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Marlon E. Cobos edited this page Mar 13, 2020 · 39 revisions

Marlon E. Cobos, Hannah L. Owens, and A. Townsend Peterson

Getting started

Preparing your data

Some of the main functions of nichevol use data that need to be loaded from a local directory and others produce results that need to be written in a local directory. Loading data from a local directory and writing the results outside the R environment helps to avoid problems related to RAM limitations. The figure below shows how the directory needs to be structured when working on a project considering multiple variables and species.


Figure 1. Suggested structure of data in a working directory for starting analyses, when more than one environmental variable needs to be considered. An example of how each file should look like is presented.


Setting a directory

The code below helps to set a working directory before starting analyses:

directory <- "DRIVE:/YOUR/DIRECTORY" # change the characters accordingly
setwd(directory)

Installing the package

Stable version

The stable version of nichevol is in CRAN and it can be installed using the code below:

install.packages("nichevol")

Latest version

The most recent version of nichevol is available at a GitHub repository and can be installed using the code below. Please, have in mind that updates will be done on this version continuously, which may introduce problems and instabilities.

Note: Try the code below first… If you have any problem during the installation, restart your R session, close other sessions you may have open, and try again. If during the installation you are asked to update packages, please do it. If any of the packages gives an error, please install it alone using install.packages(), then try re-installing nichevol again. Also, it may be a good idea to update R and RStudio (if you are using it).

# Installing and loading packages
if (!require(devtools)) {
    install.packages("devtools")
}
if (!require(nichevol)) {
    devtools::install_github("marlonecobos/nichevol")
}

An example using one variable

Loading the package

Once nichevol is installed you can load the package with the following line.

library(nichevol)

Example data

The following code helps in getting the variable mean annual temperature from the WorldClim database. The example data for species occurrences and spatial polygons representing their accessible areas will be loaded here. If you have your own data in your working directory you may want to load them using raster::raster() for a variable, rgdal::readOGR() for spatial polygons, and read.csv() for occurrences.

# getting temperature at coarse resolution
tmpd <- file.path(tempdir(), "bios") # temporal directory
dir.create(tmpd)
temp <- raster::getData("worldclim", var = "bio", res = 10, path = tmpd)[[1]]

# example data
data("m_list", package = "nichevol") # list of accessible areas
data("occ_list", package = "nichevol") # list of occurrence records

Exploring example data

Knowing how the data looks like is an important first step before running further analyses.

First, let's see some descriptive statistics of environmental values in the accessible areas and in the species occurrences.

# learn how to use the function
help(stats_eval)

# running stats
stat <- stats_eval(stats = c("mean", "sd", "median", "range"),
                   Ms = m_list, occurrences = occ_list, species = "species",
                   longitude = "x", latitude = "y", variable = temp)

# see stats
stat

#> $M_stats
#>   Species     mean       sd median range1 range2
#> 1 RD 9830 218.4736 55.10421    240     14    263
#> 2 RD 3351 244.6283 14.77594    249    175    272
#> 3 RD 6933 152.9771 76.81137    168     -1    269
#> 4 RD 761  222.2735 52.64984    238    -27    263
#> 5 RD 6773 245.2930 10.68543    248    160    269
#> 6 RD 7516 211.0188 78.19016    256    -31    288
#> 
#> $Occurrence_stats
#>   Species     mean       sd median range1 range2
#> 1 RD 9830 255.5667 4.279523    255    245    263
#> 2 RD 3351 254.9320 4.120879    254    244    266
#> 3 RD 6933 259.9392 5.271185    260    243    269
#> 4 RD 761  255.4200 4.279293    255    246    263
#> 5 RD 6773 250.1370 5.173714    250    240    268
#> 6 RD 7516 259.0946 6.296892    260    241    278

Now, let's produce a graphical representation of environmental values in the accessible areas and in the species occurrences. The plots are produced by species.

# learn how to use the function
help(hist_evalues)

# creating the plot for one species
hist_evalues(M = m_list[[1]], occurrences = occ_list[[1]], species = "species",
             longitude = "x", latitude = "y", variable = temp,
             CL_lines = c(95, 99), col = c("blue", "red"))

Preparing tables of ecological niche characters

The code below helps in creating tables of niche characters considering one environmental variable. Potential values of characters are: 0 = not present, 1 = present, and ? = uncertain.

# learn how to use the function
help(bin_table)

# preparing binned characters of niche for all species
bin_tab <- bin_table(Ms = m_list, occurrences = occ_list, species = "species",
                     longitude = "x", latitude = "y", variable = temp, 
                     percentage_out = 5, bin_size = 10)

# the table
bin_tab[, 1:8]

#>         20 to 30 31 to 40 41 to 50 51 to 60 61 to 70 71 to 80 81 to 90 91 to 100 
#> RD 9830 "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"       
#> RD 3351 "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"      
#> RD 6933 "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"       
#> RD 761  "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"       
#> RD 6773 "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"       
#> RD 7516 "0"      "0"      "0"      "0"      "0"      "0"      "0"      "0"
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