|projects > across trophic level system simulation program for the everglades/big cypress region > work plan
U.S. Geological Survey, Greater Everglades Priority Ecosystems Science (GE PES)
Fiscal Year 2006 Study Work Plan
Project Start Date: 2004 Project End Date: Ongoing
Web Site: atlss.org, sofia.usgs.gov
Location (Subregions, Counties, Park or Refuge): Total System
Funding Source: USGS Greater Everglades Priority Ecosystems Science (GE PES), ENP CESI
Other Complementary Funding Source(s): CESI, NSF.
Funding History: FY04, FY05, FY06
Principal Investigator(s): Donald L. DeAngelis
Email address: email@example.com
Mail address: Department of Biology, University of Miami, P. O. Box 249118, Coral Gables, Florida 33124
Supporting Organizations: University of Florida, University of Tennessee, University of Miami
Other Investigator(s): Dr. Lou Gross
Other Investigator(s): Dr. Jimmy Johnston
Other Investigator(s): Kenneth G. Rice, USGS; Frank J. Mazzotti, University of Florida
Overview and Objectives: An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS's ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers.
ATLSS (Across Trophic Level System Simulation) program addresses CERP's need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations.
ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. This landscape modeling approach is the work of USGS scientists and collaborators from several universities.
The South Florida Water Management Model provides hydrology for ATLSS models at a 2 x 2 mile spatial resolution. The ATLSS multimodeling approach starts with models that translate this coarse-scale hydrology output to a finer resolution appropriate for biotic components. This is achieved through use of GIS vegetation maps and empirical information relating hydroperiods with vegetation types, to develop an approximate hydrology at 500 x 500 m resolution from the 2 x 2 mile hydrology model.
The simplest ecological models in the ATLSS family are the Spatially Explicit Species Index (SESI) models, which compute indices for breeding or foraging potential for key species. These models use the fine resolution hydrology output, combining several attributes of hydrology that are relevant to the well-being of particular species to derive an index value for every 500 x 500 spatial cell in the landscape. This can be done for hydrology data for any given year under any alternative water management scenario. SESI models have been constructed and applied during the Central and Southern Florida Comprehensive Review Study (Restudy) to the Cape Sable seaside sparrow, the snail kite, short and long legged wading birds, the white tailed deer, the American alligator, two species of crayfish, and the Florida panther.
A considerably more spatially explicit simulation model, ALFISH, has been developed for the distribution of functional groups of fish across the freshwater landscape. This model considers the size distribution of large and small fish as important to the basic food chain that supports wading birds. It has been applied to assess the spatial and temporal distribution of availability of fish prey for wading birds. This simulation modeling approach is being extended to crayfish.
Spatially explicit individual based (SEIB) models, which track the behavior, growth and reproduction of individual organisms across the landscape, have been constructed for the Cape Sable seaside sparrow (SIMSPAR), the snail kite (EVERKITE), the white tailed deer (SIMDEL), the Florida panther, the American crocodile (CROCMOD), and various wading bird species. The models include great mechanistic detail on the behavioral and physiological aspects of these species. An advantage of these detailed models is that they link each individual animal to specific environmental conditions on the landscape. These conditions (e.g., water depth, food availability) can change dramatically through time and from one location to another, and determine when and where particular species will be able to survive and reproduce. ATLSS models have been developed and tested in close collaboration with field ecologists who have years of experience and data from working with the major animal species of South Florida.
The ATLSS integrated suite of models has been used extensively in Everglades Restoration planning. Restoration goals include recovery of unique Everglades species, including snail kites and Florida panthers. The quantity, quality, timing, and distribution of deliveries of water to the Greater Everglades are keys to the restoration of natural functions. The challenge is to provide the hydrologic conditions needed by communities of plants and animals, while maintaining water supplies and flood control for a large and expanding human population. The role of USGS's ATLSS Program is to predict the effects of changes in water management on Greater Everglades species and biological communities, as an aid to identifying and selecting those changes most effective for the restoration effort.
To date, the focus of ATLSS to date has been on the freshwater systems, with emphasis on the intermediate and upper trophic levels. ATLSS will be extended estuarine and near shore dynamic models once physical system models for these regions are completed. Modeling of the mangrove vegetative community and estuarine fish is now underway.
There are four tasks in this project. The first (DeAngelis) involves the coordination of the other tasks. The second task (Gross) involves the development and running of the ATLSS computer simulation models. The third task (Rice) involves developing restoration success indicators for the amphibian community. The fourth task (Johnston) involves upgrading of a ATLSS Data Visualization system.
Specific Relevance to Major Unanswered Questions and Information Needs Identified:
Many of the ATLSS models were used during scenario evaluation (1997-99). In this process, hydrology model output for scenarios was sent from the SFWMD to the U. of Tennessee. Hydrology output was used to drive the following ATLSS models: SESI models: Cape Sable seaside sparrow, snail kite, American alligator, long- and short-legged wading birds, white-tailed deer. SEIB model: Cape Sable seaside sparrow (SIMSPAR). Spatially explicit number/biomass density model: Freshwater fish (ALFISH). ATLSS output was sent to the Alternative Evaluation Team (AET), composed of representatives of agencies in South Florida, and used extensively in its evaluations and recommendations.
ATLSS models will continue to be used for scenario evaluations for the Comprehensive Everglades Restoration Plan.
