Small Ecological Modelling Workshop

Overview

The “small modelling workshop” will take place from Wednesday 2015-03-11 until Friday 2015-03-13. The idea is to have a creative and interactive atmosphere to explore ideas, to learn modelling techniques and to develop students projects. If you want to prepare you yourself, read sections 2.1 to 2.9 from http://www.simecol.de/courses/elements_en.pdf.

The slides below show you already some of the material. More will follow in the next days or directly during the workshop.

You are welcome!

Thomas and David
TU Dresden, Institute of Hydrobiology

Schedule

The plan is to meet at 9:00 and to work until 17:00 in Drude-Bau, Room 72, and to have enough breaks in between for recreation and discussion.

Wednesday 2015-03-11

• Basic R
• User-defined functions
• Ecological models with differential equations (mixed reactor model)
• Plotting, forcing functions and other selected topics from the useR!Tutorial

Tuesday 2015-03-12

• Streeter-Phelps: The basic model
• Streeter-Phelps: Solutions, limitations, extensions
• A standard notation for ODE models (Petersen-Matrix)
• Slides

Friday 2015-03-13

• Partial differential equations and the method-of-lines
• http://desolve.r-forge.r-project.org/user2014
• Example 1: Fixed-phase / fluid phase model
• Example 2: Elements of a river model (1D)
• Code generation techniques (optional)

Optional Topics and Exercises

• Reports and Presentations with R Studio and R Markdown
• Solvers for stiff systems: Vanderpol
• Model fitting with FME: Pharmacokinetic

More information

• deSolve-Website: http://desolve.r-forge.r-project.org
• TU Dresden, Institute of Hydrobiology http://tu-dresden.de/hydrobiologie

A typical learning curve as ODE – thanks to K.S. ;-)

library(deSolve)

learning <- function(t, Knowledge, p) {
  with (as.list(p), {
    # knowledge increase
    dKnowledge <- rate*Knowledge*(1-Knowledge/maxKnowledge)
    list(dKnowledge)
  })
}
parms <- c (
   rate         = 0.1,    # rate at which new knowledge is acquired
   maxKnowledge = 1)      # maximal knowledge that can be acquired

# student 1
y1 <- c(Knowledge = 0.01)  # initial knowledge about the subject
times <- 1:100
out   <- ode(y = y1, times = times, parms = parms, func = learning)

# student 2: more initial knowledge
y2 <- c(Knowledge = 0.1)
out2  <- ode(y = y2, times = times, parms = parms, func = learning)

# student 3: good basic knowledge but too many other things
y3 <- c(Knowledge = 0.1)
parms2 <- parms; parms2["rate"] <- 0.05
out3  <- ode(y = y3, times = times, parms = parms2, func = learning)

plot(out, out2, out3)