Meta-analysis shows that plant mixtures reduce pathogens and invertebrate herbivores and increase plant productivity

Domain: biology
Published: 2026-03-17
Editorial outcome: major_revision

Abstract
Pathogens and herbivores are key to diversity-productivity relationships. However, the extent to
which plant diversity maintains higher productivity by reducing biomass loss from pathogens
and herbivores remains unclear at the global scale. Based on a meta-analysis of 2,315
observations from 316 studies, we show that, compared to monocultures, plant mixtures on
average reduce pathogens abundance and damage to plants by 30.1% and 31.7% and those of
invertebrate herbivores by 21.6% and 25.1%, while increasing plant productivity by 40.1% and
35.7%, respectively. Mixture effects on specialist pathogens and invertebrate herbivores become
more negative with increasing plant taxonomic, functional, and phylogenetic diversity in
mixtures, while those on generalist pathogens and i nvertebrate herbivores show no significant
relationships with any diversity metrics. Mixture effects on pathogens decrease with stand age
but those on invertebrate herbivores shift from negative to positive with stand age. Mixture
effects on pathogens and invertebrate herbivores are negatively associated with those on
productivity. Our findings highlight that conserving plant diversity could reduce biotic damage
to plants and enhance global primary productivity.

Introduction
In the face of the continuous decline of plant diversity due to human activities,
understanding the relationship between biodiversity and ecosystem functions is critical for
optimizing management and conservation strategies 1,2. Biotic feedback from other trophic levels
(e.g. pathogens and herbivores) has been proposed to be a key mechanism for the biodiversity-
ecosystem productivity relationships 3. However, on a global scale, the effects of plant diversity
on pests and diseases as well as productivity may be highly dependent on the use of plant
diversity metrics, types of pathogens and invertebrate herbivores (i.e. fungal vs. bacteria; foliar
vs. soil vs. stem; specialists vs. generalists), stand age, ecosystem type and background climate.
A better understanding of the overall effects and so urces of variation of plant diversity (plant
mixtures compared to the average of constituent monocultures) on pathogens and invertebrate
herbivores could help us formulate plant diversity strategies that not only control pest and
disease outbreaks but also enhance ecosystem functions.

Pathogens and invRertebrate herbivores may be less abundant in more taxonomically,
functionally and phylogenetically diverse plant communities. Compared to two-species mixtures,
those with higher taxonomic diversity (TD) may experience lower abundance of pathogens or
invertebrate herbivores due to the dilution effect 4,5, which makes it harder for specialist
pathogens or invertebrate herbivores to locate their host plants. Associational resistance concepts
also predict that specific plant association may decrease the likelihood of detection by, and/or
vulnerability to, pathogens and herbivores 6,7. However, with the same level of taxonomic
diversity, the abundance and damage of pathogens and invertebrate herbivores may still be
influenced by the extent of trait variation because the physical and chemical traits of plants are
closely related to the susceptibility to pathogens and the feeding preference for invertebrate
herbivores 8-10. More diverse traits can involve different defense mechanisms, growth forms, or
phenologies, which can reduce pathogens or herbivores damage. Although functional diversity
(FD) can explain some aspects of the species trait dissimilarities, these dissimilarities of a large
number of species’ physical and chemical attributes may not be easily measured. In contrast,
phylogenetic diversity (PD) can capture phylogenetically conserved dissimilarity of traits (often
unmeasured) among species 11,12. Thus, we expect that greater FD or PD within plant
communities may reduce the likelihood of shared pathogens or invertebrate herbivores species
among plant species, thereby reducing their accumulation and Sdamage.

The mixture effects on pathogens and invertebraRte herbivores may change with stand age,
ecosystem types (croplands, forests, grasslands, and pots) and background climate. Monocultures
provide high host densities for pathogens or iInvertebrate herbivores, thus promoting the build-up
of these populations over time 13,14, which progressively impairs biomass production 15,16.
Conversely, diverse communities may take time to establish beneficial interactions (e.g.
mutualists, predators of herbivores) and foster more suppressive soil microbiomes that inhibit
pathogens over time 17,18. Thus, we expect that the reduction in pathogens and invertebrate
herbivores in plant mixtures would be more pronounced over time compared to monocultures
due to cumulative effects such as enhanced natural enemy populations and improved plant
health. The responses of pathogens and invertebrate herbivores to plant mixtures could vary
between ecosystem types as a result of differences in vegetation types (e.g. trees vs herbs). For
example, in diverse plant communities, the presence of larger non-host trees in forests as
physical barriers might limit the spread or dispersal of pathogens or invertebrate herbivores more
effectively than the herbaceous species in grasslands 19,20. Furthermore, pathogens and
invertebrate herbivores in warm and humid conditions have higher abundance and activities 21,22
and compete more intensively for specific food resources, causing them to feed on a wider range
of plants 23,24. Therefore, we expect that the decline in pathogens and invertebrate herbivores in plant
mixtures may be less pronounced in warm and humid climates due to the generally higher
activity, reproduction, and survival rates of pathogens and herbivores under such climatic
conditions 25,26.

Previous meta-analyses have reported plant diversity (i.e. species richness or genetic
diversity) reduces plant diseases 27, pathogens damage 28, insect herbivory 29, herbivores
abundance 30, pest abundance 31,32, and positive effects of planSt intraspecific diversity on
arthropods 33. Although these syntheses have advanced Runderstanding of how plant diversity—
primarily species richness, genetic diversity and intraspecific diversity—on average influences
pathogens, and herbivores, it remains unclearI how these influences vary with different
confounding covariates. For example, the effects of diversity may depend on the magnitude of
diversity (e.g. two species versus a higher number of species), other facets of diversity such as
FD and PD, the type of pathogens and invertebrate herbivores (e.g., fungal vs. bacteria; foliar vs.
soil vs. stem; specialists vs. generalists), stand age, and background climate. Moreover, previous
meta-analyses have demonstrated positive effects of plant diversity on productivity 34-37.
However, how these productivity gains are linked to diversity-driven changes in pathogens and
herbivores pressure remains largely unknown at a global scale (but see Beillouin, et al. 38 for
evidence in croplands).

Here, we conduct a global meta-analysis with 2,315 observations of plant mixtures and
constituent monocultures from 316 studies across four ecosystem types (croplands, forests,
grasslands, and pots), to examine how plant taxonomic, functional and phylogenetic diversity
affects the performance (abundance and damage to plants) of pathogens and invertebrate
herbivores to further influence plant productivity (Fig. 1; Supplementary Data 1). To compare
the relative importance of diversity indicators on the same dimension, we employ unified Hill
diversity indices 39. Specifically, we test the following hypotheses: (a) plant mixtures would, on
average, decrease the abundance and damage of pathogens and invertebrate herbivores; (b) the
magnitude of the negative mixture effects would increase with multifaceted diversity (TD, FD,
and PD) in mixtures and stand age, and are less pronounced in warm and humid climates; (c) the
positive mixture effects on plant productivity would be associated with the reduction of the
pathogens and invertebrate herbivores. Here, TD for q = 0 is sSimply species richness, for q = 1, it
is the Shannon index, and for q = 2, it is the Simpson inRdex. The Hill diversity metrics of q = 1
and 2 are influenced by the dominant species that are not biased due to the sample size and are
sufficiently accurate in estimating diversity wIithout the need for rarefaction 40. Hill diversity
metrics with q = 1 including functional diversity (FDq1), phylogenetic diversity (PDq1), and
taxonomic diversity (TDq1) explained greater amounts of variation in our focal measures than
their alternatives (q = 0 and 2) (Table S1; Table S2), hence we focus on them hereafter.

