Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting

• Published in: Frontiers in Marine Science Vol. 6
• Main Authors: Jossart, Jonathan, Theuerkauf, Seth J., Wickliffe, Lisa C., Morris Jr, James A.
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 22.01.2020; Frontiers Media S.A
• Subjects: Aquaculture; Area; Autocorrelation; Case studies; Chlorophyll; Chlorophyll a; Coastal zone management; Datasets; Decision making; Ecosystem approach to fisheries; Fisheries; Local Indicator of Spatial Association; Longline fishing; Management decisions; Marine aquaculture; Mollusks; Moran’s I; Multi-Criteria Decision Analysis; Ocean space; Oceanographic data; Planning; Remote sensing; Renewable resources; Satellites; Sea surface; Spatial analysis; spatial autocorrelation; Spatial data; Spatial planning; Statistical analysis; Statistical methods; Surface temperature; United States > US; Massachusetts; Japan
• ISSN: 2296-7745; 2296-7745
• DOI: 10.3389/fmars.2019.00806

Interest and growth in marine aquaculture is increasing around the world, and with it, advanced spatial planning approaches are needed to find suitable locations in an increasingly crowded ocean. Standard spatial planning approaches, such as a Multi-Criteria Decision Analysis (MCDA), may be challenging and time consuming to interpret in heavily utilized ocean spaces. Spatial autocorrelation, a statistical measure of spatial dependence, may be incorporated into the planning framework, which provides objectivity and assistance with the interpretation of spatial analysis results. Here, two case studies highlighting applications of spatial autocorrelation analyses in the northeast region of the United States of America are presented. The first case study demonstrates the use of a Local Indicator of Spatial Association (LISA) analysis within a relative site suitability analysis—a variant of a MCDA—for siting a mussel longline farm. This case study statistically identified 17% of the area as highly suitable for a mussel longline farm, relative to other locations in the area of interest. Use of a clear, objective, and efficient analysis provides improved confidence for industry, coastal managers, and stakeholders planning marine aquaculture. The second case study presents an incremental spatial autocorrelation analysis with Moran’s I that is performed on modeled and remotely sensed oceanographic data sets (e.g., chlorophyll a, sea surface temperature, and current speed). The results are used to establish a maximum area threshold for each oceanographic variable within the online decision support tool, OceanReports, which performs automated spatial analysis for a user-selected area (i.e., drawn polygon) of ocean space. These thresholds provide users guidance and summary statistics of relevant oceanographic information for aquaculture planning. These two case studies highlight practical uses and the value of spatial autocorrelation analyses to improve the siting process for marine aquaculture.