Dysbiosis in the urban tree microbiome

Dysbiosis in the urban tree microbiome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dysbiosis in the urban tree microbiome Kathryn Atherton, Chikae Tatsumi, Isabelle Frenette, David Heaton, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5939048/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Abstract The tree microbiome is a critical determinant of tree and ecosystem functioning, but human disturbances can disrupt natural microbe-tree relationships. Here, we show that urban trees exhibit microbial dysbiosis along a model urbanization gradient, with declines in mutualistic root and leaf symbionts, but increases in decomposers and pathogens, including those relevant to plant, animal, and human health. These shifts correlate with urban stressors such as heat, drought, and atmospheric deposition. Urban tree microbiomes also show altered biogeochemical cycling capabilities, with high potential for nitrogen loss through greenhouse gas (N2O) production and reduced capacity for methane consumption relative to rural trees. Additionally, urbanization reduces overall tree microbiome diversity, particularly among non-pathogenic microbes, potentially diminishing the ecological and health benefits of diverse environmental microbiomes in cities. These findings underscore the need to consider the microbiome in urban forestry management practices to maximize the ecological and health benefits of city greening and forest conservation efforts. Biological sciences/Ecology/Microbial ecology Biological sciences/Ecology/Urban ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 MAIN TEXT Similar to the human 1 – 3 and animal microbiome 4 – 7 , microbial communities associated with trees (i.e., the tree microbiome) play a critical role in tree health by regulating tree nutrition, metabolism, immunity, and stress tolerance 8 . Cities around the world are investing in tree planting and forest conservation efforts to protect residents from urban heat, manage stormwater runoff, and improve human wellness, perception of place and safety, and property values 9 – 11 . However, urbanization creates environmental conditions that threaten tree health, including forest edge effects 12 , which increase air and soil temperatures 13 and pollution levels 10 , 14 . Nevertheless, we have a poor understanding of how urbanization impacts the tree microbiome, leaving a critical knowledge gap about the sustainability and long-term efficacy of city greening and forest protection projects. Greening initiatives are ongoing in at least 100 cities around the world, with a global goal of including 1000 cities by 2030 15 , making urban tree health one of the most economically, socially, and environmentally important scientific topics of the next quarter century. Evidence is accumulating that one of the main impacts of urbanization on the tree microbiome may be to induce whole-tree dysbiosis, or an imbalance in microbial equilibrium that leads to a loss of beneficial microbes and an increase in harmful ones 16 . Many trees engage in belowground symbiosis with ectomycorrhizal (ECM) fungi, which colonize live tree roots, aid in nutrient and water uptake from soils 17 , and protect the trees from pathogens 18 . Aboveground, microbes on leaves can also have symbiotic relationships with plants, aiding in stress response 19 , 20 , protection from disease 21 , 22 , and overall tree fitness 19 , 22 . However, urbanization creates stressful aboveground and belowground environments for trees and their associated microbes; for example, urbanization increases ambient pollutant concentrations and surface temperatures (i.e. known as the “heat island effect” 13 ). In soils of rural ecosystems, intense warming can shift fungal community composition away from ECM fungi 23 , 24 and towards free-living decomposers 24 and plant pathogens 24 – 26 . Recent observations support the concept of “systemic induced resistance” by mycorrhizal fungi 18 , whereby ECM fungi can induce plant defenses against insects or pathogens in aboveground tissues. In cities, plant pathogens can cause catastrophic damage – the spread of microbial plant pathogens can infect hundreds of thousands of trees in a single county, costing millions of dollars to remove and replant impacted trees 27 . If urbanization disrupts the whole tree microbiome the way it disrupts the soil microbiome 23 , 28 , the loss of ECM fungi may lead to widespread plant pathogen presence among urban trees. Tree microbiomes may also serve as bioindicators of Urban One Health, or the intersection and interconnection of human, animal, and ecosystem health in urban ecosystems 29 . While the spread of zoonotic diseases in urban systems has historically been attributed to characteristics of human populations, infrastructure, public