Quantitative Plant Ecology (2018)


ECTS credits: 5

 

Course parameters:

Language: English

Level of course: PhD course

Time of year: Fall 2018

No. of contact hours/hours in total incl. preparation, assignment or the like: 35/80

Capacity limits: 16 participants

 

Objectives of the course:
The PhD students will be introduced to state-space models and structural equation models, which are becoming increasingly popular for fitting ecological models to empirical data.

The aim of the course is to introduce the students to:
i) the applied use of likelihood functions and Bayesian statistics in plant ecology,
ii) setting up advanced statistical models with latent parameters, and
iii) making quantitative predictions with a known degree of uncertainty.


Learning outcomes and competences:
At the end of the course, the student should be able to:

  • Assess the possible value of using advanced Bayesian methods in the students own scientific work
  • Critically evaluate scientific literature using advanced statistical models

 

Compulsory programme:
Preparation, active participation, assignment

 

Course contents:

  • Introduction to plant abundance data
  • Introduction to likelihood functions typically used in plant ecology
  • State-space and structural equation models
  • Fitting models to ecological data using Bayesian MCMC methods
  • Ecological prediction

 

Prerequisites:
Plant ecology, population ecology, statistics

 

Name of lecturer:
Christian Damgaard (http://pure.au.dk/portal/en/cfd@bios.au.dk)

 

Type of course/teaching methods:
Seminars and exercises

 

Literature:
Electronic notes and supplementary original literature. In the course the software Mathematica will be used. Before the course the student should have installed Mathematica on his or her portable computer and have followed some of the tutorials at http://www.wolfram.com/broadcast/#Tutorials. The software can be bought from “www.wolfram.com” (student prize ca. 800 kr.), but it can also be downloaded as a free 15-day trial version, which will be sufficient to follow the course (do not download the free trial-version before two days prior to the start of the course).

 

Course homepage:
None

 

Course assessment:
Personalized reports (approximately 20-40 pages, corresponding to a work load of 20 hours outside, and in the week after the end of the scheduled classes) has to be completed and submitted for approval (pass/fail).

 

Provider:
Department of Bioscience, Aarhus University

 

Special comments on this course:
All expenses for accommodation and travel are paid by the individual PhD student.

 

Time:
8 – 12 October 2018


Place:
Department of Bioscience, Aarhus University, Vejlsøvej 25, DK-8600 Silkeborg, Denmark

 

Registration:
Deadline for registration is 1 October 2018 (first come, first served).

For registration: Christian Damgaard, e-mail: cfd@bios.au.dk

If you have any questions, please contact Christian Damgaard.


Course Programme:

The topics of the 5 days are as detailed below, and each topic starts with a lecture followed by computer exercises which are carried out in teams of two-three participants. Each participant has to produce a personalized report of the exercises. During the course, the participants should be prepared to work outside the scheduled classes in order to complete the computer exercises.

 

Monday

10:00 – 10:15                       Coffee

10:15 – 12:00                       Lecture 1: Welcome, Introduction to Course and Mathematica

12:00 – 13:00                       Lunch

13:00 – 15:00                       Lecture 2: Plant abundance data

15:00 – 15:15                       Coffee

15:15 – 16:00                       Discussion

 

Tuesday

08:30 – 10:00                        Lecture 3: Likelihood functions typically used in plant ecology

10:00 – 10:15                       Coffee

10:15 – 12:00                       Computer Exercises

12:00 – 13:00                       Lunch

13:00 – 15:00                       Lecture 4: Likelihood functions typically used in plant ecology

15:00 – 15:15                       Coffee

15:15 – 16:00                       Computer Exercises

 

Wednesday

08:30 – 10:00                        Lecture 5: State-space and structural equation models

10:00 – 10:15                       Coffee

10:15 – 12:00                       Computer Exercises

12:00 – 13:00                       Lunch

13:00 – 15:00                       Computer Exercises

15:00 – 15:15                       Coffee

15:15 – 16:00                       Computer Exercises

 

Thursday

08:30 – 10:00                       Lecture 6: Fitting models to ecological data using Bayesian MCMC methods

10:00 – 10:15                       Coffee

10:15 – 12:00                       Computer Exercises

12:00 – 13:00                       Lunch

13:00 – 15:00                       Computer Exercises

15:00 – 15:15                       Coffee

15:15 – 16:00                       Lecture 7: Ecological predictions

 

Friday

08:30 – 10:00                       Lecture 8: Presenting other cases

10:00 – 10:15                       Coffee

10:15 – 12:00                       Student Discussion of Papers

12:00 – 13:00                       Lunch

13:00 – 14:00                       Evaluation and departure

 

Next Monday                         Submission of final report by e-mail to Christian Damgaard (cfd@dmu.dk)