Regional drivers of fish tissue mercury concentrations in the Great Plains, USA

Author:

Larréy Matthew1,Manning David2

Affiliation:

1. University of Nebraska Lincoln

2. University of Nebraska Omaha

Abstract

Abstract Mercury, a highly toxic metal, is deposited onto land from the atmosphere through wet and dry deposition, and can be transported into waterways. In lakes and streams, mercury bioaccumulates and then biomagnifies in sediments and aquatic food webs. Thus, mercury from food web sources can be biomagnified in fish tissue, and humans can be exposed through consuming contaminated fishes. Several biological, chemical, and physical factors influence the concentration of mercury in fish tissues, such as species identity, and watershed land use. We accessed publicly available fish tissue mercury data for six states in the Great Plains, USA (Iowa, Kansas, Missouri, Minnesota, Nebraska, and South Dakota), and linked them to watershed characteristics such as land use. We used mixed-effect regression analysis and model selection approaches to test the prediction that the presence of wetlands and agriculture in the watershed increases the mercury concentration found in fish tissue. Fish tissue mercury depended on species identity foremost and secondarily on spatial variables (e.g. land use/land cover, state). Presence of wetlands and mixed-forest habitat in the watershed increased fish tissue mercury concentration, but other land uses showed weaker relationships. Overall, our results emphasized the importance of species traits in predicting fish tissue mercury concentrations. Analysis of watershed land use and land cover variables also explained variation in fish contamination, highlighting the importance of watershed-scale parameters in evaluations of mercury exposure from wild-caught fish.

Publisher

Research Square Platform LLC

Reference63 articles.

1. Allaire, J.J, Xie, Y., McPherson, J., Luraschi, J., Ushey, K. Atkins, A. Wickham, H., Cheng, J., Chang, W., and Iannone, R. (2021). rmarkdown: Dynamic Documents for R. R package version 2.11. https://rmarkdown.rstudio.com.

2. Ahmed, F. E. (1991). Seafood Production, Distribution, and Consumption. In Seafood Safety. National Academies Press (US).

3. Microbial sulfate reduction in littoral sediment of Lake Constance;Bak F;FEMS Microbiology Letters,1991

4. Bartón K. (2016) MuMIn: Multi-Model Inference. Available from: https://cran.r-project.org/web/packages/MuMIn/index.html.

5. Bivand, R., Keitt, T., and Rowlington, B. (2021). rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5–23. https://CRAN.R-project.org/package=rgdal

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3