Results
Diversity metrics influences plant mixture effects
For pathogens, compared to monocultures, plant mixtures on average reduced pathogens
abundance and damage by 30.1% (95% confidence intervals, CI, -58.1% – -2.13%) and 31.7%
(CI, -41.7% – -21.7%), respectively (Fig. 2a). The effect of plant mixtures on pathogens damage
was consistently negative across different host specificity (specialists vs. generalists), habitat
(foliar vs. soil vs. stem) and taxonomy (fungal vs. bacteria), with the only exception being
generalist pathogens, for which no significant differences were observed (Fig. 2a). For
invertebrate herbivores, plant mixture on average reduced abundance and damage of invertebrate
herbivores by 21.6% (CI, -28.2% – -15.0%) and 25.1% (CI, -36.2% – -14.0%), respectively.
Similarly, the effect of plant mixtures on herbivores was consistently negative across different
host specificity and habitat except for generalist herbivores (Fig. 2b). Overall, plant mixtures had
higher productivity than the average of corresponding monocultures (Fig. 2).

The negative effects of plant mixture on pathogens performance (abundance and damage
to plants) became more pronounced with increases in all diversity metrics (TDq1, FDq1, PDq1)
in mixtures (Fig. 3a, c, e; Table S3; Fig. S4a). In contrast, the eSffect of plant mixture on
invertebrate herbivores performance (abundance and daRmage to plants) remained non-significant
with TDq1, FDq1 and PDq1 (Fig. 3b, d, f; Table S3; Fig.S4c). Similar results were obtained
when assessing the effect of plant diversity vaIriables at q=0 and q=2 on the performance of
pathogens and invertebrate herbivores (Fig. S2; Fig. S3). When categorizing pathogens and
invertebrate herbivores into specialist and generalist groups, the negative effects of plant
mixtures on specialist pathogens and invertebrate herbivores performance became more
pronounced with increasing TDq1, FDq1 and PDq1 (Fig. 4), while those on generalist pathogen
and invertebrate herbivores showed no significant relationships with any diversity metrics in
mixtures (Fig. 4). When categorizing pathogens and invertebrate herbivores into foliar and soil-
borne groups, plant species mixtures consistently reduced the performance of both foliar and soil
pathogens with increasing TDq1, FDq1 and PDq1 (Fig. S5a, c, e), while their effects on foliar
and soil invertebrate herbivores remained non-significant with TDq1, FDq1 and PDq1 (Fig. S5b,
d, f). TDq1, FDq1 and PDq1 do not significantly influence the effect of plant mixture on stem
pathogens and invertebrate herbivores (Table S7). When categorizing pathogens into fungal and
bacteria, plant mixture effects on fungal pathogens performance decreased with all diversity
metrics in mixtures (Fig. S6).

Stand age influences plant mixture effects
With increasing stand age, plant mixture effects on pathogens performance became more
negative (Fig. 5a; Fig. S4b), while the effects on invertebrate herbivores performance shifted
from negative to positive (Fig. 5b; Fig. S4d), with consistent trends between foliar and soil-borne
groups (Fig. 5e, f). Stand age do not significantly influence the effect of plant mixture on stem
pathogens and invertebrate herbivores (Table S7). The impact Sof stand age on plant mixture
effects on pathogen and invertebrate herbivore performaRnce does not differ between specialists
and generalists (Fig. 5c, d). When categorizing pathogens into fungal and bacteria groups, the
effect of plant mixtures on fungal pathogens dIecreased with stand age, whereas no significant
relationship was observed for bacterial pathogens (Fig. S6b). The effects of plant species
mixtures on pathogens performance were consistently negative across all ecosystem types (Fig.
S7a). The MAT and MAP do not significantly influence the effect of plant mixture on pathogens
and invertebrate herbivores (Table S4; Table S5).

Plant diversity enhances productivity by regulating pathogens and herbivores
The structural equation models based on the 134 studies (169 observations for pathogens,
572 observations for invertebrate herbivores) that simultaneously reported plant mixture effects
on pathogens (or invertebrate herbivores) and plant productivity showed negative associations
between the mixture effects on pathogens (or invertebrate herbivores) and those on productivity
(standardized r = -0.15 for pathogens and r= -0.12 for invertebrate herbivores) (Fig. 6; Fig. S8a,
d). Increasing FDq1 and PDq1, reduced pathogens performance, resulting in positive indirect
effects of FDq1 and PDq1 on plant productivity (r = -0.30 × -0.15 = 0.05 for FDq1 and = -0.35 ×
-0.15 = 0.05 for PDq1, Fig. 6a), while the total effects of FDq1 and PDq1 (r = 0.05 + 0.05 =
0.10) are similar with that of TDq1 (r = -0.60 × -0.18 = 0.11) (Fig. S9a). PDq1 also had a
positive direct effect on productivity (r =0.45, Fig. 6a; Fig. S8b). In addition, mixture effects on
pathogens performance decreased with stand age and those on plant productivity increased with
stand age (Fig. 6a; Fig. S8c). For specialist pathogen, increasing PDq1 and TDq1 reduced
pathogens performance, resulting in their positive indirect effects on plant productivity (r = -0.38
× -0.33 = 0.13 for PDq1 and r = -0.32 × -0.34 = 0.11 for TDq1, Fig. 6b; Fig. S9b). For generalist
pathogens, higher PDq1 also reduced pathogen performance (r = -0.59, Fig. 6c), but it did not
result in statistically significant indirect effects on plant productivity (Fig. 6c). Increasing FDq1,
PDq1 and TDq1 in mixtures did not significantly influence invertebrate herbivores performance
(r = -0.01 for FDq1, r = -0.08 for PDq1 and r= -0.03 for TDq1, Fig. 6d, Fig. S9d). Moreover,
PDq1 had a positive direct effect on productivity (r = 0.21, Fig. 6d; Fig. S8e), and the mixture
effect on invertebrate herbivores performance decreased and that of productivity increased with
stand age (Fig. 6d; Fig. S8f). For specialist invertebrate herbivores, increasing FDq1 and TDq1
reduced invertebrate herbivores performance, resulting in their positive indirect effects on plant
productivity (r = -0.35 × -0.26 = 0.09 for FDq1 and r = -0.30 × -0.28 = 0.08 for TDq1, Fig. 6e;
Fig. S9e). For generalist invertebrate herbivores, increasing FDq1, PDq1 and TDq1 did not
significantly reduce invertebrate herbivores performance (Fig. 6f, Fig. S9f).

Methods
Data collection
Studies were selected through a search on the Web of Science and the China National
Knowledge Infrastructure (CNKI; https://www.cnki.net) (peer-reviewed journal articles until
January 2025) using the Boolean search string 1: AB=((plant diversity OR tree diversity OR crop
diversity OR grass diversity OR plant mix* OR "species diversity" OR “species richness” OR
“species mix*” OR “mixed cult*” OR “pure and mix*”) AND (pathogen* OR pest OR insect
OR herbiv* OR beetle* OR disease OR infect*)) and the Boolean search string 2: AB=((species
diversity OR tree diversity OR plant diversity OR species richness OR species mix* OR mixed
cult* OR crop diversity OR mix* plantation OR pure and mix*) AND (pathogen* OR pest OR
insect OR herbiv* OR disease OR infection) AND (product* OR biomass OR yield OR growth
OR basal area OR volume)). The literature search was performed following guidelines from
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses 57; Fig. S1). In
this study, the term "invertebrate herbivores" refers to organisms that include not only
herbivorous insects but also phytophagous nematodes and mites. To accurately assess the effects
of pathogens and invertebrate herbivores on the diversity-ecosystem productivity relationship,
we reviewed each article to determine whether the studies met the following criteria: (1) the
study was purposely designed to investigate the effects of plant mixture on pathogens,
invertebrate herbivores; (2) the study had at least one plant mixture treatment and corresponding
monocultures; (3) papers focused merely on the effects of diversity on plants without other
trophic level were excluded; (4) studies were purposely implemented to separate the effects of
plant species diversity from other factors, such as water treatment and nutrition addition.

