HCNSO Student Theses and Dissertations

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Defense Date


Document Type

Thesis - NSU Access Only

Degree Name

M.S. Coastal Zone Management

Second Degree Name

M.S. Marine Biology


Oceanographic Center

First Advisor

Bernhard Riegl

Second Advisor

Michael B. Robblee

Third Advisor

Richard E. Spieler


Modeling is a powerful tool that can be used to identify important relationships between organisms and their habitat (Guisan & Zimmermann, 2000). Understanding the dynamics of how the two relate to one another is important for conserving and managing ecosystems, but the extreme complexity of those ecosystems makes it very difficult to fully diagram. Unlike many other modeling techniques, Multivariate Regression Trees (MRTs) are not limited by a prior assumptions, pre-determined relationships, transformations, or correlations. MRTs have the power to provide both explanation and prediction of ecological data by producing simple models that are easy to interpret. This study proposed to use MRTs to evaluate and model relationships between Lucania parva and the environment and habitat of Florida Bay. Counts were transformed to presence-absence and abundance groupings. Models were first run using a variety of combination of response variables and all explanatory variables. Results of these models were used to select the best combination of response and explanatory variables in an effort to create a best fit model. Models indicated that Lucania parva populations are found in the dense (cover ≥50%), shallow water (<1.8 m) grass beds that occur in the western portion of Florida Bay. A best fit model was able to explain 63.7% of the variance with predictive error of 0.43.


Funding provided by the US Army Corps of Engineers through the Comprehensive Everglades Restoration Plan (CERP) and its Monitoring and Assessment Plan (MAP). Funding to the United States Geological Survey (USGS, Dr. Michael Robblee, Work Orders #19, #15) and the National Oceanic and Atmospheric Administration (NOAA, Dr. Joan Browder, Work Orders #3, #12) supported MAP activities and These activities were consolidated as the South Florida Fish and Invertebrate Assessment Network project (FIAN).

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