Tea Bag Index (TBI)
Published January 15, 2024 | Version 1
Image Open

Global mapped Stabilization factor (STBI)

Authors/Creators

Description

Global map of modeled initial decomposition rates as described in Sarneel et al., (xxxx) Reading tea leaves worldwide: decoupled drivers of initial litter decomposition mass-loss rate and stabilization. Ecology letters

META INFORMATION

TBI_S-xxx.tiff are separate pieces of the maps. all maps are tiled and contain 8 bands (starting with “S”), they are orderd according the list below:

S_Ensemble_mean > prediction mean from 10 ensemble models 

S_Bootstrapped_mean > bootstrapped mean from 100 bootstrap samples, using best performing model from cross-validation 

S_Bootstrapped_lower > lower 2.5% confidence interval from 100 bootstrap samples 

S_Bootstrapped_upper > upper 97.5% confidence interval from 100 bootstrap samples

S_Bootstrapped_stdDev > standard deviation from 100 bootstrap samples 

S_Bootstrapped_coefOfVar > coefficient of variation (std dev / mean) from 100 bootstrap samples 

univariate_pct_int_ext > proportion of interpolation in univariate space 

PCA_pct_int_ext > proportion of interpolation in bivariate combinations for PCA-transformed axes- feature importances

Other (English)

if (!require("pacman")) install.packages("pacman")
pacman::p_load(raster,mapplots,rgdal,tidyverse)
mapdata<-"C:/..."

#read a specific band from the files (in the case below band 2; bootstrapped mean)

S1<-raster(("C:/.../TBI_S-0000000000-0000000000.tif"), band = 2) %>% aggregate(fact=20)
S2<-raster(("C:/.../TBI_S-0000000000-0000011776.tif"), band = 2) %>% aggregate(fact=20)
S3<-raster(("C:/.../TBI_S-0000000000-0000023552.tif"), band = 2) %>% aggregate(fact=20)
S4<-raster(("C:/.../TBI_S-0000000000-0000035328.tif"), band = 2) %>% aggregate(fact=20)
S5<-raster(("C:/.../TBI_S-0000011776-0000000000.tif"), band = 2) %>% aggregate(fact=20)
S6<-raster(("C:/.../TBI_S-0000011776-0000011776.tif"), band = 2) %>% aggregate(fact=20)
S7<-raster(("C:/.../TBI_S-0000011776-0000023552.tif"), band = 2) %>% aggregate(fact=20)
S8<-raster(("C:/.../TBI_S-0000011776-0000035328.tif"), band = 2) %>% aggregate(fact=20)

#glue them together

S_all<-mosaic(S8,S7,S6,S5,S4,S3,S2,S1,tolerance=1,fun=mean)

#plot the map

plot(S_all, col=colorRampPalette(c( "aquamarine","mediumaquamarine", "palegreen4","darkslategrey","black"))( 11 ),breaks = c(0,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.6,0.8),
     legend=F, axes=FALSE,xlim=c(-200,200),ylim=c(-60,100))
ticks<- c(0, 0.2, 0.4,0.6, 0.8)
plot(S_all, legend.only=TRUE, col=colorRampPalette(c( "aquamarine","mediumaquamarine", "palegreen4","darkslategrey","black"))( 11 ),breaks = c(0,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.6,0.8),
     legend.width=1, axis.args=list( at=ticks, labels=ticks), legend.shrink=0.7,legend.args=list(text='STBI', side=3, font=2, line=0.2, cex=1),
     smallplot=c(0.07,0.10, 0.15,0.50)); par(mar = par("mar"))

#DONE

Methods (English)

Geospatial modelling

To explore spatial patterns of early mass-loss dynamics of plant litter and derive global maps of predicted TBI proxies, separate random forest models were built for STBI and ln-transformed k1TBI, following the procedure outlined in van den Hoogen et al. (2019). We performed a grid search procedure to tune the random forest models across a range of 30 hyper-parameter settings (with 2–10 variables per split and 2–6 as a minimum leaf population). For each of the 30 models, we assessed the model performance using k-fold cross-validation (using k=10; folds assigned randomly, stratified per biome to ensure equal representation of each bioclimatic zone). The mean coefficient of determination R2 across the tested models was the basis for choosing the best model (van den Hoogen et al. 2019). The final image was subsequently calculated as a mean of the top 10 best performing hyperparameter settings. To generate coefficients of variation images (standard deviation divided by mean) that provides a per-pixel accuracy of our predicted TBI, we followed a stratified bootstrapping procedure (stratified per biome). After classifying the composite raster data 100 times, we used these to create per-pixel mean and standard deviation images. The resulting maps of predicted TBI proxies and associated models should be used to address large rather than small spatial scales.

To quantify the potential extrapolation of our TBI maps we assessed if the pixels with measurements covered the environmental conditions of the pixels without measurements, taking into account combinations of two environmental variables. To this end, we first performed a PCA using the 125 covariate layers for all pixels for which we had measurements (van den Hoogen et al. 2019). Second, we transformed all terrestrial pixels without measurements into the same PCA space by using scaling and centring the eigenvectors and values of the PCA. Third, we represented the sampled environmental conditions (interpolation) by creating PCA convex hulls enclosing the pixels with measurements. We did this for all bivariate combinations of the first 28 PCA axes (explaining >90% of the PCA-variation and resulting in 378 combinations). Last, for each pixel without measurements, we quantified a per-pixel degree of interpolation as the % of the convex hulls that included this pixel. Geospatial analyses and extrapolation were performed in Google Earth Engine and Python

Files

TBI_S-map.zip

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Additional details

Related works

Is derived from
Dataset: 10.5281/zenodo.10514225 (DOI)
Is supplemented by
Image: 10.5281/zenodo.10513802 (DOI)

Funding

Swedish Research Council
Teatime4science 2014-04270
Swedish Research Council
TeaTales 2021-02449

References

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  • Keuskamp et al., (2013). Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods in Ecology and Evolution, 4, 1070-1075.