Department of Geography

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https://hdl.handle.net/10919/23966

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Browsing Department of Geography by Department "Forest Resources and Environmental Conservation"

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    Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians
    Evans, Andrew M.; Odom, Richard H.; Resler, Lynn M.; Ford, W. Mark; Prisley, Stephen P. (Hindawi, 2014-08-28)
    The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for den sites and corridor habitats between boreo-montane conifer patches foraging areas. Our study related terrain data to presence of northern hardwood forest type in the recovery areas of CNFS in the southern Appalachian Mountains of western North Carolina, eastern Tennessee, and southwestern Virginia. We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. Terrain variables analyzed included elevation, aspect, slope gradient, site curvature, and topographic exposure. We used an information-theoretic approach to assess seven models based on associations noted in existing literature as well as an inclusive global model. Our results indicate that, on a regional scale, elevation, aspect, and topographic exposure index (TEI) are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. Our elevation + TEI model was the best approximating model (the lowest AICc score) for predicting northern hardwood forest type correctly classifying approximately 78% of our sample points. We then used these data to create region-wide predictive maps of the distribution of the northern hardwood forest type within CNFS recovery areas.
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    Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands
    Jensen, Carrie K.; McGuire, Kevin J.; Shao, Yang; Dolloff, C. Andrew (Wiley, 2018-05-25)
    Despite the advancement of remote sensing and geospatial technology in recent decades, maps of headwater streams continue to have high uncertainty and fail to adequately characterize temporary streams that expand and contract in the wet length. However, watershed management and policy increasingly require information regarding the spatial and temporal variability of flow along streams. We used extensive field data on wet stream length at different flows to create logistic regression models of stream network dynamics for four physiographic provinces of the Appalachian Highlands: New England, Appalachian Plateau, Valley and Ridge, and Blue Ridge. The topographic wetness index (TWI) was the most important parameter in all four models, and the topographic position index (TPI) further improved model performance in the Appalachian Plateau, Valley and Ridge, and Blue Ridge. We included stream runoff at the catchment outlet as a model predictor to represent the wetness state of the catchment, but adjustment of the probability threshold defining wet stream presence/absence to high values for low flows was the primary mechanism for approximating network extent at multiple flow conditions. Classification accuracy was high overall (> 0.90), and McFadden's pseudo R2 values ranged from 0.69 for the New England model to 0.79 in the Appalachian Plateau. More notable errors included an overestimation of wet stream length in wide valleys and inaccurate reach locations amid boulder deposits and along headwardly eroding tributaries. Logistic regression was generally successful for modeling headwater streams at high and low flows with only a few simple terrain metrics. Modification and application of this modeling approach to other regions or larger areas would be relatively easy and provide a more accurate portrayal of temporary headwaters than existing datasets.
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    Trust ecology and the resilience of natural resource management institutions
    Stern, Marc J.; Baird, Timothy D. (The Resilience Alliance, 2015)
    The resilience of natural resource management (NRM) institutions are largely contingent on the capacities of the people and organizations within those institutions to learn, innovate, and adapt, both individually and collectively. These capacities may be powerfully constrained or catalyzed by the nature of the relationships between the various entities involved. Trust, in particular, has been identified repeatedly as a key component of institutional relationships that supports adaptive governance and successful NRM outcomes. We apply an ecological lens to a pre-existing framework to examine how different types of trust may interact to drive institutional resilience in NRM contexts. We present the broad contours of what we term “trust ecology,” describing a conceptual framework in which higher degrees of diversity of trust, as conceptualized through richness and evenness of four types of trust (dispositional, rational, affinitive, and systems based), enhance both the efficacy and resilience of NRM institutions. We describe the usefulness and some limitations of this framework based on several case studies from our own research and discuss the framework's implications for both future research and designing more resilient governance arrangements.