1. Code and information sharing

To improve project documentation and quality standards for the work done in the modelling sections of CASCADE, we proposed that all model development should use git as a version control system. We invited all model developers to join Github and opened a Github organisation account for CASCADE that was meant to provide a hosting space for our projects. This facilitates internal pre-publication code review and portability of the projects to open source with the publication of articles.

• Organization account on Github: https://www.github.com/cascade-wp6

In particular, source code for simulation and analyses of the grazing model [25] is

• publicly available online at: https://github.com/cascade-wp6/2015_schneider_kefi/

The code for the fire model for the CASCADE Portuguese site [46] is

• publicly available online at: https://github.com/anavasques/ModelFirePT

We also gave workshop about Github to CASCADE members whose documentation is all online:

• presentation on git: https://fdschneider.github.io/git_intro/#/
• public repository of the git workshop : https://github.com/fdschneider/git_intro

2. Development of a visual, interactive version of the grazing model

For easier communication of cellular automata models, we implemented the spatially explicit models of grazing in dryland ecosystems in NetLogo. NetLogo is a user-friendly simulation environment which allows to transfer the model into an interactive ‘game’ that can be used to communicate the model’s mechanisms and the emergent alternative stable states.

• Available online at: https://github.com/cascade-wp6/netlogo_models

Netlogo can be used to communicate and explore the dynamics of the model, since the user can modify the parameter settings live and see directly how the model responds. Such interactive “games” allow to communicate the complexity of the dynamics in drylands under different sources of pressure (like drought, grazing, or fire) to stakeholders, to access their knowledge and to teach them about the nature of catastrophic shifts in drylands.

3. Development of a R package to run cellular automata models: caspr

As an end product of the CASCADE modelling work, Florian Schneider wrapped the R code that he developed for running cellular automata simulations of his grazing model into an R package. The package allows to run cellular automata with a spatially explicit pressure and apply spatial indicators (from ‘spatial_warnings’) on the temporal and spatial output.

• Code available at: https://github.com/fdschneider/caspr

caspr allows to run cellular automata in R with a few lines of code. The speed is considerable fast, depending on the model implementation (C code can be used for better performance). The output can be a full timeseries, a timeseries of snapshots of the lattice, a gif-animation, an array of simulations along a parameter gradient, etc.

The package will contain functions to transfer the output of caspr into valid input objects for the spatial warnings package (see »Appropriate indicators for critical thresholds).

A direct installation from Github is possible via the devtools package

install.packages("devtools")
devtools::install_github("fdschneider/caspr")
library(caspr)

The package is capable of running a couple of different models, but most importantly the grazing model that was developed for CASCADE. It’s parameters and specifications can be reviewed by typing ?grazing.

To run a model simulation, an initial landscape object needs to be provided, i.e. a grid of cells that matches the specifications of the model.

l <- init_landscape(states = c("+","0", "-"), cover = c(0.7,0.15,0.15), width = 100)
par(mar = c(1,1,1,1))
plot(l)

Then, the simulation is started using the default parameters (returning a warning at the end of the simulation) by employing the function ca():

run <- ca(l, model = grazing, t_max = 60, t_min = 60)
par(las = 1, bty = "n")
plot(run)

The plot() function plots the timeseries of the vegetation cover (cover of cell state “+”) and the summary() function returns the mean cover of all potential cell states.

4. Socio-ecological models

In another section of CASCADE, (see »Costs and benefits of interventions) different land use management practices are implemented and evaluated in a probabilistic manner over time. In contrast to the work described here, in that section we look at transient dynamics rather than steady state situations, to answer the questions for the probability and quantitative improvement after application of management methods. A version of the livestock resilience model [25] for this kind of analysis is

• available online at: https://github.com/cascade-wp6/socialecological

The functions were wrapped into a small R package. It can be installed from Github from within R.

install.packages("devtools")
devtools::install_github("cascade-wp6/socialecological")
library("socialecological")

This makes all functions available in R. A package vignette with a full set of examples

• can be found here: https://github.com/cascade-wp6/socialecological/raw/master/inst/doc/socialecological.pdf

Regarding the fire model of Vasques et al. [46], discussions were initiated with the members of CASCADE socio-ecological modelling team in December 2015. The management options considered in Portugal are conservative and non-conservative logging and possibly mulching with pine needles. The effect of these management techniques under different fire severity scenarios and fire recurrence could be evaluated using the model developed here [46]. For the cost-benefit analysis, the most interesting variable is pine biomass as a proxy of timber production (although other variables such as pine cover and age could also be interesting). The current model output is focused on pine cover; pine biomass therefore has to be estimated from pine cover and age. Simulation scenarios were discussed regarding the resilience of pine stands to different management options.

Note: For full references to papers quoted in this article see

» References