DOI: 10.5061/dryad.1vhhmgr35
title: Data from: Drought effects in Mediterranean forests are not alleviated by diversity-driven water source partitioning

description: Tree species diversity in forest ecosystems could reduce their
 vulnerability to extreme droughts through improved microclimate and
 belowground water source partitioning driven by contrasting
 species-specific water use patterns. However, little is known about the
 seasonal dynamics of belowground water uptake that determine whether
 diversity positively or negatively impacts tree carbon assimilation and
 water exchange. Using a network of 30 permanent plots in Mediterranean
 forests with increasing tree species diversity (from monospecific to
 four-species mixtures), we examined the seasonal patterns
 of in-situ aboveground carbon and water relations and belowground
 water sources on 265 trees from four pine and oak species over two years
 using hydraulic and stable isotope approaches. We found that increasing
 species diversity in broadleaf and conifer mixtures induced strong soil
 water source partitioning between oak and pine species. As conditions
 became drier during the summer in mixed stands, oak species took up water
 from deeper soil sources, while pines were systematically limited to
 shallow ones. Despite significant belowground moisture partitioning,
 stronger drought-induced reductions in photosynthesis, stomatal
 conductance, and leaf water potential were still observed in diverse
 compared to monospecific stands for pines but with some benefits for oaks.
 Synthesis: Our findings reveal that tree species diversity promoted
 belowground water source partitioning in mixed oak and pine stands,
 potentially reducing competition for water in more diverse ecosystems.
 Yet, our results show that it is insufficient to buffer the adverse
 impacts of severe droughts on aboveground tree carbon and water use,
 leading to higher water stress, especially for pines.
description: <em>Site description</em> The
 study was conducted in Mediterranean forests in the Alto Tajo natural park
 (Guadalajara, Castilla La Mancha, 40.66°N, 02.27°W) in central Spain,
 where 30 plots (30m x 30m) within a 20 km<sup>2</sup> area
 were selected from the FunDivEUROPE Exploratory Platform (Fig. S1, Baeten
 et al., 2013). To assess the effect of tree diversity, the plots were
 established in 2011 in non-managed mature even-aged (i.e., more than 50
 years) forests with limited variation in altitude (i.e., from 980 to 1300
 m a.s.l.), topography, soil type, and density (see Table S1 and Baeten et
 al. (2013) for more details on plot selection). The soils in all plots
 were shallow (from 20 to 40 cm) calcic cambisol soils (FAO/UNESCO soil
 classification) on a cracked limestone bedrock but with plant roots that
 may extend down to several meters through the fractured bedrock (Peñuelas
 & Filella, 2003). This area has a continental Mediterranean
 climate with hot and dry summers and cold winters. The long-term annual
 precipitation sum was 416 mm (2011-2022), with 516 and 367 mm in 2021 and
 2022, respectively. The long-term mean annual temperature was 11°C
 (2011-2022), with 12°C and 11°C in 2021 and 2022, respectively. Maximum
 daily temperatures were 31°C and 33°C for 2021 and 2022, respectively
 (Fig.1). The soil aridity index (P/PET) was calculated monthly using
 meteorological data from the nearest station (Molina De Aragon, Castilla
 La Mancha, ES; 20 km away, Fig. S1). We first computed the monthly
 potential evapotranspiration (PET) using the Thornthwaite equation (Yates
 & Strzepek, 1994) based on the average monthly temperature,
 daylight length, and heat index. P/PET was calculated by dividing the
 monthly precipitation sum by the monthly PET. P/PET varied from 0.34 and
 0.38 in May (i.e., corresponding to wet soil conditions) to 0.11 and 0.05
 in July (i.e., the driest and hottest period) for 2021 and 2022,
 respectively. The area is characterized by the natural
 dominance of four tree species, i.e., two coniferous species
 (<em>Pinus nigra subsp. salzmannii (Dunal) Franco</em> and
 <em>Pinus sylvestris L.</em>) and two broadleaved ones
 (<em>Quercus faginea Lam.</em> and <em>Quercus ilex
 L.</em>), which were selected for this study. We selected plots with
 increasing tree species richness, including monospecific (where the target
 species represents more than 90% of the total basal area by itself),
 monofunctional (i.e., two-species mixtures of either coniferous or
 broadleaved species), multifunctional (i.e., two-species mixtures of
 coniferous and broadleaved species) and mixtures of the four species. Each
 richness level was replicated three times for all species with all
 possible species combinations, except for the two-species mixture between
 <em>P. sylvestris</em> and <em>Q. ilex</em>,
 resulting in 30 plots (Table S1). In mixed plots, the target species had
 similar abundances with a lower limit of 60% of maximum evenness in the
 basal area (Baeten et al., 2013). The understory vegetation (representing
 less than 10% of the total basal area) was mainly composed of shrub
 species (<em>Arctostaphylos uva-ursi, Buxus sempervirens,
 </em>and<em> Genista scorpius</em>) and juveniles of the
 dominant tree species. We randomly selected five
 dominant or co-dominant trees per species in each plot, leading to 265
 trees in total. To assess the seasonal dynamics of aboveground and
 belowground tree water and carbon use, we
 conducted <em>in-situ</em> measurements (detailed below)
 repetitively on each tree in 2021 and 2022 at the beginning (i.e., in May
 when soil moisture was high, Fig.1), middle (i.e., in July, corresponding
 to the driest and hottest period), and the end of the growing season
 (i.e., in September, representing the recovery transition from dry to wet
 soils) (n=6 measurements per physiological traits and tree in total). The
 two years presented contrasting climatic conditions with a rather mild
 summer drought in 2021 and an extremely long drought in 2022 (Fig. 1),
 allowing us to assess tree carbon and water dynamics under different
 environmental conditions. Indeed, the late rain events in 2022 led to
 lower soil moisture during the measurement campaign of September, while
 conditions were wetter during the last campaign in 2021, representing full
 recovery after the summer drought (Fig.1).
 <em>Leaf-level gas exchange and water
 potential</em> We measured the leaf-level
 light-saturated net photosynthesis (A<sub>net</sub>, µmol
 m<sup>-2</sup> s<sup>-1</sup>) and stomatal
 conductance (g<sub>s</sub>, mol m<sup>-2</sup>
 s<sup>-1</sup>) on one fully developed leaf (or multiple
 needles for conifers) per tree from a 50cm to 1m-long (for pine and oak
 species, respectively) sun-exposed branch. The branches were sampled using
 an extension pole, directly placed in a water bucket, and recutted under
 water to restore water flow (Lange et al., 1986). Within 15 min after
 sampling, A<sub>net</sub> and g<sub>s</sub> were
 measured using one infrared gas exchange analyzer (LI-6800 or LI-6400,
 LICOR Biosciences, USA), according to the method described by Bayar
 & Özçelik (2024). The measurements were done between 9 am and 1 pm
 (local time). The relative humidity inside the chambers was set between 30
 to 50% (to match the average daily ambient environmental conditions during
 the measurements), the CO<sub>2</sub> concentration to 400
 ppm, the photosynthetic photon flux density (PPFD) to 1500 μmol
 m<sup>-2 </sup>s<sup>-1 </sup>(to ensure
 saturating light conditions), and the air temperature inside the cuvette
 from 20 to 30°C depending of the sampling dates (to fit the mean midday
 air temperature during the measurements; Fig. S2). Each leaf was measured
 when the gas exchange values were stable (i.e., after max. 5 minutes).  On
 the same day as gas exchange measurements, one twig per tree was collected
 before sunrise (Ψ<sub>pd</sub>) and at midday
 (Ψ<sub>md</sub>) to measure the leaf water potential (MPa)
 with a Scholander-type pressure chamber (M1505D, PMS Instruments, USA).
