Evaluating The Impacts Of Land Use And Climate Change On The Hydrology Of Headwater Wetlands In The Coastal Plain Of Virginia

Located at the interface between uplands and surface water networks, headwater wetlands act as a natural filter to improve downstream water quality and play a critical role in maintaining the ecological integrity of downstream aquatic ecosystems. Vulnerable to development pressure, as well as indirect impacts from land use and climate change, the loss and alteration of headwater wetlands has been linked to the loss of biodiversity and regional water quality declines worldwide. The overall goal of this dissertation is to address some of the challenges associated with the management and conservation of headwater wetlands in the coastal plain of Virginia including: the identification of palustrine forested wetlands in flat coastal landscapes (Chapter II); and improved understanding of the impacts of land use (Chapter III) and climate change (Chapter IV) on the hydrologic regime of headwater wetlands. First, a simple model of wetland distribution was developed by characterizing the depth to groundwater using widely available geospatial data, including surface water features and a high-resolution digital elevation model. Comparison with the National Wetland Inventory (NWI) and targeted field validation indicated that this model provides an effective approach to identify palustrine forested wetlands often unmapped by NWI. Results from this study indicate that there may be at least 37% more wetland area than is currently mapped within the study area; and that in the future, modeling approaches should be used in addition to NWI mapping to better understand the full extent and distribution of wetlands in forested areas. The impacts of land use and climate change were then investigated through field studies of headwater wetland hydrology and community composition. Potential differences in headwater wetland hydrology were evaluated through an index of hydrophytic vegetation occurrence, the wetland prevalence index (PI). Changes in PI between sapling and canopy strata, with respect to local land use, indicated that decreased forest cover was associated with a shift in plant community composition, and that increasing road density was associated with a shift towards more upland type species, while increasing agricultural cover was associated with a shift towards more wetland type species. The effects of climate change, including rising temperatures and altered precipitation patterns were evaluated by developing an empirical model of water table depth for coastal headwater wetlands. Wetland water levels were simulated under current and potential future conditions to evaluate the impact of climate change on the hydrologic regime of headwater wetlands. Based on the model scenarios applied in this study, it appears that decreasing water availability may lead to drier conditions at headwater wetlands by the end of the 21st century, with a substantial decline in minimum water levels and a 3-10% decline in average annual percent saturation. Collectively, the results of this dissertation provide practical insights for improving the conservation and management of coastal headwater wetlands. Improved understanding of the extent and distribution of previously unmapped forested wetlands can improve the capacity to monitor wetland loss and degradation. Additionally, clarifying the influence of land use and climate on the hydrologic regime of these wetlands, can help improve the capacity to forecast and then mitigate potential future impacts to wetland hydrology

2016 Greenhouse Gas Inventory Report: Virginia Institute of Marine Science

During summer 2016 the VIMS Green Team completed an inventory of greenhouse gas (GHG) emissions from the Virginia Institute of Marine Science (VIMS) Gloucester Point campus during FY2015. GHG emissions were estimated using the Campus Carbon Calculator maintained by the Sustainability Institute at the University of New Hampshire, and compared to a previous GHG audit from FY2010

Summary Tables: Lancaster County, Virginia Shoreline Inventory Report

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes)

Lancaster County, Virginia Shoreline Inventory Report Methods and Guidelines

The data inventory developed for the Shoreline Inventory is based on a three tiered shoreline assessment approach. This assessment characterizes conditions that can be observed from a small boat navigating along the shoreline or by using observations made remotely at the desktop using high resolution imagery. The three tiered shoreline assessment approach divides the shorezone into three regions: the immediate riparian zone, evaluated for land use the bank, evaluated for height, cover and natural protection the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. The 2015 Inventory for Lancaster County was generated using on-screen, digitizing techniques in ArcGIS&tm; - ArcMap v10.2.2 while viewing conditions observed in 2015 Bing high resolution oblique imagery and 2013 imagery from the Virginia Base Mapping Program (VBMP). These data sources allowed the inventory to be generated without additional field work. Three GIS shapefiles are developed. The first describes land use and bank conditions (LancasterCo _lubc_2015). The second reports shoreline structures that are described as arcs or lines (LancasterCo _sstru_2015). The final shapefile includes all structures that are represented as points (LancasterCo_astru_2015). The shapefiles use a shoreline basemap updated in-house from the 2013 VBMP high resolution digital terrain model. The shoreline is re-coded to reflect features and attributes observed. The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain to data

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

Objective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and crossvalidated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS metaanalysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. Methods: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Results: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values &lt;5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. Conclusions: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.</p

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values \u3c5×10 CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

GIS Data: Lancaster County, Virginia Shoreline Inventory Report

The data inventory developed for the Shoreline Inventory is based on a three tiered shoreline assessment approach. This assessment characterizes conditions that can be observed from a small boat navigating along the shoreline or by using observations made remotely at the desktop using high resolution imagery. The three tiered shoreline assessment approach divides the shorezone into three regions: the immediate riparian zone, evaluated for land use the bank, evaluated for height, cover and natural protection the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. The 2015 Inventory for Lancaster County was generated using on-screen, digitizing techniques in ArcGIS&tm; - ArcMap v10.2.2 while viewing conditions observed in 2015 Bing high resolution oblique imagery and 2013 imagery from the Virginia Base Mapping Program (VBMP). These data sources allowed the inventory to be generated without additional field work. Three GIS shapefiles are developed. The first describes land use and bank conditions (LancasterCo _lubc_2015). The second reports shoreline structures that are described as arcs or lines (LancasterCo _sstru_2015). The final shapefile includes all structures that are represented as points (LancasterCo_astru_2015). The shapefiles use a shoreline basemap updated in-house from the 2013 VBMP high resolution digital terrain model. The shoreline is re-coded to reflect features and attributes observed. The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain to data

GIS Data: Lancaster County, Virginia Tidal Marsh Inventory

The 2015 Tidal Marsh Inventory update for Lancaster County, Virginia was generated using on-screen digitizing techniques in the most recent version of ArcGIS® - ArcMap while viewing conditions observed in the most recent imagery from the Virginia Base Mapping Program (VBMP). Dominant plant community types were primarily determined during field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations. One shapefile is developed that portrays tidal marsh areas represented as polygons. A metadata file accompanies the shapefile to define attribute accuracy, data development, and any use restrictions that pertain to the data

GIS Data: Lancaster County, Virginia Shoreline Management Model

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the intertidal shoreline level (Accomack_SMM_Preferred_BMPs_2016)