大数据时代下的生态系统观测发展趋势与挑战.PDF

生物多样性 2016, 24 (1): 85–94 doi: 10.17520/biods.2015150 Biodiversity Science http: // —————————————————— 收稿日期: 2015-06-03; 接受日期: 2015-09-05 基金项目: 国家自然科学基金 * 通讯作者 Author for correspondence. E-mail: zhaobin@ 大数据时代下的生态系统观测发展趋势与挑战 戴圣骐 赵 斌* (复旦大学长江河口湿地生态系统野外科学观测研究站, 生物多样性与生态工程教育部重点实验室, 上海 200438) 摘要: 随着观测技术的发展, 生态学研究尺度不断扩大。生态系统观测从小规模合作、短时间个人观测向大规模、 长时间、跨学科、多因子联合观测转变。传感器技术的革新带来了生态观测在时空尺度的扩展与精确度上的提升, 致使生态学观测数据的容量、产生速度与数据种类飞速增长。对生态系统数据获取、存储与管理的传统方法无疑 不再能满足现代生态学研究的要求。因此, 我们建议以大数据时代的数据存储、管理与处理技术为基础, 整合生 态物联观测网络(Internet of Ecology)、公民科学观测网络以及基于标准化数据管理的研究者网络互联, 建立整合生 态系统观测平台来应对这一困境。为生态学研究者打造一站式生态观测服务, 是大数据时代下生态系统观测的大 势所趋。 关键词: 传感器; 观测网络; 物联网; 公民科学; 社交网络 Trends and challenges of ecosystem observations in the age of big data Shengqi Dai, Bin Zhao* Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiver- sity Science and Ecological Engineering, Fudan University, Shanghai 200438 Abstract: With the development of observation technology, the scale of ecological research is increasing. Ecological observations have turned from small-scale, short-time individual observations into broad-scale, long-term, interdisciplinary, multi-factor group observations. Innovation in sensor techniques has led to a profound evolution in time and space precision of ecological observations, while the volume, type, and gen- erating speed of these observational data are increasing, which indicates that traditional ecological data ac- quisition, storage and management methods cannot afford the demands of modern ecological research. With the assistance of new big data storage, management and processing techniques, integrated with the Internet of Ecology, a citizen science observational network and standardized data management network, we can build an ecological observation system to resolve these iss