Development and application of tidal creek ecosystem models

Abstract

Abstract Tidal creek ecosystems integrate the coastal landscape by linking upland environments to the coastal ocean. These ecosystems feature a combination of sinuous creek beds with wide lateral wetlands interspersed with mud flats and oyster reefs. Previous studies demonstrated negative relationships between habitat quality and indicators of watershed urbanization. However, predicting trends in ecological robustness for a number of irregularly branched tidal ecosystems with different watershed attributes, material inputs, and flushing is a difficult task. This study began to address this task by using a simulation framework to assess water column and sediment ecological processes in two distinct tidal creek ecosystems in South Carolina (Malind Creek versus Okatee Creek). Biogeochemical cycling in these creeks fluctuates with hourly, daily, and seasonal changes in tidal exchange, freshwater and material inputs, and autochthonous primary production. Over 2 years simulation based on 2001–2002 input data an estimated 4–5 times more freshwater entered Okatee Creek than Malind Creek even though Okatee Creek watershed and basin areas are only 2.5X larger. Phytoplankton consumed approximately 70% of all water column dissolved nitrogen annually with autochthonous production the primary source of particulate and dissolved organic carbon (OC) to the sediments. Okatee Creek had approximately 7X the net deposition of OC to the sediments were it combined with sediment microalgal biomass to drive benthic secondary production. Differences in physical transport in salt marsh dominated tidal creeks influence the capacity to process, transform, and sequester introduced materials. Modifications to the simulation model will include improved depth, volume, and tidal exchange for more realistic prediction of effective concentrations. This modeling framework connects watershed and atmospheric material loading to estuarine productivity, has been used to assess differences in ecosystem metabolism with changes in environmental drivers, and provides the foundation for a suite of sub-models to forecast the potential effects of relative sea level rise for a variety of nearshore environments.