plant functional diversity increases grassland

Milcu et al 2015 - main manuscript

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Plant functional diversity increases grassland productivity-related water 1

vapor fluxes: an Ecotron and modeling approach 2

Alexandru Milcu1,2, Werner Eugster3, Dörte Bachmann3, Marcus Guderle4, Christiane Roscher6, 3

Annette Gockele5, Damien Landais1, Olivier Ravel1, Arthur Gessler8, Marcus Lange7, Anne 4

Ebeling8, Wolfgang W. Weisser9, Jacques Roy1, Anke Hildebrandt4 & Nina Buchmann3 5

6

1CNRS, Ecotron (UPS-3248), Campus Baillarguet, F-34980, Montferrier-sur-Lez, France 7

2Centre d’Ecologie Fonctionnelle et Evolutive, CEFE-CNRS, UMR 5175, Université de Montpellier – 8

Université Paul Valéry – EPHE, 1919 route de Mende, F-34293, Montpellier Cedex 5, France 9

3Institute of Agricultural Sciences, ETH Zurich, Universitaetsstrasse 2, 8092, Zurich, Switzerland 10

4 Friedrich Schiller University Jena, Institute of Geoscience, Burgweg 11, 07749 Jena, Germany 11

5Albert-Ludwigs-Universität Freiburg, Institut für Biologie II – Geobotanik, Schänzlestr. 1, D-79104 12

Freiburg, Germany 13

6 UFZ, Helmholtz Centre for Environmental Research, Department Community of Ecology, Theodor-14

Lieser-Str. 4, 06120 Halle, Germany 15

7Max Planck Institute for Biogeochemistry, Hans-Knoell-Str. 10, 07745, Jena, Germany 16

8 Swiss Federal Research Institute WSL, Zürcherstr. 111, 8903 Birmensdorf, Switzerland 17

9Friedrich Schiller University Jena, Institute of Ecology, Dornburger Str. 159, 07743, Jena, Germany. 18

10Terrestrial Ecology Research Group, Department of Ecology and Ecosystem Management, School of 19

Life Sciences Weihenstephan, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 20

Freising, Germany. 21

22

Corresponding author: Alexandru Milcu, CNRS, Ecotron - UPS 3248, Campus Baillarguet, 34980, 23

Montferrier-sur-Lez, France, email: [email protected], phone: +33(0)467-613-243. 24

25

26

mailto:[email protected]


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Abstract 27

The impact of species richness and functional diversity of plants on ecosystem water 28

vapor fluxes has been little investigated. To address this knowledge gap, we combined a 29

lysimeter setup in a controlled environment facility (Ecotron) with large ecosystem samples/ 30

monoliths originating from a long-term biodiversity experiment (“The Jena Experiment”) and a 31

modelling approach. We aimed at (1) quantifying the impact of plant species richness (4 vs. 16 32

species) on day- and night-time ecosystem water vapor fluxes, (2) partitioning ecosystem 33

evapotranspiration into evaporation and plant transpiration using the Shuttleworth and Wallace 34

(SW) energy partitioning model, and (3) identifying the most parsimonious predictors of water 35

vapor fluxes using plant functional trait-based metrics such as functional diversity and 36

community weighted means. Day-time measured and modeled evapotranspiration were 37

significantly higher in the higher diversity treatment suggesting increased water acquisition. The 38

SW model suggests that at low plant species richness, a higher proportion of the available energy 39

was diverted to evaporation (a non-productive flux), while at higher species richness the 40

proportion of ecosystem transpiration (a productivity-related water flux) increased. While it is 41

well established that LAI controls ecosystem transpiration, here we also identified that the 42

diversity of leaf nitrogen concentration among species in a community is a consistent predictor 43

of ecosystem water vapor fluxes during day-time. The results provide evidence that, at the peak 44

of the growing season, higher LAI and lower percentage of bare ground at high plant diversity 45

diverts more of the available water to transpiration – a flux closely coupled with photosynthesis 46

and productivity. Higher rates of transpiration presumably contribute to the positive effect of 47

diversity on productivity. 48


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Keywords: biodiversity-ecosystem functioning, evapotranspiration, functional traits, 49

plant species richness, ecosystem evaporation, ecosystem transpiration, leaf area index, 50

Shuttleworth and Wallace model, lysimeter, The Jena Experiment, Ecotron. 51

52

INTRODUCTION 53

The evidence that biodiversity loss reduces the functioning of ecosystems has grown over the last 54

two decades (Cardinale et al. 2012, Hooper et al. 2012). Many experimental studies established 55

that increasing plant species richness in a community generally leads to higher biomass 56

production, an easily measurable end-product of ecosystem functioning. However, only little is 57

known about the ecosystem-level energy and mass fluxes underpinning the biomass production 58

(Duffy 2003, Cardinale et al. 2012, Milcu et al. 2014). To date, the selection (Huston 1997) and 59

complementarity (Tilman et al. 1997, Cardinale et al. 2007) effects are the main mechanisms put 60

forward to explain the positive biodiversity-ecosystem functioning relationship. While the 61

selection effect can lead to enhanced ecosystem functioning due to an increased probability of 62

including a highly-performant dominant species with increasing species richness, the 63

complementarity hypothesis is asserting that increasing plant diversity should lead to a more 64

complete exploitation of resources. Complementarity is expected to arise in more species rich 65

communities due to increased probability of including species that exhibit: (i) various forms of 66

niche partitioning leading to complementary resource capture in space and time; or (ii) 67

interspecific facilitative/ positive interactions that enhances the capture of resources. In 68

particular, ecosystem evapotranspiration (ET) and transpiration (T), two productivity-related 69

fluxes strongly coupled with photosynthesis, are expected to increase with plant diversity due to 70


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their well-documented positive effect on biomass production and leaf surface area per ground 71

area (the LAI) (Obrist et al. 2003, Hu et al. 2009). Belowground, complementarity in rooting 72

patterns (von Felten and Schmid 2008) could also potentially lead to increased water acquisition 73

and hence increased ET and T. 74

To our knowledge, the importance of plant species richness for water vapor fluxes has 75

only been investigated in a few studies. With some notable exceptions (Stocker et al. 1999, 76

Leimer et al. 2014), indications of more complete water use as well as increased water use 77

efficiency have been found at higher diversity levels (Caldeira et al. 2001, Van Peer et al. 2004, 78

Lemmens et al. 2006 and Verheyen et al. 2008). However, it is unclear whether these results can 79

be generalized since most of these studies included experimental constraints related to the use of 80

relatively small containers (Van Peer et al. 2004, De Boeck et al. 2006), short establishment 81

phase (Stocker et al. 1999, Van Peer et al. 2004, De Boeck et al. 2006) and indirect estimates of 82

evapotranspiration (ET) using water balance models (Verheyen et al. 2008, Leimer et al. 2014). 83

Furthermore, no attempt has been made to partition the ecosystem ET into evaporation (E), a 84

non-productivity related flux, and T. In addition to being controlled by different biological and 85

physical processes, by not partitioning the ecosystem water vapor fluxes in E and T, one cannot 86

disentangle the direct (water acquisition-related) effects of species richness on T from canopy 87

structure-mediated effects on ecosystem E. Consequently, we only have an incomplete 88

understanding of the underlying mechanisms influencing non-productive (E) and productive (T) 89

water vapor ecosystem fluxes, which could potentially play an important role in explaining the 90

biodiversity-ecosystem function relationship (Verheyen et al. 2008). 91

There is mounting evidence that consideration of plant morphological, biochemical, 92

behavioral, and phenological traits known to affect performance and fitness can improve our 93