Al-Rabai'ah, H. A.-K., H.-L. Koh, D. L. DeAngelis, and H.-L. Lee. 2002. Modeling fish community dynamics in Florida Everglades: Role of temperature variation. Water Science and Technology 46(9).
Al-Rabai'ah, Hussam, H.- L. Koh, D. L. DeAngelis, H.-L. Lee, Modeling long-term effects of PCBs on the Everglades fish communities. (In press, Wetland Ecology and Management)
Basset, A., M. Fedele, and D. L. DeAngelis. 2002. Optimal exploitation of spatially distributed trophic resources and population stability. Ecological Modelling 151:245-260.
Binshamlan, M., H.-L. Koh, L.-H. Lee, and D. L. DeAngelis. 2005. Modeling bioaccumulation of mercury in the Everglades fishes. Proceedings of International Conference on Reservoir Operation and River Management, Guangzhou & Three Gorges, China, September 17-23, 2005. Published in: Advances in Reservoir Operation and River Management (Yangbo Chen, ed.)
Comiskey, E. J., O. L. Bass, Jr., L. J. Gross, R. T. McBride, and R. Salinas. 2002. Panthers and forests in South Florida: an ecological perspective. Conservation Ecology 6(1): 18. [online] URL: http://www.consecol.org/vol6/iss1/art18
Comiskey, E. J., A. C. Eller, Jr., and D. W. Perkins. 2004. Evaluating impacts to Florida panther habitat: how porous is the umbrella. Southeastern Naturalist. 3(1):51-74.
Curnutt, J. L., E.J. Comiskey, M. P. Nott and L. J. Gross. 2000. Landscape-based spatially explicit species index models for Everglades restoration. Ecological Applications 10:1849-1860.
DeAngelis, D. L., L. J. Gross, W. F. Wolff, D. M. Fleming, M. P. Nott and E. J. Comiskey. 2000. Individual-based models on the landscape: applications to the Everglades. P. 199-211 in J. Sanderson and L. D. Harris (eds.), Landscape Ecology: A Top-Down Approach. Lewis Publishers, Boca Raton, FL.
DeAngelis, D. L., W. M. Mooij, M. P. Nott, and R. E. Bennetts. 2001. Individual-based models: The importance of variability among individuals. Pages 171-195. In: A. Franklin and T. Schenk (editors), Modeling in Natural Resource Management: Development, Interpretation, and Application. Island Press, Covello, California.
DeAngelis, D. L., and J. H. Petersen. 2001. Importance of the predator's ecological neighborhood in modeling predation on migrating prey. Oikos 94:315-325
DeAngelis, D. L. 2002. Community Food Webs. Pages 368-371. In: A. H. El-Shaarawi- and W. W. Piegorsch (eds.), Encyclopedia of Environmetrics, Vol. 1. John Wiley & Sons. Ltd., Chichester, UK.
DeAngelis, D. L., S. Bellmund, W. M. Mooij, M. P. Nott, E. J. Comiskey, L. J. Gross, M. A. Huston and W. F. Wolff. 2002. Modeling Ecosystem and Population Dynamics on the South Florida Hydroscape. P. 239-258 in: The Everglades, Florida Bay, and Coral Reefs of the Florida Keys: An Ecosystem Sourcebook, J. W. Porter and K. G. Porter (eds.). CRC Press, FL.
DeAngelis, D. L., and J. L. Curnutt. 2002. Integration of population, community, and landscape indicators for assessing effects of stressors. Page 509-532. In: (S. Marshall Adams, editor), Biological Indicators of Stress in Fish (2nd edition). American Fisheries Society, Bethesda, Maryland
DeAngelis, D. L. 2003. Mathematical modeling relevant to closed artificial ecosystems. Advances in Space Research 31(7):1657-1665.
DeAngelis, D. L., L. J. Gross, E. J. Comiskey, W. M. Mooij, and M. P. Nott. 2003. The Use of Models for a Multi-Scaled Ecological Monitoring System. In: David E. Busch and Joel C. Trexler (eds.), Interdisciplinary Approaches to Ecological Monitoring of Ecosystem Initiatives. Island Press.
DeAngelis, D. L. and W.M. Mooij. 2003. In praise of mechanistically-rich models. Pp. 63-82, in C. D. Canham, J. J. Cole, and W. K. Lauenroth (eds.) Models in Ecosystem Science. Princeton University Press, Princeton, New Jersey.
DeAngelis, D. L., W. M. Mooij, and A. Basset. 2003. The importance of spatial scale in the modeling of aquatic ecosystems. Chapter 24. In: L. Seuront and P. G. Strutton (eds.), Handbook of Scaling Methods in Aquatic Ecology: Measurement, Analyses, Simulation. CRC Press, Boca Raton, FL.
DeAngelis, D. L., and P. J. Mulholland. 2004. Dynamic consequences of allochthonous nutrient input into freshwater systems. Pages 12-24, In: G. A. Polis, M. E. Power, and G. R. Huxel (eds.), Food Webs at the Landscape Level. University of Chicago Press.
DeAngelis, D. L., and W. M. Mooij. 2005. Individual-based modeling of ecological and evolutionary processes. Annual Reviews of Ecology and Evolutionary Systematics 36
DeAngelis, D. L., J. C. Trexler, and W. F. Loftus. 2005. Life history trade-offs and community dynamics of small fishes in a seasonally pulsed wetland. Canadian Journal of Fisheries and Aquatic Sciences 62:781-790.
