# Crop genetic diversity benefits farmland biodiversity in cultivated fields

Carole Chateil, Isabelle Goldringer, Léa Tarallo, Christian Kerbiriou, Isabelle Le Viol, Jean-François Ponge, Sandrine Salmon, Sophie Gachet, Emmanuelle Porcher

► To cite this version:

Carole Chateil, Isabelle Goldringer, Léa Tarallo, Christian Kerbiriou, Isabelle Le Viol, et al.. Crop genetic diversity benefits farmland biodiversity in cultivated fields. Agriculture, Ecosystems & Environment, 2013, 171, pp.25-32. 10.1016/j.agee.2013.03.004. hal-00816466

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1 **Title:** Crop genetic diversity benefits farmland biodiversity in cultivated fields

2

3 **Authors**

4 Carole Chateil<sup>a, b</sup>, Isabelle Goldringer<sup>c</sup>, Léa Tarallo<sup>a</sup>, Christian Kerbiriou<sup>a</sup>, Isabelle Le Viol<sup>a</sup>, Jean-

5 François Ponge<sup>b</sup>, Sandrine Salmon<sup>b</sup>, Sophie Gachet<sup>d</sup> and Emmanuelle Porcher<sup>a</sup>

6

7 <sup>a</sup> CERSP, UMR 7204 MNHN – CNRS – UPMC, 55 rue Buffon, 75005 Paris, France

8 <sup>b</sup> MABiodiv, UMR 7179 MNHN – CNRS, 4 avenue du Petit Château, 91800 Brunoy, France

9 <sup>c</sup> UMR de génétique végétale, INRA – UPS – CNRS – AgroParisTech, Ferme du Moulon, 91190

10 Gif-sur-Yvette, France

11 <sup>d</sup> Aix-Marseille Université, Institut Méditerranéen de Biodiversité et d'Ecologie continentale et

12 marine (IMBE), UMR CNRS 7263 - IRD 237, Campus St-Jérôme, Case 421, 13397 Marseille

13 Cedex 20, France.

14

15 **Corresponding author**

16 Emmanuelle Porcher

17 Muséum national d'Histoire naturelle, UMR 7204, 55 rue Buffon, 75005 Paris, France

18 E-mail address: porcher@mnhn.fr

19 Phone number: + 33 1 40 79 53 61

20 Fax number: + 33 1 40 79 38 35

21

22 **Email addresses**
1 carole.chateil@gmail.com (C. Chateil), isa@moulon.inra.fr (I. Goldringer), kerbiriou@mnhn.fr

2 (C. Kerbiriou), ileviol@mnhn.fr (I. Le Viol), ponge@mnhn.fr (J.F. Ponge), ssalmon@mnhn.fr (S.

3 Salmon), sophie.gachet@imbe.fr (S. Gachet), porcher@mnhn.fr (E. Porcher)

4 Abstract

5 This study tested whether increasing crop genetic diversity benefited farmland biodiversity in

6 bread wheat (*Triticum aestivum*) fields, using an experimental approach in which arthropod and

7 wild plant diversity were compared in a genetically homogeneous wheat variety vs. a variety

8 mixture. The diversity of wild plant species was not affected by crop genetic diversity. However,

9 we showed for the first time a positive impact of crop genetic diversity on below (Collembolla)

10 and aboveground arthropod (Spiders and predatory Carabids) diversity at field scale in

11 agroecosystems, which may be caused by a wider variety of food resources or more complex

12 crop architecture. Increasing crop genetic diversity could therefore be an easy-to-implement

13 scheme benefiting farmland biodiversity.

14

15 Highlights

16

* Plots with higher crop genetic diversity hosted more diverse arthropod communities

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* Crop homogenization may thus contribute to biodiversity loss in agroecosystems

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19

20 Key words

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Carabids; crop management practices; farmland biodiversity; genetic resources; organic farming;

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spiders; springtails; sustainable farming.

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# 1. Introduction

Crop genetic diversity has been decreasing steadily in the agricultural landscapes of developed

countries since the early 20th century (FAO, 1997; Secretariat of the Convention on Biological

Diversity, 2006). This is mostly due the widespread replacement of genetically diverse traditional

varieties or landraces by homogeneous modern varieties (Hoisington et al., 1999), leading to

decreased genetic diversity in the fields, both within and between varieties. Hence, in spite of an

increasing number of registered crop varieties since the sixties, the majority of agricultural land

in developed countries is now covered with a few “winning” productive varieties, with generally

a single crop per field, so that the actual cultivated diversity is in fact low (e.g. FranceAgriMer

and ARVALIS Institut du Végétal, 2009 in France). The resulting crop genetic homogenization is

postulated to threaten the sustainability of production systems, and several studies now

emphasize the importance of both inter- (e.g. Altieri, 1999; Lin, 2011) and intra-specific crop

diversity (e.g. Hajjar et al., 2008; Macfadyen and Bohan, 2010) to increase and stabilize crop

yield, via e.g. improved pest control (see e.g. Tooker and Frank, 2012 for a review).

Another potential consequence of decreased crop genetic diversity that has received little

attention so far is erosion of wild biodiversity in agroecosystems. Previous studies in natural

systems (e.g. Whitham et al., 2006) have shown that the phenotype (hence the genotype) of some

plant species may affect the composition of the dependent community. These particular species,

referred to as foundation species, are abundant in the ecosystem (often, but not always, tree

species, Whitham et al., 2006). Because they represent a large fraction of the biomass of an

ecosystem, they structure a community by creating locally stable conditions for other species

