Field-scale biostimulation and desorption tracer experiments conducted in a uranium (U) contaminated, shallow alluvial aquifer have provided insight into the coupling of microbiology, biogeochemistry, and hydrogeology that control U mobility in the subsurface. Initial experiments successfully tested the concept that Fe-reducing bacteria such as Geobacter sp. could enzymatically reduce soluble U(VI) to insoluble U(IV) during in situ electron donor amendment (Anderson et al., 2003; Williams et al., 2011). In parallel, in situ desorption tracer tests using bicarbonate amendment demonstrated rate-limited U(VI) desorption (Fox et al., 2012). These results and prior laboratory studies underscored the importance of enzymatic U(VI)-reduction and suggested the ability to combine desorption and bioreduction of U(VI). Here we report the results of a new field experiment in which bicarbonate-promoted uranium desorption and acetate amendment were combined and compared to an acetate amendment-only experiment in the same experimental plot. Results confirm that bicarbonate amendment to alluvial aquifer sediments desorbs U(VI) and increases the abundance of Ca-uranyl-carbonato complexes. At the same time, the rate of acetate-promoted enzymatic U(VI) reduction was greater in the presence of added bicarbonate in spite of the increased dominance of Ca-uranyl-carbonato aqueous complexes. A model-simulated peak rate of U(VI) reduction was ~3.8 times higher during acetate-bicarbonate treatment than under acetate-only conditions. Lack of consistent differences in microbial community structure between acetate-bicarbonate and acetate-only treatments suggest that a significantly higher rate of U(VI) reduction in the bicarbonate-impacted sediment may be due to a higher intrinsic rate of microbial reduction induced by elevated concentrations of the bicarbonate oxyanion. The findings indicate that bicarbonate amendment may be useful in improving the engineered bioremediation of uranium in aquifers.

Environmental variation favors the evolution of phenotypic plasticity. For many species, we understand the costs and benefits of different phenotypes, but we lack a broad understanding of how plastic traits evolve across large clades. Using identical experiments conducted across North America, we examined prey responses to predator cues. We quantified five life-history traits and the magnitude of their plasticity for 23 amphibian species/populations (spanning three families and five genera) when exposed to no cues, crushed-egg cues, and predatory crayfish cues. Embryonic responses varied considerably among species and phylogenetic signal was common among the traits, whereas phylogenetic signal was rare for trait plasticities. Among trait-evolution models, the Ornstein-Uhlenbeck (OU) model provided the best fit or was essentially tied with Brownian motion. Using the best fitting model, evolutionary rates for plasticities were higher than traits for three life-history traits and lower for two. These data suggest that the evolution of life-history traits in amphibian embryos is more constrained by a species' position in the phylogeny than is the evolution of life history plasticities. The fact that an OU model of trait evolution was often a good fit to patterns of trait variation may indicate adaptive optima for traits and their plasticities.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.