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Landscape genetics test datasets were simulated using the software SimAdapt (Rebaudo et al., 2013) under four different landscape configurations.

  1. Uniform: same habitat over the study area, 50x50 cells.
  2. Chessboard: A favourable and an unfavourable habitat distributed evenly over the study area, 50x50 cells, 2500 cells favourables (50%).
  3. Random: A favourable and an unfavourable habitat both distributed randomly over the study area, 50x50 cells, 1261 cells favourables (50.44%).
  4. Fragmented: a favourable habitat highly fragmented by an unfavourable habitat, 55x41 cells, 701 cells favourable (31.09%).
  5. Gradient: a spatial transition from a favourable to an unfavourable habitat, 58x52 cells, 1507 cells favourable (49.97%).
Shared SimAdapt parameters:
ParameterValue
nb_agent3
biall?off
sd_H1.414
num_microsat10
mutation_rate1E-4
proba_dispersion1.000
r_growth0.25
nb_move2
num_locus_per_habitat0
var_num_generations1000
output_freq1000

The costs raster is uniform with 1 in each cell.
Under uniform conditions the carrying capacity was set to 10.
For other landscapes, favourable habitat have a carrying capacity of 20 individuals vs. 2 in not favourable.
For each landscape genetics scenario simulated, 20 independent datasets were produced. For all scenarios and replicates, three spatial sampling strategies and two level of sampling effort were considered.
  1. Random: sampling of 200 or 500 individuals anywhere across the landscape
  2. Balanced: sampling of 100 or 250 individuals from each habitat
  3. Gridded: 22 or 56 individuals from each cell of a 3x3 grid encompassing the landscape

References: