using plant traits to understand the contribution of ... · 17/06/2020 · 1 1 using plant traits...

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Using plant traits to understand the contribution of biodiversity effects to 1

community productivity in an agricultural system 2

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Nadine Engbersen1, Laura Stefan1, Rob W. Brooker2, Christian Schöb1 4

1 Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, 5

Universitätstrasse 2, 8092 Zurich, Switzerland. 6

2 The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, Scotland, UK 7

Summary 8

Increasing biodiversity generally enhances productivity through selection and complementarity 9

effects not only in natural but also in agricultural systems. However, explaining why diversity 10

enhances productivity remains a central goal in agricultural science. 11

In a field experiment, we constructed monocultures, 2- and 4-species mixtures from eight crop 12

species with and without fertilizer and both in temperate Switzerland and semi-arid Spain. We 13

measured environmental factors and plant traits and related these in structural equation models 14

to selection and complementarity effects to explain yield differences between monocultures and 15

mixtures. 16

Increased crop diversity increased yield in Switzerland. This positive biodiversity effect was 17

driven to almost same extents by selection and complementarity effects, which increased with 18

plant height and C:N ratio, respectively. Also, ecological processes driving yield increases from 19

monocultures to mixtures differed from those responsible for yield increases through the 20

diversification of mixtures. 21

While selection effects were mainly driven by one species, complementarity effects were linked 22

to higher productivity per unit N. Yield increases due to mixture diversification were driven 23

only by complementarity and were not mediated through the measured traits, suggesting that 24

ecological processes beyond those measured in this study were responsible for positive diversity 25

effects on yield beyond 2-species mixtures. 26

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Key words: agroecology, biodiversity–productivity, complementarity and selection effects, 28

intercropping, plant traits 29

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1. Introduction 30

Plant primary productivity increases with higher species diversity (e.g. Tilman et al. (2001); Cardinale 31

et al. (2006)). While the majority of research in this area has been done in perennial systems (Tilman et 32

al. 2001; Weisser et al. 2017; Huang et al. 2018), recent studies have demonstrated similar effects in 33

annual systems (Li et al. 2014; Brooker et al. 2015; Stomph et al. 2020). Intercropping of annual crops, 34

where at least two crop species are grown in close proximity at the same time, is therefore a promising 35

application of agroecological concepts. Intercropping can make use of species complementarity and 36

beneficial above- and belowground facilitation and niche differentiation, which can ultimately lead to 37

overyielding, i.e. increased yield in mixture compared with the average of the monocultures 38

(Vandermeer 1989). 39

Two main mechanisms have been proposed to explain positive biodiversity–productivity 40

relationships. First, sampling or selection effects (hereafter: selection effects) encompass the greater 41

probability that more diverse communities include highly productive species or functional groups, which 42

then account for the majority of productivity (Huston 1997; Tilman et al. 1997). Enhanced ecosystem 43

functioning in diverse agroecosystems can be driven by selection effects (i.e. communities with more 44

species are more likely to host a high-performing species). For instance, in China, a hotspot of 45

intercropping, a recent meta-analysis has shown that 10% of all yield gain of intercropping compared to 46

sole cropping was due to selection effects (Li et al. 2020). 47

The second mechanism is complementarity through resource partitioning or facilitation. Resource 48

partitioning involves more diverse communities containing species with contrasting demands on 49

resources, which leads to a more complete exploitation of available resources in diverse plant 50

communities compared with monocultures and hence increased productivity (Tilman et al. 1997; Loreau 51

and Hector 2001). Facilitation involves plants altering their environment in a way that is beneficial to at 52

least one co-occurring species (Brooker et al. 2008). However, knowledge about the precise mechanisms 53

that lead to overyielding in crop mixtures and how environmental conditions can alter these mechanisms 54

still remains incomplete (Duchene et al. 2017). 55

Resource partitioning can occur across spatial, temporal or chemical gradients. For example, 56

different rooting depths allow plants to take up water or nutrients from different soil layers, thus limiting 57

competition. While this has been observed for water uptake (Miyazawa et al. 2009), the evidence of 58

resource partitioning for soil nutrients as a driver of biodiversity effects is less clear (Von Felten et al. 59

2012; Jesch et al. 2018). Aboveground, diverse communities have been observed to harbor more diverse 60

canopy growth forms allowing for a more complete use of photosynthetically active radiation (PAR) 61

(Spehn et al. 2002; Fridley 2003), leading in some cases to yield advantages in mixtures compared with 62

monocultures (Bedoussac and Justes 2009). 63

Facilitation involves plants altering their environment in a way that is beneficial to at least one 64

co-occurring species (Brooker et al. 2008). Facilitation among plants can happen via either the 65

enrichment of resource pools by one plant for neighboring plants or the mediation of physical stress. 66



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The facilitative benefits of legume neighbors for enhancing soil N are well known, for example for 67

cereals intercropped with legumes (Jensen 1996; Hauggaard-Nielsen et al. 2001; Spehn et al. 2002; 68

