Plant species richness promotes the decoupling of leaf
and root defence traits while species-specific responses
in physical and chemical defences are rare

Leonardo Bassi, Justus Hennecke, Cynthia Albracht, Marcel Dominik Solbach, Akanksha Rai, Yuri Pinheiro Alves de Souza, Aaron Fox, Ming Zeng, Stefanie Döll, Van Cong Doan, et al.

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To cite this version:

Leonardo Bassi, Justus Hennecke, Cynthia Albracht, Marcel Dominik Solbach, Akanksha Rai, et al. Plant species richness promotes the decoupling of leaf and root defence traits while species-specific responses in physical and chemical defences are rare. New Phytologist, 2025, 246 (2), pp.729-746. 10.1111/nph.20434 . hal-05164835

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# Plant species richness promotes the decoupling of leaf and root defence traits while species-specific responses in physical and chemical defences are rare

Leonardo Bassi<fnref n="1" />,² ID, Justus Hennecke<fnref n="1" />,² ID, Cynthia Albracht³,⁴,⁵ ID, Marcel Dominik Solbach⁶ ID, Akanksha Rai⁷, Yuri Pinheiro Alves de Souza⁸,⁹ ID, Aaron Fox⁹,¹⁰ ID, Ming Zeng²,¹¹,¹² ID, Stefanie Döll²,¹¹ ID, Van Cong Doan²,¹¹,¹³ ID, Ronny Richter<fnref n="1" />,² ID, Anja Kahl<fnref n="1" /> ID, Lea Von Sivers<fnref n="1" /> ID, Luise Winkler<fnref n="1" /> ID, Nico Eisenhauer²,¹⁴ ID, Sebastian T. Meyer¹⁵ ID, Nicole M. van Dam²,¹¹,¹⁶ ID and Alexandra Weigelt<fnref n="1" />,² ID

## Summary

* The increased positive impact of plant diversity on ecosystem functioning is often attributed to the accumulation of mutualists and dilution of antagonists in diverse plant communities. While increased plant diversity alters traits related to resource acquisition, it remains unclear whether it reduces defence allocation, whether this reduction differs between roots and leaves, or varies among species.

* To answer these questions, we assessed the effect of plant species richness, plant species identity and their interaction on the expression of 23 physical and chemical leaf and fine root defence traits of 16 plant species in a 19-yr-old biodiversity experiment.

* Only leaf mass per area, leaf and root dry matter content and root nitrogen, traits associated with both, resource acquisition and defence, responded consistently to species richness. However, species richness promoted a decoupling of these defences in leaves and fine roots, possibly in response to resource limitations in diverse communities. Species-specific responses were rare and related to chemical defence and mutualist collaboration, likely responding to species-specific antagonists' dilution and mutualists' accumulation.

* Overall, our study suggests that resource limitation in diverse communities might mediate the relationship between plant defence traits and antagonist dilution.

Author for correspondence:
Leonardo Bassi
Email: leonardo.bassi@uni-leipzig.de

Received: 18 August 2024
Accepted: 13 January 2025

New Phytologist (2025) **246**: 729–746

doi: 10.1111/nph.20434

**Key words:** biodiversity, chemical defences, fine root, functional traits, leaf, physical defences, untargeted metabolome.

## Introduction

Biodiversity is vital for the functioning of an ecosystem and its services to humanity (Cardinale et al., 2012; Tilman et al., 2014; Isbell et al., 2017). Researchers have demonstrated a strong increase over time in the positive relationship between biodiversity and ecosystem functioning (BEF), both in forests (Guerrero-Ramírez et al., 2017; Huang et al., 2018) and in grasslands (Cardinale et al., 2007; Reich et al., 2012; Ravenek et al., 2014; Meyer et al., 2016; Wagg et al., 2022), suggesting greater

importance of biodiversity than previously presumed from shorter-term studies (Eisenhauer et al., 2019). Over the past decade, we have accumulated evidence that multitrophic interactions, particularly plant-soil feedback, are important drivers of the strengthening biodiversity-functioning relationships over time (Kulmatiski et al., 2012; Eisenhauer et al., 2012b; van der Putten et al., 2013; Thakur et al., 2021). The two most likely mechanisms through which biotic interactions influence BEF relationships are the dilution of antagonists (Maron et al., 2011; Schnitzer et al., 2011; Wang et al., 2023; Mahon et al., 2024)

<footnote n="1">¹Systematic Botany and Functional Biodiversity, Institute of Biology, Leipzig University, Leipzig, 04103, Germany; ²German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, 04103, Germany; ³Department of Soil Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle, 06120, Germany; ⁴Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098XH, The Netherlands; ⁵Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institute, Quedlinburg, 06484, Germany; ⁶Terrestrial Ecology Group, Institute of Zoology, University of Cologne, Cologne, 50674, Germany; ⁷Department of Biogeochemical Processes, Max Planck Institute for Biogeochemistry, Jena, 0774526, Germany; ⁸Research Unit Comparative Microbiome Analysis, Helmholtz Zentrum München, Neuherberg, 85764, Germany; ⁹TUM School of Life Science, Chair of Environmental Microbiology, Technische Universität München, Freising, 85354, Germany; ¹⁰Environment, Soils and Land Use, Teagasc, Johnstown Castle, Co, Wexford, Y35HK54, Ireland; ¹¹Institute of Biodiversity, University Jena, Jena, 07743, Germany; ¹²Université de Bordeaux, INRAE, BFP, UMR 1332, Villenave d'Ornon, 33140, France; ¹³Plant Physiology Unit, Life Sciences and Systems Biology Department, University of Turin, Torino, 10123, Italy; ¹⁴Experimental Interaction Ecology, Institute of Biology, Leipzig University, Leipzig, 04103, Germany; ¹⁵Terrestrial Ecology Research Group, School of Life Sciences, Technical University Munich, Freising, D-85354, Germany; ¹⁶Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, 14979, Germany</footnote>
and the accumulation of plant mutualists (van der Heijden
et al., 1998) with increasing diversity. However, it is still
unknown whether changes in antagonists and mutualists impose
differing selective pressures on plants along diversity gradients
(Stamp, 2003) and allow plants to reduce resource allocation to
defence to promote growth in more diverse communities. Using
a trait-based approach, we aim to test how plant defences change
in response to the dilution of antagonists and the accumulation
of mutualists along a long-term plant diversity gradient in experi-
mental grasslands.

Despite the consensus that the negative effects of below- and
aboveground antagonists decrease, while the positive effects of
mutualists increase along diversity gradients, studies reveal incon-
sistencies (Halliday & Rohr, 2019). For example, while the dilu-
tion of antagonists appears to affect primarily specialists
(Mommer et al., 2018; Wang et al., 2023), possibly due to host
dilution, (resource concentration hypothesis; Root, 1973; Civi-
tello et al., 2015), generalists may benefit from increased plant
diversity through expanded feeding opportunities (Keesing
et al., 2006). Nonetheless, several studies have consistently shown
that antagonist pressure decreases regardless of specialisation.
This was shown for soil antagonists, such as soil-borne fungal
pathogens (Mills & Bever, 1998; Yang et al., 2015; Wang
et al., 2019), root-feeding nematodes (Cortois et al., 2017; Die-
trich et al., 2021) and arthropods (Amyntas et al., 2025), as well
as for aboveground antagonists, such as arthropods and patho-
gens (Mitchell et al., 2002; Rottstock et al., 2014; Muiruri
et al., 2019; Strauss et al., 2024). This effect may be attributed to
enhanced top-down predator control on herbivores, which can
impact both generalists and specialists (Barnes et al., 2020;
Amyntas et al., 2025). Similarly, the positive effect of mutualists,
such as mycorrhizal fungi, biocontrol bacteria and decomposers,
seems independent of specialisation (van der Heijden
et al., 1998; Latz et al., 2012; Eisenhauer et al., 2012a). Overall,
these findings suggest that antagonistic pressure on plants
decreases, while support from mutualists increases with plant
diversity.