(e.g. habitats and food sources) and by modulating and stabilizing fundamental ecosystem

processes (see Ellison et al., 2005 for definitions). The impact of the genotype of a single species

on a community is known as a “community phenotype”, i.e. an effect of genes at the community
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1 level ("Community genetics", Whitham et al., 2003). High genetic and phenotypic diversity in
2 foundation species can result in a diversity of local environments, thereby benefiting species
3 diversity in the dependent community and affecting ecosystem processes (Bangert et al., 2005;
4 Whitham et al., 2006; Wimp et al., 2004). Although well documented in natural ecosystems, the
5 influence of genetic diversity on community diversity has never been investigated in
6 agroecosystems. Several lines of evidence nonetheless suggest that crop genetic diversity can
7 greatly affect wild species diversity. First, crops are dominant in terms of biomass in a field and
8 can be considered foundation species. Second, crops are known to be involved in numerous
9 (though altered, Macfadyen and Bohan, 2010) interactions with non-crop species, which may
10 create community phenotypes similar to wild foundation species. These interactions include
11 exploitation and interference competition with weed species (e.g. allelopathy, demonstrated for
12 several cereals: Belz, 2007; Bertholdsson, 2010), trophic interactions with pest or non-pest
13 species (e.g. species that feed on root and leaf secretions or excreta), or mutualistic interactions
14 via the creation of microhabitats for predators by below and aboveground vegetative architecture
15 (e.g. Johnson, 2008). As a result, decreased crop genetic diversity should alter farmland
16 biodiversity within and among fields, via a reduction in the diversity of available ecological
17 niches or food sources (Bangert et al., 2005).

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The present study assessed the relationship between in-field crop genetic diversity and the
species diversity of several taxonomic groups (springtails, ground-dwelling macroarthropods and
plants), using an experimental approach in the field. We worked with winter wheat (*Triticum
aestivum* L.), the main crop in the study region, which also exhibits large phenotypic variation
among varieties. Species diversity was compared between plots sown with a pure line variety and
plots sown with a combination of several varieties. The following predictions were tested:
(1) local (α) diversity at each sampling point should be higher in the variety mixture than in the
1 pure line variety, due to the diversity of genotypes (and therefore phenotypes) surrounding each

2 sampling point; (2) β-diversity (between sampling points) should be low within the pure variety

3 because one wheat genotype should be preferentially associated with one community phenotype,

4 whereas β-diversity in the variety mixture should be higher due to high spatial heterogeneity of

5 wheat genotype associations in the field; (3) consequently, γ-diversity, the sum of α- and β-

6 diversity, should be higher in the variety mixture than the pure line variety. We discuss the most

7 likely underlying mechanisms, as well as possible consequences for ecosystem services and

8 opportunities for agricultural sustainability.

9

## 2. Methods

11 The experiment took place in an organic farm in northern France. In winter 2007-2008 ten

12 contiguous square plots (60 m wide) were sown with either a “pure line” bread wheat variety

13 (*Triticum aestivum* “Renan”, INRA, five plots) or a genetically diverse seed mixture including 30

14 landraces and several pure line varieties, among which the “Renan” variety. Pure line varieties

15 are obtained by successive (usually 6-10) self-fertilizations of a few selected plants so that all

16 plants are eventually highly homozygous and genetically identical. These two crop diversity

17 treatments were distributed in a checkerboard-like pattern (see Appendix A). This limited

18 confounding spatial effects but did not fully discard them, owing to the partly unbalanced

19 experimental design that was constrained by field shape. However, these confounding effects

20 appeared to be minor: for example, the only plot not bordered by the surrounding matrix (plot R2,

21 Appendix A) did not exhibit extreme ecological diversities compared to other plots in the same

22 treatment. No mechanical or chemical treatment was applied between sowing and harvest, as is
1 often the case in organic farming; plots were surrounded by a wheat variety not used in the

2 experiment itself.

## 2.1. Community sampling

Springtails (Collembola) were sampled at the beginning of May 2008, using five soil cores (5 cm diameter, 12 cm deep) per plot (one in the center of the plot, the other four at the center of each quarter). After ten days of extraction with the Berlese method (Edwards and Fletcher, 1971), individuals were counted and identified to species level. Water content (dry weight / wet weight) and pH (method NF ISO 10390) were also measured in each soil core.

Ground-dwelling macroarthropods were sampled twice in May and June 2008, during two-week trapping sessions separated by a two-week interruption. To this end, five pitfall traps (9.5 cm diameter, 11.4 cm deep, filled with ethylene glycol) were located at the corners and center of a 10x10 m square centered in each plot. This distribution reduced the capture of individuals from neighboring plots, while maintaining enough distance between the five pitfall traps of a same plot to consider them as relatively independent replicates. The two most abundant groups, carabids and spiders, were identified to species level. All individuals of small carabid species (≤ 4 mm) without identification were grouped (190 individuals, 6.8% of total carabids) and six larger individuals could be identified to genus level only. All spider juveniles that could not be identified to species level were discarded (1674 individuals, 20% of total spiders); the number of discarded individuals was however not significantly different across crop diversity treatments ($F_{1,8} = 4.41, P = 0.07$). Three mature individuals could be identified to genus level only. For aboveground and belowground invertebrates, other taxonomic groups were observed in the samples (including flies, ants, non-carabid Coleoptera and slugs above ground; mites and
1 earthworms below ground), but these represented a small fraction of total abundance (1 to 5%)

2 and a small number of species.

Finally, all wild plants growing in the experimental area were sampled twice, at the beginning of May and June 2008. At each sampling date, all plant species were recorded in 25 1 m² quadrats evenly distributed within each plot, i.e. a total of 500 quadrats. All individual plants were identified to species level. The quadrats were divided into 25 20x20 cm squares to estimate abundance as the number of squares where a species was present.

## 2.2. Wheat individual measurements

Morphological and phenological characters that are classically used to describe phenotypes in wheat (e.g. IBPGR Secretariat, 1985; Murphy et al., 2008; UPOV, 1996) were measured to assess wheat phenotypic diversity within each crop diversity treatment: tiller number (five quadrats per plot, 1068 individuals), flowering date (eight quadrats per plot, 2205 individuals), total height at maturity, length, width and position along the stalk of the first leaf, and spike number (four samples of ten individuals per plot). Although these traits were not chosen on the basis of involvement in interspecific interactions, but to provide a general index of phenotypic diversity, some are nonetheless known to influence plant-plant interactions (e.g. plant height and competition for light) or plant-invertebrate interactions (e.g. plant architecture creating microhabitats, Langellotto and Denno, 2004).

