Ben is a biologist at the Eastern Ecological Science Center in Kearneysville, West Virginia
Benjamin P Gressler
Ben is a biologist at the Eastern Ecological Science Center in Kearneysville, West Virginia
Benjamin Gressler began working with the Eastern Ecological Science Center in 2019 with an interest in using GIS and remote sensing techniques. His main focus has been on summarizing landscape data and environmental stressors within aquatic systems using the Python and R scripting languages. He has also worked on collating fish passage data within the eastern United States with a focus on the Chesapeake Bay watershed. Additional work includes managing spatial data and spatial data workflows, creating software to assist in spatial product development, and many other projects.
Professional Experience
2022 to present, USGS Eastern Ecological Science Center (formerly USGS Leetown Science Center), Kearneysville, WV, Biologist
2019 to 2022, USGS Eastern Ecological Science Center (formerly USGS Leetown Science Center), Kearneysville, WV, Biologist (contractor)
2017 to 2019, U.S. Forest Service, Medicine Bow-Routt National Forest, Saratoga, WY, Field Crew Leader
Education and Certifications
M.S. Biology, 2018, Indiana University of Pennsylvania, Indiana, PA, Thesis: A Landscape Movement and Gene Flow Model of the Fisher (Pekania pennanti) in Pennsylvania Using Circuitscape
B.S. Natural Sciences, 2016, University of Pittsburgh, Pittsburgh, PA
Geographic Information Systems Certificate, 2016, University of Pittsburgh, Pittsburgh, PA
Affiliations and Memberships*
West Virginia Association of Geospatial Professionals
Science and Products
Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed
This data release links fish survey data from a suite of programs in the Chesapeake Bay watershed to the NHDPlus High Resolution Region 02 networks, hereafter referred to as NHDPlusHR. The data set contains site name, survey program, coordinates of sample, ancillary information such as sample date and site location information where available, and HR Permanent Identifier. It also includes a confid
“ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of natural environment related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as natural environment related include topography, soils/geology, hydrology/geomorphology, and other physical aspects of surface waters (temperature, flow, etc.). Outputs consist of tabular comma-separated
“ChesBay 24k – LU": Land Use/Land Cover Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of land use/land cover related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as land use/land cover related contain all land use and land cover data including datasets that are split off or combined from those data (eg. agriculture or impervious classes only datasets). Outputs cons
“ChesBay 24k – CL": Climate Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of climate related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as climate related include temperature and precipitation by both annual and monthly values. Outputs consist of tabular comma-separated values files (CSVs) for the local catchment and network summaries linked to the Na
“ChesBay 24k – HU": Human Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of human related landscape variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as human related include agriculture, barriers, road density and road/stream crossing data. Outputs consist of tabular comma-separated values files (CSVs) for both local catchment and network summaries linked
Spatial Hydro-Ecological Decision System Summarized PRISM 30-year Normal Precipitation and Temperature Data for SHEDS Region 2
This data release is a summarization within the USGS Spatial Hydro-Ecological Decision System (SHEDS) framework of the Oregon State University PRISM Climate Group 30-year normal 800 meter resolution monthly, minimum and maximum temperature data and monthly precipitation data between 1991 and 2020. The output is a table consisting of the summarized values of these continuous variables for each loca
Fishway Structure Data in the Eastern United States
These data are a compilation of fishway structures collected by the Atlantic States Marine Fisheries Commission state representatives at the request of the U.S. Geological Survey. The variables included within this dataset range from locality information and structure metadata (eg. latitude/longitude and year of construction) to metrics specifically about the fishway structure (eg. fishway width).
Attribution of Chessie BIBI and fish sampling data to NHDPlusV2 Catchments within the Chesapeake Bay Watershed
This data release links fish survey data from a suite of programs in the Chesapeake Bay watershed as well the benthic macroinvertebrate sites included in the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI) developed by the Interstate Commission on the Potomac River Basin (ICPRB) and available from the Chesapeake Bay Program. The data set contains site name, survey program, coord
Spatial Hydro-Ecological Decision System Summarized Designing Sustainable Landscapes and National Land Cover Database 2001 - 2016 Data for SHEDS Region 2
This data release is a summarization of the US Geological Survey National Land Cover Database (NLCD) 2001, 2004, 2006, 2008, 2011, 2013 and 2016 Land Cover datasets and the Landscape Ecology Lab at the University of Massachusetts Designing Sustainable Landscapes (DSL) datasets within the USGS Spatial Hydro-Ecological Decision System (SHEDS) framework. The output is a series of tables consisting of
Benjamin P Gressler
Ben is a biologist at the Eastern Ecological Science Center in Kearneysville, West Virginia
Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA
Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤2
Authors
Kelly O. Maloney, Claire Buchanan, Rikke Jepsen, Kevin P. Krause, Matt J. Cashman, Benjamin Paul Gressler, John A. Young, Matthias Schmid
Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA
The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as a
Authors
Kelly O. Maloney, Kevin P. Krause, Matt J. Cashman, Wesley Daniel, Benjamin Paul Gressler, Daniel J. Wieferich, John A. Young
Non-USGS Publications**
A Landscape Movement and Gene Flow Model of the Fisher (Pekania pennanti) in Pennsylvania Using Circuitscape
Gressler, Benjamin Paul. Indiana University of Pennsylvania ProQuest Dissertations Publishing, 2018. 10841988. https://www.proquest.com/openview/cbf32f55d6d787c7c3dfddcf64183eda/1.pdf
Gressler, Benjamin Paul. Indiana University of Pennsylvania ProQuest Dissertations Publishing, 2018. 10841988. https://www.proquest.com/openview/cbf32f55d6d787c7c3dfddcf64183eda/1.pdf
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
xstrm_local
This Python package is intended to assist with summarization of landscape information to stream watershed drainages (local summaries). Methods are built in a generalized way and are intended to support efforts for any stream network having polygon based drainage watersheds. The output of these methods can be used to calculate stream network summaries using xstrm.