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understanding of ecosystem functioning (Cadotte et al. 2009, Díaz et al. 2007, Milcu et al. 2014). 94

To date, the most common community-level functional predictors/ metrics derived from plant 95

functional traits include the community weighted mean-trait value (CWMs) and functional 96

diversity (FD). CWMs, computed as the average of the trait values weighted by the relative 97

abundances of each species, can be used to identify the relative importance of a functional trait in 98

driving ecosystem processes. Theoretically, this metric is related to the mass ratio hypothesis of 99

Grime (1998) stating that ecosystem functioning is primarily determined by trait values of the 100

dominant contributors to plant biomass (Díaz et al. 2007). In contrast, FD metrics quantifies the 101

variety, range and evenness of the functional traits present in communities, and have been 102

proposed to be linked to niche complementarity hypothesis since a greater range of trait-values is 103

generally considered to indicate less niche overlap (Díaz et al. 2007). However, while functional 104

trait-based metrics have been extensively used to predict biomass production, the role of 105

functional traits for water vapor fluxes has not been yet explored despite evidence that this 106

approach may help to achieve a better predictive framework for ecosystem functioning (Wright 107

et al. 2004, Violle et al. 2007, Reiss et al. 2009). Based on our current understanding we would 108

expect the ecosystem water vapor fluxes to be best predicted by plant functional traits affecting 109

the LAI and the rooting patters (Reichstein et al. 2014). 110

To address the aforementioned knowledge gaps we took advantage of twelve large 111

lysimeters in a controlled environment facility for ecosystem research (Ecotron) hosting intact 112

vegetation-soil monoliths from a long-term biodiversity experiment (“The Jena Experiment”) 113

(Roscher et al. 2004). This was combined with a modeling approach, based on the Shuttleworth 114

and Wallace (1985) energy partitioning model (henceforth SW), which allowed to separately 115

quantify the proportion of energy that is dissipated in T, E and sensible heat flux (H). In order to 116


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derive a mechanistic and predictive understanding of plant diversity effects, alongside species 117

richness we also tested several predictors based on above- and belowground vegetation 118

properties and functional trait-based indices (Appendix A). Specifically, we aimed at (1) 119

quantifying the effect of plant diversity on day- and night-time ecosystem water fluxes during the 120

peak of the growing season, (2) partitioning the ecosystem ET in E and T using a modeling 121

approach to test the importance of plant species richness (4 vs. 16 species) on the partitioned 122

fluxes and (3) identifying the most parsimonious statistical models of water vapor fluxes using 123

above- and belowground vegetation properties and functional trait-based metrics [CWMs and 124

Rao’s quadratic entropy index of functional diversity (FDQ)]. 125

126

MATERIALS AND METHODS 127

The Ecotron facility 128

The experiment was conducted in the Montpellier European Ecotron 129

(http://www.ecotron.cnrs.fr), an experimental infrastructure developed by the Centre National de 130

la Recherche Scientifique (CNRS, France), to study the response of ecosystems to global 131

environmental changes. The lysimeters (2 m2, circular with a diameter of 1.6 m, weighing 7 to 8 132

tonnes) were located in 12 controlled environment units of the macrocosms platform. Each unit 133

consists of a 30 m3 dome-shaped chamber situated on top of a dedicated lysimeter room [see 134

Milcu et al. (2014) and Appendix B for more information on the macorocosms platform of the 135

Ecotron facility]. The soil surface and canopy of the lysimeters were exposed to natural sunlight 136

within each dome as a highly transparent material to light and UV radiation (250µm thick 137

Teflon-FEP film, DuPont, USA) was used as cover. Automatically controlled feedback loop 138

http://www.ecotron.cnrs.fr/


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algorithms based on industrial grade Proportional-Integral-Derivative (PID) controllers were 139

used to achieve the desired set-points for air temperature, air humidity and air CO2%. 140

Plant communities 141

The soil monoliths containing the plant communities originated from the long-term Jena 142

Experiment (50° 57.1 'N, 11° 37.5' E, 130 m above sea level; mean annual temperature 9.3 °C, 143

mean annual precipitation 587 mm). The site is located on the floodplain of the Saale River 144

(Jena, Germany), and was a former arable field until 2000, then kept fallow before 82 large plots 145

(20 ×20 m) varying in plant species richness (1 to 60 species), plant functional groups (1 to 4, 146

grasses, small herbs, tall herbs and legumes) and plant species composition were established in 147

May 2002 (Roscher et al. 2004). Twelve plant communities from two sown diversity levels (4 148

and 16 species) with six independent replicates per diversity level (Appendix C), were selected 149

according to the following criteria: (1) at least three functional groups (legumes, grasses and 150

herbs) were present, (2) realized species numbers were close to sown species richness, and (3) 151

plots were equally distributed across the experimental blocks of the field site to account for the 152

variability in soil texture. The monoliths were selected to be representative (as percentage 153

vegetation cover and standing biomass) of the plots they originate from. The monoliths were 154

sampled and placed in lysimeters in December 2011 following an established non-compacting 155

extraction method (see supplementary methods in Appendix D for more information on the 156

procedure used to extract the soil monoliths). At the end of March 2012 the lysimeters were 157

transported to the Ecotron facility in Montpellier. 158

Experimental conditions 159


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During the four months when the lysimeters where hosted in the Ecotron, we aimed to simulate 160

the average climatic conditions at the Jena Experiment field site since 2002. As the recorded 161

spring and summer climatic conditions of the year 2007 were very close to the average 162

temperature and precipitation regimes in Jena for the period 2002-2010, they were used as set-163

points (at 10-minute intervals). The average air temperature achieved was close to the set-point 164

(14.0 ºC vs. 14.9 ºC in Jena). The achieved average air humidity (RH) however, was somewhat 165

lower (58.9% RH vs.73.4% RH in Jena) because during night-time the humidifying system in the 166

Ecotron had to be stopped occasionally to prevent wetting the vegetation when the set-points 167

were higher than 80% RH. Because of this and also, as the monoliths were exposed to slightly 168

higher temperatures during transport and prior to installation in the Ecotron, we opted for 169

increasing the precipitation by +28% relative to 2007 (Appendix E). This allowed achieving 170

similar soil moisture conditions (Appendix F). The precipitation was applied by manual watering 171

using a hose equipped with a sprinkler and a flowmeter. As the incoming short-wave radiation 172

estimated from the HelioClim-1 database (Blanc et al. 2011) was on average 37% lower in Jena 173

than in Montpellier between April and July, a black shading mesh was added on the inside of 174

each dome, which reduced the incoming radiation by 44%. Unwanted plant species (weeds) were 175

removed every three weeks to maintain the targeted diversity levels. To recreate the mowing 176

management of the Jena Experiment, the aboveground biomass was mown at the end of April 177

and at the end of July. The final harvest took place at the time of the July mowing and included 178

destructive vegetation and soil sampling. As the modeling exercise relied on a period of six days 179

with undisturbed water vapor fluxes (28th of June to 3rd of July, 2012), the continuous 180

environmental conditions (air RH, air temperature, VPD and radiation) during this period are 181

presented in Fig. 1ab). 182


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Water vapor flux measurements 183

Ecosystem ET was measured as lysimeter weight changes over time. Four shear beam load cells 184

per lysimeter (CMI-C3, Precia-Molen, France), with an accuracy of ± 200 g, were used to 185

measure the changes in weight. The raw weight changes were corrected for temperature effects 186

on the sensitivity of the bean load cells, with correction factors provided by the manufacturer. 187