For each measured character, we checked that wheat diversity was actually different between the two crop diversity treatments with a non-parametric Fligner test for homogeneity of variance. Wheat phenotypic diversity was then summarized within each plot by normalizing and combining the five morphological characters describing vegetation structure (total height, length, width and position of the first leaf, spike number) to calculate Rao's diversity coefficient (Rao,
1 1982) with the Mahalanobis distance, which removes correlations between morphometric characters measured on the same individual. A permutational test of homogeneity of multivariate dispersion (R, library vegan; Anderson, 2006; Anderson et al., 2006) was used to verify that the diversity measured over these five characters was highest in the variety mixture.

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## 2.3. Statistical analyses

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Linear models were analyzed with SAS software (version 9.1, SAS Institute Inc. 2006, Cary; proc GLM and MIXED) and multivariate analyses as well as resampling procedures with R software (version 2.10.1, R Development Core Team 2009, Vienna; library vegan).

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1
fixed effects, and 60x60m plot as a random effect, to account for the spatial clustering of data
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.replicates clustered within plots). This general model was used a couple of times and is
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subsequently referred to as "Model 1". A number of fixed covariates were added to this core
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structure depending on the taxonomic group but discarded if non-significant: soil pH and water
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content for springtails (not retained in the final model: P > 0.60 for both, and not influenced by
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the crop diversity treatment: P > 0.10), distance to field edges for ground-dwelling arthropods to
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account for spring recolonization (not retained), and spatial coordinates for all taxa to account for
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spatial autocorrelation of data (not retained for springtails only).

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Second, the relationship between species diversity (richness or Shannon index) and wheat
phenotypic diversity was examined to get a more quantitative picture of the effect of wheat
diversity on communities. This was done for each above-ground taxon using linear mixed effects
models in which community diversity (richness or Shannon) was explained by Rao's coefficient
of phenotypic diversity (calculated on wheat aerial architecture) and sampling session (fixed
effects); plot was included as a random effect.

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We first checked that observed differences in the number of individuals were not a mere
by-product of differences in wheat productivity (“more individuals hypothesis”, Srivastava and
Lawton, 1998): a greater biomass offers more resources, hence potentially more individuals.
These more abundant resources can be fresh or dead plant tissues for herbivorous carabids
(Harvey et al., 2008) and springtails (Hopkin, 1997) or preys for all predatory species owing to
cascading effects (Scherber et al., 2010). The relationship between number of individuals and
wheat biomass was tested with a linear mixed effects model where abundance was a function of
either of two proxies for wheat biomass (mean crop height per plot or mean individual spike
1 number per plot) and session (fixed effects), as well as plot (random effect). This was done for all
2 taxa except springtails, which were sampled too early for wheat phenotypic measures to be
3 meaningful. We also tested whether the abundance of taxonomic groups differed between wheat
4 diversity treatments using Model 1 with total abundance as a dependent variable.
5 Finally, for all taxa with a significant or marginally significant effect of wheat genetic
diversity on species richness or Shannon Index, permutation analyses were performed to separate
the effects of abundance vs. diversity. From the original dataset, 1000 rearranged datasets were
produced by randomly reallocating each sampled individual to one local community (sampling
unit), while keeping the sample size (number of individuals, i.e. abundance) of sampling units
constant. Each rearranged dataset was analyzed using Model 1. We then compared the observed
$F$-value for crop diversity treatment (pure line vs. variety mixture) obtained from the original
dataset to the distribution of $F$-values from permuted communities (see Appendix C). When the
$F$-value for the original dataset fell outside of the 95% percentile, the effect of crop diversity on
richness or Shannon Index was not explained by differences in abundance.
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1 to generate a permutation distribution of *F* under the Null hypothesis of no difference in

2 dispersion between groups.

3

### 2.3.3. Total species richness

5 To test for differences in total richness independently of differences in abundance, we used the

6 resampled communities and we compared the mean γ-diversity in these communities to the

7 observed species richness within each crop diversity treatment using one-tail t-tests. The

8 observed richness was expected to be higher (respectively lower) than the resampled richness in

9 the variety mixture (respectively in the pure line variety).

## 3. Results

12 Whereas there was no difference in wheat density between crop diversity treatments (*F*<sub>1,8</sub> = 0.00,

13 *P* = 0.97), the two wheat diversity treatments exhibited contrasting levels of phenotypic diversity:

14 for almost all morphological and phenological measures, individual variance and multivariate

15 dispersion were higher in the variety mixture than in the pure line variety (see Appendix B).

16 There was also a significant difference in biomass between the two crop diversity treatments,

17 with taller (111 ± 23 cm vs. 66 ± 6 cm, *P* < 0.001) and more ramified (4.5 ± 2.4 vs. 3.7 ± 2.0

18 tillers, *P* = 0.02) plants in the variety mixture than in the pure line variety.

19 A total of 48 plant species, 1057 springtail individuals from 19 species, 2781 carabid

20 individuals from 20 species and one group of very small carabids, and 6625 spider individuals

21 from 48 species (linyphiids: 4723 individuals, 15 species; other spiders: 1902 individuals, 33

22 species) were sampled.

23
## 1.3.1. Local community diversity

2 There was a significant effect of wheat diversity treatment on α-diversity (Fig. 1), with higher

3 diversity in the variety mixture for springtails (species richness only, $F_{1,8} = 5.38, P = 0.049$),

4 linyphiids (species richness only, $F_{1,8} = 7.48, P = 0.026$; marginal significance for Shannon

5 Index) and other spiders (Shannon Index only); regardless of statistical significance, diversity

6 was always higher in the variety mixture than in the pure line variety in these taxa. In contrast,

7 plants and carabids were not influenced by wheat diversity ($P > 0.10$), regardless of trophic level

8 for the latter (no difference between predators and herbivores). Community diversity was in

9 general higher during the second sampling session for both crop diversity treatments (non-significant interaction between wheat diversity treatment and session). When the effect of wheat

10 diversity was examined more quantitatively, via a correlation with Rao's diversity coefficient,

11 there were significant positive effects of phenotypic diversity on linyphiid species Shannon Index ($F_{1,8} = 7.85, P = 0.023$) and predatory carabid species richness ($F_{1,8} = 5.37, P = 0.049$). No effect

12 was found for the other taxa ($P > 0.10$).

