Management‐dependent effects of pollinator functional diversity on apple pollination services: A response–effect trait approach

Abstract

Functional traits mediate the response of communities to disturbances (response traits) and their contribution to ecosystem functions (effect traits). To predict how anthropogenic disturbances influence ecosystem services requires a dual approach including both trait concepts. Here, we used a response–effect trait conceptual framework to understand how local and landscape features affect pollinator functional diversity and pollination services in apple orchards.
We worked in 110 apple orchards across four European regions. Orchards differed in management practices. Low‐intensity (LI) orchards were certified organic or followed close‐to‐organic practices. High‐intensity (HI) orchards followed integrated pest management practices. Within each management type, orchards encompassed a range of local (flower diversity, agri‐environmental structures) and landscape features (orchard and pollinator‐friendly habitat cover). We measured pollinator visitation rates and calculated trait composition metrics based on 10 pollinator traits. We used initial fruit set as a measure of pollination service.
Some pollinator traits (body size and hairiness) were negatively related to orchard cover and positively affected by pollinator‐friendly habitat cover. Bee functional diversity was lower in HI orchards and decreased with increased landscape orchard cover. Pollination service was not associated with any particular trait but increased with pollinator trait diversity in LI orchards. As a result, LI orchards with high pollinator trait diversity reached levels of pollination service similar to those of HI orchards.
Synthesis and applications. Pollinator functional diversity enables pollinator communities to respond to agricultural intensification and to increase pollination function. Our results show that efforts to promote biodiversity provide greater returns in low‐intensity than in high‐intensity orchards. The fact that low‐intensity orchards with high pollinator functional diversity reach levels of pollination services similar to those of high‐intensity orchards provides a compelling argument for the conversion of high‐intensity into low‐intensity farms.

Full text

J Appl Ecol. 2021;58:2843–2853.
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2843wileyonlinelibrary.com/journal/jpe
Received: 6 November 2020
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Accepted: 16 August 2021
DOI : 10.1111/136 5-2664.1402 2
RESEARCH ARTICLE
Management- dependent effects of pollinator functional diversity
on apple pollination services: A response– effect trait approach
Laura Roquer- Beni1,2 | Georgina Alins3 | Xavier Arnan1,4 | Virginie Boreux5 |
Daniel García6 | Peter A. Hambäck7 | Anne- Kathrin Happe8 | Alexandra- Maria Klein5 |
Marcos Miñarro9 | Karsten Mody8,10 | Mario Porcel11,12 | Anselm Rodrigo1 |
Ulrika Samnegård7,13,14 | Marco Tasin11,15 | Jordi Bosch1
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
1CREAF, Universitat Autònoma de Barcelona,
Bellaterra, Spain
2BETA, Universit y of Vic– Central Universit y
of Catalonia, Vic, Spain
3IRTA Fruitcentre, PCiTAL, Lleida, Spain
4Department of Biologic al Sciences,
University of Pernambuco, Garanhuns, Brazil
5Chair of Nature Conservation and
Landscape Ecology, University of Freiburg,
Freiburg, Germany
6Instituto Mixto de Investigación en
Biodiversidad (CSIC- Uo- PA), Oviedo, Spain
7Department of Ecology, Environment
and Plant Sciences, Stockholm University,
Stockholm, Sweden
8Department of Biology, Technical University
of Darmstadt, Darmstadt, Germany
9Servicio Regional de Investigación y
Desarrollo Agroalimentario, Villaviciosa,
Spain
10Department of Applied Ecolog y,
Hochschule Geisenheim University,
Geisenheim, Germany
11Depar tment of Plant Protection Biology,
Integrated Plant Protection Unit, Swedish
University of Agricultural Sciences, Alnarp,
Sweden
12Corporación Colombiana de Investigación
Agropecuaria, Meta, Colombia
13Depar tment of Biology, Lund University,
Lund, Sweden
14School of Environmental & Rural Sciences,
University of New England, Armidale,
Australia
15Department of Chemistry, University of
Padova, Padova, Italy
Abstract
1. Functional traits mediate the response of communities to disturbances (response
traits) and their contribution to ecosystem functions (effect traits). To predict
how anthropogenic disturbances influence ecosystem services requires a dual
approach including both trait concepts. Here, we used a response– effect trait
conceptual framework to understand how local and landscape features affect pol-
linator functional diversity and pollination services in apple orchards.
2. We worked in 110 apple orchards across four European regions. Orchards dif-
fered in management practices. Low- intensity (LI) orchards were certified organic
or followed close- to- organic practices. High- intensity (HI) orchards followed in-
tegrated pest management practices. Within each management type, orchards
encompassed a range of local (flower diversity, agri- environmental structures) and
landscape features (orchard and pollinator- friendly habitat cover). We measured
pollinator visitation rates and calculated trait composition metrics based on 10
pollinator traits. We used initial fruit set as a measure of pollination service.
