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Project Work Plan

Department of Interior USGS GE PES and ENP CESI

Fiscal Year 2008 Study Work Plan

Project Title: Across Trophic Level System Simulation Program for the Everglade/Big Cypress Region
Project Start Date:   Project End Date: 2008
Web Site: atlss.gov, sofia.usgs.gov
Location (Subregions, Counties, Park or Refuge): Total System
Funding Source: USGS GE PES
Other Complementary Funding Source(s): CESI, NSF.
Funding History: FY04, FY05, FY06, FY07, FY08
Principal Investigator(s): Donald L. DeAngelis
Email address: ddeangelis@umiami.ir.miami.edu
Phone: 305-284-3973
Fax: 305-284-3039
Mail address: Department of Biology, University of Miami, P. O. Box 249118, Coral Gables, Florida 33124
Project Personnel:
Supporting Organizations:
University of Florida, University of Tennessee, University of Miami
Associated/Linked Projects:

Other Investigator(s): Dr. Lou Gross
Email address: gross@tiem.utk.edu
Phone: 865-974-4295   Fax: 865-974-3067
Mail address:
Department of Ecology and Evolutionary Biology, The University of Tennessee, Knoxville, TN 37996

Other Investigator(s): Steve Hartley
Email address: steve_hartley@usgs.gov
Phone: 337-266-8543   Fax: 337-266-8616
Mail address:
USGS-BRD National Wetland Research Center, 700 Cajundome Road, Lafayette, Louisiana 70506

Other Investigator(s): Kenneth G. Rice, USGS; Frank J. Mazzotti, University of Florida
Email address: Ken_R_Rice@usgs.gov
Phone:
954-577-6305   Fax: 954-577-6347
Mail address:
IFAS, University of Florida, 3205 College Avenue, Fort Lauderdale 33314

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 collected 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 an 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.

Recent Products:

Publications (2005-2007):

Basset, A., and D. L. DeAngelis. 2007. Body size mediated coexistence of consumers competing for resources in space. Oikos 116:1363-1377.

Mooij, W. M., J. Martin, W. M. Kitchens, and D. L. DeAngelis. 2007. Exploring the temporal effects of seasonal water availability on the snail kite of Florida. Pages 155-173, in Pulsed Resources and Wildlife Population Response: The Importance of Time. Editors: John Bissonette and Ilse Storch. Springer-Verlag Publisher.

Rashleigh, B, and D. L. DeAngelis. 2007. Conditions for coexistence between parasitic freshwater mussels.   Ecological Modelling 201(2):171-178.

DeAngelis, D. L, M. Vos, W. M. Mooij, and P. A. Abrams. 2007. Feedback effects between the food chain and induced defense strategies. Pages 213-236. In: From Energetics to Ecosystems: The Dynamics and Structure of Ecological Systems N. Rooney, K. McCann and D. Noakes (eds). Springer-Verlag.

Call, E. M., L. A. Brandt, and D. L. DeAngelis. 2007. Old World climbing fern (Lygodium microphyllum) spore germination in natural substrates. Florida Scientist 70:55-61.

Volker Grimm, Uta Berger, Finn Bastiansen, Sigrunn Eliassen, Vincent Ginot, Jarl Giske, John Goss-Custard, Tamara Grand, Simone Heinz, Geir Huse, Andreas Huth, Jane U. Jepsen, Christian Jorgensen, Wolf M. Mooij, Birgit Müller, Guy Pe'er, Cyril Piou, Steven F. Railsback, Andrew M. Robbins, Martha M. Robbins, Eva Rossmanith, Nadja Rüger, Espen Strand, Sami Souissi, Richard Stillmann, Rune Vabo, Ute Visser, Donald L. DeAngelis. 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198:115-126.

Holland, J. N., T. Okuyama, and D. L. DeAngelis. 2006. Comment on "Asymmetric coevolutionary networks facilitate biodiversity maintenance". Science 313:1887b.

Koslow, J., and D. L. DeAngelis. 2006. Host mating system and the prevalence of a disease in a plant population. Proceedings of the Royal Society of London 273: 1825-1831.

