Marlon E. Cobos, Vijay Barve, Narayani Barve, Alberto Jimenez-Valverde, and Claudia Nunez-Penichet
This repository is for the GSoC 2018 project “Species range maps in R”.
The rangemap R package presents various tools to create species range maps based on occurrence data, statistics, and SpatialPolygons objects. Other tools of this package can be used to analyze environmental characteristics of the species ranges and to create high quality figures of these maps. All the functions that create species ranges can also generate representations of species extents of occurrence (using convex hulls) and areas of occupancy according to the IUCN criteria. Shapefiles of the resulting polygons can be saved in the working directory if it is needed.
Starting with v 0.1.17, rangemap allows optional calculations of extents of occurrence and areas of occupancy in all main functions (see below). To avoid performing analysies related with these areas, set arguments extent_of_occurrence and area_of_occupancy to FALSE. This change can save time, specially if a large number of occurrences is used.
A stable version of rangemap can be installed from CRAN as follows:
rangemap can also be installed from its GitHub repository using the following code:
if(!require(remotes)){
install.packages("remotes")
}
remotes::install_github("marlonecobos/rangemap")The following code chunk loads the pacakges needed to perform some example-analyses with rangemap.
The working directory will also be defined in this part.
The rangemap_explore function generates simple figures to visualize species occurrence data in the geographic space before using other functions of this package. The figure created with this function helps to identify countries involved in the species distribution. Other aspects of the species distribution can also be generally checked here; for instance, disjunct distributions, general dimension of the species range, etc.
The function’s help can be consulted using the following line of code:
An example of the use of this function is written below.
# getting the data
data("occ_d", package = "rangemap")
# simple figure of the species occurrence data
rangemap_explore(occurrences = occ_d)
Same figure with country codes.
# simple figure of the species occurrence data
rangemap_explore(occurrences = occ_d, show_countries = TRUE)
The rangemap_buff function generates a distributional range for a given species by buffering provided occurrences using a user-defined distance.
The function’s help can be consulted using the following line of code:
An example of how to use this function is written below.
# getting the data
data("occ_p", package = "rangemap")
# species range
buff_range <- rangemap_buffer(occurrences = occ_p, buffer_distance = 100000)
summary(buff_range)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Buffer
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Peltophryne empusa 27 106241.2 66357.71
#> Area_of_occupancy
#> 92
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 1 SpatialPolygonsDataFrame S4
#> species_unique_records 27 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 23 SpatialPolygonsDataFrame S4The function rangemap_plot generates customizable figures of species range maps using the objects produced by other function of this package. Let’s see how the generated range looks like.
For further details see the function documentation.
The rangemap_boundaries function generates a distributional range for a given species by considering all the polygons of administrative entities in which the species has been detected.
The function’s help can be consulted using the following line of code:
Examples of the use of this function with most of its variants are written below.
Following there is an example in which administrative areas will be selected using only occurrences. The rangemap_explore function will be used for obtaining a first visualization of the species distributional range.
# occurrence data was obtained in the first example using
# data("occ_d", package = "rangemap")
# checking which countries may be involved in the analysis
rangemap_explore(occurrences = occ_d, show_countries = TRUE)
# getting an example of SpatialPolygonsDataFrame to be used as polygons
data("adm_boundaries", package = "rangemap")
# species range
bound_range <- rangemap_boundaries(occurrences = occ_d, polygons = adm_boundaries)
summary(bound_range)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Boundaries
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 55 12217732 4076154
#> Area_of_occupancy
#> 176
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 7 SpatialPolygonsDataFrame S4
#> species_unique_records 55 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 44 SpatialPolygonsDataFrame S4Figure of the generated range.
Following there is an example in which administrative areas will be selected using only the names of the administrative entities known to be occupied by the species. This approach may be useful in circumstances where geographic coordinates or accurate locality descriptions do not exist.
# administrative areas invloved
adm <- c("Ecuador", "Peru", "Venezuela", "Colombia", "Brazil")
# species range
bound_range1 <- rangemap_boundaries(adm_areas = adm, polygons = adm_boundaries)
summary(bound_range1)
#>
#> Summary of sp_range object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Boundaries
#>
#> Species Range_area
#> Species 12043257
#>
#>
#>
#> Species range
#> Object of class SpatialPolygonsDataFrame
#> Coordinates:
#> min max
#> x -91.66389 -29.84000
#> y -33.74067 13.37861
#> Is projected: FALSE
#> proj4string : [+proj=longlat +datum=WGS84 +no_defs]
#> Data attributes:
#> X.Species. rangekm2
#> Length:5 Min. : 255493
#> Class :character 1st Qu.: 909405
#> Mode :character Median :1135524
#> Mean :2408651
#> 3rd Qu.:1294369
#> Max. :8448466Map of the generated range.
