Shallow Water Fish Communities and Coastal Development Stressors in the Lynnhaven River

Coastal development pressures in the Mid-Atlantic have been attributed to significant negative impacts to aquatic ecosystems. The Lynnhaven River watershed, located in the southernmost extent of the Chesapeake Bay and encompassing Virginia Beach, is an example of a shallow-water tidal system under intense development pressure that is confronted with multiple and often conflicting coastal management issues. Rapid development in and around the City of Virginia Beach over the past few decades has led to the loss of natural buffers and habitat (e.g. oyster, wetlands and seagrasses), increased sedimentation, and degraded water quality. The Lynnhaven Ecosystem Restoration Project, led by U.S Army Corps of Engineers, is an effort to collaborate with State and federal partners over a 5-year period to identify and implement the most effective strategies for improving water quality, restoring oysters and seagrasses, and managing siltation. Limited quantitative information exists on the nekton assemblages utilizing shallow water habitats, such as tidal creeks, within the Lynnhaven River restoration area. To document nekton composition, and to investigate potential effects of development stressors, such as dredging and shoreline modification, three sets of paired dredged and undredged tidal creeks were surveyed in the Western Branch of the Lynnhaven River. Fish communities were sampled with multiple gear types once per month for three months (August, September, October, 2006). Abundance, average length and weight, diversity, and fish community indices were estimated for each creek and time period, and dredged compared with undredged systems for resemblance in fish composition and abundance. Tidal creeks within Lynnhaven Bay support diverse and similar fish communities. Slight differences in community structure among creeks may be attributable to the location and size of watersheds. The effects of dredging were not apparent in fish community responses measured as abundance, biomass, diversity, and fish community indices. However, anthropogenic effects may be obscured in the shortterm by the background variability of physical and water quality features of Lynnhaven Bay estuary, and long-term or cumulative effects are not quantifiable due to the dearth of historic information on fish communities. Available historic information may indicate a shift in fish community structure that could be associated with coastal development pressures, such as shoreline alteration and habitat loss of wetlands and oyster reefs. Accordingly, restoration and preservation of critical nursery habitats may augment fish productivity in Lynnhaven Bay

Ecosystem Approaches to Aquatic Health Assessment: Linking Subtidal Habitat Quality, Shoreline Condition and Estuarine Fish Communities

In the Chesapeake Bay, there is currently no comprehensive assessment of aquatic habitat heterogeneity or understanding of the effects of multiple stressors on the viability of these habitats. To assess the use of side-scan sonar technology with specially designed classification software, QTC SIDEVIEW developed by Quester Tangent Corporation as a tool to define subtidal nearshore habitat, two representative watersheds of the Chesapeake Bay were surveyed. Relationships between subtidal habitat and shoreline condition as well as linkages of habitat condition to fish community indices were assessed. Side-scan technology had the ability to image habitat at a resolution of less than 1 meter. Automated seabed classification shows promise as a delineation tool for broad seabed habitat classes. In the James River, relationships between shoreline condition and fish community indices were observed, while no association with bottom type was reflected in the data possibly due to the limited availability of vertical structure in this system. Observed relationships and habitat mapping protocols have the potential to be extrapolated to additional watersheds in the coastal plain, and become tools for future development of habitat indices and ecosystem management

Evaluation Framework for Water Quality Trading Programs in the Chesapeake Bay Watershed

Water quality trading programs are being proposed and implemented across the US in a variety of forms and with differing objectives. The programs being proposed and implemented in the Chesapeake Bay region are no exception. Against this background the Chesapeake Bay Program's Scientific and Technical Advisory Committee and the Mid-Atlantic Water Program requested a general framework to inform and guide the evaluation of the performance trading programs. This resulting report was developed by a workgroup comprised of ten individuals with extensive experience in the study, design, and evaluation of trading programs. While the impetus for this report was to improve evaluation of trading programs in the Chesapeake Bay region, the evaluation framework is broad enough to apply to trading programs in general

Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain

General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA.We investigated prediction of general cognitive ability (GCA) based on fMRI task activation patterns with 15 task contrasts in the Human Connectome Project dataset. The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in ten10‐fold cross‐validation analysis. Additionally, we found that task contrasts that produce greater fronto‐parietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in GCA prediction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/2/hbm25007.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/1/hbm25007_am.pd