Cryptic species complex shows population-dependent, rather than lineage-dependent tolerance to a neonicotinoid.

Cryptic species are rarely considered in ecotoxicology, resulting in misleading outcomes when using a single morphospecies that encompasses multiple cryptic species. This oversight contributes to the lack of reproducibility in ecotoxicological experiments and promotes unreliable extrapolations. The important question of ecological differentiation and the sensitivity of cryptic species is rarely tackled, leaving a substantial knowledge gap regarding the vulnerability of individual cryptic species within species complexes. In times of agricultural intensification and the frequent use of pesticides, there is an urgent need for a better understanding of the vulnerability of species complexes and possible differences in adaptive processes. We used the cryptic species complex of the aquatic amphipod Gammarus roeselii, which comprises at least 13 genetic mtDNA lineages and spans from small-scale endemic lineages in Greece to a large-scale widely distributed lineage in central Europe. We exposed eleven populations belonging to four lineages to the neonicotinoid thiacloprid in an acute toxicity assay. We recorded various environmental variables in each habitat to assess the potential pre-exposure of the populations to contaminants. Our results showed that the populations differed up to 4-fold in their tolerances. The lineage identity had a rather minor influence, suggesting that the cryptic species complex G. roeselii does not differ significantly in tolerance to the neonicotinoid thiacloprid. However, the observed population differentiation implies that recent pre-exposure to thiacloprid (or similar substances) or general habitat contamination has triggered adaptive processes. Though, the extent to which these mechanisms are equally triggered in all lineages needs to be addressed in the future. Our study provides two key findings: Firstly, it shows that observed phylogenetic differences within the G. roeselii species complex did not reveal differences in thiacloprid tolerance. Second, it confirms that differentiation occurs at the population level, highlighting that susceptibility to toxicants is population-dependent. The population-specific differences were within the range of accepted intraspecific variability from a regulatory standpoint. From an evolutionary-ecological perspective, it remains intriguing to observe how persistent stresses will continue to influence tolerance and whether different populations are on distinct pathways of adaptation. Given that the potential selection process has only lasted a relatively short number of generations, it is crucial to monitor these populations in the future, as even brief exposure periods significantly impact evolutionary responses.

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.

Time series of freshwater macroinvertebrate abundances and site characteristics of European streams and rivers.

Freshwater macroinvertebrates are a diverse group and play key ecological roles, including accelerating nutrient cycling, filtering water, controlling primary producers, and providing food for predators. Their differences in tolerances and short generation times manifest in rapid community responses to change. Macroinvertebrate community composition is an indicator of water quality. In Europe, efforts to improve water quality following environmental legislation, primarily starting in the 1980s, may have driven a recovery of macroinvertebrate communities. Towards understanding temporal and spatial variation of these organisms, we compiled the TREAM dataset (Time seRies of European freshwAter Macroinvertebrates), consisting of macroinvertebrate community time series from 1,816 river and stream sites (mean length of 19.2 years and 14.9 sampling years) of 22 European countries sampled between 1968 and 2020. In total, the data include >93 million sampled individuals of 2,648 taxa from 959 genera and 212 families. These data can be used to test questions ranging from identifying drivers of the population dynamics of specific taxa to assessing the success of legislative and management restoration efforts.

Workplace violence, work-related exhaustion, and workplace cognitive failure among nurses: A cross-sectional study.

AIM: To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure. DESIGN: A cross-sectional study. METHODS: An online questionnaire was administered in April 2023 to nurses in Michigan, US. Structural equation modelling was used to examine effects of physical and non-physical workplace violence (occupational stressors) and work efficiency and competence development (occupational protective factors) on workplace cognitive failure. RESULTS: Physical violence was a significant predictor of the action subscale of cognitive failure. There were no direct effects of non-physical violence, workplace efficiency, or competence development on any of the workplace cognitive failure dimensions. Both types of violence and efficiency had significant indirect effects on workplace cognitive failure via work-related exhaustion. Work-related exhaustion predicted significantly higher scores for workplace cognitive failure. CONCLUSION: Workplace violence and work efficiency exhibited primarily indirect effects on workplace cognitive failure among nurses via work-related exhaustion. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses experiencing workplace violence may be at increased risk for workplace cognitive failure, especially if they are also experiencing work-related exhaustion. Workplaces that nurses perceive as more efficient can help to mitigate the effects of violence on nurses' cognitive failure. IMPACT: This study addressed the possible effects of workplace violence as well as work efficiency and competence development on nurses' cognitive failure at work. Analyses revealed primarily indirect effects of workplace violence, and indirect protective effects of work efficiency, on nurses' cognitive failure via work-related exhaustion. This research has implications for healthcare organizations and suggests that efforts made by healthcare workplaces to prevent violence and work-related exhaustion, and to enhance work efficiency, may help to mitigate workplace cognitive failure among nurses. REPORTING METHOD: We have followed the STROBE checklist in reporting this study. PATIENT OR PUBLIC CONTRIBUTION: No Patient or public contribution.

Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics.

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.

Assessing the impact of technological change on similar occupations: Implications for employment alternatives.

BACKGROUND: The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. METHOD: We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. RESULTS: The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. CONCLUSION: Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.

The recovery of European freshwater biodiversity has come to a halt.

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

Flushing away the future: The effects of wastewater treatment plants on aquatic invertebrates.

Wastewater treatment plants (WWTP) are essential infrastructure in our developing world. However, with the development and release of novel entities and without modern upgrades, they are ineffective at fully removing micropollutants before treated effluents are released back into aquatic environments. Thus, WWTPs may represent additional point source impacts to freshwater environments, further pressuring aquatic fauna and already vulnerable insect communities. Previous studies - mostly focusing on single WWTPs - have shown general trends of freshwater invertebrate communities becoming dominated by pollution tolerant taxa. To expand on these findings, the current study is the first to comprehensively investigate data on the effects of 170 WWTPs on invertebrate taxonomic composition. We compared data for several diversity and pollution indices, as well as the taxonomic composition both upstream and downstream of the WWTPs (366 sampling sites). In terms of abundance, the three most frequent and negatively impacted orders were the Plecoptera, Trichoptera and Gastropoda, while the Turbellaria, Hirudinea and Crustacea increased in abundance. Although strong changes in community composition were observed between upstream and downstream sites (mean species turnover of 61%), commonly used diversity indices were not sensitive to these changes, highlighting their potential inadequacy in accurately assessing ecological health. Our results indicate that WWTPs change downstream conditions in favour of pollution tolerant taxa to the detriment of sensitive taxa. Order-level taxonomic responses can be informative but should be interpreted with caution, since they can be driven by a few taxa, or opposing responses of species in the same group can result in an overall low order-level response. Upgrading WWTPs via additional treatment steps or merging may be beneficial, provided upstream sections are unimpacted and/or are in a good chemical and structural condition.

Multidecadal changes in functional diversity lag behind the recovery of taxonomic diversity.

While there has been increasing interest in how taxonomic diversity is changing over time, less is known about how long-term taxonomic changes may affect ecosystem functioning and resilience. Exploring long-term patterns of functional diversity can provide key insights into the capacity of a community to carry out ecological processes and the redundancy of species' roles. We focus on a protected freshwater system located in a national park in southeast Germany. We use a high-resolution benthic macroinvertebrate dataset spanning 32 years (1983-2014) and test whether changes in functional diversity are reflected in taxonomic diversity using a multidimensional trait-based approach and regression analyses. Specifically, we asked: (i) How has functional diversity changed over time? (ii) How functionally distinct are the community's taxa? (iii) Are changes in functional diversity concurrent with taxonomic diversity? And (iv) what is the extent of community functional redundancy? Resultant from acidification mitigation, macroinvertebrate taxonomic diversity increased over the study period. Recovery of functional diversity was less pronounced, lagging behind responses of taxonomic diversity. Over multidecadal timescales, the macroinvertebrate community has become more homogenous with a high degree of functional redundancy, despite being isolated from direct anthropogenic activity. While taxonomic diversity increased over time, functional diversity has yet to catch up. These results demonstrate that anthropogenic pressures can remain a threat to biotic communities even in protected areas. The differences in taxonomic and functional recovery processes highlight the need to incorporate functional traits in assessments of biodiversity responses to global change.

Employees' reactions toward COVID-19 information exposure: Insights from terror management theory and generativity theory.