When different publications included the same data from one study, we recorded the data
only once. When a study included plant mixtures of different numbers of species, we considered
them distinct observations. Also, when a publication included several experiments under
different abiotic conditions, such as different locations, treatments, stand age and ecosystem
type, we considered them different observations. We used WebPlotDigitizer
(https://automeris.io/WebPlotDigitizer) to digitally extract data from figures when the results
were graphically reported. We obtained a meta-dataset of 2,315 observations from 316 studies
that involved the mixing of live plants in croplands (155 studies), forests (95 studies with 8 in
natural forests and 87 in plantations), grasslands (43 studies) and pots (26 studies) (Fig. 1;
Supplementary Data 1, Supplementary Note 1).

We obtained plant species name, species richness, the species proportions in plant
mixtures, plant productivity, abundance and damage of pathogens or invertebrate herbivores,
geographical location (latitude, longitude, and altitude), ecosystem type, stand age (SA), the
types of pathogens (fungal vs. bacteria; foliar vs. soil vs. stem; specialists vs. generalists) and
invertebrate herbivores (foliar vs. soil vs. stem; specialists vs. generalists) from original
publications (Supplementary Data 1). Due to data limitations, we followed the approach of
previous meta-analyses 35,36 and used a range of proxy variables—including biomass,
production, height, cover, yield, and volume—to represent productivity. These metrics are all
measures or proxies of primary productivity and can thus be collectively analyzed, in contrast to
secondary productivity, which reflects energy conversion by heterotrophs. This allowed us to
include a broader set of studies and observations. We used the initial plant density of each species in
forests, grasslands, croplands and pot experiments to calculate the species proportions in the
species mixtures. Similar to previous studies 58, SA was recorded as the number of years since
the establishment of the stand/pot experiment. For those studies conducted under natural climates
including croplands, forests and grasslands, we obtained mean annual temperature (MAT) and
mean annual precipitation (MAP) for each study site from original publications; when
unavailable, it was derived based on geographic locations using the WorldClim version 2 dataset
59.

We categorized pathogens and invertebrate herbivores as specialists or generalists based on
the classifications provided in the original literature sources. Specialist pathogens or invertebrate
herbivores are species that feed on a specific type of plant or a narrow range of related plants,
usually confined to a single family or genus. In contrast, generalist pathogens or invertebrate
herbivores can feed on a wide variety of plants spanning multiple families 60. The dataset on
pathogens included 42 studies on specialists and 11 on generalists. Invertebrate herbivores
included 36 studies on specialists and 38 on generalists. Most studies focused on fungal
pathogens (74 studies), with only 9 studies addressing bacteria pathogens. We gathered data
from 51 studies on foliar pathogens and 33 on soil pathogens, 4 on stem pathogens, as well as
213 studies on foliar invertebrate herbivores, 31 on soil invertebrate herbivores and 10 on stem
invertebrate herbivores (Fig. 2). Previous studies indicated that an increase in the abundance of
pathogens and invertebrate herbivores was positively correlated with their damage to plants 61,62.
Thus, we refer to the abundance and damage caused by pathogens or invertebrate herbivores as
their performance, following the terminology used by Wan et al. 9,30.

Functional and phylogenetic diversity quantification
Our dataset includes a total of 514 plant species, comprising 151 crops, 136 grasses, and
229 woody plants. We obtained species mean trait values for the specific leaf area (SLA), leaf N
(Nmass), root diameter (RD) and specific root length (SRRL) from the TRY Plant Trait Database
63, Botanical Information and Ecology Network (BIEN) Database 64, Global Inventory of Floras
and Traits (GIFT) Database 65, Community Responses to Resource Experiments (CoRRE)
Database 66, Fine-Root Ecology Database (FRED) 67 and the Global Root Traits (GRooT)
Database 68. These plant traits are among the most frequently measured and have been previously
reported to be related to pathogens infection 69,70 or feeding by invertebrate herbivores 48,71.
When trait data were unavailable in the above database, we obtained them from published data
sets and literature (131 species).

We used Hill numbers to quantify taxonomic diversity (TD), functional diversity (FD) and
phylogenetic diversity (PD) through the iNEXT.3D standardisation with iNEXT.3D package 40.
The Hill numbers provide a unified and comparable framework for measuring biodiversity across
these three dimensions. We obtained the phylogenetic tree via the phylo.maker function from the
V. PhyloMaker package and then calculate the values of TD, FD and PD using the ObsAsy3D
function from the iNEXT.3D package. In this study, FD was calculated based on leaf and root
traits, including SLA, Nmass, RD and SRL. Our sensitive analysis showed that FD calculated
specifically for leaf and root traits strengthened the negative mixture effects on pathogen
performance (Fig. S10) but did not affect invertebrate herbivores (Fig. S11), consistent with our
main results using FD based on all traits.

Calculating effect size
We used a natural log-transformed response ratio (𝑙𝑛𝑅𝑅) to quantify the effects of plant
mixture 72. The 𝑙𝑛𝑅𝑅 was calculated as:
𝑋̅
𝑙𝑛𝑅𝑅 = 𝑙𝑛( 𝑡) (1)
𝑋̅
𝑐
where 𝑋̅ is the mean observed pathogens performance or in vertebrate herbivores performance or
t
plant productivity in the mixture and 𝑋̅ is the mean expected pathogens performance or
c
invertebrate herbivores performance or plant productivity of the mixture in each study. The 𝑋̅
c
was calculated based on the performance of each component species in monoculture weighted by
their relative proportions in mixture following Loreau and Hector 73.
𝑋̅ = ∑(𝑝 × 𝑚 ) (2)
c i i
where 𝑚 is the observed value of the monoculture of species 𝑖 and 𝑝 is the proportion of
i i
species 𝑖 density in the corresponding mixture. When various species richness levels were
reported in a single study, we calculated the 𝑙𝑛𝑅𝑅 for each species richness level.

The weight for each observation is usually calculated by the inverse of sampling variance
when it was available. In our database, some sampling variances were missing and weightings
based on sampling variance had a very wide range which could assign undue importance to a few
extreme observations, and consequently, the average 𝑙𝑛𝑅𝑅 would be biased by a small number
of studies. An alternative weighting function was to calculate the weight of each observation
(𝑊) 74,75.
r
𝑁 ×𝑁
𝑊 = c t (3)
r
𝑁 +𝑁
c t
where 𝑊 is the weight associated with each 𝑙𝑛𝑅𝑅 observation and 𝑁 and 𝑁 are the number of
r c t
replications in monocultures and corresponding mixtures, respectively.

Then, using the 𝑙𝑛𝑅𝑅 of pathogens performance, 𝑙𝑛𝑅𝑅 of invertebrate herbivores
performance as response variables separately, we compared the linear and log-linear
relationships between 𝑙𝑛𝑅𝑅 versus individual pedictors, including species diversity in mixtures
(𝐷, that is, 𝑇𝐷, 𝐹𝐷 or 𝑃𝐷), 𝑆𝐴, 𝑀𝐴𝑇, 𝑀𝐴𝑃 according to the lowest Akaike information criterion
(AIC) values. The linear 𝐷 and 𝑆𝐴 were selected due to lower or similar AIC values (difference
< 2) (Table S1; Table S2). The following model was applied to examine the effects of the 𝐷, 𝑆𝐴
and environmental variables (𝐸, that is, ecosystem type (𝐸𝑇) or 𝑀𝐴𝑇or 𝑀𝐴𝑃) on 𝑙𝑛𝑅𝑅 of
pathogens performance, 𝑙𝑛𝑅𝑅 of invertebrate herbivores performance:
𝑙𝑛𝑅𝑅~𝛽 + 𝛽 × 𝐷 + 𝛽 × 𝑆𝐴 + 𝛽 × 𝐸 + 𝜋 + 𝜋 + 𝜀 (4)
0 1 2 2 studyID obsID
where 𝛽 is the coefficient to be estimated; 𝜋 and 𝜋 are the between-study and within-
studyID obsID
study random effects indicated by study ID and observation ID, respectively; 𝜀 is sampling error.
We first conducted the analysis using a weighted, mixed-effect model with the rma.mv
function in the R package metafor, applying the hybrid method 76,77. To overcome the issue of
non-independence among the weights in the same study, we implemented the robust variance
estimation algorithms for all meta-regression models 36,78. We additionally conducted the
analysis using restricted maximum likelihood estimation with the lme4 package with 𝑊 as the
r
weight for each corresponding observation 79 (Table S2). To prevent overfitting 80, we selected
the most parsimonious model by comparing the AICs among all alternatives with the condition
to keep 𝐷 and 𝑆𝐴, as they were part of our core hypotheses to be tested. Among alternatives (q =
0, 1, and 2), we found that plant diversity variables at q = 1 were the best to predict pathogens
and invertebrate herbivores performance, based on AIC (Table S1; Table S2).

