Physical Sciences and Mathematics Commons^™

Environmental Factors Associated With Triploid Aspen Occurrence In Intermountain West Landscapes, Karen E. Mock, James A. Walton

Browse all Datasets

Polyploidy is common among plants and can contribute to physiological and morphological differences, altering how plants respond to environmental changes, promoting genetic diversification, and even species radiation. Quaking aspen (Populus tremuloides), a keystone species associated with high plant and animal diversity is frequently found in mixed diploid/triploid populations in the Intermountain West. High mortality rates and widespread population declines in aspen are of increasing concern in the Intermountain West, often ascribed to changing climates and drought stress events. The goal of this study was to better understand environmental factors influencing the distribution of triploid aspen population in the Intermountain West. …

199965, David Kerstetter

PERC Albacore sPAT Data

Datasets (and supporting material) from 4sPAT electronic tags deployed on albacore caught by pelagic longline gear in the western North Atlantic.

199953, David Kerstetter

PERC Albacore sPAT Data

Datasets (and supporting material) from 4sPAT electronic tags deployed on albacore caught by pelagic longline gear in the western North Atlantic.

199949, David Kerstetter

PERC Albacore sPAT Data

Datasets (and supporting material) from 4sPAT electronic tags deployed on albacore caught by pelagic longline gear in the western North Atlantic.

199946, David W. Kerstetter

PERC Albacore sPAT Data

Datasets (and supporting material) from 4sPAT electronic tags deployed on albacore caught by pelagic longline gear in the western North Atlantic.

Data From: Root Distributions Predict Shrub-Steppe Responses To Precipitation Intensity, Andrew Kulmatiski, Karen H. Beard

Browse all Datasets

Precipitation events are becoming more intense around the world, changing the way water moves through soils and plants. Plant rooting strategies that sustain water uptake under these conditions are likely to become more abundant (e.g., shrub encroachment). Yet, it remains difficult to predict species responses to climate change because we typically do not know where active roots are located or how much water they absorb. Here, we applied a water tracer experiment to describe forb, grass, and shrub root distributions. These measurements were made in 8 m by 8 m field shelters with low or high precipitation intensity. We used …

Elizabeth River Basin Environmental Justice Indicators, Molly Mitchell, Andrew M. Scheld, Sarah Stafford, Tamia Rudnicky, Joseph Snitzer

Data

This data is a portion of the data included in the Elizabeth River Environmental Justice Tool (https://cmap22.vims.edu/EREJTool/) The Elizabeth River Environmental Justice map viewer contains a variety of layers that will help planners target vulnerable locations and populations within the Elizabeth River Watershed. This data was developed specifically to support the Elizabeth River Project’s decision making in this region.

Gis Data: Charles County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Worcester County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions: 1) the …

Gis Data: St Mary’S County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Somerset County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions: 1) the …

Data From: Yellow Air Day Advisory Study, Arthur J. Caplan

Browse all Datasets

Using a dataset consisting of daily vehicle trips, PM2.5 concentrations, along with a host of climactic control variables, we test the hypothesis that “yellow air day” advisories provided by the Utah Division of Air Quality resulted in subsequent reductions in vehicle trips taken during northern Utah’s winter-inversion seasons in the early 2000s. Winter inversions occur in northern Utah when climactic conditions are such that PM2.5 concentrations (derived mainly from vehicle emissions) become trapped in the lower atmosphere, leading to unhealthy air quality (concentrations of at least 35 µg/m3) over a span of what are called “red air days”. When concentrations …

Data For "Arch_Covid_Crowding_Vc", Wayne Freimund, Zachary D. Miller

Browse all Datasets

Monitoring of visitor use in Arches National Park to assess social distancing behaviors of visitors during the COVID-19 pandemic.

