ArticlePDF Available

A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes

  • Centre for Ecological Research

Abstract and Figures

Agricultural intensification is a leading cause of global biodiversity loss, which can reduce the provisioning of ecosystem services in managed ecosystems. Organic farming and plant diversification are farm management schemes that may mitigate potential ecological harm by increasing species richness and boosting related ecosystem services to agroecosystems. What remains unclear is the extent to which farm management schemes affect biodiversity components other than species richness, and whether impacts differ across spatial scales and landscape contexts. Using a global metadataset, we quantified the effects of organic farming and plant diversification on abundance, local diversity (communities within fields), and regional diversity (communities across fields) of arthropod pollinators, predators, herbivores, and detritivores. Both organic farming and higher in-field plant diversity enhanced arthropod abundance, particularly for rare taxa. This resulted in increased richness but decreased evenness. While these responses were stronger at local relative to regional scales, richness and abundance increased at both scales, and richness on farms embedded in complex relative to simple landscapes. Overall, both organic farming and in-field plant diversification exerted the strongest effects on pollinators and predators, suggesting these management schemes can facilitate ecosystem service providers without augmenting herbivore (pest) populations. Our results suggest that organic farming and plant diversification promote diverse arthropod metacommunities that may provide temporal and spatial stability of ecosystem service provisioning. Conserving diverse plant and arthropod communities in farming systems therefore requires sustainable practices that operate both within fields and across landscapes.
Content may be subject to copyright.
A global synthesis of the effects of diversified farming
systems on arthropod diversity within fields and across
agricultural landscapes
Elinor M. Lichtenberg
Christina M. Kennedy
Claire Kremen
eter Bat
Frank Berendse
Riccardo Bommarco
Nilsa A. Bosque-P
ısa G. Carvalheiro
William E. Snyder
Neal M. Williams
Rachael Winfree
orn K. Klatt
Faye Benjamin
Claire Brittain
Rebecca Chaplin-Kramer
Yann Clough
Bryan Danforth
Tim Diek
Sanford D. Eigenbrode
Johan Ekroos
Elizabeth Elle
Breno M. Freitas
Yuki Fukuda
Hannah R. Gaines-Day
Heather Grab
Claudio Gratton
Andrea Holzschuh
Rufus Isaacs
Marco Isaia
Shalene Jha
Dennis Jonason
Vincent P. Jones
Alexandra-Maria Klein
Jochen Krauss
Deborah K. Letourneau
Sarina Macfadyen
Rachel E. Mallinger
Emily A. Martin
Eliana Martinez
Jane Memmott
Lora Morandin
Lisa Neame
Mark Otieno
Mia G. Park
Lukas Pfiffner
Michael J. O. Pocock
Carlos Ponce
Simon G. Potts
Katja Poveda
Mariangie Ramos
Jay A. Rosenheim
Maj Rundl
Hillary Sardi~
Manu E. Saunders
Nicole L. Schon
Amber R. Sciligo
C. Sheena Sidhu
Ingolf Steffan-Dewenter
Teja Tscharntke
Milan Vesel
Wolfgang W. Weisser
Julianna K. Wilson
David W. Crowder
Department of Entomology, Washington State University, Pullman, WA, USA
Department of Ecology & Evolutionary Biology, The University of Arizona, Tucson, AZ, USA
Global Lands Program, The Nature Conservancy, Fort Collins, CO, USA
Department of Environmental Sciences, Policy and Management, University of California, Berkeley, CA, USA
Agroecology, University of Goettingen, G
ottingen, Germany
Nature Conservation and Plant Ecology Group, Wageningen University, Wageningen, the Netherlands
Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
Department of Entomology, Plant Pathology and Nematology, University of Idaho, Moscow, ID, USA
Departamento de Ecologia, Universidade de Bras
ılia, Bras
ılia, Brazil
Center for Ecology, Evolution and Environmental Changes (CE3C), Faculdade de Ciencias, Universidade de Lisboa, Lisboa, Portugal
Department of Entomology and Nematology, University of California, Davis, CA, USA
Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, NJ, USA
Centre for Environmental and Climate Research, Lund University, Lund, Sweden
Department of Biology, Lund University, Lund, Sweden
Norwegian Institute for Nature Research (NINA), Trondheim, Norway
Natural Capital Project, Stanford University, Stanford, CA, USA
Department of Entomology, Cornell University, Ithaca, NY, USA
Department of Landscape Ecology, Kiel University, Kiel, Germany
Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
Departamento de Zootecnia, Universidade Federal do Cear
a, Fortaleza, CE, Brazil
Received: 18 September 2016
Accepted: 17 March 2017
DOI: 10.1111/gcb.13714
Glob Change Biol. 2017;112. ©2017 John Wiley & Sons Ltd
Centres for the Study of Agriculture Food and Environment, University of Otago, Dunedin, New Zealand
Department of Entomology, University of Wisconsin-Madison, Madison, WI, USA
Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Würzburg, Germany
Department of Entomology, Michigan State University, East Lansing, MI, USA
Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy
Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
Department of Physical Geography, Stockholm University, Stockholm, Sweden
Department of Entomology, Tree Fruit Research and Extension Center, Washington State University, Wenatchee, WA, USA
Nature Conservation and Landscape Ecology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Department of Environmental Studies, University of California, Santa Cruz, CA, USA
CSIRO, Acton, ACT, Australia
CORPOICA, Centro de Investigaci
on Obonuco, Pasto, Colombia
School of Biological Sciences, University of Bristol, Bristol, UK
Pollinator Partnership Canada, Victoria, BC, Canada
Alberta Environment and Parks, Regional Planning Branch, Edmonton, AB, Canada
Department of Agricultural Resource Management, Embu University College , Embu, Kenya
Department of Humanities & Integrated Studies, University of North Dakota, Grand Forks, ND, USA
Department of Crop Science, Research Institute of Organic Agriculture, Frick, Switzerland
NERC Centre for Ecology & Hydrology, Wallingford, UK
Department of Evolutionary Ecology, Museo Nacional de Ciencias Naturales, CSIC, Madrid, Spain
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, UK
Department of Agricultural Technology, University of Puerto Rico at Utuado, Utuado, PR, USA
Institute for Land Water & Society, Charles Sturt University, Albury, NSW, Australia
AgResearch, Lincoln Research Centre, Christchurch, New Zealand
University of California Cooperative Extension, San Mateo & San Francisco Counties, Half Moon Bay, CA, USA
Department of Zoology, Faculty of Science, Palacký University, Olomouc, Czech Republic
Terrestrial Ecology Research Group, Department for Ecology and Ecosystem Management, School of Life Sciences Weihenstephan, Technical Universityof
Munich, Freising, Germany
Elinor M. Lichtenberg, Department of
Integrative Biology, University of Texas at
Austin, Austin, TX, USA.
Present address
Elinor M. Lichtenberg, Department of
Integrative Biology, University of Texas at
Austin, Austin, TX, USA
Funding information
National Institute of Food and Agriculture,
U.S. Department of Agriculture, Grant/
Award Number: 2014-51106-22096;
National Council for Scientific and
Technological Development-CNPq, Grant/
Award Number: 305062/2007-7; Felix
Trust; STEP Project, Grant/Award Number:
EC FP7 244090
Agricultural intensification is a leading cause of global biodiversity loss, which can
reduce the provisioning of ecosystem services in managed ecosystems. Organic
farming and plant diversification are farm management schemes that may mitigate
potential ecological harm by increasing species richness and boosting related
ecosystem services to agroecosystems. What remains unclear is the extent to which
farm management schemes affect biodiversity components other than species rich-
ness, and whether impacts differ across spatial scales and landscape contexts. Using
a global metadataset, we quantified the effects of organic farming and plant diversi-
fication on abundance, local diversity (communities within fields), and regional diver-
sity (communities across fields) of arthropod pollinators, predators, herbivores, and
detritivores. Both organic farming and higher in-field plant diversity enhanced
arthropod abundance, particularly for rare taxa. This resulted in increased richness
but decreased evenness. While these responses were stronger at local relative to
regional scales, richness and abundance increased at both scales, and richness on
farms embedded in complex relative to simple landscapes. Overall, both organic
farming and in-field plant diversification exerted the strongest effects on pollinators
and predators, suggesting these management schemes can facilitate ecosystem ser-
vice providers without augmenting herbivore (pest) populations. Our results suggest
that organic farming and plant diversification promote diverse arthropod metacom-
munities that may provide temporal and spatial stability of ecosystem service provi-
sioning. Conserving diverse plant and arthropod communities in farming systems
therefore requires sustainable practices that operate both within fields and across
agricultural management schemes, arthropod diversity, biodiversity, evenness, functional
groups, landscape complexity, meta-analysis, organic farming, plant diversity
Simplification of agricultural landscapes, and increased use of fertiliz-
ers and pesticides, threaten arthropod communities worldwide (Mat-
son, Parton, Power, & Swift, 1997; Potts et al., 2016; Tscharntke,
Klein, Kruess, Steffan-Dewenter, & Thies, 2005). This could impair
agricultural sustainability because declines in arthropod abundance
and diversity are often associated with reduced provisioning of
ecosystem services including pollination, pest control, and nutrient
cycling (Kremen & Miles, 2012; Oliver et al., 2015). Two strategies
purported to mitigate this ecological harm are organic farming and
in-field plant diversification (Table S1). We refer to these strategies
as farm management schemes, both of which include a host of prac-
tices that promote biological diversification (Kremen & Miles, 2012;
Puech, Baudry, Joannon, Poggi, & Aviron, 2014). We refer to organic
farming, conventional farming, high in-field plant diversification, and
low in-field plant diversification as separate field types. Mounting
evidence indicates that arthropod communities are more diverse and
abundant in fields lacking synthetic fertilizers and pesticides, and in
those with greater plant diversity (e.g., intercropped or having non-
crop vegetation like hedgerows or floral strips) (Bat
ary, Dicks, Kleijn,
& Sutherland, 2015; Crowder, Northfield, Gomulkiewicz, & Snyder,
2012; Fahrig et al., 2015; Garibaldi et al., 2014; Kennedy et al.,
2013; Letourneau et al., 2011).
The benefits of diversified farming practices may manifest at dif-
ferent scales, such as within individual fields (local diversity) or
across multiple fields in a landscape (regional diversity) (Table S1).
One observational study of 205 farms across Europe and Africa, for
example, found that although organic farming provided strong bene-
fits for local richness of plants and pollinators, these benefits faded
at regional scales (Schneider et al., 2014). This suggests that while
farmers may promote local diversity on their field(s) by using organic
practices, their efforts may not enhance biodiversity across multiple
fields. Conversely, the addition of hedgerows to crop fields has been
shown to increase community heterogeneity and species turnover
(measures of local diversity), which are important components of
regional diversity (Ponisio, MGonigle, & Kremen, 2016). The effects
of farm management for particularly mobile arthropods, such as polli-
nators, may also transcend individual fields if the improved quality of
habitats on one field boosts abundance, with organisms spilling over
to nearby fields (Kennedy et al., 2013; Tscharntke et al., 2012).
