Paper discussion on Tuesday 11 Oct

Please make sure to have thoroughly read the Krebs et al. paper BEFORE coming to class on Tuesday 11 Oct. You will discuss various aspects of this paper in small groups before reconvening to share your findings with the class. The paper is posted in the ‘schedule’ part of the web site under 11 Oct.

New homework set (DUE 13 Oct 2016)

ECOL 4000: Q1-3 from R* reading, Lotka-Volterra competition question (see below) and Q1-3 from predator-prey reading (bonus Q4-5)

ECOL 6000: As above, but including predator-prey reading Q4 (bonus Q5)

Lotka-Volterra competition question: In eastern Africa, lions and hyenas are in competition with one another. For the lion, K1 = 75 and for the hyena, K2 = 300. Competition parameters are α12 =2.5 and α21 = 3.5 (where α12 represents the effect of species 2 on species 1). Suppose the initial population sizes at a site are 30 lions and 75 hyenas. Plotting lion abundance on the x-axis, and hyena abundance on the y-axis, plot the isoclines (a.k.a. nullclines, ZNGIs) for each species, and plot these initial population sizes paying attention to details such as intercepts on the x and y-axis and accurate placement of initial conditions. Predict the short-term dynamics of each population and the final outcome of interspecific competition.

Email solutions to Dr Park by 5pm, Thurs 13 Oct 2016

Age-structured code: help getting started with loops

This help is relevant to Homework Set 3 (Dynamics of age-structured populations) Q2. To get started you can try the R code below. To see the effects of transient dynamics you will find it easier to look at about 10-20 time steps (not 50). You can do that by changing “barplot(m)” to “barplot(m[,1:10])” – where 10 is just an example (i.e. the first 10 time steps)
———–
# Choose one of the initial states:
n.0<-c(60,0,0,0,0,0)
# Create a 6(rows)x50(time steps) matrix to store results, and at first just fill it with zeros:
m<-matrix(rep(0,6*50),nrow=6)
# Put the first population data in first column
m[,1]<-n.0
# Now we create a loop to calculate the other 49 time steps (and store them in the next 49 columns)
for (i in 2:50){
  m[,i]<-LeslieSolve(m[,i-1])
}
# Make a barplot as before
barplot(m)