Comparative spatially explicit approach for testing effects of soil chemicals on terrestrial wildlife bioindicator demographics. (1st January 2023)

• Record Type: Journal Article
• Authors: Murphy, Sean M.; Hathcock, Charles D.; Espinoza, Tatiana N.; Fresquez, Philip R.; Berryhill, Jesse T.; Stanek, Jenna E.; Sutter, Benjamin J.; Gaukler, Shannon M.
• Is Part Of: Environmental pollution. Volume 316(2023)part 2
• Publication Date: 2023-01-01
• Subjects: Bioindicator monitoring -- Environmental contamination -- Normalized difference vegetation index -- Peromyscus -- Population density -- Spatial capture-recapture; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.120541
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Wildlife species are often used as bioindicators to evaluate the extent and severity of environmental contamination and the effectiveness of remediation practices. A common approach for investigating population- or community-level impacts on bioindicators compares demographic parameter estimates (e.g., population size or density) between sites that were subjected to different levels of contamination. However, the traditional analytical method used in such studies is nonspatial capture-recapture, which results in conclusions about potential relationships between demographics and contaminants being inferred indirectly. Here, we extend this comparative approach to the spatially explicit framework, allowing direct estimation of said relationships and comparisons between study areas, by applying spatial capture-recapture (SCR) models to bioindicator (deer mice [ Peromyscus spp.]) detection data from two study areas that were subjected to different industrial activities and remediation practices. Bioindicator density differed by 178% between the neighboring study areas, and the area with the highest soil concentrations of polychlorinated biphenyls, chromium, and zinc had the highest bioindicator density. Under the traditional nonspatial approach, we might have concluded that soil chemical levels had negligible influences on demographics. However, by modeling density as a spatial function of select chemical concentrations using SCR models, we found strong support for a positive relationship between density and soil chromium concentrations in one study area (β = 0.82), which was not masked by or associated with habitat-related metrics. To obtain reliable inferences about potential effects of environmental contamination on bioindicator demographics, we contend that a comparative spatially explicit approach using SCR ought to become standard. Highlights: Linking pollutants to disruption of demographic processes remains a challenge. Comparing bioindicator demographics between sites is a foundational approach. We extend the comparative approach to the spatially explicit framework. Bioindicator density differed between sites and varied spatially with soil chemicals. Demographic-pollutant relationships can be elucidated via spatial capture-recapture.