# Alley cropping agroforestry systems: Reservoirs for weeds or refugia for plant diversity?

Sébastien Boinot, Guillaume Fried, Jonathan Storkey, Helen Metcalfe, Karim

Barkaoui, Pierre-Eric Lauri, Delphine Meziere

► To cite this version:

Sébastien Boinot, Guillaume Fried, Jonathan Storkey, Helen Metcalfe, Karim Barkaoui, et al. Alley cropping agroforestry systems: Reservoirs for weeds or refugia for plant diversity?. Agriculture, Ecosystems & Environment, 2019, 284, pp.106584. 10.1016/j.agee.2019.106584. hal-02620662

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1 **Alley cropping agroforestry systems: reservoirs for weeds or refugia for plant diversity?**

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3 Sébastien Boinotª, Guillaume Friedᵇ, Jonathan Storkeyᶜ, Helen Metcalfeᶜ, Karim Barkaouiª,

4 Pierre-Éric Lauriª, Delphine Mézièreª

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6 ªSystem, Univ Montpellier, INRA, CIRAD, CIHEAM-IAMM, Montpellier SupAgro,

7 Montpellier, France

8 ᵇAnses, Laboratoire de la Santé des Végétaux, Unité Entomologie et Plantes invasives, 755

9 avenue du campus Agropolis, CS30016, 34988 Montferrier-sur-Lez Cedex, France

10 ᶜSustainable Agricultural Sciences, Rothamsted Research, Harpenden, Hertfordshire AL5

11 2JQ, UK.

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17 Corresponding author: Sébastien Boinot, INRA–UMR SYSTEM, 2 place Viala, 34060

18 Montpellier Cedex 1, France. Tel: +33 4 99 61 26 84; e-mail: sebastien.boinot@inra.fr

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Alley cropping agroforestry is a land use practice in which arable crops are grown between tree rows. In such agroforestry systems, non-crop herbaceous vegetation develops on the tree rows, resulting in understory vegetation strips (UVS). UVS are perceived both as reservoirs for weeds and opportunities for biodiversity conservation. The purpose of this study was to assess the contribution of UVS to (i) plant spillover and (ii) plant diversity conservation, depending on their functional structure and the farming system. Vegetation surveys were carried out in May 2017 in South-Western France over 16 winter cereal fields (8 alley cropping agroforestry systems and 8 pure crop controls), half under conventional farming and half under organic farming. Using data on plant functional traits related to dispersal strategies and response to agricultural disturbances, we explained the mechanisms involved in plant spillover between habitats. The study revealed that very few species were able to disperse far into crop alleys, except perennial species producing rhizomes and stolons whose spread has been favored by tillage. The presence of UVS in agroforestry fields did not increase weed-crop ratio (i.e. weed coverage / weed and crop coverage) in adjacent crop alleys. On the other hand, UVS harbored richer and more abundant floras (with high proportions of species rarely found in arable habitats) compared to crop alleys and pure crop controls, especially under conventional farming. The functional approach provided insights for weed management in alley cropping agroforestry systems in order to optimize plant diversity conservation without increasing weed-crop ratio. This study showed the relevance of using the functional approach to understand the mechanisms behind plant spillover in cropping systems that integrate semi-natural habitats.

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**Keywords:** temperate region, semi-natural habitat, understory vegetation strip, hemerophobic species, spillover, functional trait

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## 1. Introduction

The post-war agricultural intensification has led to worldwide losses of biodiversity due to the increase of both agrochemicals' application and croplands, to the detriment of semi-natural habitats such as woodlots, grasslands, hedges and field boundaries (Stoate et al., 2001). Since then, many studies have demonstrated that semi-natural habitats provide food resources, reproduction and overwintering sites and refuges from agricultural disturbances for many organisms. For example, Aavik and Liira (2010) showed that field boundaries are home to hemeromorphic plant species, i.e. species sensitive to tillage and/or herbicides, as opposed to agrotolerant species. Such species have a high conservation value as they are declining in the context of intensive agriculture (Aavik et al., 2008). Beneficial arthropods such as pollinators and natural enemies of pests also depend on the presence of semi-natural habitats to complete their life cycle (Pfiffner and Luka, 2000; Hass et al., 2018). On the other hand, it has been shown that non-crop habitats could host weeds, pathogens and pests (Norris and Kogan, 2000; Wisler and Norris, 2005). If the presence of nearby semi-natural habitats impacts the functioning of agroecosystem, the spillover of organisms between semi-natural and arable habitats is also of major importance and can be positive or negative for crop production (Blitzer et al., 2012). Indeed, in the case of arthropods, the higher the spillover of beneficial arthropods towards arable fields is, the better pest control and crop pollination can be achieved (Woodcock et al., 2016). On the other hand, pests coming from alternative host plants in adjacent habitats could disperse towards the arable fields, potentially causing crop yield losses (e.g. Johnson, 1950). In the case of spontaneous plants, which are at the basis of agroecosystem food web, their spillover in arable fields could promote biodiversity conservation, but also induce yield losses through competition with crops (Petit et al., 2011). Many studies have assessed the negative effects of various adjacent habitats on crop
77 production, often suspected to supply arable fields with weeds. Overall, the abundance and diversity of weed communities were enhanced up to 2.5 m and 4 m from field margins
78 (Marshall, 1989; Wilson and Aebischer, 1995 respectively), 3 m from forests (Devlaeminck et al., 2005), 3.5 m from road verges (Chaudron et al., 2016) and 7 m from grasslands (Hume and Archibold, 1986), thus only in crop edges in every case. Furthermore, the intensity of 81 organisms' spillover in arable fields, and hence the intensity of ecosystem processes associated, depends on the nature of adjacent semi-natural habitats. Indeed, Metcalfe et al. 83 (2019) observed a higher plant spillover in fields next to grasslands or in the presence of field 84 margins, compared to fields next to woodlots, bare ground (ploughed fields or urban) or 85 without field margins. Woodcock et al. (2016) showed that the spillover of beneficial 86 arthropods was higher in fields next to wildflowers strips, compared to fields next to grass 87 strips. Conversely, some habitats can even constitute a barrier to the dispersal of organisms 88 into arable fields (e.g. Mauremooto et al., 1995; Cordeau et al., 2012). Besides, the spillover 89 of organisms between arable and semi-natural habitats is likely to be increased by small-scale 90 agriculture and landscape fragmentation, which are characterized by higher proportion of 91 edges (Blitzer et al., 2012; Mitchell et al., 2015). That could explain the higher weed diversity 92 observed in smaller fields (Gaba et al., 2010). In the same idea, Hatt et al. (2017) showed that 93 the presence of semi-natural habitats located within fields' core themselves favored the 94 spillover of organisms farther into the crops. 95

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97 In temperate regions, agroforestry systems are gaining renewed interest as they can provide a wide range of ecosystem services from the same area of land, such as sustainable 98 food and biomass production, soil and water protection, biodiversity conservation and carbon 99 sequestration (Jose, 2009; Quinkenstein et al., 2009; Torralba et al., 2016; Kay et al., 2019). 100 Agroforestry systems can take multiple faces given the wide range of practices they cover 101
102 (e.g. hedge farmland, silvoarable and silvopastoral systems), the diversity of species that can
103 be associated (herbaceous plants, shrubs, trees) and the spatial configurations conceivable (i.e.
104 playing on the area covered by the different strata and their position within fields). Such
105 plasticity allows agroforestry systems to be implemented in many regions and for multiple
106 objectives. If promoted by agricultural and environmental policies, agroforestry systems are
107 expected to help meet Europe policy objectives on greenhouse gas emissions while providing
108 multiple ecosystem services (Kay et al., 2019). Among agroforestry systems, alley cropping
109 agroforestry, in which arable crops are grown between tree rows, represent a great opportunity
110 for the reintegration of semi-natural habitats within fields. Indeed, the presence of trees rows
111 leads to increased edges amount and field fragmentation, which is expected to enhance
112 ecosystem (dys-)services flows (Mitchell et al., 2015). Further, to prevent any damage on
113 trees, farmers avoid tilling the soil close to the trees, resulting in the development of non-crop
114 herbaceous strips under the trees, hereafter called understory vegetation strips (UVS) (Figure
115 1). UVS are poorly disturbed by crop management and so are comparable to other linear
116 semi-natural habitats such as field boundaries, except that they are located within fields and
117 occupy about 3 to 13% of the available agricultural area. Given the spatial configuration and
118 the important extent of UVS, it is likely that both the intensity of plant spillover and the
119 amount of refugia for biodiversity are increased in alley cropping agroforestry compared to
120 pure crop systems. Many works have assessed the ecosystem services supplied by (semi-
121 )permanent herbaceous vegetation in other systems such as pure crops (e.g. Hatt et al., 2017),
122 vineyards (e.g. Winter et al., 2018; Garcia et al., 2019) and orchards (e.g. Forey et al., 2016;
123 Cahenzli et al., 2019). However, research in temperate alley cropping agroforestry is recent
124 and has focused mainly on interactions between trees and crops. Works considering UVS are
125 still scarce, but we can mention Burgess (1999), Cardinael et al. (2015), Mézière et al. (2016),
126 Pardon et al. (2019) for example. If most farmers perceive agroforestry systems as a solution
127 to wildlife habitats conservation, others fear that UVS constitute reservoirs for weeds that colonize crop alleys (Graves et al., 2017). To our knowledge, very few studies have assessed the effects of alley cropping agroforestry on arable weed community structure and plant diversity conservation in temperate regions. Mézière et al. (2016) showed that an alley cropping agroforestry system can harbor higher plant diversity than a pure crop control, without enhancing weed coverage in crop alleys. However, these results were restricted to one pair of fields under conventional farming in a Mediterranean French context.

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135 The purpose of this study was to assess the contribution of UVS (i) to plant spillover into crop alleys and (ii) to plant diversity conservation in the agroecosystem, under conventional vs organic farming and taking into account the functional structure of understory vegetation. We hypothesized that 1) the ability of a plant species to colonize crop alleys from UVS depends on both its tolerance to agricultural disturbances and its dispersal strategies. So, we would expect a species that can tolerate tillage and herbicides and that also has good dispersal abilities (anemochory or vegetative dispersal) to be more likely to colonize crop alleys from UVS. Further, we hypothesized that 2) plant spillover from UVS would enhance the abundance of weed flora in alley cropping agroforestry fields compared to pure crop controls (hereafter called “weed reservoirs” hypothesis) and that 3) UVS would constitute refugia for plant diversity, particularly for hemeromorphic species (hereafter called “plant diversity refugia” hypothesis). Our final hypothesis was that 4) the role of UVS as weed reservoirs would be more important in organic farming fields given the lack of herbicide treatments and mineral fertilizers, whereas their role as refugia for plant diversity would be more important in conventional farming fields, where agricultural intensification drastically reduces the ecological niches available for spontaneous plants (Hyvönen and Salonen, 2002; Gabriel et al., 2006; Andreasen and Streibig, 2011).

