Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

AIM: To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. METHODS AND RESULTS: We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. CONCLUSIONS: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. SIGNIFICANCE AND IMPACT OF THE STUDY: This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.

Linking optimization and ecological models in a decision support tool for oyster restoration and management.

Restoration of ecologically important marine species and habitats is restricted by funding constraints and hindered by lack of information about trade-offs among restoration goals and the effectiveness of alternative restoration strategies. Because ecosystems provide diverse human and ecological benefits, achieving one restoration benefit may take place at the expense of other benefits. This poses challenges when attempting to allocate limited resources to optimally achieve multiple benefits, and when defining measures of restoration success. We present a restoration decision-support tool that links ecosystem prediction and human use in a flexible "optimization" framework that clarifies important restoration trade-offs, makes location-specific recommendations, predicts benefits, and quantifies the associated costs (in the form of lost opportunities). The tool is illustrated by examining restoration options related to the eastern oyster, Crassostrea virginica, which supported an historically important fishery in Chesapeake Bay and provides a range of ecosystem services such as removing seston, enhancing water clarity, and creating benthic habitat. We use an optimization approach to identify the locations where oyster restoration efforts are most likely to maximize one or more benefits such as reduction in seston, increase in light penetration, spawning stock enhancement, and harvest, subject to funding constraints and other limitations. This proof-of-concept Oyster Restoration Optimization model (ORO) incorporates predictions from three-dimensional water quality (nutrients-phytoplankton zooplankton-detritus [NPZD] with oyster filtration) and larval transport models; calculates size- and salinity-dependent growth, mortality, and fecundity of oysters; and includes economic costs of restoration efforts. Model results indicate that restoration of oysters in different regions of the Chesapeake Bay would maximize different suites of benefits due to interactions between the physical characteristics of a system and nonlinear biological processes. For example, restoration locations that maximize harvest are not the same as those that would maximize spawning stock enhancement. Although preliminary, the ORO model demonstrates that our understanding of circulation patterns, single-species population dynamics and their interactions with the ecosystem can be integrated into one quantitative framework that optimizes spending allocations and provides explicit advice along with testable predictions. The ORO model has strengths and constraints as a tool to support restoration efforts and ecosystem approaches to fisheries management.

Precipitation thresholds for fecal bacterial indicators in the Chesapeake Bay.

Many coastal states of the United States restrict harvest of shellfish from select areas based on some environmental trigger. Such areas are classified as being conditionally approved. In Maryland, the trigger is an inch or more of rainfall that has fallen in the last 24 h. This study used 11 years of monitoring data to test the relationship between daily rainfall totals and densities of fecal indicators in Maryland shellfish harvest waters. Precipitation and fecal coliform (FC) water monitoring data from 2004 to 2014 were matched by date and watershed. The influence of antecedent rainfall conditions (i.e. rainfall in the preceding days or weeks) and the distance of each monitoring station to land were compared to the percent of samples exceeding the FDA criterion for managing shellfish harvest areas. Sample stations beyond 1000m from land had FC densities consistently below the FDA criterion and were excluded from further analysis. Rainfall events greater than an inch tended to result in significantly elevated FC for the following two days, followed by lower levels thereafter. The total amount of rain in the last three weeks was positively related to the proportion of samples with FC greater than the FDA criterion. Bay-wide, the percent of samples exceeding the FDA criterion rose from seven percent for rainfall less than an inch to 37% following one or more inches of rain. Watersheds were classified based on the percent of FC densities over the criterion when rainfall was an inch or more, with 41 of 81 watersheds showing FC responses indicative of potential conditionally approved areas, those shellfish growing areas where the one inch precipitation trigger may be applied. These areas largely overlapped the current conditionally approved areas defined by Maryland. The percent of open water, wetlands, and poorly drained soils explained a significant amount of the variability (R2 = 0.72) in the difference in percent of samples exceeding the FDA criterion when rainfall was greater than an inch and when it was less than an inch. Logistic regression analysis showed that the current trigger of one inch of rain in 24 h is predictive of FC densities over the FDA criterion, though the appropriate threshold will most likely depend on how far the particular shellfish growing area is from land and antecedent rain conditions. In watersheds with relatively high percentages of open water to total watershed size, higher rainfall thresholds might be appropriate. The approach taken in this study could be applied to individual stations and sub-watersheds, potentially allowing the reclassification of some shellfish harvest areas.

Ocean biogeochemistry modeled with emergent trait-based genomics.

Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and "omics" data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.

Microfluidic remote loading for rapid single-step liposomal drug preparation.

Microfluidic-directed formation of liposomes is combined with in-line sample purification and remote drug loading for single step, continuous-flow synthesis of nanoscale vesicles containing high concentrations of stably loaded drug compounds. Using an on-chip microdialysis element, the system enables rapid formation of large transmembrane pH and ion gradients, followed by immediate introduction of amphipathic drug for real-time remote loading into the liposomes. The microfluidic process enables in-line formation of drug-laden liposomes with drug : lipid molar ratios of up to 1.3, and a total on-chip residence time of approximately 3 min, representing a significant improvement over conventional bulk-scale methods which require hours to days for combined liposome synthesis and remote drug loading. The microfluidic platform may be further optimized to support real-time generation of purified liposomal drug formulations with high concentrations of drugs and minimal reagent waste for effective liposomal drug preparation at or near the point of care.