Parasitological Data as Monitors of Environmental Health

When an appropriate fish host is selected, analysis of its parasites offers a useful, reliable, economical, telescoped indication or monitor of environmental health. The value of that information increases when corroborated by another non-parasitological technique. The analysis of parasites is not necessarily simple because not all hosts serve as good models and because the number of species, presence of specific species, intensity of infections, life histories of species, location of species in hosts, and host response for each parasitic species have to be addressed individually to assure usefulness of the tool. Also, different anthropogenic contaminants act in a distinct manner relative to hosts, parasites, and each other as well as being influenced by natural environmental conditions. Total values for all parasitic species infecting a sample cannot necessarily be grouped together. For example, an abundance of numbers of either species or individuals can indicate either a healthy or an unhealthy environment, depending on the species of parasite. Moreover, depending on the parasitic species, its infection, and the time chosen for collection/examination, the assessment may indicate a chronic or acute state of the environmental health. For most types of analyses, the host should be one that has a restricted home range, can be infected by numerous species of parasites, many of which have a variety of additional hosts in their life cycles, and can be readily sampled. Data on parasitic infections in the western mosquitofish (Gambusia affinis), a fish that meets the criteria in two separate studies, illustrate the usefulness of that host as a model to indicate both healthy and detrimentally influenced environments. In those studies, species richness, intensity of select species, host resistance, other hosts involved in life cycles, and other factors all relate to site and contaminating discharge.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.