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Highlighted Publications

Last Update: 2017-09-20
Effects of surgically implanted transmitters on reproduction and survival in mallards [Details] [Full Publication]
Neotectonics of interior Alaska and the late Quaternary slip rate along the Denali fault system [Details] [Full Publication]
The Peters Hills basin, a Neogene wedge-top basin on the Broad Pass thrust fault, south-central Alaska [Details] [Full Publication]
Surveillance for highly pathogenic influenza A viruses in California during 2014–2015 provides insights into viral evolutionary pathways and the spatiotemporal extent of viruses in the Pacific Americas Flyway [Details] [Full Publication]

Latest Data

Last Update: 2017-09-20
Eklutna River at Glenn Highway Bridge, Alaska Cross-Section Survey, 2016 and 2017

This dataset consists of survey data, plots, photos, and a photo index from a cross section survey of the Eklutna River at the Glenn Highway bridge in Alaska. The cross sections were surveyed in 2016 and 2017 using a total station, then adjusted to match datums for a Municipality of Anchorage orthophoto and Alaska Department of Transportation and Public Facilities bridge as-built elevations.

Gulf Watch Alaska, Benthic Monitoring Component: Sea Otter Aerial Survey Data Kenai Fjords National Park, 2002-2016

These data are is part of the Gulf Watch Alaska (GWA) long term monitoring program, nearshore monitoring component. Specifically, these data describe sea otter (Enhydra lutris) aerial survey observations from the waters around Kenai Fjords National Park between 2002 and 2016. Sea otters are a keystone predator, well known for structuring the nearshore marine ecosystem through their consumption of invertebrate prey. The dataset consists of 3 comma delimited files exported from Microsoft Excel. The data consists of 1. Strip transect counts, 2. Intensive Search Unit (ISU) counts, and 3. Transect coordinates. For each aerial survey, a pilot flew an airplane at an altitude of 91m over pre-determined transects while an observer searched on one side of the plane and recorded sea otter group counts and locations. Sea otters observed within 400 m of each transect were later used to estimate abundance. Sea otters sighted beyond the confines of designated transect swaths were also counted and mapped, time permitting. To estimate the number of sea otters in small groups (<20) not detected along transect swaths (e.g., due to diving behavior or the presence of kelp canopy), 400m diameter circles (i.e. ISUs) were searched intensively by periodically flying 5 concentric circles around an initiating group. These ISUs were distributed throughout the survey area in an attempt to accurately represent the full range of observation conditions encountered during the survey. When large groups of sea otters (≥20) were sighted on transect, they were circled until a complete count was made.

Influenza A Viruses and Antibody Response in High-Latitude Urban Wintering Mallards (Anas platyrhynchos), Alaska, 2012-2015

This data set contains information regarding the sampling of avian influenza viruses from mallard ducks at locations in Anchorage and Fairbanks, Alaska 2012-2015. Data pertaining to wild birds (mallards) sampled includes band numbers, age and sex, location and timing of sampling. Laboratory specific data is also included and used to identify presence and absence of avian influenza viruses either during active infection or previous exposure (serostatus).


Last Update: 2017-09-20
USGS Sea Ice Email Script, 2017

Daily sea ice imagery and charting benefits logistics and navigational planning in the Alaskan Arctic waters, yet access to these data often requires high bandwidth data access and substantial GIS processing. This software script acquires, processes and delivers these data in a format that may be manipulated by openly available virtual globe software, be visualized by software commonly installed on all smart phones and computers, and that may be accessed through moderate band-width data communications available in remote Alaskan communities and offshore research vessels. The script sends daily or weekly e-mails with attached maps images and virtual globe data files of sea ice products, including the National Ice Center Marginal Ice Zone chart, and images of the 6.25 Km resolution passive microwave reflectance optimized to visualize sea ice. In the emailing the script provides links to the NASA MODIS imagery corrected to enhance visualization of sea ice; National Weather Service sea ice and surface forecast products, the NOAA NECP forecasted 24 h sea ice drift map, and the latest NOAA images from the POES AVHRR satellite.

qfasar: Quantitative Fatty Acid Signature Analysis in R

An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the fatty acid composition of adipose tissue. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of potential prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options, goodness-of-fit diagnostic procedures to assess the suitability of estimates, and simulation capabilities are implemented. Please refer to the package vignette and the documentation files for individual functions for details and references.

QFASA Robustness to Assumption Violations: Computer Code

Quantitative fatty acid signature analysis (QFASA; Iverson et al. 2004. Ecological Monographs 74:211-235) has become a common method of estimating diet composition, especially for marine mammals, but the performance of the method has received limited investigation. This software was developed to compare the bias of several QFASA estimators using computer simulation and develop recommendations regarding estimator selection (Bromaghin et al. 2015. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods in Ecology and Evolution (publication expected in late 2015 or early 2016).

slope in the Susitna Basin - photo by Jamey Jones, USGS

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