13 No effect of wheat biomass ($P > 0.10$) was found on total abundance for all taxa except linyphiids, whose abundance was positively affected by mean crop height ($F_{1,8} = 9.44, P = 0.015$). In fact, only linyphiid abundance was positively affected by wheat genetic diversity

14 ($F_{1,8} = 10.52, P = 0.012$). For all other taxa, there was no difference in abundance between the

15 variety mixture and the pure line variety ($P > 0.60$). When the effect of abundance was removed by comparing observed communities to randomly assembled communities with the same local

16 abundances, the positive effect of wheat diversity on springtail species richness and spider (other than linyphiids) Shannon Index remained (springtail richness: observed $F: 5.38, simulated F$

17 ($F_{1,8} = 10.52, P = 0.012$). For all other taxa, there was no difference in abundance between the variety mixture and the pure line variety ($P > 0.60$). When the effect of abundance was removed by comparing observed communities to randomly assembled communities with the same local abundances, the positive effect of wheat diversity on springtail species richness and spider (other than linyphiids) Shannon Index remained (springtail richness: observed $F: 5.38, simulated F$
<table>
  <tbody>
    <tr>
      <td>1</td>
      <td>1</td>
      <td>contrast, the F-values of the ANOVAs on linyphiid species richness and Shannon Index in</td>
    </tr>
    <tr>
      <td>2</td>
      <td>2</td>
      <td>observed communities were not different from random communities (observed F: 7.48, simulated</td>
    </tr>
    <tr>
      <td>3</td>
      <td>3</td>
      <td>F: 11.69 and observed F: 4.92, simulated F: 4.93 respectively), suggesting that the positive effect</td>
    </tr>
    <tr>
      <td>4</td>
      <td>4</td>
      <td>of wheat diversity on linyphiids resulted from an increase in local abundance and not diversity</td>
    </tr>
    <tr>
      <td>5</td>
      <td>5</td>
      <td>per se.</td>
    </tr>
    <tr>
      <td>6</td>
      <td>6</td>
      <td></td>
    </tr>
    <tr>
      <td>7</td>
      <td>7</td>
      <td>3.2. Community similarity</td>
    </tr>
    <tr>
      <td>8</td>
      <td>8</td>
      <td>The homogeneity of multivariate community dispersion between the pure line variety and the</td>
    </tr>
    <tr>
      <td>9</td>
      <td>9</td>
      <td>variety mixture was tested for each taxon: for non-linyphiid spiders, community dispersion across</td>
    </tr>
    <tr>
      <td>10</td>
      <td>10</td>
      <td>sites was higher in the variety mixture compared to the pure line variety (P = 0.004, average</td>
    </tr>
    <tr>
      <td>11</td>
      <td>11</td>
      <td>distance to spatial median in the variety mixture: 0.37; in the pure line variety: 0.30; Fig. 2),</td>
    </tr>
    <tr>
      <td>12</td>
      <td>12</td>
      <td>indicating that local communities were more similar to one another in the pure line variety. For</td>
    </tr>
    <tr>
      <td>13</td>
      <td>13</td>
      <td>plants there was a significant but small difference (P = 0.036, average distance to spatial median</td>
    </tr>
    <tr>
      <td>14</td>
      <td>14</td>
      <td>in the variety mixture: 0.45; in the pure line variety: 0.47) with a higher dispersion in the pure</td>
    </tr>
    <tr>
      <td>15</td>
      <td>15</td>
      <td>line variety, but this effect was mainly caused by inter-plot differences in species composition,</td>
    </tr>
    <tr>
      <td>16</td>
      <td>16</td>
      <td>consistent with above-mentioned spatial autocorrelation. For other taxa (springtails, linyphiids</td>
    </tr>
    <tr>
      <td>17</td>
      <td>17</td>
      <td>and carabids), there was no difference in dispersion between the two crop diversity treatments</td>
    </tr>
    <tr>
      <td>18</td>
      <td>18</td>
      <td>(P > 0.10).</td>
    </tr>
    <tr>
      <td>19</td>
      <td>19</td>
      <td></td>
    </tr>
    <tr>
      <td>20</td>
      <td>20</td>
      <td>3.3. Total species richness</td>
    </tr>
    <tr>
      <td>21</td>
      <td>21</td>
      <td>When γ-diversity in the variety mixture and the pure line variety were compared to γ-diversity in</td>
    </tr>
    <tr>
      <td>22</td>
      <td>22</td>
      <td>randomly permutated communities, total species richness in the pure line variety was lower than</td>
    </tr>
    <tr>
      <td>23</td>
      <td>23</td>
      <td>expected for all taxa (P < 0.0001, Table 1) whereas total species richness in the variety mixture</td>
    </tr>
  </tbody>
</table>
1 was almost always higher than expected (*P* < 0.0001 except for predatory carabids and plants).

2 For plants, observed species richness was significantly lower than expected in both crop diversity

3 treatments, reflecting local specific spatial aggregation as commonly observed for weed species

4 (Rew and Cousens, 2001).

5

## 4. Discussion

7 Overall, there was a general positive correlation between wheat diversity and all levels of species

8 diversity (α, β, γ) that was consistent across most studied arthropod taxa. Although the intensity

9 and significance of the effect varied, species richness and/or species diversity of arthropod communities were consistently higher in the variety mixture than in the pure line variety. Plants

10 were the only group to exhibit no difference between the two wheat diversity treatments. This

11 result was however not surprising because most plants were typical annual weed species; the

12 latter generally exhibit patchy distributions caused by environmental heterogeneities or demographic factors (e.g. limited dispersal, Rew and Cousens, 2001). Hence, in comparison to

13 studies that demonstrated an impact of genetic diversity on plant community structure or

14 invasibility (e.g. Booth and Grime, 2003; Crutsinger et al., 2008; Fridley and Grime, 2010), the short time span of the experiment (six months between wheat sowing and the last plant survey)

15 was probably not sufficient for a spatial reorganization of plant communities via seed dispersal and differential growth. In addition, environmental variation is likely to have a much stronger

16 impact than genetic diversity on competition among plant species (Johnson et al., 2008) and

17 theory predicts variable effects of the genetic diversity of a foundation species on the diversity of subordinate species, depending on environmental heterogeneity (Gibson et al., 2012). This

18 homogeneity of weed communities between the two crop genetic diversity treatments also
1 suggests that the observed differences in arthropod diversity were mostly attributable to

2 differences in wheat, not weeds.