Holland, J. N., and D. L. DeAngelis. 2006. Interspecific population regulation and the stability of mutualism: fruit abortion and density-dependent mortality of pollinating seed-eating moths. Oikos 113:563-571.

DeAngelis, D. L., and J. N. Holland. 2006 Emergence of ratio-dependent and predator-dependent functional responses for pollination mutualism and seed parasitism. Ecological Modelling 191:551-556.

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.   2005. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science 310:987-991.

DeAngelis, D. L., and W. M. Mooij. 2005. Individual-based modeling of ecological and evolutionary processes. Annual Reviews of Ecology and Evolutionary Systematics 36:147-168.

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.

Vos, M., B. W. Kooi, D. L. DeAngelis and W. M. Mooij. 2005 . Inducible defenses in food webs. Pages 114-127, in "Dynamic Food Webs". P. de Ruiter, V. Wolters and J. Moore (eds.) Elsevier.

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.)

In press or submitted:

Sternberg, L. da S. L, S.-Y. Teh, S. Ewe, F. Miralles-Wilhelm, and D. L. DeAngelis. Competition of hardwood hammock and mangrove vegetation.   (In Press, Ecosystems).

Petersen, J. H., D. L. DeAngelis, and C. P. Paukert. Developing bioenergetics and life history models for rare and endangered species. (In press, Transactions of the American Fisheries Society.)

Cantrell, R. S., C. Cosner, D. L. DeAngelis, and V. Padron. The ideal free distribution as an evolutionarily stable strategy. (In press, Journal of Biological Dynamics).

Elderd, B. D., and M. P. Nott. Hydrology, habitat, and population demography: an individual-based model for the endangered Cape Sable seaside sparrow (Ammodramus maritimus mirabilis). Journal of Applied Ecology (in press.)

Feng, Z., R. Liu, and D. L. DeAngelis. Plant-herbivore interactions mediated by plant toxicity. (Submitted to Theoretical Population Biology, revision requested.)

Teh, S. Y., D. L. DeAngelis, L. S. L. Sternberg, F. R. Miralles-Wilhelm, and T. J. Smith. Disturbance events can cause regime shifts between vegetation types: A model study of mangroves and hardwood hammocks. (Submitted to Ecological Modelling.)

Liu, R., Z. Feng, H..Zhu, and D. L. DeAngelis. Bifurcation analysis of a plant herbivore model with toxin-determined functional response. (Submitted to Journal of Differential Equations)

Beeck, P., D. L. DeAngelis, H. Doerner, and J. Borcherding. Piscivory in combination with bimodal size distribution in young-of-the-year Eurasian perch: an overlooked phenomenon? (Submitted to Oikos.)

DeAngelis, D. L.. J. M. Koslow, and S. Ruan. Host mating and the spread of a disease-resistant allele in a population. (Submitted to Theoretical Population Biology.)

Presentations (2006 – 2007):

Fernandes, Miguel V., Donald DeAngelis, and Michael S. Gaines. 2007. The effects of tree island size and water depth on population patterns of the cotton rat (Sigmodon hispidus) and marsh rice rat (Oryzomys palustris) in the Florida Everglades. Poster at Ecological Society of America Annual Meeting, San Jose, CA.

DeAngelis, D. L., J. C. Trexler and W. F. Loftus. 2007. Monitoring and Modeling the Small Fish Community in the Florida Everglades. National Conference on Environmental Restoration, Kansas City, MO.

DeAngelis, D. L. 2006. Coupling population and biomass in individual-based models. Keynote Speech, International Society of Ecological Modelling, Yamaguchi, Japan.

Mooij, W. M., J. Martin, W M. Kitchens, and D. L. DeAngelis. 2006. Modeling snail kites in a variable environment. GEER Meeting, Lake Buena Vista, FL.

Gaines, M. S., D. L. DeAngelis, M. Fernandes, J. Warren, and H. Beck. 2006. Effects of Patch Size and Hydrology on Population Dynamics of Small Mammals in the Everglades. GEER Meeting, Lake Buena Vista, FL.

Planned Products: See tasks below

Time Frame:

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.

