population | R Documentation |

Defines the parameters of a population (non-spatial and spatial).

```
population(
name,
time,
N,
parent = NULL,
map = FALSE,
center = NULL,
radius = NULL,
polygon = NULL,
remove = NULL,
intersect = TRUE,
competition = NA,
mating = NA,
dispersal = NA,
dispersal_fun = NULL,
aquatic = FALSE
)
```

`name` |
Name of the population |

`time` |
Time of the population's first appearance |

`N` |
Number of individuals at the time of first appearance |

`parent` |
Parent population object or |

`map` |
Object of the type |

`center` |
Two-dimensional vector specifying the center of the circular range |

`radius` |
Radius of the circular range |

`polygon` |
List of vector pairs, defining corners of the polygon range or
a geographic region of the class |

`remove` |
Time at which the population should be removed |

`intersect` |
Intersect the population's boundaries with landscape features? |

`competition` , `mating` |
Maximum spatial competition and mating choice distance |

`dispersal` |
Standard deviation of the normal distribution of the distance that offspring disperses from its parent |

`dispersal_fun` |
Distribution function governing the dispersal of offspring. One of "normal", "uniform", "cauchy", "exponential", or "brownian" (in which vertical and horizontal displacements are drawn from a normal distribution independently). |

`aquatic` |
Is the species aquatic ( |

There are four ways to specify a spatial boundary: i) circular range
specified using a center coordinate and a radius, ii) polygon specified as a
list of two-dimensional vector coordinates, iii) polygon as in ii), but
defined (and named) using the `region`

function, iv) with just a world
map specified (circular or polygon range parameters set to the default
`NULL`

value), the population will be allowed to occupy the entire
landscape.

Note that because slendr models have to accomodate both SLiM and msprime back ends, population sizes and split times are rounded to the nearest integer value.

Object of the class `slendr_pop`

, which contains population
parameters such as name, time of appearance in the simulation, parent
population (if any), and its spatial parameters such as map and spatial
boundary.

```
# spatial definitions -----------------------------------------------------
# create a blank abstract world 1000x1000 distance units in size
map <- world(xrange = c(0, 1000), yrange = c(0, 1000), landscape = "blank")
# create a circular population with the center of a population boundary at
# [200, 800] and a radius of 100 distance units, 1000 individuals at time 1
# occupying a map just specified
pop1 <- population("pop1", N = 1000, time = 1,
map = map, center = c(200, 800), radius = 100)
# printing a population object to a console shows a brief summary
pop1
# create another population occupying a polygon range, splitting from pop1
# at a given time point (note that specifying a map is not necessary because
# it is "inherited" from the parent)
pop2 <- population("pop2", N = 100, time = 50, parent = pop1,
polygon = list(c(100, 100), c(320, 30), c(500, 200),
c(500, 400), c(300, 450), c(100, 400)))
pop3 <- population("pop3", N = 200, time = 80, parent = pop2,
center = c(800, 800), radius = 200)
# move "pop1" to another location along a specified trajectory and saved the
# resulting object to the same variable (the number of intermediate spatial
# snapshots can be also determined automatically by leaving out the
# `snapshots = ` argument)
pop1_moved <- move(pop1, start = 100, end = 200, snapshots = 6,
trajectory = list(c(600, 820), c(800, 400), c(800, 150)))
pop1_moved
# many slendr functions are pipe-friendly, making it possible to construct
# pipelines which construct entire history of a population
pop1 <- population("pop1", N = 1000, time = 1,
map = map, center = c(200, 800), radius = 100) %>%
move(start = 100, end = 200, snapshots = 6,
trajectory = list(c(400, 800), c(600, 700), c(800, 400), c(800, 150))) %>%
set_range(time = 300, polygon = list(
c(400, 0), c(1000, 0), c(1000, 600), c(900, 400), c(800, 250),
c(600, 100), c(500, 50))
)
# population ranges can expand by a given distance in all directions
pop2 <- expand_range(pop2, by = 200, start = 50, end = 150, snapshots = 3)
# we can check the positions of all populations interactively by plotting their
# ranges together on a single map
plot_map(pop1, pop2, pop3)
# gene flow events --------------------------------------------------------
# individual gene flow events can be saved to a list
gf <- list(
gene_flow(from = pop1, to = pop3, start = 150, end = 200, rate = 0.15),
gene_flow(from = pop1, to = pop2, start = 300, end = 330, rate = 0.25)
)
# compilation -------------------------------------------------------------
# compile model components in a serialized form to dist, returning a single
# slendr model object (in practice, the resolution should be smaller)
model <- compile_model(
populations = list(pop1, pop2, pop3), generation_time = 1,
resolution = 100, simulation_length = 500,
competition = 5, mating = 5, dispersal = 1
)
```

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