Similarly, we modeled the factors of environmental variables (𝐸𝑇, 𝑀𝐴𝑇, and 𝑀𝐴𝑃)
separately to avoid high multicollinearity 81. Following the model selection procedure described
above, we derived the most parsimonious models. To further examine the effects of ecosystem
type, we conducted an analysis with the ecosystem type as the only fixed factor and different
studies as the random factor 82. We also explored whether the effects of diversity (TDq1, FDq1,
and PDq1) and stand age (SA) vary between pathogens types (fungal vs. bacteria; foliar vs. soil
vs. stem; specialists vs. generalists) and invertebrate herbivores (foliar vs. soil vs. stem;
specialists vs. generalists). We compared the model estimates obtained using the metafor and
lme4 packages and found that the results were highly consistent across the two methods (Tables
S3).

The potential for publication bias was evaluated using Egger’s regressions 83, with
sample size as the predictor. Results of Egger’s regressions indicate no significant publication
bias in our meta-analyses (Table S6). Since most of our models did not meet the assumption of
normality, we bootstrapped the fitted coefficients by 1,000 iterations 84. All continuous
predictors were scaled (observed values minus the mean and divided by one standard deviation)
85. In this way, 𝛽 is the overall mean 𝑙𝑛𝑅𝑅 at the means of all predictors. To facilitate
0
interpretation, the 𝑙𝑛𝑅𝑅 and its corresponding 95% confidence interval was transformed to a
percentage change using:
(𝑒𝑙𝑛𝑅𝑅 − 1) × 100% (5)
If the 95% CIs of average effect size did not cross 0, the plant mixture effects were considered
significant at α = 0.05.

