README.md

In nichevol: Tools for Ecological Niche Evolution Assessment Considering Uncertainty

nichevol: Tools for Ecological Niche Evolution Assessment Considering Uncertainty ================ Marlon E. Cobos, Hannah L. Owens, and A. Townsend Peterson

• Package description
• Installing the package
• Stable version
• Latest version
• Exploring the nichevol package
• Setting a directory
• Loading the package
• Functions in nichevol
• Using nichevol with simple examples
• Packages needed for data management
• Initial data (example data)
• Preparing data for analyses
• Ancestral reconstructions and smoothing of results
• Representations of results
• References

Package description

The nichevol R package helps users to perform critical steps in the process of assessment of species’ ecological niche evolution, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risks of overestimation of amounts or rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, comparative phylogenetic analyses, and plotting for interpretation.

Installing the package

Stable version

The stable version of nichevol is in CRAN, and can be installed using the code below (we are working on this):

install.packages("nichevol")

Latest version

The most recent version of nichevol is available from this GitHub repository and can be installed using the code below. Please, have in mind that updates will be done on this version continuously.

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 this last).

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

Exploring the nichevol package

Setting a directory

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 to 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. That is why setting a working directory is recommended before starting, as follows:

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

Loading the package

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

library(nichevol)

Functions in nichevol

Three main types of functions are included in nichevol: (1) ones that help in preparing data (exploration plots and tables of characters) for ancestral reconstruction; (2) ones that perform the ancestral reconstructions (maximum parsimony and maximum likelihood); and (3) some complementary functions that help in performing post-reconstruction steps (reconstruction smoothing, and niche and niche evolution representations). Of course, other helper functions are used in the package, but they won’t be used as commonly.

A complete list of the functions in the nichevol package can be found in the package documentation. Use the following code to see the list.

help(nichevol)

Functions for data preparation

These functions are used to explore numerically and graphically the environments of the areas accessible to the species (M) and their occurrences. They also help users to prepare tables of characters that represent species’ ecological niches considering used and non-used conditions, as well as conditions where the use is uncertain. Most of the functions in this module can consider all species of interest and multiple environmental variables at the time. For that reason, they read data from a local directory and have the option to write results to such directories as well. The functions that work with data from the R environment are the ones specifically designed to work with multiple species but only one variable. These last functions do not write results to local directories. We have intentionally designed some of our functions to work interacting with local directories to avoid RAM-related limitations (especially when working with multiple environmental raster layers at high resolution).

Functions for ancestral reconstruction

This module contains functions that help in performing ancestral reconstruction of species’ ecological niches. These functions use as inputs the results of the ones from the previous module (tables of characters) and phylogenetic trees, as in objects of class “phylo” (see the package ape). There are two types of reconstructions available to date (maximum parsimony and maximum likelihood), but at least one other type will be included. All these functions use inputs and produce outputs in the R environment.

Functions for post-reconstruction processes

Functions in this module are intended to help with two main processes. First, one of these functions helps in smoothing results from ancestral reconstructions. This is necessary to prevent misinterpretations of results from comparing reconstructed niches of ancestors with niches of descendants. Other functions help in representing results of previous analyses. For instance, they help in producing bar-like labels that represent the niches of species, or they can be used to represent how niches have evolved across the phylogeny.

Using nichevol with simple examples

Packages needed for data management

The following packages are needed for specific tasks. They are used internally by nichevol, and parts of the code for the examples below will require them. Notice that nichevol is already loaded, but these other packages need to be loaded separately.

library(terra) # for reading environmental layers and spatial objects
library(ape) # for plotting phylogenetic trees and node labels
library(geiger) # for merging a phylogenetic tree with a table of niche characters

Initial data (example data)

The following lines of code will help to get example data prepared for demonstrating the usage of nichevol. These data are similar to the ones used in an article in which the methods implemented in nichevol were proposed, illustrated, and explained (see Owens et al. 2020). These data are included in the package, so their use is straightforward.

## list of species records
data("occ_list", package = "nichevol")

## list of species accessible areas
m_files <- list.files(system.file("extdata", package = "nichevol"),
                      pattern = "m\\d.gpkg", full.names = TRUE)

m_list <- lapply(m_files, terra::vect)

## raster variable
temp <- rast(system.file("extdata", "temp.tif", package = "nichevol"))

# a simple phylogenetic tree for demonstrations
data("tree", package = "nichevol")

Preparing data for analyses

Before starting to play with the functions, consider that nichevol allows distinct ways to prepare data, depending on the user’s needs. The example data downloaded before can be used with the functions designed to work with multiple variables and all taxa at a time (histograms_env, stats_evalues, bin_tables, bin_tables0). However, examples with the functions that work with data in the R environment and only for one variable at a time are shown in detail here.