 The difference between Ψ<sub>pd</sub> and
 Ψ<sub>md</sub> (Δ<sub>Ψ</sub>) was calculated to
 describe the tree’s stomatal regulation strategy.
 <em>Water uptake patterns</em> On
 each sampling date, in the monospecific and four-species mixtures (n=15
 plots), we collected three 10 cm-long twig samples across the canopy from
 each tree between 9 am and 3 pm (local time). After removing the bark, the
 samples were immediately sealed in airtight vials (Exetainer, Labco
 Limited, UK). The vial lid was wrapped with parafilm and placed in cool
 conditions to avoid evaporation. On the same day as the twig sampling,
 soil samples were collected every 10 cm at four depths (0–10, 10–20,
 20–30, and 30-40 cm) and three random positions in each plot, using a
 manual soil corer or/and a pickaxe when the soil was too rocky. For each
 stand, the soil samples per depth were immediately pooled together in 50mL
 vials and stored like twigs. As the depth of the limestone bedrock varied
 within and between plots (between 20 and 70 cm; Table S1), the maximum
 depth of soil sample collection varied by date, plot, and position.
 Nevertheless, we could extract all the soil layers from 0 to 40 cm in
 every plot at each campaign, except for the <em>Q.ilex</em>
 monocultures in spring 2021, due to technical limitations. Precipitation
 water (used as a proxy of the groundwater isotopic values, see below) was
 collected by two Tube-dip-in-water collector types with pressure
 equilibration (RS1, Palmex, HR), spread into the study area (Fig. S1). Due
 to the unique design of the rain samplers avoiding evaporation for up to
 one year (Gröning et al., 2012), we collected the rainwater every two
 months during the growing season and once during the winter each year. The
 water was placed in vials and sealed with a lid and parafilm.
 Water from xylem and soil samples was extracted using a
 custom-made cryogenic vacuum distillation system at the Swiss Federal
 Institute for Forest, Snow, and Landscape Research (WSL, Birmensdorf, CH)
 (Diao et al., 2022). The extraction system consisted of 20 tubes connected
 to 20 U-shaped collection tubes specifically designed for this system. A
 frozen sample was placed in the extraction tube and submerged in water at
 80°C, while the associated collection tube was submerged in liquid
 nitrogen. The system was then evacuated to 5.10<sup>-2</sup>
 mBar. The extraction was maintained for 2 h for both xylem and soil
 samples to achieve a complete extraction following the recommendations of
 West et al. (2006) (i.e., a minimum of 60 min extraction time for a broad
 range of plant and soil materials). This process led to an extraction of
 99.96% of the water in the samples, with more than 1 mL extracted for each
 sample, limiting the uncertainties in plant water isotopic composition due
 to cryogenic vacuum distillation (Diao et al., 2022). After the
 extraction, water samples were transferred into cap-crimp 2-ml vials and
 stored at −20 <sup>◦</sup>C until the isotopic analysis.
