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

该项目的目标是让PI在新墨西哥大学休假期间获得地理信息系统（GIS）和景观格局分析的新技能。PI将利用这些新技能，结合她在模拟建模方面的专业知识，分析和预测过渡带或生态系统类型边界的植物物种优势。具体目标是：(1)获取GIS专业知识并熟悉新墨西哥州草木植物过渡带可用的空间数据层；(2)更好地理解交错带的性质和空间显式过程对交错带动态的重要性；(3)提高PI在景观模式分析和边缘检测技术方面的知识。PI将参加地理信息系统课程，并将与在美国国家科学基金会支持的塞维莱塔长期生态研究站工作的众多调查人员合作。获得这种新的专业知识是PI实现其开发空间明确建模工具以理解和预测过渡带植被响应的长期目标的不可或缺的第一步。该项目具有广泛的科学重要性，原因有二。首先，过渡带是景观和区域中最具活力和最重要的区域之一，在这个空间尺度上，全球气候变化和土地利用的区域变化预计对植物群落和生态系统的影响最大。目前，我们对气候和土地利用等环境因素如何与生态过程相互作用以产生植被模式缺乏很好的理解。需要一个空间明确的、机械的工具来预测植被格局随环境变化的变化。这个项目将允许PI开发这样一个工具。其次，该项目将为PI提供定量方法组合的专业知识，这在生态学家中并不常见。此外，妇女在这些领域的代表人数不足。这种不同方法的结合将为生态问题提供一个独特的视角，PI可以通过教授本科和研究生水平的课程以及指导研究生，将这个视角传递给未来的生态学家。

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.