Studies further suggest that this dilution of antagonists and
accumulation of mutualists along plant diversity gradients may
intensify over time (Eisenhauer et al., 2019). However, recent
research found limited support for such temporal changes for
belowground antagonists (Amyntas et al., 2025) and mutualists
(Albracht et al., 2024). This implies that initial shifts in soil com-
munities in response to plant diversity may stabilise relatively
quickly (Eisenhauer et al., 2012b), maintaining belowground
antagonist pressure relatively constant over the years. On the con-
trary, Bröcher et al. (2024) found that the relationship between
aboveground herbivores and plant diversity is highly variable over
time, suggesting that aboveground antagonists may be more sus-
ceptible to seasonal and interannual biotic and abiotic fluctua-
tions than belowground antagonists (De Deyn & Van der
Putten, 2005). These fluctuations may lead to periodic die-offs of
aboveground antagonists, potentially hampering their dilution
and thus selection pressure in diverse plant communities.
Furthermore, aboveground antagonists generally exert milder
pressure on plants than belowground antagonists (Johnson

et al., 2016b). Thus, belowground antagonists may be a stronger
and more constant driver of plant productivity than
aboveground ones.

Given that differing selection pressures along plant diversity
gradients are known to promote changes in plant phenotypes and
genotypes over time (Miehe-Steier et al., 2015; van Moorsel
et al., 2018, 2019), changes in antagonists and mutualists along
plant diversity gradients likely lead to changes in plant resource
allocation to defence. Based on the assumption that there may be
trade-offs between growth and defence (Stamp, 2003; Lind
et al., 2013; Cappelli et al., 2020; Zaret et al., 2024) and that
growth is closely related to plant fitness, a dilution of antagonists
and an accumulation of mutualists in more diverse plant commu-
nities would reduce the need for investment into defence
(Fig. 1a). Considering that Bassi et al. (2024) identified a strong-
ger influence of belowground mechanisms on long-term mono-
culture plant performance, we also postulate that the effect of
plant diversity on root defences may be stronger than on leaves
(Fig. 1b). Furthermore, while Mraja et al. (2011) observed a
reduction in some leaf chemical defences along a plant diversity
gradient for Plantago lanceolata L., it remains unclear whether
this phenomenon can be generalised across multiple species
(Fig. 1c).

Plant defence traits, that is functional traits that promote plant
fitness in the presence of antagonists relative to the absence of
antagonists (Didiano et al., 2014), are often divided into physical
and chemical defences (Table 1; Moore & Johnson, 2017). Phy-
sical defences are those that deter herbivores from feeding on
plant tissues through morphological or anatomical traits (Hanley
et al., 2007), while chemical defences encompass traits related to
the tissue's nutritional quality (Mattson, 1980; Poorter
et al., 2004) and defensive phytochemicals (Raguso et al., 2015).
Another strategy to counteract antagonists is collaboration with
mutualists. Along the recently defined 'root economics space'
(Bergmann et al., 2020), this is captured by the 'collaboration
gradient', represented by a trade-off between specific root length
(SRL) and root diameter (RD), which is related to the root colo-
nisation by mycorrhizal fungi. Despite mycorrhizal fungi enhan-
cing plant physical defence by limiting antagonist access to roots
through competition for space and resources (Rasmann
et al., 2011), they may also promote plant chemical defences by

<figure><img src="image_5.png" /><figcaption>**Fig. 1** Graphical illustration of the hypothesised reduction in defence traits along the plant diversity gradient (a), a stronger response of fine root compared with leaf defence traits (b) and species-specific responses of three hypothetical species (1, 2 and 3) (c).</figcaption></figure>
<table><caption>**Table 1** Leaf and fine root defence traits examined in this study.</caption><thead><tr><th>Tissue</th><th>Traits</th><th>Defence correlation</th><th>Defence type</th><th>Defence mechanisms</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Leaf</td><td>Water repellency</td><td>+</td><td rowspan="3">Physical defences</td><td>Surface barrier: reduced attachment and mobility of antagonists</td><td>Hanley et al. (2007), Gorb & Gorb (2017)</td></tr><tr><td>Hair density</td><td>+</td><td>Mechanical strength</td><td>Poorter et al. (2004), Hanley et al. (2007), Johnson et al. (2010), Loranger et al. (2012), Schuldt et al. (2012), Caldwell et al. (2016), Moore & Johnson (2017), Bröcher et al. (2023)</td></tr><tr><td>Toughness</td><td>+</td><td></td><td></td></tr><tr><td></td><td>LMA</td><td>+</td><td></td><td></td><td></td></tr><tr><td>Leaf/fine root</td><td>DMC</td><td>+</td><td></td><td></td><td></td></tr><tr><td></td><td>SRL</td><td>—</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Fine root</td><td>Diameter</td><td>+</td><td></td><td>Protection through AMF<sup>†</sup> (root collaboration gradient)</td><td>Rasmann et al. (2011), Jung et al. (2012), Cameron et al. (2013), Cortois et al. (2016), Johnson et al. (2016a), Bergmann et al. (2020), Frew et al. (2022)</td></tr><tr><td>MC</td><td>+</td><td></td><td></td><td></td></tr><tr><td rowspan="4">Leaf / fine root</td><td>Nitrogen</td><td>—</td><td rowspan="4">Chemical defences</td><td>Palatability: the nutritional quality of the tissue</td><td>Mattson (1980), Poorter et al. (2004)</td></tr><tr><td>Alkaloids</td><td>+</td><td>Toxicity; Inducing or priming plant resistance or tolerance against plant antagonists (pathogens and insects)</td><td>Hol et al. (2004), Steppuhn et al. (2004), Alves et al. (2007), Nuringtyas et al. (2014), Dugé de Bernonville et al. (2017)</td></tr><tr><td>Terpenoids</td><td>+</td><td></td><td>Marak et al. (2002), Nakashita et al. (2003), Zhang et al. (2015), Zahid et al. (2017), Lackus et al. (2018), Murata et al. (2019)</td></tr><tr><td>Shikimates</td><td>+</td><td></td><td>Alonso et al. (2009), Koskimäki et al. (2009), Hölscher et al. (2014), Prasannalaxmi & Rani (2016), Olivier et al. (2018), Lea et al. (2021), Grover et al. (2022), Zhang et al. (2022), Yang et al. (2023)</td></tr></tbody></table>

The table reports the tissues (leaf or fine root), the traits and the defence correlation (i.e. the expected positive or negative correlation between trait and defence). As we hypothesise defence to be negatively correlated with diversity, the hypothesised trait-diversity relationships are exactly reversed. The table further reports the classification in defence type (physical or chemical), the corresponding defence mechanisms and relevant references. For 'Alkaloids', 'Terpenoids', and 'Shikimates and phenylpropanoids', we refer to the chemical compounds within those chemical pathways (detailed in Table S2). It is important to note that this table is based on studies using targeted metabolome quantification of one group of compounds within those pathways, while our study employs a broader untargeted metabolomic approach to assess phytochemical richness or the sum of range-scaled feature intensity within these pathways. DMC, dry matter content; LMA, leaf mass per area; MC, mycorrhizal colonisation; Shikimates, Shikimates and phenylpropanoid; SRL, specific root length. Part of this table is derived from Bassi et al. (2024).

†While we have classified mycorrhizal colonisation as a physical defence, we acknowledge that mycorrhizas may also promote plant chemical defences through priming.

inducing the production of defensive metabolites (Jung et al., 2012; Cameron et al., 2013; Frew et al., 2022).

