Managing large-scale invasions: Simulation-optimization with Gaussian dispersal Kernels and stochastic seed establishment
Onal, Sevilay Bushaj, Sabah Smith, Jennifer Houseman, Gregory R. Buyuktahtakin, Esra
Onal, Sevilay
Bushaj, Sabah
Smith, Jennifer
Houseman, Gregory R.
Buyuktahtakin, Esra
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2026-03-23
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Article
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Keywords
Agricultural production economics,Bio-economic models,Environmental sustainability,Invasive species,Random pop-up operations research,Sericea (Lespedeza cuneata),Simulation-optimisation,Stochastic dispersal and establishment
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Citation
Onal, S., Bushaj, S., Smith, J., Houseman, G. R., & Büyüktahtakın, I. E. (2026). Managing large-scale invasions: Simulation-optimization with Gaussian dispersal Kernels and stochastic seed establishment. Journal of the Operational Research Society, 1–25. https://doi.org/10.1080/01605682.2026.2641676
Abstract
Biological invaders, such as Sericea lespedeza, cause over $21 billion (about $65 per person in the US) in annual losses for the US, necessitating effective control methods. To our knowledge, this article is the first to integrate random occurrences of an invader that are not attributable to biophysical impacts, within an integrated simulation-optimisation model to control Sericea. Specifically, we introduce a novel dispersal framework that integrates predictable Gaussian seed spread with a random sprout algorithm, explicitly addressing the long-standing question of random pop-ups of new invaders and capturing long-distance establishment events that traditional models miss. The simulation models the species’ biological growth and integrates both predictable dispersal and unpredictable establishment events into a unified framework. Our optimisation model minimises economic damage by determining optimal search and treatment locations under budget constraints. The case study data and parameter calibration are based on large-scale field data collected in Kansas and Oklahoma. We simulate Sericea growth over a 2,500-acre landscape for 25 years, representing a 25-fold increase in spatial coverage and more than double the temporal scope compared to former studies, substantially increasing problem complexity while demonstrating the scalability of our model. Results, averaged over 10 independent replications, show that prioritising searches in low-density areas and treating infestations immediately upon detection yield the greatest benefits. The framework highlights the value of early detection, search speed, and cost-effective control, offering a generalisable tool for invasive species management. © 2026 The Operational Research Society.
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Taylor and Francis Ltd.
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Journal of the Operational Research Society
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01605682