health systems, and pathogens in cities 30 , trees may also influence zoonotic pathogen loads in urban areas. Past studies have utilized trees as passive biomonitors of human exposure to air pollutants 31 , 32 and recent research shows that antibiotic-resistant bacteria that act as zoonotic pathogens of humans and animal pathogens can be transported on pollution particulates 33 . If urban trees collect zoonotic pathogens of humans and animal pathogens, this may lead to cross-kingdom infections in humans, as has been found in agricultural and other plant systems like indoor plants 34 , 35 . Impacts of urbanization on total microbiome diversity hosted by trees may also impact human health in cities: the Old Friends Hypothesis posits that exposure to a broad diversity of microbes trains human immune systems to stop inflammatory responses to harmless allergens, microorganisms, or the human body itself, potentially reducing chronic disease burdens 36 . The percentage of people living in urban areas will increase to nearly 70% worldwide and almost 90% within the U.S. alone by the year 2050, such that a loss of microbiome diversity on urban trees could have widespread consequences for public health 37 – 39 . Microorganisms play a major role in controlling greenhouse gas fluxes from the land to the atmosphere, such that shifts in the tree microbiome with urbanization could alter the capacity of urban trees to mitigate climate change. For example, denitrifying microorganisms can accelerate climate change by increasing the release of nitrous oxide (N 2 O), a potent greenhouse gas, to the atmosphere 40 . Cities often experience increased inorganic nitrogen deposition 41 , which could promote the activity of these denitrifying bacteria 42 . In addition, urbanization could impact the ability of microorganisms to sequester carbon (C) from the atmosphere and store it in their biomass. Most tree-associated microorganisms use dead plant matter – such as leaf litter – as their primary C resource, using it for both biomass production and respiration of carbon dioxide (CO 2 ) to the atmosphere. However, aboveground (e.g., leaf, branch, bark) litter is actively removed from city streets to prevent clogging of infrastructure like drains and pipes, creating an ecological opening for other types of C-cycling microbes (e.g., pathogens, methanogens) to be hosted by urban trees, with unclear consequences for the urban C cycle. Plant-associated microbes are now recognized to play significant roles in modes of C and N cycling that are atypical for trees, such as N and methane fixation 43 , 44 , but their response to urbanization is unknown. To understand urbanization effects on the tree microbiome, we conducted a field study of trees, their associated microbial communities, and environmental conditions along an urban-to-rural gradient (Supplementary Table 1). We hypothesized that urbanization reduces tree-microbial mutualists and increases pathogen loads within the tree microbiome as a result of the unique, severely stressful environmental conditions in urban areas. We also expected that urbanization increases the abundance of zoonotic pathogens and decreases total microbial diversity across the tree microbiome, lowering the potential of tree microbiomes to mitigate climate change by selecting for populations of microbes that have greater capacity to release C and N from plant-soil systems to the atmosphere through processes such as plant pathogenesis and denitrification. To test these hypotheses, we characterized bacterial and fungal communities in soils, leaves, and roots of oak trees across the City of Boston and across the Urban New England (UNE) study 45 – 48 , which includes eight fragmented temperate forest sites along an urban-to-rural gradient in the State of Massachusetts (MA). While previous work has investigated the impact of urbanization on a singular component of the tree microbiome (i.e. in leaves 49 – 51 or the soil 24 , 51 – 53 ), our study characterized changes to the microbiome of multiple tree habitats – leaves, roots, and soils – of a singular host (oak trees) across the urban-to-rural gradient. This gradient includes trees from the edge (0–15 meters from a forest edge) and interior (60–90 meters from a forest edge) of both urban and rural forests (n = 8 sites; 6 trees at each site), as conditions at a forest edge can exacerbate urban stressors, such as temperature and pollution deposition, as compared to the forest interior 45 , 54 , 55 . In our study, we also sampled trees planted along Boston streets in both sidewalk pits and grassy medians in nine different neighborhoods (3–6 trees at each site). For each tree, we measured bacterial and fungal communities in roots, soils, and leaves using high-throughput amplicon sequencing, as well as tree size, root