While increases in local diversity have been shown to provide the
strongest benefits to individual ecosystem services (i.e., pollination
and biological control), regional diversity can support the simultane-
ous provision of multiple ecosystem services over space and time
(Pasari, Levi, Zavaleta, & Tilman, 2013). Thus, to mitigate the effects
of biodiversity loss across agroecosystems, farm management
schemes should ideally benefit both local and regional diversity.
Research on the impacts of organic farming and in-field plant
diversity has primarily focused on beneficial functional groups such
as natural enemies and pollinators (Crowder, Northfield, Strand, &
Snyder, 2010; Kennedy et al., 2013) across intensively sampled
regions of Europe and North America (De Palma et al., 2016; Shack-
elford et al., 2013). Moreover, almost all studies rely on richness (the
number of taxa; Table S1) as a proxy for biodiversity but ignore met-
rics such as evenness (the relative abundances among species;
Table S1) (e.g., Bengtsson, Ahnstr
om, & Weibull, 2005; Tuck et al.,
2014). Yet, richness poorly reflects overall community diversity
(Duncan, Thompson, & Pettorelli, 2015; Loiseau & Gaertner, 2015),
and its measurement is strongly confounded by abundance (Chao &
Jost, 2012). Variation in richness has also been shown to have mini-
mal impacts on ecosystem functioning when richness increases are
driven primarily by rare species that contribute little to ecosystem
services (Kleijn et al., 2015; Winfree, Fox, Williams, Reilly, & Cari-
veau, 2015). While common species may provide the majority of
ecosystem services on some farms (Kleijn et al., 2015; Schwartz
et al., 2000), rare species can provide redundancy (Kleijn et al.,
2015) or support provisioning of multiple ecosystem services (Soli-
veres et al., 2016). Assessing evenness can help determine whether
richness increases are driven by rare or common species. Richness,
evenness, and abundance can also independently or interactively
affect ecosystem function (Crowder et al., 2010; Northfield, Snyder,
Ives, & Snyder, 2010; Wilsey & Stirling, 2006; Winfree et al., 2015;
Wittebolle et al., 2009). Thus, teasing apart the effects of farm man-
agement schemes on abundance and each diversity metric are criti-
cal. While existing studies find that organic farming and in-field plant
diversification tend to boost abundance and richness of certain taxa,
whether these effects are consistent for other biodiversity compo-
nents such as evenness, for functional groups other than pollinators
and natural enemies, and for less-well studied regions of the world
(e.g., the tropics and Mediterranean) remains unclear.
Here, we present a comprehensive synthesis of studies that
explore how organic farming and in-field plant diversification influ-
ence arthropod communities across global agroecosystems. We
determine whether community responses to these management
schemes vary based on different metrics (abundance, local richness
and evenness, regional richness, and evenness) and arthropod func-
tional groups (detritivores, herbivores, pollinators, and predators).
We investigate if these responses depend on landscape complexity
(i.e., the proportion of natural and seminatural habitat surrounding
the farm; Fig. S1, Table S1), because landscape heterogeneity has
been shown to influence the effectiveness of farm management
schemes (Bat
ary, B
aldi, Kleijn, & Tscharntke, 2011; Kennedy et al.,
2013; Kleijn, Rundl
of, Scheper, Smith, & Tscharntke, 2011; Tuck
et al., 2014). We also explore whether farm management schemes
have similar impacts on relatively rare compared to common taxa.
Our results demonstrate whether local and regional diversities and
abundance of different functional groups are similarly affected by
on-farm management and landscape complexity, and the extent of
covariance between biodiversity within and across fields in a land-
scape. Broadly, our findings further reveal the role of farm manage-
ment in mitigating biodiversity loss and maintaining healthy
arthropod communities in agroecosystems under global change.
Literature survey
We compiled data from studies on arthropod diversity in agroecosys-
tems that compared one or both of the farm management schemes of
interest: (i) organic vs. conventional farming and (ii) high vs. low in-field
plant diversity. We defined organic agriculture as fields that were
organically certified or met local certification guidelines (Table S1).
These guidelines involve, at minimum, maintaining production systems
free of synthetic pesticides and fertilizers. We defined conventional
agriculture as fields or farms that used recommended rates of syn-
thetic, or a mix of synthetic and organic, pesticides and fertilizers.
Other types of farming systems, such as integrated, that fit neither cat-
egory where excluded from the analysis. Fields were defined as having
high in-field plant diversity if they had diverse crop vegetation or man-
aged field margins to include non-crop vegetation (e.g., hedgerows,
border plantings, and flower strips) (Table S1). We also classified small
(<4 ha) fields as diverse because they yield small-scale crop diversity
(across several fields) even if the target field is a monoculture (Pasher
et al., 2013). Fields were defined as having low in-field plant diversity
if they had none of these features. Studies that compared these
schemes were identified by (i) searching the reference lists of recent
meta-analyses (Bat
ary et al., 2011; Chaplin-Kramer, ORourke, Blitzer,
& Kremen, 2011; Crowder et al., 2012; Garibaldi et al., 2013; Kennedy
et al., 2013; Scheper et al., 2013; Shackelford et al., 2013), (ii) search-
ing ISI Web of Knowledge (April and May 2013) using the terms
evenness or richnessand organic and conventionalor local diver-
sity,and (iii) directly contacting researchers who study arthropods in
agricultural systems.
We identified 235 relevant studies that we examined for inclu-
sion based on five criteria: (i) sampling was performed in the same
crop or crop type (e.g., cereals) for organic and conventional fields,
or fields with high and low in-field plant diversity; (ii) sampling was
conducted at the scale of individual crop fields rather than using
plots on experiment stations; (iii) the study included at least two
fields of each type; (iv) all organisms collected were identified to a
particular taxonomic level (i.e., order, family, genus, species, or mor-
phospecies), such that no taxa were lumped into groups such as
other,and (v) at least three unique taxa were collected. We use
taxonto refer to a single biological type (e.g., species,
morphospecies, genus, and family), determined as the finest taxo-
nomic resolution to which each organism was identified in a particu-
lar study (see examples in Table S1). A total of 60 studies met our
criteria, representing 43 crops, 21 countries, and five regions (Asia,
Europe, North and Central America, South America, and Oceania)
(Fig. S2, Table S2). For studies that investigated both management
scheme comparisons, we included the data in both analyses only
when the field types were independently assigned (Table S3); other-
wise we selected the scheme that the authors indicated the study
was designed to address (Table S2). Across these 60 studies, our
meta-analysis included 110 unique data points: 81 comparing organic
and conventional fields and 29 comparing fields with high vs. low in-
field plant diversity (Fig. S2, Tables S2, S4, archived data). Among
organic vs. conventional studies, the numbers with high in-field plant
diversity, low in-field plant diversity, and both levels of plant diver-
sity were independent of organic vs. conventional management
=0.47, p=.79).
Calculation of effect sizes
Unlike traditional meta-analyses that extract summary statistics from
studies, we gathered and manipulated raw data, which enabled us to
calculate evenness and classify taxa into functional groups. For each
study, we compiled data on the abundance of all taxa in each field.
For studies conducted across multiple years or crop types, separate
values were compiled for each year and crop. To avoid pseudorepli-
cation, for multiyear studies we selected a single year to analyze
based on maximizing the number of (i) sites that met the evenness
criterion (at least three taxa), (ii) fields, or (iii) individuals (in decreas-
ing priority order; Garibaldi et al., 2013). Each collected taxon was
classified into one of four functional groups: detritivore, herbivore,
pollinator, or predator (see Supporting Methods for details). These
taxon-level data were used to calculate effect sizes for abundance,
local diversity, and regional diversity in paired organic vs. conven-
tional or high vs. low in-field plant diversity systems. For local and
regional calculations, we defined diversity as both richness and even-
ness, and treated each functional group separately (Fig. S1).
Local diversity reflects the average diversity within each field
and was calculated using individual crop fields as the sampling unit
(Fig. S1, Table S1). In studies with subsamples at a scale smaller than
a field (i.e., plots within fields), values across these subsamples were
averaged before calculating local diversity. Abundance was the num-
ber of arthropods, and richness the number of unique taxa, in a field.
Evenness was calculated using the metric E
, which ranges from 0
(one taxon dominant) to 1 (uniform abundance for all taxa). This
metric was chosen for its desirable statistical properties, particularly
independence from richness, and its use in similar previous meta-
analyses (Crowder et al., 2012). After calculating abundance, rich-
ness, and evenness for each field, we averaged values across all
fields of a particular type in a study to obtain the values for effect
size calculations.
Regional diversity values were calculated based on individuals
pooled across all fields in a study (Fig. S1, Table S1). Thus, regional
richness and evenness are measures of diversity of metacommunities
across fields in a landscape, while local diversity measures communi-
ties in a single field (Wang & Loreau, 2014). We note that regional
diversity is not a direct indication of spatial scale, as the geographical
extent of sampling varied among studies. Some studies were not
designed to assess regional diversity specifically, and sampled
unequal numbers of fields of each type. To correct for this sampling
bias, we used sample-based rarefaction with 1,000 random samples
taken from the set of fields in a given study to determine pooled
species assemblages (Gotelli & Colwell, 2011). For example, if a
study had 10 conventional and six organic fields, regional diversity
values for the conventional management schemes would be based
on the average pooled community taken from 1,000 random draws
of six field sites. Regional abundance is simply local abundance multi-
plied by the number of sites, thus we reported only one abundance
value per study.
To compare effects of farm management schemes on diversity
and abundance, we used the log-response ratio as an effect size
metric (Hedges, Gurevitch, & Curtis, 1999). We used this metric,
rather than a weighted effect size, for three reasons. First, weighted
effect sizes could not be calculated for regional diversity because
these calculations were based on a single value (without replication)
from each study, such that there was no estimate of variability. Sec-
ond, our studies classified arthropods at varying levels of taxonomic
resolution. Studies classified at the family level had less variability
than studies classified at the species level, so using a weighted met-
ric would give studies conducted at a coarser taxonomic resolution
greater weight. Finally, preliminary analysis showed that weighted
and unweighted analyses of local diversity and abundance were
qualitatively similar (Table S5). In the Results, we back-transformed
log-response ratio effect sizes to percentages.
We assessed funnel plot asymmetry to test for publication bias.
