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#' Species distributional ranges based on trend surface analyses
#'
#' @description rangemap_tsa generates a distributional range for a given species
#' using a trend surface analysis. An approach to the species extent of occurrence
#' (using convex hulls) and the area of occupancy according to the IUCN criteria
#' is also generated. Shapefiles can be saved in the working directory if it is
#' needed.
#'
#' @param occurrences a data.frame containing geographic coordinates of species
#' occurrences, columns must be: Species, Longitude, and Latitude. Geographic
#' coordinates must be in decimal degrees (WGS84).
#' @param region_of_interest a SpatialPolygonsDataFrame object on which the trend
#' surface analysis will be performed. For instance, a country, an ecoregion, or
#' a biogeographic region. Projection must be WGS84 (EPSG:4326).
#' @param cell_size (numeric) vector of length 1 or 2, defining the size of cells
#' (km) at which the resultant trend surface will be created; default = 5.
#' \code{cell_size} will depend on the extent of \code{region_of_interest}.
#' Values lower than 1 are only recommended when the species is locally distributed.
#' @param threshold (numeric) percentage of occurrence records to be excluded
#' when deciding the minimum value trend surface output to be considered as part
#' of the species range. Default = 0.
#' @param simplify (logical) if \code{TRUE}, polygons of suitable areas will be
#' simplified at a tolerance defined in \code{simplify_level}. Default =
#' \code{FALSE}.
#' @param simplify_level (numeric) tolerance at the moment of simplifying polygons
#' created using the trend surface model. Lower values will produce polygons more
#' similar to the original geometry. Default = 0. If simplifying is needed, try
#' numbers between 0 and 1 first.
#' @param extent_of_occurrence (logical) whether to obtain the extent of occurrence
#' of the species based on a simple convex hull polygon; default = \code{TRUE}.
#' @param area_of_occupancy (logical) whether to obtain the area of occupancy
#' of the species based on a simple grid of 4 km^2 resolution;
#' default = \code{TRUE}.
#' @param final_projection (character) string of projection arguments for resulting
#' Spatial objects. Arguments must be as in the PROJ.4 documentation. See
#' \code{\link[sp]{CRS-class}} for details. If \code{NULL}, the default, projection
#' used is WGS84 (EPSG:4326).
#' @param save_shp (logical) if \code{TRUE}, shapefiles of the species range,
#' occurrences, extent of occurrence, and area of occupancy will be written in
#' the working directory. Default = \code{FALSE}.
#' @param save_ts_layer (logical) if \code{TRUE}, the TSA layer will be included
#' as part of the object returned. If \code{save_shp} = TRUE, the TSA layer will
#' be written in GeoTiff format. Default = \code{FALSE}
#' @param name (character) valid if \code{save_shp} = TRUE. The name of the
#' geographic files to be exported. A suffix will be added to \code{name}
#' depending on the object as follows: species extent of occurrence = "_extent_occ",
#' area of occupancy = "_area_occ", occurrences = "_unique_records", and,
#' if \code{save_ts_layer} = \code{TRUE}, trend surface layer "_tsa".
#' @param overwrite (logical) whether or not to overwrite previous results with
#' the same name. Default = \code{FALSE}.
#' @param verbose (logical) whether or not to print messages about the process.
#' Default = TRUE.
#'
#' @return
#' A sp_range object (S4) containing: (1) a data.frame with information about
#' the species range, and SpatialPolygons objects of (2) unique occurrences,
#' (3) species range, (4) extent of occurrence, and (5) area of occupancy.
#' If \code{save_ts_layer} = TRUE, a (6) TSA layer will be included as well.
#'
#' If \code{extent_of_occurrence} and/or \code{area_of_occupancy} = \code{FALSE},
#' the corresponding spatial objects in the resulting sp_range object will be
#' empty, an areas will have a value of 0.
#'
#' @details
#' All resulting Spatial objects in the results will be projected to the
#' \code{final_projection}. Areas are calculated in square kilometers using the
#' Lambert Azimuthal Equal Area projection, centered on the centroid of occurrence
#' points given as inputs.
#'
#' Trend surface analysis is a method based on low-order polynomials of spatial
#' coordinates for estimating a regular grid of points from scattered observations.
#' This method assumes that all cells not occupied by occurrences are absences;
#' hence its use depends on the quality of data and the certainty of having or
#' not a complete sampling of the \code{regiong_of_interest}.