3. Some pollinator traits (body size and hairiness) were negatively related to orchard
cover and positively affected by pollinator- friendly habitat cover. Bee functional
diversity was lower in HI orchards and decreased with increased landscape or-
chard cover. Pollination service was not associated with any particular trait but
increased with pollinator trait diversity in LI orchards. As a result, LI orchards with
high pollinator trait diversity reached levels of pollination service similar to those
of HI orchards.
4. Synthesis and applications. Pollinator functional diversity enables pollinator com-
munities to respond to agricultural intensification and to increase pollination func-
tion. Our results show that efforts to promote biodiversity provide greater returns
in low- intensity than in high- intensity orchards. The fact that low- intensity or-
chards with high pollinator functional diversity reach levels of pollination services

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1 | INTRODUCTION
Environmental disturbances such as agricultural intensification alter
ecosystem function through changes in functional composition of
plant and animal communities (Larsen et al., 2005; McGill et al., 2006).
Species have traits that affect their ability to cope with environmental
changes (response traits) and traits that contribute to specific func-
tions such as pollination (effect traits; Violle et al., 2007). Thus, the
degree to which disturbances will affect ecosystem functioning will
depend on the overlap between response and effect traits (Schleuning
et al., 2015; Suding et al., 2008). For this reason, a response– effect
trait framework is necessary to fully understand how specific drivers
affect ecosystem function and services (Lavorel & Garnier, 2002). At
the community level, the response to environmental changes and the
maintenance of ecosystem functions may be affected by the identity
and abundance of specific traits (functional identity) but also by the
diversity of traits (functional diversity; Leps et al., 2006).
Pollinator diversity and abundance have declined over the last
century, and agricultural intensification is considered one of the
main drivers of these declines (IPBES, 2016). Agricultural intensifi-
cation affects pollinators through landscape simplification, includ-
ing isolation and loss of natural and semi- natural habitats, leading to
decreased availability of feeding resources and nesting substrates
(Roulston & Goodell, 2011; Shuler et al., 2005). In addition, the in-
creased use of pesticides associated with intensive agriculture has
direct negative effects on pollinator fitness and survival (Woodcock
et al., 2017). To reverse these effects and enhance on- farm biodiver-
sity, agri- environmental measures have been promoted at both local
and landscape scales (Primdahl et al., 2003). These measures include
reducing pesticide use, preserving historical land uses and implement-
ing agri- environmental structures (hereafter AES) such as hedgerows
and buffer strips to increase connectivity with semi- natural habitats
(Ekroos et al., 2016). The effects of agri- environmental measures on
pollinator richness and abundance have been widely studied (Marja
et al., 2019; Scheper et al., 2013), but much less is known about their
consequences on pollinator functional composition.
Pollinator responses to agricultural intensification depend on
traits related to mobility, feeding and nesting requirements and
physiological tolerance (De Palma et al., 2015; Forrest et al., 2015;
Rader et al., 2014). For example, large mobile bee species may be
better suited to find floral resources in disturbed habitats com-
pared to small species (Jauker et al., 2013; Klein et al., 2008), but
they may also have higher levels of exposure to pesticides (Brittain
& Potts, 2011). Although no general patterns have been found be-
tween single trait identity and responses to environmental changes
(Bartomeus et al., 2018; Bommarco et al., 2010), there is evidence
that landscape intensification acts as a filter of specific traits caus-
ing decreases in pollinator trait diversity (Forrest et al., 2015; Geslin
et al., 2016). Trait diversity is crucial, as it allows for a variety of re-
sponses to disturbances over space and time (Mori et al., 2013).
As for effect traits, functional composition appears to be a better
predictor of pollination function than taxonomic composition (Gagic
et al., 2015). Pollinator traits such as body size, flower- handling be-
haviour and hairiness have been associated with pollination success
(Phillips et al., 2018; Roquer- Beni et al., 2020; Stavert et al., 2016). In ad-
di tion , ba sed on the co mplem e ntar ity hypot hesis (Día z & Cabi d o, 20 01;
Tilman, 2001), communities with high trait diversity should be better
suited to provide pollination services under a variety of environmental
scenarios (Blüthgen & Klein, 2011; Woodcock et al., 2019).