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## 2. Materials and Methods

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### 2.1. Vegetation survey

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The study was conducted in Gers and Pyrénées-Atlantiques Departments (South-Western France), which is a hilly region (altitude about 300 meters) characterized by a sub-Atlantic climate with hot summers and cool winters. Agricultural areas are mainly composed of clay-limestone and clay-to-silt soils and annual precipitation usually varies from 700 to 900 mm. Vegetation surveys were carried out in May 2017 over 16 winter cereal fields (8 pairs of alley cropping agroforestry systems and pure crop controls) growing either winter wheat (*Triticum aestivum* L.) or winter barley (*Hordeum vulgare* L.), half under conventional farming and half under organic farming. Vegetation surveys in conventional farming fields were carried out at least one month after the last herbicide treatment. Thus, observed flora in these fields is mostly composed of species surviving herbicide treatments or emerging later. Each pair of fields (alley cropping agroforestry vs pure crop control) was located within the same perimeter, similar in terms of pedo-climatic conditions and surrounding land use (see Figure S1 in Supplementary material). Each pair was also cultivated by the same farmer, with similar crop managements over the three years preceding the study (see Table S1 in Supplementary material). Three fields under organic farming also contained leguminous crops, either garden pea (*Lathyrus oleraceus* Lam.) and/or common vetch (*Vicia sativa* L.) but the proportion of legumes was always very low compared to cereals. For both organically and conventionally farmed fields, UVS were either unmanaged (*n* = 2 fields), mown before sowing and after harvest (*n* = 1 field) or sown with competitive perennial species (*n* = 1 field): *Schedonorus arundinaceus* under organic farming, *Festuca rubra* under conventional farming. Features of
177 agroforestry fields (i.e. tree species and basic metrics) are given in Table S2 in Supplementary material. In each agroforestry field, UVS were surveyed in three zones distant from 20 m.
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179 Each zone was sampled with four quadrats (0.25 m²) separated by two meters each. Then, on both sides of these zones, adjacent crop alleys were sampled on transects running perpendicular to UVS, at three distances from UVS (0.5 m, 2 m and 8 m). For each distance, we visually estimated the coverage of each species found in three quadrats (0.25 m²) separated by two meters each (Figure 1), with an accuracy of ± 5%. Plants were mostly at vegetative or floral stage during the survey. In total, 66 quadrats (16.5 m²) were sampled per agroforestry field. This sampling design was located at around 50 m and 100 m from the two nearest field boundaries to exclude their effect on weed communities. The crop alleys on either side of the UVS were sampled to take into account the potential effect of slopes and prevailing wind directions on seed dispersal. The same protocol was used for pure crop controls with the transects placed at equivalent locations in the field in the absence of the UVS, resulting in 54 quadrats (13.5 m²) sampled per pure crop control. In total, 960 quadrats (240 m²) were sampled during the vegetation survey.

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193 **Figure 1.** The principal compartments of alley cropping agroforestry systems and the
194 sampling protocol used for the vegetation survey.

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196 2.2. *Functional structure, potential harmfulness and diversity of plant communities*

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198 Functional traits related to dispersal strategies and tolerance to agricultural disturbances were collected from databases and reference books of French flora (Table 1), along with Raunkiaer life forms. If an individual was identified to the genus only, the mean attributes of congeneric

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201 species found in the survey and predominant in the region were used (Association Botanique

202 Gersoise, 2003).

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204 **Table 1.** List of selected functional traits related to dispersal strategies and tolerance to disturbances (see Gaba et al., 2017 and references therein) along with their sources and associated references.

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<table><thead><tr><th>Traits, life forms</th><th>Dispersion abilities and/or expected response to disturbances</th><th>Sources</th></tr></thead><tbody><tr><td>Specific leaf area (mm².mg⁻¹)</td><td>Fertilization, crop harvesting and vegetation mowing favor species with high resources acquisition capacity (high SLA).</td><td>LEDA (Kleyer et al., 2008)</td></tr><tr><td>Plant height at maturity (cm)</td><td>Vegetation mowing favors short species.</td><td>(Coste, 1937)</td></tr><tr><td>Seed mass (g)</td><td>Seed mass/number trade-off; disturbances favor species producing numerous small seeds whereas stable habitats favor competitive species producing fewer but bigger seeds.</td><td>SID (Royal Botanical Gardens Kew, 2017)</td></tr><tr><td>Flowering onset and range (month)<sup>a</sup></td><td>Determines species ability to flower and produce seeds before crop harvest or vegetation mowing.</td><td>BaseFlor (Julve, 1998)</td></tr><tr><td>Emergence onset and range (month)<sup>b</sup></td><td>Trade-off between escaping tillage and herbicide treatment (late emergence) and avoiding crop competition (early emergence). Successful weeds often emerge simultaneously with the crop.</td><td>Internal compilation of traits in a weed-oriented database</td></tr><tr><td>Raunkiaer life forms</td><td>Tillage favors therophyte species (i.e. annual species spending winter in the form of seeds) and geophyte ones (i.e. perennial species spending winter in the form of bulbs, tubers or rhizomes).</td><td>(Jauzein, 2011)</td></tr><tr><td>Seed dispersal strategies</td><td>Spillover of animal-dispersed plants increases in response to connectivity provided by ecological corridors. Spillover of wind-dispersed plants increases in response to higher edge-to-interior ratio of habitats.</td><td>BaseFlor (Julve, 1998)</td></tr><tr><td>Presence of runners (rhizomes and/or stolons)</td><td>Tillage favors the dispersal of species with runners. Once these organs are cut into fragments, they can heal and form new plants.</td><td>(Jauzein, 2011)</td></tr></tbody></table>

208 <sup>a</sup>Flowering onset was coded from 1 (January) to 12 (December).

209 <sup>b</sup>Emergence onset was coded from 1 (October) to 12 (September) since winter cereals were sown in

210 October-November. Data were collected from observations at SupAgro Dijon and based on expert

211 opinion.

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To assess the potential harmfulness of weed communities (here defined as plant communities found in crops), total weed and crop coverage (0 to 100%) were estimated within each 0.25 m² quadrat. A weed-crop ratio was then computed for each quadrat and used as a proxy of the competitive effects of weeds on yield loss (Lutman et al., 1996) :

$$ \text{weed-crop ratio} = \frac{\text{weed coverage}}{\text{weed coverage} + \text{crop coverage}} $$

To assess the diversity and conservation value of plant communities, total coverage, species richness and evenness of both agrotolerant and hemerophobic communities were measured within each quadrat. Following Aavik et al. (2008), each species was classified as agrotolerant or hemerophobic (see Table A1 in Appendix) based on its frequency of occurrence in arable fields at national scale, using data of the Biovigilance Flore network 2002–2012 (Fried et al., 2008). A species was considered as hemerophobic if its frequency of occurrence in the sample plots of arable fields was lower than 10%. We used this classification rather than functional diversity indices because (i) it provides efficient and integrative indicators of diversity and conservation value of plant communities in response to agricultural land use intensity, (ii) data is available for most species thanks to national scale surveys, and (iii) functional diversity indices are based on a restricted number of relevant traits given specific objectives (e.g. favoring beneficial arthropods, protecting soil and water quality).

## 2.3. Data analysis

To assess the hypothesis n°1 (plant species' ability to colonize crop alleys from UVS depends on both its tolerance to agricultural disturbances and its dispersal strategies), we combined
RLQ and fourth-corner analysis following Dray et al., (2014). RLQ analysis aims to identify the main co-structures between traits (Q-table) and environmental variations (R-table) considering species abundances (L-table), while fourth-corner analysis provide tests for the correlations between each trait and each environmental variable. By combining RLQ and fourth-corner analysis we could test the correlations (i) between each trait and combination of environmental variables obtained from RLQ axes, and (ii) between each environmental variable and trait syndromes obtained from RLQ axes. First, a combination of RLQ and fourth-corner analysis was performed on the plant communities located in the UVS to analyze their taxonomic and functional structures in response to different management practices. We only considered dominant species, occurring in at least 5 quadrats (i.e. whose frequency of occurrence was superior to 5%), because rare species may unduly influence the results (Kenkel et al., 2002). Dominant species represented 90% of the total coverage observed in UVS. The Q-table contained 23 species described by 9 functional traits related to dispersal ability and tolerance to disturbances, along with Raunkiaer life forms. The R-table contained 96 quadrats characterized by farming system (conventional vs organic), the age of UVS and its management (i.e. sowing and mowing considered as binomial variables). Finally, the L-table contained the coverage of each species within each quadrat. Second, a combination of RLQ and fourth-corner analysis was performed on the plant communities located in the crop alleys to assess which life strategies were dispersing from UVS towards crop alleys. Because hypothesis 1 concerns plant species' ability to colonize crop alleys from UVS, this analysis was restricted to the same set of species that were dominant in UVS, therefore eliminating rare species and arable weed species persisting mostly in the seedbank of crop alleys (the relative coverage of these two groups can be seen in Figure S2 in Supplementary material). Again, we considered only species occurring in at least 5 quadrats in the crop alleys (i.e. whose frequency of occurrence was superior to 1%). The Q-table contained 18 species
described by the same functional traits as the first analysis, along with Raunkiaer life forms.

In this second analysis, the R-table contained 432 quadrats characterized by the farming system, the distance from UVS (0.5 m, 2 m, 8 m) and the direction from UVS (east or west).

For both analyses, Monte-Carlo tests were used to assess the global link between traits and environment tables by comparing the observed total inertia (i.e. the sum of eigenvalues of

RLQ axes) to a null distribution obtained from 999 random permutations of species and quadrats. Then, fourth-corner analysis was used to test the significance of correlations

between each trait and each environmental variable, by comparing each bivariate correlation with its null distribution obtained from 49 999 random permutations of species and quadrats.

