Using expert judgment to estimate marine ecosystem vulnerability in the California Current.

As resource management and conservation efforts move toward multi-sector, ecosystem-based approaches, we need methods for comparing the varying responses of ecosystems to the impacts of human activities in order to prioritize management efforts, allocate limited resources, and understand cumulative effects. Given the number and variety of human activities affecting ecosystems, relatively few empirical studies are adequately comprehensive to inform these decisions. Consequently, management often turns to expert judgment for information. Drawing on methods from decision science, we offer a method for eliciting expert judgment to (1) quantitatively estimate the relative vulnerability of ecosystems to stressors, (2) help prioritize the management of stressors across multiple ecosystems, (3) evaluate how experts give weight to different criteria to characterize vulnerability of ecosystems to anthropogenic stressors, and (4) identify key knowledge gaps. We applied this method to the California Current region in order to evaluate the relative vulnerability of 19 marine ecosystems to 53 stressors associated with human activities, based on surveys from 107 experts. When judging the relative vulnerability of ecosystems to stressors, we found that experts primarily considered two criteria: the ecosystem's resistance to the stressor and the number of species or trophic levels affected. Four intertidal ecosystems (mudflat, beach, salt marsh, and rocky intertidal) were judged most vulnerable to the suite of human activities evaluated here. The highest vulnerability rankings for coastal ecosystems were invasive species, ocean acidification, sea temperature change, sea level rise, and habitat alteration from coastal engineering, while offshore ecosystems were assessed to be most vulnerable to ocean acidification, demersal destructive fishing, and shipwrecks. These results provide a quantitative, transparent, and repeatable assessment of relative vulnerability across ecosystems to any ongoing or emerging human activity. Combining these results with data on the spatial distribution and intensity of human activities provides a systematic foundation for ecosystem-based management.

[1]  Rebecca G. Martone,et al.  Mapping cumulative human impacts to California Current marine ecosystems , 2009 .

[2]  I. Côté,et al.  Quantifying the evidence for ecological synergies. , 2008, Ecology letters.

[3]  Benjamin S Halpern,et al.  Interactive and cumulative effects of multiple human stressors in marine systems. , 2008, Ecology letters.

[4]  Carrie V. Kappel,et al.  A Global Map of Human Impact on Marine Ecosystems , 2008, Science.

[5]  Karen L. McLeod,et al.  Managing for cumulative impacts in ecosystem-based management through ocean zoning , 2008 .

[6]  Roger M. Cooke,et al.  Modeling stakeholder preferences with probabilistic inversion , 2008 .

[7]  Carrie V. Kappel,et al.  Evaluating and Ranking the Vulnerability of Global Marine Ecosystems to Anthropogenic Threats , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[8]  J. Carlton The Light and Smith manual : intertidal invertebrates from central California to Oregon , 2007 .

[9]  E. Barbier,et al.  Impacts of Biodiversity Loss on Ocean Ecosystem Services , 2006, Science.

[10]  Baruch Fischhoff,et al.  Analyzing disaster risks and plans: An avian flu example , 2006 .

[11]  L. Crowder,et al.  Resolving Mismatches in U.S. Ocean Governance , 2006, Science.

[12]  Roger M. Cooke,et al.  Uncertainty Analysis with High Dimensional Dependence Modelling , 2006 .

[13]  Roger M. Cooke,et al.  Techniques for generic probabilistic inversion , 2006, Comput. Stat. Data Anal..

[14]  G. De’ath,et al.  Establishing Representative No‐Take Areas in the Great Barrier Reef: Large‐Scale Implementation of Theory on Marine Protected Areas , 2005 .

[15]  Baruch Fischhoff,et al.  Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[16]  Baruch Fischhoff,et al.  Cognitive Processes in Stated Preference Methods , 2005 .

[17]  R. Leemans,et al.  A Multidisciplinary multi-scale framework for assessing vulnerability to global change , 2004 .

[18]  R. Cooke,et al.  Expert judgement elicitation for risk assessments of critical infrastructures , 2004 .

[19]  W. Peterson,et al.  An overview of interactions among oceanography, marine ecosystems, climatic and human disruptions along the eastern margins of the Pacific Ocean , 2004 .

[20]  B. Worm,et al.  Rapid worldwide depletion of predatory fish communities , 2003, Nature.

[21]  G. Ruiz,et al.  Invasive species: vectors and management strategies. , 2003 .

[22]  Nicholas J. Bax,et al.  The Control of Biological Invasions in the World's Oceans , 2001 .

[23]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[24]  M. Morgan,et al.  Categorizing Risks for Risk Ranking , 2000, Risk analysis : an official publication of the Society for Risk Analysis.

[25]  D. Wilcove,et al.  QUANTIFYING THREATS TO IMPERILED SPECIES IN THE UNITED STATES , 1998 .

[26]  D. Olson,et al.  The Global 200: A Representation Approach to Conserving the Earth’s Most Biologically Valuable Ecoregions , 1998 .

[27]  D. Pauly,et al.  Fishing down marine food webs , 1998, Science.

[28]  H. Mooney,et al.  Human Domination of Earth’s Ecosystems , 1997, Renewable Energy.

[29]  Barry Smit,et al.  Methods for cumulative effects assessment , 1995 .

[30]  Barry Smit,et al.  Cumulative environmental change: Conceptual frameworks, evaluation approaches, and institutional perspectives , 1993 .

[31]  M. J. Quadrel,et al.  Risk perception and communication , 2008 .

[32]  Eric J. Johnson,et al.  Behavioral decision research: A constructive processing perspective. , 1992 .

[33]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[34]  R. Dawes,et al.  Heuristics and Biases: Clinical versus Actuarial Judgment , 2002 .

[35]  Colin F. Camerer,et al.  General conditions for the success of bootstrapping models , 1981 .

[36]  R. Dawes Judgment under uncertainty: The robust beauty of improper linear models in decision making , 1979 .

[37]  I. Csiszár $I$-Divergence Geometry of Probability Distributions and Minimization Problems , 1975 .

[38]  C. Coombs A theory of data. , 1965, Psychological review.