title: Quantitative aspects of evaluating the consequences of pollution for parasite populations and communities.
abstract: The utility of parasites as indicators of environmental health depends upon scientists' ability to understand how environmental stress may cause changes in parasite populations and communities. Predicting the changes due to pollution calls for dynamic models and experimentally determined knowledge of the consequences of pollution on the parameters of the model. Through an interplay of modeling, laboratory experiments, and field comparisons scientists can develop a theory of pollution and parasites and perhaps use it to assess the impact of pollution. To develop a theoretical framework for the effects of pollution on host-parasite relationships will require an identification of the appropriate scale and a determination of whether the host and parasite populations should be treated as open and recruitment-driven or closed and reproduction-driven. A theory can then be developed using epidemiology models. Those models are divided into prevalence models that track the prevalence of infection and abundance models that track the number of parasites per host. Whether a deterministic model or a stochastic counterpart should be used depends upon the scale but generally a stochastic model will give a more realistic picture. Mimicking the variability inherent in natural systems is the goal of stochastic models. The models emphasize that field tests of their predictions can only be made by studies designed with extensive replication of samples from reference and stressed sites.
authors.affiliation: Gulf Coast Research Laboratory, Institute of Marine Sciences, University of Southern Mississippi, Ocean Springs 39566-7000, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Lotz
mesh_terms: D000818:Animals; D004787:Environmental Pollution; D006790:Host-Parasite Interactions; D008954:Models, Biological; D010273:Parasitic Diseases, Animal / Q000453:epidemiology / Q000469:parasitology*; D015995:Prevalence; D013269:Stochastic Processes