sTraitChange

The goal of sTraitChange is to provide the functions needed to reproduce the analyses for the manuscript ‘Changes in phenology mediate vertebrate population responses to climate globally’ by Radchuk et al. (submitted).

The package was conceived to perform analyses presented in the above-mentioned paper. Therefore, the package will only be updated if necessary to keep compatibility with other packages, so that the code represented here works well.

System requirements

Hardware requirements

The sTraitChange package requires only a standard computer with enough RAM to support the operations defined by a user. We recommend a computer with following specs:

• RAM: 16+ GB
• CPU: 4+ cores.

Software requirements

This package is supported for macOS and Windows operating systems. The development version of sTraitChange was tested on:

• macOS: Sequioa 15.0.1
• Windows:

Installation guide

The package depends on the following packages, that must be installed prior to installing the sTraitChange:

install.packages(c('climwin', 'dplyr', 'tibble', 'ggplot2', 'magrittr', 'metafor', 'broom', 'piecewiseSEM', 'purrr', 'spaMM', 'tidyr', 'tidyselect', 'ape', 'sp', 'lubridate', 'openxlsx', 'ggnewscale', 'psych', 'ggtext', 'raster', 'ggExtra', 'data.table'))

The versions of these packages with which the package sTraitChange was tested, are:

"ape 5.8"
"broom 1.0.7"
"climwin 1.2.3"
"data.table 1.16.2"
"dplyr 1.1.4"
"ggExtra 0.10.1"
"ggnewscale 0.5.0"
"ggplot2 3.5.1"
"ggtext 0.1.2"
"lubridate 1.9.3"
"magrittr 2.0.3"
"metafor 4.6.0"
"openxlsx 4.2.7.1"
"piecewiseSEM 2.3.0.1"
"psych 2.4.6.26"
"purrr 1.0.2"
"raster 3.6.30"
"sp 2.1.4"
"spaMM 4.5.0"
"tibble 3.2.1"
"tidyr 1.3.1"
"tidyselect 1.2.1"

You can install the development version of sTraitChange from GitHub with:

# install.packages("devtools")
devtools::install_github("radchukv/sTraitChange")

This should take about 20 seconds on the computer with the recommended specs.

Demo

To access the documentation on a specific function, type ‘?’ and a function name, for example:

?fit_SEM

The main functions of importance to the analyses performed for the paper are:

• climwin_proc(): runs the sliding window analyses on each specific study;
• fit_SEM(): fits the structural equation model to each study;
• fit_meta_phylo(): fits the meta-analytical (mixed-effects model that accounts for phylogenetic relatedness) for one specific SEM path as a response variable;
• fit_all_meta(): fits several meta-analytical models for several specified paths that will be used as response variables in the respective models.

And the main functions used for visualisation of the obtained results are:

• plot_concept(): plots the findings in an analogous way as expectations laid out in the conceptual figure;
• plot_hist_points(): plots the results of meta-analysis (i.e. global effect sizes) overlaid over the histogram of the study-specific effect sizes for specified paths from the SEM.

Here is an example of how SEM can be fitted to the 1st study in the dataset:

mod_SEM <- fit_SEM(biol_data = dataSEM, ID = 1,
                   out_SEM = 'output_forSEM',
                   DD = 'n_effectGR', weight = TRUE,
                   correlation = TRUE,
                   standardize = TRUE,
                   Trait = FALSE,
                   simpleSEM = TRUE)

And another example of how a meta-analysis that allows to account for phylogenetic relatedness can be fitted to the phenological response to temperature:

# prepare dataset, select only studies with phenological traits
Coefs_phenClim <- subset(dataPaths, 
                         Relation == 'Trait_mean<-det_Clim' & 
                           Trait_Categ =='Phenological')
# some standardization needed because phylogeny has different names for some species
Coefs_phenClim <- Coefs_phenClim %>%
                   dplyr::mutate(Species = dplyr::case_when(
                          Species == 'Cyanistes caeruleus' ~ 'Parus caeruleus',
                          Species == 'Thalasseus sandvicensis' ~ 'Sterna sandvicensis',
                          Species == 'Setophaga caerulescens' ~ 'Dendroica caerulescens',
                          Species == 'Thalassarche melanophris' ~ 'Thalassarche melanophrys',
                          Species == 'Ichthyaetus audouinii' ~ 'Larus audouinii',
                          Species == 'Stercorarius maccormicki' ~ 'Catharacta maccormicki',
                          TRUE ~ Species))
#  formatting sp names to have them exactly same way as on the phylogeny
Coefs_phenClim$Species <- unlist(lapply(1:nrow(Coefs_phenClim), FUN = function(x){
binary <- strsplit(as.character(Coefs_phenClim$Species[x]), " ")
Underscore <- paste(binary[[1]][1], binary[[1]][2], sep = "_")}))

# creating the object Sp_phylo that will account for the part in data variation
# purely due to phylogenetic relation
Coefs_phenClim$Sp_phylo <- Coefs_phenClim$Species

# fit meta-analysis
test_noCovar <- fit_meta_phylo(data_MA = Coefs_phenClim,
                               Type_EfS = 'Trait_mean<-det_Clim',
                               Cov_fact = NULL, COV = NULL,
                               DD = 'n_effectGR',
                               simpleSEM = TRUE,
                               A = phyloMat)

Instructions for use

The applications of all the functions is demonstrated in detail in the GitHub repo sTraitChange_Analyses that implements the full workflow required to reproduce the results presented in Radchuk et al. (submitted).
Please check the help of the required functions and the repository sTraitChange_Analyses for a specific type of the analyses you would like to apply on your data.

Support

For any further information, please contact [email protected].