Alaska Science Center
These data are the data for age, skull size, body length, and lean mass for Gates of the Arctic, Lake Clark, Kodiak, and Katmai, Alaska, 2013-2016.
These data represent estimates of den entrance and exit dates for female polar bears in the southern Beaufort and Chukchi Seas based on temperature sensor data obtained from satellite collars. An algorithm described in Olson et al. (2016) was used to determine whether the female entered a den and further analyses using temperature data as described in Olson et al. (2017) were used to assess den entry and emergence dates. The algorithm used identified periods in which temperature remained consistently above a temperature threshold. As such, users of these data should be aware that bear behavior can affect den entrance and exit date. If a bear moves in and out of a den frequently, the algorithm identifies that bear as not denning. Den substrate was determined by examining satellite locations from collars during the identified denning period and classify dens as occurring on landfast ice, sea ice, or land. This data set also includes direct observations of females post-den emergence including whether or not she was accompanied by cubs as an indicator of reproductive success.
This dataset contains data from the use of bioelectrical impedance analysis and deuterium injection as methods to estimate the body composition of female polar bears in the southern Beaufort Sea subpopulation. Data are provided on bioelectrical impedance resistance measures, the enrichment level of deuterium oxide that was injected and measured in blood samples, and morphological measures.
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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