the MIKE-SHE/MIKE11 WMS simulations as well, the errors from MIKE-SHE/MIKE11 were combined with the errors from the hydroecological models. 62

63 Results Median-Based Hydroecological Models The analysis using the median to scale from point to wetland scale included only non-zero (present) data, and the correlations and models reflect estimations of ecological variables when they are present. From here, a median model (e.g. median fish hydroecological model) refers to the hydroecological model developed using the median and includes only non-zero ecological data. The first step in developing the hydroecological models was to do a best subsets regression. The best subsets regressions identified the best hydrological variables to use in the development of median hydroecological models for prediction of non-zero biodiversity service responses. For the median analysis, all ecological variables except weedy vegetation were positively correlated (P<0.05) to IA. Additionally, all ecological variables were also correlated to a different hydrologic variable. Table 2-5 shows the multivariate linear model along with the model performance measures (R 2, RMSE, and Mallows Cp). All models were significant (P<0.001). Since zeroes were not included in the data sets, there are potential inflations of performance measures (R 2 specifically) and predictions by the hydroecological models. All R 2 values were 0.40 or higher. If a best subsets regression analysis indicated that the best model was one with hydrologic variables with high correlation coefficient, like IA and Vol (R>±0.50), the next best model was chosen based on the Mallows Cp value. For example, the best subsets regressions for the fish and amphibian models showed that the best hydrological variables for simulation purposes were IA and Vol. However, IA and Vol had a correlation coefficient of greater than 0.50 and were therefore not used together in the hydroecological model. The next best model was chosen based on the 63

64 Mallows Cp value. Some of the models had relatively high errors as indicated by the Mallows Cp value, and some of the Mallows Cp values are higher than the number of predictor variables. For example, the fish model had a relatively high R 2 value than the other models but it also had a high Mallows Cp value (Table 2-5). This indicates that although the fish model had a high R 2, it is a relatively weaker model. The RMSE in Table 2-5 represents the error for the log-transformed data. The ANOVA tables for the hydroecological models were evaluated to examine if individual hydrologic predictors in the model were significant (P<0.05). Most median models contain more than one spatial variable (e.g. fish with IA and d) but some (e.g. wetland vegetation with IA and DC) contain spatial and temporal variables. Median wetland vegetation The median hydroecological model for wetland vegetation (Table 2-5) contained one spatial (IA) and one temporal (DC) hydrologic variable with an R 2 of It shows that vegetation coverage increases with IA and decreases with DC. Increased water availability represented by an increase in IA leads to an increase in wetland vegetation, and this is consistent with known hydrologic drivers of wetland vegetation. The inverse relationship between vegetation and DC indicates that when wetlands and ditches are connected, the depth of the water in wetland as well as the ditch is high. It would seem that wetland vegetation would be positively correlated to this variable, but it isn t. One explanation for this could be that connectivity indicates drainage of the wetland. Of the ranches that were studied, wetland vegetation cover is more likely to increase with increasing spillage level at Alderman or BIR due to fewer DC observed at these ranches compared to Pelaez and Williamson ranches. The Pelaez and Williamson ranches have 64

65 deep ditches spanning their large wetlands contributing to longer periods of connectivity (Table 2-4). Median forage vegetation The median hydroecological model contained two spatial variables, IA and %IA with an R 2 of The forage cover model (Table 2-5) shows that while forage cover is positively correlated to IA, it is negatively correlated with %IA. The %IA can be thought of as the area of wetland s edge impacted by the wetland s water. Therefore an increase in %IA results in less wetland edge available for forages. Since most forages are upland plants that do not tolerate wet conditions, the model seems to follow the current understanding of factors impacting the distribution of forage plants. Percent inundation area can be larger than 100% if the inundation area on a specific day is larger than the delineated wetland boundary. There were several instances of %IA being greater than 100. A study by Henszey et al. (2004) demonstrated an increase in upland plant frequency with ample water availability (IA), but too much flooding area (%IA) caused decreases in upland plant frequency. It should be noted that the IA and %IA included in the forage vegetation model are not equivalent (Wei and Chow-Fraser, 2008). It is true that they may have some correlation since %IA is derived using IA, but %IA accounts for microtopography and effects of edge of wetland exposure which IA does not. Of the four ranches, Williamson will likely have a larger cover of forage vegetation since the wetlands at this ranch are large and shallow. The large shallow wetlands result in lower %IA with increased spillage level compared to a ranch like BIR which has smaller but deeper wetlands and higher %IA. 65

66 Median weedy and exotic vegetation The median hydroecological model developed for weedy and exotic vegetation includes two spatial hydrologic variables, %IA and Vol, with an R 2 of A negative relationship with %IA suggests that the higher the inundated area fraction, the lower is the weedy and exotic plant cover. However, weedy and exotic plants are positively correlated to Vol suggesting that with more depth variation and more water in the wetland, there will be a larger weedy and exotic plant cover. Depth variation in the wetlands can be explained by Vol (area x depth). Volume was calculated using DEMs for the wetlands which includes the changes in elevation (depth). A ranch like Williamson or Pelaez with large and shallow wetlands would see a larger cover of weedy and exotic vegetation. Median fish The median hydroecological model for the fish abundance includes two spatial variables (IA and d) with a R 2 of Fish abundance in the wetlands increases with IA but is limited by d. This suggests that with more edge habitat available (indicated by IA) and shallower water (indicated by d), fish populations will increase. Since an increase in IA indicates an increase in d, positive effects to fish abundance due to increase in the edge habitat will be reduced by the inverse relationship with d. Similar results were observed by Reid et al. (2013) who indicated that edge habitat, where the water is shallow, increases fish abundance; as IA increases, fish abundance also increases. Reid et al. (2013) showed that edge habitat, where the water is shallow, was important for fish population abundances and the deeper areas of the wetlands proved to have lower density of fish. The model indicates that topography will play an important role in 66

67 influencing fish abundance. Large and shallow wetlands seem to provide habitat to support larger fish populations. Another explanation for the decline in fish population due to depth is the species of fish captured during the sampling. Similar to the study conducted by Bennett and Conway (2002), most of the fish species trapped for use in the model were about 50 mm (2 inches) in length at maturity.. The fish species include the golden topminnow (Fundulus chrysotus), the lake chubsucker (Erimyzon sucetta), the eastern mosquitofish (Gambusia holbrooki), the least killifish (Heterandria formosa), the flagfish (Jordanella floridae), the warmouth (Lepomis gulosus), and the sailfin molly (Poecilia latipinna). These species are known to be preyed upon by larger fish, waterfowl, and wading birds (Jordan et al., 1998; Troutman et al., 2007; Bennett and Conway, 2010; and MacRae and Travis, 2013). Larger predatory fish were not captured during data collection and are therefore, not included in the model. The inverse relationship between fish and depth may also be explained by dispersal of the small fish throughout the wetland, rather than a decrease in fish abundance with depth (Jordan et al., 1998; Troutman et al., 2007). Fish are also known to be dependent on other ecological factors such as plant type (Troutman et al., 2007) which were not considered due to the need for a simpler model. Spillage levels play a role in fish population abundances. With more boards there will likely be more inundation area. However, the same spillage level at two different ranches may result in different fish populations due to differences in topography (shallow or deep wetlands). For example, BIR contains smaller and deeper wetlands than Pelaez. The same spillage level on both ranches will not provide the same 67

68 hydrologic conditions favorable for larger fish populations with Pelaez ranch likely to have more fish due to a larger, shallow wetland with more edge habitat available to the fishes. Median amphibian The median amphibian hydroecological model contains two spatial variables, IA and d, with an R 2 of The model for amphibians shows positive correlations with both IA and d. This suggests that an increase in shallow edge habitat (indicated by IA) and depth (d) will increase the abundance of amphibians. Amphibian abundance and richness have been reported to be affected by hydroperiod (Snodgrass et al., 2000; Babbitt et al., 2003). The model does support the available literature, since longer inundation periods are generally associated with greater depths. Therefore, the hydroecological model for amphibians agrees with the findings of other studies concerning amphibians and the predictive ability is improved by including both IA and d spatial characteristics. Similar to the fish abundance model, the model predicts that amphibian abundance will be greater at ranches which have large and shallow wetlands (e.g. Pelaez). Median mosquito The median hydroecological model for mosquitoes has both spatial (IA) and temporal (TI) variables with an R 2 of The model shows that mosquitoes have a positive correlation with IA and a negative correlation with TI. The model suggests that mosquito abundance will increase with an increase in inundation and will be limited as the hydroperiod increases. Longer hydroperiods and larger wetlands have shown to reduce mosquito reproduction due to predation (Arav and Blaustein, 2006). Therefore, at a ranch with longer hydroperiods (Williamson and Pelaez), mosquito populations will 68

69 be less than at a ranch which has shorter hydroperiods (BIR). Longer hydroperiods may support larger fish and amphibian populations which prey upon mosquitoes and their larvae (Arav and Blaustein, 2006). Median macroinvertebrate The median hydroecological model for macroinvertebrates has two spatial variables (IA and d) with an R 2 of Positive correlations with both IA and d suggest that abundance increases with an increase in IA and d. An increase in water storage will also result in increased IA and d. Therefore, this result suggests that increased water storage will increase macroinvertebrate abundance. This is an important and positive outcome for a PES program considering that macroinvertebrates have shown to be important food sources for wading birds and other animals (Steinman et al., 2003; Davidson et al., 2012). Models without Inundation Area Using simple regression models can over simplify a complex ecosystem and results can be misleading. As stated before, IA was used to convert the point scale data to wetland scale abundance to facilitate the development of wetland scale hydroecological models. Inundation area is also used in the regression models as a hydrological predictor variable. The question arises as to whether or not the hydroecological models are predicting ecological abundance or if they are predicting IA. Therefore, another set of multivariate linear regression analyses were conducted to determine the difference in the models by excluding IA as a predictor variable. The resulting models are presented in Table 2-6. The models in Table 2-6 were developed using the same procedure as the previous models (log-transformed, best multivariate subsets regression). 69

70 In general, the models in Table 2-6 have lower R 2 values compared to the models in Table 2-5 which include IA as a hydrologic predictor variable. All models except weedy and exotic vegetation have a lower R 2 value compared to the models (Table 2-5) developed with IA as a predictor variable. The model for weedy and exotic vegetation didn t change as IA was not included in the previous model (Table 2-5). While R 2 values decreased, the P-values remained significant for all models with the exception of macroinvertebrate for which there was only moderate evidence (p < 0.1). Given that use of IA in the models improves the accuracy of the hydroecologic model prediction shown by the R 2, it is concluded that IA needs to be included in the hydroecological models. Thus, inundation area can simultaneously be used to scale data from point scale to wetland scale data. Mean-Based Hydroecological Modeling A third set of hydroecological models where the mean was used to scale ecological data from point scale measurements to wetland scale were also developed. One reason to develop hydroecological models using the mean of the data is the inclusion of zero (not present) data which is a better description of the ecological data. Hereafter, the hydroecological models developed using the mean instead of the median will be referred to as the mean hydroecological model. The R 2 values observed for the mean of the plant and animal populations compared to the median are lower (Table 2-6), but P-values remain significant (all P<0.05). The lower R 2 is likely due to the inclusion of the zeroes in the data, but the mean is a better representation of what is occurring in the wetlands of the ranches since it includes zeroes. The range of accepted R 2 values in ecological modeling has been debated. Some argue that to is the average R 2 reported in ecological 70

71 modeling literature (Møller and Jennions, 2002), while others claim that 0.50 (Peek et al., 2003) is the average R 2 reported. A minimum R 2 value of 0.20 is generally accepted to explain significant variation in an ecological model (personal communication with EPA STAR team ecologists). Mean wetland vegetation The mean wetland vegetation model has one spatial (IA) and one temporal (DC) hydrologic variable with an R 2 of Wetland vegetation coverage increases with IA and is controlled by DC. Increased water availability represented by IA results in an increase of wetland vegetation cover, and this is consistent with known hydrologic controls on wetland vegetation. The mean wetland vegetation model shows a negative correlation to DC. This suggests that the longer the time of connectivity the longer the wetlands are being drained which results in decreased inundation area in the wetlands. The mean model has a lower R 2 value compared to the median model (R 2 =0.62). However, the relationship developed using the mean includes zero data that were excluded from the median model. Therefore, the mean wetland vegetation model is likely to better represent what is occurring in the wetlands. Given the results of the mean wetland vegetation model, a ranch with less connectivity or shallower ditches will have a greater cover of wetland vegetation. Alderman and BIR are more likely to have higher wetland vegetation cover due to fewer observed days of connectivity at these ranches compared to Pelaez and Williamson. Pelaez and Williamson ranches have deeper ditches spanning large sections of their wetlands and longer periods of connectivity were observed (Table 2-4). 71

72 Mean forage vegetation The mean hydroecological model for forage has one spatial and one temporal variable (Vol and TSI) with an R 2 of 0.33 (Table 2-7). The mean forage vegetation model suggests that increased water availability (Vol) and longer periods of wetland dryout (TSI) will increase forage vegetation cover. These findings are consistent with current knowledge of hydrologic drivers of forage (flood intolerant) vegetation (Henszey et al., 2004). The forage vegetation needs water to survive and grow, but saturating the soil for too long deprives the forage vegetation of necessary chemicals like oxygen. Of the four ranches, Pelaez or Williamson will likely have larger covers of forage vegetation since the wetlands at these ranches are large and can hold large volumes of water. There were also longer periods of TSI at Pelaez reducing the amount of time the forages are flooded (Table 2-4). Mean weedy and exotic vegetation The mean hydroecological model for weedy and exotic vegetation has one spatial and one temporal variable (IA and DC) with an R 2 of The model shows a positive correlation to IA suggesting that with more water available to the weedy and exotic vegetation, more wetland area will be covered. The mean weedy and exotic vegetation model showed a positive correlation to DC: with more connectivity, there will be more weedy and exotic vegetation cover. Often, the drainage ditches span the center of the wetland (e.g. Palaez Ranch wetlands, Fig. 2-6), and this is usually the lowest elevation in the wetland. The drainage ditches reduce the inundation in the higher elevations of the wetland (the edges of the wetland). This dried out edge of wetland allows for less flood tolerant plant species to thrive. Another explanation for the increase in weedy and exotic vegetation with 72

73 increases in DC could be that the connectivity allows for increased movement of nutrient-rich water through the ranches and wetlands. Weedy and exotic vegetation may be better adapted at utilizing high concentrations of nutrients more quickly and efficiently than native or other established vegetation (Schonbeck, 2013). A ranch like Williamson or Pelaez with large and shallow wetlands would likely see a larger cover of weedy and exotic vegetation due to their large wetlands with a greater IA and deep ditches with potentially longer periods of DC (Table 2-4). Mean fish The mean hydroecological model for fish abundance has two spatial variables (IA and d) and one temporal variable (DSC) with an R 2 of Although the mean model has a significantly lower R 2 than the median model (R 2 =0.71), the relationship developed in the mean model is more ecologically intuitive. Like the median model, the mean model contains IA and d. However, the mean model shows that fish abundance increases with increasing both IA and d, while the median model showed a positive relationship to IA but a negative relationship to d. Generally, the mean model shows that with more water in the wetlands, there will be more fish. The mean model is also positively correlated to DSC. When a wetland is connected it is being drained thus, the overall IA is reduced. Longer periods of DSC indicate that the wetlands are less connected and the edges of the wetlands are not receding which allows for larger fish abundances. Ranches like BIR with shallower ditches connecting the wetlands compared to a ranch like Pelaez or Williamson would likely have larger fish abundances. BIR also has many small wetlands (more than 600 individual wetlands), and the aggregated IA of all of BIR s small wetlands could be 73