Because we used an unweighted effect size metric, we plotted
effect sizes against sample sizes (i.e., number of fields; Figs S3, S4)
(Sterne & Egger, 2001), and visually assessed asymmetry as formal
statistical tests require effect size variances (Jin, Zhou, & He, 2015)
and measures of regional diversity had no variance component.
Based on our visual assessment, we did not find areas of missing
non-significant results, a directional bias to effects, or a strong rela-
tionship between effect and sample sizes. We did not detect any
sign of publication bias; funnel plots were sufficiently symmetrical.
Finally, we ensured the sampling method (active vs. passive sampling
techniques) did not influence results (see Supporting Methods,
Table S6). We calculated abundance and diversity values with Rv.
3.1.1 (R Core Team, 2014), using packages BIODIVERSITYR (Kindt &
Coe, 2005), doBy (Højsgaard & Halekoh, 2013), and reshape (Wick-
ham, 2007). Data and R scripts are available at
Study variables
We gathered data on three categorical variables and assessed
whether they mediated arthropod responses to farm management
schemes: (i) landscape complexity (simple, complex), (ii) biome (bo-
real, Mediterranean, temperate, and tropical), and (iii) crop cultivation
period (annual, perennial). Landscape complexity (see Fig. S1,
Table S1) was determined from land cover data on the percentage
of natural and seminatural habitat within 1 km of sampled fields.
Natural and seminatural habitats were defined as areas dominated
by forest, grassland, shrubland, wetlands, ruderal vegetation, or non-
agricultural plantings (i.e., previously cultivated areas where vegeta-
tion is regenerating, hedgerows, field margins, and vegetation along
roadways or ditches). For each study, we calculated the mean per-
centage of natural habitats across fields using locally relevant land
cover databases. Landscapes were classified as simple if they aver-
aged 20% natural habitat, and complex if they averaged >20% nat-
ural habitat, following Tscharntke et al. (2005) and common practice
(e.g., Bat
ary et al., 2011; Scheper et al., 2013) (see Supporting Meth-
ods for additional details). Biome was based on the geographic loca-
tion of the study. Crop cultivation periods were derived from several
sources (FAO AGPC, 2000; Garibaldi et al., 2013). Table S4 shows
the distribution of data points across each of these descriptive vari-
Data analyses
Table S7 summarizes specific questions we addressed and the
approach we used to test each one. We first used one-sample t-
tests (Crowder & Reganold, 2015) to determine if the mean effect
sizes for abundance, local richness and evenness, and regional rich-
ness and evenness differed significantly from 0. For each manage-
ment scheme comparison (organic vs. conventional or high vs. low
in-field plant diversity), these analyses were conducted for the
overall arthropod community and for each functional group sepa-
rately. We also explored correlations between local and regional
richness, and between local and regional evenness, to determine if
these metrics responded similarly to each of the management
schemes. We used a=0.10, to describe effect sizes that appeared
ecologically important but did not meet the somewhat arbitrary
a=0.05. This accords with a recent policy statement by the Amer-
ican Statistical Association (Wasserstein & Lazar, 2016), which
notes that reliance on arbitrary alpha values can lead to erroneous
In subsequent analyses, we used metaregression to examine
whether effect sizes were influenced by functional group and other
study characteristics. We excluded studies lacking landscape com-
plexity data (see archived data) from metaregressions. For each man-
agement scheme and response, we ran a linear mixed model (LME4
package; Bates, M
achler, Bolker, & Walker, 2014) that included eight
fixed effect variables: (i) functional group (detritivore, herbivore,
predator, and pollinator), (ii) diversity scale (local, regional), (iii) land-
scape complexity (simple, complex), (iv) biome (boreal, Mediter-
ranean, temperate, and tropical), (v) crop cultivation period (annual,
perennial), (vi) functional group 9diversity scale interaction, (vii)
functional group 9landscape complexity interaction, and (viii) diver-
sity scale 9landscape complexity interaction. These models included
study ID as a random effect. We used information-theoretic model
selection to determine the set of best-fit models for each response
variable (MUMIN package; Barton, 2014), which contained models with
AICc values within 2 of the smallest value (Burnham & Anderson,
1998). We examined significance of the fixed effects in each model
in the best-fit set (a=0.10) with likelihood ratio tests, and used post
hoc planned contrasts (with p-values adjusted to control the overall
Type I error rate using Holms sequential Bonferroni procedure; see
Supporting Methods) (PHIA package; Rosario-Martinez, 2013) to test
for (i) differences in effect size among functional groups and biomes,
(ii) differences in effect size between the local and regional scales
within each functional group, and (iii) landscape complexity differ-
ences between each pair of functional groups.
We also tested whether abundance and richness effect sizes dif-
fered for rare and common taxa. Following Kleijn et al. (2015),
within each study we classified taxa as common if their relative
abundance was at least 5% of the total community; other species
were categorized as rare. We then calculated local abundance and
richness as well as regional abundance and richness separately for
rare and common taxa. We used one-sample t-tests to determine if
mean effect sizes differed significantly from zero, and paired t-tests
to determine whether mean effect sizes differed between rare and
common taxa.
Effects of management schemes on overall
arthropod communities
Organic farming increased arthropod abundance (45% change), local
richness (19%), and regional richness (11%) (Figure 1a, Table S8).
These positive effects were stronger for local compared to regional
richness (Figure 1a, Tables S9, S10). Arthropod communities on
organic farms had significantly but only moderately lower local even-
ness (6%) and regional evenness (8%) than on conventional farms
(Figure 1a, Table S8). Fields with high in-field plant diversity
increased local richness (23%) and regional richness (19%), with simi-
lar magnitude (Figure 1b, Tables S8, S11, S12). In-field plant diver-
sity did not significantly affect abundance (27%), local evenness
(6%), or regional evenness (13%) (Figure 1b, Table S8). Overall,
there were strong positive correlations between local and regional
richness (r =.87), and between local and regional evenness (r =.57;
Fig. S5).
Organic farming increased abundance and richness of both rare
and common arthropods at the local and regional scales (Fig. S6a, c,
Table S13). At the local scale, organic farming increased arthropod
richness by promoting rare taxa (27% increase) more strongly than
common taxa (14% increase) (Fig. S6c, Table S14). In-field plant
diversification also had differential effects on rare and common taxa,
increasing richness of both at the local scale, but only of rare taxa at
the regional scale (Fig. S6d, Table S13). Fields with higher in-field
plant diversity increased abundance of common arthropods, but not
of rare arthropods (Fig. S6b, Table S13).
Effects of management schemes on arthropod
functional groups
Organic farming substantially increased the abundance (90%), local
richness (55%), and regional richness (32%) of pollinator communities
but did not impact pollinator evenness (Figure 2a, Table S15). For
predator communities, organic farming increased abundance (38%)
and local richness (14%), lowered local (9%), and regional (14%)
evenness (Figure 2c, Table S16), but did not affect regional richness
(Figure 2c, Table S16). Organic farming also did not impact abun-
dance, local or regional richness, or local or regional evenness for
herbivore (Figure 2e, Table S17), or detritivore (Figure 2g, Table S18)
communities. For all biodiversity components and functional groups,
effect sizes in response to organic farming did not differ between
the local and regional scales (Figure 2a, c, e, f, Tables S9, S10). The
diversity scale 9landscape complexity interaction was never
retained in a best-fit model (Tables S9, S11).
High in-field plant diversity promoted the abundance (45%), local
richness (44%), and regional richness (29%) of pollinator communi-
ties, but decreased local pollinator evenness (11%) (Figure 2b,
Table S15). In-field plant diversity did not affect regional pollinator
evenness (Figure 2b, Table S15). In addition, in-field plant diversity
did not alter abundance, local or regional richness, or local or regio-
nal evenness for predator (Figure 2d, Table S16) or herbivore
Abundance Richness Evenness
(a) *
Effect size
Abundance Richness Evenness
In−field plant diversity
FIGURE 1 Effects of farm management schemes on arthropod abundance, local diversity, and regional diversity. Values shown are for the
entire arthropod community, and indicate the mean log-response ratio (SE) of (a) adopting organic farming and (b) promoting in-field plant
diversity on abundance, richness, and evenness. A *above a mean effect size denotes a significant difference from zero (determined via one-
sample t-tests; a=0.1; statistical details in Tables S8), while one below a pair of means indicates a significant difference between local and
regional diversity (determined via linear mixed models; a=0.1; Tables S9S12)
(Figure 2f, Table S17) communities. In-field plant diversity increased
the regional richness (69%) of detritivores and lowered regional
detritivore evenness (65%), but did not impact detritivore abun-
dance, local richness, or local evenness (Figure 2h, Table S18). The
low sample size for detritivores, however, limits our ability to make
inferences about this group.
Effects of landscape complexity, biome, and
crop cultivation period on arthropod communities
Landscape complexity did not mediate the influences of organic
farming or in-field plant diversity on arthropod abundance or even-
ness (Figure 3, Tables S9S12). However, both management
schemes had stronger positive effects on local and regional arthro-
pod richness in complex relative to simple landscapes: organic farm-
ing 26% vs. 9%, in-field plant diversification 29% vs. 11%,
respectively (Figure 3c, d, Tables S9S12). The effects of landscape
complexity were similar in both direction and magnitude for local
and regional diversity (Figure 3ce, Tables S9S12). Organic farming
promoted herbivore richness to a greater extent in simple than com-
plex landscapes (Table S10), but other effects of landscape complex-
ity on abundance and diversity were similar across functional groups
(Tables S9S12).
Stronger richness gains in complex than simple landscapes were
driven predominantly by rare taxa (Figure 4). In complex landscapes,
both organic farming and in-field plant diversification had stronger
positive effects on local richness of rare (organic 44%, plant diversifi-
cation 68%) than of common (organic 21%, plant diversification
18%) arthropod taxa (Figure 4c, d, Table S19). Organic farming
within complex landscapes also increased local abundance and regio-
nal richness of rare taxa (78% and 17%, respectively) to a greater
extent than common taxa (33% and 4%, respectively) (Figure 4a,
Table S19). Neither management scheme differentially affected
abundance or richness of rare and common taxa in simple landscapes
(Figure 4, Table S19).
