lets.midpoint: Compute the midpoint of species' geographic ranges

In letsR: Data Handling and Analysis in Macroecology

Compute the midpoint of species' geographic ranges

Description

Calculate species distribution midpoint from a presence-absence matrix using several methods.

Usage

lets.midpoint(pam, planar = FALSE, method = "PC", inside = FALSE)

Arguments

pam	A presence-absence matrix (sites in the rows and species in the columns, with the first two columns containing the longitudinal and latitudinal coordinates, respectively), or an object of class PresenceAbsence.
planar	Logical, if FALSE the coordinates are in Longitude/Latitude. If TRUE the coordinates are planar.
method	Default option, "PC" (polygon centroid) will generate a polygon from the raster, and calculate the centroid of this polygon based on the function terra::centroids. Note that for the "PC" method, users can only use PresenceAbsence objects. Note also that this method will not be the best for PresenceAbsence objects made from occurrence records, or that have multiple disjoint distributions. Users can also choose the geographic midpoint, using the option "GM". "GM" will create a bounding box across the extremes of the distribution and calculate the centroid. Alternatively, the midpoint can be calculated as the point that minimize the distance between all cells of the PAM, using the method "CMD"(centre of minimum distance). The user can also calculate the midpoint, based on the centroid of the minimum convex polygon of the distribution, using the method "MCC". This last method is useful when using a PresenceAbsence object made from occurrence records.
inside	logical. If TRUE the points returned are guaranteed to be inside the polygons or on the lines, but they are not the true centroids. True centroids may be outside a polygon, for example when a polygon is "bean shaped", and they are unlikely to be on their line

Value

A data.frame containing the species' names and geographic coordinates (longitude [x], latitude [y]) of species' midpoints.

Author(s)

Fabricio Villalobos & Bruno Vilela

See Also

lets.presab

lets.presab.birds

Examples

## Not run: 
data(PAM)
mid <- lets.midpoint(PAM, method = "PC")
mid2 <- lets.midpoint(PAM, method = "GM")
mid3 <- lets.midpoint(PAM, method = "CMD")
mid4 <- lets.midpoint(PAM, method = "MCC")
mid5 <- lets.midpoint(PAM, method = "PC", planar = TRUE)
mid6 <- lets.midpoint(PAM, method = "GM", planar = TRUE)
mid7 <- lets.midpoint(PAM, method = "CMD", planar = TRUE)
mid8 <- lets.midpoint(PAM, method = "MCC", planar = TRUE)

for (sp in seq_len(nrow(mid))) {
 #sp = 4 # Or choose a line or species
 plot(PAM, name = mid[sp, 1])
 points(mid[sp, -1], col = adjustcolor("blue", .8), pch = 20, cex = 1.5)
 points(mid2[sp, -1], col = adjustcolor("green", .8), pch = 20, cex = 1.5)
 points(mid3[sp, -1], col = adjustcolor("yellow", .8), pch = 20, cex = 1.5)
 points(mid4[sp, -1], col = adjustcolor("purple", .8), pch = 20, cex = 1.5)
 points(mid5[sp, -1], col = adjustcolor("orange", .8), pch = 20, cex = 1.5)
 points(mid6[sp, -1], col = adjustcolor("black", .8), pch = 20, cex = 1.5)
 points(mid7[sp, -1], col = adjustcolor("gray", .8), pch = 20, cex = 1.5)
 points(mid8[sp, -1], col = adjustcolor("brown", .8), pch = 20, cex = 1.5)
 Sys.sleep(1)
}

## End(Not run)

letsR documentation built on Nov. 23, 2023, 9:07 a.m.