Breaking new ground in Quantitative training for students of ecology

Introducing data–model assimilation to students of ecology 

Hobbs, N. Thompson, and Kiona Ogle

Ecological Applications, Volume 21, Issue 5

Here is a paper that advocates a new approach to quantitative training for students of ecology.

Quantitative training for students of ecology usually comprises two sets of topics.

The components are

1) Mathematical modeling

2) Statistical analysis.

Traditionally these two topics are taught separately, modeling courses stressing on mathematical techniques for symbolic analysis and statistics courses stressing on procedures for analyzing data.

Here the researchers plumb for a merger of two separate courses by outlining a curriculum for an introductory course in data–model assimilation. Traditional introductory material in statistics is replaced by an emphasis on principles needed to develop hierarchical models of ecological systems, fusing models of data with models of ecological processes.

Here is the outline of the course devised by the researchers

 (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

 (9) Problem solving.

The researchers say 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.