The resulting data were then smoothed using a symmetric loess smoothing function with a span 188

of 0.1 (as available in the R 3.1.2 software environment) to reduce the impact caused by minute 189

differential pressure changes between the dome and the lysimeter chamber where the bean load 190

cells are installed. Since not all weight changes can be associated with water vapor fluxes (e.g. 191

during activities that add or remove mass from the lysimeter such as sampling, weeding or when 192

a measuring device is placed on the lysimeter), we restricted our analysis to a time period with 193

the highest lysimeter data quality obtained during six consecutive undisturbed days (28th of June 194

to 3rd of July, 2012). This period was also close to the final harvest of the plant communities 195

(17th-20th of July) when all plant traits and vegetation properties were measured. The lysimeter 196

measured ET for this period is presented in Fig. 1c. These measurements were used to estimate 197

average day- (ETday) and night-time (ETnight) ecosystem evapotranspiration as well as the 198

evapotranspiration over 24h (ET24h). For more information on the measurement and sampling 199

methodology, see Milcu et al. (2014) and Appendix F. 200

The Shuttleworth and Wallace model 201

Probably the most widely used model for ecosystem-scale ET simulations is the Penman (1948) 202

model with Monteith (1981) extension to include stomatal resistance as a term that allows one 203

simulate the plant control of ET, today known as the Penman-Monteith equation (PM). 204

Shuttleworth and Wallace (1985) further developed a canopy model based on PM to allow 205


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partitioning of the radiative energy in sensible heat flux (H) and ecosystem evapotranspiration 206

(ET) which can be further partitioned in evaporation (E) and transpiration (T). This is done by 207

expressing PM (a) for the top of the vegetation canopy, (b) for the soil surface below the canopy, 208

and (c) for bare ground considering that in any canopy there is a certain fraction of projected 209

surface area that is not entirely covered by the plant community. PM uses available energy, air 210

temperature, atmospheric moisture and a series of transfer resistances, including the stomatal 211

resistance of vascular plants to predict ET (see Appendix F for equations and full model details). 212

213

Vegetation properties and functional trait-based metrics 214

We measured the following vegetation properties: shoot biomass (ShootBM), root biomass 215

(RootBM), root biomass and length by soil volume and depth (0-5, 5-10, 10-20, 20-30, 30-60 216

cm), total biomass (TotalBM), shoot biomass of legumes (LegBM), shoot biomass of grasses 217

(GrassBM), shoot biomass of herbs (HerbBM), leaf area index (LAI), leaf biomass (LeafBM) 218

and percentage bare ground (%bare-ground). 219

The functional–trait based metrics included functional diversity indices (FD) and community 220

weighted means (CWM) calculated from ten plant functional traits that have been previously 221

shown to be linked to plant transpiration, photosynthetic rates and light interception (see 222

Appendix A for an overview of tested predictors). Rao's quadratic entropy (FDQ) (Botta Dukát 223

2005) was preferred as an index of functional diversity as it incorporates information about 224

functional distance as well as functional evenness (abundance-weighted ) of a community. 225

Species-specific aboveground biomass was used for abundance-weighting. For each available 226

plant species the following traits were measured in-situ in each dome before the final destructive 227

harvest: stomatal conductance (gs; µmol m-2 s-1), specific leaf area (SLA; mm2 mg-1), leaf 228


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greenness (dimensionless measure of foliar chlorophyll content), leaf dry matter content (LDMC; 229

mg g-1), leaf N concentration (% N in leaf DW), species-specific plant height (cm) and specific 230

leaf nitrogen (SLN; g N m−2 leaf). See Appendix D for further details on the sampling and 231

procedure. Literature surveys were used for seasonality of foliage (ordinal, 1 = summer green, 2 232

= partly evergreen, 3 = evergreen), rooting type (ordinal, 1 = long-living primary root system, 2 233

= secondary fibrous roots in addition to the primary root system, 3 = short living primary root 234

system, extensive secondary root system) and rooting depth (cm) as used by Roscher et al. 235

(2004). FDQ was calculated for each of the ten functional traits separately, all available traits 236

simultaneously (FDQ-all) and only leaf-related traits (FDQ-leaf). FDQ and CWM were calculated 237

using the “FD” package (Laliberté and Shipley 2010) available through the R (Team 2013) 238

statistical package version 3.1.2. 239

Statistical analyses 240

Statistical analyses were performed in R (version 3.1.2.). T-tests were used to compare measured 241

and modeled water vapor fluxes. To test for plant species richness effects we used repeated 242

measures analysis of variance (ANOVA) on daily averaged data with dome as a random factor 243

with a temporal autocorrelation function (corAR1) for individual domes as available through the 244

“lme” function. To identify the most important predictors we fitted simple linear regression 245

models on fluxes averaged over six days for each predictor as well as available soil texture-246

related covariates (see Appendix A) on the averaged water vapor fluxes of the selected six days. 247

The resulting models were then simultaneously run through a model averaging procedure 248

(dredge function in MuMIn package) which computes Akaike weights (AICw), that represent the 249

probability that a particular model is the best fit to the observed data(Burnham and Anderson 250

2001). The predictors found within the 95% confidence interval (cumulative AICw ≤ 0.95) were 251


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selected and simultaneously included in a multivariate linear fitting regression model using the 252

lm function. The resulting regression models were then simplified to reach the most 253

parsimonious models by using the automatic model simplification “step” procedure based on 254

AICc (Venables and Ripley 2002). 255

256

RESULTS 257

Results of the Shuttleworth and Wallace model 258

Time traces of ecosystem soil evaporation (E) and plant transpiration (T) fluxes modeled with 259

the SW model are displayed in Fig. 2a, 2b as averages for the two plant species richness levels. 260

Although over 24h the modeled ET slightly underestimated the measured ET (Fig. 2c), when 261

restricted to only day-time, the model agreed well with the measured data (Fig. 2d), with average 262

day-time ET over six days within ± 10% of measured values for most of the lysimeters (r2modeled, 263

measured = 0.79) and a Root Mean Square Error of Approximation (RMSEA) of 1.45. T-tests based 264

on average daily ET showed that there was no significant difference between measured and 265

modeled day-time (t = -0.58, P = 0.568) and 24h ET (t = 1.33, P = 0.197). 266

As expected, modeled night-time evapotranspiration (ETnight) values were lower than day-267

time values (Fig. 2a). However, we found that the modeled ETnight values were on average 52% 268

lower (t = 6.37, P < 0.001) than the values measured with the lysimeters (Fig. 2b). Since we 269

argue that discrepancy between the measured and modeled ETnight is likely related to the 270

controlled environment in the Ecotron (see Appendix F), we restricted the further assessment of 271

the effect of species richness and functional diversity on the modeled T and E to day-time only. 272

273

Species richness effects 274


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Measured day-time ecosystem evapotranspiration (ETday) was significantly higher in the 275

lysimeters hosting monoliths from communities sown with 16 species relative to 4 species 276

(+16.6%, F1/10, P = 0.049, Fig. 2a). This is also visible in Fig. 1c with exception of the last day 277

after the rainfall/ irrigation of 20mm. However, no significant difference in ETday was found 278

between species richness during night-time (F1/20 = 0.25, P = 0.625), or when the ET was 279

integrated over 24h (F1/10 = 2.84, P = 0.122, Fig 2a). 280

Modeled ETday was also significantly higher in the treatment with 16 species relative to 4 281

species richness (+14.5%, F1/10 = 6.86, P = 0.026; Fig. 3b), a result in accordance with the 282

measured results. Averaged over 24h, modeled ET was also significantly higher in the treatment 283

