52 research outputs found

    Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

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    The Sierra Nevada red fox Vulpes vulpes necator is listed as a threatened species under the California Endangered Species Act. It originally occurred throughout California’s Cascade and Sierra Nevada mountain regions. Its current distribution is unknown but should be determined in order to guide management actions. We used occurrence data from the only known population, in the Lassen Peak region of northern California, combined with climatic and remotely sensed variables, to predict the species’ potential distribution throughout its historic range. These model predictions can guide future surveys to locate additional fox populations. Moreover, they allow us to compare the relative performances of presence-absence (logistic regression) and presence-only (maximum entropy, or Maxent) modeling approaches using occurrence data with potential false absences and geographical biases. We also evaluated the recently revised Maxent algorithm that reduces the effect of geographically biased occurrence data by subsetting background pixels to match biases in the occurrence data. Within the Lassen Peak region, all models had good fit to the test data, with high values for the true skill statistic (76–83%), percent correctly classified (86–92%), and area under the curve (0.94–0.96), with Maxent models yielding slightly higher values. Outside the Lassen Peak region, the logistic regression model yielded the highest predictive performance, providing the closest match to the fox’s historic range and also predicting a site where red foxes were subsequently detected in autumn 2010. Subsetting background pixels in Maxent reduced but did not eliminate the effect that geographically biased occurrence data had on prediction results relative to the Maxent model using full background pixels

    LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES

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    We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections

    Science Librarian Internship as a Way to Get Started in EScience

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    The Science Bibliographers’ Group of Boston College first proposed the creation of a paid science librarian internship position in Summer 2008. Since then, the three interns hired over time have gained exposure to a wide variety of activities undertaken by science librarians, and, at the same time, have significantly furthered the Library’s understanding of, and participation in, eScience. In addition to important contributions in reference and collection development activities, intern contributions have included an environmental scan/best practices review of relevant eScience initiatives, design of an eScience brochure, development of a faculty survey to gauge interest in library data management, and a capstone presentation on eScience for all library staff. Building upon that work, the Science Bibliographers’ Group developed a Vision Statement and Action Plans for eScience. Our current intern is working closely with members of the group on the creation of a LibGuide focused on data management and, concurrently, develop-ment of curricular materials for data management workshops to be implemented during the 2011/12 academic year. Ideally, these increased efforts in eScience-related work will result in an enhanced profile for eScience on the Boston College campus, and, ultimately, creation of a new, eScience-focused position in the Boston College Libraries. An internship program can provide current knowledge and skills to educate and support a university research library through the early learning stage of developing an eSciences program, while simultaneously providing a valuable hands-on learning experience for a potential science librarian

    Science Librarian Internship as a Way to Get Started in E-Science

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    OBJECTIVETo demonstrate how a science librarian internship program can be used to jumpstart an e-sciences initiative in a university research library.METHODSCurrent library science students were hired, as paid interns, to work with an established Science Librarian Bibliographers Group. While the position included exposure to the wide variety of activities undertaken by science librarians, the most recent intern, arriving with a strong interest in e-Science, was also tasked with assisting in specific assignments designed to further the Library’s understanding of and participation in the area of e-Science. Specifically, the intern was asked to design a brochure about e-Science, develop a faculty survey to gauge interest in library involvement in data management, assist Science Librarians in an environmental scan/best practices review of relevant e-science initiatives, to serve as a roadmap in this area for the Boston College Libraries, and, finally, to further the education of all library staff with a presentation on e-Science.RESULTSBuilding upon the intern’s extensive literature review, draft brochure and PowerPoint presentation/synthesis, the Science Bibliographers’ Group has continued work on next steps in e-Science, with the development of a Vision Statement and Action Plans, as well as draft faculty/student/staff survey. The intern was exposed to a wide variety of typical science librarian job functions.CONCLUSIONSAn internship program can provide current knowledge and skills to educate and support a university research library through the early learning stage of developing an e-Sciences program, while simultaneously providing a valuable hands-on learning experience for a potential science librarian

    Neighbourhood deprivation and small-for-gestational-age term births in the United States

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    Residential context has received increased attention as a possible contributing factor to race/ethnic and socioeconomic disparities in birth outcomes in the United States. Utilizing vital statistics birth record data, this study examined the association between neighbourhood deprivation and the risk of a term small-for-gestational-age (SGA) birth among non-Hispanic whites and non-Hispanic blacks in eight geographic areas: Baltimore City, Baltimore County, Montgomery County and Prince Georges County in Maryland, 16 pooled cities in Michigan, Durham County and Wake County in North Carolina, and Philadelphia, Pennsylvania. Multilevel random intercept logistic regression models were employed and statistical tests were performed to examine if the association between neighbourhood deprivation and SGA varied by race/ethnicity and study site. The risk of term SGA was higher among non-Hispanic blacks (range: 10.8%–17.5%) than non-Hispanic whites (range: 5.1%–9.2%) in all areas and it was higher in cities than in suburban locations. In all areas, non-Hispanic blacks lived in more deprived neighbourhoods than non-Hispanic whites. However, the adjusted associations between neighbourhood deprivation and term SGA did not vary significantly by race/ethnicity or study site. The summary fully-adjusted pooled odds ratios, indicating the effect of one standard deviation increase in the deprivation score, were 1.15 [95% CI: 1.08–1.22] for non-Hispanic whites and 1.09 [95% CI: 1.05–1.14] for non-Hispanic blacks. Thus, neighbourhood deprivation was weakly associated with term SGA among both non-Hispanic whites and non-Hispanic blacks

    Point Reyes Peninsula and Vicinity Ecosystem Field Trip

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    This electronic field trip examines the biogeography and natural history of the Point Reyes Peninsula in California. The field trip gives students a broad overview of the peninsulas geography including its geology, climate, distribution of plant communities, and its historical land use. Areas discussed include the coastal redwood forest, the Bishop pine forest, the coastal scrubs, fire damage, coastal prairie and rangeland, coastal beach, dune and strand, the Douglas fir forest, and the marshland habitat. There is a field guide to the landforms, flora and fauna of this region along with questions for students to answer about the area and many references. Educational levels: High school, Undergraduate lower division, Undergraduate upper division

    BLUE OAK COMMUNITIES IN CALIFORNIA

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    Volume: 38Start Page: 80End Page: 9
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