As the coronavirus disease (COVID-19) has imposed significant risks to our health and affected our social and economic order, information on COVID-19 becomes readily accessible via various mass media and social media. In the current research, we aim to understand the impacts of employees' exposure to COVID-19 information on their workplace behaviors. Integrating Terror Management Theory (TMT; Becker, 1973; Greenberg et al., 1986) with Generativity Theory (Erikson, 1963, 1982), we proposed and investigated two psychological mechanisms (i.e., death anxiety and generativity-based death reflection) that account for the effects of employees' COVID-19 information exposure on their work withdrawal and helping behaviors toward coworkers. We also examined organizational actions [internal and external corporate social responsibility (CSR) activities] that served as a context for employees to make sense of their COVID-19 information exposure. We conducted two studies with samples of full-time employees (N1 = 278; N2 = 382) to test our predictions. Results in both studies showed that employees' exposure to COVID-19 information was positively related to their death anxiety and generativity-based death reflection, which in turn predicted their work withdrawal and helping behaviors, respectively. Further, employees' perceived internal CSR of their organization mitigated the positive association between COVID-19 information exposure and their death anxiety, weakening the positive indirect effect of COVID-19 information exposure on their work withdrawal. Our study offers new insights to the understanding of work and employment in the COVID-19 pandemic and sheds light on how individuals' death-related experiences shape work-related behaviors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Long-term data reveal unimodal responses of ground beetle abundance to precipitation and land use but no changes in taxonomic and functional diversity.

While much of global biodiversity is undoubtedly under threat, the responses of ecological communities to changing climate, land use intensification, and long-term changes in both taxonomic and functional diversity over time, has still not been fully explored for many taxonomic groups, especially invertebrates. We compiled time series of ground beetles covering the past two decades from 40 sites located in five regions across Germany. We calculated site-based trends for 21 community metrics representing taxonomic and functional diversity of ground beetles, activity density (a proxy for abundance), and activity densities of functional groups. We assessed both overall and regional temporal trends and the influence of the global change drivers of temperature, precipitation, and land use on ground beetle communities. While we did not detect overall temporal changes in ground beetle taxonomic and functional diversity, taxonomic turnover changed within two regions, illustrating that community change at the local scale does not always correspond to patterns at broader spatial scales. Additionally, ground beetle activity density had a unimodal response to both annual precipitation and land use. Limited temporal change in ground beetle communities may indicate a shifting baseline, where community degradation was reached prior to the start of our observation in 1999. In addition, nonlinear responses of animal communities to environmental change present a challenge when quantifying temporal trends.

Workplace Interventions in Response to COVID-19: an Occupational Health Psychology Perspective.

The outbreak of COVID-19 has imposed significant threats to individuals' physical health and has substantially changed the socioeconomic order and the nature of our work and life all over the world. To guide organizations to design effective workplace interventions to mitigate the negative impacts of COVID-19, we take the occupational health psychology (OHP) perspective to propose a framework that highlights important areas for organizations to intervene in order to better protect workers' physical health and safety and to promote workers' psychological well-being. Specifically, we integrate the prevention-based public health model with the Total Worker Health (TWH) and OHP-based approaches to propose a comprehensive set of primary, secondary, and tertiary interventions that target different groups of employees with varied exposure risks to the new coronavirus. We believe these proposed interventions can contribute positively to the development of healthy and safe work. Implications of these proposed interventions for workers, organizations, and policy makers are also discussed.

Toward Quantum Computing for High-Energy Excited States in Molecular Systems: Quantum Phase Estimations of Core-Level States.

This paper explores the utility of the quantum phase estimation (QPE) algorithm in calculating high-energy excited states characterized by the promotion of electrons occupying core-level shells. These states have been intensively studied over the last few decades, especially in supporting the experimental effort at light sources. Results obtained with QPE are compared with various high-accuracy many-body techniques developed to describe core-level states. The feasibility of the quantum phase estimator in identifying classes of challenging shake-up states characterized by the presence of higher-order excitation effects is discussed. We also demonstrate the utility of the QPE algorithm in targeting excitations from specific centers in a molecule. Lastly, we discuss how the lowest-order Trotter formula can be applied to reducing the complexity of the ansatz without affecting the error.

Recovery from air pollution and subsequent acidification masks the effects of climate change on a freshwater macroinvertebrate community.

