权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

EAGER: Managing our expectations: quantifying and characterizing misleading trajectories in ecological processes

EAGER：管理我们的期望：量化和描述生态过程中的误导性轨迹

基本信息

批准号：
1838807
负责人：
Christine Bahlai
金额：
$ 17.56万
依托单位：
Kent State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838807&HistoricalAwards=false
关键词：
EAGER Managing our expectations quantifying

项目摘要

A fundamental problem in ecology is understanding how to scale discoveries: from patterns observed in the lab or the plot to the field or the region, or bridging between short term observations to long term trends and trajectories. The PIs propose a method to directly address the temporal aspects of scaling ecological observations, which involves reusing data from the two dozen Long Term Ecological Research (LTER) sites, an NSF program in place since the early 1980s. The PIs intend to bridge the gap between short-term observations and the long-term trends using an automated approach of repeatedly sampling moving windows of data from existing long-term time series, and analyzing these sampled data as if they represented the entire dataset. By compiling typical statistics used to describe the relationship in the sampled data and through repeated samplings, the results will provide insights to the questions, how often are the trends observed in short term data misleading, and can we use characteristics of these trends to predict our likelihood of being misled? The experiences in reusing the LTER data will be captured and shared with the ecology and open science community. This project is supported by the National Science Foundation's Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态学的一个基本问题是理解如何扩大发现：从实验室或地块观察到的模式到实地或区域，或者在短期观察与长期趋势和轨迹之间建立桥梁。PI提出了一种方法来直接解决缩放生态观测的时间方面，其中涉及重新使用来自20多个长期生态研究（LTER）站点的数据，这是自20世纪80年代初以来的NSF计划。 PI旨在使用自动化方法弥合短期观测和长期趋势之间的差距，该方法从现有的长期时间序列中重复采样移动数据窗口，并分析这些采样数据，就像它们代表整个数据集一样。通过汇编用于描述抽样数据中的关系的典型统计数据，并通过重复抽样，结果将为以下问题提供见解：在短期数据中观察到的趋势多久会产生误导，以及我们是否可以使用这些趋势的特征来预测我们被误导的可能性？将收集和与生态学和开放科学界分享再利用LTER数据的经验。该项目由美国国家科学基金会的公共访问计划支持，该计划由美国国家科学基金会高级网络基础设施办公室代表基金会管理。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。