An example of using both occurrences and administrative areas for creating species ranges with the function rangemap_boundaries is presented below. This option may be useful when these two types of information complement the knowledge of the species distribution.
# other parameters
adm <- "Ecuador" # Athough no record is on this country, we know it is in Ecuador
# species range
bound_range2 <- rangemap_boundaries(occurrences = occ_d, adm_areas = adm,
polygons = adm_boundaries)
summary(bound_range2)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Boundaries
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 55 12473215 4076154
#> Area_of_occupancy
#> 176
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 8 SpatialPolygonsDataFrame S4
#> species_unique_records 55 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 44 SpatialPolygonsDataFrame S4Now the plot.
The rangemap_hull function generates a species range polygon by creating convex or concave hull polygons based on occurrence data.
The function’s help can be consulted using the following line of code:
Examples of the use of this function with most of its variants are written below.
With the example provided below, a species range will be constructed using convex hulls. After that this range will be split based on two distinct algorithms of clustering: hierarchical and k-means. Convex hull polygons are commonly used to represent species ranges, however in circumstances where biogeographic barriers for the species dispersal exist, concave hulls may be a better option.
# occurrences were obtained in prvious examples using
# data("occ_d", package = "rangemap")
# species range
hull_range <- rangemap_hull(occurrences = occ_d, hull_type = "convex",
buffer_distance = 100000)
#> Hull type: convex
summary(hull_range)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Convex_hull
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 4860934 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 6 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4
# disjunct distributions
# clustering occurrences with the hierarchical method
# species range
hull_range1 <- rangemap_hull(occurrences = occ_d, hull_type = "convex",
buffer_distance = 100000, split = TRUE,
cluster_method = "hierarchical",
split_distance = 1500000)
#> Clustering method: hierarchical
#> Hull type: convex
summary(hull_range1)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Convex_hull_split
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 2115826 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 7 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4
# clustering occurrences with the k-means method
# species range
hull_range2 <- rangemap_hull(occurrences = occ_d, hull_type = "convex",
buffer_distance = 100000, split = TRUE,
cluster_method = "k-means", n_k_means = 3)
#> Clustering method: k-means
#> Hull type: convex
summary(hull_range2)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Convex_hull_split
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 2115826 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 7 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4Now the figure of the species range.
With the following examples, the species range will be constructed using concave hulls. The species range will be calculated as an only area and as disjunct areas by clustering its occurrences using the k-means and hierarchical methods.
# occurrences were obtained in prvious examples using
# data("occ_d", package = "rangemap")
# species range
hull_range3 <- rangemap_hull(occurrences = occ_d, hull_type = "concave",
buffer_distance = 100000)
#> Hull type: concave
summary(hull_range3)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Concave_hull
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 4327930 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 4 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4
# disjunct distributions
# clustering occurrences with the hierarchical method
# species range
hull_range4 <- rangemap_hull(occurrences = occ_d, hull_type = "concave",
buffer_distance = 100000, split = TRUE,
cluster_method = "hierarchical",
split_distance = 1500000)
#> Clustering method: hierarchical
#> Hull type: concave
summary(hull_range4)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Concave_hull_split
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 1878355 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 6 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4
# clustering occurrences with the k-means method
# species range
hull_range5 <- rangemap_hull(occurrences = occ_d, hull_type = "concave",
buffer_distance = 100000, split = TRUE,
cluster_method = "k-means", n_k_means = 3)
#> Clustering method: k-means
#> Hull type: concave
summary(hull_range5)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: Concave_hull_split
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Dasypus kappleri 56 1878355 4095146
#> Area_of_occupancy
#> 184
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 6 SpatialPolygonsDataFrame S4
#> species_unique_records 56 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 46 SpatialPolygonsDataFrame S4Checking the figure.
The rangemap_enm function generates a distributional range for a given species using a continuous raster layer produced with an ecological niche modeling algorithm. This function binarizes the model into suitable and unsuitable areas using a user specified level of omission (a given threshold value).
The function’s help can be consulted using the following line of code:
An example of the use of this function is written below.