Our results represent the first experimental evidence that crop genetic diversity can benefit

farmland biodiversity in agroecosystems, and one of the rare examples where the effects of the

genetic diversity of a foundation species are observed at a relatively large scale under realistic

environmental conditions (but see references on *Populus* species, e.g. Wimp et al., 2004; and a

number of field studies examining the impact of crop genetic diversity on herbivore abundance,

not diversity, e.g. Hamback et al., 2010; reviewed in Tooker and Frank, 2012). The higher local

diversity in the variety mixture for most arthropod groups (springtails, spiders, and, to a lesser

extent, predatory carabids) is however consistent with comparable small-scale experimental

studies on non-crop herbaceous species (Crutsinger et al., 2006; Johnson et al., 2006; Jones et al.,

2011) and may involve similar mechanisms. First, differences in diversity may arise as a direct

consequence of differences in abundance (“more individuals hypothesis”, Srivastava and Lawton,

1998) caused by differences in biomass between diversity treatments (e.g. higher aboveground

net primary productivity with higher genotypic diversity of *Solidago altissima* cultures in

Crutsinger et al.'s experiment, 2006). Here, this explanation could be rejected for most taxonomic

groups: the observed differences in diversity were generally independent of arthropod abundance

and wheat productivity, showing that resource quality and not quantity caused higher

biodiversity. For linyphiids, however, the higher species richness and Shannon Index in the

variety mixture vs. the pure line variety were mainly mediated by an increase in the number of

individuals, partly (but not only) due to higher wheat biomass. Hence for this group the relative

roles of wheat biomass vs. genetic diversity per se cannot be differentiated. Although it is likely

that differences in biomass are the key factor that controls lyniphiid species richness by affecting

the number of individuals, we cannot exclude that crop genetic diversity also increases
1 productivity of in-field communities via a more efficient use of resources (including web-fixing sites), with consequences on community diversity (Crutsinger et al., 2006; Genung et al., 2010).
2
3 In the cases where differences in species diversity were not controlled by the number of individual arthropods, at least two alternative mechanisms may explain higher species diversity in the mixture of wheat varieties. First, the taxonomic diversity hypothesis states that higher genetic diversity of the foundation species increases the diversity of resources available to herbivores (e.g. Bukovinszky et al., 2008 in Brassica lines; or Crutsinger et al., 2006 in Solidago altissima plots). This mechanism may apply to springtails, which may be offered a wider variety or more accessible food resources in the variety mixture, directly through biochemical variation in root systems or indirectly through associated microflora. The taxonomic diversity hypothesis is however a much less likely explanation for differences in aboveground communities, where the only herbivore group (carabids) was unaffected by wheat genetic diversity. Instead, the structural heterogeneity hypothesis states that higher genetic diversity of the foundation species generates more complex vegetation architecture (Brose, 2003; Harvey et al., 2008). Several studies have shown that ground-dwelling arthropods, especially spiders, were sensitive to vegetation structural diversity (Brose, 2003; Hansen, 2000; Jeanneret et al., 2003; Langellotto and Denno, 2004) or that arthropod abundance and richness were higher on structurally complex plant genotypes (Johnson and Agrawal, 2005 in Oenothera biennis L.). Here we observed a significant effect of wheat morphological diversity on spiders and predatory (but not herbivorous) carabids, which provides support for a possible role of wheat architectural diversity in increasing species diversity, e.g. by creating multiple micro-habitats, although other mechanisms may be involved.

At the crop diversity treatment level, total (γ) species diversity was also higher in the variety mixture than in the pure line variety, which was much more due to the increase in local