DeAngelis, D. L., and J. N. Holland. Emergence of ratio-dependent and predator-dependent functional responses for pollination mutualism and seed parasitism. Ecological Modelling (in press)
DeAngelis, D. L, M. Vos, W. M. Mooij, and P. A. Abrams. Feedback effects between the food chain and induced defense strategies. In: From Energetics to Ecosystems: The Dynamics and Structure of Ecological Systems N. Rooney, K. McCann and D. Noakes (eds). Springer-Verlag (in press.)
Diffendorfer, J. E., P. S. Richards, G. H. Dalrymple, and D. L. DeAngelis. 2001. Applying linear programming to estimate fluxes in ecosystems or food webs: An example from the herpetological assemblage of the freshwater Everglades. Ecological Modelling 144 (2-3):99-120.
Dong, Q., P. V. McCormick, F. H. Sklar, and D. L. DeAngelis. 2002. Structural instability, multiple stable states, and hysteresis in periphyton driven by phosphorus enrichment in the Everglades. Theoretical Population Biology 61:1-13.
Dreitz, V. J., J. D. Nichols, J. E. Hines, R. E. Bennetts, W.M. Kitchens, and Donald L. DeAngelis. 2002. The use of resighting data to estimate the rate of population growth of the snail kite in Florida. Journal of Applied Statistics 29: 609-623
Dreitz, V. J., W. M. Kitchens, and D. L. DeAngelis. 2004. The effects of natal departure and water level on survival of juvenile snail kites in Florida. The Auk 121:894-903.
Duarte, C. M., J. Amthor, D. DeAngelis, L. A. Joyce, R. Maranger, M. L. Pace, J. Pastor, and S. Running. 2003. Pp. 437-451, in C. D. Canham, J. J. Cole, and W. K. Lauenroth (eds.) Models in Ecosystem Science. Princeton University Press, Princeton, New Jersey.
Duke-Sylvester, S. and L. J. Gross. 2002. Integrating spatial data into an agent-based modeling system: ideas and lessons from the development of the Across Trophic Level System Simulation (ATLSS). Chapter 6 in: Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Stimulating Social and Ecological Processes, H. R. Gimblett (ed.), Oxford University Press.
Duke-Sylvester, S. M. 2004. LygoMod : A model for the spread and optimal treatment of Lygodium microphyllum in the Arthur R. Marshall Loxahatchee National Wildlife Refuge; version 1.0.. Delivered to U.S. Fish and Wildlife Service staff at the A.R.M. Loxahatchee NWR, August 2004.
Duke-Sylvester, S. M. 2004. The ATLSS High Resolution Multi-Data Source Topography (HMDT). http://www.atlss.org/~sylv/HTML/Everglades/HMDT-ShortReport/index.html
Duke-Sylvester, S. M. and E. J. Comiskey. 2004. Comparison of Calibration/Verification v5.0 to Calibration/Validation v3.4. http://www.atlss.org/~sylv/HTML/Everglades/HydroComparison/index.html
Fa, J. E., C. M. Sharples, D. J. Bell, and D. L. DeAngelis. 2001. An individual-based model of rabbit viral haemorrhagic disease on European rabbit (Oryctolagus cuniculus) populations. Ecological Modelling 144:121-138
Gaff, H., D. L. DeAngelis, L. J. Gross, R. Salinas and M. Shorrosh. (2000) A dynamic landscape model for fish in the Everglades and its application to restoration. Ecological Modelling 127:33-52.
Gaff, H., J. Chick, J. Trexler, D. DeAngelis, L. Gross, and R. Salinas. 2004. Evaluation of and insights from ALFISH: a spatially explicit landscape-level simulation of fish populations in the Everglades. Hydrobiologia 520:73-86.
Gentile, J. H., M. A. Harwell, W. Cropper, Jr., C. C. Harwell, D. DeAngelis, S. Davis, J. C. Ogden, and D. Lirman. 2001. Ecological conceptual models: a framework and case study on ecosystem management for South Florida sustainability. The Science of the Total Environment 274:231-253.
Grimm, V, E.. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H.-H. Thulke, J. Weiner, T. Wiegand, and D. L. DeAngelis. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science (in press).
Gross, L. J. and D. L. DeAngelis. 2002. Multimodeling: new approaches for linking ecological models. Chapter 40, pp. 467-474, in Predicting Species Occurrences: Issues of Scale and Accuracy, (Scott, J. M., P. J. Heglund, M. Morrison, M. Raphael, J. Haufler, B. Wall, editors). Island Press, Covello, CA.
Holland, J. N., and D. L. DeAngelis. 2001. Population dynamics and the ecological stability of obligate pollination mutualisms. Oecologia:126:575-586.
Holland, J. N., and D. L. DeAngelis. 2002. Ecological conditions for fruit abortion to regulate pollinator/seed-predators and increase plant reproduction. Theoretical Population Biology 61(3): 251-263.
Holland, J. N., D. L. DeAngelis, and J. L. Bronstein. 2002. Population dynamics and mutualism: Functional responses of benefits and costs. The American Naturalist 159:231-244.
