simulate_kindist_composite {kindisperse}

Simulate kin dispersal distance pairs with composite sigmas

Description

Simulates intergenerational dispersal made up of composite dispersal stages in a species with a defined breeding and dispersal structure similar to that of Ae. aegypti - i.e. with initial, breeding, gravid & ovipositional dispersal phases, approximately non-overlapping life cycles, and defined sampling points.

Usage

simulate_kindist_composite(
  nsims = 100,
  initsigma = 100,
  breedsigma = 50,
  gravsigma = 50,
  ovisigma = 25,
  dims = 100,
  method = "Gaussian",
  kinship = "FS",
  lifestage = "immature",
  shape = 0.5
)

Arguments

nsims

(integer) - number of pairs to simulate

initsigma

(numeric) - size of pre-breeding (axial) sigma

breedsigma

(numeric) - size of breeding (axial) sigma

gravsigma

(numeric) - size of post-breeding (axial) sigma

ovisigma

(numeric) - size of oviposition (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 H1C1 or H2C

lifestage

(character) 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_composite() function is designed to enable modeling of the composite dispersal events that occur within the breeding cycle of an organism, and enables the separate treatment of the PO, FS, and HS phases (where, for example, the final distributions of full and half siblings are different in contexts where males mate with multiple females but females primarily carry the offspring of one male). This function has been designed primarily in the context of modelling dispersal in the mosquito Ae. aegypti; parameter names and the structure of kinship phases reflect a single-generational breeding organism with an initial dispersal phase, a mating phase (where HS individuals branch), a gravid phase, and an oviposition phase (where FS individuals branch). The sampling options ('immature' & 'ovipositional') also reflect common mosquito trapping methods (i.e. ovitraps & gravitraps) which both target individuals dispersing in the defined oviposition phase. This function should be easily adaptable to a vast number of other animals, especially insects, where breeding occurs in one generation and parameters such as this hold. For slightly more complex scenarios (multiple breeding cycles, differing sample points, more or less dispersal components making up a lifespan, different FS/HS branchpoints, etc.), the enhanced capabilities of the simulate_kindist_custom function may be required.

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 KinPairSimulation containing simulation details and a tibble (tab) of simulation values

See Also

Other simulate_kindist: simulate_kindist_custom(), simulate_kindist_simple()

Examples

simulate_kindist_composite(nsims = 100)
simulate_kindist_composite(
  nsims = 10000, initsigma = 20, breedsigma = 30, gravsigma = 30,
  ovisigma = 12, dims = 500, method = "Laplace", kinship = "1C", lifestage = "immature"
)