16 sown species richness (+ 14.6%, F1/10 = 6.62, P = 0.028), while no diversity effect was found 284

for ETnight. 285

Modeled ecosystem transpiration during day-time (Tday), either expressed as fraction of 286

the available energy (+65.2%, F1/10 = 6.77, P = 0.026, Fig. 2c) or mean water vapor loss per day 287

(+68.4%, F1/10 = 6.79, P = 0.026, Fig. 2d) was significantly higher in the monoliths with higher 288

plant species richness (Table 1). Furthermore, Tday correlated well with the total plant biomass at 289

the final harvest (r2 = 0.72, P < 0.001; Fig. 3f). In contrast, modeled day-time evaporation (Eday) 290

was significantly lower at high species richness expressed as both fraction of available energy (-291

22.2%, F1/10 = 6.09, P = 0.033, Fig. 2d) and mean water vapor loss per day (-15.9%, F1/10 = 6.45, P 292

= 0.029, Fig. 2d). Consequently, the ratio of day-time ecosystem transpiration to evaporation 293

(Tday /Eday) was also significantly affected by sown plant species richness, with a significantly 294

higher ratio in the monoliths with 16 sown species (F1/10 =7.28, P = 0.022, Fig 2c). The 295

proportion of total energy dissipated as sensible heat flux (Hday) was significantly lower (-28.4%; 296

F1/10 = 7.30, P = 0.022) in the monoliths with 16 compared to 4 sown plant species (Table 1). 297


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Vegetation properties and functional trait-based metrics as predictors for water vapor 298

fluxes 299

With one exception (ETnight, which was best predicted by the community weighted means of the 300

height of the species), among the vegetation properties, LAI proved to be the most consistent 301

predictor of water vapor fluxes (Table 1). Fig. 3e depicts the role of LAI in the modeled partition 302

of the available energy in Tday, Eday and Hday. Higher LAI also led to higher measured ET24h and 303

ETday (Table 1). Alongside LAI, the percentage of bare ground was retained in the most 304

parsimonious models for all of the modeled variables and influenced positively the Eday and Hday 305

and negatively the Tday and the Tday/Eday ratio. 306

The minimal adequate models for water vapor fluxes that directly focused on functional 307

trait based metrics had generally a weaker predictive power than those based on the LAI (Table 308

1). However, the diversity of leaf N concentration in the canopy (FDQ-leafN%) stood out as a 309

consistent predictor of ecosystem water vapor fluxes; increasing FDQ-leafN% was correlated 310

with an increase in measured and modeled fluxes. The relationships between FDQ-leafN%, LAI, 311

percentage bare ground and modeled daytime ecosystem transpiration (Tday) are depicted in 312

Figure 4. 313

314

DISCUSSION 315

Ecosystem ET is an important process for water and energy balance, and is closely linked to 316

ecosystem productivity (Hu et al. 2008, Verheyen et al. 2008). The Shuttleworth and Wallace’s 317

(SW) energy partitioning model has been widely used with good performance to partition the ET 318

in its component water vapor fluxes (T and E), in crops (Shuttleworth and Wallace 1985, Brisson 319

et al. 1998) and natural grasslands (Hu et al. 2009), but it has not been so far used in experiments 320


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addressing the role of biodiversity for ecosystem functioning. Here, we applied the SW model to 321

plant communities of contrasting species richness (4 vs. 16 species) in a controlled environment 322

facility (Ecotron) equipped with lysimeters. This setup allowed us to compare the performance of 323

the SW model with the lysimeter measured fluxes. Considering the large daily variability of 324

environmental variables, and particularities of the Ecotron environment (e.g. top down air flow), 325

the overall agreement of measured and modeled ET during the six days of our study was good (r2 326

= 0.79), and within the range found by previous studies (Brisson et al. 1998, Tourula and 327

Heikinheimo 1998, Kato et al. 2004). 328

Overall, our results show an increase of water acquisition in the high diversity treatment 329

(16 plant species) as indicated by higher ETday as well as higher modeled Tday. Although 330

speculative, since we provide no direct evidence for complementarity, increased water 331

acquisition can be interpreted as an indicator of complementarity, however, the jury is still out on 332

the importance of complementarity for grassland water acquisition (Verheyen et al. 2008, 333

Bachmann et al. 2015). In parallel with the higher modeled Tday—the productive fraction of the 334

ET— communities with 16 species also exhibited lower proportions of energy diverted into non-335

productive water and energy fluxes (Eday and Hday) and an increase in the ratio of energy diverted 336

to transpiration over evaporation during daytime (Tday/Eday; Table 1). This suggests that plant 337

diversity effects mediated by the structure of the canopy (by the LAI as indicated by Table 1) 338

occurred by reducing the propensity to dissipate the energy via the non-productive evaporative 339

process. Since Tday occurs only during the period of photosynthetic activity, a higher proportion 340

the T implies that more of the ET water was used to acquire carbon, which would increase the 341

water use efficiency (C gain/ET). Indeed, this is consistent with the results of Milcu et al. (2014) 342

from same experiment in the Ecotron showing a 37.6% increase in water use efficiency in 343


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communities with 16 plant species relative to 4 species. These findings are also in line with 344

several previous studies (Van Peer et al. 2004, De Boeck et al. 2006, Lemmens et al. 2006) 345

documenting an increase in ET and water use efficiency with increasing plant diversity. 346

However, our results are not in agreement with the findings of Leimer et al. (2014) which 347

indirectly estimated ecosystem ET using a simple soil water balance model in the same filed site 348

from where the soil monoliths of our study have been extracted (the Jena Experiment). By 349

modelling ecosystem ET from fluctuations in soil water content measured in two soil layers (0.0 350

– 0.3 m and 0.3 – 0.7 m), the study of Leimer et al. (2014) fund no plant diversity effects on 351

modeled ET with monthly resolution from 2005 to 2008. This is different from our results which 352

showed significantly higher modeled ET as well as a tendency of higher ET in measured ET. 353

While not fully comparable due to very different methodology and temporal resolution, it is 354

worth noting that the Leimer et al. (2014) study was only calibrated/ tested against the results of 355

a hydrological model and not against measured ET. Another possible explanation for the 356

discrepancy could be that, by using a controlled environment approach and direct measurements 357

of ET, we were able to reduce more confounding factors. Alternatively, we cannot discount the 358

possibility that the study of Leimer et al. (2014), which presented monthly aggregated data for 359

four years, also incorporated longer-term processes that are not included in our study. For 360

example, unaccounted potential discrepancies in the phenology of plant development throughout 361

the growing season might alter the differences in water use between the communities with low 362

and high diversity. 363

Of all tested predictors of water vapor fluxes based on vegetation properties such as 364

canopy structure and belowground rooting patters we found that the LAI and the percentage of 365

bare ground on the soil surface were the two predictors retained in the most parsimonious 366


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models. While it has been long established that the LAI is a consistent driver of water vapor 367

fluxes, and is actually factored into the SW energy partition model (Shuttleworth and Wallace 368

1985), here we show that plant diversity effects on ecosystem water vapor fluxes are mediated by 369

the leaf area index (LAI). Furthermore, to our knowledge, this is the first study attempting to link 370

ecosystem water vapor fluxes to plant functional trait-based metrics. By doing this we found that 371

of all functional trait-based metrics, the index of functional diversity based on leaf nitrogen 372

concentration in the canopy (FDQ-leafN%) predicted best the water vapor fluxes and was 373

strongly correlated to the LAI (Fig. 4). Moreover, it was a better predictor of measured day-time 374

