Zhang, Shuhua and Liu, Ming and Li, Dong (2026) An integrated Data-Driven-Simulation-Optimization model : Insights into controlling invasive plants in China. Expert Systems with Applications. ISSN 0957-4174 (In Press)
Zhang_et_al_2026_ESA.pdf - Accepted Version
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Abstract
The rapid spread of invasive plants such as Spartina alterniflora has emerged as a major ecological and economic threats to coastal wetlands, while existing management strategies often fail to adapt to dynamic invasion processes and limited financial resources. To address this challenge, this study develops a novel data-driven-simulation-optimization (DDSO) framework that enables dynamic and spatially explicit management of biological invasions. The core innovation lies in coupling data-driven ecological parameterization based on multi-source observations with a simulation model that captures life-cycle transitions and spatial dispersal, and a mixed-integer optimization module that allocates control budgets and intervention intensities across space and time. By integrating heterogeneous environmental, biological, and management data, the framework constructs time-varying ecological parameters that reflect evolving invasion conditions and underlying ecological processes. The optimization component then generates cost-effective intervention schedules under fixed budget constraints. Comparative evaluation against system dynamics (SD) and simulation-optimization (SO) models shows that DDSO outperforms conventional approaches not only in budget efficiency, but also by revealing counterintuitive management logics: management effectiveness hinges more on the presence of a coordinated optimization framework than on investment scale, and economically efficient strategies inherently favor highly uneven spatial resource allocation. These mechanism-level insights underscore the importance of early intervention and cross-regional coordination, establishing DDSO as a policy-relevant framework for adaptive invasive species management.