Marlon E. Cobos, Luis Osorio-Olvera, Jorge Soberón, Vijay Barve, Narayani Barve, and A. Townsend Peterson
This repository is for the project “Grinnellian ecological niches and ellipsoids in R” developed during the program GSoC 2019.
Student: Marlon E. Cobos
GSoC Mentors: Luis Osorio-Olvera, Vijay Barve, and Narayani Barve
Motivation:
Ecological niche modeling represents a set of methodological tools that allow researchers to characterize and analyze species ecological niches. Currently, these methods are used widely and their applications include disease risk mapping, climate change risk predictions, conservation biology, among others. Physiological data suggests that Grinnellian ecological niches are convex in nature and they may probably have an ellipsoidal form when multiple dimensions are considered. However, among the diverse available software to characterize ecological niches, algorithms to model these niches as ellipsoids in the environmental space are scarce. Given the need for more solutions, the ellipsenm package aimed for developing specialized tools to perform multiple analyses related to ecological niches of species.
At the moment we have completed the three phases of the project. We have made few modifications to the list of original products that have helped us to improve the package functionality. The package is fully functional but improvements can be done and in the future, they will be included. Future improvements will include: options to allow reproducibility, production of HTML reports of other main analyses, and generic methods for summarizing and plotting resultant objects. Future plans also include submitting the package to CRAN.
All commits made can be seen at the complete list of commits.
Following you can find a brief description of this R package, as well as some examples of its use (still in development).
The ellipsenm R package implements multiple tools to help in using ellipsoid envelopes to model ecological niches of species. Handly options for calibrating and selecting models, producing models with replicates and projections, and assessing niche overlap are included as part of this package. Other functions implemented here are useful to perform a series of pre- and post-modeling analyses.
ellipsenm is in a GitHub repository and can be installed and/or loaded using the code below (make sure to have Internet connection). One of the functions to evaluate model performance in this package needs compilation. That is why you must install a compilation tools before installing the package, Rtools for Windows or other tools in other Operative Systems. A guide for downloading and installing Rtools can be found here. IMPORTANT note: Add Rtools to the PATH during its installation.
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 (select the option that says All). If any of the packages gives an error, please install it alone using install.packages(), then try re-installing ellipsenm 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(ellipsenm)){
devtools::install_github("marlonecobos/ellipsenm")
}
library(ellipsenm)The main functions of the ellipsenm package produce results that need to be written in a directory in your computer. Writing the results outside the R environment helps to avoid problems related to RAM limitations. That is why, setting a working directory is recommended before starting. You ca do that using the code below:
setwd("Drive:/Your/Directory") # change the characters accordinglyAn ecological niche, from a Grinnellian perspective, is the set of environmental conditions that allow a species to maintain populations for long periods of time, without immigration events. Models created with the ellipsenm package are ellipsoid envelope models and assume that a species ecological niche is convex, has an only optimum, and the species response to each variable covaries with the response to other variables. Mahalanobis distances are used to represent how far is each combination of environmental conditions from the optimum (ellipsoid centroid). Suitability values result by default from a multivariate normal transformation of Mahalanobis distances. Therefore, maximum values of suitability will be close to the centroid and minimum values will be close to the border of the ellipsoid.
A complete list of the main functions in the ellipsenm package can be found in the package documentation. Use the following code to see the list.
help(ellipsenm)Model calibration is a process in which various candidate models, created with distinct (coherent) parameter settings, are tested based on metrics that reflect their performance. After that models the best models are selected, based on user defined selection criteria, to represent the phenomenon of interest. The function ellipsoid_calibration will help to create candidate models, evaluate them, and select the best according to the selected criteria. The performance of models in this process is assessed based on statistical significance (partial ROC), predictive power (omission rates), and prevalence. Where good models are the ones statistically significant, with low omission rates, and with low prevalence.
We encourage the users to check the function’s help before using it. This is possible using the code below:
help(ellipsoid_calibration)The code below helps users to perform a small exercise using various functions to prepare the data and to perform the model calibration process.
# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
package = "ellipsenm"))
colnames(occurrences)
# raster layers of environmental data (this ones are masked to the accessible area)
# users must prepare their layers accordingly if using other data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
pattern = "bio", full.names = TRUE))
# preparing training and testing data
data_split <- split_data(occurrences, method = "random", longitude = "longitude",
latitude = "latitude", train_proportion = 0.75,
save = TRUE, name = "occ")
# sets of variables (example)
sets <- list(set_1 = c("bio_1", "bio_7", "bio_15"),
set_2 = c("bio_1", "bio_12", "bio_15")) # change as needed
variable_sets <- prepare_sets(vars, sets)
# methods to create ellipsoids
methods <- c("covmat", "mve1")
# model calibration process
calib <- ellipsoid_calibration(data_split, species = "species", longitude = "longitude",
latitude = "latitude", variables = variable_sets,
methods = methods, level = 99, selection_criteria = "S_OR_P",
error = 5, iterations = 500, percentage = 50,
output_directory = "calibration_results")
class(calib)
# check your directory, folder "calibration_results"Once you have decided what are the best parameter settings for your models either using a model calibration process or other approach, your final models can be produced. This models will help to represent the ecological niche of a species in environmental and geographic space. The function ellipsoid_model will help to perform all necessary analyses.