WORK PLAN

Title of Task 1: Coordination of the projects and tasks under ATLSS
Task Funding: USGS Priority Ecosystem Science
Task Leaders: Donald L. DeAngelis
Phone: 305-284-1690 Email address: ddeangelis@umiami.ir.miami.edu
Task Status (proposed or active): Active
Task priority: High
Time Frame for Task 1: 10/01/2004 - 9/31/2007
Task Personnel: D. L. DeAngelis

Task Summary and Objectives: Coordinate all of the projects and tasks under ATLSS. Work with collaborators in planning their projects. Interact with agencies and interagency teams in South Florida to ascertain their needs for modeling and evaluation of restoration plans and determine how ATLSS can best meet those needs. Lay the groundwork for a decision support system.

Work to be undertaken during the proposal year and a description of the methods and procedures:

During the next year there will be especially heavy need for working with the DOI agencies (National Park Service and Fish and Wildlife Service) to perform the needed ATLSS model simulations for CERP evaluations. Part of this work will involve making ATLSS models more directly accessible to agencies. A meeting with agency representatives on September 22, 2005, indicated that there is now an urgent need to test a number of different water regulation scenarios in a short time, as well as to be able to make minor alterations in the models. This requires rapid turnaround of results (within a day or two).

The leader of this task has is working to achieve this goal both through interactions with Lou Gross of the University of Tennessee as well as by improving the capabilities at the Joint Ecosystem Modeling (JEM) Center. Currently the USGS's Across Trophic Level System Simulation (ATLSS) models are run at the University of Tennessee using 2 x 2 mile hydrology provided by the South Florida Water Management Model (SFWMM). But the DOI agencies need to have ATLSS models working in South Florida on PCs. Currently the USGS's Across Trophic Level System Simulation (ATLSS) models are run at the University of Tennessee using 2 x 2 mile hydrology provided by the South Florida Water Management Model (SFWMM). The initial step in this process is converting SFWMM topography and hydrology to the 500 x 500 meter scale of resolution used by the ATLSS models. The 500-m hydrology is used in the ATLSS models, which include Spatially Explicit Species Index (SESI) models for wading birds, snail kites, white-tailed deer, American alligator, Cape Sable seaside sparrow, crayfish, Florida panthers, and apple snails, as well as population demographic models of the American alligator and the forage fish functional group (ALFISH model). In order to transfer these functions to DOI agencies, the University of Tennessee is now cooperating in training between two and four persons from these agencies during a series of visits to the University of Tennessee beginning in early 2006. In addition, source code and documentation of the models and procedures will be transferred to the involved agencies. The task leader has been organizing and participating in visits by DOI staff to the University of Tennessee to learn how the processes needed to run ATLSS models.

The task leader will also coordinate with other modeling research in order to incorporate new models in the ATLSS framework.

The task leader is also engaged in other project related to Everglades research and restoration.

1. Working with a Ph. D. student at the University of Miami (Shu Ju) to create a nutrient cycling model for tree islands in the Everglades to understand the differences in productivity, tree communities, and other properties between and within tree islands.

3. Working with Prof. Joel Trexler of FIU to create a simple, more flexible version of ALFISH, called GEFISH that can be used to predict fish biomasses on subregions of the Everglades. This model is complete and currently in the testing phase.

4. Working with a University of Miami post-doc (Dr. Irene van der Stap) to upgrade and apply to scenarios the snail kite individual-based model (EVERKITE). The model is run using data from the SFWMD on a 2 x 2 mile grid extending across Central and South Florida. A semi-annual report has been sent to the U. S. Fish and Wildlife Service. The model will be turned over to the USFWS in January, 2008.

5. Working with Dr. Michael Gaines of the University of Miami to complete the report on 11 years of study of effects of tree island size and hydroperiod on abundance, survival, reproduction, and movement of rice rats and cotton rats. A poster was presented at the 2007 Ecological Society of America meeting on this work.

Deliverables:

- A grid based version of EVERKITE that incorporates the new empirical knowledge with documentation. The input will consist of grid based hydrological scenarios, the output of grid based kite numbers, vital rates, population structure, etc.

- A simple user-interface to run the model developed in close contact with the agencies with documentation.

