Modelling Population Dynamics
Chapter 6: Modelling population dynamics using closed-population abundance estimates

To run the code presented, simply press Run in the upper right-hand corner of the frame. You can explore the code further by pressing Edit. This will take you to a website where you can edit the code and explore all of the figures produced by the code. The code will take longer to run on the server than on your local machine, so have some patience.

If the code fails to run or is subject to delays on r-fiddle.org, run it on your local machine, after installing R. To download the code to your machine, select all text inside the frame, copy to clipboard, and paste into an editor on your machine. Note each piece of code has file(s) containing helper functions listed in the top two lines of code. Download those files onto your machine as well for the code to function.

6.4 Population dynamics model examples

6.4.1 Wildebeest

Logistic growth model, matrix model with dependencies of model parameters upon a number of rainfall as well as independent estimates of survival. Note the model with constant adult survival, constant lambda and calf survival dependent upon previous year’s rainfall has been commented out to save computing time.

6.4.2 Gray whales

Deterministic logistic population growth model for gray whale survey data along with age-structured deterministic matrix model and trajectories from parametric bootstrap resamples of estimated matrix model parameters. Note number of bootstrap replicate trajectories has been limited to 100 for illustration here.

6.4.2.2 Kalman filter estimates for gray whale analysis

The following code produces Figure 6.5 for a normal dynamic state-space model fitted to gray whale data.