Plant defence traits provide a framework that enables us to investigate how plant resource allocation to defence changes along plant diversity gradients. Similarly, functional traits, related to collaboration with mutualists, enable us to investigate plant investment in mutualistic interactions. For instance, some leaf and root traits related to physical and chemical defences, such as leaf mass per area (LMA), leaf toughness, nitrogen and phenolics content, and metabolome, as well as traits related to collaboration with mutualists, such as SRL, have been shown to change along plant diversity gradients (Scherling et al., 2010; Mraja et al., 2011; Ristok et al., 2019; Peng & Chen, 2021; Weinhold et al., 2022; Felix et al., 2023). Although several of these traits are also related to resource acquisition (Weigelt et al., 2021), their changes often affect plant-antagonist and plant-mutualist interactions (Muiruri et al., 2019; de Vries et al., 2021; Bröcher et al., 2023; Ristok et al., 2023b). However, plant species frequently deploy different types of defences: While grasses are often defended through

physical defences, forbs are more commonly defended through chemical defences (Eichenberg et al., 2015; Bassi et al., 2024). Furthermore, plant-antagonist interactions are also affected by associational effects, where the herbivory rate experienced by a focal plant species depends on the characteristics, such as defence traits, of the surrounding plant community and herbivores' feeding preferences, leading to either associational susceptibility or resistance (Barbosa et al., 2009; Underwood et al., 2014). For example, the 'neighbour contrast susceptibility and defence' hypothesis (Alm Bergvall et al., 2006) suggests that if a focal plant species is better defended than the surrounding plant community, it will experience lower herbivory compared with when it grows among plants that are better defended than itself. Indeed, changes in herbivory rates along plant diversity gradients have been shown to differ between plant species in magnitude and direction (Bröcher et al., 2023). It is thus reasonable to hypothesise that changes in defences along plant diversity gradients are species-specific (Fig. 1c). Indeed, despite consistent trait-diversity relationships among plant species (Roscher et al., 2018), species-specific responses have also been
observed (Gubsch et al., 2011; Roscher et al., 2011b; Lipowsky et al., 2015).

In this study, we measured for the first time a comprehensive set of physical and chemical defence traits in leaves and fine roots (summarised in Table 1) of 16 grassland plant species growing along a 19-yr-old plant species richness gradient in the Jena Experiment (Roscher et al., 2004). We hypothesised that the observed dilution of antagonists and accumulation of mutualists along plant species richness gradients would trigger:

(1) a reduction in plant defence traits (Fig. 1a);

(2) a more pronounced defence reduction in fine roots compared with leaves (Fig. 1b); and

(3) species-specific defence responses (Fig. 1c).

## Materials and Methods

### Study site and experimental design

The investigation took place within the Jena Experiment, a grassland biodiversity experiment initiated in 2002 (Roscher et al., 2004). The Experiment is situated in the floodplain of the Saale River near Jena, Germany (latitude: 50.95, longitude: 11.62, altitude: 130 m above sea level), a region with a mean annual air temperature of 9.9°C and annual precipitation of 660 mm (Hoffmann et al., 2014). The experimental site had formerly been an arable field for 40 yr. The experiment consists of 80 experimental plant communities representative of the Arrhenatherion mesophilic grassland (Ellenberg, 1988), with varying species and functional richness. The six species richness (SR) levels, 1, 2, 4, 8, 16 and 60, are nearly orthogonally crossed with the four functional richness levels consisting of grasses, legumes, small herbs and tall herbs (Roscher et al., 2004). Each SR level had 16 replicates (except for the monocultures with 14 and the 60-species mixtures with 4 replicates). Species compositions were randomly determined. In addition, monocultures of all 60 species were established. Plots are arranged in four blocks to account for variations in soil texture caused by distances to the river. Plots were mown twice a year in June and September, following the typical extensive meadow management of the region, and were not fertilised during the experimental period. Weeding occurred at least twice per growing season. Additional details on the experiment are reported in Roscher et al. (2004).

### Field sampling of leaf and fine root samples

Sixteen species (Table S1), four of each functional group, were selected and harvested at peak biomass from 17 to 31 May 2021 in all plots where they were present (totalling 128 species per plot combinations). Due to the randomness of species selection in the experimental design and the local extinction of certain species in specific plots, not all species were uniformly represented across the diversity gradient. In addition, the number of sampled species per plot combination varied depending on the species (refer to Table S1 for detailed species occurrences).

In each plot, we sampled above- and belowground parts of three individuals per species. An additional one or two individuals were

harvested for small species to collect enough leaf and fine root material for chemical analyses. The 428 sampled individuals were randomly selected and marked in early spring, avoiding plot edges. The aboveground plant part was harvested by cutting the stem 1–2 cm above the ground and placed in a sealed plastic bag along with a wet paper towel to rehydrate the leaves to their full potential (Pérez-Harguindeguy et al., 2013). We sampled the roots of the same individuals by extracting a soil core with a depth of 10 cm and a diameter of 6.9 cm. All collected samples were quickly put into a cooling box and transferred to a 4°C refrigerator within 6 h. Samples were processed from 6 to 26 h after sample collection. Subsamples for the metabolome analysis were always collected within 14 h after field collection.

### Leaf sample preparation and morphological trait measurements

All leaf morphological and chemical trait measurements were performed on fully expanded, undamaged and nonsenescent leaves excluding petioles and rachis. The leaves of each individual were divided into three subsamples according to their position (internode). For grasses without flowering stems, this was not possible, and random leaves were taken instead. One subsample comprising one or three leaves or leaflets (depending on leaf size) collected from the second or third internode from the top was frozen in liquid nitrogen and stored at –80°C to be used for the untargeted metabolome analysis. A second subsample consisting of one to five leaves (depending on leaf size) from the third to the fifth internode from the top was used for morphological trait measurements. The third subsample consisted of all the remaining leaves, pooled at the species per plot level, was frozen in liquid nitrogen and stored at –80°C for nitrogen quantification.

Leaf morphological traits were measured at the individual level and then averaged at the species per plot level. Leaves were first weighed and scanned with a flatbed Epson Expression 11000XL scanner at 600 DPI (Epson, Tokyo, Japan). After the scan, one leaf per individual was used for water repellency (WR; deg.), hair density (no. of hairs mm⁻²) and toughness (N mm⁻¹; the ratio of maximum shearing force to thickness) measurements (Pérez-Harguindeguy et al., 2013). Leaf images for determining WR and hair density were analysed in IMAGE (v.1.53a; Schneider et al., 2012). During the storing or handling process, five samples were damaged, resulting in missing WR measurements for those samples. Leaves were weighed after being oven-dried at 70°C for 72 h. We determined LMA (g m⁻²) as the ratio of dry weight to leaf area and leaf dry matter content (LDMC; g g⁻¹) as the ratio of dry to fresh weight (Pérez-Harguindeguy et al., 2013). Further details on the leaf morphological trait measurements are reported in Methods S1.

### Fine root sample preparation, morphological trait and mycorrhizal colonisation measurements

Before proceeding with the root washing, soil cores were soaked in cold water for 15 min. Roots were washed carefully inside a water-filled bucket to prevent root damage. We repeatedly
refreshed the water by filtering out soil debris until all roots were free from soil. We collected only roots attached to the stem of the target individual and discarded all the rest. For further analyses, we retained all fine roots with a diameter lower than 2 mm. A random subsample of the fine roots of each individual was collected, frozen in liquid nitrogen and stored at -80°C to be used for the untargeted metabolome analysis. The remaining fine roots of each individual were pooled at the species per plot level. From this bulked sample, we extracted a random subsample for morphological trait measurements and a random subsample for the estimation of mycorrhizal colonisation (MC). All remaining fine roots were frozen in liquid nitrogen and stored at -80°C to be used for nitrogen quantification.

To assess fine root morphological traits, the bulked subsample was scanned with a flatbed scanner at 600 DPI (Expression 11000XL; Epson, Düsseldorf, Germany) and weighed after being carefully dried with a paper towel. Fine roots were then freeze-dried to estimate the dry weight. We retrieved mean RD (mm) and root length from the scans using RHIZOVISION (v.20.0.3; Seethepalli *et al.*, 2021). We estimated SRL (m g⁻¹) as the ratio of root length to root dry weight and root dry matter content (RDMC; g g⁻¹) as the ratio of dry to fresh weight (Freschet *et al.*, 2021). Four SRL, three RDMC and three RD observations were excluded due to contamination of roots from other species, the inclusion of roots with a diameter larger than 2 mm, loss of sample material before the dry weight measures, or missing scan.