Gis Data: Anne Arundel County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: Calvert County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: Talbot County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Sars-Cov-2 Exposure In Wild White-Tailed Deer (Odocoileus Virginianus), Jeffrey C. Chandler, Sarah N. Bevins, Jeremy W. Ellis, Timothy J. Linder, Rachel M. Tell, Melinda Jenkins-Moore, J. Jeffrey Root, Julianna B. Lenoch, Suelee Robbe-Austerman, Thomas J. Deliberto, Tom Gidlewski, Mia Kim Torchetti, Susan A. Shriner

USDA Wildlife Services: Staff Publications

Widespread human SARS-CoV-2 infections combined with human–wildlife interactions create the potential for reverse zoonosis from humans to wildlife. We targeted white-tailed deer (Odocoileus virginianus) for serosurveillance based on evidence these deer have angiotensin-converting enzyme 2 receptors with high affinity for SARS-CoV-2, are permissive to infection, exhibit sustained viral shedding, can transmit to conspecifics, exhibit social behavior, and can be abundant near urban centers. We evaluated 624 prepandemic and postpandemic serum samples from wild deer from four US states for SARS-CoV-2 exposure. Antibodies were detected in 152 samples (40%) from 2021 using a surrogate virus neutralization test. A subset of samples …

Gis Data: Dorchester County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Genetic Variation At The Species And Population Levels In The Rocky Mountain Ridged Mussel (Gonidea Angulata) – Supplementary Material, James A. Walton, Karen E. Mock, Steven F. R. Brownlee, Jon H. Mageroy, Greg Wilson, Ian R. Walker

Browse all Datasets

Freshwater mussels in western North America are threatened by water diversions, climate change, loss of required host fish, and other factors, and have experienced marked decline in the past several decades. All four of the primary lineages (potentially species) of freshwater mussels in the western U.S. and Canada are widespread and have somewhat generalist host fish requirements. Of these lineages, perhaps the most poorly understood and of greatest conservation concern is Gonidea angulata (Rocky Mountain ridged mussel). Gonidea is a monotypic genus occurring only in the western continental U.S. and southern Canada. Here we describe the patterns of genetic variation …

Coqui Frog Predator Avoidance And Recognition, Karen H. Beard

Browse all Datasets

The purpose of this study was to determine whether coqui frogs from their non-native range responded to native predators the same way as frogs from their native range. Frogs were collected from two sites in Puerto Rico (El Yunque and Rio Abajo) in May 2006 and one site in Hawaii (Hilo) in June 2006. At each site, frogs were collected from a high (> 700 m) and low (< 300 m) elevation population. Of the total number of frogs collected, 100 males were randomly selected to be used in this study (45 and 55 from Hawaii and Puerto Rico, respectively). Three tailless whipscorpions (Phrynus longipes) and three tarantulas (Avicularia laeta) were also collected in Puerto Rico in field sites where frogs were collected and shipped back to a laboratory.

Isotope Summary Data, Andrew Kulmatiski

Browse all Datasets

Data includes deuterium tracer uptake data from plant species at the US Sheep Experiment Station.

Gis Data: King George County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Kory Angstadt, Karen A. Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for King George County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (King_George_lubc_2017). The second portrays the presence of beaches (King_George _beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(King_George _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(King_George_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any …

Gis Data: City Of Fredericksburg,Virginia, Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for the City of Fredericksburg was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (Fredericksburg_lubc_2017). The second portrays the presence of beaches (Fredericksburg_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Fredericksburg _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Fredericksburg _astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and …

Gis Data: Hanover County, Virginia, Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for Hanover County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed.The first describes land use and bank conditions (Hanover _lubc_2017). The second portrays the presence of beaches (Hanover _beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Hanover _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Hanover _astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any …

Gis Data: Surry County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, David Weiss, Carl Hershner

Data

The 2017 Inventory for Surry County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed.The first describes land use and bank conditions (Surry_lubc_2017). The second portrays the presence of beaches (Surry_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Surry_sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Surry_astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain …

Gis Data: Henrico County, Virginia, Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for Henrico County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and 2013 imagery from the Virginia Base Mapping Program (VBMP). Four GIS shapefiles are developed.The first describes land use and bank conditions (Henrico _lubc_2017). The second portrays the presence of beaches (Henrico _beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Henrico _sstru_2017). The final shapefile includes all structures that are represented as points(e.g. piers)(Henrico _astru_2017).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and …

Gis Data: City Of Fredericksburg, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen A. Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

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 …

Gis Data: Hanover County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

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 …

Gis Data: King George County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Kory Angstadt, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

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 …

Gis Data: Surry County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Stanhope, David Weiss, Carl Hershner

Data

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 …