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Context-dependent response of crop pests to landscape
composition
Long Yang1, Yunfei Pan1, Kris A. G. Wyckhuys1,2, Minlong Li1, Kaitao Wang1, Bing Liu1,
Yangtian Liu1, Shuangshuang Jia1, Qian Li1, Yan Li1, Nicolas Desneux3, Yanhui Lu1,*
1 State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of
Agricultural Sciences, Beijing 100193, China
2 School of the Environment, University of Queensland, Saint Lucia, Australia
3 Université Côte d’Azur, INRAE, CNRS, UMR ISA, 06000 Nice, France
* Corresponding author: luyanhui@caas.cn
With 3 gures and 1 table
Abstract: Landscape composition aects the performance and pest status of insect herbivores in farmland, though these
interactions are mediated by population-level processes and agroecological context. These context-dependent responses
contribute to the variability of pest reactions to landscape composition across studies, which arise from a complex set of
biotic or abiotic factors that are poorly understood. Here, we provide a systematic literature review on the key determinants
of context-specicity and a hierarchical meta-analysis to quantitatively assess the organismal and habitat-related determi-
nants of pest pressure in agricultural landscapes globally. We discussed the potential eects of pest species traits, population
processes, habitat quality, and the spatiotemporal scale of research inuence pest responses to landscape composition in
landscape-level studies. The hierarchical meta-analysis of 227 eect sizes from 70 studies for 58 herbivorous pests showed
that pest success in crop elds is greatly aected by feeding mode, the exact stage of its population dynamic and crop habi-
tat quality. In general, landscapes with more semi-natural habitats tend to enhance the colonization level of specialist pests,
while more crops land increase season-long population density of the generalist. Large areas of low-quality crop habitats
within the landscape reduce pest abundance and these eects are clearer for season-long population density. By account-
ing for these parameters, landscape-level processes can be harnessed to strengthen the ecological regulation of pests and
thereby advance the ecological intensication of agriculture.
Keywords: Agroecology; context dependency; ecological based pest management; ecological intensication; host quality
1 Introduction
Though variable across geographical, crop and socio-eco-
nomic contexts, the composition of agricultural landscapes
aects the success of benecial and deleterious organisms
(Haan et al. 2020; Tscharntke et al. 2021). As such, land use
changes lead to modications in habitats that supply food and
non-food resources to these organisms, with ensuing conse-
quences at the individual, population and community levels
(Wu & Hobbs 2002; Duarte et al. 2018; Milovanović et al.
2020). This can greatly aect the relative success of crop-
feeding herbivores, crop yield resilience and the delivery
of (arthropod mediated) ecosystem (dis)services (Redhead
et al. 2020; Zhang et al. 2020; Haan et al. 2021).
Understanding the eects of landscape composition on
crop pests and biological control agents is of critical impor-
tance in the advancement of more sustainable forms of crop
protection (Corcos et al. 2017; Wyckhuys et al. 2022, 2023;
Deguine et al. 2023; Hatt & Doring 2023). Heterogeneous
landscapes that comprise diversied cropland, small elds
and critical amounts of semi-natural habitats benet from
greater natural enemy abundance and associated biologi-
cal control services (Bianchi et al. 2006; Chaplin-Kramer
et al. 2011; Tscharntke et al. 2021). In principle, this could
translate into higher levels of pest suppression and less pest
pressure. Yet, the population dynamics of pestiferous herbi-
vores are not solely aected by top-down control from natu-
ral enemies but also by bottom-up eects and by farm-level
disturbances such as pesticide use, harvest cycles or tillage
regimes (Lawton et al. 2020; Tooker et al. 2020). The two
ecological forces act in concert to dictate agricultural pest
pressure, and both landscape- and eld-level drivers fur-
ther shape this outcome (e.g., Yang et al. 2021; Rosero et al.
2024). Studies that focused on the landscape-level drivers
Entomologia Generalis Open Access
Published online February 2025
© 2025 The authors
DOI: 10.1127/entomologia/2025/3010 E. Schweizerbart’sche Verlagsbuchhandlung, 70176 Stuttgart, Germany, www.schweizerbart.de
Early Access Review Article
of pest pressure usually report conicting results and fail to
detect consistent patterns (Chaplin-Kramer et al. 2011; Veres
et al. 2013; Karp et al. 2018). More recent work has shown
how the traits of pests, i.e., native or non-native status and
host or habitat breadth, and environmental context deter-
mine the eects of surrounding landscapes on pest pressure
at the eld level (Tamburini et al. 2020). This phenomenon
is termed as context dependence, and poses an obstacle in
eorts to predict pest pressure or natural pest control across
landscapes (Chamberlain et al. 2014; Catford et al. 2022).
