Abstract
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.
Highlights
The demand for marine aquaculture products in the United States is growing, with domestic sales from 2007 to 2012, increasing 13% per year (National Marine Fisheries Service [National Marine Fishery Service (NMFS)], 2017)
Radiarta et al (2011) created a suitability model for Japanese kelp (Laminaria japonica) in Hokkaido, Japan, using Moderate Resolution Imaging Spectroradiometer (MODIS) Sea Surface Temperature (SST) data and suspended solid concentrations calculated from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data to identify suitable locations
A few outliers with unsuitable cells adjacent to highly suitable cells were identified; these were either aids to navigation or other navigational obstructions
Summary
The demand for marine aquaculture products in the United States is growing, with domestic sales from 2007 to 2012, increasing 13% per year (National Marine Fisheries Service [NMFS], 2017). Marine aquaculture development in the United States has been increasing 3.3% annually from 2009 to 2011 (National Marine Fisheries Service [NMFS], 2017) and the use of traditional siting analyses will aid development by identifying. Spatial Autocorrelation Analyses for Marine Aquaculture optimal locations that minimize conflict with other industries and environmental constraints. Spatial autocorrelation analyses may be incorporated into planning for marine aquaculture to increase the confidence of spatial planners, stakeholders, and coastal managers overseeing development. Vessel traffic information derived from VMS or Automatic Identification Systems (AIS) is used to characterize navigation-related ocean space-use conflicts among ocean industries, such as renewable energy (Rawson and Rogers, 2015), commercial fishing (Rouse et al, 2017), and marine aquaculture (Tlusty et al, 2018). Reliable remote sensing data combined with authoritative or regulatory data, such as shipping lanes or marine protected areas, are essential for MSP
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