Holland, J. N. and D. L. DeAngelis. 2004. Evolutionary stability of mutualism: interspecific population regulation as an evolutionarily stable strategy. Proceedings of the Royal Society of London B 271:1807-1814.
Immanuel, A., M. W. Berry, L. J. Gross, M. Palmer, and D. Wang. 2004. A parallel implementation of ALFISH: simulating hydrological compartmentalization effects on fish dynamics in the Florida Everglades. Simulation Theory and Practice (to appear).
Jost, C., C. Rhodes, F. Campolongo, W. van de Bund, S. Hill, and D. L. DeAngelis. 2004. The effects of mixotrophy on the stability and dynamics of a simple planktonic food web. Theoretical Population Biology 66(1):37-51.
Kitchens, W. M., Bennetts, R. E., DeAngelis, D. L. 2002. Linkages between the snail kite population and wetland dynamics in a highly fragmented South Florida landscape. Pages 183-204. In: Porter, J. W., and Porter, K. G., (Eds.), The Everglades, Florida Bay and Coral Reefs of the Florida Keys: An Ecosystem Sourcebook, CRC Press, Delray Beach, FL, pp. 183-204.
Matsinos, Y. G., W. F. Wolff, and D. L. DeAngelis. 2000. Can individual-based models yield a better assessment of population viability? Pages 188-198. In: S. Ferson and M. Burgman (eds.), Quantitative Methods in Conservation Biology. Springer-Verlag, New York.
Mooij, W. M., and D. L. DeAngelis. 1999. Individual-based modelling as an integrative approach in theoretical and applied population dynamics and food web studies. Pages 551-575. In: H. Olff, V. K. Brown, and R. H. Drent. Herbivores Between Plants and Predators: 38th Symposium Volume of the British Ecological Society.
Mooij, W. M., and D. L. DeAngelis. 1999. Error propagation in spatially explicit population models: A reassessment. Conservation Biology 13:930-933.
Mooij, W. M., R. E. Bennetts, W. M. Kitchens, and D. L. DeAngelis. 2002. Exploring the effect of drought extent and interval on the Florida snail kite: Interplay between spatial and temporal scales. Ecological Modelling 149:25-39.
Mooij, W. M., and D. L. DeAngelis. 2003. Uncertainty in spatially explicit animal dispersal models. Ecological Applications 13:794-805.
Mooij, W. M. , J. Martin, W. M. Kitchens, and D. L. DeAngelis. Exploring the Temporal Effects of Seasonal Water Availability On the Snail Kite of Florida. In: John Bissonette and Ilse Storch (eds.), Temporal Variability and Ecological Populations, Springer-Verlag. (Invited paper has been submitted, September 2005).
Petersen, J. H., D. L. DeAngelis, and C. P. Paukert. Developing bioenergetics and life history models for rare and endangered species. Submitted to the Transactions of the American Fisheries Society.
Richards, P. M., W. M. Mooij, and D. L. DeAngelis. 2004. Evaluating the effect of salinity on a simulated American Crocodile (Crocodylus acutus) population with applications to conservation and Everglades restoration. Ecological Modelling 180:371-394.
Richards, P. M. Evaluating the relative effects of life history stages in the conservation of the American crocodile (Crocodylus acutus) in Florida. Florida Scientist 66(4):273-286.
Richards, P. M. 2003. The American crocodile (Crocodylus acutus) in Florida: Conservation issues and population modeling. Ph. D. Dissertation, Department of Biology, University of Miami, Coral Gables, FL
Sternberg, L., and D. L. DeAngelis. 2002. Carbon isotope composition of ambient CO2 and recycling: a matrix simulation model. Ecological Modelling 154:179-192.
Sternberg, L. D. L. DeAngelis, S. Ewe, and F. Wilhelm-Miralles. A dynamic model of ecosystem shifts at the saline/freshwater vegetation ecotone. (Submitted to Ecosystems).
Trexler, J. C., and D. L. DeAngelis. 2003. Resource allocation in offspring provisioning: An evaluation of the conditions favoring the evolution of matrotrophy. The American Naturalist 162:574-585.
Vanni, M. J., D. L. DeAngelis, D. E. Schindler, and G. R. Huxel. 2004. Introduction: Cross-habitat flux of nutrients and detritus. Page 3-11, In: G. A. Polis, M. E. Power, and G. R. Huxel. (eds.) Food Webs at the Landscape Level.
Vos, Matthijs, B. W. Kooi, D. L. DeAngelis, and W. M. Mooij. 2004. Inducible defences and the paradox of enrichment. Oikos 105:471-480.
Vos, M., B. W. Kooi, D. L. DeAngelis and W. M. Mooij. 2005. Inducible defenses in food webs. Chapter XX, in "Dynamic Food Webs". P. de Ruiter, V. Wolters and J. Moore (eds.) Elsevier.
Wang, D., M. Berry, E. Carr and L. Gross. 2003. Design and Implementation of a Parallel Fish Model for South Florida. Proceedings of the 37th Hawaii International Conference on System Sciences.
Wang, D., L. Gross and M. Berry. 2003. Parallel Landscape Fish Model for South Florida Ecosystem Simulation. Proceedings of SuperComputing2003, extended abstract.
