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Title: Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Abstract

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densitiesmore » for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.« less

Authors:
ORCiD logo; ORCiD logo; ; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1664445
Alternate Identifier(s):
OSTI ID: 1762729
Report Number(s):
LA-UR-19-24214
Journal ID: ISSN 1932-6203; 10.1371/journal.pone.0238870
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Published Article
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Name: PLoS ONE Journal Volume: 15 Journal Issue: 9; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Peromyscus; Soil chemistry; Ecological remediation; Bioindicators; Population density; Mammals; Wildlife; Contaminants

Citation Formats

Gaukler, Shannon M., Murphy, Sean M., Berryhill, Jesse T., Thompson, Brent E., Sutter, Benjamin J., Hathcock, Charles D., and Yue, ed., Bi-Song. Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models. United States: N. p., 2020. Web. https://doi.org/10.1371/journal.pone.0238870.
Gaukler, Shannon M., Murphy, Sean M., Berryhill, Jesse T., Thompson, Brent E., Sutter, Benjamin J., Hathcock, Charles D., & Yue, ed., Bi-Song. Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models. United States. https://doi.org/10.1371/journal.pone.0238870
Gaukler, Shannon M., Murphy, Sean M., Berryhill, Jesse T., Thompson, Brent E., Sutter, Benjamin J., Hathcock, Charles D., and Yue, ed., Bi-Song. Thu . "Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models". United States. https://doi.org/10.1371/journal.pone.0238870.
@article{osti_1664445,
title = {Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models},
author = {Gaukler, Shannon M. and Murphy, Sean M. and Berryhill, Jesse T. and Thompson, Brent E. and Sutter, Benjamin J. and Hathcock, Charles D. and Yue, ed., Bi-Song},
abstractNote = {Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.},
doi = {10.1371/journal.pone.0238870},
journal = {PLoS ONE},
number = 9,
volume = 15,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
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https://doi.org/10.1371/journal.pone.0238870

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