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

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...

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Main Authors: Shannon M Gaukler, Sean M Murphy, Jesse T Berryhill, Brent E Thompson, Benjamin J Sutter, Charles D Hathcock
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0238870
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author Shannon M Gaukler
Sean M Murphy
Jesse T Berryhill
Brent E Thompson
Benjamin J Sutter
Charles D Hathcock
author_facet Shannon M Gaukler
Sean M Murphy
Jesse T Berryhill
Brent E Thompson
Benjamin J Sutter
Charles D Hathcock
author_sort Shannon M Gaukler
collection DOAJ
description 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.
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spelling doaj.art-ad336990afa5405885705549e370237e2022-12-21T21:52:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01159e023887010.1371/journal.pone.0238870Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.Shannon M GauklerSean M MurphyJesse T BerryhillBrent E ThompsonBenjamin J SutterCharles D HathcockMonitoring 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.https://doi.org/10.1371/journal.pone.0238870
spellingShingle Shannon M Gaukler
Sean M Murphy
Jesse T Berryhill
Brent E Thompson
Benjamin J Sutter
Charles D Hathcock
Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
PLoS ONE
title Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
title_full Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
title_fullStr Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
title_full_unstemmed Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
title_short Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models.
title_sort investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture recapture models
url https://doi.org/10.1371/journal.pone.0238870
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