The FLELMR Spatial Decision Support System for ...

The FLELMR Spatial Decision Support System for Coastal Resources Management

Eleventh Annual Symposium on Geographic Information Systems

Rubec. Peter J.. Florida Department of Environmental Protection. Florida Marine Research Institute, I 0 0 Eighth Ave. SE. St. Petersburg, Florida 33701. Tel. (8 131 896-8626, Fax. (8 13) 823-0166.

Monaco, Mark E., and Christensen, John D., U.S. Department of Commerce. National Oceanic and Atmospheric Administration, Strategic Environmental Assessments Division, 1305 East-West Highway, 9th Floor, Silver Spring. Maryland 20910.

Abstract The Florida Estuarine Living Marine Resources (FLELMR)System is a spatial decision support system (SDSS)being developed by the Florida Department of Environmental Protection to summarize information concerning environmental associations of marine fish and invertebrate species in coastal areas of Floridn. The goal is to extend the system statewide. FLELMR has been created as a source of synthesized biological information needed for fisheries management and for assessing potential impactsfrom oil spills and other perturbations. The system contains information pertaining to the life histories, reproduction, and habitat requirements of 91 species of marine fish and invertebratesfound in Tampa Bay. Sarasota Bay, Indian River Lagoon, and Florida Bay. Text and numeric data are being added to an Oracle database. The system is being expanded to include more species, so that researchers can assess biodiversity and biological integrity of coastal ecosystems. Habitat suitability index (HSl)models are under development, and are used with the ARCIINFO geographic information system (CIS)to map the distributions of species by life stages. The SDSS will support spatial queries with G1S sofnvare to facilitate decision-making.

Introduction Ecosystem management is being implemented by the Florida Department of Environmental Protection (EMISC-FDEP 1995). The Florida Marine Research Institute (FMRI) within FDEP is largely responsible for providing the estuarine and coastal marine information used by fisheries and environmental managers. FMRI has adopted a holistic approach for managing the marine environment by conducting monitoring and research of coastal marine ecosystems. The Florida Bay Program includes studies at all tmphic levels (Rubec and McMichael 1996). Unfortunately, there is insufficient funding to do this in all coastal areas. The Coastal and Marine Resources Assessment (CAMRA) ,pup at FhIRI has created a coast wide CIS database, the

Marine Resources Geographic Lnformation System (MRGIS). The MRGIS supports studies using geographic information systems (GIS), remote sensing, and modeling to link the seascape to marine resources in Florida. The CAMRA group has been collaborating with NOAA's Stategic Environmental Assessments (SEA) Division within the National Ocean Service (NOS) to develop spatially-oriented desktop SDSS systems. The Florida Coastal Planning and Assessment System (COMPAS) was developed using ARCView GIS software (LaPointe et al. 1996). Florida COMPAS was developed to suit the needs of managers concerned with water quality and pollution. More recently, CAMRA developed the ARCView Marine Spill Analysis System (AVMSAS) (FDEP 1996). The AVMSAS incorporates environmental sensitivity index (ESI) maps of shore-

Vancouver British Columbia Canada

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Eleventh Annual Symposium o n Geographic Information Systems

, line habitats, plus resources at risk from oil spills. The biological

resources at risk include marine bird, reptile, and mammal species distributions. The ESI maps presently lack fish and invertebrate species distributions.

Methods The developing FLELMR SDSS is focused on marine ffih and invertebrates, while building on past work in order to relate habitats to living marine resources. It involves the creation of an integrated database that makes the best use of available quahtative and quantitative infonnafion. Quantitative data from Fisheries Independent in Choctawahatchee Bay, Tampa Bay, Charlotte Monitoring Harbor, Florida Bay, and the Indian River Lagoon are being tied to spatial habitat coverages in the MRGIS. Modeling is being used to create predicted maps of fish and invertebrate species distributions in Florida estuaries that are not quantitatively surveyed.

ELMR Program Currently, FLELMR is very similar to the Estuarine Living Marine Resources (ELMR) Program that was developed by the NOAA SEA Division (Nelson et al. 1991, 1992). Information compiled by SEA Division on the life h i s t q characteristics of 4 4 species of fish and invertebrates, and their associated spatial and temporal distributions by three salinity zones, were supplied to FMRI to form the initial database on which FLELMR has been built.

