ATLSS: ACROSS-TROPHIC-LEVEL SYSTEM SIMULATION: An Approach to Analysis of South Florida Ecosystems Biological Resources Division United States Geological Survey South Florida/Caribbean Ecosystem Research Group Miami, Florida ACROSS-TROPHIC-LEVEL SYSTEM SIMULATION (ATLSS): APPROACH FOR ANALYSIS OF SOUTH FLORIDA ECOSYSTEMS Progress Report January 1997 DRAFT South Florida/Caribbean Ecosystem Research Group Biological Resources Division United States Geological Survey Table of Contents Executive Summary 1 I. Introduction and Background 2 A. Overview 2 B. General description of ATLSS 2 C. How ATLSS developed 4 D. Objectives of ATLSS 4 E. Geographic scope of ATLSS 5 F. Empirical support for calibration and testing of ATLSS modules 6 G. Timelines for completion of ATLSS projects 6 II. Uses of ATLSS 8 A. ATLSS as a tool for managers 8 B. ATLSS as a tool for scientists in forming and testing hypotheses 9 III. Connection of ATLSS with Everglades Landscape Model (ELM) 10 IV. Limitations and Data Gaps 11 V. Individual Components within the ATLSS Framework 12 A. Landscape 12 B. Abiotic components modeling 16 C. Vegetation modeling 18 C1. Seasonal vegetation dynamics model 19 C2. Plant succession and diversity modeling 22 C3. Disturbance modeling 24 D. Lower trophic level modeling 26 E. Fish and aquatic macroinvertebrates modeling 28 F. Reptile and amphibian assemblage modeling 31 G. Crocodilian modeling 33 H. Wading bird assemblage modeling 35 I. Cape Sable seaside sparrow modeling 37 J. Snail kite population modeling 40 K. White-tailed deer and Florida panther trophic interaction modeling 43 VI. Integration of ATLSS Models 45 A. Need for integration 45 B. ATLSS integration design 45 C. ATLSS integration progress 45 D. Trophic network analysis 47 VII. Model Testing Procedures 49 A. Plans for model rationale justification, calibration, and validation of models 49 B. Considerations of error multiplication 50 VIII Long-Term Plans 51 A. Future additions to the biotic components of ATLSS 51 B. Future additions of environmental conditions 51 C. Broader roles for ATLSS 52 IX. Project Titles, Principal Investigators, and Institutions 53 X. Funding Agencies 55 XI. ATLSS Publications 56 XII. Other References 58 XIII. ATLSS Presentations 62 XIV. ATLSS Home Page Information 64 Executive Summary The Across Trophic Level System Simulation program, or ATLSS, is an integrated set of computer simulation models representing the biotic community of the Everglades/Big Cypress region and the abiotic factors that affect this community. The models are spatially explicit, using GIS map layers of topography, soil, vegetation type, etc. The spatial extent of the models is the entire Everglades/Big Cypress region and some surrounding areas, and the spatial resolution is generally 500 x 500 meter cells, though sometimes finer . Relevant abiotic processes, such as hydrology, fire, and major storms are modeled. The biotic community is represented by a hierarchy of models, beginning with the process models of the biota constituting the energy base, including vegetative biomass, lower trophic level invertebrates, and decomposers. Models that contain relevant detail on size and age structure simulate several important functional groups, fishes, macroinvertebrates, and small reptiles and amphibians, which utilize the production of the energy base and provide food for some of the top consumers. Higher trophic level species populations, such as the Florida panther (and an important prey species, the white-tailed deer), several species of wading birds, the American alligator, the Cape Sable seaside sparrow, and the snail kite are modeled using individual-based models. These populations do not include all the high trophic level consumers that might ultimately be of interest in ATLSS, but because of their ecological importance, or importance as indicator species, these species provide a good representation of the higher trophic components of the Everglades food web. The primary goal of the ATLSS Program is to produce an integrated set of models for assessment of the biological effects of water delivery scenarios. The ATLSS models will be both calibrated and validated, and ready for use by scientists and managers. Because ATLSS is intended for use by managers, it will have a user-friendly interface with a simple menu to guide the user both in selecting biotic components to simulate and analyzing the results. The ATLSS Program has been adopted by the Federal, State, and Tribal Taskforce for the Restoration of the South Florida Ecosystem as the primary ecological tool for assessing the ecological effects of alternative water management changes. The objectives of the ATLSS program over the longer term are to aid in understanding how the biotic communities of South Florida are affected by the hydrologic regime and by other abiotic factors, and to provide a predictive tool for both scientific research and ecosystem management. The distribution, volume, and timing of water flow influences the energy and material transfers among ecological components within and across the trophic levels of these systems. The ATLSS integrated model simulates mechanistically the causal relationships between hydrology and the biotic components of the Everglades/Big Cypress region. ATLSS is composed of several individual modeling projects that provide modules for ATLSS. These projects are matched where necessary with empirical studies to support the model with empirical information. The information available from ATLSS empirical studies and other data sources will be divided into parts that can be used, respectively, for calibration and testing of the component ATLSS modules. The ATLSS Program is scheduled to produce by the end of 1997 and early part of 1998 usable models of all of the biotic components that are part of the program. In subsequent years, the models of these components will be refined and models of additional components (e.g., small mammals, raptors such as ospreys) will be added to the program. In addition, other threats to the environment, such as mercury and global warming, may be modeled. I. Introduction and Background A. Overview The Everglades Forever Act calls for land acquisition, the rerouting of water flow, and other actions to be undertaken for the purpose of restoring Everglades National Park and Big Cypress Swamp National Preserve. Most specific decisions concerning possible management restoration actions have not yet been made and await evaluation their potential long-term effects. Unfortunately, restoration experiments are prohibited because of the large spatial extent and long time horizons required to restore these ecosystems. Quantitative modeling is one management tool that can be used to evaluate the long-term outcomes of specific restoration actions. Modeling is an indispensable tool for understanding complex ecological systems and for providing guidance in their protection. A model is a mathematical or computer code representation of the real world. The computer simulation models described in this report incorporate cause-and-effect relationships between the components of the biota and aspects of the environment that are influenced by humans. These models can make predictions about how these biotic components will respond to human manipulations. In particular we are interested in predicting the responses of selected biotic components in the Everglades/Big Cypress region of South Florida to such abiotic conditions as the seasonally varying water levels across the South Florida landscape. The modeling is designed to provide a basis for management decisions in this region. New modeling approaches for populations and communities have been developed, including individual-based models (e.g., DeAngelis and Gross 1994, also see definition in Table 1) and there is an increasing emphasis on a landscape-level viewpoint (e.g., Schafer 1990, Turner and Gardner 1991, Huston 1994, Forman 1996). Individual-based models allow the enormous store of knowledge of physiological and behavioral ecologists to be incorporated into models of populations and communities. The landscape perspective recognizes that the dynamics of a population depend on the landscape in which the population exists, so that population and community models must incorporate the features and heterogeneity of the landscape to an extent and degree of resolution that depend on the questions being asked. Enormous advances have also been made in GIS technology (e.g., Miller 1994). Mapping techniques using satellite and aerial photography, together with ground truthing, are providing detailed maps of vegetation and other characteristics of regions to a high degree of resolution. These GIS maps can be used as the underlying landscapes for regional scale models of populations and communities. Simulation modeling can describe how individuals, populations, and communities utilize the landscape and can help one determine the viability of these biotic entities over time scales of interest. This is the basic approach being used for developing predictive models for the ecosystems of South Florida. B. General Description of ATLSS The Across Trophic Level System Simulation program, or ATLSS, is an attempt to apply these new conceptual and technological advances in modeling and GIS to regional scale ecosystem analysis. ATLSS is an integrated set of computer simulation models. These models are set in the landscape of South Florida, represented by GIS map layers of topography, soil, vegetation type, etc. There are underlying models of the relevant abiotic quantities, such as hydrology, fire, and major storms. "On top" of these abiotic models are models of the energy base of the region, including vegetative biomass, lower trophic level invertebrates, and decomposers. Models that contain some detail concerning size and age structure simulate the main macrofaunal components, fish, small reptiles and amphibians that utilize the production of the food web base. At the very top are models of the higher trophic levels that utilize the production from the lower trophic levels. ATLSS is this integrated set of models, where the term "integrated" means that the different models and map layers, or "modules" (see definitions in Table 1) within ATLSS will be able to pass information back and forth to each other when necessary (e.g., when an herbivore module consumes a plant, or when a predator module consumes an herbivore). ATLSS uses a spatially-explicit landscape scale, modeling approach (see definitions in Table 1). This means that the distribution of the components of the biotic community are modeled across the landscape. This landscape structure is based on remote sensing information on elevation, vegetation, etc., across the region. There is an integrated system of landscape scale models: hydrologic models, plant community succession models, models of fire and other disturbances, and models of lower, intermediate, and selected higher trophic level functional groups or species populations. The hydrologic model used is interchangeable, and can be either the Natural System Model (Fennema et al. 1994), the Water Management Model (MacVicar et al. 1984), the Everglades Landscape Model (ELM, Fitz et al. 1993), which already exist in validated form, or any other model that is available, validated, and accepted by the research and management communities. ATLSS predicts how the ecosystem responds to different hydrologic regimes. ATLSS is a natural extension of the way in which spatial modeling is applied to predict hydrology across the South Florida landscape. It extends this spatial modeling to the biotic community. ATLSS is, therefore, a spatially explicit, landscape level modeling approach. ATLSS also embodies a food web approach, as it incorporates all trophic levels and simulates energy flow through the food web. ATLSS uses different modeling approaches at different trophic levels in the major food webs (aquatic and terrestrial) of the system. The different modeling approaches used in ATLSS include (see Figure I.1): 1) process-oriented models for key functional groups of lower trophic level organisms (periphyton and macrophytes, detritus, micro-, meso-, and macroinvertebrates); 2) size- and age-structured population models for key functional groups and species of intermediate trophic levels (five functional groups of macroinvertebrates and fish, four functional groups of amphibians and small reptiles); and 3) individual-based or -oriented models for key higher consumer species (American alligator, colonial wading birds, the Cape Sable Seaside Sparrow, the Snail Kite, White-tailed deer, Florida panthers). A method of linking together models that need information from, or provide information to, other models, is also needed. This integration of ATLSS model components in a common framework or integration shell is required to provide a system level approach to addressing the many scientific and management questions affecting South Florida wetlands and uplands. Because of this, we refer to ATLSS as an "integrated model". The computer code for the fully integrated model is in a standard, object-oriented language (C++) to allow easy modifications to incorporate future research and the need for possible changes in algorithms. The fully integrated ATLSS model will also have the capability of interacting through data passing with other landscape scale models, such as the Everglades Landscape Model of the South Florida Water Management District. It will provide user-friendly interfaces to allow regional managers to analyze the ecological effects of alternative hydrologic scenarios. A GIS object-oriented data storage structure will enable the saving of outputs of the integrated model for conducting multivariate analyses on the resulting database using statistical software packages. Such analyses also provide the scientific basis for conducting risk and cost-benefit analyses for each alternative hydrologic scenario evaluated. C. How ATLSS Developed ATLSS officially began as a fully funded program in 1995. However, the roots of ATLSS began a few years earlier with the need to formally conceptualize the Everglades/Big Cypress region as a dynamic, heterogeneous landscape. It is a landscape characterized by changes on several temporal scales, including within-season short-term changes, seasonal patterns, multi-year cycles, and long- term directional changes. This view of the Everglades/Big Cypress region was reflected in the proceedings volume, Everglades: The Ecosystem and Its Restoration (Davis and Ogden 1994). A second influence in the early development of ATLSS was the emergence of individual-based spatially explicit modeling as a way of modeling the dynamics of some populations (Huston et al. 1988, DeAngelis and Gross 1994), particularly higher trophic level species, in which there is rich physiological and behavioral information. In this approach, every member of a population is individually modeled. This allows population ecologists to model the spatial demographics of populations, in a realistic way, on temporally varying, heterogeneous landscapes. These models also relate directly to the research and monitoring data collected by field biologists, and make predictions that can be compared directly to field observations. The details of the time/energy budgets of the individuals, the heterogeneous distribution of food resources on the landscape, and stochastic factors are all simulated in determining the growth, reproduction, and mortality of individual members of the population. To obtain population level statistics, such as total population size through time, sums over all of the individuals being simulated are computed. The first components of the ATLSS integrated model were spatially explicit, individual-based models of wood storks (Wolff 1994, Fleming et al. 1994) and white-tailed deer/Florida panthers (Abbott 1995, Abbott et al. 1995, Comiskey et al. 1996). However, ATLSS was expanded as the need was seen to know the spatio-temporal pattern of food availability for these high-level consumers. First, a primary intermediate trophic level functional group, freshwater fishes, were modeled (DeAngelis et al. 1996). Then, to provide a mechanistic way of providing carrying capacities for the fish, the lower aquatic trophic levels (periphyton, meso- and macro-invertebrates) were modeled. Vegetation biomass production was modeled to serve as a forage base for the white-tailed deer. Progress in developing the lower trophic level and abiotic basis of the ATLSS integrated model has been aided by close collaboration with the ELM program (see section III. B.). Subsequently, other components have been added to ATLSS, both to fill out the trophic structure and to add higher-level components that are important for conservation reasons (e.g. Cape Sable seaside sparrow, snail kite). D. Objectives of ATLSS The immediate objective of the ATLSS program is to provide a quantitative, predictive modeling package for guidance of the South and Central Florida restoration effort. Given the complexity of this immense ecological system and the uncertainties involved in restoration, this program has been deemed absolutely critical to the restoration effort (Weaver et al. 1993). The ATLSS Program has been adopted by the Federal, State, and Tribal Taskforce for the Restoration of the South Florida Ecosystem as the primary ecological tool for assessing the ecological effects of alternative water management changes. The relationship of this and other modeling projects to the integrated set of tasks comprising the Central and South Florida Restudy Project is portrayed in Figure I.2. The objectives of the ATLSS program over the longer term are to aid in understanding how the biotic communities of South Florida are linked to the hydrologic regime and to other abiotic factors, and to provide a predictive tool for both scientific research and ecosystem management. The distribution, volume, and timing of water flow influences the energy and material transfers among ecological components within and across the trophic levels of these systems. The ATLSS integrated model simulates mechanistically the causal relationships between the hydrology and the biotic components of the Everglades/Big Cypress region. Stated in scientific terms, the ATLSS Program has the goal of predicting the spatial and temporal patterns of biota in response to changes in the hydrology and other physical aspects of the environment by simulating mechanistically the causal relationships between hydrology and the biotic components of the Everglades/Big Cypress and surrounding ecosystems. To help with inferring the causes for declines in key species over the past few decades, ATLSS will be used to compare trophic responses to the natural (pre-drainage) patterns of water flow and to the current (post-drainage) patterns, simulated with the same time series of rainfall data. Analyses of such comparisons will allow: 1) identification of the effects of altered landscape characteristics; 2) testing of related hypotheses concerning landscape alterations as possible causes of species declines; and 3) qualitative evaluations of the minimum hydrologic threshold requirements of the biota as a guide to restoration efforts. ATLSS will then be used to predict the responses of biotic communities to several proposed alterations of the hydrologic regime in South Florida and, from these predictions, to provide advice to the South Florida Ecosystem Restoration effort. Beyond the immediate aims of providing information for the restoration, ATLSS has as its long-term goals the study of other impacts on the South Florida ecosystem, such as pollution (e.g., mercury, phosphorus), land-use change, global warming, and the invasion of non-native species. ATLSS will also form a framework for the design of empirical studies and testing of hypotheses. E. Geographic Scope of ATLSS The geographic scope of ATLSS is currently confined to the area of South Florida that is included in hydrologic models. This geographic area is pictured in Figure I.3. All ecosystem types within this area except the urban and agricultural areas to the north and east are included in the ATLSS integrated model. Although Figure I.3 excludes the mangrove estuaries to the west, ATLSS modules are being developed for these areas in anticipation of hydrologic models being available at some time in the future. ATLSS must also be expanded to the northwest as soon as possible, to incorporate some of the habitat of the Florida panther that is currently excluded. It will eventually be extended farther north, and, in fact, the snail kite model does include patchy sites from Lake Okeechobee north to the Kissimmee lakes and Upper St. John's River. Expansion southward into Florida Bay, or linkage to proposed models of the Florida Bay ecosystem, is also planned. F. Empirical Support for Calibration and Testing of ATLSS Modules The individual modeling projects that provide modules for ATLSS are matched where necessary with empirical studies to support the model with empirical information. The information available from ATLSS empirical studies and other data sources will be divided into parts that can be used, respectively, for calibration and testing of the component ATLSS modules. The ATLSS package will provide the best possible forecast of how the ecosystems of South Florida should respond after the implementation of the restoration has begun. As the ecosystem response to restoration is monitored, this will provide further data to test and refine the ATLSS integrated model. In the sense of adaptive management, the combination of monitoring data and improved model predictions will be used to recommend modifications of the restoration scenario where necessary, a procedure shown schematically in Figure I.4. G. Timelines for Completion of ATLSS Projects Originally, ATLSS was scheduled to produce an integrated set of models for use in the analysis of hydrologic restoration scenarios by the middle of the year 2000. However, the recent Water Resources Development Act sets a schedule for a final feasibility report by the Restoration Task force for July, 1999. Therefore, to produce useful input for this report, ATLSS models must be completed much faster than the original schedule for completion of the overall package. In fact, most of the component models that make up the integrated ATLSS will be completed long before the integrated ATLSS is finished. We expect most of the component models to become available in usable form during 1997 and 1998. These can feasibly be used in aspects of the restoration evaluation prior to final completion of the overall integration. There are some caveats, however: 1) It is unlikely that all of the component models will be linked together in a central shell and accessible through an easily used menu before the middle of 1998. Each application will have to be "hard-wired", rather than being selected merely by used of a menu, in order to meet the revised and shortened time frame. 2) Some of the models will be missing sound information on key parameters, as these data are still being collected. In particular, some models will depend on having the hydrologic models extended to the coastal estuaries. 