The false discovery rate method was used to adjust p-values for multiple comparisons (Benjamini and Hochberg, 1995). Finally, we combined RLQ and fourth-corner analysis

(49 999 permutations). Seed mass was very skewed and was therefore log-transformed as

suggested by Kenkel et al. (2002). RLQ and fourth-corner analysis were performed using the package ade4 (Dray and Dufour, 2007).

To assess the hypotheses n°2, 3, and 4, we used generalized linear mixed effects

models (random intercept GLMMs). Transects and fields were included as random effects on

the intercept, with transects nested within fields. These models take into account the spatial

auto-correlation between quadrats located in a same transect or a same field. For the “weed

reservoirs” hypothesis (n°2), we compared total weed coverage, crop coverage and weed-crop

ratio per quadrat (response variables) between crop alleys (i.e. the cropped part of the

agroforestry system) and pure crop controls under conventional vs organic farming, over 16

fields. For the “plant diversity refugia” hypothesis (n°3), we compared total coverage, species

richness and evenness of agrotolerant and hemerophobic communities per quadrat (response

variables) between UVS, crop alleys and pure crop controls, under conventional vs organic
farming. In this analysis, the two fields with sown UVS and their pure crop controls were
removed because sown species had high coverage and reduced the development of other
species within UVS. They were therefore not relevant for comparing diversity indices.
Moreover, given that they were hemerophobic species, it would lead to an overestimation of
the total coverage of hemerophobic species within UVS. This resulted in a dataset of 12 fields
and 720 quadrats. Evenness was computed using the index of Williams (1977) based on the
species proportions p₁, ..., pₕ and species richness S in each quadrat, as suggested by Kvålseth
(2015):

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$$
evenness = 1 - \left[ \frac{S \sum_{i=1}^{S} (p_i^2 - 1)}{S - 1} \right]^{1/2}
$$

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On the agroforestry dataset, other GLMMs were performed to investigate the effect of the
distance from UVS (natural logarithms + 1) on all variables, under organic vs conventional
farming. All GLMMs revealed a strong effect of farming system and in some cases
interactions with other explanatory variables (Table 2). Therefore, each model was performed
on organic farming fields and conventional ones separately to facilitate the comparison
between habitats (UVS, crop alleys, pure crop controls). Species richness was assumed to
follow a Poisson distribution and all other variables (proportions between 0 and 1) were
assumed to follow a Beta distribution. When proportional variables included 0 and/or 1
value(s), the transformation (Y × (N − 1) + 0.5) / N was employed following Zuur et al.
(2013), where Y is the response variable and N is the sample size. If a variable was bound
between a and b, it was rescaled to lie between 0 and 1 by the transformation (Y − a) / (b − a).
This was the case for the total coverage of agrotolerant and hemerophobic communities
(corresponding to the summed coverage of all agrotolerant or hemerophobic species present
within each quadrat), whose maximum values were greater than 1. We used the package

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313 glmmTMB (Brooks et al., 2017) for fitting Poisson and Beta GLMMs, with the link functions
314 log and logit respectively. Poisson GLMMs revealed under-dispersion, therefore Conway-
315 Maxwell-Poisson GLMMs were fitted instead as suggested by Lynch et al. (2014). All
316 analyses were performed using the statistical software R 5.1 (R Core Team, 2018).

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318 **3. Results**

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320 A total of 88 plant species were recorded during the whole survey. Pure crop controls
321 harbored 61 species whereas 70 species were found in crop alleys of agroforestry fields, over
322 108 m² sampled per system. In UVS, 55 species were found over 24 m² sampled. The five
323 most frequent species in UVS were *Galium aparine*, *Anisantha* sp., *Avena* sp., *Lolium* sp. and
324 *Convolvulus arvensis*. A list of all species recorded along with their occurrences in each
325 habitat is given in Table A1 in Appendix A.

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327 **3.1. Functional structure of plant communities of understory vegetation strips under different**
328 management practices

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330 A Monte-Carlo permutation test revealed no significant link between traits and environment
331 tables ($P_{max}$ = 0.342). Fourth-corner analysis revealed that animal-dispersed species are
332 significantly and positively associated with unmown UVS ($r$ = 0.240, $P_{adj}$ = 0.0021, Figure
333 2a). The combination of RLQ and fourth-corner revealed that RLQ axis 1 was negatively
334 correlated with the age of UVS and sowing or no mowing management (Figure 2b). RLQ axis
335 1 was positively correlated with mowing or no sowing management. Older quadrats, whatever
336 they were unmown or sown, contained significantly more animal-dispersed species whereas
337 mown and unsown quadrats contained more barochorous species. The second RLQ axis
338 significantly separated quadrats according to the farming system but this variable had no
339 significant effect on the functional structure of plant communities within UVS.

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341 **Figure 2.** (a) Results of the fourth-corner analysis performed on dominant species of plant
342 communities located in the understory vegetation strips (UVS). (b) Results of the fourth-
343 corner analysis testing the link between RLQ axes and traits and environmental variables. Red
344 cells correspond to positive significant relationships while blue cells correspond to negative
345 significant relationships.

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347 3.2. Identification of trait syndromes enabling species to colonize crop alleys from understory
348 vegetation strips

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350 The first two axes of the RLQ accounted for 99.2% of the total inertia (84.5 and 14.7%
351 respectively, Figure 3a). The first two RLQ axes accounted for most of the variance explained
352 by separate analyses of environmental variables (97.5% for the analysis of the R-table) and
353 species traits (80.5% for the analysis of the Q-table). Coefficients of environmental variables
354 and traits (illustrated in Figure 3b and Figure 3c) are given in Table S4 in Supplementary
355 Material, along with their basic statistics. Monte-Carlo permutation test revealed a significant
356 link between traits and environment tables ($P_{max}$ = 0.003). Fourth-corner analysis revealed no
357 significant correlation between individual pairs of traits and environmental variables (Figure
358 4a). Testing the link between RLQ axes and traits or environment (Figure 4b) showed that
359 RLQ axis 1 was negatively correlated with conventional farming, direction from UVS (west)
360 and distance from UVS (2 m). RLQ axis 1 was positively correlated with organic farming,
361 direction from UVS (east) and distance from UVS (0.5 m). The species that were dominant in
362 UVS and also found in crop alleys of organic fields were perennial species characterized by
363 relatively high seed mass and plant height, later emergence and flowering. On the other hand,
364 UVS species found in crop alleys of conventional fields were much fewer and characterized
365 by large emergence and flowering ranges, along with a short life cycle (therophyte species
366 with high SLA). The second RLQ axis clearly separated quadrats at 0.5 m from those at 2 m
367 and 8 m. The vast majority of species dominant in UVS were found at 0.5 m from UVS, their
368 occurrences and abundances decreasing at 2 m and 8 m. They were mostly animal-dispersed
369 species without runners. Conversely, *Convolvulus arvensis* and *Potentilla reptans* scored
370 negatively on RLQ axis 2 (Figure 3a). These are barochorous species dispersing by means of
371 runners. They emerge later and have relatively larger flowering ranges.

372

373 **Figure 3.** RLQ analysis performed on plant communities *located in the crop alleys*. Results
374 are given on the first two axes for (a) species' scores, (b) environmental variables' loadings,
375 and (c) traits' loadings. Only species that were dominant in the understory vegetation strips
376 were considered, therefore eliminating rare species and arable weed species persisting mostly
377 in the seedbank of crop alleys. Species marked with a star were sown in UVS. Grey and black
378 labels correspond to agrotolerant and hemerophobic species respectively. Codes for species
379 are given in Table A1 in Appendix.

380

381 **Figure 4.** (a) Results of the fourth-corner analysis performed on plant communities *located in the crop alleys*, restricted to the set of species that were also dominant in the understory vegetation strips (UVS). (b) Results of the fourth-corner analysis testing the link between RLQ axes and traits and environmental variables. Red cells correspond to positive significant relationships while blue cells correspond to negative significant relationships.

386
387 **3.3. Comparison of weed-crop ratio between alley cropping agroforestry and pure crop controls**
388

389
390 Conventional fields had significantly lower total weed coverage (on average -33% per quadrat) and higher crop coverage (on average +22% per quadrat) than organic ones (Table 2). As a consequence, weed-crop ratio was much lower in conventional fields (on average -36% per quadrat) (Table 2). In conventional fields, crop and weed coverage along with weed-crop ratio were similar between crop alleys and pure crop controls (Figure 5, Table S5 in Supplementary Material). On the other hand, in organic fields, total weed coverage was significantly lower (-12%) in crop alleys compared to pure crop controls, while crop coverage and weed-crop ratio were comparable between both systems (Figure 5, Table S5 in Supplementary Material). The effect of the distance from UVS on weed-crop ratio was significant in conventional fields. Indeed, weed coverage and weed-crop ratio decreased when farther from UVS while crop coverage increased (see Table S6 and Figure S4 in Supplementary Material). However, no effect of the distance from UVS was detected in organic fields.

403
404 **3.4. Comparison of plant diversity between habitats**
405

406
407 Coverage and species richness of agrotolerant and hemerophobic communities were lower in conventional fields than in organic ones (Table 2). On the one hand, in conventional fields all diversity variables were very low and similar between pure crop controls and crop alleys, except species richness of hemerophobic communities that was slightly higher in crop alleys (Figures 6a, 6b, 6c). By contrast, UVS supported a richer and more abundant flora than cropped areas, containing both agrotolerant and hemerophobic species (Figures 6a, 6b). On
the other hand, in organic fields the coverage of both agrotolerant and hemerophobic communities was higher in the UVS (Figure 6a). Species richness of both agrotolerant and hemerophobic communities was similar between pure crop controls, crop alleys and UVS (Figure 6b). Evenness of agrotolerant and hemerophobic communities was higher in cropped areas (pure crop controls and crop alleys) than in UVS (Figure 6c). Evenness of hemerophobic communities was even higher in crop alleys than in pure crop controls (Figure 6c). The effect of the distance from UVS on plant diversity was significant only in conventional fields (see Table S6 in Supplementary Material). Furthermore, only hemerophobic communities were impacted by the distance from UVS. Indeed, the coverage and species richness of hemerophobic communities decreased when farther from UVS, while thes variables remained constant regarding agrotolerant communities (see Figures S5a, S5b in Supplementary Material).

