Ecology

Classifying seed dormancy is an essential task for plant propagation; however, several plant species lack information about the kind of dormancy the seeds have or inaccurate reports are passed on without an in-depth investigation. We investigated Copaifera langsdorffii, a widespread tropical species with several contrasting reports about seed dormancy in the literature, particularly on the role of their hard seed coat on germination control. The effect of aril (seed appendage related to the prevention of germination) and dormancy-break treatments on germination were evaluated. Seed coat permeability and the role of seed size and aril on imbibition have been investigated. Seed drying and storage were carried out to investigate a possible acquisition of dormancy. The influence of aril and seed scarification on seedling emergence was also investigated. The hard seed coat has juxtaposed palisade cells, a similar feature found in seeds with physical dormancy (PY). However, intact seeds had high germination (> 70%). Seeds had a slow imbibition pattern but did not prevent it. The aril hastens imbibition, but the seed size did not affect water uptake. Hilar region is the main permeable part of the seed coat, since the dye only enters the seed in this region. Reducing seed water content or storage did not make the seeds water-impermeable. The presence of aril or scarification decreased seedling emergence. Although PY is common in leguminous trees from seasonal tropical areas, it is not present in this species. This non-dormant seed has a main permeable area in the hilar region, which controls imbibition but does not prevent it. Low germination in arillated C. langsdorffii seeds is due to high seed death caused by fungi, not an imposed dormancy. The hard coat controls water imbibition and regulate germination timing in this tropical species.

