Persistence of Antibiotic Resistance Genes Varies with Particle Size and Substrate Conditions in Recirculating Streams.

Antibiotic resistance (AR) determinants are enriched in animal manures, a significant portion of which is land-applied as a soil amendment or as fertilizer, leading to potential AR runoff and microbial pollution in adjacent surface waters. To effectively inform AR monitoring and mitigation efforts, a thorough understanding and description of the persistence and transport of manure-derived AR in flowing waters are needed. We used experimental recirculating mesocosms to assess water-column removal rates of antibiotic resistance genes (ARGs) originating from a cow manure slurry collected from a dairy farm. We quantified the effect of three benthic (i.e., bottom) substrate variations and particle sizes of manure slurry on water column removal rates. Overall, we observed variation in ARG behavior across substrate treatments and particle sizes. For ARGs associated with small particles, removal rates were higher in mesocosms with a substrate. tetW was typically removed at the highest rates across particle size and treatment, followed by ermB and blaTEM. Our data suggests that both substrate character and particle size exert control on the fate and transport of ARGs in surface waters, laying the foundation for future research in this area to establish a predictive framework for AR persistence and fate in flowing waters.

Suspended Materials Affect Particle Size Distribution and Removal of Environmental DNA in Flowing Waters.

Environmental DNA (eDNA) in aquatic systems is a complex mixture that includes dissolved DNA, intracellular DNA, and particle-adsorbed DNA. Information about the various components of eDNA and their relative proportions could be used to discern target organism abundance and location. However, a limited knowledge of eDNA adsorption dynamics and interactions with other materials hinders these applications. To address this gap, we used recirculating stream mesocosms to investigate the impact of suspended materials (fine particulate organic matter, plankton, clay, and titanium dioxide) on the eDNA concentration and particle size distribution (PSD) from two fish species in flowing water. Our findings revealed that eDNA rapidly adsorbs to other materials in the water column, affecting its concentration and PSD. Nonetheless, only particulate organic matter affected eDNA removal rate after 30 h. Moreover, we observed that the removal of larger eDNA components (≥10 µm) was more strongly influenced by physical processes, whereas the removal of smaller eDNA components was driven by biological degradation. This disparity in removal mechanisms between larger and smaller eDNA components could explain changes in eDNA composition over time and space, which have implications for modeling the spatial distribution and abundance of target species and optimizing eDNA detection in high turbidity systems.

Climate change and the opportunity cost of conflict.

A growing empirical literature associates climate anomalies with increased risk of violent conflict. This association has been portrayed as a bellwether of future societal instability as the frequency and intensity of extreme weather events are predicted to increase. This paper investigates the theoretical foundation of this claim. A seminal microeconomic model of opportunity costs-a mechanism often thought to drive climate-conflict relationships-is extended by considering realistic changes in the distribution of climate-dependent agricultural income. Results advise caution in using empirical associations between short-run climate anomalies and conflicts to predict the effect of sustained shifts in climate regimes: Although war occurs in bad years, conflict may decrease if agents expect more frequent bad years. Theory suggests a nonmonotonic relation between climate variability and conflict that emerges as agents adapt and adjust their behavior to the new income distribution. We identify 3 measurable statistics of the income distribution that are each unambiguously associated with conflict likelihood. Jointly, these statistics offer a unique signature to distinguish opportunity costs from competing mechanisms that may relate climate anomalies to conflict.

Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality.

Implementing effective policy to protect human health from the adverse effects of air pollution, such as premature mortality, requires reducing the uncertainty in health outcomes models. Here we present a novel method to reduce mortality uncertainty by increasing the amount of input data of air pollution and health outcomes, and then quantifying tradeoffs associated with the different data gained. We first present a study of long-term mortality from fine particulate matter (PM2.5) based on simulated data, followed by a real-world application of short-term PM2.5-related mortality in an urban area. We employ information yield curves to identify which variables more effectively reduce mortality uncertainty when increasing information. Our methodology can be used to explore how specific pollution scenarios will impact mortality and thus improve decision-making. The proposed framework is general and can be applied to any real case-scenario where knowledge in pollution, demographics, or health outcomes can be augmented through data acquisition or model improvements to generate more robust risk assessments.

Comprehensive comparison of pore-scale models for multiphase flow in porous media.

