DOI: 10.5061/dryad.pvmcvdp00
title: A comparison of water use strategies between pure and mixed forests on the Chinese Loess Plateau

description: To elucidate how pure and mixed forests adjust their water use strategies
 under interannual precipitation variability, we conducted a systematic
 investigation of root architecture, water dynamics, and physiological
 regulation in pure Pinus tabuliformis, pure Robinia pseudoacacia, and
 their mixed forests on the Chinese Loess Plateau during a wet year (2022)
 and a normal year (2023). By analyzing fine root vertical distribution,
 root length density, soil moisture, stand transpiration, stable
 isotope-based water sources, and stomatal behavior, we found that mixed
 forests accumulated higher fine root biomass than either pure forest type,
 while exhibiting intermediate root length density. Seasonal fluctuations
 in soil water content and stand transpiration were more pronounced in
 mixed forests than in pure P. tabuliformis stands, though less variable
 than in pure R. pseudoacacia stands. Transpiration dynamics closely
 tracked root length density patterns and responded strongly to
 environmental drivers such as air temperature, net radiation, and vapor
 pressure deficit. Importantly, as conditions shifted from the wet to the
 normal year, all forests reduced surface water use, but mixed forests
 showed a markedly greater shift toward deep soil water extraction compared
 to pure stands. Stomatal conductance analysis further indicated that mixed
 forests adopted more stringent stomatal regulation, reflecting a more
 conservative water use approach relative to pure forests. These results
 demonstrate that mixed forests develop a more efficient and conservative
 water-use strategy by optimizing root distribution for deep-water access
 and enhancing stomatal control. Our findings offer key physiological and
 ecological insights for predicting the stability and adaptability of
 plantation ecosystems under future climate variability on the Loess
 Plateau.
description: # A comparison of water use strategies between pure and mixed forests on
 the Chinese Loess Plateau Dataset DOI: 10.5061/dryad.pvmcvdp00 ##
 Description of the data and file structure This dataset
 (doi-10.5061dryad.pvmcvdp00.zip)includes eight data sheets for statistical
 analyses. Files and variables ### File: Dataset1-Environmentaldata.csv
 Description: This data table contains the precipitation (P), vapor
 pressure deficit (VDP), air temperature (Ta), relative humidity in air
 (RH) during the tree growing seasons of 2022 and 2023, as well as the
 relative extractable water of pure *Pinus tabuliformis* forest (PT), pure
 *Robinia pseudoacacia* forest (RP), and *Pinus tabuliformis* * *Robinia
 pseudoacacia* forest (PT*RP). Variables * Variables-2022/2023: Variables
 in 2022 or in 2023 * DOY: Day of year * P: Precipitation (mm) * Ta: Air
 temperature (°C） * VPD: Vapor pressure deficit (kPa) * RH: Relative
 humidity (%) * REW: Relative extractable soil water * PT: *Pinus
 tabuliformis* forest * RP: *Robinia pseudoacacia* forest * PT*RP: *Pinus
 tabuliformis* * *Robinia pseudoacacia* forest ### File:
 Dataset2-Fineroots.csv Description: This data sheet contains the dry fine
 root biomass and fine root length density of each forest at different soil
 depths. Variables * Depth: Soil depth (cm) * DB: Dry biomass (kg m ^-3^) *
 RLD: Root length density (cm cm ^-3^) * PT-PT*RP: PT trees in the PT*RP
 forest * RP-PT*RP: RP trees in the PT*RP forest ### File:
 Dataset3-Soilwatercontent.csv Description: This data sheet contains data
 on soil water content for each forest at different soil layers. Variables
 * Variables-2022/2023: Variables in 2022 or in 2023 * Surface: Soil water
 content in surface layer (cm^3^ cm ^-3^) * Middle: Soil water content in
 the middle layer (cm^3^ cm ^-3^) * Deep: Soil water content in the deep
 layer (cm^3^ cm ^-3^) ### File: Dataset4-Isotope.csv Description: This
 data sheet contains isotopic data of soil water for each forest. Variables
 * Depth: Soil depth (cm) * 2H: δ2H value (‰) * 18O: δ18O value (‰) *
 17O: δ17O value (‰) ### File: Dataset5-Plantisotope.csv Description: This
 data sheet contains isotopic data of plant xylem samples for each forest.
 Variables * Plants: Plant xylem samples * 2H: δ2H value (‰) * 18O: δ18O
 value (‰) * 17O: δ17O value (‰) ### File: Dataset6-Watersources.csv
 Description: This data sheet contains data on soil water resources from
 different soil layers for each forest. Variables * Forests: Forest types *
 Surface: Surface layer contribution (%) * Middle: Middle layer
 contribution (%) * Deep: Deep layer contribution (%) ### File:
 Dataset7-Dailytranspiration.csv Description: This data sheet contains data
 on daily transpiration for each forest in 2022 and 2023. Variables *
 DOY-2022(2023): Day of year in 2022 or in 2023 * PT-2022(2023): Daily
 transpiration in PT in 2022 or in 2023 (mm) * RP-2022(2023): Daily
 transpiration in RP in 2022 or in 2023 (mm) * PT-PT*RP-2022(2023): Daily
 transpiration for PT trees in PT*RP forest in 2022 or in 2023 (mm) *
 RP-PT*RP-2022(2023): Daily transpiration for RP trees in PT*RP forest in
 2022 or in 2023 (mm) ### File: Dataset8-Canopyconductance.csv
 Description: This data sheet contains data on canopy stomatal conductance
 for each forest in 2022 and 2023. Variables * DOY-2022(2023): Day of year
 in 2022 or in 2023 * PT-2022(2023): Canopy stomatal conductance for PT in
 2022 or in 2023 (mm s-1) * RP-2022(2023): Canopy stomatal conductance for
 RP in 2022 or in 2023 (mm s-1) * PT-PT*RP-2022(2023): Canopy stomatal
 conductance for PT trees in PT*RP forest in 2022 or in 2023 (mm s-1) *
 RP-PT*RP-2022(2023): Canopy stomatal conductance for RP trees in PT*RP
 forest in 2022 or in 2023 (mm s-1) ## Code/software All the analyses were
 done in SPSS 22.0 (SPSS Inc., Chicago, USA), and all figures were produced
 using Origin 2018 (OriginLab Corporation, USA).