The purpose of this project is to carry out research that ensures the long-term sustainability of the Gulf of Mexico (GoM) ecosystem and socioeconomic landscape. As part of this mission, a three-year grant has been awarded to investigate fisheries management for Gulf of Mexico Red Snapper through development of a decision support tool based on a management strategy evaluation (MSE) framework. The tool will help quantify the risks and trade-offs among the various alternative long-term management strategies and potential short-term regulations that may be utilized in the rebuilding and sustainable management of the GoM Red Snapper resource. The project will seek to actively engage and implement stakeholders’ opinions and suggestions, while providing them with a powerful tool that can be directly used to weigh the trade-offs of various management measures. The end product of the research will be a user-friendly GUI-based decision support tool that can explore various management and policy decisions on an array of short- and long-term biological, economic, social, and political metrics.
The applicant will be tasked with developing the MSE operating model with supervision from the various PIs. Additionally, the applicant will assist PIs with organizing workshops with stakeholders. A critical component of the research will be directly collaborating and communicating with various stakeholders in order to determine desired capabilities of the decision support tool. Funding will be provided to attend stakeholder meetings around the Gulf region along with support to attend a yearly national or international scientific conference to present results of the work. The applicant will be expected to prepare and submit multiple peer-reviewed articles stemming from the MSE research. Secondary requirements involve mentoring Ph.D. students and participating in various ongoing research projects of the PIs, which focus on management strategy evaluation and multi-species modeling.
The applicant must have completed a Ph.D. in fisheries science, ecology, or a related field. Applicants should have a strong quantitative background and computer programming skills. Experience in statistical modeling and proficiency with programming languages (R, Python, or SAS along with ADMB or TMB) are desired. Applicants are expected to have a proven record of peer-reviewed publications and verbal communication skills.