 Uncertainties associated with bulk water extractions using cryogenic
 distillation systems could occur that would underestimate the
 contributions of soil water and overestimate the ones from groundwater
 (Barbeta et al., 2021). Yet, as all samples were treated similarly, the
 errors would only affect the actual values, not the comparison between
 mixtures and seasons. The δ<sup>2</sup>H
 and δ<sup>18</sup>O of all water samples (i.e., twig, soil,
 and precipitation) were measured with a high-temperature conversion
 elemental analyzer coupled to a DeltaPlus XP isotope ratio mass
 spectrometer (TC/EA-IRMS; Thermo, DE). Isotope ratios were reported in per
 mil (‰) relative to Vienna Standard Mean Ocean Water (VSMOW). Calibration
 versus the international standards was achieved by analysis of a range of
 certified water of different isotope ratios, resulting in a precision of
 2‰ for δ<sup>2</sup>H and 0.3‰ for
 δ<sup>18</sup>O. The combined use of
 δ<sup>2</sup>H and δ<sup>18</sup>O provides a more
 comprehensive and nuanced understanding of the water movement through
 ecosystems as they can differently account for potential fractionation
 effects (Meißner et al., 2014). <em>Water source
 contribution</em> From the natural abundance of
 δ<sup>2</sup>H and δ<sup>18</sup>O in plant xylem
 and soil water, we used a Bayesian stable isotope mixing model to quantify
 the contribution of potential tree water sources for each species in the
 monospecific and four-species mixtures for each sampling date. As the
 rainwater isotope ratio differs throughout the season (i.e., isotopically
 more depleted rainwater in winter compared to summer) and the water
 evaporation decreases with soil depth, each water source has a
 significantly different stable isotopic composition (Fig. S3). These
 distinct soil isotopic profiles allow us to determine the contribution of
 each water source to the tree xylem water under the assumption that there
 is no isotopic fractionation during water uptake by the roots (Dawson
 & Ehleringer, 1991). Therefore, the natural isotopic abundance of
 xylem sap should reflect the water sources used by the plant. We used the
 package <em>simmr</em> in R (Parnell, 2019), where the
 isotopic composition (δ<sup> 18</sup>O and δ<sup>
 2</sup>H) for each potential source (i.e., 0-10 cm, 10-20 cm, 20-30
 cm, 30-40 cm, rainwater) and each target tree were assigned into the
 model. We set the TEF (trophic enrichment factor) and the concentration
 dependence to 0 due to the presumed absence of isotopic fractionation by
 the roots (but see Barbeta et al., 2020; Ellsworth & Williams,
 2007). Using the isotopic values from each plant and the soil water source
 of the corresponding plot for each date, we ran the model where 3600
 iterations out of 10000 runs were produced over 4 Markov chain Monte Carlo
 (MCMC) (Sun et al., 2022). To increase the clarity of presentation, the
 contributions from the water sources were grouped <em>a
 posteriori</em> into three layers: shallow (i.e., 0–20 cm), deep
 (i.e., 20–40 cm), and water stored in the fractured bedrock (i.e.,
 rainwater). Indeed, the winter precipitation that penetrates deep soil
 layers and bedrock cracks could be a substantial water source for trees in
 Mediterranean forests during summer droughts (Eliades et al., 2018). As
 the bedrock water is not subjected to evaporation (Ehleringer &
 Dawson, 1992), we used the precipitation collected at our site as a proxy,
 similar to Grossiord et al., (2017). The cumulative rainwater collected
 during the winter of 2021 (i.e., January-April) and 2022 (i.e.,
 October-April) was considered bedrock water source for the May campaigns
 of 2021 and 2022, respectively. During the remaining growing season, the
 stable isotope composition of the rainwater accumulated until the sampling
 date was added to the winter precipitation (e.g., values from winter, May,
 and June rainwater were used for the campaigns in July).
 <em>Water source partitioning</em>
 To estimate the plot-level vertical water source distribution in
 the four-species mixture plots, we calculated the belowground water source
 partitioning at the community level (P<sub>W</sub>, unitless)
 from the sum of the differences in either the natural abundance of
 δ<sup>2</sup>H or δ<sup> 18</sup>O in plant water
 between all interacting species in every four-species mixtures for each
 sampling date. As different water uptake depths between trees correspond
 to greater differences in the xylem water stable isotope within a tree
 cluster, higher P<sub>W</sub> indicates that the trees are
 taking up water from more distant water sources from each other.
