Data from: What controls forest litter decomposition? A coordinated distributed teabag experiment across ten mountains
Data files
Aug 13, 2024 version files 74.10 KB
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all.dat.csv
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README.md
Abstract
Litter decomposition in mountainous forest ecosystems is an essential process that affects carbon and nutrient cycling. However, the contribution of litter decomposition to terrestrial ecosystems is difficult to estimate accurately because of the limited comparability of different studies and limited data on local microclimatic and non-climatic factors. Here, we designed a coordinated experiment within subtropical and tropical forests across ten mountains to evaluate variation in litter decomposition rates and stabilization. We tested whether elevations, soil microclimate, soil physiochemistry, tree species diversity, and microhabitat affect decomposition rates and stabilization by using the Tea Bag Index as a standardized protocol. We found that the associations of decomposition rates and stabilization with elevation and each environmental factor varied between mountains. Elevation significantly affected decomposition rates and stabilization in the western mountains, where soil microclimate also played a dominant role due to relatively cold environments. Across all mountains, decomposition rates decreased while stabilization increased with increasing elevation. In terms of microclimate, decomposition rates increased with increasing soil temperature and temperature variation during the growing season, whereas stabilization decreased with increasing soil temperature and moisture variation. In terms of non-climatic factors, decomposition rates increased with increasing tree species diversity, whereas stabilization decreased with soil pH and slope. Our findings enhance the general understanding of how different factors control forest litter decomposition, highlighting the dominant role of soil microclimate in controlling carbon and nutrient cycling in cold environments and high elevations.
README: Environmental data and decomposition index
Author/Principal Investigator Information
Name: Shiyu Ma
ORCID: https://orcid.org/0000-0003-1116-4812
Institution: East China Normal University
Email: shiyu.ma_ecology@outlook.com
Date of data collection: 2021-05-01 to 2021-09-30
Geographic location of data collection: Tropical and subtropical regions of China
Information about funding sources that supported the collection of the data: National Science Foundation of China
DATA & FILE OVERVIEW
File List: all.dat.csv, R script_Figure2.R, R script_Figure3_4.R, R script_Figure5_6.R
all.dat.csv includes the metadata of each subplot, the ten environmental factors, and focal response variables (decomposition rate-K and stabilization-S).
The three R scripts are code for statistical analyses and visualization of Figures 2, 3-4, and 5-6.
DATA-SPECIFIC INFORMATION FOR: all.dat.csv
This data file contains 16 columns and 488 rows, with each row representing one experimental site.
Columns 1-4 are metadata for each site, including:
Mt.abbr: the abbreviation of mountain names (GMT: Gaoligong Mountain, YMT: Jade Dragon Snow Mountain, EMS: E'mei\
Mountain, JFS: Jinfo Mountain, DBS: Dabie Mountain, DMS: Daming Mountain, DYS: Daiyun Mountain, GS: Guan Mountain,\
BWL: Bawangling, TMS: Tianmu Mountain)
Plot: Plot ID in each mountain
Subplot: Subplot ID in each plot
Elev: the elevation of each plot (meter)
Columns 5-6 are response variables, including:
Decomposition rate (K): K is the decomposition constant (unitless), representing the mass loss of the truly decomposed hydrolyzable fraction over the burial period.
Stabilization (S): S indicates the fraction of the recalcitrant litter material that stabilized from a theoretically hydrolyzable fraction owing to environmental constraints.
The two variables are known as Tea bag index, which can be calculated by following the protocol described by Keuskamp et al. (2013).
Columns 7-16 are environmental factors, including:
Column 7 Tree species diversity
Species.richness: the number of species within each plot.
Column 8-9 Soil Physiochemistry
Soil.pH: Soil pH of each plot
Soil.Total.P: Soil total phosphorus
Soil samples were collected at a depth of 0–10 cm using a 5 cm-diameter auger.
In each plot, the four sampling sites were distributed as evenly as possible, similar to the decomposition sites. One soil sample was collected from the center of the plot. Finally, the five soil cores were mixed homogeneously after removing visible roots, debris, and stones. Soil samples were air-dried and sieved through a 1 mm mesh. Soil pH and total phosphorus (P) were measured following the standard protocol described by Ma et al. (2019).
Columns 10-12 Microhabitat
Slope: the slope of each subplot. This was measured by using the phone application compass (Huawei Terminal Co.,
Ltd.).
Litter.THK: ground litter thickness. This variable was defined as the vertical extent of dead leaves and debris
covering the soil surface at each burial site. This was quantified by using measuring tapes with a minimum scale in
millimeters.
CanopyCover: The overstorey canopy cover of each subplot. The canopy cover at each site was measured 50 cm above the
soil surface on a sunny day (avoiding solar radiation at noon) by capturing hemispherical photographs using a
fisheye lens (238°, wide-angle view). The canopy pictures were digitalized using the software Gap Light Analyzer
(Frazer et al. 1999).
Columns 13-16 Microclimate
The microclimate variables were measured as soil temperature (℃) and moisture (%) during the litter decomposition period (three months). Measurements were taken at the center of each plot at a depth of 8 cm relative to the soil surface. Data were recorded every 15 minutes using logger TMS4 (TMS4, TOMST Ltd.; for all mountains except EMS) and iButtons (Maxim Integrated DS1925; for EMS). At the end of decomposition, we retracted data and averaged temperature and moisture at the month level (Temp_month and Mois_month) for each plot and calculated four microclimate variables, including:
soiltemp_bio1: the monthly mean soil temperature (Temp_month/3).
soiltemp_bio4: the variation of soil temperature, which was calculated as the standard deviation of Temp_month*100 (SD of Temp_month * 100).
soilMois_bio1: the monthly mean soil moisture (Mois_month/3).
soilMois_bio4: the variation of soil moisture over the decomposition period, which was calculated as the ratio between the standard deviation of Mois_month and soilMois_bio1 (SD of Mois_month / soilMois_bio1).
References:
Keuskamp, J. A. et al. 2013. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. – Methods Ecol. Evol. 4: 1070–1075.
Ma, S. et al. 2019. Plant species identity and soil characteristics determine rhizosphere soil bacteria community composition in European temperate forests. – FEMS Microbiol. Ecol. 95: fiz063.
Frazer, G. W. et al. 1999. Gap light analyzer (GLA): imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, users manual and program documentation. – Burnaby: Simon Fraser University.
Huawei Terminal Co., Ltd. (2021). Compass (Version 14.1.0.316) [Mobile app]. Huawei App Store. https://appgallery.huawei.com/.
Missing data codes: NA (not available)
Methods
Environmental data and litter decomposition data were collected from a coordinated experiment across ten mountains in the tropical and subtropical regions of China. We used standardized litter bags "red tea" and "green tea" as decomposing materials. The calculated Tea bag index included decomposition rate (k) and stabilization (S). We modeled k and S on the function of each environmental variable or multiple variables depending on the research hypotheses.