Agriculture, Ecosystems and Environment 195 (2014) 36–43

Non-linear responses of functional diversity and redundancy toagricultural intensification at the field scale in Mediterranean arableplant communities

Irene Guerrero, Carlos P. Carmona, Manuel B. Morales, Juan J. Oñate *, Begoña PecoTerrestrial Ecology Group (TEG), Departament of Ecology, Universidad Autónoma de Madrid, C/Darwin 2, Madrid 28049, Spain

A R T I C L E I N F O

Article history:Received 16 December 2013Received in revised form 16 May 2014Accepted 29 May 2014Available online xxx

Keywords:Agricultural intensificationWeedsSpecies richnessCereal cropCentral Spain

A B S T R A C T

Despite their key roles in agroecosystems, species diversity of arable plants is being severely reduced byagricultural intensification, although it remains unclear whether functional diversity is affected in asimilar way. We analyzed the response of four functional traits of arable plants (specific leaf area, canopyheight, seed mass and flowering onset) to agricultural intensification in Mediterranean arable plantcommunities. Two intensification gradients were obtained by PCA analysis on variables related either tomanagement practices at the individual field scale or to the surrounding landscape structure andcomposition. Shifts in the community weighted mean (CWM) and the functional diversity (FD) of eachtrait along these intensification gradients were explored. The relationship between species richness andthe FD of each trait (i.e. functional redundancy) along the same gradients was also analyzed. Theresponses of species richness and the considered functional traits were driven by intensification at theindividual field scale, but not by intensification at the scale of the surrounding landscape. Speciesrichness and FD of all the studied traits decreased with intensification, which favoured tall, heavy-seededand early flowering species. The decrease of FD was non-linear for specific leaf area and seed mass, withmaximum reductions at intermediate levels of intensification. Species richness and FD responses weredecoupled, indicating that the functional redundancy in the studied communities responds toagricultural intensification in a non-linear fashion. Along the first stages of intensification, there was animportant reduction in species richness that was not accompanied by changes in FD. Further levels ofintensification resulted in substantial reductions in FD without changes in species richness. Thesefindings provide new insights on how agricultural management interacts with plant communitiesthrough its non-linear effects on functional diversity and functional redundancy.

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1. Introduction

Species richness and its supporting role in connection toecosystem functions and services have been the focus of a greatbody of literature studying the impacts of agricultural intensifica-tion on biodiversity (see review by Kleijn et al., 2011). Themultifactorial process of agricultural intensification (Chamberlainet al., 2000) involves management changes at the spatial scale ofindividual agricultural fields (such as the increasing use of high-yielding crop varieties, chemical fertilizers and pesticides, irriga-tion and mechanization), aimed to maximize yields. Intensificationactions also affect the surrounding landscape, with changes in itsstructure and composition (simplification, homogenization,

* Corresponding author. Tel.: +34 91 4972780; fax: +34 91 4978001.E-mail address: juan.onate@uam.es (J.J. Oñate).

http://dx.doi.org/10.1016/j.agee.2014.05.0210167-8809/ã 2014 Elsevier B.V. All rights reserved.

artificialisation and abandonment). As a consequence, complexand detrimental effects on biodiversity and agroecosystemproperties are usually associated to agricultural intensification(e.g. Donald et al., 2006; Geiger et al., 2010; Guerrero et al., 2012;Kremen et al., 2002 and Stoate et al., 2009).

In the last decades, farmland biodiversity has been the focus ofimportant conservation efforts in Europe, including variouscommon policy tools, such as the Nitrates, Birds and HabitatsDirectives and agri-environment schemes (Beaufoy, 1998; Bulleret al., 2000 and Primdahl et al., 2003). Despite these efforts thenegative effects of agricultural intensification on Europeanfarmland biodiversity persist (EEA, 2010).

