├── Ancillary └── Tallo logo.jpg ├── DB ├── Reference_look_up_table.csv ├── Tallo.csv.zip └── Tallo_metadata.csv ├── Jucker et al. 2022 ├── .DS_Store ├── R code to replicate case studies.R ├── Tallo_environment.csv.zip ├── Tallo_environment_metadata.csv ├── current_climate.csv.zip └── future_climate_ssp245.csv.zip ├── LICENSE.txt └── README.md /Ancillary/Tallo logo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Ancillary/Tallo logo.jpg -------------------------------------------------------------------------------- /DB/Reference_look_up_table.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/DB/Reference_look_up_table.csv -------------------------------------------------------------------------------- /DB/Tallo.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/DB/Tallo.csv.zip -------------------------------------------------------------------------------- /DB/Tallo_metadata.csv: -------------------------------------------------------------------------------- 1 | Field,Description 2 | tree_id,Unique tree identifier code 3 | division,Major phylogenetic division (Angiosperm or Gymnosperm) 4 | family,Family name 5 | genus,Genus name 6 | species,Species binomial name 7 | latitude,Latitude (in decimal degrees) 8 | longitude,Longitude (in decimal degrees) 9 | stem_diameter_cm,Stem diameter (in cm). For multi-stemmed trees values for individual stems (Di) were pooled into a single value calculated as: sqrt(sum(Di^2)) 10 | height_m,Tree height (in m) 11 | crown_radius_m,Crown radius (in m) 12 | height_outlier,Identifier for trees with height values flagged as outliers (Y = outlier; N = non-outlier) 13 | crown_radius_outlier,Identifier for trees with crown radius values flagged as outliers (Y = outlier; N = non-outlier) 14 | reference_id,Reference code corresponding to the data source from which a record was obtained (see 'Reference_look_up_table.csv' for details on data sources). 15 | -------------------------------------------------------------------------------- /Jucker et al. 2022/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Jucker et al. 2022/.DS_Store -------------------------------------------------------------------------------- /Jucker et al. 2022/R code to replicate case studies.R: -------------------------------------------------------------------------------- 1 | 2 | ######################################################################################################################## 3 | ######################################################################################################################## 4 | ##### ##### 5 | ##### Citation: Jucker et al. (2022). Tallo - a global tree allometry and crown architecture database. ##### 6 | ##### Global Change Biology ##### 7 | ##### ##### 8 | ##### This R code replicates the three case studies presented in Jucker et al. (2022). Please cite the ##### 9 | ##### original paper when using this code in your work. ##### 10 | ##### ##### 11 | ######################################################################################################################## 12 | ######################################################################################################################## 13 | 14 | # Load libraries ---------------------------------------------------------- 15 | 16 | library(dplyr) 17 | library(tidyr) 18 | library(lme4) 19 | library(merTools) 20 | library(MuMIn) 21 | 22 | # Case study 1 ------------------------------------------------------------ 23 | 24 | ## Load Tallo database and merge with associated environmental data 25 | setwd("C:/") 26 | Tallo<-read.csv("Tallo.csv",header=T, na.strings=c("NA")) 27 | Tallo_env<-read.csv("Tallo_environment.csv",header=T, na.strings=c("NA")) 28 | Tallo<-merge(Tallo,Tallo_env) 29 | 30 | ## Remove outliers (set values to NA and remove any trees with NA values for both height and crown radius) 31 | Tallo$height_m[Tallo$height_outlier=="Y"]<-NA 32 | Tallo$crown_radius_m[Tallo$crown_radius_outlier=="Y"]<-NA 33 | Tallo<-filter(Tallo, !is.na(height_m)|!is.na(crown_radius_m)) 34 | 35 | #### H:D scaling 36 | 37 | ## Subset data and assign tree from tropical rain forests with no taxonomic information as angiosperms 38 | Tallo_hd<-filter(Tallo,!is.na(height_m)) 39 | Tallo_hd$division<-ifelse(is.na(Tallo_hd$division)&Tallo_hd$biome=="Tropical rain forest","Angiosperm",Tallo_hd$division) 40 | Tallo_hd<-filter(Tallo,!is.na(division)) 41 | Tallo_hd$biome_division<-as.factor(paste(Tallo_hd$biome,Tallo_hd$division,sep="_")) 42 | table(Tallo_hd$biome_division) 43 | 44 | ## Fit model 45 | M<-lmer(log(height_m)~log(stem_diameter_cm)+(log(stem_diameter_cm)|biome_division),data=Tallo_hd) 46 | summary(M) 47 | r.squaredGLMM(M) 48 | coef(M) 49 | 50 | ## Calculate CIs for slopes 51 | slope_ci_h <- REsim(M, n.sims = 1000) 52 | slope_ci_h<-filter(slope_ci_h,term!="(Intercept)") 53 | slope_ci_h$slope_mean<-NA 54 | slope_ci_h$slope_2.5<-NA 55 | slope_ci_h$slope_97.5<-NA 56 | slope_ci_h$slope_10<-NA 57 | slope_ci_h$slope_90<-NA 58 | slope_random_draw<-rnorm(1000,coef(summary(M))[2,1],coef(summary(M))[2,2]) 59 | 60 | for (i in 1:dim(slope_ci_h)[1]){ 61 | 62 | ## Mean slope 63 | slope_ci_h$slope_mean[i]<-mean(slope_random_draw+rnorm(1000,slope_ci_h$mean[i],slope_ci_h$sd[i])) 64 | ## 2.5% slope 65 | slope_ci_h$slope_2.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_h$mean[i],slope_ci_h$sd[i]),0.025) 66 | ## 97.5% slope 67 | slope_ci_h$slope_97.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_h$mean[i],slope_ci_h$sd[i]),0.975) 68 | ## 10% slope 69 | slope_ci_h$slope_10[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_h$mean[i],slope_ci_h$sd[i]),0.1) 70 | ## 90% slope 71 | slope_ci_h$slope_90[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_h$mean[i],slope_ci_h$sd[i]),0.9) 72 | 73 | } 74 | 75 | ## Assign biome and division names 76 | names(slope_ci_h)[2]<-"biome_division" 77 | slope_ci_h$division<-gsub(".*_","",slope_ci_h$biome_division) 78 | slope_ci_h$biome<-substr(slope_ci_h$biome_division, 1, regexpr("\\_", slope_ci_h$biome_division)-1) 79 | 80 | ## Add mean aridity index value 81 | aridity_data<-Tallo_hd %>% 82 | dplyr::group_by(biome_division) %>% 83 | dplyr::summarise(aridity_index_m = mean(aridity_index)) 84 | slope_ci_h<-merge(slope_ci_h,aridity_data) 85 | 86 | ## Correlation between slope and aridity 87 | cor.test(slope_ci_h$slope_mean,slope_ci_h$aridity_index_m) 88 | 89 | #### CR:D scaling 90 | 91 | ## Subset data and assign tree from tropical rain forests with no taxonomic information as angiosperms 92 | Tallo_cr<-filter(Tallo,!is.na(crown_radius_m)) 93 | Tallo_cr$division<-ifelse(is.na(Tallo_cr$division)&Tallo_cr$biome=="Tropical rain forest","Angiosperm",Tallo_cr$division) 94 | Tallo_cr<-filter(Tallo,!is.na(division)) 95 | Tallo_cr$biome_division<-as.factor(paste(Tallo_cr$biome,Tallo_cr$division,sep="_")) 96 | table(Tallo_cr$biome_division) 97 | 98 | ## Fit model 99 | M<-lmer(log(crown_radius_m)~log(stem_diameter_cm)+(log(stem_diameter_cm)|biome_division),data=Tallo_cr) 100 | summary(M) 101 | r.squaredGLMM(M) 102 | coef(M) 103 | 104 | ## Calculate CIs for slopes 105 | slope_ci_cr <- REsim(M, n.sims = 1000) 106 | slope_ci_cr<-filter(slope_ci_cr,term!