Marine Resource Management

Decision support challenge:
To communicate both the state of marine populations (e.g., fish, krill, and other targeted species), the uncertainties inherent to model projections, and the costs associated with alternative policy decisions. To enable policy-makers and other stakeholders to conduct scenario planning and compare alternative policies by developing interactive visualization tools.

Decision makers or end users:
Decision-makers include trained fisheries scientists serving on science and statistical committees or fisheries management councils, US State Department representatives and politicians, as well as fishermen, tour operators, and seafood processors. These stakeholders may have sharply divergent perspectives, especially when resources are shared across national boundaries.

Currently, scientists assessing current population size and future productivity fail to fully convey uncertainty in population models and the likely impact of management decisions on the productivity of a natural resource. This non-interactive approach fails to both address the consequences of particular management decisions and overcome major cultural barriers between stakeholders. 

Research challenges and data skills relating to decision making-process:
There are many exciting avenues for innovation in the area of quantitative fisheries management, including but not limited to the application of “deep-learning” methods for improved population forecasting. Marine resource management data encompass spatially-resolved information on species’ abundances, bathymetry, demographic data, satellite or oceanographic data recorded on the scale of minutes to days, and even the output from Global Climate Models. These advances in computational analysis will have far greater impact if students can tailor outputs to address the needs of policy-makers. To really translate scientific findings into better decision making, students must work with stakeholders throughout the modeling process and thus overcome the cultural barriers that often disconnect the social science and natural science teams working on these complex socioeconomic challenges. Interactive applications and visualization tools, for example, can allow stakeholders to test various management scenarios, but modelers often lack the programming skills required to build interactive websites. Improvements to science and communication will enable participatory Management Strategy Evaluation (MSE), a process by which multiple models or scenarios are tested under the assumption that no one model or scenario is “right” but rather represents a range of options. This process, which would be taught to trainees through their required coursework, forces stakeholders to verbalize their goals and recognize that some goals come at the expense of others or are not fully attainable. 

Assessing improved decision making:
The effectiveness of these approaches to management can be assessed by the number of overfished stocks and other metrics indicating improved status of wildlife or ecosystems such as a decrease in the number of invasive species, increases in biodiversity and higher profitability of fisheries. Natural resource management is often contentious, especially when resources are not sustainably managed. The well-being of fishing communities and satisfaction of stakeholders can also be quantified to indicate whether management is effective in both.