U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2004-3109

October 2004

Progress on the Across Trophic Level System 

Simulation (ATLSS) Program

Printed on recycled paper

The U.S. Geological Survey (USGS) Across Trophic Level 

System Simulation (ATLSS) Program has produced a set of 

models, including spatially explicit species index models, 

population demography models, and ecosystem process models 

that are designed to evaluate the ecological effects of hydrologic 

scenarios on selected Everglades biota during the Central and 

South Florida Project Comprehensive Review Study (Restudy). 

The program also supported field studies that produced data 

relevant to model construction and validation. The ATLSS 

Program has continued to develop, refine, and test models to 

provide information about the ecological effects for the 

Comprehensive Everglades Restoration Plan (CERP). Progress 

has been made in several aspects of the ATLSS Program during 

fiscal year 2004, including: (1) upgrading of the ATLSS landscape topography model (HMDT), (2) development of 

methods for testing ATLSS model data against empirical data, 

(3) development of a model for Everglades vegetation 

succession, and (4) development of an approach to allow 

ATLSS models to be run remotely on personal computers. 

ATLSS High Resolution Multi-Data Source 

Topography (HMDT)

A challenge in Everglades modeling is providing hydrologic data to ATLSS models at a spatial resolution that is 

relevant to modeling ecological processes. The primary source 

of hydrologic projections for the region is the South Florida 

Water Management Model (SFWMM). This model is used to 

estimate potential hydrologic conditions for various restoration scenarios. The spatial resolution of this model is 2 x 2 mi 

(miles); that is, the model provides a single daily estimate for 

elevation and hydrologic variables for each 2 x 2-mi plot on the 

landscape. Although this resolution is appropriate for the task of 

hydrologic management in southern Florida, it lacks sufficient 

resolution for modeling the effects of hydrologic change on 

Everglades species under various restoration scenarios.

To provide the geospatial basis for hydrologic data at a 

500 x 500-m (meter) spatial resolution, ATLSS has developed 

topography using elevations from the USGS High Accuracy 

Elevation Data (HAED) and Light Detection and Ranging 

(LIDAR) data where available. In areas where such data are 

not available, estimates of deviations of elevations within each 

SFWMM 2 x 2-mi spatial cell from the mean of that cell are 

created at the 500 x 500-m scale of resolution based on the vegetation maps and known relation between hydroperiod and vegetation. This is referred to as ATLSS High Resolution Topography (HRT). Finally, SFWMM elevations are used directly in 

urban and agricultural areas. The resulting ATLSS 

topographic map (fig. 1) is then used to refine the SFWMM 

2 x 2-mi resolution hydrology to provide estimates of water 

depth at a 500 x 500-m resolution. In producing this higher 

resolution hydrology, emphasis is placed on preserving the 

integrity of the basic assumptions within the SFWMM by 

preserving total water volume in each 2 x 2-mi cell on a daily 

basis. 

Figure 1. ATLSS High Resolution Multi-Source Topography (HMDT).

Progress in Verification of ATLSS Spatially 

Explicit Species Index Models

ATLSS has produced a set of Spatially Explicit Species 

Index (SESI) models that are designed to assess the relative 

potential for breeding and/or foraging success of modeled 

species across the greater Everglades landscape under various 

proposed hydrologic scenarios. The species modeled are:

• Short-Legged Wading Bird

• Long-Legged Wading Bird

EXPLANATION

Elevation, in meters

relative to sea level

-16.7 - 0.2

>0.2 - 1.0

>1.0 - 1.6

>1.6 - 1.9

>1.9 - 2.2

>2.2 - 2.5

>2.5 - 2.7

>2.7 - 3.0

>3.0 - 3.3

>3.3 - 3.6

>3.6 - 4.0

>4.0 - 4.4

>4.4 - 4.9

>4.9 - 5.6

>5.6 - 8.0
• White-Tailed Deer

• American Alligator

• Cape Sable Seaside Sparrow

• Snail Kite

• Apple Snail

• Florida Panther

• Crayfish

SESI models incorporate information, such as vegetation 

type, and relating suitable conditions for these species to 

local habitat conditions and yearly hydrologic conditions, 

producing yearly performance values between 0 and 1 for each 

500 x 500-m cell in the model area. 

