Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics.

Abstract

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.