Ecological Modelling SRP Task 1

0. Software Prerequisites

Please install Python3 including the following packages:

• numpy
• pandas
• pytest

1. Basic Git commands

First of all, you need a github account. Please make one and send me the name of your account.

Then you can read the following website to learn about github:

https://guides.github.com/introduction/git-handbook/

Please familiarise yourself with the basic operations, especially:

• git clone
• git fetch
• git merge
• git add
• git commit

Another useful command that is important to learn is opening a pull request. You can learn more about it here:

https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request

2. Generating Forest Data from Stochastic Processes

Implement the following functions in Landscape.py:

1. LSP.sample: Samples a species randomly from the species list in the species_list attribute.

2. LSP.simulate: Simulates t years of local succession where the overall recruitment and mortality must equal to the recr_trajectory and mort_trajectory attributes, respectively. At every year, the choice of species to be recruited is a random draw from the species in the local species pool. Whilst, the choice of species to die at every year is a random draw from the species that are already established in the plot.
   The function should return M, a n x t matrix where n is the number of species in the local species pool and t is the number of years (speciefied by recr_trajectory and mort_trajectory).

3. GSP.initialise_LSPs: Initialises local species pools from the list of species in the global species pool. n is the number of local pools and k is the number of species in each pool. Assume that the local species pool is sampled randomly from the global species pool.

4. GSP.simulate: Simulates t years of global succession where the overall recruitment and mortality must equal to the recr_trajectory and mort_trajectory attributes, respectively.
   The function should return M, a n x m x t matrix where n is the number of species in the local species pool, m is the number of localities and t is the number of years (speciefied by recr_trajectory and mort_trajectory). (Hint: use LSP.simulate)

3. Testing

Once the implementation is done, test your implementation with the provided test in test.py. You are encouraged to add additional tests to make sure that your code is correct.

The test can be run using the command:

pytest test.py

Once the program is done and tested, stage a pull request on this github.

4. Generating Result

To save the n x m x t matrix output of GSP.simulate, run:

python main.py --outfile results/out_matrix.pkl –-params params.json