A Theoretical Framework for Environmental Similarity and Vessel Mobility as Coupled Predictors of Marine Invasive Species Pathways

2025-11-05

AI Summary

Predicting marine invasive species spread in global shipping is hindered by incomplete ballast water and maritime traffic data, limiting comprehensive global risk assessment. To address this, we propose a novel theoretical framework integrating port environmental similarity with vessel mobility: ports are characterized by climate features; dynamic shipping networks are constructed from AIS data; climate-analogous ports are identified via clustering and metric learning; and a temporal link prediction model captures traffic evolution. This work pioneers the coupled modeling of environmental suitability and transport connectivity, enabling high-resolution invasion exposure assessment at both port and voyage levels. By decoupling analysis from sparse empirical ballast data, our approach significantly improves the accuracy of global-scale invasion risk quantification. It provides a scalable, data-efficient scientific tool for targeted monitoring, shipping route optimization, and ecosystem management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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.

Problem

• Predicting marine invasive species spread using environmental similarity and shipping mobility
• Overcoming incomplete ballast water records in traditional risk assessments
• Estimating species survival likelihood along shipping routes through climate analogues

Innovation

• Environmental similarity and vessel mobility predict invasion risk
• Climate-based features and mobility networks characterize port conditions
• Link prediction model captures traffic changes under environmental shifts