Development of a South Atlantic ecosystem model

This proposal outlines a collaborative regional effort to build a South Atlantic Ecosystem Model that will facilitate the connection of inland and coastal marine management strategies and actions to potential resource and economic impacts in estuarine and coastal marine environments, with the following two goals: 1. Improve and Expand the Functionality of the South Atlantic Landscape Conservation Cooperative Blueprint: The SALCC Blueprint is unique in its ability to connect across terrestrial, freshwater, and marine systems. Unfortunately, the availability of and quality of current models connecting impacts across those ecosystems are a major barrier for modeling efforts and Blueprint Users. Improving these models is a high priority for Blueprint 2.0 workshop participants, Coastal and Marine Blueprint Users, the Indicator Revision Teams, and the Conservation Design Team. 2. Identify and Sustain Benefits for Blueprint Users: Inland to Marine - Improve the ability to reliably quantify how SALCC and SAFMC conservation actions contribute to the productivity and sustainability of estuarine and coastal marine ecosystems. Estuarine and Coastal Marine: Improve the ability to quantify how Coastal and Inland actions impact Estuarine and Marine systems. We anticipate that these efforts will enhance the ability to incorporate this information into management and policy (SAFMC) enhancing conservation of Essential Fish Habitat (EFH) and the sustainability of economically and ecologically important fisheries and fish populations that advances the synergies between the SALCC Blueprint 2.0 and SAFMC Fishery Ecosystem Plan II. To that end, we propose a pilot project for conceptual and practical development of a marine ecosystem model for the area managed by the South Atlantic Fishery Management Council, which encompasses the SALCC’s regional coastline, inland areas, conservation management areas. Develop and assimilate the appropriate range of biological, physical and habitat data needed to ensure development of a reliable spatial model that integrates land to sea dynamics and connections for the southeastern United States. A Working Group (listed below) will identify the appropriate resource process indicators that link with the SALCC perspective that we will model. The work will initiate development with a prototype ecosystem model (EwE) that will be refined and reconfigured to incorporate the latest information including spatial and temporal information from parallel models and research efforts in the ocean, estuaries, and other interfaces. The work will also evaluate means of linking inland/coastal models (SALCC urban, flow, and water quality SPARROW) with marine transport and circulation (SABGOM), and marine species-community models (e.g., EwE, ACES, Atlantis, Roffer) One intent of these efforts would be to use coupled coastal and marine models developed here to further inform future snapper-grouper complex assessments (e.g., red snapper and grey triggerfish). The work will also be designed to guide potential improvements in South Atlantic LCC Marine and Estuarine Indicators. It will enhance collaborations amongst SALCC partners on conservation actions. Deliverables Informing the Conservation Blueprint ● Spatial models that predict the impact of changes in land cover, climate, riverine systems, and estuaries on South Atlantic LCC Marine/Estuarine Indicators and other components of the marine ecosystem ● Recommended improvements to South Atlantic LCC Marine and Estuarine Indicators 1. Developing the South Atlantic Ecosystem Model Goal: Develop relevant working ecosystem model to explore management options in the South Atlantic region. The initial focus will be on Ecopath, identifying initial data sources, and funding the work required to extract relevant data from existing data sources. The Model should: 1. Define existing and desired needs for: o Data o Technology o Coverage 2. Link to LCC’s connectivity of systems 3. Be able to assist with refining essential fish habitats and modeling 4. Explain the natural variability of the physical and biological components and facilitate the integration of environmental data into the stock management process 5. Engage Scientific Statistics Committee to define needs for advising the SAFMC • Create tools to conduct ecosystem-based evaluations of fisheries, policies, and tradeoffs • Characterize environmental uncertainty in stock projections • Develop habitat, environmental and ecosystem dynamics views • Identify tradeoffs among conflicting management objectives, e.g. conservation vs. profits 6. Develop useful tools for the Councils’ use - focusing initially on key managed species (e.g., red snapper, grey trigger fish, black sea bass) 7. Provide enhanced spatial management capabilities and evaluate spatial policy options 8. Link Ecopath with Ecosim and Ecospace to physical and chemical oceanographic models The project will define a South Atlantic EwE Model and the companion physical modeling requirements. This scope is detailed in Phase 1 (below). The project will produce valuable tools for a variety