Bio 292 – Problem Set 3
Exercises (do ONE of the following – or more if you’ve got lots of free time!):
Use the data for Yellowstone grizzly bear and the program checkerspot.R to test for density dependence in the YGB data. Then use the program theta.logistic.R to compute the quasi-extinction time CDF, and compare it to the one for the density independent model produced by ygb.R.
Use data for the JRH population of checkerspot butterflies (stored in checkerspot_jrh.csv) to test for density dependence, then simulate extinction risk using the appropriate model and compare the CDF to that of the JRC population studied in class.
Eliminate the extreme value of lambda for the checkerspot, re-test for density dependence, and use the result to compare extinction risk with and without the extreme value.
Compare extinction risk for the JRC population of checkerspot butterflies using the best fit DI, Ricker, and TLog models.
Use the program ricker.corr2.R to explore how different levels of autocorrelation would affect extinction risk for the JRC checkerspot population.
ADVANCED:
Use the program extremes.R to examine how including - or not including - the extreme values of lambda for the Yellowstone grizzly bear or the checkerspot influence extinction risk. Note that to use this program correctly for the checkerspot, you will need to make the model density dependent.
Modify the program checkerspot.R to fit the Allee effect model from the program allee.R, using data for the checkerspot or for Yellowstone grizzly bear. Does AIC support a model with an Allee effect?
Add environmental stochasticity to the Allee effect program (allee.R) and explore how the strength of the Allee effect influences extinction risk with different starting numbers.
Modify ricker.corr2.R to include non-linear density dependence by incorporating the theta logistic model, and then use the new program to explore how non-linear density dependence and positive environmental autocorrelation interact to affect extinction risk.