You are here:
Development of a generalized pseudo-probabilistic approach for characterizing ecological conditions in estuaries using secondary data
Citation:
Harwell, L., C. McMillion, A. Lamper, AND J. Summers. Development of a generalized pseudo-probabilistic approach for characterizing ecological conditions in estuaries using secondary data. Gulf of Mexico Conference (GOMCON) 2024, Tampa, FL, February 19 - 22, 2024.
Impact/Purpose:
The pseudo-probabilistic approach, using secondary data, offers a readily transferable way to increase information about ecological conditions in estuaries without compromising existing monitoring priorities. By leveraging existing tools, this method allows resource managers to quickly produce estuarine condition assessments using data on hand, or combined with other sources, in a manner consistent with EPA’s NCCA program—a highly successful national coastal monitoring program. The pseudo-probabilistic approach is intended to serve as an intermediate step toward filling critical data gaps in ecological baseline conditions to support efforts in sustaining or improving the long-term resilience of our estuaries.
Description:
Under the best of circumstances, achieving or sustaining optimum ecological conditions in estuaries is challenging. Persistent information gaps in estuarine data make it difficult to differentiate natural variability from potential regime shifts. Long-term monitoring is key for tracking ecological change overtime. In the United States (U.S.), many resource management programs are working at maximum capacity and have little room to expand routine sampling efforts to conduct periodic ecological baseline assessments, especially at state and local scales. Alternative design, monitoring and assessment approaches are needed to increase understanding about estuarine system resilience when existing monitoring data are sparse or spatially limited. The approach presented allows the use of found or secondary data, such as data on hand and other acquired data, to generate statistically viable characterizations of ecological conditions in estuaries. Using a generalized pseudo-probabilistic framework, data from different contributors are synthesized to inform probabilistic-like baseline assessments. The methodology relies on simple geospatial techniques paired with R code libraries developed to support national probabilistic monitoring for the U.S. Environmental Protection Agency’s National Aquatic Resource Surveys. Based on secondary estuarine water quality data collected in the Northwest Florida (U.S.), demonstrations suggest that the pseudo-probabilistic approach produces estuarine condition assessment results with reasonable statistical confidence; improved spatial representativeness; and value-added information. While the pseudo-probabilistic framework is not a substitute for traditional monitoring, it offers a scalable alternative to bridge the gap between limitations in resource management capability and optimal monitoring strategies to track ecological baselines in estuaries, over time.