Some studies have addressed the response of pollinator functional
composition to agricultural intensification (De Palma et al., 2015; Forrest
et al., 2015; Geslin et al., 2016; Rader et al., 2014; Williams et al., 2010)
whereas others have addressed the effects of functional composition
on pollination service (Gagic et al., 2015; Hoehn et al., 2008; Woodcock
et al., 2019). However, studies analysing pollinator responses and effects
simultaneously remain scarce (Bartomeus et al., 2018; Klein et al., 2008).
similar to those of high- intensity orchards provides a compelling argument for the
conversion of high- intensity into low- intensity farms.
KEYWORDS
agricultural intensification, agri- environmental structures, integrated pest management,
organic management, response– effect trait framework, trait diversity, trait identity
Correspondence
Laura Roquer- Beni
Email: laura.roquer@uvic.cat
Funding information
BiodivERsA/FACCE- JPI, Grant/Award
Number: 2014- 74; Agència de Gestió
d'Ajuts Universitaris i de Recerca,
Grant/Award Number: FI; Stiftelsen
Lantbruksforskning, Grant/Award Number:
H1256150; Bundesministerium für Bildung
und Forschung, Grant/Award Number:
01LC1403 and PT- DLR /BMBF; Ministerio
de Economía y Competitividad, Grant/
Award Number: CGL2015- 68963- C2- 2- R,
P C I N - 2 0 1 4 - 1 4 5 - C 0 2 a n d R Y C - 2 0 1 5 - 1 8 4 4 8 ;
Instituto Nacional de Investigación y
Tecnología A graria y Alimentaria, Grant/
Award Number: RTA2013- 00039- C03- 0 0;
Svenska Forskningsrådet Formas, Grant/
Award Number: 2013- 934 and 2014- 1784
Handling Editor: Guadalupe Peralta

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ROQUER- BEN I Et al.
To address this knowledge gap, we measured functional trait identity
and diversity of apple pollinator assemblages in 110 orchards differing in
management and in local and landscape features in four European apple-
growing regions. Apples Malus domestica are one of the most important
fruit crops worldwide and are highly dependent on insect pollination
(Garratt et al., 2016; Martins et al., 2014). We used a response– effect
trait framework to determine how local and landscape features affect
pollination service through changes in pollinator functional composition.
Our objectives were: (a) to identify functional metrics responding to en-
vironmental features and affecting pollination services; b) to establish
whether traits that determine pollinator responses to changes in local
and landscape factors overlap with traits that affect pollination; (c) to de-
termine whether pollination services are dependent on certain pollinator
functional traits and/or on trait diversity; (d) to establish whether the
relationship between pollinator trait composition and pollination service
is dependent on orchard management.
2 | MATERIALS AND METHODS
2.1 | Study sites
The study was conducted in 2015 in 110 commercial apple orchards
from four European apple- growing regions: Skåne (Sweden), Baden-
Württemberg (Germany), Asturias (Spain) and Catalonia (Spain) (see
Figure S1, Table S1; Happe et al., 2019; Miñarro & García, 2018;
Samnegård, Alins, et al., 2019 for further details on the study orchards).
In each region, orchards were selected to encompass a range of
local and landscape features (Table S1). In Sweden, Germany and
Cat alo nia, ha lf of th e orch ards fol lowed IO BC guidel ine s for in tegr ated
pest management (Cross, 2002) and used chemical insecticides, fun-
gicides, fertilizers and herbicides (see Happe et al., 2019 for details).
These orchards were considered high- intensity (henceforth HI) or-
chards. The rest of the orchards in Sweden, Germany and Catalonia
and all orchards in Asturias were either certified organic or followed
close- to- organic guidelines, with very low levels of synthetic inputs
and mechanical weed control. These orchards were grouped into a
low- intensity orchard management category (henceforth LI).
2.2 | Local features
In each orchard, we established two consecutive 20- m transects on
which we conducted pollinator counts (see below). To assess local fea-
tures, we used aerial photographs combined with on- site inspection
to measure the area occupied by AES within a 20- m buffer from the
first transect trees. AES included hedgerows, forests fallow lands, or-
chard meadows and semi- natural grasslands (Table S1). During apple
bloom, we estimated flower cover and diversity of entomophilous
plant species within and around each orchard. These measures were
taken using 12– 14 gridded quadrats (1 m2 in Sweden, Germany and
Catalonia; 0.25 m2 in Asturias). Half of the quadrats were laid between
the two orchard transects, and the other half within the 20- m buffer
surrounding the orchard. To estimate flower diversity (Shannon's
index), we used the mean cover of each flower species. Quadrat size
differences among regions were not expected to affect comparative
trends in flower diversity because region variability was integrated in
the statistical design (see below).