This can partially be resolved by a functional grouping of
agroecosystems to generate contextually-bound generaliza-
tions or so-called archetypes (Alexandridis et al. 2021, 2022)
or by expert-based spatial models (Riggi et al. 2024). As the
intermediate generality of these models and/or their depen-
dence upon rather subjective and increasingly scarce expert
perspectives may constrain their applicability, there is ample
potential to clarify and unravel the underlying biotic and abi-
otic determinants (i.e., context-dependent factors) of context
dependence. In this regard, an in-depth analysis of the rela-
tive contribution of pest and habitat-specic traits can help to
further the renement of modeling eorts.
In the following sections, we rst provide a qualitative
description of the potential context-dependent factors and
how these factors modulate landscape-scale processes for
pests. Second, we quantitatively assess the response of pest
pressure to landscape composition through a global meta-
analysis that takes these context-dependent factors into
account. The combination of a systematic literature review
with a global meta-analysis enhances our understanding of
the impact of landscape on crop pests and leveraging land-
scape-level approaches for sustainable pest management.
2 Context-dependent factors
Pest species-level traits, population-level processes, local
and landscape habitat characteristics, as well as the spatio-
temporal scale of research, are important context-dependent
factors inuencing pest responses to landscape composition.
2.1 Species traits
The traits of pest species are important context-dependent
factors that modulate landscape-level responses, and which
include feeding habit, dispersal capacity, ability to escape
natural enemies, voltinism, etc. First, feeding habits deter-
mine the extent to which a given pest species can exploit
the landscape-level occurrence and diversity of alternative
host plants (Tamburini et al. 2020). In addition to feeding
upon the target crop, polyphagous pests (or dietary gen-
eralists) can use a wider variety of host plant resources.
Habitats that contain those alternative host plants can thus
act as “source” or “sink” habitats according to their relative
importance within population processes (Kennedy & Storer
2000; Pan et al. 2013; Tsafack et al. 2016). The exact role of
specic habitats within a farming landscape can shift over
seasons, mediating the exact onset and magnitude of crop
colonization by generalist feeders (Carrière et al. 2012; Li
et al. 2020). Meanwhile, pests with more restricted feeding
habits (i.e., oligophagous or specialists) only rely upon one
or few cultivated or non-cultivated plant species to complete
their life cycles. Presence and relative coverage of a spe-
cic habitat that contains these (few) alternative host plants
is determinant of the eventual success of an oligophagous
pest within the entire landscape, while specialist feeders rely
upon spatiotemporal occurrence patterns of their sole (crop)
host (Veres et al. 2013).
Pest dispersal abilities, both active and passive, signi-
cantly aect eld colonization success and rate. The pressure
exerted by pests with greater dispersal capabilities is more
signicantly inuenced by landscape variables at larger
scales compared to less mobile species, as the former can
move across greater distances and exploit resources across a
broader area (Chaplin-Kramer et al. 2011; Martin et al. 2016;
Zhang et al. 2020). For example, weak iers such as Cydia
pomonella show a stronger response to landscape habitat
composition at the 1.0 km scale compared to highly mobile
species such as Helicoverpa armigera, which respond to pat-
terns at larger scales (Li et al. 2024; Song et al. 2024).
Multivoltine pests have multiple generations within a
growing season and often feed on dierent host plants. For
these species, temporal changes in host plant can be facul-
tative or obligatory (such as for the heteroecious soybean
aphid, Aphis glycines which overwinters on shrub of the
genus Rhamnus). For those pests that require a secondary
host, its landscape-level occurrence will determine overall
pest success and temporal dynamics (Moreno et al. 2022;
Yang et al. 2024). For some pests, the “top-down” eects
of natural enemies are limited and their population-level
dynamics are primarily shaped by the “bottom-up” eects
that are provided by one or more habitats within the land-
scape (Veres et al. 2013; Li et al. 2017, 2020).
2.2 Population processes
The population density of pests in the eld is the reection
of the combined eects of early colonization numbers and
subsequent population growth that inuenced signicantly
by birth and death rates (Dainese et al. 2017; Guedes et al.