ET than species richness. Previous studies from the same experiment showed that FDQ-leafN% 375

was an important predictor of C fluxes and community biomass (Roscher et al. 2012, 2013, 376

Milcu et al. 2014), and it was suggested it might capture the cumulative investment in light 377

acquisition by the community represented by the LAI, and hence, indirectly affect the 378

ecosystem-level plant transpiration. Alternatively, it has also been suggested that this index of 379

diversity measuring the unevenness of N allocation in the canopy could be linked to a more 380

optimal N distribution in the canopy (Field 1983, Hirose and Werger 1987, Milcu et al. 2014) 381

and not necessarily with the LAI. However, additional manipulative experiments are needed to 382

clarify the exact mechanism through which FDQ-leafN% affects the water vapor fluxes. 383

To conclude, our results show higher water acquisition (higher ET) in more diverse 384

communities during day-time, at least at the peak at the growing season. More novel, we 385

identified a plant functional diversity metric capturing the diversity of leaf N% in the canopy 386

(FDQ-leafN%) as a consistent predictor of several day-time ecosystem-level water and energy 387

fluxes (ET, T, E and H), presumably via its effect on LAI, a measure of the community 388

investment in light acquisition. Furthermore, since the SW model is showing that higher plant 389


18/29

diversity leads to a higher T rates – a flux closely coupled with photosynthesis and hence 390

productivity-related – this presumably contributes to the well-documented positive effect of 391

diversity on productivity. This is also supported by the positive correlation between modeled Tday 392

and total plant biomass (Fig. 3f). Allocating more of the available water to the productivity-393

related flux is likely going to become more important in the future, since extreme climatic events 394

such as droughts and heatwaves are predicted to increase in intensity and frequency in many 395

regions (Seneviratne et al. 2012). Under these conditions, plant communities containing more 396

functionally diverse species will likely use more of the available water for growth and cooling. 397

398

Acknowledgements 399

We thank DFG for funding the Jena-Ecotron experiment (FOR 456) and A. Milcu. A. 400

Hildebrandt and M. Guderle were supported by the ProExzellenz initiative of the German 401

Federal State of Thuringia. M. Guderle’s visit to the Ecotron was supported by the ExpeER 402

Transnational Access program (EU-FP7 I3). D. Bachmann and N. Buchmann acknowledge 403

funding of the SNF (315230E-131194). This study benefited from the CNRS human and 404

technical resources allocated to the ECOTRONS Research Infrastructures and the state allocation 405

'Investissement d'Avenir’ ANR-11-INBS-0001. The Languedoc-Roussillon region and the 406

Hérault Conseil Général are acknowledged for funding and supporting the Ecotron. 407

408

409

410

411


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Tables: Table 1. 526

ANOVA results for the effect of sown species richness (Sdiv, i.e. 4 vs. 16 species) alongside the 527

most parsimonious multiple regression models. ET24h, ETday and ETnight represent lysimeter 528

measured evapotranspiration values during 24h, day-time and night-time, respectively. Tday, Eday 529

and Hday represent the day-time transpiration, evaporation and sensible heat flux, respectively. 530

Vegetation properties (LAI = leaf area index, Bare = % bare ground, hrGR = biomass of grasses) 531

and functional trait-based predictors (FDQ-leafN% = diversity of leaf N concentration, CWM-532

height = community weighted means of species height) were separately analyzed in different 533

models. Non-significant effects are marked as “ns”. See Appendix A for the list of all predictors 534

included in the initial models before the AICc-based model simplification. 535

Response

variables

Sdiv

(ANOVA)

Best model based on canopy, root and

soil texture

Best model based on

functional-trait predictors

Measured

ET24h ns 2.788 + 0.342*LAI

P < 0.001, r2 = 0.78

ns

ETday P = 0.049

r2 = 0.34

2.138 + 0.344*LAI

P < 0.001, r2 = 0.93

2.46 + 0.67*FDQ-leafN%

P = 0.015, r2 = 0.46

ETnight ns 0.700 - 0.002*grBM

P = 0.094, r2=0.26

0.883 – 0.111*CWM-height

P = 0.015, r2 = 0.46

Modeled

Tday P = 0.026

r2 = 0.40

1.109 + 0.28*LAI – 0.017*Bare

P < 0.001, r2 = 0.99

0.893+ 0.918*FDQ-leafN%

P = 0.040, r2 = 0.36

Eday P = 0.029

r2 = 0.39

1.642 – 0.101*LAI + 0.006*Bare

P < 0.001; r2 = 0.97,

1.706 – 0.295*FDQ-leafN%

P = 0.076, r2 = 0.29

Tday /Eday P = 0.022

r2 = 0.42

0.597 + 0.276*LAI – 0.011*Bare

P < 0.001, r2 = 0.99

0.541 + 0.769*FDQ-leafN

P = 0.034, r2 = 0.37

Hday P = 0.022

r2 = 0.42

0.319 – 0.032*LAI+0.002*Bare

P < 0.001, r2 = 0.98

0.357– 0.111*FDQ-leafN%

P = 0.054, r2 = 0.32


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Figure legends 536

Figure 1. (a) Ecotron environmental conditions during the selected six days averaged for the two 537

plant diversity levels (4 vs. 16 species). RH represents the air relative humidity and VPD the 538

vapor pressure deficit. (b) Lysimeter measured evapotranspiration (ET) in the Ecotron. 539

Figure 2. Time trace of ecosystem evaporation (E) and transpiration (T) fluxes as modeled by 540

the SW model in (a) communities with 16 and (b) 4 plant species. T (grey area) and E (black 541

area) are plotted additively so that their sum represents total ET. Comparison of averaged 542

modeled and lysimeter ET with (c) and without (d) night-time values. 543

Figure 3. Plant diversity (4 vs. 16 species) effects on day-time, night-time and 24h 544

evapotranspiration fluxes (a) measured in lysimeters and (b) modeled by the SW model. (c) Plant 545

diversity effects on modeled partition of the available radiative energy in evaporation (E) 546

transpiration (T) and sensible heat flux (H). (d) Modeled E and T as affected by plant diversity. 547

(e) LAI and plant diversity effects on modeled E (squares), T (circles) and H (triangles). (f) 548

Relationship between modeled day-time T and plant biomass. 549

Figure 4. Scatterplot and correlation matrix depicting the relationships between the most 550

important predictors (FDQ-leafN% = diversity of leaf N concentration, LAI, % bare ground) of 551

water vapor fluxes and modeled daytime ecosystem transpiration (Tday). The line in the 552

scatterplots represents a locally weighted scatter-plot smoother (loess) fitting. 553


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Figure 1 554

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Figure 2 570

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Figure 3 577

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Figure 4 579

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Milcu et al. 2015 – appendices

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Appendices (A to F) 586

Appendix A. Overview table presenting the mean ± 1*SD of the tested predictors and covariates for the two levels of sown species 587

richness. FDQ abbreviation indicates a functional diversity index estimated using Rao’s quadratic entropy index (Botta Dukát 2005). 588

CMW indicates a community weighted means estimated for the respective functional trait. 589

Functional

trait-based

metrics

Description Type of trait / source Unit 4 species

(mean ± SD)

16 species

(mean±SD)

FDQ-leafN%, based on leaf N% continuous / measured in situ dimensionless 0.40 ± 0.28 0.79 ± 0.42