Wetzel, P. R. 2001. Plant Community Parameter Estimates and Documentation for the Across Trophic Level System Simulation (ATLSS). Technical Report http://www.atlss.org/~sylv/HTML/Everglades/VSMod-HTML/index.html
Wetzel, P. R. 2003. Nutrient and Fire Disturbance and Model Evaluation Documentation for the Across Trophic Level System Simulation (ATLSS). Technical Report. http://www.atlss.org/~sylv/HTML/Everglades/VSMod-HTML/index.html
Planned Products: See tasks below
Collaborators: Collaborators have included the following: Florida International University, Southwestern Louisiana University, University of Florida, University of Maryland, University of Miami, University of Tennessee, University of Washington, University of West Florida, National Wetland Research Center (USGS), Institute for Bird Populations, Everglades Research Group, and the Netherlands Institute of Ecology.
Clients: National Park Service, U.S. Fish and Wildlife Service.
Title of Task 1: Coordination of the projects and tasks under ATLSS
Work to be undertaken during the proposal year and a description of the methods and procedures:
Title of Task 2: Development of Selected Model Components of an Across-Trophic-Level System Simulation (ATLSS) for the Wetland Systems of South Florida
Task Summary and Objective(s): The ongoing goals in this project have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; 5) a variety of visualization and evaluation tools to aid model development, validation, and comparison to field data, and 6) developing a Web-based interface for ATLSS models, so that agencies can utilize these models through remote terminals. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. The objectives of the proposed study are as follows:
Work to be undertaken during the proposal year and a description of the methods and procedures:
Subtask 1. Integration of ATLSS Vegetation Succession Models into ATLSS SESI Models
An ATLSS vegetation succession model, VSMod, encompassing 24 vegetation types, has been developed. VSMod simulates the pattern of spatial and temporal changes in the distribution of vegetation in the Greater Everglades landscape as a function of the hydrologic regime, patterns of fire disturbance and nutrients. A primary goal is to quantify the relative differences between various hydrologic scenarios as reflected in their impacts on vegetation succession. VSMod incorporates a spatially explicit, stochastic cellular automata model to simulate vegetation succession. At any given time, each 500x500-meter plot is in one of a finite number of states. The transition between states occurs with a probability that varies in both space and time, dependent on local hydrologic and fire history as well as on the current vegetation. The model runs on a yearly time step, synchronized with the fire model, and produces annual maps of vegetation over the model area. VSMod rules are based on reports by Wetzel (2001a, 2001b). Three modeled factors influence the succession of one plant association to another: fire, nutrient change, and prolonged change in hydrologic conditions.
The type of vegetation in a plot affects succession by determining hydroperiod ranges for persistence, rate of transition to a new type, fire frequency required for persistence, maximum probability of burning, and time to recover following a fire. Hydroperiod and fire frequency ranges for most herbaceous and forested vegetation types in the model area are presented in Table 1, ordered horizontally by the range of hydroperiods and vertically by the range of fire frequencies. Each color represents similar or identical groups of vegetation types. When more than one plant community can occur within a given hydroperiod and fire interval, transitional probabilities are computed based on the proportional aerial representation of plant associations within the model area. Succession can move forward or backward within the scale of possible transitions. Pending availability of output from more complex nutrient models, the nutrient component of VSMod is based on an empirical relationship described in Reddy (1993, 1998) that estimates nutrient concentration as a function of distance from water control structures.
An ATLSS fire model was developed simultaneously with VSMod. Its purpose is to provide annual estimates of the spatial distribution of areas burned by naturally occurring fires in the Florida Everglades. The fire model provides input for the ATLSS vegetation succession model, VSMod, while VSMod provides local vegetation information for the fire model, simulating the effects of feedback between fire history and vegetation. The yearly time step of the model ends on May 31-the end of the natural fire season. The ability to input yearly map layers of historical fire distribution has also been incorporated into VSMod to investigate historical patterns of vegetation change. The available data are incomplete, however, lacking information for some regions and time periods, and historical data do not permit projecting fire distributions under alternative hydrologic management scenarios. Historical data can be leveraged to parameterize the fire model, which will be used to estimate the annual area burned across the entire landscape. The model allows future fire conditions to be estimated and the effects incorporated into Everglades restoration planning.
The planned work for FY05 will be the integration of the vegetation dynamics (linked to fire, nutrients and hydro) model with the other appropriate ATLSS models, notably the SESI ones. Associated with this is getting the veg dynamics model in a completely tested state, since we have very little funding to deal with this right now.
Subtask 2. Couple all ATLSS models to new landscape class structure being developed under CESI funding.
Under a project funded by CESI, the University of Tennessee is developing a new ATLSS landscape class structure that will allow ATLSS runs to be effectively carried out on any hydrology model output, including hydrologic models that do not use uniform grid elements (e.g., finite element methods). As part of the CESI funding, some effort will be made to upgrade the SESI models to use this more general hydrology. However, there are numerous scaling issues that will require careful thought in all the models. Some GE PES FY05 can be used to adapt the SESI models to the more general hydrologic output.
Subtask 3. Continued Testing and Validation of ATLSS Models.
Part a. Collect in a format appropriate for model evaluation and further calibration the variety of data on wading birds, snail kite, Cape Sable Seaside Sparrow, crayfish, alligators, and white-tailed deer collected over recent years.
