Domestication reduces root VOC abundance and diversity in crops with species specific effects

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Secondary metabolites involved in plant interactions, such as volatile organic compounds (VOCs), may have been reduced in domesticates compared with crop wild relatives (CWR), with possible effects on root-associated bacterial communities and crop resilience. However, the effects of domestication in root VOCs and the implications of such changes remain unknown. Methods We conducted a greenhouse experiment with eight crops (barley, beet, fava bean, cucumber, lentil, lettuce, sunflower and tomato), comparing landraces and CWR. Root VOCs were collected with SPME fibres and analysed by GC–MS; root-associated bacterial diversity was also characterized. Results Domesticated plants emitted almost three times less total root VOCs and had lower compound diversity than CWR. Effects on VOC uptake were crop-specific (reduction in barley, beet and tomato, but not generalized). Chemical composition was more dependent on crop identity than on domestication status. VOC diversity was negatively related to bacterial diversity, and VOC profiles explained bacterial composition. Conclusions In summary, domestication has reduced the quantity and diversity of root VOCs, potentially impairing chemical communication and response to soil biota; CWRs emerge as reservoirs of chemical and genetic traits essential for restoring crop resilience and sustainability. crop domestication root-associated microbes root SPME VOC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction For most of human history, food came from wild plants and animals, until independent agricultural origins ~ 10,000 years ago initiated crop domestication (Alger et al., 2023 ; Crittenden & Schnorr, 2017 ; Diamond, 2002 ; Fuller et al., 2011 , 2023 ; Gowdy & Krall, 2014 ; Zeder, 2015 ). This was a long and intense coevolutionary process between humans and some plant species that were able to thrive in nutrient-rich, emergent anthropogenic soils (Jones et al., 2021 ; Smith, 2007 ; Zeder, 2016 ). This potentially unintentional selection begun around 10,000 years ago in multiple locations (Fuller et al., 2023 ) and was followed by the selection of yield-enhancing traits in the domesticated lineages derived from wild relatives by making a progressively more controlled habitat combined and a selective breeding for yield (Purugganan, 2022 ). As a result, modern domesticated crops show many common traits that can be defined as the domestication syndrome. This includes several morphological and physiological traits such as taller growth, increased apical dominance, larger organs, synchronized flowering, reduced seed shattering, and a loss of dispersal mechanisms and defences (Doebley et al., 2006 ; Meyer & Purugganan, 2013 ). However, belowground domestication traits relating to plant-soil interactions are far less well documented. Although domestication supported larger human populations, it also contributed to a reduction in genetic, agroecosystem and dietary diversity (Krug et al., 2023 ). Consequently, some crops may present a loss of resilience to stress factors such as drought and salinity (Roy et al., 2011 ). Furthermore, it has also altered plant ecological traits and interactions with other plants, herbivores and natural enemies (Milla et al., 2015 ), through changes in morphology and secondary metabolism that ultimately led to plant vulnerability (Chen et al., 2015 ). For instance, it has been seen that high fertilization regimes can contribute to changes in plant-microbe interactions decreasing mycorrhizal colonization efficiency in crops compared to CWR (Martín-Robles et al., 2018 ). Moreover, maize ( Zea mays L.) resistance to Western corn rootworm has declined through breeding under pesticide-intensive regimes (Bernal & Medina, 2018 ). The interactions between plants and their biotic environment are universally mediated by volatile organic compounds (VOCs; Peñuelas et al., 2014 ), a diverse group of secondary metabolites, such as terpenes, methanol and aldehydes, involved in defence, communication and pollination (Abbas et al., 2022 ). Furthermore, they have important roles in plant-environment interactions, because increasing atmospheric CO 2 might lead to the synthesis of some allelopathic terpenoids (Bae et al., 2019 ). Also, VOCs can directly affect atmospheric chemistry by participating in ozone and secondary organic aerosols formation (Wang et al., 2024 ). Many organs within the plant can emit VOCs such as leaves, flowers and roots, where they can be accumulated in secretory tissues forming a reservoir or, otherwise, gradually emitted or released under stress conditions (Peñuelas et al., 2014 ). However, the emission amount and the VOC profile show a high amount of variability explained by taxa, physiological state, plant tissue or study effort (Z. Liu et al., 2023 ). Aboveground VOC emissions have been more widely studied than belowground ones, despite roots often releasing a greater amount and a higher diversity of compounds, such as glucosinolates and terpenoids, than leaves (Bourtsoukidis et al., 2018 ; Van Dam et al., 2016 ). Roots are not the sole source of belowground VOCs. These compounds can also be synthesized or transformed by soil microbes (Del Giudice et al., 2008 ) or released abiotically through degradation of organic matter and soil solutions, especially for VOCs such as methanol and terpenes (Gray et al., 2010 ; Schade & Custer, 2004 ). Soils can both emit and absorb volatiles (Peñuelas et al., 2014 ) through microbial metabolism, diffusion, adsorption, and passive root uptake, allowing plants to perceive and respond to chemical cues (Jiao et al., 2023 ; Karban et al., 2014 ; Yang et al., 2024 ). Similar to aboveground emissions, belowground VOCs mediate signalling, defence, and regulation of plant-microbe interactions (Delory et al., 2016 ; Insam & Seewald, 2010 ) and influence biogeochemical processes by stimulating microbial respiration and altering nitrogen and methane dynamics (Amaral et al., 1998 ; Ramirez et al., 2010 ; Smolander et al., 2006 ). Despite their ecological importance, belowground VOCs’ functions and mechanisms remain poorly understood (Dicke & Baldwin, 2010 ). Domestication has likely reshaped plant biochemical pathways, altering secondary metabolite profiles between crops and wild relatives (Meyer et al., 2012 ). Selection for yield and palatability often reduced pest resistance and chemical diversity, including defensive VOCs (Doebley et al., 2006 ; Milla et al., 2015 ; Whitehead et al., 2016 ). For instance, β-caryophyllene root emissions, which attracts entomopathogenic nematodes, are lower in domesticated maize compared to wild relatives (Bernal et al., 2023 ). Additionally, VOC diversity in maize and cranberry ( Vaccinium macrocarpon Aiton) domesticates is reduced under herbivory (Rodriguez-Saona et al., 2011 ; Rowen & Kaplan, 2016 ). While leaf VOC changes under domestication are well documented, root VOC dynamics remain largely unexplored despite their role in ecologically important processes such as microbiome assembly, plant defence and signalling. Identifying differences between crops and their wild relatives could reveal new strategies to enhance beneficial soil interactions that may have been altered or lost during domestication. Ultimately, improved understanding of these patterns may contribute to improved crop resilience and fostering food security under current global change scenarios (Preece & Peñuelas, 2020 ). Here we provide, to our knowledge, the first comparative assessment across eight crops and their wild relatives (CWRs or wild relatives, from now on) on how domestication has shaped belowground VOC emissions, uptake (or absorption, henceforth used as synonyms), composition, and diversity. By simultaneously quantifying both VOC exchange and root-associated microbiome diversity, this study tests whether domestication-driven shifts in belowground chemical profiles are linked to microbial assemblage. We hypothesize that domesticated plants - exposed to less heterogeneous environments - emit and absorb fewer and less diverse VOCs than CWRs (H1). Additionally, because VOCs influence soil microbiota, we further expect that VOC profiles correlate with root-associated microbiome diversity, linking chemical composition with microbiome assemblage (H2). 2. Methods 2.1 Plant material and growth conditions We selected eight annual crops representing six different families (Table 1 ). The chosen crops were barley, beet, broad bean, cucumber, lentil, lettuce, sunflower and tomato, each with two levels of domestication: the landrace varieties (domesticated lineage, without modern breeding) and its closest wild relative. Seeds were obtained from the IPK Liebniz-Institut (GBIS, last updated August 13, 2025) and USDA Germplasm Resources Information Network (GRIN, last updated February 24, 2025) seed banks. Seeds were selected from origins close to the known domestication site whenever it was possible to minimize confounding effects of geographic adaptation. Table 1 Crops used in this study, indicating the binomial name for both the domesticated plant and the wild relatives, and its botanic family. Between 1 and 5 different seed accessions were used for each species depending on availability. Crop Domesticated species Wild relative Family Barley Hordeum vulgare L. subsp. v ulgare cv. Hordeum vulgare L. subsp. spontaneum Poaceae Beet Beta vulgaris L. subsp. vulgaris cv. Beta maritima L. Amaranthaceae Broad bean Vicia faba L. cv. Vicia narbonensis L. Fabaceae Cucumber Cucumis sativus L. var. s ativus cv. Cucumis sativus var. hardwickii (Royle) Alef. Cucurbitaceae Lentil Lens culinaris Medik. subsp. culinaris cv. Lens culinaris subsp. orientalis (Boiss.) Ponert Fabaceae Lettuce Lactuca sativa L. cv. Lactuca serriola L. Asteraceae Sunflower Helianthus annuus L. cv. Helianthus annuus L. Asteraceae Tomato Solanum lycopersicum L. cv. Solanum pimpinellifolium L. Solanaceae Seeds were directly germinated in 7 L pots (20 cm × 20 cm × 25 cm) filled with a substrate consisting of washed river sand (maximum particle size of 4 mm), compost and field soil following a 5:4:1 proportion, respectively. Each species included two treatments and initally there were ten replicates per treatment. The compost component consisted of a peat-free universal substrate (Geolia Universal Substrate; 70% bark and wood fibre, 20% coconut fibre, 5% perlite, 5% seaweed humus) (Geolia Universal Substrate). The field soil was obtained from a one-year fallowed field at the experimental fields of the Autonomous University of Barcelona. All three components were mixed until homogenous. 2.2 Experimental design The experiment was conducted during spring–summer (from March to August) 2022 in a greenhouse at the Autonomous University of Barcelona (Bellaterra, northeastern Iberian Peninsula). Mean daytime temperature and relative humidity were 31.9 ± 8.2°C and 46.8 ± 23.4%, respectively, while mean nighttime temperature and relative humidity were 19.1 ± 4.9°C and 81.9 ± 13.5%, respectively. Each species was initially represented by ten replicate pots (one plant per pot). Due to aphid and mite infestations during the growing season, final replication varied between two and nine individuals per species (see Table S1 ). Pots were evenly distributed across four tables. Within each table, pot positions were randomized, and air temperature and relative humidity were continuously monitored (Lascar EasyLog EL-USB-2 data loggers). All pots were irrigated via drip system under identical conditions, with watering volume adjusted to plant growth. Each table also included two bare-soil control pots receiving the same irrigation regime. Soil moisture was measured weekly in all pots using a Delta-T ML3 probe. Mean soil moisture during plant growth was of 14.7 ± 4.7%. Plants were harvested at the onset of fruit or seed production, corresponding to the stage of maximum vegetative development. Harvest time therefore varied among individuals depending on species-specific phenology. At the time of sampling, most plants were reaching full maturity and starting to show the expected signs of leaf senescence typical of annuals. Beta vulgaris individuals did not reach full reproductive maturity, likely reflecting their facultative biennial growth habit. 2.3 VOCs sampling using SPME Before harvesting, VOCs were sampled from a subset of mature plants (n = 79) representing all crop species and domestication levels. We used solid phase microextraction (SPME) fibres (100 µm polydimethylsiloxane, Torion Technologies Inc., Utah, USA) directly inserted into the soil to passively adsorb VOCs emitted under natural, non-induced conditions (Tholl et al., 2021 ). Before sampling, fibres were preconditioned at 250ºC for 20 minutes under helium flow rate of 100 ml min − 1 and preserved in glass tubes sealed with a Teflon-coated cap. A small hole (~ 2 mm diameter, 10 cm depth) was made in the soil adjacent to the plant stem using a sterilized stainless-steel rod. A pipette tip was gently inserted to hold the fibre in place, and the SPME fibre was exposed to the soil atmosphere for 20 min. The fibre exposure time was based on pilot tests to avoid saturation. Control samples included bare soil controls (1–3 per sampling day), air controls (1–2 per day), and blank fibres that were transported to the field but not exposed, to detect possible contamination. After sampling, SPME fibres were returned to the glass tubes and stored at 4ºC until laboratory analysis. 2.3.1 GC-MS analysis The VOCs sampled with SPME fibres were analysed using a 7890A gas chromatograph coupled to a 5975C mass spectrometer inert 154 MSD/DS Performance Turbo EI System (Agilent Technologies, Santa Clara, USA) equipped with a HP5MS capillary column (30 m × 0.25 mm × 0.25 µm; Agilent J&W) using a transfer line at 250 ºC. VOCs were thermally desorbed from the SPME fibres in the injector port at 250°C in splitless mode. Analyses were conducted in full-scan mode, ranging from 35 to 350 m/z. For the chromatographic analysis, helium was used as the carrier gas with a column flow of 1 mL min − 1 . Oven temperature was programmed from 35°C (5 min hold) to 150°C at 15°C min⁻¹ (5 min hold), then to 250°C at 15°C min⁻¹ (3 min hold), and finally to 280°C at 30°C min⁻¹ (5 min hold). Chromatograms analyses were conducted using Enhanced ChemStation software (Agilent Technologies). Each peak was assigned a compound using mass spectra contrasted in the NIST 05a and Wiley 275 mass spectral data and PARADISe software. Compounds were identified by comparing mass spectra with the NIST 05a and Wiley 275 libraries using PARADISe software. The criterion to select which compounds were retained is further explained later in the section 2.6.1 . When available, retention indices were compared with literature values for confirmation (Johnsen et al., 2017 ). Mean blank values were subtracted from all samples to remove background signals. Air control data were available for one sampling date and applied as correction factors across all measurements, since variation among days was negligible. Mean peak areas from bare-soil controls were subtracted from plant samples to isolate VOCs of root and rhizosphere origin. Quantification was based on calibration curves obtained from pure standard solutions of 3-hexen-1-ol (RT 7.98), α-pinene (9.18), myrcene (10.01), 3-carene (10.29), limonene (10.53), dodecane (12.40), and α-humulene (15.30), prepared in hexane at different dilution factors and analysed under the same conditions as the experimental samples (Llusià et al., 2022 ). Calibration curves (R² > 0.95) were used to calculate a response factor ( RF ) for each compound class (non-terpenoid, monoterpene, sesquiterpene) and sampling date. The VOC quantity (µg g⁻¹ root L⁻¹ soil) for each compound was estimated as described in (1). $$\:Q\:=\frac{{S}_{obs}\times\:\:RF}{{S}_{ref}\times\:{M}_{root}\times\:{V}_{soil}}$$ (1) where Sobs is the observed peak area (a.u.), RF the response factor (µg), Sref the reference area (a.u.), Mroot the root mass (g), and Vsoil is the soil volume (L). Positive values were interpreted as VOC emission, and negative values as VOC uptake, following correction by air and soil controls. 2.4 Root and microbial sampling and sequencing After harvesting, roots were gently shaken and rinsed with sterile distilled water to remove loosely adhering soil. Root-associated microbial communities were sampled by collecting 5–10 fine root fragments per individual (≥ 0.25 g fresh weight). Samples were immediately stored at − 20°C, ground in liquid nitrogen, and transferred to sterile Eppendorf tubes at − 80°C until DNA extraction. Microbial DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen) following the manufacturer’s protocol. Extraction blanks were included to monitor potential contamination. Amplification and sequencing were conducted at the University of Antwerp following Radujković et al. ( 2025 ). The bacterial 16S rRNA gene region was amplified using the 515F–806R primer pair (Caporaso et al., 2011 ), and the fungal ITS1 region using the ITS1–ITS2 primers (Stoeck et al., 2010 ). PCR reactions (25 µL total volume) contained 1 µL of DNA template, 0.5 µM of each primer, 1× PCR buffer, 200 µM dNTPs, and 1 U of Phusion High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA, USA). Libraries were prepared and sequenced on an Illumina MiSeq platform (2 × 300 bp paired-end). Sequence reads were processed with the DADA2 pipeline to generate amplicon sequence variant (ASV) tables, including quality filtering and chimera removal. Taxonomic assignment of prokaryotic ASVs (composed predominantly of bacteria) was performed against the SILVA database (version 138.1; Quast et al., 2013 ), and fungal ASVs were assigned using the UNITE database (release April 2024; Kõljalg et al., 2005 ). 2.5 Plant biomass measurements After microbial sampling, plants were separated into leaves, stems, roots, and reproductive structures. Each fraction was oven-dried at 60°C for three days and weighed to determine organ-specific and total dry biomass. These data were subsequently used for correlation analyses with VOC emissions and microbial community composition. 2.6 Data analysis 2.6.1 General data treatment For VOC analyses, the dataset included all compounds detected per pot. Only compounds with a spectral match quality ≥ 90% (NIST library) were retained. Potential contaminants such as column siloxanes and phthalates were identified based on their occurrence in blanks and known artefact profiles and were excluded. This filtering yielded a final set of 52 compounds out of 310 initially detected (Table S2). For these compounds, additional detections with match quality between 80–90% were retained when supported by co-detection in other samples and bibliographical evidence of their presence as root VOCs (following standard practice in ecological VOC studies; Tholl et al., 2021 ). The resulting dataset was divided into emission and uptake subsets by summing all positive and negative compound values per pot, respectively. Compounds that displayed both positive and negative values across samples were included in both subsets according to their direction per observation. One sample (a domesticated cucumber) showing abnormally high total emission values -two orders of magnitude above the dataset mean - was identified as an outlier (exceeding the 99th percentile of total VOC emissions) and excluded from analyses. All analyses were performed in R version 4.3.0 (R Core Team, 2025 ). Model diagnostics were evaluated using the DHARMa package (version 0.4.6; Hartig, 2024 ), and model selection was based on AIC, adjusted R², and statistical significance. Residuals were visually inspected for deviations from model assumptions, and models showing overdispersion or zero-inflation were discarded. Interaction terms that were not significant were removed from the final models. 2.6.2 Total emission and uptake concentrations To evaluate differences in total VOC emission and absorption, generalized linear models (GLMs) with a Gamma distribution and log link were used, given the strong right-skewness and heteroscedasticity of the data. Each model included domestication status and crop as fixed effects. Random effects for crop identity and table position were initially tested, but the models did not converge due to a not large enough sample size and were therefore excluded. Model residuals and fitted value relationships were visually inspected to confirm the adequacy of the Gamma distribution. Models were fitted using the glmmTMB() function from the glmmTMB package (version 1.1.7; Brooks et al., 2025 ). Adjusted R² values were computed using the delta method (r.squaredGLMM() in MuMIn, version 1.48.4; Bartoń, 2024 ). Type II Wald χ² tests were applied to models without interaction terms, and Type III tests when interactions were present, using the car package (version 3.1-2; Fox et al., 2024 ). When significant interactions were detected, pairwise contrasts were performed with the emmeans package (version 1.8.8; Lenth, 2024 ). Predicted means and 95% confidence intervals on the response scale were obtained using the ggpredict() function from ggeffects (version 2.3.0; Lüdecke, 2024 ). Observed and predicted values were visualized on log-transformed y-axes using ggplot2 (version 3.4.4; Wickham, 2016 ), with units expressed as µg g⁻¹ root L⁻¹ soil. 2.6.3 Composition differences Differences in VOC composition between domesticated crops and their wild relatives were explored separately for emissions and absorptions using non-metric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarities. Ordinations were computed with two dimensions and 1000 random starts using the metaMDS() function in the vegan package (version 2.6-4; Oksanen et al., 2024 ). Three pots without detected emissions and one pot without any detected uptake were excluded. No additional data transformation was applied because VOC abundances were already standardized by root mass and soil volume, and preliminary analyses showed consistent ordination patterns with or without transformation. The final stress value was reported to assess ordination quality. Centroids were calculated as the mean NMDS scores for each domestication status within crop type. To test for compositional differences, permutational multivariate analyses of variance (PERMANOVA) were performed using the adonis2() function (vegan) with 9999 permutations, including domestication status and crop type as predictors. Homogeneity of multivariate dispersions was assessed using the betadisper() function in vegan based on Bray–Curtis distances. No significant differences were found among groups (p > 0.05), indicating that the PERMANOVA assumptions were met. Separate analyses were run for emission and absorption datasets. When overall effects were significant (p < 0.05), pairwise PERMANOVA comparisons were performed with the pairwiseAdonis package (version 0.4; Martinez Arbizu, 2017 ), applying false discovery rate (FDR) correction for multiple testing. To visualize the dominant contributors to observed differences, the five compounds with the highest mean emission or absorption values were identified for each crop type and domestication level. These were represented using bar plots created with ggplot2. The same approach was applied to chemical groups classified by compound type (Table S2). These visualizations were intended to aid interpretation rather than to represent formal statistical significance. 