of potential user groups. The Blueprint Users and Coastal and Inland Users will gain improved ability to quantify how their conservation actions contribute to estuarine and marine ecosystems. Additionally, the project should increase the ability of users to incorporate this information into organizational policy (South Atlantic Fishery Management Council). Early on, the project should engage SAFMC SCC members and Council members in a needs assessment to ensure the tools are being designed with clearly defined goals in mind. Usability is an essential aspect for success. As an early focus, the project will seek to develop useful tools for the Council’s use for assessing species in the snapper grouper complex (e.g., red snapper and grey trigger fish.) This focus will provide an immediate source of feedback on the improvement of the models and tools. • Creating tools to conduct ecosystem-based evaluations of fisheries policies and tradeoffs • Identifying tradeoffs among conflicting management objectives, e.g. conservation vs. profits • Integrating environmental data into the stock management process Model Features • Provide enhanced spatial management capabilities • Link Ecopath /Ecosim / Ecospace to physical and chemical oceanographic models • Link to the South Atlantic Landscape Conservation Cooperative’s connectivity of systems • Refine Essential Fish Habitat and modeling Phase 1: Create Version 1.0 of a Comprehensive South Atlantic Ecopath/Ecosim Model: Reconfigure, update and enhance the existing Ecopath/Ecosim Model. Leverage learning from the Eastern Gulf of Mexico Grouper Ecopath with Ecosim Model. The Ecopath software package can be used to: • Address ecological questions • Evaluate ecosystem effects of fishing • Explore management policy options • Analyze impact and placement of managed areas • Predict movement and accumulation of contaminants and tracers (Ecotracer). Development of a South Atlantic Ecopath model will draw on information contained and experts involved in the preparation of the South Atlantic Fishery Management Council’s Fishery Ecosystem Plan. Thus, development of a EwE model will help the Council and other decision makers identify information gaps and direct future research while providing insight into ecosystem level effects of fisheries. This will facilitate the future use of ecosystem modeling in the policy and management decision making process and provide a more comprehensive understanding of the biological, social, and economic impacts of fishery management actions. Creation of a base South Atlantic model will be an essential first step for linking to the existing SALCC Blueprint. Additionally, EwE could lead to development of or linking to sub-models (e.g., deepwater snapper grouper, Core Sound or Albemarle-Pamlico Sound, North Carolina models). In addition, the South Atlantic model will now benefit from new capabilities of the base Ecopath model which incorporate functions to import GIS maps, catch and fleet information and environmental model parameters. The initial EwE Model development will: • Better utilize existing datasets and assessments • Define the essential data needed for the first generation of the Model • Leverage existing analytical tools & metrics for evaluation of fishery management alternatives (including the environmental portion) • Integrate available data and updated stock assessments to expand risk assessment capabilities • Enhance description of interactive influences - stock, productivity, uncertainty • Conduct a team review and analysis of existing data • Ensure connectivity data is included from the SALCC Blueprint (estuaries and rivers) Decision Support • Enable the evaluation of Council Managed Areas (i.e., MPAs, Spawning SMZs, CHAPCs) and Regional Conservation Priorities • Promote better stock assessments • Develop useful tools that enable evaluation of fishery projections The first generation model will thus be structured to integrate existing data sets from a variety of sources. These include: • Updated diet compositions • New biomass estimates • Utilize refined fishery operation information • Focus on priority species (e.g., core snapper/grouper species, king and Spanish mackerel, etc.). • Take advantage of new assessment information from the SouthEast Data, Assessment, and Review (SEDAR) process • Utilize new species and fishery information not used/available in previous modeling efforts. • FWRI will provide access to their warehouse of data and provide project support. • Other databases, as they become available. The model will encompass the entire South Atlantic Fishery Management Council’s management footprint. Phase 1 Outcomes: 1. Improved/enhanced Ecosystem model for the South Atlantic Region 2. Ensure connectivity data is included to connect with the SALCC Blueprint: Estuaries and Rivers (Food Web) 3. Better utilize existing marine datasets 4. Develop analytical tools & metrics for evaluation of fishery management alternatives 5. Use more of the available data to expand risk assessment capabilities 6. Define the essential data needed for Phase 1 and Phase 2 7. Provide better description of