用环境相似性和船舶流动预测海洋物种入侵路径。
A Theoretical Framework for Environmental Similarity and Vessel Mobility as Coupled Predictors of Marine Invasive Species Pathways
- 结合港口气候特征与船舶航行数据建模
- 预测航运路线上的物种存活概率和传播风险
- 适合海洋生态管理与航运决策者使用
海洋入侵物种通过全球航运传播，造成重大生态与经济损失。传统风险评估依赖详细的压载水和航行记录，但数据常不完整，限制了全球覆盖。本文提出一种理论框架，通过融合港口间环境相似性与观测及预测的航运流动性来量化入侵风险。基于气候的特征表示刻画各港口海洋条件，而来自自动识别系统（AIS）的数据构建航运网络，反映船舶流动与潜在传播路径。聚类与度量学习识别气候类似港口，估算物种在航线上存活的可能性。时间链接预测模型捕捉交通模式在环境变化下的演变。环境相似性与预测流动性融合后，可在港口与航程层面提供暴露度估计，支持针对性监测、航线调整与管理干预。
原文摘要 · Abstract (English)
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.
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