Introducing data-model assimilation to students of ecology.
Introducing data-model assimilation to students of ecology.
复制标题
向生态学学生介绍数据模型同化。
DOI：
10.1890/09-1576.1
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
K. Ogle
中科院分区：
文献类型：
--
作者：
N. Thompson Hobbs;K. Ogle
传统上，生态学学生的定量培训强调两组主题：数学建模和统计分析。直到最近，这些主题被单独教授，建模课程强调符号分析的数学技术，统计课程强调分析数据的程序。我们主张在生态教育中合并这些传统，概述了数据模型同化的入门课程的课程。本课程取代了传统的统计学入门材料的程序重点，重点是开发生态系统的层次模型所需的原则，将数据模型与生态过程模型融合在一起。我们勾勒出这样一门课程的九个要素：（1）模型作为洞察力的途径，（2）不确定性，（3）基本概率论，（4）分层模型，（5）数据模拟，（6）似然和贝叶斯，（7）计算方法，（8）研究设计，（9）解决问题。教授这些组合元素的结果可以是学生为广泛的研究问题创建揭示性分析所需的基本理解和定量信心。
Quantitative training for students of ecology has traditionally emphasized two sets of topics: mathematical modeling and statistical analysis. Until recently, these topics were taught separately, modeling courses emphasizing mathematical techniques for symbolic analysis and statistics courses emphasizing procedures for analyzing data. We advocate the merger of these traditions in ecological education by outlining a curriculum for an introductory course in data-model assimilation. This course replaces the procedural emphasis of traditional introductory material in statistics with an emphasis on principles needed to develop hierarchical models of ecological systems, fusing models of data with models of ecological processes. We sketch nine elements of such a course: (1) models as routes to insight, (2) uncertainty, (3) basic probability theory, (4) hierarchical models, (5) data simulation, (6) likelihood and Bayes, (7) computational methods, (8) research design, and (9) problem solving. The outcome of teaching these combined elements can be the fundamental understanding and quantitative confidence needed by students to create revealing analyses for a broad array of research problems.