Until recently, information on populations had been too 

sparse to calibrate and test these models. Empirical data are 

now becoming available, and the SFWMM output data extends 

beyond its previous period of 1965-95 to 1965-2000, which 

allows comparisons between models and data over a longer time 

period than was previously possible. Even with these increases 

in available data on population sizes, it is not possible to calibrate and test SESI models directly against such data, as the 

SESI models only provide relative indices of habitat suitability, 

not population estimates. 

The reliability of SESI models can nonetheless be 

evaluated by comparing year-to-year trends in SESI outputs 

with year-to-year trends in empirical data. As an example of 

how such comparisons can be made, consider the yearly 

empirical estimates from survey counts (that is, observed 

singing males multiplied by 16 to estimate total population) 

of the Cape Sable sparrow population for a given subregion, 

the "western subpopulation," of its range. Data are available 

for 1981 and the period 1992-2004. Because the SESI model 

produces values between 0 and 1, the SESI values must first 

be normalized to the population data in order to compare SESI 

model trends with population data. To do this, the SESI index is 

averaged over the entire western subregion and then rescaled to 

be comparable to population data. This was done for 

figure 2 where the normalized SESI index is calibrated to the 

1981 count of sparrows. If the SESI model is a good representation of the sparrow breeding conditions, then the normalized 

SESI index (black dots) would be expected to track subsequent 

survey counts (red crosses). The SESI index does seem to track 

population values over most of the period from 1992 to 2002, 

although observed values are lower than model projections during 2001 and 2002. This deviation may reflect environmental 

changes in the western subpopulation’s habitat that are not yet 

incorporated in the model, such as changes in vegetation.

The Systematic Reconnaissance Flight (SRF) dataset is an 

important source of empirical population data for testing some 

of the SESI models. A protocol has been developed by the 

ATLSS Program for using SRF data on wading bird nesting and 

foraging distributions to test the SESI models for the wading 

birds. Because subperiods within the wading bird breeding 

season may be important determinants of wading bird foraging 

sites, SESI output will be generated for monthly periods within 

each breeding season. Graphical output of monthly SESI component metrics will be compared to monthly SRF abundance 

counts. The graphical time-series comparison of yearly SESI 

potential and observed abundance (by subregion of interest) 

also will be made. Finally, three-panel comparisons between 

empirical data and SESI values will be made after aggregating 

to compatible scales.

Progress in Testing the EVERKITE Snail Kite 

Demographic Model

In addition to SESI models, the ATLSS program has 

developed population demographic models for a number of 

species. One example is the individual-based model 

EVERKITE, which has been developed to project population 

change of the Florida snail kite (Rostrhamus sociabilis) under 

future hydrologic scenarios. The Florida snail kite is an 

endangered raptor that occurs as a closed population of about 

2,000 birds in the wetlands of southern and central Florida. 

Its demography is severely affected by the hydrology of these 

wetlands. As basic information for any predictive model, good 

empirical studies are required. A large number of empirical 

studies have been done on the Florida snail kite. These studies 

provide the correlative relations between specific aspects of the 

snail kite life history and behavior with the hydrology of the 

system. These relations form the building blocks of any 

hydrology-driven population dynamical model.

Figure 2. Water level during 

sparrow breeding season at 

NP205 gage (blue). Observed 

population (red) and simulated population (black) are 

expressed as proportion of 

maximum potential population 

that is realized. Population 

deficit curves follow the general pattern of intensity of drydowns, except for 1994, a year 

when water depths measured 

at sparrow sampling stations 

deviated from the NP205 gage 

1975 1980 1985 1990 1995 2000 trends.