2.3 | Landscape features
We used ArcView 10.3.1, MiraMon v8.2e, R 3.2.3 (R Core Team, 2015)
and digital maps (see Table S1) to measure the area covered by dif-
ferent habitat types within 1- km buffers from the surveyed trees and
calculated two landscape variables. First, the per cent area occupied
by pollinator- friendly habitats (henceforth PFH), defined as habitats
free of pesticides and hosting abundant floral resources and potential
bee nesting substrates. PFH included shrublands, orchard meadows,
semi- natural grasslands, abandoned orchards and hedgerows. Second,
the area occupied by orchards, as a proxy of agricultural landscape
homogenization (Table S1).
2.4 | Pollinator surveys
Pollinator surveys were conducted during apple bloom (April– May).
In Sweden, Germany and Catalonia, observers walked along the two
20- m transect s (M ± SE: 35 ± 1.3 trees/transect) and recorded all apple
visitors contacting the reproductive parts of apple flowers. Transect
walks lasted 5 min and were repeated three times throughout the day,
amounting to 30 min of pollinator survey per orchard. In Asturias, ob-
servers sur veyed a 1- m diameter canopy area for 5 min in five trees per
orchard three times throughout the day, for a total of 75 survey min-
utes per orchard. Pollinators were mostly visually identified in the field,
but some specimens were captured and identified in the laboratory.
From these surveys, we calculated abundance of each pollinator
spec ies (number of indivi duals observe d visiting flowe rs) and pollina-
tor visitation rate (number of visits/100 flowers/5 min).
2.5 | Pollination service
At the onset of bloom, we marked two to three branches per tree on
six to seven trees per orchard and counted the number of flower buds
(1,200– 1,300 per orchard). About 3 weeks after petal fall, we assessed
initial fruit set as the percentage of flowers that developed into a fruit-
let. Initial fruit set is a better measure of pollination service than fruit
set at harvest because it is less influenced by post- pollination factors
such as natural and/or artificially induced fruit abscission.
2.6 | Pollinator traits
We selected pollinator traits that, based on our knowledge and/or
previous studies (Table S2), could either influence the response of

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pollinators to environmental conditions (response traits, hereafter R),
pollination service (effect traits, hereafter E) or both (hereafter R- E). All
pollinator species recorded (99 species, Table S3) were characterized
with three traits: body length (R- E), hairiness (R- E) and larval diet (R- E).
Because bees are the most frequent and effective apple pollinators
(Garratt et al., 2016; Vicens & Bosch, 2000a), we conducted separate
analyses including only bees. We measured eight traits in this group
(45 species): intertegular span (a proxy of body size; R- E), hairiness
(R- E), proboscis length (E), forewing aspect ratio (maximum length/
maximum width; R), sociality (R), voltinism (R), nesting substrate (R)
and pollen transportation structure (E) (see Table S2 for details).
Quantitative traits were measured on pinned specimens from the
survey orchards. Categorical traits were based on literature records
and our expert knowledge (see Table S2 for methods and sample
sizes). We worked with a single mean value per trait and species. We
explored correlation between each pair of numerical traits (Table S4).
In bees, proboscis length was highly correlated to body size (r > 0.7).
Thus, we only used body size in the analyses.
2.7 | Functional composition metrics
For each orchard, we calculated the community- weighted mean
(CWM) of each trait, a measure of functional identity (Garnier
et al., 2004). CWM of numerical traits was calculated as the mean
value across species weighted by the relative abundance of each spe-
cies (Ricotta & Moretti, 2011). CWM of categorical traits was calcu-
lated as the proportion of individuals belonging to each trait category.
To measure functional diversity, we used the Rao quadratic diversity
index (RaoQ), which measures the dissimilarity between two randomly
selected individuals and calculates the sum of weighted abundance
dissimilarity between each pair of species (Rao, 1982). The RaoQ index
has some advantages over other functional diversity metrics: (a) It is
easy to interpret (it ranges from 0 to the maximum of Simpson's diver-
sity index); (b) in general, it is relatively unaffected by species richness
(Botta- Dukát, 2005) and (c) it can be used to measure both single- and
multi- trait diversity (Ricotta & Moretti, 2011).
Honeybees were excluded from calculations of response trait met-
rics because their presence was mostly (or solely) attributable to man-
aged colonies in all four regions. Conversely, honeybees were included
in calculations of effect trait metrics because they affect apple polli-
nation (Vicens & Bosch, 2000b). Measures of CWM and RaoQ were
conducted using the dbFD and functomp functions from FD library
(Laliberté & Legendre, 2010) with R (see Appendix S1 for details on
functional composition metric calculations). In addition to the RaoQ
index, we also measured trait diversity with the Functional Divergence
Index (FDiv; Villéger et al., 2008). The results were similar to those ob-
tained with the RaoQ and are provided in the Supporting Information.
