Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals. Temporal graph series comprising plankton genera or environmental drivers as node features and their relationships for edge features from two distinct water bodies, a reservoir and a river, were utilized to develop these models. To assess the predictability, the performances of the GC-LSTM models on community dynamics were compared those of LSTM and GCN models at various lead times. Moreover, GNNExplainer was used to examine the global and local importance of the nodes and edges for all predictions and specific predictions, respectively. The GC-LSTM models outperformed the LSTM models, consistently showing higher prediction accuracy. Although all the models exhibited performance degradation at longer lead times, the GC-LSTM models consistently demonstrated better performance regarding each graph signal and plankton genus. GNNExplainer yielded interpretable explanations for important genera and interaction pairs among communities, revealing consistent importance patterns across different lead times at both global and local scales. These findings underscore the potential of the proposed modeling approach for forecasting community dynamics and emphasize the critical role of graph signals with interaction terms in plankton communities.

中文翻译：

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使用图卷积嵌入式长短期记忆对具有静态和动态交互作用的淡水浮游生物群落动力学进行建模


鉴于淡水浮游生物与水质退化之间的频繁关联，已经设计了几种预测模型来理解和估计它们的动态。然而，生物和非生物相互作用的重要性被忽视了。在这项研究中，我们旨在通过应用图卷积嵌入式长短期记忆网络 （GC-LSTM） 模型来解决交互项在预测浮游生物群落动态中的重要性，该模型可以将交互项作为图信号。利用由浮游生物属或环境驱动因素组成的时间图系列作为节点特征，以及它们与来自两个不同水体（水库和河流）的边缘特征的关系，来开发这些模型。为了评估可预测性，比较了 GC-LSTM 模型在不同交货时间对社区动态的性能。此外，GNNExplainer 用于分别检查节点和边对所有预测和特定预测的全局和局部重要性。GC-LSTM 模型的性能优于 LSTM 模型，始终显示出更高的预测准确性。尽管所有模型在较长的交货时间下都表现出性能下降，但 GC-LSTM 模型在每个图形信号和浮游生物属方面始终表现出更好的性能。GNNExplainer 对群落之间的重要属和交互对产生了可解释的解释，揭示了全球和局部尺度上不同交货时间的一致重要性模式。这些发现强调了所提出的建模方法在预测群落动态方面的潜力，并强调了图形信号与交互项在浮游生物群落中的关键作用。