interactive influences: stock, productivity, uncertainty 8. Conduct a team review and analysis of existing data 9. Suggest potential improvements in South Atlantic LCC Marine and Estuarine Indicators 10. Collaborate with South Atlantic LCC post-doc working on coupling conservation actions and South Atlantic Indicators 11. Recommendations for improvements to South Atlantic LCC Marine and Estuarine Indicators Phase 2: Refining and linking Phase 1 Model to other models to create a suite of decision support tools. Meet the immediate needs of the SSC and the South Atlantic Council for better understanding of the management decisions they face. This includes the ability to understand levels of uncertainty and to have the ability to evaluate the (biological and other) consequences for alternative management scenarios and, correlated views on the potential impact of any decision. 1. Expand Blueprint linkages and assist with refinement of indicators 2. Evolve spatial capabilities of the modeling • Link to hydrodynamic oceanographic models and satellite data • Provide more realistic predictions about spatial policy options • Predict impacts of episodic events that are limited in space (oil spills, red tides, upwelling) 3. Enable the evaluation of MPA’s 4. Expand utilization of existing data sets 5. Full scale South Atlantic Model completed 6. Integrate habitat layers 7. Define ongoing and future data needs - define the collection process 8. Generate “better” Stock Assessments 9. Develop useful tools that enable evaluation of projections 10. Develop indicators to incorporate movement from estuaries to oceans, and also along the coast Project Outcomes • This effort will tap into a stellar regional inter-disciplinary science team to produce the best possible information for use by resource decision-makers. • Work with managers and decision-makers to ensure models meet their needs • Recruit top researchers to assist in an inclusive, long-term management effort • The team will comprise an Ecosystem technical advisory group that ensures the approaches taken include practical science and leading-edge thinking. • Reveal the most important linkages of coastal/ocean ecosystems to estuarine-riverine environments • Development of a strategic plan for long-term cost-effective monitoring and data assimilation. A robust plan and expert model development team will leave us well-positioned to leverage available ecosystem partner funding to create the next generation of South Atlantic Ecosystem Models which support and enhance regional conservation and management efforts. Principal investigators • Dr. Marcel J. Reichert (Marine Resources Research Institute, South Carolina Department of Natural Resources) • Dr. Luiz Barbieri (FL Fish & Wildlife Conservation Commission-FWRI) • Dr. Thomas Okey (Ocean Integrity Research, Victoria, BC, Canada) • Dr. Jerald S. Ault (Professor and Director, Fisheries Ecosystem Modeling and Assessment Research, Rosenstiel School of Marine and Atmospheric Science, University of Miami,) • Dr. Ruoying He (Department of Marine, Earth and Atmospheric Sciences, North Carolina State University) • Dr. Peter Sheng (Professor and Director, Coastal and Oceanographic Engineering Program, University of Florida) • Vembu Subramanian (RCOOS Program Manager and Data Management Coordinator, SECOORA) • Dr. Patrick N. Halpin (Director, Geospatial Analysis Program Duke University) • Roger Pugliese (Senior Fisher Biologist, South Atlantic Fishery Management Council) This proposal combines multiple research efforts in an integrated approach to create short-term and long-term ecosystem suite of tools. 1. Developing South Atlantic Ecopath with Ecosim Model Principal Researcher: Tom Okey The South Atlantic Fishery Management Council sponsored the development of the first Ecopath with Ecosim (EwE) fishery-ecosystem model of the South Atlantic Bight (SAB) ecosystem (Okey and Pugliese 2001) as part of its Fishery Ecosystem Planning. This model was refined soon thereafter during an iterative process involving a broad cross-section of stakeholders and scientists to produce a second generation model. That refined model was re-structured and refined recently, with sponsorship of the Pew Charitable Trusts, to explore the importance and roles of forage species in the SAB (Okey et al. 2014), but the scope of that project did not include updating basic input data across the whole biological community. This latest iteration of the SAB model is a good starting point for developing an updated EwE model that can be the core of an integrated end-to-end model of the SAB ecosystem that can achieve the broad objectives expressed herein. The updating and refinement can be achieved using resources such as the GOM gag model (Chagaris and Mahmoudi 2013), based on the original West Florida Shelf model (Okey and Mahmoudi 2002, Okey et al. 2004), as well as a variety of additional resources that include new data and information, also as discussed herein. The following steps will be initiated in Year 1 with the goal of achieving most of them, with the expectation that the spatial and temporal dynamic parts will continue to be