- Grid based output files of EVERKITE with documentation for the existing hydrological scenarios. These output files can be inspected with the existing ATLSS dataviewer or other GIS software already available at the agencies.

- Report of 11-year study of small mammals (with Gaines).

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 Funding
: USGS Priority Ecosystem Science
Task Leaders: Louis J. Gross, University of Tennessee
Phone: 865-974-4295
FAX: 865-974-3067
Task Status (proposed or active): Active
Task priority: High
Time Frame for Task 2: 2004-2008
Task Personnel: Louis J. Gross, Director, The Institute for Environmental Modeling, Univ. Tenn. Staff of The Institute for Environmental Modeling including: Jane Comiskey and Eric Carr.

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 an efficient way for agencies in South Florida to use models. 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:

  • Continue the effort started in FY06 to provide training of local DOI staff in South Florida to run ATLSS models with hydrologic scenarios from the SFWMM

Work to be undertaken during the proposal year and a description of the methods and procedures:

The emphasis on work being done under Task 2 shifted during FY06 due to the changing needs of agencies in South Florida. It was originally intended to continue development of a system (through an NSF-funded project at the University of Tennessee) to allow dispersed resource managers to access remotely, through the Web, the capabilities of the SInRG (Scalable Intracampus Research Grid) at the University of Tennessee. This would have allowed users at resource agencies in South Florida, with relatively little computer expertise, to initiate ATLSS simulations on the computers at the University of Tennessee. The Web-based use of the ATLSS models was progressing rapidly, using software, called NetSolve, developed at the University of Tennessee. Completion of this task would have left time to continue development of ATLSS (Version 3) and a number of other subtasks.

However, a meeting with agency representatives on September 22, 2005, indicated that there is now an urgent need to test a number of different water regulation scenarios in a short time, as well as to be able to make minor alterations in the models. This requires rapid turnaround of results (within a day or two). This requires having ATLSS models to run locally on agency PCs. In order to provide this capability the objectives of Task 2 were changed during FY06. Work is now underway to transfer the expertise in using the ATLSS (Version 2.0) models and in performing the model runs to DOI agencies in southern Florida. In order to transfer these functions to DOI agencies, the University of Tennessee is training between two and four persons from DOI agencies and collaborators during a series of visits to the University of Tennessee that began in early 2006. In addition, source code and available documentation of the models and procedures are being shared with the involved agencies.

The specific objectives of the proposed work begun during FY06 were specified as the following,

  1. A team of at least two DOI designated scientists from southern Florida will develop an understanding of the process for creating the ATLSS 500-m resolution topography and hydrology. (Expected time, two days.)
  2. Together with staff of the Institute for Environmental Modeling, University of Tennessee (TIEM), the DOI team should run usable ATLSS models against a standard 2x2 hydro scenario, converted to 500-m hydrology. This will include the following SESI models: white-tailed deer, snail kite, wading bird, alligator, Cape Sable seaside sparrow, crayfish, and apple snail. This will also include the models ALFISH and SIMSPAR. (Expected time, three days.)
  3. The team will, along with the TIEM staff, write down a standard operating procedure for updating the 500-m topography for SFWMM hydrology input, as well as the 500-m conversion program provided to ENP previously. This is important, because this topography may be used in the future not only for the Cape Sable seaside sparrow model, but may be incorporated into a marl prairie performance measure. (Expected time, one to two weeks.)
  4. The team, together with TIEM staff, will proceed to rapidly determine if a transition to Visual C++ in Windows is feasible, and concurrently move forward to develop a Linux-based version of ATLSS V2 to run on DOI computers.
  5. The team, together with TIEM staff, will examine and discuss the present ATLSS input and output files, in order to see if there are ways to better facilitate parameter sensitivity analysis and post-run data analysis.
  6. A written agreement will be developed by the team as a group that sets up a protocol for software versioning management that protects the source code while still allowing for needed updates and modifications to be performed both at UT and DOI agencies.

The following progress on these objectives has been made through August 2006.

