Cyanobacterial blooms management: A modified optimization model for interdisciplinary research

Ming Liu, Jiani Wu, Shuhua Zhang and Jing Liang

Ecological Modelling, 2023, vol. 484, issue C

Abstract: Controlling cyanobacterial blooms is a complex problem due to the biological features of cyanobacteria and the management objective of minimizing economic damages under a limited budget. Although bio-economic models provide hopeful methods for cyanobacteria control, the existing spatial-temporal optimization models do not consider both the heterogeneity of the natural loss rate of cyanobacteria in various growth stages as well as the heterogeneity of cyanobacteria spore migration between different regions. In this paper, we develop a new spatial-dynamic model to describe the reproduction and dispersal behavior of cyanobacteria. We consider the above heterogeneities and propose an integrated bio-economic optimization model as a valuable decision framework for water managers to allocate limited resources for cyanobacteria control. Specifically, our model incorporates maximum capacity constraints in terms of resource constraints, ensuring that cyanobacterial growth follows the fundamental biological principle of survival of the fittest. The potential applicability of our model is illustrated by a computational experiment. The results demonstrate that the investment exhibits diminishing marginal returns while allocating resources to the initially infected area can obtain the best bang for the buck under most test scenarios. This study is an interdisciplinary research that can assist managers in making more accurate decisions regarding budget allocation, the timing, and the location of cyanobacteria removal operations.

Keywords: Cyanobacterial blooms; Computational experiment; Integrated bio-economic optimization; Resources allocation; Interdisciplinary research (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:484:y:2023:i:c:s0304380023002107

DOI: 10.1016/j.ecolmodel.2023.110480

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