# parameters
sp_mod <- raster::raster(list.files(system.file("extdata", package = "rangemap"),
pattern = "sp_model.tif", full.names = TRUE))
data("occ_train", package = "rangemap")
# species range
enm_range <- rangemap_enm(occurrences = occ_train, model = sp_mod,
threshold_omission = 5)
summary(enm_range)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: ENM
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Amblyomma_americanum 89 2824883 3535078
#> Area_of_occupancy
#> 356
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 11 SpatialPolygonsDataFrame S4
#> species_unique_records 89 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 89 SpatialPolygonsDataFrame S4Let’s see how this range looks like.
The rangemap_tsa function generates species range polygons for a given species using a trend surface analysis. 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 region of interest.
The function’s help can be consulted using the following line of code:
An example of the use of this function is written below.
# data
data("occ_f", package = "rangemap")
CU <- simple_wmap("simple", regions = "Cuba")
# species range
tsa_r <- rangemap_tsa(occurrences = occ_f, region_of_interest = CU)
summary(tsa_r)
#>
#> Summary of sp_range_iucn object
#> ---------------------------------------------------------------------------
#>
#> Species range derived from: TSA
#>
#> Species Unique_records Range_area Extent_of_occurrence
#> Peltophryne fustiger 18 6825 1630.968
#> Area_of_occupancy
#> 48
#>
#>
#>
#> Other contents:
#> Length Class Mode
#> species_range 1 SpatialPolygonsDataFrame S4
#> species_unique_records 18 SpatialPointsDataFrame S4
#> extent_of_occurrence 1 SpatialPolygonsDataFrame S4
#> area_of_occupancy 12 SpatialPolygonsDataFrame S4Let’s take a look at the results.
The rangemap_plot function can be used to plot not only the generated species ranges but also the extent of occurrence and the species records in the same map. The species range will be plot on a simplified world map, but other SpatialPolygons objects can be used.
The function’s help can be consulted using the following line of code:
Examples of the use of this function are written below.
rangemap_plot(hull_range5, add_EOO = TRUE, add_occurrences = TRUE,
legend = TRUE, scalebar = TRUE, scalebar_length = 500,
zoom = 0.5, northarrow = TRUE)
The ranges_emaps function represents one or more ranges of the same species on various maps of environmental factors (e.g. climatic variables) to detect implications of using distinct types of ranges when looking at environmental conditions in the area. Figures can be saved using some of the function’s arguments.
The function’s help can be consulted using the following line of code:
An example of the use of this function is written below.
# example data
data("buffer_range", package = "rangemap")
data("cxhull_range", package = "rangemap")
data("cvehull_range", package = "rangemap")
vars <- raster::stack(system.file("extdata", "variables.tif",
package = "rangemap"))
names(vars) <- c("bio5", "bio6", "bio13", "bio14")
# plotting
ranges_emaps(buffer_range, cxhull_range, cvehull_range, variables = vars)
The ranges_espace function generates a three dimensional comparison of a species’ ranges created using distinct algorithms, to visualize implications of selecting one of them if environmental conditions are considered.
The function’s help can be consulted using the following line of code:
An example of the use of this function is written below.
# example data
data("buffer_range", package = "rangemap")
data("cxhull_range", package = "rangemap")
vars <- raster::stack(system.file("extdata", "variables.tif",
package = "rangemap"))
names(vars) <- c("bio5", "bio6", "bio13", "bio14")
## comparison
ranges_espace(buffer_range, cxhull_range, variables = vars,
add_occurrences = TRUE)
# you can zoom in and rotate the figure for understanding it betterSaving this figures for publication can be done using functions from the package rgl (e.g., rgl.postscript() and rgl.snapshot()) .
Student: Marlon E. Cobos
Mentors: Narayani Barve, Vijay Barve, and Alberto Jimenez Valverde
The species range maps project is motivated by the importance of information about species distribution for processes of conservation planning and the study of spatial patterns of biodiversity. In the face of multiple threats related to Global Change, protection and mitigation actions are crucial for maintaining the health of the planet, and knowing where species are located constitutes in primary information for starting these efforts. Currently, generation of species ranges maps may take several steps and the use of specialized software. Thanks to the recent development of specialized packages, R is rapidly becoming an excellent alternative for analyzing the spatial patterns of biodiversity. Taking advantage of these packages and the versatility of R, the aim of this project was offering handily and robust open source tools to obtain reliable proposals of species distribution ranges and to analyze their geographical patterns. A large community of students, researchers, and conservation managers can be benefited by this project since these tools will be freely available and will improve the way in which studies of species distributions are developed.
A version of the package rangemap (the result of the project) is in CRAN and can be found here.
All commits made can be seen at the complete list of commits.