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Expanding metal mixture toxicity models to natural stream and lake invertebrate communities

Overview of attention for article published in Environmental Toxicology & Chemistry, March 2015
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2 tweeters

Citations

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35 Dimensions

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47 Mendeley
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Title
Expanding metal mixture toxicity models to natural stream and lake invertebrate communities
Published in
Environmental Toxicology & Chemistry, March 2015
DOI 10.1002/etc.2824
Pubmed ID
Authors

Laurie S. Balistrieri, Christopher A. Mebane, Travis S. Schmidt, Wendel Bill Keller

Abstract

A modeling approach that was used to predict the toxicity of dissolved single and multiple metals to trout is extended to stream benthic macroinvertebrates, freshwater zooplankton, and daphnia magna. The approach predicts the accumulation of toxicants (h, al, cd, cu, ni, pb, and zn) on organisms using three equilibrium accumulation models that define interactions between dissolved cations and biological receptors (biotic ligands). These models differ in the structure of the receptors and include a 2-site biotic ligand model, a bidentate biotic ligand or 2-pka model, and a humic acid (ha) model. The predicted accumulation of toxicants is weighted using toxicant-specific coefficients and incorporated into a toxicity function called tox, which is then related to observed mortality or invertebrate community richness using a logistic equation. All accumulation models provide reasonable fits to metal concentrations in tissue samples of stream invertebrates. Despite the good fits, distinct differences in the magnitude of toxicant accumulation and biotic ligand speciation exist among the models for a given solution composition. However, predicted biological responses are similar among the models because there are interdependencies among model parameters in the accumulation-Tox models. To illustrate potential applications of the approaches, the three accumulation-Tox models for natural stream invertebrates are used in Monte Carlo simulations to (1) predict the probability of adverse impacts in catchments of differing geology in central Colorado (USA), (2) link geology, water chemistry, and biological response, and (3) demonstrate how this approach can be used to screen for potential risks associated with resource development. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Poland 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 28%
Student > Ph. D. Student 8 17%
Student > Bachelor 7 15%
Student > Master 5 11%
Student > Doctoral Student 3 6%
Other 5 11%
Unknown 6 13%
Readers by discipline Count As %
Environmental Science 23 49%
Agricultural and Biological Sciences 8 17%
Engineering 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 2 4%
Unknown 9 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 March 2015.
All research outputs
#13,934,961
of 18,150,220 outputs
Outputs from Environmental Toxicology & Chemistry
#3,869
of 4,987 outputs
Outputs of similar age
#206,586
of 317,902 outputs
Outputs of similar age from Environmental Toxicology & Chemistry
#39
of 80 outputs
Altmetric has tracked 18,150,220 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,987 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,902 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.