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Date of Award

Spring 2024

Document Type

Masters Thesis

Department or Program Affiliation

Environmental Science

Degree Name

Master of Science (MS)


Environmental Sciences

First Advisor

Landis, Wayne G.

Second Advisor

Bauman, Jenise

Third Advisor

Kapustka, Lawrence


The Federal Natural Resource Damage Assessment and Restoration (NRDAR) program gives Tribes and certain government agencies the authority to assess injury to natural resources and to pursue and implement compensatory action for any resources lost or injured due to unlawful releases of chemicals into the environment. This study was centered around the development of a Bayesian network (BN) decision support tool tailored to the needs of NRDAR practitioners. The goal was to design a probabilistic BN tool that could lend quantitative insight into natural resource injury. A case study was used to develop and demonstrate the tool’s functionality and propriety for NRDAR purposes. This case study focused on the fish resources of an inactive polychlorinated biphenyl (PCB)-contaminated Superfund Site in mid-eastern Indiana, the Little Mississinewa River (LMR), and the larger Mississinewa River, into which the LMR drains.

A BN framework was created to model the causal relationships between PCBs released into the LMR environment and the resulting injury to fish resources across the study site. The BN model includes three common adverse effect pathways for PCB exposure in fish - mortality, growth, and reproductive effects. The BN also includes a combined mortality + growth (M+G) effects pathway and a combined largest effects model (CLEM) pathway. Each pathway’s endpoint is an injury determination node which gives a probabilistic estimation of an injured or uninjured decision based on site-specific fish tissue concentration and toxicity data for the specified pathway.

The probability distributions from the Bayesian network’s CLEM percent effects results have been linked to spreadsheets that automate injury quantification with measurement in units of discount service acre years (DSAYs). Using the BN tool, probabilistic injury determinations and quantifications were performed for individual spatial subregions as well as the entire LMR study site. This study demonstrates that BNs can be used to characterize relative injury across a spatial gradient as well as over time. The sensitivity analyses indicate that, in all subregions, fish species is an important factor related to injury determination and that bottom-feeding fish species such as catfish are more prone to injury than non-bottom-feeding species such as largemouth bass.




NRDAR, natural resources, Bayesian network, environmental assessment, contaminated site


Western Washington University

OCLC Number


Subject – LCSH

Natural resources--United States; Environmental impact analysis--Indiana--Mississinewa River; Mississinewa River (Ind.); Bayesian statistical decision theory

Geographic Coverage

United States; Mississinewa River (Ind.)




masters theses




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