3) Most models will not have undergone full testing ("validation"), so there will be continuing modifications of these models to improve their predictive capabilities. (It should be stressed that model testing must always be an ongoing process in any case.) Table 2 shows the current schedule for completion of components of ATLSS. In some cases the table distinguishes two or three versions of the model. These cases are elaborated in somewhat more detail below, and is discussed in Section V. It should be understood that at least minor modifications will continue to be made in all models after their scheduled completion data. Vegetation biomass production - The first version of the model is complete now (denoted as Cv1). But this does not include certain geographic regions. The next version will extend the model to the coastal mangrove (tidal) areas (denoted by Cv2). The final version will extend the model to the western uplands, outside the coverage of the currently available hydrologic models. Vegetation succession - The first version with be available by the end of 1997. The final version, which will add disturbance sensitivity, will be available in the middle of 1998. Fish functional groups - The first version of the model is available now. The final version will add models of two important aquatic macroinvertebrates; apple snails and crayfish. This version will also extend the model from the freshwater marshes to the coastal estuaries. Reptile and amphibian community - The first version, or "descriptive phase," will be completed in May 1997. This will describe the standing stocks of key functional groups and the energy fluxes between these groups. The second, or "predictive phase," of the model will be completed by June 1998. This will allow predictions to be made concerning how these standing stocks and fluxes will respond to abiotic scenarios. Wading birds - The first version of this model, simulating a single nesting colony of one species (wood storks), has been completed. The final version will model simultaneously many breeding colonies of up to five species, including mixed colonies. Snail kite - An initial version of the snail kite model is available. This operates on yearly time steps. The final version will be available in December 1997. This will include more behavioral details and will operate on daily or 5-day time steps. Florida panther/deer - The first version of this model is available. The final version will include more behavioral details, such as panther marking behavior. This will be available in April, 1997. II. Uses of ATLSS A. ATLSS as a Tool for Managers The primary goal of the ATLSS Program is to produce an integrated set of models for assessment of water delivery scenarios, where the models are calibrated and validated, and ready for use by scientists and managers. Because ATLSS is intended for use by managers, it will have a user-friendly interface with a menu to guide the user both in selecting biotic components to simulate and analyzing the results. For example, suppose the user wishes to assess the effect of a given water delivery scenario on the population of Florida panthers. In making this choice, a suite of modules will automatically be selected to be run interactively; 1) topographic map 2) rainfall data from historical record (20-year period) 3) hydrologic module with given water delivery scenario 4) vegetation change module a. disturbance module b. vegetation succession module 5) vegetative biomass production module 6) white-tailed deer module 7) Florida panther module The user will be able to choose certain parts of the ATLSS landscape, including the entire landscape, on which to simulate the panther. The user will also be able to specify the duration of the simulation. This will normally be a 20-year period. Because the ATLSS biotic modules for higher trophic levels are stochastic, Monte Carlo models, each simulation is only one possible realization. To calculate means and variances for population behavior, a large number of simulations will be performed for a given water delivery scenario. For each of these simulations, a different pseudo-random number generator initiator will be used. The model will perform a number of simulations, where this number of scenarios will be chosen to give a chosen level of confidence. The user will be able to choose to observe a wide array of the output produced both by individual simulations and the summary data averaged over many simulations. In particular, the output data on spatial locations for each model panther through time can be observed on a GIS map and related to various environmental conditions across the landscape. It is the further objective of ATLSS to present these results in a form that allows scientists and managers to easily compare how each of the scenarios affects each of our model outputs, and to compare also each of these scenarios in terms of costs and of effects on other functions, such as flood control. This will be done through an interface that will take the output from our models and allow the scientists and managers (users) to view it in a convenient format that makes comparisons easy. For example, the user may want to compare the effects of each of the water delivery scenarios on Cape Sable seaside sparrows. The user should be able to select "Cape Sable seaside sparrow" and be presented with GIS maps and summary statistics showing the yearly variations in Cape Sable seaside sparrow densities over 20 years for each of the scenarios. As another example, the user may want to look at all of the effects of a single scenario. In this case, the user selects "Scenario 1" and is presented with GIS maps showing how the densities of each of the above outputs change through time over 20 years for Scenario 1. The user may also want to determine which scenario maintains all key species populations above certain critical levels, for the least amount of costs and impairment of other functions, such as flood control. This should also be possible through the interface. B. ATLSS as a Tool for Scientists in Forming and Testing Hypotheses An important long-term benefit of ATLSS will be in its use in formulating and exploring, through simulation modeling, key scientific hypotheses regarding South Florida ecosystems (some of which hypotheses may have important practical implications as well). A brief list of such questions or hypotheses, ranging from the ecosystem to the individual species level, is as follows: 1) the issue of spatial extent and population viability. What are the threshold hydrologic conditions for viability of the species? To what extent can populations be maintained on ranges that are smaller than their historical ranges, through manipulation of the functional attributes, such as landscape heterogeneity or productivity, of the shrunken range? 2) what are the effects of habitat fragmentation on key species in South Florida ecosystems? 3) what are the relative importances in the overall energy budget of South Florida ecosystems of the main biotic communities? For example, it has been hypothesized that the herpetological community may play a larger role in the Everglades than in most other ecosystems. 4) both the loss of short-hydroperiod wetlands and decreases in coastal estuary productivity have been hypothesized to be key causes in the decline of wading birds in the southern Everglades. ATLSS will attempt to compare the two factors through simulation. 5) there are numerous questions regarding the adaptations of particular species. For example, alligators are highly sexually dimorphic. Various hypotheses are possible and some may involve energetic and dispersal constraints that could be studied in a spatially explicit individual-based model such as ATLSS. Migratory patterns of snail kites and site-selection for colonial nesting wading birds are also phenomena that may be elucidated by the modeling of large numbers of individuals on complex landscapes to compare the advantages of various strategies. III. Connections of ATLSS with Everglades Landscape Model A key step in the development of a complete modeling approach for ecosystem analysis in South Florida is collaboration and possible eventual integration with the Everglades Landscape Model (ELM) of the South Florida Water Management District (e.g., Fitz et. al. 1993). The primary objectives of the ELM are to: 1) provide a spatial modeling tool to estimate water demands of the Everglades. 2) predict changes in the landscape pattern of vegetation that is associated with hydrology, water quality, and fire frequency. ELM includes the following processes: 1) water movement vertically in a cell 2) nutrient fluxes through compartments 3) primary production 4) decomposition 5) organic/inorganic sediment suspension and deposition 6) vegetation succession (function of hydrology and fire frequency) ELM thus provides information that is complementary and of use to ATLSS (items 1,2,3,4) and provides the opportunity for useful collaboration in areas that are important to both models (items 5,6) that are not well developed in the current ATLSS integrated model. The ATLSS and ELM modeling groups plan initial collaboration in three phases: Phase 1. ELM results will be used to drive the fish "resource" component of the ATLSS fish model (development of file sharing system; no model modifications). Phase 2. ELM hydrology, nutrients, and vegetation will be used to drive the ATLSS fish model (some modification of ELM and the fish model will be necessary). Phase 3. ELM and ATLSS will be coupled, so that ELM nutrients and vegetation include feedbacks from the ATLSS fish model. Interactions on the fire disturbance model will be undertaken. Status of the three phases. Phase 1 should be complete by January 1997, Phase 2 by May 1997, and Phase 3 by August 1997. Long-range plans The ELM and ATLSS groups will work together towards plans formulating general improved methodologies of ecosystem modeling and object-oriented design. IV. Limitations and Data Gaps It is important to point out gaps that exist in data needed to quantify the models within ATLSS. These gaps impose limitations on the accuracy of the models. Some of the most important gaps are described below. 1) Perhaps the most pressing general need is for higher resolution of ground surface topography and hydrology, both at a macro- and microscale. Current hydrologic resolution is 2 mile x 2 mile in the South Florida Water Management Model and 1 km x 1 km (ELM) model. Improvement is needed as follows: a) Macroscale topography and hydrology to at least 500m x 500m resolution for responses of some of the higher level consumers where water level can directly impact nest sites (e.g., Cape Sable seaside sparrows, alligators) and food availability (e.g., wading birds). Also, this or finer resolution topography will allow more detailed predictions of the water conditions that various vegetation types experience across the landscape. b) Microscale topography within 500m x 500m cells. Microtopography is needed, at least in a statistical way, to predict the occurrence of local refugia for fish, amphibians, and alligators during drydowns, or for terrestrial organisms during floods. 2) There is a need for development of further GIS layers, such as soil nutrient content and hydrologic properties, is needed. 3) More information is needed on the properties and dynamics of South Florida vegetation. Specifically: a) Information on growth rates, maximum biomass, response to water level, annual seasonality, soil type, etc. b) Responses of vegetation to stresses and disturbances such as salinity and extreme temperature. 4) Basic physiological knowledge is needed on some organisms (e.g., alligators). 5) Behavioral/physiological information is needed; e.g., conditions that trigger nesting behavior in wading birds or dispersal in snail kites. 6) Insect biomass is of direct importance to some of the modeled biotic components (e.g., Cape Sable seaside sparrows) and is indirectly important to nearly all. However, little is known about insect standing stocks and dynamics, so this functional component is omitted from the present ATLSS integrated model. V. Individual Components within the ATLSS Framework Below, the individual component modules within ATLSS are briefly described. It will be noticed that especially with regard to the higher trophic levels, the model coverage of the biotic community is not complete. This is due ultimately to the huge amount of effort that would be required to model all higher level species. Choices had to be made and these were based in part on the ecological importance of the species, its usefulness as an indicator species, and data availability. In some cases species satisfying these criteria coincided with species that are Federally listed as threatened or endangered. However, each of the higher- level trophic level species in ATLSS has some significance besides its own intrinsic value. The Florida panther is representative of species that require large home ranges. The wading birds, Cape Sable seaside sparrow, and white-tailed deer are species that utilize patchily distributed resources. The American alligator requires a relatively stable hydrologic environment, and the snail kite is an example of an extreme ecological specialist. A. Landscape Structure 1. Purpose of this component Restoring and preserving the aesthetic and functional properties of the South Florida landscape is the central goal of the South Florida Restoration. Consequently, developing a predictive understanding of the biological and physical processes that define this landscape is a central goal of the research and modeling that supports the restoration effort. The vast extent of this landscape, along with its high spatial and temporal variability, provide a major modeling challenge. The high spatial variability of the landscape requires that the basic spatial unit be relatively small, while the large extent requires a very large number of the basic spatial units. There must always be some compromise between the time required to run a model and the size and/or spatial resolution of the model. The goal of the ATLSS landscape structure is to represent the basic physical properties and processes of the landscape as simply as possible while still providing the information needed for the much more complex biological models. The general objectives of the ATLSS landscape structure are: 1) To provide information on the spatial and temporal distribution of the key physical properties of the South Florida landscape in the form that is needed by the various biological and physical components of the ATLSS modeling program. 2) To provide the capability to calculate and record temporary and permanent changes in the physical properties (e.g., water depth, topography, soil depth, vegetation biomass) of the South Florida landscape that result from physical or biological processes. 3) To provide an appropriate interface between specific biological models and the physical properties of the landscape (e.g., water depth) that are relevant to that model. 4) To provide an appropriate interface within sets of biological models that interact with one another through their distribution across the landscape (e.g., interaction between deer and panthers, or between fish and wading birds). The ATLSS landscape structure is not a single "model" but is rather a group of components, some of which function as models, others which are digital maps and datasets, and others which serve as interfaces between individual-based models and the specific landscape properties that are important to them. 2. Modeling approach The basic modeling approach has been to develop an object-oriented (C++) structure that is sufficiently general to store spatial information of any type (along with standard FGDC metadata) and to allow appropriate calculations to be preformed between different types of information. The "landscape structure" includes: 1) "landscape classes" that store spatial (GIS) information that can be accessed (and changed, if appropriate) through interactions with other ATLSS models; 2) "data structure operators," which are system level processes that handle the mechanics of data input-output, maintain structure and associated metadata; and 3) "interface operators" that modify the landscape classes, either by updating a landscape class through internal processes (e.g., changing water levels, vegetation biomass, or fish density) or by exchanging information between the landscape classes and the various ATLSS plant and animal models, some of which may simply respond to the landscape, while others may actually alter the landscape. As a simple example, the high-resolution water depth map updates itself internally through the subtraction of ground surface elevations from water stage heights, which allows calculation of surface water depth (or depth of water table). Note that in addition to storing such standard GIS information as surface elevations, soil types, and road locations, the landscape structure also stores model outputs that are distributed across the landscape, such as the output of the vegetation model or the fish model. The landscape model structure is not limited to a specific set of spatial and temporal scales, and could operate, if necessary, at very fine spatial scales over limited portions of the entire region. However, for typical applications, the landscape model runs at a spatial resolution of 100 x 100 meter cells, at temporal resolutions of 1 to 5 days. For computational efficiency, the landscape model is designed to operate over a set of scales that can be simply aggregated through nesting (e.g., sizes of 1, 4, 16, 64, 256 units). Any environmental variable can potentially be included as information in the landscape structure, if adequate data on its spatial (and possibly temporal) distribution are available. However, the most important environmental variable in the landscape structure is the one with the greatest spatial and temporal variability across South Florida: water depth. Consequently, one of the major functions of the landscape structure is to predict the spatial and temporal variation in water depth across the entire region at scales of resolution that are relevant to the biological processes being modeled. The ATLSS project is not attempting to develop an independent hydrologic model for the region. Rather, the landscape model is designed to use standard stage height output from scientifically-reviewed and accepted hydrologic models as input for model development, testing, and scenario evaluation. Currently, the landscape model is being run using stage height output from the South Florida Water Management District's Water Management Model (SFWMM). As a consequence, the spatial domain of the ATLSS landscape model, and thus all of the component biological models of ATLSS, is currently limited to the area covered by the SFWMM. The spatial scale at which hydrology is modeled in the SFWMM (with 2 x 2 mile cells as the basic unit) is much larger than the scales at which the landscape must be modeled to predict ecological responses. It is well known that small scale variation in topography interacts with larger-scale varation in water level to determine the timing and patterns of drought, wetness, and water depth that are critical to understanding the population dynamics of different organisms. Because currently available topographic data for the region are far too coarse to be relevant to the fine-scale spatial biological patterns that characterize the Everglades, Big Cypress, and other critical areas in South Florida, the ATLSS landscape model has taken the approach of inferring the land surface elevation from current vegetation patterns. This approach generates "pseudotopography," which is ground surface elevations that are predicted on the basis of vegetation maps, rather than being measured directly. As direct measurements of topography become available at the scales of resolution needed by biological models, direct topographic data will replace "pseudotopography." 3. Current progress The ATLSS landscape structure has been completed and tested for internal consistency. It is now being used as the underlying structure of the vegetation model, the deer model, and the panther model, all of which now operate over the entire region of the SFWMM. In addition, the landscape structure is being used as the basis of the fish model, which is currently operating over portions of the entire region. A key component of the landscape structure, the "pseudotopography," has been completed and is now being tested and refined (Fig. V.A.1). It is expected that refinement of the "pseudotopography" will continue as more information becomes available, until eventually it will be completely replaced by direct, high resolution topographic measurements. Until that time, however, some form of pseudotopography will be necessary to provide sufficient spatial resolution of water depth variability for biological models. We have developed and tested a program for calculating pseudotopography, and have prepared a manuscript based on the approach described (briefly) below. The method we have used for generating pseudotopography is based on the relationship between hydroperiod and vegetation type that has been documented by research in South Florida over the past 30 years. The basic assumption is that the average hydroperiod of an area (the amount of time that the area is under water each year) can be predicted from the vegetation of that area. While this relationship apparently holds over a range of spatial scales, our application is based on a vegetation classification derived from standard LandSat imagery. We are using a new vegetation map recently prepared by the Gainesville unit of the USGS/BRD (Pearlstine et al.) for the Florida GAP Analysis Project, with a spatial resolution of approximately 28 x 28 m cells. We will continue to update our pseudotopography as the vegetation map is tested and revised and new vegetation maps become available. In particular, the new vegetation maps being prepared from aerial photography by the University of Georgia and the South Florida Water Management District will provide both higher spatial resolution and more accurate vegetation classifications that can be used to generate improved pseudotopography. The two primary steps in the generation of pseudotopography are: 1) calculation of the relationship between water level (stage height) and hydroperiod using the annual hydrograph for the area, and 2) for each vegetation type within the area, calculation of the land surface elevation that is required to produce the appropriate hydroperiod for that vegetation type. The first step is relatively straightforward, and requires the creation of a cumulative frequency curve for hydroperiod generated by the SFWMM. This is accomplished by summing the number of days that the water is at or above a specific level, from the highest water level that occurs during the year (corresponding to the elevation with the shortest hydroperiod) to the lowest water level (corresponding to the elevation with the longest hydroperiod). This calculation produces a curve of the potential hydroperiod for all elevations within the range of the annual hydrograph, and is performed independently of the average ground surface elevations used by the Water Management Model (i.e.,potential hydroperiod can be calculated whether the water level is above or below the ground surface elevation assumed by the model). This calculation is repeated for each of the 2 x 2 mile cells within the area covered by the Water Management Model (~1877 cells for the current version of the model). The calculations are independent of the spatial scale of the data, and could be repeated for water level elevations at different scales produced by other hydrologic models, or other versions of the SFWMM. The second step is somewhat more complex, because of the necessity of preserving the water volumes produced by the hydrologic model (SFWMM). For a given volume of water, the actual elevation of its surface will depend on the shape of volume it is allowed to occupy, i.e., it will be deep (have a high elevation) if confined to a volume with a small surface area, or shallow if confined to a volume with a large surface area. As the topography of the ground surface underlying the water volume in a given area is altered to produce the appropriate hydroperiod for each vegetation type, the actual elevation of the water surface will change as the volume that it can occupy is altered. The water volume output of the SFWMM is partitioned between surface water (which occupies 100% of the available volume) and subsurface water (which occupies some fraction of the available volume). In the SFWMM, the subsurface waterstorage capacity of bedrock is approximately 20%. However, because most of the small-scale topographic variability in the Everglades (except for pine rocklands) results from variation in marl and peat accumulation, we use storage capacities ranging from 20 to 85% for generating pseudotopography. Water volume is maintained by adjusting the water surface elevation as the land surface elevation is altered to produce the hydroperiod required for each vegetation type, beginning with the longest hydroperiod vegetation (occupying the lowest positions on the landscape) and progressing through vegetation types with decreasing hydroperiods (higher elevations). We are continuing to test the pseudotopography against the directly measured elevations that are becoming available for selected parts of the region. 