2.8 | Statistical analysis
All statistical analyses were conducted with R v3.2.3.
2.8.1 | Response traits
To assess the response of pollinator functional composition to local
and landscape features, we performed separate linear mixed- effect
models (LMMs) for each functional composition metric (CWM of
each trait and RaoQ of all traits combined). These analyses were
done separately for all pollinator species and for bee species only.
Full models included three local variables (orchard management,
AES cover, flower diversity) and two landscape variables (orchard
cover, PFH cover) as fixed effects, and region as a random effect.
Numerical explanatory variables were not highly correlated (r < 0.7,
Table S5) and VIF was <5 for all full models.
2.8.2 | Effect traits
To analyse the effect of functional traits on pollination service,
we conducted LMMs with initial fruit set as the response variable.
Beca use all pollinators potentially co nt ribute to fru it set, these analy-
ses included honeybees. To discriminate between trait identity and
trait diversity effects, we run one model with trait CWMs and an-
other with RaoQ as predictor variables. We checked for correlation
between pairs of predictors and excluded variables as required until
VIF was <5 (Table S6; Zuur et al., 2010). Whenever two predictors
were strongly correlated, we kept the one that we considered more
likely to be associated with pollination services. As a result, CWM
of hairiness and pollinivorous larvae and RaoQ were the only func-
tional predictor variables included. These models also included two
addition al variable s that could af fect in itial fruit set: overall pollinator
visitation rate and orchard management. To establish whether the
relationship between functional composition and pollination ser-
vices was management dependent, these models also included the
interaction between management and the selected functional com-
position metrics. Finally, apple variety and region were included as
independent random effects. Fruit set data were not available for
German orchards, so n = 81 orchards for these analyses.
Following model selection procedures (MuMin package; Barton &
Barton, 2019), we tested all possible explanatory variable combinations
(see above) through a multimodel inference approach (Anderson &
Burnham, 2004). We then used a model averaging approach (with av-
eraged variable coefficients) based on AICc to assign a relative impor-
tance to each variable. Models with ΔAICc < 2 were conside red eq ually
suitable (see Appendix S1 for details on model selection procedures).
Normality and homoscedasticity assumptions were graphically
evaluated by plotting the distribution of residuals of each model.
Response variables were square- root- or log- transformed as
needed. Numerical explanatory variables were standardized to fa-
cilitate comparison across variables. To detect model outliers, we
calculated Cook's distance and excluded sites with distances >4/N
(Cook, 1977). The exclusion of outliers provided better model ad-
justments, but trends remained similar (Tables S7 and S8). To rule
out spatial autocorrelation, we applied a Moran's I test with the re-
siduals of all our models (Table S9).

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TABLE 1 Statistical outputs of model averaging relating wild pollinator and wild bee functional composition response variables to local and landscape features. Estimated coefficients,
their 95% intervals (in parentheses) and relative importance (in brackets) are provided. Significant terms are in bold. ‘— ’ denotes variables not appearing in the model average.
R2
m
and
R2
c
are the
range of marginal and conditional R2 of the best- fitted models respectively. R2 of the best model is indicated in parentheses. ‘Sites’ indicates the number of orchards included in the model after
outlier exclusion (Table S7)
Response variable Management*Flower diversity AES cover % Orchard cover
% Pollinator friendly
habitat cover
R2
m
R2
c
Sites
All pollinators
CWM body length — 0.145 [0.31]
(−0.201, 0.491)
— −0.683 [1](−1.078,
−0.288)
0.9 86 [1](0.423, 1.550) 0.28– 0.29
(0.28)
0.28– 0.29
(0.28)
98
CWM Hairinessa— — −0.397 [0.44]
(−0.889, 0.095)
−1.046 [1](−1.627,
−0.465)
0.676 [0.83](0.054,
1.299)
0.16– 0.23
(0.23)
0.28– 0.35
(0.35)
99
CWM Pollinivorous larvae 0.026 [0.18]
(−0.076, 0.129)
— — −0.031 [0.33]
(−0.083, 0.021)
0.1 54 [1](0.073, 0.235) 0.11– 0.13
(0.11)
0.56– 0.57
(0.56)
94
CWM Insectivorous larvaea−0.026 [0.14]
(−0.099, 0.047)
0.033 [0.84]
(−0.000, 0.066)
0.075 [1](0.040, 0.110) 0.023 [0.20]
(−0.018, 0.063)
−0.017 [0.17]
(−0.050, 0.017)
0.14– 0 .17
(0.16)
0.36– 0.39
(0.38)
99
Bees
RaoQ 4.946 [1](0.655, 9.236) — — −3.478 [1](−5.704,
−1. 2 52 )
1.657 [0.36]
(−1.488, 4.802)
0.18– 0.20
(0.18)
0.24– 0.26
(0.24)
105
Note: aSquare- root Log(X + 1).
