Spawning Aggregations

Full Title: Cooperative monitoring program for spawning aggregations in the Gulf of Mexico: an assessment of existing information, data gaps and research priorities

Lead Investigator (Institution): Brad Erisman (The University of Texas at Austin)

Co-investigators (Institution): William Heyman (LGL Ecological Research Associates, Inc.) and Shinichi Kobara (Texas A&M University)

Collaborators (Institution): Mandy Karnauskas (NOAA), Nick Farmer (NOAA), Susan Lowerre-Barbieri (Florida Fish and Wildlife Conservation Commission), and Jorge Brenner (The Nature Conservancy)

Technical Monitor: Nick Farmer (

Award Amount: $391,021

Award Period: September 1, 2015 to August 31, 2018

Summary: This project will compile and evaluate existing information on fish spawning aggregations in the Gulf of Mexico as the basis to design a cooperative, Gulf-wide conservation and monitoring program focused on fish spawning aggregations. The investigators will first compile existing biological and fisheries information for Gulf of Mexico species known or likely to form spawning aggregations and identify existing datasets and monitoring programs in the Gulf of Mexico that could inform regional monitoring of spawning aggregations. The investigators will then synthesize this information and convene a regional workshop where stakeholders can review it and prioritize a suite of species, habitats, monitoring methods, and areas of research moving forward. The investigators will also engage in a comprehensive outreach and data-sharing program to ensure all data and project outputs are available to inform management. The project will be led by a diverse group of experts representing academia, federal and state government, and non-governmental organizations from throughout the Gulf of Mexico region. Workshop participants and reviewers of the results of this work will include academics from diverse fields; state, federal, and regional fishery and marine resource managers; and leaders in the fishing and oil and gas industries.