At the time the ATLSS SESI models were developed, much of the parameterization and determination of model rules were decided based upon the expertise of those with long field experience in the system. This was done in part due to the very limited and spatially-sparse data available at that time. Since then, a wide variety of data sets for the above mentioned species have been elaborated, and some of these have been done in a spatially-explicit manner (particularly the SRF data sets). We propose to collect these new data in a format amenable to comparison to particular outputs of the SESI models, in order to evaluate and modify the models as needed. This involves placing the data in an appropriate geo-referenced format, and applying needed spatial averaging in order to utilize it in comparisons to model output. In particular, we wish to have the data readable in the ATLSS Dataviewer so that independent assessments of the model performance may be made by various researchers external to UT.
Part b. Statistical comparisons of the various SESI models to data collected in Subtask 1.
The spatially- and temporally-explicit comparison of data to model output is a complex task. We propose to first carry out summary comparisons using averages over various ATLSS regions (there are approximately 20 of these across the SFWMM output region). These comparisons will be done both for the entire time series of available data as well as for time-averaged data sets. We will investigate as well correlation functions as a means to describe the temporal dependencies within various spatial regions. We will also carry out spatially-explicit sub-sampling of particular model pixels where these are local spatial data to compare to the models. These comparisons will be carried out for all SESI models for which data are available.
Part c. SESI model revision based upon the statistical comparisons in Subtask 2.
We propose to utilize a Monte-Carlo fitting approach to evaluate the effect of variations in SESI model assumptions and parameters on the statistical fits obtained through the methods in Task 2. We have already carried out a variety of parameter uncertainty studies for the SESI models, but the proposed effort in this task goes well beyond those efforts in that we propose to choose models which are "optimal" in that they best fit the available data, under appropriate constraints. These constraints will be set in collaboration with field experts on the particular species, many of whom we have had long-running collaborations with. Although we do not expect that any one particular model form or parameter set will be found to be "best" (e.g. there is likely a smooth dependence on the parameters in these models, indicating no sharp transition in model output as a parameter is varied slightly), we do expect to be able to classify how well one particularly chosen model parameterization performs relative to others and to be able to choose model forms and parameterizations (as was done for the models used in the Restudy) which have overall high quality fits for various data situiations, and do not perform in unusual manners (e.g. Produce far outliers) when the model is stressed by using inputs that are just at the edge of historically reasonable conditions.
Subtask 4. Development of new SESI models, where practical
New SESI models are developed as sufficient information becomes available. In addition the nine SESI models already developed, there is the potential now to develop a SESI model for small fishes, based on field studies of Joel Trexler and Bill Loftus. This SESI model will be planned for FY05 funding. In addition, the SESI models for long-legged and short-legged wading birds are now receiving input from the ALFISH model for fish biomass. This revision of these two SESI models will be developed into assessment tools
Subtask 5. Performance of CERP Scenario Evaluations of ATLSS Models for Client Agencies
This task will be making ATLSS runs for CERP scenarios quickly and getting them posted for agency use during FY06. A plan for doing this has been developed, but to be effective will require that a main some current problems be resolved. The main difficultly in getting fast turnaround on ATLSS models is that changes are frequently made in the topography used in South Florida Water Management Model (SFWMM) simulations. This requires time-consuming changes in the ATLSS HRT topography and hydrology. If the topography can be stabilized, the University of Tennessee will be able to make about 2 or 3 CERP scenario runs, using a suite of 5-6 standard SESI models, and put out the binaries, CSV, and Arc Shapefiles of these and the ATLSS hydrodynamic output for the Data viewer. If supplemental funding is available for a computer processor cluster, then it will be possible also to run the fish model, ALFISH, as well as perform the PALFISH/Wading Bird model runs (so there might be 4 Wading Bird SESI runs then, Long, Short, and each linked to ALFISH). They will also provide alligator SESI model output for use in Ken Rice's alligator simulation model.
Work will also begin on the longer-term issue of moving the major part of this work to the IMC or the Joint Ecological Modeling (JEM) laboratory.
Other funding to support much of Subtask 5 is being sought.
Recent Products: See earlier list
Specific Task Products:
Title of Task 3: Use of Amphibian Communities as Indicators of Restoration Success
We will use established sampling methodologies such as PVC refugia trapping to investigate amphibian occupancy rates, develop new methods for sampling across hydroperiod gradients (drift fence arrays, PVC arrays), and use newly developed statistical techniques to estimate the proportion of area occupied by and to define amphibian communities.
Work to be undertaken during the proposal year and a description of the methods and procedures:
During FY06, we will concentrate our work on:
Duellman and Schwartz (1958) produced the first scientific survey of the amphibians of south Florida. This work serves as an excellent reference for the historical distribution of many species before the extensive habitat loss in south Florida during the second half of the 20th century. Meshaka et al. (2000) produced a species list of the herpetofauna for ENP, but little information about the habitat associations and population status of the species was contained in that report. Dalrymple (1988) provided a good description of the herpetofauna of the Long Pine Key area in ENP, but no attempt has been made to sample amphibians throughout the Everglades.
