We present here a simple model, with the simplest parameters and
decisions.
The main objective of this run is to introduce the main arguments and
help with some details which are required to run the function.
We will use the files available at the package example, so that
everyone can easily reproduce those steps!
ENMTML example data set consists of five bioclimantic variables for the current period and five different virtual species.
In order to simulate the files and folders needed for an standard ENMTML run we will create different folders containing the example data.
For this we will use the raster package:
if (!"raster"%in%installed.packages()){install.packages("raster")}
require(raster)
#> Loading required package: raster
#> Loading required package: sp
require(ENMTML)
#> Loading required package: ENMTML
#> Please note that rgdal will be retired during October 2023,
#> plan transition to sf/stars/terra functions using GDAL and PROJ
#> at your earliest convenience.
#> See https://r-spatial.org/r/2023/05/15/evolution4.html and https://github.com/r-spatial/evolution
#> rgdal: version: 1.6-7, (SVN revision 1203)
#> Geospatial Data Abstraction Library extensions to R successfully loaded
#> Loaded GDAL runtime: GDAL 3.6.2, released 2023/01/02
#> Path to GDAL shared files: C:/Users/santi/AppData/Local/R/win-library/4.3/rgdal/gdal
#> GDAL does not use iconv for recoding strings.
#> GDAL binary built with GEOS: TRUE
#> Loaded PROJ runtime: Rel. 9.2.0, March 1st, 2023, [PJ_VERSION: 920]
#> Path to PROJ shared files: C:/Users/santi/AppData/Local/R/win-library/4.3/rgdal/proj
#> PROJ CDN enabled: FALSE
#> Linking to sp version:2.1-1
#> To mute warnings of possible GDAL/OSR exportToProj4() degradation,
#> use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
#> Registered S3 methods overwritten by 'adehabitatMA':
#> method from
#> print.SpatialPixelsDataFrame sp
#> print.SpatialPixels sp
#> rgeos version: 0.6-4, (SVN revision 699)
#> GEOS runtime version: 3.11.2-CAPI-1.17.2
#> Please note that rgeos will be retired during October 2023,
#> plan transition to sf or terra functions using GEOS at your earliest convenience.
#> See https://r-spatial.org/r/2023/05/15/evolution4.html for details.
#> GEOS using OverlayNG
#> Linking to sp version: 2.1-1
#> Polygon checking: TRUE
# First we will create a folder (ENMTML_example) within the working directory
getwd() #' Working directory of R session
#> [1] "C:/Users/santi/Documents/GitHub/ENMTML_RStudio/vignettes"
d_ex <- file.path(getwd(), 'ENMTML_example')
d_ex
#> [1] "C:/Users/santi/Documents/GitHub/ENMTML_RStudio/vignettes/ENMTML_example"
dir.create(d_ex)
# We will now save ENMTML data set within the ENMTML_example folder
# Virtual species occurrences
data("occ")
# See the format of the .TXT file(tab-separated)!
# We only need three columns:
# one containing species name
# one containing longitude information
# one containing latitude information
knitr::kable(occ[c(1:4,43:46,90:94,127:130),])| species | x | y | |
|---|---|---|---|
| 1 | Sp_14 | -55.37526 | -31.708447 |
| 2 | Sp_14 | -54.20860 | -25.041806 |
| 3 | Sp_14 | -58.29191 | -29.125124 |
| 4 | Sp_14 | -57.29192 | -27.291798 |
| 43 | Sp_17 | -73.12519 | -15.041847 |
| 44 | Sp_17 | -70.87520 | -15.458512 |
| 45 | Sp_17 | -69.12520 | -17.708503 |
| 46 | Sp_17 | -70.45853 | -15.541844 |
| 90 | Sp_18 | -61.37523 | -26.625134 |
| 91 | Sp_18 | -61.29190 | -26.958465 |
| 92 | Sp_18 | -61.37523 | -23.125148 |
| 93 | Sp_18 | -61.04190 | -24.125144 |
| 94 | Sp_18 | -62.29190 | -26.458468 |
| 127 | Sp_34 | -67.29188 | -5.041887 |
| 128 | Sp_34 | -71.20853 | -7.458544 |
| 129 | Sp_34 | -68.20854 | -7.208545 |
| 130 | Sp_34 | -68.95854 | -4.291889 |
d_occ <- file.path(d_ex, 'occ.txt')
utils::write.table(occ, d_occ, sep = '\t', row.names = FALSE)
# Five bioclimatic variables for current conditions
data("env")
d_env <- file.path(d_ex, 'current_env_var')
dir.create(d_env)
raster::writeRaster(env, file.path(d_env, names(env)), bylayer=TRUE,
overwrite=TRUE, format='GTiff')
# shell.exec(d_ex) # open the directory and folders created
rm(list = c('env','occ'))
# We now we have the minimum data required to create models with ENMTML package!
# A directory with environmental rasters and a .txt file with occurrence.The following objects contains the path to the folder with predictors and the path to the occurrence file (TXT)
We will now fit models for with the following specifications:
require(ENMTML)
ENMTML(
pred_dir = d_env,
proj_dir = NULL,
result_dir = file.path(d_ex,"Result"),
occ_file = d_occ,
sp = 'species',
x = 'x',
y = 'y',
min_occ = 10,
thin_occ = NULL,
eval_occ = NULL,
colin_var = NULL,
imp_var = FALSE,
sp_accessible_area = NULL,
pseudoabs_method = c(method = 'RND'),
pres_abs_ratio = 1,
part=c(method= 'KFOLD', folds='2'),
save_part = FALSE,
save_final = TRUE,
algorithm = c('BIO','GLM', 'RDF', 'SVM', 'MXD'),
thr = c(type='MAX_TSS'),
msdm = NULL,
ensemble = NULL,
extrapolation = FALSE,
cores = 1
)ENMTML function will create a folder named Result within the ENMTML_example folder containing the Algorithms sub-folder and several .txt files:
We will see next several methodological advances for the Standard
model and how those are incorpoated in our modelling routine.
We will also cover projection for other periods, extent and include
other algorithms and produce and ensemble model!
Hope you understood the logic behind ENMTML and were able to produce your first models!
See you next time!
Feel free to contact us by mail (you can find the paths to André and Santiago e-mails at the end of the GitHub page)