Use of Arcellinida (testate lobose amoebae) arsenic tolerance limits as a novel tool for biomonitoring arsenic contamination in lakes. (June 2020) - Nasser, Nawaf A.; Patterson, R. Timothy; Roe, Helen M.; Galloway, Jennifer M.; Falck, Hendrik; Sanei, Hamed
Graphical abstract: Highlights: Arcellinidan response to legacy arsenic contamination in Canadian subarctic lake sediments ( n = 93) was investigated. Arsenic tolerance and optima limits were determined for 25 arcellinidan taxa. A shift toward healthier arcellinidan assemblages in lakes >10 km from the former Giant Mine was identified. Three arsenic-tolerance-based faunal assemblages were used to infer As concentrations in a test dataset. Arcellinida can be used to monitor As contamination and the progress of remedial efforts. Abstract: Arsenic (As) contamination from legacy gold mining in subarctic Canada poses an ongoing threat to lake biota. With climatic warming expected to increase As bioavailability in lake waters, developing tools for monitoring As variability becomes essential. Arcellinida (testate lobose amoebae) is an established group of lacustrine bioindicators that are sensitive to changes in environmental conditions and lacustrine ecological health. In this study, As-tolerance of Arcellinida (testate lobose amoebae) in lake sediments ( n = 93) in subarctic Northwest Territories, Canada was investigated. Arcellinida assemblage dynamics were compared with the intra-lake As distribution to delineate the geospatial extent of legacy As contamination related to the former Giant Mine (Yellowknife). Cluster analysis revealed five Arcellinida assemblages that correlate strongly with ten variables (variance explained = 40.4%), with As (9.4%) and S1-carbon (labile organic matter; 8.9%) being the most important ( p -value = 0.001, n = 84). Stressed assemblages characterized proximal lakes <10 km from the former mine site, consistent with a recently identified, geochemically-based zone of high As impact. The weighted average tolerance and optima (WATO) analysis led to identification of three arcellinidan groups based on the As-sensitivity: Low-Moderate Tolerance Group (As = 0–350 ppm); High Tolerance Group (As = 350–760 ppm); and, Extreme Tolerance Group (As >750 ppm). The predictive capability of the Low-Moderate and Extreme tolerance groups is particularly strong, correlating with As concentrations in 66.6% ( n = 20/30) of a test dataset. We propose that As influences the spatial distribution of the more nutrient-sensitive Arcellinida taxa (e.g., Cucurbitella tricuspis and Difflugia oblonga strain "oblonga") through suppression of preferred microbial food sources. These findings, which indicate that there is a variable species-level arcellinidan response to As contamination, showcases the potential of using the group as a reliable tool for inferring historical variability in As concentrations in impacted lakes, not possible using As itself due to the redox driven sensitivity of the metalloid to post-depositional remobilization. Arcellinida can also provide insight into the overall impact of As contamination on the ecological health of lakes, a metric not readily captured using instrumental analyses. Lakes with As-stressed arcellinidan faunas and high As concentrations may then be targeted for further As speciation analysis to provide additional information for risk assessment.