Collaborative Research: Spatial Inference and Prediction with Biogeographical Data

合作研究：利用生物地理数据进行空间推断和预测

• 批准号: 0832367
• 负责人: Jennifer Miller
• 金额: $ 0.98万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0832367&HistoricalAwards=false
• 关键词: Collaborative; Research; Spatial; Inference; Prediction

Maps of actual or potential species distributions are required for many aspects of resource management and conservation planning including biodiversity assessment, habitat management and restoration, single- and multiple species and habitat conservation plans, population viability analysis, modeling community and ecosystem dynamics, and predicting the effects of climate change on species and ecosystems. A growing number of quantitative methods are being used both inferentially, to identify the parameters that determine habitat suitability, and predictively, to assign habitat value to locations where biological survey data are lacking (most of the earth's surface). There are three impediments to the effective use of these modeling tools by both researchers and conservation and resource managers: a) too few of the existing applications explicitly incorporate the spatial dependence inherent in biospatial data into the modeling methods b) the statistical and GIS modeling tools are not always well integrated, and, c) the proliferation of potential methods and conflicting results regarding their efficacy is daunting to users. The investigators will 1) synthesize existing information on spatial prediction using biogeographical data, 2) strategically plan and execute a set of modeling experiments, and, based on these, 3) develop a framework to guide the operational use of these methods for biodiversity assessment and landscape management. Comparative modeling experiments will be executed using species distribution and abundance data spanning the three major ecological regions in southern California (desert, mountain, coastal), for plants from vegetation surveys and reptiles and amphibians (herptiles) surveyed in a multi-year monitoring program. The methods tested will include parametric and non-parametric statistical (generalized) models, machine learning approaches, and those incorporating spatial dependence (regression kriging, spatial autoregressive models).The proposed research is innovative because it will provide a broad comparison of modeling methods for real biological datasets that vary in their sample design, measurement scale, and spatial dependence, but were collected in the same bioregion, and will focus on biogeographical modeling of spatial dependence in plant and animal species distribution and abundance. It will result in a framework that can be used by researchers and resource managers to select an approach to modeling that is best suited to their biogeographical data and questions. The project will directly benefit society because it is collaborative with the Biological Resources Division of the US Geological Survey, the federal agency with a leadership role in spatial data archiving and analysis and biological information infrastructure. Thus, the framework and recommendations will be directly conveyed to resource and data managers.

资源管理和保护规划的许多方面都需要实际或潜在物种分布图，包括生物多样性评估、栖息地管理和恢复、单一和多个物种和栖息地保护计划、种群生存力分析、群落和生态系统动态建模以及预测气候变化对物种和生态系统的影响。 越来越多的定量方法被用于推断，以确定确定栖息地适宜性的参数，并预测性地为缺乏生物调查数据的位置（地球表面的大部分）分配栖息地价值。 研究人员以及保护和资源管理者有效使用这些建模工具存在三个障碍：a）现有应用程序中很少有明确地将生物空间数据固有的空间依赖性纳入建模方法中b）统计和GIS建模工具并不总是很好地集成，并且，c）潜在方法的激增和与其功效相关的相互矛盾的结果令用户望而生畏。 研究人员将 1) 使用生物地理数据综合有关空间预测的现有信息，2) 战略性地规划和执行一组建模实验，并在此基础上 3) 开发一个框架来指导这些方法在生物多样性评估和评估中的操作使用。景观管理。 将利用跨越南加州三个主要生态区（沙漠、山区、沿海）的物种分布和丰度数据，对来自植被调查的植物以及在多年监测计划中调查的爬行动物和两栖动物（爬行动物）进行比较建模实验。 测试的方法将包括参数和非参数统计（广义）模型、机器学习方法以及包含空间依赖性的方法（回归克里金法、空间自回归模型）。所提出的研究具有创新性，因为它将提供建模方法的广泛比较真实的生物数据集，其样本设计、测量规模和空间依赖性各不相同，但在同一生物区域收集，并将重点关注动植物物种分布和丰度的空间依赖性的生物地理建模。 它将形成一个框架，研究人员和资源管理者可以使用该框架来选择最适合其生物地理数据和问题的建模方法。 该项目将直接造福社会，因为它是与美国地质调查局生物资源部合作的，该部门是在空间数据归档和分析以及生物信息基础设施方面发挥领导作用的联邦机构。 因此，框架和建议将直接传达给资源和数据管理者。