Biome mediated the impacts of in-field plant diversity on arthro-
pod richness (pooled across local and regional scales) (Tables S11,
S12). Post hoc tests failed to indicate significant differences among
biomes when considering all studies; but when the single boreal
study was removed from the analysis, high in-field plant diversity
more strongly promoted richness in Mediterranean (53%) than in
temperate studies (2%) (Table S12). Biome did not mediate the
effects of organic farming or in-field plant diversification on arthro-
pod abundance or evenness (Tables S9S12). Organic farming
increased arthropod abundance to a greater extent in annual (70%)
than in perennial (1%) crops (Tables S9, S10). The effects of in-field
(a) *
Effect sizeEffect sizeEffect sizeEffect size
In−field plant diversity
(c) *
0.75 (e)
0.75 (f)
Abundance Richness Evenness
Abundance Richness Evenness
FIGURE 2 Effects of farm management
schemes on abundance, local diversity, and
regional diversity of arthropod functional
groups. Mean log-response ratios (SE)of
(left column) adopting organic farming and
(right column) promoting in-field plant
diversity for (a, b) pollinators, (c, d)
predators, (e, f) herbivores, and (g, h)
detritivores. A *above a mean effect size
denotes a significant difference from zero
(determined via one-sample t-tests;
a=0.1; Tables S15S18). Metaregressions
indicated that differences between local
and regional values did not vary with
functional group (Tables S9S12)
plant diversification on abundance and diversity were consistent
across crop cultivation periods (Tables S11, S12).
Our global meta-analysis showed that both organic farming and in-
field plant diversification strongly increased arthropod abundance
and richness, but had weaker effects on evenness. The minimal
evenness decreases on diversified farms reflected the presence of
more rare taxa. Emerging evidence suggests that rare taxa contribute
to individual ecosystem services less than common taxa (Kleijn et al.,
2015; Schwartz et al., 2000), although they may be important for
maintenance of multiple ecosystem services across time and space
(Isbell et al., 2011; Soliveres et al., 2016). Thus, while organic farm-
ing and plant diversification promote arthropod biodiversity conser-
vation goals, their impacts on ecosystem services may be nuanced.
The positive effects of both organic farming and in-field plant diver-
sification were greatest for two groups of beneficial arthropods: pol-
linators and predators. Thus, both schemes may increase
agroecosystem sustainability by promoting key ecosystem service
providers without boosting pest (herbivore) densities.
Previous meta-analyses have investigated how organic farming
and, to a lesser extent, in-field plant diversification, affect arthropod
abundance and richness (e.g., Bat
ary et al., 2011; Bengtsson et al.,
2005; Chaplin-Kramer et al., 2011; Scheper et al., 2013; Shackelford
et al., 2013; Tuck et al., 2014). Our study extends upon this work by
(i) combining data on multiple arthropod functional groups (but see
Shackelford et al., 2013), and (ii) examining the type and scale of
diversity across a variety of crop types. As such, we offer a more
comprehensive understanding of when and how farm management
schemes alter arthropod biodiversity. Our findings caution that the
frequent use of richness as the sole proxy for biodiversity fails to
reflect the full impacts of farming practices on biologic communities.
0.75 (a)
Regional –0.25
0.75 (b)
In−field plant diversity
0.75 (c)
0.75 (d)
Simple Complex
Effect size Effect size Effect size
Simple Complex
FIGURE 3 Effects of landscape
complexity on the entire arthropod
community in organic vs. conventional
farms (left column) and fields with high vs.
low in-field plant diversity (right column).
Each graph shows the mean log-response
ratio (SE) for studies in simple (20%
natural habitat) or complex (>20% natural
habitat) landscapes for (a, b) abundance, (c,
d) richness, and (e, f) evenness. A *
below a set of means indicates a
significant difference between means at
the habitat complexity levels (determined
via paired t-tests; a=0.1; Tables S9S12)
1.00 (a)
1.00 (b)
In−field plant diversity
Simple Complex
Effect size Effect size
Simple Complex
FIGURE 4 Effects of farm management
schemes on abundance (a, b) and richness
(c, d) of common vs. rare taxa in simple
and complex landscapes. Mean log-
response ratios (SE) of (left column)
adopting organic farming and (right
column) promoting in-field plant diversity.
A*below a pair of means indicates a
significant difference between rare and
common taxa within a landscape
complexity category (determined via paired
t-tests; a=0.1; Tables S19)
While multiple studies have shown that organic farming boosts rich-
ness (e.g., Bengtsson et al., 2005; Tuck et al., 2014), we found that
evenness decreased: an outcome that was due mainly to promotion
of rare species. Species richness might be increased by conservation
practices that target specific taxa, but the promotion of evenness
requires practices that can simultaneously balance the abundances
of many taxa (Crowder et al., 2010, 2012). Finally, our results high-
light the necessity of targeting farm management within the context
of local conditions (Cunningham et al., 2013; Saunders, Peisley,
Rader, & Luck, 2016). For example, our results suggest that farmers
in Mediterranean biomes might see greater arthropod richness gains
by increasing in-field plant diversity than by farming organically,
while farmers growing annual crops may be more likely to boost
arthropod abundance with organic farming.
Disentangling relationships between biodiversity components at
local and regional scales can inform patterns of community assembly
and mechanisms that shape community structure (Gering & Crist,
2002; Wang & Loreau, 2014). We found that regional diversity posi-
tively correlated with local diversity under both management schemes.
Further, organic farming increased richness at both scales, although
local effects were stronger than regional ones. One possible explana-
tion is that diversified farming practices increase the heterogeneity of
local communities (e.g., Ponisio et al., 2016), which could lead to
greater regional diversity. Another possibility is that diversified fields
serve as source habitats within a matrix of crop and non-crop habitats
across farming landscapes (MGonigle, Ponisio, Cutler, & Kremen,
2015). Further, the benefits of diversification practices on local com-
munities in fields can be strongly mediated by regional species pools
across farming landscapes (Gering & Crist, 2002).
Our results, in combination with another recent meta-analysis
(Schneider et al., 2014), suggest that mobility of organisms can
determine whether the benefits of farm diversification accrue at
both local and regional scales. While we show that organic farming
can boost arthropod diversity at local and regional scales, Schneider
et al. (2014) found that organic farming increased plant, earthworm,
and spider richness at field but not regional scales. These groups of
organisms tend to have limited dispersal capacity, particularly plants
and earthworms. Thus, their local communities may be structured
more by competition than long-distance dispersal (Gering & Crist,
2002), which would limit the similarity between communities within
and across fields. At the same time, Schneider et al. (2014) found
that organic farming boosted the richness of bees, a more mobile
group of organisms, by approximately 25% at the local scale and
15% at the regional scale. We likewise found that diversified farming
increased abundance, and local and regional richness, of mobile polli-
nators, but had less impact on detritivores that tend to have lower
mobility (Sattler, Duelli, Obrist, Arlettaz, & Moretti, 2010).
Overall, our results are consistent with mounting evidence that
farm management and landscape complexity interactively affect
arthropod biodiversity (e.g., Bat
ary et al., 2011; Kennedy et al.,
2013; Rusch, Valantin-Morison, Sarthou, & Roger-Estrade, 2010;
Tuck et al., 2014), although results across studies reveal sometimes
conflicting patterns (Kleijn et al., 2011; Tscharntke et al., 2012; Tuck
et al., 2014). For example, agri-environment schemes that promote
low input, low disturbance, and diverse farms are sometimes most
effective in fostering biodiversity in structurally simple landscapes
ary et al., 2011; Scheper et al., 2013). This presumably occurs
because simple landscapes fail to satisfy the resource needs of many
species, such that these species may disperse into diverse farms to
seek resources (Kremen & Miles, 2012; Tscharntke et al., 2005). In
contrast, we found that impacts of organic farming and plant diversi-
fication on arthropod richness were heightened for fields embedded
in complex landscapes. This could occur if complex landscapes sup-
port more diverse species pools that can respond positively to farm
management (Duelli & Obrist, 2003; Hillebrand, Bennett, & Cadotte,
2008; Kennedy et al., 2013). Consistent with this hypothesis, we
showed that organic farming in complex landscapes preferentially
increased richness of rare taxa locally (i.e., in fields) and regionally
(i.e., across landscapes). Importantly, the interactive effects of land-
scape complexity and on-farm management may differ across arthro-
pod functional groups with varying capacity to move across
landscapes (Chaplin-Kramer et al., 2011; Tscharntke et al., 2005).
However, the only interaction between landscape complexity and
management schemes we found was for richness of herbivores, a
group with considerable variation in mobility among taxa (Sattler
et al., 2010).
Ideally, increases in abundance and diversity of arthropods on
farms would enhance the provisioning of ecosystem services (Kre-
men & Miles, 2012). However, empirical studies have provided
mixed evidence. In-field plant diversification and increased landscape
complexity have been found to promote predator abundance and
diversity with no change in pest control levels (Chaplin-Kramer et al.,
2011; Rusch et al., 2016) or reduced crop damage (Letourneau et al.,
2011). The relationship between biodiversity and ecosystem services
on farms is thus likely strongly mediated by speciesabundances and
functional roles. For example, Northfield et al. (2010) found that
greater predator richness increased pest control, but only with high
predator densities where complementarity among predator species
was fully realized. Increases in pollinator richness can have minimal
impacts on ecosystem services when richness gains are associated
with rare species that contribute little to pollination (Kleijn et al.,
2015; Winfree et al., 2015). Increasing wild pollinator richness on
large farms (>14 ha) only increases fruit set when wild pollinator
density is also high (Garibaldi et al., 2016). Higher predator species
evenness on organic farms has also been shown to translate to
increased pest control, with the potential to reduce yield gaps com-
pared with conventional agriculture (Crowder et al., 2010). However,
models suggest that decreased evenness could also lead to greater
ecosystem services when abundance of common species that are
effective ecosystem services providers increases at the expense of
rare species that are functionally less important (Crowder & Jabbour,
2014), a result seen with pollinators in agricultural systems (Kleijn
et al., 2015; Winfree et al., 2015). The combination of context-speci-
fic responses to farm management schemes shown by this study and
biodiversity-ecosystem functioning relationships that depend on spe-
ciesabundances and functional traits suggest that the effects of
diversified farming on ecosystem services are likely to depend on
taxon, biome, landscape, and crop characteristics.
By promoting biodiversity and abundance of arthropods, diversi-
fied agriculture could provide a multitude of other benefits (Oliver
et al., 2015). Biodiversity can help maintain stability of ecosystem
processes through mechanisms such as response diversity and func-
tional redundancy (Cardinale et al., 2012; Mori, Furukawa, & Sasaki,
2013). Arthropod richness gains in response to organic farming and
plant diversification, such as those documented here, could guard
against the loss of ecological function by supporting multiple species
that occupy similar functional niches (functional redundancy) or that
are functionally similar but respond differentially to environmental
change (response diversity; Elmqvist et al., 2003). The abundance
and richness increases we detected for pollinators and predators but
not for herbivores suggest that the two former groups may benefit
more from these stabilizing processes. Resilient systems must also
exhibit multiple ecosystem functions (multifunctionality) as environ-
mental conditions and arthropod populations fluctuate. Increases in
rare taxa, as detected in this study, may be critical for multifunction-
ality (Isbell et al., 2011; Soliveres et al., 2016) and even for single
ecosystem functions (Mouillot et al., 2013; Zavaleta & Hulvey,
2004). Thus, regional-scale refuges for rare species may ensure resili-
ent agricultural systems.