YEAR

0

50

100

150

200

250

WATER LEVEL, IN CENTIMETERS T

E

R LE

V

EL, IN C

E

N

TIM

ETE

R

S WA

ABOVE NGVD 1929

1.00

0.75

0.50

0.25

1-SCALED POPULATION

Water level at NP205 gage

Unused population potential:

Observed

Simulated
With this tool in hand, we approached the challenge of making reliable projections of future population development of the 

snail kite under various hydrologic scenarios. The reliability 

of the model could be judged by comparing its predictions for 

the historical hydrologic patterns with observed demographic 

patterns during the past decades. The most logical dataset to be 

used for this comparison is the annual count of snail kites that 

was performed during 1969-95. This dataset has been criticized, 

however, for being biased in several ways. Without an unbiased 

dataset on kite demography, it is hardly possible to check the 

reliability of the kite model for the historical situation, which 

affects the faith one should have in the model in discriminating 

among hydrological scenarios.

Rather than implementing a single version of the model, we 

implemented a suite of model versions, representing the structural uncertainty in our understanding of the dynamics of the 

kite population. For each model version we developed a number 

of parameterizations, representing the uncertainty in the model 

parameters. We will evaluate each hydrological scenario for 

each model version for each parameterization, focusing on the 

long-term population growth rate (lambda) as the main response 

parameter. This analysis will enable us to evaluate whether 

structural uncertainty and parameter uncertainty interact with 

differences due to hydrological scenarios. If these interactions 

are absent, they will prove to be a reliable tool for scenario 

evaluation, despite considerable structural and parameter 

uncertainty.

Development of an ATLSS Vegetation 

Succession Model

Because changes in hydrology over a period of years are 

likely to result in significant changes in vegetation across the 

landscape, an ATLSS vegetation succession model (VSMod) 

has been developed. The model is designed to estimate 

changes in the distribution of vegetation in the Everglades 

resulting from changes in hydrology, fire disturbance, and 

nutrient levels. The model covers most of the natural areas of 

southern Florida, including the three water-conservation areas, 

Everglades National Park (ENP) and Big Cypress National 

Preserve (BCNP). Additionally, a few smaller surrounding 

natural areas also are included. The model excludes urban and 

agricultural areas, as well as portions of mangrove forest along 

the southern and western coasts of the Florida.

Like most of the other ATLSS models, the spatial 

resolution of VSMod is 500 x 500 m. The temporal resolution 

of the model is 1 year, and the beginning of each model year is 

synchronized with the end of the natural fire/dry season of the 

Everglades (April/May). VSMod uses a number of inputs with 

the primary ones being annual hydroperiod estimates, annual 

area burned by hot and cool fires, vegetation distribution, and 

parameters that describe the environmental tolerances of each 

input vegetation type. The vegetation distribution input provides 

two pieces of information for the succession model: the vegetation classes used and an initial condition. Currently, the ATLSS 

VSMod uses FGAP v6.6 as the vegetation input. This input uses 

a 30 x 30-m grid. It contains 78 habitat types, of which about 58 

represent natural vegetation communities. From these 58 types, 

the VSMod uses only 25. Most of the 33 types not used by the 

VSMod are outside the ATLSS study area. A few have very 

low representation within the ATLSS study area, and these types 

are lost when the FGAP data are rescaled to the 500 x 500-m 

resolution. For this reason we omitted them from the model. 

For each of the vegetation types in the vegetation map, the 

model contains parameters that represent the environmental 

tolerances of the community represented by each type. 

Parameters include:

• HPmin, HPmax (minimum and maximum suitable 

hydroperiod),

• HPdrought and HPflood (number of years of increased and 

decreased hydroperiod that constitutes a disturbance),

Figure 3. Output of ATLSS 

vegetation succession model, 

VSMod. The left-hand panel 

shows the vegetation cover 

predicted for 1990. The righthand panel indicates cells that 

have undergone change from the 

cover of the preceding year.