4. Empirical data Ideally, the basic information in the ATLSS landscape structure would be completely based on field data. Unfortunately, actual measurements at the needed spatial and temporal resolutions are simply not available for most of the region. Consequently, much of the physical information in the landscape structure is based on the output of the best models currently available (e.g., the SFWMM for hydrology, the GAP Analysis vegetation map for vegetation, and the Everglades Landscape Model for nutrients). As these models are further developed and improved, the ATLSS models will become more accurate in their predictions of spatial and temporal patterns. The ATLSS landscape structure is designed to be as flexible as possible, and will be able to exchange information with models being created by other groups as part of the South Florida Restoration Project, as well as exchange modules or subroutines with other models as appropriate. Any additional data that are collected on high resolution topography, soil depth and nutrient content, and other landscape properties can be directly incorporated into the landscape GIS layers. Better topographic data are particularly important for improving model accuracy. 5. Timeline for completion of work The ATLSS landscape structure is now operational. Minor modifications will be made as new or better data become available, as well as to meet the specific information needs of other ATLSS submodels. B. Abiotic Components Modeling 1. Hydrologic models The seasonal hydrologic pattern in space and time is the key driving force in the Everglades. Changes in the hydropattern will affect other abiotic factors, such as fire severity and nutrient fluxes, as well as all biotic components of the system. The ATLSS integrated model uses the output from existing hydrologic models of the Everglades/Big Cypress region. There are three available models, all covering most of the area south of Lake Okeechobee, excepting the urban, agricultural, and coastal mangrove estuary areas. South Florida Water Management Model (SFWMM) (MacVicar et al. 1984). The SFWMM was developed by the South Florida Water Management District to describe coupled surface and ground water movement and stage across the region shown in Figure I.3. The model includes the complete network of canals, levees, pumps, and well fields. The area modeled is divided into 2-mi x 2-mi grid cells. The main input driving variables are rainfall and potential evaporation. The model includes as its main processes overland flow across the marsh, infiltration, evapotranspiration, ground water flow, and channel flow through canals. The Natural System Model (NSM) (Fennema et al. 1994). This was developed by the South Florida Water Management District using the calibrated algorithms and parameters from the South Florida Water Management Model. It resembles the SFWMM in most ways (e.g., a cell size of 2-mi x 2-mi), but in the NSM all of the water regulation structures built by humans are removed. The NSM does not simulate the natural Everglades, however, as it does not reliably estimate the overflow from Lake Okeechobee. Everglades Landscape Model (ELM) (Fitz et al. 1993). The hydrology model component of the ELM was developed primarily to study interactions between water and vegetation and the transport of nutrients. This model uses the same algorithms as the SFWMM, but omits some of the canal structure of the system. The spatial cell is 1-km x 1-km. 2. Nutrient models The two nutrients thought to be most limiting in the Everglades/Big Cypress region are phosphorus and nitrogen. The ELM simulates the kinetics of both of these within and between individual spatial cells. Both of these nutrients are divided into dissolved components in the surface water and sediment pore water. The surface water component can be carried by horizontal flows between cells. These movements are modeled as mass flows, but concentrations of the nutrients are calculated at each time step. Mineralization and biotic uptake by plants and microbes are simulated, but most of the detailed kinetics of nitrogen chemistry are omitted. 3. Salinity The ELM model also can simulate the movement of NaCl, though no effects on biota are currently simulated. 4. Dissolved oxygen and temperature A module, PhysDyn, has been developed (Fitz et al. 1996) that simulates the changing oxygen concentration and temperature of representative water parcels. The temperature is simulated by a function involving the water column and the atmospheric temperature. The oxygen in the water column is modeled using the processes of exchange with the atmophere, input from plant production, and depletion due to detrital decomposition. C. Vegetation Modeling General overview Of all the component models of the ATLSS program, the vegetation models are most closely tied to the landscape. Virtually the entire landscape is covered at a detectable density with vegetation of some type. Most of the vegetation is relatively permanent, or at least does not move from one location to another very frequently. Virtually all animals, on the other hand, move from one area to another, and many occur at extremely low densities across most the landscape. Consequently, the ATLSS vegetation models are closely integrated with the ATLSS landscape structure (see Section V.A). The basic landscape properties such as elevation and sediment depth directly affect the vegetation, and the amount and type of vegetation on the landscape are extremely important to the distribution, growth, and survival of virtually all animal species. Information on vegetation properties is one of the major types of information that is supplied to the ATLSS animal models by the ATLSS landscape structure. The following subsections describe three distinct types of models that address different aspects of the vegetated landscape of South Florida. All three are essential to meet the goal of understanding and managing the South Florida landscape, but each addresses a separate set of processes. C1. Seasonal Vegetation Dynamics Model - addresses the spatial and temporal variation in the amount and quality of forage available for herbivores, the fuel available for fire, the structure available for animals, and the light available at the water surface. C2. Plant Succession and Diversity Models - address the changes in the abundance of particular plant species in response to changing environmental conditions or as a result of successional change under relatively constant conditions. This approach is essential for predicting how species distributions, including rare species and exotics, are likely to change in response to natural or human-caused environmental change, and how the overall patterns of biodiversity will respond to water manipulation and other management activities. C3. Disturbance Models - South Florida is subject to several types of disturbance that can suddenly decrease the amount of living plant material on the landscape, and thus indirectly (as well as directly) affect many types of animals. Existing disturbance models for fire and hurricanes will be integrated into the ATLSS landscape structure, and development of a freeze model is planned. C1. Seasonal Vegetation Dynamics Model 1. Purpose of the component The energy provided by plants (net primary production or NPP) is the foundation for nearly all of the animal life on Earth. In South Florida, the high temporal variability of the environment (both within a year and between wet versus dry years) and its high spatial variability (from deepwater sloughs to pinelands) produce tremendous variation in the amount of food available to animals, both the herbivorous animals that feed directly on plants and the carnivores that feed on the herbivores. This variation in food availability determines the movement patterns, the population sizes and growth rates, and ultimately the survival of animal species ranging from mosquitofish to panthers. The purpose of the seasonal vegetation model dynamics is to predict the spatial and temporal availabililty of the biomass produced by vascular plants. Vascular plants (herbaceous macrophytes, shrubs, and trees) produce most of the net primary production available to terrestrial herbivores, and directly influence the productivity of aquatic algae (an important source of energy for aquatic food chains) through their effect on light and nutrient availability. Vascular plants respond strongly to variation in water and nutrient availability that result from both natural processes and human activities (e.g., water management). Plant material that is not directly eaten by animals is important as a source of energy for detritus-based food chains (e.g., bacteria, crustaceans, some fish), as a source of organic matter for peat formation, as fuel for fires, and as a source of structure in the environment (e.g., mangrove systems, hardwood hammocks). Specific issues being addressed by the ATLSS vegetation and related lower trophic level models include the amount and quality of food available to deer and other herbivores, and the distribution and structure of vegetation that affects the aquatic food chain through its impacts on temperature, light availability, oxygenation, nutrients, and foraging habitat. Because some of the birds and mammals in South Florida move great distances in their search for food, the landscape vegetation model is designed to predict the seasonal and interannual changes in biomass of all vegetation types across the entire region. The landscape vegetation model is not designed to predict how plant species composition and biodiversity may change in response to variation in water, nutrients, climate, and disturbances such as hurricanes, fire, and frost. These issues will be addressed by the vegetation succession and diversity models described in the next section. 2. Modeling approach The requirements that the landscape vegetation model predict vegetation properties over a large region dictates that the model be relatively simple. Simple predictive models of this type are often described as "empirical models", which use mathematical descriptions of observed responses, (e.g., plant growth in response to water level), in contrast to "mechanistic models," which use mathematical descriptions of the processes that produce the observed responses (e.g., photosynthesis, transpiration, individual processes). In reality, there is no clear dividing line between empirical and mechanistic models, since most "mechanistic" models include empirical descriptions of at least some of the mechanisms. The ATLSS seasonal vegetation dynamics model is designed to produce high spatial and temporal resolution across the entire South Florida region for a few vegetation properties of importance to the higher trophic levels in both aquatic and terrestrial food chains. These vegetation properties relate to the amount and quality of different types of plant tissue, with the types classified on the basis of their quality as forage. The definition of forage quality used in the model is based on the energy content that is available to a ruminant herbivore, specifically white-tailed deer. Although the three vegetation classes are defined based on their digestible energy content (e.g., Kcal/kg), this classification relates directly to a number of important vegetation properties and ecosystem processes in addition to energy content. In general, vegetation with high digestible energy content also has relatively high availability of mineral nutrients and protein. Digestibility by herbivores is usually highly correlated with digestibility by microbes and fungi, and hence correlated with rates of decomposition and nutrient cycling. Within some vegetation types, digestibility may be correlated with fuel properties, such as flammability (i.e., leaves ignite more readily than wood), but not necessarily with gross energy content. The three classes of vegetation are defined separately for each of the vegetation types, based on differences in the digestible energy content of the vegetation (the energy content per unit mass is 2000, 1500, 800 kcal/kg for classes 1,2, and 3). Although the properties of individual species are not specifically represented by the vegetation classes, in most vegetation types, the most palatable class is composed of only a few species that represent a very small proportion of the total plant biomass (e.g., Crinum, Hymenocallis, Salix). Consequently, most of the biomass in all vegetation types is in the lowest quality class, and represents primarily mature and dead herbaceous tissue plus wood. This vegetation class can thus be used to track total dead and woody biomass as it relates to vegetation structure, successional stage, and fuel availability for fire. The spatial variability of vegetation properties in the model is determined by the resolution of available information on the vegetation types of South Florida. Currently, the highest resolution vegetation maps are based on satellite images with a maximum spatial resolution of 28.5 meters. These approximately 30 x 30 meter cells are the basic spatial unit of the vegetation map (produced by the NBS Gainesville Laboratory, L. Pearlstein et al.) being used in ATLSS, although for interaction with many of the ATLSS submodels (e.g., deer, panther, wading birds) this scale is aggregated to coarser resolutions of 100 m or 500m. Each vegetation type is assigned a local elevation consistent with its hydroperiod in relation to the other vegetation types in the area (see discussion of landscape models and pseudotopography, Section A). Surface elevation for any cell on the landscape is subtracted from water stage height information (from the SFWMD or ELM) to provide water depth (above or below the surface) which determines how rapidly any specific vegetation class grows or senesces. The temporal dynamics of the vegetation are driven primarily by water level, as it influences plant growth, senescence, and drying. Water level influences the rate of increase in plant biomass as a multiplier (from 0 to 1) of the maximum growth rate for a particular vegetation class (e.g., class 1 vegetation in the sawgrass vegetation type). Seasonal variation in the maximum plant growth (independent of water level effects) is driven by seasonal variation in total solar radiation (a multiplier from 0.8 to 1.0). The temporal resolution of vegetation change can be varied within the model structure, but for most purposes is changed in weekly increments (Figure V.C.1). Because the dynamics of the vegetation are driven by water, the spatial scale of the water data (actually, the output of the SFWMD Water Management Model) allows a substantial increase in computational efficiency. Since the water surface is essentially level over areas much larger than the 28.5 x 28.5 meter units of the vegetation map, the water levels and vegetation responses of many cells can be calculated simultaneously. Specifically, all of the 30x30m cells of a particular vegetation type within the spatial area defined by the Water Management Model (with output as 2 mile x 2 mile cells), can be assumed to experience the same water conditions and thus be calculated in a single step. Thus, rather than performing separate calculations for each of the 12,752 of the 28.5 x 28.5m cells within a 2 x 2 mile area, the calculations are performed only once for each of vegetation types within the 2 x 2 mile area (Figure V.C.2). Typically, there are less than 6 vegetation types, with a maximum of 25 (depending on the total number of vegetation or land use types in the current map). Individual 28.5m cells need be considered separately only when they become unique as a result of herbivory by deer or other disturbances such as fire. Unpredictable events that occur on real landscape, such as fires, hurricanes, freezes and lightning strikes will always cause detailed model predictions to diverge from observations, unless the models are rerun with the actual events included. Predictions of future conditions can include the effect of stochastic disturbance processes in order to quantify the potential variation in future conditions, but the exact details of future conditions can never be predicted. 3. Current progress The basic model has been completed and checked for internal consistency. Future work will involve: 1) testing the model against empirical data on spatial and temporal variation in plant biomass that will be provided by the new monitoring and research program; 2) expansion of the model to areas beyond the current extent of the SFWMM; 3) refinement of model parameters, as better data become available from field research and monitoring, plus possible modification or addition of processes that influence seasonal variation in vegetation growth and biomass (e.g., anoxia in flooded systems, seasonal nutrient dynamics). 4. Empirical data The primary limitation for vegetation modeling is the lack of adequate information on the properties and dynamics of the South Florida vegetation. Specifically, the information needed to parameterize and test the vegetation model with regard to growth rates, maximum biomass, responses to water level, annual seasonality, soil type, nutrient availability, etc., across the system over a multi-year period, is not available at a sufficient level of spatial, temporal, and functional resolution to meet the needs of restoring and managing South Florida's ecosystems. Continued model development, testing, and application must proceed in concert with a coordinated field research and monitoring program that can provide long-term records of the conditions and process rates of all the major vegetation types and environments of the region. A network of long-term permanent sites for monitoring seasonal and/or annual changes in biomass, along with process-level research on plant growth and nutrient cycling is essential both for further model development and empirical input into adaptive management decisions. Addition of new vegetation types, such as mangroves and salt marsh vegetation, will require appropriate parameter sets (including groundsurface topography), plus the development of functions for responses to environmental conditions such as salinity. Higher quality vegetation maps that may be developed in the future, as well better data on growth response parameters can easily be incorporated into the existing model. Improved information on physical drivers, such as salinity and water quality, will be essential for extension of the model into the intertidal and marine areas. Because of the critical importance of water-borne nutrients in parts of the South Florida system, ATLSS will require quality-assured input data for nutrient concentrations in order to model vegetation in areas where nutrient concentrations are high or highly variable. Currently, the only potential source of such inputs is the ELM model being developed by the SFWMD. The ATLSS vegetation model could potentially use nutrient concentration output from ELM as input, and incorporate a high resolution nutrient uptake and flux component that could be tested against the coarser resolution of the ELM model. C2. Plant succession and diversity modeling 1. Purpose of component In addition to the food they provide to herbivores, other aspects of vegetation are important to the landscape of South Florida, including both functional and aesthetic properties. Many of these vegetation properties are associated with individual species that may vary in abundance across a number of different vegetation "types." Some individual species are important because they cause a problem, such as Melaleuca, Schinus, and Eichornia. Other species are important because they are attractive, unusual, or rare, such as many of the tropical hardwoods, orchids, and threatened or endangered species. A few species are important because they are so abundant that they dominate the ecosystem function and structure of the landscape over large area, such as sawgrass. Plant species differ in many ways, such as size, growth rates, physiological tolerances, chemical composition, and importance to particular species of birds, mammals, and insects. These differences must be taken into account in resource management and assessments, since they influence how a species responds to either management actions or natural variation in conditons. In addition, since individual plants may live many years and grow from small seeds to large sizes, it is often necessary to know not simply how the species as a whole is responding, but the relative number and condition of individuals of different sizes and/or ages. For example, a population composed of all mature individuals and no young individuals may indicate a population headed toward local extinction. The need for predictions about the status and distribution of particular plant species (including endemic species and exotic species), as well as population structure and overall species composition as it relates to biodiversity, requires a very different modeling approach than that used in the ATLSS seasonal vegetation dynamics model. 