U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2004-3112

October 2004

Projecting Future Population Dynamics of the Florida 

Snail Kite in Relation to Hydrology Using EVERKITE

Printed on recycled paper

EVERKITE is a spatially explicit, individual-based model 

designed as a management scenario evaluation tool for the 

endangered Florida snail kite (Rostrhamus sociabilis). Specifically, the model is designed to project how the movement, 

reproductive success, and mortality of snail kite populations 

respond to different scenarios of hydrologic change in the major 

wetlands of southern and central Florida. EVERKITE has been 

developed at the Netherlands Institute of Ecology, Centre for 

Limnology, under the U.S. Geological Survey (USGS) Across 

Trophic Level System Simulation (ATLSS) Program.

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. The hydrology of these wetlands 

directly affects snail kite demography, and an individual-based 

model has been developed to project population change under 

proposed future hydrologic scenarios. A large number of empirical studies have been done on the Florida snail kite, providing 

basic information for the predictive model. These studies also 

provide the correlative relations between the hydrology of the 

system and specific aspects of snail kite life history and 

behavior. These relations form the building blocks of any 

hydrologically driven population dynamics model.

Two alternatives are available for modeling the life history of 

a population and involve either: (1) a system-wide deterministic matrix model, or (2) a spatially explicit, stochastic 

individual-based model. Rather than choosing between 

these two approaches, they were combined and implemented in a spatially explicit matrix model that can be 

run both in a deterministic and in a stochastic mode. 

This tool was used to address the challenge of making 

reliable projections of future population development 

of the snail kite under various hydrologic scenarios.

The reliability of the model can be determined 

by comparing model-predicted demographic patterns (using historical hydrologic data) with observed 

demographic patterns during recent decades. The 

annual count of snail kites that was performed during 

1969-95 provides the type of data needed for such a 

comparison. This dataset has been criticized, however, 

for being biased in several ways. Without an unbiased 

dataset on kite numbers, it is not feasible to check the 

reliability of the kite model against the historical situation; this potentially limits the utility of the model in 

discriminating among hydrologic scenarios.

To circumvent this problem, we adopted the 

following research strategy. Rather than using 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 then developed a number of 

parameterizations, representing the uncertainty in the model 

parameters. We will evaluate each hydrologic scenario for each 

model version for each para-meterization, focusing on the longterm population growth rate (λ) as the main response parameter. This analysis will enable us to evaluate whether structural 

uncertainty and parameter uncertainty interact with differences 

due to hydrologic scenarios. If these interactions are absent, 

they will prove to be a reliable tool for scenario evaluation, 

despite considerable structural and parameter uncertainty.

How Does EVERKITE Work?

The deterministic and stochastic versions of EVERKITE 

use the same basic mechanisms to simulate snail kite population dynamics in southern and central Florida. The model runs 

on a weekly time step, and each of the 15 major wetlands of 

southern and central Florida is represented. Additionally, there 

is a spatial unit called “peripheral habitat,” which represents 

the small pockets of kite habitat scattered over southern and 

central Florida. These small pockets are assumed to form an 

important refuge for kites during system-wide droughts (Mooij 

and others, 2002), and therefore, to be an essential component 

of the model. The fate of each kite for each week is evaluated 

using the following schedule (fig 1). First, each kite is checked 

to determine whether it makes the transition to a new life stage 

Figure 1. Flow chart of the logic applied in EVERKITE to each kite for each week.

Week in the life of a Kite New life stage?

Breeding?

Nest initiation? Nest failure? Nest desertion? Nest success?

Change life stage

Lay eggs Release fledglings

Breeding Not breeding Breeding

Move?

Select wetland

Die?

Go to next week Kill bird

Yes

Yes

Yes Yes Yes

No

No

Yes No

No No No

Yes

No

No Yes

Deterministic if/then

Deterministic process

Stochastic if/then

Stochastic process
during a given week. Second, the current breeding status is 

checked, and a new breeding status is assigned if necessary. The 

probability of a change in breeding status is determined by the 

hydrologic conditions in the wetland where the kite is located. 

Third, nonbreeding birds are given the opportunity to move to 

another wetland. The probability of movement and probability 

of choosing a given wetland are both determined by the hydrologic conditions. Finally, each kite has a probability of dying 

in a given week, depending on its life stage and the hydrologic 

conditions.

How Is EVERKITE Implemented?

EVERKITE simulations have been implemented in C++ as 

an application of the OSIRIS modeling framework. An interface 

to input the model with the relevant parameters, initial conditions, and hydrologic data has been developed both in Microsoft 

EXCEL and ACCESS (fig. 2). Numerical output produced by 

the model can be retrieved using the same interface. Graphical 

output has to be created by the end users from the numerical 

output, using the graphical capacities of EXCEL, ACCESS, or 

any other program of choice.

What Output Does EVERKITE Produce?

EVERKITE produces a number of kites in each wetland for 

each week over the duration of the hydrologic scenario. A good 

way to characterize a given scenario in a single number is to 

calculate the long-term population growth rate (λ) for the whole 

period covered by the scenario. An additional advantage of 

using the long-term population growth rate rather than absolute 

numbers is that the latter depends on an estimate of the initial 

population size, which is likely to be fairly unreliable. 

For scientific use, further breakdown of the number by life 

stage, breeding status, and wetland is possible both in the stochastic and the deterministic version of the model. EVERKITE 

has been run for each of the scenarios that were considered 

during the Restudy, and its output has been provided to the 

agencies involved. It also will be used to evaluate new Comprehensive Everglades Restoration Plan (CERP) scenarios.

Reference

Mooij, W.M., Bennett, R.E., Kitchens, W.M., and DeAngelis, 

D.L., 2002, Exploring the effect of drought extent and interval on the Florida Snail Kite: Interplay between spatial and 

temporal scales: Ecological Modelling, v. 149, p. 25-39.

For more information contact:

Figure 2. Main screen of the Microsoft Access interface to EVERKITE.

Wolf M. Mooij 

Netherlands Institute 

 of Ecology 

Centre for Limnology

Rijksstraatweg 6 

3631 AC Nieuwersluis

The Netherlands

w.mooig@nioo.knaw.nl

Donald L. DeAngelis

U.S. Geological Survey

Biological Resources 

 Division

University of Miami

Coral Gables, FL 33124

ddeangelis@umiami.ir.miami.edu