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Existing FLELMR System

textual SLH summaries and numeric tables on marine fish, benthic invertebrates, and marine plants so that managers can access information on dominant species, keystone and lower trophic level species important for sustaining fisheries, and indicator species sensitive to environmental degradation. A more comprehensive SLH outline was created. The number and types of textual and numeric tables were expanded, and associated with the main headings in the SLH outline. Textual information will be retrieved by topic. Tables were added concerning growth, mortality, population densities, and catch rates (CPUEs) by season, between years, and across environmental gradients (Figure I). Diversity tables have also been created that will allow FMRI staff to summarize the literature concerning marine communities associated with various benthic habitats. There are 13 types of textual and 13 numeric tables associated with 5 geographic regions. Most of the textual and numeric tables have the same headings to facilitate relating information in the database. The new FLELMR is designed so that a compiler can summarize information from the scientific literature into the textual tables. Key environmental parameters are then transferred to the numeric tables. Both types of tables have windows for citation numbers that tie to the master bibliography. FLELMR will be used on personal computers as part of FMRI's local area network. Specialists for different species at FMRI will revise and update various parts of the database (Figure 1). 3 W m l nrblal ME= 4 IS NEI

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T~~~~ FLELMR was developed for FMRI by the Mote Marine A~EINJM~~ + wsuar ~ r ~ m 0.0. Laboratory and Harbor Branch Oceanographic Institution Florida -F CC from 1991- 1994. It summarizes data on the seasonal abunCIS t n r a a 8 .AVMSAS dance of fish and invertebrate species by life stage, envi.c.cous 7 sW.~rnp. I ronmental occurrences, and habitat associations. It is priN a l Gmerakm marily based on qualitative information, scattered in puboat= R E LE~~~ U~ . Emen R W ~ . Ss- ?+mr ~ m m r lished and unpublished literature, as well as the expert Oronnrar,vc 1 ;:CcEarnma, L*mke knowledge of biologists familiar with Florida's marine 5 ns1 hGDI PC a ~ ~ d resources. It consists of text and numeric categorical data FLELMR CACIS in an Oracle relational database for Tampa Bay, Sarasota .~ : I O U W 0p":cllrn.e - LI* ,*mv FLE~URD D : DJIC ~ Bay, Florida Bay, and the Indian River Lagoon. Swmum om + DLI~W(XI Td*, FMRI has expanded the number of species and the numL It n*,* stmnnm rack, an.3 ber of tables in FLELMR. Information can be related within and between 91 species of marine fish and invertebrates. Figure 1. FLELMR Components and Integration. Specifically, the textual Species Life History (SLH) table summarizes information on species throughout the northern Gulf of Mexico, with emphasis on Florida literature. There Coupling Species To Habitat are 6 types of numeric tables. The Abundance, Value & Status, Habitat Suitability Index (HSI) models are used to generate Reproduction, Habitat, Reproduction, and Attributes tables premaps of habitat quality and, by inference, relative abundance sent categorical data describing the seasonal abundance, social, or carrying capacity for single species life stages (Monaco and and economic importance of each species, as well as their habitat Christensen 1996). Developing HSI models requires specific and environmental requirements by life stages. The Habitat and information about the habitat requirements of species. The Attributes tables are repeated by Egg, Larvae, Juvenile, Adult, HSI concept centers around the assumption that the "imporand Spawning Adult life stages, and Maturation and Parturition tance" of a geographic area can be defined by estimating the life stages also exist for shark species. Hence, there are 13-15 habitat requirements of a species and quantifying habitat tables associated with each species for each estuary. availability.

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Expanded FLELMR System FLELMR is being expanded to integrate FMRI's and NOAA's biological and physical/chemical databases. Information is being added concerning population dynamics, ecological interactions, stock enhancement, and species diversity. FLELMR will include

Biologically Relevant Environmental Zones FMRI's FIM Program collects hydrological and biological data at known sampling stations. NOAA SEA Division's National Estuarine Inventory also maintains a database of environmental

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GIs in Aquatic, Ocean and Coastal Applications data for Florida estuaries. These data are being integrated to support modeling and management of marine resources. SEA Division has conducted Principal Components Analysis (PCA) to determine biologically relevant ranges of salinity for fish communities in mid-Atlantic and Gulf of Mexico estuaries (Bulger et al. 1993, Lowery et al. 1996). PCA was used to develop a biosdinicy index (BSI)for assessing the potentid impacts of changes in freshwater inflow in Gulf of Mexico estuaries (Klein et al. 1995, Christensen et al. 1996). PCA analyses have allowed SEA to define five biologically relevant ranges of salinity associated with different fish assemblages for each area studied. Bulger et al. (1993) noted that the methodology could be expanded to include information on other environmental parameters. Biologically relevant ranges of salinity, temperature, depth, dissolved oxygen, and substrate type are being determined for Florida estuaries using PCA. The biologically relevant ranges of each environmental parameter are being used to contour the data using GIs. The environmental coverages are then being added to the MRGIS database (Figure 1).