2. Modeling approach An approach that is well-suited to address questions about individual species status, population structure and aggregate biodiversity is individual-based modeling, which is being used in many of the higher trophic level components of ATLSS. Individual-based models of plant communities are particularly good for predicting changes in plant size and species abundance across spatial gradients in environmental conditions (e.g., water depth, nutrient, or salinity gradients) as well as for environmental conditions that change through time (e.g., secondary succession, or response to physical changes in the environment). Numerous individual-based plant models have been developed and published over the past 25 years, so it will not be necessary to develop an entirely new model concept and structure, as was the case for many of the ATLSS animal models. Adaptation of the best features of existing models, and rewriting them into the object-oriented structure of C++ compatible with the ATLSS landscape structure, will be relatively simple. Although many of the individual-based plant models that have been developed in the past were models of tree and forest dynamics, the basic conceptual approach and model structure is the same for any form of vascular plant, from herbs and grasses, through shrubs and trees. The primary differences between models applied to different vegetation life forms are in time step (shorter for herbs than for trees, to capture more rapid competitive interactions), in growth and size parameters, and in light extinction patterns (different for grasses versus broad leafed plants). Individual-based plant models are not appropriate for completely modeling areas as large as South Florida (the number of individual plants would overwhelm even the largest computers). Rather, these models are useful for predicting the detailed changes that would be expected to occur in relatively small areas under a specific set of conditions, which can then be extrapolated to other areas with the same conditions. In addition, a well-validated individual-based plant model could be used to refine the parameters and predictions of a less detailed vegetation model, such as for seasonal dynamics. The plant succession models will differ significantly from the seasonal dynamics model. Although the spatial area that any particular implementation of the succession model will directly address is much smaller that the total area covered by the seasonal dynamics model, it will be much more detailed in its representation of plant structural and process complexity, and thus can be considered to be much more "mechanistic." The plant succession models will not be based on specific vegetation types or classification, but rather will create their own vegetation types on the basis of which species survive the physical conditions that are input to the model and the modifications to those conditions that result from interactions with other plants. This continuum of species composition can be classified into vegetation types on the basis of the same criteria used by The Nature Conservance and other public and private conservation organizations to classify actual vegetation (and which were used to define the classes of the satellite-based vegetation map used by the ATLSS landscape structure). Addressing the species-specific effects of the major natural disturbances will be an important effort, which may contribute to the restoration of areas damaged by human activities as well. This model may be useful for specific management applications where interspecific interactions are important for either endangered species or exotic species. Issues of plant dispersal and plant genetics could potentially be addressed this model. Detailed plant species composition and vegetation structure predictions may be important for some ATLSS submodels that require higher spatial resolution than the Phase I model. These include small vertebrates such as reptiles, amphibians, and small birds, as well as insects. 3. Current progress No funds have yet been allocated for this modeling effort. However, the well-developed state of individual-based plant competition models, along with the experience and working models of several of the plant ecologists already involved in South Florida (e.g., Doyle 1981, 1994, Chen and Twilley, in review), suggest that these models could be developed relatively quickly. As with the seasonal dynamics model, the primary limitations to the development and application of these models are in the data currently available for parameterization and testing, and not in the conceptual or code developments aspects of the model. 4. Empirical data Fortunately, the data requirements of both types of vegetation models can be met with the same research and monitoring program. Data on vegetation biomass and growth should always be collected on a species-specific basis, so the same sampling design will meet most of the needs of both models. The individual-based vegetation models will always be subject to improvement with better parameters for individual species, and better mathematical representation of critical plant processes, such as growth responses to environmental resources, allocation responses, responses to disturbance etc. The primary environmental drivers of water level, solar radiation, and nutrients that are used for the seasonal dynamics model will also be used for the succession models. The accuracy of both types of model can be increased through more accurate input for the environmental drivers. The succession models will also be linked directly to output from the SFWMM and ELM models. In addition, vegetation changes predicted by the individual-based models could be used to adjust the aggregate vegetation parameters using in the seasonal dynamics model, or in ELM. C3. Disturbance Modeling 1. Purpose of the component Disturbances such as fires, freezes, and hurricanes are integral features of the environment of South Florida and shape the vegetative structure of the landscape. Extensive disturbances that can cause massive mortality of plants and animals, as well as substantial damage to the infrastructure of civilization, are also a characteristic feature of South Florida. The biological and economic impacts of disturbances such as hurricanes, floods, fires, and freezes are obvious to anyone at all familiar with the region. Predictive models of any component of the future condition of the region must include the potential effects of major disturbances as a factor, even though the precise details of any disturbance can never be predicted in advance. Consideration of both the positive and negative impacts of the major disturbance types is essential for any type of resource management or restoration planning. 2. Modeling approach The two components of any disturbance model are 1) the differential responses of organisms of different types and sizes, and 2) the physical disturbance process itself. The responses of organisms to disturbance is extremely context-specific, with small differences in the location, disturbance properties, and species-specific and even individual-specific properties having a major effect on the impact of the disturbance. This level of detail can be very important for predicting disturbance impacts for management purposes, such as the effect of a particular fire regime or hurricane intensity on the size distributions of different species. Individual-based models are virtually the only feasible approach for predicting disturbance impacts and recovery in a way relevant to natural resource or wildlife management. Thus, the plant succession and diversity models described above are a prerequisite for developing disturbance models for the South Florida landscape. The physical process models for each of the major disturbance types present very different challenges. While hurricanes and freezes occur independently of the condition or properties of the vegetation, fires are highly dependent on the amount and flammability of the vegetation, as well as the immediate weather conditions. Fortunately, these physical disturbance processes operate in basically the same way wherever they occur (in contrast to the varying sensitivity of different species of plants and animals). Consequently, disturbance models that have been developed for application in other regions or to other issues are likely to be applicable to South Florida vegetation with relatively little modification. Hurricanes: Hurricanes affect large areas, with fairly predictable consequences based on distance and direction from the eye, as well as local topography and surface conditions. The importance of including hurricane disturbance in models of tropical forest succession has been recognized for some time, and such individual-based models have been effective at explaining forest structure and predicting future patterns of forest succession (Doyle 1981, 1994). The recent increase in detailed studies of hurricane impacts on forests (Loope et al., 1994; Smith et al., 1994, Doyle et al., 1995a and b) as well as progress in calculating hurricane dynamics using historical meteorological data, suggests that hurricane disturbances can be readily incorporated into the ATLSS landscape structure through collaboration with ongoing research and modeling projects. Fires: Fire has long been a feared natural phenomenon that was thought to be so destructive that massive investments were made in preventing and fighting fires across the United States. Recently, there has been increasing recogition that fire is an important, and in many cases essential, process for maintaining desirable forest and landscape properties. A major revision of resource management fire policies is underway across the entire country, with the goal of using the positive effects of fires to enhance desireable ecosystem properties. Because of the ubiquity and potential destructive power of fires, a major effort has been made to understand and model the dynamics of natural wildland fires under a wide variety of conditions, particularly by the US Forest Service (e.g., Rothermel, 1972). This work has been the basis for the development of fire models for many of the vegetation types of North America, including the Florida Everglades (Wu et al., 1996). Existing fires models will be modified in collaboration with their developers and adapted for use with the ATLSS landscape and vegetation models. Freezes: The effects of occassional cold air masses that penetrate to South Florida can be quite dramatic. Depending on the temperature and duration of the freezing conditions, aboveground plant parts of increasing diameter are killed, particularly of cold-sensitive tropical species such as mangroves. Because belowground parts are rarely killed, regrowth following a freeze can be rapid, although the initial effects appear devastating. Freeze impact models have been developed for agricultural purposes, and we will modify one of the models for use with the woody and herbaceous plant communities of South Florida. As with the above two disturbance types, modeling the potential impacts of freezes is only feasible in the context of individual-based plant models that include the relevant size and sensitivity properties of different species and individuals of different sizes and degrees of exposure. 3. Current Progress No funding has yet been provided for this component of the ATLSS project. However, as discussed above, the work that has already been accomplished by other research and modeling groups working on these issues should allow rapid development of this component of ATLSS through collaboration with these programs. 4. Empirical data The effects of major disturbances on the vegetation types of South Florida are quite well documented in the publications of Everglades National Park and Big Cypress National Preserve, including those based on the recent impacts of Hurricane Andrew. These data represent an invaluable resource for parameterizing and testing the disturbance models described above. In addition, ongoing research related to fire management will continue to provide important data for refining species-level predictions. The major efforts that have already gone into developing disturbance models for South Florida, both from the perspectives of model development and model testing, will allow this component of the ATLSS project to be developed rapidly. D. Lower Trophic Level Modeling 1. Purpose of the component The primary goal of ATLSS is to assess the consequences that changes in hydrology exert upon the higher animals of the South Florida wetlands, such as, deer, panther, alligators, and wading birds. Variations in water level do affect such "charismatic megafauna" directly; however, many effects of hydroperiod upon the larger animals are more indirect. The amount of standing water, and especially the lack thereof, can have strong influence upon faunal species, such as fish, prawns, and apple snails, that serve as forage for the large predators. Furthermore, these food items depend in turn upon vegetational and microscopic resources -- collectively referred to as "lower trophic level (LTL) components." These include submerged aquatic vegetation (macrophytes and periphyton), detritus, and very small heterotrophs, e.g., insect larvae, small snails and copepods. It is of utmost importance to the success of ATLSS that how the forage and LTL elements mediate the effects of changes in water level upon higher species be included in the modeling package. Accordingly, the modeling of forage resources will be described in the next section (V.E). The focus here is upon models of how LTL compomnents respond to changing water levels. To begin with, it should be noted these LTL elements also depend upon physical factors other than water level, so that account must also be taken of how they respond to changing temperature (or season), nutrients, and (where appropriate) salinity. At the heart of the model lie the trophic interactions among the several LTL entities and the predation by higher level species modelled in other objects of ATLSS. 2. Modeling approach In comparison with ATLSS modules for the higher trophic elements, the models of the LTL populations appear far simpler. Whereas the species of megafauna are modelled on the level of the individual organism, and the size and/or age structures of the forage resources are followed closely, the populations of LTL elements are reckoned simply as lumped variables in a set of coupled, ordinary differential equations. That is, the biomass level of any LTL entity is considered not to vary over the spatial domain (ca. 500m square) of each ATLSS cell in which it appears. Trophic interactions among LTL variables are simulated on an continuous and instantaneous basis, whereas physical factors and predation from above are updated only at 5-day intervals. The LTL model consists of five "objects" or components: (1) Macrophytes, such as Utricularia, or sawgrass, (2) Periphyton, the mostly diatomaceous film of algal "slime" that coats almost all submerged vegetation, (3) Detritus, or dead plant and animal matter,(4) Mesoheterotrophs, such as water fleas, copepods and nematodes, and (5) Macroinvertebrates, which consist mostly of insect larvae. The principal trophic interactions among these LTL components consist of grazing by meso- and macroheterotrophs upon periphyton. The remaining transfers are of dead material from each LTL component to the detrital compartment (See Ulanowicz 1994 and Figure V.D.1.) Losses of LTL elements to predation by species higher in the food chain will be effected by the predator object modules. For example, a fish or prawn object module will call the LTL module once every five days. From the amounts of LTL resources presented to it, the predator routine will subtract its 5-day consumption of items and pass the remainders back to the LTL routine. The remainders then serve as initial conditions for the next 5-day interval of LTL growth, which will be simulated within the LTL module. The parameters that govern growth, predation and death all vary on a seasonal basis. The average magnitude and amplitude of seasonal change for each parameter was determined from curve- fitting techniques that were applied to seasonal data on variations of these components, as collected by Dr. Joan Browder of NOAA and Mr. William Loftus of USGS/ENP. The response of all components to changes in water level resembles a threshold "ramp- function." That is, stocks of all components are assumed to be independent of water level until the depth falls to 7cm. Beyond that depth, biomass is transferred from all living stocks to the detrital pool in proportion to how far the level has dropped below the 7cm threshold. For example, by the time the water level falls to 1.75cm, 75% of the biomass in each stock will have died off and become detritus. Such "die-off" continues until only 5% of the original stock remains, and that residual is maintained for the duration of drought as "seed- stock" for the subsequent episode of reflooding. Models for the LTL communities in four different habitat types have been constructed. Separate models exist for gramminoid wetlands with either short or long- hydroperiods. The models for both gramminoid biotypes are identical and differ only in the values of their dynamical parameters. Models also have been created to represent the ecosystems of the forested (cypress) wetlands and the mangrove estuaries (Ulanowicz 1995a.) These latter models have essentially the same components as in the gramminoid models, except that autotrophic production (of macrophytes and periphyton) is held very low due to the dense tree canopies that shade these wetlands. They are driven instead via a seasonally-varying input of detrital "litterfall", which is decomposed and processed by the heterotrophic compartments. The responses of ecosystem processes to dissolved nutrients (phosphorus), salinity (in the mangroves), and temperature have been built into the models. These physical variables are to be generated by other object modules of ATLSS. (See Section B above.) 3. Current progress Models of the LTL communities in all four habitats are now operational. The modules were coded originally in FORTRAN and subsequently translated into C++ by the University of Tennessee ATLSS contingent. The gramminoid models already have been coupled with the fish and other forage resource models, and the combination yields plausible outputs. The models for the cypress wetland and mangrove biotopes have been written in FORTRAN, "calibrated", and currently are being translated into C++. They soon will be linked to the forage resource models now being created for those habitats. Initial results indicate that all LTL models behave in stable fashion (as designed.) The recovery of the gramminoid ecosystems from drydown appears to mimic reality well, when compared with the little independent data that are available (Figure V.D.2.) 4. Empirical data All LTL models have been calibrated using data that antedate the ATLSS project. In some cases, data available for calibration remain sparse to nonexistent. At some date prior to incorporating these particular modules into the ATLSS shell, these components should be recalibrated using data to be acquired in the interim. Special attention is drawn to the lack of almost any data pertaining to the faunal communities of the mangrove estuaries. 5. Time line for completion of work The LTL modeling task is essentially complete. Recalibration will be done as new data appear. E. Fish and Aquatic Macroinvertebrate Modeling 1. Purpose of the component Fish biomass constitutes a major energy resource for the wading bird communities and other top-level predators of the Everglades and Big Cypress ecosystems of southern Florida. Fish communities are exposed to annual fluctuations in water level (Loftus and Kushlan 1987). Fish populations expand during the period of flooding, while the annual drydown concentrates many of the fishes in shallow waterbodies, where they are easily available to predators. The purpose of the fish computer simulation model in ATLSS is to predict the fish population responses to this seasonal pattern of water levels in all the spatial cells across the landscape, and thereby the pattern of prey availability for wading birds. Long-term hydrology can have important effects on this pattern, so modeling is important to predict changes that various restoration water management scenarios would produce. For example, droughts can produce massive losses of fish numbers, and threaten the residual "seed" populations of fishes needed to repopulate the marshes in the next wet season. One of the factors helping to mitigate the severity of the effects of drought are the quasi- permanent waterbodies that exist in many areas, such as creek channels, alligator holes, solution holes, and other depressions, that are refugia for fish during these "drydowns". However, the small fish are exposed to high predation, mostly from larger fishes in the larger refugia. Prediction of speed of recovery of the fish population in a cell following a drought is one of the main goals of the model. 2. Modeling approach The model describes the seasonal dynamics of the community of small fishes (e.g., mosquitofish and killifishes) as water levels change through the year for each 500 x 500 meter cell in the Everglades/Big Cypress region. Each cell is modeled as having a statistical distribution of depressions that can serve as refugia for fish if the cell dries down during a year. There are two fish functional groups in the model; small fishes, which are a primary prey of wading birds, and large fishes, which are predators on the small fishes. The fish in each of these two functional types are modeled as 1-month age-classes. Growth in age, growth in size, and mortality occurs on these 5-day time steps, but an increase to the next age-class occurs only on the first time step within a given month. For a given age the size of the fish is calculated from a von Bertalanffy equation and weight in grams dry weight is given by a weight-length relationship. The changes in water level are modeled, as are the interactions of the fishes with their resource base of periphyton, macrophytes, mesoinvertebrates, macroinvertebrates, and detritus (see Figure V.E.1 and see section V.D for a description of the resource base of the fish). The simulation also includes the interaction of large and small fishes to address the question of whether the larger fishes may be a major regulating factor for the small fishes. Movement of fish into and out of ponds and depressions is modeled as a function of changing water levels. Intercell movement can also occur. Reproduction and probability of mortality of fish are modeled as dependent on the age of the fish, the season of the year, and water depth. 3. Current progress The model has been used to demonstrate changes in fish population and biomass numbers over multi-year periods under a variety of hydrologic conditions. The response of the model to the different scenarios of water depth (or hydroperiods) such as shown in Figure V.E.2, is especially important. Figure V.E.3 shows the model predictions of number densities (numbers per square meter, averaged over the entire 25-ha spatial cell) of the small type fish greater than 30 days of age throughout the year, for each of the scenarios, corresponding to hydroperiod scenarios a, b, c, d, e, and f of Figure V.E.2. The population density for hydroperiods a and b reach a maximum of only slightly more than 1 fish/m2 and slightly more than 2 fish/m2, respectively. Apparently, a flooded period of only seven or eight months is not sufficient for large population growth. A strong periodicity is shown in the two intermediate scenarios, c and d, with maximum population densities toward the end of the flooded period of more than 10 fish/m2. When the cell is flooded continuously, the populations of the small type fish fluctuate between about 13 and 20 fish/m2. The reason for the seasonal changes in this case is that the lower trophic levels are undergoing seasonal variations. The model makes three further predictions that appear to us to be fairly robust in the model system. First, there appears to be a threshold of about nine or so months in the length of the hydroperiod. If the hydroperiod is less than this, the small- fish-type population stays small, no more than a few fish per square meter. For longer hydroperiods, the fish population can reach levels of 10 to 20 fish per square meter (averaged over the whole spatial cell) by the end of the hydroperiod, roughly what the population would be under continuously flooding (but during the drydown these fish will be concentrated by the receding waters to much higher local densities). The second prediction is that the large, piscivorous fish do not have a significant impact on small fish populations in the marsh, even though they do in the pond. The third prediction is that the repopulation of the marsh by small fishes following a drydown, even a prolonged drydown, occurs very rapidly, within a little less than a year (though this year could be a critical one for wading birds dependent on fish prey). If the above predictions are, indeed, robust and general ones, then the prediction of the impact of fluctuating water levels on the numbers of small fishes, aggregated over species, in a marsh will be made much more simple. The ATLSS single-cell lower trophic level (LTL) and fish models have been combined with the general landscape structure code to produce an integrated model that may be run across any suitable portion of the South Florida landscape. This allows for the spatially-explicit simulation of LTL and Fish functional group responses to hydrology by coupling together many spatial cells, within each of which the LTL and Fish cell models are run. For the LTL model, cells are treated as independent, in that there is no flux of LTL components across cells. This does not imply that there are not strong spatial correlations in LTL components however, since hydrology is a strong driving variable in the LTL model and there are spatial correlations in hydrology. In the Fish components, while the underlying dynamic model of the functional groups is the same for each cell of the landscape, movement of fish does occur between cells as well as between locations within cells (e.g. the proportion within a cell in ponds, marsh and solution holes changes as a function of hydrology). The model has been coded in object-oriented form in C++. There are significant memory requirements in order to track the size (or age) structure of functional groups in marsh, pond and solution holes across a landscape. Thus, although the model may be run across any size grid, to date simulations have been limited to regions of about 50x50 grid cells of 500m each (thus covering a spatial extent of about 625 square kilometers). The model currently requires about 1 hour of cpu time per year of simulation on an Ultra Sparc 1 with 256 MB RAM and a 167MHz processor for this size grid. A variety of issues arise in the landscape model that are required to extend the single cell models. These include: 1) The solution holes distribution must be randomized across the spatial extent of the landscape. The model assumes 10 depth classes of solution holes. For each cell the model randomly assigns (according to a distribution chosen a priori that could be estimated from appropriate field data) the total fraction of the area of the cell that is occupied by each solution hole depth class. 