Exploring data numerically

First check the function documentation:

help(stats_eval)

Now, to run the code,

stat <- stats_eval(stats = c("mean", "sd", "median", "range", "quantile"), 
                   Ms = m_list, occurrences = occ_list, species = "species",
                   longitude = "x", latitude = "y", variable = temp, 
                   percentage_out = 0)

knitr::kable(stat[[1]], caption = "Table of descriptive statistics of temperature (x 10) in accessible areas for the species of interest.", digits = 2) # %>% kable_styling(font_size = 12)

Table of descriptive statistics of temperature (x 10) in accessible areas for the species of interest. Species mean sd median range1 range2 quantile.0. quantile.25. quantile.50. quantile.75. quantile.100. RD 9830 21.84 5.54 23.81 1.56 27.01 1.56 21.69 23.81 25.35 27.01 RD 3351 24.36 1.65 24.84 17.56 27.01 17.56 23.45 24.84 25.54 27.01 RD 6933 15.67 7.47 17.53 -0.01 27.16 -0.01 8.49 17.53 21.51 27.16 RD 761 22.28 5.35 23.71 -3.20 27.01 -3.20 22.52 23.71 25.21 27.01 RD 6773 25.00 1.05 25.14 16.67 27.01 16.67 24.41 25.14 25.74 27.01 RD 7516 20.95 7.68 25.12 -3.72 28.91 -3.72 16.40 25.12 26.07 28.91

knitr::kable(stat[[2]], caption = "Table of descriptive statistics of temperature (x 10) in occurrences of the species of interest.", digits = 2) #%>% kable_styling(font_size = 12)

Table of descriptive statistics of temperature (x 10) in occurrences of the species of interest. Species mean sd median range1 range2 quantile.0. quantile.25. quantile.50. quantile.75. quantile.100. RD 9830 25.67 0.54 25.59 23.79 27.01 23.79 25.36 25.59 26.02 27.01 RD 3351 25.49 0.67 25.54 23.67 27.00 23.67 25.08 25.54 25.90 27.00 RD 6933 25.95 0.73 25.91 24.47 27.11 24.47 25.35 25.91 26.72 27.11 RD 761 25.59 0.59 25.59 24.02 26.89 24.02 25.21 25.59 26.02 26.89 RD 6773 25.34 0.69 25.41 23.61 26.98 23.61 24.82 25.41 25.83 26.98 RD 7516 25.78 0.65 25.91 24.09 27.72 24.09 25.31 25.91 26.14 27.72

To work with multiple variables check the function stats_evalues.

Exploring data graphically

First check the help:

help(hist_evalues)

Now, to run the code,

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"))

To work with multiple variables check the function histograms_env.

Preparing tables of ecological niche characters

First check the help:

help(bin_table)

Now, to run the code,

bin_tabl <- bin_table(Ms = m_list, occurrences = occ_list, species = "species",
                      longitude = "x", latitude = "y", variable = temp, 
                      percentage_out = 5, n_bins = 20)

knitr::kable(bin_tabl, caption = "Table characters for ecological niches of the species of interest.") #%>% kable_styling(font_size = 12)

Table characters for ecological niches of the species of interest. 3.93 to 5.179 5.179 to 6.428 6.428 to 7.677 7.677 to 8.926 8.926 to 10.175 10.175 to 11.424 11.424 to 12.673 12.673 to 13.922 13.922 to 15.171 15.171 to 16.42 16.42 to 17.669 17.669 to 18.918 18.918 to 20.167 20.167 to 21.416 21.416 to 22.665 22.665 to 23.914 23.914 to 25.163 25.163 to 26.412 26.412 to 27.661 27.661 to 28.91 RD 9830 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 ? RD 3351 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 6933 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 6773 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 7516 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1

To work with multiple variables check the functions bin_tables0 and bin_tables.

Ancestral reconstructions and smoothing of results

These functions work with one variable at the time, but users can perform several analyses in a loop if needed. The variable to be explored here is temperature.

Phylogenetic tree and data

With the following code, the phylogenetic tree will be plotted, and its nodes will be added.

# exploring tree
tree$tip.label <- rownames(bin_tabl)
plot.phylo(tree, label.offset = 0.04)
nodelabels()

Maximum parsimony reconstruction

First check the help:

help(bin_par_rec)
help(smooth_rec)

Now, to run the code,

# tree and data together
tree_data <- treedata(tree, bin_tabl)

# reconstruction
par_rec_table <- bin_par_rec(tree_data)

# smoothing
s_par_rec_table <- smooth_rec(par_rec_table)

# results
knitr::kable(s_par_rec_table, caption = "Table characters for ecological niches of the species of interest and maximum parsimony reconstructions for their ancestors.") #%>% kable_styling(font_size = 12)

Table characters for ecological niches of the species of interest and maximum parsimony reconstructions for their ancestors. 3.93 to 5.179 5.179 to 6.428 6.428 to 7.677 7.677 to 8.926 8.926 to 10.175 10.175 to 11.424 11.424 to 12.673 12.673 to 13.922 13.922 to 15.171 15.171 to 16.42 16.42 to 17.669 17.669 to 18.918 18.918 to 20.167 20.167 to 21.416 21.416 to 22.665 22.665 to 23.914 23.914 to 25.163 25.163 to 26.412 26.412 to 27.661 27.661 to 28.91 RD 9830 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 ? RD 3351 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 6933 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 6773 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 7516 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 ? 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 ? 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 ? 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 ?