="(Intercept)") 107 | slope_ci_cr$slope_mean<-NA 108 | slope_ci_cr$slope_2.5<-NA 109 | slope_ci_cr$slope_97.5<-NA 110 | slope_ci_cr$slope_10<-NA 111 | slope_ci_cr$slope_90<-NA 112 | slope_random_draw<-rnorm(1000,coef(summary(M))[2,1],coef(summary(M))[2,2]) 113 | 114 | for (i in 1:dim(slope_ci_cr)[1]){ 115 | 116 | ## Mean slope 117 | slope_ci_cr$slope_mean[i]<-mean(slope_random_draw+rnorm(1000,slope_ci_cr$mean[i],slope_ci_cr$sd[i])) 118 | ## 2.5% slope 119 | slope_ci_cr$slope_2.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_cr$mean[i],slope_ci_cr$sd[i]),0.025) 120 | ## 97.5% slope 121 | slope_ci_cr$slope_97.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_cr$mean[i],slope_ci_cr$sd[i]),0.975) 122 | ## 10% slope 123 | slope_ci_cr$slope_10[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_cr$mean[i],slope_ci_cr$sd[i]),0.1) 124 | ## 90% slope 125 | slope_ci_cr$slope_90[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_cr$mean[i],slope_ci_cr$sd[i]),0.9) 126 | 127 | } 128 | 129 | ## Assign biome and division names 130 | names(slope_ci_cr)[2]<-"biome_division" 131 | slope_ci_cr$division<-gsub(".*_","",slope_ci_cr$biome_division) 132 | slope_ci_cr$biome<-substr(slope_ci_cr$biome_division, 1, regexpr("\\_", slope_ci_cr$biome_division)-1) 133 | 134 | ## Add mean aridity index value 135 | slope_ci_cr<-merge(slope_ci_cr,aridity_data) 136 | 137 | ## Correlation between slope and aridity 138 | cor.test(slope_ci_cr$slope_mean,slope_ci_cr$aridity_index_m) 139 | 140 | #### CR:H scaling 141 | 142 | ## Subset data and assign tree from tropical rain forests with no taxonomic information as angiosperms 143 | Tallo_crh<-filter(Tallo,!is.na(crown_radius_m)&!is.na(height_m)) 144 | Tallo_crh$division<-ifelse(is.na(Tallo_crh$division)&Tallo_crh$biome=="Tropical rain forest","Angiosperm",Tallo_crh$division) 145 | Tallo_crh<-filter(Tallo,!is.na(division)) 146 | Tallo_crh$biome_division<-as.factor(paste(Tallo_crh$biome,Tallo_crh$division,sep="_")) 147 | table(Tallo_crh$biome_division) 148 | 149 | ## Fit model 150 | M<-lmer(log(crown_radius_m)~log(height_m)+(log(height_m)|biome_division),data=Tallo_crh) 151 | summary(M) 152 | r.squaredGLMM(M) 153 | coef(M) 154 | 155 | ## Calculate CIs for slopes 156 | slope_ci_crh <- REsim(M, n.sims = 1000) 157 | slope_ci_crh<-filter(slope_ci_crh,term!="(Intercept)") 158 | slope_ci_crh$slope_mean<-NA 159 | slope_ci_crh$slope_2.5<-NA 160 | slope_ci_crh$slope_97.5<-NA 161 | slope_ci_crh$slope_10<-NA 162 | slope_ci_crh$slope_90<-NA 163 | slope_random_draw<-rnorm(1000,coef(summary(M))[2,1],coef(summary(M))[2,2]) 164 | 165 | for (i in 1:dim(slope_ci_crh)[1]){ 166 | 167 | ## Mean slope 168 | slope_ci_crh$slope_mean[i]<-mean(slope_random_draw+rnorm(1000,slope_ci_crh$mean[i],slope_ci_crh$sd[i])) 169 | ## 2.5% slope 170 | slope_ci_crh$slope_2.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_crh$mean[i],slope_ci_crh$sd[i]),0.025) 171 | ## 97.5% slope 172 | slope_ci_crh$slope_97.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_crh$mean[i],slope_ci_crh$sd[i]),0.975) 173 | ## 10% slope 174 | slope_ci_crh$slope_10[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_crh$mean[i],slope_ci_crh$sd[i]),0.1) 175 | ## 90% slope 176 | slope_ci_crh$slope_90[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci_crh$mean[i],slope_ci_crh$sd[i]),0.9) 177 | 178 | } 179 | 180 | ## Assign biome and division names 181 | names(slope_ci_crh)[2]<-"biome_division" 182 | slope_ci_crh$division<-gsub(".*_","",slope_ci_crh$biome_division) 183 | slope_ci_crh$biome<-substr(slope_ci_crh$biome_division, 1, regexpr("\\_", slope_ci_crh$biome_division)-1) 184 | 185 | ## Add mean aridity index value 186 | slope_ci_crh<-merge(slope_ci_crh,aridity_data) 187 | 188 | ## Correlation between slope and aridity 189 | cor.test(slope_ci_crh$slope_mean,slope_ci_crh$aridity_index_m) 190 | 191 | # Case study 2 ------------------------------------------------------------ 192 | 193 | ## Load Tallo database and merge with associated environmental data 194 | setwd("C:/") 195 | Tallo<-read.csv("Tallo.csv",header=T, na.strings=c("NA")) 196 | Tallo_env<-read.csv("Tallo_environment.csv",header=T, na.strings=c("NA")) 197 | Tallo<-merge(Tallo,Tallo_env) 198 | 199 | ## Remove outliers (set values to NA and remove any trees with NA values for both height and crown radius) 200 | Tallo$height_m[Tallo$height_outlier=="Y"]<-NA 201 | Tallo$crown_radius_m[Tallo$crown_radius_outlier=="Y"]<-NA 202 | Tallo<-filter(Tallo, !is.na(height_m)|!is.na(crown_radius_m)) 203 | 204 | ## Filter Tallo and create a unique site x species identifier 205 | Tallo_hd<-filter(Tallo,!is.na(species)&!is.na(height_m)) 206 | Tallo_hd$sp_site<-paste(round(Tallo_hd$latitude,2),round(Tallo_hd$longitude,2),Tallo_hd$species,sep="_") 207 | Tallo_hd$sp_site<-factor(Tallo_hd$sp_site) 208 | 209 | ## Identify site x species combinations with >=10 trees 210 | sp_site_table<-as.data.frame(table(Tallo_hd$sp_site)) 211 | names(sp_site_table)<-c("sp_site","n_tree_site") 212 | sp_site_table<-filter(sp_site_table,n_tree_site>=10) 213 | 214 | ## Remove trees from sites that have <10 of a given species 215 | Tallo_hd<-merge(Tallo_hd,sp_site_table[1]) 216 | Tallo_hd$site<-paste(round(Tallo_hd$latitude,2),round(Tallo_hd$longitude,2),sep="_") 217 | Tallo_hd$site<-factor(Tallo_hd$site) 218 | 219 | ## Summarise by species and calculate relative change in aridity across sites 220 | species_list<-Tallo_hd %>% 221 | dplyr::group_by(species) %>% 222 | dplyr::summarise(d_max = max(stem_diameter_cm), 223 | d_range = max(stem_diameter_cm)-min(stem_diameter_cm), 224 | n_trees = length(stem_diameter_cm), 225 | aridity_sp_mean = mean(aridity_index), 226 | aridity_change = min(aridity_index)/max(aridity_index), 227 | n_sites = length(unique(site))) %>% 228 | drop_na() 229 | 230 | ## Filter species list 231 | species_list<-species_list[species_list$d_range>=20,] 232 | species_list<-species_list[species_list$n_sites>=2,] 233 | species_list<-species_list[species_list$aridity_change<=0.80,] 234 | 235 | ## Remove trees from non-target species and add mean species aridity values to data 236 | Tallo_hd<-merge(Tallo_hd,species_list[c(1,5)]) 237 | length(unique(Tallo_hd$species)) 238 | 239 | ## Group-mean center the aridity value of each tree by subtracting its aridity value from the species' mean 240 | Tallo_hd$aridity_gc<-Tallo_hd$aridity_index-Tallo_hd$aridity_sp_mean 241 | 242 | ## Fit model (predictors scaled to compare model coefficents) 243 | M_arid_s<-lmer(log(height_m)~scale(log(Tallo_hd$stem_diameter_cm))+scale(Tallo_hd$aridity_gc)+scale(Tallo_hd$aridity_sp_mean)+ 244 | (scale(log(Tallo_hd$stem_diameter_cm))+scale(Tallo_hd$aridity_gc)|species),Tallo_hd) 245 | summary(M_arid_s) 246 | 247 | ## Fit model (unscaled predictors to generate predictions) 248 | M_arid<-lmer(log(height_m)~log(stem_diameter_cm)+aridity_gc+aridity_sp_mean+ 249 | (log(stem_diameter_cm)+aridity_gc|species),Tallo_hd) 250 | 251 | #### Generate predictions for each species 252 | 253 | ## Prediction data 254 | new.data<-Tallo_hd %>% 255 | dplyr::group_by(species) %>% 256 | dplyr::summarise(stem_diameter_cm = 30, 257 | aridity_sp_mean = median(aridity_index), 258 | aridity_gc_m = median(aridity_index) - median(aridity_index), 259 | aridity_gc_l = quantile(aridity_index,prob=0.10) - median(aridity_index), 260 | aridity_gc_h = quantile(aridity_index,prob=0.90) - median(aridity_index)) 261 | 262 | ## Add model coefficents to data 263 | M_coefs<-as.data.frame(coef(M_arid)$species) 264 | M_coefs<-M_coefs[-c(1,4)] 265 | names(M_coefs)<-c("slope_diameter","slope_aridity") 266 | M_coefs$species<-row.names(M_coefs) 267 | new.data<-merge(new.data,M_coefs) 268 | 269 | ## Calculate 95% confidence intervals for random slopes 270 | slope_ci <- REsim(M_arid, n.sims = 1000) 271 | slope_ci<-filter(slope_ci,term=="aridity_gc") 272 | slope_ci$slope_aridity_2.5<-NA 273 | slope_ci$slope_aridity_97.5<-NA 274 | slope_random_draw<-rnorm(1000,coef(summary(M_arid))[3,1],coef(summary(M_arid))[3,2]) 275 | 276 | for (i in 1:dim(slope_ci)[1]){ 277 | 278 | ## 2.5% slope 279 | slope_ci$slope_aridity_2.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci$mean[i],slope_ci$sd[i]),0.025) 280 | ## 97.5% slope 281 | slope_ci$slope_aridity_97.5[i]<-quantile(slope_random_draw+rnorm(1000,slope_ci$mean[i],slope_ci$sd[i]),0.975) 282 | 283 | } 284 | 285 | names(slope_ci)[2]<-"species" 286 | new.data<-merge(new.data,slope_ci[c(2,7:8)]) 287 | 288 | #### Height predictions 289 | 290 | ## Mean aridity 291 | names(new.data)[4]<-"aridity_gc" 292 | new.data$height_aridity_m<-exp(predict(M_arid,new.data)) 293 | names(new.data)[4]<-"aridity_gc_m" 294 | 295 | ## Low aridity 296 | names(new.data)[5]<-"aridity_gc" 297 | new.data$height_aridity_l<-exp(predict(M_arid,new.data)) 298 | names(new.data)[5]<-"aridity_gc_l" 299 | 300 | ## High aridity 301 | names(new.data)[6]<-"aridity_gc" 302 | new.data$height_aridity_h<-exp(predict(M_arid,new.data)) 303 | names(new.data)[6]<-"aridity_gc_h" 304 | 305 | ## Add division, family and genus names 306 | phylo_structure<-merge(new.data[1],Tallo_hd[c(1,6,5,4)]) 307 | phylo_structure<-unique(phylo_structure) 308 | new.data<-merge(phylo_structure,new.data) 309 | table(new.data$division) 310 | new.data$Pch<-ifelse(new.data$division=="Angiosperm",16,21) 311 | 312 | ## Classify species based on response to aridity 313 | new.data$aridity_effect<-ifelse(new.data$slope_aridity_97.5<0,"Negative", 314 | ifelse(new.data$slope_aridity<0&new.data$slope_aridity_97.5>0,"Negative_ns", 315 | ifelse(new.data$slope_aridity>0&new.data$slope_aridity_2.5<0,"Positive_ns","Positive"))) 316 | table(new.data$aridity_effect,new.data$division) 317 | pos<-filter(new.data,aridity_effect=="Positive") 318 | neg<-filter(new.data,aridity_effect=="Negative") 319 | neutral<-filter(new.data,aridity_effect!="Negative"&aridity_effect!