R/MSDM_Priori.R
MSDM_Priori.RdThis function creates rasters that, together with environmental variables, can be used to construct constrained species distribution models spatial
MSDM_Priori(
records,
x = NA,
y = NA,
sp = NA,
method = c("XY", "MIN", "CML", "KER"),
rasterlayer = NULL,
dirsave = NULL
)data.frame. A database with geographical coordinates of species presences used to create species distribution models.
character. Column name with longitude values.
character. Column name with latitude values.
character. Column name with species names. It would be desirable that the species names are as simple as possible and with no space between the genus and the specific epithet (e.g., Alchornea_glandulosa). Do not use author names, symbols, or accents. For example, substitute names like Senna chacoensis (L.Bravo) H.S.Irwin & Barneby or Erythrina crista-galli L., for Senna_chacoensis and Erythrina_cristagalli. The species names and the raster must be the same.
character. A character string indicating which MSDM the method that must be used. The next methods are available: XY, MIN, CML, and KER. Usage method = 'CML'
raster object. A raster, stack, or brick object used to construct species distribution models. This object will be used to create MSDM variables with the same resolution, extent, and pattern of empty cells that the environmental variables. It is advisable to use a raster of an environmental layer that will be used in the future to create the species distribution models in order not to have problems (e.g., resolution, extent, cells with NA) between environmental and constraining raster.
character. A character string indicating the directory where the result must be saved.
This function saves raster files (with geotiff format) in a folder named with the MSDM method. Such raster/s have to be used together with environmental variables at the moment to construct species distribution models. The XY approach creates a single pair of raster layers that can be used for all species that share the same study region. Otherwise, CML, MIN, and KER create a species-specific raster layer.
XY (Latlong method). It assumes that spatial structure can partially explain species distribution (Bahn & Mcgill, 2007). Two raster layers will be created, containing the latitude and longitude of pixels in decimal degrees, respectively. These raster layers should be included as covariates with the environmental layers to construct species distribution models.
MIN (Nearest neighbor distance). Compiled and adapted from Allouche et al. (2008), this method calculates for each cell the Euclidian geographic distance to the nearest presence point.
CML (Cumulative distance method). Compiled and adapted from Allouche et al. (2008), it assumes that pixels closer to presences are likely included in species distributions. A raster layer will be created containing the sum of Euclidian geographic distances from each pixel to all occurrences of a species. Obtained values are normalized to vary from zero to one. This raster layer should be included as covariates with the environmental layers to construct species distribution models.
KER (Kernel method). Also compiled and adapted from Allouche et al. (2008), this method, like CML, assumes that pixels located in areas with a higher density of occurrences are likely included in the actual species distribution. Therefore, a raster layer will be created containing the Gaussian values based on the density of occurrences of a species. The standard deviation of the Gaussian distribution was the maximum value in a vector of minimum distances between pairs of occurrences of a species. Gaussian values are normalized to vary from zero to one. This raster layer should be included as covariates with the environmental layers to construct species distribution models.
Further methodological and performance information of these methods see Mendes et al. (2020).
Mendes, P.; Velazco S.J.E.; Andrade, A.F.A.; De Marco, P. (2020) Dealing with overprediction in species distribution models: how adding distance constraints can improve model accuracy, Ecological Modelling, in press. https://doi.org/10.1016/j.ecolmodel.2020.109180
Allouche, O.; Steinitz, O.; Rotem, D.; Rosenfeld, A.; Kadmon, R. (2008). Incorporating distance constraints into species distribution models. Journal of Applied Ecology, 45(2), 599-609. doi:10.1111/j.1365-2664.2007.01445.x
Bahn, V.; Mcgill, B. J. (2007). Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16(6), 733-742. doi:10.1111/j.1466-8238.2007.00331.x
if (FALSE) {
library(MSDM)
library(raster)
# Raster data and a data.frame with occurrences will be loaded
data("sp_sdm")
data("occurrences")
head(occurrences)
# Some changes will be done on sp_sdm object to be used as example
plot(sp_sdm)
class(sp_sdm)
sp_sdm <- sp_sdm[[1]] # a layer of this RasterBrick will be selected
class(sp_sdm)
plot(sp_sdm)
tmdir <- tempdir()
tmdir # temporal directory where will be saves raster layers
# # XY method----
MSDM_Priori(
records = occurrences,
x = "x", y = "y", sp = "sp", method = "XY",
rasterlayer = sp_sdm, dirsave = tmdir
)
# open directory were raster were saved
rdir <- paste(tmdir, "MSDM_XY", sep = "/")
rdir
# plot results
new_var <- list.files(rdir, pattern = ".tif", full.names = TRUE)
new_var <- stack(new_var)
plot(new_var)
# CML method----
MSDM_Priori(
records = occurrences,
x = "x", y = "y", sp = "sp", method = "CML",
rasterlayer = sp_sdm, dirsave = tmdir
)
# open directory were raster were saved
rdir <- paste(tmdir, "MSDM_CML", sep = "/")
rdir
# shell.exec(rdir)
# plot results
new_var <- list.files(rdir, pattern = ".tif", full.names = TRUE)
new_var <- stack(new_var)
plot(new_var)
# Note that a raster is created for each species
}