In order to generate the right datasets, here are the bifurcation diagrams for the 4 models we have so far (some of them work flawlessly while some others might not) :
Forest gap model (alpha: 0.2, d: 0.01, delta: 0-1)
ggplot(results[['forestgap']]) +
geom_point(aes(delta, mean_cover_.)) +
ylab('Forest global cover')
ggplot(results[['grazing']]) +
geom_point(aes(b, mean_cover_.)) +
ylab('Veg global cover')
Mussel bed model (r: 0.7, d: 0.1, delta: 0-0.75)
ggplot(results[['musselbed']]) +
geom_point(aes(delta, mean_cover_.)) +
ylab('Mussel global cover')
Predator-prey model (betaf: 1/3, betas: 0.1, delta: 0-0.1)
ggplot(results[['predprey']]) +
geom_point(aes(delta, mean_cover_f)) +
ylab('Prey global cover')
grazing and forestgap models seem to work flawlessly. I'm a bit more hesitant about the other two (and there is no consensus implementation across all the articles/code snippets available), so any previous experience with those model is appreciated.
In order to generate the right datasets, here are the bifurcation diagrams for the 4 models we have so far (some of them work flawlessly while some others might not) :
Forest gap model (alpha: 0.2, d: 0.01, delta: 0-1)
Grazing model (del: 0.9, b: 0-1, c_: 0.2, m0: 0.05, g: 0.2, r: 0.01, f: 0.9, d: 0.1, p: 1)
Mussel bed model (r: 0.7, d: 0.1, delta: 0-0.75)
Predator-prey model (betaf: 1/3, betas: 0.1, delta: 0-0.1)
grazing and forestgap models seem to work flawlessly. I'm a bit more hesitant about the other two (and there is no consensus implementation across all the articles/code snippets available), so any previous experience with those model is appreciated.