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IntroductionSeeds can have a blockage to prevent germination, especially for species living in a seasonal climate region, which affects germination timing and the plant life cycle1. The blockage (i.e. seed dormancy) presents distinct structural and physiological features allowing the classification of dormancy into different classes2,3. The current classification system for seed dormancy includes five classes inside two subdivisions (exogenous and endogenous) with subclasses and levels for most kinds of germination blockage2,3. The diversity and complexity of these dormancy classes were updated recently4. However, seed (or dispersal unit) features may hamper seed dormancy classification, as in the case of the stony endocarp in palm diaspores and seeds with a hard and thick coat. Additionally, other seed features, such as the presence of aril, may affect seed germination, impacting an accurate classification.Physiological dormancy (PD) is the most common kind of dormancy in all habitats all over the world; however, in the tropical zone, there is a high percentage of species having seeds with physical dormancy (PY), notably in more seasonal habitats3. In tropical deciduous forests and savannas, the recurrence of PD and PY is similar3. However, the presence of seed dormancy decreases considerably in aseasonal environments (e.g., in lower latitudes as in tropical rainforests) since this germination blockage is not an adaptive trait in habitats with a longer growing season1,3. The other kinds of dormancy, such as morphological (MD), morphophysiological (MPD) and combinational (i.e., PD + PY), are not prevalent in any habitat worldwide3. In Brazilian Cerrado, dormancy in seeds is highly frequent, with PY occurring in several species5,6,7. Leguminous species are widespread in the Cerrado vegetation, and PY is also frequently associated with this plant family1,8,9. Copaifera is a pantropical genus of plants comprising 33 species, wherein 27 species are distributed all over Brazil10,11,12. From this genus, C. langsdorffii Desf. is a widespread tree species living in tropical forests, and it can colonize dissonant environments such as the Amazon rainforest and Cerrado (Brazilian savanna)12. C. langsdorffi produces dark brown/black seeds with a bright yellowish orange aril covering the hard seed coat (Fig. 1A, B). Most of the seed comprises a large embryo with aligned seed structures (lens, hilum and micropyle) in the seed coat (Fig. 1C). These seeds are desiccation tolerant (orthodox)13,14 and in some reports have slow germination (i.e., ≥ 30 d to 50% germination)15, but there is conflicting information in the literature about the presence or absence of dormancy.Fig. 1Morphology of Copaifera langsdorffii seeds. (A, C) General view under a stereomicroscope; note that the orange aril partially covers the dark brown seed coat. (A) Lateral view. (B) Antirapheal view. (C–F) Surface detail under SEM. (C) Hilar region, showing the micropyle, hilum, and lens. (D, E) Fracture lines. (E, F) Pores. (G) Cross-section of the seed coat. ar, aril; arrow, pores; arrowhead, micropyle; asterisk, remaining layers; co, cotyledon; et, exotesta; hi, hilum; le, lens; mt, mesotesta; sc, seed coat. Scale bar (A, B) = 3 mm, (C, D) = 500 μm, (E, G) = 50 μm, (F) = 10 μm.Full size imageSome authors have reported dormancy in C. langsdorffii seeds or the need for pre-germinative treatments to achieve higher germination16,17,18,19,20,21,22,23,24. Additionally, reports in the literature describe C. langsdorffii seeds as possibly having more than one kind of dormancy. PY, PD + PY, ‘chemical dormancy’ or even the generalistic classification “occasional dormancy” have been reported for this species22,23,25. In contrast, other authors described these seeds as non-dormant7,26,27,28. The information that the seed aril interferes in C. langsdorffii germination is also found in the literature. According to Carvalho20 and Souza et al.27, aril removal is required for germination since this pulpy part of seeds has inhibitory substances avoiding germination. Non-dormancy (ND) is prevalent in tropical rainforests, reducing the proportion of ND in tropical savannas3; however, the investigated species inhabits both environments. Plant species can also produce seeds with different levels of dormancy or vary the proportion of dormant and non-dormant seeds to spread germination over time (‘bet-hedging strategy’, see Gremer & Venable29; Pausas et al.30). If this occurs for C. langsdorffii, variation in the proportion of dormant seeds could be related to the inconsistencies in dormancy classification for this species. Thus, C. langsdorffii could serve as a model for seed dormancy studies, particularly on the ecological strategy behind the role of the hard seed coat regulating germination.Seed dormancy classification proposed by Baskin and Baskin2 and recently updated4 makes the comprehension of distinct kinds of seed germination blockage clearer; however, some researchers still do not follow this guide for an accurate dormancy classification. Additionally, some difficulties are found, particularly for seeds (or dispersal units) with a hard coat, or pivotal tests are forgotten, such as the imbibition tests for identifying seed permeability. Permeability tests determine if the coat confers a blockage to the water entrance (i.e., PY) or only a mechanical barrier to radicle protrusion (in this case, conferring PD), making the dormancy classification more assertive31. Thus, we carried out this work aiming to understand the ecological strategy to regulate germination in a widespread tropical species (C. langsdorffii), contributing to the efforts for the precise understanding of the hard coats on germination control. It may also help clarify whether the term “hardseededness” is appropriate to describe physical dormancy or water-impermeable seed coats.MethodsSeed collectionC. langsdorffii seeds were collected in 2020 (2020 C, August-September) during the natural dispersal period in Brazil (Iraí de Minas city, Minas Gerais State – 18º59’ 23” S, 47º28’33” W). The seed aril, when still present, was manually removed. The seeds were kept in plastic boxes and then stored in plastic bags under laboratory conditions until the beginning of the experiments (right after collection). A second collection was carried out in 2021 (2021 C) (at the same local) to obtain arillate seeds (seed + aril). The investigations using the 2021 seedlot started right after collection, aiming to investigate the influence of seed aril on germination.Morphological characterization of seedsFor the external morphology study, seeds of C. langsdorffii were removed from the fruits and photographed using a stereomicroscope (Zeiss Stemi 2000 C, Carl Zeiss Microscopy, Jena, Germany) equipped with a digital camera (Taida TD-HU708A, Shenzhen Sanqiang Taida Optical Instrument, Shenzhen, China). For scanning electron microscopy investigation, de-arillated seeds were mounted directly onto aluminium stubs, coated with gold using a sputter coater (Leica EM SCD050, Leica Microsystems, Wetzlar, Germany), examined under a scanning electron microscope (Zeiss EVO MA 100, Zeiss, Jena, Germany) and the images were digitally recorded. For light microscopy study, de-arillated seeds were imbibed, fixed in FAA 5032, dehydrated in an ascending ethanolic series to ethanol absolute, and embedded in historesin (Historesin, Leica Microsystems, Heidelberg, Germany) following the manufacturer’s instructions. The material was sectioned using a rotary microtome (Leica RM 2235, Leica Biosystems, Nussloch, Germany) into slices approximately 6 μm thick, stained with 0.05% toluidine blue33, modified with acetate buffer (pH 4.7), and mounted in a synthetic mount media (Entellan, Merck, Darmstadt, Germany). The slides were examined and photographed using a microscope (Olympus BX51, Olympus, Southall, UK) with a digital camera (Olympus DP70, Olympus, Southall, UK), and images were digitally recorded. The images were organized into plates using image editing software (Photoshop, Adobe, Redwood City, USA) and some images backgrounds were replaced.Seed dormancy and germination in C. langsdorffiiThe following experiments aimed to investigate the presence/absence of seed dormancy in the species, whether seed aril influences dormancy and germination, and whether seed size affects seed coat permeability (i.e., PY). Germination tests using intact (without aril) and scarified (using sandpaper, on the opposite side to the hilum) seeds were carried out for both seed collections. An additional treatment using intact arillate seeds was carried out for 2021 C. The effects of thermal treatments on seed germination were also investigated (for 2020 C) using immersion in water at 100 °C for 15 s and 80 °C (initial temperature) for 15 minutes8,34. The seeds were then kept in Gerbox© on moistened germination paper using distilled water and incubated at 25 °C and constant light. Germinated seeds were scored at 3-d intervals over 30 days, and the criterion for germination was the protrusion of the radicle. The number of imbibed, intact (without imbibition) and dead seeds were also evaluated. Imbibition leads to a noticeable change in seed color and size, making imbibed seeds easy to detect. The seeds were considered dead when the tissues began to liquefy and/or were surrounded by fungi. These seeds were cut to verify if the embryo was firm and white or deteriorated.Seed coat permeability was also investigated through imbibition tests. Seeds (2020 C) were separated into (1) intact and (2) scarified seeds. For 2021 C, seeds were then separated into three groups: (1) intact seeds without aril, (2) scarified seeds without aril, and (3) intact seeds + aril. Additionally, 2020 C seeds were separated into two groups: (1) large or (2) small seeds. These two groups were selected based on the seed weight using a precision scale (0,0001 g). The weight of large (heavy) seeds was ≥ 0.7 g and for small (light) seeds was ≤ 0.2 g (based on a previous characterization of the seedlot using 200 seeds). Thirty seeds for each group, for both seed collections, were individually weighed and kept in germination conditions at 25 °C under constant light. Seeds were blotted dry before each weighing, which occurred during 240 h. Intact seeds absorbing water indicate the absence of PY.Investigation of water entrance in the seedsTo investigate water entrance through the seed coat, a dye-tracking experiment [based on Jayasuriya et al.35, Gama-Arachchige et al.36,37, Rodrigues-Junior et al.8 was carried out using methylene blue 0.1% [modified from Johansen32. Seeds were immersed in the solution for 12, 24 and 48 h, blotted dry and sectioned longitudinally to observe the presence of dye and its route in the seed tissues. Seeds were analysed under a stereomicroscope (Zeiss Stemi 2000-C), and pictures were taken with a digital camera (Taida TD-HU708A).In a second investigation, seed structures were sealed to determine if the water penetrated the seed in a specific region [based on Jayasuriya et al.35, Turner et al.38, Rodrigues-Junior et al.34. Five treatments were selected for this experiment, using super glue (ethyl cyanoacrylate) to block the following seed structures: (1) lens, (2) micropyle, (3) hilum + micropyle, (4) hilar region (lens + hilum + micropyle), and (5) control (non-blocked seeds). The seeds were kept in lab conditions for 48 h to allow the superglue to dry. Twenty-five seeds of each treatment were individually weighed and then kept in germination conditions at 25 °C. Seed weight was measured again after 1, 2, 4, 6, 8, 10, and 15 days. Variation of seed weight was evaluated individually during all the experimental period.Effect of drying and storage on seed germinationAs drying can induce seed dormancy, the purpose of this experiment was to investigate if seed drying can induce the acquisition of dormancy in C. langsdorffii. Seeds (2020 C) were kept in a closed plastic box (32 × 19 × 9.5 cm, 5 L) containing dry silica gel (826 g) to reach approximately 5% of relative humidity (RH). Temperature and RH inside the drying box were measured continuously during the experiment using a datalogger (AKSO AK174). Seed samples remained in the drying box on Petri dishes (six samples of 45 seeds) for 1, 2, 4, 8, 12 and 16 days. Non-dried seeds were the control in this test. After each sample removal, 20 seeds (four replicates of five seeds) were used for water content determination39 and 25 for imbibition test. For imbibition test, dried seeds were individually weighed and kept in germination conditions for five days before another weighing to investigate if seed drying can induce dormancy (i.e., physical dormancy). For the seeds dried for 8, 12, and 16 days, an additional weighing was carried out after 10 days in germination conditions to evaluate the seed weight.Additionally, intact seeds without aril (2020 C) were stored in laboratory conditions (25 ± 3 °C) for 1, 1.5, 2 and 3.5 years. After storage, the seeds were kept in germination conditions at 25 °C under constant light. Four replicates of 25 seeds were used for each test, and the germination was evaluated at 3-d intervals for 30 days. The results were compared to non-stored (fresh) seeds (control) to investigate seed viability and storage tolerance, as well as a possible induction of seed dormancy during storage.Influence of Aril and scarification on seedling emergenceSeeds (2021 C) were separated into three groups: (1) arillate seeds, (2) seeds without aril, and (3) scarified seeds without aril. The seeds were then buried at a 2 cm-depth in plastic pots containing soil from Cerrado and kept in a covered (60% shade cloth cover) greenhouse with an automated watering system. A clear plastic cover above the experiment was used to avoid seed removal by the rain. The emergence evaluation occurred every week for 60 weeks, and all pots were verified at the end of the experiment to check seed mortality.Statistical analysesThe experimental design for all essays was completely randomized, except for the seedling emergence test, which was designed in randomized blocks. Germination, imbibition and seedling emergence data were analyzed with a generalized linear models (GLMs) (negative binomial), and the means were compared using Tukey’s test using software R40. To analyze the data of blocking experiment, a regression analysis was performed, and the fit of the model evaluated using the coefficient of determination (R2) (P ≤ 0.05) Sigmaplot® software was used to design the graphs (Systat, San José, CA, USA).ResultsMorphological characterization of seedsThe seeds of C. langsdorffii are ellipsoid, with a rigid, dark brown, slightly glossy seed coat (Fig. 1A, B). They are partially covered by an orange aril (a hilar-originated outgrowth) (Fig. 1A, B). The micropyle is punctiform and sometimes covered by remnants of the aril (Fig. 1C). The hilum is linear with remnants of the funiculus and aril (Fig. 1C). The lens is inconspicuous, showing a slight elevation at the base of the raphe (Fig. 1C). The seed coat exhibits fracture lines (Fig. 1D, E) and tiny pores (Fig. 1E, F). The seed coat consists of an exotesta with juxtaposed palisade cells covered by a thin mucilaginous layer (Fig. 1G). The mesotesta comprises three distinct regions. The outermost layer consists of hourglass-shaped cells (Fig. 1G). The median and inner layers are composed of crushed cells (Fig. 1G). The median layer has conspicuous intercellular spaces and slightly thickened walls (Fig. 1G), while the inner layer consists of cells with thinner walls and cytoplasm containing phenolic compounds (Fig. 1G). The remnants of crushed cells can be observed between the seed coat and the embryo, resulting from embryo growth (Fig. 1G).Seed dormancy and germination in C. langsdorffiiFor 2020 C, intact and scarified seeds had the highest germination percentages (P < 0.001; CV = 0.8), 71 and 67%, respectively. Seeds subjected to 80 °C for 15 min also had high germination (Fig. 2A). However, those seeds subjected to 100 °C for 15 s had a strong decrease in germination, reducing to 7% (Fig. 2A). Intact and scarified 2021 C seeds had the average germination of 43 and 9%, respectively, whereas arillate seeds had 23% of germination (Fig. 2B). The percentage of dead seeds increased significantly for the seeds that were subjected to mechanical scarification. All parameters evaluated for the seeds, germinated (P < 0.001; CV = 1.9), imbibed (P < 0.001; CV = 7.1) and dead seeds (P < 0.001; CV = 0.2) differed statistically among the treatments (Fig. 2B).Fig. 2Germination of Copaifera langsdorffii 2020 C seeds subjected to different dormancy-breaking treatments (A). Percentage of germinated (yellow), imbibed (blue), and dead (black) seeds for intact, scarified and arillate C. langsdorffii 2021 C seeds (B). Different letters indicate significant differences (P ≤ 0.05) for each parameter evaluated among the treaments.Full size imageScarified seeds of both 2020 C and 2021 C had a noticeable increase in seed weight right after 3 h of imbibition, exceeding 100% weight increase at the end of 240 h in germination conditions (Fig. 3A, B). In contrast, intact seeds had a slower imbibition, with a weight increase only starting after 48 h in germination conditions (Fig. 3A, B). Despite this initial slow imbibition, intact seeds increased seed weight substantially after 240 h, with 83 and 63% of weight increase for 2020 C (P = 0.002, CV = 0.2) and 2021 C (P < 0.001, CV = 1.0) seeds, respectively (Fig. 3A, B). However, the increase in weight for intact seeds was statistically lower than scarified seeds after 240 h of imbibition.Fig. 3Increase seed weight for Copaifera langsdorffii (mean ± s.e.). Intact and scarified 2020 C seeds (A). Intact, scarified, and arillate 2021 C seeds (B). Large and small intact seeds (2020 C) (C). Different letters indicate significant differences among the treatments (P ≤ 0.05).Full size imageThe presence of aril does not prevent water uptake, but it affects the imbibition (Fig. 3B). A high increase in seed weight at the first hours of imbibition occurred, followed by a constant decrease due to aril degradation. Regarding seed size, large seeds had an average seed weight of 0.76 g, whereas small seeds had 0.29 g. Despite having a similar pattern of weight increase, there was a difference in relation to water absorption after 240 h of imbibition between the seed sizes (P = 0.002, CV = 0.3); small seeds had a 95% weight increase while large seeds had 87% (Fig. 3C).To identify if weight increase occurred in every single intact seed (i.e., not only in the average seed weight), the individual pattern of imbibition for each seed was analysed. Some intact seeds (2020 C and 2021 C) exceeded 100% weight increase after 240 h of imbibition, while most seeds exceeded 50% weight increase. However, few seeds had a little weight increase after 240 h of imbibition. For 2020 C, one seed (from those 25) had only a 2% weight increase, whereas for 2021 C, seven seeds did not surpass a 3% weight increase (Supplementary Data Fig. S1A, B).Investigation of water entrance in the seedsFigure 4 details a longitudinally sectioned C. langsdorffii seed after 48 h of immersion in the methylene blue. The dye penetrated the seed exclusively through the hilar region (Fig. 4A, B). At this time, the embryo was not stained, only the seed coat in the hilar region—particularly under the hilum and micropyle (Fig. 4B).Fig. 4Longitudinal section of a Copaifera langsdorffii seed submerged in methylene blue and observed under a stereomicroscope. (A) General view showing seed coat and embryo. (B) Detail of the hilar region from figure A, note the blue staining in the outer seed coat near the hilum due to dye penetration. arrowhead, micropyle; asterisk, methylene blue-stained tissue; co, cotyledon; ea, embryonic axis; hi, hilum; le, lens; rb, rapheal bundle; sc, seed coat. Scale bar (A) = 1 mm, (B) = 300 μm.Full size imageThe blockage of seed structures did not prevent water absorption by the seed. However, water uptake rates had little difference among treatments (Fig. 5). Seeds with lens or micropyle sealed had higher weight increase than the other seed structures. Seeds had 117% and 114% weight increase when the micropyle or lens were blocked, respectively. However, control seeds (non-blocked seeds) had a 107% increase in weight. Seeds with the hilar region or hilum + micropyle blocked had a lesser increase in weight (Fig. 5).Fig. 5Increase in seed weight for unblocked seeds (control) or those with lens, hilum + micropyle, hilar region (lens + hilum + micropyle) and micropyle blocked (mean ± s.e.).Full size imageEffect of drying and storage on seed germinationThe conditions in the drying box were 4.9 ± 1.8% RH and 23 ± 0.5 °C during the evaluation period. The water content for non-dried seeds was 11.4% and dropped down until day 12 (7.9%), and then stabilized until 16 d of drying (Fig. 6A). The increase in weight after 5 d of imbibition following distinct drying periods is shown in Fig. 6A. Non-dried seeds had 49% of weight increase after 5 d imbibition. The increase in weight reduced along with the extension of drying, attained 28% of weight increase after 16 d of drying (Fig. 6A); however, there was no statistical difference amongst drying periods (P = 0.078, CV = 3.1). Additionally, the increase in weight after 10 d of imbibition has no statistical difference following 8, 12 and 16 d of drying, with an increase of 40, 48 and 55% in seed weight, respectively (P = 0.421, CV = 1.0) (Fig. 6B).Fig. 6Water content and increase in seed weight (after 5 d of imbibition) following drying for different periods (mean ± s.e.) (A). Increase in seed weight during 10 d of imbibition following 8, 12 and 16 days of drying (B). No significant differences among the treatments.Full size imageFreshly harvested seeds and those stored for 1, 1.5, 2 and 3.5 years had the first seeds germinated between the sixth and ninth days, but the germination of stored seeds reduced drastically (Fig. 7). For non-stored seeds, 71% of germination was attained, whereas 57, 42, 18 and 5% of seeds germinated for 1, 1.5, 2 and 3.5 year stored seeds (P < 0.01, CV = 1.1) (Fig. 7A). The decrease in germination percentage was due to the increase in seed mortality in the germination tests. For non-stored seeds, 16% of seed death was attained, increasing to 36, 40, 75 and 81% after seed storage for 1, 1.5, 2 and 3.5 y, respectively (P < 0.001, CV = 1.3) (Fig. 7B).Fig. 7Germination (A) and mortality (B) for freshly collected and stored seeds during 1, 1.5, 2 and 3.5 years (mean ± s.e.). Different letters indicate significant differences among the treatments (P ≤ 0.05).Full size imageInfluence of Aril and scarification on seedling emergenceSeedling emergence started in the second week of evaluation for all treatments. Intact (without aril) and scarified seeds had higher emergence in the first weeks compared to the arillate seeds (Fig. 8). In the subsequent weeks, intact seeds still had a higher emergence percentage, reaching 25% after eight weeks, and then seedling emergence has stabilized. There was no difference between scarified and arillate seeds regarding seedling emergence, with higher seedling emergence for intact seeds (P = 0.001, CV = 1.0). For scarified seeds, the emergence stabilized in the fourth week, reaching 15%, whereas for arillate seeds the emergence continued until the tenth week, but also reached only 15% of seedling emergence (Fig. 8). At the end of 60 weeks, there were no intact or imbibed seeds in the pots, only remains of the seed coat.Fig. 8Seedling emergence from arillate, non-arillate (intact), and scarified (without aril) seeds (mean ± s.e.). The experiment was evaluated until week 60, but with no additional emergence. Different letters indicate significant differences among the treatments (P ≤ 0.05).Full size imageDiscussionSeed dormancy is recurrent in leguminous trees, and plant species living in seasonal tropical areas have a significant probability of producing dormant seeds1,3. Amongst the five seed dormancy classes in Baskin’s classification2,3, PY is frequently reported for trees, especially for leguminous trees41,42, and this kind of dormancy is commonly reported for species in seasonal tropical environments, such as the Cerrado5,6,43. Seeds of C. langsdorffii are quite hard and have slow imbibition (seed weight may not vary after 48 h in germination conditions, or even longer). All information described above seems to lead to a possible presence of dormancy in the seeds. However, our results have confirmed the absence of seed dormancy, exogenous or endogenous, for the studied species. These seeds have a fully developed embryo at the time of dispersal (lacking MD or MPD), and their seed coat is not water-impermeable but regulates imbibition (i.e., does not exhibit PY). High germination in 30 d of evaluation also excludes the low growth potential of the embryo or a possible mechanical restriction to germination (lacking PD).Wet heat treatments are known to be efficient in breaking dormancy in seeds44,45,46. Additionally, similar treatments whose C. langsdorffii seeds were subjected has been reported for other species as effective in breaking PY2,8,34,35,37. Mechanical scarification is one of the most effective treatments to release PY in seeds because it results in removal of a part of the water-impermeable seed coat that prevents germination2,3. However, all treatments described did not increase germination for C. langsdorffii seeds. Thus, these results corroborate our statement that the investigated species does not have dormant seeds, particularly PY. However, a fraction of seeds still do not germinate within 30 days of germination tests (Fig. 2B). As shown in Supplementary Data Fig. S1A and B, certain seeds exhibited restricted water uptake, displaying only a slight increase in weight even after several days under germination conditions. These seeds likely require an extended period to complete germination.Imbibition test is fundamental for classifying seed dormancy – without this test we cannot investigate seed impermeability accurately31. Imbibition tests must be conducted carefully since seed components such as aril or mucilage can absorb water, leading to a misinterpretation of the test9. For C. langsdorffii, intact, arillate or scarified seeds absorbed water during the imbibition test, thus excluding PY. These distinct conditions only affected the water absorption rate (see Fig. 3). The aril speeds up this process, and intact seeds still absorb water, but at a slower rate. Seed scarification also hastens imbibition, indicating that the seed coat controls water uptake; however, this fast imbibition does not mean higher germination percentages (see Figs. 2 and 3). Additionally, there was no acquisition of dormancy following seed drying – PY is only acquired with a reduction of seed water content47 – but only delayed water uptake with the prolongation of drying time (Fig. 6A, B).Distinct types of diaspore tissues impose mechanical resistance and/or impermeability to water in seeds, postponing germination41,48. Hard (or stony) coat as those presented by the palm dispersal unit (seeds + endocarp) provides a strong mechanical resistance to embryo elongation but does not prevent water entrance into the seeds, only controlling imbibition rate49,50,51,52. On the other hand, those of Rhus (Anacardiaceae) and Nelumbo (Nelumbonaceae) species act as a water-impermeable barrier to prevent the start of germination37,53,54. Regarding the germination blockage by water-impermeability coverings (i.e., PY), fruit coat (e.g., pericarp or endocarp) can prevent water absorption by the seeds, as in the plant families Anacardiaceae, Lauraceae and Nelumbonaceae, or (in most cases) the seed coat, as the case of the water-impermeable palisade layer recurrently described for legume plants8,34,37,41,55. Some seeds also have both physical and physiological components of dormancy (PY + PD, combinational dormancy), and in this case, even after breaking PY by scarification, the permeable seeds still do not germinate until releasing PD3. However, the seed coat in C. langsdorffii does not exert enough resistance to hinder germination or prevent water uptake. The juxtaposed palisade layer only limits water entry through most of the seed coat (Fig. 1G), while the hilar region acts as the main permeable zone in the seed, influencing imbibition dynamics (Fig. 4).Rubio-de-Casas et al.1 stated that nondormant seeds evolved in climates with long growing seasons or lineages with larger seeds. C. langsdorffii is a species widespread in the tropics, including the Amazon Forest, an environment with a long suitable condition for seedling growth. Additionally, this species produces large seeds. However, even lacking dormancy, the slow germination (due to the limited water uptake by the hard seed coat) may enhance the establishment success for the species inhabiting contrasting conditions, as the highly seasonal region of Cerrado in Brazil (also known as the Brazilian savanna). Slow water absorption prevents germination out of the growing season caused by a short suitable condition due to occasional rainfall. Then, this species did not stagger germination over time by producing seeds with different levels of dormancy (since they are non-dormant), but rather by controlling water flux into the seeds (see Supplementary Data Fig. S1). Regarding persistence in the soil, the high predation rate could be a serious problem for a large seed with a relatively slow germination. However, the physical defensive trait conferred by the thick/hard seed coat in C. langsdorffii associated with the slow imbibition (avoiding the release of olfactory cues) may reduce predation risk, potentially contributing to seed survival. Thus, seeds of the leguminous C. langsdorffii exhibit physical traits that contribute to protection against predation while remaining non-dormant [see Dalling et al.56 for seed defence]. This permeable seed is protected from predation but ready to germinate when the conditions for seedling establishment are suitable.