Temperton et al. 2007). Other below-ground facilitative mechanisms shown to occur in intercrops 69

include enrichment of resource pools by hydraulic lift, which not only facilitates water uptake (Sekiya 70

and Yano 2004), but can also enhance nutrient mobilization and lead to increased nutrient status of the 71

intercrop (Sun et al. 2013). Other evidence of facilitation mediating physical stress in crop systems come 72

from studies of barley variety mixtures, where a denser canopy structure, shading of the soil surface, 73

and thus reduced evaporation were observed to decrease the soil temperature due (Cooper et al. 1987). 74

Also, plant species can alleviate the microclimate for their neighbors by mediating wind, heat or 75

photoinhibition (Wright et al. 2017). 76

However, despite these examples of different resource partitioning and facilitation mechanisms 77

occurring in crop systems, knowledge about the precise mechanisms that lead to overyielding in crop 78

mixtures, and how environmental conditions can alter these mechanisms, still remains incomplete 79

(Duchene et al. 2017). While there is abundant evidence for the presence of facilitation and 80

complementarity in diverse agricultural systems, the presence of these processes alone does not 81

guarantee overyielding. 82

Here, we applied ecological knowledge and methods to agriculture by assessing to what extent 83

complementarity and selection effects drive yield gains in crop mixtures compared to crop monocultures 84

and how these effects are related to plant functional traits and environmental factors. By linking 85

frequently-used plant traits to yield gains in mixtures, we can improve our ability to predict optimal 86

species combinations which can help to promote sustainable agricultural production through 87

intercropping. We used four plant traits indicative of resource use: leaf dry matter content (LDMC), 88

specific leaf area (SLA), carbon to nitrogen ratio (C:N ratio) and plant height and two environmental 89

factors: PAR and volumetric soil water content (VWC). We used a structural equation model (SEM) to 90

assess whether overyielding is driven by selection or complementarity effects or by a combination of 91

both. Furthermore, we used this hierarchical model to understand the context–dependence of the 92

complementarity and selection effects and how they are linked to plant functional traits. 93

94

2. Materials and Methods 95

2.1. Site description 96

The study was carried out in two outdoor experimental gardens in Zürich, Switzerland and Torrejón el 97

Rubio, Cáceres, Spain. The Swiss site was located at an altitude of 508 m a.s.l. (47°23’45.3” N 98

8°33’03.6” E), the Spanish site at 290 m a.s.l. (39°48’47.9” N 6°00’00.9” W). The regional climate in 99

Switzerland was classified according to Köppen-Geiger (Kottek et al. 2006) as warm temperate, fully 100

humid with warm summers while in Spain it was classified as warm temperate, summer dry with hot 101

summers. Precipitation in Zürich during the growing season 2018 (April – August) was 572 mm, and in 102

Spain 218 mm (February – June). Daily average hours of sunshine during the growing season were 5.8h 103



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in Zürich and 8.4h in Cáceres. Temperatures between the two sites were similar: average of daily mean, 104

minimum and maximum temperatures were 14.0 °C, 9.3 °C and 18.6°C in Zürich and 14.6 °C, 9.6°C 105

and 19.6°C in Cáceres. All climatic data are from the Deutsche Wetterdienst (www.dwd.de). 106

The experimental garden at each location covered 98 m2, divided into 392 square plots of 0.25 m2 107

and 40 cm depth. However, plots were open at the bottom and allowed root growth beyond 40 cm. In 108

Switzerland, the plots were arranged in 14 beds of 7 × 1 m, with two rows of 14 plots, resulting in 28 109

plots per bed. In Spain, the plots were arranged in 14 beds of 10 × 1 m, with two rows of 20 plots, 110

resulting in 40 plots per bed. The plots were filled with local, unenriched agricultural soil. Soil structure 111

and composition therefore differed between the sites. In Switzerland, soil was composed of 45% sand, 112

45% silt, 10% clay and contained 0.19% nitrogen, 3.39% carbon, and 333 mg total P/kg with a mean 113

pH of 7.25. Spanish soils consisted of 78% sand, 20% silt, 2% clay and contained 0.05% nitrogen, 0.5% 114

carbon, 254 mg total P/kg with a mean pH of 6.3. 115

The experimental gardens were irrigated throughout the growing season with the aim of 116

maintaining the differences in precipitation between the two sites but assuring survival of the crops 117

during drought periods. In Switzerland, the dry threshold was set to 50% of field capacity, with a target 118

of 90% of field capacity, while in Spain the thresholds were 17% and 25% of field capacity, respectively. 119

Automated irrigation was configured such that irrigation would start if the dry threshold was reached, 120

and irrigate until the target threshold was reached. 121

At each site, half of the beds were chosen randomly to be fertilized with N-P-K (1-1.7-1) while 122

the other half served as unfertilized controls. Fertilizer was applied three times: 50 kg/ha just before 123

sowing, another 50 kg/ha when wheat was tillering and 20 kg/ha when wheat was flowering. 124

2.2. Crop species and cultivars 125

At each site, experimental communities were constructed with eight annual crop species of agricultural 126

interest. The crop species belonged to four different phylogenetic groups: Triticum aestivum (wheat, C3 127

grass) and Avena sativa (oat, C3 grass), Lens culinaris (lentil, legume) and Lupinus angustifolius (lupin, 128

legume), Linum usitatissimum (flax, herb [superrosids]) and Camelina sativa (false flax, herb 129

