DOI: 10.5061/dryad.wwpzgmssd
title: Data from: Biogeographical variation in termite distributions alters global deadwood decay

description: Fungus-growing termites (subfamily Macrotermitinae) have a distinct
 geographical distribution, found only in the old-world tropics. Where
 present, they are considered to be major contributors and regulators of
 decomposition, with consumption rates often greater than other termite
 groups. This study sought to understand the relative roles of termite
 distribution (specifically the presence or absence of fungus-growing
 termites) and climatic variables (mean annual temperature, mean annual
 precipitation and mean annual aridity) on global patterns in deadwood
 decay. To answer this question, we added new salient data to an existing
 dataset on global wood decay by Zanne et al. (2022) available
 at https://doi.org/10.6084/m9.figshare.19920416.v1. We filtered
 the data to only include sites where termites were present and thus
 analysed a dataset containing 102 sites across 16 countries. We found that
 termite-driven decay of deadwood increased with aridity but was higher in
 sites with fungus-growing termites than sites without fungus-growing
 termites. Our results also showed that the relative role of fungus-growing
 termites increased with aridity, as rates of wood-discovery by termites
 increased with aridity but only in sites where fungus-growing termites
 were present. Our findings indicate that the inclusion of biogeographical
 differences in termite distribution could potentially alter global
 estimates of deadwood turnover. This repository contains new datasets on
 termite-driven deadwood decay of Pinus radiata wood blocks and code used
 for all data analyses and production of figures.
description: All experimental data collected on the decay of wood blocks in
 the datasets 'new_global_wood_decay.csv' and
 'pine_shade.csv' followed a standard protocol outlined by
 Cheesman et al., (2018), https://doi.org/10.1111/aec.12561. Data in 'new_global_wood_decay.csv' was collected from 140 sites across 20 countries by different researchers. A complete description of how data was collected in this dataset is provided in Zanne et al., (2022), https://www.science.org/doi/10.1126/science.abo3856. All researchers followed the same method except for one difference: data from the original Zanne et al., (2022) dataset covered wood blocks with 70% green shade cloth to reduce solar radiation degradation of mesh bags while new data did not use green shade cloth.  Environmental parameters for each site were extracted from global databases, mean annual temperature (MAT) and mean annual precipitation (MAP) was extracted from WorldClim, https://doi.org/10.1002/joc.5086, and mean annual aridity (MAA) from the Global-Aridity Index by Zomer et al. (2022), https://doi.org/10.1038/s41597-022-01493-1. Data in 'pine_shade.csv' was used to analyse if the inclusion of shade cloth had any affect on decay rates of wood blocks. In the dataset 'pine_shade.csv' the decay of <em>Pinus radiata</em> wood blocks is measured in a rainforest site (named DRO) and savanna site (named PNW) in Queensland, Australia, following the methods described in Wijas et al., (2024), https://doi.org/10.1111/1365-2435.14494, but with the inclusion of a 70% green shade cloth. The sites in 'pine_shade.csv' are the same sites named ‘wet rainforest’ and ‘dry savanna’ in Wijas et al. (2024) and site descriptions are also provided in Clement et al., (2021), https://doi.org/10.3389/fevo.2021.657444.  For analyses, we processed the 'new_global_wood_decay' dataset to only include sites where termites were known to be present (102 of the 140 sites). The presence of fungus-growing termites (subfamily Macrotermitinae) was assigned to sites if sites were within the geographical distribution of fungus-growing termites, i.e. if they were in Afrotropical, Oriental, or Malagasy realms. Additionally, we checked for the presence of fugus-growing termites by reviewing published termite transect surveys at the same sites and through personal communication with researchers based at sites. Termite decomposition of deadwood was considered a two step process: first we looked at termite discovery of deadwood, wood blocks were considered discovered by termites if researchers had noted imported soil on wood blocks by termites, termite related damage to wood blocks or termite presence on wood blocks; second we looked at decay rates of wood blocks discovered by termites. Decay of undiscovered wood blocks was attributed primarily to microbial decay. Decay of discovered wood blocks includes microbial decay but is refered to as termite-driven decay. We compared termite discovery of deadwood and termite-driven decay rates in sites where fungus-growing termites were present and absent. We used R software to run linear regression models to compare differences in termite discovery and termite-driven decay rates with climatic variables (MAT, MAP and MAA) and with the presence or absence of fungus-growing termites. 