*Low intensity: reference level of management.

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3 | RESULTS
We recorded 8,253 pollinator individuals visiting apple flowers.
Most pollinators were honeybees (77.6%), followed by wild bees
(9.1%), hoverflies (5.9%), other flies (6.1%), beetles (1%) and others
(0.4%) (Table S3). Honeybee visitation rates were similar in LI and
HI orchards (LMM, estimate β = 0.08, p = 0.24), but overall pollina-
tor visitation rates were higher in LI orchards (β = 0.15, p < 0.05;
Table S10).
3.1 | Response traits
At the local scale, AES cover favoured pollinators with insectivorous
larvae (Table 1). There were no effects of local features, landscape
features or their interactions on pollinator trait diversity (RaoQ).
Landscape orchard cover was negatively associated with body length
and hairiness CWMs (Table 1). Pollinator- friendly habitat cover was
positively associated with body length, hairiness and proportion of
pollinators with pollinivorous larvae (bees).
When considering bees only, the best- fitted models for all
CWM variables were the null models. Thus, we could not attribute
any effect of local or landscape features to any specific bee trait
(Table 1). However, local and landscape features had important
effects on functional diversity, which was negatively affected by
landscape orchard cover (Figure 1a) and enhanced by LI manage-
ment (Figure 1b).
3.2 | Effect traits
Models including CWM metrics showed that initial fruit set was en-
hanced by HI management but was not affected by any particular
trait (Table 2). Models including RaoQ revealed an interaction effect
between trait diversity and management on initial fruit set (Figure 2).
There was a positive effect of trait diversity on initial fruit set in LI
orchards but not in HI orchards (Table 2). When trait diversity was
low, initial fruit set was lower in LI orchards, but when trait diver-
sity was high, initial fruit set was similar in the two orchard types. In
other words, differences between LI and HI orchards in initial fruit
set disappeared as pollinator trait diversity increased (Figure 2).
Models computed with FDiv showed similar results (Tables S12
and S13), except that FDiv was not affected by management and was
enhanced by PFH cover.
4 | DISCUSSION
Our results show that bee trait diversity was enhanced by LI orchard
management and negatively affected by landscape orchard cover.
At the same time, pollinator trait diversity enhanced pollination ser-
vice in LI, but not in HI orchards. Our results provide evidence that
pollinator functional diversity is an important mechanism linking re-
sponses to agricultural intensification and contribution to pollination
services in agricultural systems.
Local factors were important determinants of pollinator func-
tional composition. AES enhanced the abundance of pollinators with
insectivorous larvae, probably by providing prey for aphidophagous
hoverflies and predatory wasps (Rodríguez- Gasol et al., 2019). Some
studies have reported AES to also benefit pollinators with pollini-
vorous larvae (bees; Blaauw & Isaacs, 2014). However, we found
no such effect, suggesting that floral resources may not be limiting
during apple bloom.
LI management enhanced bee trait diversity. Various studies
show positive effects of organic farming on bee abundance and tax-
onomic diversity, particularly in homogeneous landscapes, and these
results are attributed to reduced use of chemical inputs (Forrest
et al., 2015; Rundlöf et al., 2008). As far as we know, effects of agri-
cultural management on bee trait diversity have not been previously
reported. Our results show that the increase in species richness
caused by LI management also results in an increase in functional
diversity.
FIGURE 1 Effects of landscape
orchard cover (a) and orchard
management (high intensity vs. low
intensity) (b) on bee multi- trait functional
diversity (RaoQ). Grey bands indicate 95%
confidence intervals. Vertical bars indicate
standard deviations

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TABLE 2 Statistical outputs of model averaging relating initial fruit set to management (low intensity vs. high intensity), functional composition metrics, the interaction between
management and functional composition metrics and pollinator visitation rate. The first model includes single- trait metrics (CWM of hairiness and pollinivorous larvae). The second model
includes functional diversity (multi- trait RaoQ). Estimated coefficients, their 95% intervals (in parentheses) and relative importance (in brackets) are provided. Significant terms are in bold.
‘— ’ denotes variables not appearing in the model average.
