Cooperative Ecosystem Studies Unit, Gulf Coast CESU | Apply Online

Opportunity Summary:

This grant opportunity, titled "Cooperative Ecosystem Studies Unit, Gulf Coast CESU" (Funding Opportunity Number 10HQPA0083), was issued by the U.S. Geological Survey (USGS), specifically through the National Wetlands Research Center (NWRC). It supports applied research and development aimed at improving how ecological and hydrologic modeling data are managed, processed, shared, and displayed. The overall emphasis is on advanced spatial analysis and modeling methods, with a strong focus on practical software and technology outcomes that can be used by Department of the Interior (DOI) partners for real-world restoration and resource management work.

The project is framed as a cooperative agreement, meaning the work is expected to be collaborative and closely coordinated with NWRC rather than performed in isolation. The planned effort is meant to extend existing desktop modeling software suites while also producing new working prototype programming solutions. In practice, this points to a blend of research and engineering: evaluating state-of-the-art approaches, experimenting with new methods, and turning those findings into functional tools or enhancements that improve performance, flexibility, and usability of modeling and mapping systems. The announcement also suggests that colocating the recipient's research staff with NWRC's applications team would maximize the impact of the work, indicating a preference for hands-on integration with the center's ongoing development activities.

The technical scope centers on several connected themes. One is modernizing spatial data visualization and web mapping, including high-performance web mapping improvements that use innovative caching techniques to speed up access to map layers and model outputs. Another is improving access, distribution, and dissemination of scientific modeling datasets, particularly through standards-based global data sharing approaches. This includes investigating the current landscape of computer science technologies that enable interoperable data access and exchange, as well as developing or testing new technologies that broaden user access to modeling datasets. A related idea is optimizing how modeling data are delivered to end users, which could involve data services, metadata and catalog practices, or system architectures that make large scientific datasets easier to find and use.

A second major thrust is addressing the computational demands of complex ecological and hydrologic models. The opportunity calls for examining advanced computing technologies that can handle computationally heavy modeling workloads, including explicit attention to computational limitations and the use of grid computing to distribute processing. This implies interest in approaches that can reduce run times, scale to larger datasets or higher resolution models, and support more sophisticated scenario testing without becoming prohibitively slow or resource-intensive.

The announcement also highlights biological modeling improvements using neural networks, reflecting interest in machine learning methods to enhance ecological and hydrologic modeling within DOI contexts. The grant asks how neural networks could be applied to improve modeling efforts, which could include pattern recognition in environmental data, predictive habitat or species distribution modeling, surrogate modeling to approximate expensive simulations, or classification and anomaly detection across large remote-sensing and monitoring datasets. The aim is not just experimentation, but meaningful integration of these techniques into modeling workflows that agencies can use.

Another specific objective is improving model flexibility by decoupling models from their inputs. The grant explicitly calls for technologies that separate a model from the datasets it consumes, making it easier to swap inputs and generate alternative output scenarios. This is often a barrier in applied modeling where hard-coded dependencies or tightly coupled data pipelines slow down scenario planning; the opportunity is pushing toward architectures and standards that make models more modular, reusable, and adaptable to new data sources and management questions.

From an administrative standpoint, the opportunity was posted on June 22, 2010, and closed on July 1, 2010, with an archive date of July 31, 2010. The expected number of awards was one, with an estimated total funding amount of $100,000. There was no cost sharing or matching requirement listed. The CFDA number associated with the program is 15.808 (U.S. Geological Survey Research and Data Collection). Eligibility was limited to partners of the Gulf Coast Cooperative Ecosystem Studies Unit (CESU) Program, reflecting the CESU structure as a partnership network designed to provide research, technical assistance, and education through collaboration among federal agencies and their partner institutions.

In short, this NWRC-led CESU opportunity sought one Gulf Coast CESU partner to collaborate with USGS on advancing the technology foundation behind ecological and hydrologic modeling: faster and more scalable computation, better data access and standards-based sharing, stronger visualization and web mapping performance, and next-generation modeling methods (including neural networks). The deliverables were intended to be practical and implementable, strengthening existing software capabilities while producing prototypes that could be adopted and extended within DOI science and management programs.

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Previous opportunity: Cooperative Ecosystem Studies Unit, Californian CESU

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