Overall, our results suggest that both organic farming and in-field
plant diversification promote biodiversity on farms. Moreover, these
two schemes might have interactive effects on farm productivity.
Practices such as multicropping (plant diversification) and longer,
more diverse, crop rotations can reduce the yield gaps between
organic and conventional agriculture (Ponisio et al., 2015), and
increase the profitability of organic relative to conventional systems
(Crowder & Reganold, 2015). Diversified small farms are increasingly
being replaced by large, simplified, and intensive monoculture pro-
duction systems (Bennett, Bending, Chandler, Hilton, & Mills, 2012;
Tscharntke et al., 2005). This is problematic because intensified
farming reduces the long-term sustainability of agroecosystems,
thereby threatening global food security (Ray, Ramankutty, Mueller,
West, & Foley, 2012). One of the greatest challenges of the 21st
century is meeting the food, fiber, and energy needs of a growing
human population, while maintaining farm sustainability and ecosys-
tem functioning (Tilman, Balzer, Hill, & Befort, 2011). Our study
underscores that adopting organic farming or in-field plant diversifi-
cation practices might aid society in attaining these goals.
We thank Kayla Fillion and Gavin Smetzler for data assistance, and
Lea D. Schneider for help with the graphical abstract. EML and DC
were supported by the National Institute of Food and Agriculture,
U.S. Department of Agriculture, under award number 2014-51106-
22096. BMF was supported by the National Council for Scientific
and Technological Development-CNPq, Bras
ılia, Brazil #305062/
2007-7. SGP and MO were supported by the Felix Trust and STEP
Project (EC FP7 244090).
Data and scripts available at:
Barton, K. (2014). MuMIn: Multi-model inference. R package v. 1.13.14.
Retrieved from
ary, P., B
aldi, A., Kleijn, D., & Tscharntke, T. (2011). Landscape-moder-
ated biodiversity effects of agri-environmental management: A meta-
analysis. Proceedings of the Royal Society B: Biological Sciences,278,
ary, P., Dicks, L. V., Kleijn, D., & Sutherland, W. J. (2015). The role of
agri-environment schemes in conservation and environmental man-
agement. Conservation Biology,29, 10061016.
Bates, D., M
achler, M., Bolker, B., & Walker, S. (2014). Fitting linear
mixed-effects models using lme4. arXiv:1406.5823.
Bengtsson, J., Ahnstr
om, J., & Weibull, A.-C. (2005). The effects of
organic agriculture on biodiversity and abundance: A meta-analysis.
Journal of Applied Ecology,42, 261269.
Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S., & Mills, P. (2012).
Meeting the demand for crop production: The challenge of yield
decline in crops grown in short rotations. Biological Reviews,87,5271.
Burnham, K. P., & Anderson, D. R. (1998). Model selection and multimodel
inference: A practical information-theoretic approach, 2nd edn. New
York: Springer-Verlag. 488 pp.
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C.,
Venail, P., ... Naeem, S. (2012). Biodiversity loss and its impact on
humanity. Nature,486,5967.
Chao, A., & Jost, L. (2012). Coverage-based rarefaction and extrapolation:
Standardizing samples by completeness rather than size. Ecology,93,
Chaplin-Kramer, R., ORourke, M. E., Blitzer, E. J., & Kremen, C. (2011). A
meta-analysis of crop pest and natural enemy response to landscape
complexity. Ecology Letters,14, 922932.
Crowder, D. W., & Jabbour, R. (2014). Relationships between biodiversity
and biological control in agroecosystems: Current status and future
challenges. Biological Control,75,817.
Crowder, D. W., Northfield, T. D., Gomulkiewicz, R., & Snyder, W. E.
(2012). Conserving and promoting evenness: Organic farming and
fire-based wildland management as case studies. Ecology,93, 2001
Crowder, D. W., Northfield, T. D., Strand, M. R., & Snyder, W. E. (2010).
Organic agriculture promotes evenness and natural pest control. Nat-
ure,466, 109112.
Crowder, D. W., & Reganold, J. P. (2015). Financial competitiveness of
organic agriculture on a global scale. Proceedings of the National Acad-
emy of Sciences,112, 76117616.
Cunningham, S. A., Attwood, S. J., Bawa, K. S., Benton, T. G., Broad-
hurst, L. M., Didham, R. K., ... Lindenmayer, D. B. (2013). To close
the yield-gap while saving biodiversity will require multiple locally
relevant strategies. Agriculture, Ecosystems and Environment,173,
De Palma, A., Abrahamczyk, S., Aizen, M. A., Albrecht, M., Basset, Y.,
Bates, A., ... Purvis, A. (2016). Predicting bee community responses
to land-use changes: Effects of geographic and taxonomic biases. Sci-
entific Reports,6, 31153.
Duelli, P., & Obrist, M. K. (2003). Regional biodiversity in an agricultural
landscape: The contribution of seminatural habitat islands. Basic and
Applied Ecology,4, 129138.
Duncan, C., Thompson, J. R., & Pettorelli, N. (2015). The quest for a
mechanistic understanding of biodiversityecosystem services
relationships. Proceedings of the Royal Society B: Biological Sciences,
282, 20151348.
Elmqvist, T., Folke, C., Nystr
om, M., Peterson, G., Bengtsson, J., Walker,
B., & Norberg, J. (2003). Response diversity, ecosystem change, and
resilience. Frontiers in Ecology and the Environment,1, 488494.
Fahrig, L., Girard, J., Duro, D., Pasher, J., Smith, A., Javorek, S., ... Tis-
chendorf, L. (2015). Farmlands with smaller crop fields have higher
within-field biodiversity. Agriculture, Ecosystems and Environment,200,
FAO AGPC (2000). Ecocrop. Retrieved from
Garibaldi, L. A., Carvalheiro, L. G., Leonhardt, S. D., Aizen, M. A., Blaauw,
B. R., Isaacs, R., ... Winfree, R. (2014). From research to action:
Enhancing crop yield through wild pollinators. Frontiers in Ecology and
the Environment,12, 439447.
Garibaldi, L. A., Carvalheiro, L. G., Vaissi
ere, B. E., Gemmill-Herren, B.,
olito, J., Freitas, B. M., ... Zhang, H. (2016). Mutually beneficial
pollinator diversity and crop yield outcomes in small and large farms.
Science,351, 388391.
Garibaldi, L. A., Steffan-Dewenter, I., Winfree, R., Aizen, M. A., Bom-
marco, R., Cunningham, S. A., ... Klein, A. M. (2013). Wild pollinators
enhance fruit set of crops regardless of honey bee abundance.
Science,339, 16081611.
Gering, J. C., & Crist, T. O. (2002). The alphabetaregional relationship:
Providing new insights into localregional patterns of species richness
and scale dependence of diversity components. Ecology Letters,5,
Gotelli, N. J., & Colwell, R. K. (2011). Estimating species richness. In A. E.
Magurran, & B. J. McGill (Eds.), Biological diversity (pp. 3954). New
York: Oxford University Press.
Hedges, L. V., Gurevitch, J., & Curtis, P. S. (1999). The meta-analysis of
response ratios in experimental ecology. Ecology,80, 11501156.
Hillebrand, H., Bennett, D. M., & Cadotte, M. W. (2008). Consequences
of dominance: A review of evenness effects on local and regional
ecosystem processes. Ecology,89, 15101520.
Højsgaard, S., & Halekoh, U. (2013). doBy: doBy - Groupwise summary
statistics, LSmeans, general linear contrasts, various utilities. R package
v. 4.5-11. Retrieved from
Isbell, F., Calcagno, V., Hector, A., Connolly, J., Harpole, W. S., Reich, P.
B., ... Loreau, M. (2011). High plant diversity is needed to maintain
ecosystem services. Nature,477, 199202.
Jin, Z.-C., Zhou, X.-H., & He, J. (2015). Statistical methods for dealing
with publication bias in meta-analysis. Statistics in Medicine,34, 343
Kennedy, C. M., Lonsdorf, E., Neel, M. C., Williams, N. M., Ricketts, T. H.,
Winfree, R., ... Kremen, C. (2013). A global quantitative synthesis of
local and landscape effects on wild bee pollinators in agroecosystems.
Ecology Letters,16, 584599.
Kindt, R., & Coe, R. (2005). Tree diversity analysis. A manual and software
for common statistical methods in ecology and biodiversity studies.
World Agroforestry Centre (ICRAF), Nairobi, 196 pp.
Kleijn, D., Rundl
of, M., Scheper, J., Smith, H. G., & Tscharntke, T. (2011).
Does conservation on farmland contribute to halting the biodiversity
decline? Trends in Ecology and Evolution,26, 474481.
Kleijn, D., Winfree, R., Bartomeus, I., Carvalheiro, L. G., Henry, M.,
Isaaces, R., ... Potts, S. G. (2015). Delivery of crop pollination ser-
vices is an insufficient argument for wild pollinator conservation. Nat-
ure Communications,6, 7414.
Kremen, C., & Miles, A. (2012). Ecosystem services in biologically diversi-
fied versus conventional farming systems: Benefits, externalities, and
trade-offs. Ecology and Society,17, 40.
Letourneau, D. K., Armbrecht, I., Rivera, B. S., Lerma, J. M., Carmona, E.
J., Daza, M. C., ... Trujillo, A. R. (2011). Does plant diversity benefit
agroecosystems? A synthetic review. Ecological Applications,21,921.
Loiseau, N., & Gaertner, J.-C. (2015). Indices for assessing coral reef fish
biodiversity: The need for a change in habits. Ecology and Evolution,
5, 40184027.
Matson, P. A., Parton, W. J., Power, A. G., & Swift, M. J. (1997).
Agricultural intensification and ecosystem properties. Science,277,
MGonigle, L. K., Ponisio, L. C., Cutler, K., & Kremen, C. (2015). Habitat
restoration promotes pollinator persistence and colonization in inten-
sively managed agriculture. Ecological Applications,25, 15571565.
Mori, A. S., Furukawa, T., & Sasaki, T. (2013). Response diversity determi-
nes the resilience of ecosystems to environmental change. Biological
Reviews,88, 349364.
Mouillot, D., Bellwood, D. R., Baraloto, C., Chave, J., Galzin, R., Harmelin-
Vivien, M., ... Thuiller, W. (2013). Rare species support vulnerable
functions in high-diversity ecosystems. PLOS Biology,11, e1001569.
Northfield, T. D., Snyder, G. B., Ives, A. R., & Snyder, W. E. (2010). Niche
saturation reveals resource partitioning among consumers. Ecology
Letters,13, 338348.
