Supplementary Material
Supplementary Data Fig. S1- Tomato fields were located throughout the province of Grosseto
(Tuscany, Italy). The farm identity and location of fields sampled for vegetation diversity, natural
enemies and crop damage in (a) 2010 and (b) 2011 are shown. Sites from the same farms
appear to overlap at this scale but surveys were carried out in different fields each year. (c) Land
use map for a buffer of 1 km surrounding a central field.
1
Supplementary data table S1 - Land use cover classification system used for the landscape analysis.
General Category
Urban and built-up land
Land Use Categories
Individual residential units (normally within an agricultural matrix)
Large-scale residential areas (e.g. city centres)
House and public gardens
Roads and streets
Agricultural Land
Fields cultivated with herbaceous/annual crops (e.g. tomato, grain,
maize, sunflower, potatoes, etc.)
Olive groves or orchards with fruit trees
Orchard which also include the cultivation of herbaceous crops
(intercropping).
Natural and semi-natural
vegetation patches
Herbaceous field margins (with width greater than 1.5m)
Hedges (linear patches of closely spaced shrubs and tree species)
Recently abandoned agricultural fields
Woodland and maquis habitats and long agricultural fields which have
been abandoned for a longer time (characterised by the presence of
dense shrubs)
Riparian vegetation
Surface water bodies
Streams, ponds, rivers and other surface water bodies.
Ecologically Disturbed
Ground
Any other land use which is not included in the above and which is
characterised by human disturbance (e.g quarries).
2
Supplementary Data Fig. S2: The full path analysis model for (a) richness and (b) abundance data.
Percentage cropped land was used as a measure of landscape complexity and plant species richness
in field margins as a measure of local vegetation diversity. Richness of predator and parasitoid
groups was used to indicate natural enemy diversity, given that we have observed a positive effect
of local vegetation diversity on predator and parasitoid abundance and this reduced the number of
paths in the model increasing statistical power. Time of harvest and year, shown in grey boxes,
were also included as exogenous variables, while all other factors in black boxes are endogenous
variables. The model investigates the influence of all three exogenous variables on endogenous
variables and the direct and indirect paths through which these variables regulate crop damage,
measured as the number of damaged fruit. We include direct paths for measuring the influence of
landscape complexity (Chaplin-Kramer et al. 2011) and local plant species (Letourneau et al. 2011)
and flower richness on natural enemies (Balzan et al. 2014) since these were shown to influence
enemy diversity. Moreover, given that it has been shown that landscape complexity affects
vegetation diversity at the local scale (Carlesi et al. 2013; Gaba et al. 2010; Gabriel et al., 2005),
we investigate the effect of the former on natural enemies and crop damage through an indirect
effect. The availability of alternate prey has also been shown to increase the abundance of
generalist predators in tomato crops within the Mediterranean region (Balzan and Moonen 2014,
Torres and Boyd 1999). Diversity of sap-sucking bugs (abundance of member of the Order
Hemiptera and species richness of Heteroptera bugs, of the families Rhopalidae, Miridae,
Pentatomidae, Lygaeidae, Nabidae, Geocoridae, Anthocoridae, which accounted for 87.83% of
Hemiptera recorded within this study) which accounted for most of the herbivores recorded from
the field margins was included as a measure of alternative prey. Previous studies have
demonstrated that increased natural enemy diversity at the field scale leads to a higher natural pest
control (Rusch et al. 2013; Jonsson et al. 2012; Letourneau et al. 2012; Mitchell et al. 2014).
Richness of predator and parasitoid groups was used to indicate natural enemy diversity. We
decided to pool predator and parasitoid data together since our analyses indicate similar effects of
local and landscape management on these functional groups and this reduced the number of paths
in the model increased statistical power. The direct influence of field margin vegetation richness
on crop damage is also investigated since increased plant diversity at the local scale has been
shown to influence the pest directly, for example by providing food resources and shelter (Balzan
and Wäckers, 2013; Winkler et al. 2009) or by disrupting the pests' ability to locate or access the
host crop (Finch and Collier 2000). The path analysis also investigates the direct influence of
landscape complexity on crop damage, since simpler landscapes tend to be more attractive to pests
(Poveda et al. 2012; Grez et al. 2014), as suggested by the resource concentration hypothesis (Root
1973). Based on previous observations within the study area, the direct influence of time of harvest
on Lepidoptera-caused damage is also included within the model (Balzan and Moonen 2012). The
path analysis models described above were also carried out with Noctuidae and T. absoluta-caused
crop injury.
3
(a)
(b)
4
Supplementary data table S2 - Plant species composition of uncropped edges and the relative
abundance in the surveyed fields.
Species
Relative Abundance (%)
1
Cynodon dactylon (L.) Pers.
13.116
2
Convolvolus arvensis L.
11.049
3
Picris echioides L.
5.556
4
Daucus carota L.