2) The ponds distribution must be randomized across the spatial extent of the landscape. The model randomly assigns (according again to an a priori distribution that could be estimated from data) pond area for each cell of the landscape, and assumes a constant water level for all the ponds in the landscape for all the time steps. 3) The initial density of LTL components and fish functional group size structure must be assigned across the landscape. These may be assigned to be equal across cells, or may be read in from a previously saved output file from a simulation. 4) The landscape model allows movement of fish functional group density between cells. Fish are assumed to only move between marsh areas of adjacent cells, so there is no movement from the pond of one cell to any area within an adjacent cell. Fish movement is based on a combination of water level differences and functional group density differences between neighboring cells, with higher fluxes at higher values of these differences. Several alternative assumptions may be made about movement at the edge of the simulated area - presently it is assumed that there is no movement across this boundary. 4. Empirical data This modeling work is being done in collaboration with empirical scientists studying fish in the Everglades/Big Cypress pregoin (William Loftus, Joel Trexler, Jerry Lorenz), who helped in estimating parameter values for the model. Data on the seasonal changes of fish number and biomass densities are available for some areas of Everglades National Park (Loftus and Eklund 1994), and these data can be used for calibrating and testing the model. Conveniently, these authors have divided the fishes they surveyed into the two types of large and small fishes that are used in the model. Loftus and Eklund (1994) sampled fish populations in the Everglades over a period of several years, and found mean annual densities of the small-fish type to range from 15.5 to 17.1 fish/m2 in the early part of the study and 30.2 to 34.5 in the later part of the study. This compares with values of about 5 to 15 small- type fish/m2 in the model for the longer hydroperiod simulations. Loftus and Eklund estimated the mean annual density of large-type fishes at about 0.012 fish/m2. The model densities ranged from about 0.005 to 0.013 for the longer hydroperiod simulations. The empirical data of Loftus and Eklund (1994) showed strong seasonal oscillations, with the highest small-fish densities within a year approaching four or five times the lowest densities. This contrasts with the oscillations over a year in the model, where dry season densities dropped to a very small fraction of the peak densities. It may be that the model drydown periods were more severe, or at least more continuous, than those occurring in the Everglades areas sampled by Loftus and Eklund. The model simulations are in general comparable with data from the field in the Everglades (Loftus and Eklund 1994), even though the only part of the model that has been calibrated to data was that for the lower trophic levels. This indicates that the model is probably correctly transferring the energy into the two fish functional groups. 5. Timeline for completion of work Currently the fish model has been parameterized only for the freshwater areas of the Everglades/Big Cypress. Because fish production in the coastal mangrove areas is probably critically important for wading birds at certain times, the model must be extended to these areas. One of the main hindrances to doing this is the absence of a hydrologic model for the coastal estuaries. As soon as a model for major parts of the coastal estuary zone is available, the current model can be extended. F. Reptile and Amphibian Assemblage Modeling 1. Purpose of the component The herpetological assemblage of the Everglades and Big Cypress is a understudied but vital component of these systems. Populations of reptiles and amphibians can be large (G. H. Dalrymple, unpublished data), and a keystone species in the region, the American alligator (Alligator mississippiensis), is a member of this assemblage. The herpetological assemblage, by itself, may constitute a major pathway for energy flow and its interaction with other groups (e.g., insects and fishes) may have large impacts on the overall structure of energy flow in these aquatic ecosystems. In particular, amphibians and reptiles are a primary prey item of alligators (B. Barr, unpublished data) and may play a vital role in the population dynamics of this species. Hydroperiod and periodic drydowns alter the structure of the herpetological assemblage in the freshwater regions of the Everglades and Big Cypress (Duellman and Schwartz 1958, Dalrymple 1988, Dalrymple et al 1991a, b). Some individuals find solution holes, depressions, subterranean habitats or other microsites during drydowns and remain active, while other individuals leave dry areas (Dalrymple 1988, Dodd 1993). During severe droughts, specific members of the assemblage aestivate (Hansen 1958, Gibbons et. al 1983, Etheridge 1990). Furthermore, egg and larval stages of the amphibians require varying durations of standing water to successfully survive and mature into adults. Given the potential impacts reptiles and amphibians can have in the freshwater aquatic systems of southern Florida, and their observed responses to hydrology, our goal is to develop a computer simulation that will allow us to predict population level responses in the herpetological assemblage to fluctuations in water level. 2. The modeling approach The modeling of the herpetological assemblage has a both a descriptive and a predictive phase. Descriptive phase. Because so little is known about the herpetological assemblage, we are developing a food web and description of energy flow using extensive field data provided by Dr. George Dalrymple and linear programming techniques. Linear programming is an optimization routine and is excellent for estimating energy fluxes in food webs. In our linear programs, we assume a steady state in each functional group (i.e., energy in equals energy out). Furthermore, we constrain the solution set using empirically derived ranges of standing crop biomass, diet, and respiration for each herpetological functional group, thus assuring the results fall within biologically reasonable bounds. Constraints on non-herpetological components, as well as alligators have currently been left broad, because data are lacking. Thus, the results indicate the possible energy flows and biomasses, given the internal constraints found just within the herpetological assemblage. To develop the food webs presented here, we minimized the differences between the empirically derived estimates of energy fluxes, and those possible given the constraints on the system. We have three general goals for the descriptive phase. First, we will describe the pattern of energy flow within the herpetological food web. Second, we will determine how the structure of the web and the energy fluxes change with hydroperiod and habitat type. Third, we will determine the energetic interaction between the herpetological assemblage and other assemblages in the Everglades as well as estimate the potential flow of energy to alligators. Predictive phase. Using the results from phase 1 to generate the general ranges in biomasses as well as the potentially key members of the assemblage, a simulation will be created to predict the impacts of hydrology on the herpetological assemblage. The structure of the model will be based on additional empirically derived estimates of demographic parameters of the functional groups supplied by Dr. George Dalrymple. The model will function with 500 m x 500 m spatial cells, and time steps of 5 days and include the impacts of spatial heterogeneity within and across cells, responses to changes in water level (including dry-downs), and biological interactions within the herpetological assemblage (Figure V.F.1). The model will interact with the fish, crocodilian, and lower trophic level models. 3. Current progress We are currently analyzing the data necessary to complete phase 1; the description of energy flow through the food web. We have constructed food webs and run linear programs for three generalized habitat types; marsh, prairie and upland. The structure of the food web changes, as interactions between members of the herpetological assemblage are more common in the marsh than in the other habitat types, which are similar in structure (Figure V.F.2). Biomass of the more aquatic functional groups generally decreases in the upland habitats relative to the prairie and marsh. Lizards make up the largest biomass of any functional group in the prairie and upland habitats, whereas snakes and medium frogs have the highest biomass in the marsh. Despite these differences across habitats, the total amount energy flowing through the assemblage is similar across all habitat types though there is a slight trend for more energy flow in wetter habitats (marsh = 27,566 g/ha; prairie = 25,089 g/ha, upland = 22,105 g/ha). A common trend in all three habitat types is that estimated energy fluxes within the herpetological assemblage can be strongly influenced by changes in the constraints or estimated biomasses of non-herpetological functional groups. Thus, there is indeed a need to link the disparate trophic groups (i.e., birds, alligators, fishes, reptiles and amphibians) under one general simulation. Work on phase 2, the predictive simulation, has not formally begun. However, we have developed a preliminary conceptual basis for the model (described above, Figure V.F.1). 4. Empirical data Both phases of the modeling are based on a large amount of empirical data. Dr. George Dalrymple is supplying the data based on long-term censuses, analyses of large numbers of preserved specimens, and literature reviews. He has estimated upper and lower bounds, as well as average and median estimates of biomass, consumption, respiration and diets for each functional group in marsh, prairie and upland habitat types. Dr. Dalrymple will also supply data on reproduction, turnover, survival, and other demographic information for each functional group for the purposes of parameterizing the predictive simulation in phase 2. 5. Time line to completion Phase 1 will be completed by May of 1997 and will be presented in at least two deliverables in the form of journal articles. Once these results are complete, we will have nearly 18 months to develop and test the simulation, thus completing phase 2 near the end of 1998. G. Crocodilian Modeling 1. Purpose of the component The American alligator (Alligator mississippiensis) is a keystone species in the Everglades and Big Cypress Swamp, as defined by its role as a top-level carnivore and architect within these systems and its influence on the structure, distribution and abundance of native plant and animal communities. Although the alligator is a large, mobile carnivore that represents a versatile and selectively opportunistic predator, it depends upon stable resources within its local environment, e.g. presence of surface water and related prey resources. During a long life span of nearly continuous growth, an individual feeds on a variety of prey species and sizes that vary according to the alligator's size. As alligator populations consist of overlapping size classes, they are consumers at all trophic levels. The alligator also modifies its environment through construction and maintenance of "alligator holes" to regulate its body temperature. These holes also serve as critical dry-season refugia for a variety of other aquatic animals upon which wading birds and other predators feed. Because loss of such a keystone species can lead to drastic re-ordering of some parts of the floral and faunal community, the recent severe declines in the abundance of the American alligator in South Florida are of concern. These declines are attributed to alterations in the natural hydrologic regime of the region. 2. The modeling approach The alligator model simulates alligator responses to varying hydrologic regimes in a variety of freshwater, local environments. These local environments include short- and long-hydroperiod wetlands. The model consists of two parts. One part simulates the life stages of individual adult and subadult alligators and, in particular, nesting female alligators and their reproductive performance. The second part simulates the life stages of hatchlings and juveniles in typical nest areas. Hatchlings and juveniles can be modeled as cohorts because small alligators suffer from a high mortality. The module tracks their daily growth and survival based upon a set of environmental factors and stochastic survival probabilities. Adults and sub-adults, however, are modeled as individuals. The basic time step to model individual alligators is a day. Food intake during each day is monitored, as well as the amount of energy spent for activities during the day (and the night). An average assimilation coefficient from feeding experiments is used to determine the daily energy budget. These budgets are accumulated over a longer time period (e.g. a month) and are then converted into growth or regression, depending on whether the accumulated energy budget is positive or negative. The model simulated individual alligators, in particular females at their nesting areas (a schematic is shown in Figure V.G.1). Nest areas are categorized according to some basic characteristics, such as location, size and depth of pond, food availability, and suitability for building a nest. An alligator pond, or the presence of surface water provides a means for the alligator to regulate its body temperature. A simple limnological model is used to calculate approximate water temperatures in relation to weather conditions. The availability of food is simulated using output from the models for fish and aquatic macroinvertebrates. Food intake also depend on an alligator's body temperature and its current nutritional status. The model assumes that alligators do not eat every day, but capture some larger prey items once every several days, and a few smaller prey items in between. The smaller prey items are treated on an average basis, whereas the larger prey captured are treated as separate and distinct food items. The model assumes that these larger prey are 'offered' to the individual alligator, which accepts or rejects them, depending on its state variables, e.g. satisfaction/hunger. Given the food intake and the composition of the diet, the model then calculates the energy budget of the animal using food conversion rates and maintenance energy requirements determined from feeding experiments. 3. Empirical data The relationships between the amount and composition of the alligator's diet and its growth and reproductive performance have been determined for captive animals in experimental studies as well as for animals in the wild. Because the model for adults and sub- adults is based on individuals, it provides a variety of outputs that may be directly compared to the field data on individuals, in addition to comparisons of data on nesting success under differing environmental conditions. The model does not include detailed individual-based components for the hatchling and juvenile stages. 4. Timeline to completion of work The alligator model is currently under construction. The model has been coded, but not yet parametrized and calibrated. There are few data available on characteristic properties of alligator ponds, e.g. size and depth distribution, temperature profiles, faunal characteristics. The majority of the data available were obtained for ponds in the Big Cypress swamp (e.g. Kushlan and Hurt, 1979). Although it is probably not appropriate to generalize these data over the entire Everglades/Big Cypress ecosystem, they are nevertheless used to parametrize parts of the model. However, studies to obtain data on limnological characteristics of alligator ponds are currently underway and will be used in a reparametrization of the model. In addition, telemetry studies to determine the movement of alligators, in particular during the dry season, were begun in the fall of 1996. These studies will provide the data necessary for a full parametrization of the alligator model. H. Wading Bird Assemblage Modeling 1. Purpose of the component As top-level carnivores, wading birds are important components in the aquatic food web. Wading birds are highly mobile animals that influence the structure and dynamics of their freshwater and estuarine prey communities, and transport nutrients from their feeding sites to their colonies. Wading birds are also highly dependent on patchy resources with a high prey density. Since water management regulation ponding of overland flows in northern reaches of the Everglades catchment area and severe overdrainage of the southern reaches downstream of these impoundments has occurred. Taken in combination, these have altered the locations or spatial arrangement, as well as reduced the areal extent of seasonal foraging habitats for wading birds. Declines in wading bird populations have occurred in all feeding guilds concurrent with these landscape changes. Decreasing numbers attempt to nest each year, particularly at traditional colony sites within the downstream reaches of the southern Everglades. The focus of present modeling efforts for wading birds in ATLSS is to develop simulation models that allow one to investigate the dynamics of colonies and the nesting success of wading birds in relation to different hydrologic scenarios and the resulting spatial and temporal distribution of their prey. The species selected for modeling in ATLSS are tactile (wood stork, white ibis) and visually (great egret, great blue heron) feeding species and represent the majority of the wading bird population in the Everglades and Big Cypress ecosystem of southern Florida. 2. The modeling approach Because wading birds depend on patchy resources and can travel long distances during their foraging flights, aggregated model are inappropriate to describe the dynamics of colonies in a heterogeneous and rapidly changing environment as the Everglades/Big Cypress ecosystem. Although most activities of wading birds, such as whether to forage solitarily or in flocks, occur on small time scales (minutes/hours), they nevertheless have a large influence on the foraging success of individual birds as well as the depletion of local prey resources. This necessitates the use of an individual-oriented modeling approach in which individual birds are described by a set of species-specific rules that govern their behavioral activities. Contrary to other ATLSS models, the wading bird models do not operate on fixed time scales, because the various activities of individual birds are of different duration. Instead, the models use an event-driven simulation approach where each bird sets its own individual time-scales that depend on the duration of its current activities. The model consists of species-specific sets of rules for the behavior and the energetics of nesting adults, as well as rules for the energetics and growth of their nestlings. The model makes extensive use of the output of the landscape, hydrology and fish models within ATLSS. These data are then used by the individual birds; for example, to determine where they can forage and how successful they are at these sites. The wading bird model thus operates on the same spatial grid but not on the same time scale as other ATLSS models. 3. Current progress A first version simulating a single species nesting colony (wood storks) has been completed (Wolff, 1994) and was already applied to test various hypotheses about the influence of hydrological patterns on the timing of nesting and the subsequent nesting success (Fleming et al. 1994). We modeled a single colony at a traditional colony site and ran different scenarios in which we decreased the areal extent of peripheral wetlands that represent early shallow water foraging habitat for wading birds (Figure V.H.1). Colonies are formed if the birds find sufficient food in high density patches which then triggers a nesting response and provides female birds with the additional energy required for egg production. In our simulation, the larger the reduction in peripheral wetlands area, the later was colony formation (Figure V.H.2). In addition, the number of nesting attempts decreased for larger delays in colony formation (Figure V.H.3). Both of these patterns can be attributed to a shortage of high density food patches during the early dry season. There was enough food available for the birds to meet their basic energy requirements, but not enough to trigger nesting and energy required for egg production. The number of fledglings in a colony can serve as an indicator for the possible number of new recruits into the breeding population. This number decreased sharply with the reduction in peripheral wetlands area and was always well below the possible production if every nesting attempt had produced three fledglings (Figure V.H.4). If the parents could not provide their chicks with sufficient food, brood reduction occurred. If the adult birds could not meet their own energy requirements they subsequently deserted the colony as well. These general patterns, delay in colony formation, reduction in the number of nesting attempts, and lower numbers of fledglings, were quite robust for all scenarios in which the areal extent of peripheral wetlands was decreased. The current version simulates wading birds in several, mixed- species colonies and has been largely completed for the freshwater areas of the Everglades/Big Cypress ecosystem. The rules describing the behavioral activities and the energetics of individual birds for wading birds other than wood storks were parametrized as far as existing data permitted. Fine tuning of the model as well as interfacing with the landscape, hydrology and fish models will proceed as soon as high-level code for these models becomes available. These models were specifically constructed to evaluate the relation between hydrology and nesting colony success. Due to this focus, the time span in the model is one breeding season, and therefore a variety of assumptions must be made to evaluate colony success over several years. In particular, to date there is no explicit model component dealing with survival outside of the nesting season because data about interannual survival of wading birds are virtually nonexistent. In addition, the model does not yet include any adult or nestling mortality that originates from disease or predation. 