2.6.4 Changes in VOC diversity VOC alpha-diversity per pot was quantified using the Shannon index (H′), based on the relative abundance of detected compounds, calculated with the diversity() function in the vegan package. Shannon diversity was chosen because it integrates both compound richness and evenness, providing a robust representation of chemical complexity. Separate indices were calculated for emitted and absorbed VOC datasets. Homoscedasticity was assessed using the Fligner–Killeen test (p < 0.05), and model residuals were visually inspected for normality. When necessary, log-transformation of H′ values was applied to meet model assumptions. Linear models were fitted with domestication status and crop type as fixed predictors. Random effects for species and table position were initially tested but the models failed to converge due to a not large enough sample size and were therefore excluded. Type II Wald χ² tests were applied to models without interactions, and Type III tests when interactions were present, using the car package. Pairwise contrasts were performed with the emmeans package when significant interaction effects were detected. Model predictions and 95% confidence intervals were obtained using the ggpredict() function in ggeffects. Observed and predicted values were plotted in the original diversity scale using ggplot2. 2.6.5 Root microbial diversity relationships with VOC diversity The relationship between root VOC α-diversity (Shannon index) and the α-diversity of root-associated bacterial and fungal communities was evaluated using linear models. Bacterial and fungal Shannon diversity indices were computed with the diversity() function in the vegan package. One pot lacking microbial data due to sampling failure was excluded. Each model used bacterial or fungal diversity as predictor of VOC diversity, following the same procedures as described for VOC α-diversity analyses. Observed data and fitted regression lines with 95% confidence intervals were visualized using ggplot2. To explore associations between VOC and microbial community composition, we tested if VOC composition could explain microbial assemblages. For this, microbial ASV tables were rarefied to correct for amplification bias using the rrarefy() function, with depths of 10,000 reads for bacteria and 6,000 for fungi, chosen to retain over 90% of samples while minimizing loss of low-abundance ASVs. Bray–Curtis dissimilarity was used for both VOC and microbial matrices, as suitable for non-normal and zero-inflated ecological data. Then we performed distance-based redundancy analyses (dbRDA) using the capscale() function in vegan, with microbial community matrix as the response variable and VOC matrix as the explanatory variable. Variable selection for VOC compounds was performed with ordiR2step() (9999 permutations), and model significance and adjusted R² values were obtained via permutation ANOVA tests. Correlations between dissimilarity matrices of VOCs and microbial communities were further assessed using Mantel tests (Bray–Curtis distance, Spearman correlation, 9999 permutations) implemented in vegan. All analyses were conducted separately for bacterial and fungal datasets. Graphical representations of dbRDA ordinations were generated with ggplot2. 2.6.6 Effect of air temperature and humidity To evaluate potential environmental influences on VOC fluxes, we tested whether total VOC emission and absorption values correlated with ambient air temperature (°C) and relative humidity (%). Mean temperature and humidity values recorded during VOC sampling were used as predictors in linear models, with total VOC emission and uptake concentrations as response variables. Model residual diagnostics (DHARMa adjusted quantile test, p < 0.05) indicated deviations from the expected distribution, suggesting violation of model assumptions, likely due to data heteroscedasticity and skewness. Moreover, no consistent pattern was detected between VOC flux rates and either temperature or humidity, visually or statistically. Consequently, these variables were not considered for further analyses or corrections, as their inclusion did not improve model fit or alter the interpretation of VOC patterns (Figure S2). 3. Results 3.1 Total VOC emission and uptake concentrations Overall, domestication was associated with a reduction in total root VOC emissions across crop lineages (GLM, χ² = 6.76, p = 0.009), with domesticated plants emitting markedly less than their wild relatives. On average, CWRs emitted nearly three times more VOCs than domesticated plants (0.076 µg g⁻¹ root L⁻¹ soil vs. 0.025 µg g⁻¹ root L⁻¹ soil; p < 0.01). Crop type also had a significant effect (GLM, χ² = 18.14, p = 0.011, R² = 0.27), indicating that total emissions differed among crop species (Fig. 1 a). Wild relatives of cucumber, beet, and lettuce exhibited the highest mean emission levels (predicted means = 0.16, 0.14, and 0.12 µg g⁻¹ root L⁻¹ soil, respectively), whereas their domesticated counterparts showed consistently lower values (0.05, 0.05, and 0.04 µg g⁻¹ root L⁻¹ soil; Table S3). The interaction term was non-significant, suggesting that the domestication effect was generally consistent across crop types. For root VOC uptake, a significant interaction between crop type and domestication status was detected (GLM, χ² = 17.70, p < 0.05, R² = 0.53), indicating that the domestication effect on VOC uptake varied among crops. Significant differences between domesticated and wild forms were observed for barley ( p = 0.0001), beet ( p < 0.05), and tomato ( p < 0.0001), all showing greater uptake in wild relatives than in domesticated varieties (Fig. 1 b). For example, in tomato, the predicted mean uptake was 0.03 µg g⁻¹ root L⁻¹ soil for the wild relative compared to 5.12 × 10⁻³ µg g⁻¹ root L⁻¹ soil for the domesticated variety. In contrast, lentil displayed the opposite trend, with domesticated plants showing higher uptake (0.05 µg g⁻¹ root L⁻¹ soil) than their wild relative (0.01 µg g⁻¹ root L⁻¹ soil), although this difference was not significant ( p = 0.2011). Among all crops, wild cucumber varieties exhibited the highest predicted uptake values (0.14 µg g⁻¹ root L⁻¹ soil), followed by broad bean (0.09), beet (0.08), and lettuce (0.07) progenitors (Table S4). Altogether, domestication affected root VOC uptake in a crop-specific manner, with a general tendency toward reduced uptake in domesticated varieties. 3.2 VOC composition differences The NMDS ordination of VOC emissions (2D stress = 0.19) showed no clear separation according to domestication status (PERMANOVA, p = 0.66, pseudo- R² = 0.01). In contrast, crop type significantly explained compositional variation (PERMANOVA, p = 0.0001, pseudo- R² = 0.20), indicating that VOC profiles could be distinguished based on crop type. Broad bean and tomato displayed particularly distinct profiles, differing significantly from each other ( p = 0.028, pseudo- R² = 0.23; Fig. 2 a). Patterns for VOC uptake partially differed from those observed for emissions. Clusters of domesticated and wild samples showed partial but non-significant separation (PERMANOVA, p = 0.0535, pseudo- R² = 0.18), indicating only a marginal trend towards differentiation. Crop type again had a strong and significant effect (PERMANOVA, p < 0.0001, pseudo- R² = 0.23), with highly distinct profiles between certain species, such as cucumber and broad bean (adjusted p = 0.028, pseudo- R² = 0.26; Fig. 2 b). Analysis of the top five emitted compounds per crop and domestication status revealed pronounced variation among species and treatments (Figure S1 ). Members of the Asteraceae family (lettuce and sunflower) displayed particularly similar emission profiles across domesticated and wild forms, dominated by 2-methoxy-[1]benzothieno[2,3-c]quinolin-6(5H)-one and 1,1,2-trifluoro-2,5-bis(trifluoromethyl)-hexane. In contrast, other crops showed divergent profiles between domesticated and wild relatives, although no consistent directionality was observed across species. Uptake profiles showed greater overlap between domesticated and wild plants (Figure S3). For example, three compounds were shared between wild and domesticated cucumber (nonanal, benzothiazole, and salicylaldehyde) and between wild and domesticated broad bean (pentane, hexane, and β-ionone epoxide). Benzothiazole (detected in six of eight crops) and nonanal (five of eight) were among the most frequently absorbed compounds across species. When VOCs were grouped by chemical family, several crop-specific trends in relation to domestication emerged (Fig. 3 ). Lettuce and sunflower (Asteraceae) were dominated by heterocyclic and halogenated compounds in both domesticated and wild forms. Although these compound classes were also abundant in barley, they decreased in the domesticated variety. In lentil, terpenoids were prevalent across both domestication levels, but domesticated plants exhibited a higher relative abundance of aliphatic hydrocarbons. Cucumber wild relatives emitted high levels of terpenoids that were markedly lower in domesticated forms, where heterocyclic compounds became relatively more abundant. In broad bean, reduced total emissions in domesticated plants were primarily driven by lower fatty acid derivatives. For uptake, patterns by chemical class were largely consistent across crops and domestication statuses, with a few notable exceptions. Broad bean wild relatives showed higher terpenoid uptake than their domesticated counterparts, which lacked detectable heterocyclic compounds. In lentil, domesticated varieties exhibited a marked increase in the proportion of absorbed halogenated compounds. In tomato, while the relative proportions of compound classes were similar, total VOC uptake was substantially lower in domesticated individuals. 3.3 Diversity of VOC profiles The diversity of emitted VOCs, calculated using the Shannon index, was significantly influenced by both crop type ( p < 0.001) and domestication status ( p < 0.05) in a linear model (adjusted R² = 0.39). Wild relatives consistently exhibited higher chemical diversity in their emitted VOC profiles (mean H’ = 1.21 ± 0.1) compared to domesticated varieties (H’ = 0.86 ± 0.1). The interaction between crop type and domestication status was not significant, indicating that the domestication effect was consistent across species (Fig. 4 a). Among individual crops, tomato showed the highest VOC emission diversity overall, with mean H’ values of 2.22 for wild and 1.87 for domesticated individuals, followed by lettuce (1.34 and 0.99, respectively) and sunflower (1.32 and 0.92; Table S5). In contrast, VOC uptake diversity was significantly explained by the interaction between crop type and domestication status ( p < 0.001, adjusted R² = 0.54; Fig. 4 b). This indicates that domestication effects on VOC uptake diversity were species-specific. Only barley showed a significant reduction in diversity in domesticated plants compared with their wild relatives ( p < 0.0001). For the remaining crops, no consistent or significant domestication trend was detected. Despite a significant overall domestication effect in the model ( p < 0.0001), the strong interaction with crop type underscores the variable impact of domestication across species. The highest mean uptake diversity was observed in domesticated sunflower (mean H’ = 2.07), followed by domesticated beet (2.03) and cucumber (1.98), while the lowest diversity occurred in the wild lentil (H’ = 0.51; Table S6). 3.4 Root microbiota relationship with VOCs Linear models linking VOC and microbial α-diversity revealed a significant negative relationship between the Shannon diversity of emitted VOCs and bacterial diversity (β = − 0.48, p < 0.001; Figure S3a), whereas no relationship was detected for fungal diversity (β = − 0.08, p = 0.43; adjusted R² = 0.18; Figure S3b). A similar pattern was observed for absorbed VOCs, which showed a negative association with bacterial diversity (β = − 0.31, p < 0.01; Figure S3c), but not with fungal diversity (β = − 0.15, p = 0.09; adjusted R² = 0.11; Figure S3d). These results indicate that higher bacterial α-diversity was generally linked to lower VOC diversity, while fungal α-diversity appeared unrelated to root VOC richness. Multivariate analyses further demonstrated that root VOC composition significantly structured bacterial community composition. A dbRDA based on emitted VOCs explained a large proportion of bacterial compositional variance (adjusted R² = 0.079, p = 0.001; Mantel test p = 0.0014; Fig. 5 a). The main compounds driving this relationship were tribromo-methane ( p = 0.001), dodecane ( p = 0.001), β-ionone-epoxide ( p = 0.005), 7,8-dimethoxy-1,2,4,5,10a,10b-hexahydrobenzo(de)pyrrolo(3,2,1-ij)quinoline ( p < 0.05), and tridecane ( p < 0.05). Absorbed VOCs also accounted for significant variation in bacterial composition (adjusted R² = 0.065, p = 0.001; Mantel p < 0.001; Fig. 5 b), mainly explained by β-ionone-epoxide ( p = 0.001) and β-bisabolene ( p < 0.01). In contrast, no meaningful relationship was detected between VOC composition and fungal community structure. dbRDA models explained negligible variance for both emitted (adjusted R² = 0, Mantel p = 0.07) and absorbed VOCs (adjusted R² = 0.026, p < 0.01; Mantel p = 0.003). Only one compound, β-ionone-epoxide, was retained as a significant predictor in the latter model ( p = 0.001), suggesting that root VOCs exerted limited influence on fungal community composition. 4. Discussion Belowground volatile organic compounds (VOCs) play a central role in mediating plant–soil interactions, influencing root–microbe communication, allelopathy, and plant responses to environmental cues. The domestication process, by selecting for traits related to yield and productivity, may have unintentionally reduced plants’ investment in chemical signalling. In this study, we examined how domestication shaped root VOC emission and uptake across eight major crops and their wild relatives, aiming to determine whether these changes could have altered the chemical interface between roots and their environment. Our results broadly confirmed this hypothesis. Domesticated plants emitted substantially lower total amounts of VOCs and exhibited reduced chemical diversity of emitted compounds compared with their wild relatives. These patterns suggest that selection under cultivation may have relaxed the need for chemically mediated belowground interactions, as crops increasingly relied on managed environments rather than ecological signalling for resource acquisition or defence. In contrast, changes in VOC composition were largely crop-specific, with significant differences among species but no consistent domestication trend. This indicates that, while domestication generally reduced emission quantity and diversity, the qualitative makeup of emitted compounds was shaped by lineage-specific metabolic constraints or selection histories. Similarly, VOC uptake responses to domestication were variable. Some species, such as barley, showed the expected reduction in both total uptake and compound diversity, whereas others displayed no detectable trend or even slight increases. This suggests that VOC uptake may be governed by physiological or structural traits that evolved differently across crop lineages, rather than being uniformly affected by domestication pressures. 4.1 Total VOC emission and uptake concentrations Our results provide the first evidence of a consistent, cross-species reduction in root VOC emissions associated with domestication, with potential consequences for both biotic and abiotic interactions in the rhizosphere. These findings align with the generalized loss of defences during domestication, as selection for yield trades off with secondary metabolite production Meyer et al. ( 2012 ). Root VOC emission mechanisms remain poorly understood, as roots lack both cuticular barriers and stomatal structures that modulate volatile fluxes aboveground (Bergman et al., 2025 ). Moreover, rhizosphere microbes also contribute substantially to total VOC emissions (Chowdhury et al., 2019 ), further complicating the interpretation of plant versus microbial sources. Thus, the biochemical and ecological processes underlying the observed reductions remain to be fully resolved. Substantial differences in emission rates were also observed among the eight studied species, likely reflecting species-specific physiological or morphological traits. Because emission rates were normalized to root mass rather than surface area, variation in root architecture could have influenced apparent emission intensity. Nevertheless, emission rate does not appear to be a phylogenetically conserved trait, as even closely related species within the same genus (e.g., Centaurea L.) exhibit contrasting sesquiterpene emission profiles (Gfeller et al., 2019 ). In contrast, patterns of VOC uptake were more variable across crops. Domesticated barley, beet, and tomato showed markedly lower uptake rates than their wild relatives, potentially indicating a loss of signal perception capacity that could reduce defence priming (Brosset & Blande, 2021 ). Differences in root-associated microbiota may also contribute, since microbial metabolism can serve as a major sink for soil-borne VOCs (Jiao et al., 2023 ). However, the other five species showed no significant domestication effect on uptake, suggesting that VOC absorption is more resilient or more strongly modulated by microbial or physicochemical soil factors. Discrepancies between uptake and emission patterns suggest domestication disproportionately affects plant-derived VOC synthesis over root and rhizosphere VOC uptake. Furthermore, while aboveground absorption occurs mainly via stomata, the mechanisms enabling VOC uptake by roots remain largely unknown (Matsui, 2016 ). Future work integrating microbial community composition with plant physiological traits will be crucial to disentangle these processes and assess whether VOC exchange capacity represents an overlooked component of crop functional diversity. 4.2 Compositional changes in VOC profiles The plants studied exhibited a wide diversity of compounds in their root VOC profiles, both for emission and absorption. Contrary to expectations, no consistent domestication effect was associated with specific compositional changes. Instead, both emitted and absorbed volatile blends differed primarily among crop species and were generally conserved within each crop regardless of domestication status. This pattern indicates that root VOC composition is largely shaped by phylogenetic constraints rather than domestication, suggesting that the metabolic pathways underlying secondary metabolite synthesis were already fixed within each lineage prior to agricultural selection pressures (Thompson et al., 2024 ). These findings contrast with the hypothesis that VOC compositional shifts would primarily reflect adaptive responses to the functional and environmental differences between wild and cultivated habitats (Bernal et al., 2023 ). The chemical similarity observed between the two Asteraceae species, lettuce and sunflower, further supports a phylogenetic influence. Both taxa, irrespective of domestication status, shared characteristic compounds such as 2-methoxy[1]benzothieno[2,3-c]quinolin-6(5H)-one and 1,1,2-trifluoro-2,5-bis(trifluoromethyl)-hexane. Although little is known about the ecological roles of these heterocyclic and halogenated compounds, their conserved occurrence across the family points to lineage-specific biosynthetic constraints that may override environmental modulation (Courtois et al., 2016 ). A notable domestication-related shift was observed in broad bean, where emissions of fatty acid derivatives markedly decreased compared with its wild relative. These compounds, commonly detected in root VOC blends (Peñuelas et al., 2014 ), are linked to 13-lipoxygenase activity and are central to defence-related signalling against both biotic and abiotic stress (Van Dam et al., 2016 ). Their reduction in domesticated broad bean may therefore indicate a diminished capacity for belowground chemical defence. Among absorbed VOCs, two compounds—nonanal and benzothiazole—were nearly ubiquitous across all species. Nonanal, a hydrophobic fatty acid–derived aldehyde, plays a well-established role in plant defence signalling during fungal infection (Brambilla et al., 2022 ; Li et al., 2021 ), yet it is also emitted constitutively under unstressed conditions (Wildt et al., 2003 ). Exposure to nonanal has been shown to trigger bacterial and fungal resistance responses in diverse crops (Girón-Calva et al., 2012 ; Sharifi et al., 2022 ) and has recently been proposed as a sustainable elicitor to enhance yield and seed quality in common bean (Razo-Belmán et al., 2024 ). In our study, domesticated barley and beet showed reduced nonanal absorption compared with their wild relatives, suggesting a lower capacity to respond to defensive cues and potentially reduced resilience to soil-borne pathogens. Similarly, β-ionone-epoxide—a defence-related apocarotenoid (Brambilla et al., 2022 )—was absorbed at lower levels in domesticated broad bean. Conversely, uptake of nonanal remained comparable between wild and domesticated lettuce, sunflower, and cucumber, underscoring the species-specific nature of VOC uptake profiles. The dynamics of nonanal in soil further explain part of this variability. Its moderate hydrophobicity (log Kow ≈ 3.5–3.6) promotes adsorption to soil organic matter and root surfaces, increasing local availability while limiting volatilization (Degtyarenko et al., 2008 ). Moreover, aldehydes such as nonanal are rapidly metabolized by soil microbes or oxidized to nonanoic acid, and can also react with root exudates (Jiao et al., 2023 ). Thus, both chemical reactivity and microbial degradation likely contribute to species-specific uptake patterns. Benzothiazole, another widely absorbed compound, is of particular interest because it can originate from both microbial activity and anthropogenic pollution (e.g., tyre additives). It is commonly reported as an antibacterial VOC (Rani et al., 2023 ) and has been shown to be absorbed and metabolized by carrot roots (Wu et al., 2024 ). Other less abundant absorbed compounds, including 2-ethyl-1-hexanol (Y. Zhang et al., 2024 ) and tridecane (Lee et al., 2012 ), also exhibit antipathogenic properties. The widespread presence of these volatiles in agricultural soils suggests that plants may have evolved conserved uptake mechanisms to detect and metabolize stress-related or contaminant VOCs, a capacity that could be adaptive in anthropogenically altered environments (Oikawa & Lerdau, 2013 ). Finally, domesticated lentil displayed high uptake values for a halogenated compound, 5,5′-dicarboxy-3′-(2-chloroethyl)-4-(2-acetoxyethyl)-3,4′-dimethylpyrromethane, for which no ecological information is currently available. However, legumes such as peanut ( Arachis hypogaea ) have been shown to absorb and translocate halogenated contaminants (Fan et al., 2020 ) Increased absorption of this compound in domesticated lentil may be related to changes in root lipid composition or structure (Zhang et al., 2017 ), possibly reflecting adaptation to prolonged exposure to agrochemical residues. Such patterns could support the proposed role of domesticated legumes in phytoremediation processes (Liu et al., 2025 ), a hypothesis that remains underexplored. Overall, compositional analyses indicate that root VOC chemistry is strongly constrained by phylogeny, with domestication producing limited but functionally relevant modifications in specific compound classes, particularly those linked to defence and pollutant metabolism. 