Relative contribution of different diversity metrics on productivity
Because TDq1 are significantly correlated with FDq1 and PDq1, we firstly employed
structural equation modeling (SEM) with R package piecewiseSEM 86 to examine the
simultaneous effects of FDq1, PDq1, SA and their interaction on 𝑙𝑛𝑅𝑅 of pathogens
performance, 𝑙𝑛𝑅𝑅 of invertebrate herbivores performance and the 𝑙𝑛𝑅𝑅 of plant productivity
both directly and indirectly. As recommended 87, we assessed the conceptual model (full model)
vs. reduced models by goodness of fit statistics and selected the final model with the lowest AIC
value among alternative models. We then evaluated the relationship between the TDq1, SA and
their interaction on 𝑙𝑛𝑅𝑅 of pathogens performance, 𝑙𝑛𝑅𝑅 of invertebrate herbivores
performance and the 𝑙𝑛𝑅𝑅 of plant productivity using the same method as above. We also
included a hierarchical structure by including “Study” as a random factor. We used
piecewiseSEM because it is based on mixed-effects models (e.g., lmer), which can be coherently
integrated into a structural equation modeling framework to evaluate direct and indirect
pathways while accounting for hierarchical data structure. In contrast, meta-analytic models
implemented in metafor cannot be incorporated into SEM without losing their weighting and
variance structure, and thus are not suitable for SEM-based inference. In our study, mixed-effects
models fitted using lmer produced effect size estimates comparable to those from metafor,
therefore providing the additional advantage of supporting SEM-based evaluation of direct and
indirect pathways. The taxonomic diversity SEM explained a similar magnitude of variation in
𝑙𝑛𝑅𝑅 of plant productivity and the 𝑙𝑛𝑅𝑅 of pathogens performance (or 𝑙𝑛𝑅𝑅 of invertebrate
herbivores performance) (Fig. S9). R packages including data.table, metafor, lme4, lmerTest,
ggmap, maps, iNEXT.3D, V.PhyloMaker, ggplot2, piecewiseSEM were used to assist with
graphing and statistical analyses, which were conducted in R v.4.3.3 (R Development Core
Team, Vienna, Austria).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their study, Huang and colleagues sought to show how plant diversity can reduce damage from insect pests and fungal pathogens, and how this associational resistance (a key concept in this field, e.g. Barbosa et al. 2099, but never cited by the authors) translates into gains in primary productivity. To do this, they used a meta-analysis. Each of these plant diversity response variables (resistance and productivity) has already been the subject of many meta-analyses, and it is surprising that they are not cited in the introduction or discussion. For example, for the effect of plant diversity on pest damage, among many others: Beillouin et al. 2021; Jactel et al. 2021; Hahn et al. 2024; Yousefi et al. 2024. See also the recent meta-analysis by Field et al. 2025 on pathogens. For the effect on productivity, again these are just several examples in forests: Warner et al. 2023; Jactel et al. 2018; Zhang et al. 2012. This should be acknowledged and, above all, one should avoid saying in the introduction that the results of the studies are inconsistent. In hundreds of cases, it is clear that some effects will be positive, others negative, others neutral. It is precisely the role of meta-analyses to highlight the direction (the sign) of the overall mean effect size.\nMore original is the authors' attempt to link the effect of plant diversity on pest damage with its effect on the productivity of the same plants. The problem is that the methodology employed is too flawed to produce reliable and convincing results. I list the problems below in the order in which the methods are described in the manuscript.\n1. Data collection: I'm familiar with the bibliography used for forestry, and I'm sure that in many cases the combination of keywords (product* OR biomass OR yield) would not have found them. 23 articles on forest insects were selected, whereas the meta-analysis by Jactel et al. 2021 is based on 69 articles (3 times as many). 9 articles deal with forest pathogens, whereas the meta-analysis by Field et al. 2025 uses 44 (5 times more). These discrepancies are significant and raise the question of the exhaustiveness of the bibliographic search.\n2. Functional diversity: the inclusion of root traits is interesting in itself, but none of the insect pests or pathogens cited for the forest cases are present in the soil. The functional diversity index is therefore not relevant for testing the effects on damage reported in the selected publications.\n3. Calculating effect size: this is one of the most important methodological problems in the manuscript. With regard to effect size for pest damage, the calculation of Xc is unclear, as 1) it is not clear why a sum (Sigma) should be used and 2) multiplying an average damage in the monoculture by the proportion of the species in the mixture does not work if, for example, the damage is expressed in %. As far as the effect size for productivity is concerned, the Xc formula is similarly unclear, and in particular which (i) effects are to be summed. More importantly, the simple ratio of tree sizes is not sufficient to estimate productivity. Primary productivity corresponds to the increase in volume or biomass over a given time step. To calculate productivity, you therefore need to have data on the volume or above-ground biomass (AGB) of trees at two dates and calculate the difference, divided by the number of years between two measurements. This is not done here.\n4. Calculating effect size: there is also an inconsistency in L395-396, where it is stated that there is no significant difference in the effect of diversity between specialist and generalist herbivores, whereas table S5 shows the opposite.\n5. Relative contribution of diversity metrics on productivity: this is undoubtedly the major problem with the study. Correlation does not mean causality. In the SEM, the direction of the downward arrow from pest performance to productivity implies that the reduction in damage inflicted on plants would explain (in part) the increase in their productivity. But it's just as possible that the increase in plant productivity explains the reduced damage inflicted by herbivores or pathogens. Indeed, there are numerous studies showing that more vigorous plants, i.e. those with stronger growth, are more resistant to attacks by pests, particularly secondary pests. The role of associational resistance is therefore by no means demonstrated by this model. To do so would require more complicated studies, such as monitoring plant growth over several years after a severe pest attack followed by an absence of attack, or manipulating herbivory intensity. Here, at best, the SEM model should use a two-way arrow between pest performance and productivity (but we have seen the limits of calculating this productivity), and the discussion should seek to understand the common or pleiotropic effects of plant diversity rather than its cascading effects.\nFor these various methodological reasons, I don't think this manuscript can be accepted for publication without a major revision.", "reviewer_2": "The manuscript by Huang and colleagues is a quantitative synthesis of published articles on the effects of plant diversity on pathogen and invertebrate herbivores and plant productivity. The main result is that plant biodiversity reduces biotic damage to plants and increases productivity. This conclusion is based on an analysis of 103 studies published between 2000-2023. While this is an important and timely question, my major concern lies with the novelty of the findings and the authors' awareness of prior research on the subject. I was particularly surprised that the authors did not reference earlier meta-analyses on related topics. For example:\nJactel, H., Moreira, X., & Castagneyrol, B. (2021). Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Annual Review of Entomology, 66(1), 277-296.\nJactel, H., Gritti, E. S., Drössler, L., Forrester, D. I., Mason, W. L., Morin, X., ... & Castagneyrol, B. (2018). Positive biodiversity–productivity relationships in forests: climate matters. Biology letters, 14(4), 20170747.\nChen, C., Xiao, W. & Chen, H.Y.H. Meta-analysis reveals global variations in plant diversity effects on productivity. Nature (2025). https://doi.org/10.1038/s41586-024-08407-8\nFor example, the aforementioned studies on tree diversity show that there were 69 published papers on tree diversity’s effect on insect pests, 126 case studies on productivity, and 452 studies on plant diversity’s effect on productivity. This raises the question of whether the authors applied sufficiently strict criteria for the selection of studies in their meta-analysis. There also appear to be several more recent studies (post-2000) that could have been included in their analysis but are notably absent from the manuscript.\nAdditionally, I noticed some references were incorrectly cited. For example, the paper by Koricheva & Hayes (2018) in line 92 is cited as though it pertains to species richness, but it focuses on plant intraspecific diversity.\nHowever, despite these concerns, I believe the manuscript does offer an original contribution by analyzing the link between plant diversity and productivity through the lens of reduced insect and pathogen damage. This unique angle, which considers both biotic stressors and productivity together, is underrepresented in the literature and could add value to the field. Nevertheless, this aspect of the study is not discussed in sufficient depth.\nSome additional comments:\nLn 44-48 : the effects of other facets of diversity as FD and PD have already been discussed in several studies (ex. Castagneyrol, B., Jactel, H., Vacher, C., Brockerhoff, E. G., & Koricheva, J. (2014). Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. Journal of Applied Ecology, 51(1), 134-141.)\nLn 49 : what do you mean by « types » of pathogens and invertebrate herbivores ? their lifestyle ? feeding guild ? death breath ? organ affected ?\nLn 67-69 : why ? please develop more\nLn 76-78 : why ? some references and explanations are needed.\nLn 80 : what do you mean by vegetation type ? trees vs herbs ?\nLn 89 : again, some references and details are missing here. There are also several papers assessing climate effects on abiotic damage to plants that could be cited.\nLn 93- 99 : this is not correct, there are several references on this (see previous comments).\nLn 199 : this paper focus on tropical forest insects !\nLn 271 : why only from 2000 to 2023 ?\nLn 300 : what about wood organs ?\nLn 309-310 : are you sure of this ? insect feeding in more than one species from the same genera are often considered as specialist too (ex. see Ali, J. G., & Agrawal, A. A. (2012). Specialist versus generalist insect herbivores and plant defense. Trends in plant science, 17(5), 293-302.)\nLn 322 : and how many of them were trees or herbs ?"}, "author_response": "R: We greatly appreciate your comments. As suggested, we have acknowledged and cited the aforementioned references in the introduction (Lines 58-59, 95-96, 97, 105-106, 108) and discussion sections (Line 209). Regarding the inconsistent statements, we have removed them from the introduction to avoid redundancy.