USGS ScienceSearch

USGS · 70266873

Selenium differentially influences methylmercury retention across mayfly life stages

Abstract

Though high mercury and selenium concentrations are individually toxic to organisms, there is a hypothesized antagonistic relationship. This potential mercury–selenium interaction is under-studied in aquatic macroinvertebrates, particularly in relation to complex life histories. We examined the proposed effect of selenium on methylmercury accumulation between four life stages for a parthenogenetic mayfly ( Neocloeon triangulifer ). We exposed diatoms to elevated methylmercury concentrations and fed them to mayflies exposed to elevated aqueous selenomethionine. We found some support for the mercury–selenium antagonism hypothesis, but it was context-specific. Selenium reduced methylmercury accumulation in high but not low methylmercury environments. Though terrestrial adult life stages had higher mercury concentrations compared to aquatic larval life stages, cumulative life history transfer factor (LHTF; ratio of methylmercury in adult imago to late instar larvae) differed by treatment. LHTF was constant for all aqueous selenium exposure levels at high dietary methylmercury (selenium impacts on methylmercury uptake and loss) but increased with aqueous selenium exposures at low dietary methylmercury (selenium impacts on methylmercury uptake only), suggesting a synergistic enhancement of MeHg transfer between life stages with increased aqueous Se exposure levels. These results suggest that animals eating adult aquatic insects are exposed to higher concentrations of methylmercury than those feeding on larval insects across selenium and methylmercury levels, but interference of selenium on methylmercury accumulation is only present at high methylmercury environments.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jacqueline R. Gerson, Rebecca A. Dorman, Collin A. Eagles-Smith, David M. Walters. 2025-04-16. Selenium differentially influences methylmercury retention across mayfly life stages. https://doi.org/10.1021/acs.est.5c00338

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Digging into soil: Effects of soil texture on RT-QuIC performance for environmental prion surveillance.

Chronic wasting disease (CWD) is a fatal neurodegenerative disease caused by infectious prions affecting wild and captive cervids. Transmission occurs directly between hosts or indirectly through contact with prion-contaminated environments. Soils, particularly those rich in clay, are hypothesized to enhance prion stability, retention, and bioavailability. Accurate detection of prions is therefore important for understanding environmental transmission risks. Real-time quaking-induced conversion (RT-QuIC) is a sensitive assay used to detect PrP CWD in tissue, excreta, and environmental materials. However, RT-QuIC performance across soil textures has not been evaluated. This study assessed RT-QuIC sensitivity and specificity using laboratory-prepared soils spiked with CWD-positive brain homogenate or water controls under a standardized extraction method. Conditional on the extraction method used, results suggest that RT-QuIC performance depends on soil texture, and thus, an optimal time-to-threshold (TTT) cutoff required to balance sensitivity and specificity will also vary with soil texture. RT-QuIC exhibited higher sensitivity and moderate specificity in soils with low clay (<20%) and moderate to high silt content, whereas high-clay soils (>20%) with low to moderate silt content (2%–60%) reduced both sensitivity and specificity, and required shorter TTT cutoffs. These findings highlight the importance of accounting for soil texture in environmental CWD surveillance.

Environmental Science and Technology

Remote sensing enables basin-scale inventories of coal mine methane

Underground coal mines are important global sources of methane, but emission estimates are uncertain. We show that emission estimates for individual mines from aircraft remote-sensing surveys in the United States agree within 40% with direct measurements used for national emission reporting (IPCC Tier 3 estimate). Such direct measurements are unavailable in most countries, which rely on estimated emission factors (EFs) applied to coal-production rates. We find that EFs from IPCC Tier 1 and the Model for Calculating Coal Mine Methane (MC2M) methods overestimate U.S. emissions 3-fold due to incorrect dependence on mine depth. An IPCC Tier 2 method using measured basin-specific mine gas content agrees with direct emission measurements but does not account for gob well emissions and requires gas content data that are generally unavailable. We show that aircraft remote sensing for a small sample of mines can successfully estimate basin-specific EFs for ventilation shafts and gob wells, enabling estimates of basin- and national-scale emissions. We discuss how the method can be applied with satellite remote sensing to quantify coal emissions worldwide.

Alabama, Colorado, Kentucky, New Mexico, Ohio, Pen

Fifteen years of WRTDS for advancing water-quality science: A critical review of methodological developments and global applications

Contamination by nutrients, major ions, and metals poses a major threat to global water sustainability. Understanding how these pollutants vary across time and space requires long-term monitoring and robust statistical approaches. Traditional methods, however, often struggle to account for streamflow variability, seasonality, and nonlinear responses. Introduced in 2010, the Weighted Regressions on Time, Discharge, and Season (WRTDS) method offers a flexible, data-driven framework that generates both observed and flow-normalized estimates of concentration and load. Over the past 15 years, WRTDS has become a state-of-the-art tool for water-quality science and management, with applications spanning a wide range of hydrologic, climatic, and policy contexts─including major watersheds across North America, Europe, Asia, Australia, and the Arctic. In this review of WRTDS, we document the method’s major advancements, examine its expanding geographic and thematic applications, and summarize its relevance to water-quality management programs and policies worldwide. We also discuss its performance relative to other regression and machine-learning approaches. Finally, we identify key priorities for future development to support the continued evolution of WRTDS as a trusted and practical tool for scientists and managers working to protect and sustain water resources.

Environmental Science and Technology