Acknowledgements

We thank the North Carolina Division of Marine Fisheries and the United States Geological Survey for providing datasets used in this study. We also thank editor A. Manning for helpful comments that improved the manuscript. Funding for this project was provided by the Environmental Defense Fund (Program Manager Pam Baker), North Carolina Coastal Recreational Fishing License Program (Grant No. 2010-H-004), North Carolina Sea Grant (R12-HCE-2) and the National Science Foundation (OCE-1155609) to D. Eggleston. A. Nail was supported as a VIGRE Postdoctoral Fellow by NSF grant DMS 0354189.

Author details

United States

181

Christina L. Durham<sup>1</sup>

University, Raleigh, NC, United States

provided the original work is properly cited.

\*Address all correspondence to: eggleston@ncsu.edu

, David B. Eggleston<sup>1</sup>

Process-Based Statistical Models Predict Dynamic Estuarine Salinity

DOI: http://dx.doi.org/10.5772/intechopen.89911

1 Department of Marine, Earth and Atmospheric Sciences, North Carolina State

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 Department of Statistics, North Carolina State University, Raleigh, NC,

\* and Amy J. Nail<sup>2</sup>
