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).

Radio-telemetryTracking Tools

Abiotic Conditions Models

Spatially-Explicit Species Index Models

Linked Cell Models

Process Models

Age/Size Structured Models

Individual-Based Models

High Resolution HydrologyHigh Resolution Topography Disturbance

Cape Sable Seaside Sparrow

Snail Kite

Long-legged Wading Birds

Short-legged Wading Birds

White-tailed Deer

Alligators

Lower Trophic Level Components Vegetation

Fish Functional Groups Alligators Reptiles and Amphibians

White-tailed Deer

Florida Panther

Snail Kite

Wading Birds

© TIEM / University of Tennessee 1999

Cape Sable Seaside Sparrow

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

• 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

As a proxy for uncertainty of future weather, develop scenarios by rearranging water conditions over 30 years

ATLSS Restudy Area Broken Down by Sub-regions

0.0

0.01

0.02

0.03

0.04

ATLSS SESI Uncertainty Evaluation – Hydrology EffectsCape Sable Seaside Sparrow Index Values

Restudy Area

F2050 Alt D 13R

A

D

W

D - Dry

A

W - Wet

- Average

- Base

A

D

W

HydrologyTypes

0.0

0.05

0.1

0.15

0.2

ATLSS SESI Uncertainty Evaluation – Hydrology EffectsWading Bird Index Values

Restudy AreaShort-legged Wading Birds

F2050 F2050Alt D 13R Alt D 13R

A

DW D - Dry

A

W - Wet

- Average

- Base

AD

W

WW

AA

D

D

HydrologyTypes

Restudy AreaLong-legged Wading Birds

0.0

0.1

0.2

0.3

0.4

0.5

ATLSS SESI Uncertainty Evaluation – Hydrology EffectsWhite-tailed Deer Index Values

EvergladesNational Park Big CypressRestudy Area

F2050 F2050 F2050Alt D 13R Alt D 13R Alt D 13R

D

A

D

W

D - Dry

A

W - Wet

- Average

- BaseA

D

W

WW

W

W

AA

A

A

DDD

HydrologyTypes

0.0

0.2

0.4

0.6

0.8

1.0

ATLSS SESI Uncertainty Evaluation – Hydrology EffectsAmerican Alligator Index Values

Water ConservationAreas 3A and 3B

Shark River, NE SharkRiver, and Taylor SloughsRestudy Area

F2050 F2050 F2050Alt D 13R Alt D 13R Alt D 13R

D

AD

W

D - Dry

A

W - Wet

- Average

- BaseA

D

W

W

W

WW

A

A

A

A

D

DD

HydrologyTypes

0.0

0.05

0.1

0.15

0.2

0.25

ATLSS SESI Uncertainty Evaluation – Hydrology EffectsSnail Kite Index Values

Restudy Area

F2050 F2050 F2050Alt D 13R Alt D 13R Alt D 13R

D

A

D

W

D - Dry

A

W - Wet

- Average

- Base

A

DW

WW

W

W

A

A

A

A

DDD

HydrologyTypes

Water ConservationAreas 3A and 3B

Water ConservationAreas 1, 2A, and 2B

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