thermophilization

This repository includes the data and R scripts to reproduce analyses and figures found in the article Disturbances amplify tree community responses to climate change in the temperate-boreal ecotone by Brice, Cazelles, Legendre and Fortin in Global Ecology and Biogeography.

Installation

The analyses were carried out with R (a free software environment for statistical computing and graphics) and require the installation of a recent version of it.

Analyses were reproduced in two different environments:

1. MacOSX Mojave

R version 3.5.1 (2018-07-02) Platform: x86_64-apple-darwin18.0.0 (64-bit) Running under: macOS 10.14.4

Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib

locale: [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] graphicsutils_1.2-1 kableExtra_1.0.1 knitr_1.21 plotrix_3.7-4
[5] scales_1.0.0 RColorBrewer_1.1-2 effects_4.0-3 zoo_1.8-4
[9] gtools_3.8.1 car_3.0-2 carData_3.0-2 adespatial_0.3-2
[13] FD_1.0-12 geometry_0.3-6 magic_1.5-9 abind_1.4-5
[17] ape_5.2 ade4_1.7-13 vegan_2.5-4 lattice_0.20-35
[21] permute_0.9-4 raster_2.8-4 sp_1.3-1 sf_0.7-1
[25] stringr_1.3.1 reshape2_1.4.3 plyr_1.8.4 dplyr_0.7.8

loaded via a namespace (and not attached): [1] minqa_1.2.4 colorspace_1.4-0 seqinr_3.4-5 deldir_0.1-15
[5] class_7.3-14 rio_0.5.10 rprojroot_1.3-2 rstudioapi_0.9.0
[9] xml2_1.2.0 codetools_0.2-15 splines_3.5.1 nloptr_1.2.1
[13] phylobase_0.8.4 cluster_2.0.7-1 shiny_1.2.0 readr_1.1.1
[17] compiler_3.5.1 httr_1.3.1 backports_1.1.3 adegraphics_1.0-12 [21] assertthat_0.2.0 Matrix_1.2-14 lazyeval_0.2.1 survey_3.34
[25] later_0.7.5 htmltools_0.3.6 prettyunits_1.0.2 tools_3.5.1
[29] bindrcpp_0.2.2 igraph_1.2.2 coda_0.19-2 gtable_0.2.0
[33] glue_1.3.0 gmodels_2.18.1 Rcpp_1.0.0 cellranger_1.1.0
[37] spdep_0.7-9 gdata_2.18.0 nlme_3.1-137 xfun_0.4
[41] adephylo_1.1-11 rvest_0.3.2 openxlsx_4.1.0 lme4_1.1-19
[45] mime_0.6 XML_3.98-1.16 LearnBayes_2.15.1 MASS_7.3-50
[49] hms_0.4.2 promises_1.0.1 parallel_3.5.1 expm_0.999-3
[53] yaml_2.2.0 curl_3.2 ggplot2_3.1.0 latticeExtra_0.6-28 [57] stringi_1.2.4 e1071_1.7-0 boot_1.3-20 zip_1.0.0
[61] spData_0.2.9.4 rlang_0.3.1 pkgconfig_2.0.2 rncl_0.8.3
[65] evaluate_0.12 purrr_0.2.5 bindr_0.1.1 tidyselect_0.2.5
[69] magrittr_1.5 R6_2.3.0 DBI_1.0.0 pillar_1.3.1
[73] haven_1.1.2 foreign_0.8-70 mgcv_1.8-24 units_0.6-1
[77] survival_2.42-3 nnet_7.3-12 tibble_2.0.1 crayon_1.3.4
[81] uuid_0.1-2 KernSmooth_2.23-15 rmarkdown_1.10 progress_1.2.0
[85] RNeXML_2.2.0 adegenet_2.1.1 grid_3.5.1 readxl_1.1.0
[89] data.table_1.12.0 forcats_0.3.0 webshot_0.5.1 digest_0.6.18
[93] classInt_0.2-3 xtable_1.8-3 tidyr_0.8.2 httpuv_1.4.5
[97] munsell_0.5.0 viridisLite_0.3.0

2. Debian Buster

R version 3.5.2 (2018-12-20) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster)