74 larger than the IA of a large wetland like at Williamson, resulting in larger fish abundance. Mean amphibian The mean hydroecological model for amphibian abundance has one spatial and one temporal variable (Vol and DSC) with an R 2 of The mean model is positively correlated to both Vol and DSC. This shows that amphibian abundances increase with water availability (Vol) and with longer periods in between connectivity (DSC). The Vol variable in the mean model demonstrates the amphibians dependence on changes in the microtopography of the wetlands. Changes in topography and depth of water are important to amphibians due to their life cycle from egg, to the various stages of tadpole, and eventually a frog. Watson et al. (2003) identified topography and water level as the critical variables in limiting the successful reproduction cycle of the Oregon Spotted Frog (Rana pretiosa). The variable DSC also has implications for the life cycle of amphibians. Longer periods of DSC indicate that the wetlands are not being drained and the amphibians are able to reproduce and increase in abundance. A ranch with shallower drainage ditches like BIR could have large abundances of amphibians. However, a ranch with large wetlands able to hold large volumes of water like Williamson or Pelaez (Table 2-4) could also have large abundances of amphibians. Mean mosquito The mean hydroecological model for mosquito abundance has one spatial and temporal variable (%IA and TSI) with an R 2 of With a negative correlation to %IA and a positive correlation to TSI, mosquito abundance increases with increasing wetland edge exposure and longer periods of TSI. The median model for mosquitoes showed similar results: a decrease in mosquito abundance with longer periods of TI. 74

75 Knight (2011) described that mosquitoes need periods of inundation (for eggs to hatch, larval development, and maturation) followed by periods of drier conditions (oviposition and egg-conditioning). After a wetting period, mosquito larvae are likely to increase (Knight, 2011), but the model showed a positive relationship to direr wetland conditions. Therefore, it is likely that longer periods of inundation allow for predation of mosquito larvae. As previously stated, longer hydroperiods and larger wetlands have shown to reduce mosquito reproduction due to predation (Arav and Blaustein, 2006). Therefore, at a ranch with longer hydroperiods (Williamson and Pelaez, Table 2-4), mosquito populations will be less than a ranch which has shorter hydroperiods (BIR). Longer hydroperiods may support larger fish and amphibian populations which prey upon mosquitoes and their larvae (Arav and Blaustein, 2006). Mean macroinvertebrates The mean hydroecological model for macroinvertebrates has two spatial variables (%IA and d) with an R 2 of A positive correlation with both %IA and d suggests that macroinvertebrate abundance increases with decreases in exposed wetland edge and increases in d. Since many types of macroinvertebrates such as the Dobson fly (Hellgrammite), water scorpion (Nepidae), crane fly (Tipulidae), or the creeping water bug (Naucoridae) survive in inundated conditions, these results are consistent with known factors which control macroinvertebrates. Mean Models without Inundation Area To evaluate the effects of multiplying by IA to obtain a total abundance of each plant and animal population as well as using IA as a hydrologic predictor, a set of regression models was developed which did not multiply the mean of the daily measurements by IA (Table 2-8). The hydroecological models in Table 2-8 predict the 75

76 percent cover or abundance of plants and animals per meter squared (m 2 ). This is unlike the models in Table 2-7 which predict whole-of-wetland scale abundance. The same methods which were used in the median and mean hydroecological models were used. In all cases the R 2 values of the hydroecological models significantly dropped below the ecological minimum acceptance of This does not indicate that the relationships developed without using IA to scale to wetland abundance are invalid, but it does indicate that there is little to no predictability of biodiversity services when using the models in Table 2-8. This suggests that either the hydrologic predictors chosen are not the most influential hydrologic variables to the plant and animal groups, or that other methods of developing hydroecological models are needed. Water Storage Payment for Ecosystem Services Program The hydroecological models developed above used the data under a specific spillage level and environmental (e.g. climate) conditions. However, for these models to be useful for evaluating multiple PES, they need to be used for simulating a variety of hydrological conditions in the wetland and at different spillage levels maintained at the ranch outlet. To predict biodiversity under a variety of WMS, the first step was to estimate the changes in inundation characteristics of wetlands for different spillage levels. These estimations were achieved by using the outputs from MIKE-SHE/MIKE11. The results from MIKE-SHE/MIKE11 for the water management boundary of BIR indicated an increase of water storage of 1 million m 3 (886 acre-feet) from the baseline WMS to the top of the structure WMS (Table 2-9). The increase in storage indicates an increase in the hydrologic variables (IA, d, TI, etc.). The results for BIR also showed a decrease in storage for four of the WMS (2 Board, 3 Board, 4 Board, and 5 Board). This 76

77 is due to low antecedent water storage conditions on BIR (Hendricks et al., 2014). At Pelaez, the lowest spillage level WMS (baseline) stored the least amount of water. As the spillage level increased, more water was stored with the highest spillage level WMS (6-Board) showing the highest water storage (Table 2-10). Using the outputs from the MIKE-SHE/MIKE11, the hydrologic variables were estimated for each WMS for BIR (Table 2-9) and Pelaez (Table 2-10). Biodiversity Predictions with Median Models Using the MIKE-SHE/MIKE11 results, input variables for the median hydroecological models (e.g. wetland IA and d) were estimated to predict biodiversity services on BIR (Table 2-11) and Pelaez (Table 2-12). Results in Table 2-11 for BIR showed that among the ecological services and dis-services considered, the greatest differences in abundance of animals or cover of vegetation were evident between the baseline and maximum spillage levels. Wetland vegetation, amphibian, and macroinvertebrate abundances were highest for the highest spillage level WMS at BIR. An increase in the cover of wetland plants showed that increased water storage in the wetlands would benefit wetland productivity. Along with wetland plant cover, both amphibian and macroinvertebrate abundances increased by 4.4% and 7.6%, respectively from baseline to maximum board scenarios. Increased amphibian and macroinvertebrate abundances were desirable outcomes as both species are food sources for wading birds and contribute to overall wetland health and productivity (Snodgrass et al., 2000; Babbitt et al., 2003; and Davidson et al., 2012). Some species decreased in cover and abundance. Forage and weedy vegetation cover, fish abundance, and mosquito abundance, all decreased with an increase in the spillage level and water storage on BIR. It is well documented (e.g. Henzsey et al., 77

78 2010) that forage vegetation cover decreases as inundation increases. A decrease of 11.8% cover of forage vegetation is a dis-service to the rancher. However, a 13.6% decrease in weedy and exotic vegetation due to increased inundation is a service. A decrease in fish populations due to increased spillage level and water storage is unexpected, but the median hydroecological model showed an inverse relationship to depth. Depth increased as a result of increased spillage level and water storage. Again, the fish could be dispersing throughout the wetland rather than declining in abundance (Jordan et al., 1998; and Troutman et al., 2007). Mosquito predictions also showed a decrease in abundance with increased water storage due to an inverse relationship with hydroperiod (increased hydroperiod with higher spillage levels). A decrease in mosquito abundance is likely a service for the rancher. While directionally similar results are possible at other ranches the actual changes may not be the same due to differences in size, hydrology, topography, and management. These differences can be observed when comparing the results found at BIR to the results found at Pelaez (Table 2-12). Unlike BIR, all ecological abundances or coverages were highest at a higher spillage level WMS. Also, different from BIR, the WMS providing the most services and dis-services is not the maximum spillage level WMS, it is the second highest (5-Board WMS). This suggests that there is a sweet spot for providing biodiversity services at Pelaez. Although all services (wetland and forage cover, fish, amphibian, and macroinvertebrate abundances) were highest at the 5-Board WMS, all dis-services (weedy and exotic cover and mosquito) were also highest at the 5-Board WMS. 78

79 At both ranches trade-offs had to be made. At BIR, the highest wetland vegetation cover, amphibian abundance, macroinvertebrate abundance, and the lowest weedy and exotic vegetation cover and mosquito abundance were traded-off with the lowest forage cover and fish abundance. At Pelaez, the highest wetland vegetation cover, forage cover, fish abundance, amphibian abundance, and macroinvertebrate abundance were traded-off with the highest weedy and exotic cover and mosquito abundance. The results in Tables 2-10 and 2-11 show how the median models can inflate predictions. For example, the wetland area for which the models were used to predict biodiversity services totaled 1.9 ha for BIR. The lowest wetland vegetation cover was almost 30 ha. The extreme inflation is also apparent at Pelaez, where the wetland area modeled was 29.3 ha. The lowest prediction for the weedy and vegetation cover was 82 ha. The inflation at Pelaez becomes larger when the models predict higher vegetation coverages. While the lowest prediction of weedy and exotic vegetation at Pelaez is 82 ha, the highest prediction is 2,900 ha for the 5-Board WMS. Since the insect and animal responses cannot be compared to reference population size, it is unclear as to how inflated the insect and animal predictions are. These inflations are likely caused by the lack of zeroes in the models. Biodiversity Predictions with Mean Models Predictions of biodiversity services were estimated using the mean hydroecological models for BIR (Table 2-13) and Pelaez (Table 2-14). The predictions using the mean models were made using the same estimates of the hydrologic variables in Table 2-9 and 2-9. Similar to the median-based hydroecological model predictions for BIR, there was a large difference in most of the biodiversity service or 79

80 dis-service provision when comparing the maximum board and baseline WMS (Table 2-13). At BIR, the wetland vegetation cover, fish, amphibian, and macroinvertebrate biodiversity services were highest at the 6-Board WMS. The dis-service of weedy and exotic vegetation cover was also highest at the 6-Board WMS. Mosquito abundance and forage cover were highest at an intermediate WMS. The mean predictions of biodiversity services at BIR indicated that trade-offs had to be made. High wetland vegetation, fish, amphibian, and macroinvertebrates were traded-off with high weedy and exotic vegetation and low forage. The predictions of biodiversity services at Pelaez using the mean (Table 2-13) are similar to the results using the median. All ecological responses were maximum at the second highest spillage level WMS (5 Board). Similar to the results using the median, at Pelaez, the highest wetland vegetation, forage, fish, amphibian, and macroinvertebrates were traded-off with the highest weedy and exotic and mosquitoes. The prediction values of the biodiversity services were not as high using the mean models compared to the median models. This indicates that the mean models do not inflate predictions of biodiversity services as much as the median models. While predictions of vegetation coverages for BIR are still greater than the total area of wetland modeled, Pelaez seems to have reasonable results. When using the mean models, the predictions of biodiversity services are more reasonable because the mean models included the zero data. Uncertainty in Hydroecological Models The RMSE in Tables 2-14 and 2-15 are based on back-transformed predicted and observed values. The RMSE values in Tables 2-4 and 2-6 represent the error of the predicted and observed data on the natural log scale. The RMSE values are difficult to 80

81 understand by the stakeholders who are concerned with how well the models performed at predicting abundances of animals or vegetation cover. Returning the predicted value back to the point-scale data (median or mean) measure allowed for the calculation of RMSE of the hydroecological models without considering errors in measurement or the prediction errors of the MIKE-SHE/MIKE11 model. The RMSE values of the median models explain how well the hydroecological models perform in predicting animal abundance and vegetation cover in the wetlands when they are present. The RMSE of 48.5 for median wetland vegetation suggests that the median wetland vegetation model can predict the wetland percent cover within ±49% cover/m 2. Forage and weedy vegetation cover are predicted within ±54% cover/m 2 and ±42% cover/m 2, respectively. The RMSE value of 7 for fish abundance suggests that the hydroecological model can predict within ±7 fish/m 2 when present. Amphibian, mosquitoes, and macroinvertebrates (all per m 2 ) are predicted within ±2, ±9, and ±32, respectively. The RMSE values represent the efficiency of the hydroecological model to predict the median of the given ecological response variable, but the RMSE values are not on the same spatial scale for which biodiversity service predictions are made. Therefore, the averages of the observed medians were compared to the calculated RMSE values (Table 2-15) to show the percent error in predictions by the hydroecological models. The RMSE values are within ±105%, ±184%, and ±174%, of the average observed median for wetland, forage, and weedy vegetation respectively. The RMSE values of the fish, amphibian, mosquito, and macroinvertebrate hydroecological models compared to their respective average observed median showed 81

82 errors of ±82%, ±93%, ±150%, and ±85% respectively. The percentages can be translated to covers or abundances in the wetlands. For example, if the median hydroecological model predicts there will be 1,000 fish in a wetland, the fish abundance may be between 180 fish to 1,820 fish given a percent error of 82%. If the percent error is 100% or greater, the range of the predicted quantity will start from zero, since the fish numbers cannot be negative. For example, if the median hydroecological model predicts there will be 10 acres of wetland vegetation coverage, the actual area may range from zero to 20.5 acres. Results for the mean hydroecological models resulted in higher RMSE values compared to the median hydroecological model results and higher percent errors (Table 2-16). Overall, conclusions for the mean models were similar to those for the median hydroecological models. In comparing the RMSE and percent error values for the mean versus the median hydroecological models, precision (median models) is compromised for more consistent relationships (mean models) between ecology and hydrology. Considering that the RMSE and percent error values are already high for the median hydroecological models, it was concluded that the models that are more consistent with current understanding of ecological drivers would be a better option. Integrating Errors in Hydroecological and Hydrological Models Since MIKE-SHE/MIKE11 is also a model and therefore has inherent uncertainty, the hydrologic predictor variables used in the median hydroecological models also have errors associated with them. The mean square error (MSE) for each of the hydrological predictor variables (IA, %IA, Vol, d, TI, TSI, DC, and DSC) was calculated for the BIR and Palaez ranches for which MIKE-SHE/MIKE11 models were developed. The MSE is the squared product of the RMSE. These along with the MSE from the median 82

83 hydroecological models were combined to conduct a holistic error analyses. Table 2-16 shows how different the errors are when only considering the error in the median hydroecological model and the combined errors in the MIKE-SHE/MIKE11 predictions and hydroecological models. To estimate the combined error in the median hydroecological models and MIKE-SHE/MIKE11 estimated hydrologic variables, mixed linear models were developed using SAS/STAT (SAS Institute Inc., Cary, NC, USA). The mixed linear models combined the MSE of the median hydroecological models and the MSE of the MIKE-SHE/MIKE11 hydrologic variables to estimate a total error in predictions of biodiversity services. The percentages in Table 2-16 are the percent increase in error when considering measurement error in the median hydroecological models. The percent increase in error in Table 2-16 shows substantial increases in error when errors in the hydrologic model (MIKE-SHE/MIKE11) were included. Model performance for the MIKE-SHE/MIKE11 calibration and validation for BIR was satisfactory to very good model performance (Hendricks et al., 2014). Therefore, errors are introduced in another aspect of model development and biodiversity service prediction. Since each ecological response variable was multiplied by IA to convert point-scale data to wetland scale, it is concluded that most of the additional error in the simulated hydrological variables is attributed to the prediction of inundation area and volume in the wetlands using the ArcGIS (ESRI, Redlands, CA, USA). The results indicated that errors will vary from ranch to ranch and that ranch specific models could reduce errors and increase predictability. Substantial errors can be observed (Table 2-16) suggesting a need to improve the model. Additional variables (hydrologic, ecologic, or climatic) may need to be 83

84 considered. The hydrologic variables chosen in this study are relatively easy to measure or simulate for the stakeholders in a PES. Considering that MIKE-SHE/MIKE11 is a complex and resource intensive model, stakeholders may not have access to or the ability to develop MIKE-SHE models for a ranch. Simpler models, based on the results from the MIKE-SHE model, may need to be developed for their use in a PES. However, simpler models are likely to further increase the already large errors presented in Table Summary and Conclusion Wetland-scale hydroecological models were developed using hydrological and ecological data from 15 wetlands at four ranches participating in a water storage PES program in Florida s NE basin. Hydrological data collected included groundwater and surface water stages. These data were used within ArcGIS to estimate the wetland IA, %IA, Vol, TI, TSI, DC, and DSC. These spatiotemporal variables have been shown to be important variables influencing ecological activity in wetland systems. Ecological data were collected for vegetation (wetland, forage, and weedy and exotic) and for animals (fish, amphibian, mosquito, and macroinvertebrates) by a team of researchers from UCF and ABS. Best subsets regressions of the hydrological predictor variables for each of the ecological response variables were used to select the best hydrologic variables for predicting biodiversity services and dis-services. Two sets of models, using the median and the mean of the ecological data, were developed. All hydroecological models (median-based and mean-based) were statistically significant (P<0.05). The median models (using non zero data) demonstrated that hydrologic variables can predict wetland ecological responses with 84