It remains unclear whether changes in species diversity areaccompanied by similar changes in the provisioning of associatedecosystem services in agroecosystems (e.g. Letourneau andBothwell, 2008 and Macfadyen et al., 2009). Recently, researchers'attention has shifted to functional diversity (FD; i.e. the value and

I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43 37

range of functional traits of the organisms in a community) as areliable proxy of the range of functions provided by a community(Díaz and Cabido, 2001 and Hooper et al., 2005). It is generallyassumed that the loss of species associated to land use intensifi-cation results in a similar loss of functional diversity. However, thisassumption has been recently challenged (Mayfield et al., 2010),since changes in species richness and functional diversity afterintensification, although often positively correlated, could followdifferent trajectories, depending on the degree of functionalredundancy in the community (i.e. the number of speciespossessing similar functional traits, see Rosenfeld, 2002) and onhow changes in land management affect community assemblyprocesses. In fact, species richness may decline without acorresponding loss of functional diversity in communities withhigh functional redundancy (Flynn et al., 2009) and it may even bepossible for functional diversity to increase without a change inspecies richness, thanks to changes in the environmental filtersfavouring the appearance of functionally different species (May-field et al., 2010). These models have recently been tested alongintensification gradients in grassland ecosystems (Sasaki et al.,2009 and Peco et al., 2012), but not in arable systems.

In this paper we explore the relationships between speciesrichness and functional diversity along agricultural intensificationgradients, focusing on wild plants growing on agricultural fields(from now on, arable plants, sensu Storkey, 2006). Arable plants arewell adapted to disturbed and resource-rich environments such asagricultural fields, thanks to a functional trait composition thatmakes them good colonizers, reproducers and/or survivors(Sutherland 2004). Due to their position at the base of trophicwebs, arable plants play key roles in agroecosystems (Marshall2003), being essential to other taxa (Brooks et al., 2012; Ebelinget al., 2012; Evans et al., 2011 and Scherber et al., 2010), includingspecies providing important ecosystem services, such as biologicalpest control and pollination (Kremen et al., 2002; Thies et al., 2011and Winqvist et al., 2012). They contribute as well to vegetationstructure within cropped fields, determining the quality ofagricultural habitats for invertebrates, birds and mammals (Bentonet al., 2003 and Marshall et al., 2003). Diversity of arable plants atthe individual field scale is not only negatively affected by theintensification of practices aimed at combating their potentiallynegative impact on crop yield (Guerrero et al., 2010 and Storkeyet al., 2012), but it also depends on the configuration of thesurrounding landscape, with local diversity increasing withlandscape complexity (Gabriel et al., 2005).

The response of four arable plant functional traits (specific leafarea, canopy height, seed mass and flowering onset) to agriculturalintensification gradients was analyzed, separating field andlandscape scales. We also analyzed the changes in the relationshipbetween arable plant species richness and functional diversity ofeach trait (functional redundancy) along the same intensificationgradients. Specifically, we expected that: (i) increased productivityrelated to agricultural intensification induces changes in functionaltrait composition of arable plant communities towards trait valuesassociated to faster resource-use strategies, such as higher SLA, aswell as trait values associated to increased competition for light,such as earlier flowering, higher canopies and heavier seeds; (ii)intensification of agricultural practices reduces functional diversi-ty, eliminating functional types poorly adapted to high nutrientlevels and competition for light.

2. Materials and methods

2.1. Study area

The study was conducted in a ca. 500 km2 dry cereal farmlandarea in central Spain (40�400N, 3�250W; altitudes ranging from 600

to 800 m.a.s.l.). Average annual temperature is 14.1 �C with hotsummers and mild winters and average annual rainfall is ca.400 mm, concentrated in spring and autumn (AEMET, 2008). Thesecharacteristics define a rather homogeneous area for rainfed cerealcropping (see Table S1, electronic supplementary data). Traditionalland use has produced a dynamic agricultural mosaic in the area,with fields of different size (in the range of 0.5–30 ha) and varyingdevelopment of field boundaries. Non-irrigated winter wheat andbarley and annual fallow (fields left unsown in a given year)dominate, covering ca. 86% of total area. The rest is long-term fallow(more than two years old), shrubs and, marginally (0.60% of totalarea), olive groves and vineyards. Typical rotation on a given fieldhas a two-year cycle, with alternating cereals and fallow. Cerealyield in the studyarea is around 3000 kg ha�1, lying within the rangeof central Spanish drylands (average � SD is 3256 � 710 kg ha�1;MARM, 2008), but still low enough to consider this system as low-intensity in the European context (Bignal and McCracken, 1996).

