The 3rd USGS Modeling Conference (7-11 June 2010)

Paper No. 4
Presentation Time: 4:05 PM

MODELING TRENDS IN NEKTON AND ASSOCIATED CHANGES IN NORTHEAST COASTAL SALT MARSHES


POOLER, Penelope S.1, TYRRELL, Megan C.2, LELLIS-DIBBLE, Kimberly A.3, BAYLEY, Holly K.2 and STEVENS, Sara1, (1)Department of the Interior, National Park Service, Northeast Coastal and Barrier Network, 1 Greenhouse Rd, Rm. 105, Kingston, RI 02881, (2)Department of the Interior, National Park Service, Cape Cod National Seashore, 99 Marconi Site Road, Wellfleet, MA 02667, (3)Department of Natural Resources Science, University of Rhode Island, 1 Greenhouse Rd, Rm. 105, Kingston, RI 02881, Penelope_Pooler@nps.gov

As part of the National Park Service Inventory and Monitoring program, the Northeast Coastal and Barrier Network (NCBN) and the Cape Cod Ecosystem Monitoring program have implemented long term monitoring of nekton in salt marshes. The goal of monitoring nekton and other ecosystem indicators is to gain information about the condition of salt marshes within Northeast coastal parks over time. Detecting trends in salt marsh nekton community structure, abundance, and species richness and relating these trends to changes in the physical aspects of salt marshes can be challenging. Collecting quantitative nekton data is expensive and time consuming and these data are highly variable both seasonally and annually. Our goal in modeling nekton data is to differentiate between temporal variability and sustained or consistent trends associated with changes in salt marsh condition. Although the nekton monitoring program is relatively new throughout the NCBN, we were able to model trends using data collected over five years at a salt marsh site in Cape Cod National Seashore. We developed and evaluated models using a multivariate response of nekton community structure as well as univariate responses such as species richness and abundance of specific nekton species and communities. Our preliminary model results indicate that after accounting for seasonal variability and physical variables such as dissolved oxygen, water temperature, and salinity, we are able to detect trends after as few as four years of monitoring. We present the different models along with the interpretation of each and how we plan to utilize them as additional nekton and other salt marsh data are collected over time.