Multiphase flows in porous media are important in many natural and industrial processes. Pore-scale models for multiphase flows have seen rapid development in recent years and are becoming increasingly useful as predictive tools in both academic and industrial applications. However, quantitative comparisons between different pore-scale models, and between these models and experimental data, are lacking. Here, we perform an objective comparison of a variety of state-of-the-art pore-scale models, including lattice Boltzmann, stochastic rotation dynamics, volume-of-fluid, level-set, phase-field, and pore-network models. As the basis for this comparison, we use a dataset from recent microfluidic experiments with precisely controlled pore geometry and wettability conditions, which offers an unprecedented benchmarking opportunity. We compare the results of the 14 participating teams both qualitatively and quantitatively using several standard metrics, such as fractal dimension, finger width, and displacement efficiency. We find that no single method excels across all conditions and that thin films and corner flow present substantial modeling and computational challenges.

Transient surface hydration impacts biogeography and intercellular interactions of non-motile bacteria.

There are many hydrated surface niches that are neither static nor continuously flowing that are colonized by microbes such as bacteria. Such periodic hydrodynamic regimes are distinct from aquatic systems where microbial dissemination is reasonably predicted by assuming continuous flow or static systems where motile microbes largely control their own fate. Here we show how non-motile bacteria exhibit rapid, dispersive bursts of movement over surfaces using transient confluent hydration from the environment, which we term "surface hydrodispersion" where cells traverse thousands of cell lengths within minutes. The fraction of the population disseminated by surface hydrodispersion is small-on order of 1 cell per million. Thus, surface hydrodispersion can promote isolated distribution of single cells, which is unlike other characterized active and passive surface motilities. We describe this translocation using a continuous time random walk modeling approach and find in computational simulations that transient fluid accumulation, dilution, and gravitational pull are the contributing factors. Surface hydrodispersion, consistent with advection, is unlike simple colony expansion as it dramatically alters spatial relationships, shown here with Staphylococcus aureus, which becomes increasingly virulent when isolated from Corynebacterium striatum Surface hydrodispersion of non-motile bacteria exploiting transient fluid availability and gravity is a mechanism that can result in sporadic and sudden shifts in microbial community behavior. To better understand how this movement can impact biogeography on the millimeter scale, this work describes a system for study of primary factors behind this movement as well as a stochastic model describing this dispersal.Importance: Understanding the dynamics within microbiome communities is a challenge. Knowledge of phylogeny and spatial arrangement has led to increased understanding of numerous polymicrobial communities yet, these snapshots do not convey the dynamics of populations over time. The actual biogeography of any microbiome controls the potential interactions, governing any possible antagonistic or synergistic behavior. Accordingly, a shift in biogeography can enable new behavior. Little is known about the movement mechanisms of "non-motile" microbes. Here we characterize a universal means of movement we term hydrodispersion where non-motile bacteria are transported thousands of cell lengths in minutes. We show that only a small fraction of the population is translocated by hydrodispersion and describe this movement further using a random-walk mathematical model approach in silico We demonstrate the importance of hydrodispersion by showing that Staphylococcus aureus can separate from a coculture inoculation with Corynebacterium striatum thus permitting transition to a more virulent state.

Water Flow and Biofilm Cover Influence Environmental DNA Detection in Recirculating Streams.

The increasing use of environmental DNA (eDNA) for determination of species presence in aquatic ecosystems is an invaluable technique for both ecology as a field and for the management of aquatic ecosystems. We examined the degradation dynamics of fish eDNA using an experimental array of recirculating streams, also using a "nested" primer assay to estimate degradation among eDNA fragment sizes. We introduced eDNA into streams with a range of water velocities (0.1-0.8 m s-1) and substrate biofilm coverage (0-100%) and monitored eDNA concentrations over time (∼10 d) to assess how biophysical conditions influence eDNA persistence. We found that the presence of biofilm significantly increased initial decay rates relative to previous studies conducted in nonflowing microcosms, suggesting important differences in detection and persistence in lentic vs lotic systems. Lastly, by using a nested primer assay that targeted different size eDNA fragments, we found that fragment size altered both the estimated rate constant coefficients, as well as eDNA detectability over time. Larger fragments (>600 bp) were quickly degraded, while shorter fragments (<100 bp) remained detectable for the entirety of the experiment. When using eDNA as a stream monitoring tool, understanding environmental factors controlling eDNA degradation will be critical for optimizing eDNA sampling strategies.