\nR: We are sorry for the methodological shortcomings that have undermined the strength and clarity of our results. In this revision, we have carefully addressed all the concerns you raised regarding the methods used in the manuscript to ensure that our results are more reliable and convincing. We believe that the manuscript has been significantly improved thanks to your insightful comments. Please find our point-by-point responses below.\n1. Data collection: I'm familiar with the bibliography used for forestry, and I'm sure that in many cases the combination of keywords (product* OR biomass OR yield) would not have found them. 23 articles on forest insects were selected, whereas the meta-analysis by Jactel et al. 2021 is based on 69 articles (3 times as many). 9 articles deal with forest pathogens, whereas the meta-analysis by Field et al. 2025 uses 44 (5 times more). These discrepancies are significant and raise the question of the exhaustiveness of the bibliographic search.\nR: Thank you for your comment. We acknowledge that the choice of keywords in a meta-analysis can significantly influence data collection. As suggested, we have revised the search terms (Lines 293-300) to broaden the scope of our literature search and invested considerable time in re-collecting data. As a result, we have nowcollected 299 articles comprising 2,279 observations, representing an increase of 196 articles over the previous collection of 103. Specifically, for forest ecosystems, our updated dataset includes 94 articles—20 focused on forest pathogens and 86 on forest invertebrate herbivores. We sincerely thank for your valuable advice on data collection and believe this more comprehensive dataset adequately addresses the concern regarding limited data coverage.\n2. Functional diversity: the inclusion of root traits is interesting in itself, but none of the insect pests or pathogens cited for the forest cases are present in the soil. The functional diversity index is therefore not relevant for testing the effects on damage reported in the selected publications.\nR: Thank you for your comment. After re-collecting the data, we now have a total of 64 articles reporting on soil biotic agents—33 on soil pathogens and 31 on soil invertebrate herbivores (Lines 343-344). Given this, we believe it is necessary to include root traits in the analysis to better capture belowground plant–pathogens/herbivores interactions. However, we also acknowledge that compared to aboveground data, belowground data—particularly from forest ecosystems—remain relatively limited. We have now noted this limitation and highlighted the need for future research in the Discussion section (Lines 225-230).\n3. Calculating effect size: this is one of the most important methodological problems in the manuscript. With regard to effect size for pest damage, the calculation of Xc is unclear, as 1) it is not clear why a sum (Sigma) should be used and\nR: Thank you for your constructive comments.\nFor your first question. We realize that our initial explanation of the calculation of 𝑋̅ may not have been sufficiently clear. In our analysis, 𝑋̅ represents the mean expected value of a given response variable (e.g., pathogen performance, invertebrate herbivore performance, or plant productivity). This expected value was estimated based on the performance of each component species in monoculture, weighted by their relative proportions in the mixture (see Eq. 3). This method of calculating expected values in mixtures was developed by Loreau and Hector (2001), and has been widely used in both empirical studies and meta-analyses to quantify mixture effects (Chen et al. 2019; Chen et al. 2021; Gong et al. 2021; Chen et al. 2022; Xiang et al. 2022; Rahman et al. 2025). We have revised the relevant wording for clarity in our method section (Lines 380-381).\nTo further illustrate the calculation of 𝑋̅ (expected value in mixtures), we present the above graph comparing arithmetic and weighted mean approaches for estimating expected yields in mixed plant communities. The analysis demonstrates the critical importance of incorporating species proportions in such calculations. Our system comprises three components: (1) a dominant species (representing 60% of the community with a monoculture yield of 120 g/m²), (2) a common species (30%, 60 g/m²), and (3) a rare species (10%, 40 g/m²). The arithmetic mean (73.3 g/m²) substantially underestimates the weighted mean (94 g/m²) by 20.7 g/m² (representing a 28.2% relative difference). This discrepancy clearly shows how unweighted averages systematically distort ecological predictions by disproportionately weighting the contributions of rare species.\n2) multiplying an average damage in the monoculture by the proportion of the species in the mixture does not work if, for example, the damage is expressed in %. As far as the effect size for productivity is concerned, the Xc formula is similarly unclear, and in particular which (i) effects are to be summed.\nR: For your second question, we believe that the calculation of 𝑋̅ remains valid even when damage is expressed as a percentage. For example, consider a mixture of two plant species, A and B, present in equal proportions. If the damage observed in their respective monocultures is 50%, then the expected damage in the mixture would be:\n𝑋̅ = 50%×0.5+50%×0.5= 25%+25%=50%\nThis illustrates that the weighted average approach still applies when using percentage-based metrics. Furthermore, when calculating effect sizes using the log-transformed response ratio between 𝑋̅ and 𝑋̅ , the units become irrelevant, as the ratio is dimensionless. This allows for consistent comparison across studies regardless of the original measurement scales.\nMore importantly, the simple ratio of tree sizes is not sufficient to estimate productivity. Primary productivity corresponds to the increase in volume or biomass over a given time step. To calculate productivity, you therefore need to have data on the volume or above-ground biomass (AGB) of trees at two dates and calculate the difference, divided by the number of years between two measurements. This is not done here.\nR: For your third question regarding productivity. When calculating the mixture effect size on productivity, we first computed 𝑋̅ , the expected productivity in the mixture, derived as the weighted mean of the component species’ productivity based on their proportions in the mixture. 𝑋̅ represents the observed productivity in the mixture. The effect size was then calculated as the log-transformed response ratio between 𝑋̅ and 𝑋̅ , which quantifies the deviation of mixture performance from the expected value. We have revised the relevant wording in the manuscript to clarify this calculation (Lines 377-379).\nWe apologize for any confusion. In this study, the term “productivity” does not strictly refer to primary productivity. Due to data limitations, we followed the approach of previous meta-analyses and used a range of proxy variables—including biomass, production, height, cover, yield, and volume—to represent productivity, all of which are primary productivity measures or proxies and thus can be collectively analyzed, unlike secondary productivity that measures heterotrophs' energy conversion. This allowed us to include a broader set of studies and observations. We have clarified this definition in the Methods section and other relevant places to avoid potential misunderstandings (Lines 325-330).\n4. Calculating effect size: there is also an inconsistency in L395-396, where it is stated that there is no significant difference in the effect of diversity between specialist and generalist herbivores, whereas table S5 shows the opposite.\nR: Thank you for your comment. Since our dataset has trebled following the recollection, we reanalyzed the data and updated the results accordingly. The results for specialist and generalist pathogens and herbivores are now presented in Fig. 2, Fig. 4 and Fig. 5c, d.\n5. Relative contribution of diversity metrics on productivity: this is undoubtedly the major problem with the study. Correlation does not mean causality. In the SEM, the direction of the downward arrow from pest performance to productivity implies that the reduction in damage inflicted on plants would explain (in part) the increase in their productivity. But it's just as possible that the increase in plant productivity explains the reduced damage inflicted by herbivores or pathogens. Indeed, there are numerous studies showing that more vigorous plants, i.e. those with stronger growth, are more resistant to attacks by pests, particularly secondary pests. The role of associational resistance is therefore by no means demonstrated by this model. To do so would require more complicated studies, such as monitoring plant growth over several years after a severe pest attack followed by an absence of attack, or manipulating herbivory intensity. Here, at best, the SEM model should use a two-way arrow between pest performance and productivity (but we have seen the limits of calculating this productivity), and the discussion should seek to understand the common or pleiotropic effects of plant diversity rather than its cascading effects.\nR: Thank you for your insightful comment. We fully agree that correlation does not imply causation and must be interpreted with caution. As you rightly pointed out, it is indeed possible that increased plant productivity could also contribute to reduced damage by herbivores or pathogens. To better reflect this complexity, we have adopted a bidirectional arrow between pest performance and productivity in Fig. 6 and Fig. S10, as suggested. Additionally, we have revised the Discussion section (Lines 209-218, 233-244, 277) to place greater emphasis on the shared or pleiotropic effects of plant diversity, rather than its cascading effects.\nFor these various methodological reasons, I don't think this manuscript can be accepted for publication without a major revision.\nR: Thank you very much for your constructive and insightful comments. As suggested, we have invested considerable time in re-collecting the data and clarifying our methodology. We believe the manuscript has significantly improved as a result of your feedback, and we hope that our responses to each of your comments satisfactorily address your concerns. We also welcome any additional comments or suggestions that could help us further improve the manuscript.\nR: We greatly appreciate your comments. The choice of keywords in a meta-analysis can significantly influence data collection. We have now revised the search terms (Lines 293-300) to broaden the scope of our literature search and invested considerable time in re-collecting data. As a result, we have now collected 299 articles comprising 2,279 observations, representing an increase of 196 articles over the previous collection of 103. This includes 84 articles on pathogens (526 observations), 232 on herbivores (1752 observations), and 127 on plant productivity (744 observations). We sincerely thank for your valuable advice on data collection and believe this more comprehensive dataset strengthens the robustness of our findings. In addition, we have cited the references you kindly shared in the relevant sections (e.g. lines 96, 105-106, 209, 249, 326) of the Introduction and highlighted the specific focus of our research (lines 94-108).