R/deprecated/deprecated_simulate_seed_dispersal.R

In mhesselbarth/rABMP: R agent-based model for plants

#' #' deprecated_simulate_seed_dispersal
#' #'
#' #' @description Simulate seed dispersal
#' #'
#' #' @param input Tibble with input data
#' #' @param threshold Minimum DBH threshold for reproduction
#' #'
#' #' @details
#' #' Simulates seed dispersal by first calculating the number of seeds for each tree
#' #' and following distributing them around parental trees following a seed kernel.
#' #'
#' #' @return vector
#' #'
#' #' @examples
#' #' \dontrun{
#' #' names(example_input_data)
#' #' df_trees <- prepare_input(input = example_input_data, x = "x_coord", y = "y_coord",
#' #' species = "spec", type = "Class", dbh = "bhd")
#' #' deprecated_simulate_seed_dispersal(df_trees)
#' #' }
#' #'
#' #' @aliases deprecated_simulate_seed_dispersal
#' #' @rdname deprecated_simulate_seed_dispersal
#' #'
#' #' @references
#' #' Clark, J.S., Silman, M., Kern, R., Macklin, E., HilleRisLambers, J., 1999. Seed
#' #' dispersal near and far: Patterns across temperate and tropical forests. Ecology 80, 1475-1494.
#' #'
#' #' Bilek 2009 Paper
#' #'
#' #' Millerón, M., De Heredia, U.L., Lorenzo, Z., Alonso, J., Dounavi, A., Gil, L.,
#' #' Nanos, N., 2013. Assessment of spatial discordance of primary and effective seed
#' #' dispersal of European beech (Fagus sylvatica L.) by ecological and genetic methods. Mol. Ecol. 22, 1531-1545.
#' #'
#' #' Ribbens, E., Silander, J. A., & Pacala, S. W. (1994). Seedling recruitment in forests:
#' #' Calibrating models to predict patterns of tree seedling dispersion. Ecology, 75(6), 1794-1806.
#' #'
#' #' @export
#' deprecated_simulate_seed_dispersal <- function(input, threshold = 30){
#'
#'   # unnest data
#'   input <- tidyr::unnest(input)
#'
#'   # get most recent time step
#'   max_i <- max(input$i)
#'
#'   # only get living trees of current timestep above threshold
#'   current_living <- input[which(input$type != "Dead" &
#'                                   input$i == max_i &
#'                                   input$dbh > threshold), ]
#'
#'   # Number of seedlings for each tree
#'   number_seedlings <- calculate_seeds(species = current_living$species,
#'                                       dbh = current_living$dbh)
#'
#'   number_seedlings <- floor(number_seedlings * stats::runif(n = 1, min = 0.812, max = 0.83) * 0.0236)
#'
#'   # which trees produce surviving seedlings?
#'   id <- which(number_seedlings > 0)
#'
#'   # only number seedlings > 0
#'   number_seedlings <- number_seedlings[id]
#'
#'   species <- current_living$species[id]
#'
#'   # calculate seedlings coordinates
#'   seedlings <- deprecated_rcpp_create_seedlings(coords = as.matrix(current_living[id, 2:3]),
#'                                                 number = number_seedlings,
#'                                                 species = species)
#'
#'   # create seedlings id
#'   id <- seq(from = max(input$id) + 1, to = max(input$id) + nrow(seedlings))
#'
#'   # create tibble
#'   seedlings <- tibble::tibble(id = id,
#'                               x = seedlings[, 1],
#'                               y = seedlings[, 2],
#'                               species = rep(x = species, times = number_seedlings),
#'                               i = max_i,
#'                               type = "Seedling",
#'                               dbh = 1.0,
#'                               ci = 0.0)
#'
#'   # combine to one data frame
#'   result <- rbind(input, seedlings)
#'
#'   # nest dataframe
#'   result <- tidyr::nest(result, -c(id, x, y, species), .key="data")
#'
#'   return(result)
#' }