M.Sc. Thesis 2018-2020

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GitHub page (latest)	Thesis PDF (latest)	Initial Submission Release	Final Submission Release

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This repository contains all the code and some of the data that is needed to reproduce the work of my M.Sc. Thesis titled:

Integrating land use and land cover change simulations, stakeholder understanding of the landscape, and connectivity modelling: a case study in the Montérégie region in southern Québec.

It is packaged under the form of a Docker image and makes use of the renv package.

The goal of this repository is to allow future users to rerun the analysis and build on it if necessary, but also to enforce good reproducibility practices as early as possible in the publication process.

Most of the figures in the thesis are reproducible. This repo is set up for continuous (daily) deployment of a github page that contains the main figures of the thesis.

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Reproducibility

Disclaimer: Although all the code for the thesis is archived here, parts of the analysis are not reproducible, as some of the data used is not in open access. The simulation code is designed to be paralellized on a remote linux machine (not an HPC or local environment) of the type Arbutus Compute Cloud from Compute Canada. However, the final steps (analysis, figure making) can be ran on computer of a much more modest size.

1. Install Docker

You will need to install Docker for running the analysis. The image will contain all dependencies necessary.

2. Pull the Docker image

Open a terminal and pull the image. The tag 3.6.2-1 corresponds to the version of R the image contains. The image is built on top of [rocker/geospatial:3.6.2](https://github.com/rocker-org/geospatial). See further below for a detailed breakdown of the docker image. Warning: the image is voluminous as it contains a custom (and large) R library.

docker pull vlucet/land_con_monteregie:3.6.2-1

3. Run the Docker image

Once the image is downloaded, you can run it in a container. Two options are available. For simulation code, it is important to increase the access to shared memory for the container with the option --shm-size (give as much as you can afford, I usually use 50G).

• **Run docker image interactively (recommended) ** (docker run [options] vlucet/land_con_monteregie:3.6.2-1 /bin/bash && cd ~/land_con_monteregie), executing a command that starts bash and move to the repo directory (/bin/bash && cd ~/land_con_monteregie), with options to run an interactive terminal (-it) with authentification disabled (DISABLE_AUTH=true).

docker run -it -e DISABLE_AUTH=true vlucet/land_con_monteregie:3.6.2-1 \
  /bin/bash && cd ~/land_con_monteregie

• Run docker image with Rstudio in your browser (see this post for another example). Here you run the container in a detached mode (-d) on a local port (-p 8787:8787). Then, go to http://192.168.99.100:8787 in your browser to access Rstudio.

docker run -d -p 8787:8787 -e DISABLE_AUTH=true vlucet/land_con_monteregie:3.6.2-1

Note that with docker, you have the option to link a local volume.

Reproducing figures

The workflow is controlled with the help of the main bash utility landcon.sh in the top directory. This utility is used to run all the steps in the thesis analysis. Help can be accessed with landcon.sh -h

LAND USE CHANGE AND CON MODEL - 2020
Valentin Lucet - Thesis McGill University


Usage: landcon.sh [-a run all] [-p prep] [-m model no prep] [-f fit & predict]
                  [-s stsim] [-r reclassify] [-c circuitscape] [-d post process] [-g make figures]

Subcommands: prep, fitpred, stsim, cs, figs (use -h to see usage on each subcommand)

In order to reproduce the main figures of the thesis, simply run:

./landcon.sh figs

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Detailed thesis workflow

The utility has multiple options, each one to run a separate step of the analysis. The first steps cover data preparation and Land Use change modeling (see figure below):

• -p Prepare the model data by transforming all raw data in processed model spatial (and some non-spatial) inputs.
• -m Prepare model data by transforming processed spatial inputs into tabular input for use with the [tidymodels](https://www.tidymodels.org/) package.
• -f Fit the land use change statistical model (Random Forest) and prepare STSIM spatial multipliers
• -s Build STSIM library and run model, default to 10 timestep and 10 iterations.

The rest of the steps cover habitat suitability analysis and connectivity analysis (see second figure below):

• -a Run ALL steps (again, provided data is provided)
• -r Reclassify all STSIM outputs into Circuitscape inputs (resistance maps)
• -c Run Circuitscape (Julia package)
• -d Post process Circuitscape outputs into tabular data
• -g Make figures, knitting the Rmd document that is the base of the GitHub page for this repo (link at the top). This is the only fully reproducible step. in the absence of additional data.

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