Using Bayesian statistics to analyze environmental toxicology data from native freshwater mussels
Abstract
Scientists at the US Geological Survey are looking at the effects of the chemical
combination 3-trifluoromethyl-4-nitrophenol (TFM) with 1% niclosamide on native
freshwater mussels’ behavior and reproduction. Mussels were exposed to the chemicals
for 24 hours and reproductive and behavioral outcomes were measured throughout the
exposure and 10-day recovery period. The objective of the study is to compare the
treatments with respect to these outcome variables. This is usually done using traditional
statistical methods like the analysis of variance (ANOVA) or the Kruskal-Wallis test. In
this thesis, alternative methods are proposed, using Bayesian methodology. To compare
the six treatments, a Bayesian linear regression procedure, a Bayesian logistic mixed
effects procedure, and a Bayesian ordinal model were used. This thesis will illustrate how
these Bayesian procedures can be implemented using two new R packages. These two R
packages use MCMC methods to simulate from the posterior distributions. These
simulated values allowed the construction of credible intervals, which can be used in
comparing the treatments. Results obtained using the Bayesian methods applied to the
native freshwater mussel toxicology data were similar to the results from traditional
statistical methods. Some additional benefits of using Bayesian statistics are also
discussed in this thesis.
Subject
Bayesian statistical decision theory
Environmental toxicology
Freshwater mussles