2022). During the colonization phase, immigration events
usually play the dominant role in determining eld pest den-
sity, given that natural enemies – usually signicantly raise
death rates – sometimes immigrate into crop elds later than
do pests (Welch & Harwood 2014). Consequently, natural
enemies provide limited control during this early coloniza-
tion stage. When immigrating pests emanate from non-crop
habitats, crop-dominated landscapes may dilute the pest
population, lowering the pest density in any given patch of
the crop (Josso et al. 2013; Gilabert et al. 2017; Yang et al.
2019). In some cases, for instance, in complex agricultural
landscapes with a high proportion of non-crop habitats, the
2 Long Yang et al.
colonization of pests is higher compared to simplied land-
scapes (Thies et al. 2005; Yang et al. 2019). During the pest
reproductive periods, however, the presence of more host
crop habitats in the landscape tends to promote pest popula-
tion growth as it is easier for pests to access continuous food
resources (Rand et al. 2014; Schmidt-Jeris & Nault 2018).
However, the lower occurrence of natural enemies in sim-
plied landscapes cannot eectively control the subsequent
pest population growth. In such scenarios, pests maintain
faster growth rates in the simplied landscape, resulting in
no signicant dierence in pest numbers between dierent
landscapes (Thies et al. 2005; Yang et al. 2018, 2019).
2.3 Habitat quality
In agricultural ecosystems, pest occurrence is jointly regu-
lated by factors operating at both the local eld and land-
scape levels. The quality of crop habitats, both within the
wider landscape scale and at the eld level, can inuence
the outcomes of landscape-level research (Ricci et al. 2019;
Beaumelle et al. 2021; Serée et al. 2022).
At the landscape level, the composition and distribu-
tion of dierent habitats inuence pest occurrence, funda-
mentally because of the ecological functions these habitats
provide to both pests and their natural enemies (such as pro-
viding food resources, shelter environments, etc.) (Bianchi
et al. 2013). Habitats that promote pest occurrence directly
or indirectly can be referred to high-quality habitats, while
those that do not are known as low-quality habitats (Münsch
et al. 2019). Vegetation composition, degree of disturbance,
and even changes over time in plant growth within habitats
could aect their qualities and regulate pest occurrence at a
larger landscape scale (Li et al. 2020; Klimm et al. 2024).
Plant crop varieties with high nitrogen and sugar content
usually provide abundant food sources for pests, and plant-
ing large areas of high-quality host crop plants is suitable for
pest population growth. Moreover, provision of consistent
water and fertilizer management in farming systems keeps
crop tissues in a tender state for a longer time, making it
easier for pests to feed, lay eggs, and develop and provides
a suitable environment for the establishment of pest popula-
tions (Aqueel & Leather 2011; Bernal & Medina 2018). On
the other hand, lack of high-quality primary hosts within the
landscape may force pests to choose secondary hosts for ovi-
position and feeding, increasing the pest population density
on these secondary hosts (Yang et al. 2022).
Farm management practices are key factors determining
habitat quality in agricultural production systems. Human
interference and the expression of resistance genes usually
reduce the quality of host plants for pests, thereby suppress-
ing its area-wide populations (Carrière et al. 2012; Arends
et al. 2022). For example, frequent use of chemical pesti-
cides in crop elds kills immigrant pests, leading to a nega-
tive correlation between the proportion of specic crops in
the landscape and pest population densities (Madeira et al.
2021). On the other hand, some non-crop habitats with
lower pesticide use can provide food resources for pests,
facilitating the growth and occurrence of pest populations
(Santoiemma et al. 2019; Madeira et al. 2022). Intensive
interventions in crop elds to control pests also weakens
ecological services provided by natural enemies, potentially
leading to a resurgence of pests across the landscape (Batáry
et al. 2010). Areas with low management intensity, such as
semi-natural habitats and organic crop elds, can enhance
the conservation of natural enemies, that, in turn, may reduce
pests in crop elds within the landscape (Muneret et al.
2018; Katayama et al. 2023). Therefore, well targeted, envi-
ronmentally friendly insecticides usually have few negative
eects on natural enemies, while still controlling pests. Such
approaches can result in crops being low-quality habitats for
pests and reduce pest occurrences through both “top-down”
and “bottom-up” eects (Wu et al. 2008; Lu et al. 2012).