Oliver, T. H., Isaac, N. J. B., August, T. A., Woodcock, B. A., Roy, D. B., &
Bullock, J. M. (2015). Declining resilience of ecosystem functions
under biodiversity loss. Nature Communications,6, 10122.
Pasari, J. R., Levi, T., Zavaleta, E. S., & Tilman, D. (2013). Several scales of
biodiversity affect ecosystem multifunctionality. Proceedings of the
National Academy of Sciences,110, 1021910222.
Pasher, J., Mitchell, S. W., King, D. J., Fahrig, L., Smith, A. C., & Lindsay,
K. E. (2013). Optimizing landscape selection for estimating relative
effects of landscape variables on ecological responses. Landscape
Ecology,28, 371383.
Ponisio, L. C., MGonigle, L. K., & Kremen, C. (2016). On-farm habitat
restoration counters biotic homogenization in intensively managed
agriculture. Global Change Biology,22, 704715.
Ponisio, L. C., MGonigle, L. K., Mace, K. C., Palomino, J., de Valpine, P.,
& Kremen, C. (2015). Diversification practices reduce organic to con-
ventional yield gap. Proceedings of the Royal Society B: Biological
Sciences,282, 20141396.
Potts, S. G., Imperatriz-Fonseca, V. L., Ngo, H. T., Biesmeijer, J. C.,
Breeze, T. D., Dicks, L. V., ... Viana, B. F. (Eds.) (2016). Summary for
policymakers of the assessment report of the Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem Services on pollinators,
pollination and food production. Secretariat of the Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services,
Bonn, Germany, 36 pp.
Puech, C., Baudry, J., Joannon, A., Poggi, S., & Aviron, S. (2014). Organic
vs. conventional farming dichotomy: Does it make sense for natural
enemies? Agriculture, Ecosystems and Environment,194,4857.
R Core Team (2014).R: A language and environment for statistical comput-
ing. R Foundation for Statistical Computing, Vienna.
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C., & Foley, J. A.
(2012). Recent patterns of crop yield growth and stagnation. Nature
Communications,3, 1293.
Rosario-Martinez, H. (2013). phia: Post-Hoc Interaction Analysis. R package
v. 0.2-0. Retrieved from
Rusch, A., Chaplin-Kramer, R., Gardiner, M. M., Hawro, V., Holland, J.,
Landis, D., ... Bommarco, R. (2016). Agricultural landscape simplifica-
tion reduces natural pest control: A quantitative synthesis. Agriculture,
Ecosystems and Environment,221, 198204.
Rusch, A., Valantin-Morison, M., Sarthou, J.-P., & Roger-Estrade, J.
(2010). Biological control of insect pests in agroecosystems: Effects
of crop management, farming systems, and seminatural habitats at
the landscape scale: A review. In D. L. Sparks (Ed.), Advances in agron-
omy,Vol.109 (pp. 219259). Cambridge, MA, USA: Academic Press.
Sattler, T., Duelli, P., Obrist, M. K., Arlettaz, R., & Moretti, M. (2010).
Response of arthropod species richness and functional groups to urban
habitat structure and management. Landscape Ecology,25, 941954.
Saunders, M. E., Peisley, R. K., Rader, R., & Luck, G. W. (2016). Pollina-
tors, pests, and predators: Recognizing ecological trade-offs in agroe-
cosystems. Ambio,45,414.
Scheper, J., Holzschuh, A., Kuussaari, M., Potts, S. G., Rundl
of, M., Smith,
H. G., & Kleijn, D. (2013). Environmental factors driving the effective-
ness of European agri-environmental measures in mitigating pollina-
tor loss a meta-analysis. Ecology Letters,16, 912920.
Schneider, M. K., L
uscher, G., Jeanneret, P., Arndorfer, M., Ammari, Y.,
Bailey, D., ... Herzog, F. (2014). Gains to species diversity in organi-
cally farmed fields are not propagated at the farm level. Nature Com-
munications,5, 4151.
Schwartz, M. W., Brigham, C. A., Hoeksema, J. D., Lyons, K. G., Mills, M.
H., & van Mantgem, P. J. (2000). Linking biodiversity to ecosystem
function: Implications for conservation ecology. Oecologia,122, 297
Shackelford, G., Steward, P. R., Benton, T. G., Kunin, W. E., Potts, S.
G., Biesmeijer, J. C., & Sait, S. M. (2013). Comparison of pollinators
and natural enemies: A meta-analysis of landscape and local effects
on abundance and richness in crops. Biological Reviews,88, 1002
Soliveres, S., Manning, P., Prati, D., Gossner, M. M., Alt, F., Arndt, H., ...
Allan, E. (2016). Locally rare species influence grassland ecosystem
multifunctionality. Philosophical Transactions of the Royal Society B:
Biological Sciences,371, 20150269.
Sterne, J. A. C., & Egger, M. (2001). Funnel plots for detecting bias in
meta-analysis: Guidelines on choice of axis. Journal of Clinical Epi-
demiology,54, 10461055.
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand
and the sustainable intensification of agriculture. Proceedings of the
National Academy of Sciences,108, 2026020264.
Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I., & Thies, C.
(2005). Landscape perspectives on agricultural intensification and bio-
diversity ecosystem service management. Ecology Letters,8, 857
Tscharntke, T., Tylianakis, J. M., Rand, T. A., Didham, R. K., Fahrig, L.,
ary, P., ... Westphal, C. (2012). Landscape moderation of biodi-
versity patterns and processes - eight hypotheses. Biological Reviews,
87, 661685.
Tuck, S. L., Winqvist, C., Mota, F., Ahnstr
om, J., Turnbull, L. A., & Bengts-
son, J. (2014). Land-use intensity and the effects of organic farming
on biodiversity: A hierarchical meta-analysis. Journal of Applied Ecol-
ogy,51, 746755.
Wang, S., & Loreau, M. (2014). Ecosystem stability in space: a,band c
variability. Ecology Letters,17, 891901.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASAs statement on p-
values: Context, process, and purpose. The American Statistician,70,
Wickham, H. (2007). Reshaping data with the reshape package. Journal of
Statistical Software,21,120.
Wilsey, B., & Stirling, G. (2006). Species richness and evenness respond
in a different manner to propagule density in developing prairie
microcosm communities. Plant Ecology,190, 259273.
Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R., & Cariveau, D. P.
(2015). Abundance of common species, not species richness, drives
delivery of a real-world ecosystem service. Ecology Letters,18, 626
Wittebolle, L., Marzorati, M., Clement, L., Balloi, A., Daffonchio, D., Hey-
len, K., ... Boon, N. (2009). Initial community evenness favours func-
tionality under selective stress. Nature,458, 623626.
Zavaleta, E. S., & Hulvey, K. B. (2004). Realistic species losses dispropor-
tionately reduce grassland resistance to biological invaders. Science,
306, 11751177.
Additional Supporting Information may be found online in the sup-
porting information tab for this article.
How to cite this article: Lichtenberg EM, Kennedy CM,
Kremen C, et al. A global synthesis of the effects of
diversified farming systems on arthropod diversity within
fields and across agricultural landscapes. Glob Change Biol.
... Our finding of higher taxonomic richness and diversity in the agroforestry alleys compared with the arable fields is supported by previous findings in agricultural systems which have a lower management intensity (Attwood et al. 2008;Lichtenberg et al. 2017). We also found a stronger benefit to natural enemy Shannon and phylogenetic diversity early in the season, which suggests that agroforestry could play an important role for overwintering natural enemies. ...
... Sustainable intensification and the diversification of farming systems are suggested as possible solutions to global food security (Mbow et al., 2018.;Charles et al., 2014) and the biodiversity crisis (IPBES 2018; Lichtenberg et al. 2017), and as a way to increase resilience to predicted climate change (Gil et al. 2017;Kremen & Miles 2012). Agroforestry is one such example of a diversified farming system, and is defined as "the intentional integration of trees or shrubs with crop and animal production to create environmental, economic, and social benefits" (United States Department of Agriculture 2019). ...
... Ecological research on agroforestry systems has traditionally focussed on the biodiversity benefits of trees as providers of food sources, such as flowers, fruits, and organic matter, in addition to indirect benefits such as alternative prey/hosts and favourable microclimates for both soil and arboreal insects (Jose 2012;Tsonkova et al. 2012). In-field plant diversification is known to provide benefits to pollinators and predators of pests (Lichtenberg et al. 2017); therefore, management of the understorey beneath the trees that would promote plant diversity could contribute to invertebrate diversity by providing ground-level cover for overwintering, and additional food sources such as pollen and nectar (Boinot et al. 2019b;Staton et al. 2019). ...
Full-text available
The intensification of agricultural production in recent decades is widely recognised to have contributed substantially to global declines in biodiversity and associated ecosystem services, such as natural pest control and pollination. Agroforestry systems, where trees and/or shrubs are integrated into agricultural fields or landscapes, have the potential to increase biodiversity and associated ecosystem services. This thesis therefore aims to evaluate how agroforestry systems affect invertebrate pests, their natural enemies, and pollinators, in addition to productivity and farm income, relative to arable monocultures. A review and meta-analysis of the literature revealed that arthropod pests are significantly suppressed, and natural enemies significantly enhanced, in agroforestry systems relative to arable monocultures. However, the results were equivocal with high heterogeneity. Empirical data collected from three agroforestry sites with paired arable controls confirmed higher levels of plant and invertebrate biodiversity in agroforestry systems, and also revealed that the agroforestry systems led to a change in plant and invertebrate communities. These changes could be explained in terms of life-history traits, for example, plant communities in agroforestry systems were more perennial while invertebrates were less likely to be winged. Functional trait diversity of natural enemies was significantly higher in the agroforestry systems, indicating a higher level of biological control. Furthermore, species-level pollinator data from the same sites revealed that additional bee species in agroforestry contributed to functional trait diversity through niche complementarity. To further explore causes of heterogeneity, understorey management was manipulated at one agroforestry site, and was found to significantly affect natural enemy abundance and diversity, aphid suppression, and pollinator visitation. Although arable yields were up to 11% lower in agroforestry than arable systems, financial modelling predicted that agroforestry systems were capable of increasing farm income after at least seven years. Agroforestry systems therefore represent a viable option to restore farmland biodiversity and improve agricultural sustainability.
... More recently, the EU Farm to Fork strategy aims at halving the risk and use of synthetic pesticides by 2030, largely by promoting the adoption of organic management and IPM (European Commission, 2020). However, while there is substantial evidence to support organic management as a less harmful alternative to conventional farming, with several quantitative syntheses providing consensus about its benefits for biodiversity (e.g., Bengtsson et al., 2005;Lichtenberg et al., 2017), the effects of IPM have been subject to far less scrutiny (but see Katayama et al., 2019). ...