4. Empirical data There exists an extensive data base of published and unpublished data on wading birds. Nevertheless, some critical data (for all species!) are still missing from the literature. The most prominent missing information is foraging success in relation to prey availability. However, field studies are currently under way to fill most of these data gaps so that individual rules can be more accurately formulated and parametrized. 5. Timeline to completion of work The mangrove areas are assumed to be critically important during the early part of the breeding season and the wading bird model must therefore be extended to these areas as well. An extension to these areas will be possible as soon as the corresponding models for the hydrology and the prey resources are available for these areas. I. Cape Sable Seaside Sparrow Population Modeling 1. Purpose of the component The Cape Sable seaside sparrow (Ammodramus maritima mirabilis) is an ecologically isolated subspecies of the seaside sparrow (Beecher 1955, Funderburg and Quay 1983, Post and Greenlaw 1994). Its range is restricted to the extreme southern portion of the Florida peninsula almost entirely within the boundaries of the Everglades National Park and Big Cypress National Preserve (Werner 1975, Bass and Kushlan 1982). The sparrow breeds in marl prairies either side of Shark River Slough (Figure V.I.1). Marl prairies are typified by dense mixed stands of graminoid species usually below 1m in height, naturally inundated by freshwater for two to four months annually. The potential of such habitat, for sparrow breeding, is dependent upon regimes of fire, hydrology and catastrophic events (hurricane and frost). Recent declines in the sparrow population across its entire range, especially the western portion, highlight the need for an effective ecological management strategy. The remaining core of the population occupies approximately 60-70 sq.km. in the area adjacent to the south east of Mahogany hammock (Area B in Figure V.I.1). This sub-population currently represents 73% of the total population (1996 estimate), and because of the spatial restriction it is seriously at risk to the effects of hurricane or wildfire. Changes to the hydrology of the southern Everglades may also increase the threat of extinction. Increased hydroperiods affect the sparrow in two ways : a) directly shorten the potential breeding season, and b) indirectly by causing changes in the vegetation. Recent studies (Nott et al. in press) show that wetter conditions cause typically short-hydroperiod vegetation (Muhlenbergia) to become dominated by sawgrass (Cladium jamaicense) and spikerush (Eleocharis spp.). This kind of habitat is less suitable for breeding purposes but remains available for foraging. The main objective of the model is to investigate the effects of fire and hydrology regimes upon various measurements of the sparrow population. These include lifetime reproductive success of individuals, movement patterns and spatial distributions of the population, fluctuations in the size and structure of the population and local densities. The model adopts an individual- based spatially explicit approach. Such an approach is preferable for modeling small populations that are dependent upon limited resources (Uchmanski and Grimm 1996). In this model, individual sparrows in the population explore a variable landscape consisting of 100m x 100m cells. This resolution is ecologically appropriate, considering the minimum territory size, the scale of many landscape features, and the length of typical 'neighborhood' flights. 2. The modeling approach A set of state variables describes each individual in the population. They differ from one another and respond to both the landscape, and to other individuals in the population.The minimum set required to model the observed complexity of the sparrow's behavior include spatial location, age, sex, weight, reproductive status, fitness and associations with others. The model updates each individual's status daily according to movement and behavior rules. The spine of the model is a simple flow of decisions and actions that affect individuals. A flow diagram for the breeding season portion of the model is shown in Figure V.I.2. At each step the model updates the breeding status and tracks associations between individuals. Each individual (in random order) moves around the landscape according to a simple set of movement rules. These are dependent upon the time of year, water levels, the status of the individual, the attributes of the cells it encounters and the attributes of neighboring cells. Important landscape attributes include elevation, the vegetation classification, and fire history. Some types of cells represent 'reflective' barriers to movement (pine forest, hammock and open water), other 'transparent' cell types allow movement but do not represent breeding habitat (sawgrass/spikerush marsh). Temporal and spatial patterns in water levels represent the main environmental driving force behind the model. A set of behavioral rules mimics observed interactions between individuals. The outcome probability of encounters between individuals is dependent upon their relative status. This determines the next movement of each individual, and updates the associations between individuals. For instance, early in the breeding season two neighboring males may fight over the borders of their respective territories. After this stage they reinforce the limits of their territories by countersinging and other less physical behavior. However, males chase neighboring males more often when they are caring for nestlings (Lockwood et al. in press). Fighting may also be triggered when a bachelor male or juvenile enters an established territory, normally the resident male will drive off the intruder. The direction of unpaired female movements is influenced by the proximity of territorial males, this simulates the fact that male song can be heard (at least by humans) from several hundred meters away. Subsequent encounters between unpaired territorial males and unpaired females may result in successful mating. As breeding activity diminishes the sparrows form small cohesive groups, and associations between individuals become more complex. The combination of simple behavioral and movement rules give rise to more complex patterns. For example, Figure V.I.3 depicts a number of possible scenarios representing typical patterns of movement and encounter. Once a breeding pair enters the breeding cycle, each egg laid is treated as an individual. Subsequently, they are vulnerable to daily probabilities of hatching, predation, and mortality based on the ecological parameters given in Table 1. At this stage the model updates familial associations to avoid unnecessary encounters and to ensure spatial adjacency during the fledgling stage. 3. Current status A reduced form of the model investigates the response of a sparrow population to changing hydrologic conditions. It is driven by the availability of breeding habitat and describes each individual using a reduced set of state variables (location, sex and age). The spatial extent of this model is an area approximately 20 x 15 kilometers to the west of Shark Slough (Figure V.I.1). Water level is the only environmental factor included. A model of the availability of breeding habitat uses daily water level data collected from the NP205 hydrological monitoring station. The breeding patterns of a population of sparrows respond to this changing hydrology. The topology of this area was estimated from field measurements of water depth (Pimm et al. 1995, Nott et al. in press). In the second phase of development an energetic component will be incorporated into the basic landscape model. Two years of dietary studies and invertebrate sampling form the basis of this component (Lockwood et al. in press, Pimm et al. 1996). The energetically based model will track energy input through food intake and energy losses through basal metabolism, movement, territoriality and other activities. The status of an individual changes daily, based upon the behavioral and physiological responses of individuals to changes in environmental conditions. The model will track their daily growth and survival, with reference to a set of environmental factors and stochastic survival probabilities. 4. Empirical data Results of field observations provide the ecological parameters of the model (Curnutt et al. in press, Lockwood et al. in press, Werner et al. 1975). Significantly, recent estimates of survivorship (based on 122 banded birds over 3 years) reveal a possible correlation between survivorship and reproductive success (Pimm et al. 1996). Specifically, males that breed successfully have a greater chance of survival. Also, survivorship varied considerably between study areas (0.31 - 0.75). Estimates of philopatry show that males breeding in one year return to within 200m of the same site the next year. Importantly, recent field work has revealed hitherto unknown post-breeding dispersal patterns (Balent, Fenn and Lockwood pers. comm.). Small cohesive groups of sparrows forage in sawgrass swathes within 500m of the breeding territories (at least from July until December). The vegetation landscape currently available appears to have two major problems. Firstly the extent of Muhlenbergia dominated prairie appears to be greater than that seen in the field. Secondly, the satellite image from which the vegeatation classifications were resolved was not taken in the driest of years, hence some marl priairie is not shown. We can assess the accuracy of the vegetation classification for areas commonly utilized by sparrows using a DGPS based microhabitat survey of sparrow breeding habitat. This will also give us a reference point with which to compare future surveys in order to assess vegetation change at 100m resolution where factors such as the effects of shrub invasion and fire effects can be followed. The pseudo-topographical landscape is modeled with a resolution of 2 miles, this is 3 orders of magnitude coarser than the 100m resolution defined in this, and other ATLSS models. A wet season survey of the distribution of water depth (equivalent to elevation) should be carried out in the same areas, and performed at a variety of scales it will provide estimates of within cell and between cell heterogeneity. A more realistic topography should improve the accuracy of estimating the effects of hydrology on foraging and breeding habitat. Currently, we only know the range of nest heights relative to ground level. An important question is - do sparrows maximise nest height relative to local, or absolute elevation? In order to obtain estimates of absolute height we have set water level indicators at the site of several old nests. Wet season water levels are recorded at these sites and compared with data from nearby hydrological monitoring index stations to obtain the absolute height of nests. We suggest that future field work should expand our knowledge of the distribution of relative and absolute nest heights. In addition we need to assess whether there are threshold water levels below which nesting activity starts. These measurements will provide us with estimates of ground height, as well as spatial and temporal patterns of drainage. We also need to increase the number of nest observations to obtain better estimates of clutch size probability and nest cycle duration (especially of the interval between broods). 5. Timeline for completion of work In the broader context of ATLSS the sparrow model is relatively independent, it only links to the hydrology, fire and vegetation models. In addition, we have developed our own hydrology model linked to a realistic topology. For these reasons we can continue to develop the model without reference to other models until the hydrology and vegetation platforms become available. We estimate that a first draft of the model should be available July 1997 and completed by January 1998. J. Snail Kite Population Modeling 1. Purpose of the component The snail kite (Rostrhamus sociabilis plumbeus Ridgway) is a wetland hawk whose distribution in United States is limited to the freshwater marshes of southern and central Florida (Bennetts et al, 1994). It is listed as an endangered species in the United States, although its numbers appear to be increasing in recent years. Because of its endangered status, a spatially-explicit, individual-based model of the dynamics of the snail-kite population will be developed within the ATLSS project. Because the snail kite feeds almost exclusively on the apple snail (Pomacea paludosa), it is subject to environmental variations that affect apple snail dynamics and, therefore, the availability of the apple snails to snail kites. Apple snails occur in areas of extended inundation and their availability to kites is greatly reduced during droughts. Thus, snail kite populations are sensitive to climatic fluctuations. Within southern and central Florida snail kites are highly mobile and they may escape local drought conditions through movement, provided that suitable refugia are available. The main research question concerning the snail kite model is best described in a hypothesis formulated by Bennetts and Kitchens (in prep.) on the relationship between drought severity (i.e. intensity, spatial extend, and duration) and the type of response by snail kites. They hypothesize that droughts of low intensity and spatial extend are more likely to produce a behavioral response (i.e., birds move to a new location), but do not necessarily have a numerical response (i.e., change in survival and/or reproduction). As the severity of a drought increases, the probability of a numerical response also increases, because birds become unable to behaviorally escape the effects of the drought. 2. Modeling approach The dynamics of the kite population under different management regimes will be analysed by means of a spatially-explicit individual-based model (Figure V.J.1). Given the total size of the kite population (order of magnitude 100-1000) it will be possible to represent each individual kite in the model. In the first implementation of the model, focusing on general demographic processes, only the age of the kites was recorded (Mooij and DeAngelis, submitted). In an extended version, model kites will be given some form a memory. This will allow them to show a behavioral response to local environmental conditions, based on previous experiences in the various major wetlands of southern and central Florida. In the final version, including bio-energetical details, parameters describing the condition of the kite will be included. The first version of the model employs a yearly time step. In the second version of the model, which concentrates n demographic and behavioral details, this time step will be refined to weeks-months. In the final version of the model, including bio-energetical details, the life-functions of individual kites will be evaluated on a daily time scale. All versions of the model will have a relative coarse spatial structure. In the model kites will live in one the fifteen major wetlands of southern and central Florida or in one aggregated peripheral habitat. The choice for not taking further spatial details into account has been made primarily because kites will move around within wetlands on a daily basis, and therefore it will be hard to link their behaviour and survival to specific grid cells within a wetland. This aspect of the dynamics of the kites will be emulated in the model by supplying the kites with an aggregated information of the hydrological status of the various grid cells which fall within the wetland in which they are currently living. The first version of the model, taking only general demographic processes into account is independent of other ATLSS components. The second version of the model, including demographic and behavioral details, will depend on information of the hydrological status of the fifteen major wetlands under various management regimes. The final version will also depend on information of the availability of snails in the fifteen major wetlands. 3. Current progress Phases 1 thru 3 (see below) were realized in 1996 and the main results will be discussed briefly. Phases 4 thru 6 are planned for 1997. In phase 1 of the project we fitted a simple non-spatially explicit multiplicative simulation model to the observed number of snail kites. This model, which can be seen as an extension and modification of the model of Beissinger (1995), relates the number of counted birds to the environmental state, derived from the yearly lowest water levels during the years 1970-94. We tested the fit of the simulation model by means of a Monte-Carlo permutation test and found a significant relation between snail kite numbers and the environmental state (Figure V.J.2). Moreover, we showed that some of the observations of the annual count should be considered as an outlier. Finally, we were able to detect in one year (1987) a large unexpected decrease in snail kite numbers that cannot be attributed to the processes occurring in the other years. This result may indicate a hitherto overlooked catastrophic event in the snail kite population in that year. In phase 2, we have build a simple individual-based simulation model of the kite population of central and southern Florida. This version of the model is hosted in a framework for individual-based simulations (OSIRIS), developed at the Netherlands Institute of Ecology. Although the spatial structure of the fifteen major wetlands of south and central Florida is already included, currently the effects of spatial heterogeneity are not taken into account; instead, one hydrology (that of WCA-3A) is currently imposed on all wetlands. The purpose of this first simple individual-based model was to analyze the effects of demographic stochasticity on the predictions of Kite dynamics for a given hydrological scenario. The first results of this analysis show that in fact demographic stochasticity will be a major component in the total stochasticity of a predictive snail kite model, due to the fact that within the current range of numbers, kite population-dynamics apparently show no signs of density-dependence. In phase 3, we concentrated on the effects of environmental stochasticity on our capacity to predict the number of kites, assuming a full understanding of the dependence of kite population dynamics on the hydrological conditions. This analysis shows that due to environmental stochasticity within less than a decade range of the possible population sizes ranges from unrealistically high values to values close to extinction. This analysis stresses the importance to include density-dependent processes in the kite model. Inclusion of these processes will decrease the range of model outcomes by imposing a increased kite mortality at high densities and a reduced kite mortality at low densities. 4. Empirical data Annual counts were conducted on the snail kites from 1969 to 1994 in November and December of each year (Sykes et al. 1995). Although there is a great deal of uncertainty in these data (Bennetts and Kitchens, in prep), they provide an indication of population levels and the only basis for assessing the changing status of the population on the long term. A detailed study on the demography and movements of the kites has been performed by Bennetts and Kitchens (in prep.). Their two primary field methods were radio telemetry and mark-resighting (banding). These data will be used to estimate demographic parameters with an emphasis on survival, to evaluate the influences of environmental conditions (e.g., hydrology) on survival, and to evaluate the movement patterns of snail kites in Florida including what environmental conditions are correlated with these movements. 5. Timeline for completion of work Phase 1: Statistical analysis of the population-dynamics of the snail kite during the last 25 years in relation to environmental conditions (time investment 2 weeks, delivered in May 1996). Phase 2: First simple version of the spatial-explicit individual-based model of the snail kite, developed within the OSIRIS framework (time investment 2 weeks, delivered in May 1996). Phase 3: Probabilistic projections of the expected population dynamics for the next 25 years, based on the results of the analysis in phase 1 (time investment 2 weeks, delivered in August 1996). Phase 4: Transfer of the simple spatial-explicit individual-based snail kite model of phase 2 from OSIRIS to ATLSS (time investment 2 weeks, delivered in January 1997). Phase 5: Extended second version of the spatially-explicit individual-based snail kite model, including demographic and behavioral details, given the hydrological scenarios of the major wetlands of southern and central Florida, supplied by other ATLSS submodels (time investment 4 weeks, delivered in May 1997). Phase 6: Final version of the spatially-explicit individual-based snail kite model, also including bio-energetical details, given the availability of snails in the major wetlands of southern and central Florida, supplied by other ATLSS-submodels (time investment 4 weeks, delivered December 1997). K. Florida Panther and White-tailed Deer Trophic Interaction Modeling 1. Purpose of the component As one of the most charismatic endangered species in the U.S., the Florida panther has attracted widespread attention to some of the environmental challenges facing us in any attempt to modify conditions in South Florida. As the largest terrestrial carnivore in South Florida, and an animal that depends upon large home range sizes, the Florida Panther (Felis concolor coryi) serves as a key species for assessing the health of these ecosystems. Dealing with the implications to the Florida panther of plans for Everglades restoration has been a central concern of the ATLSS project since it's inception. Our objective therefore has been to develop a model that allows us to determine the potential long-term impacts (e.g. over 30 years or more) of spatially-explicit modifications in habitat (particularly hydrology) on the panther population. The spatio-temporal dynamics of habitat coupled with small population size and long-distance movements of panthers, implied that an individual-based modeling approach offered the best hope to utilize the extensive available empirical data on panthers to produce a model which appropriately tracks the effects of alternative hydrologic scenarios. Additionally, such a model offers the capability to provide cost-effective methods to help guide any potential captive release program, in terms of comparing the effect of alternative release programs on the population throughout the region. Individual panther success (e.g. survival and reproduction) in South Florida is closely linked to a panther's ability to obtain large prey items, notably White-tailed deer (Odocoileus virginianus seminolus) and feral hogs. Thus, in order to produce a panther model, it was essential to obtain reasonable methods to determine how hydrologic changes would affect these key prey resources, and link this to the panthers. White-tailed deer, the only large (native) herbivore in the region, has a significant localized impact on vegetation, so including it as a major component of the project is justified independent of the importance of deer to panthers. Accounting for the impacts on vegetation of feral hogs in the system would also be appropriate, however funding limitations have restricted us to a simplified method that allows only coarse-scale implications of hog density on panthers. The main objective of this modeling component, therefore, is to allow for prediction of the relative impacts of alternative hydrologic scenarios over a 30 year time frame on the spatial and temporal (e.g., seasonal) distribution of panther and deer across South Florida, and to produce relative comparisons of mortality, reproduction, individual movement patterns and territory size across the landscape for both species. 