DOI: 10.5061/dryad.ns1rn8q2z
title: Multifaceted precipitation patterns impact biocrust functionality in drylands: A cascade of variability via species replacement in soil microbiota

description: 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.
description: # Precipitation regimes impacts dryland multifunctionality: A cascade of
 historical precipitation regimes via species replacement in multitrophic
 microbiota 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.