#'
#' @usage
#' rangemap_tsa(occurrences, region_of_interest, cell_size = 5,
#' threshold = 0, simplify = FALSE, simplify_level = 0,
#' extent_of_occurrence = TRUE, area_of_occupancy = TRUE,
#' final_projection = NULL, save_shp = FALSE,
#' save_ts_layer = FALSE, name, overwrite = FALSE, verbose = TRUE)
#'
#' @export
#'
#' @importFrom sp CRS SpatialPointsDataFrame SpatialPolygonsDataFrame
#' @importFrom sp proj4string spTransform
#' @importFrom raster disaggregate area extent rasterize res values mask
#' @importFrom raster writeRaster extract rasterToPolygons
#' @importFrom rgeos gUnaryUnion
#' @importFrom rgdal writeOGR
#' @importFrom spatial surf.ls predict.trls
#' @importFrom stats na.omit
#'
#' @examples
#' # data
#' data("occ_f", package = "rangemap")
#'
#' CU <- simple_wmap("simple", regions = "Cuba")
#'
#' # running
#' tsa_range <- rangemap_tsa(occurrences = occ_f, region_of_interest = CU,
#' cell_size = 5)
#'
#' summary(tsa_range)
rangemap_tsa <- function(occurrences, region_of_interest, cell_size = 5,
threshold = 0, simplify = FALSE, simplify_level = 0,
extent_of_occurrence = TRUE, area_of_occupancy = TRUE,
final_projection = NULL, save_shp = FALSE,
save_ts_layer = FALSE, name, overwrite = FALSE,
verbose = TRUE) {
# testing potential issues
if (missing(occurrences)) {
stop("Argument 'occurrences' is necessary to perform the analysis")
}
if (dim(occurrences)[2] != 3) {
stop("'occurrences' must have the following columns: \nSpecies, Longitude, Latitude")
}
if (missing(region_of_interest)) {
stop("Argument 'region_of_interest' is necessary to perform the analysis")
}
if (threshold > 0) {
warning("As 'threshold' > 0, some occurrences may be excluded from the species range.")
}
if (save_shp == TRUE) {
if (missing(name)) {
stop("Argument 'name' must be defined if 'save_shp' = TRUE.")
}
if (file.exists(paste0(name, ".shp")) & overwrite == FALSE) {
stop("Files already exist, use 'overwrite' = TRUE.")
}
}
# initial projection
WGS84 <- sp::CRS("+init=epsg:4326")
# final projection
if (is.null(final_projection)) {
final_projection <- WGS84
} else {
final_projection <- sp::CRS(final_projection) # character to projection
}
# erase duplicate records
occ <- as.data.frame(unique(occurrences))[, 1:3]
colnames(occ) <- c("Species", "Longitude", "Latitude")
# making spatial points
occ_sp <- sp::SpatialPointsDataFrame(coords = occ[, 2:3], data = occ,
proj4string = WGS84)
# keeping only records in land
occ_sp <- occ_sp[region_of_interest, ]
# project the points using their centriods as reference
LAEA <- LAEA_projection(spatial_object = occ_sp)
occ_pr <- sp::spTransform(occ_sp, LAEA)
# region of interest projected
region <- region_of_interest
region_of_interest <- sp::spTransform(region_of_interest, LAEA)
# preparing variables
## creating a grid
grid <- raster::raster(raster::extent(region_of_interest))
## grid resolution and values
raster::res(grid) <- cell_size * 1000
raster::values(grid) <- 0
## grid projection
sp::proj4string(grid) <- sp::proj4string(region_of_interest)
## extract grid with region
grid_reg <- raster::mask(grid, region_of_interest)
## grid for region of interest
grid_r_pol <- raster::rasterToPolygons(grid_reg)
## points for region of interest
matrix_pa <- raster::rasterToPoints(grid_reg)
## selecting grids with occurrences
grid_r_pol <- grid_r_pol[occ_pr, ]
## grid to points
ras_grid <- raster::rasterize(grid_r_pol, grid_reg, "layer")
ras_grid <- raster::rasterToPoints(ras_grid)[, 1:2]
## asigning 1 to cells occupied by occurrenes
matrix_pa[, 3] <- ifelse(paste(matrix_pa[, 1], matrix_pa[, 2]) %in%
paste(ras_grid[, 1], ras_grid[, 2]), 1, 0)
## data for models
condition <- nrow(matrix_pa) > (10000 + nrow(ras_grid))
if (condition) {
all_matrix <- matrix_pa[, 1:2]