We measured the MC rate (%) using the method developed by Trouvelot *et al.* (1986). Fine roots were first rinsed in water and destained by incubation in a 10% KOH solution at 95°C for 10 min. We stained the roots with an acidic ink solution (5% acetic acid and 5% Pelican blue ink). We removed the excess by rinsing them in water and incubating them twice at 75°C in a 20% acetic acid solution. Roots were mounted on microscope slides for analysis with a digital microscope (Keyence VHX, Osaka, Japan) to collect at least nine images per sample at 200× magnification. The images were used to estimate the MC rate in six cover classes as described in Trouvelot *et al.* (1986). Cover classes were converted to percentages and all replicates averaged to the plot level. For some samples, the destaining procedure did not work properly. This resulted in 21 missing data points: four for *Arrhenaterum elatius* L., *Geranium pratense* L. and *Vicia cracca* L.; three for *Lotus corniculatus* L.; two for *Galium mollugo* L. and *Taraxacum officinale* L.; and one for *Medicago × varia* Martyn and *Plantago media* L.

## Nitrogen measurements

We estimated leaf and root relative nitrogen content (N; % of dry weight) by combining two methods to reduce laboratory workload and costs. For 59% of the samples, we used 10 mg of milled material and an elemental analyser (VarioEL II, Elementar, Hanau, Germany). For the remaining 41% of the samples, we predicted nitrogen content with a bootstrapped CARS-PLSR model (Richter & Bassi, 2023) using near-infrared spectral data collected with a Multi-Purpose FT-NIR-Analyzer (MPA, Bruker Corp., Billerica, MA, USA). The model was calibrated with spectral data from this

experiment and published data (Bassi *et al.*, 2023) and achieved excellent prediction accuracy in internal model validation (R² = 98%, RMSE = 0.16%, RPD = 7.44). Details of the measurements are given in the Methods S2.

## Untargeted metabolomics analyses

The untargeted metabolome analysis was performed on nonvolatile, polar, semipolar and apolar metabolites between 100 and 1600 Dalton, extracted from leaf and fine root with a solution comprising 75% methanol and 25% water acetate buffer using an ESI-UHR-Q-ToF-MS (maXis impact, Bruker Daltonics, Hamburg, Germany) in positive mode, following the procedure described in Weinhold *et al.* (2022) with minor modifications (Bassi *et al.*, 2024). Sample preparation included freeze-drying, manual homogenisation with scissors, and milling in 2-ml tubes with ceramic beads. The obtained raw data were processed in Bruker Compass MetaboScape Mass (2022b v.9.0.1; Build 11 878; Bruker Daltonics, Hamburg, Germany) using MetaboScape's T-ReX algorithm.

Raw data processing was performed on leaf and root together. This caused features with high-intensity values in one tissue type to appear at low-intensity in the other. To minimise the influence of low-intensity features, we applied the same MetaboScape feature filtering criteria to the leaf and root datasets separately in R. The entire process, including sample extraction, LC-MS analysis, MetaboScape settings and feature filtering, is reported in the Method S3.

After removing features derived from blanks and internal standards (1177 features) and applying the feature filtering at the tissue level, the dataset included 10 667 features of which 9551 with an MS/MS fragmentation pattern. For these features, we performed a de-novo feature annotation and classification based on the MS/MS fragmentation pattern with SIRIUS v.5.7.2 (Dührkop *et al.*, 2019). Annotations were re-ranked with ZODIAC (Ludwig *et al.*, 2020), and molecular structures were assigned with CSI:FingerID (Dührkop *et al.*, 2015; Hoffmann *et al.*, 2021) and classified using the deep neural network-based natural product classifier (NPClassifier; Kim *et al.*, 2021). We used 5 ppm accuracy and limited the element to CHNPOS and left the other settings to default for SIRIUS and ZODIAC. For CSI:FingerID, formulas from all databases were selected. We discharged features with a ZODIAC score lower than 0.5 and NPC pathway probability lower than 0.5.

## Estimation of chemical defence traits from untargeted metabolomic analysis

We used an untargeted metabolome analysis focusing on secondary metabolites to quantify chemical richness, calculated as the number of mass signals of putative metabolites, or features (hereafter feature richness), as well as the sum of the range-scaled feature intensity (hereafter feature sum) within three major chemical defence pathways: alkaloids, terpenoids, and shikimates and phenylpropanoids (NPClassifier; Kim *et al.*, 2021). The selection of these pathways was informed by an extensive literature review
(reported in Table S2) and chosen due to their pivotal roles in plant defences. However, we acknowledge that compounds within those pathways are involved also in other biological processes and that our approach represents a very global estimate of the allocation to defence.

While feature richness does not provide a direct quantitative measure of defence phytochemicals, it serves as a proxy of the overall diversity and number of final products within that pathway (Müller & Junker, 2022). Plant individuals showing higher feature richness within a specific defence pathway are expected to possess enhanced defence against a broader array of antagonists compared with those with lower feature richness (Whitehead et al., 2021; Fernandez-Conradi et al., 2022). However, the concentration of highly toxic compounds may act as a stronger determinant than the richness of potentially less toxic compounds. To extract a more robust proxy of total allocation to defence, we summed the range-scaled feature intensity within each selected pathway. Range scaling was calculated separately for roots and leaves. For each feature, scaling was performed by subtracting the minimum intensity value across all samples from the intensity of each sample and then dividing by the intensity range across all samples ($y_i = \frac{x_i - \min(x)}{\max(x) - \min(x)}$), and was chosen to account for discrepancies in feature intensity detection caused by differences in ionisation (Smilde et al., 2005). While this scaling results in the loss of absolute feature intensity information, it gives equal weight to each feature (Sun & Xia, 2024). Thus, the sum of range-scaled intensities within a pathway does not provide an absolute measure of the total concentration of phytochemicals within a pathway (i.e. alkaloids), but it rather indicates whether the concentration of features within a pathway increases or decreases.

We derived feature richness and the sum within the three selected pathways from 428 individual leaf and 391 individual root samples, averaging the data per plot at the species level. Due to limited root material, 22 individuals could not be analysed, and 15 individuals were excluded from the analysis due to contamination from roots of other species or moss rhizoids. This resulted in a missing value in our final data for *Vicia cracca* in a 60-species mixture.

## Statistical analysis

We performed all statistical analyses in R (v.4.3.2; R Core Team, 2023). We used principal component analysis (PCA) to evaluate the coordination of leaf and root defence traits across species, with missing values imputed by the median. We used linear mixed models (LME4 R package v.1.1–35.1; Bates et al., 2015) to examine the effect of plant community sown SR (log scale), plant species identity (ID) and their interaction (SR: ID) on each plant defence traits separately. The random effect consisted of the experiment plot nested within block to account for repeated measurements within each plot (multiple species per plot) and the distance of the plot to the river (block). To assess term significance, we used ANOVA type III sum of squares (LMERTEST R package v.3.1-3; Kuznetsova et al., 2017) and adjusted *P*-values for false discovery rate (FDR) due to multiple

testing (Benjamini & Hochberg, 1995). The FDR correction was done separately for the *P*-values relative to the three terms in the model (SR, ID and SR:ID). The beta coefficients (slopes), their confidence interval and *P*-values of each defence trait changes along the plan SR gradient for each species were extracted from the models with the *EMTRENDS* function from the *EMMEANS* R package (v.1.10.0; Lenth, 2024). To obtain standardised regression beta coefficients of each defence trait along the plant SR gradient among all species, we ran additional linear mixed models using Z-transformed (mean-centred and scaled) plant defence traits and only plant community sown SR (log scale) as the explanatory variable. To consider variations in species defence trait means and response to the SR gradient, ID was added as a random intercept only or as a random intercept and slope. Specifically, the random slope for ID was applied only to traits for which the SR:ID term was significant (FDR *P*-values < 0.05) in the first set of models. The proportion of variance explained, marginal and conditional *R*², was extracted from the models using the *MUMIN* R package (v.1.47.5; Bartón, 2023).

## Results

### Features classification

Of the 9551 features with an MS/MS fragmentation pattern, 5187 (54%) were classified according to our accuracy threshold (ZODIAC score and NPC pathway probability above 0.5). Shikimates and phenylpropanoids (2173 features), terpenoids (1618 features) and alkaloids (258 features) accounted for 20, 15 and 2% of the total features, respectively (Table S3). The other pathways, amino acids and peptides, carbohydrates, fatty acids and polyketides, accounted for 2 to 3% of the total features. The superclasses with the highest number of features within shikimates and phenylpropanoids were flavonoids, phenylpropanoids, lignans, phenolic acids and coumarins. The superclasses with the highest number of features for terpenoids were tri-, sesqui-, mono- and di-terpenoids. Within alkaloids, the superclasses with the highest number of features were pseudoalkaloids, and ornithine, tyrosine and anthranilic acid alkaloids. The detailed number of features classified within each pathway is shown in Figs 2, S1; Tables S4.

