Transcript ATLSS and Uncertainty: relativity and spatially-explicit ecological models as methods to aid management planning in Everglades restoration Louis J.

ATLSS and Uncertainty: relativity and
spatially-explicit ecological models as
methods to aid management planning in
Everglades restoration
Louis J. Gross, Mark Palmer and Jane Comiskey
The Institute for Environmental Modeling
Departments of Ecology and Evolutionary
Biology and Mathematics
University of Tennessee
ATLSS.org
Acknowledgements
• USGS
• National Science Foundation
• UT Center for Information Technology
Research
• UT Scalable Intra-Campus Network Grid
Dealing with trade-offs
Two general approaches to multicriteria optimization:
1. Define a common currency for all criteria, e.g.
economic. An example is the Wading Bird Habitat
Value Assessment Model and the General
Ecological Risk Assessment Model (see 2002
Everglades Consolidated Report, Chap. 6)
2. Maintain a variety of measurement units, without
forcing any single weighting between alternative
criteria, and allow different stakeholders to
determine their own summaries, possibly assisted
by a decision support tool (the ATLSS approach,
aided by the ATLSS DataViewer).
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
Snail Kite
Abiotic Conditions
Models
High Resolution Topography High Resolution Hydrology
White-tailed Deer
Alligators
Disturbance
© TIEM / University of Tennessee 1999
Spatially-Explicit Species Index (SESI)
Models
These are designed as extensions of habitat
suitability index models, to provide yearly
assessments of the effects of within and
between year hydrology variation on basic
requirements for foraging and breeding in a
spatially-explicit manner. They allow
comparisons of alternative scenarios, and
allow different stakeholders to focus on their
own criteria.
Uncertainties and Relative Assessment
• Uncertainties include:
– Lack of knowledge of future weather
– Imperfect understanding and representation of major
processes in the physical and biotic models
– Imprecise measurement of important physical and
biological parameters used in the equations or as
initial conditions.
• We do not claim that ATLSS can accurately
predict future changes in the system, but rather
that a relative comparison of two alternative
scenarios provides an accurate assessment of the
relative impacts of the scenarios.
How we reduce the impact of
uncertainties on regional planning?
• Adaptive management
• Extensive sensitivity analysis of models and
their robustness to modifications
• Use Relative Assessment – do not claim that
results from any one model are accurate
predictors of the future changes in the system
but rather that a relative comparison of two or
more alternative scenarios provides an accurate
assessment of the relative impacts of different
scenarios
Uncertainties and Relative Assessment
• Our objective therefore is to provide a means for
public choice of the relative ranking of
alternative scenarios.
• The rationale is that when uncertainties do not
interact differentially with changes in scenarios,
then errors should propagate similarly in model
runs on different scenarios.
• This methodology is testable by varying
uncertain components in the same way in two
scenarios and seeing if the ranking is altered.
Hydrologic uncertainty alternatives
As a proxy for uncertainty of future weather, develop scenarios
by rearranging water conditions over 30 years
• Two Base plans – F2050 and AltD13R
• Wet – choose 5 wettest years of the 30 year Base
plan, reorder them randomly, repeat 6 times to
produce a 30 year plan, then repeat 28 times
• Dry - choose 5 driest years of the 30 year Base
plan, reorder them randomly, repeat 6 times to
produce a 30 year plan, then repeat 28 times
• Average - choose 5 years closest the average of
the 30 year Base plan, reorder them randomly,
repeat 6 times to produce a 30 year plan, then
repeat 28 times
ATLSS Restudy Area Broken Down by Sub-regions
ATLSS SESI Uncertainty Evaluation – Hydrology Effects
Cape Sable Seaside Sparrow Index Values
Restudy Area
0.04
0.03
D
D
Hydrology
Types
A
0.02
A
D - Dry
W - Wet
A - Average
- Base
W
0.01
W
0.0
F2050
Alt D 13R
ATLSS SESI Uncertainty Evaluation – Hydrology Effects
Wading Bird Index Values
Restudy Area
Short-legged Wading Birds
Restudy Area
Long-legged Wading Birds
0.2
0.15
A
A
0.1
D
A
A
W
W
W
D
D
W
Hydrology
Types
D - Dry
W - Wet
A - Average
- Base
D
0.05
0.0
F2050
Alt D 13R
F2050
Alt D 13R
ATLSS SESI Uncertainty Evaluation – Hydrology Effects
White-tailed Deer Index Values
Restudy Area
Everglades
National Park
Big Cypress
0.5
0.4
D
D
A
D
A
W
0.3
A
D
A
W
W
D
D
A
A
W
W
0.2
D - Dry
W - Wet
W
A - Average
- Base
0.1
0.0
F2050
Alt D 13R
Hydrology
Types
F2050
Alt D 13R
F2050
Alt D 13R
ATLSS SESI Uncertainty Evaluation – Hydrology Effects
American Alligator Index Values
Restudy Area
Water Conservation Shark River, NE Shark
Areas 3A and 3B River, and Taylor Sloughs
1.0
0.8
W
W
A
A
0.6
D
D
W
W
A
D
0.4
W
A
D
A
D - Dry
D
W
A
- Base
D
0.0
Alt D 13R
W - Wet
A - Average
0.2
F2050
Hydrology
Types
F2050
Alt D 13R
F2050
Alt D 13R
ATLSS SESI Uncertainty Evaluation – Hydrology Effects
Snail Kite Index Values
Restudy Area
Water Conservation
Areas 3A and 3B
Water Conservation
Areas 1, 2A, and 2B
0.25
0.2
Hydrology
Types
A
A
0.15
A
D - Dry
W - Wet
W
0.1
A - Average
- Base
W
A
0.05
0.0
A
A
W
W
D
D
F2050
Alt D 13R
D
F2050
D
Alt D 13R
W
W
D
D
F2050
Alt D 13R
Take-Home Messages
• Resource management at regional extent requires
spatially-explicit assessments which allow different
stakeholders to rank alternative scenarios based
upon criteria of their choice
• Modelers can account for uncertainty and maintain
realism by comparing rankings of alternative
scenarios under different assumptions about
uncertain components
• Spatial averaging can modify rankings of
alternatives so stakeholders comparisons must
account for the spatial scale of interest to that
stakeholder