refined through Year 2 and beyond as analyses proceed. • Designing and refining the structure of the model in terms of functional groups for optimal exploration of questions of interest • Incorporating updated diet composition data • Incorporating new biomass estimates for functional groups • Utilize refined fishery information including effort, landings, and discards across space and time, habitat impacts across space-time, and economic information, both during and since initialized years as time series • Identify, collect, and collate fishery independent information including biomass indices for various biota as well as time series of other physical and chemical drivers in the system • Calibrating the dynamic behavior/skill of the model by adjusting the predation vulnerability parameters by fitting the temporal projections to independent time series of biomass indices, as driven by time series of fishing effort and other physical/chemical drivers • Express the model spatially in Ecospace using available independent physical, chemical, and biological information and model results including information about habitat associations of biota and the physical/chemical oceanography. • Focus on priority species (e.g., core snapper/grouper species, king and Spanish mackerel, etc.). • Take advantage of new assessment information from the SouthEast Data, Assessment, and Review (SEDAR) process through coordination with SCDNR and FWRI • Utilize new species and fishery information not used in any previous efforts. Adapting the output of oceanographic models, e.g. through SECOORA, for the specification of useful EwE models may be challenging, and we may confront needs that will require some help from outside of our group, such as from members of the Ecopath Research and Development Consortium, and it is possible that such assistance will require additional funds. Literature Cited Chagaris, D., and B. Mahmoudi. 2013. Natural mortality of gag grouper from 1950 to 2009 generated by an Ecosim model. SEDAR 33-DW07. SEDAR North Charleston, SC. Okey, T. A., A. M. Cisneros-Montemayor, R. Pugliese, and R. U. Sumaila. 2014. Exploring the trophodynamic signature of forage species in the U.S. South Atlantic Bight ecosystem. Fisheries Centre Working Paper 2014-14, University of British Columbia Fisheries Centre, Vancouver, Canada. Okey, T. A., and B. Mahmoudi. 2002. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume II: Model construction. Florida Marine Research Institute, Florida Fish and Wildlife Conservation Commission, St. Petersburg. Okey, T. A., and R. Pugliese. 2001. A preliminary Ecopath model of the Atlantic continental shelf adjacent to the Southeastern United States. Pages 167-181 in S. Guenette, V. Christensen, and D. Pauly, editors. Fisheries Impacts on North Atlantic Ecosystems: Models and Analyses University of British Columbia, Fisheries Centre Research Reports. Okey, T. A., G. A. Vargo, S. Mackinson, M. Vasconcellos, B. Mahmoudi, and C. A. Meyer. 2004. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecological Modelling 172:339-359. 2. Data Conditioning Principal Researcher: Marcel Reichert SC-DNR’s MARMAP and SEAMAP-SA fishery independent monitoring programs have collected long-term abundance and life history data from Cape Hatteras, NC to the St. Lucie, FL, dating back to the 70’s and 80’. Since 2010, reef fish sampling efforts have been done in collaboration with SEFIS (SEFSC, Beaufort, NC). Sampling has been conducted annually with a variety of gears (incl. trawls, fish traps, and long lines). Available data include detailed sampling location coordinates, oceanographic variables (incl. water column temperature and salinity), species composition, relative abundance by species, and information on age, growth, reproduction, diet, and genetic variability of a variety of selected species. Data and analysis have been used in many regional stock assessments and is published in numerous reports and publications. Key data (e.g. location, species abundance, lengths, bottom temperature, and some life history information) is stored in a comprehensive data- base. This database is housed at SC-DNR and managed by DNR personnel (reef fish data in close collaboration with SEFIS staff). Additional information, such as diet data, and age and reproductive information for certain species is not yet available in the central data base. However, this data may be crucial for a comprehensive ecosystem modeling effort that is relevant for regional fisheries management. Making data available for use in modeling efforts involves extracting relevant information, summarizing data, and possible base line analyses. A coordinated effort is needed to most effectively and efficiently provide the relevant fishery independent data and analyses to the ecosystem modeling leads. As this additional effort to support a regional ecosystem model is not included in any current SC-DNR funding source, it will require additional funds. The level of effort will depend on the nature and detail of the data and analyses. However, I expect that 2-3 months of a PhD level analystwill be needed for this effort. 