Data availability

Data is provided within the manuscript and supplementary material.
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Download referencesAcknowledgementsG.F.P. thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the scholarship. G.S.O. thanks Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) for the scholarship. We thank the staff of the Laboratório Multiusuário de Microscopia Eletrônica of the Faculdade de Engenharia Química (UFU) for assistance in SEM images.FundingThis study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.Author informationAuthors and AffiliationsUniversidade Federal de Uberlândia, Instituto de Biologia, Uberlândia, 38405-302, BrazilG. F. Pereira, M. C. Sanches, O. C. De-Paula & G. S. OliveiraUniversidade Federal do Triângulo Mineiro, Central de Laboratórios, Uberaba, 38025-180, BrazilT. A. A. VazInstituto Federal de São Paulo, São José do Rio Preto, 15030-070, BrazilT. A. A. VazUniversidade Estadual Paulista “Júlio de Mesquita Filho”, Instituto de Biociências, Letras e Ciências Exatas, São José do Rio Preto, 15054-000, BrazilA. G. Rodrigues-JuniorAuthorsG. F. PereiraView author publicationsSearch author on:PubMed Google ScholarM. C. SanchesView author publicationsSearch author on:PubMed Google ScholarO. C. De-PaulaView author publicationsSearch author on:PubMed Google ScholarT. A. A. VazView author publicationsSearch author on:PubMed Google ScholarG. S. OliveiraView author publicationsSearch author on:PubMed Google ScholarA. G. Rodrigues-JuniorView author publicationsSearch author on:PubMed Google ScholarContributionsA.G.R.-J. conceived the work. G.F. Pereira performed the germination and dormancy experiments. O.C. De-Paula and G.S. Oliveira performed the anatomical and structural characterization of seeds. T.A.A.Vaz analysed the data. A.G.R.-J., G.F. Pereira, O.C. De-Paula, T.A.A.Vaz and M.C. Sanches wrote the manuscript. All authors revised and approved the final version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articlePereira, G.F., Sanches, M.C., De-Paula, O.C. et al. Germination control by a hard seed coat: insights from a tropical legume.
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KeywordsGermination controlHard coatSeed dormancyWater uptake More

AbstractSoil microbiomes regulate critical ecosystem functions, yet their relationship with agronomic practices and farmer beliefs remains unclear. Through surveying 85 organic farms, we identified five practices that reshaped soil microbiomes and linked these changes to plant defense functions. Compost and organic pesticide use were associated with decreased levels of two plant defense compounds, jasmonic and salicylic acid, while targeted irrigation, grass cover crops, and no tillage were linked to increased jasmonic acid, through changes in three microbial taxa (Fusarium chlamydosporum; Paenibacillus senegalensis; Microtrichales spp.) and two beta diversity metrics. Structural equation modeling suggested no tillage, pesticide, and compost use were influenced by farmers’ beliefs in the microbiome, while adoption of targeted irrigation and grass cover crops was shaped by abiotic and economic factors. Our work indicates that soil microbiomes and their ecosystem services can be managed through farming practices and highlights sustainable pest management strategies to prioritize for outreach programs.

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IntroductionMicrobial communities are essential for the health of ecosystems and promote numerous beneficial interactions between hosts and the environment1. Thus, the global loss of microbial biodiversity jeopardizes beneficial interactions and the ecosystem functions they support2,3. More diverse microbiomes are generally considered to be more stable and to provide more benefits for hosts and the ecosystem4, however, a deeper understanding of links between microbiome diversity and ecosystem services is still required to harness soil microbiome benefits and promote plant health. For instance, while greater soil microbiome diversity is associated with increased plant defenses and pest suppression5 and functional redundancy in the microbiome mediates the stability of ecosystem services generally6, the specific dimensions of microbiome diversity driving these benefits remain largely unknown and may depend on agroecosystem conditions. This lack of knowledge linking the microbiome with function limits our ability to predict how biodiversity loss will affect ecosystem services and constrains management efforts to steer microbiomes in agroecosystems1,2,3.The function of microbiome biodiversity can be further informed by socio-ecological models. Socio-ecological models recognize that beliefs along with economic, demographic, and farming system characteristics influence ecosystem services7. Organic agriculture is a key example of a socio-ecological system where the dimensions of microbiome biodiversity and associated ecosystem services are influenced by social, political, and economic variables8. For example, organic farmers may adopt different practices to meet the requirements of federal regulations, based on the cost of practice implementation, and the abiotic and economic contexts of their farms each year9. The theory of planned behavior explains that variation in practice adoption is also driven by beliefs, which include the benefits farmers assume to be true about a practice10,11. This suggests the adoption of microbiome supportive management may vary by farmers beliefs in the economic benefits of the microbiome, and beliefs in which practices are microbiome-friendly.Accumulating evidence indicates that a range of practices used within organic farming can increase soil microbial diversity and enhance plant associations with beneficial microbes in the rhizosphere, promoting ecosystem services and plant health when compared to conventional farms12,13. Increased soil microbiome diversity on organic farms has been shown to mediate increased crop plant resilience to insect herbivores via changes in plant chemistry5,14. While general practices that support microbiome diversity are increasingly well understood15, there remains a need to move beyond canonical organic and conventional comparisons towards a holistic understanding of agroecosystems that steer soil microbiomes towards pest suppression. Organic farms present such an opportunity because of variation in their management9 creating a natural experiment to identify farmers who are adopting practices that support soil microbiome biodiversity and functions on-farm, which can be shared with the farming community to enhance sustainable regulation of pest populations across farms.Previously, we surveyed 85 organic farmers across New York (NY) state on their beliefs in the microbiome and farm characteristics9. In this published work, we clustered farmers by their beliefs in the factors mediating the microbiome population on their farm9, a process that classified participants into seven farmer groups (Fig. 1, Supplementary Figs. 1, 2). In this study, we used the same 85 organic farms to test the impact of differences in agronomic practice adoption across farms on soil microbiome diversity and functions using farmer-collected samples. To begin, we conducted soil microbiome metabarcoding with samples from across the 85 farms, and performed lab bioassays with field soil. Next, we used machine learning, which identified five farming practices (no tillage, cover cropping with grasses, pesticide use, composting, and targeted irrigation) that were linked to different aspects of microbiome diversity and plant defense responses. Specifically, we found loss of bacterial groups, replacement of fungal taxa, reduced abundance of Fusarium chlamydosporum, and increased abundance of Microtrichales spp., were all associated with increases in the plant defense compound jasmonic acid (JA), while increased abundance of Paenibacillus senegalensis was linked with decreases in two plant defense compounds, JA and salicylic acid (SA), across organic farms. We then used structural equation models (SEMs) to understand how farmer beliefs and other farm characteristics influence the adoption of these practices and their ecological outcomes. These SEMs revealed practices where adoption was driven by farmer beliefs, yielding insights for future extension approaches to support sustainable, microbiome-based pest management.Fig. 1: Farmer beliefs, soil microbiomes, plant defenses, and pest suppression vary across NY state organic farms.a Surveys and soil samples were collected from farmers across NY state (n = 85). The red region on the continental map of the United States indicates the study region. b Using the surveys farms were clustered by farmer microbiome beliefs. Exemplars are the most representative farm for that cluster, as determined by microbiome beliefs. Due to low sample size, the sixth cluster was excluded from further analysis. Relative abundance of (c) bacterial and d fungal OTUs were found for each soil sample and displayed at the family level. OTUs where the relative abundance was less than 0.35 when summed by family for each cluster are included in the “others” classification. e Jasmonic acid (JA) and f salicylic acid (SA) content in pea plants grown in the microbiome of the exemplar farm for each belief cluster. JA and SA content were determined in undamaged systemic leaves directly above aphid cages eight days after starting the assay. Letters in (e, f) indicate statistical differences (GLM with alpha level = 0.05). Statistics were calculated with log-transformed phytohormone concentrations and plotted in the response scale. Grey points are mean values with red lines indicating the standard error. g The direct relationship between belief clusters and aphid populations on the day of phytohormone collections. Shapes in e–g indicate experimental repetitions (n = 3). Correlations between phytohormones and aphid populations are given in Supplementary Fig. 4. “b” is reprinted from ref. 9 following the Creative Commons CC BY license terms.Full size imageResultsSoil microbial communities vary across organic farms in New York stateFarmers were asked to submit up to two soil samples with distinct suites of practices to ensure broad representations of different practices across NY state 9. We received 136 soil samples with practice use characteristics and 85 completed surveys on microbiome beliefs and general farmer/farm characteristics from across NY state (Fig. 1a). Across the soil samples submitted, 9343 and 6740 unique organizational taxonomic units (OTUs) were identified for bacteria and fungi, respectively. Seven microbiome farmer belief clusters were identified using participants perceptions of 13 statements regarding the factors that mediating the microbiome on their farm in a previous study (Fig. 1b) (see Supplementary Table 1 for cluster details)9. Points oriented on the first and third rotated components (RC1, RC3) (Fig. 1b) indicate farmer perceptions of on- and off-farm factors for influencing their soil microbiome (Supplementary Table 1)9. Bacterial and fungal OTUs varied taxonomically and statistically across belief clusters, and were composed of approximately 260 and 507 unique families, respectively (Fig. 1c, d). Approximately half of bacteria were distributed across three phyla, with 24.79%, 12.85%, and 12.67% of OTUs belonging to Planctomycetota, Proteobacteria, and Chloroflexota, respectively. Whereas, fungi were dominated by two phyla, where 56.86% and 21.25% of OTUs belonged to Ascomycota and Basidiomycota. Next, we conducted Monte Carlo reference-based consensus clustering, principal coordinates analysis (PCoA), and permutational multivariate analysis of variance (PERMANOVA) to understand microbiome structure relationships to belief clusters and other variables (Supplementary Fig. 3). Both farmer belief clusters and Monte Carlos consensus clusters explained a modest amount of variation in the microbiome in the unconstrained ordinations (Supplementary Fig. 3). When fungal and bacterial groups identified through consensus clustering were visualized on a map, they revealed geographic structuring, indicating abiotic factors contributed to regional variation (Supplementary Fig. 3).Microbiome-mediated plant defense induction varies across organic farmsTo evaluate the impact of different soil microbiomes on plant defenses and pest suppression, we grew peas (Pisum sativum) in soil microbiomes extracted from the exemplar farmers’ soil samples for six of the seven microbiome belief clusters (cluster 6 was excluded due to low membership; magenta points; Fig. 1a, b) (Supplementary Table 1)9. Based on the unsupervised clustering approach, exemplar microbiomes used in the bioassays represented diverse fungal and bacteria consensus clusters (Supplementary Fig. 3). Five weeks after planting, pea aphid (Acyrthosiphon pisum) reproduction (progeny/adult) and systemic induction of the plant defense hormones JA and SA were quantified from leaf tissues (Fig. 1e-g). Statistics were calculated with log transformed phytohormone concentrations. Both phytohormone concentrations and reproduction were analyzed using generalized linear models (GLMs) (package = glmmTMB; family = gaussian)16. As expected, JA and SA concentrations in leaf tissues inversely correlated with aphid populations (GLMs JA: Estimate (Est) = −0.507, Standard Error (SE) = 0.236; SA: Est = −0.348; SE = 0.174). The highest JA and SA levels were observed in plants inoculated with the soil microbiome from cluster four, which is the exemplar for farmers who believed external factors (e.g., changes in weather patterns, increases in extreme weather, conventional pesticides applied in bordering lands, amount of natural area in bordering lands) impacted their microbiome (Fig. 1e, f; Supplementary Table 1; see blue points; log values for phytohormone data available in Supplementary Fig. 4). Soil from this farm also produced plants with a lower standard deviation for aphid populations compared to their peers (Fig. 1g; cluster 4).When compared with cluster four, less variable SA levels were observed in plants inoculated with soil extracts from the farms in cluster three or five, which included farmers that believed farm characteristics and management (e.g., compost application, time in organic farming) were the most important factors for microbiome-mediated pest suppression (Fig. 1f; Supplementary Table 1; see green and teal points). Plants inoculated with the soil microbiome from cluster seven, where farmers believed farm characteristics and management were not important for their microbiome, had the lowest JA levels (Fig. 1e; Supplementary Table 1). Soils from these farmers also produced plants with aphid reproduction that was 50% more variable when compared to the farmer cluster with the highest JA levels (Fig. 1e, g; see blue points).Three farming practices reduce microbiome alpha diversity across sitesNext we used machine learning to determine the most important bivariate relationships between nine different aspects of microbiome diversity for both bacterial and fungal sequencing and the 16 farming practices that were widely adopted but also varied considerably across the submitted soil samples (Supplementary Fig. 1; Supplementary Table 2)9. Machine learning suggested the adoption of three practices correlated with decreases in alpha diversity across sites (Fig. 2a). Specifically, preplanting practices (e.g., tarping and solarization), which are used to manage soil pathogens17, were associated with a ≈ 9% decrease in fungal richness (118.26 fewer OTUs) (Fig. 2a). The adoption of mineral fertilizers was associated with a ≈ 24% decrease in Shannon’s diversity for fungi (21.6 fewer OTUs), indicating reductions in common taxa (Fig. 2a). Finally, adoption of no tillage was associated with a ≈ 12% decrease in Simpson’s diversity for bacteria (22.72 fewer OTUs), suggesting fewer dominant taxa, a pattern shown previously by18 (Fig. 2a).Fig. 2: Microbiome diversity measures and taxa correlated with farming practice adoption.a Statistically significant alpha and beta diversity measures that correlated with the adoption or loss of specific management practices across all farms (GLM with alpha level = 0.05). Negative estimates mean the beta diversity measure decreased with practice adoption or loss while positive estimates mean the measure increased with practice adoption or loss. Light blue and yellow shading in (a) indicate diversity measures of the bacterial and fungal microbiome, respectively. b Counts of differentially abundant taxa (OTUs) that significantly correlated with adoption of farming practices for fungi and bacteria. Solid and hatched bars in (b) indicate the number of OTUs that had positive and negative relationships with the adoption of specific practices, respectively. Colors in (b) are grouped by practices and ordered following the legend. Log fold change and statistical significance values for each taxa are found in Supplementary Table 3 and Supplementary Fig. 5.Full size imageIrrigation was linked to bacterial turnover, while diverse practices were linked to fungal turnoverOur machine learning analysis indicated the gain or loss of five practices correlated with changes in different microbiome beta diversity measures across farms (Fig. 2a; diversified cropping, cover cropping with grasses, cover cropping with legumes, compost application, and targeted irrigation). Bacterial beta diversity was primarily regulated by adopting or losing practices related to water management (Fig. 2a). Targeted forms of irrigation (e.g., drip irrigation) were associated with increases in bacterial loss (OTU and phylogenetic), decreases in bacterial replacement (OTU and phylogenetic), and decreases in OTU bacterial balance (Fig. 2a). Loss of only one practice was associated with microbiome turnover measures (Fig. 2a). Here, the abandonment of drip irrigation negatively correlated with phylogenetic bacterial loss. Compost application was the only non-irrigation practice associated with bacterial beta diversity. Here, the adoption of compost resulted in less phylogenetic bacterial replacement (Fig. 2a). In contrast to bacteria, fungal beta diversity measures were associated with more diverse practices (diversified cropping, different cover crops, or compost application) (Fig. 2a). The adoption of legume or grass cover crops was associated with increases in fungal balance and fungal replacement (OTU and phylogenetic) suggesting these practices reassort the numerical distribution of fungal OTUs and shift taxa identity, respectively (Fig. 2a). Conversely, the adoption of compost or crop diversification was associated with less fungal replacement and less fungal loss (OTU and phylogenetic), respectively (Fig. 2a).Organic practices regulated the assemblage of 109 microbial taxa across soil samplesFinally, to determine which taxa may be underlying changes in microbiome diversity, we evaluated bivariate links between farming practice adoption and the abundance of specific microbial OTUs. Overall, twelve practices were identified that correlated with the differential abundance of 109 specific taxa across sites (27 fungal and 82 bacterial OTUs; Fig. 2b; Supplementary Fig. 5; Supplementary Table 3). Adoption of no tillage was associated with 54% of all differentially abundant OTUs (Fig. 2b; Supplementary Table 3; 18 fungal + 41 bacterial = 59/109 total OTUs) and primarily had negative impacts on fungal OTUs (Fig. 2b; Supplementary Table 3; 17/18 negative), and positive impacts on bacterial OTUs (Fig. 2b; Supplementary Table 3). The adoption of plant-based fertilizers and biological mulch was associated with the greatest number of increases in bacterial taxa. In contrast, targeted irrigation and compost applications were associated with the greatest number of decreases in bacterial taxa (Fig. 2b, Supplementary Table 3). Half the number of farming practices were associated with fungal compared to bacterial OTU differential abundance (6 practices fungi; 12 practices bacteria).Cover crops and irrigation were associated with increases in plant defensesTo determine potential functions, we next leveraged only the microbiome changes identified above (Fig. 2), and examined bivariate associations between these nine biodiversity measures and 109 taxa with changes in plant defense compounds (Fig. 1; JA and SA). We used model-based imputation to extend the defense and pest suppression capacity correlations observed for the exemplar farms (Fig. 1e–g) to all farms using cluster classifications and microbiome measures as predictor variables. Because beta diversity was examined across farm pairs, we chose two microbiome turnover measures associated with loss of defense compounds across pairs (Fig. 3a). None of the three alpha diversity measures were associated with concentrations of either defense-related phytohormone, however two beta diversity measures were associated with loss in phytohormones across farm pairs (Fig. 3a). Specifically, increased fungal replacement (OTU and phylogenetic) was associated with decreases in JA and SA loss (i.e., higher levels of JA and SA). Similarly, increased bacterial OTU loss correlated with decreases in JA loss across paired sites (i.e., higher levels of JA; Fig. 3a). Collectively, these results suggest compost applications may reduce plant defense compounds through decreases in fungal replacement (Figs. 2a, 3a), while the adoption of grass cover cropping and targeted irrigation could increase plant defense compounds, via fungal replacement (OTU and phylogenetic) and bacterial loss, respectively (Figs. 2a, 3a).Fig. 3: Microbiome beta diversity measures and three microbial taxa are correlated with changes in plant susceptibility or resistance.Relationships between the microbiome and plant defenses for a beta diversity and b key microbial taxa (GLM with alpha level = 0.05). In (a) beta diversity, values are bound between 0 and 1, with greater values indicating increasing microbiome turnover (dissimilarity) between site pairs. Changes in JA and SA concentrations were found for each pairwise farm comparison (n = 9120) and ranged from positive to negative differences. Positive values indicate decreases in hormones, and negative values represent increases in hormones. In (b) the log corrected abundance for each taxon is correlated with JA and SA concentrations from undamaged systemic leaves collected directly above aphid cages eight days after starting the assay. Statistics displayed were calculated with log-transformed phytohormone concentrations for exemplars. Dashed red lines are empirical values generated through laboratory assays using exemplars, while solid red lines are model-based predictions for all sites (n = 85 farms).Full size imageNo tillage adoption and reduced pesticide use is associated with increases in plant defenseAmong the 109 OTUs evaluated, bivariate correlations were established between three taxa and changes in total plant defense concentrations. Of these taxa, Fusarium chlamydosporum (OTU 5), a known fungal plant