  • A series of three visits to the University of Tennessee (UT) by a team from DOI in South Florida has been started, with the first two visits occurring in April and June 2006. A final version of the SOW was developed and the team learned the process of developing ATLSS's high resolution topography (HRT) and hydrology (HRH). The UT staff started the documentation of the process of developed HRT and HRH, and also of converting ATLSS SESI models from UNIX work stations to LINUX boxes.
  • The source code for a suite of the ATLSS models was provided to the DOI agencies by UT. This included ALFISH, and the wading bird, alligator, Cape Sable seaside sparrow, and snail kite SESI models. The source code will be the versions of ATLSS which utilize Version 2 of the ATLSS Landscape classes built for Sun Microsystems Solaris Forte C++ compiler. These will be provided by March 31, 2006.
  • As part of the preparation for the third meeting, UT staff delivered to Everglades National Park and the JEM lab in July, 2006, the following handbooks:
    • ATLSS High Resolution Topography (HRT) Manual
    • ATLSS High Resolution Multi-Source Topography (HRMST) Implementation Manual
    • ATLSS HydroSuite (HS) Implementation Manual
  • These draft versions of instruction manuals cover the complete ATLSS hydrology creation process from SFWMM Calibration/Verification to generation of ATLSS hydrology from Scenario runs.  In the draft version, separate manuals are provided for HRT, HMDT, and HydroSuite. 
  • In addition, a copy of the Linux version of the ATLSS white-tailed SESI has been delivered to Everglades National Park and the JEM lab.
The effort proposed here is an expansion of the ongoing effort carried out at UTK to transfer information and methods on ATLSS to staff members of JEM/NPS/IMC, as well as translate the model codes of ATLSS to run on Linux rather than Solaris machines. In this process, UTK produced a document of SOP for ATLSS Hydrology, trained two individuals from JEM/NPS in the necessary methods for HRH and ATLSS SESI model production, and developed Linux versions of the ATLSS SESI models requested by the NPS/JEM.

While successful in the above, it has become evident that the development of ATLSS HMDT for a variety of SFWMM versions that are being used in CERP and for other planning purposes by DOI agencies requires further detailed efforts at UT and in collaboration with DOI partners.

Objectives:

The objectives of the proposed work are the following,

1. UTK will develop a complete new HMDT for which ever version of the SFWMM the DOI partners decide, and for which the appropriate detailed input and data source information is provided by the DOI partners.

2. The process in #1 will be documented, with particular attention to development of standardized methods to compare the resulting topography and hydrology to SFWMM inputs and results. As part of this, a re-analysis in particular of the soil saturation inputs utilized in ATLSS will be developed.

3. An evaluation will be made as to whether an alternative to the ATLSS HRH might be utilized in areas for which the SFWMM does not utilize HAED and Lidar data. This would imply removing the current HRH methodology and replacing it with a methodology that utilizes HAED or other data as available for these regions.

4. A versioning system will be developed to clarify the variety of inputs and assumptions included in the development of the HMDT, accounting for the variety of potential differences in any single SFWMM version.

Work to be undertaken during future FY's and proposed funding:

Recent Products: See earlier list

Specific Task Products:

Delivery of a complete new HMDT for which ever version of the SFWMM the DOI partners decide, and for which the appropriate detailed input and data source information is provided by the DOI partners.

Documentation of the above, with particular attention to development of standardized methods to compare the resulting topography and hydrology to SFWMM inputs and results. As part of this, a re-analysis in particular of the soil saturation inputs utilized in ATLSS will be developed.

Replacing the current HRH methodology if necessary.

A versioning system will be developed to clarify the variety of inputs and assumptions included in the development of the HMDT, accounting for the variety of potential differences in any single SFWMM version.

