ScholarWorks@UA

ScholarWorks@UA is University of Alaska's institutional repository created to share research and works by UA faculty, students, and staff

  • Working with Individuals with Fetal Alcohol Spectrum Disorders: A Meta-Synthesis

    Rehmer, Shelby (University of Alaska Southeast, 2014)
    This meta-synthesis of the literature on working with individuals with Fetal Alcohol Spectrum Disorders (FASD) examines the characteristics of individuals with FASD and the need for supports and services for these individuals in the classroom. There are behavior characteristics unique to FASD and these behaviors coupled with sensory processing deficits lead to distinctive challenges for individuals with FASD. Early identification, supports and services are critical to address challenges for individuals with FASD in the classroom, yet are often unavailable or unidentified.
  • Even in Arcadia: stories

    Wood, Rebecca N.; Farmer, Daryl; Johnson, Sara; Schell, Jennifer (2021-05)
    Even in Arcadia: Stories is a short story collection that follows adolescent and young adult women as they navigate growing up and growing out of the spaces they inhabit. Set in the American southwest, specifically unnamed suburban and rural cities of Arizona, the collection challenges the culturally popular narratives that surround the West-- the idealized cowboy, rugged individualism, and conquest of nature. Drawing on long-standing myths, serialized TV shows, and classic literature, the collection asks the reader to evaluate the stories they consume and are willing to take as truth. The stories range from realistic to speculative, employing horror and surrealistic elements as they descend into a sort of hellscape that draws on natural elements of desert landscapes juxtaposed against urban spaces. The collection focuses on gender, adolescence, and trauma set in the aftermath of the 2008 recession and the decline of small-town America from the perspective of youth.
  • The development and initial testing of the vertical comet assay, a novel technique for the study of DNA damage and repair

    Williams, Robert T. D.; Pdlutsky, Andrej; Chen, Cheng-fu; Drew, Kelly (2021-05)
    Gene-specific repair is the idea that certain segments of the genome repair at a faster rate than others. This idea, if demonstrated with adequate evidence, would have large implications for the field of biology as a whole, with special significance for the fields of oncology, gerontology, and molecular and cell biology. The concept of gene-specific repair is not new, with the earliest references in the literature dating back to 1985, and there is a small volume of evidence derived over the years. However, the evidence generated so far is not enough to conclusively prove the existence of gene-specific DNA repair. Generally, the reason for the lack of evidence is that currently available assays and techniques are not adequate for the study of gene-specific repair on a large scale as the techniques that are available require a great deal of time, funding, and skill to generate a reliable and conclusive data set for a single gene, let alone the entire genome. The vertical comet technique described here-in is a response to the perceived need for a robust and relatively high-throughput technique for the study of gene-specific DNA repair. In the traditional comet assay, cells are fixed in agarose gel. Electrophoresis is performed, following several treatment steps, to create a ball of nuclear material embedded in the agarose gel with a 'tail' of smaller pieces of nominally damaged DNA extending to one side. The vertical comet captures this tail DNA in a buffer, allowing for its further analysis with processes such as quantification, PCR/qPCR, and sequencing. The capture of the tail DNA not only makes genespecific repair studies possible, it also allows the vertical comet to fulfill the role of the traditional comet assay with a number of advantages - a reduction in human bias, a reduction in labor-hours required for work, and a reduction in inter-lab variability of results.
  • Exploring the use of machine learning for daily fire growth prediction in Alaska