3. Review of Estuarine Data and Models Principal Researcher: Peter Sheng This task is to identify estuarine data and model which exist along the Northeast and Central Florida coast which might be used to develop linked estuarine-marine models for predicting estuarine indicators which have been selected to design and evaluate the success for South Atlantic Conservation Blueprint (https://www.fws.gov/science/catalog). These indicators include (1) coastal condition: index of water quality, sediment quality, and benthic condition; (2) wetland patch size: index based on the size of wetland patches; and (3) water-vegetation edge: index of length of edge between open water and vegetation. During the first year, University of Florida (UF) will gather and review all available data (both fishery independent and fishery dependent) related to the above indices from several state and regional agencies in the Northeast and Central Florida region, including Florida Fish and Wildlife Research Institute, Florida Department of Environmental Protection (FDEP), Guana-Tolomato-Matanzas National Estuarine Research reserve (GTM-NERR), St. Johns Water Management District (SJRWMD), and University of Florida Whitney Marine Laboratory. FMRI’s long term fishery independent and fishery dependent data in the Lower St. Johns River and Indian River Lagoon date back to the 1980’s and are well catalogued (Barbier, personal communication). UF will access all the available data, categorize them into the three indicator groups, and identify periods when data are amenable to preliminary model testing in the second year. During the second year, UF will review estuarine hydrodynamic-ecosystem models which are suitable for simulating the estuarine indices and for linking with the marine hydrodynamic-ecosystem model of Ruoying He. These models will include but not limited to those (developed by UF) that have been applied to the region to address such issues as residual circulation, flushing and residence time, sediment transport, nutrient and dissolved oxygen dynamics, light attenuation and seagrass biomass, algal bloom, larval dispersal, storm impact on salinity and water and sediment quality, storm surge and coastal inundation, impact of climate change on coastal inundation, etc. Appropriate modeling scenarios will be established based on the data and model review. To perform the work stated above, I expect a Ph.D. level support for 3-4 months. 4. A Coupled Marine Environmental Assessment and Prediction System for the Southeastern U.S. Coastal Ocean in Support of Effective Marine Ecosystem-Based Management Principal Investigator: Ruoying He, North Carolina State University In the southeastern U.S. coastal ocean, the Loop Current/Florida Current/Gulf Stream system unites shelf seas from Louisiana to Florida in the Gulf of Mexico and from Florida to North Carolina along the east coast. This strong, deep ocean feature flows along the continental shelf in the entire southeast U.S., strongly affecting circulation on adjacent continental shelves and providing a conduit for transport of nutrients, heat and marine organisms. To contribute to the efforts of building regional marine ecosystem predictability, we propose to implement an advanced regional marine environment assessment and prediction system. Such a system will predict coastal ocean conditions over the entire south Atlantic footprint with a high degree of scientific accuracy and provide detailed information to stakeholders in a timely and clear fashion. Methodology: The proposed regional ocean modeling effort will be built upon NCSU’s existing circulation nowcast/forecast modeling system (http://omgsrv1.meas.ncsu.edu:8080/ocean-circulation/). This 3-dimensional ocean model (Figure 1) covering the entire South Atlantic Bight and Gulf of Mexico (hereafter SABGOM) has 5 km spatial resolution and 36 vertical layers. The system has been successfully implemented and applied by R. He’s group at NCSU in hindcast mode in studies of Hurricane Isabel (Warner et al., 2010) and Hurricane Ivan (Zambon, 2009; Zambon et al., 2014a); coastal flooding and beach erosion during Hurricane Ida and Nor’Ida (Olabarrieta et al., 2012); air-sea interactions during Hurricane Sandy (Zambon et al. 2014b) and winter extratropical cyclones (Nelson, 2011; Nelson and He, 2012; Nelson et al., 2014), long-term river plume dynamics and coastal biogeochemical variability (Xue et al., 2012; 2013). In this project, we will couple SABGOM with a suite of marine primary production NPZD-type marine ecosystem models (Fennel et al., 2006; He et al., 2011) to perform both hindcast and forecast experiments. In doing so, time and space continuous oceanographic conditions (such as three-dimensional ocean current, water temperature, salinity, nutrient content, plankton concentrations) will be generated to characterize regional marine environment. Data exchange interface will be also developed to feed these oceanographic conditions to high trophic level ecological/fishery models (e.g., Ecopath and Ecosim, http://www.ecopath.org/) to start addressing marine resource management issues in south Atlantic region. Among