CWM-leafN% based on leaf N% continuous / measured in situ mg N g-1 leaf

DW

2.55 ± 0.67 2.36 ± 0.35

FDQ-SLN based on N specific leaf area continuous / measured in situ dimensionless 0.17 ± 0.25 0.54 ± 0.34

CWM-SLN based on N specific leaf continuous / measured in situ g N m−2 leaf 1.50 ± 0.25 1.26 ± 0.34

FDQ-SLA, based on specific leaf area continuous / measured in situ dimensionless 0.14 ± 0.14 0.49 ± 0.22

CWM-SLA based on specific leaf area continuous / measured in situ mm2 mg-1 16.91 ± 2.25 19.77 ± 2.77

FDQ-GS, based on stomatal conductance continuous / measured in situ dimensionless 0.57 ± 0.72 0.77 ± 0.67

CWM-GS based on stomatal conductance continuous / measured in situ µmol m-2 s-1 469 ± 155 557 ± 167

FDQ-greenness, based on leaf greenness continuous / measured in situ dimensionless 0.16 ± 0.24 0.60 ± 0.29

CWM-greenness based on leaf greenness continuous / measured in situ dimensionless 37.56 ± 7.36 34.86 ± 3.41

FDQ-LDMC, based on leaf dry matter content continuous / measured in situ dimensionless 0.42 ± 0.30 0.92 ± 0.35

CWM-LDMC based on leaf dry matter content continuous / measured in situ mg g-1 231.55 ± 41.38 229.38 ± 31.47

FDQ-height, based on recorded species height continuous / measured in situ dimensionless 0.25 ± 0.35 0.58 ± 0.50

CWM-height based on recorded species height continuous / measured in situ cm 15.7 ± 10.48 16.6 ± 4.02

FDQ-rootdepth, based on rooting depth continuous / literature dimensionless 0.73 ± 0.88 0.67 ± 0.56

CWM-rootdepth based on rooting depth continuous / literature cm 3.51 ± 0.74 3.70 ± 0.35

FDQ-typeroot, based on rooting type ordinal / literature dimensionless 0.40 ± 0.17 0.74 ± 52

CWM-typeroot based on rooting type ordinal / literature NA 2.55 ± 0.30 2.37 ± 0.31


2/12

590

FDQ-leaf based on all six leaf related traits continuous / measured in situ dimensionless 2.05 ± 1.15 4.77 ± 1.76

FDQ-all based on all ten functional traits continuous and ordinal / in situ

and literature

dimensionless 3.44 ± 1.73 6.77 ± 2.41

Vegetation

properties

Description Type of variable / source

Sdiv sown species richness categorical (4 and 16 species) /

in situ

counts 4 ± 0 16 ± 0

Rdiv realized species richness at July

harvest

counts / measured in situ counts 3.16 ± 0.75 11.3 ± 1.5

LAI leaf area index continuous / measured in situ dimensionless 1.46 ± 0.97 2.78 ± 0.84

%bare percentage bare ground percentage / measured in situ % 26.83 ± 20.50 6.25 ± 9.23

ShootBM shoot biomass continuous / measured in situ g DW m-2 148.71 ± 61.07 193.01 ± 43.31

RootBM total root biomass continuous / measured in situ g DW m-2 100.52 ± 39.87 154.07 ± 37.77

RootBM/L root biomass per layer (0-5, 5-10,

10-20, 20-30, 30-40, 40-60cm)

continuous / measured in situ g DW m-2 layer dependent layer dependent

RootS/V root surface per volume soil (by

layer)

continuous / measured in situ cm2/cm3 layer dependent layer dependent

TotalBM total biomass (shoot + root biomass) continuous / measured in situ g DW m-2 249.24 ± 93.34 347.08 ± 55.75

LegBM biomass of legumes continuous / measured in situ g DW m-2 18.24 ± 24.65 42.86 ± 40.27

Leaf biomass biomass of leaves continuous / measured in situ g DW m-2 93.89 ± 50.93 124.21 ± 28.62

Leaf-to-

ShootBM-ratio

ratio of leaf to shoot biomass ratio / measured in situ dimensionless 0.63 ± 0.14 0.67 ± 0.09

HerBM biomass of herbs continuous / measured in situ g DW m-2 83.15 ± 71.47 95.58 ± 54.15

GraBM biomass of grasses continuous / measured in situ g DW m-2 34.12 ± 61.66 36.81 ± 52.14

Covariates Description Type of variable / source

Soil sand % soil sand content at 0-20 cm depth percentage / measured in situ % 21.16 ± 17.63 17.87 ± 15.89

Soil clay % soil clay content at 0-20 cm depth percentage / measured in situ % 26.09 ±7.09 24.42 ± 6.25

Soil silt % soil silt content at 0-20 cm depth percentage / measured in situ % 52.74 ± 10.94 57.70 ±12.28


3/12

Appendix B. Top image: simplified schematic of one controlled environment unit (macrocosm) 591

of the CNRS Ecotron facility. Red arrows represent the direction of airflow (image by Marc 592

Saubion). Bottom image: closeup of the vegetation from dome no. 1 at the July harvest (photo by 593

Alexandru Milcu). 594

595

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602

603

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Appendix C. Table with sown species richness (Sdiv) and functional group (G = grasses, H = 620

herbs and L = legumes) composition of the twelve selected plots from the Jena Experiment. The 621

species present at the final harvest are marked in bold. 622

Grasses (G): Ae = Arrhenatherum elatius L. (J. et C. PRESL), Ao= Anthoxantum odoratum L., 623

Ap= Alopecurus pratensis L., AVp = Avenula pubescens HUDS. (DUM.), Be= Bromus erectus 624

HUDS., Bh= Bromus hordeaceus L., Cc= Cynosurus cristatus L., Dg= Dactylis glomerata L., 625

Fp= Festuca pratensis HUDS., Fr= Festuca rubra L., Hl= Holcus lanatus L., LUc = Luzula 626

campestris L (DC.), PHp= Phleum pratense L., Pp= Poa pratensis L., Pt= Poa trivialis L., Tf= 627

Trisetum flavescens L. (P. BEAUV.); 628 629

Herbs (H): Am= Achillea millefolium L., Bp= Bellis perennis L., Cb= Crepis benis L., CAc= 630

Carum carvi L., CAp = Campanula patula L., Cj= Centaurea jacea L., Co= Cirsium oleraceum 631

L., Cp= Cardamine pratensis L., Dc= Daucus carota L., Gm= Galium mollugo L., Gh= 632

Glechoma hederacea L., Hs = Heracleum sphondylium L., Ka= Knautia arvensis L., La= 633

Leontodon autumnalis L., Lh= Leontodon hispidus L., Lv= Leucanthemum vulgare Lam., PIm = 634

Pimpinella major L. (HUDS.), Pl= Plantago lancelolata L., Pm= Plantago media L., PRv = 635

Primula veris L., Pv= Prunella vulgaris L., Ra= Rumex acetosa L., To= Taraxacum officinale 636

WEBER, TRp= Tragopogon pratensis L., Vc= Veronica chamaedrys L; 637

638

Legumes (L): Lp= Lathyrus pratensis L., Lc= Lotus corniculatus L., Ml= Medicago lupulina L., 639

Ms=Medicago x varia MARTYN, Ov= Onobrychis viciifolia SCOP., Td= Trifolium dubium 640

SIBTH., TRf = Trifolium fragiferum L., Th= Trifolium hybridum L., Tr= Trifolium repens L., 641