85 acceptable uncertainty (0.40 R ). However, the median models did not include zero data, and therefore inflated R 2 values and the predictions of biodiversity services and dis-services. The mean hydroecological models (using all ecological data) demonstrated that although certainty in predictions decreases (0.05 R ), the relationships modeled are more consistent with known hydrologic drivers of ecological form and function compared to the median models. The uncertainty was evaluated in the hydroecological models (median-based and mean-based). Significant errors were found in both sets of models. Considering that significant uncertainties were found in both sets of models and that the mean models showed relationships more consistent with current literature, it is concluded that the mean models are a better option for predicting biodiversity services on NE basin ranchlands. To predict the biodiversity services and dis-services at different spillage levels (WMS), the hydrologic model MIKE-SHE/MIKE11 was used to simulate surface and ground water levels. The outputs from MIKE-SHE/MIKE11 were used to estimate hydrologic predictor variables for BIR and Palaez. Application of the hydroecological models demonstrated that trade-offs of biodiversity services were evident at both BIR and Pelaez. Because the median models did not include zero data, predictions of biodiversity services were inflated when using the median models. Inflation of predictions was obvious when the median models were used to predict large amounts of ecological responses. The mean models did not show as much inflation of predictions because the mean model development included zero data. The mean models were more consistent with known relationships of ecology and 85

86 hydrology. Therefore, it is recommended that the mean models be used for predicting biodiversity services. At BIR, when using the mean hydroecological models to predict biodiversity services, wetland vegetation, weedy and exotic vegetation, fish, amphibian, and macroinvertebrates were all highest at the maximum spillage level WMS, while mosquitoes and forage were highest at an intermediate WMS. Trade-offs among services and dis-services were apparent: high amounts of wetland vegetation, fish, amphibians, and macroinvertebrates were traded-off with a high amount of weedy and exotic vegetation and a low amount of forage. The mean hydroecological models were also used to predict biodiversity services at Pelaez. Results showed that there was a sweet spot of biodiversity service provision. The highest spillage level WMS was not predicted to provide the highest amounts of biodiversity services. The second highest spillage level WMS that was modeled in MIKE-SHE/MIKE11 provided the highest amounts of all ecological responses. Like BIR, trade-offs among services and dis-services were apparent: high amounts of wetland vegetation, forage, fish, amphibians, and macroinvertebrates were traded-off with high amounts of weedy and exotic vegetation and mosquitoes. The prediction results from BIR and Pelaez indicated that biodiversity services can be successfully linked to water storage services with different levels of uncertainty. Results showed that simple hydroecological models can be developed to predict biodiversity services and dis-services which when combined with water storage in a PES can help develop and evaluate a multiple ecosystem services program. 86

87 For ecosystem service programs, quantification must be simple and effective. For example, while ranch specific factors may result in different ecological responses, site specific models are not likely to be useful for any PES or other multiple ecosystem services program. Simplicity was a key component for the outcomes of the hydroecological models in this study since they will be used by many different stakeholders with varying familiarity with hydrology, ecology, and modeling. Simplicity was also important while selecting the hydrologic predictor variables which were easyto-measure or predict. Many studies have shown the importance of ecological factors influencing other ecological communities (Troutman et al., 2007; MacRae and Travis, 2013), but the intended users of the hydroecological models are the stakeholders such as ranchers, state agencies, and conservation groups. It is much easier for any one of these stakeholders (or others) to go into the wetlands and measure water depth or simulate inundation area metrics using a simple hydrologic model than it is for them to measure fish populations, vegetation cover, or mosquito abundance. This study showed that simple, multivariate linear regressions can quantify ecologic responses due to changes in wetland hydrology arising from increases in storage on ranchlands. Simplicity combined with the nature of modeling (estimating relationships based on available data) inherently involves uncertainty. However, the hydroecological models showed that simple models can be used to evaluate a ranch s potential for providing biodiversity services. Even with inherent uncertainties in model predictions, a stakeholder involved in a multiple ecosystem services program could gauge the general direction of increased or decreased biodiversity services as a function of increased or decreased water storage on a ranch. This was demonstrated in the examples from BIR 87

88 and Pelaez. Without the hydroecological models developed in this study, all ranches participating in a water storage program would be valued the same: a ranch s ability to provide water storage services. However, some ranches may have more ecosystem service potential than others. For instance, two ranches might provide the same water storage, but one may contain wetlands which provide topographic and hydrologic conditions more favorable to increases of amphibian and fish populations with increased water storage. Depending on the stakeholder, these biodiversity services may be valued more than water storage alone. In south Florida, the only services the state is currently willing to buy are water storage and treatment. If a new collaboration began in which there was a participating buyer of biodiversity services, the value of a ranch s ecosystem services could be reflected in the water storage payments: higher payments to ranches which provide water storage as well as greater biodiversity services. Example ranches used in this study show that ranches like BIR and Pelaez provide water storage services, multiple biodiversity services, along with some biodiversity disservices. The trade-offs will have to be evaluated by the stakeholders. A clear and easy to follow DSS which utilizes the hydroecological models to simultaneously evaluate all services and dis-services can aid in decision making processes that are part of a multiple ecosystem services project. 88

89 Table 2-1. The three vegetation groups for which hydroecological models were developed and the plants that were in each group with their scientific names. Vegetation Group Common Name Scientific Name Wetland pickerelweed Pontederia cordata duck potato Sagittaria latifolia and Sagittaria graminea soft rush Juncus effusus fire flag Thalia geniculata wetland grasses Sacciolepis striata, Paspilidium geminatum, Paspalum distichum, Paspalum acuminatum, Panicum rigidulum, Panicum repens, Panicum longifolium, Panicum hemitomon, Panicum dichotimiflorum, Hymenachne amplexicaulis, Hemarthria altissima, Echinochloa walteri, Dichanthelium erectifolium, Axonopus furcatus, Aristida palustris Forage bahia grass Paspalum notatum floralta Hemarthria altissima Weedy and exotic limpograss other forages dog fennel annuals exotics Setaria geniculata, Paspalum urvillei, Paspalum conjugatum, Digataria serotina, Cynodon dactylon, Aristida patula, Andropogon sp. Eupatorium capillifolium Aster subulatus, Azolla caroliniana, Commelina diffusa, Diodia virginiana, Echinochloa walteri, Eclipta prostrata, Juncus repens, Linaria caroliniana, Polygonum punctatum, Ptilimnium capillaceum, Salvinia minima, Senecio glabellus, Sesbania sp. Alternanthera philoxeroides, Cuphea carthenogensis, Cynodon dactylon, Eichhornia crassipes, Hermarthria altissima, Hymenachne amplexicaulis, Ludwigia peruviana, Panicum repens, Paspalum acuminatum, Paspalum notatum, Paspalum urvelii 89

90 Table 2-2. Description of the measured and simulated hydrological variables used for developing the hydroecological models. Data Type Groundwater (May 2010 Feb 2012) Measured or Simulated Measured Ranches Institution/Project Use All UF/IFAS [a] and EPA STAR [b] Estimate hydroperiod (TI and TSI) and surface water connectivity (DC and DSC) Surface water flow out of WMA (May 2010 Feb 2012) Measured All UF/IFAS and EPA STAR MIKE-SHE modeling by UF/IFAS 2 Surface water in ditches (May 2010 Feb 2012) Measured All UF/IFAS and EPA STAR Estimating connectivity variables (DC and DSC) Wetland depth (May 2010 Feb 2012) Measured All UF/IFAS and EPA STAR Average depth in wetland Simulating inundation area (IA and %IA) and volume (Vol) MIKE-SHE Outputs Simulated Buck Island Ranch Palaez Ranch UF/IFAS, Hendricks et al., 2014 [c] UF/IFAS, Wu, 2014 [c] Development of hydrological variables (d, IA, %IA, Vol, TI, TSI, DC, DSC) for many levels of water storage. Use of the simulated hydrological variables in the hydroecological models for prediction of biodiversity services. [a] UF/IFAS is the University of Florida/Institute of Food and Agricultural Sciences. [b] EPA STAR refers to the group for which the United States Environmental Protection Agency (EPA) funded the grant G08K10487 and includes investigators from the University of Central Florida, the Archbold Biological Station, and UF/IFAS. [c] Hendricks et al., 2014 and Wu, 2014 are in preparation for publication. 90

91 Table 2-3. A sample of culvert riser board structures from watershed 35 of Buck Island Ranch and their elevations and the increases in spillage elevation (spillage level) from baseline (ditch bottom) to the top of the structure. Structure ID Ditch Bottom Elevation [a] Culvert Invert Elevation [b] Top of Structure Elevation Maximum Difference from Baseline [c] Pumpfield Pumpfield Middlefield Middlefield Bullfield Bullfield Bullfield-35B [a] All elevations are in NAVD88 reference and all units are in meters (m). [b] Culvert invert is the elevation of the bottom of the ditch plus the height of the culvert with no riser boards. [c] Maximum difference = top of structure elevation ditch bottom elevation. 91

92 Table 2-4. Observed and estimated hydrologic data for four ranches Alderman, Buck Island Ranch, Pelaez, and Williamson. Obs Date Ranch [a] Pond d IA %IA Vol TI TSI DC DSC 19-May-10 ALD ap Jul-10 ALD ap , Sep-10 ALD ap Apr-11 ALD ap Jul-11 ALD ap , , Aug-11 ALD ap , , Oct-11 ALD ap , , Nov-11 ALD ap , , Jan-12 ALD ap , , May-10 ALD ap , , Jul-10 ALD ap , , Sep-10 ALD ap , Apr-11 ALD ap Jul-11 ALD ap , , Aug-11 ALD ap , , Oct-11 ALD ap , Nov-11 ALD ap , , Jan-12 ALD ap , , May-10 ALD ap , , Jul-10 ALD ap , , Sep-10 ALD ap , , Apr-11 ALD ap Jul-11 ALD ap , , Aug-11 ALD ap , , Oct-11 ALD ap , , Nov-11 ALD ap , , Jan-12 ALD ap , , Feb-12 ALD ap , , May-10 BIR bp , , Jul-10 BIR bp , , Sep-10 BIR bp , , Apr-11 BIR bp , Oct-11 BIR bp , Nov-11 BIR bp , May-10 BIR bp , Jul-10 BIR bp , Sep-10 BIR bp , Apr-11 BIR bp Oct-11 BIR bp , Nov-11 BIR bp ,

93 Table 2-4 continued. Obs Date Ranch [a] Pond d IA %IA Vol TI TSI DC DSC 04-Jan-12 BIR bp , May-10 BIR bp , Jul-10 BIR bp , , Sep-10 BIR bp , Apr-11 BIR bp Oct-11 BIR bp , Nov-11 BIR bp , Jan-12 BIR bp , , Feb-12 BIR bp , May-10 BIR bp , , Jul-10 BIR bp , Sep-10 BIR bp , Apr-11 BIR bp , Nov-11 BIR bp , , Jan-12 BIR bp , May-10 BIR bp , , Jul-10 BIR bp , , Sep-10 BIR bp , Apr-11 BIR bp Nov-11 BIR bp , May-10 BIR bp , , Jul-10 BIR bp , Sep-10 BIR bp , , Apr-11 BIR bp , May-10 BIR bp , , Jul-10 BIR bp , , Sep-10 BIR bp , , Apr-11 BIR bp Nov-11 BIR bp , May-10 BIR bp , , Jul-10 BIR bp , , Sep-10 BIR bp , , Apr-11 BIR bp Jul-10 PEL p , , Sep-10 PEL p , , Apr-11 PEL p Aug-11 PEL p , , Oct-11 PEL p , , Nov-11 PEL p , , Jan-12 PEL p , , Feb-12 PEL p ,

94 Table 2-4 continued. Obs Date Ranch [a] Pond d IA %IA Vol TI TSI DC DSC 03-Sep-10 PEL p , , Apr-11 PEL p Oct-11 PEL p , , Jun-10 WIL wp , , Aug-10 WIL wp Sep-10 WIL wp , , Apr-11 WIL wp , Jul-11 WIL wp , , Aug-11 WIL wp , , Oct-11 WIL wp , , Nov-11 WIL wp , , May-10 WIL wp , , Jun-10 WIL wp , , Aug-10 WIL wp Sep-10 WIL wp , , Apr-11 WIL wp , Jul-11 WIL wp , , Aug-11 WIL wp , , Oct-11 WIL wp , , Nov-11 WIL wp , , [a] The different ranch abbreviations are ALD = Alderman, BIR = Buck Island Ranch, PEL = Pelaez, and WIL = Williamson. d = depth (m), IA = inundation area (m 2 ), %IA = percent inundated area (fraction), Vol = volume (m 3 ), TI = time inundated (days), TSI = time since inundated (days), DC = days connected (days), DSC = days since connected (days) 94

95 Table 2-5. Wetland-scale median hydroecological models developed by aggregating hydrological data and the median of non-zero ecological data for the four ranches. Ecological N [a] Hydrologic Response Variables [b] R 2 Cp Values RMSE [c] Model ln(wetland) 75 IA, DC E-5*IA - 1.6E-3*DC ln(forage) 47 IA, %IA E-5*IA *%IA ln(weeds) 72 %IA, Vol *%IA + 2E-4*Vol ln(fish) 50 IA, d E-5*IA *d ln(amphibian) 47 IA, d E-5*IA *d ln(mosquito) 75 IA, TI E-5*IA E-3*TI ln(macro [d] ) 44 IA, d E-5*IA *d [a] N is the number of data points used to develop the hydroecological model [b] d = depth (m), IA = inundation area (m 2 ), %IA = percent inundated area (fraction), Vol = volume (m 3 ), TI = time inundated (days), TSI = time since inundated (days), DC = days connected (days), DSC = days since connected (days) [c] Root mean square error (RMSE) of the model developed using natural logarithmic transformed data. [d] Macro an abbreviation for macroinvertebrates. Units of vegetation predictions are vegetation coverages in m 2 and the units of animal predictions are abundance (number) of animal in the wetland. Table 2-6. Wetland-scale median hydroecological models developed by aggregating hydrological data and the median of non-zero ecological data for the four ranches without using inundation area as a predictor variable. Ecological N Response Hydrologic Cp Variables [b] R2 Values RMSE [c] Model ln(wetland) 75 Vol, DC E-4*Vol -1.6E-3*DC ln(forage) 47 %IA, Vol *%IA E-4*Vol ln(weeds) 72 %IA, Vol *%IA + 2.0E-4*Vol ln(fish) 50 Vol, d E-5*Vol *d ln(amphibian) 47 Vol, TSI E-5*Vol *TSI ln(mosquito) 75 Vol, TI E-5*Vol E-3*TI ln(macro [d] ) 44 d, TI *d E-3*TI [a] N is the number of data points used to develop the hydroecological model [b] d = depth (m), IA = inundation area (m 2 ), %IA = percent inundated area (fraction), Vol = volume (m 3 ), TI = time inundated (days), TSI = time since inundated (days), DC = days connected (days), DSC = days since connected (days) [c] Root mean square error (RMSE) of the model developed using natural logarithmic transformed data. [d] Macro an abbreviation for macroinvertebrates. Units of vegetation predictions are vegetation coverages in m 2 and the units of animal predictions are abundance (number) of animal in the wetland. 95