\nAdditionally, I noticed some references were incorrectly cited. For example, the paper by Koricheva & Hayes (2018) in line 92 is cited as though it pertains to species richness, but it focuses on plant intraspecific diversity.\nR: Thank you for your comment. We have carefully reviewed all references cited in the manuscript to ensure they are accurately cited and appropriately support the corresponding statements. Any inconsistencies have been corrected to improve the clarity and credibility of the manuscript.\nHowever, despite these concerns, I believe the manuscript does offer an original contribution by analyzing the link between plant diversity and productivity through the lens of reduced insect and pathogen damage. This unique angle, which considers both biotic stressors and productivity together, is underrepresented in the literature and could add value to the field. Nevertheless, this aspect of the study is not discussed in sufficient depth.\nR: Thank you for your positive feedback. As suggested, we have expanded the discussion on the link between plant diversity and productivity, specifically through the lens of reduced damage from pathogens and herbivores (Lines 233-244, 280, 287). We believe this addition provides a more comprehensive understanding of the potential mechanisms underlying the biodiversity–productivity relationship.\nSome additional comments:\nLn 44-48: the effects of other facets of diversity as FD and PD have already been discussed in several studies (ex. Castagneyrol, B., Jactel, H., Vacher, C., Brockerhoff, E. G., & Koricheva, J. (2014). Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization. Journal of Applied Ecology, 51(1), 134-141.)\nR: Thank you for your comment. Our original intention was to convey that the inconsistent results across studies may stem from a failure to account for the effects of functional diversity (FD) and phylogenetic diversity (PD), rather than to imply a lack of studies examining the influence of FD and PD on pathogens and herbivores. However, in response to a suggestion from the other reviewer, we have removed the relevant statements to avoid any potential misunderstanding.\nLn 49: what do you mean by « types » of pathogens and invertebrate herbivores ? their lifestyle? Feeding guild? Death breath? Organ affected?\nR: Sorry for the confusion. Types here represent fungal vs. bacterial; foliar vs. soil vs. stem; specialists vs. generalists. We have rephrased the sentence for clarity (Lines 45-47).\nLn 67-69: why? Please develop more\nR: We have added further reasoning to better support our initial expectation and clarify the underlying rationale (Lines 57-59, 63-65).\nLn 76-78: why? Some references and explanations are needed.\nR: We have provided more explanations and added the relevant references (Lines 77-79, 81-82).\nLn 80: what do you mean by vegetation type? Trees vs herbs?\nR: Yes, we have revised the sentences for clarity (Lines 84).\nLn 89: again, some references and details are missing here. There are also several papers assessing climate effects on abiotic damage to plants that could be cited.\nR: We have provided more explanations and added the relevant references (Lines 90-93).\nLn 93-99: this is not correct, there are several references on this (see previous comments).\nR: Thank you for your comment. We have cited the relevant literature based on your previous comments and adjusted the wording accordingly to improve clarity and accuracy (Lines 94-108).\nLn 199: this paper focus on tropical forest insects!\nR: Thank you for your comment. Based on the updated and more comprehensive dataset and analyses, the effects of taxonomic diversity (TD) on herbivores are no longer statistically significant. Therefore, we have removed the corresponding discussion to ensure the manuscript accurately reflects the results.\nLn 271: why only from 2000 to 2023?\nR: Thank you for your comment. Systematic screening revealed that studies conducted before 2000 generally lacked accessible raw data and essential metadata (e.g., species composition and proportions), leading to their exclusion from our meta-analysis. We conducted our data collection in July 2023, and spent over a year writing and revising the manuscript, which was submitted in December 2024. As a result, literature from the most recent two years was not initially included. We have now updated the search time range to address this limitation and ensure a more complete dataset (Lines 293).\nLn 300: what about wood organs?\nR: Thank you for your comment. We have supplemented the dataset accordingly. Specifically, we identified only 4 articles on stem pathogens and 10 on stem herbivores, resulting in a relatively limited dataset. Our results show that plant mixtures significantly reduce damage from stem pathogens and herbivores compared to monocultures (Fig. 2). However, these mixture effects did not vary significantly with TDq1, FDq1, or PDq1 (Table S5). We acknowledge that these results may be influenced by the limited number of available studies, leading to a low statistical power.\nLn 309-310: are you sure of this? insect feeding in more than one species from the same genera are often considered as specialist too (ex. see Ali, J. G., & Agrawal, A. A. (2012). Specialist versus generalist insect herbivores and plant defense. Trends in plant science, 17(5), 293-302.)\nR: Thank you for your comment. We agree that insects feeding on multiple species within the same genus are often classified as specialists. In our study, insect herbivores were categorized as specialists or generalists primarily based on the classifications provided in the original literature sources. We have revised the relevant sentence in the manuscript to clarify this point (Lines 338-339).\nLn 322: and how many of them were trees or herbs?\nR: Our dataset includes 140 crop species, 136 grass species, and 229 tree species. We have now added these numbers to the manuscript to improve clarity (Lines 351-352)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In their revision of the manuscript, Huang and colleagues did a good job of increasing the number of references used in the meta-analysis, improving the robustness of the method, and providing a more in-depth interpretation of the results.\nHowever, I believe that further effort is needed to clarify some points that remain unclear, which I list below:\nMethods\n• The literature search was limited to the years 2000-2025 (line 293), why not go further back?\n• The most commonly used R library for meta-analyses is metafor: did they use it?\n• In the publication, several mixtures are compared to the same control = monoculture, the variance must be corrected to take into account non-independence: was this done?\n• Throughout the manuscript, the author uses the term ‘invertebrate herbivores’: does this refer only to herbivorous insects or to other organisms as well?\n• The definition and criteria for distinguishing between specialist and generalist should be clarified.\n• Was FD calculated based on all traits or on traits relevant to pests or diseases, e.g. leaf traits for foliar herbivores, root traits for root herbivores?\nIntroduction\n• Line 94: What are ‘negative effects’? A reduction in damage?\n• Line 95: What are ‘infectious diseases’? Plant diseases?\n• Line 96: What are the ‘herbivores’? Large and/or small herbivores?\n• Line 101-104: a verb is missing\n• Line 121: it would be helpful for the reader if the names or definitions of TD, FD and PD q1 were given here (to avoid having to look them up in the ‘Methods’ section).\nResult\n• Line 136: based on the ‘Methods’ section, I understand that productivity here refers to a single plant species, not the plant community (at the field or stand level). The authors should clarify this point by explaining that this is a different estimate from the more traditional estimate of overyielding.\n• Why was TQ(q=1) not tested in the SEM models?\n• The authors now clearly show that the response of invertebrate herbivores to increased diversity depends on their degree of host specificity. However, once they demonstrate that specialists and generalists differ in the magnitude and direction of their response to plant diversity, this factor (moderator) cannot be ignored when testing the effect of other covariates. It is therefore important that the effect of stand age (SA) be tested separately (or better, in interaction) with host specificity (results and Figure 5). The same applies to the SEM model (results and Figure 6). This is clearly my main remaining concern regarding this study.\nDiscussion\n• Lines 185-188: a word is missing (before ‘than’)\n• Line 192: “decreased” instead of ‘increased’?\n• The authors repeatedly highlight the ‘dilution effect’ as the main mechanism explaining the negative effect (reduction) of damage caused by pests and pathogens in plant mixtures. However, they have not actually tested this hypothesis. They should therefore explain that this is a possible explanation. In addition, they should mention other potential mechanisms, such as reduced plant apparency (among heterospecific neighbours) or the role of natural enemies."}, "author_response": "R: Thank you very much for your positive evaluation and constructive comments. We have carefully considered and addressed each of your comments to increase the clarity of our manuscript. Please see our responses to each of your comments below.\nMethods\n• The literature search was limited to the years 2000-2025 (line 293), why not go further back?\nR: Thank you for your comment. Although raw data were unavailable for most studies conducted before 2000, we were still able to collect an additional 17 eligible studies from that period and have included them in our analysis (Line 312).\n• The most commonly used R library for meta-analyses is metafor: did they use it?\nR: We did not use ‘metafor’ package; instead, we used lmer function of the ‘lme4’ package, which has also been widely used in meta-analyses (van Dijk et al. 2021; Zhao et al. 2022; Vendig et al. 2023). A main difference between the analysis using ‘metafor’ and ‘lme4’ is that ‘metafor’ assumes that the sampling variances are exactly known, whereas the ‘lme4’ assumes that the sampling variances are known only up to a proportionality constant (Lines 432-435) (https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer). Specifically for this study, the sampling variances are not exactly known as they were not available in 58% of all studies included in our dataset. Therefore, we applied ‘lme4’ in this study.\nTo strengthen the robustness of our results, we compared the outcomes obtained using ‘metafor’ with those from ‘lme4’, and found that both approaches yielded similar coefficient estimates (Table A).\n• In the publication, several mixtures are compared to the same control = monoculture, the variance must be corrected to take into account non-independence: was this done?