Ecology and Society Ecology and Society
E&S Home > Vol. 20, Iss. 1 > Art. 30 > Abstract Open Access Publishing 
Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

Karen M Thorne, USGS Western Ecological Research Center
Brady J. Mattsson, Department of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences, Vienna, Austria
John Takekawa, USGS Western Ecological Research Center, San Francisco Bay Estuary Field Station
Jonathan Cummings, University of Vermont
Debby Crouse, USFWS, Endangered Species Recovery Program
Giselle Block, USFWS, Inventory & Monitoring
Valary Bloom, USFWS, Sacramento Field Office
Matt Gerhart, State Coastal Conservancy
Steve Goldbeck, Bay Conservation and Development Commission
Beth Huning, San Francisco Bay Joint Venture
Christina Sloop, Blue Earth Consultants
Mendel Stewart, USFWS, Carlsbad Fish and Wildlife Office
Karen Taylor, California Department of Fish & Wildlife, Napa-Sonoma Marshes Wildlife Area
Laura Valoppi, USGS Western Ecological Research Center

DOI: http://dx.doi.org/10.5751/ES-07018-200130

Full Text: HTML   
Download Citation


Abstract

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

Key words

Bayesian decision network; climate change; expert elicitation; San Francisco Bay; sea-level rise; structured decision making; tidal marsh

Copyright © 2015 by the author(s). Published here under license by The Resilience Alliance. This article  is under a Creative Commons Attribution-NonCommercial 4.0 International License.  You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

Top
Ecology and Society. ISSN: 1708-3087