Literature DB >> 23727279

A model for the impact of contaminants on fish population dynamics.

Qihua Huang1, Laura Parshotam, Hao Wang, Caroline Bampfylde, Mark A Lewis.   

Abstract

Mathematical models have been widely applied to perform chemical risk assessments on biological populations for a variety of ecotoxicological processes. In this paper, by introducing a dose-dependent mortality rate function, we formulate a toxin-dependent aquatic population model that integrates mortality as toxin effect in addition to considering the effects of toxin on growth and recruitment. The model describes the direct effect of toxin on population by treating the concentration of toxin in the environment as a parameter. The model is more convenient to connect with data than traditional differential equation models that describe the interaction between toxin and population. We analyze the positive invariant region and the stability of boundary and interior steady states. The model is connected to experimental data via model parametrization. In particular, we consider the toxic effects of mercury on rainbow trout (Oncorhynchus mykiss) and obtain an appropriate range for each model parameter. The parameter estimates are then used to illustrate the long-time behavior of the population under investigation. The numerical results provide threshold values of toxin concentration in the environment to keep the population from extirpation. The findings are consistent with surface water quality guidelines. It may be appropriate to apply our model to other species and other chemicals of interest to consider guideline development.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Contaminants; Fish; Mercury; Model; Rainbow trout

Mesh:

Substances:

Year:  2013        PMID: 23727279     DOI: 10.1016/j.jtbi.2013.05.018

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  3 in total

1.  Direct and indirect effects of toxins on competition dynamics of species in an aquatic environment.

Authors:  Chunhua Shan; Qihua Huang
Journal:  J Math Biol       Date:  2018-08-29       Impact factor: 2.259

2.  Exclusion of the fittest predicts microbial community diversity in fluctuating environments.

Authors:  Shota Shibasaki; Mauro Mobilia; Sara Mitri
Journal:  J R Soc Interface       Date:  2021-10-06       Impact factor: 4.293

3.  A switching model for the impact of toxins on the spread of infectious diseases.

Authors:  Lulu Wang; Zhen Jin; Hao Wang
Journal:  J Math Biol       Date:  2018-05-09       Impact factor: 2.259

  3 in total

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