### Defence traits coordination

The leaf trait PCA's first two principal components (PCs) explained 35 and 20% of the variance, respectively, while those of the root PCA explained 42 and 28%, respectively (Fig. 3). The first leaf PC revealed a trade-off between physical (toughness, dry matter content and WR) and chemical defences (shikimates and alkaloids; Table S5). The second leaf PC showed a similar trade-off between physical (LMA) and chemical defences (alkaloids and terpenoids), although not all chemical defences (nitrogen and shikimates) followed this trade-off.

Several root physical and chemical defences (shikimates, alkaloids, MC and RD) were positively associated along the first root PC, except for root nitrogen. The second root PC highlighted a
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Sunburst plot showing the proportion of features for the chemical superclasses (outer circle) that could be classified with the NPClassifier within three selected pathways (inner circle; alkaloids, terpenoids and shikimates and phenylpropanoids) across all samples. Colours depict the three pathways. Only superclasses with the highest number of features within each pathway are reported. Interactive sunburst plots, including chemical classes of leaves and fine roots samples are available in the Figure S1. Ornith., Ornithine alkaloids; Pseudoalk., Pseudoalkaloids; Tyro., Tyrosine alkaloids.</figcaption></figure>

trade-off between physical (RDMC, RD and SRL) and chemical defences (terpenoids). Overall, the PCA analysis revealed multiple trade-offs between physical and chemical defences.

## Species richness effects on defence traits

Among the 23 defence traits measured in this study, only four exhibited a significant change along the SR gradient (Table 2, column SR). With increasing plant SR, plants showed lower LMA (FDR *P*-value = 0.0004), LDMC (FDR *P*-value = 0.0043) and root N (FDR *P*-value = 0.0001) compared with plants growing in low-diversity communities (Table 2; Fig. 4; note that in Fig. 4, the coefficients for nitrogen are multiplied by -1). In contrast to LDMC, RDMC increased along the SR gradient (FDR *P*-value = 0.0229). Similar to root N, leaf N tended to decrease along the SR gradient (Fig. 4), albeit not significantly (FDR *P*-value = 0.2248). None of the proxies for chemical defence allocation within the three selected pathways changed significantly along the SR gradient after FDR correction (Table 2; Fig. 4). However, without FDR correction, the production of defensive phytochemicals within the terpenoids (*P*-value = 0.0490) and shikimates and phenylpropanoids (*P*-value = 0.0745) pathways in leaves showed a significant or marginally significant increase along the SR gradient, for the feature sum parameter of the two pathways. In summary, while two out of five leaf physical defences showed a

clear decrease along the SR gradient, two out of seven leaf chemical defences tended to increase, albeit not significantly when correcting for FDR. On the contrary, one out of four root physical defences and one out of seven root chemical defences increased along the SR gradient.

As expected, ID explained a notably large amount of variation in plant defence traits and the term was significant for all traits, except leaf toughness (FDR *P*-value = 0.0727). When ID was included in the fixed effect, the marginal *R*² ranged from a minimum of 58% for leaf toughness to a maximum of 96% for the terpenoid sum (Table 2). Conversely, plant SR explained only a modest amount of variation in plant defence traits, as indicated by the marginal *R*² of the models with ID included in the random term, and only plant SR in the fixed term. For these models, the marginal *R*² was 4% for LMA and 1% for the other significant traits, LDMC, root N and RDMC (Table S6).

Despite the low variance explained by SR, LMA, LDMC and root N responses to SR were independent of ID as the interactions between SR and ID (SR:ID) were not significant (Table 2). This suggests that, at least for these traits, most species responded in the same direction to the plant richness gradient. For RDMC, the interaction term, SR:ID, was marginally significant (FDR *P*-value 0.0908), suggesting that the response of RDMC to SR partially differed between species (Table 2). Indeed, marginal trend estimation revealed that 9 of the 16 species sampled showed an increase in RDMC along the diversity gradient. This was only
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Principal component analysis (PCA) of leaf (left) and root (right) defence traits. Variance explained by each component is reported in the axis titles. Different shapes indicate plant functional groups and species within each functional group are depicted in different colours. Traits negatively correlated with defence (nitrogen and specific root length; Table 1) were multiplied by -1 to ensure that loading vectors indicate increased defence. For chemical traits related to the metabolome, alkaloids, terpenoids and shikimates, only the sum of range-scaled feature intensity (not feature richness) was used to avoid over-representation of chemical defences. Several traits are log-transformed to enhance linearity among traits (Supporting Information Table S5). Shikimates, Shikimates and phenylpropanoids. Full species names: Arr.ela, *Arrhenatherum elatius* L.; Bro.ere, *Bromus erectus* Huds.; Fes.rub, *Festuca rubra* L.; Gal.mol, *Galium mollugo* L.; Ger.pra, *Geranium pratense* L.; Kna.arv, *Knautia arvensis* (L.) Coult.; Lat.pra, *Lathyrus pratensis* L.; Lot.cor, *Lotus corniculatus* L.; Med.var, *Medicago × varia* Martyn; Pla.lan, *Plantago lanceolata* L.; Pla.med, *Plantago media* L.; Pru.vul, *Prunella vulgaris* L.; Ran.acr, *Ranunculus acris* L.; Tar.off, *Taraxacum officinale* L.; Tri.fla, *Trisetum flavescens* (L.) P.Beauv.; Vic.cra, *Vicia cracca* L.</figcaption></figure>

significant for one species, *Gallium mollugo*, while the remaining seven species showed neutral or slightly negative trends (Figs 5, S2).

The only other three traits that showed a significant or margin-
ally significant interaction between SR and ID were MC rate
(FDR *P*-value: 0.0173), terpenoid richness in leaves (FDR *P*-
value: 0.0353; Table 2), and the sum of alkaloids in roots (FDR
*P*-value: 0.0908). For MC rate and the sum of alkaloids in roots,
species showed negative, neutral and positive responses to SR
(Fig. 5). Species richness increased the rate of MC in *Trisetum
flavescens* (L.) P.Beauv and *Lathyrus pratensis* L., but decreased it
in *Plantago lanceolata*. In addition, SR increased alkaloid produc-
tion in the roots of *Lathyrus pratensis* but decreased it in the roots
of *Taraxacum officinale*. The number of features within terpe-
noids was mostly neutral among species, with only one species,
*Trisetum flavescens*, showing a clear increase in the number of leaf
terpenoids (Fig. 5). Overall, these results suggest that while physi-
cal defence traits tend to respond similarly among species to the
SR gradient, chemical defence traits and MC rate responses to
the SR gradient show more species-specific responses.