 Therefore, to calculate P<sub>W</sub> based on<sub>
 </sub>δ<sup>2</sup>H, we used the following equation
 developed by Grossiord et al. (2018): P<sub>W
 </sub>= |δ<sup>2</sup>H<sub>PN</sub> -
 δ<sup>2</sup>H<sub>PS</sub>| +
 |δ<sup>2</sup>H<sub>PN</sub> -
 δ<sup>2</sup>H<sub>QF</sub>| +
 |δ<sup>2</sup>H<sub>PN</sub> -
 δ<sup>2</sup>H<sub>QI</sub>| +
 |δ<sup>2</sup>H<sub>PS</sub> -
 δ<sup>2</sup>H<sub>QF</sub>| +
 |δ<sup>2</sup>H<sub>PS</sub> -
 δ<sup>2</sup>H<sub>QI</sub>| +
 |δ<sup>2</sup>H<sub>QF</sub> -
 δ<sup>2</sup>H<sub>QI</sub>|                 
 (Equ. 1) where
 δ<sup>2</sup>H<sub>PN</sub>,
 δ<sup>2</sup>H<sub>PS</sub>,
 δ<sup>2</sup>H<sub>QF,</sub> and
 δ<sup>2</sup>H<sub>QI</sub> correspond to
 δ<sup>2</sup>H of <em>P. nigra, P. sylvestris, Q.
 faginea,</em> and <em>Q. ilex</em>, respectively. For
 the monospecific plots, we applied the same formula but with the sum of
 the differences in δ<sup>2</sup>H or δ<sup>
 18</sup>O in plant water between the tree individuals for each
 sampling date. P<sub>W</sub> resulting from
 δ<sup>18</sup>O and δ<sup>2</sup>H weighted
 similarly (Fig. S4), supporting the absence of significant differences in
 using δ<sup>18</sup>O and δ<sup>2</sup>H to
 determine water source partitioning found by Wang et al. (2019). Hence, as
 δ<sup>2</sup>H had higher range of values increasing the
 vizualisation of the data, only the P<sub>W</sub> based on
 δ<sup>2</sup>H was further used in this paper.
 <em>Statistical analyses</em> The
 effects of tree species diversity on Ψ<sub>pd</sub>,
 Ψ<sub>md</sub>, Δ<sub>Ψ</sub>,
 A<sub>net</sub>, g<sub>s, </sub>xylem
 δ<sup>2</sup>H and δ<sup>18</sup>O were determined
 through linear mixed-effects models for each species using the
 package<em> lmer</em>. The effect of the season (i.e., spring,
 summer, fall), year (i.e., 2021, 2022), and species diversity (i.e.
 monospecific/monofunctional/ multifunctional/four-species mixtures) were
 used as fixed effects, and the individual plot and tree was treated as a
 random intercepts. Similar models were used to determine differences in
 soil water δ<sup>2</sup>H and δ<sup>18</sup>O.
 Sampling dates, species diversity, species, and soil depth were used as
 explanatory factors in the fixed part of the model. Significant
 differences in soil water δ<sup>2</sup>H and
 δ<sup>18</sup>O between depths for each species'
 diversity and sampling dates were found, allowing us to use the Bayesian
 isotope mixing model to determine the water source contribution of trees
 as described above (Fig. S3). The output of this model was analyzed
 similarly with linear mixed-effects models for each species. First, the
 effect of soil depth (i.e., shallow, deep, bedrock), season, year, and
 species diversity (i.e., monospecific and four-species mixture) were set
 as fixed effects, and the individual plot as a random effect.  Then, we
 ran similar models for each soil depth and species where the season, year,
 and species diversity were used as fixed effects and the plots as random
 effects. To reveal significant differences between species richness for
 each measurement at each sampling date and each species, post hoc analyses
 were performed with Tukey's HSD test, with FDR correction for
 multiple testing. Linear regressions were used to test the relationships
 between Δ<sub>Ψ</sub>, Ψ<sub>pd</sub>,
 g<sub>s</sub>, xylem δ<sup>18</sup>O, P/PET, and
 P<sub>W</sub>. All analyses were performed using the R v.4.2.2
 statistical software (R Development Core Team, Vienna, Austria, 2022).