ma_a <- matrix_pa[matrix_pa[, 3] == 1, ]
matrix_pa <- matrix_pa[matrix_pa[, 3] == 0, ]
matrix_pa <- matrix_pa[sample(nrow(matrix_pa), 10000), ]
matrix_pa <- rbind(matrix_pa, ma_a)
}
# tsa
## tsa model
tsa <- spatial::surf.ls(np = 3, x = matrix_pa[, 1], y = matrix_pa[, 2],
z = matrix_pa[, 3])
# tsa prediction to region of insterest
if (condition) {
tsa_reg <- spatial::predict.trls(tsa, all_matrix[, 1], all_matrix[, 2])
tsa_model <- raster::rasterize(all_matrix, grid_reg, tsa_reg)
} else {
tsa_reg <- spatial::predict.trls(tsa, matrix_pa[, 1], matrix_pa[, 2])
tsa_model <- raster::rasterize(matrix_pa[, 1:2], grid_reg, tsa_reg)
}
# tsa thresholded
tsa_t <- tsa_model
occ_val <- na.omit(raster::extract(tsa_t, occ_pr@coords))
val <- ceiling(nrow(occ) * threshold / 100) + 1
thres <- sort(occ_val)[val]
tsa_t <- tsa_t >= thres
# only presence
tsa_t[tsa_t[] == 0] <- NA
# tsa to spatial polygon
tsa_t <- raster::rasterToPolygons(tsa_t)
tsa_t <- rgeos::gUnaryUnion(tsa_t, tsa_t$layer)
tsa_t <- raster::disaggregate(tsa_t)
if (simplify == TRUE) {
tsa_t <- suppressWarnings(rgeos::gSimplify(tsa_t, tol = simplify_level))
}
# calculate areas in km2
area <- raster::area(tsa_t) / 1000000
areakm2 <- sum(area) # total area of the species range
# adding characteristics to spatial polygons
species <- as.character(occurrences[1, 1])
clip_area <- sp::SpatialPolygonsDataFrame(tsa_t,
data = data.frame(species, area),
match.ID = FALSE)
# extent of occurrence
if (extent_of_occurrence == TRUE) {
eooc <- eoo(occ_sp@data, region)
eocckm2 <- eooc$area
extent_occurrence <- eooc$spolydf
extent_occurrence <- sp::spTransform(extent_occurrence, final_projection)
} else {
eocckm2 <- 0
extent_occurrence <- new("SpatialPolygonsDataFrame")
}
# area of occupancy
if (area_of_occupancy == TRUE) {
aooc <- aoo(occ_pr, species)
aocckm2 <- aooc$area
area_occupancy <- aooc$spolydf
area_occupancy <- sp::spTransform(area_occupancy, final_projection)
} else {
aocckm2 <- 0
area_occupancy <- new("SpatialPolygonsDataFrame")
}
# reprojection
clip_area <- sp::spTransform(clip_area, final_projection)
occ_pr <- sp::spTransform(occ_pr, final_projection)
# exporting
if (save_shp == TRUE) {
if (verbose == TRUE) {
message("Writing shapefiles in the working directory.")
}
rgdal::writeOGR(clip_area, ".", name, driver = "ESRI Shapefile",
overwrite_layer = overwrite)
rgdal::writeOGR(occ_pr, ".", paste(name, "unique_records", sep = "_"),
driver = "ESRI Shapefile", overwrite_layer = overwrite)
if (extent_of_occurrence == TRUE) {
rgdal::writeOGR(extent_occurrence, ".", paste(name, "extent_occ", sep = "_"),
driver = "ESRI Shapefile", overwrite_layer = overwrite)
}
if (area_of_occupancy == TRUE) {
rgdal::writeOGR(area_occupancy, ".", paste(name, "area_occ", sep = "_"),
driver = "ESRI Shapefile", overwrite_layer = overwrite)
}
if (save_ts_layer == TRUE) {
if (verbose == TRUE) {
message("Writing trend surface layer in the working directory.")
}
raster::writeRaster(tsa_model, paste0(name, "_ts_layer.tif"),
format = "GTiff", overwrite = overwrite)
}
}
# return results
sp_dat <- data.frame(Species = species, Unique_records = dim(occ_pr)[1],
Range_area = areakm2, Extent_of_occurrence = eocckm2,
Area_of_occupancy = aocckm2)
if (save_ts_layer == TRUE) {
results <- sp_range_iucnextra(name = "TSA", summary = sp_dat,
species_unique_records = occ_pr,
species_range = clip_area,
extent_of_occurrence = extent_occurrence,
area_of_occupancy = area_occupancy,
trend_surface_model = tsa_model)
}else {
results <- sp_range_iucn(name = "TSA", summary = sp_dat,
species_unique_records = occ_pr,
species_range = clip_area,
extent_of_occurrence = extent_occurrence,
area_of_occupancy = area_occupancy)
}
return(results)
}
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