Data from: Seasonally changing interactions of species traits of termites and trees promote complementarity in coarse wood decomposition
Data files
Sep 30, 2024 version files 133.79 KB
-
Data_code_Readme_Guo_et_al._el.2024.rar
125.72 KB
-
README.md
8.07 KB
Abstract
Complementary resource use by functionally different species may accelerate ecosystem processes. However, how co-variation in plant traits and animal traits promotes complementarity through temporal plant-animal interactions is poorly understood, even less so in detrital systems, thereby hampering our fundamental understanding of decomposition and carbon turnover. We hypothesized that, in seasonal subtropical forests where termites are major deadwood decomposers, trait complementarity of both termite species and tree species should promote overall deadwood decomposition through different seasons and years. Findings from a four-year coarse wood decomposition experiment involving 27 tree and 5 termite species support this hypothesis. Phenological and mandibular traits of the two most abundant termite species controlled wood decomposition of tree species differing in wood traits, through the seasons over four years, thereby promoting overall deadwood decomposition rates. Our findings indicate that complementarity in functional trait co-variation in plants and animals plays an important role in carbon cycling.
README: Data from: Seasonally changing interactions of species traits of termites and trees promote complementarity in coarse wood decomposition
Description of the data files
A. File list：
wdter_F.csv
termite_coexistence.csv
mass loss_termites.csv
wood traits.csv
wood mass loss.csv
Code_Termites_paper_30thAug.R
B. File descriptions:
(1) For 'wdter_F.csv':
This document contains five key pieces of information.
The first column is 'site', which represents our experimental sampling points, including PT site and TT site. And site ‘PT’ refers to Putuo Island, which is situated in Zhoushan City, Zhejiang Province, China. Site ‘TT’ denotes Tiantongshan National Forest Park, located in Ningbo City, Zhejiang Province, China.
The second column is 'species', which details the species of 20cm diameter wood used in our decomposition experiments.
The third column is 'WES', which stands for Wood Economic Spectrum; it represents the scores of each species obtained from a principal component analysis of wood traits.
The fourth column is 'Types', which contains information on termite species. There are five dominant termite species listed: DBY is Macrotermes barneyi, RBY is Coptotermes formosanus, SBY is Reticulitermes periflaviceps, TBY is Odontotermes formosanus, and JBXBY is Nasutitermes tiantongensis.
Columns five to fifteen are labeled by year_season, representing the abundance of termites during those specific seasons.This termite abundance information is obtained through field monitoring conducted each season, where we count the termites found on decomposing wood.
(2) For 'termite_coexistence.csv':
This file includes seven columns.
The first column is 'site', which represents the experimental site information, including PT and TT sites. And site ‘PT’ refers to Putuo Island, which is situated in Zhoushan City, Zhejiang Province, China. Site ‘TT’ denotes Tiantongshan National Forest Park, located in Ningbo City, Zhejiang Province, China.
The second column is 'WES', which stands for Wood Economic Spectrum, representing the position rankings of 27 wood species based on wood trait data obtained from the first principal component analysis (PCA) axis scores.
The third column is 'Time', composed of year and month, marking the timing of 11 field monitoring surveys.
The fourth column is 'Season', indicating the season of each field survey.
The fifth column is 'CumulativeVolumeloss', which represents the total volume loss of wood from the start to the survey time point, expressed as a percentage.
The sixth column is 'PeriodVolumeloss', representing the volume loss between the survey time and the previous period, also expressed as a percentage.
The seventh column is 'Termite species', detailing which termite species were found at the respective survey times and on the wood samples, including options such as S, L, S+L, and None, where 'S' corresponds to 'small' in the paper, referring to SBY (Reticulitermes periflaviceps), 'L' corresponds to 'large', referring to TBY (Odontotermes formosanus), 'S+L' indicates both types of termites were present, and 'none' means no termites were detected.
(3) For 'mass loss_termites.csv':
This file includes 13 columns.
The first column is 'site', which represents the experimental plot information, including PT and TT sites. And site ‘PT’ refers to Putuo Island, which is situated in Zhoushan City, Zhejiang Province, China. Site ‘TT’ denotes Tiantongshan National Forest Park, located in Ningbo City, Zhejiang Province, China.
The second column is 'Species', which details the species of 20cm diameter wood used in our decomposition experiments.
The third column is 'WES', which stands for Wood Economic Spectrum, representing the positional ranking of 27 wood species based on wood trait data, obtained from the scores of the first principal component analysis (PCA) axis.
The fourth column is 'year', indicating the year of the field monitoring survey, including 2018, 2019, 2020, 2021, and 2022.
The fifth column is 'Season', representing the season of the field survey, including spring, summer, and late autumn.