4.3 Emitted and absorbed VOC diversity In line with total emission trends, the diversity of emitted VOCs was consistently lower in domesticated varieties compared with their wild relatives. This finding indicates that domestication has reduced the chemical complexity of root volatile blends. Interestingly, this pattern contrasts with reports in other systems. For example, Thompson et al. ( 2024 ) found increased diversity of herbivory-induced volatiles in six Cucurbitaceae species following domestication, although they also observed that prolonged coexistence with herbivores reduced root volatile diversity. Similarly, Bernal et al. ( 2023 ) reported that domestication increased the diversity of maize root VOCs, influencing herbivore preference. These discrepancies suggest that the direction of domestication effects on chemical diversity may depend on the ecological context and plant compartment studied. Aboveground tissues, which are directly exposed to herbivory and pollination pressures, may have experienced selective maintenance or enhancement of chemical diversity, whereas belowground tissues, under the protection of managed soils and reduced biotic stress, may have undergone relaxed selection for secondary metabolite variability. The observed skewed relative abundances could result from metabolic canalization, where specific biosynthetic pathways become upregulated or fixed during domestication at the expense of others. This narrowing of chemical profiles may have consequences for ecological interactions in the rhizosphere, potentially reducing the range of microbial or signalling partners that plants can effectively engage with. 4.4 Microbial diversity relationship with compound diversity VOC alpha diversity, both for emitted and absorbed compounds, was negatively correlated with bacterial alpha diversity but showed no significant relationship with fungal alpha diversity. This suggests that plants hosting more diverse bacterial communities tend to emit and absorb a less chemically diverse set of VOCs. A plausible explanation is that a more diverse bacterial community may be able to metabolize or transform a broader range of volatile compounds, thereby reducing the observable diversity of VOCs in the rhizosphere (Raza et al., 2021 ). Competitive use of VOC-derived carbon sources and co-metabolic degradation could also contribute to this pattern. The absence of a similar relationship for fungi might reflect the generally slower metabolic response and lower catabolic diversity of fungal communities toward volatiles, or a lesser role of VOCs as signalling cues in fungal recruitment. Evidence from other systems supports this interpretation. In wheat ( Triticum durum Deff.), domesticated varieties exhibited reduced VOC diversity but higher root endophytic bacterial diversity, although with lower functional diversity compared to their wild relatives (Deng et al., 2024 ). This suggests that domestication may have promoted more taxonomically diverse but functionally convergent microbial assemblages, potentially linked to simplified VOC emission patterns. It is important to note that the use of Shannon diversity indices may mask more specific associations between individual VOCs and microbial taxa. Indeed, several emitted (n = 5) and absorbed (n = 2) compounds were significantly associated with bacterial community composition. This finding supports the idea that plant-specific metabolic traits influencing VOC synthesis and uptake pathways can shape the establishment of distinct bacterial communities during coevolution (Sharifi et al., 2022 ). The interplay between VOCs and microbiota is highly reciprocal. Plants can use VOCs to recruit or stabilize specific microbial partners, as shown in tomato, where root-associated bacterial inoculation induced leaf emissions of β-caryophyllene that, in turn, promoted microbiome similarity among neighbouring plants (Kong et al., 2021 ). Conversely, microbes can modulate plant VOC metabolism, altering plant–plant and plant–herbivore interactions (Russo et al., 2022 ). Genetic and functional shifts in microbiomes under domestication further illustrate this bidirectional relationship: wild rice relatives harbour microbiomes richer in nitrogen-fixation genes (Chang et al., 2025 ), while bacterial taxa specific to certain cultivars may alter VOC emission or uptake patterns (Kumar et al., 2024 ). Such effects would remain undetected when relying solely on diversity indices, highlighting the need for integrative approaches combining metabolomic, genomic, and network-based analyses. Overall, our findings suggest that domestication may have decoupled the diversity of root-emitted VOCs from microbial community complexity. This chemical–microbial imbalance could have consequences for soil communication networks, nutrient cycling, and the resilience of cultivated plants to environmental stress. 4.5 Limitations and future perspectives Measurements of VOCs are highly sensitive to sampling methods. In this exploratory study, we selected direct SPME fibre injection because it enables minimally invasive detection of root emissions and uptake under non-induced stress conditions (Tholl et al., 2021 ). However, SPME adsorption efficiency is strongly affected by fibre coating chemistry, temperature, and humidity (Fontez et al., 2025 ), and soil matrices can substantially reduce VOC recovery by adsorption and diffusion limitations (Voyard et al., 2024 ). These factors may have biased absolute flux estimates, particularly for low-molecular-weight or highly polar compounds, which could partially explain the lower emission values recorded in some species. Standardizing exposure times and using the same fibre type across all samples minimized comparability issues, but complementary quantitative methods such as Proton Transfer Reaction–Time of Flight–Mass Spectrometry (PTR-ToF-MS) would provide real-time dynamic measurements to refine flux estimates when combined with SPME (Brennan et al., 2022 ). Although ambient air temperature did not correlate with VOC emissions or uptake concentrations (Figure S2), temperature is known to be a key modulator of VOC synthesis and volatility (Jiao et al., 2023 ). Future work should include soil temperature and moisture monitoring to reduce microclimatic bias. Given that VOC responses to temperature often display nonlinear or threshold behaviour, such high-resolution measurements may reveal subtle treatment effects masked in this study. The present work compared landraces and their wild relatives, thereby focusing on early domestication effects. However, most modern cultivars have experienced additional selective pressures due to intensive breeding for yield and the chronic use of fertilizers and pesticides (Krug et al., 2023 ). Including modern varieties would allow discrimination between initial domestication changes and more recent agroecological adaptations. Wild relatives often display greater resistance to abiotic and biotic stress and harbour valuable genetic diversity for crop improvement (F. Zhang & Batley, 2020 ). Understanding how domestication has altered belowground chemical and microbial traits could therefore inform breeding programs aimed at restoring lost functional diversity (Preece & Peñuelas, 2020 ). Finally, belowground VOCs remain poorly integrated into terrestrial carbon and signalling models. Incorporating soil and root VOC fluxes into atmospheric carbon budgets could improve predictions of ecosystem feedbacks, as these compounds represent an overlooked yet potentially significant carbon source (Isidorov & Zaitsev, 2022 ). In conclusion, by demonstrating that domestication consistently reduced root VOC diversity and emission fluxes, this study highlights belowground chemical diversity as a potentially eroded functional trait. Moreover, differences among crops underscore the need to understand the crop-specific patterns in belowground traits that may shape distinct interactions and physiological pathways. Further research into how these changes affect microbial recruitment, nutrient cycling, and plant resilience will be essential for developing more sustainable and self-regulating crop systems in the face of global environmental change. Statements and Declarations 5.1 Funding This work was supported by the Research Foundation Flanders (FWO) MSCA SoE Fellowship (grant number 42899), a Kleine Projecten BOF UAntwerpen 2022, the European Research Council project ERC-2023-COG-101125455-WILD-ROOTS, a Ramón y Cajal fellowship (RYC2022-037008-I) funded by the Spanish Ministry of Science, Innovation and Universities and the Spanish State Research Agency (AEI) and co-financed by the European Social Fund Plus (FSE+). 5.2 Author contributions (CRediT) Jordi Cercós : Conceptualization, Methodology, Formal Analysis, Data Curation, Writing - Original Draft Joan Llusià : Investigation, Resources, Writing - Review & Editing Laura Márquez : Methodology, Investigation Josep Peñuelas : Conceptualization, Resources, Writing - Review & Editing Erik Verbruggen : Investigation, Resources, Writing - Review & Editing Ana María Yáñez-Serrano : Conceptualization, Resources, Writing - Review & Editing Catherine Preece : Conceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing – Original Draft and Review & Editing, Supervision, Project administration. As the study consisted of a greenhouse experiment placed in the home region of most of the authors involved, there was no local data collection. 5.3 Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 5.4 Data availability The data that support the findings of this study are available from the corresponding author on reasonable request. References Abbas, F., O’Neill Rothenberg, D., Zhou, Y., Ke, Y., & Wang, H. C. (2022). Volatile organic compounds as mediators of plant communication and adaptation to climate change. In Physiologia Plantarum (Vol. 174, Issue 6). John Wiley and Sons Inc. https://doi.org/10.1111/ppl.13840 Alger, I., Dridi, S., Stieglitz, J., & Wilson, M. L. (2023). The evolution of early hominin food production and sharing. Proceedings of the National Academy of Sciences of the United States of America , 120 (25), e2218096120. https://doi.org/10.1073/PNAS.2218096120/SUPPL_FILE/PNAS.2218096120.SAPP.PDF Amaral, J. A., Ren, T., & Knowles, R. (1998). Atmospheric methane consumption by forest soils and extracted bacteria at different pH values. Applied and Environmental Microbiology , 64 (7), 2397–2402. https://doi.org/10.1128/AEM.64.7.2397-2402.1998/ASSET/725B0046-8FC5-4E8D-9B42-1DD2F4E3DC1C/ASSETS/GRAPHIC/AM0781843007.JPEG Bae, J., Byun, C., Ahn, Y. G., Choi, J. H., Lee, D., & Kang, H. (2019). Effect of elevated atmospheric carbon dioxide on the allelopathic potential of common ragweed. Journal of Ecology and Environment , 43 (1), 1–7. https://doi.org/10.1186/S41610-019-0116-5/FIGURES/1 Bartoń, K. (2024). MuMIn: Multi-Model Inference . https://CRAN.R-project.org/package=MuMIn Bergman, M. E., Huang, X. Q., Baudino, S., Caissard, J. C., & Dudareva, N. (2025). Plant volatile organic compounds: Emission and perception in a changing world. Current Opinion in Plant Biology , 85 , 102706. https://doi.org/10.1016/J.PBI.2025.102706 Bernal, J. S., Helms, A. M., Fontes-Puebla, A. A., DeWitt, T. J., Kolomiets, M. V., & Grunseich, J. M. (2023). Root volatile profiles and herbivore preference are mediated by maize domestication, geographic spread, and modern breeding. Planta , 257 (1), 1–18. https://doi.org/10.1007/S00425-022-04057-0/METRICS Bernal, J. S., & Medina, R. F. (2018). Agriculture sows pests: how crop domestication, host shifts, and agricultural intensification can create insect pests from herbivores. Current Opinion in Insect Science , 26 , 76–81. https://doi.org/10.1016/J.COIS.2018.01.008 Bourtsoukidis, E., Behrendt, T., Yañez-Serrano, A. M., Hellén, H., Diamantopoulos, E., Catão, E., Ashworth, K., Pozzer, A., Quesada, C. A., Martins, D. L., Sá, M., Araujo, A., Brito, J., Artaxo, P., Kesselmeier, J., Lelieveld, J., & Williams, J. (2018). Strong sesquiterpene emissions from Amazonian soils. Nature Communications , 9 (1). https://doi.org/10.1038/s41467-018-04658-y Brambilla, A., Sommer, A., Ghirardo, A., Wenig, M., Knappe, C., Weber, B., Amesmaier, M., Lenk, M., Schnitzler, J. P., & Vlot, A. C. (2022). Immunity-associated volatile emissions of β-ionone and nonanal propagate defence responses in neighbouring barley plants. Journal of Experimental Botany , 73 (2), 615–630. https://doi.org/10.1093/JXB/ERAB520 Brennan, D. L., Kollar, L. M., Kiel, S., Deakova, T., Laguerre, A., McDaniel, S. F., Eppley, S. M., Gall, E. T., & Rosenstiel, T. N. (2022). Measuring volatile emissions from moss gametophytes: A review of methodologies and new applications. Applications in Plant Sciences , 10 (2), e11468. https://doi.org/10.1002/APS3.11468 Brooks, M., Bolker, B., Kristensen, K., Maechler, M., Magnusson, A., Skaug, H., Nielsen, A., Berg, C., & van Bentham, K. (2025). glmmTMB: Generalized Linear Mixed Models using Template Model Builder . https://github.com/glmmTMB/glmmTMB Brosset, A., & Blande, J. D. (2021). Volatile-mediated plant–plant interactions: volatile organic compounds as modulators of receiver plant defence, growth, and reproduction. Journal of Experimental Botany , 73 (2), 511. https://doi.org/10.1093/JXB/ERAB487 Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., Fierer, N., & Knight, R. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences , 108 (supplement_1), 4516–4522. https://doi.org/10.1073/PNAS.1000080107 Chang, J., Costa, O. Y. A., Sun, Y., Wang, J., Tian, L., Shi, S., Wang, E., Ji, L., Wang, C., Pang, Y., Yao, Z., Ye, L., Zhang, J., Chen, H., Cai, Y., Chen, D., Song, Z., Rong, J., Raaijmakers, J. M., … Kuramae, E. E. (2025). Domesticated rice alters the rhizosphere microbiome, reducing nitrogen fixation and increasing nitrous oxide emissions. Nature Communications 2025 16:1 , 16 (1), 1–14. https://doi.org/10.1038/s41467-025-57213-x Chen, Y. H., Gols, R., & Benrey, B. (2015). Crop domestication and its impact on naturally selected trophic interactions. Annual Review of Entomology , 60 (Volume 60, 2015), 35–58. https://doi.org/10.1146/ANNUREV-ENTO-010814-020601/CITE/REFWORKS Chowdhury, F. T., Islam, M. R., Islam, M. R., & Khan, H. (2019). Diversity of Plant Endophytic Volatile Organic Compound (VOC) and Their Potential Applications. Reference Series in Phytochemistry , 307–333. https://doi.org/10.1007/978-3-319-90484-9_10 Courtois, E. A., Dexter, K. G., Paine, C. E. T., Stien, D., Engel, J., Baraloto, C., & Chave, J. (2016). Evolutionary patterns of volatile terpene emissions across 202 tropical tree species. Ecology and Evolution , 6 (9), 2854. https://doi.org/10.1002/ECE3.1810 Crittenden, A. N., & Schnorr, S. L. (2017). Current views on hunter-gatherer nutrition and the evolution of the human diet. American Journal of Physical Anthropology , 162 , 84–109. https://doi.org/10.1002/AJPA.23148 Degtyarenko, K., De matos, P., Ennis, M., Hastings, J., Zbinden, M., Mcnaught, A., Alcántara, R., Darsow, M., Guedj, M., & Ashburner, M. (2008). ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Research , 36 (suppl_1), D344–D350. https://doi.org/10.1093/NAR/GKM791 Del Giudice, L., Massardo, D. R., Pontieri, P., Bertea, C. M., Mombello, D., Carata, E., Tredici, S. M., Talà, A., Mucciarelli, M., Groudeva, V. I., De Stefano, M., Vigliotta, G., Maffei, M. E., & Alifano, P. (2008). The microbial community of Vetiver root and its involvement into essential oil biogenesis. Environmental Microbiology , 10 (10), 2824–2841. https://doi.org/10.1111/J.1462-2920.2008.01703.X Delory, B. M., Delaplace, P., Fauconnier, M. L., & du Jardin, P. (2016). Root-emitted volatile organic compounds: can they mediate belowground plant-plant interactions? Plant and Soil 2016 402:1 , 402 (1), 1–26. https://doi.org/10.1007/S11104-016-2823-3 Deng, L., Zhang, A., Wang, A., Zhang, H., Wang, T., Song, W., & Yue, H. (2024). Wheat domestication alters root metabolic functions to drive the assembly of endophytic bacteria. Plant Journal , 120 (4), 1263–1277. https://doi.org/10.1111/TPJ.16972;CTYPE:STRING:JOURNAL Diamond, J. (2002). Evolution, consequences and future of plant and animal domestication. Nature , 418 (6898), 700–707. https://doi.org/10.1038/NATURE01019;KWRD=SCIENCE Dicke, M., & Baldwin, I. T. (2010). The evolutionary context for herbivore-induced plant volatiles: beyond the ‘cry for help’. Trends in Plant Science , 15 (3), 167–175. https://doi.org/10.1016/J.TPLANTS.2009.12.002/ASSET/2867EB56-187E-4A50-833C-41F7C5D70F92/MAIN.ASSETS/GR2.JPG Doebley, J. F., Gaut, B. S., & Smith, B. D. (2006). The Molecular Genetics of Crop Domestication. Cell , 127 (7), 1309–1321. https://doi.org/10.1016/J.CELL.2006.12.006/ATTACHMENT/70A57389-00B1-4546-9D74-45C55AB36610/MMC1.PDF Fan, Y., Chen, S. J., Li, Q. Q., Zeng, Y., Yan, X., & Mai, B. X. (2020). Uptake of halogenated organic compounds (HOCs) into peanut and corn during the whole life cycle grown in an agricultural field. Environmental Pollution , 263 , 114400. https://doi.org/10.1016/J.ENVPOL.2020.114400 Fontez, M., Huguenin, J., Jullien, F., Moja, S., Proffit, M., & Nicole, F. (2025). A practical workflow to facilitate analysis of the emission dynamics of non-target BVOCs in terrestrial ecosystems by PTR-TOF-MS. Frontiers in Ecology and Evolution , 13 , 1536032. https://doi.org/10.3389/FEVO.2025.1536032/BIBTEX Fox, J., Weisberg, S., & Price, B. (2024). car: Companion to Applied Regression . https://r-forge.r-project.org/projects/car/ Fuller, D. Q., Denham, T., & Allaby, R. (2023). Plant domestication and agricultural ecologies. Current Biology , 33 , R636–R649. https://doi.org/10.1016/j.cub.2023.04.038 Fuller, D. Q., Willcox, G., & Allaby, R. G. (2011). Cultivation and domestication had multiple origins: Arguments against the core area hypothesis for the origins of agriculture in the Near East. World Archaeology , 43 (4), 628–652. https://doi.org/10.1080/00438243.2011.624747;REQUESTEDJOURNAL:JOURNAL:RWAR20;WGROUP:STRING:PUBLICATION Gfeller, V., Huber, M., Förster, C., Huang, W., Köllner, T. G., & Erb, M. (2019). Root volatiles in plant–plant interactions I: High root sesquiterpene release is associated with increased germination and growth of plant neighbours. Plant, Cell & Environment , 42 (6), 1950–1963. https://doi.org/10.1111/PCE.13532 Girón-Calva, P. S., Molina-Torres, J., & Heil, M. (2012). Volatile Dose and Exposure Time Impact Perception in Neighboring Plants. Journal of Chemical Ecology , 38 (2), 226–228. https://doi.org/10.1007/S10886-012-0072-3/FIGURES/1 Gowdy, J., & Krall, L. (2014). Agriculture as a major evolutionary transition to human ultrasociality. Journal of Bioeconomics , 16 (2), 179–202. https://doi.org/10.1007/S10818-013-9156-6/METRICS Gray, C. M., Monson, R. K., & Fierer, N. (2010). Emissions of volatile organic compounds during the decomposition of plant litter. Journal of Geophysical Research: Biogeosciences , 115 (G3), 3015. https://doi.org/10.1029/2010JG001291 Hartig, F. (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models . http://florianhartig.github.io/DHARMa/ Insam, H., & Seewald, M. S. A. (2010). Volatile organic compounds (VOCs) in soils. Biology and Fertility of Soils 2010 46:3 , 46 (3), 199–213. https://doi.org/10.1007/S00374-010-0442-3 Isidorov, V. A., & Zaitsev, A. A. (2022). Reviews and syntheses: VOC emissions from soil cover in boreal and temperate natural ecosystems of the Northern Hemisphere. Biogeosciences , 19 (19), 4715–4746. https://doi.org/10.5194/BG-19-4715-2022 Jiao, Y., Kramshøj, M., Davie-Martin, C. L., Albers, C. N., & Rinnan, R. (2023). Soil uptake of VOCs exceeds production when VOCs are readily available. Soil Biology and Biochemistry , 185 , 109153. https://doi.org/10.1016/J.SOILBIO.2023.109153 Johnsen, L. G., Skou, P. B., Khakimov, B., & Bro, R. (2017). Gas chromatography – mass spectrometry data processing made easy. Journal of Chromatography A , 1503 , 57–64. https://doi.org/10.1016/J.CHROMA.2017.04.052 Jones, G., Kluyver, T., Preece, C., Swarbrick, J., Forster, E., Wallace, M., Charles, M., Rees, M., & Osborne, C. P. (2021). The origins of agriculture: Intentions and consequences. Journal of Archaeological Science , 125 , 105290. https://doi.org/10.1016/J.JAS.2020.105290 Karban, R., Yang, L. H., & Edwards, K. F. (2014). Volatile communication between plants that affects herbivory: A meta-analysis. Ecology Letters , 17 (1), 44–52. https://doi.org/10.1111/ELE.12205;PAGE:STRING:ARTICLE/CHAPTER Kõljalg, U., Larsson, K. H., Abarenkov, K., Nilsson, R. H., Alexander, I. J., Eberhardt, U., Erland, S., Høiland, K., Kjøller, R., Larsson, E., Pennanen, T., Sen, R., Taylor, A. F. S., Tedersoo, L., Vrålstad, T., & Ursing, B. M. (2005). UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytologist , 166 (3), 1063–1068. https://doi.org/10.1111/J.1469-8137.2005.01376.X Kong, H. G., Song, G. C., Sim, H. J., & Ryu, C. M. (2021). Achieving similar root microbiota composition in neighbouring plants through airborne signalling. The ISME Journal , 15 (2), 397–408. https://doi.org/10.1038/S41396-020-00759-Z Krug, A. S., Drummond, E. B. M., Van Tassel, D. L., & Warschefsky, E. J. (2023). The next era of crop domestication starts now. Proceedings of the National Academy of Sciences of the United States of America , 120 (14). https://doi.org/10.1073/pnas.2205769120 Kumar, A., Kuznetsova, O., Gschwendtner, S., Chen, H., Alonso-Crespo, I. M., Yusuf, M., Schulz, S., Bonkowski, M., Schloter, M., & Temperton, V. M. (2024). Shifts in plant functional trait dynamics in relation to soil microbiome in modern and wild barley. Plants, People, Planet , 6 (6), 1398–1412. https://doi.org/10.1002/PPP3.10534 Lee, B., Farag, M. A., Park, H. B., Kloepper, J. W., Lee, S. H., & Ryu, C. M. (2012). Induced Resistance by a Long-Chain Bacterial Volatile: Elicitation of Plant Systemic Defense by a C13 Volatile Produced by Paenibacillus polymyxa. PLOS ONE , 7 (11), e48744. https://doi.org/10.1371/JOURNAL.PONE.0048744 Lenth, R. V. (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means . https://rvlenth.github.io/emmeans/ Li, Q., Zhu, X., Xie, Y., & Liang, J. (2021). Antifungal properties and mechanisms of three volatile aldehydes (octanal, nonanal and decanal) on Aspergillus flavus. Grain & Oil Science and Technology , 4 (3), 131–140. https://doi.org/10.1016/J.GAOST.2021.07.002 Liu, H., Ciric, L., & Bhatti, M. (2025). Phytoremediation of organic pollution using leguminous plants and auxiliary additives: principles and advantages. Chemical and Biological Technologies in Agriculture , 12 (1), 1–15. https://doi.org/10.1186/S40538-025-00797-9/FIGURES/4 Liu, Z., Wang, M., Wu, M., Li, X., Liu, H., Niu, N., Li, S., & Chen, L. (2023). Volatile organic compounds (VOCs) from plants: From release to detection. TrAC Trends in Analytical Chemistry , 158 , 116872. https://doi.org/10.1016/J.TRAC.2022.116872 Llusià, J., Asensio, D., Sardans, J., Filella, I., Peguero, G., Grau, O., Ogaya, R., Gargallo-Garriga, A., Verryckt, L. T., Van Langenhove, L., Brechet, L. M., Courtois, E., Stahl, C., Janssens, I. A., & Peñuelas, J. (2022). Contrasting nitrogen and phosphorus fertilization effects on soil terpene exchanges in a tropical forest. Science of The Total Environment , 802 , 149769. https://doi.org/10.1016/J.SCITOTENV.2021.149769 Lüdecke, D. (2024). ggeffects: Create Tidy Data Frames of Marginal Effects for ggplot from Model Outputs . https://strengejacke.github.io/ggeffects/ Martinez Arbizu, P. (2017). pairwiseAdonis: Pairwise Multilevel Comparison using Adonis . https://github.com/pmartinezarbizu/pairwiseAdonis Martín-Robles, N., Lehmann, A., Seco, E., Aroca, R., Rillig, M. C., & Milla, R. (2018). Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species. New Phytologist , 218 (1), 322–334. https://doi.org/10.1111/NPH.14962 Matsui, K. (2016). A portion of plant airborne communication is endorsed by uptake and metabolism of volatile organic compounds. Current Opinion in Plant Biology , 32 , 24–30. https://doi.org/10.1016/J.PBI.2016.05.005 Meyer, R. S., Duval, A. E., & Jensen, H. R. (2012). Patterns and processes in crop domestication: An historical review and quantitative analysis of 203 global food crops. New Phytologist , 196 (1), 29–48. https://doi.org/10.1111/J.1469-8137.2012.04253.X;JOURNAL:JOURNAL:14698137;WGROUP:STRING:PUBLICATION Meyer, R. S., & Purugganan, M. D. (2013). Evolution of crop species: Genetics of domestication and diversification. Nature Reviews Genetics , 14 (12), 840–852. https://doi.org/10.1038/NRG3605;SUBJMETA=208,2491,2492,449,480,631;KWRD=PLANT+DOMESTICATION,PLANT+GENETICS,QUANTITATIVE+TRAIT Milla, R., Osborne, C. P., Turcotte, M. M., & Violle, C. (2015). Plant domestication through an ecological lens. Trends in Ecology and Evolution , 30 (8), 463–469. https://doi.org/10.1016/J.TREE.2015.06.006/ASSET/C2A09C40-CD5A-4508-8916-40B88ECF4212/MAIN.ASSETS/GR1B2.SML Oikawa, P. Y., & Lerdau, M. T. (2013). Catabolism of volatile organic compounds influences plant survival. Trends in Plant Science , 18 (12), 695–703. https://doi.org/10.1016/J.TPLANTS.2013.08.011/ASSET/C3801A19-3910-4324-8891-E2BC6640EAC8/MAIN.ASSETS/GR3.SML Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., … Weedon, J. (2024). vegan: Community Ecology Package . https://github.com/vegandevs/vegan Peñuelas, J., Asensio, D., Tholl, D., Wenke, K., Rosenkranz, M., Piechulla, B., & Schnitzler, J. P. (2014). Biogenic volatile emissions from the soil. In Plant, Cell and Environment (Vol. 37, Issue 8, pp. 1866–1891). Blackwell Publishing Ltd. https://doi.org/10.1111/pce.12340 Preece, C., & Peñuelas, J. (2020). A Return to the Wild: Root Exudates and Food Security. In Trends in Plant Science (Vol. 25, Issue 1, pp. 14–21). Elsevier Ltd. https://doi.org/10.1016/j.tplants.2019.09.010 Purugganan, M. D. (2022). What is domestication? Trends in Ecology & Evolution , 37 (8), 663–671. https://doi.org/10.1016/J.TREE.2022.04.006 Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research , 41 (D1), D590–D596. https://doi.org/10.1093/NAR/GKS1219 R Core Team. (2025). R: A Language and Environment for Statistical Computing . https://www.R-project.org/ Radujković, D., Portillo-Estrada, M., Hendrickx, B., Campetella, G., Emsens, W. J., Höhn, M., Jurasinski, G., Chelli, S., Vochten, T., & Verbruggen, E. (2025). Soil biodiversity and ecosystem functions in grasslands: Is more always better? Soil Biology and Biochemistry , 211 . https://doi.org/10.1016/j.soilbio.2025.109988 Ramirez, K. S., Lauber, C. L., & Fierer, N. (2010). Microbial consumption and production of volatile organic compounds at the soil-litter interface. Biogeochemistry , 99 (1), 97–107. https://doi.org/10.1007/S10533-009-9393-X/FIGURES/6 Rani, A., Rana, A., Dhaka, R. K., Singh, A. P., Chahar, M., Singh, S., Nain, L., Singh, K. P., & Minz, D. (2023). Bacterial volatile organic compounds as biopesticides, growth promoters and plant-defense elicitors: Current understanding and future scope. Biotechnology Advances , 63 , 108078. https://doi.org/10.1016/J.BIOTECHADV.2022.108078 Raza, W., Wei, Z., Jousset, A., Shen, Q., & Friman, V.-P. (2021). Extended Plant Metarhizobiome: Understanding Volatile Organic Compound Signaling in Plant-Microbe Metapopulation Networks. MSystems , 6 (4). https://doi.org/10.1128/MSYSTEMS.00849-21 Razo-Belmán, R., Castañeda-Rodríguez, R., Heil, M., & Ozuna, C. (2024). Nonanal, a volatile organic compound, improves the techno-functional, nutritional, and nutraceutical properties of the common bean (Phaseolus vulgaris L.). Food Chemistry Advances , 4 , 100707. https://doi.org/10.1016/J.FOCHA.2024.100707 Rodriguez-Saona, C., Vorsa, N., Singh, A. P., Johnson-Cicalese, J., Szendrei, Z., Mescher, M. C., & Frost, C. J. (2011). Tracing the history of plant traits under domestication in cranberries: potential consequences on anti-herbivore defences. Journal of Experimental Botany , 62 (8), 2633–2644. https://doi.org/10.1093/JXB/ERQ466 Rowen, E., & Kaplan, I. (2016). Eco-evolutionary factors drive induced plant volatiles: a meta-analysis. New Phytologist , 210 (1), 284–294. https://doi.org/10.1111/NPH.13804 Roy, S. J., Tucker, E. J., & Tester, M. (2011). Genetic analysis of abiotic stress tolerance in crops. Current Opinion in Plant Biology , 14 (3), 232–239. https://doi.org/10.1016/J.PBI.2011.03.002 Russo, A., Pollastri, S., Ruocco, M., Monti, M. M., & Loreto, F. (2022). Volatile organic compounds in the interaction between plants and beneficial microorganisms. Journal of Plant Interactions , 17 (1), 840–852. https://doi.org/10.1080/17429145.2022.2107243 Schade, G. W., & Custer, T. G. (2004). OVOC emissions from agricultural soil in northern Germany during the 2003 European heat wave. Atmospheric Environment , 38 (36), 6105–6114. https://doi.org/10.1016/J.ATMOSENV.2004.08.017 Sharifi, R., Jeon, J. S., & Ryu, C. M. (2022). Belowground plant–microbe communications via volatile compounds. Journal of Experimental Botany , 73 (2), 463–486. https://doi.org/10.1093/JXB/ERAB465 Smith, B. D. (2007). Niche construction and the behavioral context of plant and animal domestication. Evolutionary Anthropology: Issues, News, and Reviews , 16 (5), 188–199. https://doi.org/10.1002/EVAN.20135 Smolander, A., Ketola, R. A., Kotiaho, T., Kanerva, S., Suominen, K., & Kitunen, V. (2006). Volatile monoterpenes in soil atmosphere under birch and conifers: Effects on soil N transformations. Soil Biology and Biochemistry , 38 (12), 3436–3442. https://doi.org/10.1016/J.SOILBIO.2006.05.019 Stoeck, T., Bass, D., Nebel, M., Christen, R., Jones, M. D. M., Breiner, H. W., & Richards, T. A. (2010). Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Molecular Ecology , 19 (SUPPL. 1), 21–31. https://doi.org/10.1111/J.1365-294X.2009.04480.X Tholl, D., Hossain, O., Weinhold, A., Röse, U. S. R., & Wei, Q. (2021). Trends and applications in plant volatile sampling and analysis. In Plant Journal (Vol. 106, Issue 2, pp. 314–325). Blackwell Publishing Ltd. https://doi.org/10.1111/tpj.15176 Thompson, M. N., Cohen, Z. P., Merrell, D., & Helms, A. M. (2024). Eco-evolutionary factors contribute to chemodiversity in aboveground and belowground cucurbit herbivore-induced plant volatiles. Plant Biology . https://doi.org/10.1111/PLB.13709 Van Dam, N. M., Weinhold, A., Garbeva, P., Van Dam, N. M., Weinhold, A., & Garbeva, P. (2016). Calling in the Dark: The Role of Volatiles for Communication in the Rhizosphere . 175–210. https://doi.org/10.1007/978-3-319-33498-1_8 Voyard, A., Ciuraru, R., Lafouge, F., Decuq, C., Fortineau, A., Loubet, B., Staudt, M., & Rees, F. (2024). Emissions of volatile organic compounds from aboveground and belowground parts of rapeseed (Brassica napus L.) and tomato (Solanum lycopersicum L.). Science of the Total Environment , 955 . https://doi.org/10.1016/j.scitotenv.2024.177081 Wang, L., Lun, X., Wang, Q., & Wu, J. (2024). Biogenic volatile organic compounds emissions, atmospheric chemistry, and environmental implications: a review. Environmental Chemistry Letters 2024 22:6 , 22 (6), 3033–3058. https://doi.org/10.1007/S10311-024-01785-5 Whitehead, S. R., Turcotte, M. M., & Poveda, K. (2016). Domestication impacts on plant-herbivore interactions: a meta-analysis. Phil. Trans. R. Soc . https://doi.org/10.1098/rstb.2016.0034 Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis . Springer-Verlag New York. https://ggplot2.tidyverse.org Wildt, J., Kobel, K., Schuh-Thomas, G., & Heiden, A. C. (2003). Emissions of oxygenated volatile organic compounds from plants part II: Emissions of saturated aldehydes. Journal of Atmospheric Chemistry , 45 (2), 173–196. https://doi.org/10.1023/A:1024030821349/METRICS Wu, J., Lai, Y., Yang, X., Zhou, Q., Qian, Z., Zhang, A., Sun, J., & Gan, J. (2024). Structure-Dependent uptake and metabolism of Tire additives Benzothiazoles in carrot plant. Environment International , 193 , 109075. https://doi.org/10.1016/J.ENVINT.2024.109075 Yang, K., Llusià, J., Preece, C., Tan, Y., Peñuelas, J., Yang, K., Llusià, J., Preece, · C, Peñuelas, · J, Llusià, · J, Tan, Y., & Guilin, G. (2024). Exchange of volatile organic compounds between the atmosphere and the soil. Plant and Soil 2024 501:1 , 501 (1), 509–535. https://doi.org/10.1007/S11104-024-06524-X Zeder, M. A. (2015). Core questions in domestication research. Proceedings of the National Academy of Sciences of the United States of America , 112 (11), 3191–3198. https://doi.org/10.1073/PNAS.1501711112/-/DCSUPPLEMENTAL Zeder, M. A. (2016). Domestication as a model system for niche construction theory. Evolutionary Ecology 2015 30:2 , 30 (2), 325–348. https://doi.org/10.1007/S10682-015-9801-8 ZHANG, C., FENG, Y., LIU, Y. wang, CHANG, H. qing, LI, Z. jun, & XUE, J. ming. (2017). Uptake and translocation of organic pollutants in plants: A review. Journal of Integrative Agriculture , 16 (8), 1659–1668. https://doi.org/10.1016/S2095-3119(16)61590-3 Zhang, F., & Batley, J. (2020). Exploring the application of wild species for crop improvement in a changing climate. Current Opinion in Plant Biology , 56 , 218–222. https://doi.org/10.1016/J.PBI.2019.12.013 Zhang, Y., Li, Z., Wei, S., Xu, C., Chen, M., Sang, J., Han, Y., Yan, H., Li, Z., Cui, Z., & Ye, X. (2024). Antifungal Activity and Mechanisms of 2-Ethylhexanol, a Volatile Organic Compound Produced by Stenotrophomonas sp. NAU1697, against Fusarium oxysporum f. sp. cucumerinum. Journal of Agricultural and Food Chemistry , 72 (27), 15213–15227. https://doi.org/10.1021/ACS.JAFC.3C09851/ASSET/IMAGES/MEDIUM/JF3C09851_0010.GIF Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8828517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589290511,"identity":"695da64d-1709-4b69-9b09-6a6c64dd5653","order_by":0,"name":"Jordi Cercós Tuset","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACxgYGBokEGO+DAalaGGcYMEiAWQcIaJOAMZh5GIjQwtze+/DGAwY7OfP2w4c/2xTcq+NvYH74+QM+h/UcN7ZIYEg2ljmTliadY1AsIXGAzVgCny2MM9LYgH45kDhDgseMOccgQYLhAA8DUVrqgVqMP1sAtcgf4GH+QYyWBAkJHgNpBqAWgwM8bPht6TnGbJFgkGw4gyctTbLHIEFy42E2M4szeLQYtrcx3vxRYScvwX748IcffxL45Y43P75RgU9LA4hEiXRmPMpBQJ6A/CgYBaNgFIwCBgYAAN1DnAFu96AAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-7495-1057","institution":"IRTA: Institut de Recerca i Tecnologia Agroalimentaries","correspondingAuthor":true,"prefix":"","firstName":"Jordi","middleName":"Cercós","lastName":"Tuset","suffix":""},{"id":589290512,"identity":"86502575-0a7d-49fd-8101-6ac5cf8e5e15","order_by":1,"name":"Joan Llusià","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Llusià","suffix":""},{"id":589290513,"identity":"fd8ec422-450f-47da-88bb-04b32bddbeaa","order_by":2,"name":"Laura Márquez Tur","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"Márquez","lastName":"Tur","suffix":""},{"id":589290514,"identity":"086b47f7-89c5-42e7-b89c-e75734cc050c","order_by":3,"name":"Josep Peñuelas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Peñuelas","suffix":""},{"id":589290515,"identity":"77bbdecf-826d-4a23-b6de-5e19571f1516","order_by":4,"name":"Erik Verbruggen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Verbruggen","suffix":""},{"id":589290516,"identity":"4e9e910b-60d5-4df0-943e-6265463f85d3","order_by":5,"name":"Ana María Yáñez Serrano","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"María Yáñez","lastName":"Serrano","suffix":""},{"id":589290517,"identity":"770119a6-f256-4c8c-af87-c5583deb4d11","order_by":6,"name":"Catherine Preece","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Preece","suffix":""}],"badges":[],"createdAt":"2026-02-09 09:35:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8828517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8828517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102773700,"identity":"4741c619-009a-4864-8f69-fbe0ff6241e0","added_by":"auto","created_at":"2026-02-16 13:18:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25592471,"visible":true,"origin":"","legend":"\u003cp\u003eVOC emissions (a) and uptake concentration(b) per gram of root and litre of soil (on a natural log scale) for crop wild relatives (CWR, in orange) and domesticated (Dom, in green) of the eight studied species. Each point represents a plant pot. Mean predicted values with its 95% IC from the GLM are represented with a cross and whiskers, respectively. Asterisks show significant differences (·, p \u0026lt; 0.06; *, p \u0026lt; 0.05; ***, p \u0026lt; 0.001) from emmeans.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/480723cf7efc15276a37906e.png"},{"id":102773698,"identity":"5dbff58b-85cf-4ad6-b09d-c0b94313b051","added_by":"auto","created_at":"2026-02-16 13:18:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19706045,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of the scores of the NMDS conducted with the emitted (a) and taken up (b) VOC composition (mean ± SE) with stress = 0.195 and stress = 0.219, respectively. Colour grading is attributed by crop species. Domestication status is indicated by shape; circle for crop wild relatives (CWRs) and triangle for domesticates.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/8682f89d8675dd9e2478c1f0.png"},{"id":102773701,"identity":"79759765-bba5-42f4-a1b4-77be706a9866","added_by":"auto","created_at":"2026-02-16 13:18:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13840525,"visible":true,"origin":"","legend":"\u003cp\u003eMean of the emitted and taken up chemical groups of all compounds per gram of root and litre of soil for each species and domestication status. Each chemical family is represented as a coloured bar as listed in the legend on the right side: aliphatic hydrocarbons (orange), fatty acid derivatives (light orange), aromatic (amber), halogenated (teal), heterocyclic compounds (cyan-blue) and terpenoids (navy blue).\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/d3971185bf48c2fbda4a925e.png"},{"id":103503784,"identity":"e1226690-ea4c-4e9c-8be8-0cca064d3a4c","added_by":"auto","created_at":"2026-02-26 13:00:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22197565,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity of VOCs emitted (a) and taken up (b) VOCs in Shannon index for crop wild relatives (CWR, in orange) and domesticated (Dom, in green) of the eight studied species. Each point represents a plant pot. Mean predicted values with its 95% IC from the linear model are represented with a cross and whiskers, respectively. Asterisks show significant differences (***, p \u0026lt; 0.001) from emmeans.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/d14174deb664b4a610b534ff.png"},{"id":102962437,"identity":"1131ab2b-6675-42ef-b38c-68fee0e4b393","added_by":"auto","created_at":"2026-02-19 04:08:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20966617,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial composition explained by root emitted (a) and absorbed (b) VOCs. Each point represents a plant pot, colour indicates crop species and shape indicates the domestication status. Significant compound relations to the CAP axis are shown in blue arrows with the compound name on its tip. The cumulative proportion of squared Bray distance for CAP1 and CAP2 axis are of 0.10 for emissions and 0.09 for uptake concentrations.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/ef969214619738caff7a4502.png"},{"id":102773697,"identity":"3119866c-d3d0-4000-b029-d54c325e1ed1","added_by":"auto","created_at":"2026-02-16 13:18:20","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":4880583,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8828517/v1/87e3f8c10a465eaa0221894e.docx"}],"financialInterests":"","formattedTitle":"Domestication reduces root VOC abundance and diversity in crops with species specific effects","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFor most of human history, food came from wild plants and animals, until independent agricultural origins\u0026thinsp;~\u0026thinsp;10,000 years ago initiated crop domestication (Alger et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Crittenden \u0026amp; Schnorr, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Diamond, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Fuller et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gowdy \u0026amp; Krall, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zeder, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This was a long and intense coevolutionary process between humans and some plant species that were able to thrive in nutrient-rich, emergent anthropogenic soils (Jones et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Smith, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zeder, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This potentially unintentional selection begun around 10,000 years ago in multiple locations (Fuller et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and was followed by the selection of yield-enhancing traits in the domesticated lineages derived from wild relatives by making a progressively more controlled habitat combined and a selective breeding for yield (Purugganan, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, modern domesticated crops show many common traits that can be defined as the domestication syndrome. This includes several morphological and physiological traits such as taller growth, increased apical dominance, larger organs, synchronized flowering, reduced seed shattering, and a loss of dispersal mechanisms and defences (Doebley et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Meyer \u0026amp; Purugganan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, belowground domestication traits relating to plant-soil interactions are far less well documented.\u003c/p\u003e \u003cp\u003eAlthough domestication supported larger human populations, it also contributed to a reduction in genetic, agroecosystem and dietary diversity (Krug et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, some crops may present a loss of resilience to stress factors such as drought and salinity (Roy et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, it has also altered plant ecological traits and interactions with other plants, herbivores and natural enemies (Milla et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), through changes in morphology and secondary metabolism that ultimately led to plant vulnerability (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, it has been seen that high fertilization regimes can contribute to changes in plant-microbe interactions decreasing mycorrhizal colonization efficiency in crops compared to CWR (Mart\u0026iacute;n-Robles et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, maize (\u003cem\u003eZea mays\u003c/em\u003e L.) resistance to Western corn rootworm has declined through breeding under pesticide-intensive regimes (Bernal \u0026amp; Medina, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interactions between plants and their biotic environment are universally mediated by volatile organic compounds (VOCs; Pe\u0026ntilde;uelas et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a diverse group of secondary metabolites, such as terpenes, methanol and aldehydes, involved in defence, communication and pollination (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, they have important roles in plant-environment interactions, because increasing atmospheric CO\u003csub\u003e2\u003c/sub\u003e might lead to the synthesis of some allelopathic terpenoids (Bae et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, VOCs can directly affect atmospheric chemistry by participating in ozone and secondary organic aerosols formation (Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Many organs within the plant can emit VOCs such as leaves, flowers and roots, where they can be accumulated in secretory tissues forming a reservoir or, otherwise, gradually emitted or released under stress conditions (Pe\u0026ntilde;uelas et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the emission amount and the VOC profile show a high amount of variability explained by taxa, physiological state, plant tissue or study effort (Z. Liu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Aboveground VOC emissions have been more widely studied than belowground ones, despite roots often releasing a greater amount and a higher diversity of compounds, such as glucosinolates and terpenoids, than leaves (Bourtsoukidis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Van Dam et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoots are not the sole source of belowground VOCs. These compounds can also be synthesized or transformed by soil microbes (Del Giudice et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) or released abiotically through degradation of organic matter and soil solutions, especially for VOCs such as methanol and terpenes (Gray et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schade \u0026amp; Custer, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Soils can both emit and absorb volatiles (Pe\u0026ntilde;uelas et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) through microbial metabolism, diffusion, adsorption, and passive root uptake, allowing plants to perceive and respond to chemical cues (Jiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Karban et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar to aboveground emissions, belowground VOCs mediate signalling, defence, and regulation of plant-microbe interactions (Delory et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Insam \u0026amp; Seewald, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and influence biogeochemical processes by stimulating microbial respiration and altering nitrogen and methane dynamics (Amaral et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ramirez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Smolander et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Despite their ecological importance, belowground VOCs\u0026rsquo; functions and mechanisms remain poorly understood (Dicke \u0026amp; Baldwin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDomestication has likely reshaped plant biochemical pathways, altering secondary metabolite profiles between crops and wild relatives (Meyer et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Selection for yield and palatability often reduced pest resistance and chemical diversity, including defensive VOCs (Doebley et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Milla et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Whitehead et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, β-caryophyllene root emissions, which attracts entomopathogenic nematodes, are lower in domesticated maize compared to wild relatives (Bernal et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, VOC diversity in maize and cranberry (\u003cem\u003eVaccinium macrocarpon\u003c/em\u003e Aiton) domesticates is reduced under herbivory (Rodriguez-Saona et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rowen \u0026amp; Kaplan, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While leaf VOC changes under domestication are well documented, root VOC dynamics remain largely unexplored despite their role in ecologically important processes such as microbiome assembly, plant defence and signalling. Identifying differences between crops and their wild relatives could reveal new strategies to enhance beneficial soil interactions that may have been altered or lost during domestication. Ultimately, improved understanding of these patterns may contribute to improved crop resilience and fostering food security under current global change scenarios (Preece \u0026amp; Pe\u0026ntilde;uelas, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere we provide, to our knowledge, the first comparative assessment across eight crops and their wild relatives (CWRs or wild relatives, from now on) on how domestication has shaped belowground VOC emissions, uptake (or absorption, henceforth used as synonyms), composition, and diversity. By simultaneously quantifying both VOC exchange and root-associated microbiome diversity, this study tests whether domestication-driven shifts in belowground chemical profiles are linked to microbial assemblage. We hypothesize that domesticated plants - exposed to less heterogeneous environments - emit and absorb fewer and less diverse VOCs than CWRs (H1). Additionally, because VOCs influence soil microbiota, we further expect that VOC profiles correlate with root-associated microbiome diversity, linking chemical composition with microbiome assemblage (H2).