For instance, the expression of Bt proteins in insect-resistant
crops eectively suppresses target pests, reducing eld pes-
ticide use and conserving natural enemies, and the properly
adoption of Bt crops eectively controls the occurrence of
target pest populations over whole landscapes (Wu et al.
2008).
At the eld level, types of vegetation, plant diversity, and
management intensity inuence the local habitat and how the
surrounding landscape aects pests (Daelemans et al. 2023).
For example, pests that use grass crops as hosts can more
easily migrate from semi-natural habitats to host crop elds.
In herbaceous crops, the number of pests increases with the
proportion of semi-natural habitats in the surrounding land-
scape, whereas in woody crop elds, the number of pests
is negatively correlated with the proportion of semi-natural
habitats in the landscape (Tamburini et al. 2020). Intensive
eld management practices such as pesticide application and
plowing can negate the positive eects provided by the sur-
rounding landscape on natural enemies or pests. However,
appropriate eld management methods (such as functional
plant strips and organic planting) can also compensate for
the functional deciencies of landscape (Geiger et al. 2010;
Gagic et al. 2019; Ricci et al. 2019). Additionally, environ-
mental conditions at the eld scale sometimes interact with
landscape conditions and enhance the impact of landscape
eects on pests. For example, high weed diversity in local
walnut orchards further exacerbated cotton bollworm larval
damage, which was intensied by a landscape dominated by
secondary hosts, such as walnut (Yang et al. 2022).
2.4 Spatiotemporal scales
The responses of pests to landscape habitats also vary
along temporal scales, and a growing body of literature has
examined such temporal dynamics (Settele & Settle 2018;
Clemente-Orta et al. 2020). Top-down and bottom-up eects
on eld pest densities vary across study periods because of
the changing functions of habitats for arthropods (Carrière
et al. 2012; Schellhorn et al. 2015). Throughout the growing
season, meteorological conditions aect the microclimate of
Responses of crop pests to landscape 3
crop and non-crop habitats, phenology of cultivated and wild
plants, and farming practice. This determines whether or to
what extent a specic habitat (crop or non-crop) assumes a
role as “source” or “sink” in the population dynamics of a
given pest and/or its resident natural enemies. These changes
in source-sink dynamics for dierent trophic guilds over
time will mediate the outcomes in terms of natural pest con-
trol or biological control (Santoiemma et al. 2018; Li et al.
2020). Interannual and intra-annual variance of abiotic fac-
tors, such as temperature and precipitation, usually lead to
signicant dierences in pest occurrence. These temporal
variations in pest density may aect the dynamic balances
of direct “bottom-up” eects and indirect “top-down” eects
on pests contributed by surrounding agricultural landscape
(Almdal & Costamagna 2023). For example, in years with
severe soybean aphid infestations, increasing crop diversity
in the landscape reduced resource concentration and thereby
alleviated aphid outbreaks. However, in years with lighter
aphid infestations, increasing edge density in the landscape
promoted the biocontrol eective of natural enemies and
reduced aphid damage (Almdal & Costamagna 2023). In
addition, the eects of landscape and eld level factors on
the current generation may be due to how earlier conditions
aected the density of pest in the previous generation, result-
ing in a lag eect (Boetzl et al. 2023; Yang et al. 2024).
Eects of landscape patterns on pests in dierent ecologi-
cal regions, at various spatial scales, or over dierent gradients
of landscape variables usually yield inconsistent conclusions
about eects on pests (Chaplin-Kramer et al. 2011; Karp et al.
2018). The relationship between pest population and land-
scape variables is not always linear. Rates of change vary over
gradients of landscape variables and even opposite trends may
appear in dierent parts of gradient ranges (Schneider et al.
2015; Gonzalez et al. 2022). Additionally, changes in land-
scape patterns in dierent ecological regions (such as climate
zones, altitude, latitude, etc.) may also show signicant varia-
tion in their eects on pest populations.
3 Hierarchical meta-analysis
We performed a hierarchical, global meta-analysis of the
extent to which context-dependent factors modied the
responses of pests to landscape composition. Species traits
(i.e., generalist vs. specialist feeding habits), population-
level variables (i.e., population dynamics stage, such as the
colonization phase, the peak population stage, and the sea-
son-long population stage), and habitat quality parameters
(i.e., high or low for a given pest species) were treated as
context-dependent factors to quantify the eects of land-
scape-level parameters on pest density within a focal crop.
