Transcript No Slide Title

Spatially-explicit species index models in application to
Everglades restoration
ATLSS and the Planning
Process for Everglades
Restoration
E. JANE COMISKEY and LOUIS J. GROSS
The Institute for Environmental Modeling,
Department of Ecology and Evolutionary Biology
University of Tennessee, Knoxville, TN 37996-1610
The ATLSS Hierarchy of Models
This poster is based in part on: “Landscape-based spatially-explicit species
index models for Everglades restoration”, J. L. Curnutt, E. J. Comiskey,
M. P. Nott and L. J. Gross. Ecological Applications 10:1849-1860(2000).
Abstract
The ATLSS (Across Trophic Level System Simulation) hierarchy of models
is designed to utilize varying levels of detail and data availability to
assess the relative impact of alternative hydrological plans on the biotic
components of South Florida. ATLSS is being used regularly in the
ongoing planning for Everglades restoration (see http://atlss.org/). A
key segment of the ATLSS hierarchy includes models that make use of
information on breeding and foraging requirements for species and how
these relate to localized habitat conditions and within-year dynamics of
hydrology. These Spatially-Explicit Species Index (SESI) models
compare the relative potential for breeding and/or foraging across the
landscape without tracking in detail the population dynamics or
behavior of individuals for these species. SESI models are viewed as
approximations which are useful in coarse evaluations of scenarios and
are an aid in interpreting more detailed ATLSS models. We describe the
construction and application of SESI models for the Cape Sable Seaside
Sparrow, the Snail Kite, Short- and Long-Legged Wading Birds, Whitetailed Deer, and Alligators. These models have been applied on a
regular basis during the Central and South Florida Comprehensive
Restudy to assess the relative effects of alternative scenarios compared
to various the base scenarios. The SESI approach offers advantages
over Habitat Suitability Index (HSI) methods as it can incorporate both
static and dynamic landscape features.
Objectives of SESI Models
The Plan includes:
Individual-Based
Models
Age/Size Structured
Models
Cape Sable
Seaside Sparrow
Snail Kite
White-tailed Deer
Wading Birds
Florida Panther
Fish Functional Groups
Alligators
Radio-telemetry
Tracking Tools
Reptiles and Amphibians
Linked Cell
Models
Lower Trophic Level Components
Process Models
Spatially-Explicit
Species Index Models
Cape Sable
Seaside Sparrow
Long-legged
Wading Birds
Short-legged
Wading Birds
High Resolution Topography
Alligators
High Resolution Hydrology
Disturbance
© TIEM / University of Tennessee 1999
Shown in red are the ATLSS components which are used for SESI Models
Constructing SESI Models
Habitat Suitability Index (HSI) models have been developed
for many wildlife species, with the objective of evaluating
the potential effects of management decisions which modify
habitat conditions for these species. SESI models differ
from traditional HSI models in that they:
(1) have a temporal component and, thus, incorporate both
static and dynamic landscape features;
•Allow for the input of static and dynamic landscape features
importable from a geographic information system (GIS) or spatial
models of physical
(2) are based on a 'landscape structure' which, once
established, can be used to model the responses of any
species in the system; and
•Provide a spatially-explicit representation of the relative impact of
different scenarios
(3) can provide a relatively easy means of comparing species
responses to more complex ATLSS models, including process
models, size-structured population models, and individualbased models.
•Provide a relatively easy method to compare the spatial impacts
of different scenarios on different species, compared to more
complex demographic and individual-based models
•Provide a method to rapidly assess alternative local indices, in
conjunction with individuals highly familiar with the species being
modeled, to speed model development
•Provide a method to compare simple assessments with those
derived from more complex models as part of a multimodeling
approach to regional management
•Provide a method for assessment of alternative monitoring
schemes by determining the relative variability of model
components in different spatial regions
Inputs to SESI Models
•Government agencies and programs provide spatial
representations of the landscape including vegetation, land use,
and elevation
•Field researchers provide historical observations of breeding,
foraging behavior, and abundances as affected by hydrology
•Hydrology planners provide scenarios for future ponding over
31 years at a 2-mile by 2-mile resolution
•Reducing the average of 1.7 billion gallons of fresh water discharged
every day to the ocean
White-tailed Deer
Snail Kite
Abiotic Conditions
Models
•Reconnecting over 80 percent of the remaining Everglades by
removing over 240 miles of internal levees and canals.
•Additional land purchases of 47,000 acres as an addition to
Everglades National Park
Vegetation
•Provide a method to assess the impact of management scenarios
on species’ habitat
•Base the models around a landscape structure that can be
readily reused for other applications and regions
In 1992 Congress authorized the Comprehensive Review Study
(Restudy) of the Central and Southern Florida Project to
develop modifications to the current drainage system in order
to restore the Everglades and Florida Bay ecosystems while
providing for the other water-related needs of the region.
The resulting plan was submitted to Congress in July, 1999.
Step 1: Provide a 'landscape structure' using a GIS. This
structure includes both static information (e.g., surface
elevations, aspect, soil type, vegetation type and
structure, physical structures) and dynamic information
(e.g., changing water levels, fire, vegetation dynamics).