Title of Task 3: Use of Amphibian Communities as Indicators of Restoration Success
Task Funding:
USGS Priority Ecosystems Science
Task Leaders: Kenneth G. Rice, USGS; Frank J. Mazzotti, University of Florida
Phone: 954-577-6305
FAX: 954-577-6347
Task Status (proposed or active): Active
Task priority: High
Time Frame for Task 2: 2004-2008
Task Personnel: Replacement for Hardin Waddle, GS-11

Task Summary and Objectives: Declines in amphibian populations have been documented by scientists worldwide from many regions and habitat types. No single cause for declines has been demonstrated, but stressors like acid precipitation, environmental contaminants, the introduction of exotic predators, disease agents, parasites, and the effects of ultraviolet radiation have all been suggested. Because of their susceptibility to these and other stressors, amphibians are important as indicators of ecosystem health. Amphibians are present in all habitats and under all hydrologic regimes in the Everglades. The species present and the occupancy rate of a given species differ greatly across those gradients. These differences are due to hydropattern, vegetation, and other environmental factors. The combination of species composition and proportion of each habitat occupied at a given time form unique communities defined by those environmental factors. Therefore, if these communities can be reliably defined and measured, Everglades restoration success can be evaluated, restoration targets can be established, and restoration alternatives can be compared. This study will develop methodologies for defining and measuring the membership and area occupancy of amphibian communities. Further, we will investigate the relationship of occupancy of amphibians with hydroperiod and other environmental factors. Finally, we will provide a method for measuring restoration success based on these communities. The importance of amphibian communities to Everglades restoration has been recognized and listed as critical priority research needs (see USGS Ecological Modeling Workshop and the DOI Science Plan in Support of Greater Everglades Ecosystem Restoration).

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. Our objectives include:

  • Define amphibian communities appropriate for evaluating restoration success.
  • Develop methods for measuring the area occupancy of amphibian communities across habitats and environmental gradients.
  • Investigate the relationship of occupancy with hydroperiod and other environmental factors.
  • Develop restoration targets for the amphibian community of the Everglades.
  • Develop a restoration tool for amphibian communities that measures restoration success and compares restoration alternatives.
  • Develop an index of biological integrity for amphibians that provides a framework for scientifically defensible decisions by restoration managers.

Work to be undertaken during the proposal year and a description of the methods and procedures:

During FY08, we will concentrate our work on:

  • Establishing restoration targets for amphibian communities in appropriate habitats.
  • Finalizing models and methods to measure restoration success across these communities and compare restoration alternatives.
  • Finalizing an overall index of amphibian community integrity.

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.

We used 2 primary methods to accomplish the objectives of the project:

  • Proportion area occupied (PAO) by a species.
    • Vocalization survey
    • Time-constrained searches
  • Proportion area occupied by a community.

Proportion area occupied by a species (Field work FY04-FY05, First Analysis FY06, new data added (from Southwest Florida study) and models re-analyzed FY07-08).-- 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 FY08. - 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 – Finalized in FY08. – 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.

Figure 1. Conceptual view of proportion of area occupied by communities of amphibians across a gradient of hydroperiod in the Everglades. [larger image]

We have seen this pattern begins to emerge in preliminary data from the Everglades (Table 1).

Table 1. Proportion Area Occupied values for amphibian species in the Everglades across a gradient of hydroperiod (values are an estimate of the proportion of a stratum occupied by that species):

Hydroperiod

Cricket Frog

Southern Toad

Squirrel Treefrog

Pigfrog

Leopard Frog

Short

0.0000

0.5277

0.7058

0.0000

0.3101

0.0000

0.5155

0.6495

0.3123

1.0000

0.1525

1.0000

0.1865

1.0000

0.8564

0.3391

0.0000

1.0000

0.8708

0.8646

Long

0.7080

0.4333

0.1718

0.7068

0.3558

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. -- 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 FY08.

Literature Cited:

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,' Enironmental 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):

  • Tools and scientific data necessary for evaluation of restoration success and comparison of restoration alternatives.
  • Methods and data necessary for RECOVER's adaptive assessment process and monitoring program.
  • Development of a cost-effective monitoring program for amphibians.
  • Development of performance measures for amphibian communities.
  • Peer-reviewed publications and published methodology for evaluation of restoration success.

Title of Task 4: Development of an Internet Based GIS to Visualize ATLSS Datasets For Resource Managers
Task Funding: USGS Priority Ecosystem Sciences
Task Leaders: Steve Hartley
Phone: 337-266-8543
Fax: 337-266-8616
e-mail: steve_hartley@usgs.gov
Task Status (proposed or active): Active
Task priority: High
Time Frame for Task 3: Year 4 - 1/10/04-9/31/08
Task Personnel: Steve Hartley

Introduction

The ATLSS Data Viewer System (DVS) is an application developed to allow resource managers and scientists to display and analyze the outputs of some Across Trophic Level System Simulation (ATLSS) models.