3.1 Literature search and study selection
An extensive literature search was conducted on the Web of
Science (last updated in October 2023), using the follow-
ing keywords: “AB = [agri* OR crop] AND [landscape]
AND [proportion OR percentage OR area OR composition
OR structure OR complex* OR intensi* OR simpl* OR
heterogene*] AND [pest OR insect OR herbivore OR con-
sumer OR enem* OR predat* OR parasit* OR biological
control]”, and English as language. A total of 2677 studies
were obtained; we ltered the articles by reading the title and
abstract according to the criteria described below. A total of
246 articles remained for full-text screening, and 70 papers
were ultimately included in the meta-analysis (Fig. S1).
We identied relevant studies for inclusion in the meta-
analysis based on the following criteria:
(1) Empirical studies that were carried out in agroecosys-
tems to evaluate the response of insect pest density,
plant damage or crop yield in local elds to the propor-
tion of semi-natural or cultivated crop habitat at land-
scape scale. Landscape sectors ranged from 0.5 km
radii to several kilometers around a focal eld, with
the smaller radii likely to pick up the comparatively
stronger eects of eld borders.
(2) The focal pest was either specically identied to
species-level or its identity could be deduced from
studies that reported abundance at the guild level (e.g.,
aphids), in which case we chose the dominant species
i.e., with eld abundance surpassing 70% of the entire
guild.
(3) Statistics were reported as the univariate relationship
between the proportion of landscape-level habitats and
pest response or the partial contribution of habitats
among other factors. Statistical parameters (e.g. F, t, r,
R2, χ2, Spearman’s rho or Z-score and sample size) for
landscape habitats were extracted from gures, tables,
text or supporting information. WebPlotDigitizer
(https://automeris.io/WebPlotDigitizer/) was used to
extract data from gures.
(4) If several studies for particular crop and pest species
were conducted by the same group of researchers in the
same region, we only considered the publication where
data could be extracted easiest.
3.2 Landscape habitats and response variables
Landscape-level habitats were categorized into two groups:
semi-natural habitat and crop habitat. Both categories are
commonly used in landscape ecology studies and we consid-
ered the proportion of either habitats surrounding each study
site as landscape variables (Haan et al. 2020). Semi-natural
habitat included natural or non-crop habitats, such as forest,
woodlands, grassland, fallow land, hedgerows or eld mar-
gins. Meanwhile, crop habitat referred to a particular crop or
the combination of several or all crops within a landscape.
Pest population density, pest-induced plant damage, and
crop yield (inverted) were treated as response variables. For
studies that used more than one response variable or assessed
landscape-level eects at dierent spatial scales, we chose
the correlation coecient with the greatest value. If a study
4 Long Yang et al.
performed the research for multiple years and analyzed data
separately, we only considered the most recent data. As some
studies examined eects for dierent landscape habitats, we
included “study” as a random variable in the analysis.
3.3 Context dependent factors that used as
moderators
For each study, we grouped the target pest according to its
feeding habit and population processes. Based upon pub-
lished information on the population dynamics of a given
pest in a particular crop, we divided pest population dynam-
ics into three periods: the colonization stage, the peak popula-
tion stage, and the season-long population stage. If sampling
was done during the colonization stage or at the peak popula-
tion density, the resulting data were logged according to that
stage. Meanwhile, where sampling was done throughout the
growing season and sampling data were averaged, they were
treated as season-long population stage. For studies that con-
ducted several surveys within a specic stage, we chose the
correlation coecient with the greatest value. Based on the
herbivores’ feeding habits, we divided pests into generalist
(polyphagous) species that feed on several hosts from dier-
ent plant families or specialist (monophagous and oligopha-
gous) species that feed on one or a few plant species from
the same family.
For studies that assessed the eects of dierent crop
habitats separately, we categorized habitats based upon their
quality for the focal pest. Low quality habitats refer to crops
that are unsuitable for particular pests e.g., due to frequent
insecticide applications or varietal resistance. Land manage-
ment information was derived from the published article or
other relevant publications in the same system. Publications
in which data from dierent types of crops were combined or
where limited information was provided on pesticide usage
intensity or other agronomic practices were not included in
our analysis.
3.4 Data analysis
For each study, Pearson’s correlation coecient r for the
relationship between the proportion of landscape habitats
and pest responses was extracted as a measure of eect size.