96 Table 2-7. Wetland-scale mean hydroecological models developed by aggregating hydrological data and the mean of ecological data for the four ranches. Ecological N [a] Hydrologic Response Variables [b] R 2 Cp Values RMSE[c] Model ln(wetland) 100 IA, DC E-5*IA E-3*DC ln(forage) 101 Vol, TSI E-4*Vol E-2* TSI ln(weeds) 101 IA, DC E-5*IA E-3*DC ln(fish) 88 IA, d, DSC E-5*IA+15.06*d+1.67E-2*DSC ln(frog) 88 Vol, DSC E-5*Vol + 7.6E-3*DSC ln(mosquito) 88 %IA, TSI *%IA *TSI ln(macro [d] ) 88 %IA, d *%IA *d [a] N is the number of data points used to develop the hydroecological model [b] d = depth (m), IA = inundation area (m 2 ), %IA = percent inundated area (fraction), Vol = volume (m 3 ), TI = time inundated (days), TSI = time since inundated (days), DC = days connected (days), DSC = days since connected (days) [c] Root mean square error (RMSE) of the model developed using natural logarithmic transformed data. [d] Macro an abbreviation for macroinvertebrates. Units of vegetation predictions are vegetation coverages in m 2 and the units of animal predictions are abundance (number) of animal in the wetland. Table 2-8. Wetland-scale hydroecological models developed by aggregating hydrological data and the mean of ecological data for the four ranches. The ecological data were not multiplied by inundation area. Ecological N [a] Hydrologic Response Variables [b] R 2 Cp Values RMSE [c] Model ln(wetland) 100 Vol, DSC E-4*Vol + 2.7E-2*DSC ln(forage) 101 IA, d E-5*IA *d ln(weeds) 101 d, DSC *d E-2*DSC ln(fish) 88 IA, TI, DSC E-2+1.0E-5*IA+1.23E-2*TI+1.63E-2*DSC ln(frog) 88 %IA, DSC *%IA + 5.8E-3*DSC ln(mosquito) 88 %IA, d E-1*%IA *d ln(macro [d] ) 88 %IA, d E E-2*%IA *d [a] N is the number of data points used to develop the hydroecological model [b] d = depth (m), IA = inundation area (m 2 ), %IA = percent inundated area (fraction), Vol = volume (m 3 ), TI = time inundated (days), TSI = time since inundated (days), DC = days connected (days), DSC = days since connected (days) [c] Root mean square error (RMSE) of the model developed using natural logarithmic transformed data. [d] Macro an abbreviation for macroinvertebrates. Units of vegetation predictions are percent cover of vegetation per m 2 and the units of animal predictions are abundance (number) of animal per m 2. 96

97 Table 2-9. Hydrologic variable estimations using outputs from MIKE-SHE/MIKE11 for eight water management scenarios for Buck Island Ranch (Hendricks et al., 2014). WMS [a] d [b] IA (m 2 Vol TI TSI DC DSC Water ) %IA (m) (m 3 ) (days) (days) (days) (days) Storage (m 3 ) BL , Board , , Board , , FRESP , , Board , , Board , , Board , , Board , , [a] WMS is water management scenario. [b] d = depth, IA = inundation area, %IA = percent inundated area (fraction), Vol = volume, TI = time inundated, TSI = time since inundated, DC = days connected, DSC = days since connected. Table Hydrologic variable estimations using outputs from MIKE-SHE/MIKE11 for seven water management scenarios for Pelaez (Wu, 2014). WMS [a] d [b] IA (m 2 ) %IA Vol (m 3 TI TSI DC DSC Water ) (m) (days) (days) (days) (days) Storage (m 3 ) BL , , Board , , , Board , , , Board , , , Board , , , Board , , , Board , , , [a] WMS is water management scenario. [b] d = depth, IA = inundation area, %IA = percent inundated area (fraction), Vol = volume, TI = time inundated, TSI = time since inundated, DC = days connected, DSC = days since connected. 97

98 Table Predictions of biodiversity services from hydroecological models developed using the median of ecological data for Buck Island Ranch for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure. Plant Cover (acres [a] ) Animal Abundance WMS Wetland Forage Weeds Fish Frog Macro [b] Mosq [c] BL 29.90* 16.30** 7.30** 49,065** 16,118* 26,933* 22,711** 1 Board ,021 16,139 26,995 22,604 2 Board ,922 16,190 27,143 22,383 FRESP [d] ,828 16,236 27,279 22,328 3 Board ,861 16,226 27,250 22,329 4 Board ,856 16,227 27,253 22,329 5 Board ,447 16,432 27,861 22,318 6 Boards or higher 30.34** 14.38* 6.31* 47,664* 16,863** 29,156** 22,113* Max Min Services Change in biodiversity service or dis-service (%) [a] Total acreage of wetlands considered was 4.8 acres (1.9 ha, 1ha = 2.47 acres). [b] Macro is macroinvertebrates. [c] Mosq is mosquitoes. [d] FRESP is the WMS implemented during the FRESP program. * is the lowest prediction of a service or disservice. ** is the highest prediction of a service or dis-service. Table Predictions of biodiversity services from hydroecological models developed using the median of ecological data for Pelaez for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure. Plant Cover (acres [a] ) Animal Abundance WMS Wetland Forage Weeds Fish Frog Macro [b] Mosq [c] BL * ,654 19,798* 35,875* 22,705 1 Board 28.39* * 74,760* 20,806 39,110 16,277* 2 Board ,715 24,619 51,317 18,515 3 Board ,988 28,825 65,979 21,351 4 Board ,311 40, ,659 29,857 5 Board 69.12** ** 2,931.99** 122,221** 45,677** 136,833** 37,868** 6 Board , ,758 32,266 80,941 21,208 Max Min Services , ,461 25, ,958 21,591 Change in biodiversity service or dis-service (%) [a] Total acreage of wetlands considered was acres (29.29 ha, 1ha = 2.47 acres). [b] Macro is macroinvertebrates. [c] Mosq is mosquitoes. [d] FRESP is the WMS implemented during the FRESP program. * is the lowest prediction of a service or disservice. ** is the highest prediction of a service or dis-service. 98

99 Table Predictions of biodiversity services from hydroecological models developed using the mean of ecological data for Buck Island Ranch for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure. Plant Cover (acres [a] ) Animal Abundance on BIR WMS Wetland Forage Weeds Fish Frog Macro [b] Mosq [c] BL * 434,879* 1,099 16,740* 595,679 1 Board * ,605 1,098* 16, ,377* 2 Board ** ,537 1,196 17, ,820 FRESP [d] 29.01* ,878 1,241 17, ,528 3 Board ,196 1,242 17, ,141** 4 Board ,473 1,241 17, ,558 5 Board ,192 1,195 18, ,548 6 Boards or higher 29.07** ** 784,279** 1,245** 20,737** 610,706 Max Min Services , , ,443 Change in biodiversity service or dis-service (%) [a] Total acreage of wetlands considered was 4.8 acres (1.9 ha, 1ha = 2.47 acres). [b] Macro is macroinvertebrates. [c] Mosq is mosquitoes. [d] FRESP is the WMS implemented during the FRESP program. * is the lowest prediction of a service or disservice. ** is the highest prediction of a service or dis-service. Table Predictions of biodiversity services from hydroecological models developed using the mean of ecological data for Pelaez for different water management scenarios ranging from baseline ditch bottom (BL) to the highest flashboard in the culvert riser board structure. Plant Cover (acres) Animal Abundance WMS Wetland Forage Weeds Fish Frog Macro [b] Mosq [c] BL * 18, ,587* 22,705 1 Board 20.96* 0.28* ,933* 251* 5,168 16,277* 2 Board , ,289 18,515 3 Board , ,315 21,351 4 Board , ,785 29,857 5 Board 29.87** 8.46** 8.01** 78,052** 1,432** 10,904** 37,868** 6 Board , ,426 21,208 Max Min Services ,119 1,180 63,17 21,591 Change in biodiversity service or dis-service (%) [a] Total acreage of wetlands considered was acres (29.29 ha, 1ha = 2.47 acres). [b] Macro is macroinvertebrates. [c] Mosq is mosquitoes. [d] FRESP is the WMS implemented during the FRESP program. * is the lowest prediction of a service or disservice. ** is the highest prediction of a service or dis-service. 99

100 Table Back-transformed root mean square error (RMSE) values and percent errors for the median-based hydroecological models. The median-based models did not consider zero values. Hydroecological Model RMSE Units Percent Error [a] Wetland Vegetation 48.5 percent cover 105% Forage Vegetation 53.5 percent cover 184% Weedy Vegetation 42.2 percent cover 174% Fish 7 fish/m 2 82% Amphibian 2 amphibian/m 2 93% Mosquito 9 mosquito/m 2 150% Macroinvertebrates 3 macro/m 2 85% [a] Percent error of the model calculated from the predicted median RMSE and observed average median. Table Back-transformed root mean square error (RMSE) values and percent errors of mean hydroecological models. Hydroecological Model RMSE [a] Units Percent Error [b] Wetland Vegetation percent cover 170% Forage Vegetation percent cover 368% Weedy Vegetation percent cover 3761% Fish 4.32 fish/m 2 207% Amphibian 1.36 amphibian/m 2 236% Mosquito mosquito/m 2 806% Macro [c] 2.83 macro/m 2 144% [a] Back-transformed root mean square error (RMSE) of the predicted median. [b] Percent error of the model based on the predicted median RMSE and observed average median. [c] Macro is macroinvertebrates. 100

101 Table The percent increase in error of biodiversity predictions when considering the error in estimations of hydrologic variables plus the error in the median hydroecological models. Percent Increase in Error [a] Total Percent Error [b] Hydroecological Model BIR [c] PEL [d] BIR [c] PEL [d] Wetland cover 13% 67% 118% 172% Upland cover 35% 34% 219% 218% Weeds cover 31% 64% 205% 238% Fish abundance 36% 17% 118% 99% Frog abundance 33% 38% 126% 131% Mosquito abundance 50% 32% 200% 182% Macroinvertebrate abundance 63% 62% 148% 147% [a] Percent increase in error is the error difference between the simple hydroecological model and the combined error in the hydroecological models and estimations of hydrologic variables from MIKE-SHE/MIKE11. [b] Total percent error is the combined error from the hydroecological model and the error in the estimated hydrologic variables from MIKE-SHE/MIKE11. [c] BIR is Buck Island Ranch. [d] PEL is Pelaez and Sons Ranch. 101

102 Figure 2-1. A culvert with riser boards (CRB) structure installed in a wetland at the Palaez and Sons Ranch. 102

103 Figure 2-2. Flowchart of process for developing hydroecological models and integrating them with hydrologic models for decision analysis. 103

104 Figure 2-3. Northern Everglades basin map comprising of Lake Okeechobee, Caloosahatchee, and St. Lucie watersheds and the study ranch locations. 104

105 Figure 2-4. Alderman Deloney Ranch water management alternative boundary and groundwater locations and wetland ecological point sampling locations (Boughton et al., 2011). Figure 2-5. Maps of Buck Island Ranch with the water management alternative boundary of the project area (yellow line), wetlands where ecological data were collected (underlined numbers indicate wetland identification numbers), and culvert locations. Green circles represent culvert riser board (CRB) structures (table 2-3). On the right is Buck Island Ranch groundwater monitoring sites and FRESP CRB structure locations (Boughton et al., 2011). 105

106 Figure 2-6. Point sampling locations in wetlands on Buck Island Ranch. Location of wetlands are shown in Fig Figure 2-7. Palaez Ranch wetlands with ecological sampling locations at wetland p1 and p4. The monitoring period for wetland p4 is only up to November of 2010 (orange points) (Boughton et al., 2011). 106

107 Figure 2-8. Water management alternative boundary at the Williamson Cattle Co. ranch (yellow lines) and groundwater sampling locations. Ecological sampling locations denoted by the green points(boughton et al., 2011). 107

108 CHAPTER 3 DECISION SUPPORT SYSTEM FOR A MULTIPLE ECOSYSTEM SERVICE PAYMENT FOR ECOSYSTEM SERVICES (PES) PROGRAM Introduction Ecosystem services have become the subject of environmental decision making due to their importance to human well-being and the negative impacts felt when the services are not provided (Carpenter et al., 2006; Ribaudo et al., 2010). Ecosystem services are processes or products naturally provided by the ecosystems of the earth (Carpenter et al., 2006; EPA, 2012). Depending on the scale and because the different ecosystems encompass many types of land uses and users of the land, the decision making to improve and protect ecosystem services involves many stakeholders with risk. Stakeholders involved in an environmental decision process are individuals, companies, agencies, or other social groups which are invested financially, sentimentally, or otherwise and will be impacted (positively or negatively) if a change is made. Making decisions to alter land management for provision of ecosystem services using information about systems with significant unpredictability and uncertainty is difficult (Poch et al., 2003). Ecosystem services can be of various types; provisioning services, cultural services, supporting services, and regulatory services (Millennium Ecosystem Assessment, 2005; Ribaudo et al., 2010). Specific ecosystem services include, but are not limited to, water storage and treatment, biodiversity, carbon sequestration, and promotion or preservation of cultural services (Ribaudo et al., 2010). To encourage landowners to change land management for enhanced provision of ecosystem services, payment for ecosystem services (PES) programs have recently become popular. Payment for ecosystem service programs adopt a market-like system in which there is a 108

109 buyer and a seller of an (or multiple) ecosystem service where the seller is paid based on documented service provided (Bohlen et al., 2009; Ribaudo et al., 2010; Shabman and Lynch, 2013;). The buyer in a PES program could be governmental agencies or the public and the seller could be farmers, ranchers, and other stewards of the land. With many stakeholders, far-reaching effects, and unpredictability associated with large ecosystems, it is easy to see why planning and enacting a PES program is difficult (Lynch and Shabman, 2011). Agro-Ecosystems as a Service Provider Ecosystem services are already provided on many types of land, but have great potential to be enhanced even further on agricultural systems (agro-ecosystems) (Baylis et al., 2005; Porter et al., 2009; Ribaudo et al., 2010). Food and fiber production are services known to be provided by agro-ecosystems. Other ecosystem services which could be provided on agro-ecosystems include water storage and biodiversity services (Porter et al., 2009; Ribaudo et al., 2010). Of the United States land cover of 2.3 billion acres (930 million hectares (ha)), about 1 billion acres (405 million ha) is used for agriculture (Nickerson & Borchers, 2012). Since agro-ecosystems cover about 45% of the United States land area (Borchers, 2014), there is ample area which can be used for enhancing ecosystem services on agro-ecosystems. Florida History An example of environmental problems associated with degraded ecosystems and a loss of their ability to provide ecosystem services can be taken from southern Florida and the Northern Everglades (NE) watershed. Ecosystems impacted include Lake Okeechobee (LO), the Kissimmee River, the adjacent coastal Caloosahatchee and St. Lucie estuaries, and the Everglades National Park (ENP). In the early 20 th 109

110 century, this shallow water table region of Florida was extensively ditched and drained to support urban development and agricultural production (Steinman and Rosen, 2000; Bohlen et al., 2009). Altering the natural hydrology and storage capacity has resulted in large influxes of water and nutrients entering LO. Lake Okeechobee is surrounded by agricultural (the Everglades Agricultural Area) and urban lands to the south which is downstream from LO. To protect these areas the Herbert Hoover Dike was constructed by the United States Army Corps of Engineers around LO. When lake levels are too high for the dike to manage safely, water is pumped out of LO and into the rivers and canals. Eventually the nutrient rich fresh water reaches the estuaries and ENP. High amounts of nutrient rich, fresh water leaving the lake are damaging to the estuarine and the ENP ecology (Mitsch and Gosselink, 2007; Lynch and Shabman, 2011; Day et al., 2012). For example, increased instances of eutrophication in LO and the estuaries and overgrowth of invasive cattails (typha) in ENP are the result of increases in nutrients (Steinman and Rosen, 2000). Agencies in Florida estimated that 1.3 million acre-feet of new storage north of LO could alleviate some of the strain of flashy inflows of nutrient laden water placed upon LO, the estuaries, and ENP (SFWMD et al., 2008). Payment for Ecosystem Services Programs in Florida To explore and test the utility of PES programs for improving water quality and quantity flowing into LO, a pilot PES program, the Florida Ranchland Environmental Services Program (FRESP), began in FRESP was designed to pay ranchers for the ecosystem services of water storage and nutrient treatment. Ranchers provided the water storage service by raising the spillage level out of a designated area, or water management alternative (WMA), on their ranches by installing culverts with riser boards (CRB) structures. Different water management scenarios (WMS) could be implemented 110

