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

摘要棺材CAA 9707100这个项目的目标是让PI在新墨西哥大学休假期间获得地理信息系统和景观格局分析的新技能。PI将使用这些新技能，结合她在模拟建模方面的成熟专业知识，分析和预测交错带的植物物种优势，或生态系统类型之间的边界。要完成的具体目标是：(1)获得地理信息系统的专门知识并熟悉新墨西哥州草木植物交错带可用的空间数据层；(2)更好地了解交错带的特性以及空间显性过程对交错带动态的重要性；以及(3)增加景观中格局分析和边缘检测技术的PI知识。PI将参加一门地理信息系统课程，并将与在NSF支持的Sevilleta长期生态研究站点工作的众多研究人员合作。获得这种新的专业知识是PI实现她的长期目标的不可或缺的第一步，她的目标是开发一种空间显式建模工具来理解和预测交错带植被的响应。这个项目具有广泛的科学意义，原因有两个。首先，交错带是景观和区域中最具活力和最重要的区域之一，在空间尺度上，全球气候变化和区域土地利用变化预计将对植物群落和生态系统产生最大影响。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了解。随着环境的变化，需要一种空间上明确的机械工具来预测植被模式的变化。该项目将允许PI开发这样一个工具。其次，该项目将为PI提供在生态学家中罕见的定量方法组合方面的专业知识。此外，妇女在这些领域的任职人数都不足。这种不同方法的结合将提供一种关于生态问题的独特视角，这种视角可以通过教授本科生和研究生课程以及指导研究生来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.