pathogen, had a negative association with JA levels, while a bacterial species, Microtrichales spp. (OTU 1139), was positively associated with JA levels (Fig. 3b). In our model no tillage was associated primarily with decreases in fungal taxa, and increases in bacterial taxa, including F. chlamydosporum and Microtrichales spp. (Fig. 2b, Supplementary Table 3). These results suggest that the adoption of no tillage could be used to suppress F. chlamydosporum and promote Microtrichales spp., which should increase plant defenses. The third taxa identified, Paenibacillus senegalensis (OTU 4736), had a negative association with both SA and JA (Fig. 3b). Combined with our model results for practices, this finding suggests decreasing P. senegalensis populations through reduced soil pesticide use should increase plant defense compounds (Fig. 2b; Supplementary Table 3; Supplementary Fig. 5). Overall, our results indicate that two organic farming practices (no tillage and pesticides) alter specific soil microbes that are associated with changes in plant defenses.Farmer microbiome beliefs indirectly promote pest susceptibility and suppression within farmsUsing only the statistically important bivariate correlations leading to changes in plant defenses, we finally constructed SEMs to test the indirect impacts of farmer microbiome beliefs on microbiome-mediated pest suppression, and conditioned these models based on economic (farming as the main income source) and abiotic (time in organic and soil properties) characteristics9,10. Two separate SEMs were constructed for practices associated with beta diversity (targeted irrigation, grass cover crops, and compost) and specific taxa (no tillage and pesticides). For the beta diversity SEM, we examined loss of microbiome beliefs, time in organic, JA, SA, and progeny across farm pairs, while totals within farms were used for the specific taxa SEM. Both SEMs suggested microbiome-mediated pest suppression is indirectly correlated with farmer beliefs in the microbiome (Fig. 4a, b; Supplementary Tables 4, 5).Fig. 4: Farmer beliefs have positive and negative impacts on pest suppression mediated by practice adoption and the microbiome.a Beta diversity and b specific taxa structural equation models. The statistically important indirect mediation pathways are labeled and highlighted in yellow. P-values found with maximum likelihood estimates are displayed (<0.05*, < 0.01**, < 0.001***). Dashed ovals are single indicator latent variable predictors. Each latent variable was measured empirically (e.g., RC1 + RC3 for beliefs). Path coefficients are given to two significant digits and placed nearest the predictor variable. For a predictor values have been differenced for time in organic management and percent sand in soil, and farm income has been reclassified. OTU information for b is as follows: F. chlamydosporum (OTU 5); Microtrichales spp. (OTU 1139); P. senegalensis (OTU 4736). In a, b values for jasmonic and salicylic acid are the loss in concentration across farms and total concentrations, respectively. Additional statistics are in Supplementary Tables 4, 5.Full size imageIn the beta diversity SEM, loss of beliefs in the microbiome mediated loss of pest progeny indirectly, and this was primarily mediated through decreased adoption of compost by farmers in clusters one, two, and seven compared to their counterparts, which increased fungal replacement, increased JA, and decreased pest population (Figs. 1b, 4a; Supplementary Table 4). However, the adoption of compost applications was increased on farms serving as the main source of income, which may decrease JA and increase pest populations (Fig. 4a, Supplementary Table 4). To a lesser extent, loss of beliefs in the microbiome drove increased progeny loss indirectly through increased adoption of targeted irrigation by farmers in clusters one, two, and seven (Fig. 4a; Supplementary Table 4). The primary direct driver of the adoption of targeted irrigation in the SEM was sandiness of the soil, however, and not beliefs in the microbiome (Fig. 4a). Similarly, the adoption of grass cover crops was primarily mediated through negative associations with farming as the main source of household income and decreases in the amount of time in organic management across farm pairs, and not farmer microbiome beliefs (Fig. 4a; Supplementary Table 4). Therefore, the less time farms were in organic production or the greater dependence on farming for income, the less likely farmers were to adopt grass cover crops, which increased fungal replacement (OTU and phylogenetic), promoted SA and JA, and reduced pests (Fig. 4a; Supplementary Table 4). Taken together, this suggests adoption of some microbiome-friendly practices can promote pests and are influenced more by farmer beliefs (composting), while others are influenced more by economic and abiotic factors (e.g., targeted irrigation).In the specific taxa SEM, indirect impacts of microbiome beliefs were mediated through increased adoption of no tillage by farmers in clusters three, four and five, which promoted Microtrichales spp. and suppressed F. chlamydosporum abundances, increasing JA, and decreasing aphid populations (Fig. 4b; Supplementary Table 5). Overall, beliefs in the microbiome had the strongest direct influence on adoption of no tillage, however the longer time the farm was in organic production and the greater the amount of primary income arising from farming, the less likely farmers were to adopt no tillage, despite indirect benefits for the soil microbiome (Fig. 4b; Supplementary Table 5). Farmers who believed more factors impacted their microbiome were also less likely to use organic pesticides, which should decrease Paenibacillus senegalensis abundance (Fig. 4b; Supplementary Table 5). As Paenibacillus senegalensis had a negative association with JA concentrations, reductions in the bacteria should increase JA, and decrease pest populations (Fig. 4b; Supplementary Table 5). However, this indirect relationship was not a major driver of pest populations. In summary, the adoption of microbiome-supportive practices (no tillage) could be limited by farm characteristics and economics, while beliefs may partially counteract these patterns (Fig. 4a, b; Supplementary Tables 4, 5).DiscussionViewing farms as complex socio-ecological systems presents an underexplored opportunity to promote the adoption of practices that enhance sustainable pest management. As the system of relationships between farming practice, soil microbiomes, and pest management becomes increasingly well understood15, farmer beliefs provide a mechanism to shift adoption patterns and enhance agroecosystem sustainability9,10. Our research identified organic farming practices that shift soil microbiomes across farming systems and linked these microbiome changes with plant defenses and pest suppression. While previous research identified the benefit of organic agriculture for microbiome conservation and the regulation of pests (See refs. 5,14 among others), our study is the first linking variation in practices across organic farms to functional soil microbiome shifts. Taken more broadly, we show that agroecosystem sustainability hinges not only on production practices per se, but on farmer beliefs and the contextualizing abiotic and economic characteristics which drive farmer decision making.In our study, > 90% of the practices we evaluated mediated changes in the microbiome (alpha diversity, beta diversity, and species identity). Nevertheless, only the adoption of no tillage, targeted irrigation, and grass cover crops was associated with changes in plant defenses and pest populations through changes in the microbiome. Our results add enhanced plant defense and reduced pest populations to the known benefits of no tillage19, however, farmers were less likely to use no tillage the longer the farm was in organic management or if the farm was the main source of income (Fig. 4b; Supplementary Table 5). Bloom et al.9 also found that adoption of no-tillage decreased with farm size, which may indicate persistent barriers for scaling up no-tillage in organic systems, including difficulties with weed suppression and the economics of new equipment (e.g., interrow mowers) needed by farmers at larger scales20. Simultaneously, our findings indicated that no tillage reduced the dominance of bacterial alpha diversity and the abundance of a plant pathogen, F. chlamydosporum21. Previous evidence suggests no tillage reduces soil pathogens when combined with crop rotation22 and reduces bacterial diversity ostensibly via a lack of soil disturbance and breakdown of crop residues on soil surfaces23. Outreach efforts to better inform farmers of the full suite of biological no tillage benefits, such as microbiome-mediated pest suppression, could be an effective strategy to increase adoption, as beliefs in the microbiome were a primary driver of the adoption of no tillage in our SEM (Fig. 4b, Supplementary Table 5).Less documented are the benefits of targeted forms of irrigation for beneficial soil microbiome management. Overall, our findings suggest that the adoption of targeted forms of irrigation (e.g., drip systems) are key regulators of bacterial beta diversity. Evidence suggests drip irrigation can promote plant growth24,25 and reduce the spread of plant pathogens through the regulation of soil moisture and temperature26. Taken with our results, targeted irrigation may have other benefits for crops, such as enhanced plant defenses that are mediated through changes in bacterial populations (Figs. 2a, 3a). We suggest the adoption of targeted irrigation benefits plant defense through the loss of antagonistic and plant pathogenic bacteria from the rhizosphere, though these mechanisms need further validation. While the literature regarding the benefits of compost applications is extensive (See ref. 27 among others), the adoption of compost in our study was linked to decreases in plant defenses through the stabilization of fungal microbiomes (Figs. 2a, 3a), suggesting relationships between compost applications, plant health, and ecosystem services are nuanced. Indeed, attention to source inputs, temperature, and water are critical to destroy pathogens in compost during production27. Working with farmers to identify approaches that minimize fungal replacement after compost use will be a critical next steps for optimizing microbiome-mediated ecosystem services across farms.Our differential abundance analysis suggests some practices have contrasting impacts on bacterial and fungal OTUs (e.g., no tillage), while other practices primarily impact bacterial OTUs in both positive (vegetable fertilizer, mineral fertilizer, and biological mulch) and negative (compost and animal manure) ways. Several of these microbiome taxa were also identified as potential plant defense modulators. Specifically, our analysis suggests an abundance of F. chlamydosporum, a wilt-causing root pathogen21, correlates with reduced JA levels. Although additional studies are needed, Fusarium sp. may be preventing beneficial defense-promoting root-microbe interactions28 or this may represent a plant trade-off, where JA is being reallocated to belowground defenses during pathogen infection29, leaving above-ground structures vulnerable to pests. While F. chlamydosporum was associated with decreased plant defenses, Microtrichales spp. correlated with the upregulation of JA. Members of the Microtrichales are linked to phosphate solubilization30,31, and phosphate is key for JA regulation32. Microtrichales may indirectly regulate JA by promoting changes in soil nutrient cycling and phosphate availability for JA biosynthesis; however, additional experiments are required to dissect this. More broadly, only ≈ 0.4% and ≈ 0.9% of OTUs were associated with practice adoption for fungi and bacteria, respectively, suggesting processes mediating ≈ 99% of the OTUs in our study remain unexplained.Our findings indicate that not all measures of microbiome diversity are indicators of ecosystem services. Here, we show alpha diversity was not robustly correlated with farming practices or a primary indicator for microbe-mediated pest suppression, but instead, beta diversity offered better insights into microbiome-mediated ecosystem services in our system. While evidence suggests microbiome beta diversity is common, associated changes in function are rarer33, potentially due to functional redundancy in microbe turnover6. Indeed, our results indicate that fungal taxa may be replaced with functionally redundant or phylogenetically related taxa, because less fungal replacement were related to decreases in JA concentrations and increased pest populations (Figs. 3a, 4a, Supplementary Fig. 4). Future research and development may focus on taxa identity, cultivating beneficial microbes, and avoiding pathogen accumulation, over general increases in microbiome biodiversity which is typically a weak predictor of ecosystem function34. In more practical terms, our findings suggest key taxa could serve as indicators of plant defenses and pest suppression for rapid microbiome diagnostics within farms, and the practices we identified could be used in decision support tools paired with sequencing, allowing farmers to rapidly modify their microbiome.Additional studies are still needed to decrypt the role of microbiomes in pest suppression and ecosystem services. Namely, higher throughput approaches are needed to phenotype the defense-including capacities of diverse agricultural soils while reducing methodological bias. Our study, among others5,35,36 leverages inoculations of sterilized potting soil as the plant growth media. While this approach is standard for predicting microbiome function under field conditions35, the established microbiomes in potting soil are not completely equal to the field microbiomes they originate from36. Further, these methods take a large amount of work, limiting the number of soil microbiomes that could be evaluated. In the current study, we addressed throughput limitations by selecting one exemplar soil per belief cluster for microbiome functional analysis in the lab, and then used this data to predict the plant defense-inducing capacity of other soil microbiomes using multivariate imputation by chained equations37. Therefore, more robust validation of the links between belief clusters, microbiome diversity and functions in plant defense is still needed using additional clusters or field trials38. While advances in sequencing have made microbiome characterization rapidly accessible39, linking these microbiomes to plant function across farming systems without introducing bias presents an ongoing challenge for researchers and decision makers.To our knowledge, our research is the first to view microbiome-mediated insect pest suppression through a socio-ecological lens by linking it with farmer beliefs and adoption. We show that farmer microbiome beliefs have cascading indirect ecological consequences for pest suppression (Fig. 4a, b; Supplementary Tables 4, 5). Our model suggests that customized messaging to farmers with different characteristics and beliefs will be useful in promoting the adoption of practices that support the microbiome40. For example, recent work by Bloom et al.9 indicated only 42% of farmers in the present study have beliefs consistent with the microbiome literature, suggesting most organic farmers would benefit from extension efforts, on-farm experimentation, and farmer-focused science that reconciles discrepancies between beliefs and ecosystem services. Our findings also indicate that time in organic management and farmer income mediate practice adoption, presenting targets for farm policy instruments (e.g., the Environmental Quality Incentives Program). Policymakers can use this knowledge to incentivize the adoption of microbiome-friendly practices through subsidies that support initial investments associated with new practices, when adoption is limited by income. More broadly, our results suggest there are misalignments between farmer beliefs and practices promoting microbiome function. Therefore, enhancing farmer knowledge via microbiome extension activities may improve pest suppression through the adoption of practices that are beneficial and the optimization of the practices that are detrimental to the microbiome but are considered desirable for other properties.MethodsStudy system, soil samples, and questionnaireTo generate soil samples for metabarcoding and pest suppression assays, instructions for soil sampling were sent to 279 organic farmers in New York, USA, along a with paper survey on microbiome beliefs, practices associated with the soil sample, farm characteristics, and farmer demographics as explained in detail by Bloom et al.9. Participants were able to submit up to two soil samples with distinct management practices from their farm (n = 85 farmers). The soil microbiome and associated practices for each sample were treated as separate observations. Because not all farms contributed two samples, farmer and general farm characteristics were sometimes linked to multiple samples with different practices and associated microbiomes, while others were not. This approach generated a richer data set and increased representation of practices; however, we acknowledge that samples coming from the same farm are not fully independent. Farmers were instructed to collect 10 soil subsamples consisting of a 6” deep × 2” thick core using a spade or shovel in a transect across the field as previously described41,42. We recommended avoiding points that fell in unusual areas and spanned different soil types. Following sample collection, participants were instructed to thoroughly mix all sub-samples and transfer the homogenized mixture to a 1-quart sample bag (Ziploc; Part No. 682256). For shipping, participants were given a US Postal Service prepaid polyethylene expansion mailer (Quality Park Products; Part No. QUA46390). Immediately upon receiving the soil samples, a V-shaped sterile spatula was used to sample and store ≈ 50 ml of soil per sample at -20 °C for metabarcoding. The remainder of each sample was refrigerated at 1.6–3.3 °C until use in laboratory assays.One hundred and thirty-six samples were received over a 3-month period (Fig. 1a). Sample, farmer, and farming system characteristics were quantified by the survey instrument previously described in ref. 9. Characteristics used in this study include: (1) farming practices used in the field where soil sample(s) were collected (Supplementary Table 2); (2) time in organic production, (3) percent of income coming from farming; and (4) beliefs clusters found by Bloom et al.9. We focus on time in organic production and percent income that comes from farming because they consistently mediate the adoption of farming practices known to influence the microbiome9. Moreover, time in organic management is known to promote farming system biodiversity43. As described in ref. 9 the 85 participants who completed the survey were clustered by their beliefs using affinity propagation, and exemplar farms identified for each cluster. The soil samples from exemplars were used in bioassays (see Herbivore and plant defense assays), and cluster classifications served as predictors for farming practice adoption in our socio-ecological models. For these models, farmer beliefs were modified to a continuous variable by summing the values for each farm within the rotated component coordinate plane (Fig. 1; RC1 + RC3) (see Structural equation modeling). Due to varimax rotation, successive components no longer capture as much variance as possible; therefore (RC2) was found to explain the least variation after rotation and was