biomass, and soil properties (e.g., temperature, pH, bulk density, moisture, organic matter (SOM), available N; NO 3 − and NH 4 + ). We also assembled previously published data on atmospheric deposition rates and concentrations of nitrogen dioxide (NO 2 ), nitric oxide (NO), and tropospheric ozone (O 3 ) from nearby collection sites 48 , 56 . With these data, we tested the impact of urbanization and forest edge effects 45 on microbial diversity and abundances of key fungal and bacterial functional groups using linear regression models that accounted for spatial autocorrelation in community composition, as well as correlation analyses with environmental variables. RESULTS City trees have low symbiont and high plant pathogen loads In line with our hypothesis, we found that tree symbionts declined on city street trees compared to forest trees. City street trees had the lowest abundance of ectomycorrhizal (ECM) fungi in soil (Fig. 1 a) of any site type, consistent with previous work showing lower root colonization rates in street trees compared to rural trees 57 . ECM abundances were negatively correlated with soil temperature, pH, and available NH 4 + and NO 3 − , but positively correlated with soil moisture and organic matter content (Fig. 1 d), in line with previous observations that the abundance and activity of ECM fungi are reduced by inorganic nitrogen inputs 58 . Foliar epiphytes were also lowest on city street trees, correlating positively with the same soil properties as ECM fungi, as well as ammonium deposition rates (Fig. 1 d). Epiphytes are hypothesized to reduce plant oxidative stress by taking up ammonia in leaves 19 , which could account for their positive relationship with ammonium deposition. However, we found that epiphytes were strongly negatively correlated with the deposition of nitrate, metals, and chloride (Cl − ), consistent with the idea that they can serve as a bioindicator of air quality 19 . These data suggest that anthropogenically-induced urban heat island effects, drought, and increased atmospheric inorganic N deposition are major inhibitors of fungal tree mutualists in urban environments, but that management efforts to reverse these conditions could recover tree mutualists in urban areas 59 , 60 . In contrast to tree mutualists, plant decomposers and pathogens thrived in both belowground and aboveground tree tissues in city streets. Wood decomposers, including fungal wood saprotrophs, cellulolytic bacteria, and lignolytic bacteria, significantly increased in the soil and roots of city trees compared to forest trees (Fig. 1 b, Fig. S1 a). A complete lack of aboveground tree litter in these street tree pits suggests that these microbial taxa may be decomposing street tree roots. Wood decomposers can thrive in the soil surrounding trees infected with insect pests 54 , which our data indicate is happening in street trees. Street trees showed a sharp increase in foliar fungi (sooty molds) associated with insect damage (Fig. 1 c): these fungi consume honeydew produced by aphids 62 , which can create outbreaks of insect damage in urban trees 63 . Street tree roots may also be more vulnerable to disease than forest tree roots if they have lost their ECM fungal symbionts that can confer disease resistance 18 , 64 : we found that some of the wood decomposers that emerge underneath street trees can also double as plant pathogens (Fig. S1 c). Environmental factors that correlated negatively with ECM fungi (e.g., soil temperature, pH, and N species) correlated positively with wood decomposer abundances (Fig. 1 d), indicating that wood decomposers are effectively replacing mutualistic symbionts on city trees (Fig. S1 b). Bacterial plant pathogens also increased with urbanization in tree foliage (Fig. 1 c). These plant pathogens were correlated with high temperatures, low soil moisture content, and atmospheric deposition of nitrate and alkali and alkaline-earth metals (Fig. 1 d), consistent with potential pathogen range expansion due to climate change-induced temperature increases and drought stress 25 and increased tree susceptibility to pathogens from particulate matter (PM)- induced stomatal clogging 65 . Higher foliar pathogens in urban trees that suffer a loss of ECM fungi also supports the idea of “systemic induced resistance” by mycorrhizal fungi 18 , such that re-introducing ECM fungi to urban trees could make trees less susceptible to aboveground disease 66 . City trees collect zoonotic and animal pathogens We found that, in addition to plant pathogens, zoonotic and animal pathogens increased in the tree microbiome with urbanization. Fungal animal pathogens in leaves and soil were highest on city street trees and lowest on rural forest trees (Fig. 2 a). In leaves, fungal animal pathogens were positively associated with atmospheric