Proof-of-concept model for exploring the impacts of microplastics accumulation in the Maryland coastal bays ecosystem

Jessica Boyer, Kasondra Rubalcava, Shawn Booth and Howard Townsend

Ecological Modelling, 2022, vol. 464, issue C

Abstract: With increased global production of plastics since the 1950s, marine environments have experienced an increase in plastic pollution. This pollution has the potential to contaminate marine organisms with microplastics, which, in turn, may have deleterious effects on humans that consume seafood. Plastic pollution is often presented as a global issue; however, its sources are often based on local actions and potential health effects occur at an individual level. Environmental management to control this problem also can occur on a local scale. To draw attention to the issue and demonstrate the need to take management actions to reduce plastic inflow, we have developed a proof-of-concept model that connects inflow of plastic in a small-scale marine environment to a contaminants-based food web model. We use Ecotracer in the Ecopath with Ecosim modeling suite to estimate current organism concentrations of microplastics and then use model outputs to calculate human health effects. The model is used to project future microplastic concentrations in marine organisms and human health effects under different environmental plastic inflow rate scenarios. The model is parameterized to simulate the Maryland Coastal Bays ecosystem, which is adjacent to Ocean City, Maryland (USA) a region dependent on the tourism and seafood industries. We consider this a proof-of-concept model, because data for the system are limited. This approach helps to illustrate local consequences of a global problem. In addition, it provides a summary of pertinent regional data on the issues and helps identify gaps for future monitoring and research.

Keywords: Ecosystem modelling; Microplastics; Contaminant biotransport; Ecopath with Ecosim; Ecotracer (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:464:y:2022:i:c:s0304380021003872

DOI: 10.1016/j.ecolmodel.2021.109849

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