Maximum likelihood reconstruction

First check the help:

help(bin_ml_rec)

Now, to run the code,

# reconstruction
ml_rec_table <- bin_ml_rec(tree_data)

# smoothing
s_ml_rec_table <- smooth_rec(ml_rec_table)

# results
knitr::kable(s_ml_rec_table, caption = "Table characters for ecological niches of the species of interest and maximum likelihood reconstructions for their ancestors.", digits = 2) #%>% kable_styling(font_size = 12)

Table characters for ecological niches of the species of interest and maximum likelihood reconstructions for their ancestors. 3.93 to 5.179 5.179 to 6.428 6.428 to 7.677 7.677 to 8.926 8.926 to 10.175 10.175 to 11.424 11.424 to 12.673 12.673 to 13.922 13.922 to 15.171 15.171 to 16.42 16.42 to 17.669 17.669 to 18.918 18.918 to 20.167 20.167 to 21.416 21.416 to 22.665 22.665 to 23.914 23.914 to 25.163 25.163 to 26.412 26.412 to 27.661 27.661 to 28.91 RD 9830 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 ? RD 3351 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 6933 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 RD 6773 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 RD 7516 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 1 1 1 ? LogLik NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA -3.46573590313359 NA NA NA -5.49306144388884 Rates NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 855.964596564529 NA NA NA 557.653395948505 SE NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1658231.13989086 NA NA NA 62372.4765130361

Representations of results

Ecological niches of species on the phylogeny

plot.phylo(tree, label.offset = 0.04)
niche_labels(tree, s_par_rec_table, label_type = "tip", height = 0.8, width = 1.5)

Reconstructed ecological niches of ancestors

plot.phylo(tree, label.offset = 0.04)
niche_labels(tree, s_par_rec_table, label_type = "tip_node", height = 0.8, width = 1.5)

Evolution of ecological niches in the group

plot.phylo(tree, label.offset = 0.04)
niche_labels(tree, s_par_rec_table, label_type = "tip", height = 0.8, width = 1.5)
nichevol_labels(tree, s_par_rec_table, height = 0.8, width = 1.5)

A more informative plot

par(mfrow = c(1, 2))
plot.phylo(tree, label.offset = 0.04)
niche_labels(tree, s_par_rec_table, label_type = "tip_node", height = 0.8, width = 1.5)
niche_legend(position = "topleft", cex = 0.6)

plot.phylo(tree, label.offset = 0.04)
niche_labels(tree, s_par_rec_table, label_type = "tip", height = 0.8, width = 1.5)
nichevol_labels(tree, s_par_rec_table, height = 0.8, width = 1.5)
nichevol_legend(position = "topleft", cex = 0.6)

Mapping niches and evolution

Evolution occurred between node 9 and the species RD 6933. Let’s map and see how things look like in geography.

# preparing layers to represent niches and evolution
niche9 <- map_nichevol(whole_rec_table = s_par_rec_table, variable = temp, 
                       return = "niche", from = "9")
nichesp <- map_nichevol(whole_rec_table = s_par_rec_table, variable = temp, 
                        return = "niche", from = "RD 6933")

evol_8vssp <- map_nichevol(whole_rec_table = s_par_rec_table, variable = temp, 
                           return = "evolution", from = "9", to = "RD 6933")
nichevol_8vssp <- map_nichevol(whole_rec_table = s_par_rec_table, variable = temp, 
                               return = "nichevol", from = "9", to = "RD 6933")

par(mfrow = c(2, 2))
plot(niche9, main = "Niche node 9")
plot(nichesp, main = "Niche RD 6933")

plot(evol_8vssp, main = "Evolution 9 vs RD 6933")
plot(nichevol_8vssp, main = "Niche node 9, evolution to RD 6933")

References

• Barve, N., V. Barve, A. Jimenez-Valverde, A. Lira-Noriega, S. P. Maher, A. T. Peterson, J. Soberón, and F. Villalobos. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222:1810-1819.
• Machado-Stredel, F., M. E. Cobos, and A. T. Peterson. 2021. A simulation-based method for selecting calibration areas for ecological niche models and species distribution models. Frontiers of Biogeography, 13(4):e48814e.
• Owens, H. L., L. P. Campbell, L. L. Dornak, E. E. Saupe, N. Barve, J. Soberón, K. Ingenloff, A. Lira-Noriega, C. M. Hensz, C. E. Myers, and A. T. Peterson. 2013. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263:10-18.
• Owens, H. L., V. Ribeiro, E. E. Saupe, M. E. Cobos, P. A. Hosner, J. C. Cooper, A. M. Samy, V. Barve, N. Barve, C. J. Muñoz-R, A. T. Peterson. 2020. Acknowledging Uncertainty in Evolutionary Reconstructions of Ecological Niches. Ecology and Evolution 10(14):6967–6977.

nichevol documentation built on March 31, 2023, 5:38 p.m.