School of Forestry and Resource Conservation, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan, ROC.
Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Burwood Campus, Melbourne, Australia.
Sci Rep. 2020 Oct 12;10(1):17003. doi: 10.1038/s41598-020-73922-3.
Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2-3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.
鱼类死亡通常是由低溶解氧（DO）引起的，涉及到环境中复杂的相互作用和动态变化。在许多地方，由于缺乏持续的水质监测，大规模鱼类死亡的确切原因仍然不确定。在这项研究中，我们通过使用自组织映射（SOM）这一机器学习技术，检验了气象条件是否可以通过与灰鲻鱼（Mugil cephalus）死亡事件相关的现成气象数据来充当低 DO 的替代指标。受不同气象模式的驱动，SOM 将鱼类死亡事件分为夏季和非夏季两种类型。夏季鱼类死亡事件与长时间的低气压和高温有关，并集中在鱼类死亡事件发生前 2-3 天的风暴事件中。相比之下，非夏季鱼类死亡事件则与相对较低的气压、持续较低的风速以及鱼类死亡事件发生前 5 天的连续风暴事件有关。我们的研究结果表明，异常的气象条件可以作为管理者的预警信号，通过采取预防措施来避免鱼类死亡。虽然不能替代水质监测计划，但气象数据可以支持渔业管理，以保护河流生态系统的健康。