[superrosids]), Chenopodium quinoa (quinoa, herb [superasterids]) and Coriandrum sativum (coriander, 130

herb [superasterids]). The four phylogenetic groups were based on their phylogenetic distances: Cereals 131

diverged from the other groups 160 million years ago (mya); superasterid herbs diverged from 132

superrosid herbs including legumes 117 mya and finally, legumes diverged from the other superrosid 133

herbs 106 mya (TimeTree). Phylogenetic distance was chosen as a criterion for functional similarity as 134

it is often positively correlated with functional diversity and acts as a proxy to assess the impacts of 135

species diversity on ecosystem functions (Mouquet et al. 2015). At both sites, we grew commercial 136

cultivars typically used for organic farming in Switzerland (Table S1). While these were bred for a Swiss 137

climate, their cultivation in Spain demonstrated the ability of these cultivars to adapt to a climate change-138

type scenario, with conditions considerably warmer and drier than in Switzerland. 139



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2.3. Experimental crop communities 140

Experimental crop communities at both sites consisted of eight different monocultures, 24 different 2-141

species mixtures consisting of two different phylogenetic groups and 16 different 4-species mixtures 142

consisting of four different phylogenetic groups. Every combination of 2-species mixture with two 143

species from different phylogenetic groups and every possible 4-species mixture with species from four 144

different phylogenetic groups were planted. Each experimental community was replicated two times. 145

Plots were randomized within each country and fertilizer treatment. Each monoculture and mixture 146

community consisted of one, two or four crop species planted in four rows. The row order of the species 147

was chosen randomly. Sowing densities differed among phylogenetic groups and were based on current 148

cultivation practice (Table S1). Sowing was done by hand in early February 2018 in Spain and early 149

April 2018 in Switzerland. 150

2.4. Data collection 151

Leaf traits were measured at the time of flowering (May 2018 in Spain, 94 days after sowing; June 2018 152

in Switzerland, 65 days after sowing). Three individuals per crop species per plot were randomly marked 153

and their height measured with a ruler from the soil surface to the highest photosynthetically active 154

tissue. Of each marked individual, 1 to 10 healthy leaves were sampled and immediately wrapped in 155

moist cotton and stored overnight at room temperature in open plastic bags. For the subsequent leaf trait 156

measurements (specific leaf area [SLA] and leaf dry matter content [LDMC]) we followed standard 157

protocols (Cornelissen et al. 2003). The leaf samples were blotted dry to remove any surface water and 158

immediately weighed to produce a value for saturated weight. They were instantly scanned with a flatbed 159

scanner (CanoScan LiDE 120, Canon) and oven-dried in a paper envelope at 80 °C for at least 48 h. 160

After drying, the leaf samples were reweighed to obtain dry weight. Values for dry matter content were 161

calculated as the ratio of leaf dry mass (mg) to saturated leaf mass (g). The leaf scans were analyzed for 162

leaf area with the image processing software ImageJ 1 (Rasband 1997-2018). SLA was calculated as the 163

ratio of leaf area (cm2) to dry mass (g) and is a measure of the investment in dry mass per light-164

intercepting surface and used to assess light acquisition strategies; higher SLA values can be observed 165

under low light conditions as a larger leaf area per leaf mass (= thinner leaves) enables increased surface 166

for light capture at lower C costs (Weiher et al. 1999; Evans and Poorter 2001). LDMC is often inversely 167

related to SLA but also includes soil-water relations and hence indicates impacts of resource conditions, 168

including water (Pérez-Harguindeguy et al. 2013) 169

At the time of harvest (duration of crop growth from sowing to harvest given in table S2) all crops 170

in each plot were harvested. The three marked individuals used for the trait measurements were collected 171

separately, while all remaining plants per crop species per plot were pooled together. Plant shoots were 172

cut at the soil surface and biomass and seeds were separated. The total number of individuals per crop 173

species per plot was recorded. Fruits were air-dried. Afterwards, seeds were separated from chaff with 174

a threshing machine (in Switzerland: Allesdrescher K35, Baumann Saatzuchtbedarf, Germany; in Spain: 175

Hege 16, Wintersteiger, Austria). Vegetative biomass was oven-dried at 80 °C until constant weight. 176



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Interception of PAR by the plant canopy was measured weekly with a LI-1500 (LI-COR 177

Biosciences GmbH, Germany). In each plot, three PAR measurements were taken around noon by 178

placing the sensor on the soil surface in the center of each of the three in-between rows. Light 179

measurements beneath the canopy were put into context through simultaneous PAR measurements of a 180

calibration sensor, which was mounted on a vertical post at 2 m above ground in the middle of the 181

experimental garden. FPAR (%) indicates the percentage of PAR that was intercepted by the crop 182

canopy. VWC in the soil was measured weekly with a ML3 ThetaProbe Soil Moisture Sensor (Delta-T, 183

Cambridge). The measurements were taken in the center of each of the three in-between rows per plot. 184

For further data analysis, we used FPAR and VWC values from the week of leaf trait measurements 185

