Alaska Science Center
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).
U.S. Geological Survey researchers conducted time-series ground-penetrating radar (GPR) surveys with a Sensors and Software 500-MHz Pulse Ekko Pro system. We collected common-offset data from the ground, towed behind a researcher using a snowmobile, and from the air, strapped underneath a helicopter. We also collected common-midpoint data at specific glacier locations. All the profiles are linked to coincident GPS observations. Coincident in-situ data may provide calibration information, and may be composed of any of the following: snow pits and/or snow-pit/snow-core combinations, probe profiles, and ablation stakes. This supplemental information may provide estimates of snow properties to calibrate radar velocity.
These are genetic data collected from over 700 individual coho salmon (Oncorhynchus kisutch) from 17 streams and rivers within Glacier Bay Alaska and 2 rivers outside the bay. Data collected from all samples include one nuclear gene intron, Growth Hormone-1, and eight microsatellite loci.
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.
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.
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).
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