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant material and growth conditions\u003c/h2\u003e \u003cp\u003eWe selected eight annual crops representing six different families (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The chosen crops were barley, beet, broad bean, cucumber, lentil, lettuce, sunflower and tomato, each with two levels of domestication: the landrace varieties (domesticated lineage, without modern breeding) and its closest wild relative. Seeds were obtained from the IPK Liebniz-Institut (GBIS, last updated August 13, 2025) and USDA Germplasm Resources Information Network (GRIN, last updated February 24, 2025) seed banks. Seeds were selected from origins close to the known domestication site whenever it was possible to minimize confounding effects of geographic adaptation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrops used in this study, indicating the binomial name for both the domesticated plant and the wild relatives, and its botanic family. Between 1 and 5 different seed accessions were used for each species depending on availability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDomesticated species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWild relative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHordeum vulgare\u003c/em\u003e L. subsp. v\u003cem\u003eulgare\u003c/em\u003e cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHordeum vulgare\u003c/em\u003e L. subsp. \u003cem\u003espontaneum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBeta vulgaris\u003c/em\u003e L. subsp. \u003cem\u003evulgaris\u003c/em\u003e cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBeta maritima\u003c/em\u003e L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmaranthaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBroad bean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVicia faba\u003c/em\u003e L. cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVicia narbonensis\u003c/em\u003e L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCucumis sativus\u003c/em\u003e L. var. s\u003cem\u003eativus\u003c/em\u003e cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCucumis sativus\u003c/em\u003e var. \u003cem\u003ehardwickii\u003c/em\u003e (Royle) Alef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCucurbitaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLens culinaris\u003c/em\u003e Medik. subsp. \u003cem\u003eculinaris\u003c/em\u003e cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLens culinaris\u003c/em\u003e subsp. \u003cem\u003eorientalis\u003c/em\u003e (Boiss.) Ponert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLettuce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLactuca sativa\u003c/em\u003e L. cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLactuca serriola\u003c/em\u003e L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHelianthus annuus\u003c/em\u003e L. cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHelianthus annuus\u003c/em\u003e L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L. cv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSolanum pimpinellifolium\u003c/em\u003e L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolanaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeeds were directly germinated in 7 L pots (20 cm \u0026times; 20 cm \u0026times; 25 cm) filled with a substrate consisting of washed river sand (maximum particle size of 4 mm), compost and field soil following a 5:4:1 proportion, respectively. Each species included two treatments and initally there were ten replicates per treatment. The compost component consisted of a peat-free universal substrate (Geolia Universal Substrate; 70% bark and wood fibre, 20% coconut fibre, 5% perlite, 5% seaweed humus) (Geolia Universal Substrate). The field soil was obtained from a one-year fallowed field at the experimental fields of the Autonomous University of Barcelona. All three components were mixed until homogenous.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was conducted during spring\u0026ndash;summer (from March to August) 2022 in a greenhouse at the Autonomous University of Barcelona (Bellaterra, northeastern Iberian Peninsula). Mean daytime temperature and relative humidity were 31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u0026deg;C and 46.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.4%, respectively, while mean nighttime temperature and relative humidity were 19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u0026deg;C and 81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5%, respectively. Each species was initially represented by ten replicate pots (one plant per pot). Due to aphid and mite infestations during the growing season, final replication varied between two and nine individuals per species (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Pots were evenly distributed across four tables. Within each table, pot positions were randomized, and air temperature and relative humidity were continuously monitored (Lascar EasyLog EL-USB-2 data loggers).\u003c/p\u003e \u003cp\u003eAll pots were irrigated via drip system under identical conditions, with watering volume adjusted to plant growth. Each table also included two bare-soil control pots receiving the same irrigation regime. Soil moisture was measured weekly in all pots using a Delta-T ML3 probe. Mean soil moisture during plant growth was of 14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7%.\u003c/p\u003e \u003cp\u003ePlants were harvested at the onset of fruit or seed production, corresponding to the stage of maximum vegetative development. Harvest time therefore varied among individuals depending on species-specific phenology. At the time of sampling, most plants were reaching full maturity and starting to show the expected signs of leaf senescence typical of annuals. \u003cem\u003eBeta vulgaris\u003c/em\u003e individuals did not reach full reproductive maturity, likely reflecting their facultative biennial growth habit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 VOCs sampling using SPME\u003c/h2\u003e \u003cp\u003eBefore harvesting, VOCs were sampled from a subset of mature plants (n\u0026thinsp;=\u0026thinsp;79) representing all crop species and domestication levels. We used solid phase microextraction (SPME) fibres (100 \u0026micro;m polydimethylsiloxane, Torion Technologies Inc., Utah, USA) directly inserted into the soil to passively adsorb VOCs emitted under natural, non-induced conditions (Tholl et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Before sampling, fibres were preconditioned at 250\u0026ordm;C for 20 minutes under helium flow rate of 100 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and preserved in glass tubes sealed with a Teflon-coated cap. A small hole (~\u0026thinsp;2 mm diameter, 10 cm depth) was made in the soil adjacent to the plant stem using a sterilized stainless-steel rod. A pipette tip was gently inserted to hold the fibre in place, and the SPME fibre was exposed to the soil atmosphere for 20 min. The fibre exposure time was based on pilot tests to avoid saturation. Control samples included bare soil controls (1\u0026ndash;3 per sampling day), air controls (1\u0026ndash;2 per day), and blank fibres that were transported to the field but not exposed, to detect possible contamination. After sampling, SPME fibres were returned to the glass tubes and stored at 4\u0026ordm;C until laboratory analysis.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 GC-MS analysis\u003c/h2\u003e \u003cp\u003eThe VOCs sampled with SPME fibres were analysed using a 7890A gas chromatograph coupled to a 5975C mass spectrometer inert 154 MSD/DS Performance Turbo EI System (Agilent Technologies, Santa Clara, USA) equipped with a HP5MS capillary column (30 m \u0026times; 0.25 mm \u0026times; 0.25 \u0026micro;m; Agilent J\u0026amp;W) using a transfer line at 250 \u0026ordm;C. VOCs were thermally desorbed from the SPME fibres in the injector port at 250\u0026deg;C in splitless mode. Analyses were conducted in full-scan mode, ranging from 35 to 350 m/z. For the chromatographic analysis, helium was used as the carrier gas with a column flow of 1 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Oven temperature was programmed from 35\u0026deg;C (5 min hold) to 150\u0026deg;C at 15\u0026deg;C min⁻\u0026sup1; (5 min hold), then to 250\u0026deg;C at 15\u0026deg;C min⁻\u0026sup1; (3 min hold), and finally to 280\u0026deg;C at 30\u0026deg;C min⁻\u0026sup1; (5 min hold).\u003c/p\u003e \u003cp\u003eChromatograms analyses were conducted using Enhanced ChemStation software (Agilent Technologies). Each peak was assigned a compound using mass spectra contrasted in the NIST 05a and Wiley 275 mass spectral data and PARADISe software. Compounds were identified by comparing mass spectra with the NIST 05a and Wiley 275 libraries using PARADISe software. The criterion to select which compounds were retained is further explained later in the section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e2.6.1\u003c/span\u003e. When available, retention indices were compared with literature values for confirmation (Johnsen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mean blank values were subtracted from all samples to remove background signals. Air control data were available for one sampling date and applied as correction factors across all measurements, since variation among days was negligible. Mean peak areas from bare-soil controls were subtracted from plant samples to isolate VOCs of root and rhizosphere origin.\u003c/p\u003e \u003cp\u003eQuantification was based on calibration curves obtained from pure standard solutions of 3-hexen-1-ol (RT 7.98), α-pinene (9.18), myrcene (10.01), 3-carene (10.29), limonene (10.53), dodecane (12.40), and α-humulene (15.30), prepared in hexane at different dilution factors and analysed under the same conditions as the experimental samples (Llusi\u0026agrave; et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Calibration curves (R\u0026sup2; \u0026gt; 0.95) were used to calculate a response factor (\u003cem\u003eRF\u003c/em\u003e) for each compound class (non-terpenoid, monoterpene, sesquiterpene) and sampling date.\u003c/p\u003e \u003cp\u003eThe VOC quantity (\u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil) for each compound was estimated as described in (1).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Q\\:=\\frac{{S}_{obs}\\times\\:\\:RF}{{S}_{ref}\\times\\:{M}_{root}\\times\\:{V}_{soil}}$$\u003c/div\u003e\u003c/div\u003e(1) \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eSobs\u003c/em\u003e is the observed peak area (a.u.), \u003cem\u003eRF\u003c/em\u003e the response factor (\u0026micro;g), \u003cem\u003eSref\u003c/em\u003e the reference area (a.u.), \u003cem\u003eMroot\u003c/em\u003e the root mass (g), and \u003cem\u003eVsoil\u003c/em\u003e is the soil volume (L). Positive values were interpreted as VOC emission, and negative values as VOC uptake, following correction by air and soil controls.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Root and microbial sampling and sequencing\u003c/h2\u003e \u003cp\u003eAfter harvesting, roots were gently shaken and rinsed with sterile distilled water to remove loosely adhering soil. Root-associated microbial communities were sampled by collecting 5\u0026ndash;10 fine root fragments per individual (\u0026ge;\u0026thinsp;0.25 g fresh weight). Samples were immediately stored at \u0026minus;\u0026thinsp;20\u0026deg;C, ground in liquid nitrogen, and transferred to sterile Eppendorf tubes at \u0026minus;\u0026thinsp;80\u0026deg;C until DNA extraction. Microbial DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen) following the manufacturer\u0026rsquo;s protocol. Extraction blanks were included to monitor potential contamination.\u003c/p\u003e \u003cp\u003eAmplification and sequencing were conducted at the University of Antwerp following Radujković et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The bacterial 16S rRNA gene region was amplified using the 515F\u0026ndash;806R primer pair (Caporaso et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the fungal ITS1 region using the ITS1\u0026ndash;ITS2 primers (Stoeck et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). PCR reactions (25 \u0026micro;L total volume) contained 1 \u0026micro;L of DNA template, 0.5 \u0026micro;M of each primer, 1\u0026times; PCR buffer, 200 \u0026micro;M dNTPs, and 1 U of Phusion High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA, USA). Libraries were prepared and sequenced on an Illumina MiSeq platform (2 \u0026times; 300 bp paired-end).\u003c/p\u003e \u003cp\u003eSequence reads were processed with the DADA2 pipeline to generate amplicon sequence variant (ASV) tables, including quality filtering and chimera removal. Taxonomic assignment of prokaryotic ASVs (composed predominantly of bacteria) was performed against the SILVA database (version 138.1; Quast et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and fungal ASVs were assigned using the UNITE database (release April 2024; K\u0026otilde;ljalg et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Plant biomass measurements\u003c/h2\u003e \u003cp\u003eAfter microbial sampling, plants were separated into leaves, stems, roots, and reproductive structures. Each fraction was oven-dried at 60\u0026deg;C for three days and weighed to determine organ-specific and total dry biomass. These data were subsequently used for correlation analyses with VOC emissions and microbial community composition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 General data treatment\u003c/h2\u003e \u003cp\u003eFor VOC analyses, the dataset included all compounds detected per pot. Only compounds with a spectral match quality\u0026thinsp;\u0026ge;\u0026thinsp;90% (NIST library) were retained. Potential contaminants such as column siloxanes and phthalates were identified based on their occurrence in blanks and known artefact profiles and were excluded. This filtering yielded a final set of 52 compounds out of 310 initially detected (Table S2). For these compounds, additional detections with match quality between 80\u0026ndash;90% were retained when supported by co-detection in other samples and bibliographical evidence of their presence as root VOCs (following standard practice in ecological VOC studies; Tholl et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The resulting dataset was divided into emission and uptake subsets by summing all positive and negative compound values per pot, respectively. Compounds that displayed both positive and negative values across samples were included in both subsets according to their direction per observation.\u003c/p\u003e \u003cp\u003eOne sample (a domesticated cucumber) showing abnormally high total emission values -two orders of magnitude above the dataset mean - was identified as an outlier (exceeding the 99th percentile of total VOC emissions) and excluded from analyses.\u003c/p\u003e \u003cp\u003eAll analyses were performed in R version 4.3.0 (R Core Team, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Model diagnostics were evaluated using the DHARMa package (version 0.4.6; Hartig, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and model selection was based on AIC, adjusted R\u0026sup2;, and statistical significance. Residuals were visually inspected for deviations from model assumptions, and models showing overdispersion or zero-inflation were discarded. Interaction terms that were not significant were removed from the final models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Total emission and uptake concentrations\u003c/h2\u003e \u003cp\u003eTo evaluate differences in total VOC emission and absorption, generalized linear models (GLMs) with a Gamma distribution and log link were used, given the strong right-skewness and heteroscedasticity of the data. Each model included domestication status and crop as fixed effects. Random effects for crop identity and table position were initially tested, but the models did not converge due to a not large enough sample size and were therefore excluded. Model residuals and fitted value relationships were visually inspected to confirm the adequacy of the Gamma distribution. Models were fitted using the glmmTMB() function from the glmmTMB package (version 1.1.7; Brooks et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Adjusted R\u0026sup2; values were computed using the delta method (r.squaredGLMM() in MuMIn, version 1.48.4; Bartoń, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Type II Wald χ\u0026sup2; tests were applied to models without interaction terms, and Type III tests when interactions were present, using the car package (version 3.1-2; Fox et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When significant interactions were detected, pairwise contrasts were performed with the emmeans package (version 1.8.8; Lenth, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePredicted means and 95% confidence intervals on the response scale were obtained using the ggpredict() function from ggeffects (version 2.3.0; L\u0026uuml;decke, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Observed and predicted values were visualized on log-transformed y-axes using ggplot2 (version 3.4.4; Wickham, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with units expressed as \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3 Composition differences\u003c/h2\u003e \u003cp\u003eDifferences in VOC composition between domesticated crops and their wild relatives were explored separately for emissions and absorptions using non-metric multidimensional scaling (NMDS) based on Bray\u0026ndash;Curtis dissimilarities. Ordinations were computed with two dimensions and 1000 random starts using the metaMDS() function in the vegan package (version 2.6-4; Oksanen et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Three pots without detected emissions and one pot without any detected uptake were excluded. No additional data transformation was applied because VOC abundances were already standardized by root mass and soil volume, and preliminary analyses showed consistent ordination patterns with or without transformation. The final stress value was reported to assess ordination quality. Centroids were calculated as the mean NMDS scores for each domestication status within crop type.\u003c/p\u003e \u003cp\u003eTo test for compositional differences, permutational multivariate analyses of variance (PERMANOVA) were performed using the adonis2() function (vegan) with 9999 permutations, including domestication status and crop type as predictors. Homogeneity of multivariate dispersions was assessed using the betadisper() function in vegan based on Bray\u0026ndash;Curtis distances. No significant differences were found among groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the PERMANOVA assumptions were met. Separate analyses were run for emission and absorption datasets. When overall effects were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), pairwise PERMANOVA comparisons were performed with the pairwiseAdonis package (version 0.4; Martinez Arbizu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), applying false discovery rate (FDR) correction for multiple testing.\u003c/p\u003e \u003cp\u003eTo visualize the dominant contributors to observed differences, the five compounds with the highest mean emission or absorption values were identified for each crop type and domestication level. These were represented using bar plots created with ggplot2. The same approach was applied to chemical groups classified by compound type (Table S2). These visualizations were intended to aid interpretation rather than to represent formal statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.4 Changes in VOC diversity\u003c/h2\u003e \u003cp\u003eVOC alpha-diversity per pot was quantified using the Shannon index (H\u0026prime;), based on the relative abundance of detected compounds, calculated with the diversity() function in the vegan package. Shannon diversity was chosen because it integrates both compound richness and evenness, providing a robust representation of chemical complexity. Separate indices were calculated for emitted and absorbed VOC datasets.\u003c/p\u003e \u003cp\u003eHomoscedasticity was assessed using the Fligner\u0026ndash;Killeen test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and model residuals were visually inspected for normality. When necessary, log-transformation of H\u0026prime; values was applied to meet model assumptions. Linear models were fitted with domestication status and crop type as fixed predictors. Random effects for species and table position were initially tested but the models failed to converge due to a not large enough sample size and were therefore excluded. Type II Wald χ\u0026sup2; tests were applied to models without interactions, and Type III tests when interactions were present, using the car package. Pairwise contrasts were performed with the emmeans package when significant interaction effects were detected.\u003c/p\u003e \u003cp\u003eModel predictions and 95% confidence intervals were obtained using the ggpredict() function in ggeffects. Observed and predicted values were plotted in the original diversity scale using ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.6.5 Root microbial diversity relationships with VOC diversity\u003c/h2\u003e \u003cp\u003eThe relationship between root VOC α-diversity (Shannon index) and the α-diversity of root-associated bacterial and fungal communities was evaluated using linear models. Bacterial and fungal Shannon diversity indices were computed with the diversity() function in the vegan package. One pot lacking microbial data due to sampling failure was excluded. Each model used bacterial or fungal diversity as predictor of VOC diversity, following the same procedures as described for VOC α-diversity analyses. Observed data and fitted regression lines with 95% confidence intervals were visualized using ggplot2.\u003c/p\u003e \u003cp\u003eTo explore associations between VOC and microbial community composition, we tested if VOC composition could explain microbial assemblages. For this, microbial ASV tables were rarefied to correct for amplification bias using the rrarefy() function, with depths of 10,000 reads for bacteria and 6,000 for fungi, chosen to retain over 90% of samples while minimizing loss of low-abundance ASVs. Bray\u0026ndash;Curtis dissimilarity was used for both VOC and microbial matrices, as suitable for non-normal and zero-inflated ecological data. Then we performed distance-based redundancy analyses (dbRDA) using the capscale() function in vegan, with microbial community matrix as the response variable and VOC matrix as the explanatory variable. Variable selection for VOC compounds was performed with ordiR2step() (9999 permutations), and model significance and adjusted R\u0026sup2; values were obtained via permutation ANOVA tests.