Software
An official USGS software project is code reviewed and approved at the bureau-level for distribution.
Filter Total Items: 881
Interpolating missing water quality data Interpolating missing water quality data
This repository contains analysis codes to interpolate missing data from the Upper Mississippi River LTRM dataset.
Reproductive Success from Movement Data Reproductive Success from Movement Data
This software release includes code to conduct analyses presented in the manuscript titled: A hierarchical modeling framework for estimating individual- and population-level reproductive success from movement data
SeaLog SeaLog
SeaLog is a robust data-logging application designed to collect at-sea data of marine birds and mammals. This software was developed collaboratively with the U.S. Geological Survey, the National Park Service, the U.S. Fish and Wildlife Service, and ABR, Inc. SeaLog is Windows 10 compatible, is highly customizable, and provides user designed maps to allow visual feedback of observations.
Scenario Analysis of Management Alternatives for Free-roaming Horse Populations (Version 1.0.0) Scenario Analysis of Management Alternatives for Free-roaming Horse Populations (Version 1.0.0)
PopEquus is a predictive modeling tool to support decisions related to the management of free-roaming horse populations. It permits the simulation and comparison of how management alternatives influence horse population size and metrics associated with management. This repository uses R scripts with predictive functions from the PopEquus model to simulate how management alternatives (e.g...
ADOM: A Data Orchestration Manager ADOM: A Data Orchestration Manager
A Data Orchestration Manager (ADOM) is a user interface designed to enforce data management goals by entwining and extending the open-source file management tool rclone. The rclone tool provides data movement and management functionality in and between supported end-user-defined endpoints, also known as remotes. ADOM takes rclone a step further by wrapping rclone functionality into USGS...
Modeling widespread declines in walleye recruitment following zebra mussel invasion in Minnesota lakes Modeling widespread declines in walleye recruitment following zebra mussel invasion in Minnesota lakes
Invasive zebra mussels (Dreissena polymorpha) alter lake ecosystems and can negatively affect first-year growth of walleye (Sander vitreus), potentially lowering walleye recruitment success. We quantified walleye recruitment responses to zebra mussel invasion in Minnesota lakes using data from 1,438 electrofishing surveys across 348 lakes collected between 1993 and 2019 to measure...
Accounting for spatio-temporal variation in catchability in joint species distribution models Accounting for spatio-temporal variation in catchability in joint species distribution models
1: Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats, and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations...
Code for a hierarchical model of raven densities linked with sage-grouse nest survival to help guide management of subsidized avian predators, version 1.0 Code for a hierarchical model of raven densities linked with sage-grouse nest survival to help guide management of subsidized avian predators, version 1.0
This repository includes R code to conduct hierarchical density surface modeling of common raven (Corvus corax) point count data and hierarchical shared frailty modeling of Greater sage-grouse (Centrocercus urophasianus) nest fates. The model components are quantitatively linked, such that estimates of raven density from the hierarchical distance sampling component are spatially...
Conservation Planning Tool for the Bi-State Distinct Population Segment of Greater Sage-grouse Conservation Planning Tool for the Bi-State Distinct Population Segment of Greater Sage-grouse
The Bi-State conservation planning tool provides a science-based approach for prioritizing pinyon (Pinus spp.) and juniper (Juniperus osteosperma, J. occidentalis), hereafter, 'conifer', removal to improve habitat for greater sage-grouse (Centrocercus urophasianus) in the Bi-State Distinct Population Segment (DPS, U.S. Fish and Wildlife Service 2010). Conifer negatively affects sage...
Version 2.3.0 of Coupled Ground-Water and Surface-Water Flow Model Based on the Integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model Version 2.3.0 of Coupled Ground-Water and Surface-Water Flow Model Based on the Integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model
GSFLOW is a coupled Groundwater and Surface-Water Flow model based on the integration of the U.S. Geological Survey Precipitation-Runoff Modeling System (PRMS; Markstrom and others, 2015) and the U.S. Geological Survey Modular Groundwater Flow Model (MODFLOW-2005, Harbaugh, 2005; MODFLOW-NWT, Niswonger and others, 2011). In addition to the basic PRMS and MODFLOW simulation methods...
Code for: Leveraging local efforts to solve regional-scale ecological questions: using multiple sources of data and a multi-species occupancy model to explore bee-plant interactions Code for: Leveraging local efforts to solve regional-scale ecological questions: using multiple sources of data and a multi-species occupancy model to explore bee-plant interactions
This repository contains all of the scripts to reproduce the analyses, figures, and tables associated with the manuscript Lee et al. in prep. The scripts are organized in the order in which they should be run. Briefly, the files do the following: format the data, fit the model, create the figures, and create the tables.
UASmagpy: Python code for compensating rotary-wing sling-load UAS aeromagnetic data UASmagpy: Python code for compensating rotary-wing sling-load UAS aeromagnetic data
This notebook implements the processing of UAS aeromagnetic total magnetic intensity data as described in Phelps and others (2022). After low-pass filtering the raw data and applying a diurnal magnetic correction, a heading and altitude correction model is applied to the data, solved using regularized regression methods; heading refers to the direction in which the sensor is flying. The...