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Collection of Publications provided or contributed by SSAR programs. Selecting an item you'll find additional information and program point of contacts.

Filter Total Items: 247

Spatial extent drives patterns of relative climate change sensitivity for freshwater fishes of the United States

Assessing the sensitivity of freshwater species to climate change is an essential component of prioritizing conservation efforts for threatened freshwater ecosystems and organisms. Sensitivity to climate change can be systematically evaluated for multiple species using geographic attributes such as range size and climate niche breadth, and using species traits associated with climate change sensit
Samuel C. Silknetter, Abigail Benson, Jennifer A. Smith, Meryl C. Mims

Thermal traits of anurans database for the southeastern United States (TRAD): A database of thermal trait values for 40 anuran species

Thermal traits, or how an animal responds to changing temperatures, impacts species persistence and thus biodiversity. Trait databases, as repositories of consolidated, measured organismal attributes, allow researchers to link study species with specific trait values, enabling comparisons within and among species. Trait databases also help lay the groundwork to build mechanistic linkages between o
Traci P. DuBose, Victorjose Catalan, Chloe E. Moore, Vincent R. Farallo, Abigail Benson, Jessica Dade, William A. Hopkins, Meryl C. Mims

Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation

This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the fres
Hao Yu, Arthur R. Cooper, Jared Ross, Alexa McKerrow, Daniel J. Wieferich, Dana M. Infante

Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey

As of 2016, the U.S. Geological Survey (USGS) Fundamental Science Practices require data management plans (DMPs) for all USGS and USGS-funded research. The USGS Science Data Management Branch of the Science Analytics and Synthesis Program has been working to help the USGS (Bureau) meet this requirement. However, USGS researchers still encounter common data management-related challenges that may be
Madison Langseth, Elizabeth Sellers, Grace C. Donovan, Amanda N. Liford

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White

John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1

The John Wesley Powell Center for Synthesis & Analysis is a USGS initiative that aims to foster innovative thinking in Earth system science through collaborative analysis and synthesis of existing data and information. The Powell Center supports working groups that address some of the most pressing and complex questions facing society, such as climate change, biodiversity loss, water scarcity, nat
Jill Baron, Demi Jasmine Bingham

Help build the Protected Areas Database of the United States (PAD-US)

IntroductionPAD-US provides a comprehensive geospatial database of protected and managed areas in the United States. We assemble known protected areas whose primary purpose is biodiversity conservation, as well as lands and waters that provide public access to nature. As a National Geospatial Data Asset (https://ngda-portfolio-community-geoplatform., the PAD-US database (https://w
Roger M. Johnson

Community for data integration 2019 project report

The U.S. Geological Survey Community for Data Integration annually supports small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 14 projects supported in fiscal year 2019 and outlines their goals, activities, and accomplishments. Proposals in 2019 were encouraged to addre
Amanda N. Liford, Caitlin M. Andrews, Aparna Bamzai, Joseph A. Bard, David S. Blehert, John B. Bradford, Wesley M. Daniel, Sara L. Caldwell Eldridge, Frank Engel, Jason A. Ferrante, Amy K. Gilmer, Margaret E. Hunter, Jeanne M. Jones, Benjamin Letcher, Frances L. Lightsom, Richard R. McDonald, Leah E. Morgan, Sasha C. Reed, Leslie Hsu

Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York

Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil
Leslie A. DeSimone


No abstract available.
Xiaogang Ma, Matty Mookerjee, Leslie Hsu, Denise Hills

Update on U.S. Geological Survey Fundamental Science Practices

The U.S. Geological Survey (USGS) Fundamental Science Practices (FSP) are a set of standard principles fundamental to how USGS conducts and carries out its science activities and how resulting information products and data are reviewed, approved, and released. These policies, practices, philosophical premises, and operational principles serve as the foundation for all USGS research and monitoring

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of err
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey