Last updated: 2022-12-29
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Knit directory: freezing_cycles/
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points = data.frame(read_xlsx("./data/data_freezing.xlsx", sheet = "species_data"))[,1:3]# This script extracts how many months have the minimum temperature below zero and the maximum temperature above zero (from Bioclim rasters) and save it in a table.
# As the raw climatic rasters are very heavy (~ 10Gb), this code is not run, but only shown here.
# The tmin and tmax data rasters can be downloaded at: https://www.worldclim.org/data/monthlywth.html
tmin.list = list.files(path="./data/tmin",
pattern =".tif", full.names=TRUE)
tmin.stack = raster::stack(x=tmin.list)
tmins = data.frame(raster::extract(tmin.stack, points[,3:2]))
tmins = cbind(points$species_code, tmins)
tmins = gather(tmins, "month", "temp_min", 2:ncol(tmins))
colnames(tmins)[1] ="species"
tmins$month = substr(tmins$month,start=17, stop = 23)
tmax.list = list.files(path="./data/tmax",
pattern =".tif", full.names=TRUE)
tmax.stack = raster::stack(tmax.list)
tmax = data.frame(raster::extract(tmax.stack, points[,3:2]))
tmax = cbind(points$species_code, tmax)
tmax = gather(tmax, "month", "temp_max", 2:ncol(tmax))
colnames(tmax)[1] ="species"
tmax$month = substr(tmax$month,start=17, stop = 23)
temp_all = distinct(merge(tmins, tmax, by=c("species","month")))
low = (temp_all$temp_min<0) * (temp_all$temp_max<0) # not really necessary
mixed = (temp_all$temp_min<0) * (temp_all$temp_max>0)
high = (temp_all$temp_min>0) * (temp_all$temp_max>0)
month_class = rep("low", nrow(temp_all))
month_class[as.logical(mixed)] = "mixed"
month_class[as.logical(high)] = "high"
temp_all$month_class = month_class
sum_table = data.frame(table(temp_all$species, temp_all$month_class))
colnames(sum_table) = c("species_code", "type", "count")
sum_table = sum_table %>% filter(type == "mixed") %>% mutate(prop_mixed = count/108)
write.table(sum_table, "./data/mixedmonths_data.txt")bioclims_extracted = read.table("./data/mixedmonths_data.txt", header=T)data_merged = merge(read.table("./output/means_cycles.txt", header=T),bioclims_extracted)
data_merged$scaled_M = as.numeric(scale(data_merged$M))tree = read.tree("./data/tree.nwk")
tree = root(tree,"Milnesium_variefidum")
tree = chronos(tree)
tree = drop.tip(tree, tip = tree$tip.label[!(tree$tip.label %in% data_merged$species)])
class(tree) = "phylo"
data_merged = data_merged[data_merged$species %in% tree$tip.label,]phylo.matrix = vcv.phylo(tree)
inv.phylo.matrix = solve(phylo.matrix)
data_merged = data_merged[match(rownames(inv.phylo.matrix),data_merged$species),]
data.jags = list(M = data_merged$scaled_M,
mixprop = as.numeric(scale(data_merged$prop_mixed)),
inv.phylo.matix = inv.phylo.matrix,
nsp = nrow(inv.phylo.matrix),
zeros = rep(0, nrow(inv.phylo.matrix)))
# The model includes some truncated priors. As it is not possible to do it by specifying the modell as function in R, the moded is loaded as txt file.
parameters.jags = c("alpha","beta","sigma.phylo","sigma.res","marginalR2","conditionalR2","residualR2","phylogeneticR2")
ifelse("mod.env.rds" %in% list.files("./output"),{fit.env = readRDS("./output/mod.env.rds")},{
fit.env = jags(data = data.jags,
parameters.to.save = parameters.jags,
model.file = "./code/model.txt",
n.chains = 3, n.iter = 10000000)
write_rds(fit.env, file="./output/mod.env.rds")
})
# Make diagnostic plots (not showed but saved as pdf)
ggmcmc(ggs(as.mcmc(fit.env)), file="./output/model_fit.env.pdf", param_page=2)Download model file here:
Download mod.env.rdsDownload diagnostic plots here:
Download model_fit.env.pdfchains = data.frame(do.call(rbind,as.mcmc(fit.env)))
p_dir = p_direction(chains)
p_vals = pd_to_p(p_dir$pd)
p_vals[3:9] = rep(NA,7)
names(p_vals) = p_dir$Parameter
fit.env$BUGSoutput$summary %>% cbind(p_vals) %>% round(4) %>%datatable(class = 'cell-border stripe')The beta paramenter represent the effect of the proportion on “mixed” months on the resistance to freeze-thaw cycles. It is positive and significant, so the more the mixed months there are, the more the tardigrades are resistent.
sessionInfo()R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.utf8
[2] LC_CTYPE=English_United Kingdom.utf8
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xfun_0.34 ggmcmc_1.5.1.1 DT_0.26 patchwork_1.1.2
[5] R2jags_0.7-1 rjags_4-13 coda_0.19-4 bayestestR_0.13.0
[9] ape_5.6-2 raster_3.6-3 rgdal_1.6-2 sp_1.5-1
[13] readxl_1.4.1 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
[17] purrr_0.3.5 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[21] ggplot2_3.4.0 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] nlme_3.1-160 fs_1.5.2 lubridate_1.9.0
[4] RColorBrewer_1.1-3 insight_0.18.7 httr_1.4.4
[7] rprojroot_2.0.3 tools_4.2.1 backports_1.4.1
[10] bslib_0.4.1 utf8_1.2.2 R6_2.5.1
[13] R2WinBUGS_2.1-21 DBI_1.1.3 colorspace_2.0-3
[16] withr_2.5.0 GGally_2.1.2 tidyselect_1.2.0
[19] compiler_4.2.1 git2r_0.30.1 cli_3.4.1
[22] rvest_1.0.3 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.2 scales_1.2.1 digest_0.6.30
[28] rmarkdown_2.18 pkgconfig_2.0.3 htmltools_0.5.3
[31] dbplyr_2.2.1 fastmap_1.1.0 htmlwidgets_1.5.4
[34] rlang_1.0.6 rstudioapi_0.14 farver_2.1.1
[37] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.3
[40] crosstalk_1.2.0 googlesheets4_1.0.1 magrittr_2.0.3
[43] Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[46] abind_1.4-5 lifecycle_1.0.3 terra_1.6-17
[49] stringi_1.7.8 yaml_2.3.6 plyr_1.8.8
[52] grid_4.2.1 parallel_4.2.1 promises_1.2.0.1
[55] crayon_1.5.2 lattice_0.20-45 haven_2.5.1
[58] hms_1.1.2 knitr_1.41 pillar_1.8.1
[61] boot_1.3-28 codetools_0.2-18 reprex_2.0.2
[64] glue_1.6.2 evaluate_0.18 modelr_0.1.10
[67] vctrs_0.5.0 tzdb_0.3.0 httpuv_1.6.6
[70] cellranger_1.1.0 gtable_0.3.1 reshape_0.8.9
[73] assertthat_0.2.1 datawizard_0.6.4 cachem_1.0.6
[76] mime_0.12 broom_1.0.1 later_1.3.0
[79] googledrive_2.0.0 gargle_1.2.1 workflowr_1.7.0
[82] timechange_0.1.1 ellipsis_0.3.2