CAA: Development of Landscape Analysis and GIS Expertise to Predict Vegetation Dynamics at Ecotones

点击翻译按钮获取中文摘要

Abstract Coffin CAA 9707100 The goal of this project is for the PI to obtain new skills in geographic information systems (GIS) and landscape pattern analysis while on a sabbatical leave at the University of New Mexico. The PI will use these new skills, in combination with her established expertise in simulation modeling, to analyze and predict plant species dominance at ecotones, or boundaries between ecosystem types. The specific objectives to be accomplished are: (1) acquiring GIS expertise and familiarity with spatial data layers available for grass-woody plant ecotones in New Mexico; (2) gaining a better understanding of properties of ecotones and the importance of spatially-explicit processes to ecotone dynamics; and (3) increasing the PI's knowledge of pattern analysis and edge detection techniques in landscapes. The PI will take a class in GIS, and will collaborate with numerous investigators working at the NSF-supported Sevilleta Long Term Ecological Research site. Acquiring this new expertise is an integral first step for the PI to achieve her long-term goal of developing a spatially-explicit modeling tool to understand and predict responses of vegetation at ecotones. This project has broad scientific importance for two reasons. First, ecotones are among the most dynamic and important areas for landscapes and regions, the spatial scales where global changes in climate and regional changes in landuse are expected to have the largest impact on plant communities and ecosystems. Currently we are lacking a good understanding of how environmental factors, such as climate and landuse, interact with ecological processes to produce patterns in vegetation. A spatially-explicit, mechanistic tool is needed to predict changes in vegetation patterns as the environment changes. This project will allow the PI to develop such a tool. Second, this project will provide the PI with expertise in a combination of quantitative approaches tha t is not commonly found in ecologists. Furthermore, women are under-represented in each of these areas. This combination of different approaches will provide a unique perspective on ecological problems, a perspective that the PI can transmit to future ecologists by teaching undergraduate and graduate level courses as well as by mentoring graduate students.