(Spain: 92 days after sowing; Switzerland: 62 days after sowing). 186

2.5. Plant N analyses 187

For chemical analyses of the plant tissue we pooled the three dried leaf samples of the marked 188

individuals per plot and per crop species. Leaf samples were ground for 20 minutes in 1.2 ml tubes with 189

two stainless steel beads in a bead mill (TissueLyserII, Qiagen). Afterwards, either 100 mg (if available 190

in sufficient amount) or 4 mg (+/- 1 mg) (if the sample was too small) of ground leaf material were 191

weighed into tin foil cups or 5 × 9 mm tin capsules and analyzed for C and N contents. The 400 large 192

samples were analyzed on a LECO CHN628C elemental analyzer (Leco Co., St. Joseph, USA) and the 193

505 small samples on a PDZ Europa 20-20 isotope ratio mass spectrometer linked to a PDZ Europa 194

ANCA-GSL elemental analyzer (Sercon Ltd., Cheshire, UK), respectively. Eight samples were cross-195

referenced on both analytical devices (Fig. S1) and measured values from LECO were corrected to 196

account for the differences between the devices (correction factors are 1.0957 for N and 1.026 for C, 197

respectively). 198

2.6. Data analysis 199

Prior to data analysis the dataset was screened for missing data or incorrect data recording and the whole 200

plot was removed when any of these occurred. A total of 467 plots remained, 231 in Switzerland and 201

236 in Spain. 202

All statistical analyses were performed in R version 3.6.0. (R Core Team 2019). We used general 203

linear mixed-effects models using restricted maximum likelihood estimation to explain yield at the 204

community-level. We assessed the significance of the fixed effects using type-I ANOVA and the 205

Satterthwaite approximation of denominator degrees of freedom (lme4 (Bates et al. 2015) and lmerTest 206

(Kuznetsova et al. 2017) packages). Yield was log transformed. The fixed effects of the model were 207

country (Switzerland versus Spain), fertilizer (fertilized versus unfertilized), species number (2 versus 208

4) nested in diversity (monocultures vs mixtures), and interactions among the fixed effects (except 209

between the nested terms). Random terms were species composition, garden bed and the interactions of 210

garden bed with diversity and garden bed with species number. To test for effects of diversity within 211

each country, we performed pairwise multiple comparison Tukey tests. 212



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We used the same linear mixed-effects models as mentioned above to analyze treatment effects 213

on environmental factors (FPAR, VWC), plant traits (SLA, LDMC, height, leaf N, C:N ratio) and 214

biodiversity effects (selection and complementarity effects) with country, fertilizer, species number 215

nested in diversity and interactions among these as fixed effects. Random terms were species 216

composition, garden bed and their interaction. Response variables were log transformed, except for 217

biodiversity effects. 218

Using structural equation modeling (SEM) we analyzed the sign and intensity of treatment effects 219

and abiotic factors on plant traits, biodiversity effects and community-level yield, based on our complex 220

a priori model (Fig. S2). To explain differences in community-level yield between mixtures and 221

monocultures, we calculated Δyield as the difference between the summed community-level yields of 222

all species in a mixture plot and the average of the mean community-level yields of all monocultures 223

corresponding to the species in the mixture plot. Thus, Δyield compares the observed yield in the mixture 224

with the expected yields in a mixture based on their yields in a monoculture. Δyield was square root 225

transformed to meet the assumptions of normal distribution. We quantified the biodiversity effect 226

(Δyield) and its two components, the complementarity and selection effects according to Loreau and 227

Hector (Loreau and Hector 2001). 228

∆𝑦𝑖𝑒𝑙𝑑 = 𝑁 ∙ ∆𝑅𝑌̅̅ ̅̅ ̅̅ ∙ �̅� + 𝑁 ∙ 𝑐𝑜𝑣(∆𝑅𝑌, 𝑀) (1) 229

Where N is the number of species in the plot. ΔRY is the deviation from expected relative yield 230

of the species in mixture in the respective plot, which is calculated as the ratio of observed relative yield 231

of the species in mixture to the yield of the species in monoculture. The first component of the 232

biodiversity effect equation (𝑁 ∙ ∆𝑅𝑌̅̅ ̅̅ ̅̅ ∙ �̅�) is the complementarity effect, while the second component 233

(𝑁 ∙ 𝑐𝑜𝑣(∆𝑅𝑌, 𝑀)) is the selection effect. We calculated ΔVWC and ΔFPAR according to equation 2. 234

∆𝑉𝑊𝐶 = 𝑉𝑊𝐶̅̅ ̅̅ ̅̅ ̅𝑚𝑖𝑥 − 𝑉𝑊𝐶̅̅ ̅̅ ̅̅ ̅

𝑚𝑜𝑛𝑜 (2) 235

Where 𝑉𝑊𝐶̅̅ ̅̅ ̅̅ ̅𝑚𝑖𝑥 is the average of all three measurements of VWC per mixture plot and 236

𝑉𝑊𝐶̅̅ ̅̅ ̅̅ ̅𝑚𝑜𝑛𝑜 the average of all three measurements of VWC of the respective monoculture plots. The same 237

was calculated for ΔFPAR. To scale up plant trait measurements to the community level, community-238

weighted means in mixtures and monocultures were used to calculate ΔSLA, ΔLDMC, ΔC:N ratio and 239