An Integrated Experimental and Modeling Approach to Predict Sediment Mixing from Benthic Burrowing Behavior.

Bioturbation is the dominant mode of sediment transport in many aquatic environments and strongly influences both sediment biogeochemistry and contaminant fate. Available bioturbation models rely on highly simplified biodiffusion formulations that inadequately capture the behavior of many benthic organisms. We present a novel experimental and modeling approach that uses time-lapse imagery to directly relate burrow formation to resulting sediment mixing. We paired white-light imaging of burrow formation with fluorescence imaging of tracer particle redistribution by the oligochaete Lumbriculus variegatus. We used the observed burrow formation statistics and organism density to parametrize a parsimonious model for sediment mixing based on fundamental random walk theory. Worms burrowed over a range of times and depths, resulting in homogenization of sediments near the sediment-water interface, rapid nonlocal transport of tracer particles to deep sediments, and large areas of unperturbed sediments. Our fundamental, parsimonious random walk model captures the central features of this highly heterogeneous sediment bioturbation, including evolution of the sediment-water interface coupled with rapid near-surface mixing and anomalous late-time mixing resulting from infrequent, deep burrowing events. This approach provides a general, transferable framework for explicitly linking sediment transport to governing biophysical processes.

Influence of Stream Bottom Substrate on Retention and Transport of Vertebrate Environmental DNA.

While environmental DNA (eDNA) is now being regularly used to detect rare and elusive species, detection in lotic environments comes with a caveat: The species being detected is likely some distance upstream from the point of sampling. Here, we conduct a series of seminatural stream experiments to test the sensitivity of new digital droplet PCR (ddPCR) to detect low concentrations of eDNA in a lotic system, measure the residence time of eDNA compared to a conservative tracer, and we model the transport of eDNA in this system. We found that while ddPCR improves our sensitivity of detection, the residence time and transport of eDNA does not follow the same dynamics as the conservative tracer and necessitates a more stochastic framework for modeling eDNA transport. There was no evidence for differences in the transport of eDNA due to substrate type. The relatively large amount of unexplained variability in eDNA transport reveals the need for uncovering mechanisms and processes by which eDNA is transported downstream leading to species detections, particularly when inferences are to be made in natural systems where eDNA is being used for conservation management.

Characterization of the endemic equilibrium and response to mutant injection in a multi-strain disease model.

We explore a model of an antigenically diverse infection whose otherwise identical strains compete through cross-immunity. We assume that individuals may produce upon infection different numbers of antibody types, each of which matches the antigenic configuration of a particular epitope, and that one matching antibody type grants total immunity against a challenging strain. In order to reduce the number of equations involved in the analytic description of the dynamics, we follow the strategy proposed by Kryazhimskiy et al. (2007) and apply a low-order closure reminiscent of a pair approximation. Using this approximation, we go beyond the numerical studies of Kryazhimskiy et al. (2007) and explore the analytic properties of the ensuing model in the absence of mutation. We characterize its endemic equilibrium, comparing with the results of agent based simulations of the full model to assess the performance of the closure assumption. We show that a particular choice of immune response leads to a degenerate endemic equilibrium, where different strain prevalences may exist, breaking the symmetry of the model. Finally we study the behavior of the system under the injection of mutant strains. We find that the build up of diversity from a single founding strain is extremely unlikely for different choices of the population׳s immune response.

Mobility of Dissolved Organic Matter from the Suwannee River (Georgia, USA) in Sand-Packed Columns.

Transport of dissolved organic matter (DOM) from the Suwannee River and of synthetic polystyrene sulfonates (PSSs) was investigated in columns packed with naturally Fe/Al-oxide-coated sands from Oyster, Virginia. Surface-water samples were collected in May 2012 and processed by XAD-8 (humic substances; HPOA), XAD-4 (transphilic acids [TPIAs]), and reverse osmosis (broad range of components; NOM). Median transport time (Ro ) of PSSs increased with molecular weight (MW) from 1,000 to 8,000 Da but decreased for the largest PSS (18,000 Da), which is consistent with previous observations of MW effects on DOM adsorption and transport. Breakthrough curves (BTCs) of HPOAs and NOM were similar whereas TPIA transport was distinct; although all DOM samples had similar Ro values, BTC asymmetry and dispersivity were greater for TPIAs. All samples exhibited power-law tailing that is characteristic of heterogeneous sorbent/sorbate interactions, potentially including kinetic effects. The one-dimensional advection-dispersion equation was unable to capture the tailing but it was captured well using a continuous-time random walk (CTRW) model. CTRW parameters were similar for the NOM and HPOA samples but distinctly different for TPIAs, which had more pronounced tailing. While retardation of organics generally tends to increase with MW, the lower average MW of TPIAs did not result in decreased overall retardation, which suggests the importance of compositional differences. Results suggest that while TPIAs tend to be a relatively minor component of DOM, their transport behavior differs from that of the predominant HPOA fraction, and they might thus have different impacts on pollutant transport.