Proportion area occupied by a species (Field work FY04-FY05, Analysis FY06).-- One problem with many of the methods used to sample amphibians is the lack of any control of the myriad environmental factors that affect the behavior and activity of the animals. Abiotic factors like temperature, humidity and hydrology as well as biotic factors like the presence of predators or conspecifics can affect the observability of amphibians. The observability of species' population is a function of the population size, the behavior of the individuals, and the ability of the observer to locate the animals in the particular habitat. Many monitoring programs simply count animals and do not control for this observability or capture probability (p). Therefore, comparisons over time or space are not possible or are biased. If the monitoring program can assume the cost of marking individual animals, then p can be determined and population size or density determined (standard mark-recapture methods, see Williams, et al. 2002). However, this would be cost prohibitive in a monitoring program for all amphibian species throughout the Everglades. MacKenzie, et al. (2002) has developed a novel approach to this problem. Rather than mark the individual, we mark the species. Therefore, presence/absence data from several plots within a habitat (or along a hydroperiod gradient in our study) provided an estimate of p and will allow estimation of the proportion of a stratum occupied by a given species at a given time.
Sampling units were chosen randomly within each stratum. Within Everglades National Park these were along the Main Park Road and Context Road. We chose 5 permanent sites along each road accessed by foot. The sites were located within 300 to 900 feet of the road. In Water Conservation Area 3A, we selected 5 permanent sites in each stratum along a North-South transect from I75 to SR41. Each stratum was defined by the hydroperiod observed from existing hydrologic data and habitat type as defined by existing GIS vegetation layers. Sites were visited twice biweekly, April through September. Further sites in each stratum were visited twice during the study to provide further information on a broader geographic scale.
Our standardized sampling unit was a circular plot of 20m radius. Plots were sampled after dark to increase the probability of observing nocturnal amphibians. At each plot 2-3 person crews began by listening for anuran vocalizations for 10 minutes. The abundance of each species was categorized as: no frogs calling, one frog calling, 2-5 calling, 6-10 calling, >10 calling, or large chorus. The intensity of the vocalizations were categorized as: no frogs calling, occasional, frequent, or continuous. After the vocalization survey, we performed a 30-minute visual encounter survey (VES) in each plot. During this time, all individual amphibians observed were identified to species and captured if possible. We recorded the species, categorized the age (egg, larvae, juvenile, sub-adult, or adult), measured and recorded the snout-to-vent length and recorded the sex when possible. The animal was released at the original capture site. We also recorded the substrate and perch height of the animal. A University of Florida Institutional Animal Care and Use Committee approval was obtained for animal capture. In addition to VES, we used funnel traps to attempt to capture aquatic amphibians. We also recorded several ancillary variables at each plot (air temperature, relative humidity, presence of water, water temperature, wind speed, cloud cover).
In addition, 20-1m tall, 5 cm diameter PVC removable pipes were installed in each site for refugia of treefrog species. During each visit, animals were removed from the pipe for identification and measurement as outlined above. All animals were released into the original PVC refugia. All PVC was removed at the end of the study.
At 10 sites in ENP (5 along Context Road and 5 along Main Park Road) we installed 20m of drift fence for capture of aquatic salamanders. The drift fence consisted of removable erosion control fence with a funnel trap incorporated at each end. The fence was arrayed as 4 separate 5-m fences in a grid around the center of the site. Traps were placed along the fence for 5 consecutive days once per month during May through October. The traps were checked each day in the morning to minimize heat stress on captured animals. Animals were measured as outlined above and released at the capture site. All traps and drift fences were removed during non-capture periods and at the end of the study.
Analysis during FY06. - Individual species capture histories (matrix of presence/absence of each species at a sampling period and plot) and corresponding covariates (habitat, hydroperiod, temperature, humidity) will be assembled. We will then estimate the proportion of each stratum occupied by a species and the capture probability (using MLE and the logistic regression for covariates; MacKenzie et al. 2002). The best model will minimize AIC and adequately estimate the parameters in the model (the candidate model list will be developed a priori based on ecological knowledge and will not include all possible combinations). We can then use these estimates to construct appropriate communities for each stratum (see proportion of area occupied by a community below).
Proportion area occupied by a community (FY06). - Given that species occupancy rates differ across hydroperiod gradients and that hydrology is the controlling factor of this difference (see above), we can begin to construct communities. In Figure 1 below (letters represent species, the size of the circle represents PAO, numbers represent hydroperiod), we can see that in short hydroperiod sites, species A and D dominate. However, as we move to longer hydroperiod sites, other species emerge as the dominate species in the community. This pattern of species composition and PAO forms the set of communities along the hydroperiod gradient.
We have seen this pattern begins to emerge in preliminary data from the Everglades (Table 1).
At present, the method for defining and then predicting community composition and PAO is not complete. This study will develop this methodology for the Everglades.
Index of Biological Integrity (FY06). -- Indices of biological integrity (IBI) were originally developed to assess conditions of riverine systems (Karr 1991, 1993) and also have been developed successfully for use in terrestrial environments (O'Connell et al. 1998). The basic premise of IBI's is that a range of conditions of ecological integrity can be defined based on the structure and composition of a selected biological community (e.g. amphibians, fish, birds, macroinvertebrates). The concept of biological integrity provides an ecologically-based framework in which species-assemblage data can be ranked in a manner that is more informative than traditional measures such as richness and diversity (Karr and Dudley 1981, Brooks et al. 1998). Therefore, the final step in this project will be to develop an amphibian community index (ACI) for evaluating the success of restoration and management of Greater Everglades Ecosystems. The ACI will be modeled after previously developed IBI's (Cronquist and Brooks 1991, Karr 1991,1993, Books et al. 1998, O'Connell et al. 1998). Essentially, we will use the PAO of communities estimated above to index or define the integrity of a given stratum. As restoration proceeds, we can use changes in the index to make informed management decisions and to measure success. Further, we can use the pattern of these communities based on hydopattern to develop restoration targets and to compare alternatives. By providing a reliable and repeatable measure of ecological quality an ACI will help managers reach scientifically defensible decisions (Brooks et al. 1998).
Work to be undertaken during future FY's and proposed funding:
This project is scheduled to end in FY06.
Boughton, R. G., J. Staiger, and R. Franz. 2000. Use of PVC pipe refugia as a sampling technique for hylid treefrogs. American Midland Naturalist 144: 168-177.
Brooks, R.P., O'Connell, T.J., Wardrop, D.H., and Jackson, L.E.: 1998, 'Towards a Regional Index of Biological Integrity: The Example for Forested Riparian Systems,' Environmental Monitoring and Assessment, 51, 131-143.
Croonquist, M.J. and Brooks, R.P.: 1991, 'Use of avian and mammalian guilds as indicators of cumulative impacts in riparian-wetland areas,' Environmental Management 15, 701-714.
Dalrymple, G. H. 1988. The herpetofauna of Long Pine Key, Everglades National Park, in relation to vegetation and hydrology. Pp 72-86 In: Szaro, R. C., K. E. Stevenson, and D. R. Patton, eds. The management of amphibians, reptiles and small mammals in North America. U.S. Dept. of Agriculture, U.S. Forest Service Symposium, Gen. Tech. Rept. RM-166, Flagstaff, AZ.
Donnelly, M. A., C. Guyer, J. E. Juterbock, and R. A. Alford. 1994. Techniques for marking amphibians. In Heyer, W. R., M. A. Donnelly, R. W. McDiarmid, L. C. Hayek, and M. S. Foster, editors. Measuring and monitoring biological diversity: Standard methods for amphibians. Smithsonian Institution. Washington, D.C.
Duellman, W.E. and A. Schwartz. 1958. Amphibians and reptiles of southern Florida. Bull. Florida State Mus., no. 3.
Enge, K. M. 1997. A standardized protocol for drift-fence surveys. Florida Game and Fresh Water Fish Commission Technical Report No. 14. Tallahassee. 69 pp.
Karr, J.R. : 1991, 'Biological integrity: a long-neglected aspect of water resource management,' Ecological Applications 1, 66-84.
Karr, J.R. : 1993, 'Defining and assessing ecological integrity: beyond water quality,' Environmental Toxicology and Chemistry 12, 1521-1531.
Karr, J.R. and Dudley, D.R. : 1981, 'Ecological perspective on water quality goals,' Environmental Management 5, 55-68.
MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle, and C.A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one, Ecology. In Press.
Meshaka, W.E., W.F. Loftus, and T. Steiner. 2000. The Herpetofauna of Everglades National Park. Florida Scientist 63(2): 84-103.
O'Connell, T. J., Jackson, L.E., and Brooks, R.P. : 1998, 'A Bird Community Index of Biotic Integrity for the Mid-Atlantic Highlands,' Environmental Monitoring and Assessment, 51, 145-156.
Williams, B.K., J.D. Nichols, and M.J. Conroy. 2002. Analysis and management of animal populations. Academic Press, London. 817 pp.
Specific Task Product(s):
Title of Task 4: Development of an Internet Based GIS to Visualize ATLSS Datasets For Resource Managers
Task Summary and Objectives:
This project concerns the development of a customized spatial query and visualization tool that provide capabilities of loading ATLSS models data and showing, in the Everglades/Big Cypress area, alternative water management changes and their effects on numerous species modeled in ATLSS (i.e. Cape Sable seaside sparrow, Snail Kite, wading birds, white-tailed deer, American alligator, Florida panther), as opposed to one species, and compare numerous scenarios for one species. The overall goal is to provide an easy-to-use tool capable to access the vast amounts of data produced by the ATLSS models, display and integrate spatial and non-spatial information from different sources, interactively extract statistics for user-specified areas, allowing the users to produce easy-to-read outputs in form of maps, time series graphs, summarized tables, reports and metadata. Particular attention is being devoted in:
Continuous feedback will be requested to ATLSS models developers and potential final users to release a finished product that fulfills the initial planning tasks. This project will be used as prototype server application for an Internet based visualization tool.
The above goals have largely been completed. In addition, DVS has been upgraded to ATLSS Data Visualization System (DVS) 2.0. The upgrades include
Work to be undertaken during the proposal year and a description of the methods and procedures:
This translation will be done under FY05 funding. In addition, the investigators will work with the Interagency Modeling Center located at the South Florida Water Management District, and with the new Joint Ecological Modeling center at the University of Florida, Fort Lauderdale, to integrate the ATLSS DVS into their model development plans.
ATLSS Data Viewing System 2.0
Specific Task Products:
U.S. Department of the Interior, U.S. Geological Survey
This page is: http://sofia.usgs.gov/projects/workplans06/atlss.html
Comments and suggestions? Contact: Heather Henkel - Webmaster
Last updated: 04 September, 2013 @ 02:08 PM(TJE)