="Positive") 320 | 321 | ## Correlation between a species' predicted height at D=30cm and its mean aridity index value 322 | cor.test(new.data$height_aridity_m,new.data$aridity_sp_mean) 323 | 324 | ## Correlation between a species' height response to aridity (random slope for aridity_gc) and its mean aridity index value 325 | cor.test(new.data$slope_aridity,new.data$aridity_sp_mean) 326 | 327 | # Case study 3 ------------------------------------------------------------ 328 | 329 | ## Load environmental data 330 | setwd("C:/") 331 | Tallo_env<-read.csv("Tallo_environment.csv",header=T, na.strings=c("NA")) 332 | current_climate<-read.csv("current_climate.csv",header=T, na.strings=c("NA")) 333 | future_climate<-read.csv("future_climate_ssp245.csv",header=T, na.strings=c("NA")) 334 | 335 | ## Load Tallo 336 | Tallo<-read.csv("Tallo.csv",header=T, na.strings=c("NA")) 337 | Tallo<-merge(Tallo,Tallo_env) 338 | 339 | ## Remove outliers (set values to NA and remove any trees with NA values for both height and crown radius) 340 | Tallo$height_m[Tallo$height_outlier=="Y"]<-NA 341 | Tallo$crown_radius_m[Tallo$crown_radius_outlier=="Y"]<-NA 342 | Tallo<-filter(Tallo, !is.na(height_m)|!is.na(crown_radius_m)) 343 | 344 | ## Subset data 345 | Tallo_hd<-filter(Tallo,!is.na(height_m)) 346 | dim(Tallo_hd)[1] 347 | 348 | ## Set size threshold for prediction (biome specific) 349 | D_threshold<-Tallo_hd %>% 350 | dplyr::group_by(biome) %>% 351 | dplyr::summarise(stem_diameter_cm = round(quantile(stem_diameter_cm,0.99),0)) 352 | future_climate<-merge(future_climate,D_threshold) 353 | current_climate<-merge(current_climate,D_threshold) 354 | 355 | ## Fit model 356 | M<-lm(log(height_m)~log(stem_diameter_cm)+log(aridity_index)+rainfall_seasonality+ 357 | maximum_temperature+mean_annual_temperature+mean_annual_temperature:log(aridity_index),Tallo_hd) 358 | 359 | ## Predict current height (including Baskerville correction) 360 | baskerville_cor<-exp(sigma(M)^2/2) 361 | current_climate$height_m<-exp(predict(M,current_climate))*baskerville_cor 362 | hist(current_climate$height_m) 363 | mean(current_climate$height_m);quantile(current_climate$height_m,c(0,0.025,0.25,0.50,0.75,0.975,1)) 364 | 365 | ## Predict future height 366 | future_climate$height_m<-exp(predict(M,future_climate))*baskerville_cor 367 | hist(future_climate$height_m) 368 | mean(future_climate$height_m);quantile(future_climate$height_m,c(0,0.025,0.25,0.50,0.75,0.975,1)) 369 | 370 | ## Absolute change in height 371 | future_climate$h_change<-future_climate$height_m-current_climate$height_m 372 | hist(future_climate$h_change) 373 | mean(future_climate$h_change);quantile(future_climate$h_change,c(0,0.025,0.25,0.50,0.75,0.975,1)) 374 | 375 | ## Relative change in height 376 | future_climate$h_change_rel<-(future_climate$height_m-current_climate$height_m)/current_climate$height_m*100 377 | hist(future_climate$h_change_rel) 378 | mean(future_climate$h_change_rel);quantile(future_climate$h_change_rel,c(0,0.025,0.25,0.50,0.75,0.975,1)) 379 | 380 | ## Relative and absolute height change by biome and biogegraphic realm 381 | h_change_biome<-future_climate %>% 382 | dplyr::group_by(biome) %>% 383 | dplyr::summarise(h_change_rel_m = round(mean(h_change_rel),1), 384 | h_change_rel_25 = round(quantile(h_change_rel,0.25),1), 385 | h_change_rel_75 = round(quantile(h_change_rel,0.75),1), 386 | h_change_m = round(mean(h_change),1), 387 | h_change_25 = round(quantile(h_change,0.25),1), 388 | h_change_75 = round(quantile(h_change,0.75),1)) 389 | 390 | h_change_biome_realm<-future_climate %>% 391 | dplyr::group_by(biome,realm) %>% 392 | dplyr::summarise(h_change_rel_m = round(mean(h_change_rel),1), 393 | h_change_rel_25 = round(quantile(h_change_rel,0.25),1), 394 | h_change_rel_75 = round(quantile(h_change_rel,0.75),1), 395 | h_change_m = round(mean(h_change),1), 396 | h_change_25 = round(quantile(h_change,0.25),1), 397 | h_change_75 = round(quantile(h_change,0.75),1)) 398 | -------------------------------------------------------------------------------- /Jucker et al. 2022/Tallo_environment.