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: 0452389
• 负责人: Janet Franklin
• 金额: $ 14.24万
• 依托单位: San Diego State University Foundation
• 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: 2005-04-15 至 2009-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0452389&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，种群生存能力分析，社区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来确定确定栖息地适宜性的参数，并预测性地将栖息地价值分配给缺乏生物调查数据的地区（地球表面的大部分地区）。 研究人员和保护及资源管理人员有效使用这些建模工具有三个障碍：a）现有的应用程序很少明确地将生物空间数据中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，以及，c）潜在方法的扩散和关于其功效的相互矛盾的结果使用户望而却步。 研究人员将1）利用地理数据综合现有的空间预测信息，2）战略性地计划和执行一系列建模实验，并在此基础上，3）开发一个框架，以指导生物多样性评估和景观管理这些方法的操作使用。 将使用跨越加州南部三个主要生态区域（沙漠、山区、沿海）的物种分布和丰度数据，针对多年监测计划中调查的植被调查植物和爬行动物和两栖动物（爬虫类），进行比较建模实验。 测试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（回归克里格，空间自回归模型）。所提出的研究是创新的，因为它将提供真实的生物数据集的建模方法的广泛比较，这些数据集在样本设计，测量尺度和空间依赖性方面有所不同，但收集在同一生物区，并将侧重于植物和动物物种分布和丰度的空间依赖性的地理学建模。 它将产生一个框架，研究人员和资源管理人员可以使用该框架来选择最适合其地理数据和问题的建模方法。 该项目将直接造福于社会，因为它是与美国地质调查局生物资源司合作的，后者是在空间数据存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人员。