Data for

Abstract

Exceedingly high or low concentrations of essential elements and heavy metals can have devastating effects on homeostasis and the health of living organisms. Baseline ranges for many elements have been widely assessed and studied for domesticated species, but such a baseline is lacking for squirrels, especially in remote areas such as Alaska (United States). North American red squirrels (Tamiasciurus hudsonicus) are mesopredators that interact with a vast array of ecosystem components (fungi, seeds and vegetation, predators, and species that co-evolved with squirrels), while being able to thrive in semi-urbanized landscapes of the Anthropocene. As such, they can serve as a sentinel species for ecosystem health. To assess baseline heavy metal exposure and essential element concentrations in Interior Alaskan red squirrels, we collected 158 squirrels through citizen crowdsourcing and extracted livers from each squirrel during necropsies. We grouped the 158 squirrels into 11 composite samples (based on the location of the deceased animal) and analyzed liver tissues for 71 elements using Inductively Coupled Plasma-Sector Field Mass Spectrometry (ICP-MS). Our laboratory results mostly fall within the range reported in the few available literature records for liver tissue analyzed within the Sciuridae family, but tend to be found in the highest quarter of comparable literature records for manganese (Mn), cadmium (Cd), mercury (Hg), nickel (Ni), and lead (Pb). Of the total 71 elements measured, we analyzed 21 using the TreeNet Gradient Boosting Machine Learning algorithm in the Salford Predictive Modeler (SPM) and present three elements (As, Se, and Cd) as case studies. These 21 elements were chosen based on our judgment of their relevance for the health of red squirrels in the study area. We used 224 environmental predictors and ‘scored’ the predictions in SPM, linking the aspatial TreeNet models to the corresponding spatial location. We then generate a predicted geo-referenced element concentration value (data point) for each pixel for each assessed element. Hence, the exploratory spatial predictions enabled us to increase the original composite sample size of 11 sites to a much larger Alaskan landscape (approximately 656,000 predicted sites), leading us to the territory of Big Data approaches, representing a core novelty aspect of this study. Some of the major heavy metal prediction hotspots were found to be near active and inactive military sites (e.g., Murphy Dome and Salcha sites in Interior Alaska for Se, Cd, and As), raising potential concerns for heavy metal contaminations of nearby landscapes. This approach can be further utilized as a planning tool for future on-the-ground sampling of squirrels or other components of the landscape.