\nR: Yes, to account for the autocorrelation among observations within each study (i.e. non-independence), we used a linear-mixed-effect model where the “study” was included as a random factor following Gong et al. (2021), Chen et al. (2025), and Zhou et al. (2025) (Lines 429-430).\n• Throughout the manuscript, the author uses the term ‘invertebrate herbivores’: does this refer only to herbivorous insects or to other organisms as well?\nR: In this study, besides collecting data on herbivorous insects, we also gathered information on phytophagous nematodes and mites, which are not insects. Therefore, we use the broader term ‘invertebrate herbivores’ to encompass these organisms. We have clarified this in the Methods section (Lines 322-323).\n• The definition and criteria for distinguishing between specialist and generalist should be clarified.\nR: Thank you for your comment. We have clarified the definitions and criteria used to distinguish specialists from generalists, and revised the relevant sentence in the Methods section for improved clarity (Lines 361-365).\n• Was FD calculated based on all traits or on traits relevant to pests or diseases, e.g. leaf traits for foliar herbivores, root traits for root herbivores?\nR: FD was calculated based on all traits, and we have now made this clear in the Method section (Lines 393-394). Inspired by your comments, we also calculated FD based on leaf traits for foliar pathogen and invertebrate herbivores, and on root traits for root pathogens and invertebrate herbivores, and analyzed them separately (Lines 394-397; Fig. S11; Fig. S12). The results showed that FD (based on leaf or root traits) strengthened the negative mixture effects on leaf and root pathogen performance but had no effects on invertebrate herbivores performance, which is consistent with our main results using FD based on all traits (Fig. 3c, d; Fig. S2c, d; Fig. S3c, d; Fig. S5c, d).\nIntroduction\n• Line 94: What are ‘negative effects’? A reduction in damage?\nR: Yes, and we have revised the sentences to enhance clarity (Lines 93-94).\n• Line 95: What are ‘infectious diseases’? Plant diseases?\nR: Yes, we have revised the sentences for clarity (Line 94).\n• Line 96: What are the ‘herbivores’? Large and/or small herbivores?\nR: Since the original literature did not specify whether the herbivores were large or small, we used the general term ‘herbivores’ to avoid any potential misunderstanding.\n• Line 101-104: a verb is missing\nR: Thanks, and we have rephrased the sentence for clarity (Line 100).\n• Line 121: it would be helpful for the reader if the names or definitions of TD, FD and PDq1 were given here (to avoid having to look them up in the ‘Methods’ section).\nR: Thank you for your comment. As suggested, we have provided the definitions of TD, FD, and PD (q=0, 1, 2) in Lines 120-124.\nResult\n• Line 136: based on the ‘Methods’ section, I understand that productivity here refers to a single plant species, not the plant community (at the field or stand level). The authors should clarify this point by explaining that this is a different estimate from the more traditional estimate of overyielding.\nR: Sorry for the confusion, our measure of productivity for the species monocultures and mixtures are at the community level. When calculating expected productivity, the productivity of each component species in monoculture represents the average productivity of single-species community. This value is at the community level, not the species level (Chen et al. 2025). The calculation of mixture effects was based on a natural log-transformed response ratio (𝑙𝑛𝑅𝑅).\nWe have revised the relevant sentences for clarity (Lines 139-140; 201-202; 249-250).\n• Why was TD(q=1) not tested in the SEM models?\nR: Because TDq1 is significantly correlated with FDq1 and PDq1, including all three metrics in a single model led to high multicollinearity. Therefore, we constructed two alternative SEMs: one including FDq1 and PDq1 (please see lines 471-475 and Fig. 6), and the other including TDq1 (please see lines 475-482 and Fig. S10).\n• The authors now clearly show that the response of invertebrate herbivores to increased diversity depends on their degree of host specificity. However, once they demonstrate that specialists and generalists differ in the magnitude and direction of their response to plant diversity, this factor (moderator) cannot be ignored when testing the effect of other covariates. It is therefore important that the effect of stand age (SA) be tested separately (or better, in interaction) with host specificity (results and Figure 5). The same applies to the SEM model (results and Figure 6). This is clearly my main remaining concern regarding this study.\nR: Thank you for your insightful comment. As suggested, we have tested the effect of stand age (in interaction) with host specificity (Lines 161-163 and Fig. 5c, d). We have also conducted SEM model separately for specialists and generalists (Lines 181-185, 190-194; 250-253; Fig. 6b, c, e, f and Fig. S10b, c, e, f).\nDiscussion\n• Lines 185-188: a word is missing (before ‘than’)\nR: Thank you for your comment. We have revised the sentences for clarity (Lines 199-203).\n• Line 192: “decreased” instead of ‘increased’?\nR: Sorry for the mistake. We have revised the sentence (Line 207).\n• The authors repeatedly highlight the ‘dilution effect’ as the main mechanism explaining the negative effect (reduction) of damage caused by pests and pathogens in plant mixtures. However, they have not actually tested this hypothesis. They should therefore explain that this is a possible explanation. In addition, they should mention other potential mechanisms, such as reduced plant apparency (among heterospecific neighbours) or the role of natural enemies.\nR: Thank you for your comment. Our meta-analysis identifies patterns rather than tests mechanisms directly. We have therefore revised the text to avoid implying causal confirmation. Specifically, we have replaced the word “dilution” with “negative” when describing our findings (Lines 289, 303). We also added alternative mechanisms, including reduced apparency and the role of natural enemies, to the Discussion (Lines 253-255)."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I have read through the manuscript and the comments and responses to Reviewer 1. Overall, I enjoyed reading this manuscript and found it to be a very robust and interesting study. The authors did a great job of responding to comments from Reviewer 1, although I do have concerns regarding one of these comments about using the lme4 package rather than metafor.\nRegarding the use of the lme4 package to conduct the meta-analysis (lines 430-435): The choice of the lme4 seems odd and inappropriate based on the website link to the metafor package that the authors provided in their response letter (https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer). The linked page describes why lme4 should NOT be used, so it is a little curious why the authors would link that page and use it to justify the use of lme4. The reason why it is problematic is how it handles the weighting the variance, which in lme4 is not appropriate for meta-analysis. The metafor documentation demonstrates that the parameter estimates may be similar in some cases, although the standard errors, and therefore the p-values, will be different. The table the authors provide comparing lme4 to metafor in the response letter (Table A) also shows these properties. Table A shows models with diversity metrics but only with the covariate of stand age, which is not significant in the metafor models (lines 158-161 and Fig 5 in the revised manuscript). Models for the other covariates are not compared, but it does seem the main results of the diversity effects hold.\nSee also this recent reference (Nakagawa et al. 2023), which includes the author of the metafor package and other experts in meta-analysis (https://onlinelibrary.wiley.com/doi/10.1111/ele.14144). Their worked example of using the sample size calculation, Wr, same as used in the current manuscript (line 416) is conducted in the metafor package for the reasons outlined in the metafor package documentation. My suggestion would be read the Nakagawa et al. (2023 Ecology Letters) paper, reference it, and report the results using the metafor package, which are moderately different than the current results from lme4.\n(Remarks on code availability)\nThe \"data\" they provided seem to be processed data used to create the figures. I was able to recreate the map (Fig 1A) but the rest of the code did not work for me, based on the data in the excel file they provided. The raw data (ie. data from treatments and controls, sample sizes, etc.) were not available."}, "author_response": "R: Thank you very much for your positive evaluation and constructive comments. Following your suggestions, we have reanalyzed the data using the metafor package and updated all relevant results, including tables and figures. The main conclusions remain consistent across both analytical approaches (lme4 and metafor) showing that plant mixtures reduce pathogens and invertebrate herbivores and increase plant productivity. We believe these revisions have further strengthened the robustness of the manuscript.\nR: Thank you for your insightful comments regarding our use of the lme4 package in the meta-analysis.\nWe fully agree with your assessment that the lme4 and metafor may yield different results. In response, we have conducted a comprehensive re-analysis of all key models—particularly those incorporating additional covariates—using the rma.mv function in the R package metafor (lines 424-431). A detailed comparison of the results obtained using lme4 and metafor is provided in the Supplementary Materials (Tables S2-S6). Importantly, these analyses demonstrate that the main conclusions of the manuscript remain robust across both methodological approaches (Table S4; lines 445-447).\nWe sincerely thank you for sharing the recent reference by Nakagawa et al. (2023). Following your suggestion, we have cited this reference and reanalyzed the data using the metafor package (lines 424-431) and updated all relevant results, including tables and figures (lines 22-23, 26-28, 130-131, 136, 149-151, 159, 167, 469-477).\nWe are once again grateful for your insightful and highly constructive feedback, which has significantly strengthened the manuscript’s methodological clarity and statistical rigor.\nR: We have now updated and verified all relevant code and data to ensure that the main results can be reliably reproduced."}, {"round": 4, "reviewer_comments": {"reviewer_3": "I appreciate the authors' work on the revisions and the use of the metafor package for the analysis. The differences between lme4 and metafor were subtle, and I was glad to see the main conclusions were not affected. I am satisfied that the analysis is robust and with the changes.\n(Remarks on code availability)\nI previously reviewed the code, although I did not revisit the code. I looked at the available data and found it sufficient."}, "author_response": "R: Thank you for your positive assessment and for confirming the sufficiency of our data and code. We appreciate your time and constructive comments."}]

Decision letter