Description Usage Arguments Examples

Pre-defined functions to operate on a population during a simulation.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
simple_growth(demo_stoch = FALSE)
fast_dispersal(dispersal_kernel = exponential_dispersal_kernel(distance_decay
= 0.1), stages = NULL)
kernel_dispersal(distance_function = function(from, to)
sqrt(rowSums(sweep(to, 2, from)^2)),
dispersal_kernel = exponential_dispersal_kernel(distance_decay = 0.1),
arrival_probability = "both", stages = NULL, demo_stoch = FALSE)
cellular_automata_dispersal(dispersal_distance = list(0, 10, 10, 0),
dispersal_kernel = list(0, exp(-c(0:9)^1/3.36), exp(-c(0:9)^1/3.36),
0), dispersal_proportion = list(0, 0.35, 0.35 * 0.714, 0),
barrier_type = 0, dispersal_steps = 1, use_barriers = FALSE,
barriers_map = NULL, arrival_probability = "habitat_suitability",
carrying_capacity = "carrying_capacity")
translocation(source_layer, sink_layer, stages = NULL,
effect_timesteps = NULL)
ceiling_density_dependence(stages = NULL)
``` |

`demo_stoch` |
should demographic stochasticity be used in population change? (default is FALSE) |

`dispersal_kernel` |
a single or list of user-defined distance dispersal kernel functions |

`stages` |
which life-stages disperse, are modified (e.g. translocated), or contribute to density dependence - default is all |

`distance_function` |
defines distance between source cells and all potential sink cells for dispersal |

`arrival_probability` |
a raster layer that controls where individuals can disperse to (e.g. habitat suitability) |

`dispersal_distance` |
the distances (in cell units) that each life stage can disperse |

`dispersal_proportion` |
proportions of individuals (0 to 1) that can disperse in each life stage |

`barrier_type` |
if barrier map is used, does it stop (0 - default) or kill (1) individuals |

`dispersal_steps` |
number of dispersal steps to take before stopping |

`use_barriers` |
should dispersal barriers be used? If so, a barriers map must be provided |

`barriers_map` |
a raster layer that contains cell values of 0 (no barrier) and 1 (barrier) |

`carrying_capacity` |
a raster layer that specifies the carrying capacity in each cell |

`source_layer` |
a spatial layer with the locations and number of individuals to translocate from - note, this layer will only have zero values if individuals are being introduced from outside the study area |

`sink_layer` |
a spatial layer with the locations and number of individuals to translocate to |

`effect_timesteps` |
which timesteps in a single simulation do the translocations take place |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
library(steps)
# Use the simple growth function to modify the
# population using life-stage transitions:
test_lin_growth <- simple_growth()
# Use the fast kernel-based dispersal function to modify the
# population using a user-defined diffusion distribution and
# a fast-fourier transformation (FFT) computational algorithm:
test_kern_dispersal <- fast_kernel_dispersal()
# Use the probabilistic kernel-based dispersal function to modify the
# population using a user-defined diffusion distribution
# and an arrival probability layers (e.g. habitat suitability):
test_kern_dispersal <- probabilistic_kernel_dispersal()
# Use the cellular automata dispersal function to modify
# the population using rule-based cell movements:
test_ca_dispersal <- cellular_automata_dispersal()
# Use the translocation_population_dynamics object to modify the
# population using translocations:
test_ca_dispersal <- pop_translocation(source_layer = pop_source,
sink_layer = pop_sink,
stages = NULL,
effect_timesteps = 1)
# Use the translocation_population_dynamics object to modify the
# population using translocations:
test_pop_dd <- pop_density_dependence()
``` |

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