Summarize raster layers within overlapping polygons
Python code generated for the purpose of summarizing a defined set of discrete/categorical and/or continuous raster layers within zones that may be overlapping. Many widely used GIS tools to summarize raster layers within zones are not capable of handling overlapping zones. This code also allows for the computations to be completed over a number of separate raster layers at once and compiles the o
Science and Products
- Data
Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed
This data release links fish survey data from a suite of programs in the Chesapeake Bay watershed to the NHDPlus High Resolution Region 02 networks, hereafter referred to as NHDPlusHR. The data set contains site name, survey program, coordinates of sample, ancillary information such as sample date and site location information where available, and HR Permanent Identifier. It also includes a confid“ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of natural environment related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as natural environment related include topography, soils/geology, hydrology/geomorphology, and other physical aspects of surface waters (temperature, flow, etc.). Outputs consist of tabular comma-separated“ChesBay 24k – LU": Land Use/Land Cover Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of land use/land cover related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as land use/land cover related contain all land use and land cover data including datasets that are split off or combined from those data (eg. agriculture or impervious classes only datasets). Outputs cons“ChesBay 24k – CL": Climate Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of climate related variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as climate related include temperature and precipitation by both annual and monthly values. Outputs consist of tabular comma-separated values files (CSVs) for the local catchment and network summaries linked to the Na“ChesBay 24k – HU": Human Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments
These tabular data are summaries of human related landscape variables within catchments of the Chesapeake Bay watershed using the Xstrm methodology at 1:24,000 scale. Variables being counted as human related include agriculture, barriers, road density and road/stream crossing data. Outputs consist of tabular comma-separated values files (CSVs) for both local catchment and network summaries linkedSpatial Hydro-Ecological Decision System Summarized PRISM 30-year Normal Precipitation and Temperature Data for SHEDS Region 2
This data release is a summarization within the USGS Spatial Hydro-Ecological Decision System (SHEDS) framework of the Oregon State University PRISM Climate Group 30-year normal 800 meter resolution monthly, minimum and maximum temperature data and monthly precipitation data between 1991 and 2020. The output is a table consisting of the summarized values of these continuous variables for each locaFishway Structure Data in the Eastern United States
These data are a compilation of fishway structures collected by the Atlantic States Marine Fisheries Commission state representatives at the request of the U.S. Geological Survey. The variables included within this dataset range from locality information and structure metadata (eg. latitude/longitude and year of construction) to metrics specifically about the fishway structure (eg. fishway width).Attribution of Chessie BIBI and fish sampling data to NHDPlusV2 Catchments within the Chesapeake Bay Watershed
This data release links fish survey data from a suite of programs in the Chesapeake Bay watershed as well the benthic macroinvertebrate sites included in the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI) developed by the Interstate Commission on the Potomac River Basin (ICPRB) and available from the Chesapeake Bay Program. The data set contains site name, survey program, coordSpatial Hydro-Ecological Decision System Summarized Designing Sustainable Landscapes and National Land Cover Database 2001 - 2016 Data for SHEDS Region 2
This data release is a summarization of the US Geological Survey National Land Cover Database (NLCD) 2001, 2004, 2006, 2008, 2011, 2013 and 2016 Land Cover datasets and the Landscape Ecology Lab at the University of Massachusetts Designing Sustainable Landscapes (DSL) datasets within the USGS Spatial Hydro-Ecological Decision System (SHEDS) framework. The output is a series of tables consisting of - Multimedia
Benjamin P Gressler
Ben is a biologist at the Eastern Ecological Science Center in Kearneysville, West Virginia
Ben is a biologist at the Eastern Ecological Science Center in Kearneysville, West Virginia
- Publications
Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA
Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤2AuthorsKelly O. Maloney, Claire Buchanan, Rikke Jepsen, Kevin P. Krause, Matt J. Cashman, Benjamin Paul Gressler, John A. Young, Matthias SchmidUsing fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA
The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as aAuthorsKelly O. Maloney, Kevin P. Krause, Matt J. Cashman, Wesley Daniel, Benjamin Paul Gressler, Daniel J. Wieferich, John A. YoungNon-USGS Publications**
A Landscape Movement and Gene Flow Model of the Fisher (Pekania pennanti) in Pennsylvania Using Circuitscape
Gressler, Benjamin Paul. Indiana University of Pennsylvania ProQuest Dissertations Publishing, 2018. 10841988. https://www.proquest.com/openview/cbf32f55d6d787c7c3dfddcf64183eda/1.pdf**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
- Software
xstrm_local
This Python package is intended to assist with summarization of landscape information to stream watershed drainages (local summaries). Methods are built in a generalized way and are intended to support efforts for any stream network having polygon based drainage watersheds. The output of these methods can be used to calculate stream network summaries using xstrm.Summarize raster layers within overlapping polygons
Python code generated for the purpose of summarizing a defined set of discrete/categorical and/or continuous raster layers within zones that may be overlapping. Many widely used GIS tools to summarize raster layers within zones are not capable of handling overlapping zones. This code also allows for the computations to be completed over a number of separate raster layers at once and compiles the o
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government