Restricciones de acceso a la Merca

Aboveground plant material was harvested by cutting the plants two cm above the soil surface at the end of the drought (12th _to16th _{of June), sorted to species and dried} in the oven for 48h at 70 °C before weighing. In addition, pictures were taken at the start of the drought with an RGB camera (Sony DSC HX50) at 1.3 m above the soil surface to estimate plant community cover using the vegetation index “Excess Green minus Excess Red (ExG−ExR)” of Meyer and Neto (2008). The vegetation index was calculated using the packages “rgdal” (Bivand et al., 2017) and “raster” in R (Hijmans, 2015), using a pixel intensity threshold value of 80 for the vegetation index after visual checking and comparison with cover percentages using green band pictures and pixel thresholding in ImageJ (1.51f) (Rasband, 1997-2017).

The rooting depth of each species was determined using root biomass sampled in July 2015 in each monoculture by taking three soil cores per plot (50 cm deep x 2.5 cm diameter), divided into four layers: 0-5, 5-15, 15-30 and 30-50 cm. Samples were pooled per plot per layer and carefully rinsed with tap water using a 0.5 mm sieve to collect the fine roots (<2 mm). The root samples were oven dried for 72h at 70°C and weighted. The deep rooting fraction (DRF), the fraction of roots that was found in the deepest layer (30-50 cm) compared to total root biomass (0-50 cm), was calculated for each (monoculture) plot. Species’ deep rooting fractions are shown in Table 5.1.

In addition, soil moisture was determined gravimetrically (Reynolds, 1970) in four soil layers, 0-15, 15-30, 30-50 and 50-60 cm, by taking soil samples with a soil core (1.5 cm diameter) at the start of the drought and at the end of the drought period. Samples were collected in plastic bags, fresh weighted, dried for at least 48h at 105 °C, and weighted again for dry weight.

Temperature of the plant community was measured on a warm, sunny day in the fourth week of the drought, using an infra-red camera. The thermal camera was placed on a two meter high tripod to capture the temperature of the inner 0.32 m2 _{of the communities. We assume that community temperature is related} to the drought stress of the community, partly because leaf temperature increases when leaf transpiration is reduced due to water stress (Jackson and Hillel, 1982). In addition, community temperature shows the severity of the micro conditions that the communities experience (heath stress).

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5.3.4 Calculations

Biodiversity effects (net effects (NE), complementarity effects (CE) and selection effects (SE) were calculated for all mixtures using the additive partitioning method of (Loreau and Hector, 2001). In this method, the following equation (eq. 5.1) is used,

in which delta yield (ΔY) measures the overall difference between the observed yield in the mixture (Y_O) and the expected yield (Y_E). N is the number of species present in the mixture at the end of the drought, and ΔRY the difference between the observed and expected relative yield in mixture (RY_O - RY_E), averaged across species. RY_O is the observed yield of a species in mixture, divided by its monoculture yield, while the expected relative yield (RY_E) is 1/N. is the average monoculture yield of the species that are present in the mixture. In this equation, ΔY is similar to NE, while the left component ) measures CE and the right component ( ) SE. Positive biotic interactions in mixtures, such as complementary water use, will be measured by CE. As is shown in equation 1, SE is calculated as the covariance between deviation from expected yield and monoculture biomass. Thus, it measures if increased (or decreased) yield in mixtures is related to productivity in monoculture across the species present in a mixture. If under drought, drought tolerant species are the most productive species in monoculture and become dominant in mixtures (and thereby maintain community biomass during drought), this will lead to increased positive SE values. We calculated the average monoculture yield ( ) per drought treatment, so that biodiversity effects of control mixture plots were compared with their control monocultures and drought mixture plots with drought monocultures. It is important to note that biodiversity effects depend on monoculture biomass (M): all else equal, a larger M will mean larger biodiversity effects (see eq. 5.1). This means that if drought leads to a reduction in (monoculture) biomass, biodiversity effects will decrease as well, even if the species interactions underlying these effects do not change. To be able to compare biodiversity effects between control and drought plots, we standardized biodiversity effects by dividing them by their average monoculture yield (Craven et al., 2016). These will be referred to as relative NE (rNE) relative CE (rCE) and relative SE (rSE). Note that the value of these relative biodiversity effects

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can be interpreted as the relative contribution to mixture biomass, compared to the average monocultures biomass. For example, an rNE of 0.5 means that the net effect increased mixture biomass by 50% compared to the average monoculture biomass.

