Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting
Interest and growth in marine aquaculture are 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 challen...
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doaj-1f500e7c07754b2e9b638465a03496ac2020-11-25T01:44:36ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452020-01-01610.3389/fmars.2019.00806501839Applications of Spatial Autocorrelation Analyses for Marine Aquaculture SitingJonathan Jossart0Seth J. Theuerkauf1Lisa C. Wickliffe2James A. Morris Jr.3CSS, Inc., for National Oceanic and Atmospheric Administration, Fairfax, VA, United StatesThe Nature Conservancy, Arlington, VA, United StatesCSS, Inc., for National Oceanic and Atmospheric Administration, Fairfax, VA, United StatesNational Oceanic and Atmospheric Administration, National Ocean Service, National Center for Coastal Ocean Sciences, Beaufort, NC, United StatesInterest and growth in marine aquaculture are 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 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. The 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 an 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.https://www.frontiersin.org/article/10.3389/fmars.2019.00806/fullspatial planningmarine aquaculturespatial autocorrelationLocal Indicator of Spatial AssociationMoran’s IMulti-Criteria Decision Analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jonathan Jossart Seth J. Theuerkauf Lisa C. Wickliffe James A. Morris Jr. |
spellingShingle |
Jonathan Jossart Seth J. Theuerkauf Lisa C. Wickliffe James A. Morris Jr. Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting Frontiers in Marine Science spatial planning marine aquaculture spatial autocorrelation Local Indicator of Spatial Association Moran’s I Multi-Criteria Decision Analysis |
author_facet |
Jonathan Jossart Seth J. Theuerkauf Lisa C. Wickliffe James A. Morris Jr. |
author_sort |
Jonathan Jossart |
title |
Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting |
title_short |
Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting |
title_full |
Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting |
title_fullStr |
Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting |
title_full_unstemmed |
Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting |
title_sort |
applications of spatial autocorrelation analyses for marine aquaculture siting |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Marine Science |
issn |
2296-7745 |
publishDate |
2020-01-01 |
description |
Interest and growth in marine aquaculture are 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 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. The 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 an 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. |
topic |
spatial planning marine aquaculture spatial autocorrelation Local Indicator of Spatial Association Moran’s I Multi-Criteria Decision Analysis |
url |
https://www.frontiersin.org/article/10.3389/fmars.2019.00806/full |
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