simulate_kindist_simple: Simulate kin dispersal distance pairs with simple sigma

In kindisperse: Simulate and Estimate Close-Kin Dispersal Kernels

Description

Simulates intergenerational dispersal defined by a simple dispersal sigma (covering the entire lifecycle) and ignoring phase differences between full & half sibling dispersal categories. Returns an object of class KinPairSimulation

Usage

simulate_kindist_simple(
  nsims = 100,
  sigma = 125,
  dims = 100,
  method = "Gaussian",
  kinship = "PO",
  lifestage = "immature",
  shape = 0.5
)

Arguments

nsims	(integer) - number of pairs to simulate
sigma	(numeric) - size of simple (axial) sigma
dims	(numeric) - length of sides of (square) simulated site area
method	(character) - kernel shape to use: either 'Gaussian', 'Laplace' or 'vgamma' (variance-gamma)
kinship	(character)- kin category to simulate: one of PO, FS, HS, AV, GG, HAV, GGG, 1C, 1C1, 2C, GAV, HGAV, H1C or H2C
lifestage	(lifestage) lifestage at sample collection: either 'immature' or 'ovipositional'
shape	(numeric) - value of shape parameter to use with 'vgamma' method. Default 0.5. Must be > 0. Increment towards zero for increasingly heavy-tailed (leptokurtic) dispersal

Details

This function is one of a family of functions that implement the core intergenerational dispersal simulations contained in the kindisperse package. Each of these functions proceeds by the following steps:

1. identify the pedigree relationship, dispersal phase (FS, HS & PO) and sampling stage that must be generated;

2. randomly assign a coordinate position to the 'root' individual within the pedigree (i.e. last common ancestor of the dyad, inclusive);

3. 'disperse' both pathways from this root position via the appropriately defined phase dispersal (additively via random draws from the underlying statistical model, defined by an axial standard deviation - sigma);

4. further disperse both phased descendant branches according to the number of realised breeding dispersal cycles contained in the defining pedigree (additively via random draws from the chosen underlying statistical model);

5. add displacement caused by dispersal before the sampling point in a similar manner to above, defining the final positions of the sampled dispersed kin dyads;

6. calculating geographical distances between the resulting dyads.

These simulation functions operate under an additive variance framework: all individual dispersal events are modeled as random draws from a bivariate probability distribution defined by an axial standard deviation sigma and (sometimes) a shape parameter. At present, three such distributions are included as options accessible with the method parameter: the bivariate normal distribution 'Gaussian', the bivariate Laplace distribution 'Laplace', and the bivariate variance-gamma distribution 'vgamma'. The Gaussian (normal) distribution enables easy compatibility with the framework under which much population genetic & dispersal theory (isolation by distance, neighbourhoods, etc.) have been developed. The Laplace distribution is a multivariate adaptation of the (positive) exponential distribution, and represents a more 'fat-tailed' (leptokurtic) disperal situation than Gaussian. The vgamma distribution is a mixture distribution formed by mixing the gamma distribution with the bivariate normal distribution. The flexibility of this distribution's shape parameter enables us to model arbitrarily leptokurtic dispesal kernels, providing a helpful way to examine the impacts of (e.g.) long distance dispersal on the overall disperal distribution and sampling decisions. A vgamma distribution with shape parameter equal to 1 reduces to the bivariate Laplace distribution. As shape approaches infinity, the vgamma distribution approaches the bivariate normal distribution. As shape approaches zero, the distribution becomes increasingly leptokurtic.

The simulate_kindist_simple() function is the most basic of the simulation functions, ignoring all information about dispersal phase and treating dispersal with a single sigma corresponding to the entire lifecycle to breeding of the dispersed individuals. It is useful for exploring simple intergenerational dispersal in a stripped back context; for many typical contexts involving complex dispersal across different phases of the breeding cycle, the other dispersal simulation functions would be more suitable.

Following simulation, the results are returned as an object of the specially defined package class KinPairSimulation, which stores the simulation results along with information about all simulation parameters, and can be further passed to sample filtering & dispersal estimation functions.

Value

 returns an object of class \code{\link{KinPairSimulation}} containing simulation details and a \code{tibble} (tab) of simulation values

See Also

Other simulate_kindist: simulate_kindist_composite(), simulate_kindist_custom()

Examples

test <- simulate_kindist_simple(nsims = 10, sigma = 50, dims = 1000, method = "Laplace")
simulate_kindist_simple(nsims = 10000, sigma = 75, kinship = "PO", lifestage = "ovipositional")

kindisperse documentation built on July 28, 2021, 5:09 p.m.