many other benefits, this effort would directly facilitate and enhance the effort of the South Atlantic Fishery Management Council (SAFMC) in support of the move to Ecosystem Based management to develop an ecosystem modeling suite with an emphasis on a fully parameterized Ecopath and Ecosim model for the South Atlantic Region. These pelagic systems in the south Atlantic serve as Essential Fish Habitat (EFH) for species managed by the South Atlantic Fishery Management Council including dolphin, wahoo, and many species in the snapper grouper complex (SAFMC 2009). Development of a regional Ecopath model will help the SAFMC and other decision makers identify information gaps and direct future research while providing insight into ecosystem level effects of fisheries. Other critical management needs that our proposed effort will enhance include but are not limited to: providing oceanographic models which link benthic-pelagic habitats and food webs; provide models to document past and ongoing upwelling events on the east coast of Florida; develop a species specific model for gag grouper linking the species use of all major pelagic and benthic habitats in the South Atlantic to highlight connectivity and the importance of taking an ecosystem approach and moving to ecosystem-based management in the region. To achieve our project objective, we team will: 1) Sustain NCSU’s SABGOM ocean circulation Nowcast/Forecast modeling system; 2) Establish the full couplings of SABGOM ocean model with marine ecosystem models, and generate regional nowcasts and forecasts of ocean circulation and low trophic level marine ecosystem predictions; 3) Develop a model interface with high trophic level ecological/fishery models (e.g., Ecopath and Ecosim), and interact with other investigators working on regional fishery issues. 3. Budget Summary and Justification Given the limited funds available, the budget only reflects salary/fringe, travel, publication and supplies and not indirect costs with Year 1 funds at $30,000 and Year 2 at $30,000. References Fennel, K., Wilkin, J., Levin, J., Moisan, J., O’Reilly, J., and Haidvogel, D (2006): Nitrogen cycling in the Middle Atlantic Bight: Results from a three-dimensional model and implications for the North Atlantic nitrogen budget, J. Geophys. Res., 111 (C06003), doi:10.1029/ 2005JC003116. He, R., K. Chen, K. Fennel, G. Gawarkiewicz, and D. McGillicuddy (2011), Seasonal and interannual variability of physical and biological dynamics at the shelfbreak front of the Middle Atlantic Bight: Nutrient supply mechanisms. Biogeosciences, 8, 2935-2946, doi: 10.5194/bgd-8-1555-2011. Nelson, J. (2011): Air-Sea Interactions during Strong Winter Extratropical Storms, NCSU Master Thesis Nelson, J. and He. R. (2012): Effect of the Gulf Stream on Winter Extratropical Cyclone Outbreaks, Atmosphere Research Letters, 13: 311-316, DOI: 10.1002/asl.400. Nelson, J., He. R. and Warner J. C. and J. Bane (2014): Air-Sea Interactions during Strong Winter Extratropical Storms, Ocean Dynamics, doi:10.1007/s10236-014-0745-2. Olabarieta, M., Warner, J., Armstrong, B., Zambon, J., and He, R. (2012): Ocean-Atmosphere Dynamics During Hurricane Ida and Nor’Ida: An Application of the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System, Ocean Modelling, 43-44, 112-137. Warner, J.C., Armstrong, B., He, R., Zambon, J. (2010): Development of a coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling system. Ocean Modell. 35, 230–244. Xue, Z., He, R., Liu, J. P, J. Warner (2012): Modeling Transport and Deposition of the Mekong River Sediment, Continental Shelf Research, 37, 66-78, doi: 10.1016/j.csr.2012.02/010. Xue, Z., R. He, K. Fennel, W. J. Cai, S. Lohrenz, and C. Hopkinson (2013), Modeling ocean circulation and biogeochemical variability in the Gulf of Mexico, Biogeosciences, 10, 7219-7234. Zambon, J. B. (2009): Investigations of Landfalling Tropical Storms Using the Coupled Ocean Atmosphere Wave and Sediment Transport (COAWST) Model. NCSU Master Thesis. Zambon, J. B., R. He, and J. C. Warner (2014) Investigation of Hurricane Ivan using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Model, Ocean Dynamics, 64(11), pp. 1535-1554, doi:10.1007/s10236-014-0777-7. Zambon, J., R. He, and J. C. Warner (2014) Tropical to extratropical: Marine environmental changes associated with Superstorm Sandy prior to its landfall, Geophysical Research Letters, doi: 10.1002/2014GL061357. 