cellular_automata_dispersal | R Documentation |

The cellular_automata_dispersal function simulates movements of individuals using rule-based cell movements. In each cell that has population, every individual up to a specified proportion of the total population attempts to move. For each step from a specified minimum up to a specified maximum number of movements, a weighted draw of four directions, based on habitat suitability, is made and then the destination cell is checked for available carrying capacity. If there is carrying capacity available, the individual moves to the cell, if not, it remains in its current cell. This is repeated until the maximum number of cell movements is reached. If no cell is found with available carrying capacity, the individual remains in the source cell.

cellular_automata_dispersal( max_cells = Inf, min_cells = max_cells, dispersal_proportion = set_proportion_dispersing(), barriers = NULL, use_suitability = TRUE, carrying_capacity = "carrying_capacity" )

`max_cells` |
the maximum number of cell movements that each individual in each life stage can disperse in whole integers. |

`min_cells` |
the minimum number of cell movements that each individual in each life stage will disperse in whole integers. |

`dispersal_proportion` |
a built-in or custom function defining the proportions of individuals that can disperse in each life stage. |

`barriers` |
the name of a spatial layer in the landscape object that contains cell values between 0 (no barrier) and 1 (full barrier) Any values between 0 and 1 indicate the permeability of the barrier. |

`use_suitability` |
should habitat suitability be used to control the likelihood of individuals dispersing into cells? The default is TRUE. Note, if a barrier map is also provided, the suitability map is multiplied with the barrier map to generate a permeability map of the landscape. |

`carrying_capacity` |
the name of a spatial layer in the landscape object that specifies the carrying capacity in each cell. |

This function allows the use of barriers in the landscape to influence dispersal. The function is computationally efficient, however, because as individuals are dispersed, performance scales with the population sizes in each cell across a landscape and the maximum number of cell movements.

The maximum number of cell movements in cellular automata dispersal does not correspond exactly to the distance decay of a dispersal kernel, since cellular automata dispersal depends on the permeability of the landscape, and is interrupted on reaching a cell with available capacity (above the minimum specified number of cell movements). A heuristic that can be used to determine a reasonable number of steps from a mean dispersal distance 'd' and cell resolution 'res' is: 'max_cells = round(2 * (d / (res * 1.25)) ^ 2)'. This corresponds approximately to the number of cell-steps in an infinite, homogenous landscape with no early stopping, for which d is the mean end-to-end dispersal distance of all individuals.

Rather than relying on this value, we recommend that the user experiment with
the `max_cells`

and `min_cells`

parameters to find a value such that
the the mean dispersal distance in a reasonably realistic simulation
corresponds with field estimates of mean dispersal distances.

# Example of cellular automata dispersal where the 2nd and 3rd life stages # disperse up to a maximum of 100 cells but dispersal is affected by # barriers (in this case roads). The road rasters have values of 0 for # large roads (no dispersal across barrier) and 0.5 for smaller roads # (reduced dispersal across barrier). ## Not run: ca_dispersal <- cellular_automata_dispersal(max_cells = c(0, 100, 100), barriers = "roads") ls <- landscape(population = egk_pop, suitability = egk_hab, carrying_capacity = egk_k, "roads" = egk_road) pd <- population_dynamics(change = growth(egk_mat), dispersal = ca_dispersal, density_dependence = ceiling_density()) simulation(landscape = ls, population_dynamics = pd, habitat_dynamics = NULL, timesteps = 20) ## End(Not run)

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