424

**Table 2.** Estimates, their standard errors and p-values obtained from generalized linear mixed-effects models (GLMMs). Crop alleys and understory vegetation strips (UVS) are compared to pure crop controls (reference level in GLMMs). Conventional farming is compared to organic farming (reference level in GLMMs). *n* = number of quadrats used for each GLMM. In the case of evenness, only quadrats containing more than 1 species were considered. No quadrats containing more than one hemerophobic species were found in pure crop controls under conventional farming, therefore comparisons with agroforestry systems were impossible in this case. Bold letters indicate significance difference at 0.05 threshold (*P* ≤ 0.05, **P* ≤ 0.01, ***P* ≤ 0.001).
<table><thead><tr><th rowspan="2">Response variables</th><th colspan="3">Main terms</th><th colspan="3">Interactions with farming system</th></tr><tr><th>Crop alleys</th><th>UVS</th><th>Conventional farming</th><th>Crop alleys</th><th>UVS</th><th>Conventional farming</th></tr></thead><tbody><tr><td rowspan="3"><i>Potential harmfulness of weed communities</i></td><td>Weed coverage<br>(n = 863)</td><td>-0.49 ± 0.497</td><td>—</td><td><b>-2.84</b> ± 0.500 ***</td><td>0.90 ± 0.706</td><td>—</td></tr><tr><td>Crop coverage<br>(n = 863)</td><td>0.07 ± 0.424</td><td>—</td><td><b>2.35</b> ± 0.424 *</td><td>0.86 ± 0.600</td><td>—</td></tr><tr><td>Weed-crop ratio<br>(n = 863)</td><td>-0.31 ± 0.521</td><td>—</td><td><b>-2.84</b> ± 0.524 ***</td><td>0.64 ± 0.740</td><td>—</td></tr><tr><td rowspan="4"><i>Diversity of agrotolerant communities</i></td><td>Total coverage<br>(n = 720)</td><td>-0.00 ± 0.542</td><td><b>1.23</b> ± 0.550 *</td><td><b>-1.52</b> ± 0.544 **</td><td>0.27 ± 0.769</td><td>-0.18 ± 0.788</td></tr><tr><td>Species richness<br>(n = 720)</td><td>0.01 ± 0.618</td><td>0.15 ± 0.620</td><td><b>-2.31</b> ± 0.658 ***</td><td>0.50 ± 0.907</td><td>1.52 ± 0.909</td></tr><tr><td>Evenness<br>(n = 312)</td><td>-0.12 ± 0.390</td><td><b>-1.03</b> ± 0.422 *</td><td>0.32 ± 0.66</td><td>-1.47 ± 0.836</td><td>0.14 ± 0.829</td></tr><tr><td>Total coverage<br>(n = 720)</td><td><b>-0.62</b> ± 0.275 *</td><td><b>1.13</b> ± 0.294 ***</td><td><b>-2.14</b> ± 0.279 ***</td><td><b>0.96</b> ± 0.393 **</td><td><b>2.53</b> ± 0.419 ***</td></tr><tr><td rowspan="3"><i>Diversity of hemerophobic communities</i></td><td>Species richness<br>(n = 720)</td><td>-0.16 ± 0.432</td><td>0.27 ± 0.436</td><td><b>-4.17</b> ± 0.603 ***</td><td><b>2.64</b> ± 0.746 ***</td><td><b>3.40</b> ± 0.750 ***</td></tr><tr><td>Evenness<br>(n = 282)</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>
434

**Figure 5.** Comparison of weed-crop ratio (i.e. weed coverage / weed and crop coverage), used as a proxy for the potential harmfulness of weed communities, between pure crop controls and crop alleys, under conventional vs organic farming. See Table S5 in Supplementary material for detailed outputs of GLMMs.

435

436

437

438

439

**Figure 6.** Comparison of the variables considered for the assessment of plant diversity between habitats (pure crop controls, crop alleys, understory vegetation strips). Stars indicate significant difference at 0.05 threshold based on p-values of GLMMs comparing these variables between pure crop controls (taken as reference) and agroforestry systems (crop alleys and understory vegetation strips), under conventional and organic farming. See Table S5 in Supplementary material for detailed outputs of GLMMs.

440

441

442

443

444

445

446

**Figure 7.** Plant spillover from UVS to crop alleys in alley cropping agroforestry systems. **a)**

447

Species A is too sensitive to agricultural disturbances, thus hardly able to grow in crop alleys,

448

**b)** Species B has low tolerance to disturbances and low dispersal abilities, it relies on regular recolonization of crop alleys' edges from UVS to persist in such disturbed habitat, **c)**

449

Species C is both tolerant to agricultural disturbances and competitive in undisturbed habitats, therefore able to thrive anywhere. Species C also has high dispersal abilities (vegetative reproduction through runners), making spillover between habitats easier especially when soil tillage is performed in crop alleys. Regarding typical arable weed species persisting mostly in the soil seedbank of crop alleys, the spillover between habitats is less likely given that such species are mostly barochorous (limited dispersal ability) and are hardly able to handle the competitiveness of the already well established plant community in UVS.

450

451

452

453

454

455

456

457
458 **4. Discussion**

459

460 **4.1. How are plants able to colonize crop alleys from understory vegetation strips?**

461

462 The functional approach supported the hypothesis that the ability of a species to colonize crop alleys from UVS depends both on its tolerance to tillage and herbicide and its dispersal strategy. Very few species were able to colonize crop alleys from UVS, even under organic farming. The only species both dominant in UVS and also found ingressing into crop alleys were *Convolvulus arvensis* and *Potentilla reptans*. These are perennial species that produce runners, have relatively late emergence and larger flowering ranges. Tillage in crop alleys probably favored their spread over long distances, as cutting their roots or stems can promote new shoots. A later emergence and larger flowering range can enable them to grow in summer crops as well, making it easier to colonize fields year after year. Besides, only *Poa annua* was successful in crop alleys of conventional fields after herbicide treatment. This is a ruderal species flowering all year round, therefore able to escape herbicide pressure (Storkey et al., 2010). This result is concurring with the results of Metcalfe et al. (2019) who showed that the effects of immediate adjacent habitats on species richness were reduced after herbicide treatment in fields under conventional farming.

476

477 Regarding wind-dispersed species, such as *Picris echoides* and *Sonchus asper*, we expected them to be important contributors to spillover from UVS but they were not dispersing far into crop alleys. Although there was no significant effect of UVS management on wind-dispersed species, they tended to be found in mown UVS where they could have been prevented from producing seeds (see Figure S3 in Supplementary Material). It is likely that we have underestimated the dispersion of wind-dispersed species, that were uncommon in
483 UVS and probably well controlled by farmers in our experiment, which might be higher in
484 another context (no mowing and windier climate). Further, although the functional approach
485 was mostly based on categorical traits for which there is no concern of intra-specific variation,
486 the use of mean trait values collected from databases can be misleading for plastic traits such
487 as plant height and SLA, which are highly dependent on vegetation management,
488 environmental conditions and biological interactions. Interpretations regarding such traits
489 should be treated with caution. Finally, these results were restricted to no-plough tillage
490 systems and winter cereal crops – the most abundant crops in France – but problematic weeds
491 might be different in other crops and under different crop management, especially in the
492 absence of tillage. For example, Trichard et al. (2013) showed that direct drilling favored
493 perennial grass species such as *Poa trivialis*, which was found in UVS and could become
494 problematic under such no-tillage systems.

495

496 4.2. *Understory vegetation strips do not increase weed-crop ratio in crop alleys*

497

498 The vast majority of species dominant in UVS, such as *Galium aparine*, *Avena* sp. and
499 *Anisantha* sp., were abundant only in crop alleys' edges (i.e. less than 2 m from UVS), so we
500 rejected the “weed reservoirs” hypothesis. Consequently, weed-crop ratio was similar
501 between alley cropping agroforestry fields and pure crop controls, which shows the very weak
502 impact of UVS on the potential harmfulness of weed communities in crop alleys. This concurs
503 with the results of other studies assessing plant spillover from semi-natural habitats, such as
504 field margins (Smith et al., 1999), sown grass strips (Cordeau et al., 2012), forest edges
505 (Devlaeminck et al., 2005), road verges (Chaudron et al., 2016) or grasslands (Hume and
506 Archibold, 1986) towards cropland. These empirical studies showed that plant populations in
507 semi-natural habitats disperse only up to a few meters within the crops, generally less than 4
m. This is not surprising as most weeds have poor dispersal abilities (Benvenuti, 2007) and are more likely to be distributed by farm equipment parallel to the adjacent semi-natural habitat (Bischoff, 2005). Moreover, agricultural disturbances reduce the ecological niches available in arable fields for plants coming from semi-natural habitats (Poggio et al., 2013 and references therein), whose population retention depends on regular recolonization of the field (Metcalfe et al., 2019). In conclusion, plant spillover from semi-natural habitats towards cropland appears to be restricted to short distances, even in very fragmented systems such as alley cropping agroforestry.

Interestingly, although the weed-crop ratio was similar between alley cropping agroforestry fields and pure crop controls under conventional farming, the weed-crop ratio decreased when farther from UVS in agroforestry. This could be explained by the fact that UVS – often forming dense covers – would constitute a barrier to weed dispersal within fields, especially for species that are poorly competitive in a more stable and shadier habitat. This potential function of UVS could have stronger impacts on weed communities than the spillover itself. Indeed, some authors showed that grass margin strips reduced the dispersal of arable weed species from semi-natural habitats to cropped fields or the other way around (Cordeau et al., 2012; Marshall, 2009). This could also explain that under organic farming, weed coverage was lower in crop alleys than in pure crop controls (–12% per quadrat on average), whereas we expected a very high spillover given the lack of herbicide treatments and mineral fertilizers. Under organic farming, the fact that weed-crop ratio was constant whatever the distance from UVS can be explained by the presence of an already-established and abundant flora in crop alleys, in comparison to the plants dispersing from UVS. Further studies are needed to assess this role of barrier to weed dispersal.
533 4.3. *Understory vegetation strips: an opportunity for plant diversity conservation in agroecosystems*

534
535
536 The group of hemerophobic species constitutes a more adequate indicator of environmental
537 quality in agricultural landscapes than species richness *per se*. It includes rare weeds and
538 habitat specialists, whose abundances have decreased with intensive agriculture (Aavik et al.,
539 2008).

