CAREER: Scientific Computing for a New Generation of Ecologists

职业：新一代生态学家的科学计算

• 批准号: 1148867
• 负责人: Stefano Allesina
• 金额: $ 59.92万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148867&HistoricalAwards=false
• 关键词: CAREER; Scientific; Computing; New; Generation

Ecology is about to face the data deluge that other biological disciplines have already experienced. With ecological data increasing rapidly in quality and size, new methods are needed to extract the most relevant biological information from massive data-sets. The objective of this project is to develop new mathematical, computational and statistical tools for the analysis of three ecological problems. First, when a species goes extinct, the impact reverberates through the ecological network, possibly causing the extinction of other species. A new method to predict such "secondary extinctions" will be developed. Second, the number and size of published ecological networks is increasing rapidly, making it possible to answer one of the oldest questions in ecology: how many species traits (e.g., body size, swimming speed, metabolic rate) does one need to measure to predict whether two species will interact? A new computational method, coupled with a large dataset will attempt to answer this question. Knowing which are the critical traits determining the possibility of interactions could find application in the study of invasive species. Third, the spatial structure of ecosystems mediates many ecological processes. A new method will be developed to measure the impact of spatial heterogeneity on the structure of ecological networks.The development of these new tools require sophisticated methods, which are not typically included in the curriculum of biologists. The educational goal of the project is to train ecologists in the computational methods that will be needed to advance the discipline in the decades to come. Graduate students will learn how to automate the analysis of biological data, distribute computation over large computer clusters, organize data into relational databases, program in different languages, collaborate on data, code and manuscripts, automatically managing versions and conflicts, and pick the right tool for each task. Outreach activities will be provided through lectures and media interviews and with activities carried out in collaboration with local elementary schools and the Museum of Science and Industry.

生态学即将面临其他生物学科已经经历过的数据洪流。随着生态数据的质量和规模的迅速增加，需要新的方法来从海量数据集中提取最相关的生物信息。该项目的目标是开发新的数学、计算和统计工具，用于分析三个生态问题。首先，当一个物种灭绝时，其影响会在生态网络中产生反响，可能导致其他物种的灭绝。一种新的方法来预测这种“二次反射”将被开发。其次，已发表的生态网络的数量和规模正在迅速增加，这使得回答生态学中最古老的问题之一成为可能：有多少物种特征（例如，身体大小、游泳速度、代谢率）是否需要测量来预测两个物种是否会相互作用？一种新的计算方法，加上一个大的数据集将试图回答这个问题。了解哪些是决定相互作用可能性的关键特征可以在入侵物种的研究中找到应用。第三，生态系统的空间结构介导了许多生态过程。将开发一种新方法来测量空间异质性对生态网络结构的影响。这些新工具的开发需要复杂的方法，而这些方法通常不包括在生物学家的课程中。该项目的教育目标是培养生态学家的计算方法，这将需要在未来几十年内推进学科。研究生将学习如何自动分析生物数据，在大型计算机集群上分配计算，将数据组织到关系数据库中，用不同的语言编程，在数据，代码和手稿上进行协作，自动管理版本和冲突，并为每个任务选择正确的工具。 外联活动将通过讲座和媒体采访以及与当地小学和科学与工业博物馆合作开展的活动进行。