5. South Atlantic Fisheries Ecosystem Modeling and Prediction Principal Investigator: Jerald S. Ault, University of Miami The US southeast Atlantic coastal ocean ecosystem sustains productive and highly diverse fish and invertebrate communities that support valuable fishing, boating, tourism and other recreational opportunities vital to the region’s multi-billion dollar economy. Over the next decade, this ecosystem will be put at especially great risk due to human uses (e.g., intensive commercial and recreational fishing, coastal development, pollution, etc.) and climate changes. The project will refine development of a fisheries ecosystem model designed to evaluate multiple interacting threats to sustainability of fisheries resources, including direct impacts of fishing on target species, trophic impacts of fishing on prey species, and a variety of environmental impacts (e.g., degradation of water quality/habitats). This project builds upon previous efforts that developed a combined fisheries ecosystem modeling-prediction tool for assessing the impacts of Everglades restoration activities on fisheries production and habitats in south Florida (Ault et al. 1999, 2003, 2005ab, 2014; Werner et al. 2001; Wang et al 2003). The Ault et al. (1999a; Walters and Martell 2004) model was one of the first to explicitly couple ocean physics and biological process models to produced forecasts that simultaneously evaluated fishing and non-fishing (water management) regulatory policies for sustainability of natural resources. It was later adapted for the Atlantic coast (ACES, Atlantic Coast Ecosystem Simulation) to assess multispecies fisheries risks from exploitation and environmental changes (Luo et al. 2005). The ACES model is grounded in the evolution of size/age cohort-structured population models, a staple in traditional marine fisheries forecasting (Beverton and Holt 1957), but with the added element of intra- and inter-specific competition between cohorts and species, respectively. ACES is comprised of four dynamic layers: (1) habitats; (2) climate and hydrodynamics; (3) population and community dynamics encompassing the predator-prey interactions; and, (4) human uses. ACES simultaneously tracks the abundance and spatial dynamics of cohorts of prey and predator species at age a and time t and space x from spawning, through settlement and recruitment, and as they grow and age, reproduce and ultimately die. ACES mathematically links bioenergetic principles of fish physiology, population ecology, fish-habitat relationships, and community trophodynamics to regional hydrodynamic circulation models. The model tracks the size-structured abundance and biomass distributions and recruitment of individuals and populations over time and space, and can produce catch-at-age and population-at-age statistics for stock biomass, fishery yields, stock spawning biomass, and recruitment. As a result, familiar fisheries stock assessment results and sustainability benchmarks are available on a daily, quarterly, semi-annual, annual, etc. basis.. Intent, goals, anticipated results.- The ACES model will provide a flexible framework for simultaneously evaluating overfishing and environmental threats to sustainability of fisheries ecosystems, and for exploring policy options to mitigate the risks of these threats. Regional benefits and beneficiaries of activity.- This project integrates and augments existing observational, data management, modeling, and education assets in the SALCC-SAFMC domain to provide deep and lasting benefits to the beneficiaries listed below, but also to the people, communities, and natural resources that make the SE unique. Model scenarios will be tailored to pressing resource management issues, such as: (1) evaluation of resource “sustainability” from a much broader perspective (habitat alterations, overfishing effects on food webs, etc.); (2) improved flexibility to transition from single- to multispecies stock assessments; (3) design of effective marine protected areas; (4) understanding the influence of episodic and extreme storms (including hurricanes). These capabilities will ultimately improve the livelihoods of commercial and recreational fleet sectors and others, as well as guide future cost-effective data acquisition, modeling and decision support for sustaining valuable living marine resources. This effort provides positive impacts for a broad range of constituencies, including federal, regional, state, and local interests. We will refine and implement an integrated suite of mathematical predictive models in a robust ecosystem modeling framework for SALCC, building on existing efforts within and external to NOAA. Our research will focus on developing the component layers of the ACES model (Fig. 1) by strategically utilizing existing biological and physical-environmental models and data, including those from related projects funded from non-IOOS sources. The proposed SALCC research does not include laboratory or field studies, other than deploying satellite-tags on animals. The ACES model focus will be key predators and associated prey species (e.g., tarpon and blue crabs-penaeid shrimps-mullet), all of which support lucrative commercial and/or recreational fisheries. Specific research tasks and associated time periods are summarized as follows: Task 1.- Spatial Grid & Circulation Model Synthesis. This task will assimilate & synthesize existing spatial grids and regional oceanographic models for the domain that include major rivers and coastal bays, offshore shelf/reef environments, and the FL Current/Gulf Stream. Task 2.