deposition of nitrate and metal ions, while in soils, fungal animal pathogens were positively associated with inorganic N and ozone concentrations, available soil NO 3 − , soil temperature, and soil pH (Fig. 2 c). These results suggest that reducing atmospheric ozone and nitrogen concentrations in the environment, via restrictions to vehicular emissions aboveground and fertilizer additions to soils, for example, may be an important first step for managing animal pathogen loads in cities. Bacterial zoonotic pathogens of humans also increased with urbanization in both leaves and soils (Fig. 2 a, 2 b) and were positively associated with soil temperature, pH, and atmospheric deposition of nitrate and alkali and alkaline-earth metals, as well as soil bulk density (Fig. 2 c). While plant-associated zoonotic pathogen outbreaks have been studied in an agricultural context 67 – 69 , our results suggest a similar potential in city streets. Because trees can collect airborne pathogens 70 , we expect that elevated zoonotic pathogens in city trees reflects high zoonotic pathogen loads present generally in the city environment. Similar environmental factors correlated positively with animal, zoonotic, and plant pathogen abundances on trees (e.g. root decomposers and foliar plant pathogens (Fig. 1 d)) indicating that the environmental conditions leading to disease threat for trees also support proliferation of animal and zoonotic disease-causing microorganisms in cities. City tree microbiomes have alternative carbon and nitrogen cycling strategies ​Urbanization also increased microbial potential for ecosystem N and C loss through greenhouse gas emissions. Soil nitrate content was highest in urban street tree pits (Fig. 3 a), as were relative abundances of nitrifying and denitrifying bacteria, which possess the genomic capacity to emit N 2 O, a potent greenhouse gas 40 . High levels of denitrifiers in city street trees is consistent with observed increases in N 2 O emissions within cities 71 . Evidence is emerging that ECM fungi have the capacity to suppress nitrifying and denitrifying bacterial growth in soil through competition for ammonium 32 , 63 , 64 , such that the decline in ECM fungi in urban street tree soils (Fig. 1 ) may create suitable conditions for denitrifiers to flourish. Additionally, we found that free-living soil decomposer communities shifted towards microbes that have higher potential for CO 2 release to the atmosphere in cities (Fig. 3 c). Wood, litter, and dung decomposers that dominate street tree pits use relatively labile C (instead of soil organic matter) as their primary C source, which could lead to increased soil respiration and reduced C-residence time, or sequestration, in soils underneath city trees 74 , 75 . This is consistent with low soil organic matter content of city street tree pits (Fig. S2a) and would occur if trees reduce C allocation belowground in cities, either because of constricted root growth, low colonization by mycorrhizal fungi, increased herbivory, or other types of aboveground tree damage. In addition to sequestering CO 2 through photosynthesis and C allocation to soil, trees have the potential to directly take up methane (CH 4 ) – another potent greenhouse gas – in various tissues 44 . This capacity would be particularly useful in cities, where atmospheric CH 4 concentrations can be extremely high 76 . However, we found that CH 4 uptake capability of the foliar microbiome was significantly lower in urban street trees compared to forest trees (Fig. 3 d), consistent with low measured rates of methane consumption in city vegetation 77 . Collectively, these data suggest that dysbiosis in the urban tree microbiome reduces its ability to mitigate climate change induced by three of the most potent greenhouse gases (N 2 O, CO 2 , and CH 4 ). City trees host lower microbial diversity than forests In line with our expectations, urban tree microbiomes were significantly less diverse than rural tree microbiomes (Fig. 4 ), especially for non-pathogenic microorganisms. Urbanization reshapes tree microbiomes, where microbial communities, despite showing strong spatial autocorrelation similar to soil and aquatic microbiomes 78 , 79 were distinct among site types (i.e., street trees, urban forest edge and interior, rural forest edge and interior) (Fig. S3). While urban land use has been shown to homogenize soil microbiomes 23 , we did not find evidence of microbiome convergence in urban trees (Fig. S4). Instead, urbanization lowered bacterial and fungal diversity in leaves (Fig. 4 a, 4 b, 4 d) and in the fungal soil community, especially for non-pathogenic fungi (Fig. 4 c). This loss in tree-associated microbial diversity with urbanization may be detrimental to trees, animals, and humans, as previous studies have shown that exposure to