540
541 We confirmed the “plant diversity refugia” hypothesis. In conventional fields, the
542 weed flora was very poor. By contrast, UVS were home to a rich and abundant flora
543 containing both agrotolerant and hemerophobic species, the latter in higher proportion. In
544 organic fields, both UVS and arable habitats (i.e. pure crops and crop alleys) supported rich
545 and abundant flora containing agrotolerant and hemerophobic species in similar proportions.
546 The weed flora was more even, but less abundant, than the UVS flora. The intermediate
547 values of communities’ evenness in UVS indicate that the vegetation is generally composed
548 of a few dominant species along with a set of less abundant species.

549
550 Hemerophobic species can grow in arable fields under organic farming, independently
551 of the presence of UVS. Conversely, in conventional fields hemerophobic species were
552 concentrated in UVS, their richness and abundance quickly decreasing in crop alleys. These
553 results highlight the importance of UVS in conserving hemerophobic species associated with
554 semi-natural habitats, which are threatened in intensive agricultural landscapes. However, no
555 rare arable weeds were found during the survey, their conservation depending on targeted
556 management of arable habitats, with reduced inputs of fertilizers and herbicides and moderate
557 disturbances, rather than semi-natural habitats (Storkey and Westbury, 2007; Albrecht et al.,
2016). Further studies are needed to assess the benefits – apart from conservation purposes – of promoting botanically diverse communities within arable fields, which are likely to offer different ecosystem services than those provided by arable weed communities. Interestingly, unmanaged and older UVS were dominated by animal-dispersed species, suggesting that these habitats act as ecological corridors. This result is concurrent with the study from Brudvig et al. (2009) who showed that animal-dispersed species are favored by the connectivity between habitats. Tewksbury et al. (2002) showed that corridors in fragmented landscapes are very important to facilitate plant–animal interactions such as pollination and that the beneficial effects of corridors extend beyond their area. Acting as refugia for plant diversity and ecological corridors, UVS are thus likely to benefit higher trophic taxa.

## 4.4. Guidelines for alley cropping agroforestry farmers

This study revealed a very weak impact of plant spillover from UVS on the potential harmfulness of weed communities, even under organic farming, which is good news for alley cropping agroforestry farmers. We argue that the best way to avoid spillover from UVS towards crop alleys is to use contrasting management practices between these two habitats, in order to favor plant communities with different ecological preferences. Indeed, in this study, all farmers used contrasting management between UVS (no-tillage) and crop alleys (tillage). However, in no-tillage systems such as direct drilling, plant spillover could be enhanced, especially because of the presence of perennial grasses. In this case, mowing the vegetation of UVS could help reducing the spread of perennial grasses and favoring annual species. Regarding wind-dispersed species, which could be important contributors to plant spillover in windier climates, one solution to prevent them from dispersing towards crop alleys would be to plant the tree rows parallel to dominant winds whenever possible. Sowing competitive
583

grass species is also a very effective way to avoid the development of problematic weed

584

species in UVS, but it is clearly reducing the overall diversity and probably depriving alley

585

cropping agroforestry systems of one of their greatest assets.

586

587

Indeed, this study revealed that UVS can be home to a rich and abundant flora,

588

including hemerophobic species who suffered from agricultural intensification. We believe

589

that plant diversity conservation in UVS can even be optimized by widening UVS, in order to

590

favor perennial species to the detriment of common arable weed species which were also

591

found in UVS (Aavik and Liira, 2010; Fried et al., 2018). This could also promote the role of

592

UVS as a barrier to weed dispersal. Further, despite the resulting loss of cropland, the

593

promotion of wildlife habitats enhances ecosystem services' flows in crops by supporting

594

pollinators and natural enemies of pests, leading to even higher crop yields than in absence of

595

such habitats (Pywell et al., 2015). Mowing the vegetation could help enhancing plant

596

diversity by preventing the spread of competitive species often dominating unmanaged UVS

597

over time, such as *G. aparine*, *Avena* sp. and *Anisantha* sp., although it might also favor

598

potentially troublesome weeds. Indeed, the only species that were dominant in UVS and also

599

found far into crop alleys (*Convolvulus arvensis* and *Potentilla reptans*) tended to be found in

600

mown UVS (see Figure S3 in Supplementary Material), where their prostrate forms,

601

underground organs and resprouting capacities would have given them advantages over the

602

other species. Probably the mowing of UVS also created better light conditions by reducing

603

the canopy of herbaceous strata. It was shown that the abundance of *Convolvulus arvensis* can

604

be reduced by shading (using shade cloth) whereas mowing has no effect or can even lead to

605

positive response (see Orloff et al., 2018 and references therein). However, it seems that UVS

606

are unsuitable for the conservation of rare weeds for which alternative habitats (such as

607

conservation headlands) would need to be established in the landscape.
608

609 4.5. What can we expect in older alley cropping agroforestry fields?

610

611 The agroforestry systems studied here were relatively young (between 2 and 11 years). On the
612 one hand, it could be expected that plant spillover from UVS is higher in younger agroforestry
613 fields. Indeed, after tree plantation in a field, the vegetation of UVS is first composed of
614 typical arable weeds coming from the soil seedbank, which are adapted to agricultural
615 disturbances and therefore likely to disperse in crop alleys. Over time, hemerophobic species
616 can colonize UVS and contribute to reduce the spread of weeds. On the other hand, it could be
617 expected that plant spillover from UVS is higher for older agroforestry fields. The
618 heterogeneity of environmental conditions induced by the trees could favor the growth of
619 opportunist weeds with high plasticity to the detriment of crop varieties which remain selected
620 only in full sun conditions (Desclaux et al., 2016). For example, Boinot (2015) showed that
621 Avena sterilis and Fallopia convolvulus exhibited higher specific leaf area and lower canopy
622 height in an old agroforestry field with high shading, compared to an agroforestry field with
623 poorly developed trees. This shade-tolerance syndrome (Perronne et al., 2014) might
624 constitute a competitive advantage for weeds in agroforestry fields.

625

626 4.6. Taking advantage of understory vegetation strips to optimize the delivery of multiple
627 ecosystem services

628

629 Our study revealed that UVS promote plant diversity conservation within cropped fields.
630 Therefore, we expect that UVS can supply many additional ecosystem services like other
631 farmland vegetative strips (Cresswell et al., 2019). For example, UVS could be used to
632 provide alternative resources and overwintering habitats for pollinators, detritivores and
natural enemies of crop pests and so enhance pollination, nutrient cycling and biological control. UVS could also improve soil structure and porosity, thus reducing soil erosion. To promote the delivery of multiple ecosystem services, future research should assess not only the nature of ecosystem services provided by plant communities of UVS but also the relationships between these services (i.e. trade-off, complementarity, synergy). Indeed, if management interventions are devoted to the promotion of a single or restricted number of services, it can have unintended negative consequences on other services (Bennett et al., 2009). However, an encouraging review on interactions between biological control, pollination and nutrient cycling revealed that complementary effects between these ecosystem services were the most common, followed by synergistic effects, whereas trade-offs were rarer (Garibaldi et al., 2018). These results demonstrate that promoting multiple ecosystem services with biodiversity-friendly practices is a possibility.

The ecological engineering of UVS should focus on both the functional structure and area covered by plant communities in UVS, which are expected to be the major drivers of ecosystem services supported by plant communities. There is currently a wide range of UVS management strategies among alley cropping agroforestry farmers, resulting in different spatial configuration (i.e. UVS width, spacing between UVS) and disturbance regimes (i.e. no management, mowing, crushing, mulching, plant mixtures sowing). Further experiments are needed to determine what are the best UVS management strategies to promote multiple ecosystem services, while reducing the risk of crop pest and weed spillover within crop alleys. Taking full advantage of the presence of UVS should greatly improve the agricultural and environmental performance of alley cropping agroforestry systems in temperate regions.
656

657 **5. Conclusions**

658

659 The non-crop herbaceous strip under the tree rows is a compartment often forgotten but
660 nevertheless essential to understand the provision of ecosystem services that we can expect
661 from alley cropping agroforestry. To our knowledge, our study is the first to describe plant
662 communities associated to tree rows in temperate alley cropping agroforestry systems. We
663 demonstrated that plant spillover from understory vegetation strips towards crop alleys had a
664 very weak impact on the potential harmfulness of weed communities. We also revealed a high
665 potential of understory vegetation strips, home to a rich and abundant hemerophobic flora, for
666 preserving plant diversity in agroecosystems. The originality of alley cropping agroforestry
667 systems lies in the presence of trees and non-crop herbaceous vegetation within fields
668 themselves, which should definitely be used for biodiversity conservation purposes and for
669 the enhancement of ecosystem services flows in the crops, in the perspective of reducing our
670 dependence to agrochemicals. However, even within pure crops, farmers could establish non-
671 crop habitats to take advantage from their functions, as it has been done with beetle banks and
672 wildflowers strips. We suggest that reconnecting with non-crop vegetation is a crucial step for
673 the transition towards agroecological systems, urgently needed given the context of climate
674 change and biodiversity extinction crisis we are facing.

675

676 Acknowledgements

677

678 The doctoral research of SB is financially supported by La Fondation de France. This research
679 was also part of the project BAG'AGES (Bassin Adour-Garonne : quelles performances des
680 pratiques AGroécologiques ?) supported by Agence de l'Eau Adour-Garonne. SB would like
681 to thank the French National Institute for Agricultural Research (INRA) for funding his three-months stay at Rothamsted Research, UK. JS and HM are funded by the research programme
682 NE/N018125/1 LTS-M ASSIST. The authors are grateful for the assistance from Emilie
683 Bourgade (Arbre & Paysage 32), David Condotta (Syndicat Mixte de Gestion Adour et
684 Affluents) and agroforestry farmers and land owners. In particular, we very much appreciated
685 the technical assistance provided by Colin Bonnot and Emilie Cadet for the vegetation survey.
686