2. Modeling approach Due to the fact that individual panthers travel over wide territories with potentially very different hydrologic conditions, varying on short time scales relative to organism lifespan, using an aggregated approach to model panther population dynamics would be infeasible. A standard age-structured matrix or differential equation model could not take account of the spatial dynamics of habitat, and the fact that the population size is so small makes such aggregated approaches inappropriate. Therefore, we have constructed an individual-based model, SIMPDEL (Spatial-explicit Individual-based simulation Model of Florida Panther and white-tailed Deer in the Everglades and Big Cypress Landscape), that tracks movement, growth, reproduction and mortality of individaul deer and panther. This model is shown schematically in Figures V.K.1 and V.K.2. The model operates on a daily time step, although within this time step, deer and panther movement are simulated, taking account of local water conditions, forage and prey availability. Spatially, the model makes use of vegetation data to calculate forage availability on a 100 m scale, but tracks deer and panther locations on the daily time step at 500 m scale. The primary inputs to SIMPDEL are daily hydrology data at the 100 m scale, as modified by the pseudotopography model, the vegetation map (from which 3 classes of dynamic forage maps are constructed), a landuse map, a map of feral hog density, and a road map. The primary environmental factors driving the model are, therefore, hydrology and vegetation, which vary temporally. 3. Current progress SIMPDEL is a working model in C++ that has been verified, has been coupled to the landscape structure and vegetation models, and is ready for performance evaluation. Several papers have been written describing it and associated efforts to parallelize the prior C code version (Abbott et al. 1997, Comiskey et al. 1997, Luh et al. 1997). The model produces highly detailed spatial information on deer and panther distribution pattern changes over time that appear reasonable based upon consultation with experts (Figures V.K.3 and V.K.4, see also information on home pages, Section XII). Performance evaluation will include validation efforts such as detailed comparisons of deer distributions with historical data, comparison of aggregated variables such as age-dependent mortality, age-structure, body weight distribution and birth rates with available data, and comparison of model individual-movement patterns with radio collar data. A visualization program has been written to allow easy access to the radio collar information available, and provide a means to readily compare this to model output. 4. Empirical data There is an extensive data base on deer and hog density variation and mortality across portions of South Florida, as well as extensive radio collar information on panthers. Behavioral information, though more anecdoctal in form, has been used extensively to construct the model. 5. Timeline for completion of work Performance evaluation of SIMPDEL will be completed in 1997. Although there are a number of modifications to the model that may be mad (such as the inclusion of panther marking behavior to delineate territories), the form is essentially complete at this time, and as soon as the evaluation is completed, the model can be applied to analyze alternative restoration scenarios. Several versions of the model have already been delivered to the appropriate granting agencies, and a version fully integrated with the landscape/structure/vegetation model will be delivered to meet the April 1, 1997 deliverable due date. VI. Integration of ATLSS Modules A. Need for Model Integration As described earlier, ATLSS is a collection of models that simulate the ecosystem processes throughout all trophic levels across the landscape of South Florida (Fleming et al. 1994). The ATLSS project addresses a fundamental issue in bringing together empirical studies that typically take place in small areas, and integrating these studies into a single model that can be used to study the many diverse ecosystems of South Florida (Ewel and Myers 1990, Forman 1995). There are two principle components of the ATLSS research program. The first effort is empirically based studies designed to collect natural history data that can be used to support the second major objective, computer models. We will discuss the integration of computer models here into what we refer to as a program. A program is a fully integrated set of working computer models. B. ATLSS Integration Design ATLSS is coded in C++ (Eckel 1993), a so-called object-oriented computer language. This means that C++ supports the creation of new data types such as objects. Within ATLSS examples of objects are a deer object, a fish population object, and a wading bird object, for instance. Once an object template is created, the objects can be replicated but each object will behave differently. C++ also permits the creation of methods within objects. A method can be a rule specifying behavioral aspects of animal movement or as simple as an equation describing how the mass of periphyton changes with a cell on the landscape. The ATLSS code framework or driver is also object oriented. That is, objects within the driver program call objects within each separate model that perform various tasks. For instance, before each model can execute, certain tasks such as initialiization procedures must be performed. All models have initialization procedures but these procedures vary between models. Thus, the ATLSS framework calls on each model in turn to perform the relevant initialization procedures. Similiarly, when ATLSS is executed, all models are summoned by the framework to produce graphics output. Other important code development features include the creation of state files that allow ATLSS studies to be restarted at any time. File output creation follows protocols developed to provide ATLSS with a long life. Output files contain information that describe the contents of the file, for instance. C. ATLSS Integration Progress Several models already in various stages of development were chosen as core modules to be used within the ATLSS program. Some of these models were already executing and producing computer output that could be analyzed. Other models were in various stages of development, while most models were in conceptual stages of design. Thus, the ATLSS integration project faced the task of addressing typical program development issues. What made the ATLSS code development activity different was that a landscape-scale model spanning an entire trophic structure had never been created before. Several ATLSS integration issues had to be addressed: 1) ATLSS modules were in different stages of development. While some models were executing and producing useful output, other models were in the conceptual stage, and future models were being discussed. These models would eventually all interconnect and work in concert. 2) Legacy code existed and much effort had been expended to calibrate model results to agree with empirically derived censuses and other data. For instance, the deer and panther model had been in progress for three years and was 11,000 lines of code representing 4 person-years of work (Abbott 1995). A colonial wading bird model was written and functioning (Wolff 1993). A snail kite model was being designed. 3) Generally, models were written to execute over different spatial extents and on different time scales. That is, some models were designed, written, and coded to run on a 100 m by 100 m grid scale while other models already coded were being executed on a 2 mi by 2 mi grid scale and over a different spatial extent. 4) Some models were based on differential equations and excuted within a cell such as the six lower trophic levels (Ulanowicz 1995) while the invididual-based models allowed elements to move spatially across the entire grid. 5) Finally, different model types had to be integrated in the general framework without causing undo hardship to existing efforts. The deer and panther model was executing on a one day timestep. The fish model being developed was coded to execute on a five day timestep. Thus, integration had to proceed in two phases. The first phase was to integrate existing efforts into a coordinated framework. Both legacy code and new code had to obey standards and protocols set forth in the ATLSS integration document (Fishwick and Sanderson 1995) that required code develpment to proceed in specific ways. The second concurrent phase was to describe how integration would proceed if all code could be rewritten within a new integration framework. The ATLSS framework had to be flexible yet rigorous to provide sufficient model development flexibility within the bounds of an object-oriented design. The present ATLSS framework assures compatability over spatial extent and accommodates coding changes without the need for extensive code changes throughout ATLSS. We refer to our integration of models within the ATLSS framework as Object Oriented Physical Modeling (OOPM). No methodology exists for the kind of integrated object oriented design that had to be undertaken in ATLSS. Thus, our design serves as a "blueprint" for all modelers, whether or not code has been written. OOPM methodology provides the framework for different types of model integration including legacy code whiling avoiding the need for each model developer to know data structures in other models. D. Trophic Network Analysis 1. Purpose of the component The ATLSS endeavor is one of the most complex and sophisticated simulations ever attempted. Anyone familiar with modeling is keenly aware that with increasing model complexity comes a higher probability for unexpected, exotic and unrealistic behaviors. No one expects the full ATLSS model to perform realistically the first time it is run. Before it can ever be applied to management scenarios, it almost certainly will be necessary to correct any number of behavioral pathologies inherent in the ensemble of modules when first they are made to communicate with each other. As there are no set formulae for "calibrating" such enormous multi-models, ATLSS investigators have chosen to invoke network analysis as a set of tools to guide them in calibrating and debugging initial modeling trials. Trophic flow networks are graphical and mathematical answers to the questions, "Who eats whom, and by how much?" Typically, diagrams of flow networks are comprised of boxes that represent the major components of the ecosystem. The boxes are connected by arrows, which indicate the transfers of material or energy between the components. Each arrow usually is labelled with the magnitude of the transfer it represents as averaged over a prescribed period of time (Figure VI.D.1). 2. Modeling approach Mathematically, these transfers also can be portrayed as elements of one- and two-dimensional computer arrays. In this form the investigator may invoke a number of analytical techniques that were adapted from linear algebra. For example, it becomes possible to trace material consumed by a higher level species back through its trophic antecedents to its ultimate origins. Similarly, one may quantify the eventual fates of material that leaves a particular lower trophic component (Szyrmer and Ulanowicz 1987.) Such information on sources and sinks can be extremely useful in tracing the causes of unrealistic model behaviors. For example, field data on the stocks of ecosystem components tell one only that the predicted biomasses diverge from their values in nature. Network analysis also provides clues to where the problem may lie. Of course, some of the early problems with ATLSS may involve more than just local or bilateral dynamics. Whole ecosystem models can behave strangely for reasons that are quite diffuse. Full calibration will require more than just "pointwise" comparisons of biomasses and flows. It will be necessary as well to judge how the whole-system attributes of ATLSS are behaving. Fortunately, network analysis provides some useful system-level criteria. For example, the same techniques from linear analysis that are used to trace the distributions of origins and fates along the trophic web also can be adapted to coalesce the complicated trophic web into an equivalent simple (canonical) chain of transfers (Ulanowicz 1995b.) Thus, by comparing the canonical food chain of the ATLSS output with that of the prototype network, investigators may assess how well ATLSS is replicating system-wide efficiencies at various levels of the trophic hierarchy. Of great importance to the functioning of an ecosystem is its ability to cycle and retain material. Network techniques have been developed that identify, enumerate and quantify each pathway of material recycle (Ulanowicz 1983.) The aggregated pattern of all recycle pathways often provides strong clues as to the roles that some components play in maintaining overall system function (Baird and Ulanowicz 1989.) The pattern of recycle as it transpires in the independently estimated networks, when compared with that generated by the ATLSS model should reveal how well ATLSS is mimicking system controls. Toward the goal of better quality control over ATLSS predictions, the University of Maryland contingent of investigators is putting together very detailled networks of carbon exchanges (and those of phosphorus, if feasible) as they normally occur in the ecosystems of the South Florida wetlands. Networks comprising some 60 - 70 compartments will be estimated for each of four habitats, using existing data and ongoing field work. Separate networks for wet and dry seasons will be created for the ecosystems of the gramminoid marshes, the forested wetlands, the mangrove estuaries, and the shallows of Florida Bay. Each network will be a snapshot of the trophic flows and biomasses as averaged over the hydroperiod in question and over the entire spatial domain of the particular biotope. 3. Current progress The estimation of the forested wetlands network began in June, 1996. (The networks are being created one at a time.) The components of the cypress network already have been identified and the flows among them have been parsed. Significant progress has been made toward estimating the stocks of carbon that characterize each compartment. The network is expected to be fully quantified by the end of February, 1997. The University of Maryland investigators charged with the network subtask are using the Excel(TM) expanded spreadsheet to document their work. Spreadsheets were designed as accounting tools, and so are well- suited for building networks. Excel has one particularly useful option whereby a user may click on any particular value in the exchange matrix and read the supporting documentation on the assumptions and data sources used to calculate that item. 4. Empirical data The construction of wetland networks will proceed using mostly data retrieved from the literature. Of course, information on some elements is bound to be sparse, or possibly nonexistent. For example, almost no information exists on the densities of faunal components in forested wetlands. The investigators will be forced to rely on guesses by experts (primarily Dr. George Dalrymple), who have extensive field experience in cypress swamps. Data are also scant on the faunal compartments of the mangrove estuaries. It is hoped that ongoing field surveys currently being carried out by USGS/ENP off the SW coast of Florida will be adapted to include a brief survey of faunal densities in the mangrove habitat. The richest source of data is anticipated from the gramminoid wetlands, where an ongoing "Flume Study" to quantify the effect of nutrient additions upon this ecosystem is being directed by Dr. Dan Childers of FIU under the aegis of the SFWMD. Virtually every element of the gramminoid ecosystem will be quantified as part of this research program. 5. Timeline for completion of work Work began on the forested wetlands network in June, 1996 and should conclude by the end of February, 1997. Creating the network of the Florida Bay ecosystem should be carried out through the remainder of Calendar Year 1997, and it is anticipated that the mangrove estuarine network will be completed by September, 1998. The gramminoid wetland networks will be completed last (by the end of May, 1999) so as to await the appearance of the detailled and accurate data to be acquired under the Flume Study. VII. Model Testing Procedures A. Plans for Model Rationale Justification, Calibration, and Validation of Models The ATLSS integrated model is an applied model that is designed to make predictions concerning the effects of various restoration scenarios on the biota of the Everglades/Big Cypress region. As with any other applied model, it must be decided "if the model is acceptable for its intended use, i.e., whether the model mimics the real world well enough for its stated purpose" (Loehle 1983, Giere 1991, Rykiel 1996). This decision requires that we have criteria for deciding if the model is acceptable for its intended uses and means for testing whether the model meets those criteria. The criteria for acceptability of ATLSS as a predictive model are being developed as the individual models within the integrated model reach completion. Each modeling project within ATLSS is based on empirical data and has close associations with empirical scientists. The modelers will work closely with the empirical scientists in three basic stages: model rationale justification, calibration, and validation. Model rationale justification: By model rationale justification we mean ascertaining that the assumptions concerning the basic structure of the model are shown to be correct or appropriate for the system being modeled. This includes the choice of variables and parameters, the abiotic factors included, the spatial and temporal resolution and extent, and the mechanisms and causal interactions. At present, there are some obvious problems. For example, ATLSS does not include an explicit small mammal component, only a constant background level of small mammals to supplement the diets of Florida panthers. If small mammals are crucially important as a resource for panthers, and if they are affected to a strong degree by water regulation, then it would be important to be able to model their dynamics. This and other aspects of the model structure are being reviewed through close interaction with empirical specialists in each area. Future improvements in the ATLSS integrated model are being prioritized (see section VIII). Calibration: After the basic structure of a model is formulated and justified, the process of calibration is started. Calibration is the estimation and adjustment of model parameters and constants to improve the agreement between model output and a data set (Rykiel 1996). Each of the models within ATLSS uses published empirical data and information provided directly by empirical experts on particular components of the system to estimate parameters. Calibration is normally an ongoing process. Only parts of the data available are initially used to calibrate a model, leaving some data for testing or validation. As better information becomes available and as the models are tested against data from the field, the parameters in the models can be adjusted, with the aim of improvement. Validation: After an initial calibration, the next step in the process is validation. Validation is a demonstration that a model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model (Rykiel 1996; see also other references given by Rykiel). In the current context of the ATLSS model, this means that the models within the integrated ATLSS model must be demonstrated to give quantitative predictions of data (independent of the data used for calibration) on the biotic components and processes they are intended to describe. Validation will use of both historical and currently collected data (that has not already been used for calibration) to test model predictions against. This includes time series data and spatial distribution data, because the models attempt to predict both the temporal changes in components and processes and their spatial distribution across the landscape. The validation process has not yet begun for most of the models, but is planned to begin in 1997, as many models approach completion. B. Considerations of Error Multiplication One of the points raised with respect to large, complex models is that uncertainty in parameters and initial conditions can behave multiplicatively and produce results that are highly uncertain. There are at least two ways this can happen. First, there can be many causal linkages in a model, for example several trophic levels; uncertainty in the interactions at successive levels could, in principle, multiply such that the effects of a change in the bottom trophic level could produce highly uncertain changes in the top trophic level. Second, for simulations of population size over long periods of time, uncertainty in population parameters or initial conditions can, in principle, cause much greater uncertainty in the final population prediction. While these types of error multiplication are possible under some circumstances in models, this should not be a cause for concern in the ATLSS integrated model. The ATLSS integrated model is large when considered as a whole, but only a few modules are used for making a specific predictions. In particular, the topography map, vegetation map, and hydrology model are used in each simulation, but generally there are at most a few other components interacting with the component in question. The following generalizations apply to each situation. 1) The number of causal linkages in ATLSS are generally small. Some of the biotic components are driven almost directly by abiotic factors, or at least the connection between abiotic factors and the behavior of the component is highly predictable. This includes the Cape Sable seaside sparrow and the snail kite. 2) When there is a longer series of trophic linkages, the prey base on which the higher trophic level is dependent is subject to empirical observation, so the model of this prey base can be tested and shown to be making predictions within reasonable bounds. For example, consider the chain of effects (lower trophic levels - fish - wading birds). The food capture of wading birds depends on fish production in spatial cells surrounding a breeding colony. The model predictions of both the lower trophic levels (e.g., periphyton) and fish densities in landscape cells will be rigorously tested to guarantee that they lie within reasonable bounds for a known cell hydroperiod. Therefore, in this and other simulations, there will be no unknown intermediate variables, whose uncertainty could cause multipying effects. 