Understanding the mechanisms by which organisms adapt to environmental conditions is a fundamental question for ecology and evolution. In this study, we evaluate changes in gene expression of a marine mollusc, the eastern oyster Crassostrea virginica, associated with the physico-chemical conditions and the levels of metals and other contaminants in their environment. The results indicate that transcript signatures can effectively disentangle the complex interactive gene expression responses to the environment and are also capable of disentangling the complex dynamic effects of environmental factors on gene expression. In this context, the mapping of environment to gene and gene to environment is reciprocal and mutually reinforcing. In general, the response of transcripts to the environment is driven by major factors known to affect oyster physiology such as temperature, pH, salinity, and dissolved oxygen, with pollutant levels playing a relatively small role, at least within the range of concentrations found in the studied oyster habitats. Further, the two environmental factors that dominate these effects (temperature and pH) interact in a dynamic and nonlinear fashion to impact gene expression. Transcriptomic data obtained in our study provide insights into the mechanisms of physiological responses to temperature and pH in oysters that are consistent with the known effects of these factors on physiological functions of ectotherms and indicate important linkages between transcriptomics and physiological outcomes. Should these linkages hold in further studies and in other organisms, they may provide a novel integrated approach for assessing the impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms via relatively inexpensive microarray platforms.

Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part, this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems, and perhaps more important, is the insensitivity of the biological end points that we have used to assess these impacts. In this study, we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land-use practices in the surrounding watershed using advanced machine-learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data show that gill tissues are far more responsive and provide superior discrimination of land-use classes than hepatopancreas and that transcripts encoding proteins involved in energy production, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P-450.

Upland areas of southeastern U.S. tidal creek watersheds are popular locations for development, and they form part of the estuarine ecosystem characterized by high economic and ecological value. The primary objective of this work was to define the relationships between coastal development, with its concomitant land use changes and associated increases in nonpoint source pollution loading, and the ecological condition of tidal creek ecosystems including related consequences to human populations and coastal communities. Nineteen tidal creek systems, located along the southeastern United States coast from southern North Carolina to southern Georgia, were sampled during summer, 2005 and 2006. Within each system, creeks were divided into two primary segments based upon tidal zoning: intertidal (i.e., shallow, narrow headwater sections) and subtidal (i.e., deeper and wider sections) and then watersheds were delineated for each segment. Relationships between coastal development, concomitant land use changes, nonpoint source pollution loading, the ecological condition of tidal creek ecosystems, and the potential impacts to human populations and coastal communities were evaluated. In particular, relationships were identified between the amount of impervious cover (indicator of coastal development) and a range of exposure and response measures including increased chemical contamination of the sediments, increased pathogens in the water, increased nitrate/nitrite levels, increased salinity range, decreased biological productivity of the macrobenthos, alterations to the food web, increased flooding potential, and increased human risk of exposure to pathogens and harmful chemicals. The integrity of tidal creeks, particularly the headwaters or intertidally-dominated sections, were impaired by increases in nonpoint source pollution associated with sprawling urbanization (i.e., increases in impervious cover). This finding suggests these habitats are valuable early warning sentinels of ensuing ecological impacts and potential public health and flooding risk from sprawling coastal development. Results also validate the use of a conceptual model with impervious cover thresholds for tidal creek systems in the southeast region.

We developed the Stormwater Runoff Modeling System (SWARM) based on curve number and unit hydrograph methods of the U.S. Department of Agriculture, Natural Resources Conservation Service. SWARM models single events, targets watersheds fitting easily within hydrologic units with 12‐digit codes, and has been calibrated for low‐gradient topography of the Southeast coastal plain. We established protocols; made changes related to peak rate factors, travel time formulas, curve numbers, and the initial abstraction ratio; and then tested the output with multi‐site validation using U.S. Geological Survey measurements of discharge and rainfall. Validation results from both undeveloped and developed watersheds support the robustness of our system in quantifying and simulating runoff: rainfall to runoff differences between measured and simulated volumes ranged from 3 to 11%; r2 for hydrograph curves ranged from 0.82 to 0.98. SWARM can be a useful tool for scientific research and for coastal resource management and decision making in the Southeast coastal plain specifically and also may be applied to other areas by recalibrating parameters and modifying calculation templates. Copyright © 2012 John Wiley & Sons, Ltd.