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-telemetry Tracking Tools Abiotic Conditions Models Spatially-Explicit Species Index Models Linked Cell Models Process Models Age/Size Structured Models Individual-Based Models High Resolution Hydrology High Resolution Topography Disturbance Cape Sable Seaside Sparrow Snail Kite Long-legged Wading Birds Short-legged Wading Birds White-tailed Deer Alligators Lower Trophic Level ComponentsVegetation Fish Functional Groups AlligatorsReptiles and AmphibiansWhite-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

ATLSS SESI Uncertainty Evaluation – Hydrology Effects Cape Sable Seaside Sparrow Index Values Restudy Area F2050 Alt D 13R A D W D- Dry A W- Wet - Average - Base A D W Hydrology Types

ATLSS SESI Uncertainty Evaluation – Hydrology Effects Wading Bird Index Values Restudy Area Short-legged Wading Birds F2050 Alt D 13R A D W D- Dry A W- Wet - Average - Base A D W W W AA D D Hydrology Types Restudy Area Long-legged Wading Birds

ATLSS SESI Uncertainty Evaluation – Hydrology Effects White-tailed Deer Index Values Everglades National Park Big CypressRestudy Area F2050 Alt D 13R D A D W D- Dry A W- Wet - Average - Base A D W W W W W A A A A D D D Hydrology Types

ATLSS SESI Uncertainty Evaluation – Hydrology Effects American Alligator Index Values Water Conservation Areas 3A and 3B Shark River, NE Shark River, and Taylor Sloughs Restudy Area F2050 Alt D 13R D A D W D- Dry A W- Wet - Average - Base A D W W W W W A A A A D D D Hydrology Types

ATLSS SESI Uncertainty Evaluation – Hydrology Effects Snail Kite Index Values Restudy Area F2050 Alt D 13R D A D W D- Dry A W- Wet - Average - Base A D W W W W W A A A A DD D Hydrology Types Water Conservation Areas 3A and 3B Water Conservation Areas 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