less microbial diversity is associated with a decline in immune system function and an increase in the prevalence of chronic inflammatory diseases, such as asthma and allergies 37 , 39 , 80 . DISCUSSION Cities are investing in greening initiatives, but the sustainability of these initiatives is unclear because the impact of urbanization on tree health is not fully resolved 81 , 82 . Borrowing from concepts in human microbiome science 83 , we aimed to assess the impact of city living on the diverse tree microbiome and its potential to carry out mutualistic, pathogenic, and alternative metabolism that could impact trees and the larger urban ecosystem. We found that urban trees suffer dysbiosis of their microbiomes, losing aboveground and belowground microbial symbionts and collecting plant, zoonotic, and animal pathogens. This issue is important to address, because street trees determine pathogen loads in the air microbiome 84 , making local, individual tree-based microbiomes major controls over both the biotic and abiotic components of the environment at city and regional scales. Furthermore, though trees are important biogeochemical cyclers, planted in part for their C sequestration abilities, our results suggest that street trees’ climate change mitigation functions may be offset by their accumulation of denitrifying bacteria and fungal decomposers that could accelerate the production of greenhouse gases. A concomitant loss of methanotrophic bacteria in city trees highlights the reality that, even in long-established urban areas, human-created biomes may not metabolize and sequester as much of our most harmful environmental pollutants as previously thought. Our results have implications for managing and expanding urban street tree coverage to maximize the benefits of city greening and forest conservation efforts. Public health studies have called for the establishment of an airborne microbiome monitoring network to detect and characterize outbreaks of airborne diseases 85 . Street trees might provide such a pre-established biomonitoring system to surveil human exposure to pathogens, if future work can directly link pathogen loads on trees to disease outbreaks, similar to how COVID-19 outbreaks are surveilled by sampling wastewater 86 . In addition, “rewilding” the urban microbiome, via either planting diverse, native vegetation in urban greenspaces 87 or transplanting native plant-associated microbiomes into soils 88 , has the potential to restore microbial plant mutualist communities and improve plant and overall ecosystem health, as has been done in agricultural studies to promote nitrogen metabolism 82 , 83 and in forests to increase plant productivity 84 . Compost additions to urban trees can also increase soil moisture, lower pH, and increase soil C stocks within six years of treatment 92 , which our data suggest could reduce pathogen loads and increase tree mutualist abundances. Aboveground management practices, such as applying fungicide to leaves, have also led to positive effects on diseased plants, including reverting belowground pathogen-induced microbiome changes 93 . Our results suggest policy measures to address greenhouse gas emissions, air pollution, and urban heat island effects more broadly must also be implemented to see the full benefits of investing in city greening projects. METHODS Study system We sampled oak trees (i.e., in the genus Quercus ) across a 120 km urban-to-rural gradient in the state of Massachusetts. The gradient includes street trees within nine neighborhoods in the City of Boston (described in Smith et al. 81 ), as well as eight mid-successional mixed temperate forest sites that are part of the Urban New England (UNE) project: seven oak-dominated forest sites located along the urban-to-rural gradient from Boston into western Massachusetts (described in previous UNE studies 45 – 47 ) and a well-characterized intact rural forest located at the Harvard Forest Environmental Measurement Station (EMS) in western Massachusetts (Petersham, MA, described in Smith et al. 81 ) (Table S1 ). All sites have a humid, continental climate with warm summers (mean monthly temperatures of 18.6 ºC to 21.7 ºC) and cold, snowy winters (-4.3 ºC to -0.1 ºC), and 1,100-1,300 mm of precipitation distributed evenly throughout the year 94 . We selected 93 oak trees from multiple age classes across the urban-to-rural gradient for sampling. Trees were sampled across age classes: 5–10 cm diameter at breast height (DBH, “young”, 21 trees), 10–30 cm DBH (“mid”, 32 trees), and 30–100 cm DBH (“old”, 40 trees). Some city trees were lining streets in concrete-enclosed pits, while others were surrounded by grass, but we maintained uniformity within a neighborhood by only sampling trees that were in the same growing conditions (pit vs. grass) within each