R2
m
and
R2
c
are the marginal and conditional R2 range of the best- fitted model respectively. R2 of the best model is indicated in
parentheses. ‘Sites’ indicates the number of orchards included in the model after outlier exclusion (Table S8)
Response variable Management* CWM hairiness CWM pollinivorous
CWM
hairiness x
management
CWM
pollinivorous
x
management Visitation rate
R2
m
R2
c
Sites
All pollinators
Initial fruit seta−1.617 [1](−2.3 04,
−0.93 0)
0.113 [0.20]
(−0.251, 0.477)
— — — 0.154 [0.25]
(−0.193, 0.500)
0.24 –
0.25
(0.24)
0.24 –
0.25
(0.24)
76
Response variable Management* RaoQ RaoQ × management Visitation rate
R2
m
R2
c
Sites
Initial fruit seta−1.580 [1](−2.193, −0.966) −0.301
[0.6 4]
(−0.821,
0.219)
0.749 [0.64](0.089, 1.409) — 0. 27–
0.32
(0.32)
0. 27–
0.32
(0.32)
76
Note: Data transformations: aSquare- root.
*Low intensity: reference level of management.

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ROQUER- BEN I Et al.
Landscape features also affected pollinator functional compo-
sition. Large and hairy pollinators were negatively affected by or-
chard cover and enhanced by pollinator- friendly habitat cover. The
response of body size and other traits to landscape intensification
is controversial and appears to be context dependent. According
to some studies, small bee species are more sensitive to landscape
intensity and isolation from natural habitats (De Palma et al., 2015;
Klein et al., 2008). However, other studies show opposite trends
(Larsen et al., 2005; Rader et al., 2014) or no clear patterns
(Bartomeus et al., 2018; Forrest et al., 2015; Williams et al., 2010).
We cannot think of any reason why hairier pollinators should be
more abundant in semi- natural habitats than in agricultural areas.
We also found that landscapes with high levels of pollinator-
friendly habitat promoted the relative abundance of pollinators with
pollinivorous larvae (bees). Semi- natural habitats provide spatio-
temporal stability to flower visitors (Klein, 2009) through wide-
spread and continued accessibility to flower resources, especially
before and after the crop's flowering period. Pollinator- friendly
habitats also provide nesting resources and pesticide- free refuge
areas (Holzschuh et al., 2010), thus promoting the colonization and
establishment of bee communities in agricultural areas (Kremen
et al., 2004). Bee trait diversity was not affected by pollinator-
friendly habitat in our study, but it decreased with landscape or-
chard cover. Bee trait diversity is known to be lower in farmland
compared to natural habitats (Forrest et al., 2015; Hass et al., 2018;
Woodcock et al., 2014). In addition to higher levels of pesticide ex-
posure, orchard- dominated landscapes are characterized by land-
scape homogenization and isolation from semi- natural habitats, thus
hindering consistent spatio- temporal availability of flower resources
not only during but especially before and after apple bloom (Marini
et al., 2012). In sum, our results support the need of landscape level
initiatives to promote bee communities in agroecosystems (Cole
et al., 2020).
HI orchards had higher initial fruit set than LI orchards. Because
pollinator visitation rates were higher in LI orchards, we cannot
attribute this result to lower pollination levels in LI orchards. We
find two possible explanations for the higher initial fruit set in HI
orchards. First, the use of chemical fertilizers and lower levels of
pests and diseases may enhance fruitlet retention in HI orchards
(Peck et al., 2006; Samnegård et al., 2019). Second, to enhance the
production of large apples, fruit load is usually chemically thinned.
In HI orchards, synthetic thinners are applied during fruitlet growth
(Fallahi & Greene, 2010) whereas in LI orchards, organic thinners are
applied during bloom (Lordan et al., 2018), effectively lowering the
numbers of flowers available for fruitlet development.
No specific pollinator traits emerged as important determi-
nants of pollination service. We found that trait diversity enhanced
initial fruit set, but only in LI orchards. The positive effect of trait
diversity on pollination services has been previously recognized
(Hoehn et al., 2008; Martins et al., 2014; Woodcock et al., 2019),
but ours is, as far as we know, the first study showing interactive
effects between trait diversity and agricultural management on pol-
lination services. Functionally diverse pollinator communities may
enhance pollination function through various spatial and temporal
complementarity mechanisms (Blüthgen & Klein, 2011). Our results
show that, through the enhancement of pollinator trait diversity,
LI orchards may reach levels of initial fruit set similar to those of
HI orchards. Importantly, the positive effect of bee trait diversity
on initial fruit set in LI orchards was detected despite a very strong
background of honeybee visitation.
Some pollinator traits responded to local and/or landscape fac-
tors but, as mentioned, no specific traits influenced pollination ser-
vices. In other words, pollination service could not be explained by
sampling effects of dominant traits (Mokany et al., 2008). A recent
study shows that pollen deposition in apple flowers is influenced
by various morphological and behavioural pollinator traits, possi-
bly diluting the effect of any single trait by itself (L. Roquer- Beni,
unpublished data). Bee trait diversity responded negatively to two
important features associated with agricultural intensification: HI
management and increased orchard cover, and pollinator trait diver-
sity enhanced initial fruit set in LI orchards. These results suggest
that the response– effect framework is more relevant for integrative
measures (multi- trait diversity) than single traits (Peña et al., 2020).
