Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea.
Faculty of Marine Technology, Chonnam National University, Yeosu 59626, Korea.
Int J Environ Res Public Health. 2019 Jan 31;16(3):409. doi: 10.3390/ijerph16030409.
Our study aims to identify the spatial characteristics of water quality and sediment conditions in relation to fisheries resources, since the productivity of fisheries resources is closely related to the ambient conditions of the resource areas. We collected water quality samples and sediment contaminants from twenty-one sites at Gwangyang Bay, Korea, in the summer of 2018. Our study sites covered the area from the Seomjin River estuary to the inner and outer bays. To spatially characterize physicochemical features of Gwangyang Bay, we used Self-Organizing Map (SOM), which is known as a robust and powerful tool of unsupervised neural networks for pattern recognition. The present environmental conditions of Gwangyang Bay were spatially characterized according to four different attributes of water quality and sediment contamination. From the results, we put emphasis on several interesting points: (i) the SOM manifests the dominant physicochemical attributes of each geographical zone associated with the patterns of water quality and sediment contamination; (ii) fish populations appear to be closely associated with their food sources (e.g., shrimps and crabs) as well as the ambient physicochemical conditions; and (iii) in the context of public health and ecosystem services, the SOM result can potentially offer guidance for fish consumption associated with sediment heavy metal contamination. The present study may have limitations in representing general features of Gwangyang Bay, given the inability of snapshot data to characterize a complex ecosystem. In this regard, consistent sampling and investigation are needed to capture spatial variation and to delineate the temporal dynamics of water quality, sediment contamination, and fish populations. However, the SOM application is helpful and useful as a first approximation of an environmental assessment for the effective management of fisheries resources.
我们的研究旨在确定与渔业资源相关的水质和沉积物条件的空间特征，因为渔业资源的生产力与资源区域的环境条件密切相关。我们于 2018 年夏季在韩国光阳湾采集了 21 个地点的水质样本和沉积物污染物。我们的研究地点涵盖了从顺天江入海口到内湾和外湾的区域。为了对光阳湾的理化特征进行空间描述，我们使用了自组织映射（SOM），它是一种用于模式识别的强大而有力的无监督神经网络工具。根据水质和沉积物污染的四个不同属性，对光阳湾的当前环境条件进行了空间描述。从结果中，我们强调了几个有趣的点：（i）SOM 体现了与水质和沉积物污染模式相关的每个地理区域的主要理化属性；（ii）鱼类种群似乎与其食物来源（例如虾和蟹）以及周围的理化条件密切相关；（iii）在公共卫生和生态系统服务方面，SOM 结果可能为与沉积物重金属污染相关的鱼类消费提供指导。鉴于快照数据无法描述复杂的生态系统，本研究可能无法代表光阳湾的一般特征。在这方面，需要进行一致的采样和调查，以捕捉水质、沉积物污染和鱼类种群的空间变化，并描绘其时间动态。然而，SOM 的应用作为环境评估的初步近似值有助于对渔业资源进行有效管理。