 Before performing each model, the homogeneity of variances and the
 normality of residuals were assessed, and data were log-transformed if
 necessary (Table S2).
description: # Data from: Drought effects in Mediterranean forests are not alleviated
 by diversity-driven water source partitioning
 [https://doi.org/10.5061/dryad.1vhhmgr35](https://doi.org/10.5061/dryad.1vhhmgr35) ## Description of the data and file structure Description for the metadata in the file "Data_soil" | Column | Entry | Value | Unit | Explanation | Remark | | :----- | :--------- | :---- | :--- | :-------------------------------------------------------------------------------- | :---------------------------------------------------------- | | A | Year | 2 | | Year (2021; 2022) | | | B | Date | 6 | | Date of the measuring campaign (format: Year-Month) | | | C | Plot | 15 | | Plot number | | | D | Depth | 5 | | Soil layers ( from 0 to 10cm, 10-20, 20-30, more than 30 (30+), rain water (RW))  | Rain water was used as estimate for the groundwater | | E | Mixture | 2 | | Species richness level (1, 4) | | | F | Mean\_d18O | 366 | ‰ | Mean soil water oxygen isotopic composition | | | G | sd\_ d18O | 366 | ‰ | Standard deviation soil water oxygen isotopic composition | only one measure per plot so standard deviation is set at 0 | | H | Mean\_d2H | 366 | ‰ | Mean soil water hydrogen isotopic composition | | | I | sd\_d2H | 366 | ‰ | Standard deviation soil water hydrogen isotopic composition | only one measure per plot so standard deviation is set at 0 | Description for the metadata in the file "Data_tree" | Column | Entry | Value | Unit | Explanation | Remark | | :----- | :------------------- | :---- | :-------- | :---------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------- | | A | Year | 2 | | Year (2021; 2022) | | | B | Plot | 30 | | Plot number | | | C | Tree | 263 | | Tree number | | | D | Species | 4 | | Species (PS:Pinus sylvestris, PN: Pinus nigra, QF: Quercus faginea, QI: Quercus ilex) | | | E | Mixture | 3 | | Species richness level (1, 2, 4) | | | F | Species\_composition | 10 | | Species composition for each plot (e.g., PS-QF: mixture of Pinus sylvestris and Quercus faginea) | | | G | Group | 4 | | Type of mixture (monoculture, monofunctional (Pine-pine or oak-oak), multifunctional (pine-oak), mixture-4) | | | H | Date | 6 | | Date of the measuring campaign (format: Year-Month) | | | I | Campaign | 6 | | Campaign number | | | J | Season | 3 | | Season (Spring, Summer, Fall) | | | K | Soil\_moisture | 170 | % | Soil moisture at 10 cm depth | "n/a" indicates data not available for the plot | | L | P\_PET | 6 | unitless | Aridity index for each campaign | | | M | Predawn | 1578 | MPa | Predawn leaf water potential | "n/a" indicates data not available for the tree or obvious outliers removed | | N | Midday | 1578 | MPa | Midday leaf water potential | "n/a" indicates data not available for the tree or obvious outliers removed | | O | Delta | 1578 | MPa | Difference between predawn and midday water potential | "n/a" indicates data not available for the tree or obvious outliers removed | | P | Anet | 1578 | µmol/m²/s | Ligth-saturated net carbon assimilation at 400ppm | "n/a" indicates data not available for the tree or obvious outliers removed | | Q | gs | 1578 | mol/m²/s | Ligth-saturated stomatal conductance at 400ppm | "n/a" indicates data not available for the tree or obvious outliers removed | | R | d18O | 685 | ‰ | Xylem water oxygen isotopic composition | measured only for species richness 1 and 4, "n/a" indicates data not available for the tree or obvious outliers removed | | S | d2H | 685 | ‰ | Xylem water hydrogen isotopic composition | measured only for species richness 1 and 4, "n/a" indicates data not available for the tree or obvious outliers removed | | T | Pw | 90 | unitless | Belowground community-level water source partitioning  | measured only for species richness 1 and 4, "n/a" indicates data not available for the plot or obvious outliers removed |