The Use of Geographic Information Systems for Spatial Ecological Risk Assessments: An Example from the Athabasca Oil Sands Area in Canada

Abstract

There is an acknowledged need in ecotoxicology for methods that integrate spatial analyses in risk assessment. This has resulted in the emergence of landscape ecotoxicology, a subdiscipline of ecotoxicology. However, landscape ecotoxicology has yet to become common practice in risk assessment due to the underdevelopment of techniques and a lack of standardized methods. In the present study, we demonstrate how geographic information systems (GISs) can serve as a standardized platform to integrate data, assess spatial patterns of ecotoxicological data for multiple species, and assess relationships between chemical mixture exposures and effects on biota for landscape ecotoxicological risks assessment. We use data collected under the Joint Oil Sands Monitoring Program in the Athabasca Oil Sands Region in Alberta, Canada. This dataset is composed of concentrations of contaminants including metals and polycyclic aromatic compounds, and health endpoints measured in 1100 biological samples, including tree swallows, amphibians, gull and tern eggs, plants, and mammals. We present 3 examples using a GIS as a platform and geospatial analysis to: 1) integrate data and assess spatial patterns of contaminant exposure in the region, 2) assess spatial patterns of exposures to complex mixtures, and 3) examine patterns of exposures and responses across the landscape. We summarize the methods used in the present study into a workflow for ease of use. The GIS methods allow researchers to identify hot spots of contamination, use georeferenced monitoring data to derive quantitative exposure‐response relationships, and assess complex exposures with more realism. Environ Toxicol Chem 2019;38:2797–2810. © 2019 SETAC

Geospatial analysis of the patterns of chemical exposures among biota in the Canadian Oil Sands Region

“…This paper also presents the application of a novel method for assessing the spatial patterns of multidimensional ecotoxicology data and builds on our previous work using the MEM method to assess spatial patterns of complex metal exposure in adult wood frogs in northern Alberta [ 27 ]. The results of this analysis identify key hotspots of complex metal and PTE exposure in different species and life stages in the OSR.…”

“…Further, to integrate the datasets collected from different species and life stages, we range-normalized (scaled) by species-life stage subgroups to convert all measurements to a common scale (0–1) within each subgroup. Essentially this ranks the exposure within each subgroup [ 27 , 32 ]. This is necessary to control for the differences in bioaccumulation and biomagnification rate of metals and PTEs between species and life stages and allows for contaminant concentrations to be compared and integrated [ 27 , 33 ].…”

“…Essentially this ranks the exposure within each subgroup [ 27 , 32 ]. This is necessary to control for the differences in bioaccumulation and biomagnification rate of metals and PTEs between species and life stages and allows for contaminant concentrations to be compared and integrated [ 27 , 33 ]. A workflow of the data preparation and analysis is shown in Fig 2 .…”

“…Further, chemicals are measured in different matrices (e.g., feather, muscle, liver). The species, life stage, and matrix differences in chemical concentrations can make it challenging to make direct comparisons between measured biomarkers and can hinder data integration [27]. Additionally, contaminant variables are often highly correlated, which can be problematic for some statistical methods such as regression [28].…”

“…Geographic information systems (GIS) are useful platforms to integrate data using a normalization method and assess spatial patterns of exposures to complex mixtures using a spatial principal component analysis (sPCA) [27]. The objective of this study is to demonstrate the use of a geospatial framework and spatial analytical methods to address the challenges associated with integrating and analyzing large wildlife datasets in ecotoxicology.…”