population_dynamics_functions: Functions to modify the population in a state object.

Description Usage Arguments Examples

Description

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

Usage

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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)

Arguments

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

Examples

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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()

skiptoniam/dhmpr documentation built on Sept. 26, 2018, 3:11 p.m.