Aims: To investigate with high geographical resolution the small-scale spatial and temporal distribution of the pathogen Vibrio vulnificus throughout the water column in a temperate Texas bay where numerous V. vulnificus infections had been reported by the regional media the previous summer.



Methods and Results:  Surface and bottom water samples were collected from 19 sites between April 2005 and October 2006 from Matagorda Bay, TX. Physicochemical parameters were measured and V. vulnificus were analysed using quantitative polymerase chain reaction (Q-PCR) as a means of overcoming constraints of traditional culturing techniques. V. vulnificus was detected through out the year, although it’s temporal and spatial distribution was patchy. V. vulnificus abundances at individual sites ranged from 1·1 × 103 cells ml−1. No statistically reliable predictive model related to the physicochemical parameters could be developed for this pathogen.



Conclusions:  his study demonstrates that year round detection of V. vulnificus while likely in the viable but nonculturable (VBNC) state during the winter months and emphasizes why physicochemical factors are insufficient metrics for robust regression modelling of this pathogen.



Significance and Impact of the Study:  This study provides an effective new tool, Q-PCR, to study environmental distribution of V. vulnificus and that in the light of the patchy distribution observed, new reliable approaches and a mechanistic understanding of pathogen ecology need to be considered to effectively model the aquatic distribution of V. vulnificus.

https://sfamjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2672.2011.05229.x

Year round patchiness of Vibrio vulnificus within a temperate Texas bay.

UTILIZING MODEL INTEROPERABILITY AND SPATIAL CLOUD COMPUTING TO ENABLE THE COMPUTABILITY OF DUST STORM FORECASTING Qunying Huang, MS George Mason University, 2011 Dissertation Director: Chaowei Yang Both environmental and human challenges, such as deforestation and desertification, require scientifically sound simulations of physical phenomena to better understand the past and to better predict future trends for improved decision support. However, many scientific problems cannot be processed using a single computer and require computing capability from many distributed computers. The problems should be solved by interdisciplinary efforts instead of by a single science community. Using dust storm forecasting as a case study, I investigate how interoperability technologies can facilitate data access service, model input integration, model coupling, and output utilization and dissemination. Additionally, the research will explore how to utilize spatiotemporal patterns of phenomena, models and computing resources to improve the performance of dust storm forecasting. Finally, I adopt and optimize cloud computing platforms through spatiotemporal patterns to enable the computability of dust storm forecasting over a large area with high resolution to support geospatial decision-making. This research eventually reduce the execution time and communication for two heterogeneous models, Eta-8bin

Utilizing Model Interoperability and Spatial Cloud Computing to Enable the Computability of Dust Storm Forecasting

The University of South Carolina has a long-standing and extensive effort in the development and implementation of a range platforms and tools enabling interoperability among distributed data monitoring activities in southeastern U.S. coastal regions. These activities are central to a number of ocean observation programs, including the Carolinas Regional Coastal Ocean Observing System (Carolinas RCOOS), the Southeast Coastal Ocean Observing Regional Association (SECOORA), and the Center for Integrated Information System Testbed Project. These programs were preceded and made possible by the earlier Carolinas Coastal Ocean Observing and Prediction System (CaroCOOPS), Coastal Ocean Research and Monitoring Program (CORMP), and Southeast Atlantic Coastal Ocean Observing System (SEACOOS) collaborative programs. All of these initiatives involve a range of real time and delayed mode data on environmental conditions, which are received and managed to support a large variety of data uses and products. The capacities that have been established during the development of these programs involve the following: (1) Ability to access a variety of data types and import them into sophisticated centralized data hubs; (2) A relational data base structure for rapid access and selection of the data; (3) A system of quality assurance and quality control that helps to identify spurious data and/or interesting data anomalies; (4) A variety of platforms and tools, including map-based products that enable visualization presentations of data/information; (5) Tools that allow users to pull data of interest and that push data to users, according to user-specified needs; (6) An understanding, and initial implementation, of the infrastructure required for reliable data access, maintenance, and supply as needed. These processes and protocols are largely based on open source software and involve a variety of partners that add diversity, resources, and skills to the collective initiative. The potential to expand this to a variety of environmental monitoring systems is enormous. A wide range of federal and state government, university, and private sector programs collect enormous amounts of environmental data for a range of purposes, such as weather forecasts, beach contamination monitoring, and fisheries resource management. While each of these observing efforts serves a specific purpose, a capacity to integrate and access such disparate data streams would increase their value and application potential enormously.

/pdf/making-the-most-of-information-from-environmental-monitoring-3ogf7sm2yx.pdf

Making the most of Information from Environmental Monitoring Systems

The National Estuarine Research Reserve System (NERRS) consists of 28 coastal reserves located across the United States. A system-wide monitoring program was established in 1995 to develop quantitative measurements of short-term variability and long-term changes in abiotic and biotic properties of estuarine ecosystems for the purpose of informing effective coastal management. The hallmarks of this program are its two decades of data collection, the use of common protocols and instrumentation across all observing platforms, and a centralized approach to data quality assurance/quality control (QA/QC). By using standardized procedures at all reserves, this monitoring program generates a national database on estuarine ecosystems, and it creates a network of sentinel sites for detecting and understanding the effects of climate change. Examples of how these data inform coastal managers include water quality assessment, habitat mapping and change analysis, establishment of nutrient criteria for estuaries, and understanding the predicted impacts of climate change.

System-Wide Monitoring Program of the National Estuarine Research Reserve System: Research and Monitoring to Address Coastal Management Issues

Government agencies, non-governmental organizations, commercial enterprises, and academic research programs have established a vast array of environmental databases for programmatic and research purposes. Most of these use different data management infrastructures that prevent repurposing or integrating information to serve other uses. Creating capacity to integrate and access disparate data streams enormously increases their value and application potential. Building on existing environmental information management expertise, USC has launched a Southeast Regional Integration Testbed (SRIT), and used this new capacity to implement an end-to-end decision support application that links monitoring data with predictive models in order to provide advance warning of impending beach contamination. The Water Quality Portlet project design reported herein serves as an exemplar toward establishing a sustained Center for Integrated Information Systems and Coastal Ocean Observations (CIISCOO) that will promote flexible, real-time data interoperability, across a wide user base, to efficiently meet evolving future needs.

http://www.environ.sc.edu/sites/default/files/files/Pournelle/PID982259.pdf

A Southeast Regional testbed for integrating complex coastal and ocean information systems

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A Southeast Regional testbed for integrating complex coastal and ocean information systems