Models for the masses: bringing
computational resources for addressing
complex ecological problems to stakeholders
Louis J. Gross, Eric Carr and Mark Palmer
The Institute for Environmental Modeling
Departments of Ecology and Evolutionary Biology
and Mathematics
University of Tennessee
ATLSS.org
Policy and management at regional
spatial extent requires:
• Ecological models linked to physical process data
and models
• Spatial environmental data sets that may be at a
variety of spatial and temporal resolutions
• Decision support tools based upon the best
available science that allow stakeholders to assess
the impacts on natural systems of alternative
future actions
• Capability for different stakeholders to establish
their own decision criteria
• Methods to link models and data to monitoring
protocols
An example: ATLSS - Across Trophic
Level System Simulation for Everglades
restoration planning
• Provides a general methodology for regional
assessment of natural systems by coupling
physical and biotic processes in space and time
using a mixture of modeling approaches.
• Utilizes the best available science and intuition of
many biologists with extensive field experience to
construct models for particular system
components and link these at appropriate spatial
and temporal resolutions
ATLSS Objectives (Con’d)
• Provide a method to compare the relative impacts
of alternative management of the region on the
natural systems, so different stakeholders can
focus on sub-regions, species, or conditions of
particular interest to them.
• Ensure that the structure of the multimodel is
extensible so that as new models, data and
monitoring information becomes available, it may
be efficiently utilized.
Computational ecology offers opportunities to
develop spatially-explicit models at a variety of levels
of complexity to inform public policy on matters such
as regional water management, reserve design,
harvesting, biocontrol, and monitoring schemes.
Such models (which may be discrete or continuous
dynamical systems, individual-based, or mixtures of
these) can produce enormous amounts of spatiotemporal data, and typically include uncertainty in
inputs, model structure, and parameterization.
How can we best utilize models to inform public
policy and educate stakeholders?
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
Vegetation
Process Models
Spatially-Explicit
Species Index Models
Cape Sable
Seaside Sparrow
Long-legged
Wading Birds
Short-legged
Wading Birds
Alligators
Snail Kite
Abiotic Conditions
Models
High Resolution Topography
White-tailed Deer
High Resolution Hydrology
Disturbance
© TIEM / University of Tennessee 1999
What is computationally
challenging in this?
• Space-time linkages
• GIS very limited at dynamic modeling
• Different components operate on different scales
(resolution required differs between components)
• Model data can be huge
• Models are complex
• Large state variable dynamical systems
• Large numbers of interconnected agents
• Models are not independent - multimodeling
Difficulties faced in providing access
to complex computational models
• Agencies do not have appropriate in-house
computational facilities or support
• Agency staff wish to focus their time on
analysis of appropriate information for policy
input, not on carrying out simulations
• It is inefficient to have each agency carry out
simulations, yet each needs the capability to
obtain simulations tailored to their needs.
Agencies involved in Everglades restoration:
U.S. Army Corps of Engineers
Environmental Protection Agency
National Park Service
National Marine Fisheries Service
Natural Resources Conservation Service
U.S. Fish and Wildlife Service
Florida Department of Agriculture and Consumer
Services
Florida Department of Environmental Protection
Florida Game and Fresh Water Fish Commission
South Florida Water Management District
Miccosukee Tribe
Seminole Tribe
plus input from numerous NGO's and individuals.
What have we done to address these needs?
• Developed a Model Interface to provide
stakeholder agencies with capability to run ATLSS
models
• Utilize grid-computing to make computational
resources of Univ. of Tenn. available to approved
users
• Underlying methods are transparent to the user
• Users are given options as to models to run,
certain model parameters to be set, and scenarios
to utilize
• Provides output that users import into the ATLSS
Dataviewer on their desktop to carry out their own
assessments
SESI Output for Long-Legged Wading Birds in N. Taylor Slough: For 1993
Multiple Hardware Requirements
•Purchase Cost
•Systems Administration
•User Knowledge Base
•Storage Needs
LINUX
PC
SOLARIS
UNIX
Software Needs
•Software License
•Installation
•Compiler
•Code Control Issues
MATLAB
C++, C,
Fortran
Proprietary
Licensed
Software
ATLSS MODELS
• Solaris(primarily), PC, MATLAB based
models.
• Long run times from 30min to 36+ hours
dependent on model and parameters.
• Output data results ranging from 10 MB to
14 GB.
• Single as well as SMP and cluster based
models.
Potential ATLSS Model Users
• Primarily Windows PC based.
• Multiple locations across Florida, US, and
Internationally.
• Varying degrees of computer infrastructure
and Sys. Admin. Support.
• Desire to run models and not maintain
systems.
ATLSS-NetSolve-IBP
A WWW implementation framework
for ATLSS models under NetSolve
with IBP file management.
Netsolve
• Single Agent manages Multiple Servers on
differing platforms.
• Different servers can have different versions
that run same function (SMP, Cluster, linux).
• Allows access to run models on computers
without the need for individual system
logins or accounts.
• User has no access to actual server hardware,
model code, or datafiles.
IBP: Internet Backplane Protocol
• Data Storage Utility.
• Data accessed through an ASCII key, called an
EXNODE.
• ExNode completely provides access information for
the Data.
• Able to perform multithread file transfer for very
quick storage of large files.
• HTML, C library, and other access methods.
IBP Data Storage
• The Data File (ascii or binary)
is divided into pieces.
• Each piece is stored on an IBP
server.
• The ExNode holds location
and order information for each
piece.
• The ExNode provides the
information needed to retrieve
and reconstitute the Data File.
DATA FLOW
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
Model/Func
Infiles
Exnode Infiles
Outfiles
Exnode Outfiles
HTML User Interface
•
•
•
•
Single access point for all models on all servers.
User Login control.
Allows parameter designation.
Integrates with Database for access to previous
performed runs and inclusion of new runs.
• Provides email notification for set and forget
model runs.
Interface Entrance Login
HTML Model List
Alligator Model Page
Take-Home Messages
• Resource management at regional extent requires spatiallyexplicit assessments which allow different stakeholders to
evaluate alternative scenarios based upon criteria of their
choice
• Multimodeling, linking models at differing spatial extents,
resolutions and levels of detail, provides flexibility in dealing
with the variety of physical and ecological models and data
available
• Grid-based computational technology gets models to users,
allowing stakeholders to modify particular model
assumptions; carry out simulations focused on
species/functional groups of particular interest to them; and
assess the impacts of altered hydrologic plans altered based
upon their own assumptions
Funding support for ATLSS and the Gridcomputing effort comes from:
The US Geological Survey through a cooperative agreement
with the Cooperative Ecosystems Studies Unit at the
University of Tennessee.
The National Science Foundation through ITR award DEB0219269 to The Institute for Environmental Modeling of the
University of Tennessee and award EIA-9972889 to the
Computer Science Department.
www.tiem.utk.edu/gem/