Δheight: 240

∆𝑆𝐿𝐴 = 𝐶𝑊𝑀. 𝑆𝐿𝐴𝑚𝑖𝑥 − 𝐶𝑊𝑀. 𝑆𝐿𝐴𝑚𝑜𝑛𝑜 (3) 241

Where CWM.SLAmix is the community-weighted mean of SLA of all species in a mixture plot 242

and CWM.SLAmono the community-weighted mean of SLA in monoculture of all the species in the 243

respective plot. Aboveground biomass of each species was used as weights. ΔLDMC, ΔC:N ratio and 244

Δheight were calculated likewise according to formula 3. The SEM was constructed using LAVAAN 245

(Rosseel 2012) in R. For each variable, we report the conditional coefficient of determination (R2c), 246

which represents the variation explained by fixed and random effects and was calculated with the 247

MUMIN package (Bartoń 2019). 248

249



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3. Results 250

3.1. Response of community-level yield to treatments 251

Community-level yield was significantly affected by country, with 88% higher yields in Switzerland 252

compared with Spain (Fig. 1a, Table 1). The addition of fertilizer did not show any effect on yield in 253

either country nor on either diversity level. Mixtures produced significantly more yield than 254

monocultures –and 4-species mixtures yielded more than 2-species mixtures (Fig. 1a, Table 1). Tukey 255

post hoc tests revealed that productivity of mixtures was enhanced, particularly in Switzerland (Table 256

S3), where 4-species mixtures yielded 30% more than 2-species mixtures and 93% more than 257

monocultures. Also, 2-species mixtures in Switzerland yielded 48% more than monocultures. In Spain, 258

yield did not respond significantly to different diversity levels. The presence of a legume in mixtures 259

increased yields in Spain by 72% but decreased it by 23% in Switzerland (Fig. 1b, Table S3). The three-260

way interaction of country × fertilizer × legume indicates that mixtures without legumes yielded more 261

in Switzerland in both fertilizer treatments, while in Spain mixtures with legumes had higher yields, but 262

only in unfertilized soils (Fig. 1b, Table S3). 263

264

Figure 1: Community-level yields in kg dry weight per m2 visualizing the significant results from table 265

1. Differences in community-level yield between countries (a), diversity levels (a), mixture 266

diversification (a), between the two-way interactions country × legume (b), country × diversity (a) and 267

country × mixture diversification (a) and between the three-way interaction country × fertilizer × 268

legume (b). 269



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Table 1: Results of mixed effects ANOVA testing effects of the different treatments (country, fertilizer, 270

legume, diversity (monocultures vs. mixtures) and mixture diversification (2- vs. 4-species mixtures) on 271

community-level yield. SS: Sum of squares, MS: mean of squares, numDF: degrees of freedom of term, 272

denDF: degrees of freedom of error term, F-value: variance ratio, P: error probability. P-values in bold 273

are significant at α = 0.05 (* P < 0.05, ** P < 0.01, *** P < 0.001). n = 315 274

SS MS numDF denDF F-value P

country 4.691 4.691 1 24.98 12.64 0.002**

fertilizer 1.192 1.192 1 23.75 3.212 0.086

legume 0.201 0.201 1 43.24 0.542 0.466

diversity 1.524 1.524 1 42.67 4.107 0.049*

mixture diversification 1.933 1.933 1 43.78 5.209 0.027*

country × fertilizer 0.384 0.384 1 23.65 1.035 0.319

country × legume 16.99 16.99 1 257.1 45.77 8.91E-1***

country × diversity 15.01 15.01 1 79.23 40.45 1.20E-08***

country × mixture diversification 24.95 24.95 1 246.2 67.22 1.34E-14***

fertilizer × legume 0.356 0.356 1 254.9 0.958 0.329

fertilizer × diversity 0.059 0.059 1 76.7 0.158 0.692

fertilizer × mixture diversification 0.014 0.014 1 244.7 0.037 0.849

country × fertilizer × legume 1.518 1.518 1 253.9 4.089 0.044*

country × fertilizer × diversity 0.488 0.488 1 77.59 1.315 0.255

country × fertilizer × mixture

diversification 0.997 0.997 1 245.5 2.685 0.103

275

3.2. Environmental factors, plant traits and biodiversity effects 276

Neither environmental factors nor plant traits showed significant variation in response to the diversity 277

treatments (Fig. 2, Table S4, S5). Reduced water input resulted in a significantly lower volumetric soil 278

water content (VWC) in Spain. However, VWC did not vary significantly in response to fertilizer or 279

diversity treatments (Fig. 2a, Table S5). Both plant height and FPAR were significantly higher in 280

Switzerland than in Spain, indicating that canopy closure was more complete and that vegetative growth 281

was generally stronger in Switzerland than in Spain (Fig. 2b,e) and in fertilized compared with 282

unfertilized treatments (Table S5). SLA of crops was significantly higher in Switzerland compared with 283

Spain and LDMC showed an opposite behavior, with higher values in Spain than in Switzerland (Fig. 284

2c,d). Neither LDMC nor SLA responded significantly to fertilizer or diversity treatments. Leaf N and 285

