How these concepts will explicitly address the 3 “legs” [Human Dimension, Big Data, Risk and Uncertainty] of the Risk and Uncertainty Quantification in Marine Science Program:

Human Dimension: The human dimension “leg” of this project could include a range of issues, such as:

1. Importance of policy language/process in establishment of reserves and evaluation program.
2. Role of economic and non-economic values associated with reserves.
3. Role of “messaging” and “communication” in setting expectations about reserves.
4. Understanding of the range of definitions of “success” as defined by relevant stakeholders.

Big Data:

1. Particle tracking from individual-based model simulations of larval dispersal within and outside marine reserves
2. Oceanographic and socio-ecological data compilation, including ocean currents, temperature, salinity, turbulent mixing, fish catches outside reserves compared to experimental fishing inside (control), sociological and economic indicators of marine reserve effects on human populations.

Risk and Uncertainty Quantification:

1. Uncertainty associated with process errors in particle tracking simulations
2. Uncertainty associated with measurements of socio-ecological data
3. Risk associated with loss of commercially and recreationally valuable marine resources, loss of income opportunities
4. Uncertainty regarding new income opportunities and research value provided by marine reserves

Faculty Contacts:

Ana Spalding, School of Public Policy: ana.spalding@oregonstate.edu
Kirsten Grorud-Colvert, Integrative Biology: grorudck@science.oregonstate.edu
Alix Gitelman, Statistics: alix.gitelman@oregonstate.edu
Lorenzo Ciannelli, CEOAS: lciannelli@coas.oregonstate.edu

Topic:

What makes a marine reserve (MR) ecologically and socio-economically successful? How (can) we integrate the different types of data that are used to evaluate the different components of the socio-ecological system (e.g. recruitment, adult fish stocks, compliance, economic benefits, social acceptance, social literacy, etc.)? Can the integration of data help inform policy and ensure management success?

Beyond the explicit call to “avoid significant adverse social and economic impacts on ocean users and coastal communities”, to date there is no specific mandate to outline stakeholder expectations or perspectives of what a successful marine reserve would accomplish in Oregon. Similarly, there is a dearth of information on how these expectations/perceptions of success could be better integrated into the formal marine reserve monitoring program. In the context of the 2023 evaluation deadline, our goal for this NRT cluster is to explore mutually enforcing, interrelated outcomes of the marine reserve system through the development of assessment tools for merging social and ecological measures of marine reserve success. In order to achieve this goal, students will: (1) Identify the marine reserve goals as stated in policy documents; (2) Aggregate existing ecological, cultural, social, and economic data on marine reserves and assess similarities, differences, and effects of sampling design across data sets to establish guiding principles of success; (3) Identify key ecological and social measures of marine reserves success; (4) Identify an appropriate theoretical framework for integration, taking into account established MR goals.

Background:

In January 2016, the last of Oregon’s five marine reserve sites was implemented within state waters as part of Oregon’s marine reserve system, following OPAC policy recommendations and statutes passed by the Oregon Legislature (ORS 196.540 through 196.555). Marine reserves can be contentious, with significant differences among stakeholder groups regarding the efficacy, purpose, and inclusivity of marine reserve processes (Fox et al., 2013). However, reserves also present an opportunity to increase community cohesion and local ocean pride (Alcala and Russ, 2006), economic benefits (e.g. Sala et al., 2013), ecological benefits (e.g., Lester et al., 2009), human well-being (Reithe et al., 2014), and educational opportunities (e.g., Leisher et al., 2012).

The Oregon Legislature has called for an evaluation of the Oregon Marine Reserves Program and a report submitted to the Legislature in the year 2023. The evaluation will reflect upon all aspects of the Program in relation to the marine reserves goals, objectives, and policy directives including: site management; ecological and human dimensions scientific monitoring and research; outreach; community engagement; compliance and enforcement; and funding for the five marine reserve sites. The evaluation is to look across the five sites to examine what has worked well, where there are deficiencies, and what has been learned to date. There is general agreement from the scientific community that this timeframe is too brief for detection of substantive ecological changes due to marine reserve protections. However, this duration does provide time for constructive ecological and human dimensions research that helps inform marine reserves science and nearshore resource management in Oregon.

Existing Data:

Baseline and continuing ecological datasets are already being collected and analyzed by ODFW and other researchers for ecological monitoring and evaluation purposes. Likewise, ODFW’s human dimension’s research program focuses on studies of coastal communities, uses, attitudes and perceptions of implementation and management, and market and non-market valuation of the reserves.

Desired Area(s) of Expertise for Students which complete the three “legs” core concepts of the Risk and Uncertainty Quantification in Marine Science Program.

• Human dimensions component: Ideal areas of expertise include anthropology, geography, sociology or economics; with strong skills in the analysis of ethnographic data, and qualitative data management and coding.
• Natural system (Ecology): Ideal areas of expertise include oceanography, fisheries, bio-physical modeling of particle dispersal, biostatistics, ecology.
• Big data/statistics component: Ideal areas of expertise include sampling design, statistical analysis of uncertainty of management outcomes, combining data from different sources.