... While various individual scientific studies have compared conventional, organic and IPM in terms of their biodiversity impacts (e.g., Pekár 1999;Campos-Herrera et al., 2008;Krauss et al., 2011;Meng et al., 2016), meta-analyses have focused mainly on comparisons between conventional and organic management. These quantitative syntheses point to largely positive effects of organic management for biodiversity, particularly on service-providing organisms such as natural enemies of crop pests (Bengtsson et al., 2005;Garratt et al., 2011;Tuck et al., 2014;Lichtenberg et al., 2017;Katayama et al., 2019). Natural enemies are organisms that can exert top-down control on herbivores, which in turn can be considered pests or non-pests depending on the damage they produce to crops. ...
... Natural enemies are organisms that can exert top-down control on herbivores, which in turn can be considered pests or non-pests depending on the damage they produce to crops. Herbivorous insects (including pests) on the other hand have shown both positive or no responses to organic management (e.g., Bengtsson et al., 2005;Lichtenberg et al., 2017). Muneret et al., (2018) explicitly examined the effects of organic management on biocontrol potential and pest infestation relative to conventional management, revealing a higher level of biocontrol potential, but also of pest infestation, though only when the pests in question were weeds. ...
Agricultural policies in the European Union (EU) are increasingly promoting organic management and integrated pest management (IPM) as environmentally friendly alternatives to high-input conventional management. While there is consensus that organic management is largely beneficial for biodiversity, including the natural enemies of crop pests, IPM has been much less scrutinized. We conducted a meta-analysis based on 294 observations extracted from 18 studies to compare the effects of conventional, IPM and organic management on biocontrol potential and herbivore pressure in olive, an important cash crop in the EU. Information about the management practices used was also compiled, to assess differences in intensity between the three management strategies. Results suggest that IPM is predominantly based on intensive practices, employing chemical control rather than preventive measures as a first resort. Biocontrol potential and herbivore pressure were similar in conventional management and IPM. Moreover, biocontrol potential was higher in organic crops than in crops under IPM, especially when considering canopy-dwelling natural enemies. Although organic management enhanced biocontrol potential, it also benefitted some olive pests, and in both cases effects were more pronounced at warmer temperatures. Our results suggest that, in its current form, IPM might not significantly affect biocontrol potential or herbivore pressure when compared with conventional olive crop management. A shift to a more comprehensive implementation of IPM practices is thus needed, involving the use of proactive measures to promote natural enemies and regulate olive pests before resorting to chemical control. Moreover, greater use of non-chemical inputs might be required for effective regulation of olive pests in organic olive crops.
... We emphasise that the actions we have presented are only a starting point for change and acknowledge that there are unique and substantial challenges to restoring tropical agroecosystems, such as land use conflicts [80] and lack of education and awareness on how restoration can benefit biodiversity and livelihoods [81]. Further, it is likely that there will be serious challenges when applying individual restoration strategies across different regions, climatic conditions, surrounding landscape contexts, land-use histories, and crop systems [5,8,9,23,82]. Upscaling restoration can be further complicated when long-term financial support is lacking or when restoration initiatives cross borders [21]. ...
Well-designed approaches to ecological restoration can benefit nature and society. This is particularly the case in tropical agroecosystems, where restoration can provide substantial socioecological benefits at relatively low costs. To successfully restore tropical agroecosystems and maximise benefits, initiatives must begin by considering ‘who’ should be involved in and benefit from restoration, and ‘what’, ‘where’, and ‘how’ restoration should occur. Based on collective experience of restoring tropical agroecosystems worldwide, we present nine actions to guide future restoration of these systems, supported by case studies that demonstrate our actions being used successfully in practice and highlighting cases where poorly designed restoration has been damaging. We call for increased restoration activity in tropical agroecosystems during the current UN Decade on Ecosystem Restoration.
... There are, however, countermeasures focusing on the concept of sustainable intensification, a process (or system) where yields are increased without harmful environmental impacts. Integrated pest management and conservation agriculture (including diversified crop rotations) have been practiced for a long time already with mixed biodiversity benefits (Lichtenberg et al. 2017;Dainese et al. 2019;Beillouin et al. 2021). One approach to support biodiversity in agriculture is intercropping (Martin-Guay et al.2018;Wuest et al. 2021), where two or more crop species are grown on the same piece of land. ...
Full-text available
Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species-rich taxon providing important ecosystem services. To counteract negative consequences of agricultural intensification, creating a less hostile agricultural “matrix” through growing crop mixtures can reduce harm for arthropods without yield losses. While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and management intensity to study arthropods are lacking. Here, we experimentally manipulated crop diversity (1-3 species, fallows), crop species (wheat, faba bean, linseed, oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. Increasing crop diversity increased arthropod diversity and arthropod numbers. Mass-flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could lead to a massive increase in flower visits (up to 15 million visits/ha), indicating benefits of intercropping for insect biodiversity and associated ecosystem services.
... Mallinger et al., 2015), and changing landscapes and land management practises (e.g. Humbert et al., 2012;Kennedy et al., 2013;Lichtenberg et al., 2017). This stochastic temporal variation in abundance reduces the power of regression-based models to identify trends (Gerrodette, 1987), or, alternatively, increases the risk of erroneously identifying a trend where there is none. ...
Full-text available
1. Despite widespread recognition of the need for long-term monitoring of pollinator abundances and pollination service provision, such studies are exceedingly rare. 2. In this study, we assess changes in bee visitation and net capture rates for 73 species visiting watermelon crop flowers at 19 farms in the mid-Atlantic region of the United States from 2005 to 2012. 3. Over the 8 years, we found a 58% decline in wild bee visitation to crop flowers, but no significant change in honey bee visitation rate. Most types of wild bees showed similar declines in both the visitation and the net capture data; bumble bees, however , declined by 56% in the visitation data but showed no change in net capture rates. Trends in pollination services, that is, estimated pollen deposition, largely followed the trends in visitation and net capture rates. 4. While we detected large and significant declines in wild bees when using generalised linear mixed models (GLMMs), permutation analyses that account for non-directional variation in abundance were non-significant, demonstrating the challenge of identifying and describing trends in highly variable populations. 5. As far as we are aware, this article represents one of fewer than 10 published time-series (defined as >5 years of data) studies of changes in bee abundance, and one of only two such studies conducted in an agricultural setting. More such studies are needed in order to understand the magnitude of bee decline and its ramifications for crop pollination.
... Agriculture, the most widespread form of land use, takes up more than one-third of the global landmass and is increasingly impacting natural ecosystems (Wanger et al., 2020). The abundance, species richness and functional diversity of many plants, animals and microbes are lower on farmlands than in natural habitats, especially in monocultures with high inputs of agrochemicals (Lichtenberg et al., 2017). Specifically, the abundance and diversity of beneficial insects (pollinators and biological control agents) decline with increasing proportions of agricultural habitat in the landscape (Chaplin-Kramer et al., 2011;Kennedy et al., 2013). ...
Full-text available
Life‐history traits are increasingly used to understand how arthropod communities assemble and function under diverse conditions, for example why some species are better adapted to agricultural intensification than others. We aimed to understand which traits characterise parasitoid wasps under agricultural disturbances. To this end, we studied parasitoid communities from pomegranate orchards and nearby natural habitats in Israel. Ten sites along a climate gradient were sampled thrice along one fruit‐growing season. We compiled information on life‐history traits associated with development, adult diet and host taxa, for 27 well‐represented parasitoid species. We tested for relationships between the parasitoids' abundances, functional traits and environmental conditions, using RLQ and fourth‐corner analyses. Life‐history traits were highly related to environmental variables. Koinobionts (wasps whose parasitized hosts feed and grow), and parasitoids of aphids and whiteflies, were more common, and sugar‐feeding was less common, in orchards than in natural habitats. Parasitism of larval hosts correlated with aridity, while egg parasitism increased with herbaceous vegetation cover. Host composition and koinobiosis shape parasitoid communities in the orchards. Koinobiosis is often associated with life‐history traits such as small eggs, short life‐span, early egg maturation and high fecundity, which may be adaptive in the frequently disturbed orchard habitats. Further, dense vegetation conditions seem to favour egg parasitism (perhaps because of reduced risks of egg desication), while larval parasitism is more common in arid seasons and sites. These findings provide initial insights regarding the effects of land use and climate on the functional characteristics of parasitoid communities. Life history traits differ between habitats
... Nevertheless, we found that stripintercropping supported intermediate values of the diversity of carabid beetles and spiders, thereby enhancing the potential complementarity effects of these two important predator groups. This result adds to the growing knowledge of the benefits of combining two or more plant crops in the same area to attract predator species that would otherwise not be present (Brooker et al., 2015;Kremen & Miles, 2012;Lichtenberg et al., 2017;Qian et al., 2018). Spiders or carabid beetles that are more abundant in one crop can spill over to the second crop and promote ecological benefits. ...
Full-text available
1. Conventional agriculture in the global north is typically characterized by large monocultures, commonly managed with high levels of pesticide or fertilizer input and mechanization. Strip‐intercropping, i.e., diversifying cropland by growing strips of different crops using conventional machinery, may be a viable strategy to promote natural predator diversity and associated biological pest control in such conventional farming systems. 2. We tested the influence of strip‐intercropping of conventionally managed winter wheat with oilseed rape, using common machinery with 27‐36 m broad strips, on arthropod predator diversity and biological pest control. We characterized spider and carabid beetle communities, calculated pest aphid and pollen beetle densities, and recorded parasitism rates for both crops (number of mummified aphids on wheat and number of parasitized pollen beetle larvae on oilseed rape). 3. We observed a significant reduction in the densities of wheat aphids (50% decrease) and pollen beetle larvae (20% decrease) in strip‐intercropping areas compared to monocultures. Parasitism rates of wheat aphids increased significantly from 10% in monocultures to 25% in strip‐intercropping areas. The number of parasitized pollen beetle larvae did not show the same pattern but was higher towards the center of the oilseed rape strip. Overall, the composition of predator communities benefited from the close neighborhood of the two crop species in the strips, as carabid beetles were more abundant in oilseed rape and spiders were more abundant in wheat fields. Overall, strip‐intercropping reduced the dominance of one predator group and allowed for an equal representation of both spiders and carabid beetles in the mixture. 4. Synthesis and applications: Our study presents evidence of the benefits of adopting strip‐intercropping with relatively large strips (adapted to existing machinery) for natural predator diversity and biological pest control in a large‐scale conventionally managed farm scenario. Wheat‐oilseed rape strip‐intercropping reduced pest densities, increased parasitism of wheat aphids, and promoted equal representation of natural predator groups well beyond the areas of monoculture. Overall, by reducing the area dedicated to only one crop, the implementation of strip‐intercropping adapted to mechanized agricultural scenarios can be used to increase crop heterogeneity at regional scales and enhance biodiversity and biological control, even in simplified landscapes dominated by large‐scale conventional agriculture.
