All Data

R code for data prep, processing, and population analysis associated with manuscript of same title.

Data fall into three types: 1) individual snake scanning data (PIT tag ID, time stamp, and device) downloaded from readers, 2) individual trait data (sex, size, etc) of tested brown treesnakes, and 3) detailed transcripts of videos of behavioral trial data as snakes interacted with the readers. All data were collected in an experimental arena in Dededo, Guam in August 2021.

This data release documents the digital data used to produce flood-inundation maps for a range of gage heights (stages) for the Sabinal River near Utopia, Tex. The simulated flood-inundation maps correspond to a range in stage from 11 to 28 feet (ft) at U.S. Geological Survey (USGS) streamgage 08197970 Sabinal River at Utopia, Tex. at intervals of 0.5-ft. The maps were created for a 10...

This dataset consists of one table with sample collection locations and results of screening resident wildlife for Cryptosporidium and Giardia in the Alaskan Arctic, 2012-2017. We analyzed feces collected from polar bear, Pacific walrus, Arctic fox, and caribou with MERIFLUOR direct immunofluorescence assay to determine the presence of Cryptosporidium oocysts and Giardia cysts.

Adult female San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) were captured and examined using an ultrasound device to determine if they were gravid and count the number of embryos in those that were. Morphological data including length and mass were collected for each snake, along with the date of capture and a study site identifier. These data support the following...

This repository contains code to fit Integral Projection Models and simulate population reintroductions for San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) This repository specifically contains code to reproduce analyses in: Rose, J.P., Kim, R., Schoenig, E.J., Lien, P.C., and Halstead, B.J. in-review. Comparing reintroduction strategies for the endangered San Francisco...

A variety of new technologies are being evaluated to mitigate the spread of invasive carps from the Illinois River to the Great Lakes. In 2022, the U.S. Army Corps of Engineers, U.S. Fish and Wildlife Service, and U.S. Geological Survey undertook a field-scale interagency study to determine the efficacy of an Automated Barge Clearing Deterrent (ABCD) in flushing live fish away from...

Endangered Banbury Springs limpet and threatened Bliss Rapids snail populations in springs along the Snake River in southern Gooding County, south-central Idaho, are declining. To protect these species, the U.S. Fish and Wildlife Service (USFWS) needs to understand what affects the species' habitat such as aquatic vegetation, associated with elevated nitrate concentrations in the springs...

The U.S. Geological Survey (USGS) works closely with the Mississippi Department of Transportation (MDOT) to provide information to be used by the MDOT for design of highway-drainage structures. MDOT spends millions of dollars annually for highway construction. Streamflow records, hydrologic analyses of basins, and hydraulic analyses of flooding potential at proposed highway crossings...

Model input and computed flood depths associated with the combined influence of sea level position, tides, storm surge, and streamflow under existing conditions and projected future higher sea level and peak stream runoff are provided for the lower (Reach 1) of the Nooksack River and delta in Whatcom County, western Washington State. The flood-depth projection data are provided in a...

This data release presents calculated accumulated wastewater (ACCWW, as a percent of total streamflow) values for 43 National Hydrologic Dataset Version 2.1 (NHDPlus V2.1) stream segments coinciding with long-term smallmouth bass sampling locations (Table 1) in the Shenandoah River Watershed (encompassing parts of Virginia and West Virginia, USA). Values are calculated for quarter-year...

Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i...