Tp= Trifolium pratense L., VIc= Vicia cracca L. 642

643

Plot

ID Sdiv G H L Species composition

Ecotron

dome

B2A22 16 5 5 6 Cc, Fp, Tf, Pp, Pt, Cj, Ra, So, Am, CAp , Th, Lc, VIc, Tr, Lp, Ov

1

B4A04 4 1 2 1 Ae, Pl, As, Tc

2

B1A01 16 4 8 4 AVp, Pp, Ao, Bh, Pl, To, Ar, Rr, As, Gp, TRp, CAc, Tc, VIc, Lp, Lc

3

B1A04 4 1 2 1 Fp, Pl, CAp, Ov

4

B3A23 4 1 2 1 Bh, Rr, Lv, TRf

5

B2A18 16 4 8 4 Ap, Bh, Pp, Cc, Rr, Pm, Ar, Pv, CAp, Gp, As, Cp, Ml, Tr, Td, Tc

6

B4A18 16 4 8 4 Cc, LUc, Ap, Bh, La, Pm, Vc, To, Cb, CAc, PIm, Hs, Th, Tc, Lp, Ov

7

B2A01 4 1 2 1 Ao, Pv, Ka, Tp

8

B3A22 16 4 8 4 PHp, Fr, Ao, Be, Rr, Ar, Bp, Vc, Gp, Cb, Ra, Gm, VIc, Ov, TRf, Td

9

B2A16 4 0 3 1 Pm, La, Ka, VIc

10

B3A24 16 6 5 5 Fp, Bh, Ap, Ao, Pt, Ae, To, Rr, Ar, Pv, Gh, Lc, Tp, Tr, VIc, Ms

11

B4A11 4 1 2 1 Tf, TRp, Hs, Ms

12


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Appendix D. Comparison of cumulative precipitation (mm) recorded in the “Jena Experiment” 644

field site in 2007 and simulated in the Ecotron in 2012. 645

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(m

m)

Jena 2007

Ecotron

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6/12

Appendix E. Comparison of the soil moisture achieved in the Ecotron and the “Jena 657

Experiment” field site in 2007 at similar depths for the period with available Ecotron data form 658

2012. 659

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Appendix F - Supplementary Methods 691

Extraction of soil monoliths 692

Soil monoliths were extracted by UMS GMBH (München, Germany) following a proprietary 693

non-compacting extraction method. Using a hydraulic press, steel cylinders with cutting edges 694

were pressed down to 2 m depth (to include the average depth of the water table in summer 695

known from previous hydrological field investigations) while simultaneously digging out the soil 696

60 cm around the cylinder. The created space around the cylinders allowed to cut the bottom of 697

the soil monolith and to insert a sealing plate in order to be able to lift the monolith and to create 698

a water tight container/ lysimeter. The lysimeters were then extracted with a crane, and after 699

inspection, were buried to the surface level near the experimental field after extraction in order to 700

facilitate the recovery after the extraction disturbance, while being exposed to the same 701

environmental conditions as the field plots. At the end of March 2012 the lysimeters were 702

transported to the Ecotron facility in Montpellier. 703

704

Measurements of environmental variables 705

Air temperature and relative humidity were measured with the DT269 digital sensor (Michell 706

Instruments Ltd, Ely, UK). The soil moisture was measured with a Trime-Pico 32 time domain 707

reflectometry sensor (IMKO GmbH, Ettlingen, Germany). Solar radiation was measured with a 708

BF5 Sunshine Sensor (Delta-T Devices, Cambridge, UK). 709

710

Sampling methodology 711

Shoot biomass was estimated by clipping the vegetation at ground level in a rectangle of 0.8 x 712

1.0 m per plot and drying at 65°C for three days. Root biomass was estimated from three cores of 713

3.5 cm diameter and 60 cm depth. The soil cores were separated into six layers (0-5, 5-10, 10-20, 714

20-30 and 40-60cm) before they were pooled per layer, washed with tap water and dried 715

following the same procedure as for shoot biomass. Leaf area index (LAI) was estimated using a 716

portable LAI-2000 plant canopy analyzer (LI-COR, Lincoln, USA). LAI was measured in the 717

evening under diffused light conditions with one measurement above the canopy as a reference 718


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and the average of five measurements near ground level positioned at different places in the 719

center of each lysimeter. The zenith angle was restricted to 0-43o in order to minimize the edge 720

effect (Hyer and Goetz 2004) inherent to a canopy with a surface of 2m2. In lysimeters with a 721

high proportion of one rosulate species (Plantago media) with leaves laying very close to the 722

ground, the values measured with the LAI-2000 were replaced with the average value of two leaf 723

area measurements using a LI-3100 leaf area meter (LI-COR, Lincoln, USA) leaf area meter) 724

from two rectangles of 0.2 × 0.5 m per plot. Percentage vegetation cover and bare ground as well 725

species specific percentage cover were visually estimated for the whole lysimeter (2 m2). 726

Stomatal conductance was measured at midday using a portable porometer (SC-1 Leaf 727

porometer, Decagon Devices, Pullman, USA). Leaf greenness was estimated with a hand-held 728

chlorophyll meter (SPAD-502, Konica-Minolta, Osaka, Japan), which enables non-destructive 729

assessment of leaf greenness by measuring the absorbance by the leaf of two different 730

wavelengths (650 nm and 940 nm). The SPAD values were calibrated (r2 = 0.69) against 731

spectrophotometrically determined chlorophyll concentrations from leaf extracts following the 732

method of Moran (1982). Leaf dry mater content (LDMC) is defined as the ratio of leaf dry mass 733

to saturated fresh mass according to Vile et al. (2005). LDMC was determined with the partial 734

rehydration method following the protocol of Wilson et al. (1999). Samples were put sealed 735

plastic bags promoting rehydration by storing leaves overnight between sheets of moistened 736

tissue paper. Then, samples were blotted dry using tissue paper to remove any surface water and 737

immediately weighed. Samples were oven-dried (65°C, 3 days) and reweighed to get values for 738

dry mass. This procedure gives a good approximation in comparison to the complete rehydration 739

method (Vaieretti et al. 2007). 740

741

The Shuttleworth and Wallace model 742

Evaporation and transpiration were modeled with the approach by Shuttleworth and Wallace 743

(1985). This approach is a further development of the Penman-Monteith model, 744

745

746

747

748

(1)


9/12

where x denotes the plant canopy compartment of interest. λEx is parameterised once for the 749

canopy top (λEc), once for the soil surface below canopy (λEs), and once for the fraction of bare 750

soil not covered by the canopy (λEb) total evapotranspiration λE is expressed as the sum of 751

transpiration from the plant canopy and evaporation from the soil weighted by their respective 752

surface cover fractions fc, fs and fb, λE = fc∙λEc + fs∙λEs + fb∙λEb, with fs = fc, and fc = 1–fb.. Thus, 753

transpiration T = λEc and evaporation E = λEs + λEb. Rn is net radiation (at canopy top Rnc or soil 754

surface Rns), G is ground heat flux, Δ is the mean rate of change of saturated vapor pressure with 755

temperature, γ is the psychrometric constant, cp is specific heat at constant pressure (1005.5 J kg–756

