Marine Spatial Planning (MSP) provides a process that uses spatial data and models to evaluate environmental, social, economic, cultural, and management trade-offs when siting (i.e., strategically locating) ocean industries. Aquaculture is the fastest-growing food sector in the world. The United States (U.S.) has substantial opportunity for offshore aquaculture development given the size of its exclusive economic zone, habitat diversity, and variety of candidate species for cultivation. However, promising aquaculture areas overlap many protected species habitats. Aquaculture siting surveys, construction, operations, and decommissioning can alter protected species habitat and behavior. Additionally, aquaculture-associated vessel activity, underwater noise, and physical interactions between protected species and farms can increase the risk of injury and mortality. In 2020, the U.S. Gulf of Mexico was identified as one of the first regions to be evaluated for offshore aquaculture opportunities as directed by a Presidential Executive Order. We developed a transparent and repeatable method to identify aquaculture opportunity areas (AOAs) with the least conflict with protected species. First, we developed a generalized scoring approach for protected species that captures their vulnerability to adverse effects from anthropogenic activities using conservation status and demographic information. Next, we applied this approach to data layers for eight species listed under the Endangered Species Act, including five species of sea turtles, Rice’s whale, smalltooth sawfish, and giant manta ray. Next, we evaluated four methods for mathematically combining scores (i.e., Arithmetic mean, Geometric mean, Product, Lowest Scoring layer) to generate a combined protected species data layer. The Product approach provided the most logical ordering of, and the greatest contrast in, site suitability scores. Finally, we integrated the combined protected species data layer into a multi-criteria decision-making modeling framework for MSP. This process identified AOAs with reduced potential for protected species conflict. These modeling methods are transferable to other regions, to other sensitive or protected species, and for spatial planning for other ocean-uses.

Numerous environmental conditions may influence when a female Loggerhead sea turtle (Caretta caretta) selects a nesting site. Limited research has used Geographic Information Systems (GIS) and statistical analysis to study sea turtle spatial patterns and temporal trends. Therefore, the goals of this research were to identify areas that were most prevalent for nesting and to test social and environmental variables to create a nesting suitability predictive model. Data were analyzed at all barrier island beaches in North Carolina, USA (515 km) and several variables were statistically significant: distance to hardened structures, beach nourishment, house density, distance to inlets, and beach elevation, slope, and width. Interestingly, variables that were not significant were population density, proximity to the Gulf Stream, and beach aspect. Several statistical techniques were tested and Negative Binomial Distribution produced good regional results while Geographically Weighted Regression models successfully predicted the number of nests with an average of 75% of the variance explained. Therefore, the combination of traditional and spatial statistics provided insightful predictive modeling results that may be incorporated into management strategies and may have important implications for the designation of critical Loggerhead nesting habitats.

Marine Spatial Planning (MSP) provides a process that uses spatial data and models to evaluate environmental, social, economic, cultural, and management trade-offs when siting ocean industries. Aquaculture is the fastest-growing food sector in the world. The U.S. has substantial opportunity for offshore aquaculture development given the size of its exclusive economic zone, habitat diversity, and variety of candidate species for cultivation. However, many protected species rely upon habitats that overlap with promising aquaculture areas. Siting surveys, farm construction, operations, and decommissioning can alter the habitat and behavior of animals in the vicinity of these activities. Vessel activity, underwater noise, and physical interactions between protected species and farms can potentially increase the risk of injury or cause direct mortality. In 2020, the U.S. Gulf of Mexico was identified as one of the first regions to be evaluated for offshore aquaculture opportunities as directed by a Presidential Executive Order. We developed a generalized scoring model for protected species data layers that captures vulnerability using species conservation status and demographic information. We applied this approach to data layers for eight species listed under the Endangered Species Act, including five species of sea turtles, Rice’s Whale, Smalltooth Sawfish, and Giant Manta Ray. We evaluated several methods for scoring (e.g., arithmetic mean, geometric mean, product, lowest scoring layer) and created a combined protected species data layer that was used within a multi-criteria decision-making modeling framework for MSP. The product approach for scoring provided the most logical ordering of and the greatest contrast in site suitability scores. This approach provides a transparent and repeatable method to identify aquaculture site alternatives with the least conflict with protected species. These modeling methods are transferable to other regions, to other sensitive or protected species, and for spatial planning for other ocean-uses.