2.2. Data collection

2.2.1. Field data collectionA total of 78 agricultural fields sown with winter wheat were

sampled in spring 2007, an average year regarding temperaturesand rainfall in the area (AEMET, 2008). One to five sampling pointswere distributed in each field depending on field size. To avoid fieldmargin effects on observations, sampling points were placed at10 m from the margin. Arable plant species were surveyed betweenMay 27th and June 25th. Three 2 � 2 m2 vegetation quadrats persampling point were located parallel to the field side and 5 m apartfrom each other. Percentage of cover of each species within thequadrat was estimated and averaged for each sampling point.Finally, species richness was calculated as the number of speciesfound on each sampling point, and subsequently averaged to attaina single value for each field, which indicates the average richness atthe sampling point scale.

2.2.2. Agricultural intensification dataThree variables related to management practices at the

individual field scale and three related to the surroundinglandscape structure and composition were considered (Table 1).Information about yield, a frequently used proxy of agriculturalintensification (e.g. Green et al., 2005), and farming practices(applied nitrogen fertilizer and sowing density) during 2007 wascollected by means of a questionnaire sent out to farmersmanaging each field. Information on landscape structure variableswas obtained from digital maps and measured within 500 m radiuscircles around the centre of each sampling point and averagedwhen there were more than one sampling point per field (Guerreroet al., 2010).

2.2.3. Functional traitsFollowing Westoby (1998), three representative traits of plant

strategy for resource capture and allocation were selected: specificleaf area (SLA, mm2mg�1), mean canopy height (cm) and seedmass (mg). Flowering onset (month, ranging from March toSeptember) was further included, a trait that has been frequentlyused in studies analyzing the response of vegetation to agriculturalintensification (e.g. Peco et al., 2012 and Storkey et al., 2010).Functional trait data were extracted from LEDA and e-FLORA-sysdatabases (Kleyer et al., 2008) for 105 sampled species (seeTable S2, electronic supplementary data). Species with traitinformation represented an average percentage cover of ca. 95%.

Prior to any calculation, the values of seed mass were log-transformed to attain a normal distribution. For each functionaltrait and sampling point, community weighted mean (CWM) andfunctional diversity (FD) were calculated. CWM can reveal changes

Table 2Summary of the p-values of GAM models including simultaneously both PCA axes.

Response variable PC1 (Individual field scale) PC2 (Landscape scale)

CWMHeight <0.001 0.306SLA 0.349 0.177Seed mass 0.003 0.112Flowering onset 0.008 0.979

FDHeight <0.001 0.775SLA 0.002 0.655Seed mass <0.001 0.330Flowering onset 0.003 0.206

Species richness 0.007 0.298

Table 1Description and summary statistics of field management and landscape scale variables used to characterize sampled cereal fields (n = 78) and principal component analysisloadings in factors summarizing field-scale (PC1) and landscape-scale (PC2) characteristics of sampled fields.

Variable Description Mean � SD PC1 PC2

Field managementFertilizer (Fert) Total kg/ha nitrogen applied on focal field 59.47 � 33.46 0.797 0.125Sowing density (Sowd) Density (kg/ha) of seed sown 204.17 � 70.23 0.682 0.010Yield (Yield) Cereal grain (ton/ha) obtained in focal field 3.02 � 1.22 0.832 �0.127

Landscape characteristicsField size (Ffs) Focal field size (ha) 4.47 � 5.24 0.127 0.870Mean field size (Mfs_500) Mean size (ha) of fields with arable crops within a circle radius 500 m centered in the sampling point 3.54 � 4.21 0.050 0.857Arable land cover (Arable_500) Percentage of cultivated land within a circle radius 500 m centered in the sampling point 62.08 � 26.27 �0.383 0.519

Proportion of varianceexplained

0.326 0.299

Cumulative variance explained 0.326 0.625

All variables adjust to a normal distribution (Kolmogorof–Smirnov test, p < 0.05) except Sowd and Mfs_500, which were ln (x + 1) transformed.

38 I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43

in mean trait values along the studied intensification gradients andwas calculated for each particular trait averaging the values of allthe species present in the sampling point, weighted by theirrelative covers (Ricotta and Moretti, 2011). FD, which can be usedas an indicator of the effects of agricultural intensification on traitvalues range (e.g. Mayfield et al., 2010), was calculated as theweighted standard deviation of each trait value considering allspecies present in the sampling point:

FDt ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

piðxti � CWMtÞ2vuut

where FDt is the functional diversity of the trait t, pi is the relativeabundance of the species i, CWMt is the CWM of the consideredtrait and xti is the value of the trait t for the species i. This index hasbeen used in works studying shifts in functional diversityassociated with land use changes (Mayfield et al., 2010 and Pecoet al., 2012). Subsequently, in fields with more than one samplingpoint, the average of FD values and species richness values werecalculated, thus obtaining a single value per field for each variable.

