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Development and application of a density dependent matrix population model for Atlantic killifish (Fundulus heteroclitus)
Miller, D., B. Clark, AND D. Nacci. Development and application of a density dependent matrix population model for Atlantic killifish (Fundulus heteroclitus). SETAC North America, Orlando, FL, November 06 - 10, 2016.
Ranging along the Atlantic coast from US Florida to the Maritime Provinces of Canada, the Atlantic killifish (Fundulus heteroclitus) is an important and well-studied model organism for understanding the effects of pollutants and other stressors in estuarine and marine ecosystems. Matrix population models are useful tools for ecological risk assessment because they integrate effects across the life cycle, provide a linkage between endpoints observed in the individual and ecological risk to the population as a whole, and project outcomes for many generations in the future. We developed a density dependent matrix population model for Atlantic killifish by modifying a model developed for fathead minnow (Pimephales promelas) that has proved to be extremely useful, e.g. to incorporate data from laboratory studies and project effects of endocrine disrupting chemicals. We developed a size-structured model (as opposed to one that is based upon developmental stages or age class structure) so that we could readily incorporate output from a Dynamic Energy Budget (DEB) model, currently under development. Due to a lack of sufficient data to accurately define killifish responses to density dependence, we tested a number of scenarios realistic for other fish species in order to demonstrate the outcome of including this ecologically important factor. We applied the model using published data for killifish exposed to dioxin-like compounds, and compared our results to those using a previously published stage-based density-independent killifish matrix model. By considering how models can accommodate variation in life histories and account for ecological factors such as density dependence, we can more easily develop other fish species-specific matrix models to characterize population status and predict more realistically the ecological impacts of stressors.