Simulated Ler variability - Project notebook for the Invasion Heterogeneity project

So, we are now ready to do some analyses. Let’s look at simulated Ler mean and variability over 6 generations.

n_init <- 50
Ler_params$gap_size <- 0 
controls <- list(
  n_reps = 10,
  DS_seeds = TRUE,
  ES_seeds = TRUE,
  kernel_stoch = TRUE,
  kernel_stoch_pots = TRUE,
  seed_sampling = TRUE,
  pot_width = 7
)
Adults <- matrix(n_init, controls$n_reps, 1)
for (i in 1:6) {
  Adults <- iterate_genotype(Adults, Ler_params, controls)
}
Adults

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
 [1,]  119  182  181  192  228  277  138  219  186   113   123    60    10
 [2,]  586  374  679  944  378  886  858  612  975   439   607   336   144
 [3,]  112  158  108   90  123  299  220  193   94    87    40    19     6
 [4,]  757  524  688  719  338  642  968  878  448   799   529   482   211
 [5,]  285  353  313  461  219  389  165  401  195   461   316   210    64
 [6,]  342  661  743  608  924  649  811  811  551   485   398   731   311
 [7,]  351  539  524  630  437  756  376  703  373   613   296   316   103
 [8,]  331  464  288  314  282  442  442  439  482   339   200   179    50
 [9,]  229  231  300  188  240  115  134  112  197   136   130    52    28
[10,]  307  580  424  688  416  636  756  386  444   393   340   139    58
      [,14] [,15] [,16] [,17]
 [1,]     2     0     0     0
 [2,]    28    13     1     0
 [3,]     0     0     0     0
 [4,]   115    22     9     1
 [5,]   207    41    20     3
 [6,]   321   155    60    12
 [7,]    10     0     0     0
 [8,]    20     2     3     0
 [9,]     6     0     0     0
[10,]    13     2     0     0

The densities do not seem to be getting nearly large enough.

Calculate the distribution, and statistics, of furthest dispersal.

npot <- ncol(Adults)
rep_sum <- t(apply(Adults[, npot:1], 1, cummax))[, npot:1]
rep_sum

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
 [1,]  239  239  239  239  239  213  158  158  158   135    49    15     3
 [2,]  198  198  198  181  163   88   88   83   83    59    25    10     0
 [3,]  274  274  274  261  261  261  261  261  261   261   151    30     2
 [4,]   43   43   43   43   43   42   16   16   16     4     4     1     0
 [5,]  187  187  187  161  152  121   88   82   19     3     0     0     0
 [6,]  113  113  113  112  112  112  112  112  112   112   112    38    14
 [7,]   28   28   28   28   28   28   26   22   22    13     6     1     0
 [8,]  272  272  244  244  244  195  195  193  193   193   193   138    64
 [9,]   89   89   89   89   89   89   89   89   89    86    86    20     3
[10,]   97   97   97   97   97   72   21    2    0     0     0     0     0
      [,14] [,15]
 [1,]     0     0
 [2,]     0     0
 [3,]     0     0
 [4,]     0     0
 [5,]     0     0
 [6,]     1     0
 [7,]     0     0
 [8,]     6     0
 [9,]     1     1
[10,]     0     0

maxd <- apply(rep_sum, 1, function(x) max((1:length(x))[x > 0]))
maxd

 [1] 13 12 13 12 10 14 12 14 15  8

mean(maxd)

[1] 12.3

var(maxd)

[1] 4.233333

Now for the data:

maxd_data <- pull(subset(LerC_spread, Gap == "0p" & Generation == 6), Furthest)
maxd_data

 [1] 13 13  9 19 17 10 11 20 12 16

mean(maxd_data)

[1] 14

var(maxd_data)

[1] 14.44444

OK, so the variance is way too low. This may be related to the underproduction of seeds.