111 by raising the spillage level in the CRB structures by adding or removing riser boards. The raised spillage level dammed the water behind the structure and this increased storage was assumed to reduce the flow volume and rate entering LO. The response to FRESP from ranchers, the public, and other stakeholders along with the assumed success of providing water storage in FRESP, gave rise to the Northern Everglades Payment for Environmental Services (NE-PES) program in 2011, to pay ranchers in lieu of water storage or nutrient removal (Shabman et al, 2013). Ranchland was considered as the service provider because 0.5 million hectares (ha) of ranchland lies in the LO basin which constitutes 36% of the region s landuse area, the largest landuse in the area (Tweel and Bohlen, 2008; Bohlen et al., 2009). Ranchlands are also considered low intensity agriculture and would be less likely to raise issues of releasing agrochemicals when flooded. The buyer of the water storage services in the NE-PES program is the South Florida Water Management District (SFWMD) while the sellers are the ranchers. In 2011, eight ranches signed contracts to participate in NE-PES for providing an average of 4,800 acre-feet (1,942 ha) of water storage annually (FRESP.org, 2014). The storage is provided on an area of the ranch called the water management alternative (WMA). Programs like FRESP and NE-PES along with other similar dispersed water management (DWM) programs and projects are beginning to provide the NE basin with new water storage and to reduce the timing and loads of nutrient laden water entering LO (SFWMD et al., 2008). Multiple Ecosystem Services Programs Given that several ecosystem services are dependent upon other ecosystem services (e.g. biodiversity upon water); multiple ecosystem services could be realized and planned for in a PES program (Nelson et al., 2009; Porter et al., 2009; Ribaudo et 111

112 al., 2009). For example, in the FRESP and NE-PES programs, the payment-based services provided are water storage and nutrient removal. There was an assumption of other multiple services being provided in conjunction with the water storage. Since part of the increased storage is provided by the previously drained wetlands, plants and animals that had once naturally inhabited the original wetlands could return in greater abundance than in ditched and drained conditions (Stites et al., 2002). If biodiversity services could be quantified, biodiversity services could be an additional ecosystem service in FRESP and NE-PES water storage programs, making these a multiple PES programs. However, the current NE-PES program is only paying for water storage or nutrient treatment. Additional services provided through the NE-PES WMS, like biodiversity services, are not paid for. Should a new collaboration emerge in which there is a willing buyer of the biodiversity services and if biodiversity services could be quantified, then biodiversity could be a service paid for in a PES program like NE-PES. Since there currently is no buyer, the quantification of biodiversity services on ranchland could be used within NE-PES to select ranches to participate in the program which provide water storage services along with greater biodiversity services. Biodiversity Ecosystem Services Isolated wetlands are ubiquitous on the ranchlands of south Florida. On all ranches where there are FRESP and NE-PES contracts, natural wetlands (drained and un-drained) make up much of the landscape. Ditching and draining converted a large fraction of these wetlands to productive pasture which is now used for cattle grazing. Increases of surface water and groundwater occurred in the wetlands by implementing CRB structures. Such rehydration of the drained wetlands is likely to increase wetland biodiversity (Stites et al., 2002). 112

113 It is well-known that hydrologic parameters drive changes in ecology (Stites et al., 2002; Mitsch and Gosselink, 2007). To quantify the biodiversity services, predictive models are needed to explain ecological interactions with changes in hydrologic conditions. The models describing these interactions between hydrology and ecology are known as hydroecological models. Studies have shown that changes in wetland hydrologic conditions drive changes in plant and animal populations. Henszey et al. (2004), Loheide & Booth (2010), and Chui et al. (2011) have all shown that hydroecological models can be developed to describe some vegetation responses to hydrological variables. Hydroecological models have also been developed for animal species (fish, mosquitoes, and amphibians) to demonstrate their dependence on hydrological parameters (Snodgrass et al., 2000; DeAngelis et al., 2009; Knight, 2011;). Hydroecological models could be used to predict changes in biodiversity services on the ranches participating in the water storage PES programs. Hydrology, plant, and animal data from four ranches that participated in the FRESP, were used to develop hydroecological models (see Chapter 2). All four ranches installed CRB structures to reduce surface flow out of the ranch and increase storage in the wetlands. Although the ranchers were not paid for biodiversity services in the FRESP or NE-PES, the hydroecological models showed that biodiversity services are provided in conjunction with the water storage service. To predict the changes in biodiversity services for different WMS, hydrologic modeling tools were used to estimate the changes in wetland water storage and other hydrologic variables due to changing spillage levels in the CRB structures. Integration of the hydrologic model estimations with the hydroecological models allowed for the 113

114 prediction of biodiversity services for multiple WMS at two of the ranches studied, Buck Island Ranch (BIR) and Pelaez and Sons Ranch (Pelaez) (Chapter 2, Tables 2-11 and 2-12). Dis-Services and Trade-Offs While managing agro-ecosystems for ecosystem services, there is the possibility of causing dis-services (Nelson et al., 2009; Viglizzo et al., 2012). For example, in a water storage PES, the agro-ecosystem landscape is managed to increase water held on the land, but the increase in water availability may also increase certain undesirable effects such as increased mosquito population (Mutero et al., 2000; Schafer et al., 2006) or increases in invasive and exotic plants (Stites et al., 2002). Other examples of dis-services in PES programs could include loss of pasture or useable cropland, or increases in pest populations (Costedoat, 2012). Increases of dis-services in a PES are in effect a trade-off situation in which an ecosystem service is provided but a dis-service inherently comes with it. Broadly defined, a trade-off is getting one thing but giving up another (Yoe, 2002). Because FRESP and NE-PES operate on ditched and drained ranchland, re-flooding part of the landscape could cause adverse effects for the rancher (Costedoat, 2012). Rehydration of the wetlands is expected to increase biodiversity (Stites et al., 2002). However, rehydrating the wetland can also cause forage loss at a ranch and a need for supplementation of forage (Costedoat, 2012). Trade-offs are also present when considering what the stakeholder is or is not getting and what the stakeholder really wants. For example, a rancher may want to provide a large amount of water storage so that he could be paid for this storage, but the SFWMD has a budgetary limitation and can only pay for a certain amount of water 114

115 storage. Therefore, the rancher will have to trade-off storing less water for less money. Trade-offs among services can also occur (Viglizzo et al., 2012). For example, increased water storage may decrease food and fiber production (Viglizzo et al., 2012). There are tradeoffs to consider when planning and implementing PES programs on agro-ecosystems, and it is important that they are evaluated in a clear and effective manner to enable well-informed decision making by stakeholders (Bohlen et al., 2009; Ruckelshaus et al., 2013). Decision Support Systems (DSS) One formal approach in addressing the tradeoffs apparent in PES programs and aid in decision making is the use of a decision support system (DSS). A DSS can enable simultaneous evaluation of ecosystem services, dis-services, and trade-offs. Generally, DSS are computer tools designed to facilitate and support complex decision making and problem solving (Shim et al., 2002). Different types of DSSs are primarily driven by the types of data needed for a decision to be made. There are model-driven, data-driven, knowledge-driven, document-driven, communication-driven, inter- and intra-organizational, general purpose or function specific, and web-based DSSs (Power, 1999; Gray, 2003). The type of DSS developed is determined by the type of data available. There are several methods which can be used for solving decision-making problems (Fülöp, 2005). The selection of an appropriate tool depends on the decision problem, as well as on the objectives of the decision makers. Multicriteria decision analytics (MCDA) is one type of decision-making methodology that can take form as a DSS. Decision support systems and MCDA tools require two components: alternative decisions and the criteria for the decision to be made (Yoe, 2002). Alternatives are 115

116 different solutions to the same problem, and in a water storage PES the alternatives are the different WMS (spillage levels) in the CRB structures. Criteria are the aspects present in each alternative. The alternatives are judged based on the performance of each criterion and the importance each stakeholder places on each criterion. A DSS user gives a score or a weight to each criterion depending on its importance to them. The importance a stakeholder places on the group of criteria is the importance weighting scenario. The need for MCDA techniques arises from the contradictory value of the criteria (Triantaphyllou and Baig, 2005). For example, the water storage PES programs contain a cost of implementation criterion and water storage amount criterion: two criteria which have different scales and values to different stakeholders. The MCDA is a systematic tool to help identify the best alternative when there are dissimilar data and information available to stakeholders who each value the data differently (Kiker et al., 2005; Linhoss et al., 2013). To determine the best alternative, analysis of all the alternatives is needed. To do this, the criteria are evaluated to know how well each alternative performs at providing the criteria. For example, in a water storage PES program there are multiple WMS which can provide the water storage criterion, but not all provide amounts of this criterion which stakeholders require. Other criteria which may be addressed by the stakeholders include loss of forage and cost of implementation. One WMS may provide ample water storage but may be costly to build and cause significant forage loss. A ranking of all of the WMS could identify which WMS is best at providing water storage while remaining relatively inexpensive to build and damages the least amount of forage. Other criteria can be included to evaluate the alternatives. For example, a rancher may require the consideration of the invasion of 116

117 exotic vegetation species, and a conservation group may require inclusion of increased water fowl and wading bird activity. These three criteria set by the different stakeholders would have to be included in each alternative in the MCDA for evaluation of how well each WMS provides these criteria. One alternative will likely not provide the highest values for all criteria simultaneously. Since it is not possible for all stakeholders to achieve their ideal alternative, trade-offs have to be considered by all parties, and usually a compromise has to be made. MCDA tools can be applied to situations involving multiple stakeholders and can assess the value of each criterion and the alternatives (Kiker et al., 2005). Davies et al. (2013) found that MCDA tools can be used to aid in different types of conflict among stakeholders. Davies et al. (2013) studied four cases which utilized MCDA methods to solve ecological conservation problems and how they aided in positive outcomes for all stakeholders involved. The conflicts they considered included stakeholders who disagreed about landuse changes, establishment and management of protected areas, and conservation of specific species. Davies et al. found that the main challenges to managing conflicts among stakeholders include mistrust and unwillingness to engage; differing values and world views; idealistic solutions without compromise; gaps or mistrust in information; lack of transparency of process; issues operating across multiple spatial and temporal scales; and inflexibility of legislative tools (Davies et al., 2013). The MCDA techniques can be used to address these challenges and facilitate compromises between stakeholders with differing interests (Davies et al., 2013). A simple yet effective method for evaluating the 117

118 dissimilar criteria, evaluation of the differences in opinion among stakeholders, and providing an easy to use tool to facilitate compromise among stakeholders is needed. Simplicity in Decision Support Systems With advances in science and technology, it would seem that more data and information than ever before could feed a DSS to improve the decision-making process. However, it is questioned as to how much detail is really needed to make informed decisions (Ruckelshaus et al., 2013). Ruckelshaus et al. (2013) compiled lessons learned from the field while planning and implementing ecosystem service programs and concluded that keeping a decision process simple is the best way to inform policy makers and the public. They suggested that the best way to serve decision makers is by providing relatively simple models that have been clearly documented, published, and validated (Ruckelshaus et al., 2013). Shim et al. (2002) reports on the classic approaches to DSS development and that they include a powerful, yet simple user interface that enables interactive queries, reporting, and graphing functions. Different stakeholders will have varying levels of familiarity with computer-based DSS software. For PES programs developed for agro-ecosystems, stakeholders include the farmers/landowner, government agencies, environmental groups, and other organizations. To enable clear decision making by stakeholders with varied background, simplicity is a key component in developing the interface of a DSS (Gray, 2003; Ruckelshaus et al., 2013). Uncertainty in Decision Making While it is important to keep a decision making process simple, oversimplification can be misleading. Because of the nature of an environmental DSS or MCDA tool, uncertainty is inherent (Castro et al., 2013; Linhoss et al., 2013), and it is a 118

119 necessary aspect of decision making to consider (Castro et al., 2013). For example, incorporating uncertainty into a DSS for a PES allows a stakeholder to assess the likelihood that a decision will yield the predicted ecosystem service or dis-services. Environmental unpredictability as well as error in data collection, model development processes, and weighting scenarios all creates uncertainty in DSS and MCDA results (Castro et al., 2013). There is a need to understand what might happen in a decision making process when uncertainty is factored in. Since a simple DSS cannot consider and analyze an infinite amount of alternative weighting scenarios and their associated uncertainty, other analysis would have to be completed such as stochastic multicriteria acceptability analysis. Stochastic multicriteria acceptability analysis (SMAA) is a DSS tool that can be used to assess uncertainty in decision alternatives. SMAA techniques can be used to assess many different weighting scenarios and their associated uncertainties (Aersten et al., 2011; Linhoss et al., 2013). SMAA methodology has been used in addressing uncertainties in ecosystem service decision making (Aersten et al., 2011). Environmental Applications of Decision Support Systems Despite challenges, DSS have been developed to aid in decision-making for ecosystem service provisioning programs and other related conservation projects. Elmahdi and McFarlane (2009) developed a DSS to analyze the effects of land management and ground water extraction on the sustainability of the groundwater. They used the DSS in Perth, Western Australia (WA), Australia to assess the effects significant groundwater losses would have on multiple sectors of the area including agriculture, public use, and the environment (specifically wetland biodiversity). Land and water management changes were recommended by a multi-institution taskforce 119

120 comprised of governmental agencies in charge of urban planning, environmental protection, and water conservation. The recommended options were analyzed using Elmahdi and McFarlane s DSS software to determine which option would best suit all stakeholders and result in reduced groundwater extraction and better environmental conditions. Seven different scenarios were analyzed and they were the following: business as usual (BAU), maximizing [groundwater] recharge, maximizing biodiversity, maximizing short term economic gains, a mixed use post-pine, maximizing food security, and zero abstraction for public water supply (Elmahdi and McFarlane, 2009). They found that most scenarios led to a declining water table, and by the year 2030, estimated groundwater levels were eight meters lower than the current state. Their findings suggested that all of the recommended options would lead to a significant loss of wetlands in Perth, WA along with significant losses of biodiversity. Elmahdi and McFarlane concluded that unless extreme measures are taken in Perth, WA, the decline in groundwater levels will be difficult to stop (Elmahdi and McFarlane, 2009). Merritt et al. (2009) developed a DSS for use in Australia. The DSS was tested in an area in north central New South Wales for the wetlands of the Narran Lakes. It is known as the IBIS DSS and the primary use for this DSS is to determine the best alternative that would protect and recover the stressed wetland ecology, particularly for the Ibis water bird (Merritt et al., 2009). The IBIS DSS was used to determine how many days a wetland needs to be inundated (hydroperiod) for an Ibis to successfully fledge their young. Alternatives focused on changing land management by changing the climatic conditions and the flow volume entering the Narran Lakes. The alternatives changed the hydroperiod and conditions favorable for successful Ibis nesting and 120

