run_optimization_min_conf — run_optimization_min

Generate an optimized estimate of community composition (species presences and absences) for every site in the study area.

Usage

run_optimization_min_conf(
  alpha_list,
  total_gamma,
  target,
  max_iterations,
  partial_solution = NULL,
  fixed_species = NULL,
  autostop = 0,
  seed = NA,
  verbose = TRUE,
  interruptible = TRUE
)

Arguments

alpha_list: Matrix of predicted alpha diversity (species richness) in each cell.
total_gamma: Total number of species present throughout the entire landscape.
target: Pairwise matrix of species in common between each site by site pair. Only the upper triangle of the matrix is actually needed.
max_iterations: The maximum number of iterations that the optimization algorithm may run through before stopping.
partial_solution: Can be either the result of a previous optimization run (see value) or an (initial) matrix of species presences and absences for each site in the landscape. The total number of presences must match the estimated species richness of each site. If a result of a previous optimization is used, its optimized_grid is used as initial matrix and its error data frame will be extended with the new iterations.
fixed_species: Fixed partial solution with species that are considered as given. Those species are not going to be changed during optimization.
autostop: The optimizer will stop after this number of iterations with no improvement. Default: 0 means auto stop is disabled.
seed: Seed for random number generator. Seed must be a positive integer value. seed = NA means that a random integer is used as seed.
verbose: If TRUE (default), a progress report is printed during the optimization run.
interruptible: Allow a run to be interrupted before completion. FALSE increases the performance.#'

Value

A species presence/absence matrix of the study landscape.

Details

run_optimization_min_conf is the core function of the spectre package. The underlying algorithm of this function is adapted from Mokany et al. (2011). A pairwise commonness matrix (having the same structure as the target matrix) is calculated from the partial_solution matrix and the value difference with the target determined. If a difference is present and depending on the set stopping criteria the algorithm continues. A random site in the presence/absence matrix is selected, and a random presence record at this site replaced with an absence. Every absence in the selected site is then individually flipped to a presence and the value difference with the objective recorded. The presence record which resulted in the lowest value difference (minimum conflict) is retained. This cycle continues, with a random site selected every iteration, until the pairwise commonness and objective matrices match or the algorithm runs beyond the max_iterations.

References

Mokany, K., Harwood, T.D., Overton, J.M., Barker, G.M., & Ferrier, S. (2011). Combining \(\alpha\) and \(\beta\) diversity models to fill gaps in our knowledge of biodiversity. Ecology Letters, 14(10), 1043-1051.