Description Usage Arguments Details Examples

Pre-defined or custom functions to define population dispersal during a simulation. Each dispersal method uses different computing resources and may be applicable to different simulation scenarios.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
fast_dispersal(dispersal_kernel = exponential_dispersal_kernel(distance_decay
= 0.1), dispersal_proportion = all_dispersing())
kernel_dispersal(dispersal_kernel = exponential_dispersal_kernel(distance_decay
= 1), max_distance = Inf, arrival_probability = c("both",
"suitability", "carrying_capacity", "none"),
dispersal_proportion = all_dispersing())
cellular_automata_dispersal(max_distance = Inf,
dispersal_kernel = exponential_dispersal_kernel(distance_decay = 1),
dispersal_proportion = all_dispersing(), barriers_map = NULL,
arrival_probability = "suitability",
carrying_capacity = "carrying_capacity")
``` |

`dispersal_kernel` |
a single built-in or user-defined distance dispersal kernel function. |

`dispersal_proportion` |
proportions of individuals (0 to 1) that can disperse in each life stage. Can also be a custom function that relates the proportion dispersing to a spatial layer in the landscape object (e.g. carrying capacity). |

`max_distance` |
the maximum distance that each life stage can disperse in spatial units of the landscape (in kernel-based dispersal this truncates the dispersal curve) - must be specified. |

`arrival_probability` |
the name of a spatial layer in the landscape object that controls where individuals can disperse to (e.g. "suitability") or "none" to allow individuals to disperse to all non-NA cells. |

`barriers_map` |
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. |

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

The fast_dispersal function uses kernel-based dispersal to modify the population with a user-defined diffusion distribution and a fast-fourier transformation (FFT) computational algorithm. It is computationally efficient and very fast, however, only useful for situations where dispersal barriers or arrival based on habitat or carrying capacity are not required. In other words, organisms can disperse in all directions and to all cells in the landscape.

The kernel_dispersal function employs a probabilistic kernel-based dispersal algorithm to modify the population using a user-defined diffusion distribution, arrival probability layers (e.g. habitat suitability), and growth limiting layers (e.g. carrying capacity). This function is much slower than the fast_dispersal, however, respects dispersal limitations which may be more ecologically appropriate. Further, the kernel-based dispersal function utilises a mechanism to optimise computational performance in which it switches between pre-allocating cell movements based on the available memory of the host computer (faster but more memory intensive) or executing cell movements in sequence (slower but less memory intensive).

The cellular_automata_dispersal function modifies populations using rule-based cell movements. This function allows the use of barriers in the landscape to influence dispersal. The function is computationally efficient, however, because individuals are dispersed, performance scales with the population sizes in each cell across a landscape.

1 2 3 4 5 | ```
test_fast_dispersal <- fast_dispersal()
test_kern_dispersal <- kernel_dispersal()
test_ca_dispersal <- cellular_automata_dispersal()
``` |

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