1 K–1), ρa is the density of air, e and esat are the actual and saturation vapor pressure, and ra and rs 757

are aerodynamic and surface (stomatal) resistances, respectively. 758

759

We used the mass flux of water vapor in our analysis, which is the simple conversion from 760

energy flux densities modeled by the Shuttleworth and Wallace (1985) model to mass flux 761

densities, with plant transpiration T = λEc/Lv, and soil evaporation E = λEs/Lv, with Lv being the 762

latent heat of vaporisation (Lv = 2.501∙106 – 2370∙Tair with air temperature Tair in °C and Lv in J 763

kg–1 (Stull 1988). 764

765

Saturation vaporpressure esat was determined via the Magnus equation for nonfreezing conditions 766

at air temperature Tair measured in °C in each dome (esat = 610.7⨉10[7.5⨉Tair/(235+Tair)], in Pa), and 767

actual vapor pressure e=RH/100∙esat, with relative humidity RH in %. Density of air was 768

computed from Tair and atmospheric pressure P [in Pa], ρa = P/(Tair+273.15)/R, with the gas 769

constant for dry air R = 287.05 J kg–1 K–1. Δ was approximated as Lv∙esat, /Rv/(Tair+273.15)2 770

with the gas constant for water vapor Rv = 461.5 J kg–1 K–1. 771

772

The key parameters used to model λEc, λEs and λEb are the short-wave albedo of canopy (αc) and 773

bare soil (αs= αb ), the fractions of vegetated (fc) and unvegetated (fb) ground surface, the wind 774

speed above the canopy (u), the fraction of that wind speed observed in mid-canopy (ξc) and at 775

the ground surface (ξb and ξs; where soil evaporation takes place; we assumed that ξb = ξc, but ξs 776

≪ ξc), and the soil surface resistance (rss). The albedo was used to compute Rn from available 777

global radiation (Rg) measurements as Rn = (1–α) Rg. It was assumed that 10% of Rn is 778

dissipated to G under a vegetation canopy, and 30% on the bare-soil component. Since 779


10/12

experiments were done inside a controlled environment facility, we assumed that the dome 780

temperature is not very different from the soil temperature and thus the net longwave component 781

of Rn is negligible. 782

783

The stomatal resistance of plants (rsc) was modeled according to Wesely (1989) and Erisman et 784

al. (1994), 785

786

where ri the minimal stomatal resistance, and Ts leaf surface temperature in °C in the range 0–787

40°C. The values for ri were determined from rsc measurements carried out for each relevant 788

species of each dome at midday (see sampling methodology). The weighted average of rsc was 789

used to parameterize ri. Weighting was by the proportion of leaf biomass of each plant species, 790

individually for each of the twelve plant communities. 791

792

Wind speed posed some challenges since the conditions in the domes of the Ecotron facility does 793

not represent the typical relationships between horizontal wind speed and mechanical turbulence 794

as the Penman-Monteith model implicitly assumed. The air condition systems of the Ecotron’s 795

domes created a turbulent environment, where wind speed varied between between 0.7–2.5 m s–1 796

in a fraction of a second, and with averaged (during a few seconds) anemometer readings 797

(Almemo 2890-9, Coalville, UK) of 0.9-1 m s-1. Video records however clearly showed that the 798

turbulent motion of the plants in the domes rather corresponded to the turbulence observed at a 799

higher wind speed. Hence, horizontal wind speed was set to 2 m s-1. The wind speed above bare 800

soil patches was set to the value specified above plant canopies. Wind speed inside the canopy at 801

mid-height was set to 35% of the above-canopy wind speed, and the wind speed at ground level 802

inside the plant canopy was set to 4%. These optimum values were determined iteratively via 803

minimization of deviations of the total ET from lysimeter measurements (nonlinear least-squares 804

fitting procedure). 805

While the parameters in the physically-based model by Shuttleworth and Wallace (1985) 806

are given, the search for the best model fit to experimental data obtained from the lysimeters in 807

each Ecotron unit was done with an iterative least squares fitting procedure to find best 808

parameter estimates for which no measurements were available. The free parameters for which 809

(2)


11/12

best estimates were searched for were: albedo of the plant canopy; albedo of the bare soil 810

fraction; relative proportion of available energy partitioned to ground heat flux under plant 811

canopy and on the bare soil part; fraction of above-canopy wind speed inside the plant canopy 812

and at the ground surface (a) below plants and (b) over the bare soil fraction; soil surface 813

resistance against evaporation. This procedure was however partially restricted to not overfit the 814

model in such a way that one parameter estimate was searched for all twelve Ecotron domes/ 815

experimental units to not introduce artificial differences between the domes for parameters that 816

conceptually should be the same in the Shuttleworth and Wallace (1985) model for all of the 817

Ecotron’s domes. To model E and T separately with the SW model, additional model parameters 818

were introduced to characterize the canopy structure and plant species specific contributions to 819

T, which were quantified using the best available empirical measurements (see below). Where no 820

direct measurements were available (soil resistance to evaporation, wind speed, albedo of plants 821

and of soil), a best estimate for the respective model parameter was made under the premise that 822

the Ecotron provides controlled conditions and hence the same parameter estimate can be used 823

for all replicates. Since the soil monoliths stem from the same soil extracted from the Jena 824

experiment, we argue that this this assumption is justified, but acknowledge that it does not 825

include potential indirect feedbacks to soil density and structure. 826

There are intrinsic limitations in the simulation of the night-time environmental 827

conditions in the Ecotron. The intensive air mixing of the atmosphere in the domes prevents 828

night-time stratification of air, which means that the air, soil surface and the soil will quickly 829

arrive at similar temperatures. In a natural settings however, the soil surface would be 830

considerably cooler than the soil below or the atmosphere above, which would reduce 831

evaporation from the cool nocturnal soil surface. In addition, due to the air-humidifying system, 832

the air relative humidity during night-time was limited at 65% which may increase evaporation 833

in the Ecotron. Presumably, these limitations explain the discrepancy between the modeled and 834

measured night-time ET. However, the day-time evaporative flux is the predominant component 835

of water vapor loss over 24h since the night-time fluxes typically represent only 10-15% (Caird 836

et al. 2007) of day-time fluxes. Therefore, we restricted our analyses of plant diversity effects on 837

the modeled E and T for the day-time period where the environmental conditions simulated in 838

the Ecotron match closely the field conditions at the “Jena Biodiversity Experiment” site. In 839


12/12

addition, there was good agreement between the day-time measured and modeled ET (see 840

results). 841

842

Supplementary References 843

Caird, M. A, J. H. Richards, and L. A. Donovan. 2007. Nighttime stomatal conductance and 844

transpiration in C3 and C4 plants. Plant physiology 143:4–10. 845

Erisman, J. W., a. Van Pul, and P. Wyers. 1994. Parametrization of surface resistance for the 846

quantification of atmospheric deposition of acidifying pollutants and ozone. Atmospheric 847

Environment 28:2595–2607. 848

Hyer, E. J., and S. J. Goetz. 2004. Comparison and sensitivity analysis of instruments and 849

radiometric methods for LAI estimation: assessments from a boreal forest site. Agricultural 850

and Forest Meteorology 122:157–174. 851

Stull, R. B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer. 852

Vaieretti, M. V., S. Díaz, D. Vile, and E. Garnier. 2007. Two measurement methods of leaf dry 853

matter content produce similar results in a broad range of species. Annals of Botany 854

99:955–958. 855

Vile, D., et al. 2005. Specific leaf area and dry matter content estimate thickness in laminar 856

leaves. Annals of Botany 96:1129–1136. 857

Wesely, M. L. 1989. Parameterization of surface resistances to gaseous dry deposition in 858

regional-scale numerical models. Atmospheric Environment 23:1293–1304. 859

Wilson, P. J., K. Thompson, and J. G. Hodgson. 1999. Specific leaf area and leaf dry matter 860

content as alternative predictors of plant strategies. New Phytologist 143:155–162. 861

862

plant functional diversity increases grassland

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