DOI: 10.5061/dryad.hmgqnk9r3
title: Data from: Biogeographic patterns of soil microbial biomass in alpine ecosystems depend on local rather than regional drivers

description: Analyses for the manuscript “Biogeographic patterns of soil microbial
 biomass in alpine ecosystems depend on local rather than regional drivers”
 using soil microbial and environmental data collected for a large-scale
 sampling campaign along elevational gradients in seven mountains across
 mainland China. The dataset included information about the sample plots,
 data of soil microbial biomass, and variables of four environmental
 parameter sets (i.e., macroclimate, plant functional traits, soil
 physicochemical properties, and topography), and relevant code for
 statistical analysis.
description: In this study, we conducted a large-scale sampling
 campaign along seven elevation gradients across mainland China.
 We measured soil microbial biomass using phospholipid fatty acid
 (PLFA) analysis and the chloroform fumigation extraction
 method. We analyzed plant and soil samples to
 determine plant functional traits and soil physicochemical properties, and
 we extracted macroclimate, and terrain data from the CHELSA, and SRTM
 datasets, respectively. To improve the normality and
 homoscedasticity of the residuals before statistical analysis, we examined
 the variables of soil microbial biomass, F/B, MBC, and MBN for normality
 and homogeneity using the Shapiro-Wilk and Levene tests. We transformed
 the variables using ln(x) when necessary. All the variables of four
 environmental factors were transformed to Z-scores before fitting the
 models.
description: # Biogeographic patterns of soil microbial biomass in alpine ecosystems
 Analyses for the manuscript "Biogeographic patterns of soil microbial
 biomass in alpine ecosystems depend on local rather than regional
 drivers" using soil microbial and environmental data collected for a
 large-scale sampling campaign along elevational gradients in seven
 mountains across mainland China. Details of experimental setup, collection
 of data, and statistical methods can be found in the paper
 "Biogeographic patterns of soil microbial biomass in alpine
 ecosystems depend on local rather than regional drivers." ##
 Description of the data and file structure **Description of files:** *
 "Data.csv" --&gt; data of information about the sample plots,
 SMB, MBC, MBN, F/B, and variables of four environmental parameter sets
 (i.e., macroclimate, plant functional traits, soil physicochemical
 properties, and topography). * "Variable_names.xlsx" --&gt;
 including the short names, long names, and units of variables in Data.csv.
 Abbreviations: Mountains: AT: Altai Mountains, TS: Tian Mountains, CB:
 Changbai Mountains, QL: Qilian Mountains, BL: Balang Mountains, SL:
 Segrila Mountain, YL: Yulong Mountains, Altitude: Four key ecological
 transition zones were selected for sampling efforts: (1) the upper
 elevational limit of vegetation (High), (2) the alpine treeline
 (Treeline), (3) an intermediate elevation between the treeline and the
 upper vegetation limit (Middle), and (4) a lower elevational position
 below the treeline (Low), SDE: Standardized elevation, SMB: total
 microbial biomass, FB: the ratio of fungi to bacteria, MBC: soil microbial
 biomass carbon, MBN: soil microbial biomass nitrogen,NH4: ammonium
 nitrogen, NO3: nitrate nitrogen, TN: total nitrogen, TC: total carbon,
 SCN: ratio of soil carbon to nitrogen, MAT: mean annual air temperature,
 MTWM: maximum air temperature of warmest month, MTWQ: mean air temperature
 of warmest quarter, TS: air temperature seasonality, MAP: mean annual
 precipitation, PWQ: precipitation of warmest quarter, PS: precipitation
 seasonality, LDMC: leaf dry matter content, SLA: specific leaf area,
 leafC: leaf carbon, leafN: leaf nitrogen, leafCN: ratio of leaf carbon to
 nitrogen, TWI: topographic wetness index. ## Code/Software “R_code.R”
 shows a part of statistical analysis process in the research, which
 consists of 3 parts: * Evaluation of distribution patterns of soil
 microbial biomass along the elevation gradient * Linear mixed-effects
 models (LMMs) with univariate structures to rank the environmental
 variables * Assessments of microbial-environment relationship across local
 and regional scales All of this code runs in R (v4.3.0, R Core Team,
 2023).