Laboratory Exercise: Density-Dependent Population Growth Models, Lab Reports of Biology

Download Laboratory Exercise: Density-Dependent Population Growth Models and more Lab Reports Biology in PDF only on Docsity! FW662 Laboratory 2 – Density-dependent models 1 Computer Laboratory, Exercise 2 and Discussion. Objective: Become familiar with the density-dependent models. 1. Program a spreadsheet to generate a logistic density-dependent population growth function. Graph the resulting function. An example is given in logistic.wb2. a. Manipulate the value of R0 to see the change in the logistic growth function. b. Manipulate the value of K to see the change in the logistic growth function. c. Graph (Nt+1 - Nt)/Nt versus Nt to see the shape of the per capita growth function. d. Graph Nt+1 - Nt versus Nt to see the shape of the population growth function. e. Increase the value of R0 to generate a growth function with chaos. Substantiate the behavior outlined in the table above. f. Build a Ricker population growth function, and graphically compare the results to a logistic growth function. An example of the Ricker stock-recruitment curve is in the spreadsheet ricker.wb2. 2. Build a logistic growth function with a time delay of 2 intervals. Examine the graph of Nt to see if cycles are produced. An example is provided in timelag.wb2. 3. Build a logistic growth function where R0 and K are normally distributed random variables. You can use the @NORMINV function to compute a normal random deviate with specified mean and standard deviation by @NORMINV(@RAND, mean, SD). An example is provided in randomrk.wb2. 4. Modify the logistic model to incorporate the Richards' m exponent, and see how the shape of the curve changes. Further, how does MSY change? An example is provided in the nonlin_r.wb2 spreadsheet. 5. The spreadsheet allee.wb2 demonstrates how an Allee effect can be programmed. 6. The spreadsheet logisfit.wb2 provides an example of fitting logistic models to observed data to estimates the parameters r and K.