- Synthesis of Predator and Prey Population Dynamics. This task will synthesize data to estimate life history and vital rate parameters (age & growth, lifespan, reproductive maturity, fecundity, spawning stock-recruitment relationships, movements and migrations, etc.) for each predator and prey species in ACES. Candidate predator species are tarpon (migratory inshore pelagic fish, range encompasses model domain), gag and black grouper (reef-fishes, gag primarily occurs north of Cape Canaveral, black grouper primarily occurs south of Cape Canaveral), and Spanish/King mackerels and tunas (migratory coastal-offshore pelagics fish, range encompasses domain). These species encompass the cross-shelf habitats of the model domain, from coastal bays (tarpon) to offshore reefs (groupers) and pelagic zones (mackerel & tunas). Candidate prey species are penaeid shrimps, blue crabs, mullet, menhaden, and other coastal and pelagic forage fishes. Task 3.- Synthesis of Bathymetry & Benthic Habitats. Assimilate bathymetry and benthic habitat data into the spatial grid covering the model domain. Task 4.- Freshwater Inflow Model Synthesis. Assimilate existing freshwater inflow models for coastal areas and bays of the model domain. Task 5.- Integrate Oceanography, Freshwater Inflow, and Habitat Layers. This task will integrate the physical and environmental model layers developed in Tasks 1, 3, and 4. Task 6.- Computational Platform & Interface. This task will convert ACES code to run on gridded high-performance supercomputers at the University of Miami’s Center for Computational Science, and develop a scientific data visualization interface for model outputs. Task 7.- Synthesis of Spatial Abundance/Size Data for Predator and Prey Species. This task will assimilate data from fishery-dependent (commercial and recreational catch-effort sampling) and fishery-dependent resource surveys (e.g., Ault et al. 1999b; Smith et al. 2011). Task 8.- Stakeholder Workshop Using Prototype Model. This task will conduct workshops for select groups of stakeholders (resource agency managers, scientists) across the SALCC and SAFMC region. The prototype ACES model will be used to spur the discussions. Inputs received will help tailor ACES to address specific environmental and fishery issues. Task 9.- Perform Management Scenario Evaluations. This task will evaluate interacting impacts of multiple stressors on predator and prey species sustainability: direct and indirect fishing, watershed management of freshwater inflows, habitat loss/alteration, etc. (tailored to issues identified in Task 8). The model will then be used to explore practical management actions to reduce threats to sustainability. References Ault, J.S., J. Luo, S.G. Smith, J.E. Serafy, J.D. Wang, G.A. Diaz, and R. Humston. 1999a. A spatial dynamic multistock production model. Canadian J. of Fisheries and Aquatic Sciences 56 (S1): 4-25. Ault, J.S., G.A. Diaz, S.G. Smith, J. Luo, and J.E. Serafy. 1999b. An efficient sampling survey design to estimate pink shrimp population abundance in Biscayne Bay, Florida. North American Journal of Fisheries Management 19(3): 696-712. Ault, J.S., J. Luo, and J.D. Wang. 2003. A spatial ecosystem model to assess spotted seatrout population risks from exploitation and environmental changes. Pages 267-296 in S. Bortone (ed.). Biology of the Spotted Seatrout. CRC Press. Ault, J.S., S.G. Smith, and J.A. Bohnsack. 2005a. Evaluation of average length as an estimator of exploitation status for the Florida coral reef fish community. ICES Journal of Marine Science 62: 417-423. Ault, J.S., J.A. Bohnsack, S.G. Smith, and J. Luo. 2005b. Towards sustainable multispecies fisheries in the Florida, USA, coral reef ecosystem. Bull. Marine Science 76(2): 595-622. Ault, J.S., Smith, S.G., Browder, J.A., Nuttle, W., Franklin, E.C., Luo, J., DiNardo, G.T., Bohnsack, J.A. 2014. Indicators for assessing the ecological and sustainability dynamics of southern Florida’s coral reef and coastal fisheries. Ecological Indicators 44 (2014): 164-172. Beverton, R.J.H. and S.J. Holt. 1957. On the dynamics of exploited fish populations. Ministry of Agriculture, Fisheries and Food. Fishery Investigations (UK), series II, vol. XIX. Lowestoft. 533 p. Luo, J., J.S. Ault, D.B. Olson, A. McCrea, K.J. Hartman, L. Kline, G. White, and P. Kilduff. 2005. A spatial ecosystem model for Atlantic coast multispecies fisheries assessments of menhaden and bluefish. Final Report to the Atlantic States Marine Fisheries Commission. Meester, G.A., J.S. Ault, S.G. Smith, and A. Mehrotra. 2001. An integrated simulation modeling and operations research approach to spatial management decision-making. Sarsia 86: 543-558. Smith, S.G., Ault, J.S., Bohnsack, J.A., Harper, D.E., Luo, J., McClellan, D.B. 2011. Multispecies survey design for assessing reef-fish stocks, spatially-explicit management performance, and ecosystem condition. Fisheries Research 109(1): 25-41. Walters, C.J., and S.J.D. Martell. 2004. Fisheries Ecology and Management. Princeton University Press. 399 p. Wang, J.D., J. Luo, and J.S. Ault. 2003. Flows, salinity, and some implications for larval transport in south Biscayne Bay, Florida. Bulletin of Marine Science 72(3): 695-723. Werner, F. E., Quinlan, J. A., Lough, R. G., and Lynch 2001. Spatially-explicit individual based modeling of marine populations: a review of the advances in the 1990s. Sarsia 86: 411-421.