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

该研究项目将测量，描述和建模 生态模式和过程的变化 地理区域的方式，将测试的效用， 分析和预测气候变化的景观方法 生态过程（即生产力、演替）， 优化资源管理和评估人类影响。 这项工作将有助于建立发展和 通过检验相对湿度来检验空间生态系统模型 不同空间模拟技术的性能， 将新的计算机技术与数据丰富的沿海研究相结合 网站. 可以预测景观模式的方法 变化对景观生态学的发展至关重要， 还没有出现连贯的预测工具。 调查人员 将综合当前的生态数据，并收集新的数据， 纳入空间模型， 景观模式 这些理论包含在一系列 空间模型，从基于过程的模拟到 转移概率模型，将使用一个系统进行测试， 描述性统计和模式匹配技术， 将作为这项研究的一部分进行开发和评估。 模型 将为三种不同的沿海景观开发， 大量的历史数据和正在进行的长期研究已经 存在. 结果将提供：（1）增加对 控制景观变化的过程;（2） 调整空间和时间尺度以优化可预测性 模型的建立;（3）景观数据集的比较;（4）原则 为了确定特定的一组模型的最佳类型， 目标、规模和分辨率;（5）新的方法， 检查预测和数据之间的拟合优度， 适用于空间生态数据，需要 空间模式识别度。 最后，在某种程度上 如果可能的话，该项目将试图综合这些原则， 并将其转化为一个广义的理论， 动力学 调查人员在方法上很创新。 机构支持和设施都很好。 结果 这项工作对建立新的 基本生态学研究方向以及 土地使用规划和资源管理。

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 are 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.