2.3. Statistical analyses

Principal component analysis (PCA) was applied to the datamatrix with 6 variables on agricultural intensification and 78agricultural fields. Two orthogonal axes were retained: one wascontributed by yield, sowing density and applied nitrogen fertilizerand was thus related to management practices at the individualfield scale (PC1); the other was contributed by proportion of arableland, mean arable field size and sampled field size and was relatedto the structure and composition of the surrounding landscape(PC2). Both axes explained together 63% of total variance (Table 1).This procedure allowed us to obtain two independent gradients ofagricultural intensification, each one associated with one of theconsidered spatial scales (individual field and surroundinglandscape), while simultaneously considering the multifactorialnature of agricultural intensification.

Three sets of regression analyses were performed, with CWMand FD of the four selected traits and species richness as responsevariables and using intensification at the individual field scale(scores on PC1) and at the landscape scale (scores on PC2) asexplanatory variables. To account for possible non-linear effects ofagricultural intensification on biodiversity (see Klein et al., 2011),we fitted GAM models (Hastie and Tibshirani, 1990), initiallyincluding both PC1 and PC2 as explanatory variables, andsubsequently removing the non-significant terms.

Functional redundancy can be simply understood as the ratiobetween species richness and FD (e.g. Flynn et al., 2009).

Accordingly, we used the species richness-FD space to interpretchanges in functional redundancy along the intensificationgradient. After examining the GAM models, we divided thegradient into intervals of increasing intensification. Then, wefitted the vectors joining the centroids of the consecutiveintensification intervals (following an increasing intensificationorder) in the richness-FD space (DDSF vectors, which indicatechanges in diversity of species and functional traits; Mayfield et al.,2010). As such, DDSF vectors that depart from the 1:1 line indicatechanges in the species functional redundancy between twodifferent intensification stages (Petchey et al., 2007 and Flynnet al., 2009). This approach was just used as a visual interpretationof the regression analyses results. All the analyses were performedusing the program R version 2.15.3 (R Core Team, 2013).

3. Results

The effect of intensification at the landscape scale (PC2) was notsignificant in either GAM models, CWM, FD or species richness(Table 2), and it was subsequently discarded for further analyses.Intensification at the individual field scale (PC1) had a significanteffect on the CWM of all the studied traits, with the exception ofSLA. These traits showed a non-linear response to intensification atthe field scale as revealed by the GAM models. Given the lack ofeffects of intensification at the landscape scale, and for the sake ofclarity, hereafter we will use the term ‘intensification' to refer tointensification at the individual field scale.

The effect of intensification was greater at intermediate than athigh or low levels of intensification (Fig. 1). Intensificationselected for tall, heavy-seeded and early flowering species (Fig. 1).Intensification decreased the FD of all the studied traits (Fig. 2).

Fig. 1. GAM models representing the relationship between agricultural intensification at the individual field scale and community weighted mean (CWM) values for thestudied arable plant traits. Vertical dotted lines represent the four equal-length intervals along the intensification gradient. The proportion of deviance explained by thecorresponding GAM model is indicated in each panel.

I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43 39

However, the traits differed in the shapes of their responses;while for height and flowering onset, this relationship showed tobe linear, for SLA and seed mass the greatest reductions in FDoccurred at intermediate levels of intensification.

Species richness decreased linearly along the intensificationgradient (Fig. 3). We divided the gradient into four equal-lengthintervals of increasing intensification that roughly fitted the threedifferent trends detected by GAM models in the responses of FD(Fig. 2). We ascribed sampled fields to each of these four intervalsand plotted their species richness and FD values of each trait. Thisgraphical analysis showed that the greatest changes in FD typicallyoccurred at intermediate levels of intensification, but such changeswere not accompanied by substantial changes in species richness(Fig. 4). In summary, the DDSF vectors described a non-linearpattern of decreasing functional redundancy along the intensifica-tion gradient (Fig. 4).