687
688 Declaration of interest: none

689
690 Appendix A.
691

692 Table A1. Species classification, conservation value, and occurrence within the three
693 surveyed habitats.

<table><thead><tr><th rowspan="2">EPPO code</th><th rowspan="2">Latin name</th><th rowspan="2">Classification¹</th><th rowspan="2">Conservation value of arable weeds²</th><th colspan="2">Alley cropping agroforestry</th><th rowspan="2">Pure crop controls (n = 432)</th></tr><tr><th>Understory vegetation strips (n = 96)</th><th>Crop alleys (n = 432)</th></tr></thead><tbody><tr><td>ALOMY</td><td>Alopecurus myosuroides</td><td>agrotolerant</td><td>3</td><td>X</td><td>X</td><td>X</td></tr><tr><td>APHAR</td><td>Aphanes arvensis</td><td>agrotolerant</td><td>3</td><td>X</td><td>X</td><td>X</td></tr><tr><td>ARBTH</td><td>Arabidopsis thaliana</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>ARREL</td><td>Arrhenatherum elatius</td><td>hemerophobic</td><td>3</td><td>X</td><td>X</td><td>X</td></tr><tr><td>ATXPA</td><td>Atriplex patula</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>AVESS</td><td>Avena sp</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>BROSS</td><td>Bromus sp</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>LITAR</td><td>Buglossoides arvensis</td><td>hemerophobic</td><td>3</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CAPBP</td><td>Capsella bursa-pastoris</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CERGL</td><td>Cerastium glomeratum</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CHEAL</td><td>Chenopodium album</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CIRAR</td><td>Cirsium arvense</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CIRVU</td><td>Cirsium vulgare</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CLVVT</td><td>Clematis vitalba</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CONAR</td><td>Convolvulus arvensis</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr><tr><td>CAGSE</td><td>Convolvulus sepium</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>
<table>
  <tr>
    <td>DACGL</td>
    <td>Dactylis glomerata</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>DAUCA</td>
    <td>Daucus carota</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>DIWSI</td>
    <td>Dipsacus fullonum</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>AGRRE</td>
    <td>Elytrigia repens</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>EPIAD</td>
    <td>Epilobium tetragonum</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>EQUAR</td>
    <td>Equisetum arvense</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>ERICA</td>
    <td>Erigeron canadensis</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>EPHEX</td>
    <td>Euphorbia exigua</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>POLCO</td>
    <td>Fallopia convolvulus</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>FESRU</td>
    <td>Festuca rubra</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>FUMOF</td>
    <td>Fumaria officinalis</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>GALAP</td>
    <td>Galium aparine</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>GERCO</td>
    <td>Geranium columbinum</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GERDI</td>
    <td>Geranium dissectum</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PICEC</td>
    <td>Helminthotheca echioides</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>HOLLA</td>
    <td>Holcus lanatus</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>HOLMO</td>
    <td>Holcus mollis</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>HYPPE</td>
    <td>Hypericum perforatum</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>IUNBU</td>
    <td>Juncus bufonius</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>KICEL</td>
    <td>Kickxia elatine</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>LACSE</td>
    <td>Lactuca serriola</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>LAMPU</td>
    <td>Lamium purpureum</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>LAPCO</td>
    <td>Lapsana communis</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>LOLSS</td>
    <td>Lolium sp</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>ANGAR</td>
    <td>Lysimachia arvensis</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>MATMT</td>
    <td>Matricaria discoidea</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>MEDPO</td>
    <td>Medicago polymorpha</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>MYOAR</td>
    <td>Myosotis arvensis</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PAPRH</td>
    <td>Papaver rhoeas</td>
    <td>agrotolerant</td>
    <td>3</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>POLLA</td>
    <td>Persicaria lapathifolia</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td></td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>PHAPA</td>
    <td>Phalaris paradoxa</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PICHI</td>
    <td>Picris hieracioides</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PLALA</td>
    <td>Plantago lanceolata</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PLAMA</td>
    <td>Plantago major</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>POAAN</td>
    <td>Poa annua</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>POATR</td>
    <td>Poa trivialis</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>POLAV</td>
    <td>Polygonum aviculare</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>PTLRE</td>
    <td>Potentilla reptans</td>
    <td>agrotolerant</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>RANAR</td>
    <td>Ranunculus arvensis</td>
    <td>hemerophobic</td>
    <td>2</td>
    <td></td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>RANBU</td>
    <td>Ranunculus bulbosus</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>RANRE</td>
    <td>Ranunculus repens</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>RUBSS</td>
    <td>Rubus sp</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>RUMCR</td>
    <td>Rumex crispus</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
  <tr>
    <td>RUMOB</td>
    <td>Rumex obtusifolius</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td>X</td>
    <td></td>
  </tr>
  <tr>
    <td>SAIPR</td>
    <td>Sagina procumbens</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td></td>
    <td></td>
    <td>X</td>
  </tr>
  <tr>
    <td>FESAR</td>
    <td>Schedonorus arundinaceus</td>
    <td>hemerophobic</td>
    <td>0</td>
    <td>X</td>
    <td>X</td>
    <td>X</td>
  </tr>
</table>
<table><tr><td>FESPR</td><td>Schedonorus pratensis</td><td>hemerophobic</td><td>0</td><td>X</td><td></td></tr><tr><td>SENVU</td><td>Senecio vulgaris</td><td>agrotolerant</td><td>0</td><td></td><td>X</td></tr><tr><td>SETVI</td><td>Setaria italica</td><td>hemerophobic</td><td>0</td><td></td><td>X</td></tr><tr><td>SHRAR</td><td>Sherardia arvensis</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>SLYMA</td><td>Silybum marianum</td><td>hemerophobic</td><td>0</td><td></td><td>X</td></tr><tr><td>SINAR</td><td>Sinapis arvensis</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td></tr><tr><td>SONAS</td><td>Sonchus asper</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td></tr><tr><td>SONOL</td><td>Sonchus oleraceus</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td></tr><tr><td>TAROF</td><td>Taraxacum officinale</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td></tr><tr><td>TOIAR</td><td>Torilis arvensis</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>TROPS</td><td>Tragopogon porrifolius</td><td>hemerophobic</td><td>0</td><td>X</td><td></td></tr><tr><td>TROPR</td><td>Tragopogon pratensis</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>TRFAR</td><td>Trifolium arvense</td><td>hemerophobic</td><td>0</td><td></td><td>X</td></tr><tr><td>TRFPR</td><td>Trifolium pratense</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VLLLO</td><td>Valerianella locusta</td><td>hemerophobic</td><td>0</td><td></td><td>X</td></tr><tr><td>VEBOF</td><td>Verbena officinalis</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VERAR</td><td>Veronica arvensis</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VERPE</td><td>Veronica persica</td><td>agrotolerant</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VERPO</td><td>Veronica polita</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VICBI</td><td>Vicia bithynica</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VICHY</td><td>Vicia hybrida</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr><tr><td>VLPMY</td><td>Vulpia myuros</td><td>hemerophobic</td><td>0</td><td>X</td><td>X</td></tr></table>

694 ¹ Following Aavik et al. (2008), each species was classified as agrotolerant or hemerophobic

695 based on its frequency of occurrence in arable fields at national scale, using data of the

696 Biovigilance Flore network 2002–2012 (Fried et al., 2008). A species was considered as

697 hemerophobic if its frequency of occurrence in the sample plots of arable fields was lower

698 than 10%.

699 ² Conservation value of arable weeds according to the Archeophyt Weed National Red Lists

700 (Aboucaya et al., 2000); 1: species in real danger of extinction, 2: species that are thought to

701 have experienced significant regression but are nevertheless still common in some regions, 3:

702 species that are at best stable in at least some regions.

703

704 **Supplementary material**

705

706 Supplementary material may be found in the online version of this article:
707 **Figure S1.** Map of the agroforestry fields and their pure crop controls.

708 **Table S1.** Crop management for each pair of agroforestry fields and pure crop controls.

709 **Table S2.** Description of agroforestry fields.

710 **Figure S2.** Mean and standard deviation of the coverage of species dominant in UVS (kept in the spillover analysis) vs arable weed species persisting mostly in the seedbank (excluded from the analysis).

713 **Figure S3.** RLQ analysis performed on plant communities located in the understory vegetation strips.

715 **Table S3.** Abbreviations, units, basic statistics and RLQ axis loadings of environmental variables and traits considered in the RLQ analysis of plant communities *located in the understory vegetation strips (UVS)*.

718 **Table S4.** Abbreviations, units, basic statistics and RLQ axis loadings of environmental variables and traits considered in the RLQ analysis of plant communities *located in the crop alleys* and restricted to species that were also dominant in the understory vegetation strips (UVS).

722 **Table S5.** Regression parameters, standard errors and p-values of generalized mixed-effects models (GLMMs) performed on organic and conventional fields separately.

724 **Table S6.** Regression parameters, standard errors and p-values of generalized mixed-effects models (GLMMs) assessing the effect of the distance from understory vegetation strips (UVS) on potential harmfulness and diversity of communities *in the crop alleys*.

727 **Figure S4.** Effect of distance from understory vegetation strips (UVS) on the weed-crop ratio (weed coverage / weed and crop coverage).

729 **Figure S5.** Effect of distance from understory vegetation strips (UVS) on the variables considered for the assessment of communities' diversity.
731 **Table S7.** Total species richness observed across all fields, per habitat and under conventional vs organic farming.

733

734 **References**

735

736 Aavik, T., Augenstein, I., Bailey, D., Herzog, F., Zobel, M., Liira, J., 2008. What is the role of local landscape structure in the vegetation composition of field boundaries? Applied Vegetation Science 11, 375–386. 10.3170/2008-7-18486.

739 Aavik, T., Liira, J., 2010. Quantifying the effect of organic farming, field boundary type and landscape structure on the vegetation of field boundaries. Agriculture, Ecosystems & Environment 135, 178–186. 10.1016/j.agee.2009.09.005.

742 Albrecht, H., Cambecèdes, J., Lang, M., Wagner, M., 2016. Management options for the conservation of rare arable plants in Europe. Botany Letters 163, 389–415. 10.1080/23818107.2016.1237886.

745 Andreasen, C., Streibig, J.C., 2011. Evaluation of changes in weed flora in arable fields of Nordic countries - based on Danish long-term surveys. Weed Research 51, 214–226. 10.1111/j.1365-3180.2010.00836.x.

748 Association Botanique Gersoise, 2003. Eflore32: list of observed taxa in Gers. http://www.eflore32 botagora.fr/fr/eflore32.aspx/ (accessed October 2017)

750 Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 57, 289–300.

753 Bennett, E.M., Peterson, G.D., Gordon, L.J., 2009. Understanding relationships among multiple ecosystem services. Ecology Letters 12, 1394–1404. 10.1111/j.1461-0248.2009.01387.x.
756 Benvenuti, S., 2007. Weed seed movement and dispersal strategies in the agricultural environment. Weed Biology and Management 7, 141–157. 10.1111/j.1445-6664.2007.00249.x.

759 Bischoff, A., 2005. Analysis of weed dispersal to predict chances of re-colonisation. Agriculture, Ecosystems & Environment 106, 377–387. 10.1016/j.agee.2004.09.006.

