Alaska Science Center
This data release contains three tables of information on behavior and productivity of greater white-fronted geese nesting near Point Lonely, Alaska, 2013-2014: transcriptions of nest photographs obtained by time-lapse photography at 1-minute intervals in 2013 and 2014, and characteristics of nests monitored with cameras and via periodic nest visits during 2013-2014. Data were collected as part of the USGS Changing Arctic Ecosystems Initiative to understand the effects of industrial and researcher disturbance on Arctic-nesting geese.
The dynamics of the linked river systems draining Exit and Paradise Glaciers have been dependent on glacial and fluvial controls since the 1800s. This data release contains maps of historical Exit and Paradise creek and glacier positions, 2013 channel survey and particle size data, and a 2012 digital elevation model and orthophoto for the Exit and Paradise glacier forelands, Alaska.
To demonstrate connectivity between terrestrial and marine ecosystems, we used stable (δ13C, δ15N, δ2H) and radiogenic (∆14C) isotopes to estimate the relative contribution of glacier runoff and terrestrial-derived organic matter (OM) to marine food webs. This dataset contains information on isotopic signatures from dissolved organic matter (DOM), dissolved inorganic matter (DIC) and particulate organic matter (POM), mussels, plankton, fish and seabirds near tidewater glaciers during the peak melt in summer 2012-2013 in Prince William Sound and the Western Aleutians, Alaska.
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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