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应用生态学报 ›› 2019, Vol. 30 ›› Issue (2): 449-455.doi: 10.13287/j.1001-9332.201902.033

• 中国生态学学会2018年学术年会会议专栏 • 上一篇    下一篇

协同分布式实验2.0 (CDE 2.0):生态学野外研究新方法

李际*   

  1. 云南师范大学马克思主义学院, 昆明 650500
  • 收稿日期:2018-04-08 修回日期:2018-11-14 出版日期:2019-02-20 发布日期:2019-02-20
  • 通讯作者: E-mail:liji09b@mails.ucas.ac.cn
  • 作者简介:李际,男,1976年生,博士,讲师.主要从事生态学哲学与方法研究.E-mail:liji09b@mails.ucas.ac.cn
  • 基金资助:
    本文由中国博士后科学基金项目(2015M571099)和教育部人文社科研究基金项目(14YJCZH072)资助

Coordinated distributed experiments 2.0 (CDE 2.0): A novelty methodology of ecological field investigation.

LI Ji*   

  1. School of Marxism, Yunnan Normal University, Kunming 650500, China
  • Received:2018-04-08 Revised:2018-11-14 Online:2019-02-20 Published:2019-02-20
  • Supported by:
    This work was supported by the China Postdoctoral Science Foundation (2015M571099) and the Humanity and Social Science Foundation of Ministry of Education, China (14YJCZH072).2018-04-08 Received, 2018-11-14 Accepted.*

摘要: 全球野外实验网络是近年来兴起的生态学实验方法,产生了“协同分布式实验”(CDE)、“分布式协作实验”(DCE)的野外实验网络和英国生物学记录中心(BRC)的野外观察网络等具体方法,但均存在尺度小、周期短或数据偏差问题.野外实验网络的协议设计应秉持实验优于观察测量、数据数量优于数据质量和尺度优于操作的原则.综合上述方法,本研究提出利用公众科学(citizen science)获得大范围、多尺度和长周期的数据,采用环境因子作为协变量对公众参与者的实验数据集进行验证后,将其作为后验概率与作为主观概率的生态学家实验数据集进行比对,以完成数据有效性验证,克服公众参与者数据质量瑕疵,并提出了相应的统计学模型.讨论了生态学实验数据中应用先验概率、先验与后验概率的逻辑关系以及后验概率发现演化过程新关系的可能性.这种方法更符合统计学采样规范,并增加了大尺度时空的样本量.该方法有望为生态学研究所寻找的一般性理论提供方法支持,新方法可称之为“协同分布式实验2.0”(CDE 2.0).

关键词: 生态学实验, 研究网络, 先验概率, 公众科学, 野外实验

Abstract: The global field experiment network is rapidly growing in ecological research in recent years. Some specific methods have emerged, such as the Coordinated Distributed Experiments (CDE), Distributed Collaborative Experiments (DCE), and field observational network of British Biological Records Centre (BRC). However, problems including too small scale, short duration and biased data are criticized in these methods. Construction of the protocol of field experiment network should follow several principles: controlled experiment prior to observation, quantity prior to quality of data, and scale prior to operation. Here, I advocated the application of citizen science to the obtaining of the data in large field, at multi-scale, and with a long duration. Environmental factors could be considered as covariant to test the dataset provided by citizen participants. Furthermore, the same dataset, as posterior probability, could be compared with the priori data set provided by ecologists to test the validity of data. This methodology, with the corresponding statistical model, would overcome the shortcoming of qualitative bias of data in citizen science. The application of priori probability, logistical relation between priori and posteriori probability, and possibility of discovering new causality of evolutionary process in ecological experimental data were discussed. Compared with CDE, CED, and BRC, this method improved the match between statistical norm and sampling quantity in large spatial and temporal scales. This new method would help discover the general theory of ecology researches and it could be termed “Coordinated Distributed Experiments 2.0” (CDE 2.0).

Key words: ecological experiment, research network, prior probability, filed experiment, citizen science