Discussion

This study investigated intraspecific responses of 23 leaf and fine
root physical and chemical defence traits to a 19-yr-old plant diver-
sity gradient of sixteen grassland plant species. Our main goal was
to test whether the accumulation of mutualists and dilution of
antagonists, often observed along plant diversity gradients, would
promote a reduction in plant defences in high-diversity commu-
nities. In addition, we tested whether this reduction would be
stronger in fine root compared with leaf defences, and if it would
differ among species. Our results showed that most plant defence
traits do not respond to species richness. Furthermore, while
defence traits, which are involved in other plant functions, such as
resource uptake and usage, responded to species richness similarly
among species, chemical defence traits, such as the production of
terpenoids, or traits related to the collaboration with mutualists,
showed species-specific responses. Interestingly, some leaf and root
defences responded in opposing directions to species richness, sug-
gesting that while changes in resource availability along species
<table><caption>**Table 2** ANOVA table based on type III sum of squares for the linear mixed models with plant defence traits as response variable and plant species richness (log scale; SR), plant species identity (ID) and their interaction (SR:ID) as explanatory variables.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">SR</th><th colspan="2">ID</th><th colspan="2">SR:ID</th><th rowspan="2">R<sup>2</sup> (%)<br>mar/con</th></tr><tr><th>df</th><th>F-value</th><th>df</th><th>F-value</th><th>df</th><th>F-value</th></tr></thead><tbody><tr><td rowspan="12">Leaf</td><td>Water repellency</td><td>88.195</td><td>0.073</td><td>88.884</td><td>8.107***</td><td>88.702</td><td>0.707</td><td>84/86</td></tr><tr><td>Hair density</td><td>32.245</td><td>0.454</td><td>94.500</td><td>6.104***</td><td>90.723</td><td>0.502</td><td>73/77</td></tr><tr><td>Toughness</td><td>94.466</td><td>0.180</td><td>94.343</td><td>1.898.</td><td>94.115</td><td>1.064</td><td>58/61</td></tr><tr><td>Leaf mass per area</td><td>24.841</td><td>20.049***</td><td>92.725</td><td>12.244***</td><td>85.659</td><td>1.137</td><td>83/87</td></tr><tr><td>Dry matter content</td><td>20.334</td><td>13.213**</td><td>92.431</td><td>18.241***</td><td>84.911</td><td>1.779</td><td>90/92</td></tr><tr><td>Nitrogen</td><td>24.996</td><td>2.542</td><td>93.347</td><td>15.863***</td><td>88.198</td><td>0.980</td><td>87/90</td></tr><tr><td>Alkaloids richness</td><td>95.435</td><td>0.028</td><td>95.128</td><td>19.035***</td><td>94.907</td><td>0.888</td><td>90/90</td></tr><tr><td>Alkaloids sum</td><td>94.743</td><td>0.308</td><td>94.576</td><td>14.399***</td><td>94.327</td><td>0.341</td><td>85/86</td></tr><tr><td>Terpenoids richness</td><td>27.371</td><td>1.923</td><td>91.746</td><td>45.569***</td><td>86.813</td><td>2.150*</td><td>95/97</td></tr><tr><td>Terpenoids sum</td><td>95.218</td><td>3.975</td><td>94.925</td><td>48.528***</td><td>94.657</td><td>1.151</td><td>96/96</td></tr><tr><td>Shikimates richness</td><td>34.334</td><td>0.538</td><td>91.888</td><td>48.456***</td><td>88.817</td><td>1.649</td><td>94/96</td></tr><tr><td>Shikimates sum</td><td>28.679</td><td>3.425</td><td>85.065</td><td>17.281***</td><td>79.57</td><td>0.977</td><td>85/93</td></tr><tr><td rowspan="12">Fine root</td><td>Specific root length</td><td>24.988</td><td>0.054</td><td>90.434</td><td>4.869***</td><td>86.791</td><td>0.883</td><td>70/74</td></tr><tr><td>Diameter</td><td>9.208</td><td>0.341</td><td>91.010</td><td>6.582***</td><td>84.210</td><td>0.617</td><td>76/77</td></tr><tr><td>Mycorrhizal colonisation</td><td>73.274</td><td>1.243</td><td>72.906</td><td>10.184***</td><td>72.808</td><td>2.356*</td><td>81/83</td></tr><tr><td>Dry matter content</td><td>92.154</td><td>7.644*</td><td>92.040</td><td>4.313***</td><td>92.051</td><td>1.837.</td><td>74/74</td></tr><tr><td>Nitrogen</td><td>94.973</td><td>18.317***</td><td>94.649</td><td>18.185***</td><td>94.323</td><td>1.718</td><td>90/90</td></tr><tr><td>Alkaloids richness</td><td>22.387</td><td>0.902</td><td>92.160</td><td>10.257***</td><td>86.687</td><td>1.590</td><td>82/86</td></tr><tr><td>Alkaloids sum</td><td>7.865</td><td>0.305</td><td>92.184</td><td>18.956***</td><td>81.552</td><td>1.861.</td><td>89/90</td></tr><tr><td>Terpenoids richness</td><td>18.077</td><td>0.143</td><td>91.948</td><td>10.606***</td><td>86.300</td><td>0.872</td><td>83/86</td></tr><tr><td>Terpenoids sum</td><td>18.375</td><td>0.028</td><td>92.153</td><td>12.416***</td><td>86.051</td><td>0.287</td><td>86/88</td></tr><tr><td>Shikimates richness</td><td>93.769</td><td>0.032</td><td>93.576</td><td>7.687***</td><td>93.330</td><td>1.250</td><td>80/82</td></tr><tr><td>Shikimates sim</td><td>93.009</td><td>0.022</td><td>92.906</td><td>10.921***</td><td>92.718</td><td>0.866</td><td>84/86</td></tr></tbody></table>

The random effect included plots nested in blocks. The table reports the denominator degree of freedom (df), F-values and marginal and conditional R² (marginal before slash and conditional after) for each model (rows). The level of significance is based on FDR-adjusted P-values and reported with asterisks and dots: *P* < 0.1; **, *P* < 0.05; **, *P* < 0.01; ***, *P* < 0.001. con, conditional; mar, marginal; MC, mycorrhizal colonisation; Shikimates, Shikimates and phenylpropanoid.

richness gradients have a consistent effect among species, they promote the decoupling of defence traits between leaves and fine roots. On the other hand, the species-specific responses of chemical defence traits and traits related to collaboration with mutualists suggest that the commonly observed dilution of antagonists and accumulation of mutualists across SR gradients may not affect all species equally.

## Effect of plant species richness on leaf defence traits

The leaf defence traits with the most consistent response to the diversity gradient among species were LMA and LDMC. Similar to other studies, these two traits decreased along the diversity gradient (Gubsch et al., 2011; Roscher et al., 2011b; Lipowsky et al., 2015) and are associated with plant defence, against leaf chewers (Caldwell et al., 2016), as well as foliar pathogens (Cappelli et al., 2020). These traits' responses seem to align with our first hypothesis, that plant defence traits decrease along the plant diversity gradient, due to a reduction in antagonist pressures. However, despite being significant and marginally significant only without correcting for FDR, the production of features within the terpenoids and shikimate and phenylpropanoids pathways in leaves tended to increase with SR in line with the findings of Poeydebat et al. (2021). Several phytochemicals within those pathways are known for their defensive role against a variety of

leaf antagonists, including arthropods (Dugé de Bernonville et al., 2017), pathogens (Lackus et al., 2018) and viruses (Zhang et al., 2015). This response contradicts our first hypotheses, as it suggests that leaf chemical defences increase with plant diversity.

The opposite responses of these leaf physical and chemical defences to the diversity gradient suggest a trade-off between physical and chemical defences within species. Despite this general trend, this trade-off was consistent within all species only for LDMC and leaf terpenoids (Fig. S3). However, the PCA revealed a comparable trade-off between physical and chemical defences across species in leaf and, to a lower extent, in roots, aligning with findings from other studies (Fernandez-Conradi et al., 2022; Bassi et al., 2024). Overall, the finding that physical and chemical defences exhibit a trade-off within and across species aligns with the growth-defence trade-off hypothesis (Lind et al., 2013; Zaret et al., 2024) as it suggests that plants can simultaneously optimise resource competition and promote defence, as shown in previous studies investigating trade-offs between constitutive and induced defences (Kempel et al., 2011). In our experiment, this trade-off may arise due to the differential strengths and effects that multiple stressors, such as resource limitation and antagonist pressure, exert on leaf defence traits.