121 fledging of their young. The user explored possible land and water management scenarios and what effects different rainfall events and changes of inflows had on the wetlands of the Narran Lakes, hydroperiod, and Ibis nesting. Merritt et al. found that The DSS will be most useful in the long term if it is used as part of an evolving process where alternatives are continually updated to test hypotheses of Ibis nesting. Many environmentally focused decisions can be made through the use of MCDA techniques. Linhoss et al. (2013) used a MCDA tool to assess sea-level rise and the effects it has on the Snowy Plover shorebird. A decline in the Snowy Plover population was noted due to a loss of nesting habitat from sea-level rise and human disturbance on beaches (Lamonte et al., 2006; Linhoss et al., 2013). The MCDA techniques used in this study aimed at determining the best method for protecting the Snowy Plover shorebird from future population declines. This study utilized many dissimilar criteria with differing quantitative values and differing importance values to stakeholders. Linhoss et al. successfully analyzed several alternative land management scenarios to determine the best strategy to protect the Snowy Plover. The recommended alternative was to build nest exclosures around the Snow Plovers nests. The nest exclosures limit human and other predatory access to the nests (Linhoss et al., 2013). These examples of environmental DSS and MCDA tools show that simple yet effective DSS and MCDA tools can be developed for other programs and systems, including the water storage PES program of south Florida. Simple DSS approaches can be used to assess the effects of hydrology on ecological responses (Elmahdi and McFarlane, 2009; Merritt et al., 2009). As Linhoss et al. (2013) has shown, MCDA 121

122 techniques can also be used to evaluate many types of dis-similar data like water storage amounts, water storage payments, and ecological abundance. Objectives The goal of this study was to develop and implement a decision support system (DSS) to provide flexible and transparent trade-off evaluation of multiple ecosystem services and agricultural production functions at scales relevant to decisions by ranchers and regional decision makers specifically for a PES program. Specific objectives were to 1) develop a DSS for the Northern Everglades basin in south Florida for evaluation of water storage and biodiversity services and their associated trade-offs; and 2) to evaluate the uncertainty in a water storage and biodiversity services PES program and its implications for a multiple PES program. Methods for Decision Support System Development Study Area and Background The hydroecological models developed (chapter 2) were integrated with a DSS to predict the optimal scenario for maximizing biodiversity services, water storage ecosystem services, and trade-offs on ranches. The models and the DSS enable transparent trade-off evaluation and decision making processes among stakeholders of a PES program. The hydroecological models were developed with data collected under influence of a single WMS, the FRESP WMS. To completely understand the trade-offs and to understand which scenario could deliver optimum environmental services for a multiple PES program, hydrologic modeling was needed. Hydrologic modeling enables the evaluation of environmental service provisioning under any possible WMS, whether it is 122

123 baseline (no water storage) or maximum water storage, because it simulates hydrologic measures at many levels of water storage. Hydroecological Modeling Using the hydrological and ecological data collected from the four ranches participating in FRESP, hydroecological models were developed (Chapter 2). Ecological response data were collected and a hydroecological model was developed for wetland vegetation, forage vegetation, weedy and exotic vegetation, fish abundance, amphibian abundance, mosquito abundance, and macroinvertebrate abundance. Hydrological predictor variables used in the hydroecological model development include the depth (d) of the wetland, inundation area (IA) of the wetland, percent inundation area (%IA) of the wetland, volume (Vol) of the wetland, length of time in days the wetland was inundated (TI), length of time in days since the wetland was inundated (TSI), length of time in days that the wetland was connected (DC) via ditches and canals, and length of time in days since the wetland was last connected (DSC) via ditches and canals. The first step taken in the hydroecological model development was to adhere to assumptions about multivariate linear models; normally distributed data, constant variance among variables, and variable independence. The normality assumption was met by natural log transforming the ecology data, and the variables were tested for independence through autocorrelation testing. Inundation area and volume showed high correlation and could not be used to predict changes in ecology together. The ecological data also had to be scaled from point scale to wetland scale to allow for prediction of biodiversity services at scales relevant to stakeholders. Once assumptions were met, best subsets regressions were performed to conclude to the best subset of hydrologic variables to use in the hydroecological 123

124 models. Using the Mallows Cp value for eliminating bias, the hydroecological models were chosen and regressed using hydrologic predictor variables chosen in the best subsets regression models. The wetland vegetation hydroecological model included IA and DC with a coefficient of determination (R 2 ) of The forage vegetation model included IA and %IA with a R 2 of The weedy and exotic vegetation model included %IA and Vol with a R 2 of The fish and amphibian modeling results showed that IA and d would be the best hydrologic predictors and both with R 2 of The mosquito hydroecological model included IA and TI with a R 2 of The final hydroecological model for macroinvertebrates included IA and d with a R 2 of Complete hydroecological modeling methods and results can be found in Chapter 2. Hydrologic Modeling A hydrologic model which can provide both the predictions of water storage ecosystem services and changes in wetland hydrology was needed. MIKE- SHE/MIKE11 is one of the few integrated models that has been shown to successfully simulate the surface and groundwater flows in south Florida (Jaber and Shukla, 2005; 2007; and 2012). The model, MIKE-SHE, developed by the Danish Hydraulic Institute (DHI), is a physically-based spatially and temporally explicit hydrologic model that simulates all major processes of the land phase of the hydrologic cycle (Refsgaard and Storm, 1995). These include evapotranspiration, interception, overland flow, channel flow, unsaturated flow and saturated zone flow. The saturated flow component of MIKE- SHE allows for a three dimensional flow in heterogeneous aquifers including confined and unconfined conditions. It is based on the 3-D Darcy equation and solved by an iterative implicit finite difference method. MIKE-SHE has a coupled 1-dimensional 124

125 hydraulic model, MIKE11, to simulate surface flows through channels and rivers. MIKE11 can effectively represent hydraulic structures such as CRB structures, pumps, and drainage used for water management in south Florida including the ranchlands where FRESP was implemented. The spatially and temporally explicit nature of the model allows for quantification of hydroperiods of wetlands within a ranch as well other hydrologic metrics (Jaber and Shukla, 2012). The MIKE-SHE/MIKE11 model was used to evaluate the possible scenario effects on storage and wetland inundation characteristics a rancher could implement to store water on their ranch. In this study, WMS focus on baseline, where baseline is the ditches without any water control structure, the addition of culvert with riser board (CRB) structures with no boards, and riser board addition to the top of the CRB structure. These WMS are the alternatives in the DSS. Detailed information on model development, validation, and calibration can be found in Hendricks et al. (2014) for BIR and Wu (2014) for Pelaez. MIKE-SHE was used to simulate hydrologic data for the unobservable WMS. The data developed in MIKE-SHE/MIKE11 for the WMS was then used in the hydroecological models to quantify the changes in wetland ecological populations for different WMS (different spillage levels). Quantifying the hydrologic and biodiversity services for all WMS is but one step. Through the development and use of a DSS, evaluation of which WMS produces the optimum trade-off level of water storage, biodiversity, and dis-services can be achieved. Development of the Northern Everglades Ranchland Decision Support The DSS developed in this study was adapted from a DSS by Girrard et al., (2005). The original DSS is a simple MCDA tool and uses a simple spreadsheet 125

126 approach. Its original use was intended to provide the user with method of choosing the best culvert pipe liner to use when using trenchless techniques for repairing pipes and culverts used as drainage conduits. To achieve the objectives for this study, Girard et al. s spreadsheet-based MCDA tool was modified by integrating the hydroecological models for biodiversity service prediction for many levels of water storage and changing the criteria to fit the needs of the stakeholders involved in a multiple PES program. The DSS developed for this PES program was named the Northern Everglades Ranchland Decision Support (NERDS). The NERDS spreadsheet uses three techniques for weighting and ranking alternative decisions: the weighted average method (WAM) (Abdelrahman et al., 2008), the discrete compromise programming (CP) method (Zeleny, 1973), and the preference ranking organization method for enrichment evaluation (PROMETHEE) (Brans and Vincke, 1985). Use of these three methods ensures robust decision-making. Weighted Average Method (WAM) The weighted average method (WAM) is a value-based method where the value of the performance measure is used to assign the alternative a rank. The performance measure is how well each alternative performs given a set of importance weights for the criteria. For each criterion, an importance rating scale from 1 to 4 is used. A value of 1 is used to represent the worst (not important) and 4 is the best (very important). The importance of each criterion is assigned by the decision maker (e.g. rancher). The importance factors are then normalized giving a set of normalized criterion weights. Each alternative score is then multiplied by the corresponding normalized weight (Girard et al., 2005; Abdelrahman et al., 2008); 126

127 n S j = W i R i,j Where, S j = overall score for alternative j; W i = weight for criterion i; R i,j = relative i=1 importance of criterion i (Girard et al., 2005; Abdelrahman et al., 2008). The above equation was used in the NERDS as one of the methods to rank the alternatives given the stakeholders weighting scenarios. Discrete Compromise Programming Method (CP) The discrete compromise programming (CP) method is a value-based, distance method with a ranking scale for alternatives of 0 to 1, where 0 is the worst alternative and 1 is the best (Zeleny, 1973; Girard et al., 2005). The criteria for each alternative are weighted in the same way as in WAM by the stakeholders. R i,j = [ Actual p i,j Worst i ] Best i Worst i Where R i,j = rating metric for criterion I within alternative j; Actual i,j = actual rating of alternative for criterion I within alternative j; Worst i = worst rating of any alternative for a criterion i; Best i = best rating of any alternative for a criterion i; p = exponent determining the additional emphasis on the CP metric rating value (Zeleny, 1973; Girard et al., 2005). The CP method was used as a method to rank the alternatives in the NERDS given the stakeholders weighting scenarios. Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) Unlike the WAM and CP methods, PROMETHEE is not a value-based method, it is an outranking method. The PROMETHEE method takes each alternative and performs pairwise comparisons of each alternative. Preference values are determined from the pairwise comparisons and then are analyzed to develop an overall rating value 127

128 for each alternative. The scores range from -1 to +1, where +1 is most preferred alternative and -1 is least preferred alternative (Brans and Vincke, 1985; Girard et al., 2005). PROMETHEE was used to rank the alternatives of the NERDS. Alternatives The different WMS analyzed in this study were simulated in MIKE-SHE/MIKE11 (Hendricks et al., 2014; Wu, 2014). The model simulated reliable hydrologic data to enable prediction of ecosystem services for multiple WMS. Different alternatives include baseline, the addition of culvert with riser board (CRB) structures from baseline, the FRESP WMS (for BIR only), and riser board addition to the top of the CRB structure. The FRESP WMS was the scenario which was implemented during the FRESP program. The spillage levels in the CRB structures varied. These WMS result in differential storages as well as varying quantities of biodiversity services. Criteria As previously mentioned, criteria are what must be included in each WMS decision and are used to rank and score the alternative based on how well each WMS produces the criteria. Criteria in the NERDS include the amount of water storage and payment, cost of implementation of the PES program on the ranch, cost due to loss of forage, cover of wetland vegetation, and abundance of mosquitoes, frogs, macroinvertebrates, and fish. These were chosen as they were deemed most important for the stakeholders involved and can be measured relatively easily and inexpensively. Weighting Scenarios Stakeholders involved in a water storage PES like FRESP include the rancher, the South Florida Water Management District (SFWMD), environmental and conservation groups, and research scientists. In the NERDS, each stakeholder can 128

129 assign each of the criteria a weight of importance. The set of rankings given to all criteria for one stakeholder are defined as the weighting scenario in this study. For each stakeholder, these weights will vary from 1 (not important) to 4 (very important) for each criterion. The weighting assignments will vary from stakeholder to stakeholder portraying what criteria are most and least important to them. The DSS allows the user to analyze three different weighting scenarios. The first weighting scenario has all criteria weighted as 1. This scenario gives a standard ranking and is the same for all stakeholders to have a baseline to judge other weighting scenarios against. A Decision Support System for a Multiple Ecosystem Services Program When a user first opens the NERDS, the user sees a page that directs them through NERDS (Fig. 3-1). The user clicks on the Ranch, Wetland, and Hydrologic Information button to enter their data about the ranch, its wetlands, and hydrology (Fig. 3-2). Here, all the data needed must be entered by the user in order to continue. This page asks for the name of the ranch, the number of wetlands on the ranch, the number of wetlands that are within 100 feet of a ditch or canal, the watershed area of the ranch, the amount expected for payment per acre-foot of water storage, the cost of implementation estimated to participate in the PES, and the number of alternatives that are to be analyzed. Hydrologic data about the wetlands are needed for each WMS to be analyzed. The user will need to estimate or measure the hydrologic data independently from NERDS. The following data are needed for all WMS to be evaluated. An example data sheet is presented in Fig Data needed for NERDS describing the wetlands hydrologic conditions includes: total wetland area (m 2 ), average annual wetland depth (m), average annual inundation area (m 2 ), average 129

130 annual percent inundation area (fraction), average annual wetland volume (m 3 ), average annual number of days the wetland has at least 15 cm of water (days), and average annual number of days the wetland is connected to canals and ditches (days). Data about the storage capacity of the ranch are needed for each WMS, for which the annual average volumetric flow out of the ranch is used (m 3 ). The annual average volumetric outflow will have to be simulated for each WMS independent of NERDS. Finally, the number of boards used in the CRB for the WMS is needed, where one board measures about 18 cm in height. A metadata sheet is provided in NERDS and explains how to calculate or measure each of these variables. The example data sheet in Fig. 3-3 uses data from BIR which has six wetlands in the project area but only three within 100 feet of the ditch. The wetlands close enough to the ditch are the only wetlands considered to provide biodiversity services as these were the only wetlands to show a change in hydroperiod due to increases in water storage (Hendricks et al., 2014). Longer hydroperiods are shown to influence biologic activity (Goswami and Shukla, 2014). On BIR, the expected payment per acre-foot of water storage is $ The range of water storage payment is from $100 to $150 per acre-foot of water storage and depends on the contract between the SFWMD and the rancher. The BIR has an area of 4.76 million m 2 (476 hectares) which is managed for water storage services. The example in Fig. 3-3 has eight WMS to evaluate with the hydrologic data chart fully populated for the alternatives. After the spreadsheet is populated in correct units, NERDS uses the data to quantify water storage and biodiversity services. Using hydrological metrics and hydroecological models, biodiversity services and dis-services are predicted for trade- 130

131 off analyses. The user can view the predicted abundance of mosquitoes, fish, frogs, wetland plants, and macroinvertebrates for each WMS in the MCDA Matrix sheet (Fig. 3-4). The user can also view the payment amount for water storage, cost of implementation, and the cost of forage loss. Here, the user also will define whether or not the criterion is an ascending or descending criterion. Ascending criteria are those that are more favored when they increase in value while descending criteria are those that are more favored when they decrease in value. For example, a rancher will most likely want the water storage payment criterion to be maximized and would assign this criterion as an ascending or maximizing a criterion. In contrast, the rancher will most likely want the loss of forage criterion to be minimized and would assign this criterion as a descending or minimizing criterion. Next, the user clicks the Go to Interface button to assign importance weights for each criterion and analyze the different WMS using the different ranking methods (WAM, CP, and PROMETHEE). An example MCDA Matrix page is presented for BIR and Pelaez in Fig The results from the hydroecological modeling along with the water storage services are presented. The first criterion is the cost of implementation criterion. It can be observed that the cost of implementation is three times the cost of water storage payment for the maximum (6-Board) WMS for BIR. The cost of implementation is a one-time cost while the water storage payment is an annual payment for the length of the contract. Payment is only for the water storage services and not the biodiversity services since there are no payments currently given for the biodiversity services. Loss of forage however, has been assigned a monetary value of $53.83 per acre (Elizabeth Boughton, MacArthur Agro-ecology Research Center, personal communication, 9 September 2013). Fig