excluded from our analysis9.Soil DNA extractionsPrior to DNA extractions, soil samples were homogenized using the quartering method44. In brief, approximately 25 ml of bulk soil was placed in a sterile autoclaved glass petri dish and divided into quarters. Each quarter was mixed individually with a sterilized spatula, the two quarters from each half were mixed, and the two halves were mixed to form a homogenous matrix44. This procedure was repeated several times. After homogenization, soil samples were dried for 24 h in a biosafety cabinet (Labconco; Delta Series; Purifier Class II). Dried samples were further homogenized with a sterile autoclaved mortar and pestle until all soil aggregates were equal in size, then stored at -20 °C.To extract soil sample DNA, we used the DNeasy PowerSoil Pro Kit (Qiagen; Cat. No. 47016) followed by ethanol precipitation45 and DNA cleanup. We conducted DNA extractions using manufacturer protocols, with the following exceptions: cell lysis was conducted with a modified high speed paint shaker (Harbil; Part No. 24018) and a reduced homogenization time of 1 min. This approach promoted DNA yields and reduced shearing compared to other cell lysis methods (e.g., vortex), which we confirmed using DNA gel electrophoresis (data not shown). Further optimization was conducted during inhibitor removal and prior to DNA column binding, where samples were placed on ice for 5 min after briefly vortexing with aluminum chloride hexahydrate (Qiagen; Mat. no. 1108824) and guanidinium thiocyanate (Qiagen; Mat. No.1108825). DNA yield and purity were assessed using spectrophotometry (Thermo Scientific; NanoDropTM OneC; Cat. No. ND-ONE-W). All samples not meeting sequencing standards of the Dalhousie University Integrated Microbiome Resource (IMR) (Halifax, Nova Scotia, CA) underwent ethanol precipitation as in ref. 46. For samples not reaching purity standards after ethanol precipitation, we used a DNA cleanup kit (New England Biolabs; Cat. no. T1030S) and followed the manufacturer protocol. Prior to sequencing, DNA underwent PCR to confirm purity (e.g., absence of inhibitors), and was standardized to ≈ 10 ng/µl. At least 100 ng of soil DNA per sample meeting purity standards (260/280 > 1.80; 260/230 > 2.0) was sent for library preparation and sequencing using MiSeq at the IMR facility. The V4–V5 region of the 16S ribosomal RNA region was sequenced to characterize bacterial communities (Forward primer: 515FB = GTGYCAGCMGCCGCGGTAA; Reverse primer: 926R = CCGYCAATTYMTTTRAGTT), and the internal transcribed spacer (ITS2) region of the rRNA gene was sequenced to characterize fungal communities (Forward primer: ITS86(F) = GTGAATCATCGAATCTTTGAA; Reverse primer: ITS4(R) = TCCTCCGCTTATTGATATGC).Bioinformatics pipelineRead preprocessing, data clustering, and post-clustering were conducted in AMPtk47. Read preprocessing merged paired-end reads using usearch, removed forward and reverse primers, and concatenated samples, yielding 12,556,488 and 16,541,471 valid output reads for bacterial and fungal communities, respectively. Clustering of reads into OTUs was conducted using the unoise3 pipeline. In brief, the pipeline included filtering (maximum expected error < 1.0), dereplication, denoising (minimum size = 8), de novo chimera removal, and validation of ASV orientation. Reads were mapped to denoised ASVs (identity = 97%) and denoised sequences were clustered into biological OTUs (global identity threshold = 97%), yielding 11,669 and 7232 OTUs for bacteria and fungi, respectively. Post clustering was performed with the LULU algorithm (version = 0.1.0). To begin clustering, pairwise sequence similarity for match detection between OTUs was calculated using VSEARCH (version = 2.15.1; minimum identity threshold = 0.84; minimum query coverage = 0.90). Then, LULU merging was applied using a co-occurrence minimum ratio of 95% and minimum relative abundance of 1, yielding the final set of 9343 (2326 merged) and 6740 (492 merged) OTUs for bacteria and fungi, respectively.Taxonomy was assigned in R (function: assignTaxonomy; package: dada2) using SILVA (version: 138.1) for bacteria48 and UNITE (general dynamic release: 29.11.2022) for fungi49, with species-level assignments made for bacteria using the “addSpecies” function50. This approach implements the RDP classifier algorithm from ref. 51 with kmer size 8 and 100 bootstrap replicates. Phylogenetic tree construction was conducted starting with “muscle” for mass sequence alignment, with the “diags” argument used to enhance algorithm speed, and the current alignment returned after 24 hours52. Mass sequence alignments were trimmed using “trimal”, removing columns with gaps in more than 20% of sequences or a similarity score lower than 0.001, unless this removed more than 40% of the columns in the alignment, thus the minimum coverage was set to 60%53. Trimmed mass sequence alignments were passed to “RAxML” for phylogenetic tree construction using the “GTRGAMMA” substitution model with 100 rapid bootstrap inferences and thereafter a thorough ML search54. Importantly, due to limitations with the ITS region (e.g., sequence length variation; alignment ambiguity), we caution evolutionary conclusions from our analysis. Rather, phylogenetic tree construction was used to approximate relatedness among OTUs for our beta diversity measurements. The OTU and taxonomy tables, phylogenetic tree, and sample data from these analyses were then used for downstream analyses.Exploratory microbiome ordinationsVariance-stabilized microbiomes were analyzed using Monte Carlo reference-based consensus clustering (package = M3C) to visualize unconstrained patterns of composition55. Principal coordinates analysis (PCoA) with Bray-Curtis dissimilarities was also conducted to visualize unconstrained variation in the microbiome with farmer belief and M3C consensus clusters56. Permutational multivariate analysis of variance (PERMANOVA) was then used to test whether farmer belief clusters and M3C-derived clustering explained a significant portion of the observed variation (function = adonis2)9 (Supplementary Fig. 3).Microbiome biodiversity measuresAlpha and beta diversity for fungi and bacteria were measured using the iNext and Betapart packages, respectively57,58. For alpha diversity, we used Hill numbers parameterized by diversity orders, including species richness, Shannon diversity, and Simpson’s diversity. Post clustering OTU tables for fungi and bacteria were used to calculate the asymptotic alpha diversity metrics using the iNext function for (read) abundance data with a 95% confidence interval58 (Supplementary Fig. 6). Unlike interpolated and extrapolated values, asymptotic metrics estimate the true diversity expected under infinite and standardized sampling effort for each soil sample. Beta diversity measures for OTU tables were collected pairwise using the beta.pair, beta.pair.abund, and phylo.beta.pair functions57. For incidence-based pair-wise dissimilarities, we used the Sørensen indices that accounted for spatial replacement and nestedness of OTUs across sites. OTU replacement accounts for substitution, whereas nestedness measures OTU loss across sites. Measures of nestedness were used to evaluate microbiome patterns across and within farms, along with Wilcoxon tests (function = wilcox.test) for fungi and bacteria. Thereafter, to promote independence, our analysis included only pairwise measures across rather than within farms. For clarity, we also refer to nestedness as loss above. Incidence-based measures were complemented by their phylogenetic equivalents, which account for relatedness across samples59,60. For the loss and replacement terms, we found that microbiome beta diversity was phylogenetically structured, indicating assembly was non-neutral (Supplementary Fig. 7). In other terms, microbial beta diversity was likely driven by environmental gradients60. Abundance-based beta diversity measures were found using the Bray indices, accounting for OTU balance and gradients61. The balance and gradient terms evaluate the numerical substitution of reads and the loss of reads from the OTU tables across sites, respectively.Differential abundance analysisTo address the role of identity effects in mediating plant defenses and pest suppression, we used the practice adoption predictor variables (Supplementary Table 2) to parameterize a differential abundance analysis (package = ANCOMBC; function = ancombc2), where taxa were considered differentially abundant based on a false discovery rate-adjusted p-value < 0.05, using the default log fold change threshold of zero62 (See results in Fig. 2b). Differential abundance analysis began by merging our OTU table, farming practices (predictor variables), and taxonomy table into a SummarizedExperiment object63. We then subset our data using prevalence filtering (prevalence = 0.1; function = subsetbyprevalenttaxa; package = mia)64, and used the prevalence filtered data in our differential abundance analysis (function = ancombc2)62. We conducted the differential abundance analysis at the OTU level, with 100 bootstrap iterations62. Because ANCOMBC emphasizes statistical significance after multiple-test correction, all OTUs with significant adjusted p-values were retained for further analysis. The log corrected abundances for these taxa identified via differential abundance analysis then underwent selection as predictive variables for plant defenses (see Machine learning) and inclusion in SEMs. Log corrected abundance values are bias-adjusted log-transformed abundances, which are calculated for each OTU per sample after adjusting for sample-specific bias factors (e.g., library size, compositional biases)62. Because bias-corrected log-abundance values account for these biases, they no longer reflect absolute abundance (i.e., OTU counts), but instead offer an unbiased measure for our correlational analyses62.Herbivore and plant defense assaysWe replicated our herbivore and plant defense bioassay three times, with each assay conducted over a five-week period. Bioassays began by extracting the soil microbiome of the six exemplar farms for each farmer cluster (Fig. 1b) in a ¼ strength Hoagland’s solution as done previously by ref. 5. For microbiome extractions, 16 g of soil and 240 mL of ¼ strength Hoagland’s solution were shaken in duran flasks at 275 rpm for 1 hour. After shaking, flasks were allowed to rest at room temperature for 1 hour and then centrifuged for five minutes at 500 rpm and 4 °C (Sorvall RC 5 C Plus). Supernatants for each sample were returned to a Duran flask and kept at 4 °C until use. Peas (Pisum sativum L., variety ‘Perfection Dark Seeded’) were grown in sterilized 9 cm square plastic pots containing triple autoclaved potting soil. Exemplar farm microbiome extracts were applied at a rate of 15 ml twice per week for three weeks. At four weeks post seedling emergence, five adult pea aphids (Acyrthosiphon pisum) were caged on a single leaf, with one cage per plant (6 replications per exemplar farm per assay). After 24 hours, adult aphids were removed and F1 progeny were culled to five nymphs per cage. After 9 days, the number of F2 progeny was counted for each exemplar farm. After counting, the next undamaged developmentally matched leaf directly above aphid cages was harvested into LN2 from all plants and stored at −80 °C until systemic phytohormone extraction and quantification following5. Correlations between exemplar microbiomes, plant defenses, and progeny were assessed using GLMs in R (package = glmmTMB) (Fig. 1e-g)16.Phytohormone extraction and quantificationPrior to phytohormone extraction, leaf tissue was lyophilized (Labconco Freeze Dry System; Catalog no. 77520-00 L), weighed, and ≈ 25 mg of dried leaf tissue was homogenized with a modified paint shaker (Harbil; Part No. 24018). Phytohormone extractions were performed as in Blundel et al. 2020 using D4-SA (salicylic acid) and D5-JA (jasmonic acid) as internal standards. Dried sample extracts were resuspended in 200 μL of HPLC-grade methanol and 10 μL was injected onto a Dionex UHPLC (Thermo Fisher Scientific, Waltham, MA, USA) through a C18 reversed-phase HPLC column (Phenomenex Gemini) and an Orbitrap-Q Exactive mass spectrometer (Thermo Scientific) run on negative polarity. A gradient of 0.1% (v/v) formic acid in water (Solution A) and 0.1% (v/v) formic acid in acetonitrile (Solution B) was established at a flow rate of 600 uL per minute. A 10.5-minute gradient was established as follows: for the first minute, the composition of the liquid phase was 99% Solution A and 1% Solution B. From minute 1 to minute 8, the composition of the liquid phase started at 80% Solution A and 20% Solution B, and gradually shifted to 25% Solution A and 75% Solution B. From minute 8 to minute 9.5, the composition of the liquid phase was 0% Solution A and 100% Solution B. From minute 9.5 to minute 10.5, the composition of the liquid phase was 99% Solution A and 1% Solution B. Data acquisition and interpretation was conducted in Xcalibur (Thermo Fisher Scientific). Peak areas were recorded for internal standards and endogenous phytohormones. The endogenous peak areas were divided by the internal standard peak area and reported relative to the sample dry weights. No contamination was detected in methanol blanks with internal standards.Covariate preparation and machine learningCovariates used in the assessment of alpha diversity, beta diversity, and differential abundance included the farming practices used in the field where soil sample(s) were collected (Supplementary Table 2). For alpha diversity and OTU differential abundance practice presence or absence was recorded. For matching with fungi and bacteria beta diversity, farming practices were transformed into beta diversity terms, practice nestedness, and replacement57. Here, we interpret replacement and nestedness as the gain (adoption) or loss (abandonment) of the practice across sites in the comparison. To find these terms, we evaluated the partial contribution of each farming practice to the overall turnover terms calculated pairwise across sites65,66. This approach consists of removing one farming practice at a time, recomputing the partial turnover value for each term, and finding the percent contribution of the partial to the overall term57. Here, negative and positive values for each term indicate if the practice is contributing to similarity or dissimilarity (turnover) in the practice across sites, respectively. For example, a value of 100% indicates the practice is either being lost or gained depending on the beta diversity term, while all other practices remained static.Because using all sites for assays was untenable given space limitations, plant defense and pest population values were found for all sites using model-based estimation with the mice package in R37. Here, the fungal and bacterial asymptotic alpha diversity values at each q value (range 0 – 2), log corrected abundance of differentially abundant taxa, microbiome beta diversity variables, and summed exemplar location within the rotated component coordinate plane (RC1 + RC3) were used to estimate the plant defense and pest population values for all samples in the study using multivariate imputation by chained equations37. We then used machine learning (ML), stability selection, and stepwise AIC for model selection67, allowing us to identify important bivariate relationships between farming practices, microbiome measures, plant defenses, and pest suppression (Supplementary Fig. 1). Our ML approach first used the cv.glmnet function (nfolds = 10) from the glmnet package68 to find suitable lambda values (minimum and 1 SE), which were passed to the model fitting glmnet function. Results were visually inspected to assess top models, which were then verified using stability selection. Here, we performed a resampling procedure using the stabsel function (fit function = glmnet lasso; cutoff = 0.6; PFER = 1; sampling type = MB) from the stabs package69 to identify the most influential variables. We then used stepwise AIC (package = MASS) on the stable variables67,70 yielding the most influential predictor and response variables for use in SEMs (Supplementary Fig. 1). This process was repeated, working from farming practices to the microbiome, plant defenses, and pest suppression. Only the most influential response variables were included as predictor variables in the following model.Structural equation modelingWe hypothesize that farmer beliefs would have cascading indirect effects on herbivore populations via farming practices, the soil microbiome, and plant defenses (Supplementary Fig. 1). To test this prediction, structural equation models were constructed using the SEM function in the Lavaan package71 following72 using a single-indicator latent variable approach to represent our response and predictor variables. This approach allows for the inclusion of latent constructs in the path diagram while avoiding overparameterization by using single observed variables to represent each latent factor. Each latent variable (e.g., farmer microbiome beliefs) was measured using an empirically selected highly indicative indicator variable (e.g., the summed RCs for beliefs). Relationships between model parameters for practices, microbiome measures, plant hormones, and pest suppression were informed by prior machine learning and stability selection in a stepwise manner (Supplementary Fig. 1). To avoid fitting problems, variables were scaled ad libitum using a generic scale function73. Residual correlations between latent variables were included to account for unexplained covariance using modification indices (function = modificationIndices) until models reached acceptable fits (RMSEA > 0.05 and CFI ≈ 1). To quantify indirect effects, mediation pathways were specified algebraically in the model syntax. This approach estimates compound path coefficients for each mediation sequence. Model fits (chi-square) and parameters were estimated with maximum likelihood (estimator = MLM) and significance was accepted at p ≥ 0.05. Soil properties characterizing abiotic conditions were found using the gridded soil survey74. To match with microbiome beta diversity, continuous predictors (e.g., belief cluster) and response variables (e.g., plant defenses) were differenced and binomial predictors (e.g., farming as the main income source [no or yes coded as 0 or 1]) were transformed into categorical predictors (1 = 0 to 0; 2 = 1 to 0; 3 = 0 to 1; 4 = 1 to 1) for comparisons across sites.Soil properties characterizationsThe GPS locations of sites were intersected with the gridded soil survey geographic (gSSURGO) database rasters deployed at the county-level for NYS from the National Cooperative Soil Survey74. Via this intersection, we found the map unit key for each site, which we then related to the gSSURGO component table, which gives the soil properties by site per map unit. We then related the component table to the horizon table using the component key to derive data by horizon for each site. We then retrieved representative values for the following soil properties: pH; available water content; organic matter; percent sand, silt, and clay; and erodibility. Because there are several records for each map unit key, we then averaged the representative values for each site per soil property. Soil properties were highly correlated. Here, we collected the variance inflation factor (VIF) for our pool of seven soil properties and selected those with values below two. This VIF approach indicated that only the percent sand in the soil sample should be retained. Therefore, for our SEMs, we selected the percentage of sand estimated per farm as a proxy for soil properties in general.

