Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

[1]  Michael W. Berry,et al.  Computational Science for Natural Resource Management , 2007, Computing in Science & Engineering.

[2]  Amy W. Ando,et al.  On the Use of Demographic Models of Population Viability in Endangered Species Management , 1998 .

[3]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[4]  Michael D Mastrandrea,et al.  Dynamics of climate and ecosystem coupling: abrupt changes and multiple equilibria. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  L. Harris,et al.  Everglades: The Ecosystem and its Restoration. , 1995 .

[6]  Stephen R. Carpenter,et al.  UNCERTAINTY AND THE MANAGEMENT OF MULTISTATE ECOSYSTEMS: AN APPARENTLY RATIONAL ROUTE TO COLLAPSE , 2003 .

[7]  James S. Clark,et al.  FECUNDITY OF TREES AND THE COLONIZATION–COMPETITION HYPOTHESIS , 2004 .

[8]  Karin Frank,et al.  RANKING METAPOPULATION EXTINCTION RISK: FROM PATTERNS IN DATA TO CONSERVATION MANAGEMENT DECISIONS , 2003 .

[9]  Lutz E. Schlange Scenarios: The art of strategic conversation , 1997 .

[10]  A. Hastings,et al.  When are no-take zones an economically optimal fishery management strategy? , 2006, Ecological applications : a publication of the Ecological Society of America.

[11]  Werner A. Kurz,et al.  Habitat patterns in forested landscapes: management practices and the uncertainty associated with natural disturbances , 2000 .

[12]  E. Menges,et al.  Population viability analyses in plants: challenges and opportunities. , 2000, Trends in ecology & evolution.

[13]  L. Gross,et al.  LANDSCAPE‐BASED SPATIALLY EXPLICIT SPECIES INDEX MODELS FOR EVERGLADES RESTORATION , 2000 .

[14]  B. Taylor,et al.  The Reliability of Using Population Viability Analysis for Risk Classification of Species , 1995 .

[15]  Prabhu Pingali,et al.  Why global scenarios need ecology , 2003 .

[16]  Michael A. McCarthy,et al.  Extinction dynamics of the helmeted honeyeater: effects of demography, stochasticity, inbreeding and spatial structure , 1996 .

[17]  Michael J. Conroy,et al.  Parameter Estimation, Reliability, and Model Improvement for Spatially Explicit Models of Animal Populations , 1995 .

[18]  Octavio Aburto-Oropeza,et al.  A General Model for Designing Networks of Marine Reserves , 2002, Science.

[19]  Michael J. Wisdom,et al.  Reliability of Conservation Actions Based on Elasticity Analysis of Matrix Models , 1999 .

[20]  Helen M. Regan,et al.  Comparing predictions of extinction risk using models and subjective judgement , 2004 .

[21]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[22]  Michael W. Berry,et al.  A grid service module for natural-resource managers , 2005, IEEE Internet Computing.

[23]  Anthony W. King,et al.  Spatial Uncertainty and Ecological Models , 2004, Ecosystems.

[24]  M. Groom,et al.  The Analysis of Population Persistence: An Outlook on the Practice of Viability Analysis , 1998 .

[25]  S. Ellner,et al.  USING PVA FOR MANAGEMENT DESPITE UNCERTAINTY: EFFECTS OF HABITAT, HATCHERIES, AND HARVEST ON SALMON , 2003 .

[26]  Peter W J Baxter,et al.  Optimal eradication: when to stop looking for an invasive plant. , 2006, Ecology letters.

[27]  L. Pearlstine,et al.  Assessing state-wide biodiversity in the Florida Gap analysis project. , 2002, Journal of environmental management.

[28]  E. J. Comiskey,et al.  Landscape Modeling for Everglades Ecosystem Restoration , 1998, Ecosystems.

[29]  B. Danielson,et al.  Spatially Explicit Population Models: Current Forms and Future Uses , 1995 .

[30]  René A. Salinas,et al.  A dynamic landscape model for fish in the Everglades and its application to restoration , 2000 .