Collaborative Research: Landscape Modeling: The Synthesis of Ecological Processes Over Large Geographic Regions and Long Time Scales

合作研究：景观建模：大地理区域和长时间尺度的生态过程综合

• 批准号: 8906269
• 负责人: Robert Costanza
• 金额: $ 26.37万
• 依托单位: University of Maryland Center for Environmental Sciences
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-07-15 至 1992-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8906269&HistoricalAwards=false
• 关键词: Collaborative; Research; Landscape; Modeling; Synthesis

This research project will measure, describe, and model change in ecological patterns and processes over large geographical areas in ways that will test the utility of the landscape approach for analyzing and predicting changes in ecological processes (i.e. productivity, succession), for optimizing resource management, and for evaluating human impacts. The work will help establish guidelines for the development and testing of spatial ecosystem models by testing the relative performance of different spatial simulation techniques and by combining new computer technologies with data-rich coastal study sites. Approaches that can predict the way landscape patterns change are crucial to the development of landscape ecology, yet no coherent predictive tools have emerged. The investigators will synthesize current ecological data and gather new data for incorporation into spatial models that predict change in landscape patterns. The theories embodied within a spectrum of spatial models, ranging from process-based simulation to transition probability models, will be tested using a system of descriptive statistics and pattern matching techniques which will be developed and evaluated as part of this research. Models will be developed for three different coastal landscapes, for which much historical data and ongoing long term research already exists. Results will provide: (1) increased understanding of the processes controlling changes in landscapes; (2) principles for adjusting spatial and temporal scales to optimize predictability in models; (3) comparison of landscape data sets; (4) principles for determining the optimal type of model for a particular set of objectives, scale and resolution; and (5) new methods for examining the goodness-of-fit between predictions and data that re appropriate for spatial ecological data and which require a degree of spatial pattern recognition. Finally, to the extent possible, the project will attempt to synthesize the principles and methods into a generalized theory for predicting landscape dynamics. The investigators are innovative in their approach. Institutional support and facilities are excellent. Results from the work should be of significance to the establishment of new basic ecological research directions as well as to the areas of land use planning and resource management.

该研究项目将测量、描述和模拟大地理区域内生态格局和过程的变化，以检验景观方法在分析和预测生态过程变化（即生产力、演替）、优化资源管理、以及评估人类的影响。这项工作将通过测试不同空间模拟技术的相对性能，并通过将新的计算机技术与数据丰富的沿海研究地点相结合，帮助建立空间生态系统模型的开发和测试准则。预测景观格局变化的方法对景观生态学的发展至关重要，但目前还没有一致的预测工具出现。研究人员将综合目前的生态数据，并收集新的数据，纳入预测景观格局变化的空间模型。从基于过程的模拟到过渡概率模型，在一系列空间模型中体现的理论将使用描述性统计系统和模式匹配技术进行测试，这些技术将作为本研究的一部分进行开发和评估。将为三种不同的沿海景观开发模型，这些景观已有许多历史数据和正在进行的长期研究。结果将提供：(1)增加对控制景观变化的过程的理解；(2)优化模型可预测性的时空尺度调整原则；(3)景观数据集对比；(4)针对一组特定目标、规模和分辨率确定最优模型类型的原则；(5)研究预测与数据拟合优度的新方法，这些方法适用于空间生态数据，需要一定程度的空间模式识别。最后，在可能的范围内，本项目将尝试将这些原理和方法综合成一个预测景观动态的广义理论。研究人员的方法很有创新性。制度支持和设施非常好。研究结果对建立新的生态基础研究方向以及土地利用规划和资源管理等领域具有重要意义。