Literature DB >> 20380436

From individual to population level effects of toxicants in the tubicifid Branchiura sowerbyi using threshold effect models in a Bayesian framework.

Virginie Ducrot1, Elise Billoir, Alexandre R R Péry, Jeanne Garric, Sandrine Charles.   

Abstract

Effects of zinc were studied in the freshwater worm Branchiura sowerbyi using partial and full life-cycle tests. Only newborn and juveniles were sensitive to zinc, displaying effects on survival, growth, and age at first brood at environmentally relevant concentrations. Threshold effect models were proposed to assess toxic effects on individuals. They were fitted to life-cycle test data using Bayesian inference and adequately described life-history trait data in exposed organisms. The daily asymptotic growth rate of theoretical populations was then simulated with a matrix population model, based upon individual-level outputs. Population-level outputs were in accordance with existing literature for controls. Working in a Bayesian framework allowed incorporating parameter uncertainty in the simulation of the population-level response to zinc exposure, thus increasing the relevance of test results in the context of ecological risk assessment.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20380436     DOI: 10.1021/es903860w

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  3 in total

1.  Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

Authors:  Carole Forfait-Dubuc; Sandrine Charles; Elise Billoir; Marie Laure Delignette-Muller
Journal:  Ecotoxicology       Date:  2012-05       Impact factor: 2.823

2.  Population level effects of multiwalled carbon nanotubes in Daphnia magna exposed to pulses of triclocarban.

Authors:  Anne Simon; Thomas G Preuss; Andreas Schäffer; Henner Hollert; Hanna M Maes
Journal:  Ecotoxicology       Date:  2015-05-24       Impact factor: 2.823

3.  Developing demographic toxicity data: optimizing effort for predicting population outcomes.

Authors:  John D Stark; John E Banks
Journal:  PeerJ       Date:  2016-05-25       Impact factor: 2.984

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.