The aim of the study was to create a procedure and test it in a case study that would permit forming a predicted depiction of the cause-and-effect network of connections in the lagoon ecosystem. We propose the novelty procedure as a tool for predicting transformations in the structure of links within ecosystems and forecasting changes in the impact some ecological factors have on others. An attempt was made to combine the Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) tools to obtain the most reliable model of the dependency structure among variables that would be based on prediction data from the initial structural model and processed with a backpropagation algorithm. The subject of the explanatory-predictive analysis in this work was the Vistula Lagoon (southern Baltic) ecosystem network: physical-chemical factors – zooplankton – phytoplankton – organic carbon forms. The SEM-ANN-SEMpredict modeling sequence produced satisfactory adjustment rates of the ANN models for training and validation trials for the majority of the organic parameters analyzed. Higher matching rates and a higher number of relationships were obtained with the SEMpredict model than with the SEM model based on observed data. The role of physical factors changed in the predictive model compared to the explanatory model. Wind speed changed the direction of the impact on the picophytoplankton from a negative to one that facilitated growth and influenced on the cyanobacteria biomass increase. The role of salinity in promoting the growth of phytoplankton groups in relation to the impact of basin depth was more prominent. The analysis of both the new predictive network of direct links and the total effect of these relationships permitted us to extend our knowledge about potential phenomena that can occur in the ecosystem studied. Innovative methodology combined SEM and ANN network models to analyze the structure and prediction of ecosystem network indicated important potential properties of ecological systems.

该研究的目的是创建一个程序并在一个案例研究中对其进行测试，从而可以对泻湖生态系统中的因果关系网络进行预测描述。我们提出新颖性程序作为一种工具，用于预测生态系统内链接结构的转换以及预测某些生态因素对其他因素的影响的变化。尝试将结构方程模型（SEM）和人工神经网络（ANN）工具组合在一起，以获得变量之间最可靠的依存结构模型，这些变量将基于来自初始结构模型的预测数据并经过反向传播处理算法。这项工作中的解释性-预测性分析的主题是维斯杜拉泻湖（波罗的海南部）生态系统网络：物理化学因素–浮游动物–浮游植物–有机碳形式。SEM-ANN-SEMpredict建模序列为大多数分析的有机参数的训练和验证试验提供了令人满意的ANN模型调整率。基于观测数据，与基于SEM模型的SEMpredict模型相比，获得了更高的匹配率和更高数量的关系。与解释模型相比，物理因素在预测模型中的作用发生了变化。风速将对浮游植物的影响方向从负向改变为促进生长并影响蓝藻生物量增加的方向。与盆地深度的影响有关，盐度在促进浮游植物群生长方面的作用更为突出。对新的直接链接的预测网络以及这些关系的总体影响的分析使我们得以扩展对所研究的生态系统中可能发生的潜在现象的认识。创新的方法论结合了SEM和ANN网络模型来分析生态系统网络的结构和预测，表明了生态系统的重要潜在特性。