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Jucker et al. 2022/Tallo_environment.csv.zip -------------------------------------------------------------------------------- /Jucker et al. 2022/Tallo_environment_metadata.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Jucker et al. 2022/Tallo_environment_metadata.csv -------------------------------------------------------------------------------- /Jucker et al. 2022/current_climate.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Jucker et al. 2022/current_climate.csv.zip -------------------------------------------------------------------------------- /Jucker et al. 2022/future_climate_ssp245.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/selva-lab-repo/TALLO/95d7788d204c090fa4c425ac21db76d2b5bb25c9/Jucker et al. 2022/future_climate_ssp245.csv.zip -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | TALLO - a global tree allometry and crown architecture database 2 | ====================================================================================================== 3 | ![license](https://img.shields.io/badge/Licence-GPL--3-blue.svg) 4 | 5 | This is the repository of the Tallo database, a global collection of georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. For a full description of the database, see: *Jucker et al. (2022). Tallo - a global tree allometry and crown architecture database. Global Change Biology, doi:10.1111/GCB.16302* (https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16302). If using the Tallo database in your work please cite the original publication listed above. 6 | 7 | The repository contains two separate data sets: 8 | 9 | ### DB 10 | 11 | The Tallo database itself, along with its associatedand metadata files: 12 | 13 | - **Tallo.csv**: the Tallo database (zipped) 14 | - **Tallo_metadata.csv**: a metadata file describing the fields of the Tallo.csv file 15 | - **Reference_look_up_table.csv**: a look-up table matching reference ID codes in the Tallo.csv file to the original data sources 16 | 17 | 18 | ### Jucker et al. (2022) 19 | 20 | Associated data files and R code used in the publication accompanying the release of the Tallo database: 21 | 22 | - **R code to replicate case studies.R**: the R code that replicates the case studies in Jucker et al. (2022) GCB 23 | - **Tallo_environment.csv**: environmental data layers for each tree in the Tallo.csv file needed to replicate the case studies in Jucker et al. (2022) GCB (zipped) 24 | - **Tallo_environment_metadata.csv**: a metadata file describing the fields of the Tallo_environment.csv file 25 | - **current_climate.csv**: current-day climate data layers at 5-arc minute spatial resolution needed to replicate case study 3 in Jucker et al. (2022) GCB (zipped) 26 | - **future_climate_ssp245.csv**: projected future climate data layers at 5-arc minute spatial resolution needed to replicate case study 3 in Jucker et al. (2022) GCB (zipped) 27 | --------------------------------------------------------------------------------