Jeung Minhyuk, Jang Min-Chul, Shin Kyoungsoon, Jung Seung Won, Baek Sang-Soo
Department of Rural & Biosystems Engineering (Brain Korea 21), Chonnam National University, Gwangju, 61186, Republic of Korea.
Ballast Water Research Center, Korea Institute of Ocean Science & Technology, Geoje, 53201, Republic of Korea.
Environ Sci Ecotechnol. 2024 Nov 26;23:100514. doi: 10.1016/j.ese.2024.100514. eCollection 2025 Jan.
Mesozooplankton are critical components of marine ecosystems, acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations. They play pivotal roles in the pelagic food web and export production, affecting the biogeochemical cycling of carbon and nutrients. Therefore, accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies. However, modeling these dynamics remains challenging due to the complex interplay among physical, chemical, and biological factors, and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models. Graph neural network (GNN) models offer a promising approach to forecast multivariate features and define correlations among input variables. The high interpretive power of GNNs provides deep insights into the structural relationships among variables, serving as a connection matrix in deep learning algorithms. However, there is insufficient understanding of how interactions between input variables affect model outputs during training. Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species. We find that forecasting accuracy is closely related to interactions within ecosystem dynamics. Notably, increasing the number of nodes does not always enhance model performance; closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing. Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.
中型浮游动物是海洋生态系统的关键组成部分，通过捕食浮游植物并影响鱼类种群，充当初级生产者和较高营养级之间的关键中介。它们在远洋食物网和输出生产中发挥着关键作用，影响着碳和营养物质的生物地球化学循环。因此，准确模拟和可视化中型浮游动物群落动态对于理解海洋生态系统模式和制定有效的管理策略至关重要。然而，由于物理、化学和生物因素之间复杂的相互作用，对这些动态进行建模仍然具有挑战性，并且在理论驱动的模型中，详细的参数化和反馈机制在理论上尚未完全理解。图神经网络（GNN）模型为预测多变量特征和定义输入变量之间的相关性提供了一种有前景的方法。GNN的高解释力能够深入洞察变量之间的结构关系，在深度学习算法中充当连接矩阵。然而，对于训练过程中输入变量之间的相互作用如何影响模型输出，我们还了解不足。在这里，我们研究用于训练GNN模型的生态系统动态图结构如何影响其对中型浮游动物物种的预测准确性。我们发现预测准确性与生态系统动态中的相互作用密切相关。值得注意的是，增加节点数量并不总是能提高模型性能；联系紧密的物种在趋势和峰值时间方面往往会产生相似的预测输出。因此，我们证明纳入生态系统动态的图结构可以通过提供有关感兴趣物种的有影响力信息来提高中型浮游动物建模的准确性。这些发现将深入了解影响中型浮游动物物种的因素，并强调构建适当的图以预测这些物种的重要性。