Suitability Indices Several approaches are being evaluated to determine the best means of relating species abundances to environmental parameters in Florida. Various qualitative or quantitative techniques have been applied by the U.S. Fish and Wildlife Service to relate CPUEs of fish to environmental p d i e n t s for use in habitat suitability index (HSI) modeling (Terrell et al. 1982, Crance 1987). Suitability indices (SIs) are based on the assumption that the life stage of a species is more abundant in one zone along an environmental gradient. SIs for juvenile and adult spotted seatrout (Cynoscion nebulosus). American oyster (Crarsosnea virginica), and white shrimp (Penaeus setiferus) from Pensacola Bay were qualitatively derived from the scientific literature (Monaco and Christensen 1996).A more quantitative approach involves the determination of habitat affinity indices (HAI) based on the relative occurrence of species in a specific habitat when compared to the relative availability of that habitat (Monaco et al. 1996). FMRI is using FIM data to quantitatively relate CPUEs respectively to different levels of salinity, temperature, depth, dissolved oxygen, and sediment type. SIs are determined by normalizing the CPUEs from 0 to 1. The SIs are then added to the MRGIS database.

HSI Modeling The HSI models are currently developed in the ARClINFO Grid module, although other raster-based GIs systems could also be used. The data layers representing the different environmental coverages are overlaid in the database. The model uses rasterized environmental data layers with a cell size of 100 by 100 meters (Figure 2). The HSI formula calculates a composite HSI coefficient by taking the geometric mean of the SI indices representing all the environmental coverages for each grid cell. After all the cells across the estuary are evaluated, a map is produced that depicts the geographic distribution and abundances of the species. Species distribution maps are derived using the model in each grid cell from environmental coverages that define the habitat, and associated SIs representing abundance. HSI modeling will be conducted to produce predicted maps of approximately 50 species of juvenile and adult marine species in 12 west Florida estuaries.

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Figure 2. Predicting fish distributions from a grid using GIs in conjunction with HSI modeling.

Conclusion The integrated FLELMR SDSS is critical in the development of more complex spatial-analytical capabilities needed for living marine resources management. Spatial coverages for benthic habitats, physicaYchemical parameters, and marine species distributions are being created in the joint FMRYSEA effort to characterize, model, and map the biota of Florida estuaries. FLELMR incorporates fish and invertebrate fishery resources information in relational textual and numeric tables, images, and maps of species distributions by life stage. Consideration is also being given to how the species community information can be utilized to assess biological integrity and to map biocriteria (Angermeier and Karr 1994). The user of the FLELMR SDSS will be able to "hot-link" between textual and numeric tables, maps, images, and graphs. The developing system is designed to support i n t e p t e d coastal zone management, fisheries management, pollution assessmentlmitigation, restorationlenhancement, natural resources damage assessment, and the maintenance of healthy coastal ecosystems.

References Angermeier, P.L.. and J.R. Karr. Biological integrity versus biological diversity as policy directives: protecting biotic resources. BioScience 44(10):690-697. Bulger, AJ., B.P. Hayden, M E . Monaco, D.M. Nelson, and M.G. McCormick-Ray. 1993. Biologically-based estuarine salinity zones derived from a multi variate analysis. Estuaries 16(2):3 11-322. Christensen, J.D., T.A. Lowery, and M.E. Monaco. 1996. An index to assess the sensitivity of species to freshwater inflow changes in the Gulf of Mexico. Gulf Research Reports (in press): 13 p. Crance, J.H. 1987. Guidelines for Using the Delphi Technique to Develop Habitat Suitability Index Curves. U.S. Department of theInterior, U.S. Fish and Wildlife Service, National Ecology Center, Division of Wildlife and Contaminant Research, Biological Report 82(10.134): 1-21. EMISC-FDEP, 1995. Ecosystem Management Implementation Strategy: An Action Plan for the Department of Environmental Protection. E. Barnett, J. Lewis, J. Marx, and D. Trimble (Editors), Ecosystem Management Implementation Strategy Committee and Florida Department of Environmental Protection, Volumes 1&2.

