dispersal_n: Calculate number of individuals dispersing from patch i to j
In Jpomz/IGPtoy: Simulate Intraguild Predation Dynamics in Three Species Community

Description Usage Arguments Details Value Examples

Calculate number of individuals dispersing from patch i to j

dispersal_n(
  N,
  v_p_dispersal,
  v_theta,
  dist_mat,
  m_b_dispersal,
  m_c_dispersal,
  m_p_dispersal
)

`N`	Abundance matrix with `nrow = n_sp = 3` and `ncol = n_patch`. Rows 1:3 correspond to species B, C, and P respectively.
`v_p_dispersal`	A vector of dispersal probabilities of length = 3. in `igp_sim()`, this is controlled with the `p_dispersal` argument. Values should be from 0 to 1
`v_theta`	Vector of length = 3, Parameters controlling the distance decay function. Larger values of theta result in a faster decline of dispersal distance. `v_theta` is caluclated internally in `igp_sim` based on the `theta` argument.
`dist_mat`	a distance matrix with `nrow = ncol = n_patch` and diagonal == 0.
`m_b_dispersal`	dispersal matrix for species B. This and the following 2 matrices are calculated internally in `igp_sim()`
`m_c_dispersal`	dispersal matrix for species C.
`m_p_dispersaldispersal`	matrix for species P.

This function is used internally in igp_sim() to calculate the number of individuals of each dispersing from patch i to patch j. Emigrants from patch i are more likely to become Immigrants to patch j if the patches are closer together.

A matrix of abundances after accounting for dispersal with nrow = n_sp = 3 and ncol = n_patch. Values may be returned with decimal values, but will be converted to integers using rpois() within the simulation model.

n_patch = 5
n_sp = 3
N = matrix(rpois(n_sp * n_patch, lambda = 10), nrow = n_sp, ncol = n_patch)
# sort coordinates so you can easily see increasing distances
 x_coord = sort(runif(5, 0, 5))
 y_coord = sort(runif(5, 0, 5))
 dist_mat = data.matrix(dist(cbind(x_coord, y_coord)))
disperal_n(N = N, v_p_dispersal = 0.25, theta = 1, dist_mat = dist_mat)