\u003c/p\u003e \u003cp\u003eCorrelations between dissimilarity matrices of VOCs and microbial communities were further assessed using Mantel tests (Bray\u0026ndash;Curtis distance, Spearman correlation, 9999 permutations) implemented in vegan. All analyses were conducted separately for bacterial and fungal datasets. Graphical representations of dbRDA ordinations were generated with ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.6.6 Effect of air temperature and humidity\u003c/h2\u003e \u003cp\u003eTo evaluate potential environmental influences on VOC fluxes, we tested whether total VOC emission and absorption values correlated with ambient air temperature (\u0026deg;C) and relative humidity (%). Mean temperature and humidity values recorded during VOC sampling were used as predictors in linear models, with total VOC emission and uptake concentrations as response variables. Model residual diagnostics (DHARMa adjusted quantile test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated deviations from the expected distribution, suggesting violation of model assumptions, likely due to data heteroscedasticity and skewness. Moreover, no consistent pattern was detected between VOC flux rates and either temperature or humidity, visually or statistically. Consequently, these variables were not considered for further analyses or corrections, as their inclusion did not improve model fit or alter the interpretation of VOC patterns (Figure S2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Total VOC emission and uptake concentrations\u003c/h2\u003e \u003cp\u003eOverall, domestication was associated with a reduction in total root VOC emissions across crop lineages (GLM, χ\u0026sup2; = 6.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), with domesticated plants emitting markedly less than their wild relatives. On average, CWRs emitted nearly three times more VOCs than domesticated plants (0.076 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil vs. 0.025 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Crop type also had a significant effect (GLM, χ\u0026sup2; = 18.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.27), indicating that total emissions differed among crop species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Wild relatives of cucumber, beet, and lettuce exhibited the highest mean emission levels (predicted means\u0026thinsp;=\u0026thinsp;0.16, 0.14, and 0.12 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil, respectively), whereas their domesticated counterparts showed consistently lower values (0.05, 0.05, and 0.04 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil; Table S3). The interaction term was non-significant, suggesting that the domestication effect was generally consistent across crop types.\u003c/p\u003e \u003cp\u003eFor root VOC uptake, a significant interaction between crop type and domestication status was detected (GLM, χ\u0026sup2; = 17.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.53), indicating that the domestication effect on VOC uptake varied among crops. Significant differences between domesticated and wild forms were observed for barley (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), beet (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and tomato (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), all showing greater uptake in wild relatives than in domesticated varieties (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). For example, in tomato, the predicted mean uptake was 0.03 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil for the wild relative compared to 5.12 \u0026times; 10⁻\u0026sup3; \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil for the domesticated variety.\u003c/p\u003e \u003cp\u003eIn contrast, lentil displayed the opposite trend, with domesticated plants showing higher uptake (0.05 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil) than their wild relative (0.01 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil), although this difference was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2011). Among all crops, wild cucumber varieties exhibited the highest predicted uptake values (0.14 \u0026micro;g g⁻\u0026sup1; root L⁻\u0026sup1; soil), followed by broad bean (0.09), beet (0.08), and lettuce (0.07) progenitors (Table S4).\u003c/p\u003e \u003cp\u003eAltogether, domestication affected root VOC uptake in a crop-specific manner, with a general tendency toward reduced uptake in domesticated varieties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 VOC composition differences\u003c/h2\u003e \u003cp\u003eThe NMDS ordination of VOC emissions (2D stress\u0026thinsp;=\u0026thinsp;0.19) showed no clear separation according to domestication status (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.01). In contrast, crop type significantly explained compositional variation (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.20), indicating that VOC profiles could be distinguished based on crop type. Broad bean and tomato displayed particularly distinct profiles, differing significantly from each other (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.23; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003ePatterns for VOC uptake partially differed from those observed for emissions. Clusters of domesticated and wild samples showed partial but non-significant separation (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0535, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.18), indicating only a marginal trend towards differentiation. Crop type again had a strong and significant effect (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.23), with highly distinct profiles between certain species, such as cucumber and broad bean (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028, pseudo-\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.26; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the top five emitted compounds per crop and domestication status revealed pronounced variation among species and treatments (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Members of the Asteraceae family (lettuce and sunflower) displayed particularly similar emission profiles across domesticated and wild forms, dominated by 2-methoxy-[1]benzothieno[2,3-c]quinolin-6(5H)-one and 1,1,2-trifluoro-2,5-bis(trifluoromethyl)-hexane. In contrast, other crops showed divergent profiles between domesticated and wild relatives, although no consistent directionality was observed across species.\u003c/p\u003e \u003cp\u003eUptake profiles showed greater overlap between domesticated and wild plants (Figure S3). For example, three compounds were shared between wild and domesticated cucumber (nonanal, benzothiazole, and salicylaldehyde) and between wild and domesticated broad bean (pentane, hexane, and β-ionone epoxide). Benzothiazole (detected in six of eight crops) and nonanal (five of eight) were among the most frequently absorbed compounds across species.\u003c/p\u003e \u003cp\u003eWhen VOCs were grouped by chemical family, several crop-specific trends in relation to domestication emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lettuce and sunflower (Asteraceae) were dominated by heterocyclic and halogenated compounds in both domesticated and wild forms. Although these compound classes were also abundant in barley, they decreased in the domesticated variety. In lentil, terpenoids were prevalent across both domestication levels, but domesticated plants exhibited a higher relative abundance of aliphatic hydrocarbons. Cucumber wild relatives emitted high levels of terpenoids that were markedly lower in domesticated forms, where heterocyclic compounds became relatively more abundant. In broad bean, reduced total emissions in domesticated plants were primarily driven by lower fatty acid derivatives.\u003c/p\u003e \u003cp\u003eFor uptake, patterns by chemical class were largely consistent across crops and domestication statuses, with a few notable exceptions. Broad bean wild relatives showed higher terpenoid uptake than their domesticated counterparts, which lacked detectable heterocyclic compounds. In lentil, domesticated varieties exhibited a marked increase in the proportion of absorbed halogenated compounds. In tomato, while the relative proportions of compound classes were similar, total VOC uptake was substantially lower in domesticated individuals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diversity of VOC profiles\u003c/h2\u003e \u003cp\u003eThe diversity of emitted VOCs, calculated using the Shannon index, was significantly influenced by both crop type (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and domestication status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in a linear model (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.39). Wild relatives consistently exhibited higher chemical diversity in their emitted VOC profiles (mean H\u0026rsquo; = 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) compared to domesticated varieties (H\u0026rsquo; = 0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1). The interaction between crop type and domestication status was not significant, indicating that the domestication effect was consistent across species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Among individual crops, tomato showed the highest VOC emission diversity overall, with mean H\u0026rsquo; values of 2.22 for wild and 1.87 for domesticated individuals, followed by lettuce (1.34 and 0.99, respectively) and sunflower (1.32 and 0.92; Table S5).\u003c/p\u003e \u003cp\u003eIn contrast, VOC uptake diversity was significantly explained by the interaction between crop type and domestication status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.54; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This indicates that domestication effects on VOC uptake diversity were species-specific. Only barley showed a significant reduction in diversity in domesticated plants compared with their wild relatives (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For the remaining crops, no consistent or significant domestication trend was detected. Despite a significant overall domestication effect in the model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), the strong interaction with crop type underscores the variable impact of domestication across species. The highest mean uptake diversity was observed in domesticated sunflower (mean H\u0026rsquo; = 2.07), followed by domesticated beet (2.03) and cucumber (1.98), while the lowest diversity occurred in the wild lentil (H\u0026rsquo; = 0.51; Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Root microbiota relationship with VOCs\u003c/h2\u003e \u003cp\u003eLinear models linking VOC and microbial α-diversity revealed a significant negative relationship between the Shannon diversity of emitted VOCs and bacterial diversity (β = \u0026minus;\u0026thinsp;0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S3a), whereas no relationship was detected for fungal diversity (β = \u0026minus;\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43; adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.18; Figure S3b). A similar pattern was observed for absorbed VOCs, which showed a negative association with bacterial diversity (β = \u0026minus;\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Figure S3c), but not with fungal diversity (β = \u0026minus;\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09; adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.11; Figure S3d). These results indicate that higher bacterial α-diversity was generally linked to lower VOC diversity, while fungal α-diversity appeared unrelated to root VOC richness.\u003c/p\u003e \u003cp\u003eMultivariate analyses further demonstrated that root VOC composition significantly structured bacterial community composition. A dbRDA based on emitted VOCs explained a large proportion of bacterial compositional variance (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.079, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Mantel test \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The main compounds driving this relationship were tribromo-methane (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), dodecane (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), β-ionone-epoxide (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), 7,8-dimethoxy-1,2,4,5,10a,10b-hexahydrobenzo(de)pyrrolo(3,2,1-ij)quinoline (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and tridecane (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Absorbed VOCs also accounted for significant variation in bacterial composition (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.065, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Mantel \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), mainly explained by β-ionone-epoxide (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and β-bisabolene (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eIn contrast, no meaningful relationship was detected between VOC composition and fungal community structure. dbRDA models explained negligible variance for both emitted (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0, Mantel \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07) and absorbed VOCs (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.026, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Mantel \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Only one compound, β-ionone-epoxide, was retained as a significant predictor in the latter model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), suggesting that root VOCs exerted limited influence on fungal community composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBelowground volatile organic compounds (VOCs) play a central role in mediating plant\u0026ndash;soil interactions, influencing root\u0026ndash;microbe communication, allelopathy, and plant responses to environmental cues. The domestication process, by selecting for traits related to yield and productivity, may have unintentionally reduced plants\u0026rsquo; investment in chemical signalling. In this study, we examined how domestication shaped root VOC emission and uptake across eight major crops and their wild relatives, aiming to determine whether these changes could have altered the chemical interface between roots and their environment.\u003c/p\u003e \u003cp\u003eOur results broadly confirmed this hypothesis. Domesticated plants emitted substantially lower total amounts of VOCs and exhibited reduced chemical diversity of emitted compounds compared with their wild relatives. These patterns suggest that selection under cultivation may have relaxed the need for chemically mediated belowground interactions, as crops increasingly relied on managed environments rather than ecological signalling for resource acquisition or defence.\u003c/p\u003e \u003cp\u003eIn contrast, changes in VOC composition were largely crop-specific, with significant differences among species but no consistent domestication trend. This indicates that, while domestication generally reduced emission quantity and diversity, the qualitative makeup of emitted compounds was shaped by lineage-specific metabolic constraints or selection histories. Similarly, VOC uptake responses to domestication were variable. Some species, such as barley, showed the expected reduction in both total uptake and compound diversity, whereas others displayed no detectable trend or even slight increases. This suggests that VOC uptake may be governed by physiological or structural traits that evolved differently across crop lineages, rather than being uniformly affected by domestication pressures.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Total VOC emission and uptake concentrations\u003c/h2\u003e \u003cp\u003eOur results provide the first evidence of a consistent, cross-species reduction in root VOC emissions associated with domestication, with potential consequences for both biotic and abiotic interactions in the rhizosphere. These findings align with the generalized loss of defences during domestication, as selection for yield trades off with secondary metabolite production Meyer et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Root VOC emission mechanisms remain poorly understood, as roots lack both cuticular barriers and stomatal structures that modulate volatile fluxes aboveground (Bergman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, rhizosphere microbes also contribute substantially to total VOC emissions (Chowdhury et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), further complicating the interpretation of plant versus microbial sources. Thus, the biochemical and ecological processes underlying the observed reductions remain to be fully resolved.\u003c/p\u003e \u003cp\u003eSubstantial differences in emission rates were also observed among the eight studied species, likely reflecting species-specific physiological or morphological traits. Because emission rates were normalized to root mass rather than surface area, variation in root architecture could have influenced apparent emission intensity. Nevertheless, emission rate does not appear to be a phylogenetically conserved trait, as even closely related species within the same genus (e.g., \u003cem\u003eCentaurea\u003c/em\u003e L.) exhibit contrasting sesquiterpene emission profiles (Gfeller et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, patterns of VOC uptake were more variable across crops. Domesticated barley, beet, and tomato showed markedly lower uptake rates than their wild relatives, potentially indicating a loss of signal perception capacity that could reduce defence priming (Brosset \u0026amp; Blande, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Differences in root-associated microbiota may also contribute, since microbial metabolism can serve as a major sink for soil-borne VOCs (Jiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the other five species showed no significant domestication effect on uptake, suggesting that VOC absorption is more resilient or more strongly modulated by microbial or physicochemical soil factors. Discrepancies between uptake and emission patterns suggest domestication disproportionately affects plant-derived VOC synthesis over root and rhizosphere VOC uptake. Furthermore, while aboveground absorption occurs mainly via stomata, the mechanisms enabling VOC uptake by roots remain largely unknown (Matsui, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Future work integrating microbial community composition with plant physiological traits will be crucial to disentangle these processes and assess whether VOC exchange capacity represents an overlooked component of crop functional diversity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Compositional changes in VOC profiles\u003c/h2\u003e \u003cp\u003eThe plants studied exhibited a wide diversity of compounds in their root VOC profiles, both for emission and absorption. Contrary to expectations, no consistent domestication effect was associated with specific compositional changes. Instead, both emitted and absorbed volatile blends differed primarily among crop species and were generally conserved within each crop regardless of domestication status. This pattern indicates that root VOC composition is largely shaped by phylogenetic constraints rather than domestication, suggesting that the metabolic pathways underlying secondary metabolite synthesis were already fixed within each lineage prior to agricultural selection pressures (Thompson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings contrast with the hypothesis that VOC compositional shifts would primarily reflect adaptive responses to the functional and environmental differences between wild and cultivated habitats (Bernal et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The chemical similarity observed between the two Asteraceae species, lettuce and sunflower, further supports a phylogenetic influence. Both taxa, irrespective of domestication status, shared characteristic compounds such as 2-methoxy[1]benzothieno[2,3-c]quinolin-6(5H)-one and 1,1,2-trifluoro-2,5-bis(trifluoromethyl)-hexane. Although little is known about the ecological roles of these heterocyclic and halogenated compounds, their conserved occurrence across the family points to lineage-specific biosynthetic constraints that may override environmental modulation (Courtois et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA notable domestication-related shift was observed in broad bean, where emissions of fatty acid derivatives markedly decreased compared with its wild relative. These compounds, commonly detected in root VOC blends (Pe\u0026ntilde;uelas et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), are linked to 13-lipoxygenase activity and are central to defence-related signalling against both biotic and abiotic stress (Van Dam et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their reduction in domesticated broad bean may therefore indicate a diminished capacity for belowground chemical defence.\u003c/p\u003e \u003cp\u003eAmong absorbed VOCs, two compounds\u0026mdash;nonanal and benzothiazole\u0026mdash;were nearly ubiquitous across all species. Nonanal, a hydrophobic fatty acid\u0026ndash;derived aldehyde, plays a well-established role in plant defence signalling during fungal infection (Brambilla et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), yet it is also emitted constitutively under unstressed conditions (Wildt et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Exposure to nonanal has been shown to trigger bacterial and fungal resistance responses in diverse crops (Gir\u0026oacute;n-Calva et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sharifi et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and has recently been proposed as a sustainable elicitor to enhance yield and seed quality in common bean (Razo-Belm\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, domesticated barley and beet showed reduced nonanal absorption compared with their wild relatives, suggesting a lower capacity to respond to defensive cues and potentially reduced resilience to soil-borne pathogens. Similarly, β-ionone-epoxide\u0026mdash;a defence-related apocarotenoid (Brambilla et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026mdash;was absorbed at lower levels in domesticated broad bean. Conversely, uptake of nonanal remained comparable between wild and domesticated lettuce, sunflower, and cucumber, underscoring the species-specific nature of VOC uptake profiles.\u003c/p\u003e \u003cp\u003eThe dynamics of nonanal in soil further explain part of this variability. Its moderate hydrophobicity (log Kow\u0026thinsp;\u0026asymp;\u0026thinsp;3.5\u0026ndash;3.6) promotes adsorption to soil organic matter and root surfaces, increasing local availability while limiting volatilization (Degtyarenko et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, aldehydes such as nonanal are rapidly metabolized by soil microbes or oxidized to nonanoic acid, and can also react with root exudates (Jiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, both chemical reactivity and microbial degradation likely contribute to species-specific uptake patterns.