59
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63
64
65
<table>
  <tbody>
    <tr>
      <td>1</td>
      <td>1</td>
      <td>(α) species diversity than to among-sampling-unit differences in community composition (β-diversity): heterogeneity between communities was significantly higher in the variety mixture for non-linyphiid spiders only. This may be caused by the relatively large number of rare species found for all taxa except plants in the variety mixture vs. the pure line variety. These species represented 20 to 57% of species present in the mixture, but accounted for a small number of individuals, reflecting the strong dominance of one or a few species (e.g. <i>Pardosa agrestis</i> Westring in spiders) and explaining the high similarity between local communities. For non-linyphiid spiders, this pattern of dominance was found in the pure line variety only, hence the difference in β-diversity.</td>
    </tr>
    <tr>
      <td>10</td>
      <td>11</td>
      <td><b>4.1. Limitations and perspectives</b></td>
    </tr>
    <tr>
      <td>12</td>
      <td>12</td>
      <td>One key limitation of our approach is that two crop diversity treatments only were compared (pure line variety vs. highly diverse mixture), with a single variety in the pure line variety treatment. As a result, we cannot make firm conclusions on the true role of genetic or phenotypic diversity per se vs. a variety effect on the diversity of farmland species. However, several facts and lines of evidence suggest that the difference in species diversity observed between the two treatments is caused by differences in genetic diversity. First, the pure line variety "Renan" was included in the variety mixture, although at a low frequency (&lt;10%), so that a variety effect (repulsion of several species by the pure line variety) can be partly discarded. Second, and most important, wheat morphological diversity correlated positively with species diversity for two taxonomic groups, which cannot be explained by a mere variety effect. An additional limit of incomplete crop diversity treatments is that the actual effect of diversity vs. biomass on community diversity cannot be differentiated for the one taxonomic group (linyphiid spiders) that</td>
    </tr>
    <tr>
      <td>13</td>
      <td>13</td>
      <td></td>
    </tr>
    <tr>
      <td>14</td>
      <td>14</td>
      <td></td>
    </tr>
    <tr>
      <td>15</td>
      <td>15</td>
      <td></td>
    </tr>
    <tr>
      <td>16</td>
      <td>16</td>
      <td></td>
    </tr>
    <tr>
      <td>17</td>
      <td>17</td>
      <td></td>
    </tr>
    <tr>
      <td>18</td>
      <td>18</td>
      <td></td>
    </tr>
    <tr>
      <td>19</td>
      <td>19</td>
      <td></td>
    </tr>
    <tr>
      <td>20</td>
      <td>20</td>
      <td></td>
    </tr>
    <tr>
      <td>21</td>
      <td>21</td>
      <td></td>
    </tr>
    <tr>
      <td>22</td>
      <td>22</td>
      <td></td>
    </tr>
    <tr>
      <td>23</td>
      <td>23</td>
      <td></td>
    </tr>
    <tr>
      <td>24</td>
      <td>24</td>
      <td></td>
    </tr>
    <tr>
      <td>25</td>
      <td>25</td>
      <td></td>
    </tr>
    <tr>
      <td>26</td>
      <td>26</td>
      <td></td>
    </tr>
    <tr>
      <td>27</td>
      <td>27</td>
      <td></td>
    </tr>
    <tr>
      <td>28</td>
      <td>28</td>
      <td></td>
    </tr>
    <tr>
      <td>29</td>
      <td>29</td>
      <td></td>
    </tr>
    <tr>
      <td>30</td>
      <td>30</td>
      <td></td>
    </tr>
    <tr>
      <td>31</td>
      <td>31</td>
      <td></td>
    </tr>
    <tr>
      <td>32</td>
      <td>32</td>
      <td></td>
    </tr>
    <tr>
      <td>33</td>
      <td>33</td>
      <td></td>
    </tr>
    <tr>
      <td>34</td>
      <td>34</td>
      <td></td>
    </tr>
    <tr>
      <td>35</td>
      <td>35</td>
      <td></td>
    </tr>
    <tr>
      <td>36</td>
      <td>36</td>
      <td></td>
    </tr>
    <tr>
      <td>37</td>
      <td>37</td>
      <td></td>
    </tr>
    <tr>
      <td>38</td>
      <td>38</td>
      <td></td>
    </tr>
    <tr>
      <td>39</td>
      <td>39</td>
      <td></td>
    </tr>
    <tr>
      <td>40</td>
      <td>40</td>
      <td></td>
    </tr>
    <tr>
      <td>41</td>
      <td>41</td>
      <td></td>
    </tr>
    <tr>
      <td>42</td>
      <td>42</td>
      <td></td>
    </tr>
    <tr>
      <td>43</td>
      <td>43</td>
      <td></td>
    </tr>
    <tr>
      <td>44</td>
      <td>44</td>
      <td></td>
    </tr>
    <tr>
      <td>45</td>
      <td>45</td>
      <td></td>
    </tr>
    <tr>
      <td>46</td>
      <td>46</td>
      <td></td>
    </tr>
    <tr>
      <td>47</td>
      <td>47</td>
      <td></td>
    </tr>
    <tr>
      <td>48</td>
      <td>48</td>
      <td></td>
    </tr>
    <tr>
      <td>49</td>
      <td>49</td>
      <td></td>
    </tr>
    <tr>
      <td>50</td>
      <td>50</td>
      <td></td>
    </tr>
    <tr>
      <td>51</td>
      <td>51</td>
      <td></td>
    </tr>
    <tr>
      <td>52</td>
      <td>52</td>
      <td></td>
    </tr>
    <tr>
      <td>53</td>
      <td>53</td>
      <td></td>
    </tr>
    <tr>
      <td>54</td>
      <td>54</td>
      <td></td>
    </tr>
    <tr>
      <td>55</td>
      <td>55</td>
      <td></td>
    </tr>
    <tr>
      <td>56</td>
      <td>56</td>
      <td></td>
    </tr>
    <tr>
      <td>57</td>
      <td>57</td>
      <td></td>
    </tr>
    <tr>
      <td>58</td>
      <td>58</td>
      <td></td>
    </tr>
    <tr>
      <td>59</td>
      <td>59</td>
      <td></td>
    </tr>
    <tr>
      <td>60</td>
      <td>60</td>
      <td></td>
    </tr>
    <tr>
      <td>61</td>
      <td>61</td>
      <td></td>
    </tr>
    <tr>
      <td>62</td>
      <td>62</td>
      <td></td>
    </tr>
    <tr>
      <td>63</td>
      <td>63</td>
      <td></td>
    </tr>
    <tr>
      <td>64</td>
      <td>64</td>
      <td></td>
    </tr>
    <tr>
      <td>65</td>
      <td>65</td>
      <td></td>
    </tr>
  </tbody>
</table>
1 exhibited differences in abundance between the two treatments. More generally, we cannot
2 distinguish additive from interactive processes, i.e. processes resulting from the simple
3 combination of community phenotypes in monocultures vs. processes that are non predictable
4 from isolated genotypes only, nor can we point out the exact source of wheat variability involved
5 in community diversity (e.g. biochemical and/or architectural).

16 To confirm these results, the next essential step is therefore to consider a larger range of
17 diversity levels and to compare several wheat varieties grown in monocultures vs. plots
18 containing contrasting numbers of wheat varieties, as in similar studies exploring the relationship
19 between a number of genotypes and the diversity of associated communities (Crutsinger et al.,
20 2006; Johnson et al., 2006). Such full experimental design, ideally based on neighbor-balanced
21 21 latin square designs, is the only way to discriminate additive vs. interactive mechanisms for the
22 effects of genetic diversity (Hughes et al., 2008; Loreau and Hector, 2001). Focusing on the
23 number of genotypes alone may, however, be misleading when studying the influence of
24 intraspecific diversity on communities and ecosystems, because the relationship between the
25 number of genotypes and genetically based variance in phenotypic traits is not straightforward
26 (Hughes et al., 2008). Two genotypes can have very similar or very different phenotypic traits
27 involved in interspecific interactions, so that the genetic variance for these traits can be same on
28 average whether one compares two or many genotypes (Hughes et al., 2008). Hence, there is a
29 need to go beyond the number of genotypes and to quantify phenotypic diversity, as was done in
30 the present study. This quantification is necessary to confirm that genotypic diversity is
31 associated with phenotypic diversity, and to elucidate the underlying genetic and ecological
32 mechanisms.
33
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38
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41
42
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1 and Chable, 2009), in-field genetic diversity would maintain the evolutionary potential of crop varieties, allowing them to adapt to their local environment (including co-evolving flora, fauna and pathogens). Here, we have shown that an increase in crop genetic diversity can also improve biodiversity, which is a general aim of all signatories to the Convention on Biological Diversity, and may help slow down the functional homogenization of agroecosystems. In view of the widespread positive relationship between species diversity and ecosystem functioning (e.g. Cardinale et al., 2012), a higher diversity of taxonomic groups such as soil microarthropods (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