Leaf mass per area and dry matter content have a pivotal role in other plant functions than defence, such as the acquisition of light (Poorter et al., 2019). Thus, the response we observed is
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Standardised beta coefficients of plant defence traits change along the plant species richness gradient (log scale) based on linear mixed models with species as random intercept or random intercept and slope (Supporting Information Table S6; random effect structure was based on species richness (SR): species identity (ID) significance in Table 2). Standardisation was performed by running models with Z-transformed defence traits (centred and scaled). Traits negatively correlated with defence (nitrogen and specific root length; Table 1) were multiplied by -1 to ensure that positive beta coefficients consistently indicate increased defence, as indicated by the arrows below each panel. Error bars show the 95% confidence interval. Significant coefficients (P < 0.05), based on false discovery rate (FDR)-adjusted P-values (Table 2) are reported in black, while nonsignificant ones are in grey. Leaf defence traits are shown on the left and roots on the right. Model coefficients are reported in Table S6. Shikimates, Shikimates and phenylpropanoid.</figcaption></figure>

most likely related to the changes in light availability, as pre-
viously detected in our experiment (Roscher *et al.*, 2011a; Bach-
mann *et al.*, 2018), a major limiting resource along species
richness gradients (Hautier *et al.*, 2009), rather than to changes
in antagonistic pressure. However, the reduction in LMA and
dry matter content, to optimise light-capturing surface per car-
bon cost (Poorter *et al.*, 2009), inevitably reduces the defensive
benefits conferred by these traits. This poses the leaves of plants
growing in highly diverse mixtures under a higher risk of antago-
nist attack. Indeed, even though arthropod herbivore pressure,
measured on a biomass or energy flux basis (herbivore biomass or
herbivore energy influx divided by plant biomass) was shown to
decrease (Ebeling *et al.*, 2018; Barnes *et al.*, 2020), the

proportion of leaf area and leaf biomass eaten by invertebrate
herbivores, including gastropods, a group containing many gen-
eralists, increases with plant species richness (Meyer *et al.*, 2017;
but see Seabloom *et al.*, 2017). Indeed, at the same site, Bröcher
*et al.* (2023) found that the increase or decrease in leaf area eaten
by invertebrate herbivores along the species richness gradient was
proportional to the change in LDMC, in line with the ‘neigh-
bour contrast susceptibility and defence’ hypothesis (Alm Berg-
vall *et al.*, 2006). Given that under light limitation in diverse
communities, the relative importance of leaves should increase,
and at the same time, the probability of leaf attack increases due
to a reduction in physical defence, according to optimal defence
theory, plants should promote allocation to defence in leaves
<figure><img src="image_9.png" /><figcaption>**Fig. 5** Beta coefficients of plant defence trait changes along the plant species richness gradient (log scale) of the 16 sampled species for leaf terpenoid richness, root mycorrhizal colonisation, root alkaloids sum and root dry matter content. Error bars show a 95% confidence interval. The coefficients, confidence intervals, and *P*-value were derived from linear mixed models (summarised in Table 2) using the ‘emtrends’ function from the *EMMEANS* R package. Only defence trait for which the species richness and species identity interaction were at least marginally significant (false discovery rate (FDR) *P*-values < 0.1) are reported. Beta coefficients for all defence traits measured in this study are presented in Supporting Information Fig. S2. Species are ordered according to functional groups (grasses, small herbs, tall herbs and legumes). Full species names: Arr.ela, *Arrhenatherum elatius* L.; Bro.ere, *Bromus erectus* Huds.; Fes.rub, *Festuca rubra* L.; Gal.mol, *Galium mollugo* L.; Ger.pra, *Geranium pratense* L.; Kna.arv, *Knautia arvensis* (L.) Coult.; Lat.pra, *Lathyrus pratensis* L.; Lot.cor, *Lotus corniculatus* L.; Med.var, *Medicago × varia* Martyn; Pla.lan, *Plantago lanceolata* L.; Pla.med, *Plantago media* L.; Pru.vul, *Prunella vulgaris* L.; Ran.acr, *Ranunculus acris* L.; Tar.off, *Taraxacum officinale* L.; Tri.fla, *Trisetum flavescens* (L.) P.Beauv.; Vic.cra, *Vicia cracca* L.</figcaption></figure>

(Stamp, 2003). Increasing leaf chemical defences, such as the production of features within the terpenoids and shikimate and phenylpropanoids pathways, could be a cost-effective way to counterbalance the loss of physical defence without hampering the light-capturing capacity.

## Effect of plant species richness on root defence traits

Contrary to our first hypothesis, our study indicated that fine roots in high-diverse mixtures exhibited greater defence than those in low-diverse mixtures. While RDMC increased, root nitrogen decreased along the plant diversity gradient, suggesting that fine root defences, particularly against root chewers and root-feeding nematodes (Johnson *et al.*, 2010; Moore & Johnson, 2017; Bassi *et al.*, 2024), increase along the diversity gradient. However, similar to LMA and LDMC, root nitrogen content and RDMC are related to functions other than plant defences, such as nutrient and water uptake.

The reduction in root nitrogen content along the species richness gradient is consistent with other studies (Mulder *et al.*, 2002; van Ruijven & Berendse, 2005) and is probably due to the lower availability of nitrogen, which was previously found in our experiment (Roscher *et al.*, 2008), as well as increased productivity and thus nitrogen demand in more diverse communities. Although nitrogen access in diverse mixtures may be enhanced through facilitation effects and resource use complementarity (Bessler *et al.*, 2012), our results rather suggest that nitrogen limitation becomes more pronounced in diverse communities. Notably, the experimental plots have not been fertilised since the experiment began, and consistently, higher plant biomass removal from high-diversity plots (Wagg *et al.*, 2022) may have depleted soil nitrogen. Interestingly, the reduction in leaf nitrogen was smaller and not significant, suggesting that the decreased nitrogen content in roots may partly result from reallocation to leaves (Wang *et al.*, 2024) to maintain sufficient nitrogen for photosynthesis.
The increased RDMC along the diversity gradient may be attrib-
uted to reduced nutrient or water availability (Fischer et al., 2019).
The simultaneous increase in RDMC and decrease in LDMC may
suggest a reallocation of water from the roots to the aboveground
part of the plant to support higher biomass and construction of
shade leaves in high-diverse mixtures. A similar opposite response
of root and LDMC to light, nutrient and water availability gradi-
ents was observed by Freschet et al. (2013). However, contrary to
root nitrogen content, the observed increase in RDMC is less con-
sistent among species and inconsistent with other studies (Gould
et al., 2016; Chen et al., 2017). Hennecke et al. (2025) found no
changes in the community-level root tissue density, a trait strongly
related to RDMC (Birouste et al., 2014) in the same experiment
and at the same time as this study. This suggests that the increase in
root dry matter that we observed in our species pool, does not affect
the majority or the most dominant species. Nonetheless, the reduc-
tion in RDMC of some species is consistent with the findings of
Roeder et al. (2019, 2021), who found that species with taproots,
tend to become older and simultaneously reduce growth rates along
the species richness gradient. Thus, in high-diverse mixtures, some
species exhibit a more conservative growth strategy belowground
(Bergmann et al., 2020), whether this is related to changes in water
and nutrient availability or plant age has yet to be determined.

Decoupled response of leaf and fine root physical defence
traits

Contrary to our second hypothesis that fine root defence traits
would decrease more than leaf defence traits along diversity gradi-
ents, our study revealed that while leaf chemical defences showed
a tendency to increase, the few leaf and fine root defence traits
that responded to the diversity gradient did so in the opposing
direction. These opposite trends between leaf and root defences
were driven by traits associated with resource uptake. This sug-
gests a complex relationship between plant defence traits and
antagonist pressure, likely mediated by resource availability.
While it might be surprising that plants can simultaneously
increase root defence and reduce leaf defence, our results contri-
bute to the ongoing debate about whether leaf and root traits are
coordinated (Carmona et al., 2021; Weigelt et al., 2021, 2023;
Bueno et al., 2023), showing that, at least within species, this
may not always be the case. In addition, although other studies
have shown that plant diversity has a direct effect on above-
ground and belowground antagonist pressures (Ristok
et al., 2023a,2023b), our study suggests that the observed changes
in aboveground and belowground antagonist pressure with spe-
cies richness may be mostly driven by changes in plant defence
traits and not vice versa, as we hypothesised. Combined with the
lack of response in several defence traits, our results indicate that
the dilution or accumulation of antagonists and mutualists may
not occur uniformly (Halliday & Rohr, 2019). Instead, these
dynamics may be more complex, differing between aboveground
and belowground, as well as among antagonist groups.

Competition for resources in diverse plant communities seems
to promote fine root defences. This increase in root defences may
explain the reduction in belowground antagonistic pressure from

root-feeding nematodes and arthropods observed in the same
experiment (Cortois et al., 2017; Dietrich et al., 2021; Amyntas
et al., 2025). However, this increase in defences might also be a
response to increased pressure from other groups of antagonists,
which have not yet been documented.