When studies did not report r values, statistical results pro-
vided by the authors (F, t, R2, χ2, Spearman-rho or Z-score)
were converted to the correlation coecient r using
measures provided by Koricheva et al. (2013). We trans-
formed the r value into Fisher’s Z rather than using the cor-
relation directly to avoid skewed distributions when the
correlation coecient approaches ± 1, using equation:
Fisher’s Z =
Fisher’s Z = 1
21 +
1 − )
. The standard deviation of Z was esti-
mated as
= 1
−3,
where n is the size of sample used to cal-
culate r (Makowski et al. 2019).
A hierarchical, mixed-eects meta-analysis was per-
formed to examine the variation in eect sizes that was
explained by context dependent factors (i.e., pest population
Fig. 1. Eects of surrounding semi-natural habitats on the abundance of herbivore pests in local elds. Forest plot shows the mean
eect size (Fisher’s Z) and 95% condence interval of a mixed eects model for each moderator. Values in parentheses denote total
sample size /number of studies.
Responses of crop pests to landscape 5
Fig. 2. Eects of crop habitats on the abundance of herbivore pests in local elds. Forest plot shows the mean eect size (Fisher’s Z)
and 95% condence interval of a mixed eects model for each moderator. Values in parentheses denote the number of sample size
/number of studies, respectively.
stage, pest feeding habitat). We treated the context dependent
factors as moderators of the pest response to semi-natural
habitats and overall crop habitats. In order to test the eect
of crop quality, we treated crop quality and its interaction
with pest population stage or pest feeding habit as predictor
variables. We used study ID as a random eect to account
for heterogeneity in the study design and non-independence
of data from the same study. Additionally, the two or three-
way interactions between dependent factors were also
assessed. As the moderators were categorical variables, we
performed mixed-eects models with intercepts to examine
the between-group heterogeneity among dierent moderator
levels on eect sizes. To provide general quantitative infor-
mation about the response of crop pest to semi-natural and
crop habitats, modes without intercept were used for within-
group eect sizes. As such, we were able to test whether the
eects of landscape habitat eects on crop pests were signi-
cant in given subsets of the dataset.
We further assumed that studies were published regardless
of statistical signicance, and that authors did not selectively
reported results. That said, we recognize that studies report-
ing a signicant eect are more likely to be published than
studies nding no eects (Nakagawa et al. 2017; Gurevitch
et al. 2018). Therefore, the possibility of a publication bias
was explored through funnel plots (i.e., scatter plots of eect
sizes against a measure of their variance) and Kendall’s rank
correlation test (Begg’s test). Funnel asymmetry in graphical
tests and signicant p values in statistical tests may indicate
publication bias. However, in our meta-analysis, neither fun-
nel plots nor regression tests showed any sign of publication
bias. The funnel plot of eect size versus standard errors of
the mean showed no skewness for semi-natural habitat, crop
habitat, or habitat quality datasets (Fig. S2). Also, Kendall’s
rank correlation tests did not show any signicant relation-
ships between eect sizes and sample sizes (semi-natural
habitats: Z = 0.0256, p = 0.7037; crop habitats: Z = −0.0217,
p = 0.7316; crop habitats intensity: Z = −0.0364, p = 0.6773).
All statistical analyses and graphical presentations were car-
ried out in R environment (R Core Team), using rma.mv
function in the metafor package (Viechtbauer 2010).
3.5 Results and discussion of the meta-analysis
In the meta-analysis, we found 227 eect sizes from 70 stud-
ies for 58 herbivorous pests. Most studies were carried out
in Europe (36 studies) or USA (20), and the remainder in
Asia, Africa, or Oceania. The logged publications covered
24 dierent crops, including wheat (15 studies), oilseed rape
(8), and maize (6) commonly. Focal pests primarily belonged
to the Hemiptera (25 species), followed by Coleoptera
(15), Lepidoptera (8), Diptera (5), Hymenoptera (2) and
Thysanoptera (3), comprising 33 polyphagous, 18 oligopha-
gous and 7 monophagous species.