761 Blitzer, E.J., Dormann, C.F., Holzschuh, A., Klein, A.M., Rand, T.A., Tscharntke, T., 2012. Spillover of functionally important organisms between managed and natural habitats. Agriculture, Ecosystems & Environment 146, 34–43. 10.1016/j.agee.2011.09.005.

764 Boinot, S, 2015. Caractérisation des communautés adventices en agroforesterie intra-parcellaire. MSc thesis. Master Biodiversité, Ecologie et Evolution. Université de Montpellier, France.

767 Brooks, M.E., Kristensen, K., van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Mächler, M., Bolker, B.M., 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9, 378–400.

771 Brudvig, L.A., Damschen, E.I., Tewksbury, J.J., Haddad, N.M., Levey, D.J., 2009. Landscape connectivity promotes plant biodiversity spillover into non-target habitats. Proceedings of the National Academy of Sciences 106, 9328–9332. 10.1073/pnas.0809658106.

775 Burgess, P.J., 1999. Effects of agroforestry on farm biodiversity in the UK. Scottish Forestry 53, 24–27.

777 Cahenzli, F., Sigsgaard, L., Daniel, C., Herz, A., Jamar, L., Kelderer, M., Jacobsen, S.K., Kruczyńska, D., Matray, S., Porcel, M., Sekrecka, M., Świergiel, W., Tasin, M., Telfser, J., Pfiffner, L., 2019. Perennial flower strips for pest control in organic apple
780 orchards - A pan-European study. Agriculture, Ecosystems & Environment 278, 43-
781 53. 10.1016/j.agee.2019.03.011.

782 Cardinael, R., Chevallier, T., Barthès, B.G., Saby, N.P.A., Parent, T., Dupraz, C., Bernoux,
783 M., Chenu, C., 2015. Impact of alley cropping agroforestry on stocks, forms and
784 spatial distribution of soil organic carbon — A case study in a Mediterranean context.
785 Geoderma 259-260, 288–299. 10.1016/j.geoderma.2015.06.015.

786 Chaudron, C., Chauvel, B., Isselin-Nondedeu, F., 2016. Effects of late mowing on plant
787 species richness and seed rain in road verges and adjacent arable fields. Agriculture,
788 Ecosystems & Environment 232, 218–226. 10.1016/j.agee.2016.03.047.

789 Cordeau, S., Petit, S., Reboud, X., Chauvel, B., 2012. The impact of sown grass strips on the
790 spatial distribution of weed species in adjacent boundaries and arable fields.
791 Agriculture, Ecosystems & Environment 155, 35–40. 10.1016/j.agee.2012.03.022.

792 Coste, H., 1937. Flore descriptive et illustrée de la France, de la Corse et des contrées
793 limitrophes, P. Klincksieck ed., Librairie des Sciences Naturelles, Paris, France.

794 Cresswell, C.J., Cunningham, H.M., Wilcox, A., Randall, N.P., 2019. A trait-based approach
795 to plant species selection to increase functionality of farmland vegetative strips.
796 Ecology and Evolution 103, 1579. 10.1002/ece3.5047.

797 Desclaux, D., Huang, H.-Y., Bernazeau, B., Lavene, P., 2016. Agroforestry: new challenge
798 for field crop breeding? European Agroforestry Conference: Celebrating 20 years of
799 Agroforestry research in Europe, pp. 102–105.

800 Devlaeminck, R., Bossuyt, B., Hermy, M., 2005. Seed dispersal from a forest into adjacent
801 cropland. Agriculture, Ecosystems & Environment 107, 57–64.
802 10.1016/j.agee.2004.10.003.
803 Dray, S., Choler, P., Dolédec, S., Peres-Neto, P.R., Thuiller, W., Pavoine, S., ter Braak, C.J.F., 2014. Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. Ecology 95, 14–21. 10.1890/13-0196.1.

804
805
806 Dray, S., Dufour, A.-B., 2007. The ade4 Package: Implementing the Duality Diagram for Ecologists. Journal of Statistical Software 22. 10.18637/jss.v022.i04.

808 Forey, O., Metay, A., Wery, J., 2016. Differential effect of regulated deficit irrigation on growth and photosynthesis in young peach trees intercropped with grass. European Journal of Agronomy 81, 106–116. 10.1016/j.eja.2016.09.006.

811 Fried, G., Norton, L.R., Reboud, X., 2008. Environmental and management factors determining weed species composition and diversity in France. Agriculture, Ecosystems & Environment 128, 68–76. 10.1016/j.agee.2008.05.003.

814 Fried, G., Villers, A., Porcher, E., 2018. Assessing non-intended effects of farming practices on field margin vegetation with a functional approach. Agriculture, Ecosystems & Environment 261, 33–44. 10.1016/j.agee.2018.03.021.

817 Gaba, S., Chauvel, B., Dessaint, F., Bretagnolle, V., Petit, S., 2010. Weed species richness in winter wheat increases with landscape heterogeneity. Agriculture, Ecosystems & Environment 138, 318–323. 10.1016/j.agee.2010.06.005.

820 Gaba, S., Perronne, R., Fried, G., Gardarin, A., Bretagnolle, F., Biju-Duval, L., Colbach, N., Cordeau, S., Fernández-Aparicio, M., Gauvrit, C., Gibot-Leclerc, S., Guillemin, J.-P., Moreau, D., Munier-Jolain, N., Strbik, F., Reboud, X., 2017. Response and effect traits of arable weeds in agro-ecosystems: a review of current knowledge. Weed Research 57, 123–147. 10.1111/wre.12245.

825 Gabriel, D., Roschewitz, I., Tscharntke, T., Thies, C., 2006. Beta diversity at different spatial scales: plant communities in organic and conventional agriculture. Ecological Applications 16, 2011–2021.
828 Garcia, L., Damour, G., Gary, C., Follain, S., Le Bissonnais, Y., Metay, A., 2019. Trait-based approach for agroecology: contribution of service crop root traits to explain soil aggregate stability in vineyards. Plant Soil 435, 1–14. 10.1007/s11104-018-3874-4.

829
830
831 Garibaldi, L.A., Andersson, G.K.S., Requier, F., Fijen, T.P.M., Hipólito, J., Kleijn, D., Pérez-Méndez, N., Rollin, O., 2018. Complementarity and synergisms among ecosystem services supporting crop yield. Global Food Security 17, 38–47.
832
833
834 10.1016/j.gfs.2018.03.006.

835 Graves, A.R., Burgess, P.J., Liagre, F., Dupraz, C., 2017. Farmer perception of benefits, constraints and opportunities for silvoarable systems: Preliminary insights from Bedfordshire, England. Outlook on Agriculture 46, 74–83.
836
837
838 10.1177/0030727017691173.

839 Hass, A.L., Kormann, U.G., Tscharntke, T., Clough, Y., Baillod, A.B., Sirami, C., Fahrig, L., Martin, J.-L., Baudry, J., Bertrand, C., Bosch, J., Brotons, L., Burel, F.G., Georges, R., Giralt, D., Marcos-García, M.Á., Ricarte, A., Siriwardena, G., Batáry, P., 2018. Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in western Europe. Proceedings. Biological sciences 285. 10.1098/rspb.2017.2242.

840
841
842
843
844
845 Hatt, S., Lopes, T., Boeraeve, F., Chen, J., Francis, F., 2017. Pest regulation and support of natural enemies in agriculture: Experimental evidence of within field wildflower strips. Ecological Engineering 98, 240–245. 10.1016/j.ecoleng.2016.10.080.

846
847
848 Hume, L., Archibold, O.W., 1986. The influence of a weedy habitat on the seed bank of an adjacent cultivated field. Canadian Journal of Botany 64, 1879–1883.

850 Hyvönen, T., Salonen, J., 2002. Weed species diversity and community composition in cropping practices at two intensity levels – a six-year experiment. Plant Ecology 154, 73–81.
851
852
853 Jauzein, P., 2011. Flore des champs cultivés, Quae ed., Paris, France.

854 Johnson, C.G., 1950. Infestation of a bean field by *Aphis fabae* Scop. in relation to wind direction. Annals of Applied Biology 37, 441–450. 10.1111/j.1744-7348.1950.tb00967.x.

857 Jose, S., 2009. Agroforestry for ecosystem services and environmental benefits: an overview. Agroforestry Systems 76, 1–10. 10.1007/s10457-009-9229-7.

859 Julve, P., 1998. Baseflor: Index botanique, écologique et chronologique de la flore de France, http://philippe.julve.pagesperso-orange.fr/catminat.htm. Accessed October 2017.

861 Kay, S., Rega, C., Moreno, G., den Herder, M., Palma, J.H.N., Borek, R., Crous-Duran, J., Freese, D., Giannitsopoulos, M., Graves, A., Jäger, M., Lamersdorf, N., Memedemin, D., Mosquera-Losada, R., Pantera, A., Paracchini, M.L., Paris, P., Roces-Díaz, J.V., Rolo, V., Rosati, A., Sandor, M., Smith, J., Szerencsits, E., Varga, A., Viaud, V., Wawer, R., Burgess, P.J., Herzog, F., 2019. Agroforestry creates carbon sinks whilst enhancing the environment in agricultural landscapes in Europe. Land Use Policy 83, 581–593. 10.1016/j.landusepol.2019.02.025.

868 Kenkel, N.C., Derksen, D.A., Thomas, A.G., Watson, P.R., 2002. Review: Multivariate analysis in weed science research. Weed Science 50, 281–292.

870 Kleyer, M., Bekker, R.M., Knevel, I.C., Bakker, J.P., Thompson, K., Sonnenschein, M., Poschlod, P., van Groenendael, J.M., Klimeš, L., Klimešová, J., Klotz, S., Rusch, G.M., Hermy, M., Adriaens, D., Boedeltje, G., Bossuyt, B., Dannemann, A., Endels, P., Götzenberger, L., Hodgson, J.G., Jackel, A.-K., Kühn, I., Kunzmann, D., Ozinga, W.A., Römermann, C., Stadler, M., Schlegelmilch, J., Steendam, H.J., Tackenberg, O., Wilmann, B., Cornelissen, J.H.C., Eriksson, O., Garnier, E., Peco, B., 2008. The LEBA Traitbase: a database of life-history traits of the Northwest European flora. Journal of Ecology 96, 1266–1274. 10.1111/j.1365-2745.2008.01430.x.
878 Kvålseth, T.O., 2015. Evenness indices once again: critical analysis of properties.