neighborhood. Urban and rural site designations for forests were based on impervious surface area and population density, following previous analyses of UNE sites reported in our prior publications 47 . We sampled 37 trees from 9 Boston neighborhoods (3–6 trees per neighborhood), 14 trees from the Harvard Forest Inventory plots, and 42 trees from the forest edge (0–15 m from the edge of the forests 45 , 21 trees) and interior (60–90 m from the edge of the forests 45 , 21 trees) of the UNE forest sites. For each tree selected, we measured DBH to the nearest 0.1 cm and recorded the GPS coordinates of the tree with a Garmin GPS unit. Sample collection and processing Microbiome samples were collected during peak tree greenness (July and August) in 2021. At each tree, we collected three sample types: soil, roots, and leaves. For soil collection, we took three replicate 30 cm deep, 2.4 cm radius soil cores from within the drip line or the boundaries of the concrete tree pit. Each of the three cores was divided into two samples, the upper 15 cm (0–15 cm depth) and the lower 15 cm (15–30 cm depth), for a total of six soil samples per tree (549 soil samples in total, 93 trees x 3 cores per tree x 2 layers per core − 9 samples). Soil cores were separated by depth instead of horizon (organic vs. mineral) because street trees lacked a visible organic horizon. We recorded the depth of each core to calculate bulk density and we measured the temperature of each soil sample with a Hanna Instruments Thermistor Thermometer (± 0.4°C for one year; Waterproof Thermistor Thermometer: HI93510N, 2021). For roots, we subsampled 8–10 fine roots per soil sample, pooling roots from the same tree and sampling depth (24–30 root tips per pool) to generate enough tissue for DNA extraction. For leaves, we collected six replicates of mature, mid-canopy leaves in full sunlight from each tree using a 15’ pole pruner; seven trees were too tall to collect leaves with the pole pruner, resulting in 516 leaf samples collected in total (86 trees x 6 leaves per tree). Soil and leaf samples were kept on ice during same-day transport back to the laboratory. Upon returning to the laboratory, we weighed the soil samples for bulk density calculations, took a subsample of soil for microbial community analyses, and collected all roots from each soil sample. The soil and root subsamples, as well as the leaves, were stored at -80 ºC until DNA extraction. The remaining soil materials were stored at 4 ºC for nutrient analysis processing. Within 72 hours of sampling, we sieved the soils through a 2-mm sieve, pooling soils from the same depth and tree during sieving. What did not pass through the sieve, we stored at 4 ºC for total root biomass quantification (see Supplementary Methods). We took additional fresh soil subsamples from the pooled, sieved soils to measure soil physiochemical properties, including gravimetric moisture content, pH, organic matter concentration, and ammonium and nitrate content 46 , 47 . We ground the frozen leaves with a mortar and pestle under liquid nitrogen. A subsample of the leaves was stored at -80 ºC for DNA extraction. Additional soil and leaf subsamples were dried at 65 ºC and used for measuring total C and N content and total elemental abundance. Details for all biogeochemical assays can be found in the Supplementary Methods. DNA extractions DNA extraction procedures followed standard protocols, but were separately optimized for each sample type (upper soil, lower soil, leaf, root). To extract DNA from the upper soil (0–15 cm), we used the DNeasy PowerSoil Pro Kit (QIAGEN, Hilden, Germany) with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.25 g of soil of each sample, yielding 255 extracts in total (93 trees x 3 reps per tree − 24 samples which did not extract enough DNA). To extract DNA from the lower soil (15–30 cm), we used the DNeasy PowerLyzer PowerSoil Kit (QIAGEN, Hilden, Germany) with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.25 g of soil of each sample, yielding 253 extracts in total (93 trees x 3 reps per tree − 26 samples which did not extract enough DNA). To extract DNA from roots, we applied a cetyltrimethylammonium bromide (CTAB)/chloroform extraction and LiCl precipitation method 95 to 10–15 fine roots (approximately 0.2 g) per sample, resulting in 122 extracts in total (93 trees x 2 reps per tree − 64 samples which did not extract enough DNA) and stored the extracts at -80 º C. We cleaned the extracted root DNA with AMPure XP Bead-Based Reagent (Beckman-Coulter Life Sciences, Indianapolis, IN) with modifications (see Supplementary Methods). To extract DNA from leaves, we used the DNeasy Plant Pro Kit (QIAGEN, Hilden, Germany), with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.03 g of leaf tissue of each sample, yielding 505 extracts