Natural Organic Matter Transport Modeling with a Continuous Time Random Walk Approach.

In transport experiments through columns packed with naturally Fe/Al oxide-coated quartz sand, breakthrough curves (BTCs) of natural organic matter (NOM) displayed strong and persistent power law tailing that could not be described by the classical advection-dispersion equation. Tailing was not observed in BTCs for a nonreactive tracer (sulforhodamine B); therefore, the anomalous transport is attributed to diverse adsorptive behavior of the polydisperse NOM sample rather than to physical heterogeneity of the porous medium. NOM BTC tailing became more pronounced with decreases in pH and increases in ionic strength, conditions previously shown to be associated with enhanced preferential adsorption of intermediate to high molecular weight NOM components. Drawing from previous work on anomalous solute transport, we develop an approach to model NOM transport within the framework of a continuous time random walk (CTRW) and show that under all conditions examined, the CTRW model is able to capture tailing of NOM BTCs by accounting for differences in transport rates of NOM fractions through a distribution of effective retardation factors. These results demonstrate the importance of considering effects of adsorptive fractionation on NOM mobility, and illustrate the ability of the CTRW model to describe transport of a multicomponent solute.

Statistical-physical method for simulating the transport of microplastic-antibiotic compound pollutants in typical bay area.

Microplastics and antibiotics are emerging pollutants in the environment and have received widespread attention globally. In coastal areas, microplastic and antibiotic pollution is ubiquitous and often overlapping. Microplastic-antibiotic compound pollutants that are formed through adsorption have thus become a major concern. However, modeling knowledge of microplastic transport in coastal areas is still limited, and research on the impact of compound pollutants caused by Polythene (PE)-antibiotics in such settings is in early stages. In this study, using a lattice Boltzmann method (LBM) and temporal Markov method (TMM) under a statistical-physical framework, we simulated pollutant transport and PE-antibiotic compound pollutants in coastal areas. First, a series of models are proposed, including an LBM wave-current coupling model, an LBM antibiotic transport model, an LBM particle-tracking model, a TMM microplastic transport model and the final LBM-TMM hybrid compound pollutant model. Then, the suitability and applicability of the models was validated using experimental data and numerical simulations. Finally, the models were applied to a study area, Laizhou Bay (China). The simulation results demonstrate that adsorption will reduce the concentration of antibiotics in the water environment. Within 44 days, the adsorbed antibiotic carried by PE particles migrate further, and the width of the pollution zone escalates from 234.2 m to 689.0 m.

Flow intermittency, dispersion, and correlated continuous time random walks in porous media.

We study the intermittency of fluid velocities in porous media and its relation to anomalous dispersion. Lagrangian velocities measured at equidistant points along streamlines are shown to form a spatial Markov process. As a consequence of this remarkable property, the dispersion of fluid particles can be described by a continuous time random walk with correlated temporal increments. This new dynamical picture of intermittency provides a direct link between the microscale flow, its intermittent properties, and non-Fickian dispersion.

Communication: A full solution of the annihilation reaction A + B → [empty-set] based on time-subordination.

The connection between the governing equations of chemical reaction and the underlying stochastic processes of particle collision and transformation have been developed previously along two end-member conditions: perfectly mixed and maximally diffusion-limited. The complete governing equation recognizes that in the perfectly mixed case, the particle (i.e., molecular or macro-particle) number state evolution is markovian, but that spatial self-organization of reactants decreases the probability of reactant pairs finding themselves co-located. This decreased probability manifests itself as a subordination of the clock time: as reactant concentrations become spatially variable (unmixed), the time required for reactants to find each other increases and the random operational time that particles spend in the active reaction process is less than the clock time. For example, in the system A + B → [empty-set], a simple approximate calculation for the return time of a brownian motion to a moving boundary allows a calculation of the operational time density, and the total solution is a subordination integral of the perfectly-mixed solution with a modified inverse gaussian subordinator. The system transitions from the well-mixed solution to the asymptotic diffusion-limited solution that decays as t(-d∕4) in d-dimensions.