The landscape is divided into equal-sized spatial cells,
each with a suite of values that correspond to the
parameters included in the model. The inclusion of static
and dynamic information results in an interdependence of the
values assigned to each cell.
Step 2: Develop a local index that describes particular
aspects of the species' ecology affected by the landscape.
When considering the effects of habitat management on only
one species, breeding habitat - how much of the landscape
will be suitable for successful breeding during the breeding
season - is the most direct statistic for comparison. If
the species has special breeding requirements that can not
easily be incorporated into the structural landscape or if
breeding is not spatially limited, another aspect of the
species biology could form the basis of the index. One that
we have used is foraging potential, in which site-specific
information can be used to assess foraging success
dynamically.
How ATLSS was used during the Restudy:
1. A hydrologic alternative plan for the next 31 years was
produced by the South Florida Water Management District (SFWMD)
using their management model, based upon historical rainfall
and assuming changes in control structures that occur all at once.
2. Major hydrologic results from the plan were posted on the Web
and detailed results were provided to ATLSS staff.
3. The ATLSS staff ran the collection of ecological models for
the alternative, compared to the base plan (F2050) which
assumed only control structure changes currently planned to
be implemented by 2050. Within a week, results from these were
bound and shipped overnight to 14 agency representatives, as
well as being placed on the Web.
4. A few days later the Alternative Evaluation Team, consisting
of representatives from interested agencies, met to evaluate
the alternative and make suggestions for additional changes,
based upon ATLSS results, hydrologic performance measures,
and their agencies mandate.
5. Suggestions from the AET were sent to the Alternative
Design Team, consisting mostly of hydrologists, who then
revised the plan and ran a new hydrologic scenario.
6. Go back to step 2 and repeat many times.
There were over 15 major iterations of the above, each
iteration taking approximately 3 weeks, from September 1997
to January 1999. Additional plans taking account of phasing of
structure changes are still ongoing.
ATLSS Models are ALWAYS used in a relative
assessment framework. We do not claim these
are predictive models. Rather, they allow a
relative ranking of the effects of alternative
scenarios on the various species modeled.
SESI Output Comparison Maps:
Providing decision makers with information as to the relative
impact of hydrology scenarios upon selected species.
Cape Sable Seaside Sparrow
Breeding Potential
Snail Kite Index
Display two hydrology scenarios
(left and right maps) and their
differences (middle map).
Difference map uses colors
color-blind individuals can interpret
Short Legged Wading Bird
Foraging Condition
Data are mapped into twenty classes.
Long Legged Wading Bird
Foraging Condition
Presented maps all contain the mean
of 30 years data.
Used to compare two
hydrology scenarios.
Presents large data sets in an easy
to understand format.
White-tailed Deer
Breeding Potential
Indicate general spatial and
temporal trends
American Alligator
Breeding Potential
Permits the knowledge codified
within models to be easily
communicated to management.
Everglades SESI Models
Cape Sable Seaside Sparrow SESI Model
Focuses on breeding rules, all based on the results of intensive
field studies. Rules describe how the dynamics of hydrology
affect the duration and spatial extent of the annual dry season
during which the water level in any cell remains below the nesting
threshold level. Then estimates are made of the potential number
of breeding cycles in cells with appropriate levels of preferred
habitat.
Snail Kite Model
Estimates appropriate foraging conditions during the kite breeding
season for these raptors which are obligate predators of the apple
snail. The requirements for appropriate foraging sites include:
• having the potential for a substantial population of apple
snails
• having surface water present
• having surface water depths less than certain depths (or else
the kites cannot locate and catch snails).
Wading Bird Foraging Conditions Index Model
Computes a Foraging Conditions Index (FCI) based on how hydrologic
factors affect the concentration and availability of food
resources during the breeding season. The FCI is a composite index
of spatial and temporal patterns. It calculates the FCI for two
different types of wading birds:
• a "long-legged forager" type with a feeding depth range of 5-35
cm and a long nesting cycle (during which a major water level
reversal would cause nesting failure and decrease the index value
to zero);
• a "short-legged forager" type with a feeding depth range of 0-20
cm and a shorter nesting cycle (with potentially multiple
opportunities for nesting during a single dry season).
White-tailed
Computes how
availability
sites during
Deer Model
hydrologic factors affect the production and
of food resources and the availability of dry bedding
the breeding season.
Some References on ATLSS
ATLSS Home Page http://atlss.org/
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, M. A. Huston, W. F. Wolff, D. M. Fleming,
E. J. Comiskey, S. M. Sylvester. 1998. Landscape Modeling for Everglades
Ecosystem Restoration. Ecosystems 1:64-75.
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.
Duke-Sylvester, S. and L. J. Gross. 2000. Integrating spatial data into an
agent-based modeling system: ideas and lessons from the development
of the Across Trophic Level System Simulation (ATLSS). To appear in:
Agent-based models and GIS, R. Gimblett (ed.), Oxford University Press.
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.