Relating results of chronic toxicity responses to population-level effects: Modeling effects on wild chinook salmon populations

Authors

  • Julann A. Spromberg,

    1. Environmental Conservation Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 2725 Montlake Boulevard East, Seattle, Washington 98112, USA
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  • James P. Meador

    Corresponding author
    1. Environmental Conservation Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 2725 Montlake Boulevard East, Seattle, Washington 98112, USA
    • Environmental Conservation Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 2725 Montlake Boulevard East, Seattle, Washington 98112, USA
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Abstract

Standard toxicity tests assess the physiological responses of individual organisms to exposure to toxic substances under controlled conditions. Time and space restrictions often prohibit the assessment of population-level responses to a toxic substance. Compounds affecting various toxicity endpoints, such as growth, fecundity, behavior, or immune function, alter different demographic traits and produce different impacts on the population. Chronic effects of immune suppression, reproductive impairment, and growth reduction were examined using life history models for Chinook salmon (Oncorhynchus tshawytscha). Modeled immune suppression acted through reductions in age-specific survival, with first- and second-year survival producing the greatest changes in the population growth rate (Λ). A 10% reduction in various reproductive parameters all produced a similar Λ, but different sensitivity and stable age distributions. Growth reduction models incorporated effects to both survival and reproduction and produced additive effects. Overall, model output indicated that for Chinook salmon, alteration of first-year survival has the greatest relative impact on Λ. Results support the importance of linking toxicity endpoints to the demographic traits that they influence and help generate toxicity tests that are more relevant for the species. Life history modeling provides a useful tool to develop testable hypotheses regarding specific and comparative population-level impacts.

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