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FDEP 1996. The Florida Department of Environmental Protection Florida Marine Spill Assessment System. Florida Department of Environmental Protection. Klein, C J., M. Monaco, SP. Orlando, M. Harris, S. Holliday, and R. Ives. 1995. Freshwater Inflow to Gulf of Mexico Estuaries: Workshop Summary. U.S. EPA Gulf of Mexico Program, and NOAAMOS Strategic Environmental Assessments Division, Silver Spring MD, 26 p. LaPointe, TR. E.D. Archer, C E . Alexander, JJ. Buschek, M.S. Jacobsen. MA. 0rencia.A J. Reyer, I. Rosenthal. P.C. Stauffer, and JP. Tolsen. 1996. COMPAS and the evolution of SEA'S Desktop Information System Program. In: PJ. Rubec and J. O'Hop (eds.) GIs Applications For Fisheries And Coastal Resources Management. Proceedings of Symposium held 18 March 1993 in Palm Beach FL,Gulf States Marine Fisheries Commission, Ocean Springs MS (in press). Lowery, TA., Monaco, ME., and A J. Bulger. 1996. Biologically-based estuarine salinity zones in the north-central Gulf of Mexico. Gulf Research Reports (in review): 39 p. Monaco, ME., and J.D. Christensen. 1996. The NOAANOS biogeography program: coupling species distributions and habitat. In: G.W. Boehlert and J D . Schumacher (eds.), Changing Oceans and Changing Fisheries: Environmental Data For Fisheries Research and Management. NOAA Technical Memorandum NOAA-TM-NMFS-SWFSC (in press), 8 p. Monaco, ME., S.B.Weisberg, and T A . Lowery. 1996. Summer habitat affinities of estuarine fish in mid-Atlantic coastal systems. Manuscript Submitted to Fisheries Management and Ecology. Nelson, DM., E.A. Irlandi, LR. Settle, ME. Monaco, and L. Coston-Clements. 1991. Distribution and Abundance of Fishes and Invertebrates in Southeast Estuaries. ELMR Report No. 9. NOAAfNOS Strategic Environmental Assessments Division, Silver Spring MD, 167 p.

Nelson, DM.. M.E. Monaco, C.D. Williams, TE. Czapla, M E . Patillo. L. Coston-Clements,L.R. Settle, and EA. Irlandi. 1992. Distribution and Abundance of Fishes and Invertebrates in Gulf of Mexico Estuaries, Volume 1: Data Summaries. ELMR Report No. 10, NOAANOS Strategic Environmental Assessments Division, Rockville MD,273 p. Rubec, PJ.and R.H. McMichael, Jr. 1996. Ecosystem management relating habitat to marine fisheries in Florida. In: PJ. Rubec and J. O'Hop (eds.) GIs Applications For Fisheries And Coastal Resources Management. Proceedings of Symposium held 18 March 1993 in Palm Beach FL, Gulf States Marine Fisheries Commission, Ocean Springs MS (in press). Terrell, J.W., TE. McMahon, PD. Inskip. R.F.Raleigh, K.L. Williamson. 1982. Habitat Suitability Index Models: Appendix A, Guidelines for Riverine and Lacusmne Applications of Fish HSI Models With The Habitat Evaluation Procedures. U.S. Department Of the Interior, U.S. Fish and Wildlife Service, Biological Services Program and Division of Ecologicaf Services, FWSI0.S.-82/10.A, 55 p.

Biographies Peter J. Rubec is a Research Scientist (Oil Spill Assessment) with the Florida Department of Environmental Protection, Flori& Marine Research Institute. He is a graduate of the University of Ottawa (Horn. BSc. and MSc.) and Tern A&M University (PhD.). Mark E. Monaco is the Chiefof the Biogeographic Characterization Branrh, within the U S . Department of Commerce,Nan'onal Ocean~c and Amtospheric Adminisnation, Nananoml Ocean Service, Stategic Environmental k~essmen.tsDivision. He is a grad~rareof Ohio State University (BSc.and MSc.) and the Universiry of Mqland (PhD). John D. Christensen is a Fishery Biologist with the Biogeographic Characterization Branch. He is a gradriare of E m A&M University (BSc and MSc).

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