4. Discussion

4.1. Individual field vs. surrounding landscape intensification effectson FD

The surrounding landscape structure and composition wasfound to be important for biodiversity conservation in agricul-tural fields (e.g. Tscharntke et al., 2005), but its relativeimportance depends on the taxonomical group considered(Guerrero et al., 2010). Indeed, we did not find significantcorrelations between indicators of intensification at the land-scape scale and the analyzed traits. The lack of influence of thesurrounding landscape in this study, in which we avoided fieldedges, may be attributed to the higher agricultural pressuretowards the centre of arable fields compared to the edges, and tothe lower probability of seed arrival because of the distance to

Fig. 2. GAM models representing the relationship between agricultural intensification at the individual field scale and functional diversity (FD) values for the studied arableplant traits. Vertical dotted lines represent the four equal-length intervals along the intensification gradient. The proportion of deviance explained by the corresponding GAMmodel is indicated in each panel.

40 I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43

field margins (Marshall, 1989). This result is consistent with otherstudies considering inner areas of fields in Mediterraneanfarmland (José-María et al., 2011 and Romero et al., 2008),suggesting that arable plant conservation policies in Mediterra-nean cereal fields should be better addressed to farming practicesat the individual field scale (Armengot et al., 2011).

4.2. Arable plant functional trait composition

Our results showed that management intensification, results inarable plant communities with heavier seeds, higher canopies andearlier flowering. This combination of traits confers the species thecapacity to persist in the highly productive and competitiveenvironments dominated by crop species. Early flowering has beeninterpreted as a strategy to avoid competition (Franklin, 2008). Tallspecies have a better access to light than short ones (Grime, 2001).Finally, seedlings of heavier-seeded species are better able to

survive hazards, including deep shade, physical damage and thepresence of competing crop vegetation (Westoby et al., 1996).However, a typical weedy trait like high seed output (e.g.Sutherland, 2004) is associated to small seeds, as predicted by awell-known life history trade-off (Charnov, 2002 and Ben-Huret al., 2012). In our case, apparently, the advantages gained byhaving larger seeds would exceed those provided by the improvedcolonizing capacity conferred by a large seed output. We expectedSLA to increase with intensification, because species with high SLAshow high relative growth rate and productivity (Westoby et al.,1996), being better adapted to resource-rich environments(Ordóñez et al., 2009) like fields with a high supply of fertilizers.However, we have found no change in average SLA values along thegradient, which suggests that competition for light, and notincreased availability of nutrients, is the main driver of changes inthe functional structure of these communities. In summary, highlevels of intensification favoured species with trait values that

Fig. 3. GAM model representing the relationship between agricultural intensifica-tion at the individual field scale and species richness of the studied arable plantcommunities. The proportion of deviance explained by the GAM model is indicated.

Fig. 4. Relationship between species richness and functional diversity (FD) values for therichness for the corresponding four equal-length intervals along the field scale intensificaError bars indicate � SE. Arrows indicate the trajectories of change in species richness anan order of increasing intensification.

I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43 41

confer the ability either to cope with low light availability – tallspecies and species with heavy seeds – or to temporally avoidcompetition with the crop – early flowering species.

4.3. Functional diversity and redundancy responses to intensification

Responses of the FD of all the studied traits were consistentwith our prediction of reduced functional diversity at highintensification levels. In accordance to this, Pakeman et al.(2011) found that decreasing FD with increased productivitywas the most common pattern across the 12 traits studied, whichwould indicate reduced variance in traits and trait convergence.Therefore, our results suggest that agricultural intensification atthe individual field scale is associated with trait convergence.Additionally, and in accordance with previous works (Armengotet al., 2011; Gabriel et al., 2005; Guerrero et al., 2010 and Storkeyet al., 2012), intensification reduced species richness (Fig. 3).