... conducted in the same study area for dung and carrion beetles found that forest fragments and forest-pasture edges had the highest number of individuals and species compared to living fences (Díaz et al. 2010). For arboreal arthropods, the key factors driving this pattern have been associated with more harsh conditions, decreased food resources and suitable microhabitats caused by the reduction in canopy cover, plant abundance and diversity in altered habitats (Floren & Linsenmair 2001, Lichtenberg et al. 2017, Novais et al. 2016. Although harsh conditions have been suggested as a possible factor that could increase the magnitude of the ecosystem engineering by insects in seasonal tropical forests (Novais et al. 2018, Vieira & Romero 2013, the variations in abiotic conditions among habitats in the evergreen tropical rainforest studied are expected to be considerably smaller compared to those between seasons in seasonal forests. ...
The magnitude of facilitation by shelter-building engineers on community structure is expected to be greater when they increase limited resources in the environment. We evaluated the influence of local environmental context on the colonisation of leaf shelters by arthropods in a Mexican evergreen tropical rainforest. We compared the species richness and abundance of arthropods (total and for different guilds) colonising artificially rolled leaves in habitats differing in understory heterogeneity (forest edge > old-growth forests > living fences). Arthropod abundance of the most representative arthropod taxa (i.e., Araneae, Blattodea, Collembola and Psocoptera) colonising the rolled leaves was greater at forest edge, a trend also observed for average arthropod abundance, and for detritivore and predator guilds. In addition, fewer arthropod species and individuals colonised the rolled leaves in the living fence habitat, a trend also observed for most arthropod guilds. As forest edge is expected to have a greater arthropod diversity and stronger density-dependent interactions, a greater limitation of refuges from competitors or predators may have determined the higher colonisation of the rolled leaves in this habitat. Our results demonstrate that local environment context is an important factor that affects the colonisation of arthropods in leaf shelters.
... Many studies have measured the response of within-site species richness (that is, alpha-diversity) to global environmental change (Estes et al. 2011;McGill et al. 2015;Chase et al. 2019), while others have focused on dissimilarity in community composition among sites (beta-diversity; Baselga 2010;Dornelas et al. 2014;Magurran et al. 2015). Still others consider how disturbance affects the diversity of species' functional traits (that is, functional diversity; Suding et al. 2008;Bjorkman et al. 2018) or evolutionary histories (phylogenetic diversity; Purvis et al. 2000;Sol et al. 2017), or biomass or abundance, at both the species and community level (Dirzo et al. 2014;Lichtenberg et al. 2017;Ross et al. 2018b). Compositional change may result from a range of mechanisms including changes to species richness, evenness, and dominance, with a recent synthesis finding that global change drivers affect these underlying mechanisms equally (Avolio et al. 2021). ...
Full-text available
Through global environmental change, humans are modifying the planet at an unprecedented rate and scale, triggering the ongoing biodiversity and climate crises. Ecological stability and the consistency of nature’s contributions to people are fundamental to the continued sustainability of human societies. Stability is a complex and multidimensional concept including components such as variability in time and space and the resistance to and recovery from disturbances. Global change has the potential to destabilise ecosystems, but the form and strength of the relationship between different global change drivers and dimensions of stability remains understudied, precluding general or mechanistic understanding. Here, I combine theory, a field experiment, and observational data from a high-resolution acoustic monitoring network to reveal the potential for multiple global change drivers to erode multidimensional ecological stability. Critically, I also show how biodiversity and natural habitats can buffer the destabilising effects of global environmental change on ecosystems and soundscapes, providing vital insurance against disturbance. In an era characterised by unrelenting global change and intensifying disturbance regimes, my results provide a key step towards a generalisable understanding—and ultimately management—of the stability of ecosystems and their contributions to human wellbeing.
Full-text available
Agricultural diversification often promotes biodiversity and ecosystem services by increasing habitat diversity. However, responses to agricultural diversification are context dependent, differentially impacting functional groups of service-providing organisms and crop yields. Conservation and no tillage are promoted as agricultural diversification practices that increase soil heterogeneity and habitat diversity. Here we investigated whether soil tillage practices in canola crop fields altered arthropod biodiversity or yield, and how effects of field-scale diversification compared to landscape-scale habitat context. We focused on effects of high, medium, or no tillage on five functional groups with unique diets and reproductive strategies: (i) herbivores, (ii) kleptoparasites, (iii) parasitoids, (iv) pollinators, and (v) predators. Effects of agricultural diversification on arthropod abundance and diversity varied across functional groups. Pollinators responded to on-farm soil diversification, benefiting from medium tillage. Predators and herbivores responded most strongly to landscape-scale habitat composition and were more abundant in landscapes with more semi-natural habitat. However, variation in arthropod communities had little effect on canola crop yield, which was lowest in fields with no tillage. Policy implications: Our results indicate that natural history differences among arthropod functional groups mediate how habitat availability affects biodiversity. Crop yields, however, showed no response to biodiversity of ecosystem service providers. Our research highlights the need to determine the contexts in which soil diversification practices meet a multi-faceted goal of simultaneously supporting natural biodiversity, ecosystem services, and crop yield.
Full-text available
Species diversity promotes the delivery of multiple ecosystem functions (multifunctionality). However, the relative functional importance of rare and common species in driving the biodiversity-multifunctionality relationship remains unknown. We studied the relationship between the diversity of rare and common species (according to their local abundances and across nine different trophic groups), and multifunctionality indices derived from 14 ecosystem functions on 150 grasslands across a land-use intensity (LUI) gradient. The diversity of above-and below-ground rare species had opposite effects, with rare above-ground species being associated with high levels of multifunctionality, probably because their effects on different functions did not trade off against each other. Conversely, common species were only related to average, not high, levels of multifunctionality, and their functional effects declined with LUI. Apart from the community-level effects of diversity, we found significant positive associations between the abundance of individual species and multifunctionality in 6% of the species tested. Species-specific functional effects were best predicted by their response to LUI: species that declined in abundance with land use intensification were those associated with higher levels of multifunctionality. Our results highlight the importance of rare species for ecosystem multifunctionality and help guiding future conservation priorities. © 2016 The Author(s) Published by the Royal Society. All rights reserved.
Full-text available
This manual can be downloaded for free from URL Effective data analysis requires familiarity with basic concepts and an ability to use a set of standard tools, as well as creativity and imagination. Tree diversity analysis provides a solid practical foundation for training in statistical methods for ecological and biodiversity studies. This manual arose from training researchers to analyse tree diversity data collected on African farms, yet the statistical methods can be used for a wider range of organisms, for different hierarchical levels of biodiversity and for a variety of environments — making it an invaluable tool for scientists and students alike. Focusing on the analysis of species survey data, Tree diversity analysis provides a comprehensive review of the methods that are most often used in recent diversity and community ecology literature including: • Species accumulation curves for site-based and individual-based species accumulation, including a new technique for exact calculation of sitebased species accumulation. • Description of appropriate methods for investigating differences in diversity and evenness such as Rényi diversity profiles, including methods of rarefaction to the same sample size for different subsets of the data. • Modern regression methods of generalized linear models and generalized additive models that are often appropriate for investigating patterns of species occurrence and species counts. • Methods of ordination for investigating community structure and the influence of environmental characteristics, including recent methods such as distance-based redundancy analysis and constrained analysis of principal coordinates. The manual also introduces a powerful new software programme, BiodiversityR, that is capable of performing all the statistical analyses described in the book. The software is built using the free R language and environment for statistical computing, and several of its libraries such as the vegan community ecology package and the R-commander graphical user interface. The software is provided on CD. After publishing this manual, the BiodiversityR software was modified into a package that can be downloaded and installed from URL The vegan community ecology package can be downloaded from URL Installation guidelines for windows users are available from URL A tutorial for ensemble suitability modelling is available from URL
Full-text available
Ecological intensification, or the improvement of crop yield through enhancement of biodiversity, may be a sustainable pathway toward greater food supplies. Such sustainable increases may be especially important for the 2 billion people reliant on small farms, many of which are undernourished, yet we know little about the efficacy of this approach. Using a coordinated protocol across regions and crops, we quantify to what degree enhancing pollinator density and richness can improve yields on 344 fields from 33 pollinator-dependent crop systems in small and large farms from Africa, Asia, and Latin America. For fields less than 2 hectares, we found that yield gaps could be closed by a median of 24% through higher flower-visitor density. For larger fields, such benefits only occurred at high flower-visitor richness.Worldwide, our study demonstrates that ecological intensification can create synchronous biodiversity and yield outcomes.
Full-text available
The composition of species communities is changing rapidly through drivers such as habitat loss and climate change, with potentially serious consequences for the resilience of ecosystem functions on which humans depend. To assess such changes in resilience, we analyse trends in the frequency of species in Great Britain that provide key ecosystem functions - specifically decomposition, carbon sequestration, pollination, pest control and cultural values. For 4,424 species over four decades, there have been significant net declines among animal species that provide pollination, pest control and cultural values. Groups providing decomposition and carbon sequestration remain relatively stable, as fewer species are in decline and these are offset by large numbers of new arrivals into Great Britain. While there is general concern about degradation of a wide range of ecosystem functions, our results suggest actions should focus on particular functions for which there is evidence of substantial erosion of their resilience.
Agricultural intensification is a leading cause of global biodiversity loss, which can reduce the provisioning of ecosystem services in managed ecosystems. Organic farming and plant diversification are farm management schemes that may mitigate potential ecological harm by increasing species richness and boosting related ecosystem services to agroecosystems. What remains unclear is the extent to which farm management schemes affect biodiversity components other than species richness, and whether impacts differ across spatial scales and landscape contexts. Using a global metadataset, we quantified the effects of organic farming and plant diversification on abundance, local diversity (communities within fields), and regional diversity (communities across fields) of arthropod pollinators, predators, herbivores, and detritivores. Both organic farming and higher in-field plant diversity enhanced arthropod abundance, particularly for rare taxa. This resulted in increased richness but decreased evenness. While these responses were stronger at local relative to regional scales, richness and abundance increased at both scales, and richness on farms embedded in complex relative to simple landscapes. Overall, both organic farming and in-field plant diversification exerted the strongest effects on pollinators and predators, suggesting these management schemes can facilitate ecosystem service providers without augmenting herbivore (pest) populations. Our results suggest that organic farming and plant diversification promote diverse arthropod metacommunities that may provide temporal and spatial stability of ecosystem service provisioning. Conserving diverse plant and arthropod communities in farming systems therefore requires sustainable practices that operate both within fields and across landscapes.
Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.