Data availability

The raw data generated in this study will be available in FigShare and in the NCBI SRA at the time of publication (BioProject Accession: PRJNA1334013).
Code availability

All processing data and key analytical scripts will be available via the Figshare Digital Repository at the time of publication https://doi.org/10.6084/m9.figshare.30610085.
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Abstract

The Niepołomice Forest, though relatively natural, is affected by air pollutants transported from nearby urban areas. To assess the impact of air pollution, we analyzed the bioaccumulation of elements (Ca, Cd, Cu, Fe, Hg, Pb, S, Zn) in thalli of Hypogymnia physodes (L.) Nyl., together with oxidative stress biomarkers (SOD, TBARS) and thallus condition, at 15 sites during heating and non-heating seasons. Seasonal variability was observed: Cd and TBARS were higher in non-heating season (0.97 µg·g⁻¹ and 0.95 mmol·g⁻¹ FM respectively), while S increased during heating season (1331 µg·g⁻¹), suggesting emissions from fuel combustion. Spatial differences were most pronounced for Cd and Zn. In the western part of the forest, H. physodes was absent at some sites, and lichens showed elevated Pb and Cu concentrations with increased SOD activity, indicating strong traffic-related pollution. In the east, thalli contained a high proportion of degenerated algae, associated with elevated Cd, Hg, and S, as well as other stressors. Overall, element concentrations were comparable to values reported from other regions of Poland. The study highlights that even seemingly natural forests are subject to significant pollution pressure. Combining chemical data with biomarkers offers deeper insight into the effects of toxic elements on lichen bioindicators.

Data availability

The data are available in Open Research Data Repository of Krakow Universities RODBUK: https://doi.org/10.24917/VYJKFU.
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Download referencesAcknowledgementsThe study was founded through the statutory research subvention of UKEN: BS-472/G/2018 “Ocena stanu środowiska naturalnego Puszczy Niepołomickiej w oparciu o porost Hypogymnia physodes (Nyl)”. [Assessment of the natural environment of the Niepołomice Forest based on the lichen Hypogymnia physodes (Nyl.)].FundingThe study was founded through the statutory research subvention of UKEN: BS-472/G/2018 “Ocena stanu środowiska naturalnego Puszczy Niepołomickiej w oparciu o porost Hypogymnia physodes (Nyl)”. [Assessment of the natural environment of the Niepołomice Forest based on the lichen Hypogymnia physodes (Nyl.)].Author informationAuthors and AffiliationsInstitute of Biology and Earth Sciences, University of the National Education Commission, Krakow, Podchorążych 2, Krakow, 30-084, PolandRobert Kościelniak, Izabela Wiśniowska, Danuta Kadłub, Marzena Albrycht, Laura Betleja, Katarzyna Gawrońska, Katarzyna Kucharska & Łukasz J. BinkowskiAuthorsRobert KościelniakView author publicationsSearch author on:PubMed Google ScholarIzabela WiśniowskaView author publicationsSearch author on:PubMed Google ScholarDanuta KadłubView author publicationsSearch author on:PubMed Google ScholarMarzena AlbrychtView author publicationsSearch author on:PubMed Google ScholarLaura BetlejaView author publicationsSearch author on:PubMed Google ScholarKatarzyna GawrońskaView author publicationsSearch author on:PubMed Google ScholarKatarzyna KucharskaView author publicationsSearch author on:PubMed Google ScholarŁukasz J. BinkowskiView author publicationsSearch author on:PubMed Google ScholarContributionsRK: Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review & editing. IW: Formal analysis, Validation, Visualization, Writing – original draft.DK: Validation, Visualization, Writing – original draft. MA: Methodology, Investigation, Writing – review & editing. LB: Methodology, Investigation, Writing – review & editing. KG: Methodology, Investigation, Writing – review & editing. KK: Visualization, Writing – review & editing. ŁJB: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing.Corresponding authorCorrespondence to
Izabela Wiśniowska.Ethics declarations

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AbstractUnderstanding taphonomic patterns on skeletal remains, along with associated entomological evidence, remains a critical challenge in forensic and archaeological investigations. This study examines the specific impact of an Alpine environment on a WWI mass grave of 12 Austro-Hungarian soldiers (Cima Cady, Italy). Alongside general poor preservation caused by acidic soil erosion, a distinctive reddish staining affected over 80% of the skeletal elements. This was linked to the invasive root system of Juniperus sp., a plant species typical of Alpine zones, which had penetrated the grave and bone cavities. Additional unusual deterioration patterns were observed on tarsal elements that had been in contact with leather boots. Entomological evidence included the presence of Pterostichus multipunctatus and puparia of Protophormia terraenovae, known to mainly colonise exposed remains. The combined taphonomic and entomological findings provide important insights into the postmortem history of the remains and the environmental factors influencing their preservation. Collectively, these results support historical records and testimonies of the burial dynamics from over a century ago, and highlight the broader forensic potential of such evidence in reconstructing mass grave scenarios, including the contemporary investigations of human rights violations or war crimes.

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Data availability

The complete osteometric dataset and all derived data are available upon reasonable request. Because the skeletal remains and associated records are the exclusive property of the Italian State and classified as military heritage pursuant to D. Lgs. 42/2004 (Codice dei Beni Culturali e del Paesaggio), D.Lgs. 66/2010 art. 272 (Codice dell’Ordinamento Militare), and L. 365/1999, the release of the data requires prior written authorisation from the Ufficio Beni Archeologici of the Autonomous Province of Trento and the Ufficio per la Tutela della Cultura e della Memoria della Difesa (Ministero della Difesa). The human remains were reburied in accordance with the current Italian regulations governing military burials (e.g.: Decreto Legislativo 15 marzo 2010, n. 66). Therefore, no human remains of that mass grave are currently held in a museum or research repository. Personal and military items recovered during the excavation are stored and preserved at the Restoration Laboratory of the Archaeological Heritage Office of Trento Province. These materials are curated under the authority’s supervision and may be accessed for research purposes upon request.
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Wiktoria Baranowska or Daniel Gaudio.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleBaranowska, W., Gobbi, M., Vanin, S. et al. Taphonomic patterns of a WWI Alpine mass grave: insights from the Italian front.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32171-yDownload citationReceived: 02 July 2025Accepted: 08 December 2025Published: 22 December 2025DOI: https://doi.org/10.1038/s41598-025-32171-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsAlpine environmentForensic taphonomyMass gravePink teethReddish stainingWorld War I More

AbstractBiodiversity and ecosystem services are known to respond to large-scale urban planning, but the ecological role of fine-scale, designable habitat features remains poorly quantified We analysed bird communities across six Italian cities in relation to habitat and human-designed features measured at a 100-m scale, quantifying taxonomic diversity and trait-based proxies of cultural (e.g. aesthetic appeal) and regulating (e.g. seed dispersal, pest control) ecosystem services. Grass and water were key predictors: grass-rich areas supported more diverse bird communities, while aquatic features enhanced both diversity and regulating services. Impervious surfaces reduced diversity and cultural values, whereas intermediate vegetation height maximized diversity, highlighting the value of structural heterogeneity. Scenario modelling showed that expanding green areas improved avian diversity far more than increasing tree height, while green-area loss caused severe declines. Our findings emphasize that integrating blue-green infrastructures and habitat complexity into urban design is essential to foster biodiversity and ecosystem services, supporting the EU Biodiversity Strategy 2030 and UN Sustainable Development Goals.

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Data availability

Breeding bird data used for the analyses are available in the Zenodo repository (https://doi.org/10.5281/zenodo.15343590). This data was originally collected and published in Alba et al. 202515 and was re-used here for additional analyses addressing different research questions.
Code availability

The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.
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Download referencesAcknowledgementsWe thank Luca Bajno for his help during fieldwork and land use analysis. R.A., V.F., G.A., F.C., I.R., E.C., and D.C. were funded by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 – Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU; Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP D13C22001350001, Project title “National Biodiversity Future Center – NBFC”. L.I. and D.R. were partly supported by Ecosistema MUSA – Multilayered Urban Sustainability Action (funded by the European Union – NextGenerationEU under the NRRP M4C2 Investment Line 1.5: Strengthening of research structures and creation of R&D “innovation ecosystems”, set up of “territorial leaders in R&D”, project ECS_00000037). F.M. was supported by Foundation for Science and Technology (FCT) PhD fellowship 2020.06036.BD, DOI 10.54499/2020.06036.BD.Author informationAuthors and AffiliationsDepartment of Life Sciences and System Biology, University of Turin, Turin, ItalyRiccardo Alba, Fabio Marcolin, Valeria Ferrario, Francesca Cochis, Irene Regaiolo, Enrico Caprio & Dan ChamberlainNBFC, National Biodiversity Future Center, Palermo, ItalyRiccardo Alba, Valeria Ferrario, Francesca Cochis, Irene Regaiolo, Enrico Caprio & Dan ChamberlainForest Research Centre and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Lisboa, PortugalFabio MarcolinCIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, School of Agriculture, University of Lisbon, Lisboa, PortugalFabio MarcolinCIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, University of Porto, Vairão, PortugalFabio MarcolinDipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale “Amedeo Avogadro”, Alessandria, AL, ItalyGiacomo AssandriDepartment of Environmental Sciences and Policy, University of Milan, Milan, ItalyLuca Ilahiane & Diego RuboliniAuthorsRiccardo AlbaView author publicationsSearch author on:PubMed Google ScholarFabio MarcolinView author publicationsSearch author on:PubMed Google ScholarValeria FerrarioView author publicationsSearch author on:PubMed Google ScholarGiacomo AssandriView author publicationsSearch author on:PubMed Google ScholarLuca IlahianeView author publicationsSearch author on:PubMed Google ScholarFrancesca CochisView author publicationsSearch author on:PubMed Google ScholarIrene RegaioloView author publicationsSearch author on:PubMed Google ScholarDiego RuboliniView author publicationsSearch author on:PubMed Google ScholarEnrico CaprioView author publicationsSearch author on:PubMed Google ScholarDan ChamberlainView author publicationsSearch author on:PubMed Google ScholarContributionsR.A. and D.C. devised the study and the main conceptual ideas. G.A. and L.I. collected the data in the field, and R.A. designed the statistical methodology, performed the computations, prepared the figures and wrote the original draft, with inputs from D.C. All authors (R.A., F.M., V.F., G.A., L.I., F.C., I.R., D.R., E.C. and D.C.) read, contributed and approved the manuscript.Corresponding authorCorrespondence to
Fabio Marcolin.Ethics declarations

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The authors declare no competing interests.

