Grants and Contracts Details
General Overview: In physical geography and Earth sciences literature, there still is a critical paucity of theories, which are related to the potential effects of spatial autocorrelation (SAC) on modeling and interpreting the distribution of natural resources across landscapes. Numerous studies have shown what difference happens if SAC is explicitly incorporated into a conventional non-spatial modeling procedure, but much is not known about how (or why) that difference happens: that is, spatial processes of factors representing physical environments are still under-investigated. In this proposed research, we evaluate this latter inquiry, focusing on soil-landform, water-landscape, and diversity-environment relationships in a regression framework (i.e., soil, water, and species diversity are treated as response variables, whereas landform, landscape, and environmental parameters are regarded as predictor variables). We intend to analyze multiple spatial data sets that are collected in many different parts of the world representing different climate and vegetation gradients, in order to draw some general conclusions about the implications of SAC for soil-landform, water-landscape, and diversity-environment modeling. In this way, we aim to define a pattern of potential networks among various natural resources, such as soil, water, biota, landform, geology, and climate. Intellectual Merit: It is always one ultimate goal of physical geography to generate universal theories of Earth surface processes that can be widely applied to a wide range of environmental systems. Our preliminary research conducted at four pedogeomorphological sites has produced surprisingly consistent results: the effects of SAC on spatial modeling procedures (e.g., changing R2 values and residual autocorrelation) are predictable by examining the level of SAC inherently possessed by each soil property. It would be premature to claim that the preliminary research has fulfilled the search for universally-applicable theories in physical geography. Rather, one implication of this intended collaborative work is to propose a possible way toward achieving it. Our new methodological framework presented in the proposal could directly be applied to any other type of systems that are not explored in the preliminary work. A successful achievement of this intended research will potentially enable us to anticipate-and, further, quantify-the influences of SAC even without conducting spatially-explicit predictions, simply by examining the internal correlation structure of predictor and response variables included in the soil-landform, water-landscape, and diversity-environment modeling. In the long term and at the broad scale, we believe that our efforts to improve soil-landform, water-landscape, and diversity-environment modeling procedures will contribute to the advance of pedogeomorphological, hydrochemical, and ecological theories. Broader Impacts: One primary purpose of this research is to train two master's students (preferably women or from underrepresented groups) for two years, so that they can successfully initiate more in-depth doctoral research or smoothly start their career outside of the academia. After all, each student is expected to present her findings at professional meetings, write a master's thesis, and, eventually, publish results in reputed international journals. These products are expected to cross-fertilize different sub-disciplines of physical geography, ecology, and GIScience. Moreover, this research will provide the policy makers and researchers of government agencies with much-needed fundamental information about how to better predict and manage the spatial distribution of natural resources in this era of rapid climate change and development pressure by humans. For example, this research will contribute to the ongoing effort of federal agencies, like the Natural Resources Conservation Service (NRCS), for improving the characterization of land cover changes and landscape conservation. We note that the effects of SAC are still yet to be explicitly considered within the NRCS's Ecological Site Description Database, when defining alternative ecological states and how they transition over time. Such improvement will potentially serve as a springboard for establishing platforms for comprehensive management recommendations for various ecosystems.
|Effective start/end date||9/1/16 → 2/29/20|
- National Science Foundation: $336,478.00
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