C:N ratio both indicate significantly higher leaf N levels in Switzerland than in Spain (Fig. 2f,g, Table 286

S4, S5). Complementarity effects were stronger in Switzerland than in Spain and stronger in 4-species 287

than in 2-species mixtures in Switzerland (Fig. 2h). Selection effects showed no response to any 288

treatment factor (Fig. 2i). 289



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290

Figure 2: Community-level means for the environmental factors volumetric soil water content (VWC) 291

(a) and absorption of photosynthetically active radiation (FPAR) (b), the plant traits specific leaf area 292

(SLA) (c), leaf dry matter content (LDMC) (d), plant height (e), leaf N (f) and C:N ratio (g) and the 293

biodiversity effects, divided into complementarity (h) and selection effects (i). Data are shown for both 294

countries and separated by levels of diversity. Complementarity and selection effects are only available 295

for mixtures. Brackets indicate significant differences between treatments and labels above brackets 296

indicate which treatment (mix. div. = mixture diversification) was significant at α = 0.05 (* P < 0.05, 297

** P < 0.01, *** P < 0.001). 298

299

300

301



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3.3. Structural equation model to explain community-level yields 302

The SEM showed a good fit to the data (d.f = 5, p = 0.572, CFI = 1) and explored the links between 303

experimental treatment factors (country, fertilizer, mixture diversification), environmental factors 304

(ΔFPAR, ΔVWC) and the effect of these on the relevant plant traits (ΔSLA; ΔLDMC; ΔC:N ratio, 305

Δplant height) and finally linked these through selection and complementarity effects to yield 306

differences between crop monocultures and mixtures (Δyield) (Fig. 3). 307

The effect of complementarity on Δyield was 1.2 fold stronger than the selection effect (Fig. 3). 308

Both biodiversity effects were negatively correlated with each other, thus when one effect increased, the 309

other decreased. The selection effect was only related to Δheight, indicating that increasing plant height 310

in mixtures compared to monocultures increased the selection effect (Fig. 3). We suspect that most of 311

the selection effect is due to the tall-growing and high-yielding species Chenopodium quinoa dominating 312

the communities and their yield, particularly in Switzerland (Fig. S3). The complementarity effect was 313

positively related only to ΔC:N ratio. Increases in ΔC:N ratio indicate higher productivity per unit N in 314

mixtures compared with monocultures, which increased the complementarity effect. Overall, the model 315

explained 92% of yield differences between mixtures and monocultures (Fig. 3). 316

317

318

Figure 3: Mechanistic model showing the experimental treatment effects on the effects of environmental 319

factors on plant traits and subsequently on biodiversity effects and differences in yield between crop 320

mixtures and crop monocultures. Δ indicates differences between the respective measurements in 321

mixtures compared with monocultures, thus positive Δ values indicate higher values in mixtures 322



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compared with monocultures and vice versa. Mean values for Δ values per country, fertilizer treatment 323

and species number are given in table S6. Displayed black arrows show significant positive (solid) or 324

negative (dashed) relationships (α = 0.05), grey arrows indicate direct effects of treatment factors on 325

traits and yield. Arrow thickness indicates effect size based on standardized path coefficients. Numbers 326

next to the variables indicate their explained variance (R2). Double-headed grey dashed arrows indicate 327

significant correlations. Non-significant tested relationships are not shown. 328

329

Plant traits, excluding plant height, were correlated among each other. ΔLDMC was strongly 330

negatively correlated with ΔSLA and positively with ΔC:N ratio. This suggests that a higher water 331

content in leaves of crop mixtures than crop monocultures (low ΔLDMC) is correlated to a higher leaf 332

area per unit mass in crop mixtures than in crop monocultures (high ΔSLA) and to less leaf N in crop 333

monocultures than in crop mixtures (low ΔC:N ratio) (Fig. 3). 334

ΔLDMC was negatively related to both environmental factors, ΔFPAR and ΔVWC, indicating 335

that higher light interception by the crop canopy and higher soil water contents decreased LDMC, thus 336

increasing leaf water contents in mixtures. The negative correlation between ΔC:N ratio and ΔFPAR 337

implies that leaf N content in mixtures increased with increasing light interception. ΔSLA was positively 338

related to both environmental factors, suggesting that increasing light interception and soil water content 339

increased SLA, thus promoting higher leaf area per leaf mass. Based on standardized effect sizes, the 340

effect of ΔFPAR on leaf traits was stronger than the effect of ΔVWC; specifically, the effect of ΔFPAR 341

on ΔLDMC and ΔSLA was 1.4 and 1.5 fold stronger than the effect of ΔVWC, respectively. ΔVWC 342

was positively correlated to the selection effect, with the effect of Δheight being 1.2 fold stronger than 343

the effect of ΔVWC on the selection effect. Thus, higher soil water contents in mixtures compared with 344

monocultures increased the selection effect in mixtures compared with monocultures (Fig. 3). 345