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AbstractThe yellow-legged hornet, Vespa velutina subs. nigrithorax Buysson, 1905, is an invasive species in Europe, posing substantial ecological and economic threats. Its biology and behavior, marked by rapid reproduction and aggressive predation, endanger native insects. This has negative impacts on agriculture, biodiversity, and human safety. Effective control is therefore essential to prevent and minimize its spread and ecological impact. This study assessed the acute oral toxicity of four commercial insecticide formulations containing acetamiprid, spinosad, cypermethrin, and a mixture of natural pyrethrins as active ingredients on V. v. nigrithorax workers. Our findings indicate that acetamiprid and spinosad are the most promising compounds, with acetamiprid inducing rapid mortality at low doses, and spinosad causing both lethal and sublethal effects, potentially disrupting hornet behavior and colony viability. Given that oral exposure generally resulted in higher toxicity than contact exposure, pest control strategies should incorporate multiple exposure pathways. Further investigations are necessary to confirm efficacy under field conditions and to evaluate potential ecological risks to non-target species. Overall, this study provides critical data to improve V. v. nigrithorax control and supports the development of more effective and environmentally sustainable control strategies against this invasive species.

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Data availability

The datasets generated during and/or analyzed during the current study are all available as Supplementary Material.
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Download referencesAcknowledgementsWe thank Marco Portocarrero and Bárbara Rodrigues from Associação Nativa, Joana Lopes and all the staff from Proteção Civil de Coimbra, and Henrique Silva from Câmara Municipal da Lousã for providing nest coordinates and for their invaluable support during fieldwork. We also thank Malaika Muschler, Giovanni Cilia, Leonhard Bürger, and Kimberley Lea Ring for their invaluable support during fieldwork. We also extend our gratitude to the two anonymous reviewers for their valuable contributions in reviewing the original manuscript of this work.FundingThe study was carried out under the project “CONTROLVESPA—Development of strategies for the CONTROL of VESPA velutina invasion (PTDC/CTA-AMB/2123/2020)”, financed by FCT – Fundação para a Ciência e a Tecnologia, I.P. (DOI identifier https://doi.org/10.54499/PTDC/CTA-AMB/2123/2020). This study was also supported by the strategic plan of the Centre for Functional Ecology – Science for People and the Planet (CFE) (UIDB/04004/2020; https://doi.org/10.54499/UIDB/04004/2020) and Associate Laboratory TERRA (LA/P/0092/2020; https://doi.org/10.54499/LA/P/0092/2020).Author informationAuthor notesThese authors contributed equally to this work: Paula Malaquias Souto and Soraia Sousa Santos.Authors and AffiliationsCentre for Functional Ecology, Department of Life Sciences, Associated Laboratory TERRA, University of Coimbra, 3004-504, Coimbra, PortugalPaula Malaquias Souto, Soraia Sousa Santos, Artur Sarmento, Aline de Liz Ronsani, Ana Luísa Tomaz, Sara Leston, José Paulo Sousa & Nuno CapelaDepartment of Soils and Natural Resources, Universidade do Estado de Santa Catarina (UDESC Lages), 88520-000, Lages, SC, BrazilAline de Liz RonsaniMunicípio de Cantanhede, Praça Marquês de Marialva, 3060-133, Cantanhede, PortugalHenrique M. V. S. Azevedo-PereiraAssociated Laboratory for Green Chemistry (LAQV), Network of Chemistry and Technology (REQUIMTE), Apartado 55142, 4051-401, Porto, PortugalSara Leston & Fernando RamosFaculty of Pharmacy, University of Coimbra, Polo III, Azinhaga de Santa Comba, 3000-548, Coimbra, PortugalFernando RamosAuthorsPaula Malaquias SoutoView author publicationsSearch author on:PubMed Google ScholarSoraia Sousa SantosView author publicationsSearch author on:PubMed Google ScholarArtur SarmentoView author publicationsSearch author on:PubMed Google ScholarAline de Liz RonsaniView author publicationsSearch author on:PubMed Google ScholarAna Luísa TomazView author publicationsSearch author on:PubMed Google ScholarHenrique M. V. S. Azevedo-PereiraView author publicationsSearch author on:PubMed Google ScholarSara LestonView author publicationsSearch author on:PubMed Google ScholarFernando RamosView author publicationsSearch author on:PubMed Google ScholarJosé Paulo SousaView author publicationsSearch author on:PubMed Google ScholarNuno CapelaView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: Paula Souto, Artur Sarmento, Nuno Capela, Henrique M.V.S. Azevedo-Pereira; Data curation: Paula Souto, Artur Sarmento; Formal analysis: Paula Souto, Sara Leston; Funding acquisition: Henrique M.V.S. Azevedo-Pereira, José Paulo Sousa; Investigation: Paula Souto, Artur Sarmento, Nuno Capela, Soraia Santos, Aline Ronsani, Ana Luísa Tomaz, Sara Leston, Fernando Ramos; Methodology: Paula Souto, Artur Sarmento, Henrique M.V.S. Azevedo-Pereira, Nuno Capela, Soraia Santos, José Paulo Sousa; Project administration: José Paulo Sousa, Paula Souto; Resources: José Paulo Sousa, Fernando Ramos; Supervision: José Paulo Sousa, Paula Souto, Nuno Capela; Validation: Paula Souto, Artur Sarmento, Nuno Capela, José Paulo Sousa; Visualization: Paula Souto, Artur Sarmento, Nuno Capela; Writing – original draft: Paula Souto, Soraia Santos; Writing – review & editing: Paula Souto, Artur Sarmento, Soraia Santos, Aline Ronsani, Ana Luísa Tomaz, Nuno Capela, Henrique M.V.S. Azevedo-Pereira, Sara Leston, José Paulo Sousa. All authors read and approved the final version.Corresponding authorCorrespondence to
Paula Malaquias Souto.Ethics declarations

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The authors declare no competing interests.

Ethics approval
Individuals of V. v. nigrithorax were collected from both private properties and public spaces. For collections on private properties, consent from the owners was obtained. In public areas, permission for collection was given by local authorities responsible for treating nests. Moreover, no special authorization is necessary to keep this species in laboratory conditions or to conduct insect animal testing within the EU. The fieldwork did not involve any endangered or protected species.

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Reprints and permissionsAbout this articleCite this articleMalaquias Souto, P., Santos, S.S., Sarmento, A. et al. Assessing the oral toxicity of acetamiprid, spinosad, cypermethrin, and pyrethrins in the invasive hornet Vespa velutina nigrithorax.
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AbstractCoral reefs provide essential ecosystem services and livelihoods, particularly for small island nations like the Maldives. However, they are increasingly threatened by climate change and coastal modification. In 2022, the Greater Malé Connectivity Project (GMCP) commenced in North Malé Atoll, involving large-scale land reclamation and marine construction that affected adjacent coral reefs. As a mitigation measure, coral colonies were relocated to the reef surrounding Villimalé Island. Over two years of monitoring, relocated corals showed encouraging performance despite challenging environmental conditions. Overall survival reached 66%, with larger colonies outperforming smaller fragments and Pocillopora generally exhibiting higher growth and thermal resistance than Acropora. Growth rates declined with rising sea surface temperature, and mortality was primarily associated with tissue-loss responses rather than predation or ectosymbiotic colonisation. Health trajectories differed among coral types: Acropora fragments were more prone to bleaching, whereas Pocillopora colonies maintained tissue integrity but experienced chronic degradation. Despite these biological interactions and health challenges, many corals acclimatised to the urban reef environment, underscoring that coral relocation, when combined with species selection and size consideration, can serve as a viable short-term conservation tool in highly impacted systems.

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Data are available on request to the corresponding author due to restrictions and ownership of the NGO Save the Beach Maldives.
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Irene Pancrazi.Ethics declarations

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Reprints and permissionsAbout this articleCite this articlePancrazi, I., Tritini, D., Ahmed, H. et al. Coral relocation supports survival and growth in an urban reef of the Maldives.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33671-7Download citationReceived: 06 July 2025Accepted: 20 December 2025Published: 22 December 2025DOI: https://doi.org/10.1038/s41598-025-33671-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsCoral relocation and restorationBiodiversity conservationRestoration performanceAnthropogenic pressureMaldives More

AbstractUnderstanding microbial dispersion in the atmosphere is essential for studying microbial biogeography and ecosystem dynamics under global change. Airborne bacterial communities, shaped by exchanges between atmosphere and Earth’s surface, can originate from diverse sources and vary with meteorological conditions and air mass trajectories. In this study, we assessed airborne microbial communities in Antarctica at regional and local scales. Air samples were collected during the austral summer at two Antarctic Specially Protected Areas (ASPAs): Byers Peninsula (Livingston Island, South Shetland Islands) and Avian Island (Marguerite Bay). Bacterial composition was analysed through 16S rRNA gene sequencing using amplicon sequence variants (ASVs). Additionally, back-trajectories of the sampled air parcels were simulated with HYSPLIT. A core community was identified in 80% of Byers Peninsula samples, representing 57.91% of total ASVs. Notably, 79.4% of ASVs matched soil bacteria from the same location, suggesting a strong influence of local sources. Communities from Byers Peninsula and Avian Island showed low overall similarity. However, one sample from Byers resembled the Avian sample, likely due to similar air mass back-trajectories. These findings suggest that airborne bacterial communities are shaped by both local ecosystems, and broader regional or continental processes, such as long-range trajectories carrying microorganisms from distant locations.

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Data availability

The air sequences generated in this study are available in GenBank under BioProject accession number PRJNA1165500.
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Download referencesAcknowledgementsThe authors are grateful to the members of field teams from MICROAIRPOLAR projects Sergi González and David Velázquez, Unidad de Tecnología Marina (UTM-CSIC), and crews of BIO Hespérides (Spanish Navy) and B/O Sarmiento de Gamboa (CSIC) for the logistic support in Antarctic campaigns. The authors acknowledge the computer resources, technical expertise and assistance provided by the Centro de Computación Científica at the Universidad Autónoma de Madrid (CCC-UAM), the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model, the Norwegian Polar Institute for the Quantarctica package, and Agencia Estatal de Meteorología (AEMET) for providing meteorological data from Juan Carlos I station. Special thanks to Pablo Sanz and Sergi González for their support in obtaining back-trajectories of air masses.FundingThis work was supported by the Spanish Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), Grants PID2020-116520RB-I00 and CTM2016-79741-R. SG was supported by a PIPF-contract fellowship (PIPF-2022/ECO-25833) from Comunidad Autónoma de Madrid government’s (Spain).Author informationAuthor notesThese authors contributed equally: Antonio Quesada and Ana Justel.Authors and AffiliationsDepartment of Biology, Universidad Autónoma de Madrid, 28049, Madrid, SpainSofía Galbán & Antonio QuesadaDepartment of Plant and Microbial Ecology, University of Minnesota, Minneapolis, 55455, USAPablo AlmelaDepartment of Mathematics, Universidad Autónoma de Madrid, 28049, Madrid, SpainAna JustelAuthorsSofía GalbánView author publicationsSearch author on:PubMed Google ScholarPablo AlmelaView author publicationsSearch author on:PubMed Google ScholarAntonio QuesadaView author publicationsSearch author on:PubMed Google ScholarAna JustelView author publicationsSearch author on:PubMed Google ScholarContributionsS.G.: Conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing—original draft, writing—review and editing. P.A.: Conceptualization, methodology, writing—review and editing. A.Q.: Conceptualization, investigation, methodology, funding acquisition, project administration, resources, supervision, validation, writing—review and editing. A.J.: Conceptualization, investigation, methodology, funding acquisition, project administration, resources, supervision, validation, writing—review and editing.Corresponding authorCorrespondence to
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KeywordsAerobiologyAntarcticaBacteriaCore communityBiogeographyAir mass back-trajectories More