132 (a) shows that although some services are maximized at the maximum board WMS (water storage, wetland plant cover, amphibian abundance, and macroinvertebrate abundance); other ecosystem services are minimized at the maximum board WMS. Both forage cover and fish abundance are minimized at the maximum board WMS. The hydroecological model for forage cover shows a decrease in forage cover with increases in percent inundation area, which is increasing with increasing board height. The fish abundance hydroecological model shows a decrease in fish abundance with an increase in wetland depth, a hydrologic variable which is also increasing with increasing board height. Fig. 3-4 (b) shows that most biodiversity services are maximized at the 5- board WMS and not the maximum (6-board) WMS. The differing results from BIR (Fig. 3-4, a) and Pelaez (Fig. 3-4, b) show that biodiversity services will respond differently at different ranches even under a similar WMS. The next page of NERDS is the interface page. At the interface page, also called the MCDA Dashboard, the user defines the method of ranking to use (WAM, CP, or PROMETHEE) and defines two weighting scenarios for the criteria (Fig. 3-5). There are three columns for the user to enter different importance weighting scenarios, but as a general rule, the first set of weights should all be 1 s (Yoe, 2002; Girrard et al., 2005). The Interface page is interactive and allows the user to change the ranking method and importance weighting scenarios and view it in real-time on a graph that shows the score and rank of each WMS. The rank is based on the selected ranking method and selected weighting scenario. On the Interface page, there is a button to Calculate Results Table. This will calculate the scores and rankings of the WMS for all three weighting scenarios for one ranking method (WAM, CP, or PROMETHEE). The Go to Results 132

133 Table button takes the user to the results page (Fig. 3-6). For simultaneous viewing of the three different weighting scenarios, the Results page shows the rankings and scores of each WMS based on the ranking method selected on the interface page and all three weighting scenarios. The results in Fig. 3-6 represent the scores and rankings of the WMS for the BIR example. In this example, the scores were calculated using the WAM ranking method and WMS were ranked according to their score. All three weighting scenarios can be viewed for the ranking method chosen. The user can go back to the MCDA Dashboard to change the ranking method and weighting scenarios by clicking on the Go to MCDA Dashboard button. Java-Based Stochastic Multicriteria Acceptability Analysis (JSMAA) Although NERDS produced the rankings to enable decision-making, there are uncertainties in different components of NERDS. Depending on the ranking method chosen (WAM, CP, or PROMETHEE), rankings of the WMS can be different for the same weighting scenario, providing uncertainty in the ranking. For example, the ranking of WMS using the WAM method may indicate that the best WMS is the highest spillage level in the CRB structure and the CP method may indicate that the best WMS is the second highest spillage level. There are also uncertainties due to errors in predictions from the hydrological models (MIKE-SHE/MIKE11) and hydroecological models that were used to quantify biodiversity services. The Java-based stochastic multicriteria acceptability analysis software (JSMAA, v1.0.2) was used to evaluate uncertainty in the estimates of the hydroecological models and the rankings in NERDS. JSMAA is open-source java platform software that uses SMAA techniques (Tervonen, 2014) to evaluate uncertain criteria to determine favored 133

134 alternatives through stochastic methods (Linhoss et al., 2013). JSMAA utilizes SMAA and SMAA-2 techniques. Using utility functions, SMAA assigns a preference value to criteria for the decision maker (Lahdelma and Salminen, 2001). SMAA techniques can determine when each alternative will achieve the highest ranking by evaluating all possible weighting scenarios, enabling an understanding of the effects of uncertainty in model prediction and user preferences (Lahdelma and Salminen, 2001; Linhoss et al., 2013). The rank acceptability index, central weight vector, and confidence factor are calculated in SMAA. The rank acceptability index is a value given to an alternative to show how often the alternative is ranked as the best alternative by considering the uncertainty in the criteria and varied weighting scenarios (Tervonen, 2012). The central weight vector selects appropriate weights to make each alternative rank the highest with no consideration to preferences of a certain stakeholder. The confidence factor is a value from 0 to 1, and it is the probability that the central weight vector calculated in JSMAA for an alternative will rank that alternative as the most favored alternative (Lahdelma et al., 1998; Linhoss et al., 2013). SMAA-2 is similar to SMAA, but in addition to considering the best rank for all alternatives it also explores all ranking results and their associated rank acceptability indices, central weighting vectors, and confidence factors (Lahdelma and Salminen, 2001; Tervonen, 2012). Along with the percentage of times the alternatives are ranked as the best alternatives, through the use of SMAA-2 techniques, rank acceptability index for the second, third, up to the last ranked alternative are calculated. In other words, the percentages of times an alternative is ranked second, third, etcetera up to last place are calculated using SMAA

135 Results Northern Everglades Ranchland Decision Support (NERDS) Analysis Different weighting scenarios were applied to analyze stakeholders points of view on the criteria and the outcomes of those viewpoints on the WMS rankings. The three most common stakeholders and users of NERDS presented in this study would be the seller (rancher or landowner), the buyer (state agency, e.g. water management district), and a conservation group or entity. The resulting rankings of WMS for each stakeholder are based on weights presented in Table 3-1. All hydrologic data required for analysis were obtained from the MIKE-SHE/MIKE11 model predictions. The weighting scenarios set in Table 3-1 varies based on what is assumed to be important to each stakeholder. It is assumed that a rancher will be more likely to preserve grazing land (pasture) and minimize forage loss while obtaining maximum water storage payment. The buyer s (state agency) main goal is to optimize water storage while minimizing total cost (implementation + water storage payments). The environmental group is most concerned with what will happen to the ecosystems plant and animal species. The environmental group will most likely want native plant and animal species to increase in abundance while invasive and exotic species abundances are minimal. Buck Island Ranch Analyses From Tables 3-2, 3-3 and 3-4 it can be observed that the three ranking methods (WAM, CP, and PROMETHEE) tend to converge at the same WMS, the maximum board WMS, for all weighting scenarios and for all three stakeholders at BIR. The baseline WMS is always ranked in last place; the worst WMS choice for all stakeholders. 135

136 When comparing scores of each WMS among the intermediate WMS (2-board to 5-board), they are very similar and don t impact the overall ranking of WMS. The intermediate WMS don t always agree in ranking across ranking methods, and this likely caused by the quantities of each criterion being similar in value for the intermediate WMS. Similar quantities in criterion for the intermediate WMS can be attributed to the increase in outflow observed for those WMS from the MIKE-SHE/MIKE11 model predictions (Table 2-8). The increase in outflow from the ranch was caused by low antecedent storage conditions in the wetlands (Hendricks et al., 2014). The WMS for BIR which are consistently scored the highest are the 1-board, FRESP, and 6-Board WMS. These WMS were shown to provide the most storage from baseline according to the MIKE-SHE/MIKE11 simulation results. The 6-Board, FRESP, and 1-board WMS are always ranked 1, 2, and 3 respectively for the WAM and CP methods for all weighting scenarios for all stakeholders. PROMETHEE ranks them 1, 2, and 7 respectively. The user of NERDS observing these results will have to weigh the results and consider that for different ranking methods, different rankings will be produced. The ecosystem services of water storage (and payment), wetland vegetation cover, amphibian abundance, and macroinvertebrate abundance are all maximized at the 6-Board WMS, and the ecosystem dis-service of mosquito abundance is minimized at the 6-Board WMS. However, the biodiversity services of forage vegetation cover and fish abundance are minimized at the 6-Board WMS. The ecosystem services of abundance of the water storage, wetland vegetation, amphibian, and 136

137 macroinvertebrates are traded-off with the ecosystem dis-services of decreases in fish abundance and forage cover. In this example on BIR, all stakeholders have to accept trade-offs. The rancher sustains forage loss but obtains the maximum amount of water storage payment and minimal mosquito abundance. The environmental group sustains losses to fish populations but gains wetland vegetation, amphibian abundance, and macroinvertebrate abundance. The state agency has to help financially implement the most expensive project and pay the most expensive water storage payment, but the agency is able to receive the maximum amount of water storage services from BIR. Overall, the results from the three groups of weights for the three different stakeholders suggest that on BIR the best WMS is the maximum, 6-Board WMS for all stakeholders. Pelaez Analyses Much like the analyses done using NERDS for BIR, the analyses for Pelaez tend to converge to the same WMS. Pelaez had one less WMS than BIR to evaluate, and it does not consider the FRESP WMS. The three WMS with the highest spillage elevation (WMS 4-, 5-, and 6-board) prove to be the best WMS for the three different stakeholders (rancher, state agency, and environmental group). While the 5-boards WMS is always ranked 1 for every stakeholder and for every weighting scenario, the 4- and 6-board scenarios are ranked either second or third. The baseline WMS is usually ranked last (7 out of 7) for Pelaez. Depending on the weighting scenario, the baseline WMS is ranked higher than seventh. However, it was never ranked in the top three. When baseline was ranked higher than seventh, it is because of a highest weight (4) given to the cost-related criteria. 137

138 The rankings of WMS results for Pelaez are different from the results for BIR. Unlike BIR, Pelaez results show that 5-board WMS results in a sweet spot which satisfies the buyer, seller, and conservation group best. Although the highest spillage level (6-board WMS) provides maximum surface flow reduction and therefore the maximum payment for storage, it is not ranked highest. The reason for its second-best ranking is that other biodiversity services were maximized at the 5-board WMS (the second highest spillage level). Wetland vegetation cover, forage vegetation cover, amphibian abundance, macroinvertebrate abundance, and fish abundance were all maximized at the 5-board WMS. The 5-board WMS also maximized the ecosystem disservice of mosquito abundance, but this was traded-off with the increase of abundances of the other biodiversity services. Implementing the 5-board WMS satisfies all stakeholders. The rancher does not lose forage cover and still receives the second highest water storage payment. The state agency doesn t have to pay for the most water storage, but still receives the second highest water storage from the ranch. The environmental group receives the most biodiversity services from the 5-board WMS. Results from the two ranches show that the highest storage is not necessarily the best WMS indicating that NERDS is able to capture the between-site variations in water retention and associated biodiversity services and dis-services and integrate them for selecting the WMS that is considered the best for stakeholders based on trade-offs. The 6-Board WMS for BIR and the 5-board WMS for Pelaez will likely provide each stakeholder with maximum satisfaction in regards to trade-offs among ecosystem services, dis-services, and economics. The NERDS tool enables decision making by satisfying relevant parties for a multi-services PES program. Decision support systems, 138

139 such as NERDS, have inherent uncertainty in their rankings due to uncertainties in measurement as well as modeling of services and dis-services. An analysis of these uncertainties in the NERDS follows. Integrating the Northern Everglades Ranchland Decision Support with JSMAA In JSMAA, each criterion is assigned a distribution type, their associated values for each WMS, and whether or not they are an ascending or descending criterion. Ascending criteria are those that are more favored when they increase in value while descending criteria are those that are more favored when they decrease in value. The cost-related criteria were either considered to be Gaussian or interval distributions in JSMAA. In a multiple PES program, the seller will not owe the state agency money if the WMS causes the service to underperform expectations. However, standard deviations in predictions of water storage services and therefore of cost values can cause a negative payment to the seller (i.e. seller owes the buyer) which can mislead results in JSMAA. To ensure that a negative payment in an WMS does not occur within JSMAA, interval distributions were utilized when models predictions and standard deviations could cause a negative water storage payment. The interval range was from 0 to +σ, where σ is the standard deviation. If the WMS never caused a payment to the buyer, the distribution followed the assumption of normally distributed data and a Gaussian ±σ distribution in JSMAA. Cost due to loss of forage was evaluated differently than cost of implementation or water storage payment. Since the value of cost of forage loss is based on the results of a hydroecological model multiplied by a monetary factor, the hydroecological model outputs were used instead of the cost. This approach was taken to ensure that errors in the measurement and hydroecological modeling would be included. Therefore, the cost 139

140 due to loss of forage criterion was used in JSMAA following the methods used for the other biodiversity service criteria values based on hydroecological model outputs. Hydroecological models were developed assuming a normal distribution (with natural log-transformation), therefore, the outputs from the hydroecological models for the biodiversity service criteria were used in JSMAA with a lognormal ±σ distribution. The only exact distribution criterion was cost of implementation. The cost of implementation and the number of boards are known with high accuracy. The following analyses were performed with missing weighting scenarios to not bias toward a single stakeholder s preferences. The analyses determine which WMS perform best most often along with how often they rank in other ranking places. SMAA-2 methods used in JSMAA generated ranking results with rank acceptability indices, the central weighting vectors, and the confidence factors. The value estimations from NERDS made for each criterion for each WMS were used as the values for criteria in JSMAA. Standard deviations were included as the error terms for the criteria in the JSMAA. To include the error from the simulated hydrologic variables along with the error from the hydroecological models, the mean squared errors (MSE) for individual hydroecological models (e.g. frog, fish etc.; see Table 2-17, Chapter 2) were combined by adding them to the MSE for the hydrologic variables predicted by the MIEK- SHE/MIKE11 model. The MSEs for the hydrologic and hydroecologic models were combined to obtain a total standard deviation for each biodiversity service criterion. Buck Island Ranch JSMAA Results and Discussion JSMAA was able to evaluate uncertainty in the hydroecological models, the hydrologic variables (from MIKE-SHE), and weighting factors for a water storage PES program by using SMAA-2 methodologies. With measurement distributions available for 140

141 criterion measurements, JSMAA was able to accurately represent the range of values for each criterion and analyze that range for each criterion of each WMS. This consideration is an improvement over the spreadsheet NERDS since NERDS does not consider a range of possible outcomes for one criterion for a specific WMS. The spreadsheet NERDS assumed that each criterion value is accurate and is unaffected by measurement and modeling related errors. Stochastically applying different weights to each criterion for each WMS, JSMAA was able to account for uncertainties when considering different weighting scenarios that had not been considered in the spreadsheet NERDS. The results from JSMAA are not biased by the preferences of one stakeholder over another. It simply produces weighting scenarios which allow each WMS to be the best ranked WMS. The BIR rank acceptability bar chart (Fig. 3-7) and percentage values for each WMS in a specific rank (Table 3-8) show that the 6-Board WMS and the FRESP WMS performed as the best WMS 30% and 16% of the times, respectively and as the second best WMS 24% and 23% of the times, respectively. These values agree with NERDS results which show the 6-Board and FRESP WMS as the best and second best WMS, respectively, given the three different stakeholders weighting scenarios. The results from JSMAA also indicate that the baseline WMS ranked comparatively well with the 6-Board and FRESP WMS. The baseline WMS ranked as the best WMS 28% of the time. While the baseline WMS performed well compared to the other WMS, it is important to consider the weights that make it rank first 28% of the time. When observing the weighting scenarios that enable the baseline WMS to be the best ranking WMS, 23% of the total weight for eight criteria is placed upon the cost of 141

142 implementation criterion. The high weight on the cost of implementation criterion ensures that baseline is ranked first because the cost of implementation for the baseline WMS is zero, and there is a large jump in expenditure of dollars from baseline to WMS when considering the cost of installing the CRB structure. The 6-baord and FRESP WMS have more evenly distributed weighting scenarios as well as more weight given to biodiversity and water storage ecosystem services compared to the baseline WMS. The weighting scenario for the 6-board WMS enabling it to be the best ranking WMS, placed the most weight on the water storage payment criterion (19% of the total weight) and a near even importance given to the rest of the criteria. FRESP followed the 6-Board WMS by weighting the water storage payment criterion with 16% of the total weight and an even distribution of weights for the resulting criteria. With all criteria receiving similar weights for importance, the more evenly distributed weighting scenarios could potentially satisfy all stakeholders in a multiple PES program. JSMAA s SMAA-2 suggests with a confidence factor of 0.26 and 0.56 respectively that the FRESP and the 6-Board WMS are the best WMS to choose between for implementation on BIR for all stakeholders involved for a multiple PES program. Similar results were found using NERDS. It can also be observed from Fig. 3-7 and Table 3-8 that the next few WMS (1- board to 5-board) have similar rank acceptabilities suggesting lack of significant changes between these scenarios. It can therefore be concluded that unless a WMS provides the maximum or near maximum water storage, there is not much benefit in implementing WMS to provide water storage and biodiversity services. This suggests 142

