On the Spatial Structure of Vegetation Communities at Multiple Spatial Scales: Advancing and Testing a Comprehensive Theory of Macroecology

多空间尺度植被群落的空间结构：宏观生态学综合理论的推进和检验

• 批准号: 0516161
• 负责人: John Harte
• 金额: --
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0516161&HistoricalAwards=false
• 关键词: Spatial; Structure; Vegetation; Communities; Multiple

Macroecology is the branch of ecology that seeks to identify, and understand the origin of patterns in the distribution and abundance of species across multiple spatial scales. Numerous metrics of pattern exist, including the dependence of species richness on area censused, the dependence of range size on abundance, and the dependence of species turnover on plot area and inter-plot distance. Many macroecological theories and models have been proposed and shown to fit specific patterns at specific spatial scales, but a mechanism-based theory that explains the full array of metrics available today, with few adjustable 'fitting parameters', is lacking. We have recently developed a macroecological model that accurately predicts patterns across spatial scales. The model is purely statistical in that it is not based on information about birth, death, dispersal, competition, or predation. The primary goal of this research is to develop a mechanism-based theory that generates the outcomes of the statistical model. A secondary goal is to extend, and test against plant and animal data sets from numerous habitats around the world, its range of predictions. The research will incresase knowledge of the mechanisms underlying complex biodiversity patterns, adding to the fundamental knowledge base of ecology. Broader Impacts. Knowledge of macroecological patterns is critical to formulating local, national and global conservation and land use policies. Theoretical understanding of species turnover will contribute to our ability to estimate species richness in regions too large to thoroughly census; developing and testing a technique for reliably estimating species abundance from sparse presence-absence data will aid in estimation of the conservation status of species. Thus our work will contribute to the solution of pressing problems in the worldwide effort to conserve biological diversity. This project will also broaden representation in the field of quantitative ecology, providing training opportunities for women transferring from the physical sciences to ecology.

宏观生态学是生态学的分支，旨在识别和理解物种在多个空间尺度上的分布和丰度模式的起源。 存在许多度量模式，包括物种丰富度对区域普查的依赖，范围大小对丰度的依赖，以及物种周转率对地块面积和地块间距离的依赖。 许多宏观生态理论和模型已被提出，并证明适合特定的模式在特定的空间尺度，但机制为基础的理论，解释了今天可用的全部指标，很少有可调的“拟合参数”，是缺乏的。 我们最近开发了一种宏观生态模型，可以准确预测空间尺度上的模式。 该模型是纯统计的，因为它不是基于出生，死亡，扩散，竞争或捕食的信息。 本研究的主要目标是开发一种基于机制的理论，产生统计模型的结果。 第二个目标是扩展其预测范围，并针对来自世界各地众多栖息地的植物和动物数据集进行测试。 这项研究将增加对复杂的生物多样性模式的机制的了解，增加生态学的基本知识基础。 更广泛的影响。 了解宏观生态模式对于制定地方、国家和全球养护和土地利用政策至关重要。 对物种周转的理论理解将有助于我们在太大而无法进行彻底普查的地区估计物种丰富度的能力;开发和测试一种从稀疏的存在-不存在数据可靠地估计物种丰富度的技术将有助于估计物种的保护状况。因此，我们的工作将有助于解决全世界保护生物多样性努力中的紧迫问题。 该项目还将扩大在数量生态学领域的代表性，为从自然科学转到生态学的妇女提供培训机会。