Effects Of Lamprey Sex Ratio on Ecosystem Stability Based on Graph Neural Network and Deep Learning

Abstract

The purpose of this study was to explore the impact of the dynamic adjustment of the sex ratio of lamprey on the stability of the ecosystem. The ecological effects of the sex ratio under different resource conditions were analyzed by constructing logistic model, genetic algorithm and deep learning prediction model combined with graph neural network. The study found that the dynamic adjustment of gender ratio can significantly improve the robustness of the ecological network, and its stabilizing effect can be reflected through the network centrality and the optimization of community structure. The results of data visualization show that the coupling effect of gender ratio change and resource availability can be visualized through dynamic thermal map and three-dimensional interactive map. The innovation of this study is to introduce graph neural network and deep learning technology into ecological research, which effectively makes up for the shortcomings of traditional models in dealing with the dynamics of complex ecological networks, and provides a new analytical framework for the ecological mechanism of gender ratio regulation. In addition, the research and development of prediction tools based on deep learning has high application feasibility, which can provide accurate decision support for ecosystem management and help the sustainable management and protection of ecosystems.