description: # Data from: Biogeographical variation in termite distributions alters
 global deadwood decay
 [https://doi.org/10.5061/dryad.wwpzgmssd](https://doi.org/10.5061/dryad.wwpzgmssd) This repository contains the data and analyses used for the manuscript 'Biogeographical variation in termite distributions alters global deadwood decay'. There are three .csv files in total, including 'new_global_wood_decay.csv', 'site.covars.csv', and 'pine_shade.csv', and all R-scripts used to run the analyses and plot figures. ## Description of the data and file structure 1. The dataset 'new_global_wood_decay.csv' in the supplementary files combines new experimental data on the decay of *Pinus radiata* wood blocks with existing data from Zanne et al., (2022) [https://www.science.org/doi/10.1126/science.abo3856](https://www.science.org/doi/10.1126/science.abo3856) downloaded from [https://doi.org/10.6084/m9.figshare.19920416.v1](https://doi.org/10.6084/m9.figshare.19920416.v1). There are 12603 rows of data including 10361 rows from Zanne et al., (2022) and 2242 new rows of data, each row contains data for the decay of a single wood block. New data includes experimental data on wood decay from seven new sites in Africa (not present in the Zanne et al. 2022 dataset) and new data from five existing sites in the Zanne et al. (2022) dataset. NA values indicate no data was collected, any blanks should be interpreted as NA. Metadata for all columns in this dataset are provided in the pdf 'Metadata for new_global_wood_decay'. 2. The dataset 'site.covars.csv' describes environmental parameters for study sites (e.g. mean annual temperature, mean annual precipitation, mean annual aridity, etc.) Each row contains data for one site and there are 140 study sites in total. NA values indicate no data collected, any blanks should be interpreted as NA. Metadata for all columns in this dataset are provided in the pdf 'Metadata for site.covars'. 3. The dataset 'pine_shade.csv' contains experimental data on the decay of 40 *Pinus radiata* wood blocks in a tropical savanna site (n = 20 blocks) and a tropical rainforest site (n = 20 blocks) in Queensland, Australia from 2018 to 2020. The rainforest site is named DRO and the savanna site is named PNW. Each row contains data for the decay of a single wood block (40 rows of data in total). Wood blocks were left to decay for 12, 18 or 24 months. NA values indicate no data collected, any blanks should be interpreted as NA. Metadata for all columns in this dataset are provided in the pdf 'Metadata for pine_shade'. ## Sharing/Access information The original dataset on global wood block decay by Zanne et al., 2022 [https://www.science.org/doi/10.1126/science.abo3856](https://www.science.org/doi/10.1126/science.abo3856) is available at [https://doi.org/10.6084/m9.figshare.19920416.v1](https://doi.org/10.6084/m9.figshare.19920416.v1) Additional data was downloaded from the following sources and used in the analyses alongside the uploaded datasets described above: * The dataset named 'pine_CO2_clean.csv' from Duan et al., 2023, [https://doi.org/10.5194/egusphere-2023-1952](https://doi.org/10.5194/egusphere-2023-1952) was downloaded manually from [https://github.com/Zanne-Lab/WTF-Climate-Flux/blob/main/weather_flux/data/processed/wood_respiration/pine_CO2_clean.csv](https://github.com/Zanne-Lab/WTF-Climate-Flux/blob/main/weather_flux/data/processed/wood_respiration/pine_CO2_clean.csv). This dataset describes the decay of *Pinus radiata* wood blocks at sites in Queensland, Australia. * A raster of global mean aridity values from Zomer et al., 2022 ,[https://doi.org/10.1038/s41597-022-01493-1](https://doi.org/10.1038/s41597-022-01493-1), datafile named 'ai_v3_yr.tif ' within 'Global-AI_ET0_v3_annual', version 6, was downloaded manually from [https://doi.org/10.6084/m9.figshare.7504448.v5](https://doi.org/10.6084/m9.figshare.7504448.v5) ## Data Usage / Code The data could be processed with almost any program but we used R version 4.3.1 (2023-06-16) (R Core Team, 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) and the following packages: "readxl", "tidyverse", "ggplot2", "ggpubr", "rstatix", "raster", "RColorBrewer", "geodat", "sf", "parameters", "emmeans", "lubricate", "lme4", "car", "ggeffects", "MuMIn", "DescTools", "sjPlot" The datasets 'new_global_wood_decay.csv" and "site.covars.csv" were first combined and processed (filtering out sites without termites and assigning the presence or absence of fungus-growing termites to sites) to generate a dataset used for all analyses using the R script "download_data.R". The dataset 'pine_shade.csv' was analysed alongside an existing dataset named 'pine_CO2_clean.csv' from Duan et al., 2023, [https://doi.org/10.5194/egusphere-2023-1952](https://doi.org/10.5194/egusphere-2023-1952). Figure 1 was generated using a raster file of global aridity values from Zomer et al., 2022, [https://doi.org/10.6084/m9.figshare.7504448.v5](https://doi.org/10.6084/m9.figshare.7504448.v5). There are 12 R scripts in total, including: 1. "download_data.R" should be used first to generate a processed dataset, named 'processed_data', and used for all subsequent analyses. 2. "environmental_parameters.R" is the R script used to describe environmental differences between sites with and without fungus-growing termites, includes code to produce figure S1.1 and figure S1.3. 3. "Fig1_map.R" is the R script used to produce the map in figure 1, showing the location of sites and global aridity values, and figure S1.2. 4. "discovery_model.R" is the R script used to analyse termite discovery rates of wood blocks, includes code to produce figure 2. 5. "decay_models.R" is the script used to analyse termite-driven decay rates (i.e. decay of discovered wood blocks), includes code to produce figure 3. 6. "microbial_decay.R" is the R script used to analyse microbial decay rates (i.e. decay of undiscovered wood blocks), includes code to produce figure S3. 7. "shadecloth_models.R" is the R script that analyses the affect of shade cloth on termite-driven decay rates, includes code to produce figure S2.1 and figure S2.2. 8. "richness_models.R" is the R script that analyses the affect of aridity on termite richness and termite richness on termite-driven decay rates, includes code to produce figure S4.2. 9. "aridity_all.R" is the R script used to analyse termite-driven decay rates across a wider range of aridity values, includes code to produce figure S3.2. 10. "MAT_MAP_models.R" is the R script used to analyse the affect of MAT and MAP on termite discovery and termite-driven decay of wood blocks, includes code to produce figure S6. 11. "mixed_effects_models.R" includes code to analyse termite discovery of dead wood and termite-driven decay rates of deadwood using mixed effects models shown in supplementary materials.  12. "FigS1.4.R" is the R script used to generate Figure S1.4 in the supplementary materials.