Mixing-Driven Equilibrium Reactions in Multidimensional Fractional Advection Dispersion Systems.

We study instantaneous, mixing-driven, bimolecular equilibrium reactions in a system where transport is governed by a multidimensional space fractional dispersion equation. The superdiffusive, nonlocal nature of the system causes the location and magnitude of reactions that take place to change significantly from a classical Fickian diffusion model. In particular, regions where reaction rates would be zero for the Fickian case become regions where the maximum reaction rate occurs when anomalous dispersion operates. We also study a global metric of mixing in the system, the scalar dissipation rate and compute its asymptotic scaling rates analytically. The scalar dissipation rate scales asymptotically as t-(d+α)/α , where d is the number of spatial dimensions and α is the fractional derivative exponent.

Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation.

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1 km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

Particle size influences decay rates of environmental DNA in aquatic systems.

Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish-derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium-sized particles (1-10 µm) decayed more slowly than other size classes (i.e., <1 and > 10 µm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target-specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single-target applications.

Environmental DNA (eDNA) removal rates in streams differ by particle size under varying substrate and light conditions.

The use of environmental DNA (eDNA) as a sampling tool offers insights into the detection of invasive and/or rare aquatic species and enables biodiversity assessment without traditional sampling approaches, which are often labor-intensive. However, our understanding of the environmental factors that impact eDNA removal (i.e., how rapidly eDNA is removed from the water column by the combination of decay and physical removal) in flowing waters is limited. This limitation constrains predictions about the location and density of target organisms after positive detection. To address this question, we spiked Common Carp (Cyprinus carpio) eDNA into recirculating mesocosms (n = 24) under varying light (shaded versus open) and benthic substrate conditions (no substrate, bare substrate, and biofilm-colonized substrate). We then collected water samples from each mesocosm at four time points (40 min, 6 h, 18 h, and 48 h), and sequentially filtered the samples through 10, 1.0, and 0.2 µm filters to quantify removal rates for different eDNA particle sizes under varying light and substrate conditions. Combining all size classes, total eDNA removal rates were higher for mesocosms with biofilm-colonized substrate compared to those with no substrate or bare (i.e., no biofilm) substrate, which is consistent with previous findings linking biofilm colonization with increased eDNA removal and degradation. Additionally, when biofilm was present, light availability increased eDNA removal; eDNA levels fell below detection after 6-18 h for open mesocosms versus 18-48 h for shaded mesocosms. Among size classes, larger particles (>10 µm) were removed faster than small particles (1.0-0.2 µm). These results suggest that changes in the distribution of eDNA size classes over time (e.g., with downstream transport) and with differing environmental conditions could be used to predict the location of target organisms in flowing waters, which will advance the use of eDNA as a tool for species monitoring and management.

Visualization of mixing processes in a heterogeneous sand box aquifer.

Mixing is increasingly recognized as a critical process for understanding and modeling reactive transport. Yet, mixing is hard to characterize because it depends nonlinearly on concentrations. Visualization of optical tracers in the laboratory at high spatial and temporal resolution can help advance the study of mixing processes. The solute distribution is obtained by analyzing the relationship between pixel intensity and tracer concentration. The problem with such techniques is that grain borders, light fluctuations, and nonuniform brightness contribute to produce noisy images of concentrations that cannot be directly used to estimate mixing at the local scale. We present a nonparametric regression methodology to visualize local values of mixing from noisy images of optical tracers that minimizes smoothing in the direction of concentration gradients. This is achieved by weighting pixel data along concentration isolines. The methodology is used to provide a full visualization of mixing dynamics in a tracer experiment performed in a reconstructed aquifer consisting of two materials with contrasting hydraulic properties. The experiment reveals that mixing is largest at the contact area of regions with different permeability. Also, the temporal evolutions of mixing and dilution rates are significantly different. The mixing rate is more persistent than the dilution rate during tracer invasion, and the opposite is true during flushing, which helps in understanding the complementary nature of these two measures.