in total (86 trees x 6 reps per tree − 11 samples which did not extract enough DNA). To accurately represent leaf microbiome diversity, we paired and pooled leaf DNA extracts from the same tree in equimolar concentrations into three replicates per tree, resulting in 259 pooled extracts representing the 86 trees in total. Fungal and bacterial amplicon sequencing We used modified versions of the fITS7 and ITS4 primer set to amplify the ITS2 region of fungal rDNA 96 , 97 and modified versions of the 515f and 806r primer set to target the v4 16S region of bacterial rDNA 98 . All primers contained both Illumina adapters and unique sample indexes 99 , 100 . See Supplementary Methods for PCR amplification details. Amplicon sequencing was carried out on 853 fungal DNA amplicon samples and 866 bacterial DNA amplicon samples (66 fungal samples and 56 bacterial samples did not amplify). We verified amplicons using agarose gel electrophoresis, cleaned them with the Just-a-Plate 96 PCR Purification and Normalization Kit (Charm Biotech, MO), and quantified them with the Qubit HS-dsDNA kit (Invitrogen, Carlsbad, CA). We then mixed 16S and ITS amplicons for each sample at equimolar concentrations, and combined up to 176 16S and ITS amplicons into a single library for sequencing, resulting in eight libraries. The TUFTS Genome Sequencing Core facility performed 250 base pair (bp) paired-end sequencing on the Illumina MiSeq for each library. Sequencing resulted in a total of 49,980,377 16S sequences and 33,193,586 ITS sequences. The average read number per sample and sample read standard deviation for each sample type throughout the data cleaning method are reported in Table S2. Bioinformatics The 16S and ITS sequencing data were processed using BU16S ( https://github.com/Boston-University-Microbiome-Initiative/BU16s ), a customized QIIME2 101 pipeline designed to operate on Boston University’s Shared Computing Cluster 102 . In summary, BU16S identifies amplicon sequence variants (ASVs) that are 99% similar in rDNA sequence using DADA2 103 and then classifies ASVs with 95% or greater sequence identity to a database, SILVA99 (v138.1) 104 for bacteria and UNITE dynamic database (v9.0) 105 for fungi, using VSEARCH 106 (see Supplementary Methods). Additional bioinformatics analyses were conducted in the R software environment (version 4.3.1) 107 . To assign taxa to functional groups, fungal genera were matched to the FungalTraits database 108 , and bacterial genera were compared against an in-house functional database developed based on the presence of genes encoding enzymes involved in specific biochemical pathways, including copiotroph, oligotroph, cellulolytic, ligninolytic, methanotroph, chitinolytic, assimilatory nitrite-reducing, dissimilatory-nitrite reducing, assimilatory nitrate-reducing, dissimilatory nitrate-reducing pathways 109 , 110 (see Supplementary Methods). Fungal plant pathogens were assigned to genera identified by the FungalTraits database with any plant pathogenic capacity description, while fungal animal pathogens were assigned to genera identified by the FungalTraits database with an animal biotrophic capacity description (% identity of ASVs to fungal pathogens in each sample type are reported in Tables S3-S6). To assign pathogen status to bacterial ASVs, representative sequences for ASVs were blasted against the Multiple Bacterial Pathogen Detection database 111 and classified with 95% or greater sequence identity (% identity of ASVs to bacterial pathogens in each sample type are reported in Tables S7-S10). Additional tree and environmental factors To calculate the proximity of each tree to the City of Boston, we used the distHaversine function in the geosphere R package (v.1.5–18) 112 to find the distance between the latitude and longitude of the tree and Boston downtown, as defined by the coordinates of Park Street Massachusetts Bay Transit Authority (MBTA) subway station. Tree growth rate was calculated for street trees and trees in the Harvard Forest Biomass plots using the difference between the recorded DBH and a previously-recorded DBH from at least one year prior and annualized by dividing by the number of years since the last recorded DBH. Data from Smith et al. (2019) was used for DBH from 2014 for the street trees 81 and the Harvard Forest Data Archive was used for annual DBH for the Harvard Forest Biomass plot trees 113 . Foliar N concentration (%), foliar C to N ratio (C:N), and soil soluble salt content (µS/m) data collected in 2018–2019 for Caron et al. (2023) were averaged across oak tree samples taken from the edge (0 m from forest edge) and interior (90 m from forest edge) of each forest 46 and assigned to the UNE trees at the edge and interior of that forest, respectively. Atmospheric gas concentration (O 3 , NO, NO 2 , and total