In general, management intensification reduced both the FDand species richness of the studied communities. However, thesereductions did not happen simultaneously: the greatest loss ofspecies was not accompanied by similar changes in FD and vice-versa. As stated above, DDSF vectors that depart from the 1:1 lineindicate changes in the functional redundancy of species betweentwo different intensification stages (Petchey et al., 2007 and Flynnet al., 2009). We could distinguish two such departures in ourstudy (Fig. 4). First, species richness was substantially reducedbetween the first and second intervals of intensification, withoutsimilar changes in FD (Fig 4). This pattern can be interpreted as a

studied arable plant traits. Black circles represent the mean values of FD and speciestion gradient (PC1; smallest: lowest intensification; largest: highest intensification).d FD of each trait (DDSF vectors) in relation to agricultural intensification, following

42 I. Guerrero et al. / Agriculture, Ecosystems and Environment 195 (2014) 36–43

reduction in the functional redundancy of the species present inthe communities (i.e. a smaller number of species yield similarvalues of FD). The opposite pattern was observed between thesecond and third intervals (Fig 4): FD decreased with little changein species richness, indicating a loss of functionally non-redundantspecies or a replacement of these by more functionally similarspecies. At the highest extreme of the intensification gradient (Fig4; third to fourth intervals), FD did not experience substantialchanges, which suggests a certain resistance of these arable plantcommunities to management intensity. This result suggests thatthese remnant species and functional groups are characterized byintensification-resistant traits, presenting therefore a high degreeof functional redundancy. Overall, the decoupling between theresponses of species richness and FD indicates that the functionalredundancy of the studied communities responds to intensifica-tion in a non-linear fashion.

In summary, the trajectories of change in species richness andthe FD of each trait (DDSF vectors, Mayfield et al., 2010) point at anon-linear pattern of functional redundancy along the intensifica-tion gradient. To our knowledge, this pattern has not beenpreviously described in arable plant communities (but see Sasakiet al., 2009 for a similar one along a grazing gradient in grasslandplant communities). Redundant species are considered necessaryto ensure ecosystem resilience to disturbance (Walker, 1992).Given the key roles played by arable plants in supportingbiodiversity and service provisioning in agroecosystems (Marshallet al., 2003), the identified pattern has important implications interms of the consequences of management intensification on thefunctioning of these systems. The detected abrupt loss offunctional diversity as intensification increases suggests theexistence of ecological thresholds and probable sudden shifts tostates with a lower capacity to generate ecosystem services (Folkeet al., 2004). The combined and often synergistic effects of naturaland man-made pressures can make agroecosystems morevulnerable to changes that were previously more easily buffered,which may be critical for system functioning and the sustainableuse of ecosystem services (Hooper et al., 2005).

Our findings add to those of previous studies that highlight theimportance of considering species and functional diversitiessimultaneously in the study of ecosystem response to intensifica-tion (e.g. Carmona et al., 2012; Mayfield et al., 2010 and Peco et al.,2012). Moreover, by selecting the same index of functionaldiversity as some of these studies (Mayfield et al., 2010), we haveensured that our results are directly comparable with theirs(Mouchet et al., 2010).

5. Conclusions

Our study provides new insights on how agricultural manage-ment interacts with plant community structure and functioningthrough its non-linear effects on functional diversity andfunctional redundancy. The response of the considered functionaltraits is primarily driven by intensification of farming practices atthe field scale, with non-significant effect of the surroundinglandscape context. Although increasing levels of intensification atthe individual field scale did not select for higher SLA values, it didso for traits that indicate increased competition for light. Speciesrichness and trait diversity decreased with intensification. At thefirst stages, intensification caused a loss of redundant species, asindicated by the lack of changes in FD. Subsequent increases inintensification caused dramatic losses in the FD of the affectedcommunities. Further increases in intensification resulted only in asmall additional loss of species, suggesting that species in thesecommunities are highly adapted to the conditions imposed byintensification, but with no change in FD, indicating again a highfunctional redundancy at these later stages.

In order to enhance our ability to preserve arable plantbiodiversity, the associated services and system resilience, non-linear responses of arable plant communities, like the onespresented in this study, should be taken into consideration whendesigning and adopting conservation oriented managementstrategies.

Acknowledgements

We thank all collaborating fieldworkers and farmers. TheEuropean Science Foundation and the connected National ScienceOrganizations funded the present study through the EurodiversityAGRIPOPES program. The REMEDINAL2-S2009/AMB-1783 projectof the Comunidad de Madrid Government and the SpanishMinistries of Science and Education (Projects, CGL2006–26145-E/BOS and CGL2011 24871, FPI grant BES-2008–009821 for C.P.C.and Salvador de Madariaga grant PR2011–0491 for B.P.) alsocontributed to this study.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.agee.2014.05.021.

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