ΔFPAR varied in response to fertilizer treatment, with 10% higher values in unfertilized 346

treatments, indicating that crop mixtures intercepted more light than crop monocultures and that this 347

effect was stronger in unfertilized treatments (Table S6). ΔVWC varied significantly only among 348

countries, with more negative values in Switzerland compared with Spain. This indicates that soils in 349

crop monocultures had a higher water content than in crop mixtures and that this effect was more 350

pronounced in Switzerland than in Spain. All plant traits, except ΔSLA varied significantly among 351

countries; ΔLDMC was 163% higher and Δheight 207% higher in Spain than in Switzerland, while 352

ΔC:N was 104% higher in Switzerland than in Spain (Fig. 3, Table S6). ΔLDMC also varied 353

significantly among fertilization treatments, with 170% higher results in fertilized compared with 354

unfertilized treatments. Both, selection and complementarity effects responded significantly to country. 355

Complementarity effects were 540% higher in Switzerland than in Spain and selection effects were 356

350% higher in Switzerland than in Spain. Complementarity effect was the only variable responding to 357

mixture diversification and complementarity effects were 110% higher in 4- than in 2-species mixtures. 358



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The effect of mixture diversification on complementarity effects was as strong as the effect from ΔC:N 359

ratio on complementarity effects (Fig. 3, Table S6). 360

361

4. Discussion 362

Our study found increasing yields from crop monocultures to 2- to 4-species mixtures, in particular at 363

the temperate site in Switzerland. Community-level yield did not respond to fertilizer treatments, but 364

varied strongly between the two countries. Selection and complementarity effects were linked to plant 365

height and C:N ratio, respectively, and explained 92% of the variation in community-level crop yield 366

differences between mixtures and monocultures. While C:N ratio was linked to light use, plant height 367

showed no link to environmental factors and only differed between countries. The effect of mixture 368

diversification on crop yield was not mediated through any of the abiotic factors or plant traits measured 369

in this study, but acted directly upon complementarity effects. 370

371

Positive biodiversity–productivity relationships are context–dependent 372

In Switzerland community-level yield increased from monoculture to mixture and from 2- to 4-species 373

mixtures, while the diversity effects on yield in Spain remained weak. The differences between the two 374

countries were diverse and included differences in precipitation and irrigation, hours of sunshine, soil 375

nutrients and soil texture. Light availability in Spain was presumably not limiting, but could have been 376

an inhibiting factor. Lower SLA values in Spain indicate that the plants had less leaf area per dry leaf 377

mass, which could be the plants’ effort to reduce leaf area exposed to high irradiance. 378

From the three growth-limiting resources we focused on in this study, soil water and N availability 379

were the most promising to explain the missing positive diversity-productivity relationship in Spain. 380

Soil water content in Spain was kept low by reducing irrigation to the amount needed for plant survival 381

(see chapter 2.1) (mean VWC in Spain: 5.7 ± 0.4 %). Combined with a generally hotter climate in Spain, 382

the crops were more prone to water stress. Crop yields in intercropping under drought conditions are 383

expected to decrease (Coll et al. 2012). Also, positive diversity effects on crop water availability in 384

intercropping remain contested and Brooker et al. (2015) suggest that these effects are limited to 385

intercropping systems where at least one species has a low water demand. The crop species planted in 386

this experiment were not adapted to the dry conditions in Spain, since they were Swiss cultivars bred 387

for use under temperate climatic conditions. A further explanation for the absence of a positive diversity-388

productivity relationship in Spain can be the increased allocation of C to belowground productivity in 389

response to dry conditions. In our study we were interested in positive productivity effects on crop yield, 390

hence the focus on above-ground biomass. However, increased belowground investment can lead to a 391

decrease in aboveground productivity while maintaining overall community productivity (Kahmen et 392

al. 2005). 393



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Besides arid conditions in Spain, low soil fertility could also have reduced biodiversity effects on 394

productivity, as observed before in an agricultural experiment (Fridley 2003). The increase in 395

community-level yields in response to legume presence in Spain indicates that N sparing by the legumes 396

could increase leaf N content for neighboring crops. Since the N sparing effect was only observed in 397

unfertilized communities in Spain, we suggest that fertilizer addition reduced the formation of nodules 398

during early legume growth, inhibiting N-fixation during later growth stages (Naudin et al. 2011). Also, 399

available soil water content is an important parameter controlling N2-fixation of legumes, either directly 400

by influencing nodulation or indirectly by reducing plant growth and thus N2-fixation (Sprent and 401

Minchin 1983). Therefore, we suggest that drier conditions in Spain might have limited plant growth 402

directly and also inhibited N2-fixation, further reducing N availability for crops. 403

404

Complementarity effects increased yields in mixtures compared with monocultures 405

In our study, complementarity effects were shown to drive positive biodiversity–productivity 406

relationships in Switzerland. The effect of complementarity was mainly linked to changes in C:N ratio 407

between mixtures and monocultures, implying that crops grown in mixtures were producing more 408

biomass per unit leaf N than crops in monoculture. This increase in efficiency could partially explain 409

the higher productivity often observed at higher diversity (Fargione et al. 2007). The C:N ratio was 410

dependent on the fraction of intercepted light but not on soil water contents. The negative link between 411

C:N ratio and FPAR indicated that higher light interception was linked to higher leaf N content per unit 412

biomass. Thus, crops responded to a more complete canopy cover and thus lower light access by 413

increasing their SLA (Fig. 3) and leaf N content to have a larger photosynthetically active leaf area. 414

