A Bayesian methodological framework assessing fish tumour occurrences in Canadian Areas Of Concern

Session: Restoration and Management of Great Lakes Fishes (1)

Ariola Visha, University of Toronto Scarborough, [email protected]
Agnes Richards, Environment Canada, [email protected]
Mark McMaster, Environment and Climate Change Canada, [email protected]
George Arhonditsis, University of Toronto Scarborough, [email protected]

Abstract

Contaminated sediments have been connected with the development of liver cancer in bottom-dwelling fish species, such as brown bullhead and white sucker. The likelihood that a fish has a tumour can be causally associated with a series of important covariates, such as the fish age, fork length, liver weight, gonad weight, or total fish weight. Founded upon the implementation of Bayesian inference techniques, our study aims to predict hepatic (liver) neoplasm and preneoplasm tumours in brown bullhead and white sucker samples across several Areas of Concern (AOCs) in the Great Lakes. Our study revisits the existing delisting criteria issues by accounting for the natural variability amongst samples as well as the spatial heterogeneity across exposed and reference sites. Current results indicate that the prevalence of preneoplasm tumours is higher in comparison to neoplasm tumours. Brown bullhead preneoplasm tumours were distinctly higher in Hamilton Harbour, Bay of Quinte, and the St. Clair River. White sucker preneoplasm tumors appear more frequently in exposed sites from the St. Marys River and Thunder Bay. We anticipate that the proposed framework will advance our ability in distinguishing between impaired and non-impaired fish population conditions.