Our meta-analysis detected no signicant trend for the
response of pests to the proportion of semi-natural habitats
or crop habitats in the landscape (Figs. 1, 2 & 3). Including
context-dependent factors in the mixed-eect models, how-
6 Long Yang et al.
Fig. 3. Eects of crop quality on the abundance of herbivore pests in local elds. Forest plot shows the mean eect size (Fisher’s Z)
and 95% condence interval of a mixed eects model for each moderator. Values in parentheses denote the number of sample size/
number of studies, respectively.
ever, improved model t, resulting in lower AICc values
for models that integrated such factors or their interactions
(Table 1). By separating the studies according to the stage of
the population cycle, signicant eect sizes were detected
(Table 1). During the pest colonization stage, pest pressure
positively related to landscape-level semi-natural habitat
cover (Fig. 1). Meanwhile, when considering season-long
pest population density, pest pressure positively related to
crop area (Fig. 2). Feeding habit had signicant eects on
pest responses to landscape composition (Table 1), although
no signicant eect sizes were found (Figs. 1 & 2). When
separating studies by the stage of pest population and feed-
ing habit, semi-natural habitats showed a signicant positive
eect on specialists during colonization stage (Fig. 1), while
the season-long population density of generalist displayed a
positive response to crop habitats (Fig. 2). Crop eld quality
aected the eect sizes signicantly (Table 1), high-quality
crop habitat enhanced the occurrence of pests and their posi-
tive role was much clearer for generalists than for specialists
(Fig. 3). Low-quality crop habitats signicantly reduced pest
density and the suppression eects were more obvious for
specialists than for generalists (Fig. 3).
At the beginning of the pest infestation, initial pest popu-
lation abundance largely depends on the number of success-
ful colonizers arriving to the crop from other habitats (Thies
et al. 2005; Tscharntke et al. 2016; Yang et al. 2018). In most
studies, fewer pest control activities had been taken during
this period because the density of pest populations rarely
exceeded the economic threshold (Sivako et al. 2013).
Semi-natural habitats suer less disturbance and usually pro-
vide vital resources (shelter or food resources) to pests, espe-
cially to those species that complete part of their life cycles
outside the crop (Tscharntke et al. 2016; Santoiemma et al.
2019). Pests emigrate from semi-natural habitats once crop
hosts are available, making semi-natural habitats as “source”
that enhance the occurrence of pests in crops (Berger et al.
2018; Urbaneja-Bernat et al. 2020). Increased semi-natural
habitat cover may be paralleled by reductions in cropland
coverage, which in turn can weaken the dilution eects during
the crop colonization stage (Yang et al. 2019). Semi-natural
Responses of crop pests to landscape 7
Table 1. Summary table of the tests of moderators, associated heterogeneities (Q) and AICc values for each model with intercept.
Separated mixed-eects models are presented for the response of crop pests to semi-natural and crop habitats. The eects of crop
intensity on the responses of pest to crop habitats were also tested. Results of two or three interaction are also shown.
Dataset Moderators/Residuals d.f. Q PAICc
Semi-natural habitats
(n = 106)
Null model (no moderator) 105 609.98 < .0001 186.92
Population stage 2 17.08 0.0002 174.70
Residual 103 577.35 < .0001
Feeding habits 1 10.87 0.001 178.40
Residual 104 609.88 < .0001
Population stage × Feeding habits 5 27.89 < .0001 171.68
Residual 100 565.95 < .0001
Crop habitats (n = 119) Null model (no moderator) 118 557.79 < .0001 192.82
Population stage 2 0.38 0.8263 197.63
Residual 116 555.01 < .0001
Feeding habits 1 9.04 0.0026 186.34
Residual 117 557.52 < .0001
Population stage × Feeding habits 5 17.00 0.0045 190.39
Residual 113 548.68 < .0001
Crop management (n = 65) Null model (no moderator) 64 386.06 < .0001 129.38
Host quality 1 33.16 < .0001 101.95
Residual 63 337.42 < .0001
Host quality × Feeding habits 3 37.12 < .0001 105.30
Residual 61 336.21 < .0001
Host quality × Population stage 5 53.14 < .0001 97.61
Residual 59 321.88 < .0001
Host quality × Feeding habits ×
Population stage
9 71.00 < .0001 94.40
Residual 55 303.94 < .0001
habitats enhance densities of specialist more than generalist
pests. One explanation for this observation is that polypha-
gous pests with wide host range can easily move among dif-
ferent semi-natural or crop habitats to forage, while only a
few host crops are available for specialist pests to colonize;
consequently, the dilution eects of crops on specialists may
be much smaller (Gilabert et al. 2017). An alternative expla-
nation is that the generalist immigrants can be recruited from
many crop habitats besides semi-natural habitats, and their
population may not be enhanced by semi-natural habitats if
semi-natural habitats are not the main sources of the coloniz-
ing pest (Carrière et al. 2006; Tscharntke et al. 2012).
