Alaska Science Center
These data are nutrient concentrations of seven key forage-plant species in the ranges of three caribou herds, in northern Alaska. During the growing seasons of 2011-2014, we collected forage samples from 21 plots within the ranges of three caribou herds: Central Arctic Herd (2011-2013), Teshekpuk (2011-2013), Western Arctic (2013-2014). We also analyzed stable isotopes of carbon (13C), nitrogen (15N) and sulfur (34S) in key plants. Further, we included of soil chemistry measures within the sampling range.
These are data that characterize the distribution of polar bear denning habitat in 1002 Area of the Arctic National Wildlife Refuge, Alaska. They were generated to compare the efficacy of two different techniques: 1) from a previously published study (Durner et al., 2006) that used manual interpretation of aerial photos and 2) from computer interrogation of interferometric synthetic aperture radar (IfSAR) digital terrain models. Data include vector shapefiles of putative denning habitat derived using both methods, as well as a description of the IfSAR-derived digital terrain model (DTM) tiles used to generate the raster shapefiles. The IfSAR DTM are available for purchase through Intermap Technologies, Inc., and are not provided in the data release.
Data set containing avian influenza sampling information for late summer and early autumn waterfowl and gulls within and around the Izembek National Wildlife Refuge (NWR), Alaska, 2011-2015. Data contains species, age, sex, collection data and location of sampled migratory birds. Laboratory specific data used to identify presence and absence of influenza A viruses from collected samples are included.
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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