    White, James; Walsh, John; Thoman, Richard; Bhatt, Uma (2021-05)
    Wildfire is a natural but often hazardous part of the Alaskan ecosystems. Physically based wildfire models range from simple relationships used for rapid, in-situ fire behavior analysis to complex weather models used for prediction over several days and weeks. Physical models in Alaska, however, often struggle to integrate weather forecast information to make predictions beyond just a few days. The random forest model explored here is able to leverage an array of variables to identify days of enhanced and reduced satellite fire detections. Peaks and lulls in activity are accurately identified, though exact magnitudes are often incorrect, especially when wildfire suppression efforts occurred. This study emphasizes the use of reanalysis weather variables in addition to antecedent fire activity, highlighting the usefulness of variables like vapor pressure deficit for use in quantitative prediction. By applying weather forecast data, the model generated simulated wildfire forecasts. These forecasts show some success at identifying peaks and lulls in fire activity. Effective lead time varied widely ranging between 1 and 10 days, mostly dependent on the weather model performance. By providing specific timing and using real ensemble forecasts for medium term prediction, a model likes this fills a potential open niche in fire predictive services. Machine learning may be especially useful for its relative efficiency and ease of automation.
  • Salmonid distribution models to support restoration planning across the fragmented Chehalis River basin, WA

    Walther, Eric J.; Westley, Peter; Zimmerman, Mara; Falke, Jeffrey; Seitz, Andrew (2021-05)
    Understanding the factors that influence the distribution of species through time and across space is a fundamental goal of ecology and crucial information needed to effectively manage and recover populations. Anthropogenic fragmentation of habitat disrupts ecological processes and is an on-going threat to species persistence across taxa. River ecosystems are particularly vulnerable to disruptions in connectivity and are the focus of extensive restoration efforts and financial investment. For example, over $300 million/year is invested towards restoration in the Columbia River basin. However, restoration is often impeded by knowledge gaps in distribution that can result in omitting locations that would benefit from restoration. For mobile species within dendritic freshwater networks, the boundary that demarcates the total quantity of available habitat can be defined by the upper limit of occurrence (ULO) and is a useful metric for assessing the extent of habitat to consider for restoration. The first goal of this work was to identify the ULO boundary for three socially and ecologically important anadromous fishes (Oncorhynchus spp.) in a subset of representative streams across a complex river network in southwestern Washington State, USA, and quantify the relationship of the ULO with landscape attributes for these species. Extensive field surveys covering 669 river km across two years documented the ULO in 115 terminal streams (i.e., uppermost independent stream segment within a stream network) for coho salmon (O. kisutch), 97 terminal streams for steelhead trout (O. mykiss), and 57 terminal streams for chum salmon (O. keta). The landscape attributes associated with these ULO locations varied among species. For example, precipitation was an important predictor only for coho salmon, whereas slope was an important predictor only for steelhead trout. In contrast, drainage area, elevation, and geology were important predictors for all species; while the direction was the same for drainage area and elevation, the magnitude of the effect of each landscape attribute varied among species. I demonstrated that large-scale landscape attributes can accurately and consistently detect species-specific distribution boundaries across broad and diverse habitat (percent correct classification:78%-89%; area under the receiver operating characteristic curve: 0.87-0.96). The ability to quantify landscape attributes related to distribution boundaries illuminates how the biology and life history of a species is captured across the landscape. The second goal of this work was to predict the range of occurrence as a function of landscape attributes for coho salmon, steelhead trout, and chum salmon across a range of probability decision thresholds, that reflect different risk-tolerance scenarios and determine whether stream reaches are within or outside the range of occurrence. Generalized linear mixed models were used to compare the quantity of currently described distribution used in restoration planning in the basin and quantify the amount of habitat inaccessible due to anthropogenic barriers. The change in amount of habitat within the predicted range of occurrence across probability decision thresholds ranged from 60%-74% among species. Differences between the model predictions and the currently described distribution for each species ranged from -14% to 171%, which on a whole indicates that the amount of habitat being considered for restoration is currently underestimated. As predicted, species with a greater range of occurrence (e.g., coho salmon) had a greater percentage of predicted suitable habitat inaccessible due to anthropogenic barriers (coho salmon:17.4%-28.8%, 0.75-0.25 PDT; steelhead trout:10.2%-17.5%; chum salmon: 3.9%-12.3%), and the locations of these barriers varied among species. Modelling species distributions at multiple levels of risk-tolerance allows practitioners to weigh the ecological benefits and financial investment when considering locations for restoration. Ultimately, the effective consideration of restoration actions requires tools such that managers can weigh the trade-offs of their decisions given that not all actions equitably benefit all species.

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