Our results have important implications in the face of the new
European Common Agricultural Policy, one of whose strategic ob-
jectives is the preservation of landscapes and biodiversity (European
Commission, 2019). First, efforts to promote functional biodiver-
sity provide greater returns in low- intensity than in high- intensity
farms. Second, the fact that high levels of ecosystem ser vices can
be reached in low- intensity farms (as long as functional diversity is
preserved) provides a compelling argument for the conversion of
FIGURE 2 Effects of pollinator multi- trait diversity (RaoQ,
including wild and managed pollinators) on initial fruit set (square-
root transformed) in low- intensity (estimate β = 0.192; p = 0.05)
and high- intensity (β = −0.108; p = 0.213) orchards (Table S11).
Grey bands indicate 95% confidence intervals

|
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ROQUER- BEN I Et al.
high- intensity into low- intensity farms. Measures promoting pollina-
tor functional diversity (e.g. reduction of chemical inputs, implemen-
tation of AES enhancing diversified nesting and flower resources,
increased landscape heterogeneity and connectivity to semi- natural
habitats) could provide environmental benefits and, at the same
time, reduce farmer dependence on costly external inputs while
maintaining competitive production.
ACKNOWLEDGEMENTS
We thank all producers for permission to work in their orchards and
several fruit associations (ADV- Ponent, ADV- Fluvià, ACTEL, KOB
Bavendorf, FÖKO e.V. Äppelriket in Kivik) for their advice and as-
sistance. We are very grateful to I. Fraile, S. Muntada and several
students for their invaluable help. This research (EcoFruit project)
was funded through the 2013– 2014 BiodivERsA/FACCE- JPI joint
call (2014- 74), Spanish MinECo (PCIN- 2014- 145- C02), German
BMBF (PT- DLR/BMBF, 01LC1403) and Swedish Research Council
Formas (2014- 1784) by Formas (2013- 934 to M.T.), Stiftelsen
Lantbruksforskning (H1256150 to M.P.), INIA (RTA2013- 00039-
C03- 00 to G.A. and M.M.), MinECo/FEDER (CGL2015- 68963- C2-
2- R to D.G.), FI- AGAUR (to L.R.- B.) and MinECo (RYC- 2015- 18448
to X.A.). The use of IACS (Sweden) was developed within projects
SAPES and MULTAGRI and adapted by M. Stjernman and P. Olsson.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.
AUTHORS' CONTRIBUTIONS
A.- M.K., A.- K.H., A.R., D.G., J.B., K.M., L.R.- B., M.M., P.A.H., U.S.,
V.B. and X.A. conceived and designed the study; A .- K.H., D.G., G.A.,
J.B., L.R.- B., M.M., M.P., M.T., U.S. and V.B. collected the data; L.R.- B.
led data analysis and manuscript writing with assistance from A.R.,
X.A . and J.B. All authors contributed to draft development and gave
final approval for publication.
DATA AVAILAB ILITY STATE MEN T
Data available via the Dryad Digital Repository https://doi.
org/10.5061/dryad.63xsj 3v39 (Roquer- Beni et al., 2021).
ORCID
Laura Roquer- Beni https://orcid.org/0000-0001-8454-6745
Xavier Arnan https://orcid.org/0000-0002-9904-274X
Daniel García https://orcid.org/0000-0002-7334-7836
Peter A. Hambäck https://orcid.org/0000-0001-6362-6199
Alexandra- Maria Klein https://orcid.org/0000-0003-2139-8575
Marcos Miñarro https://orcid.org/0000-0002-5851-6873
Anselm Rodrigo https://orcid.org/0000-0001-6341-0363
Ma rco Tas in https://orcid.org/0000-0001-7379-4954
Jordi Bosch https://orcid.org/0000-0002-8088-9457
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sion of the article at the publisher’s website.
How to cite this article: Roquer- Beni, L., Alins, G., Arnan, X.,
Boreux, V., García, D., Hambäck, P. A., Happe, A.- K., Klein,
A.- M., Miñarro, M., Mody, K., Porcel, M., Rodrigo, A.,
Samnegård, U., Tasin, M., & Bosch, J. (2021). Management-
dependent effects of pollinator functional diversity on apple
pollination services: A response– effect trait approach. Journal
of Applied Ecology, 58, 2843– 2853. ht t p s : //doi.
org /10.1111/1365- 2664.14022