High leaf N and high SLA are a common plant response to lower light conditions (Lambers et al. 1998; 415

Reich et al. 1998; Evans and Poorter 2001; Funk et al. 2017). However in Switzerland, at higher mixture 416

diversification (i.e. in 4-species mixtures) the relation between FPAR and C:N ratio became positive 417

(table S6), indicating that with increasing diversification a shift in N use efficiency occurred. Thus, 4-418

species mixtures in Switzerland produced more yield per unit N than 2-species mixtures or 419

monocultures. Increasing N efficiency in more diverse communities has been observed before in semi-420

natural (van Ruijven and Berendse 2005; Fargione et al. 2007) but, to the best of our knowledge, not in 421

intercropping systems. 422

Complementarity effects were the only variable responding to mixture diversification, with an 423

increase of complementarity effects in 4- over 2-species mixtures. Strikingly, mixture diversification 424

did not affect any of the environmental factors or plant traits measured in this study. This suggests that 425

the plant traits measured in this study were not able to describe how mixture diversification affects 426

complementarity. We suspect belowground processes driving yield increases from 2- to 4-species 427

mixtures or that dynamics of nutrients other than N, or reduced impacts of pests, were possibly playing 428

a role. Concerning possible belowground processes, other studies observed that the presence of a legume 429



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15

can increase N or P availability in the soil surrounding its roots (Temperton et al. 2007; Zhang et al. 430

2019). In our study, this observation could potentially be important, as all 4-species mixtures contained 431

one leguminous crop, while not all 2-species mixtures did so. As an alternative to not measuring the 432

appropriate traits, it could also be that not enough traits were measured to fully capture niche differences. 433

As suggested by earlier studies, when linking plant traits to biodiversity effects, a large range of traits is 434

required to explain niche differences (Kraft et al. 2015; Cadotte 2017). 435

Although we did see facilitative processes in Spain, especially the N sparing effect of the legumes 436

in unfertilized plots, we propose that facilitative processes were not strong enough to compensate for 437

the difficult growing conditions in Spain. 438

439

Selection effects due to Chenopodium quinoa 440

In this study, selection effects had a similarly strong effect on yield differences between mixtures and 441

monocultures as had complementarity effects. While it is often assumed that positive biodiversity–442

productivity relationships are driven mainly by niche differentiation (Cardinale 2013), we show here 443

that selection effects are nearly as important. The selection effect was linked to plant height, thus 444

selection effects increased with increasing plant height in mixtures compared to monocultures. A 445

relation between plant height and selection effects has been observed before (Cadotte 2017) and is 446

probably due to plant height being related to competition for light, where taller plants outcompete shorter 447

plants (Westoby 1998). 448

Selection effects were significantly higher in Switzerland than in Spain. Combined with the link 449

to plant height, we concluded that one highly productive and tall-growing species was causing this effect 450

Chenopodium quinoa (Fig. S3). It has been observed before that C. quinoa was highly competitive 451

(Buckland 2016) and that abundant irrigation could increase its yields (Walters et al. 2016). Thus, the 452

selection effect was considerably less important in Spain than in Switzerland. 453

454

5. Conclusion 455

Our study showed that crop productivity increased with diversity under temperate conditions but only 456

weakly when crops were grown under semiarid conditions with limited availability of water and strong 457

irradiance. Increases in productivity in mixtures compared to monocultures were caused to almost the 458

same extent by complementarity and selection effects. Selection and complementarity effects were 459

explained by different plant trait syndromes. The selection effect was maximized in plots with tall plants 460

and was probably caused by one single species, Chenopodium quinoa, which was highly productive and 461

tall-growing. Complementarity effects were linked to an increased C capture per unit N, thus indicating 462

that crops in mixtures were more efficient. However, complementarity effects were also stronger in 4- 463

compared with 2-species mixtures and this link was not mediated through any of the measured plant 464

traits, suggesting that other ecological processes must have been responsible for the positive diversity 465



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16

effect on yield beyond two-species mixtures. This finding suggests that the drivers of diversity effects 466

from monocultures to mixtures are not the same as from 2- to 4-species mixtures and should therefore 467

be targeted specifically in future studies. Nevertheless, by identifying links between relevant plant traits 468

and positive biodiversity–productivity relationships through selection and complementarity effects, this 469

study helps pave the way to select crop species for mixtures that optimize sampling and complementarity 470

effects. 471

472

Acknowledgments 473

We are grateful to Elisa Pizarro Carbonell, Carlos Barriga Cabanillas and Anja Schmutz for their help 474

with the field experiment, and Johan Six for comments on the experimental design. We also thank the 475

Aprisco de Las Corchuelas and the University of Zurich for allowing us to use their experimental 476

gardens. The study was funded by the Swiss National Science Foundation (PP00P3_170645). 477

478

Author contribution 479

N.E. and C.S. conceived the study with input from L.S. and R.W.B. N.E., L.S. and C.S. collected the 480

data; N.E. assembled and analyzed the data with the help of C.S.; N.E. and C.S. wrote the paper. All 481

authors discussed data analyses and results and revised the manuscript. 482

483

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