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] raster_2.8-19 sp_1.3-1 kableExtra_1.1.0 knitr_1.23
[5] effects_4.1-0 FD_1.0-12 geometry_0.4.1 ape_5.3
[9] ade4_1.7-13 gtools_3.8.1 car_3.0-2 carData_3.0-2
[13] graphicsutils_1.3-0 scales_1.0.0 RColorBrewer_1.1-2 sf_0.7-3
[17] zoo_1.8-4 vegan_2.5-4 lattice_0.20-38 permute_0.9-5
[21] adespatial_0.3-4 reshape2_1.4.3 dplyr_0.8.0.1

loaded via a namespace (and not attached): [1] minqa_1.2.4 colorspace_1.4-0 seqinr_3.4-5 deldir_0.1-16
[5] class_7.3-15 rio_0.5.16 estimability_1.3 rstudioapi_0.9.0
[9] xml2_1.2.0 codetools_0.2-16 splines_3.5.2 nloptr_1.2.1
[13] phylobase_0.8.6 cluster_2.0.7-1 shiny_1.2.0 readr_1.3.1
[17] compiler_3.5.2 httr_1.4.0 adegraphics_1.0-15 assertthat_0.2.0
[21] Matrix_1.2-17 lazyeval_0.2.2 survey_3.35-1 later_0.8.0
[25] htmltools_0.3.6 prettyunits_1.0.2 tools_3.5.2 igraph_1.2.4.1
[29] coda_0.19-2 gtable_0.2.0 glue_1.3.0 gmodels_2.18.1
[33] Rcpp_1.0.0 cellranger_1.1.0 spdep_1.1-2 gdata_2.18.0
[37] nlme_3.1-137 xfun_0.7 stringr_1.4.0 adephylo_1.1-11
[41] rvest_0.3.2 openxlsx_4.1.0 lme4_1.1-21 mime_0.6
[45] XML_3.98-1.19 LearnBayes_2.15.1 MASS_7.3-51.1 hms_0.4.2
[49] promises_1.0.1 parallel_3.5.2 expm_0.999-3 curl_3.3
[53] ggplot2_3.1.0 latticeExtra_0.6-28 stringi_1.4.3 highr_0.7
[57] plotrix_3.7-4 e1071_1.7-0.1 boot_1.3-20 zip_2.0.2
[61] spData_0.3.0 rlang_0.3.1 pkgconfig_2.0.2 rncl_0.8.3
[65] evaluate_0.13 purrr_0.3.2 tidyselect_0.2.5 plyr_1.8.4
[69] magrittr_1.5 R6_2.4.0 DBI_1.0.0 pillar_1.3.1
[73] haven_2.1.0 foreign_0.8-71 mgcv_1.8-27 units_0.6-2
[77] survival_2.43-3 abind_1.4-5 nnet_7.3-12 tibble_2.1.1
[81] crayon_1.3.4 uuid_0.1-2 KernSmooth_2.23-15 rmarkdown_1.12
[85] progress_1.2.0 RNeXML_2.3.0 adegenet_2.1.1 grid_3.5.2
[89] readxl_1.3.1 data.table_1.12.0 forcats_0.4.0 webshot_0.5.1
[93] digest_0.6.18 classInt_0.3-1 xtable_1.8-3 tidyr_0.8.3
[97] httpuv_1.5.1 munsell_0.5.0 viridisLite_0.3.0 magic_1.5-9

The following packages must be installed:

• dplyr
• plyr
• reshape2
• stringr
• sf
• sp
• raster
• vegan
• FD
• adespatial
• car
• gtools
• zoo
• effects
• RColorBrewer
• scales
• plotrix
• graphicsutils (not on CRAN)
• knitr
• kableExtra

Below are the R commands to install them all:

install.packages(
  "dplyr", "plyr", "reshape2", "stringr", "sf", "sp", "raster", "vegan", "FD",
  "adespatial", "car", "gtools", "zoo", "effects", "RColorBrewer", "scales",
  "plotrix", "knitr", "kableExtra", "remotes"
)
remotes::install_github("inSileco/graphicsutils")

Guidelines

In short

To reproduce the entire analysis including the data retrieval / data cleaning steps, run

lapply(list.files("scripts", full.names=TRUE), source)

Note that data retrieval step will take a fair amount of time. Fortunately, cleaned data are included in the data folder and the first steps can be skipped:

lapply(list.files("scripts", full.names=TRUE)[-c(1:3)], source)

Comments

Several steps required for data preparation were performed using scripts available in the Quebec_data repository. The rest of the data retrieval / data cleaning operations are performed directly in this repository:

1. scripts/0_retrieve_worldclim_data.R retrieves Worldclim data (annual temperature only, this represents >1GB of data and takes a fair amount of time to be downloaded but are required to compute the community temperature index (CTI), i.e. to run scripts/2_computeCTI.R

2. Once the Worldclim data are downloaded, you can reproduce all the steps to tidy data:

source("scripts/1_dataFormatting.R")
source("scripts/2_computeCTI.R")

3. Then, all analyses and figures of the main text (as well as fig. S6) are obtained as follow:

source("scripts/3_beta_trends.R")
source("scripts/4_computeCTI.R")
source("scripts/5_computeCTI.R")

4. Finally, run the following scripts to obtain supplementary figures and table S1:

source("scripts/fig_S1_clim_trend.R")
source("scripts/fig_S2_disturb.R")
source("scripts/fig_S4_species_change.R")
source("scripts/fig_S5_ternary.R")
source("scripts/table_S1.R")

Figures and tables are saved in ms/figures.