A radiocarbon chronology of Holocene climate change and sea-level rise at the Delmarva Peninsula, US Mid-Atlantic Coast

Radiocarbon dates from 176 sites along the Delmarva Peninsula record the timing of deposition and sea-level rise, and non-marine wetland deposition. The dates provide confirmation of the boundaries of the Holocene subepochs (e.g. “early-middle-late” of Walker et al.) in the mid-Atlantic of eastern North America. These data record initial sea-level rise in the early Holocene, followed by a high rate of rise at the transition to the middle Holocene at 8.2 ka, and a leveling off and decrease in the late-Holocene. The dates, coupled to local and regional climate (pollen) records and fluvial activity, allow regional subdivision of the Holocene into six depositional and climate phases. Phase A (>10 ka) is the end of periglacial activity and transition of cold/cool climate to a warmer early Holocene. Phase B (10.2–8.2 ka) records rise of sea level in the region, a transition to Pinus-dominated forest, and decreased non-marine deposition on the uplands. Phase C (8.2–5.6 ka) shows rapid rates of sea-level rise, expansion of estuaries, and a decrease in non-marine deposition with cool and dry climate. Phase D (5.6–4.2 ka) is a time of high rates of sea-level rise, expanding estuaries, and dry and cool climate; the Atlantic shoreline transgressed rapidly and there was little to no deposition on the uplands. Phase E (4.2–1.1 ka) is a time of lowering sea-level rise rates, Atlantic shorelines nearing their present position, and marine shoal deposition; widespread non-marine deposition resumed with a wetter and warmer climate. Phase F (1.1 ka-present) incorporates the Medieval Climate Anomaly and European settlement on the Delmarva Peninsula. Chronology of depositional phases and coastal changes related to sea-level rise is useful for archeological studies of human occupation in relation to climate change in eastern North America, and provides an important dataset for future regional and global sea-level reconstructions.

Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets.

Influenza A Virus Detected in Native Bivalves in Waterfowl Habitat of the Delmarva Peninsula, USA

We evaluated the prevalence of influenza A virus (IAV) in different species of bivalves inhabiting natural water bodies in waterfowl habitat along the Delmarva Peninsula and Chesapeake Bay in eastern Maryland. Bivalve tissue from clam and mussel specimens (Macoma balthica, Macoma phenax, Mulinia sp., Rangia cuneata, Mya arenaria, Guekensia demissa, and an undetermined mussel species) from five collection sites was analyzed for the presence of type A influenza virus by qPCR targeting the matrix gene. Of the 300 tissue samples analyzed, 13 samples (4.3%) tested positive for presence of influenza virus A matrix gene. To our knowledge, this is the first report of detection of IAV in the tissue of any bivalve mollusk from a natural water body.