TROP, HAMMOCK

DECID/TROP. SWAMP FOREST

XERIC/MESIC LIVE OAK

LIVE OAK

BAY/GUM/CYPRESS

S FL SLASH PINE FOREST

MESIC/HYDRIC FOREST

SWAMP FOREST CG

CYPRESS FOREST CG

SLASHPINE WOODLAND

BRD LVD/MIXED EVERGREEN SHRU

DRY PRAIRE

CALLBERRY/PALMETTE CG

DICED, SHRUB

GRAMINCID DRY PRAIRE EC

GRAMINICID MARSH CG

CLADIUM

ELEOCHARIS

MUHLY GRASS MARSH

TYPHA

SPARSELY WOODED WET PRAIRIE C

DWARF CYPRESS SAVNNA

FORB ENERGNET MARSH

FLOATING LEAVED VE.

BURNED

1990 Change from 1989
• HPt,vt where vt is the vegetation type in a plot at time is the vegetation type in a plot at time t

(mean number of years required to transition to another 

type given a hydroperiod disturbance), and,

• Fmin, Fmax (suitable range of years since last fire used to (suitable range of years since last fire used to 

compute transition probabilities).

To show changes in vegetation type from year to year, model 

output can be represented in two-panel diagrams in which the 

map of vegetation for a particular year is beside a map showing 

the cells that have changed from the preceding year (fig. 3).

Making Remote Runs of ATLSS Models Possible

ATLSS models are designed to be used by a wide range of 

stakeholders. Most stakeholders do not have the means or desire 

to maintain and run ATLSS models on their own computer 

systems, but would like to choose hydrologic input sets for the 

ATLSS models and obtain output with a minimum amount of 

effort. To make ATLSS model results directly accessible for 

natural resource managers, a new ecosystem modeling service suite has been developed. This utilizes grid-computing 

technologies, network-based middleware access control, and 

visualization resources to remove the impediments for application by stakeholder agencies. This service suite also provides 

new tools that enhance the ability of stakeholders to carry out 

assessments based in part upon the criteria they choose. The 

goal of the suite is to assist stakeholder groups in applying both 

current and future models by: (1) incorporating new data in the 

models, (2) comparing models to data, (3) contrasting results 

from different scenarios, and (4) incorporating and contrasting 

results for two different trophic levels (such as wading bird and 

fish). Specifically, the ecosystem modeling service suite allows 

natural resource managers in southern Florida to remotely 

launch ecosystem modeling tasks on, and harvest model results 

from, a high-performance computing grid at the University of 

Tennessee with the use of minimal local resources. 

The user accesses the university computing grid through 

an HTML user interface using a web browser (fig. 4). Two 

software programs NetSolve and Internet Backplane Protocol 

(IBP) then allow models to be run remotely. NetSolve manages 

several servers at the university and allows external users to run 

models on various platforms without the need for individual 

system logins or accounts on those computers. IBP allows rapid 

access to data used in the ATLSS models. Users can specify 

which of several hydrologic files they choose to use as input to 

the models. The HTML interface allows the users flexibility in 

varying some of the ATLSS model parameters. Once ATLSS 

model output is generated, it can be stored locally by the users, 

who can then use the ATLSS Data Viewer to examine the output 

data and compare scenarios. 

For more information contact:

Donald L. DeAngelis

U.S. Geological Survey

Florida Integrated Science Center

Water and Restoration Studies

Department of Biology

University of Miami

Coral Gables, FL 33124

Phone: 305-284-1690

Fax: 305-284-3039

don_deangelis@usgs.gov

Figure 4. Flow chart of the system for producing ATLSS runs remotely.

Email

Web

Browser

Local

Storage

ATLSS

Dataviewer

Database

HTML

Interface

Control

NetSolve

Agent

NetSolve

Server(s)

IBP

Database

IBP

Database

IBP

Database