\u003c/p\u003e \u003cp\u003eBenzothiazole, another widely absorbed compound, is of particular interest because it can originate from both microbial activity and anthropogenic pollution (e.g., tyre additives). It is commonly reported as an antibacterial VOC (Rani et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and has been shown to be absorbed and metabolized by carrot roots (Wu et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Other less abundant absorbed compounds, including 2-ethyl-1-hexanol (Y. Zhang et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and tridecane (Lee et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), also exhibit antipathogenic properties. The widespread presence of these volatiles in agricultural soils suggests that plants may have evolved conserved uptake mechanisms to detect and metabolize stress-related or contaminant VOCs, a capacity that could be adaptive in anthropogenically altered environments (Oikawa \u0026amp; Lerdau, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, domesticated lentil displayed high uptake values for a halogenated compound, 5,5\u0026prime;-dicarboxy-3\u0026prime;-(2-chloroethyl)-4-(2-acetoxyethyl)-3,4\u0026prime;-dimethylpyrromethane, for which no ecological information is currently available. However, legumes such as peanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e) have been shown to absorb and translocate halogenated contaminants (Fan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Increased absorption of this compound in domesticated lentil may be related to changes in root lipid composition or structure (Zhang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), possibly reflecting adaptation to prolonged exposure to agrochemical residues. Such patterns could support the proposed role of domesticated legumes in phytoremediation processes (Liu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a hypothesis that remains underexplored.\u003c/p\u003e \u003cp\u003eOverall, compositional analyses indicate that root VOC chemistry is strongly constrained by phylogeny, with domestication producing limited but functionally relevant modifications in specific compound classes, particularly those linked to defence and pollutant metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Emitted and absorbed VOC diversity\u003c/h2\u003e \u003cp\u003eIn line with total emission trends, the diversity of emitted VOCs was consistently lower in domesticated varieties compared with their wild relatives. This finding indicates that domestication has reduced the chemical complexity of root volatile blends. Interestingly, this pattern contrasts with reports in other systems. For example, Thompson et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found increased diversity of herbivory-induced volatiles in six Cucurbitaceae species following domestication, although they also observed that prolonged coexistence with herbivores reduced root volatile diversity. Similarly, Bernal et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that domestication increased the diversity of maize root VOCs, influencing herbivore preference. These discrepancies suggest that the direction of domestication effects on chemical diversity may depend on the ecological context and plant compartment studied. Aboveground tissues, which are directly exposed to herbivory and pollination pressures, may have experienced selective maintenance or enhancement of chemical diversity, whereas belowground tissues, under the protection of managed soils and reduced biotic stress, may have undergone relaxed selection for secondary metabolite variability.\u003c/p\u003e \u003cp\u003eThe observed skewed relative abundances could result from metabolic canalization, where specific biosynthetic pathways become upregulated or fixed during domestication at the expense of others. This narrowing of chemical profiles may have consequences for ecological interactions in the rhizosphere, potentially reducing the range of microbial or signalling partners that plants can effectively engage with.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Microbial diversity relationship with compound diversity\u003c/h2\u003e \u003cp\u003eVOC alpha diversity, both for emitted and absorbed compounds, was negatively correlated with bacterial alpha diversity but showed no significant relationship with fungal alpha diversity. This suggests that plants hosting more diverse bacterial communities tend to emit and absorb a less chemically diverse set of VOCs. A plausible explanation is that a more diverse bacterial community may be able to metabolize or transform a broader range of volatile compounds, thereby reducing the observable diversity of VOCs in the rhizosphere (Raza et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Competitive use of VOC-derived carbon sources and co-metabolic degradation could also contribute to this pattern. The absence of a similar relationship for fungi might reflect the generally slower metabolic response and lower catabolic diversity of fungal communities toward volatiles, or a lesser role of VOCs as signalling cues in fungal recruitment.\u003c/p\u003e \u003cp\u003eEvidence from other systems supports this interpretation. In wheat (\u003cem\u003eTriticum durum\u003c/em\u003e Deff.), domesticated varieties exhibited reduced VOC diversity but higher root endophytic bacterial diversity, although with lower functional diversity compared to their wild relatives (Deng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that domestication may have promoted more taxonomically diverse but functionally convergent microbial assemblages, potentially linked to simplified VOC emission patterns.\u003c/p\u003e \u003cp\u003eIt is important to note that the use of Shannon diversity indices may mask more specific associations between individual VOCs and microbial taxa. Indeed, several emitted (n\u0026thinsp;=\u0026thinsp;5) and absorbed (n\u0026thinsp;=\u0026thinsp;2) compounds were significantly associated with bacterial community composition. This finding supports the idea that plant-specific metabolic traits influencing VOC synthesis and uptake pathways can shape the establishment of distinct bacterial communities during coevolution (Sharifi et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interplay between VOCs and microbiota is highly reciprocal. Plants can use VOCs to recruit or stabilize specific microbial partners, as shown in tomato, where root-associated bacterial inoculation induced leaf emissions of β-caryophyllene that, in turn, promoted microbiome similarity among neighbouring plants (Kong et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, microbes can modulate plant VOC metabolism, altering plant\u0026ndash;plant and plant\u0026ndash;herbivore interactions (Russo et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Genetic and functional shifts in microbiomes under domestication further illustrate this bidirectional relationship: wild rice relatives harbour microbiomes richer in nitrogen-fixation genes (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while bacterial taxa specific to certain cultivars may alter VOC emission or uptake patterns (Kumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such effects would remain undetected when relying solely on diversity indices, highlighting the need for integrative approaches combining metabolomic, genomic, and network-based analyses.\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that domestication may have decoupled the diversity of root-emitted VOCs from microbial community complexity. This chemical\u0026ndash;microbial imbalance could have consequences for soil communication networks, nutrient cycling, and the resilience of cultivated plants to environmental stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Limitations and future perspectives\u003c/h2\u003e \u003cp\u003eMeasurements of VOCs are highly sensitive to sampling methods. In this exploratory study, we selected direct SPME fibre injection because it enables minimally invasive detection of root emissions and uptake under non-induced stress conditions (Tholl et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, SPME adsorption efficiency is strongly affected by fibre coating chemistry, temperature, and humidity (Fontez et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and soil matrices can substantially reduce VOC recovery by adsorption and diffusion limitations (Voyard et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors may have biased absolute flux estimates, particularly for low-molecular-weight or highly polar compounds, which could partially explain the lower emission values recorded in some species. Standardizing exposure times and using the same fibre type across all samples minimized comparability issues, but complementary quantitative methods such as Proton Transfer Reaction\u0026ndash;Time of Flight\u0026ndash;Mass Spectrometry (PTR-ToF-MS) would provide real-time dynamic measurements to refine flux estimates when combined with SPME (Brennan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough ambient air temperature did not correlate with VOC emissions or uptake concentrations (Figure S2), temperature is known to be a key modulator of VOC synthesis and volatility (Jiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future work should include soil temperature and moisture monitoring to reduce microclimatic bias. Given that VOC responses to temperature often display nonlinear or threshold behaviour, such high-resolution measurements may reveal subtle treatment effects masked in this study.\u003c/p\u003e \u003cp\u003eThe present work compared landraces and their wild relatives, thereby focusing on early domestication effects. However, most modern cultivars have experienced additional selective pressures due to intensive breeding for yield and the chronic use of fertilizers and pesticides (Krug et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Including modern varieties would allow discrimination between initial domestication changes and more recent agroecological adaptations. Wild relatives often display greater resistance to abiotic and biotic stress and harbour valuable genetic diversity for crop improvement (F. Zhang \u0026amp; Batley, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding how domestication has altered belowground chemical and microbial traits could therefore inform breeding programs aimed at restoring lost functional diversity (Preece \u0026amp; Pe\u0026ntilde;uelas, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, belowground VOCs remain poorly integrated into terrestrial carbon and signalling models. Incorporating soil and root VOC fluxes into atmospheric carbon budgets could improve predictions of ecosystem feedbacks, as these compounds represent an overlooked yet potentially significant carbon source (Isidorov \u0026amp; Zaitsev, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, by demonstrating that domestication consistently reduced root VOC diversity and emission fluxes, this study highlights belowground chemical diversity as a potentially eroded functional trait. Moreover, differences among crops underscore the need to understand the crop-specific patterns in belowground traits that may shape distinct interactions and physiological pathways. Further research into how these changes affect microbial recruitment, nutrient cycling, and plant resilience will be essential for developing more sustainable and self-regulating crop systems in the face of global environmental change.\u003c/p\u003e \u003c/div\u003e"},{"header":"Statements and Declarations","content":"\u003ch3\u003e5.1 Funding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the Research Foundation Flanders (FWO) MSCA SoE Fellowship (grant number 42899), a Kleine Projecten BOF UAntwerpen 2022, the European Research Council project ERC-2023-COG-101125455-WILD-ROOTS, a Ramón y Cajal fellowship (RYC2022-037008-I) funded by the Spanish Ministry of Science, Innovation and Universities and the Spanish State Research Agency (AEI) and co-financed by the European Social Fund Plus (FSE+).\u003c/p\u003e\n\u003ch3\u003e5.2 Author contributions (CRediT)\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eJordi Cercós\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Methodology, Formal Analysis, Data Curation, Writing - Original Draft \u003cem\u003eJoan Llusià\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Investigation, Resources, Writing - Review \u0026amp; Editing\u0026nbsp;\u003cem\u003eLaura Márquez\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Methodology, Investigation \u003cem\u003eJosep Peñuelas\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Resources,\u0026nbsp;Writing - Review \u0026amp; Editing\u0026nbsp;\u003cem\u003eErik Verbruggen\u003c/em\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eInvestigation, Resources,\u0026nbsp;Writing - Review \u0026amp; Editing\u0026nbsp;\u003cem\u003eAna María Yáñez-Serrano\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Resources,\u0026nbsp;Writing - Review \u0026amp; Editing\u0026nbsp;\u003cem\u003eCatherine Preece\u003c/em\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing – Original Draft and Review \u0026amp; Editing, Supervision, Project administration. As the study consisted of a greenhouse experiment placed in the home region of most of the authors involved, there was no local data collection.\u003c/p\u003e\n\u003ch3\u003e5.3 Competing interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch3\u003e5.4 Data availability\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas, F., O\u0026rsquo;Neill Rothenberg, D., Zhou, Y., Ke, Y., \u0026amp; Wang, H. C. (2022). Volatile organic compounds as mediators of plant communication and adaptation to climate change. In \u003cem\u003ePhysiologia Plantarum\u003c/em\u003e (Vol. 174, Issue 6). John Wiley and Sons Inc. https://doi.org/10.1111/ppl.13840\u003c/li\u003e\n \u003cli\u003eAlger, I., Dridi, S., Stieglitz, J., \u0026amp; Wilson, M. L. (2023). The evolution of early hominin food production and sharing. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(25), e2218096120. https://doi.org/10.1073/PNAS.2218096120/SUPPL_FILE/PNAS.2218096120.SAPP.PDF\u003c/li\u003e\n \u003cli\u003eAmaral, J. A., Ren, T., \u0026amp; Knowles, R. (1998). Atmospheric methane consumption by forest soils and extracted bacteria at different pH values. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(7), 2397\u0026ndash;2402. https://doi.org/10.1128/AEM.64.7.2397-2402.1998/ASSET/725B0046-8FC5-4E8D-9B42-1DD2F4E3DC1C/ASSETS/GRAPHIC/AM0781843007.JPEG\u003c/li\u003e\n \u003cli\u003eBae, J., Byun, C., Ahn, Y. G., Choi, J. H., Lee, D., \u0026amp; Kang, H. (2019). Effect of elevated atmospheric carbon dioxide on the allelopathic potential of common ragweed. \u003cem\u003eJournal of Ecology and Environment\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(1), 1\u0026ndash;7. https://doi.org/10.1186/S41610-019-0116-5/FIGURES/1\u003c/li\u003e\n \u003cli\u003eBartoń, K. (2024). \u003cem\u003eMuMIn: Multi-Model Inference\u003c/em\u003e. https://CRAN.R-project.org/package=MuMIn\u003c/li\u003e\n \u003cli\u003eBergman, M. E., Huang, X. Q., Baudino, S., Caissard, J. C., \u0026amp; Dudareva, N. (2025). Plant volatile organic compounds: Emission and perception in a changing world. \u003cem\u003eCurrent Opinion in Plant Biology\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e, 102706. https://doi.org/10.1016/J.PBI.2025.102706\u003c/li\u003e\n \u003cli\u003eBernal, J. S., Helms, A. M., Fontes-Puebla, A. A., DeWitt, T. J., Kolomiets, M. V., \u0026amp; Grunseich, J. M. (2023). Root volatile profiles and herbivore preference are mediated by maize domestication, geographic spread, and modern breeding. \u003cem\u003ePlanta\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e(1), 1\u0026ndash;18. https://doi.org/10.1007/S00425-022-04057-0/METRICS\u003c/li\u003e\n \u003cli\u003eBernal, J. S., \u0026amp; Medina, R. F. (2018). Agriculture sows pests: how crop domestication, host shifts, and agricultural intensification can create insect pests from herbivores. \u003cem\u003eCurrent Opinion in Insect Science\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 76\u0026ndash;81. https://doi.org/10.1016/J.COIS.2018.01.008\u003c/li\u003e\n \u003cli\u003eBourtsoukidis, E., Behrendt, T., Ya\u0026ntilde;ez-Serrano, A. M., Hell\u0026eacute;n, H., Diamantopoulos, E., Cat\u0026atilde;o, E., Ashworth, K., Pozzer, A., Quesada, C. A., Martins, D. L., S\u0026aacute;, M., Araujo, A., Brito, J., Artaxo, P., Kesselmeier, J., Lelieveld, J., \u0026amp; Williams, J. (2018). Strong sesquiterpene emissions from Amazonian soils. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). https://doi.org/10.1038/s41467-018-04658-y\u003c/li\u003e\n \u003cli\u003eBrambilla, A., Sommer, A., Ghirardo, A., Wenig, M., Knappe, C., Weber, B., Amesmaier, M., Lenk, M., Schnitzler, J. P., \u0026amp; Vlot, A. C. (2022). Immunity-associated volatile emissions of \u0026beta;-ionone and nonanal propagate defence responses in neighbouring barley plants. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 615\u0026ndash;630. https://doi.org/10.1093/JXB/ERAB520\u003c/li\u003e\n \u003cli\u003eBrennan, D. L., Kollar, L. M., Kiel, S., Deakova, T., Laguerre, A., McDaniel, S. F., Eppley, S. M., Gall, E. T., \u0026amp; Rosenstiel, T. N. (2022). Measuring volatile emissions from moss gametophytes: A review of methodologies and new applications. \u003cem\u003eApplications in Plant Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), e11468. https://doi.org/10.1002/APS3.11468\u003c/li\u003e\n \u003cli\u003eBrooks, M., Bolker, B., Kristensen, K., Maechler, M., Magnusson, A., Skaug, H., Nielsen, A., Berg, C., \u0026amp; van Bentham, K. (2025). \u003cem\u003eglmmTMB: Generalized Linear Mixed Models using Template Model Builder\u003c/em\u003e. https://github.com/glmmTMB/glmmTMB\u003c/li\u003e\n \u003cli\u003eBrosset, A., \u0026amp; Blande, J. D. (2021). Volatile-mediated plant\u0026ndash;plant interactions: volatile organic compounds as modulators of receiver plant defence, growth, and reproduction. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 511. https://doi.org/10.1093/JXB/ERAB487\u003c/li\u003e\n \u003cli\u003eCaporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., Fierer, N., \u0026amp; Knight, R. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(supplement_1), 4516\u0026ndash;4522. https://doi.org/10.1073/PNAS.1000080107\u003c/li\u003e\n \u003cli\u003eChang, J., Costa, O. Y. A., Sun, Y., Wang, J., Tian, L., Shi, S., Wang, E., Ji, L., Wang, C., Pang, Y., Yao, Z., Ye, L., Zhang, J., Chen, H., Cai, Y., Chen, D., Song, Z., Rong, J., Raaijmakers, J. M., \u0026hellip; Kuramae, E. E. (2025). Domesticated rice alters the rhizosphere microbiome, reducing nitrogen fixation and increasing nitrous oxide emissions. \u003cem\u003eNature Communications 2025 16:1\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 1\u0026ndash;14. https://doi.org/10.1038/s41467-025-57213-x\u003c/li\u003e\n \u003cli\u003eChen, Y. H., Gols, R., \u0026amp; Benrey, B. (2015). Crop domestication and its impact on naturally selected trophic interactions. \u003cem\u003eAnnual Review of Entomology\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(Volume 60, 2015), 35\u0026ndash;58. https://doi.org/10.1146/ANNUREV-ENTO-010814-020601/CITE/REFWORKS\u003c/li\u003e\n \u003cli\u003eChowdhury, F. T., Islam, M. R., Islam, M. R., \u0026amp; Khan, H. (2019). Diversity of Plant Endophytic Volatile Organic Compound (VOC) and Their Potential Applications. \u003cem\u003eReference Series in Phytochemistry\u003c/em\u003e, 307\u0026ndash;333. https://doi.org/10.1007/978-3-319-90484-9_10\u003c/li\u003e\n \u003cli\u003eCourtois, E. A., Dexter, K. G., Paine, C. E. T., Stien, D., Engel, J., Baraloto, C., \u0026amp; Chave, J. (2016). Evolutionary patterns of volatile terpene emissions across 202 tropical tree species. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(9), 2854. https://doi.org/10.1002/ECE3.1810\u003c/li\u003e\n \u003cli\u003eCrittenden, A. N., \u0026amp; Schnorr, S. L. (2017). Current views on hunter-gatherer nutrition and the evolution of the human diet. \u003cem\u003eAmerican Journal of Physical Anthropology\u003c/em\u003e, \u003cem\u003e162\u003c/em\u003e, 84\u0026ndash;109. https://doi.org/10.1002/AJPA.23148\u003c/li\u003e\n \u003cli\u003eDegtyarenko, K., De matos, P., Ennis, M., Hastings, J., Zbinden, M., Mcnaught, A., Alc\u0026aacute;ntara, R., Darsow, M., Guedj, M., \u0026amp; Ashburner, M. (2008). ChEBI: a database and ontology for chemical entities of biological interest. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(suppl_1), D344\u0026ndash;D350. https://doi.org/10.1093/NAR/GKM791\u003c/li\u003e\n \u003cli\u003eDel Giudice, L., Massardo, D. R., Pontieri, P., Bertea, C. M., Mombello, D., Carata, E., Tredici, S. M., Tal\u0026agrave;, A., Mucciarelli, M., Groudeva, V. I., De Stefano, M., Vigliotta, G., Maffei, M. E., \u0026amp; Alifano, P. (2008). The microbial community of Vetiver root and its involvement into essential oil biogenesis. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(10), 2824\u0026ndash;2841. https://doi.org/10.1111/J.1462-2920.2008.01703.X\u003c/li\u003e\n \u003cli\u003eDelory, B. M., Delaplace, P., Fauconnier, M. L., \u0026amp; du Jardin, P. (2016). Root-emitted volatile organic compounds: can they mediate belowground plant-plant interactions? \u003cem\u003ePlant and Soil 2016 402:1\u003c/em\u003e, \u003cem\u003e402\u003c/em\u003e(1), 1\u0026ndash;26. https://doi.org/10.1007/S11104-016-2823-3\u003c/li\u003e\n \u003cli\u003eDeng, L., Zhang, A., Wang, A., Zhang, H., Wang, T., Song, W., \u0026amp; Yue, H. (2024). Wheat domestication alters root metabolic functions to drive the assembly of endophytic bacteria. \u003cem\u003ePlant Journal\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(4), 1263\u0026ndash;1277. https://doi.org/10.1111/TPJ.16972;CTYPE:STRING:JOURNAL\u003c/li\u003e\n \u003cli\u003eDiamond, J. (2002). Evolution, consequences and future of plant and animal domestication. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e418\u003c/em\u003e(6898), 700\u0026ndash;707. https://doi.org/10.1038/NATURE01019;KWRD=SCIENCE\u003c/li\u003e\n \u003cli\u003eDicke, M., \u0026amp; Baldwin, I. T. (2010). The evolutionary context for herbivore-induced plant volatiles: beyond the \u0026lsquo;cry for help\u0026rsquo;. \u003cem\u003eTrends in Plant Science\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 167\u0026ndash;175. https://doi.org/10.1016/J.TPLANTS.2009.12.002/ASSET/2867EB56-187E-4A50-833C-41F7C5D70F92/MAIN.ASSETS/GR2.JPG\u003c/li\u003e\n \u003cli\u003eDoebley, J. F., Gaut, B. S., \u0026amp; Smith, B. D. (2006). The Molecular Genetics of Crop Domestication. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(7), 1309\u0026ndash;1321. https://doi.org/10.1016/J.CELL.2006.12.006/ATTACHMENT/70A57389-00B1-4546-9D74-45C55AB36610/MMC1.PDF\u003c/li\u003e\n \u003cli\u003eFan, Y., Chen, S. J., Li, Q. Q., Zeng, Y., Yan, X., \u0026amp; Mai, B. X. (2020). Uptake of halogenated organic compounds (HOCs) into peanut and corn during the whole life cycle grown in an agricultural field. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, \u003cem\u003e263\u003c/em\u003e, 114400. https://doi.org/10.1016/J.ENVPOL.2020.114400\u003c/li\u003e\n \u003cli\u003eFontez, M., Huguenin, J., Jullien, F., Moja, S., Proffit, M., \u0026amp; Nicole, F. (2025). A practical workflow to facilitate analysis of the emission dynamics of non-target BVOCs in terrestrial ecosystems by PTR-TOF-MS. \u003cem\u003eFrontiers in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 1536032. https://doi.org/10.3389/FEVO.2025.1536032/BIBTEX\u003c/li\u003e\n \u003cli\u003eFox, J., Weisberg, S., \u0026amp; Price, B. (2024). \u003cem\u003ecar: Companion to Applied Regression\u003c/em\u003e. https://r-forge.r-project.org/projects/car/\u003c/li\u003e\n \u003cli\u003eFuller, D. Q., Denham, T., \u0026amp; Allaby, R. (2023). Plant domestication and agricultural ecologies. \u003cem\u003eCurrent Biology\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, R636\u0026ndash;R649. https://doi.org/10.1016/j.cub.2023.04.038\u003c/li\u003e\n \u003cli\u003eFuller, D. Q., Willcox, G., \u0026amp; Allaby, R. G. (2011). Cultivation and domestication had multiple origins: Arguments against the core area hypothesis for the origins of agriculture in the Near East. \u003cem\u003eWorld Archaeology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(4), 628\u0026ndash;652. https://doi.org/10.1080/00438243.2011.624747;REQUESTEDJOURNAL:JOURNAL:RWAR20;WGROUP:STRING:PUBLICATION\u003c/li\u003e\n \u003cli\u003eGfeller, V., Huber, M., F\u0026ouml;rster, C., Huang, W., K\u0026ouml;llner, T. G., \u0026amp; Erb, M. (2019). Root volatiles in plant\u0026ndash;plant interactions I: High root sesquiterpene release is associated with increased germination and growth of plant neighbours. \u003cem\u003ePlant, Cell \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(6), 1950\u0026ndash;1963. https://doi.org/10.1111/PCE.13532\u003c/li\u003e\n \u003cli\u003eGir\u0026oacute;n-Calva, P. S., Molina-Torres, J., \u0026amp; Heil, M. (2012). Volatile Dose and Exposure Time Impact Perception in Neighboring Plants. \u003cem\u003eJournal of Chemical Ecology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 226\u0026ndash;228. https://doi.org/10.1007/S10886-012-0072-3/FIGURES/1\u003c/li\u003e\n \u003cli\u003eGowdy, J., \u0026amp; Krall, L. (2014). Agriculture as a major evolutionary transition to human ultrasociality. \u003cem\u003eJournal of Bioeconomics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 179\u0026ndash;202. https://doi.org/10.1007/S10818-013-9156-6/METRICS\u003c/li\u003e\n \u003cli\u003eGray, C. M., Monson, R. K., \u0026amp; Fierer, N. (2010). Emissions of volatile organic compounds during the decomposition of plant litter. \u003cem\u003eJournal of Geophysical Research: Biogeosciences\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(G3), 3015. https://doi.org/10.1029/2010JG001291\u003c/li\u003e\n \u003cli\u003eHartig, F. (2024). \u003cem\u003eDHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) \u0026nbsp;Regression Models\u003c/em\u003e. http://florianhartig.github.io/DHARMa/\u003c/li\u003e\n \u003cli\u003eInsam, H., \u0026amp; Seewald, M. S. A. (2010). Volatile organic compounds (VOCs) in soils. \u003cem\u003eBiology and Fertility of Soils 2010 46:3\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 199\u0026ndash;213. https://doi.org/10.1007/S00374-010-0442-3\u003c/li\u003e\n \u003cli\u003eIsidorov, V. A., \u0026amp; Zaitsev, A. A. (2022). Reviews and syntheses: VOC emissions from soil cover in boreal and temperate natural ecosystems of the Northern Hemisphere. \u003cem\u003eBiogeosciences\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(19), 4715\u0026ndash;4746. https://doi.org/10.5194/BG-19-4715-2022\u003c/li\u003e\n \u003cli\u003eJiao, Y., Kramsh\u0026oslash;j, M., Davie-Martin, C. L., Albers, C. N., \u0026amp; Rinnan, R. (2023). Soil uptake of VOCs exceeds production when VOCs are readily available. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e185\u003c/em\u003e, 109153. https://doi.org/10.1016/J.SOILBIO.2023.109153\u003c/li\u003e\n \u003cli\u003eJohnsen, L. G., Skou, P. B., Khakimov, B., \u0026amp; Bro, R. (2017). Gas chromatography \u0026ndash; mass spectrometry data processing made easy. \u003cem\u003eJournal of Chromatography A\u003c/em\u003e, \u003cem\u003e1503\u003c/em\u003e, 57\u0026ndash;64. https://doi.org/10.1016/J.CHROMA.2017.04.052\u003c/li\u003e\n \u003cli\u003eJones, G., Kluyver, T., Preece, C., Swarbrick, J., Forster, E., Wallace, M., Charles, M., Rees, M., \u0026amp; Osborne, C. P. (2021). The origins of agriculture: Intentions and consequences. \u003cem\u003eJournal of Archaeological Science\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e, 105290. https://doi.org/10.1016/J.JAS.2020.105290\u003c/li\u003e\n \u003cli\u003eKarban, R., Yang, L. H., \u0026amp; Edwards, K. F. (2014). Volatile communication between plants that affects herbivory: A meta-analysis. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 44\u0026ndash;52. https://doi.org/10.1111/ELE.12205;PAGE:STRING:ARTICLE/CHAPTER\u003c/li\u003e\n \u003cli\u003eK\u0026otilde;ljalg, U., Larsson, K. H., Abarenkov, K., Nilsson, R. H., Alexander, I. J., Eberhardt, U., Erland, S., H\u0026oslash;iland, K., Kj\u0026oslash;ller, R., Larsson, E., Pennanen, T., Sen, R., Taylor, A. F. S., Tedersoo, L., Vr\u0026aring;lstad, T., \u0026amp; Ursing, B. M. (2005). UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e166\u003c/em\u003e(3), 1063\u0026ndash;1068. https://doi.org/10.1111/J.1469-8137.2005.01376.X\u003c/li\u003e\n \u003cli\u003eKong, H. G., Song, G. C., Sim, H. J., \u0026amp; Ryu, C. M. (2021). Achieving similar root microbiota composition in neighbouring plants through airborne signalling. \u003cem\u003eThe ISME Journal\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 397\u0026ndash;408. https://doi.org/10.1038/S41396-020-00759-Z\u003c/li\u003e\n \u003cli\u003eKrug, A. S., Drummond, E. B. M., Van Tassel, D. L., \u0026amp; Warschefsky, E. J. (2023). The next era of crop domestication starts now. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(14). https://doi.org/10.1073/pnas.2205769120\u003c/li\u003e\n \u003cli\u003eKumar, A., Kuznetsova, O., Gschwendtner, S., Chen, H., Alonso-Crespo, I. M., Yusuf, M., Schulz, S., Bonkowski, M., Schloter, M., \u0026amp; Temperton, V. M. (2024). Shifts in plant functional trait dynamics in relation to soil microbiome in modern and wild barley. \u003cem\u003ePlants, People, Planet\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(6), 1398\u0026ndash;1412. https://doi.org/10.1002/PPP3.10534\u003c/li\u003e\n \u003cli\u003eLee, B., Farag, M. A., Park, H. B., Kloepper, J. W., Lee, S. H., \u0026amp; Ryu, C. M. (2012). Induced Resistance by a Long-Chain Bacterial Volatile: Elicitation of Plant Systemic Defense by a C13 Volatile Produced by Paenibacillus polymyxa. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(11), e48744. https://doi.org/10.1371/JOURNAL.PONE.0048744\u003c/li\u003e\n \u003cli\u003eLenth, R. V. (2024). \u003cem\u003eemmeans: Estimated Marginal Means, aka Least-Squares Means\u003c/em\u003e. https://rvlenth.github.io/emmeans/\u003c/li\u003e\n \u003cli\u003eLi, Q., Zhu, X., Xie, Y., \u0026amp; Liang, J. (2021). Antifungal properties and mechanisms of three volatile aldehydes (octanal, nonanal and decanal) on Aspergillus flavus. \u003cem\u003eGrain \u0026amp; Oil Science and Technology\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 131\u0026ndash;140. https://doi.org/10.1016/J.GAOST.2021.07.002\u003c/li\u003e\n \u003cli\u003eLiu, H., Ciric, L., \u0026amp; Bhatti, M. (2025). Phytoremediation of organic pollution using leguminous plants and auxiliary additives: principles and advantages. \u003cem\u003eChemical and Biological Technologies in Agriculture\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1\u0026ndash;15. https://doi.org/10.1186/S40538-025-00797-9/FIGURES/4\u003c/li\u003e\n \u003cli\u003eLiu, Z., Wang, M., Wu, M., Li, X., Liu, H., Niu, N., Li, S., \u0026amp; Chen, L. (2023). Volatile organic compounds (VOCs) from plants: From release to detection. \u003cem\u003eTrAC Trends in Analytical Chemistry\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e, 116872. https://doi.org/10.1016/J.TRAC.2022.116872\u003c/li\u003e\n \u003cli\u003eLlusi\u0026agrave;, J., Asensio, D., Sardans, J., Filella, I., Peguero, G., Grau, O., Ogaya, R., Gargallo-Garriga, A., Verryckt, L. T., Van Langenhove, L., Brechet, L. M., Courtois, E., Stahl, C., Janssens, I. A., \u0026amp; Pe\u0026ntilde;uelas, J. (2022). Contrasting nitrogen and phosphorus fertilization effects on soil terpene exchanges in a tropical forest. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e802\u003c/em\u003e, 149769. https://doi.org/10.1016/J.SCITOTENV.2021.149769\u003c/li\u003e\n \u003cli\u003eL\u0026uuml;decke, D. (2024). \u003cem\u003eggeffects: Create Tidy Data Frames of Marginal Effects for ggplot from \u0026nbsp;Model Outputs\u003c/em\u003e. https://strengejacke.github.io/ggeffects/\u003c/li\u003e\n \u003cli\u003eMartinez Arbizu, P. (2017). \u003cem\u003epairwiseAdonis: Pairwise Multilevel Comparison using Adonis\u003c/em\u003e. https://github.com/pmartinezarbizu/pairwiseAdonis\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;n-Robles, N., Lehmann, A., Seco, E., Aroca, R., Rillig, M. C., \u0026amp; Milla, R. (2018). Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e218\u003c/em\u003e(1), 322\u0026ndash;334. https://doi.org/10.1111/NPH.14962\u003c/li\u003e\n \u003cli\u003eMatsui, K. (2016). A portion of plant airborne communication is endorsed by uptake and metabolism of volatile organic compounds. \u003cem\u003eCurrent Opinion in Plant Biology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 24\u0026ndash;30. https://doi.org/10.1016/J.PBI.2016.05.005\u003c/li\u003e\n \u003cli\u003eMeyer, R. S., Duval, A. E., \u0026amp; Jensen, H. R. (2012). Patterns and processes in crop domestication: An historical review and quantitative analysis of 203 global food crops. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e196\u003c/em\u003e(1), 29\u0026ndash;48. https://doi.org/10.1111/J.1469-8137.2012.04253.X;JOURNAL:JOURNAL:14698137;WGROUP:STRING:PUBLICATION\u003c/li\u003e\n \u003cli\u003eMeyer, R. S., \u0026amp; Purugganan, M. D. (2013). Evolution of crop species: Genetics of domestication and diversification. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12), 840\u0026ndash;852. https://doi.org/10.1038/NRG3605;SUBJMETA=208,2491,2492,449,480,631;KWRD=PLANT+DOMESTICATION,PLANT+GENETICS,QUANTITATIVE+TRAIT\u003c/li\u003e\n \u003cli\u003eMilla, R., Osborne, C. P., Turcotte, M. M., \u0026amp; Violle, C. (2015). Plant domestication through an ecological lens. \u003cem\u003eTrends in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(8), 463\u0026ndash;469. https://doi.org/10.1016/J.TREE.2015.06.006/ASSET/C2A09C40-CD5A-4508-8916-40B88ECF4212/MAIN.ASSETS/GR1B2.SML\u003c/li\u003e\n \u003cli\u003eOikawa, P. Y., \u0026amp; Lerdau, M. T. (2013). Catabolism of volatile organic compounds influences plant survival. \u003cem\u003eTrends in Plant Science\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12), 695\u0026ndash;703. https://doi.org/10.1016/J.TPLANTS.2013.08.011/ASSET/C3801A19-3910-4324-8891-E2BC6640EAC8/MAIN.ASSETS/GR3.SML\u003c/li\u003e\n \u003cli\u003eOksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O\u0026rsquo;Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., \u0026hellip; Weedon, J. (2024). \u003cem\u003evegan: Community Ecology Package\u003c/em\u003e. https://github.com/vegandevs/vegan\u003c/li\u003e\n \u003cli\u003ePe\u0026ntilde;uelas, J., Asensio, D., Tholl, D., Wenke, K., Rosenkranz, M., Piechulla, B., \u0026amp; Schnitzler, J. P. (2014). Biogenic volatile emissions from the soil. In \u003cem\u003ePlant, Cell and Environment\u003c/em\u003e (Vol. 37, Issue 8, pp. 1866\u0026ndash;1891). Blackwell Publishing Ltd. https://doi.org/10.1111/pce.12340\u003c/li\u003e\n \u003cli\u003ePreece, C., \u0026amp; Pe\u0026ntilde;uelas, J. (2020). A Return to the Wild: Root Exudates and Food Security. In \u003cem\u003eTrends in Plant Science\u003c/em\u003e (Vol. 25, Issue 1, pp. 14\u0026ndash;21). Elsevier Ltd. https://doi.org/10.1016/j.tplants.2019.09.010\u003c/li\u003e\n \u003cli\u003ePurugganan, M. D. (2022). What is domestication? \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 663\u0026ndash;671. https://doi.org/10.1016/J.TREE.2022.04.006\u003c/li\u003e\n \u003cli\u003eQuast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., \u0026amp; Gl\u0026ouml;ckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(D1), D590\u0026ndash;D596. https://doi.org/10.1093/NAR/GKS1219\u003c/li\u003e\n \u003cli\u003eR Core Team. (2025). \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e. https://www.R-project.org/\u003c/li\u003e\n \u003cli\u003eRadujković, D., Portillo-Estrada, M., Hendrickx, B., Campetella, G., Emsens, W. J., H\u0026ouml;hn, M., Jurasinski, G., Chelli, S., Vochten, T., \u0026amp; Verbruggen, E. (2025). Soil biodiversity and ecosystem functions in grasslands: Is more always better? \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e211\u003c/em\u003e. https://doi.org/10.1016/j.soilbio.2025.109988\u003c/li\u003e\n \u003cli\u003eRamirez, K. S., Lauber, C. L., \u0026amp; Fierer, N. (2010). Microbial consumption and production of volatile organic compounds at the soil-litter interface. \u003cem\u003eBiogeochemistry\u003c/em\u003e, \u003cem\u003e99\u003c/em\u003e(1), 97\u0026ndash;107. https://doi.org/10.1007/S10533-009-9393-X/FIGURES/6\u003c/li\u003e\n \u003cli\u003eRani, A., Rana, A., Dhaka, R. K., Singh, A. P., Chahar, M., Singh, S., Nain, L., Singh, K. P., \u0026amp; Minz, D. (2023). Bacterial volatile organic compounds as biopesticides, growth promoters and plant-defense elicitors: Current understanding and future scope. \u003cem\u003eBiotechnology Advances\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 108078. https://doi.org/10.1016/J.BIOTECHADV.2022.108078\u003c/li\u003e\n \u003cli\u003eRaza, W., Wei, Z., Jousset, A., Shen, Q., \u0026amp; Friman, V.-P. (2021). Extended Plant Metarhizobiome: Understanding Volatile Organic Compound Signaling in Plant-Microbe Metapopulation Networks. \u003cem\u003eMSystems\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4). https://doi.org/10.1128/MSYSTEMS.00849-21\u003c/li\u003e\n \u003cli\u003eRazo-Belm\u0026aacute;n, R., Casta\u0026ntilde;eda-Rodr\u0026iacute;guez, R., Heil, M., \u0026amp; Ozuna, C. (2024). Nonanal, a volatile organic compound, improves the techno-functional, nutritional, and nutraceutical properties of the common bean (Phaseolus vulgaris L.). \u003cem\u003eFood Chemistry Advances\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 100707. https://doi.org/10.1016/J.FOCHA.2024.100707\u003c/li\u003e\n \u003cli\u003eRodriguez-Saona, C., Vorsa, N., Singh, A. P., Johnson-Cicalese, J., Szendrei, Z., Mescher, M. C., \u0026amp; Frost, C. J. (2011). Tracing the history of plant traits under domestication in cranberries: potential consequences on anti-herbivore defences. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(8), 2633\u0026ndash;2644. https://doi.org/10.1093/JXB/ERQ466\u003c/li\u003e\n \u003cli\u003eRowen, E., \u0026amp; Kaplan, I. (2016). Eco-evolutionary factors drive induced plant volatiles: a meta-analysis. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e210\u003c/em\u003e(1), 284\u0026ndash;294. https://doi.org/10.1111/NPH.13804\u003c/li\u003e\n \u003cli\u003eRoy, S. J., Tucker, E. J., \u0026amp; Tester, M. (2011). Genetic analysis of abiotic stress tolerance in crops. \u003cem\u003eCurrent Opinion in Plant Biology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 232\u0026ndash;239. https://doi.org/10.1016/J.PBI.2011.03.002\u003c/li\u003e\n \u003cli\u003eRusso, A., Pollastri, S., Ruocco, M., Monti, M. M., \u0026amp; Loreto, F. (2022). Volatile organic compounds in the interaction between plants and beneficial microorganisms. \u003cem\u003eJournal of Plant Interactions\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 840\u0026ndash;852. https://doi.org/10.1080/17429145.2022.2107243\u003c/li\u003e\n \u003cli\u003eSchade, G. W., \u0026amp; Custer, T. G. (2004). OVOC emissions from agricultural soil in northern Germany during the 2003 European heat wave. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(36), 6105\u0026ndash;6114. https://doi.org/10.1016/J.ATMOSENV.2004.08.017\u003c/li\u003e\n \u003cli\u003eSharifi, R., Jeon, J. S., \u0026amp; Ryu, C. M. (2022). Belowground plant\u0026ndash;microbe communications via volatile compounds. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 463\u0026ndash;486. https://doi.org/10.1093/JXB/ERAB465\u003c/li\u003e\n \u003cli\u003eSmith, B. D. (2007). Niche construction and the behavioral context of plant and animal domestication. \u003cem\u003eEvolutionary Anthropology: Issues, News, and Reviews\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 188\u0026ndash;199. https://doi.org/10.1002/EVAN.20135\u003c/li\u003e\n \u003cli\u003eSmolander, A., Ketola, R. A., Kotiaho, T., Kanerva, S., Suominen, K., \u0026amp; Kitunen, V. (2006). Volatile monoterpenes in soil atmosphere under birch and conifers: Effects on soil N transformations. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(12), 3436\u0026ndash;3442. https://doi.org/10.1016/J.SOILBIO.2006.05.019\u003c/li\u003e\n \u003cli\u003eStoeck, T., Bass, D., Nebel, M., Christen, R., Jones, M. D. M., Breiner, H. W., \u0026amp; Richards, T. A. (2010). Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. \u003cem\u003eMolecular Ecology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(SUPPL. 1), 21\u0026ndash;31. https://doi.org/10.1111/J.1365-294X.2009.04480.X\u003c/li\u003e\n \u003cli\u003eTholl, D., Hossain, O., Weinhold, A., R\u0026ouml;se, U. S. R., \u0026amp; Wei, Q. (2021). Trends and applications in plant volatile sampling and analysis. In \u003cem\u003ePlant Journal\u003c/em\u003e (Vol. 106, Issue 2, pp. 314\u0026ndash;325). Blackwell Publishing Ltd. https://doi.org/10.1111/tpj.15176\u003c/li\u003e\n \u003cli\u003eThompson, M. N., Cohen, Z. P., Merrell, D., \u0026amp; Helms, A. M. (2024). Eco-evolutionary factors contribute to chemodiversity in aboveground and belowground cucurbit herbivore-induced plant volatiles. \u003cem\u003ePlant Biology\u003c/em\u003e. https://doi.org/10.1111/PLB.13709\u003c/li\u003e\n \u003cli\u003eVan Dam, N. M., Weinhold, A., Garbeva, P., Van Dam, N. M., Weinhold, A., \u0026amp; Garbeva, P. (2016). \u003cem\u003eCalling in the Dark: The Role of Volatiles for Communication in the Rhizosphere\u003c/em\u003e. 175\u0026ndash;210. https://doi.org/10.1007/978-3-319-33498-1_8\u003c/li\u003e\n \u003cli\u003eVoyard, A., Ciuraru, R., Lafouge, F., Decuq, C., Fortineau, A., Loubet, B., Staudt, M., \u0026amp; Rees, F. (2024). Emissions of volatile organic compounds from aboveground and belowground parts of rapeseed (Brassica napus L.) and tomato (Solanum lycopersicum L.). \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e955\u003c/em\u003e. https://doi.org/10.1016/j.scitotenv.2024.177081\u003c/li\u003e\n \u003cli\u003eWang, L., Lun, X., Wang, Q., \u0026amp; Wu, J. (2024). Biogenic volatile organic compounds emissions, atmospheric chemistry, and environmental implications: a review. \u003cem\u003eEnvironmental Chemistry Letters 2024 22:6\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(6), 3033\u0026ndash;3058. https://doi.org/10.1007/S10311-024-01785-5\u003c/li\u003e\n \u003cli\u003eWhitehead, S. R., Turcotte, M. M., \u0026amp; Poveda, K. (2016). Domestication impacts on plant-herbivore interactions: a meta-analysis. \u003cem\u003ePhil. Trans. R. Soc\u003c/em\u003e. https://doi.org/10.1098/rstb.2016.0034\u003c/li\u003e\n \u003cli\u003eWickham, H. (2016). \u003cem\u003eggplot2: Elegant Graphics for Data Analysis\u003c/em\u003e. Springer-Verlag New York. https://ggplot2.tidyverse.org\u003c/li\u003e\n \u003cli\u003eWildt, J., Kobel, K., Schuh-Thomas, G., \u0026amp; Heiden, A. C. (2003). Emissions of oxygenated volatile organic compounds from plants part II: Emissions of saturated aldehydes. \u003cem\u003eJournal of Atmospheric Chemistry\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(2), 173\u0026ndash;196. https://doi.org/10.1023/A:1024030821349/METRICS\u003c/li\u003e\n \u003cli\u003eWu, J., Lai, Y., Yang, X., Zhou, Q., Qian, Z., Zhang, A., Sun, J., \u0026amp; Gan, J. (2024). Structure-Dependent uptake and metabolism of Tire additives Benzothiazoles in carrot plant. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e193\u003c/em\u003e, 109075. https://doi.org/10.1016/J.ENVINT.2024.109075\u003c/li\u003e\n \u003cli\u003eYang, K., Llusi\u0026agrave;, J., Preece, C., Tan, Y., Pe\u0026ntilde;uelas, J., Yang, K., Llusi\u0026agrave;, J., Preece, \u0026middot; C, Pe\u0026ntilde;uelas, \u0026middot; J, Llusi\u0026agrave;, \u0026middot; J, Tan, Y., \u0026amp; Guilin, G. (2024). Exchange of volatile organic compounds between the atmosphere and the soil. \u003cem\u003ePlant and Soil 2024 501:1\u003c/em\u003e, \u003cem\u003e501\u003c/em\u003e(1), 509\u0026ndash;535. https://doi.org/10.1007/S11104-024-06524-X\u003c/li\u003e\n \u003cli\u003eZeder, M. A. (2015). Core questions in domestication research. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(11), 3191\u0026ndash;3198. https://doi.org/10.1073/PNAS.1501711112/-/DCSUPPLEMENTAL\u003c/li\u003e\n \u003cli\u003eZeder, M. A. (2016). Domestication as a model system for niche construction theory. \u003cem\u003eEvolutionary Ecology 2015 30:2\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(2), 325\u0026ndash;348. https://doi.org/10.1007/S10682-015-9801-8\u003c/li\u003e\n \u003cli\u003eZHANG, C., FENG, Y., LIU, Y. wang, CHANG, H. qing, LI, Z. jun, \u0026amp; XUE, J. ming. (2017). Uptake and translocation of organic pollutants in plants: A review. \u003cem\u003eJournal of Integrative Agriculture\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(8), 1659\u0026ndash;1668. https://doi.org/10.1016/S2095-3119(16)61590-3\u003c/li\u003e\n \u003cli\u003eZhang, F., \u0026amp; Batley, J. (2020). Exploring the application of wild species for crop improvement in a changing climate. \u003cem\u003eCurrent Opinion in Plant Biology\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e, 218\u0026ndash;222. https://doi.org/10.1016/J.PBI.2019.12.013\u003c/li\u003e\n \u003cli\u003eZhang, Y., Li, Z., Wei, S., Xu, C., Chen, M., Sang, J., Han, Y., Yan, H., Li, Z., Cui, Z., \u0026amp; Ye, X. (2024). Antifungal Activity and Mechanisms of 2-Ethylhexanol, a Volatile Organic Compound Produced by Stenotrophomonas sp. NAU1697, against Fusarium oxysporum f. sp. cucumerinum. \u003cem\u003eJournal of Agricultural and Food Chemistry\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(27), 15213\u0026ndash;15227. https://doi.org/10.1021/ACS.JAFC.3C09851/ASSET/IMAGES/MEDIUM/JF3C09851_0010.GIF\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"crop, domestication, root-associated microbes, root, SPME, VOC","lastPublishedDoi":"10.21203/rs.3.rs-8828517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8828517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlant domestication has been a long coevolutionary process with humans, profoundly shaping plant chemical traits. Secondary metabolites involved in plant interactions, such as volatile organic compounds (VOCs), may have been reduced in domesticates compared with crop wild relatives (CWR), with possible effects on root-associated bacterial communities and crop resilience. However, the effects of domestication in root VOCs and the implications of such changes remain unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a greenhouse experiment with eight crops (barley, beet, fava bean, cucumber, lentil, lettuce, sunflower and tomato), comparing landraces and CWR. Root VOCs were collected with SPME fibres and analysed by GC–MS; root-associated bacterial diversity was also characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDomesticated plants emitted almost three times less total root VOCs and had lower compound diversity than CWR. Effects on VOC uptake were crop-specific (reduction in barley, beet and tomato, but not generalized). Chemical composition was more dependent on crop identity than on domestication status. VOC diversity was negatively related to bacterial diversity, and VOC profiles explained bacterial composition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, domestication has reduced the quantity and diversity of root VOCs, potentially impairing chemical communication and response to soil biota; CWRs emerge as reservoirs of chemical and genetic traits essential for restoring crop resilience and sustainability.\u003c/p\u003e","manuscriptTitle":"Domestication reduces root VOC abundance and diversity in crops with species specific effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 13:18:15","doi":"10.21203/rs.3.rs-8828517/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4a15b84-fcba-4550-bf68-f646d0e9fe03","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T21:17:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 13:18:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8828517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8828517","identity":"rs-8828517","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}