Alaska Science Center
These are genetic data collected from replicated samples of 21 Enteroctopus (E. dofleini or a cryptic lineage) in Prince William Sound Alaska to evaluate tissue type, DNA extraction method, and time until analyses are completed on data reliability. Data collected from all samples include two microsatellites identified as possible lineage indicators, and nine microsatellite loci previously identified as polymorphic in both lineages. DNA sequence data from 528 bp of the octopine dehydrogenase (OCDE) gene were also collected.
These are genetic data collected from Peregrine falcons (Falco peregrinus) from three subspecies present in Alaska (F.p. pealei, n = 59; F. p. anatum, n = 26; F. p. tundrius, n = 47; F. p. anatum/tundrius?, n = 10), two subspecies from Canada (F. p. tundrius, n = 18 and F. p. anatum, n = 14), one subspecies present in Russia (F. p. pealei, n = 7), and samples from the San Juan Islands, Washington (n=15) thought to be a contact zone between F. p. anatum and the North Pacific segment of F. p. pealei. Data collected from all samples include twelve microsatellite loci and DNA sequence data from 559 base pairs of domain 1 of the mtDNA control region.
Crested (Aethia cristatella) and Least Auklets (Aethia pusilla) are crevice-nesting birds that breed in large mixed colonies at relatively few sites in the Aleutian Chain, Bering Sea, Gulf of Alaska and Sea of Okhotsk. Time-lapse imagery of nesting habitat was collected over the summers of 2010, 2012, and 2013 to assess use of the primary pre-eruption colony site. At-sea surveys collected relative abundance data to identify shifts in distributions of Crested and Least Auklets around Kasatochi and Koniuji Islands.
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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