Introducing data-model assimilation to students of ecology

N. Thompson Hobbs, Kiona Ogle

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1537-1545
Number of pages9
JournalEcological Applications
Volume21
Issue number5
DOIs
StatePublished - Jul 1 2011

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Keywords

  • Data assimilation
  • Data-model assimilation
  • Ecological modeling
  • Ecological theory
  • Ecology curriculum
  • Hierarchical modeling
  • Mathematical ecology
  • Pedagogy
  • Statistical ecology

ASJC Scopus subject areas

  • Ecology

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