3) The simulations will be Monte Carlo (stochastic) simulations on a landscape. Both the stochastic nature and the landscape approach of the modeling tend to cause errors to be smoothed out rather than multiplied. For example, in the terrestrial vegetation - deer - Florida panther trophic chain, individual deer sample over a variety of spatial sites, and are not sensitive to precise biomass densities in a given cell, as long as forage in a cell is below some threshold. Thus, the precision of model estimates of forage biomass in any given cell is not critically important. Spatially local uncertainties do not propagate. Instead, the top trophic levels will be sensitive, as desired, only to the changes in hydrology that significantly affect amounts and patterns of fish production across large portions of landscape. 4) Long time scale predictions are not the focus of many models, but specific effects on parts of life cycles. For example, the wading bird model considers only breeding seasons and does not attempt to make long-term predictions for whole populations. The idea is that the effect of water regulation on the reproductive success of the wading birds is a sufficient means of assesment of the regulation scenario. In other models in which long-time population dynamics are simulated, such as the model of white- tailed deer - Florida panther, density-dependent effects that limit population size generally buffer the effects of one trophic level on another. VIII. Long-Term Plans The following sections list major ATLSS-related activities that will be pursued on a long time scale (> 2 years). A. Future Additions to the Biotic Components in ATLSS Over the long term, new biotic components included in the ATLSS/ELM integrated model. These components may include: 1) functional groups of animals that may play important ecosystem roles, but are currently omitted from ATLSS. These include the small mammal assemblage, raptorial birds, and carrion-feeding birds. 2) important insect communities that perform important ecosystem functions, such as the community of native bees. 3) important species in the food chain that may be links in bioaccumulation of pollutants (e.g., raccoons for mercury). 4) additional endangered species; e.g., brown-headed nuthatch, red-cockaded woodpecker. 5) not-native invading species; e.g., Mayan cichlid, pike killifish, Melaleuca, Brazilian pepper. 6) other species of interest; e.g., the river otter. B. Future Additions of Environmental Conditions The focus of ATLSS for the next three to four years is on the prediction of ecosystem response to changes in hydrology. However, other important environmental changes may be of similar significance. ATLSS will be applied to assess their possible effects on the biotic community. These environmental changes include: 1) sources of mercury in the environment. Food webs pathways and population-level effects can be studied using ATLSS, particularly as the higher-level consumers are modeled in an individual-based approach, which allows physiological detail. 2) global warming. Both direct physiological effects of temperature and indirect effects through hydrology and through alterations of the food web can be modeled. 3) land-use changes. ATLSS can be extended to incorporate larger areas of South and Central Florida, including agricultural areas and current buffer areas, which may undergo change. The ATLSS integrated landscape model will assess both the usefulness of various types of land to wildlife and the effects of change. 4) nutrient pollution. Together will ELM, ATLSS can investigate possible changes to Everglades fauna, as well as flora, of changed inputs of phosphorus. C. Broader Roles for ATLSS The products and experience gained from ATLSS may be useful in a broader context than outlined above. The following possible future roles are envisaged: 1) ATLSS may be useful as a template for other efforts. The approach being used in ATLSS, particularly the ATLSS integration, is new, but other regional environmental assessments are in preparation or are at least contemplated, including: a) Florida Bay and other South and Central Florida areas. It would be natural to extend the ATLSS/ELM spatial cell grid over a broader area and initiate additional relevant modeling efforts. Particularly since water and nutrient output from the Everglades flows into Florida Bay, any Florida Bay model must be connected with ATLSS/ELM. b) an NBS/USGS Joint Initiative on "Aquatic Ecosystems in the Columbia River Basin" has been submitted, with explicit requests for collaboration from ATLSS. c) a proposal on modeling the Serengeti Ecosystem has been submitted to the National Center for Ecosystem Analysis and Synthesis, with explicit request for collaboration with ATLSS. 2) ATLSS will be used to help increase the outreach of USGS to universities, by offering to share or help develop software for use in landscape modeling projects by students or faculty. 3) ATLSS will try to develop educational games from at least a few of the components of the overall ATLSS model; for example, the deer/panther model. IX. Project Titles, Principal Investigators, and Institutions "Development of Selected Model Components of an Across-Trophic- Level-Systems Simulation (ATLSS) for the Wetland Ecosystems of South Florida Co-PIs: L. J. Gross and H. A. Huston, University of Tennessee. "Computer Simulation Modeling of Intermediate Trophic Levels for Across Trophic Level System Simulation of the Everglades/Big Cypress Region" Co-PIs: Michael S. Gaines, University of Miami, Donald L. DeAngelis, Biological Resources Division, USGS, Wilfried Wolff, University of Miami Contractor (snail kite model): Wolf M. Mooij, Netherlands Institute of Ecology "Cape Sable Seaside Sparrow Population Study" PI: Stuart L. Pimm, University of Tennessee "Population Size and Life History Parameters of the Everglades Crayfish (Procambarus alleni [Faxon])" Co-PIs: C. E. Grue and D. Armstrong, The University of Washington. "Experimental studies of population growth and predator-prey interactions of fishes in the Everglades National Park" PI: Joel C. Trexler, Florida International University "Population structure and spatial delineation of consumer communities in the Everglades National Park" PI: Joel C. Trexler, Florida International University "Development of Trophic Models for Amphibians and Reptiles in Southern Florida" George Dalrymple, Everglades Research Group, Inc. "Dietary Studies on the American Alligator in Southern Florida" PI: Julian C. Lee, University of Miami "The Effect of Everglades Food Items (Prey) on Crocodilian Growth, Development, and Fertility PI: Paul T. Cardeilhac, University of Florida "American Alligator Distribution, Thermoregulation, and Biotic Potential Relative to Hydroperiod in the Everglades National Park" PI: H. Franklin Percival, University of Florida "Population Studies of the Snail Kite" Wiley F. Kitchens, Biological Resources Division, USGS "Integration and Development of a Graphical User Interface for the Across-Trophic-Level-System Simulation (ATLSS)" CO-PIs: J. G. Sanderson, P. Fishwick, and L. D. Harris, Unive rsity of Flori da. "Network Analysis of Trophic Dynamics in South Florida Ecosystems". PI: R. E. Ulanowicz, University of Maryland. X. Funding Agencies Principal Funding Agencies Environmental Protection Agency National Park Service South Florida Water Management District U. S. Army Corps of Engineers U. S. Fish and Wildlife Service U. S. Geological Survey Other Organizations that Have Provided Funding The following organizations either 1) provided partial funding for certain projects before full funding began in 1995 or 2) provided funding for other projects coordinated with ATLSS modeling projects. National Science Foundation Oak Ridge National Laboratory University of Tennessee XI. ATLSS Publications General Fleming, D. M., D. L. DeAngelis, L. J. Gross, R. E. Ulanowicz, W. F. Wolff, W. F. Loftus, and M. A. Huston. 1994. ATLSS: Across-Trophic-Level System Simulation for the Freshwater Wetlands of the Everglades and Big Cypress Swamp. National Biological Service Technical Report. DeAngelis, D. L., D. M. Fleming, L. J. Gross, M. A. Huston, J. G. Sanderson, R. E. Ulanowicz, and W. F. Wolff. 1996. Multimodeling and across-scales ecosystem modeling. Submitted to Ecology. Fleming, D. M., D. L. DeAngelis, and W. F. Wolff. 1996. The importance of landscape in ecosystem integrity: the example of Everglades restoration efforts. J. Lemmons and L. Westra (eds.), Perspectives on Ecological Integrity. University of New England Press. Wading Bird Component: Fleming, D.M., W. F. Wolff, and D. L. DeAngelis. 1994. The importance of landscape heterogeneity to a colonial wading bird in the Florida Everglades. Environmental Management 18:743-757. Wolff, W. F. 1994. An individual-oriented model of a wading bird nesting colony. Ecological Modelling 72:75-114. Deer/Panther Component: Abbott, Catherine Ann M.S. (Computer Science) A Parallel Individual-Based Model of White-Tailed Deer in the Florida Everglades. M.S. Thesis. University of Tennessee, Department of Computer Science. August 1995. Abbott, C. A., M.W. Berry, E. J. Comiskey, L. J. Gross and H.-K. Luh. Computational models of white-tailed deer in the Florida Everglades. IEEE Computational Science and Engineering (to appear) Comiskey, E. J., L. J. Gross, D. M. Fleming, M. A. Huston, O. L. Bass, H.-K. Luh and Y. Wu. (1996) A spatially-explicit individual-based simulation model for Florida panther and white-tailed deer in the Everglades and Big Cypress landscapes. To appear in Florida Panther Proceedings Volume, U.S. Fish and Wildlife Service. Luh, H.-K., C. Abbott, M. Berry, E.J. Comiskey, J. Dempsey, and L. J. Gross. Parallelization in a spatially-explicit individual-based model (I) Spatial data interpolation. Computers and Geosciences (to appear). Lower Trophic Level Component: Ulanowicz, R.E. 1995. Modeling lower trophic components of the Everglades ecosystem. Chesapeake Biological Laboratory, Solomons, Maryland. Fish Component: DeAngelis, D. L., W. F. Loftus, J. C. Trexler, and R. E. Ulanowicz. Modeling fish dynamics and effects of stress in a hydrologically pulsed ecosystem. (in press, to Journal of Aquatic Ecosystem Stress and Recovery). Snail Kite Component: Mooij, W. M., and D. L. DeAngelis. A new approach to the analysis of the population growth rate of the endangered Everglades snail kite (Rostrhamus sociabilis) in relation to environmental conditions. (Submitted to Ecological Applications) Cape Sable Seaside Sparrow Component: Curnutt, J. L., A. L. Mayer, M. P. Nott, O. L. Bass, D. M. Fleming, S. Killifer, N. Fraley and S. L. Pimm. 1996. The Cape Sable seaside sparrow : an Endangered bird in a vulnerable landscape. in press. Nott, M. P., O. L. Bass, D.M. Fleming, and S.L.Pimm. Hydrologic regimes rapidly change vegetational landscape and temporal breeding patterns for an endangered bird species. (Submitted). ATLSS Integration: Fishwick, P. A. and J.G. Sanderson. 1995. Interface Specifications for Across-Trophic-Level system simulation (ATLSS) for the Everglades and Big Cypress Swamp. Fishwick, P. A., J. G. Sanderson, and W. F. Wolff. A multimodeling basis for across-trophic-level ecosystem modeling: The Florida Everglades example. (Submitted) XII. Other References Baird, D. and R. Ulanowicz. 1989. The seasonal dynamics of the Chesapeake Bay ecosystem. Ecol. Monogr. 59: 329-364. Bass, O. L., Jr., and J. A. Kushlan. 1982. Status of the Cape Sable Sparrow. Report T-672, South Fla. Res. Ctr., Everglades National Park. Homestead, Fla. 41 pp. Beecher, W.J. 1955. Late-Pleistocene isolation in salt-marsh sparrows. Ecology 36:23-28. Beissinger, S.R. 1995. Modeling extinction in periodic environments: Everglades water levels and Snail Kite population viability. 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Florida/Caribbean Research Center. Ref. No. [UMCEES]CBL 94-129, Chesapeake Biological Laboratory, Solomons, MD. Ulanowicz, R.E. 1995a. Modeling lower trophic components of the Everglades ecosystem. Final Report to NBS S. Florida/Caribbean Research Center. Ref. No. [UMCEES]CBL 95-203, Chesapeake Biological Laboratory, Solomons, MD. Ulanowicz, R.E. 1995b. Ecosystem trophic foundations: Lindeman Exonerata. pp.549- 560. In: (B.C. Patten and S.E. Jorgensen Eds.). Complex Ecology: The Part-Whole Relation in Ecosystems. Prentice-Hall, NY. Weaver, J. et al. 1993. Federal objectives for the South Florida restoration. Prepared by the Science SubGroup of the South Florida Management and Coordination Working Group. Werner, H. W. 1975. The biology of the Cape Sable Sparrow. Report to U. S. Fish and Wildlife Service, Frank M. Chapman Memorial Fund, The International Council for Bird Preservation and U.S. National Park Service, Homestead, Fl. 215 pp. Wu, Y., Sklar, F., Gopu, K., and Rutchey, K. Fire modeling in the Everglades landscape using parallel programming. Ecological Modeling, submitted. XIII. ATLSS Presentations 8/93 Madison, WI. Annual Meeting of the Ecological Society of America. SIMDEL: A Spatially explicit, individual-based model for white-tailed deer in the Everglades landscape. Y. Wu*, D. DeAngelis, L. Gross and D. M. Fleming 4/94 Raleigh, NC. SE Math Ecology Meeting. Across trophic level system simulation for the Everglades. D.L. DeAngelis, D.M. Fleming, L. J. Gross* 6/94 Knoxville, TN. Annual Meeting of the Ecological Society of America. ATLSS: Across trophic level system simulation for the freshwater areas of the Everglades. D.L. DeAngelis, D.M. Fleming, L. J. Gross 6/94 Knoxville, TN. Annual Meeting of the International Society for Ecological Modeling. SIMPDEL: A spatially explicit individual-based model for white-tailed deer and Florida panther on the Everglades landscape. Y. Wu, L. J. Gross, D. M. Fleming, J. Comiskey*, and H-K. Luh. 11/94 Trieste, Italy. Fourth Autumn Course on Mathematical Ecology. Modeling across scales from behavior to landscape: the Everglades example. L. J. Gross* 11/94 Ft. Myers, FL. Florida Panther Conference. Sponsored by: Florida Panther Interagency Committee. A Spatially-explicit Individual-based Simulation Model for Florida Panther and White-tailed Deer in the Everglades and Big Cypress Landscapes. J. Comiskey*, H-K. Luh, L. J. Gross, M. A. Huston, D. M. Fleming, O. L. Bass, Y. Wu. 3/95 Knoxville, TN. UTK Mathematics Department Colloquium. The Everglades: Modeling, Mathematical and Computational Problems arising in Across Trophic-Level System Simulation. L. J. Gross* 5/95 Nieuwersluis, The Netherlands. Netherlands Institute of Ecology, Centre for Limnology. The Everglades: An ecosystem in restoration. W. J. Mooij. 8/95 Logan, UT. Conference on Mathematical Models in Population Dynamics. ATLSS: Across Trophic Level System Simulation for the Freshwater Areas of the Everglades. D.L. DeAngelis, D.M. Fleming, L. J. Gross* 10/95 Charlotte, NC. Annual Meeting of SIAM. Modeling the Everglades: Integrating Alternative Methodologies across Scales. L. J. Gross*, D. L. DeAngelis and D. M. Fleming. 11/95 Cambridge, MD. Horn Point Environmental Laboratory, University of Maryland. Modeling the Everglades Ecosystem. D. L. DeAngelis. 2/96 Santa Barbara, CA. Opening of National Center for Ecological Analysis and Synthesis. Modeling the effects of hydrology on the Everglades biotic community. D. L. DeAngelis*, D. M. Fleming, L. J. Gross, W. F. Wolff, and R. E. Ulanowicz. 4/96 Orlando, FL. SETAC Meeting. Using Across Trophic Level Simulation Methods to Assess Biotic Impacts of Water Management in South Florida: Applying an Individual-Based Deer/Florida Panther Model. L. J. Gross* 5/96 Lubbock, TX. Texas Tech University Math/Biology Colloquium. The Everglades: Modeling, Mathematical and Computational Problems arising in Across Trophic-Level System Simulation. L. J. Gross* 8/96 Providence, RI. Annual Meeting of the Ecological Society of America. Modern computation and conservation biology: giving theory some muscle. L. J. Gross* and M. A. Huston. 8/96 Providence, RI. Annual Meeting of the International Society for Ecological Modeling. Multimodeling for South Florida Wetland Ecosystems. M. A. Huston* and L. J. Gross 9/96 Nieuwersluis, The Netherlands. Netherlands Institute of Ecology, Centre for Limnology. On ecosystems and dice: Snail kites in the casino. W. J. Mooij. 10/96 Trieste, Italy. Third Autumn Workshop on Mathematical Ecology. Modeling the Everglades: Integrating Alternative Methodologies across Scales. L. J. Gross*, D. A. DeAngelis and D. M. Fleming. 11/96 Saving the Everglades: Applications of Ecological Modeling to the South Florida Restoration Project. Univerity of Wisconsin, Milwaukee, WI. M. A. Huston* 10/96 Miami, FL. Florida International University (Invited Talk) Considerations of Scale in Modeling the Everglades Ecosystem. D. L. DeAngelis XIV. ATLSS Home Page Information A Home Page for ATLSS that includes detailed information on the white-tailed deer/Florida panther model is: http://www.tiem.utk.edu/~gross/atlss_www/atlss_frame.html A Home Page for ATLSS that contains details of the the ATLSS integration is: http://www0.cise.ufl.edu/~jgs/ATLSS/atlss.html Table 1. Some definitions of terms used in this report Functional groups Functional groups are sets of species that play similar roles in an ecosystem, and so can be lumped together as one component for convenience. Module A module will be defined here to be a component of a larger model. Modules within the ATLSS integrated model may include the various map layers (e.g., vegetation, soil, topography, land use), various models for abiotic variables and processes (e.g., temperature, dissolved oxygen, water depth), and models of biotic components (e.g., fish functional groups, snail kites, alligators) Individual-based models These are simulation models for populations or assemblages of interacting populations in which each organism in a population is modeled individually. The characteristics of each organism (age, size, sex, spatial location, condition, social status, experience, knowledge, etc.) constitute the set of variables of the system. Actions of individuals are usually determined according to a set of rules. Results of actions have a stochastic element and Monte Carlo methods are used to perform the simulations. Ecosystem process models These are models that describe particular biotic or abiotic processes at the ecosystem level, such as primary production, secondary production, decomposition and energy and nutrient fluxes. The aim of these models is to predict how environmental conditions (temperature, water availability, salinity, nutrient loading, etc.) affect these processes). Age- and size-structure models Populations or functional groups in these models are described at the level of age- and/or size-classes. Spatially-explicit models These are models that are placed in an explicit spatial setting, and can include abiotic or biotic process models, age-structure models of populations, or individual-based models. This is usually done by defining a grid of spatial cells with a fixed resolution (e.g., 500 x 500 meters). The abiotic or biotic quantity of interest is modeled in each spatial cell, as well as movements between cells. Figure Legends FIGURE I.1 - Schematic of the models included in the ATLSS integrated model, including process models, age-structured models, and individual-based models, and including inputs from hydrologic models and GIS landscape maps. Figure I.2 - Schematic showing the role of ATLSS modeling in the integrated scientific support for the Central and South Florida restoration effort. This figure portrays the original schedule for delivery of integrated scientific advice, which has now been accelerated. Figure I.3 - Geographic scope of the ATLSS model Figure I.4 - Proposed procedure for using modeling a part of an adaptive management scheme for the Central and South Florida restoration program. Figure Legends Figure V.D.1 - A schematic of the trophic relationships among the five compartments of the LTL model for the gramminoid wetlands. Dotted lines indicate trophic exchanges not included in the initial calibration process. Dotted arrows not ending in another box indicate transfers to higher trophic level modules. Figure V.D.2 - Test of the LTL model for the short hydroperiod gramminoid wetlands. Simulation is of system recovery from drydown commencing ca.julian day 120. Jagged line connects data points that were not used in the calibration of the model. Heavy black line is model output. Figure Legends Figure V.E.1 - Schematic diagram of trophic interactions in fish model Figure V.E.2 - Six assumed intra-annual water depth scenarios for the spatial cell. Curves e and f represent cases in which the cell is flooded continuously, though at changing depths. Figure V.E.3 - Number densities (number/square meter) of the small functional type fish simulated through the year. For each of the six intra-annual hydrologic scenarios shown in Figure V.E.2. The plots (a), (b), (c), (d), (e), and (f) correspond to the small fish type densities for the similarly labelled curves in Figure V.E.2. Figure Legends Figure V.F.1 - Conceptual model for predictive simulation of the herpetological assemblage Figure V.F.2 - Herpetological food web in (a) marsh and (2) prairie habitats Figure V.F.2 - Herpetological food web in (c) upland habitat Figure Legends Figure V.J.1 - Schematic flow chart of the snail kite model, indicating the main demographic (survival and breeding) and behavioral (movement) processes. Figure V.J.2 - Observed and modeled number of snail kites from 1969 to 1993. Observed numbers (solid line) refer to the annual November/December count. The first of the two modelled lines (dashed with open squares) refers to the simple multiplicative simulation model, which relates changes in kites number to the hydrological state of the system. The second of the two modeled lines (dashed line with asterixes) predicts on basis of the same model the number of kites after 1987, would the hypothesized catastrophic event in that year not taken place. Figure V.J.3 - Timeline for the development of the snail kite model. The table indicates when the six phases in the model development will take place and also gives the time investment per phase. Figure Legends Figure V.K.1 - Schematic diagram of white-tailed deer component of deer/panther model Figure V.K.2 - Schematic diagram of Florida panther component of deer/panther model Figure V.K.3 - Model predictions of spatial distributions of deer through time under hypothetical conditions. (No resemblance to real distributions is attributed to these results at this preliminary stage of the model development.) Figure V.K.4 - Model predictions of locations of Florida panthers through time under hypothetical conditions. (No resemblance to real distributions is attributed to these results at this preliminary stage of the model development.) Figure Legends Figure VI.E.1 - An example of a simple ecosystem trophic network. Boxes represent the 17 components of a Crystal River (Florida) marsh gut ecosystem. Arrows indicate transfers of carbon in mgC/m2/d. Ground symbols represent respirations, and "linked" arrows are used to simplify the returns to the detrital compartment. (After Mark Homer, W. Michael Kemp and Hank McKellar, unpublished manuscript, ca. 1976) Figure Legends Figure V.I.1 - Geographical ranges of six Cape Sable seaside sparrow sub-populations (B), located within marl prairies of the southern Everglades. The rectangle encapsulating sub-population A represents the extent of the hydrological sub-model. Figure V.I.2 - General flow chart for the Cape Sable seaside sparrow individual based model. Figure V.I.3 - Diagrammatic representation of sparrow movements on a representative landscape: a) Bachelor male encounters 'reflective' habitat cells. b) Male crosses 'transparent' sawgrass cells and establishes territory. c) Unpaired female encounters transparent and reflective cells. d) Two males dispute over territory, one moves on. e) Female chooses from three established territories and mates. Table V.I.1. Ecological parameter values for Cape Sable seaside sparrow individual-based model. (Sources: Lockwood et al. 1996, except 1Pimm et al. 1996).