A Stochastic Computational Model for Dreissenid Mussel Invasion Risk in Montana’s Waterways

Overview

Tom Ritzdorf Zebra mussels, first detected in Montana in 2016, are known to cause severe economic and environmental damage when they colonize an area. To combat their spread, an accurate computer mod el would allow decontamination resources to be placed so that water bodies most at risk of contamination could be most effectively protected, while indicating certain lakes in which mussels would not survive and could thus be prioritized below others. This study has created a computational model to determine ideal locations for watercraft inspection stations such that as many contaminated boats are intercepted as possible, and that vulnerable lakes are effectively protected. The models developed in this study will be able to assist in determining the most efficient way to prevent mussel infestation, both from the state’s interior and its exterior. The primary model described herein is a gravity model, which computes boat traffic within a given area based on the locations of nearby lakes. It also incorporates GIS data describing precise boat travel routes and stochastic procedures within a repetitive Monte Carlo framework, allowing the full range of possible outcomes to be determined, while also making clear the most probable ones. In general, it appears that major highways tend to carry the most contaminated boat traffic, and the model is equipped to identify sections of these highways over which the most boats travel. Watercraft inspection and decontamination stations placed on these sections of road would intercept the greatest numbers of boats, reducing contamination and curbing infestation spread

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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