The responses of pests to crop habitats are largely medi-
ated by the pest occurrence period in the crop and the qual-
ity of the crop for pest feeding and reproduction. A higher
proportion of area devoted to the production of high-qual-
ity crops will likely promote pest outbreaks by providing
greater resource continuity (Schellhorn et al. 2015; Iuliano
& Gratton 2020). On the other hand, increases in the cov-
erage of high-quality crops may reduce coverage by semi-
natural areas, with cascading eects on natural enemies and
then pests, because biocontrol agents usually benet from
the availability of semi-natural habitats (Landis et al. 2000).
According to the resource concentration hypothesis, the
abundance of specialized pests may be facilitated by one
or several crops, but generalist pests can benet from most
crops. Considering that some of the studies included in our
meta-analysis treated the proportion of a particular crop as a
landscape variable, when sometimes a given crop was not a
host plant for the specialized pests, it is understandable why
crop land eects were much clearer for generalist pests than
more specialized species. However, increases in the propor-
tion of land devoted to low-quality hosts for pests or which
are intensively managed (i.e., high pesticide pressure, resis-
tant crop varieties) has a negative eect on the density of
pests. Such crop habitats sometimes attract pests and func-
tion as dead-end trap crops, suppressing pest densities at the
landscape level (Wu et al. 2008; Santoiemma et al. 2019). In
northern China, for instance, increased area planted to inten-
sively managed cotton had a negative eect on the abun-
8 Long Yang et al.
dance of Apolygus lucorum, while other higher quality crops
(e.g., soybean, peanuts, and maize) are positively associated
with the mirid’s population abundance (Li et al. 2020).
Some of studies in our meta-analysis pooled their data
across sample periods to acquire average and cumulative
values, or treated sample period as a random eect, ignor-
ing the temporal variance. In these cases, responses of pest
abundance to landscape habitats were largely shaped by one
or several periods, which had higher pest abundances or
stronger relationships with landscape habitats. As a result,
the relationship between pest abundance and landscape traits
may have been aected by many other dependent factors and
without any consistent trend.
4 Implications for landscape ecology
studies
The agricultural landscape is a dynamic mosaic of crop and
non-crop habitats that varies in composition over time and
space. The impact of landscape characteristics on the occur-
rence of crop pests is regulated by a variety of biotic and abi-
otic factors and exhibit context dependency. Dierences in
contexts often lead to false or seemingly contradictory con-
clusions, which limit our understanding of the mechanisms
behind how agricultural landscape compositions aect the
ecological processes of pests, thereby hindering the general-
ization and application of research ndings across dierent
spatial and temporal extents. Addressing context depen-
dency is a key issue in ecological research, doing so will
enhance our understanding of ecological mechanisms and
our ability to predict ecological processes. It is essential to
fully consider these context-dependent factors when analyz-
ing the mechanisms by which agricultural landscape patterns
aect pest abundance.
The introduction of context-dependent factors into our
studies facilitates the identication of universal patterns
and aids in elucidating the ecological mechanisms through
which agricultural landscape patterns regulate pest presence
and density. In this meta-analysis we demonstrated that the
response of crop pests to landscapes was mediated by the
pest’s feeding habits, the pest phenology, and the crop’s
quality. When we design a landscape for pest management
at larger scales, we should take into account eects on the
pest itself, as well as relevant context dependent factors. We
should be concerned about pest shifts among dierent land-
scape habitats at a particular stage in its life history or that of
its key host plants. Meanwhile, we should make full use of
the distribution of low-quality crop habitats to suppress pest
population at landscape scales. In practical agricultural pro-
duction, it is also important to consider local characteristics,
take farm management and seasonal changes into consider-
ation, and integrate management measures across multiple
temporal and spatial scales to mitigate pest damage.
Acknowledgements: This study was nancially supported by the
National Natural Science Funds of China (Grant No. 32202300)
and China Agriculture Research System of MOF and MARA
(CARS-15-19).
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Manuscript received: September 12, 2024
Revisions requested: November 27, 2024
Revised version received: December 13, 2024
Manuscript accepted: January 23, 2025
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12 Long Yang et al.