Similarly, competition for light reduces leaf physical defences
while promoting their chemical defences. This could suggest that
under low light availability, plants maintain high chemical
defences that, in turn, promote arthropod herbivore and patho-
gen dilution along the diversity gradient. However, it also raises
the possibility that leaf chemical defences may increase due to the
accumulation of other antagonist groups, aside from arthropods
and leaf pathogens (Ebeling et al., 2018; Barnes et al., 2020). For
example, the observed increase in leaf herbivory rates (Meyer
et al., 2017; Bröcher et al., 2023) could be attributed to damage
inflicted by generalist antagonists, such as gastropods, which were
not accounted for in other measurements. This may suggest that
certain groups of antagonists, particularly generalists, may accu-
mulate rather than dilute along diversity gradients (Keesing
et al., 2006), thereby explaining the increase in certain defence
traits we observed. Further studies are needed to disentangle the
complex relationship between plant defence traits, antagonists
and mutualists accumulation or dilution.

Species-specific response of leaf and fine root defence traits

Plant species identity was the main driver of all defence
traits assessed in this study, explaining a substantial proportion of
the observed variation. However, species-specific responses to
diversity gradients were less common than anticipated under our
third hypothesis. While plant defence traits linked to resource
acquisition showed consistent responses across species, species-
specific responses were more frequent in traits with tighter links
to defence or collaboration with mutualists. Although only a few
traits exhibited this response, root chemical defences showed
stronger species-specific patterns than leaf chemical traits, align-
ing with the findings of Weinhold *et al.* (2022).

Given the known link between mycorrhiza and the metabo-
lome through the priming of defensive phytochemicals (Frew
et al., 2022), as well as the association between MC and root che-
mical defences observed across species in the PCA, it is surprising
that our results did not reveal any consistent trends between MC
and chemical defence responses to the diversity gradient. Only *T.
flavescens* showed a consistent response, with increases in both
MC and the number of leaf terpenoids, providing some evidence
for priming.

Overall, this species-specific response may indicate that the dilu-
tion of below- and aboveground antagonists and the accumulation
of belowground mutualists across plant diversity gradients are also
species-specific. Indeed, at the same site, Bröcher et al. (2023)
found that herbivory rate changes along diversity gradients were
species-specific. These species-specific responses can be explained
by the associational effects, where plant community composition
can promote resistance or susceptibility (Barbosa et al., 2009;
Underwood et al., 2014) of focal species to herbivores, depending
on whether the focal species is more or less attractive or defended
than neighbouring species. While these mechanisms are relatively
well understood for aboveground herbivores, it remains unclear to
what extent belowground antagonists and mutualists follow the
same mechanism. Further studies addressing these issues from a
belowground perspective are needed.

Conclusion

Overall, our results emphasise the complexity of plant defence
strategies and their interactions with antagonists across plant
diversity gradients. They highlight that plant responses to
resource limitation across SR gradients are probably the main dri-
vers of changes in some defence traits, which may in turn influ-
ence antagonist pressure. Conversely, chemical defence traits
appear to respond to changes in antagonist pressure and, together
with traits related to cooperation with mutualists, show more
species-specific responses. This suggests a stronger bottom-up
effect of leaf and fine root physical defence traits on invertebrate
herbivores and a top-down effect of antagonists on leaf chemical
defence traits. Finally, our results show that responses of defence
traits to plant SR can differ significantly between above- and
belowground compartments, highlighting the need to integrate
both in future studies.

Acknowledgements

We are grateful to Annette Jesch for coordinating the Jena
Experiment and to Roman Patzak, the Jena Experiment technical
staff and numerous student helpers for their dedicated efforts in
maintaining the experiment and conducting measurements. We
are grateful to Ines Hilke and the ‘RoMA’ service group at the
Max Planck Institute for Biogeochemistry in Jena (Germany), for
the nitrogen measurements, and to the Jena Experiment Research
Unit and the Systematic Botany and Functional Biodiversity
research group at the University of Leipzig for their valuable
expertise and contributions to this project. This research was
funded by the German Research Foundation (DFG) through the
Jena Experiment Research Unit (FOR 5000-1). NE, JH, SD and
NMvD acknowledge support from iDiv (German Research
Foundation (DFG)-FZT 118, 202548816). MZ acknowledge
funding support from the French government in the framework
of the IdEX Bordeaux University “Investments for the Future”
program/GPR Bordeaux Plant Sciences. Open Access funding
enabled and organized by Projekt DEAL.

Competing interests

None declared.

Author contributions

LB, AW, NMvD, STM and NE designed the study. LB, JH, CA,
MDS, AR, YPAdS, AF, VCD, AK, LvS and LW conducted the
field campaign and measured leaf and root traits. CA measured
the mycorrhizal colonisation rate. RR implemented the boot-
strapped CARS-PLSR model to predict nitrogen content. MZ,

SD and VCD performed the untargeted metabolomic analysis.
LB performed the statistical analysis and wrote the first draft of
the manuscript. All authors contributed to the data interpretation
and the revision of each draft.

ORCID

Cynthia Albracht https://orcid.org/0000-0002-8779-3749

Leonardo Bassi https://orcid.org/0000-0002-2604-8665

Stefanie Döll https://orcid.org/0000-0002-0001-8302

Van Cong Doan https://orcid.org/0000-0001-9189-1301

Nico Eisenhauer https://orcid.org/0000-0002-0371-6720

Aaron Fox https://orcid.org/0000-0002-6452-5290

Justus Hennecke https://orcid.org/0000-0002-6839-2333

Anja Kahl https://orcid.org/0000-0001-5497-8520

Sebastian T. Meyer https://orcid.org/0000-0003-0833-1472

Yuri Pinheiro Alves de Souza https://orcid.org/0000-0001-9854-6383

Ronny Richter https://orcid.org/0000-0002-8728-7918

Marcel Dominik Solbach https://orcid.org/0000-0001-7073-7428

Nicole M. van Dam https://orcid.org/0000-0003-2622-5446

Lea Von Sivers https://orcid.org/0009-0009-3932-4276

Alexandra Weigelt https://orcid.org/0000-0001-6242-603X

Luise Winkler https://orcid.org/0009-0008-0627-5968

Ming Zeng https://orcid.org/0000-0003-2836-9240

Data availability

R code, leaf and root trait data, as well as the experimental design
metadata, are available through the Jena Experiment database
(JExIS) at doi: 10.25829/4VMQ-V994, and doi: 10.25829/
J0YV-2655, respectively. The near-infrared spectral data are
available at doi: 10.25829/XVJH-HM95. The raw and processed
metabolome data are available from MetaboLights (Yurekten
et al., 2024; https://www.ebi.ac.uk/metabolights/MTBLS8464);
study identifier: MTBLS8464.

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## Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

**Fig. S1** Interactive sunburst plots showing the numbers of features for the leaf and fine root samples classified with the NPClassifer.

**Fig. S2** Beta coefficients and 95% confidence intervals depicting the impact of plant species richness (log scale) on leaf and fine root defence traits across the 16 sampled species.

**Fig. S3** Scatter plots showing the standard major axis regression slopes between leaf physical and chemical defence traits within and across species.

**Methods S1** Additional details on the leaf morphological trait measurements.
**Methods S2** Additional details on the nitrogen measurements.

**Methods S3** Additional details on the untargeted metabolome analysis.

**Table S1** Count of sampled plots across the species richness gradient, the total number of observations and per-species richness levels for each species.

**Table S2** Literature review of the impact of defensive chemical compounds within the alkaloids, terpenoids, and shikimates and phenylpropanoid pathways on plant antagonists.

**Table S3** Number and percentage of unclassified and classified features in each of the NPClassifier pathways.

**Table S4** Number and percentage of features classified in the NPClassifier superclasses with the highest numbers of features (at least 0.2% of all features).

**Table S5** Loadings of the leaf and root defence traits PCA.

**Table S6** ANOVA table for the linear mixed models with plant defence traits as the response variable and plant species richness (log scale) as predictors.

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