879 SpringerPlus 4, 232–245. 10.1186/s40064-015-0944-4.

880 Lutman, P.J.W., Risiott, R., Ostermann, H.P., 1996. Investigations into alternative methods to predict the competitive effects of weeds on crop yields. Weed Science 44, 290–297.

882 Lynch, H.J., Thorson, J.T., Shelton, A.O., 2014. Dealing with under- and over-dispersed count data in life history, spatial, and community ecology. Ecology 95, 3173–3180.

884 10.1890/13-1912.1.

885 Marshall, E.J.P., 1989. Distribution patterns of plants associated with arable field edges. Journal of Applied Ecology 26, 247–257.

887 Marshall, E.J.P., 2009. The impact of landscape structure and sown grass margin strips on weed assemblages in arable crops and their boundaries. Weed Research 49, 107–115.

889 Mauremooto, J.R., Wratten, S.D., Worner, S.P., Fry, G.L.A., 1995. Permeability of hedgerows to predatory carabid beetles. Agriculture, Ecosystems & Environment 52, 141–148. 10.1016/0167-8809(94)00548-S.

892 Metcalfe, H., Hassall, K., Boinot, S., Storkey, J., 2019. The contribution of spatial mass effects to plant diversity in arable fields. Journal of Applied Ecology 00, 1–15. 10.1111/1365-2664.13414

895 Mézière, D., Boinot, S., de Waal, L., Cadet, E., Fried, G., 2016. Arable weeds in alley cropping agroforestry systems - Results of a first year study, European Agroforestry Conference: Celebrating 20 years of Agroforestry research in Europe, pp. 66–69.

898 Mitchell, M.G.E., Suarez-Castro, A.F., Martinez-Harms, M., Maron, M., McAlpine, C., Gaston, K.J., Johansen, K., Rhodes, J.R., 2015. Reframing landscape fragmentation's effects on ecosystem services. Trends in Ecology & Evolution 30, 190–198. 10.1016/j.tree.2015.01.011.
902 Norris, R.F., Kogan, M., 2000. Interactions between weeds, arthropod pests, and their natural
903 enemies in managed ecosystems. Weed Science 48, 94–158. 10.1614/0043-
904 1745(2000)048[0094:IBWAPA]2.0.CO;2.

905 Orloff, N., Mangold, J., Miller, Z., Menalled, F.D., 2018. A meta-analysis of field bindweed
906 (Convolvulus arvensis L.) and Canada thistle (Cirsium arvense L.) management in
907 organic agricultural systems. Agriculture, Ecosystems & Environment 254, 264–272.
908 10.1016/j.agee.2017.11.024.

909 Pardon, P., Reheul, D., Mertens, J., Reubens, B., De Frenne, P., De Smedt, P., Proesmans, W.,
910 van Vooren, L., Verheyen, K., 2019. Gradients in abundance and diversity of ground
911 dwelling arthropods as a function of distance to tree rows in temperate arable
912 agroforestry systems. Agriculture, Ecosystems & Environment 270-271, 114–128.
913 10.1016/j.agee.2018.10.017.

914 Perronne, R., Gaba, S., Cadet, E., Le Corre, V., 2014. The interspecific and intraspecific
915 variation of functional traits in weeds: diversified ecological strategies within arable
916 fields. Acta Botanica Gallica: Botany Letters 161, 243–252.
917 10.1080/12538078.2013.868320.

918 Petit, S., Boursault, A., Guilloux, M., Munier-Jolain, N., Reboud, X., 2011. Weeds in
919 agricultural landscapes. A review. Agronomy for Sustainable Development 31, 309–
920 317. 10.1051/agro/2010020.

921 Pfiffner, L., Luka, H., 2000. Overwintering of arthropods in soils of arable fields and adjacent
922 semi-natural habitats. Agriculture, Ecosystems & Environment 78, 215–222.
923 10.1016/S0167-8809(99)00130-9.

924 Poggio, S.L., Chaneton, E.J., Ghersa, C.M., 2013. The arable plant diversity of intensively
925 managed farmland: Effects of field position and crop type at local and landscape
926 scales. Agriculture, Ecosystems & Environment 166, 55–64.

927 10.1016/j.agee.2012.01.013.

928 Pywell, R.F., Heard, M.S., Woodcock, B.A., Hinsley, S., Ridding, L., Nowakowski, M.,

929 Bullock, J.M., 2015. Wildlife-friendly farming increases crop yield: evidence for

930 ecological intensification. Proceedings. Biological sciences 282, 20151740.

931 10.1098/rspb.2015.1740.

932 Quinkenstein, A., Wöllecke, J., Böhm, C., Grünewald, H., Freese, D., Schneider, B.U., Hüttl,

933 R.F., 2009. Ecological benefits of the alley cropping agroforestry system in sensitive

934 regions of Europe. Environmental Science & Policy 12, 1112–1121.

935 10.1016/j.envsci.2009.08.008.

936 R Core Team, 2018. R: A language and environment for statistical computing. R Foundation

937 for Statistical Computing, Vienna, Austria.

938 Royal Botanical Gardens Kew, 2017. Seed Information Database (SID),

939 http://data.kew.org/sid. Accessed October 2017.

940 Smith, H., Firbank L. G., Macdonald, D.W., 1999. Uncropped edges of arable fields managed

941 for biodiversity do not increase weed occurrence in adjacent crops. Biological

942 Conservation 89, 107–111.

943 Stoate, C., Boatman, N.D., Borralho, R.J., Rio Carvalho, C., de Snoo, G.R., Eden, P., 2001.

944 Ecological impacts of arable intensification in Europe. Journal of Environmental

945 Management 63, 337–365. 10.1006/jema.2001.0473.

946 Storkey, J., Moss, S.R., Cussans, J.W., 2010. Using Assembly Theory to Explain Changes in

947 a Weed Flora in Response to Agricultural Intensification. Weed Science 58, 39–46.

948 10.1614/WS-09-096.1.

949 Storkey, J., Westbury, D.B., 2007. Managing arable weeds for biodiversity. Pest Management

950 Science 63, 517–523. 10.1002/ps.1375.
951 Tewksbury, J.J., Levey, D.J., Haddad, N.M., Sargent, S., Orrock, J.L., Weldon, A., Danielson, B.J., Brinkerhoff, J., Damschen, E.I., Townsend, P., 2002. Corridors affect plants, animals, and their interactions in fragmented landscapes. Proceedings of the National Academy of Sciences 99, 12923–12926. 10.1073/pnas.202242699.

955 Torralba, M., Fagerholm, N., Burgess, P.J., Moreno, G., Plieninger, T., 2016. Do European agroforestry systems enhance biodiversity and ecosystem services? A meta-analysis. Agriculture, Ecosystems & Environment 230, 150–161. 10.1016/j.agee.2016.06.002.

958 Trichard, A., Alignier, A., Chauvel, B., Petit, S., 2013. Identification of weed community traits response to conservation agriculture. Agriculture, Ecosystems & Environment 179, 179–186. 10.1016/j.agee.2013.08.012.

961 Wilson, P. J., Aebischer, N.J., 1995. The distribution of dicotyledonous arable weeds in relation to distance from the field edge. Journal of Applied Ecology 32, 295–310.

963 Winter, S., Bauer, T., Strauss, P., Kratschmer, S., Paredes, D., Popescu, D., Landa, B., Guzmán, G., Gómez, J.A., Guernion, M., Zaller, J.G., Batáry, P., 2018. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: A meta-analysis. The Journal of Applied Ecology 55, 2484–2495. 10.1111/1365-2664.13124.

968 Wisler, G.C., Norris, R.F., 2005. Interactions between weeds and cultivated plants as related to management of plant pathogens. Weed Science 53, 914–917. 10.1614/WS-04-051R.1.

971 Woodcock, B.A., Bullock, J.M., McCracken, M., Chapman, R.E., Ball, S.L., Edwards, M.E., Nowakowski, M., Pywell, R.F., 2016. Spill-over of pest control and pollination services into arable crops. Agriculture, Ecosystems & Environment 231, 15–23. 10.1016/j.agee.2016.06.023.
975 Zuur, A.F., Hilbe, J.M., Ieno, E.N., 2013. A beginner's guide to GLM and GLMM with R: a

976 frequentist and Bayesian perspective for ecologists. Highland Statistics Ltd,

977 Newburgh, UK.
<figure><img src="image_2.png" /></figure>

transect 1

transect 2

transect 3

Crop alley

Understory vegetation
strip (UVS)

Field boundary
a)

<table><thead><tr><th></th><th>Organic farm.</th><th>Conventional farm.</th><th>Age UVS</th><th>No mowing</th><th>Mowing</th><th>No sowing</th><th>Sowing</th></tr></thead><tbody><tr><th>Seed mass</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Height</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>SLA</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Disp.animals</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Disp gravitation</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Disp.wind</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Geo.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Hemicrypto.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Thero.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>No runners</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Runners</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Flow. on.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Flow. ran.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Emerg. on.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Emerg. ran.</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

b)

<table><thead><tr><th></th><th>Seed mass</th><th>Height</th><th>SLA</th><th>Disp.animals</th><th>Disp gravitation</th><th>Disp.wind</th><th>Geo.</th><th>Hemicrypto.</th><th>Thero.</th><th>No runners</th><th>Runners</th><th>Flow. on.</th><th>Flow. ran.</th><th>Emerg. on.</th><th>Emerg. ran.</th></tr></thead><tbody><tr><th>Axis 1</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Axis 2</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th></th><th>Organic farm.</th><th>Conventional farm.</th><th>Age UVS</th><th>No mowing</th><th>Mowing</th><th>No sowing</th><th>Sowing</th></tr></thead><tbody><tr><th>Axis 1</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Axis 2</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
RLQ axis 2 (14.7%)

<figure><img src="image_3.png" /></figure>

RLQ axis 1 (84.5%)

b)

<figure><img src="image_4.png" /></figure>

c)

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

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

b)

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /></figure>

Pure crop controls

Crop alleys

<figure><img src="image_15.png" /></figure>

Pure crop controls

Crop alleys
a) UNSUCCESSFUL SPILLOVER

<figure><img src="image_16.png" /></figure>

b) FREQUENT SPILLOVER TO SHORT DISTANCES

<figure><img src="image_17.png" /></figure>

c) RARE SPILLOVER TO LONG DISTANCES

<figure><img src="image_18.png" /></figure>