5.392
5
Portulaca oleracea L.
5.165
6
Equisetum palustre L.
4.235
7
Solanum nigrum L.
2.753
8
Phalaris paradoxa L.
2.592
9
Rubus fruticosus L.
2.395
10
Avena sterilis L.
2.155
11
Digitaria sanguinalis (L.) Scop.
2.142
12
Echinocloa crus-galli (L.) Beauv
2.040
13
Pulicaria dysenterica (L.) Bernh.
2.030
14
Calystegia sepium (L.) R. Br.
2.027
15
Chenopodium album L.
1.876
16
Polygonum aviculare L. (s. l.)
1.817
17
Foeniculum vulgare Mill
1.748
18
Galium album Mill
1.594
19
Beta vulgaris L. s. l.
1.551
20
Cirsium arvense (L.) Scop.
1.331
21
Medicago sativa L.
1.061
22
Elymus repens (L.) Gould.
1.061
23
Dactylis glomerata L. s. str.
0.959
24
Cuscuta sp.
0.894
25
Lolium rigidum Gaudich
0.871
26
Mentha suaveolens Ehrh
0.844
27
Symphyotrichum squamatum (Spreng.)
G.L.Nesom
0.835
28
Amaranthus blitoides S. Watson
0.805
29
Cyperus glomeratus L.
0.789
5
Species
Relative Abundance (%)
30
Coleostephus myconis (L.) Cass.
0.739
31
Setaria viridis (L.) P. Beauv.
0.733
32
Clematis vitalba L.
0.664
33
Dittrichia viscosa (L.) Greuter
0.651
34
Elaeoselinum asclepium (L.) Bertol.
0.647
35
Bromus sterilis L.
0.637
36
Phragmites australis (Cav.) Steud.
0.572
37
Rumex crispus L.
0.542
38
Xanthium italicum Moretti
0.513
39
Torilis arvensis (Huds.) Link
0.496
40
Eragrostis cilianensis (All.) Janch.
0.496
41
Festuca rubra L.
0.493
42
Agrostis gigantea Roth
0.493
43
Conyza canadensis (L.) Cronquist
0.476
44
Humulus lupulus L.
0.414
45
Taraxacum officinale Webb
0.391
46
Rosa sp.
0.378
47
Lolium multiflorum Lam.
0.378
48
Amaranthus blitum L.
0.368
49
Amaranthus albus L.
0.361
50
Ferula communis L.
0.358
51
Cyperus longus L.
0.358
52
Epilobium parviflorum Schreb.
0.315
53
Melissa officinalis L.
0.306
54
Picris angustifolia subsp. angustifolia DC
0.302
55
Crataegus monogyna Jacq.
0.296
56
Polygonum equisetiforme Sm
0.289
57
Apera spica venti (L.) P. Beauv.
0.289
58
Smilax aspera L.
0.243
59
Equisetum giganteum L.
0.243
60
Holcus lanatus L.
0.240
61
Stachys palustris L.
0.237
6
Species
Relative Abundance (%)
62
Cornus sanguinea L.
0.230
63
Trifolium arvense L.
0.210
64
Persicaria maculosa Gray
0.204
65
Andryala integrifolia L.
0.204
66
Fallopia convolvolus (L.) Á.Löve
0.200
67
Phalaris minor Retz.
0.194
68
Verbena officinalis L.
0.181
69
Kickxia commutataFritsch
0.181
70
Atriplex patula L.
0.174
71
Equisetum hyemale L.
0.164
72
Sinapis arvensis L.
0.161
73
Malva silvestris L.
0.154
74
Bromus briziformis Fisch. & C.A.Mey.
0.148
75
Chrozophora tinctoria (L.) A.Juss.
0.141
76
Mentha sp.
0.138
77
Potentilla reptans L.
0.125
78
Plantago serraria L.
0.118
79
Typha angustifolia L.
0.112
80
Plantago lanceolata L.
0.112
81
Anagallis arvensis L.
0.105
82
Lathyrus sylvestris L.
0.102
83
Salvia sp.
0.099
84
Trifolium pratense L.
0.095
85
Briza maxima L.
0.095
86
Knautia arvensis (L.) Coult
0.092
87
Equisetum arvense L.
0.082
88
Epilobium tetragonum L. s. str.
0.082
89
Dipsacus fullonum L.
0.076
90
Amaranthus retroflexus L.
0.076
91
Equisetum telmateia Ehrh.
0.069
92
Atriplex prostrata subsp. calotheca (Rafn)
M.A.Gust
0.069
7
Species
Relative Abundance (%)
93
Silene gallica L.
0.066
94
Sonchus oleraceus L.
0.059
95
Populus sp.
0.059
96
Artemisia vulgaris L.
0.056
97
Gaudinia fragilis (L.) P. Beauv.
0.053
98
Galium verum L.
0.053
99
Lactuca serriola L.