Please check the function’s help with the code below:
help(ellipsoid_model)Now the examples. Three distinct ways to create ecological niche models using ellipsenm are presented below:
Creating a simple ecological niche model
When models are created this way, the whole dataset is used for fitting the ellipsoids.
# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
package = "ellipsenm"))
# raster layers of environmental data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
pattern = "bio", full.names = TRUE))
# creating the model
ell_model <- ellipsoid_model(data = occurrences, species = "species",
longitude = "longitude", latitude = "latitude",
raster_layers = vars, method = "covmat", level = 99,
replicates = 1, prediction = "suitability",
return_numeric = TRUE, format = "GTiff",
overwrite = FALSE, output_directory = "ellipsenm_model")
class(ell_model)
# check your directory, folder "ellipsenm_model"Modeling an ecological niche with replicates
When models are replicated, sub-samples of the data are used to create each replicate. Mean, minimum, and maximum ellipsoid models are also calculated. See more details about sub-sampling in the function’s help.
# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
package = "ellipsenm"))
# raster layers of environmental data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
pattern = "bio", full.names = TRUE))
# creating the model with replicates
ell_model1 <- ellipsoid_model(data = occurrences, species = "species",
longitude = "longitude", latitude = "latitude",
raster_layers = vars, method = "covmat", level = 99,
replicates = 5, prediction = "suitability",
return_numeric = TRUE, format = "GTiff",
overwrite = FALSE, output_directory = "ellipsenm_model1")
class(ell_model1)
# check your directory, folder "ellipsenm_model1"Example of an ecological niche model with projections
Ellipsoid envelope models can also be projected to other scenarios. This is one example using a RasterStack of layers. Projections can be done to multiple scenarios in same modeling process by changing the type of projection_variables. Check the function’s help for more details.
# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
package = "ellipsenm"))
# raster layers of environmental data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
pattern = "bio", full.names = TRUE))
# creating the model with projections
pr_vars <- raster::stack(system.file("extdata", "proj_variables.tif",
package = "ellipsenm"))
names(pr_vars) <- names(vars)
ell_model2 <- ellipsoid_model(data = occurrences, species = "species",
longitude = "longitude", latitude = "latitude",
raster_layers = vars, method = "covmat", level = 99,
replicates = 3, replicate_type = "bootstrap",
bootstrap_percentage = 75,
projection_variables = list(projection = pr_vars),
prediction = "suitability", return_numeric = TRUE,
format = "GTiff", overwrite = FALSE,
output_directory = "ellipsenm_model2")
class(ell_model2)
# check your directory, folder "ellipsenm_model2"Niche overlap analyses that can be performed in ellipsenm allow to compare the degree of overlap between ecological niches in environmental space. The function ellipsoid_overlap helps is performing needed analyses. To plot results use the function plot_overlap.
Please check the function’s help with the code below:
help(ellipsoid_overlap)The code below helps to prepare data and perform an example of overlap analysis.
# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
package = "ellipsenm"))
# raster layers of environmental data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
pattern = "bio", full.names = TRUE))
# preparing data
vext <- raster::extent(vars)
ext1 <- raster::extent(vext[1], (mean(vext[1:2]) + 0.2), vext[3:4])
ext2 <- raster::extent((mean(vext[1:2]) + 0.2), vext[2], vext[3:4])
# croping variables and splitting occurrences
vars1 <- raster::stack(raster::crop(vars, ext1))
vars2 <- raster::stack(raster::crop(vars, ext2))
occurrences1 <- occurrences[occurrences$longitude < (mean(vext[1:2]) + 0.2),]
occurrences2 <- occurrences[!occurrences$longitude %in% occurrences1$longitude,]
# preparing overlap objects to perform analyses
niche1 <- overlap_object(occurrences1, species = "species", longitude = "longitude",
latitude = "latitude", method = "covmat", level = 95,
variables = vars1)
niche2 <- overlap_object(occurrences2, species = "species", longitude = "longitude",
latitude = "latitude", method = "covmat", level = 95,
variables = vars2)
# niche overlap analysis
overlap <- ellipsoid_overlap(niche1, niche2)
# niche overlap analysis with test of significance
overlap_st <- ellipsoid_overlap(niche1, niche2, overlap_type = "back_union",
significance_test = TRUE, replicates = 100)Use the following lines of code for plotting results using distinct options.
# plotting only ellipsoids
plot_overlap(overlap)
# plotting ellispodis and background for full overlap
plot_overlap(overlap, background = TRUE, proportion = 0.6, background_type = "full")
# plotting ellispodis and background for overlap based on accessible environments
plot_overlap(overlap, background = TRUE, proportion = 1, background_type = "back_union")Detailed examples for other functions of the package can be found in their respective documentation.