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Science Analytics and Synthesis (SAS) Program

Science Analytics and Synthesis (SAS) emphasizes a science data lifecycle approach to Earth systems data and information. We strive to accelerate research and decision making through data science, information delivery, advanced computing, and biodiversity analytics.

News

USGS New Supercomputer Helps Scientists Wrangle Data

USGS New Supercomputer Helps Scientists Wrangle Data

Schedule of FY23 Working Groups

Schedule of FY23 Working Groups

ScienceBase Data Release Training for USGS Authors and Data Managers

ScienceBase Data Release Training for USGS Authors and Data Managers

Publications

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
Authors
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
Authors
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
Authors
Hao Yu, Arthur R. Cooper, Jared Ross, Alexa McKerrow, Daniel J. Wieferich, Dana M. Infante