143 that the biggest decision to make would be whether or not to implement CRB structures and use several boards to increase spillage levels. Pelaez JSMAA Results and Discussion JSMAA results for Pelaez also present an interesting ranking scenario. Fig. 3-9 and Table 3-10 show a few WMS that stand out as the best ranking WMS most often. Baseline, the 5-board, and the maximum (6-board) WMS are ranked as the best WMS 27%, 22%, and 38% of the time, respectively. While the ranks for the 5 and 6-board WMS agree with the results from NERDS, the performance of the baseline WMS does not as NERDS did not rank it above the sixth place. Similar to the BIR results, the baseline WMS at Pelaez performs well compared to many of the other WMS due to the heavy weight given to the cost of implementation criterion. With 24% of the total weight and a confidence factor for the weighting scenario of 0.97, the cost of implementation criterion swings the baseline WMS for Pelaez to be the best WMS 27% of the time. While the 5-board WMS is ranked the best WMS less often than the baseline WMS, the 5-board WMS ranks as one of the top two WMS 56% of its total ranks, 22% of the time it is ranked first and 34% of the time it is ranked second. Although it may not be the best WMS more often than the baseline WMS, the 5-board WMS is still a viable option for stakeholders. The weighting scenario which enables the 5-board WMS to be the best WMS is a more evenly distributed weighting scenario with the most weight (17%) given to the wetland plant cover criterion. It is necessary to consider the 5-board WMS as a possible decision and to explore more weighting scenarios given its good performance of 34% of the time ranked as the second best WMS. 143

144 The 6-board WMS is most often the best ranked WMS with 38% of its total ranks as the best WMS with a confidence factor of It is also ranked as the second best WMS 28% of the time. The weighting scenario which makes the 6-board WMS the best ranked WMS has more evenly distributed weights than the baseline WMS with most weight (18%) given to the water storage payment criterion, similar to the results for the BIR. The results from the JSMAA analysis for Pelaez are similar to those obtained from NERDS. There may be a sweet spot for the Pelaez for which more biodiversity services can be provided. The 5-board WMS is the sweet spot when observing the NERDS and JSMAA results. Overall, results from the JSMAA and spreadsheet DSS for Pelaez, like the BIR results, indicate that higher the spillage level (more boards) higher the water storage and biodiversity services. Summary and Conclusion A decision support system (Northern Everglades Ranchland Decision Support, NERDS) was developed to evaluate biodiversity services and dis-services associated with an existing PES program that pays ranchers to store water on their property. The alternatives considered in NERDS were the different WMS, and the criteria included water storage and biodiversity services and dis-services. The WMS can provide multiple levels of both water storage and biodiversity services depending on the height of the spillage level at the ranch outlets. Specific WMS considered were baseline (ditch bottom, no storage) and increments of spillage level up to maximum spillage level (top of the CRB structure). The ecosystem services (criteria) evaluated for each WMS included water storage and wetland-scale changes in wetland and forage vegetation, 144

145 and fish, amphibian, mosquito, and macroinvertebrate abundances. To predict the responses of the biodiversity services to changes in wetland water storage services, hydroecological models were developed and incorporated into the spreadsheet-based NERDS. Hydrologic modeling of multiple WMS on BIR and Pelaez ranches enabled prediction of water storage and biodiversity services, dis-services, and trade-offs. Water management scenarios for two ranches were evaluated to test the functionality of NERDS. The ranches were Pelaez and BIR. Buck Island Ranch is currently participating in a water storage PES. Overall, the results of NERDS for BIR with eight WMS suggest that the best WMS for all the stakeholders (rancher, state agency, and environmental group) is the maximum board (6-Board) WMS. The 6-Board WMS resulted in services, dis-services, and trade-offs for the three stakeholders for BIR. The seller (rancher) obtains the services of the maximum water storage payment and a low abundance of mosquitoes. The rancher sustains dis-services in the form of significant amount of forage lost due to increased wetland inundation. However, for BIR, the costs associated from forage loss are overwhelmingly offset by the water storage payment ($115,000). The buyer (state agency) obtains the maximum amount of water storage services (106,183 m 3 ; 861 acre-feet), but also sustains the highest cost of implementation and water storage payment. The environmental and other stakeholders have to trade-off the biodiversity services of maximized wetland vegetation cover, amphibian abundance, and macroinvertebrate abundance with the dis-services of minimal fish abundance. The 6-Board WMS provides the most ecosystem services of water storage (and payment), wetland vegetation cover, frog abundance, and macroinvertebrate 145

146 abundance. However, it also minimizes the service of fish abundance. The 6-Board WMS maximizes the dis-service of forage loss, but minimizes the dis-service of mosquito abundance for BIR. JSMAA was used to evaluate the uncertainty in predictions in biodiversity services due to hydroecological model errors and estimations of hydrologic predictor variables. JSMAA also stochastically evaluated weighting scenarios which would allow each WMS to be ranked first. The JSMAA results indicated the same ranking of WMS for BIR as NERDS suggesting that NERDS is a robust tool to aid in decision-making. Similar to BIR, NERDS was used to evaluate WMS on Pelaez. The results from NERDS analysis for Pelaez indicate that the best WMS is the second highest board level, the 5-board WMS. The rancher (seller) had to trade-off not receiving the highest water storage payment and highest abundance of mosquitoes with no loss of forage. The state agency (buyer) had to settle for the second-highest water storage amount available from Pelaez, but the state agency will not have to pay for the most expensive WMs implementation. The 5-board WMS was estimated to store about 51,500 m 3 (417 acre-feet) less water than the 6-board WMS. The 5-board WMS maximizes all biodiversity services, thus the environmental groups (influence group) do not have to make any trade-offs. Different from the results found on BIR, there seems to be a sweet spot for Pelaez for which the most biodiversity services are provided. Once the water storage exceeded that limit (5-board WMS), the biodiversity service provisioning decreased. Although the rancher would not receive as much payment for water storage as s/he would for the 6-board WMS, the result of no forage loss is a desirable (economic gain) service for the rancher. Overall, the best WMS for all stakeholders is 146

147 the 5-board WMS for Pelaez. Incorporation of uncertainty from hydroecological models and hydrologic variable estimations in JSMAA resulted with the 6-board WMS as the best WMS more often than the 5-board. However, JSMAA analysis showed that the 5- board WMS was ranked first or second for 56% of its weighting scenarios, indicating that it could be considered a viable WMS for implementation in a PES program where additional services and dis-services beyond the water storage services are considered. The NERDS provides a means by which dissimilar data can be integrated (e.g. water storage payment, abundance of animals and plants) to select a WMS which can satisfy all stakeholders. JSMAA validated NERDS s findings through evaluation of uncertainty in the hydrological model predictions, the median hydroecological models, and the weighting scenarios. Best ranked WMS, with and without JSMAA, were similar for both ranches. NERDS demonstrated that although two ranches (e.g. BIR and Pelaez) provide significant amounts of water storage service, they can vary in provision of biodiversity services. No monetary value has been assigned to the biodiversity services because there is currently no willing buyer of biodiversity services in Florida. The NERDS can show stakeholders which WMS on which ranch can provide more biodiversity services. If a new collaboration began in which there was a buyer willing to pay for biodiversity services and a rancher implemented new practices targeted to specifically enhance biodiversity services, biodiversity could be part of a multiple PES program. Although there is currently no buyer for biodiversity services, a state agency, like the SFWMD, may be willing to pay a higher water storage payment to a ranch which produces more biodiversity services than another ranch where the same water storage 147

148 is provided. For example, if BIR stored more water than Pelaez, but the latter could provide twice as much biodiversity services as compared to BIR, the state agency may be willing to pay Pelaez a higher rate for water storage because of the additional biodiversity services. This study s impacts are not limited to water storage and biodiversity services. If an ecosystem service can be modeled and quantified for multiple WMS, many ecosystem services (e.g. nutrient removal, carbon sequestration, increased habitat for an endangered species) could be included in a multiple ecosystem services program and be incorporated into NERDS for the evaluation of the best WMS. The next step would be to interview or survey stakeholders who are currently participating in a water storage PES program, such as the NE-PES, to field-test their weighting criteria. In this study, assumptions about the stakeholders preferences were made to assign weights to the criteria. For example, it was assumed that a rancher would weigh the cost of loss of forage and water storage payment criteria highest; the state agency would weigh the cost of implementation and water storage payment criteria highest; and the environmental group would weigh the biodiversity criteria highest. Next, the NERDS can be presented to the stakeholders and obtain their feedback on the missing factors, such as criteria. Lastly, the NERDS needs to be evaluated by all stakeholders to evaluate if NERDS is a helpful and impactful tool for decision-making and evaluating a multiple ecosystem services program. 148

149 Criteria Table 3-1. Example importance weighting scenarios of criteria for three stakeholders (e.g. rancher, state agency, and conservation group) in a multiple ecosystem services program. Importance Weighting Scenarios Rancher State Agency Conservation Group Seller Buyer Stakeholder Weighting Scenario One Two Three One Two Three One Two Three Water Storage Payment Mosquito Abundance Frog Abundance Macro- Invertebrate Abundance Fish Abundance Cost of Implementation Wetland Plant Abundance Costs due to Loss of Forage

150 Table 3-2. Example results for the seller stakeholder (e.g. rancher) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for the Buck Island Ranch. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board [d] 5 Board [d] FRESP Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation. [d] The rankings for these alternatives were tied. 150

151 Table 3-3. Example results for the buyer stakeholder (e.g. state agency) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Buck Island Ranch. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board [d] 5 Board [d] FRESP Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation [d] The rankings for these alternatives were tied. 151

152 Table 3-4. Example results for an environmental stakeholder for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Buck Island Ranch. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board [d] 5 Board [d] FRESP Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board FRESP Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation. [d] The rankings for these alternatives were tied. 152

153 Table 3-5. Example results for a seller stakeholder (e.g. rancher) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation. 153

154 Table 3-6. Example results for a buyer stakeholder (e.g. state agency) for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation. 154

155 Table 3-7. Example results for an environmental stakeholder for different weighting scenarios and all ranking methods used in the Northern Everglades Ranchland Decision Support (NERDS) for Pelaez. Weighting Scenario 1 WAM [a] CP [b] PROMETHEE [c] WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 2 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board Weighting Scenario 3 WAM CP PROMETHEE WMS Score Rank Score Rank Score Rank Baseline Board Board Board Board Board Board [a] WAM is the weighted average method for ranking alternatives. [b] CP is the compromise programing method for ranking alternatives. [c] PROMETHEE is the preference ranking organization method for enrichment evaluation. 155

156 Table 3-8. Rank acceptability analysis from JSMAA for Buck Island Ranch which shows, as a percent of its total ranks, how often each alternative is in a given rank. WMS Percent of Times WMS is Ranked in Given Rank (%) Baseline Board Board Board Board Board FRESP Board WMS Table 3-9. Central weights for water management scenarios (WMS) on Buck Island Ranch showing the weights for each criterion when each WMS is ranked first. The confidence factor is also shown. Confidence Factor [a] Cost to Implement Water Storage Payment Cost of Loss of Forage Wetland Plants Mosquito Frogs Macros Fish Baseline Board Board Board Board Board FRESP Board [a] The confidence factor describes the probability of the alternative to be ranked first given the assigned weights. Table Rank acceptability analysis from JSMAA for Pelaez Ranch which shows, as a percent of its total ranks, how often each alternative is in a given rank. WMS Percent of Times WMS is in Given Rank (%) Baseline Board Board Board Board Board Board

157 WMS Table Central weights for water management scenarios (WMS) on Pelaez Ranch showing the weights for each criterion when each WMS is ranked first. The confidence factor is also shown. Confidence Factor [a] Cost to Implement Water Storage Payment Cost of Loss of Forage Wetland Plants Mosquito Frogs Macros Fish Baseline Board Board Board Board Board Board [a] The confidence factor describes the probability of the alternative to be ranked first given the assigned weights. 157

158 Figure 3-1. The opening screen of the Northern Everglades Ranchland Decision Support (NERDS). This page directs the user through the DSS. Each box can be clicked to take the user to different pages in the DSS to enter ranch data, view hydroecological modeling predictions, or set weighting scenarios and evaluate alternative rankings. 158

159 Figure 3-2. The Northern Everglades Ranchland Decision Support (NERDS) data entry page. The user can enter data about the ranch. The user can click on the information button for help in obtaining all hydrologic values needed. They can also go to the home page or the interface page using the Go to Home Page or Go to Interface Page buttons, respectively. 159

160 Figure 3-3. Example data entry page with data from Buck Island Ranch for eight water management scenarios (WMS). 160

161 (a) (b) Figure 3-4. The Northern Everglades Ranchland Decision Support (NERDS) Multicriteria Decision Analysis (MCDA) Matrix page for (a) Buck Island Ranch and (b) Pelaez Ranch. The MCDA Matrix page is where the results from the hydroecological modeling can be viewed along with the water storage payments for all alternatives. At the MCDA Matrix page the user can observe the cost of implementation, water storage payment, the cost due to loss of forage, and the quantities of each biodiversity service or dis-service for all alternatives with available hydrologic data. 161

162 Figure 3-5. The Northern Everglades Ranchland Decision Support (NERDS) interface or Multicriteria Decision Analysis (MCDA) Dashboard page. Here the method of ranking is selected by clicking on WAM, CP, or PROMETHEE in the list of Select Method. Wt. 1, Wt. 2, and Wt. 3 are the three weighting scenarios the user defines. The user and can choose which weighting scenario is presented on the bar graph by clicking on the radio button above Wt. 1, Wt. 2, or Wt. 3. Alternative 1, 2, 3, 4, 5, 6, 7, and 8 are the eight WMS chosen to be evaluated in the Buck Island Ranch example. The Score under each alternative is the score that the ranking method calculated given the weighting scenario chosen. The Rank is the rank of the alternative in the group of alternatives. Here, the user can change the ranking method ( Select Method ), relative importance weights ( Relative Importance ), calculate the results ( Calculate Results Table ), and go to the results page ( Go to Results Table ). The user can also navigate back to the home screen by clicking the Go to Home Page button, and can also navigate back to the MCDA Matrix by clicking on the Go to MCDA Matrix button. 162

163 Figure 3-6. The Results Table page of the Northern Everglades Ranchland Decision Support (NERDS) corresponding to the ranking method and all three importance weighting scenarios presented in Fig This page gives scores and rankings of alternatives for all three importance weighting scenarios for the ranking method selected on the MCDA Dashboard. 163

164 Figure 3-7. The JSMAA rank acceptability for Buck Island Ranch showing the percentage of times each alternative is ranked in each place of ranking. For Buck Island Ranch there are eight alternatives and eight ranks. The rank acceptability values for each alternative are presented in Table

165 Figure 3-8. Central weighting plot for Buck Island Ranch showing the weights for each criterion when each alternative is ranked first. From left to right of the criterion axis, the labels are cost of implementation (ImpCost), water storage payment (WS Pay), cost of loss of forage (ForCost), wetland plant cover (WetCov), mosquito abundance (MosqAbun), frog abundance (FrogAbun), macroinvertebrate abundance (MacroAbun), and fish abundance (FishAbun). The numerical weights are given in Table

166 Figure 3-9. The JSMAA Rank acceptability for Pelaez showing the percentage of times each alternative is ranked in each place of ranking. For Pelaez there are seven alternatives and seven ranks. The rank acceptability values for each alternative are presented in Table

167 Figure Central weighting plot for Pelaez showing the weights for each criterion when each alternative is ranked first. From left to right of the criterion axis, the labels are cost of implementation (ImpCost), water storage payment (WS Pay), cost of loss of forage (ForCost), wetland plant cover (WetCov), mosquito abundance (MosqAbun), frog abundance (FrogAbun), macroinvertebrate abundance (MacroAbun), and fish abundance (FishAbun). The numerical weights are given in Table