ATLSS models

ATLSS is a set of models developed by the U.S. Geological Survey and other agencies to predict the responses of a suite of higher trophic level species to different alterations in the Everglades and Big Cypress (South Florida) hydrology regime. The goal is to help resource managers evaluate alternative restoration plans in comparison with no restoration efforts, and through this to aid development of appropriate monitoring and adaptive management schemes. The DVS allows the display of data from the following models: Hydrology, White-tailed Deer Breeding Potential Index, Cape Sable Seaside Sparrow Breeding Potential Index, Wading Birds Foraging Condition Index, American Alligator Production Index, and Snail Kite Index. The latest version of the DVS (3.0) also includes data from the ALFISH model. Except the Hydrology and the ALFISH, all other models are included in a subgroup of the ATLSS models called Spatially-Explicit Species Index (SESI), which allow the comparison of the relative potential for breeding and/or foraging across the landscape within-year dynamics of hydrology.

ATLSS data

SESI models produce a spatial distribution of indices (0 to 1 floating point values) representing environmental conditions during critical stages of the species. The outputs from the SESI models used in the DVS are annual averages representing a simulation between 1965 and 2000. The set of 36 annual grids is available for three different hydrologic conditions: the base 2000, the base 2050, and the alternative D13R. The DVS allows the comparison of alternative conditions for the same or different time periods selected inside the 36 years. The Hydrology model generates spatial distributions of annual hydroperiods (days of inundation) while the ALFISH model generates monthly data (#fish/sqm), both for the same three alternative hydrologic scenarios. ALFISH model produces 5 monthly data sets for each of the 36 years simulation period: Feb, Apr, Jul, Oct, and Dec.

ATLSS DVS

ATLSS models generate a large amount of data, and the data are in a format that is often difficult to manage in PC-based applications. The USGS National Wetlands Research Center has developed a customized ArcGIS 9.x-based project in which the standard graphical interface and functions have been enhanced to perform visualization and analysis tasks specifically designed for ATLSS data.

ATLSS DVS allows users to:

1) Convert binary raster files, as they are generated from the ATLSS models, to ESRI-grids, which is the spatial format used by the system for visualization and analysis.

2) Process the model outputs by user-defined time periods and compare alternative hydrologic scenarios.

3) Analyze the result of processing model outputs and extract zonal statistics, such as mean index values or variances by user-defined zones of interest.

4) Generate exportable tables, line graphs, and maps representing the result of analyzing ATLSS model data.

5) Integrate base maps pertaining to particular project areas selected from a customizable list of layers.

6) Extract Zonal means from the model outputs. These values may be used to detect correlations between model outputs and empirical data collected over the same areas.

7) Extract mean index values based on user-defined areas.

8) Import ATLSS runs as monthly or even daily data sets inside the 36 years of simulation.

9) Define geographic coordinates, UTM, decimal degrees, or degree, minute, seconds imported from an Excel worksheet to create a point theme. This theme may be used to extract cell values from model outputs and perform correlation analysis with empirical data collected for the same points.

A. Goals:

  1. Continue developing the DVS interface in ArcGIS.
  2. Develop interface and functionality and upgrade code as needed to allow agencies capable of independently running ATLSS models (SFWMD and ENP) to read and display their runs into the ATLSS DVS.
  3. Develop a new DVS user's Guide based on the new interface and user's comments and suggestions.
  4. Develop interface capabilities to display and analyze the output of the ATLSS snail kite demographic model.
  5. Provide technical assistance and on-site trainings.
  6. Work with the Interagency Modeling Center and Joint Ecosystem Modeling center (UF) to help them utilize the ATLSS DVS and modify it for their specific purposes.
  7. Long Term Goals:
    1. Web-based ArcServer ATLSS DVS with data housed at NWRC or IMC.
    2. Model construction, manipulation, and execution via the DVS.



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