Marine invasive species spread through global shipping and generate substantial ecological and economic impacts. Traditional risk assessments require detailed records of ballast water and traffic patterns, which are often incomplete, limiting global coverage. This work advances a theoretical framework that quantifies invasion risk by combining environmental similarity across ports with observed and forecasted maritime mobility. Climate-based feature representations characterize each port's marine conditions, while mobility networks derived from Automatic Identification System data capture vessel flows and potential transfer pathways. Clustering and metric learning reveal climate analogues and enable the estimation of species survival likelihood along shipping routes. A temporal link prediction model captures how traffic patterns may change under shifting environmental conditions. The resulting fusion of environmental similarity and predicted mobility provides exposure estimates at the port and voyage levels, supporting targeted monitoring, routing adjustments, and management interventions.

翻译：海洋入侵物种通过全球航运传播，造成显著的生态和经济影响。传统风险评估依赖于详细的压载水和交通模式记录，但这些数据往往不完整，限制了全球覆盖范围。本研究提出一个理论框架，通过结合港口间的环境相似性与观测及预测的海事流动性来量化入侵风险。基于气候的特征表征刻画了各港口的海洋环境条件，而源自自动识别系统数据的流动性网络则捕捉了船舶流量及潜在的转移路径。聚类与度量学习揭示了气候相似港口，并支持估算物种沿航运路线的生存概率。时序链路预测模型捕捉了交通模式在变化环境条件下可能发生的变化。环境相似性与预测流动性的融合提供了港口和航次层面的暴露度估计，为针对性监测、航线调整和管理干预提供支持。