0.049
100
Polycarpon tetraphyllum (L.) L.
0.043
101
Nigella damascena L.
0.039
102
Anthemis altissima L.
0.039
103
Potentilla alba L.
0.036
104
Kickxia spuria (L.) Dumort.
0.036
105
Bromus tectorum L.
0.036
106
Aristolochia rotunda L.
0.036
107
Anthemis arvensis L.
0.036
108
Lamium purpureum L.
0.033
109
Hypericum perforatum L.
0.033
110
Sorghum halepense (L.) Pers.
0.030
111
Raphanus raphanistrum L.
0.026
112
Anagallis arvensis subsp. Foemina (Mill.)
Schinz & Thell
0.026
113
Veronica sp.
0.023
114
Hierarcium sp.
0.023
115
Ammi majus L.
0.023
116
Lathyrus annuus L.
0.020
117
Lapsana communis L.
0.020
118
Asparagus tenuifolius Lam.
0.020
119
Unidentified species
0.016
120
Tragopogon dubius Scop
0.016
121
Eragrostis cilianensis (All.) Janch
0.016
122
Sonchus asper (L.) Hill
0.013
123
Helianthus annuus L.
0.013
8
Species
Relative Abundance (%)
124
Galium lucidum All.
0.013
125
Datura stramonium L.
0.013
126
Arctium lappa L.
0.013
127
Anchusa arvensis (L.) M. Bieb. s. l.
0.013
128
Vitis sp.
0.010
129
Rhamnus alaternus L.
0.010
130
Quercus sp.
0.010
131
Mentha longifolia(L.) L.
0.010
132
Mentha aquatica L.
0.010
133
Hordeum murinum L. s. l.
0.010
134
Cichorium intybus L.
0.010
135
Cerastium fontanum Baumg. s. str.
0.007
136
Centaurium pulchellum (Sw.) Druce
0.007
137
Agrostis stolonifera L.
0.007
138
Trifolium repens L.
0.003
139
Phalaris brachystachys Link
0.003
140
Paspalum dilatatum Poir
0.003
141
Bromus hordeaceus L.
0.003
142
Blackstonia perfoliata (L.) Huds.
0.003
9
Supplementary data table S3- Influence of Shannon vegetation diversity (H) and Shannon habitat-type diversity in landscape buffers of
1000m (H), harvest date (T), year (YR) and their interactions on yield loss (number of damaged fruits) and pest damage (number of fruit
galleries). P-values were obtained from likelihood ratio tests in which deviances with and without the term in the models were compared.
Variables in bold were included in the minimum adequate model (MAM, the symbol “*” in the MAM indicates that the model includes
the shown variables and all the interactions between them).
Independe
nt
variable
(a) Crop damage
df
Vegetation Diversity (H)
χ2
Habitat diversity
(H)
χ2
p-val
(b) Noctuidae-caused fruit
injury
(c) T. absoluta-caused fruit
injury
Vegetation
Diversity (H)
Vegetation
Diversity (H)
Habitat
diversity (H)
Habitat
diversity (H)
p-val
χ2
p-val
χ2
p-val
χ2
p-val
χ2
p-val
0.31
1.34
0.25
H
1
1.43
0.23
17.20
<0.000
1
2.30
0.13
6.59
0.01
1.05
T
1
7.63
0.006
16.73
<0.000
1
8.58
0.003
13.73 0.000
2
19.41
YR
1
0.10
0.75
2.85
0.09
4.43
0.03
2.47
0.12
9.38
0.002
9.52
0.002
HxYR
1
8.75
0.003
0.00
1.00
3.18
0.07
1.03
0.31
1.71
0.19
1.91
0.17
TxYR
1
0.00
1.00
0.08
0.77
2.90
0.09
3.10
0.08
11.28
0.0008
HxT
1
2.67
0.10
0.64
0.42
2.82
0.09
0.18
0.67
4.15
0.04
3.84
0.05
HxYRxT
1
5.23
0.02
5.32
0.02
5.50
0.02
1.90
0.17
0.03
0.85
1.36
0.24
MAM
H*YR*T
H*YR*T
H*YR*T
H+T
<0.0001 30.3 <0.000
6
1
H*T+T*YR
14.8 0.0001
2
H*T+T*YR
10
Supplementary Data Fig. S3– Path diagram for measuring the direct and indirect effects on (a) T.
absoluta (df = 6, p-val 0.99, Satorra-Bentler correction = 0.90) and (b) Noctuidae-caused tomato
crop injury (df = 6, p-val 0.99, Satorra-Bentler correction = 1.22) using abundance data. Solid lines
denote significant and line width indicates strength of the relationship, while the values represent
standardised path coefficients. Parameters in a grey box are exogenous variables whilst those in
black boxes are endogenous variables. * P<0.05, ** P<0.01, *** P<0.001.
(a)
(b)
11