The sixth column is 'Time', composed of year and season, marking the timing of 11 field monitoring surveys.
The seventh column is 'CumulativeVolumeloss', which represents the total volume loss of wood from the start to the survey time point, expressed as a percentage.
The eighth column is 'PeriodVolumeloss', representing the volume loss between the survey time and the previous period, also expressed as a percentage.
Columns 9-13 are DBY, RBY, SBY, TBY, and JBXBY, respectively representing the abundance of termites at the respective survey times and on the wood samples.
JBXBY is represented as 'NA' at PT sites where it does not occur. This means that we didn't find this termite species in the PT site.
DBY is Macrotermes barneyi, RBY is Coptotermes formosanus, SBY is Reticulitermes periflaviceps, TBY is Odontotermes formosanus, and JBXBY is Nasutitermes tiantongensis.
(4) For 'wood traits.csv':
This file includes 8 columns.
The first column is 'Species', detailing the wood species involved.
Columns 2-8 contain wood trait data on the respective wood species, including Wooddensity (Wood density, unit g/cm3 ), woodN (Wood nitrogen content, unit mg/g), Lignin (Wood Lignin cotent, unit %),
Cellulose (Wood cellulose cotent, unit %), woodP (Wood phosphorus content, unit mg/g), woodDMC (Wood dry matter content, unit g/g), woodC (Wood carbon content, unit mg/g)
(5) For 'wood mass loss.csv':
This file includes 10 columns.
The first column is 'site', which represents the experimental plot information, including PT and TT sites. And site ‘PT’ refers to Putuo Island, which is situated in Zhoushan City, Zhejiang Province, China. Site ‘TT’ denotes Tiantongshan National Forest Park, located in Ningbo City, Zhejiang Province, China.
The second column is 'Species', which details the species of 20cm diameter wood used in our decomposition experiments.
The third column is 'WES', which stands for Wood Economic Spectrum, representing the positional ranking of 27 wood species based on wood trait data, obtained from the scores of the first principal component analysis (PCA) axis.
The fourth column is 'Types', which contains information on termite species. There are five dominant termite species listed: DBY is Macrotermes barneyi, RBY is Coptotermes formosanus, SBY is Reticulitermes periflaviceps, TBY is Odontotermes formosanus, and JBXBY is Nasutitermes tiantongensis.
The fifth column is the cumulative termite abundance for 2019, which is the sum of termite abundances from the summer of 2018, the spring of 2019, and the summer of 2019.
The sixth column is the cumulative termite abundance for 2020, which is the sum of termite abundances from the autumn of 2019, the spring of 2020, and the summer of 2020.
The seventh column is the cumulative termite abundance for 2021, which is the sum of termite abundances from the autumn of 2020, the spring of 2021, and the summer of 2021.
Columns 7-9 are labeled '2019_woodmassloss', '2020_woodmassloss', and '2021_woodmassloss', respectively representing the wood mass loss for the years 2019, 2020, and 2021. These data are derived from field decomposition experiments where 20cm diameter wood samples were decomposed and incubated, and samples were retrieved in the summers of 2019, 2020, and 2021, with the remaining dry matter mass calculated over those decomposition periods.
Wood mass loss is calculated as the difference between the percentage of the initial dry matter content and the percentage of the remaining dry matter content. The initial dry matter content is 100%.
Please note: If NA values appear in the dataset, they represent missing data.
(6) For 'Code_Termites_paper_30thAug.R'
This file is R code for data analysis. Includes calculations of the width and centre of termite ecological niches and the construction of wood economic spectrums using wood traits, as well as the relationship between termites and wood traits and wood volume loss.
Methods
The article describes a detailed study conducted in two forest sites in Zhejiang Province, East China, focusing on the decomposition of logs from various tree species to understand environmental and biological factors influencing wood decay. The study utilized 81 healthy adult trees from 27 species, creating 648 log samples for decomposition experiments. Data collection involved placing logs in designated plots and monitoring them over several years with the help of micro-weather stations to gather information on environmental conditions such as soil temperature, moisture, and wind.
For data on wood decomposition, the study assessed volume loss by visually estimating log surface area reduction using a grid system, and depth measurements in termite tunnels to calculate volume loss. Termite species were identified and quantified to understand their role in decomposition, and their traits like body size and mandibular width were measured to assess their feeding capabilities.
Wood traits were also meticulously recorded, including density, dry matter content, and chemical composition such as carbon, nitrogen, phosphorus, lignin, and cellulose content. This comprehensive dataset from field measurements, laboratory analysis, and environmental monitoring provided a robust foundation for evaluating the interactions between wood traits, termite activity, and environmental conditions in wood decomposition processes.