Freshwater ecosystems are dynamic, complex systems with a multitude of physical and ecological processes and stressors which drive fluctuations on the community-level. Disentangling the effects of different processes and stressors is challenging due to their interconnected nature. However, as protected areas (i.e. national parks) are less anthropogenically impacted, they are ideal for investigating single stressors. We focus on the Bavarian Forest National Park, a Long-Term Ecological Research (LTER) site in Germany, where the major stressors are climate warming, air pollution (i.e. acidification) and bark beetle infestations. We investigated the effects of these stressors on freshwater macroinvertebrates using comprehensive long-term (1983-2014) datasets comprising high-resolution macroinvertebrate and physico-chemical data from a near-natural stream. Macroinvertebrate communities have undergone substantial changes over the past 32 years, highlighted by increases in overall community abundance (+173%) and richness (+51.6%) as well as taxonomic restructuring driven by a disproportional increase of dipterans. Prior to the year 2000, regression analyses revealed a decline in sulphate deposition and subsequent recovery from historical acidification as potential drivers of the increases in abundance and richness rather than to increases in water temperature (1.5 °C overall increase). Post 2000, however, alterations to nutrient cycling caused by bark beetle infestations coupled with warming temperatures were correlated to taxonomic restructuring and disproportional increases of dipterans at the expense of sensitive taxa such as plecopterans and trichopterans. Our results highlight the challenges when investigating the effects of climate change within a multi-stressor context. Even in conservation areas, recovery from previous disturbance might mask the effects of ongoing disturbances like climate change. Overall, we observed strong community restructuring, demonstrating that stenothermal headwater communities face additional stress due to emerging competition with tolerant taxa. Conservation efforts should consider the temporal variability of communities and their recovery from disturbances to adequately identify species vulnerable to local or widespread extinction.

Data-driven molecular modeling with the generalized Langevin equation.

The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.

Predatory functional responses under increasing temperatures of two life stages of an invasive gecko.

The direct effects of temperature increases and differences among life-history might affect the impacts of native and invasive predators on recipient communities. Comparisons of functional responses can improve our understanding of underlying processes involved in altering species interaction strengths and may predict the effect of species invading new communities. Therefore, we investigated the functional responses of the mourning gecko Lepidodactylus lugubris (Duméril & Bibron, 1836) to explore how temperature, body-size and prey density alter gecko predatory impacts in ecosystems. We quantified the functional responses of juvenile and adult geckos in single-predator experiments at 20, 23 and 26 °C. Both displayed saturating Type-II functional responses, but juvenile functional responses and the novel Functional Response Ratio were positively affected by temperature as juvenile attack rates (a) increased as a function of increased temperature. Handling times (h) tended to shorten at higher temperature for both predator stages. We demonstrate that the effects of temperature on functional responses of geckos differ across ontogeny, perhaps reflecting life-history stages prioritising growth and maturation or body maintenance. This indicates that temperature-dependent gecko predatory impacts will be mediated by population demographics. We advocate further comparisons of functional responses to understand the invasiveness and future predatory impacts of geckos, and other invasive species globally, as temperatures change.

A clustering-based biased Monte Carlo approach to protein titration curve prediction.

In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished. We used a strategy to improve the efficiency of MCMC by taking advantage of the cluster structure in the interaction energy matrix to bias the sampling. We pursued two different schemes for the biased MCMC runs and show that they are valid MCMC schemes. We used both synthesized and real-world systems to show the improved performance of our biased MCMC methods when compared to the regular MCMC method. In particular, we applied these algorithms to the problem of estimating protonation ensemble averages and titration curves of residues in a protein.

Visualizing biomolecular electrostatics in virtual reality with UnityMol-APBS.

Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the Adaptive Poisson-Boltzmann Solver (APBS) software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two-dimensional external molecular visualization software. This process has inherent limitations: visualization of three-dimensional objects using a two-dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol-APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple graphical user interface (GUI) for either a standard desktop or immersive virtual reality experience.

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness.

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measures and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed a new data-driven approach to construct orthonormal polynomials for arbitrary mutually dependent randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as partial differential equations and realistic molecular systems within high-dimensional (O(10)) conformational spaces where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations.

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is underdetermined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parametrization problems, including those beyond continuum solvation calculations. The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters.