Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting

Abstract

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.

Spatio–Environmental Analysis of Vespula germanica Nest Records Explains Slow Invasion in South Africa

“…In the Western Cape, a winter rainfall area, there are regular droughts, and the dry season coincides with the seasonal peak in the wasp population [46]. The distribution of successful nests (where a foundress can survive and reproduce new queen daughters) are not random and are located mainly in moist farming valleys or sites with higher moisture availability (rivers and shady habitats, or irrigated orchards); conditions that are also favourable for growing Northern Hemisphere crops in the Western Cape.…”

“…This method uses the Getis-Ord Gi* statistic [43] to characterise the intensity of the clustering of high values (hot spots) or low values (cold spots) in the data without the use of traditional statistical tests (e.g., [44,45]). Spatial autocorrelation, using the Global Moron's I statistic, is calculated at incrementing distances in order to determine the scale of analysis (neighbourhood) (e.g., see Jossart et al [46]), after which the Getis-Ord Gi* statistic is used to determine whether the clustering of high and low values based on the neighbourhood are statistically significant (i.e., it assesses each feature within the context of its neighbouring features and then compares the derived local statistic to the global statistic) [43]. The results from the Getis-Ord Gi* statistic are automatically corrected for multiple testing and spatial dependence by making use of the false discovery rate correction method.…”

Global-Local-Comparison Approach: Understanding Marine Mammal Spatial Behavior by Applying Spatial Statistics and Hypothesis Testing to Passive Acoustic Data

“…Not only is this approach applicable to a BACI data set-for which it was originally designed and demonstrated here-but a final strength of the GLC approach is that it is not limited to the study of marine mammal behavior, or the assessment of anthropogenic noise impact. For example, the value of spatial autocorrelation analyses has been demonstrated in other applications, such as marine spatial planning (Redfern et al, 2013;Jossart et al, 2020). Within a large-scale hydrophone receiver array framework, some examples of ways the GLC approach can be extended could include spatially analyzing sound levels over different periods of time in a changing soundscape, or assessing changes in marine mammal vocalizations that are not directly linked to behavior.…”

Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

“…This was achieved after splitting the data into yearly and monthly geo-layers. The ISA results (Supplementary Figures S1-S22 for annual and monthly, respectively) helped in the detection of further spatial hot and cold spots as it provides baseline bandwidths and thresholds for such inquires [74]. Thus, to achieve the statistical significance, we executed spatial local clustering statistics (i.e., Optimized Hot Spot analysis).…”