Multifaceted precipitation patterns impact biocrust functionality in drylands: A cascade of variability via species replacement in soil microbiota
Data files
Nov 20, 2024 version files 7.12 MB
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data_package.zip
4.50 MB
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README.md
4.63 KB
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row_data.xlsx
2.62 MB
Abstract
Aim: The pervasive impacts of climate change on the biodiversity and functionality in arid ecosystems are increasingly evident in the Anthropocene. Although soil microbes are anticipated to be sensitive to changes in precipitation regimes, the mechanisms through which multifaceted precipitation variances impair the current delivery of multiple functions by multitrophic microbiota remain largely unexplored. Here, we examined the direct effects of historical precipitation regimes on the multifunctionality and their indirect impacts mediated by soil microorganisms in drylands.
Location: Northwestern China.
Time period: Precipitation records (1979-2016); Field investigation in 2020.
Major taxa studied: Multitrophic microbiota in biocrusts.
Methods: We evaluated a 38-year daily precipitation across the arid regions to quantify its multifaceted characteristics, including mean amount, frequency, and variability. The community structures of heterotrophic bacteria, eukaryotic fungi, and photoautotrophic cyanobacteria were determined through amplicon sequencing, aimed to measure taxonomic and phylogenetic α-diversity, as well as spatial turnover in soil microbiota. We explored the interactive linkages between precipitation impacts and diversity effects on soil multifunctionality.
Results: We found that α-richness exerted positive functional influences, but phylogenetic dissimilarity was a more important predictor in photoautotrophs. Species replacement, as a component of β-diversity, consistently enhanced the multifunctional dissimilarity and asynchrony of biocrusts. The legacy effect of historical precipitation variability, rather than annual precipitation, emerged as a primary driver on a convergence of less beneficial species, thereby promoting the variance in soil functioning. Importantly, the findings illuminated a cascading effect of precipitation regimes regulated by multifaceted diversity, which potentially magnifies their influences on vulnerable arid habitats.
Main conclusions: Our study highlighted the importance of precipitation variability and the indirect climate impact via soil microbiota. It contributes to a deeper understanding of the real-world consequences of altered precipitation on drylands, providing valuable insights for more accurate modeling of global change.
The historical daily precipitation records, abiotic variables and functional variables of each site are provied, and they can be found in the file named 'row_data.xlsx'.
The main code used is stored in a compressed file named "data_package.zip".
File/Folder Details
Summary of row_data.xlsx
*Description: a detail file containing the historical daily precipitation records, abiotic variables and functional variables of each site in our study.
*Sheet count: 4
*Sheet name: "Introduction", "Pre.Daily", "Abio.Var", "Func.Var"
Details for: Introduction
A summary for the file, containing the abbreviation of variables.
**Abbreviation of abiotic variables:
*Longitude: longitude of sampling site (°)
*Latitude: latitude of sampling site (°)
*Elevation: elevation of sampling site (m)
*pH: soil pH
*Salinity: soil salinity (μmol·g-1)
*C:N: the ratio of total organic carbon to total nitrogen (unitless)
*MAT: mean annual temperature (℃)
*TV_7d: Short-term temperature variability (unitless)
*TV_38y: Long-term temperature variability (unitless)
*MAP_0.1: mean annual precipitation (mm)
*PV_0.1: precipitation variability (unitless)
*Freq_0.1: mean annual precipitation frequency (days)
*MAP_1: mean annual precipitation (mm)
*PV_1: precipitation variability (unitless)
*Freq_1: mean annual precipitation frequency (days)
**Abbreviation of functional variables:
*Chla: chlorophyll-a concentration (μg·g-1)
*WHC: water-holding capacity (w/w)
*EPS: extracellular polysaccharides content (μg·g-1)
*TOC: total organic carbon (mg·g-1)
*S-SC: activity of sucrase
*S-β-GC: activity of β-glucosidase
*TN: total nitrogen (mg·g-1)
*NO3-: nitrate ion ( μg·g-1)
*NH4+: ammonium ion ( μg·g-1)
*TP: total phosphorus (mg·g-1)
*PO43+: phosphate ion ( μg·g-1)
*S-AKP: activity of alkaline phosphatase
**Abbreviation of sampling sites:
*BTL(1-4): Baituliang
*DK(1-6): Dengkou
*DLT(1-2): Dalate
*DX(1-4): Danxia
*GTX(1-2): Gaotai
*HHT(1-5): Huanghuatan
*JCX(1-3): Jiechaixian
*NHZ(1-2): Nanhuzhen
*SHW(1-4): Shahaowan
*SPT(1-5): Shapotou
*ZY(1-8): Zhangye
Details for: Pre.Daily
A dataset containing historical daily rainfall spanning 38 years for our sampling sites.
Details for: Abio.Var
Abiotic variable describing geographic location, soil characteristics and climatic variables.
Details for: Func.Var
The 12 key individual variables related to soil functions in dryland biocrusts.
Summary of data_package.zip
*Description: a detail folder containing the the data and script required for the main analysis process.
*Subfolder count: 3
*Subfolder name: "data","include" and "output"
Details for: data
The folder includes the files that need to be read for analysis.
*comm: This folder contains the community composition data, including heterotrophic bacteria(comm_Bac), cyanobacteria(comm_Cya) and fungi(comm_Fgi).
*tree: This folder contains the phylogenetic tree of representative sequences obtained from high-throughput sequencing, including heterotrophic bacteria(Bac_mafft_trim.fa.contree), cyanobacteria(Cya_mafft_trim.fa.contree) and fungi(Fgi_mafft_trim.fa.contree).
*Abiotic_difference: This file contains the difference of abiotic variables between paired sites
*Alpha_indices: This file contains the alpha indices used in this study.
*Beta_indices: This file contains the beta indices used in this study.
*Environmental_vars: This file contains the environmental variables used in this study.
*EnvPreference_FuncImportance: This file contains the species' environmental preference value, functional importance value (SES) and mean relative aboundance (RA).
*Function_vars: This file contains twelve individual functional variable and multifunctionality value based on average method (MeanFunction) and multithreshold method(Thre.0.25, Thre.0.50 and Thre.0.75).
Details for: include
The file includes the main R scripts used in our study. The defined functions were stored in the file "Source_R_code.txt".
*Beta_diversity_part: This R script can be used to decompose beta diversity into species replacement and richness difference components.
*Bootstrap of multiple regression: This R script can be used to execute bootstrapping program for multiple regression.
*piecewise regression: This R script can be used to fit the piecewise regression.
*pSEM: This R script can be used to fit the structural equation model.
Details for: output
The file includes those intermediate files output during analysis.
