rangemap_hull: Species distributional ranges based on convex or concave hull...
In rangemap: Simple Tools for Defining Species Ranges

Description Usage Arguments Details Value Examples

rangemap_hull generates a distributional range for a given species by creating convex or concave hull polygons based on occurrence data. Optionally, representations of the species extent of occurrence (using convex hulls) and the area of occupancy according to the IUCN criteria can also be generated. Shapefiles can be saved in the working directory if it is needed.

rangemap_hull(occurrences, hull_type = "convex", concave_distance_lim = 5000,
              buffer_distance = 50000, split = FALSE,
              cluster_method = "hierarchical", split_distance = NULL,
              n_k_means = NULL, polygons = NULL, extent_of_occurrence = TRUE,
              area_of_occupancy = TRUE, final_projection = NULL,
              save_shp = FALSE, name, overwrite = FALSE, verbose = TRUE)

`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).
`hull_type`	(character) type of hull polygons to be created. Available options are: "convex" and "concave". Default = "convex".
`concave_distance_lim`	(numeric) distance, in meters, to be passed to the length_threshold parameter of the `concaveman` function. Default = 5000. Ignored if `hull_type` is not "concave".
`buffer_distance`	(numeric) distance, in meters, to be used for creating a buffer around resulting hull polygons; default = 50000.
`split`	(logical) if `TRUE`, a distance (for hierarchical clustering) or a number (for K-means clustering) is used to separate distinct chunks of occurrences. Recommended when the species of interest has a disjunct distribution. Default = `FALSE`.
`cluster_method`	(character) name of the method to be used for clustering the occurrences. Options are "hierarchical" and "k-means"; default = "hierarchical". Note that this parameter is ignored when `split` = `FALSE`. See details for more information on the two available methods.
`split_distance`	(numeric) distance in meters that will limit connectivity among hull polygons created with chunks of points separated by long distances. This parameter is used when `cluster_method` = "hierarchical" and `split` = `TRUE`. Default = `NULL`.
`n_k_means`	(numeric) if `split` = `TRUE`, number of clusters in which the species occurrences will be grouped when using the "k-means" `cluster_method`. Default = `NULL`.
`polygons`	(optional) a SpatialPolygons* object to clip polygons and adjust the species range and other polygons to these limits. Projection must be WGS84 (EPSG:4326). If `NULL`, the default, a simplified world map will be used.
`extent_of_occurrence`	(logical) whether to obtain the extent of occurrence of the species based on a simple convex hull polygon; default = `TRUE`.
`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 = `TRUE`.
`final_projection`	(character) string of projection arguments for resulting Spatial objects. Arguments must be as in the PROJ.4 documentation. See `CRS-class` for details. If `NULL`, the default, projection used is WGS84 (EPSG:4326).
`save_shp`	(logical) if `TRUE`, shapefiles of the species range, occurrences, extent of occurrence, and area of occupancy will be written in the working directory. Default = `FALSE`.
`name`	(character) valid if `save_shp` = `TRUE`. The name of the shapefile to be exported. A suffix will be added to `name` depending on the object, as follows: species extent of occurrence = "_extent_occ", area of occupancy = "_area_occ", and occurrences = "_unique_records".
`overwrite`	(logical) whether or not to overwrite previous results with the same name. Default = `FALSE`.
`verbose`	(logical) whether or not to print messages about the process. Default = TRUE.

All resulting Spatial objects in the results will be projected to the 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.

If split = TRUE, some point clusters may end up having less than three points, in which cases creating hull polygons is not possible. Such point clusters will be discarded if buffer_distance = 0. To keep them as part of the final result, use a buffer_distance > 0.

The cluster_method must be chosen based on the spatial configuration of the species occurrences. Both methods make distinct assumptions and one of them may perform better than the other depending on the spatial pattern of the data.

The k-means method, for example, performs better when the following assumptions are fulfilled: Clusters are spatially grouped—or “spherical” and Clusters are of a similar size. Owing to the nature of the hierarchical clustering algorithm it may take more time than the k-means method. Both methods make assumptions and they may work well on some data sets, and fail on others.

Another important factor to consider is that the k-means method always starts with a random choice of cluster centers, thus it may end in different results on different runs. That may be problematic when trying to replicate your methods. With hierarchical clustering, most likely the same clusters can be obtained if the process is repeated.

For more information on these clustering methods see Aggarwal and Reddy (2014), here.

A sp_range object (S4) containing: (1) a data.frame with information about the species range, and Spatial objects of (2) unique occurrences, (3) species range, (4) extent of occurrence, and (5) area of occupancy.

If extent_of_occurrence and/or area_of_occupancy = FALSE, the corresponding spatial objects in the resulting sp_range object will be empty, an areas will have a value of 0.

# getting the data
data("occ_d", package = "rangemap")

# other info for running
dist <- 100000
hull <- "convex" # try also "concave"

hull_range <- rangemap_hull(occurrences = occ_d, hull_type = hull,
                            buffer_distance = dist)

summary(hull_range)