title: Evaluation of the efficacy of extrapolation population modeling to predict the dynamics of Americamysis bahia populations in the laboratory.
abstract: An age-classified projection matrix model has been developed to extrapolate the chronic (28-35 d) demographic responses of Americamysis bahia (formerly Mysidopsis bahia) to population-level response. This study was conducted to evaluate the efficacy of this model for predicting the population behavior of A. bahia held (for more than three generations) under controlled laboratory exposure conditions. The research involved the performance of a standard life-cycle test and a multigenerational (greater than three mysid generations, 55 d) assay using A. bahia to experimentally evaluate model predictions regarding population-level risks of chemical exposure. The organic compound para-nonylphenol was chosen as the chemical stressor in assays. This compound is a ubiquitous contaminant and suspected endocrine disruptor. Utilizing data obtained during the standard life-cycle test, aggregate estimates of population growth rate (lambda) and measured p-nonylphenol concentration were used to develop an exposure-response model of population-level effects. These estimates provided the basis of predictions for the long-term dynamics of mysid populations exposed to p-nonylphenol. The veracity of the mysid population model was evaluated through quantitative comparisons of model predictions based on the life-cycle test with dynamics of the experimental populations (multigenerational assay results). The results indicate that the population model was able to project within a few micrograms per liter the concentration where population-level effects would begin to occur (projected 16 micrograms/L from the model vs measured 19 micrograms/L from the multigenerational assay).
authors.affiliation: U.S. Environmental Protection Agency, Atlantic Ecology Division, 27 Tarzwell Drive, Narragansett, Rhode Island 02882, USA. kuhn.anne@epa.gov
authors.forename: A
authors.initials: A
authors.lastname: Kuhn
authors.forename: W R
authors.initials: WR
authors.lastname: Munns
authors.forename: D
authors.initials: D
authors.lastname: Champlin
authors.forename: R
authors.initials: R
authors.lastname: McKinney
authors.forename: M
authors.initials: M
authors.lastname: Tagliabue
authors.forename: J
authors.initials: J
authors.lastname: Serbst
authors.forename: T
authors.initials: T
authors.lastname: Gleason
mesh_terms: D000375:Aging / Q000502:physiology; D000465:Algorithms; D000818:Animals; D003445:Crustacea / Q000502:physiology*; D005260:Female; D008018:Life Cycle Stages / Q000187:drug effects; D008297:Male; D008954:Models, Biological; D010636:Phenols / Q000633:toxicity; D011153:Population; D014874:Water Pollutants, Chemical / Q000633:toxicity
chemical_list: D010636:Phenols; D014874:Water Pollutants, Chemical; C025256:nonylphenol