Literature DB >> 25943079

Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada.

David H Miller1, Joseph E Tietge2, Mark E McMaster3, Kelly R Munkittrick4, Xiangsheng Xia5, David A Griesmer5, Gerald T Ankley2.   

Abstract

Recovery of fish and wildlife populations after stressor mitigation serves as a basis for evaluating remediation success. Unfortunately, effectively monitoring population status on a routine basis can be difficult and costly. In the present study, the authors describe a framework that can be applied in conjunction with field monitoring efforts (e.g., through effects-based monitoring programs) to link chemically induced alterations in molecular and biochemical endpoints to adverse outcomes in whole organisms and populations. The approach employs a simple density-dependent logistic matrix model linked to adverse outcome pathways (AOPs) for reproductive effects in fish. Application of this framework requires a life table for the organism of interest, a measure of carrying capacity for the population of interest, and estimation of the effect of stressors on vital rates of organisms within the study population. The authors demonstrate the framework using linked AOPs and population models parameterized with long-term monitoring data for white sucker (Catostomus commersoni) collected from a study site at Jackfish Bay, Lake Superior, Canada. Individual responses of fish exposed to pulp mill effluent were used to demonstrate the framework's capability to project alterations in population status, both in terms of ongoing impact and subsequent recovery after stressor mitigation associated with process changes at the mill. The general approach demonstrated at the Jackfish Bay site can be applied to characterize population statuses of other species at a variety of impacted sites and can account for effects of multiple stressors (both chemical and nonchemical) and dynamics within complex landscapes (i.e., meta-populations including emigration and immigration processes).
© 2015 SETAC.

Entities:  

Keywords:  Adverse outcome pathway; Population model; Pulp mill effluent; White sucker

Mesh:

Substances:

Year:  2015        PMID: 25943079     DOI: 10.1002/etc.2972

Source DB:  PubMed          Journal:  Environ Toxicol Chem        ISSN: 0730-7268            Impact factor:   3.742


  5 in total

1.  The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology.

Authors:  Gerald T Ankley; Stephen W Edwards
Journal:  Curr Opin Toxicol       Date:  2018-06-01

2.  Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology.

Authors:  Rory B Conolly; Gerald T Ankley; WanYun Cheng; Michael L Mayo; David H Miller; Edward J Perkins; Daniel L Villeneuve; Karen H Watanabe
Journal:  Environ Sci Technol       Date:  2017-04-07       Impact factor: 9.028

3.  A Multidimensional Matrix Model for Predicting the Effects of Male-Biased Sex Ratios on Fish Populations.

Authors:  David H Miller; Daniel L Villeneuve; Kelvin J Santana-Rodriguez; Gerald T Ankley
Journal:  Environ Toxicol Chem       Date:  2022-02-16       Impact factor: 4.218

4.  A 30-Year Study of Impacts, Recovery, and Development of Critical Effect Sizes for Endocrine Disruption in White Sucker (Catostomus commersonii) Exposed to Bleached-Kraft Pulp Mill Effluent at Jackfish Bay, Ontario, Canada.

Authors:  Erin J Ussery; Mark E McMaster; Mark R Servos; David H Miller; Kelly R Munkittrick
Journal:  Front Endocrinol (Lausanne)       Date:  2021-04-22       Impact factor: 5.555

5.  How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

Authors:  Clemens Wittwehr; Hristo Aladjov; Gerald Ankley; Hugh J Byrne; Joop de Knecht; Elmar Heinzle; Günter Klambauer; Brigitte Landesmann; Mirjam Luijten; Cameron MacKay; Gavin Maxwell; M E Bette Meek; Alicia Paini; Edward Perkins; Tomasz Sobanski; Dan Villeneuve; Katrina M Waters; Maurice Whelan
Journal:  Toxicol Sci       Date:  2016-12-19       Impact factor: 4.849

  5 in total

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