2 varieties, allowing them to adapt to their local environment (including co-evolving flora, fauna and pathogens). Here, we have shown that an increase in crop genetic diversity can also improve biodiversity, which is a general aim of all signatories to the Convention on Biological Diversity, and may help slow down the functional homogenization of agroecosystems. In view of the widespread positive relationship between species diversity and ecosystem functioning (e.g. Cardinale et al., 2012), a higher diversity of taxonomic groups such as soil microarthropods (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

4 biodiversity, which is a general aim of all signatories to the Convention on Biological Diversity, and may help slow down the functional homogenization of agroecosystems. In view of the widespread positive relationship between species diversity and ecosystem functioning (e.g. Cardinale et al., 2012), a higher diversity of taxonomic groups such as soil microarthropods (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

6 widespread positive relationship between species diversity and ecosystem functioning (e.g. Cardinale et al., 2012), a higher diversity of taxonomic groups such as soil microarthropods (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

7 Cardinale et al., 2012), a higher diversity of taxonomic groups such as soil microarthropods (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

8 (springtails) and generalist predators (spiders and carabids) may result in better ecosystem services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

9 services such as soil regeneration or biological control (Hajjar et al., 2008; Whittingham, 2011), but this remains to be demonstrated.

10 but this remains to be demonstrated.

11 Although the impact of crop genetic diversity on farmland communities documented in the present study may appear moderate compared to that of chemical inputs or natural habitat loss, two major causes of biodiversity loss in agroecosystems (Tscharntke et al., 2005), it may nonetheless have significant positive impacts on biodiversity if implemented on a large scale (Macfadyen and Bohan, 2010), which would be easy for farmers. This raises the issue of extrapolating results of small-scale experiments to more natural systems: Hughes et al. (2008), for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

13 two major causes of biodiversity loss in agroecosystems (Tscharntke et al., 2005), it may nonetheless have significant positive impacts on biodiversity if implemented on a large scale (Macfadyen and Bohan, 2010), which would be easy for farmers. This raises the issue of extrapolating results of small-scale experiments to more natural systems: Hughes et al. (2008), for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

14 nonetheless have significant positive impacts on biodiversity if implemented on a large scale (Macfadyen and Bohan, 2010), which would be easy for farmers. This raises the issue of extrapolating results of small-scale experiments to more natural systems: Hughes et al. (2008), for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

15 (Macfadyen and Bohan, 2010), which would be easy for farmers. This raises the issue of extrapolating results of small-scale experiments to more natural systems: Hughes et al. (2008), for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

16 extrapolating results of small-scale experiments to more natural systems: Hughes et al. (2008), for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

17 for example, fear that the effects of genetic diversity observed in controlled, small-scale experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

18 experiments will be masked by other factors in natural situations. However, this experiment was set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

19 set-up in a real agricultural landscape and met the conditions identified by Hughes et al. (2008) as the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

20 the requirements for genetic diversity to efficiently drive ecological processes: (1) crops are dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

21 dominant in fields and therefore can be considered as foundation species; (2) genetic diversity in crop fields is controlled by farmers only and can be stable across time; (3) farmland communities are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

22 are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

23 are submitted to repeated anthropogenic disturbance, so that species and genetic diversity may be crucial for disturbance response and stability. Hence, the ecological consequences of increased

24 crucial for disturbance response and stability. Hence, the ecological consequences of increased
1 crop genetic diversity on farmland communities and its potential contribution to agricultural sustainability deserve further examination.

## Acknowledgments

5 We are very grateful to O. Ranke, M. Diaz, F. Mercier and the 'Fondation pour le progrès de l'Homme' for providing the study site and seeds, and for achieving all agronomic tasks in the project. We thank N. Galic, Y. Harmelin and Q. Chaffaux for their help with fieldwork. The project was partly funded by the Bureau des Ressources Génétiques (BRG Grant 2007-2008 #33 to IG and EP) and by the Agence Nationale de la Recherche (ANR Grant #2006-JCJC-0032 to EP).

6 l'Homme' for providing the study site and seeds, and for achieving all agronomic tasks in the project. We thank N. Galic, Y. Harmelin and Q. Chaffaux for their help with fieldwork. The project was partly funded by the Bureau des Ressources Génétiques (BRG Grant 2007-2008 #33 to IG and EP) and by the Agence Nationale de la Recherche (ANR Grant #2006-JCJC-0032 to EP).

8 We are very grateful to O. Ranke, M. Diaz, F. Mercier and the 'Fondation pour le progrès de l'Homme' for providing the study site and seeds, and for achieving all agronomic tasks in the project. We thank N. Galic, Y. Harmelin and Q. Chaffaux for their help with fieldwork. The project was partly funded by the Bureau des Ressources Génétiques (BRG Grant 2007-2008 #33 to IG and EP) and by the Agence Nationale de la Recherche (ANR Grant #2006-JCJC-0032 to EP).

