Citation:

Miller, D., D. Villeneuve, K. Santana Rodriguez, AND G. Ankley. A Multidimensional Matrix Model for Predicting the Effects of Male-Biased Sex Ratios on Fish Populations. Frontiers in Endocrinology. Frontiers, Lausanne, Switzerland, 41(4):1066-1077, (2022). https://doi.org/10.1002/etc.5287

Impact/Purpose:

Ecological effects of chemicals that may interact with the vertebrate hypothalamic pituitary gonadal and/or thyroidal (HPG/T) axes continue to be of scientific and regulatory concern throughout the world. Laboratory experiments have established that exposure to certain endocrine-active substances prior to and/or during the period of sexual differentiation can lead to skewed sex ratios in fish. However, the potential long-term population impact of a biased sex ratio depends on multiple factors including the life history of the species and whether the ratio is male biased or female biased. Herein, we describe a novel multidimensional, density-dependent matrix model that analyzes age class-structure of both males and females over time, allowing for the explicit and quantitative evaluation of the effects of biased sex ratio on population status. This approach can be used in conjunction with field monitoring efforts (e.g., effects-based monitoring programs) and/or laboratory testing to link effects on sex ratio due to chemical and/or nonchemical stressors to adverse outcomes in whole organisms and populations. For demonstration purposes, we applied the model to evaluate population trajectories for fathead minnow (Pimephales promelas) exposed to prochloraz, an aromatase inhibitor, during sexual differentiation. The model also was used to explore the population impact in a more realistic exposure scenario in which both adult and early life stages of fish are exposed concurrently to prochloraz, which, in addition to altering sex ratio during development, can decrease vitellogenin and egg production in adult females. For each exposure scenario, the model was used to analyze total population size, numbers of females and of males, and sex specific recruitment of the F1 generation. The present study illustrates the utility of multidimensional matrix population models for ecological risk assessment in terms of integrating effects across a population of an organism even when chemical effects on individuals are manifested via different pathways depending on life stage.

Description:

Laboratory experiments have established that exposure to certain endocrine-active substances prior to and/or during the period of sexual differentiation can lead to skewed sex ratios in fish. However, the potential long-term population impact of a biased sex ratio depends on multiple factors including the life history of the species and whether the ratio is male biased or female biased. Herein, we describe a novel multidimensional, density-dependent matrix model that analyzes age class-structure of both males and females over time, allowing for the explicit and quantitative evaluation of the effects of biased sex ratio on population status. This approach can be used in conjunction with field monitoring efforts (e.g., effects-based monitoring programs) and/or laboratory testing to link effects on sex ratio due to chemical and/or nonchemical stressors to adverse outcomes in whole organisms and populations. For demonstration purposes, we applied the model to evaluate population trajectories for fathead minnow (Pimephales promelas) exposed to prochloraz, an aromatase inhibitor, during sexual differentiation. The model also was used to explore the population impact in a more realistic exposure scenario in which both adult and early life stages of fish are exposed concurrently to prochloraz, which, in addition to altering sex ratio during development, can decrease vitellogenin and egg production in adult females. For each exposure scenario, the model was used to analyze total population size, numbers of females and of males, and sex specific recruitment of the F1 generation. The present study illustrates the utility of multidimensional matrix population models for ecological risk assessment in terms of integrating effects across a population of an organism even when chemical effects on individuals are manifested via different pathways depending on life stage.

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