Estimate the number of subpopulations for one or multiple species
Source:R/subpop.comp.R
subpop.comp.Rd![[Stable]](figures/lifecycle-stable.svg)
Estimate the number of subpopulations following the method
circular buffer method (overlapping buffered circles form a single
subpopulation)
subpop.comp(
XY,
resol_sub_pop = NULL,
proj_type = "cea",
export_shp = FALSE,
parallel = FALSE,
show_progress = TRUE,
NbeCores = 2
)Arguments
- XY
a data frame containing the geographical coordinates for each taxon (see Details).
- resol_sub_pop
a value defining the radius of the circles around each occurrence (in kilometres) or data frame vector containing a column 'tax' with the taxa names and a column 'radius' with the species-specific radius (in kilometre as well). Typically, this data frame is the output of
ConRfunctionsubpop.radius.- proj_type
string or numeric
- export_shp
logical. Whether the resulting shapefiles should be exported. FALSE by default.
- parallel
a logical. Whether running should be performed in parallel. FALSE by default.
- show_progress
logical. Whether progress informations should displayed. TRUE by default
- NbeCores
an integer. Register the number of cores for parallel execution. Two by default.
Value
If export_shp is TRUE,
number_subpop a numeric vector of AOO estimates for each taxa
poly_subpop a simple feature collection
a Simple feature collection with as many MULTIPOLYGON as taxa.
If export_shp is FALSE, a vector with estimated number of subpopulation per
taxa.
Details
XY as a data.frame should have the following structure:
It is mandatory to respect field positions, but field names do not matter
The first column is contains numeric value i.e. latitude in decimal degrees
The second column is contains numeric value i.e. longitude in decimal degrees
The third column is contains character value i.e. the names of the species
References
Rivers MC, Bachman SP, Meagher TR, Lughadha EN, Brummitt NA (2010) Subpopulations, locations and fragmentation: applying IUCN red list criteria to herbarium specimen data. Biodiversity and Conservation 19: 2071-2085. doi: 10.1007/s10531-010-9826-9
Examples
data(dataset.ex)
subpop.comp(dataset.ex, resol_sub_pop = 5)
#>
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#> tax subpop
#> 1 Berlinia bruneelii 88
#> 2 Oncocalamus mannii 29
#> 3 Platycoryne guingangae 34
#> 4 Psychotria minuta 10
#> 5 species_1 2
#> 6 species_2 3
rad.df <- data.frame(
tax = unique(dataset.ex$tax),
radius = seq(3,13, by=2),
stringsAsFactors = FALSE
)
subpop.comp(dataset.ex, resol_sub_pop = rad.df)
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#> tax subpop
#> 1 Berlinia bruneelii 80
#> 2 Oncocalamus mannii 29
#> 3 Platycoryne guingangae 32
#> 4 Psychotria minuta 10
#> 5 species_1 2
#> 6 species_2 2
subpop.comp(dataset.ex, resol_sub_pop = rad.df, export_shp = TRUE)
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#> $number_subpop
#> tax subpop
#> 1 Berlinia bruneelii 80
#> 2 Oncocalamus mannii 29
#> 3 Platycoryne guingangae 32
#> 4 Psychotria minuta 10
#> 5 species_1 2
#> 6 species_2 2
#>
#> $poly_subpop
#> Simple feature collection with 155 features and 1 field
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 5.655084 ymin: -15.23224 xmax: 33.79618 ymax: 5.962126
#> Geodetic CRS: WGS 84
#> First 10 features:
#> tax geometry
#> 1 Berlinia bruneelii POLYGON ((11.49359 -4.49189...
#> 2 Berlinia bruneelii POLYGON ((12.39218 -3.51255...
#> 3 Berlinia bruneelii POLYGON ((12.62548 -3.29588...
#> 4 Berlinia bruneelii POLYGON ((12.57689 -3.02518...
#> 5 Berlinia bruneelii POLYGON ((11.54218 -1.97918...
#> 6 Berlinia bruneelii POLYGON ((10.02548 -1.62918...
#> 7 Berlinia bruneelii POLYGON ((12.41019 -1.02515...
#> 8 Berlinia bruneelii POLYGON ((12.77548 -0.86250...
#> 9 Berlinia bruneelii POLYGON ((12.62048 -0.21116...
#> 10 Berlinia bruneelii POLYGON ((12.02548 -0.11250...
#>