## Supplementary data

13 Supplementary information on the experimental design (Appendix A), on the phenotypic diversity of wheat varieties (Appendix B), and on statistical tests of the difference in species diversity among wheat diversity treatments (Appendix C) can be found in the online version, at http://...
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24
# 1 Tables

## 2

## 3 Table 1. Comparison of species richness in the variety mixture vs. the pure line variety in each

## 4 taxon or trophic group.

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Number of shared species</th><th colspan="2">Number of species unique to</th><th rowspan="2">Total number of species</th><th colspan="2">Mean number of species after randomization<sup>a</sup></th></tr><tr><th>Mixture</th><th>Pure line</th><th>Mixture</th><th>Pure line</th></tr></thead><tbody><tr><td>Taxa or trophic group</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Springtails</td><td>15</td><td>4</td><td>0</td><td>19</td><td>15</td><td>16.6<br>P &lt; 0.001</td><td>17.2<br>P &lt; 0.001</td></tr><tr><td>Linyphiids</td><td>10</td><td>4</td><td>1</td><td>14</td><td>11</td><td>13.5<br>P &lt; 0.001</td><td>12.5<br>P &lt; 0.001</td></tr><tr><td>Other spiders</td><td>16</td><td>11</td><td>6</td><td>27</td><td>22</td><td>24.8<br>P &lt; 0.001</td><td>25.8<br>P &lt; 0.001</td></tr><tr><td>Carabids</td><td>12</td><td>6</td><td>3</td><td>18</td><td>15</td><td>16.6<br>P &lt; 0.001</td><td>16.8<br>P &lt; 0.001</td></tr><tr><td>Predators</td><td>7</td><td>2</td><td>1</td><td>9</td><td>8</td><td>8.9<br>P = 0.22</td><td>9.0<br>P &lt; 0.001</td></tr><tr><td>Herbivores</td><td>3</td><td>4</td><td>1</td><td>7</td><td>4</td><td>5.8<br>P &lt; 0.001</td><td>5.2<br>P &lt; 0.001</td></tr><tr><td>Plants</td><td>33</td><td>8</td><td>7</td><td>41</td><td>40</td><td>43.4<br>P = 1</td><td>43.5<br>P &lt; 0.001</td></tr></tbody></table>

<sup>a</sup>The total number of species in each crop diversity treatment is compared to the mean number of species observed in 1000 randomized datasets (see text for details). *P*-values of one-tail t-tests indicate whether the observed number of species in the variety mixture (respectively in the pure line variety) is significantly higher (respectively smaller) than expected at random.

## 5

## 6

## 7

## 8
1 **Figure captions**

2

3 **Figure 1: Shannon Diversity Index for all studied taxonomic groups in the variety mixture (white bars) and in the pure line variety (grey bars).**

4

5 **Error bars represent standard errors. *F* and *P*-values for the crop diversity treatment effect in the ANOVA are given (significant effects are in bold).**

6

7

8 **Figure 2: Principal coordinate ordination of the non-linyphiid spider data, showing assemblages from the variety mixture (circles, solid line) and the pure line variety (triangles, dashed line).**

9

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<figure><img src="image_1.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>Figure 2</figcaption></figure>
Chateil et al. Supporting Information

Appendix A: *Experimental design*

<figure><img src="image_3.png" /><figcaption>(b) Localization of the pure line vs. variety mixture plots</figcaption></figure>

<figure><img src="image_4.png" /></figure>
Appendix B: Wheat phenotypic diversity in the pure line variety vs. variety mixture

<table><thead><tr><th rowspan="2">Character</th><th colspan="2">Individual variance*</th><th rowspan="2">P-value (test for<br>homogeneity of<br>variances†)</th></tr><tr><th>Pure line variety<br>(number of samples)</th><th>Variety mixture<br>(number of samples)</th></tr></thead><tbody><tr><td>1. Tiller number</td><td>3.72<br>(n = 532)</td><td>5.74<br>(n = 536)</td><td>P &lt; 0.0001</td></tr><tr><td>2. Flowering date<br>(number of days<br>since flowering)</td><td>6.35<br>(n = 1064)</td><td>17.08<br>(n = 1141)</td><td>P &lt; 0.0001</td></tr><tr><td>3. Total height at<br>maturity (cm)</td><td>52.10<br>(n = 200)</td><td>414.89<br>(n = 200)</td><td>P &lt; 0.0001</td></tr><tr><td>4. Length of the first<br>leaf (cm)</td><td>11.49<br>(n = 199)</td><td>18.01<br>(n = 195)</td><td>P = 0.11</td></tr><tr><td>5. Width of the first<br>leaf (mm)</td><td>4.82<br>(n = 200)</td><td>8.92<br>(n = 195)</td><td>P &lt; 0.0001</td></tr><tr><td>6. Position along the<br>stalk of the first leaf<br>(cm)</td><td>32.63<br>(n = 200)</td><td>217.84<br>(n = 200)</td><td>P &lt; 0.0001</td></tr><tr><td>7. Spike number</td><td>1.76<br>(n = 200)</td><td>2.56<br>(n = 200)</td><td>P = 0.51</td></tr><tr><td>8. Multivariate<br>measure (characters<br>3 to 7 combined)</td><td>1.54<br>(n = 199)</td><td>2.24<br>(n = 195)</td><td>P &lt; 0.0001</td></tr></tbody></table>

* Univariate measures: residual variance obtained in mixed model analyses of variance with the following explanatory variables: observer (fixed effect), plot and quadrat nested within plot (random effects); multivariate measure: group mean distance to spatial median.

† Univariate measures: Fligner test for homogeneity of variances; multivariate measure: distance-based multivariate test for homogeneity of variances (multivariate analogue of Levene's test).
Appendix C: Distribution of *F*-values in linear mixed effects models testing the effect of wheat diversity treatment on species diversity or Shannon diversity index.

In the model, wheat diversity treatment, sampling session and their interaction were included as fixed effects; plot was included as a random effect. The distribution of *F*-values obtained from linear mixed effects models on 1000 rearranged datasets is plotted in grey; the 95th percentile of this distribution (*F*<sub>95%</sub>) is plotted in red; *F*-values from linear mixed effects models on original data (*F*<sub>obs</sub>) are plotted in blue.

<figure><img src="image_5.png" /></figure>

a – Springtails
Species richness

*F*<sub>95%</sub> = 3.33 *F*<sub>obs</sub> = 5.38

<figure><img src="image_6.png" /></figure>

c – Linyphiid spiders
Species richness

*F*<sub>obs</sub> = 7.48 *F*<sub>95%</sub> = 11.69

<figure><img src="image_7.png" /></figure>

b – Non-linyphiid spiders
Shannon index

*F*<sub>95%</sub> = 4.11 *F*<sub>obs</sub> = 10.67

<figure><img src="image_8.png" /></figure>

d – Linyphiid spiders
Shannon index

*F*<sub>obs</sub> = 4.92 *F*<sub>95%</sub> = 4.93