All species that died before or during the drought and had zero biomass during biomass collection (see Fig. S5.3 for survival) were excluded from the analyses. As Leontodon species shared one position in the design due seed contamination (see above), their RY_E and M were corrected using the actual number of planted individuals: RY_E of Leontodon species was calculated as the number of individuals planted divided by the total number of individuals planted in the inner 6 x 6 rows (36 individuals). To calculate M, monoculture biomass was weighted per plot using the number of individuals planted in monoculture, by multiplying the observed plot yield by the number of total planted individuals divided by the number of planted individuals in that plot. Unfortunately, all L. autumnalis individuals died in the monocultures, but not in mixtures. To determine biodiversity effects for mixtures containing this species, we used its average biomass in mixtures, separately for drought and control plots and corrected for the different planting densities, as its monoculture yield in the calculations. Thus, on average, L. autumnalis did not affect the biodiversity effects in mixtures.

Next, relative species performance in mixtures was calculated per plot using proportional deviation (D_i; Loreau, 1998) . D_i is the proportional deviation of species biomass in mixture (O_i) from its expected biomass (E_i): D_i = (O_i - E_i) / E_i. The expected biomass of a species (E_i) was calculated as the proportion of individuals planted in the mixture multiplied by its monoculture biomass. This measure is independent of the number of individuals planted, which is necessary to compare the performance of

L. hispidus. Further, we calculated monoculture drought resistance for each species as

the difference in logs between the average monoculture biomass in control plots and the average biomass in drought plots.

Finally, for each mixture we calculated the average rooting depth as the community weighted mean (CWM) deep root fraction (DRF; the fraction of root biomass in 30-50 cm compared to total root biomass in the 0-50 cm soil profile). In addition, we calculated variation in rooting depth among the species in each community as functional diversity in DRF. This diversity in DRF was calculated as functional dispersion (FDis), a functional diversity index that is independent of the

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CWM, but includes the spread of trait values within the community relative to the community weighted mean (Laliberté and Legendre, 2010). Communities’ CWMs and FDis in DRF were calculated using the “FD package” in R (Laliberté and Shipley, 2011), using species specific DRF values (Table 5.1) and species relative abundances. Species relative abundances were based on the harvested aboveground biomass (species aboveground biomass compared to whole community biomass).

5.3.5 Statistics

Soil moisture at the start and end of the drought was analysed using an linear mixed model (LME) with drought treatment, species richness and soil layer and their interactions as fixed factors and plot nested in block as random factor. Differences between layers were tested using post hoc tests (Tukey) with the “lsmeans” package (Lenth, 2016). The effects of species richness and drought on community biomass were tested using an LME with planted species richness and drought treatment and their interaction as fixed factors and block and species composition as random factors (REML). Similarly, the effect of drought treatment on rNE, rCE and rSE was tested with an LME with drought treatment as a fixed factor and species composition as a random factor.

To identify drought-tolerant species and potential shifts in species’ drought resistance in mixtures, we also analysed species-specific biomass in monoculture and mixture. Differences in the drought response between species in monoculture were tested with an LME using log biomass as response variable and species and drought treatment as fixed factors. Block was included as random factor. For the mixtures, the effect of the drought treatment on species’ D_i was tested using an LME with drought treatment as fixed variable and species composition as random factor. Thereafter, a similar test was done for all species separately. L. autumnalis was excluded from these analyses as this species did not have a monoculture yield to compare mixture yield with.

To examine the role of rooting depth in species and community drought resistance, we tested the effect of DRF on monocultures drought resistance (biomass difference between C and D plots) with an linear model. We expected that species with high DRF, indicating a vertical root distribution with a high proportion of roots

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in deeper layers, have a higher drought resistance, since species with a high DRF have potentially higher water uptake from deeper layers, where water availability is higher during drought. Further, we investigated the importance of the average DRF values (CWMs) and diversity (FDis) in DRF within a community on performance of the mixture communities (rCE) under drought and control conditions: we coupled rCE to DRF CWM and DRF FDis and drought treatment in an LME with species composition as a random factor (Maximum Likelihood).

To determine if plant cover played a role in the community drought response, we first determined the relationships between cover, soil moisture at the start and at the end of the drought, and community temperature with Pearsons correlation coefficient (package “ Hmisc” (Harrell Jr and others, 2016), using all plots. Next, we tested whether cover affected changes in rCE due to drought by including cover (continuous) and its interaction with drought as fixed factors in the LME used for rCE as described above. As a second step, we analysed the relationships between rCE and cover separately for the control and drought plots using linear models.

In all models, species richness was included as a factor with two levels: monocultures and mixtures. To meet model assumptions, biomass was ln transformed. Similarly, rNE, rCE, rSE, and D_i were log10 transformed after adding the lowest value plus one to have only positive values. In the analyses of biodiversity effects, extreme outliers – defined as values that were five or more times the interquartile range above the third quartile – were removed. This led to the removal of three rNE and rSE (same plots) and one rCE value. All statistics were done in R (version 3.1.3) with R studio (version 1.0.153), using the “lme4” (Bates et al., 2015) or “nlme” (Pinheiro et

al., 2016) package. We used anova type III with the “anova” function of the “stats”

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5.4 Results