nitrogen oxides (NO x ) parts per billion (ppb)) and annual inorganic ion throughfall deposition (NH 4 + , NO 3 − , and total inorganic nitrogen deposition) data collected in 2019 for Rindy et al. (2023) 48 was averaged between replicates at the edge and interior of a forest, separately, and assigned to the UNE trees at the edge and interior of that forest, respectively. Annual rates of atmospheric wet deposition (kg ha − 1 yr − 1 ) of hydrogen (H + ), potassium (K + ), magnesium (Mg 2+ ), chlorine (Cl − ), calcium (Ca 2+ ), sodium (Na + ), sulfate (SO 4 2− ), NH 4 + , NO 3 − , and total inorganic N deposition were previously collected at National Atmospheric Deposition Program (NADP) sites at Boston University (site MA22) and the Arnold Arboretum (MA98), reported in Conrad-Rooney et al. (2023) 56 , and assigned to any trees within 15 km of the collection sites, as a 15 km radius around the NADP sites was used to categorize the NADP sites as urban, suburban, or rural 56 . Any trees within 15 km of both collection sites were assigned the data of the closer NADP site. Similarly, atmospheric gas concentration data and inorganic ion throughfall deposition data from Rindy et al. (2023) 48 from the edge of the Arnold Arboretum forest was also assigned to the Boston street trees within 15 km of the Arnold Arboretum forest edge, and the data from the interior of the closest Harvard Forest UNE study site (Harvard Forest UNE-1) was also assigned to the Harvard Forest Biomass plot trees, as they were within 15 km of the Harvard Forest UNE-1 site. Statistical analysis Before conducting statistical analyses, contaminating sequences were removed from the amplicon sequence data based on their prevalence in sequenced negative controls using the decontam package in R (version 1.20.0) 114 . To normalize ASV counts across samples for each sample type, we conducted rarefaction with the rarefy_even_depth function in the phyloseq package in R (version 1.44.0) 115 . This normalization was done to reduce the false discovery rate of ASVs, following our previous analysis of urban forest microbiomes 45 , as samples varied more than 10-fold in sequencing depth 116 . We rarefied ASV counts for each sample type using random subsampling based on a read count that minimized dropped samples and maximized number of reads per sample: a 5,769 read cutoff was used for fungal leaf communities, a 3,395 read cutoff for fungal 0–15 cm depth soil communities, a 3,307 read cutoff for fungal 15–30 cm depth soil communities, and a 5,204 read cutoff for fungal root communities. A 200 read cutoff was used for bacterial leaf communities, a 6,508 read cutoff for bacterial 0–15 cm depth soil communities, a 6,194 read cutoff for bacterial 15–30 cm depth soil communities, and a 1,974 read cutoff for bacterial root communities. To quantify the impact of urbanization on the tree microbiome, we conducted several different analyses using two different metrics of urbanization intensity as predictor variables. In the first set of analyses, microbiomes in different sample types (root, soil, leaves) were analyzed separately. Shannon’s alpha diversity for each fungal and bacterial community was computed using the vegan package in R (version 2.6-4) 117 . Initially, we tested for urbanization effects on the relative abundance of microbial taxonomic groups, functional groups, and Shannon’s diversity using distance from Boston and distance from the forest edge, as well as their interaction, as the fixed independent variables in regression analyses, following previous analyses of forest microbiomes along the UNE gradient 45 . Regressions were run using the maximized log-likelihood method of the lme function in the nlme package (version 3.1–166) in R 118 . City street trees were categorized as the most urban and most edge (0 m to edge) trees. To account for replicate samples taken from individual trees at individual locations, tree ID nested within sampling site (either forest site at UNE or neighborhood in the City of Boston) was included as a random variable in each model. If we found a significant edge effect or urbanization x edge effect on microbiome composition, we then tested the effect of different site types (i.e., street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior) using the maximized log-likelihood method of the lme function in the nlme package (version 3.1–166) 118 . ANOVA for LMMs was performed on standardized values using the scale function in R, with the lmerTest (version 3.1-3) packages 119 , and confidence intervals on the difference in group means were calculated with the TukeyHSD function of the R stats package (version 4.3.1) 107 , with significance levels set at 5%. In a second set of analyses, we assessed urbanization and edge effects on microbiome homogenization across sample types by calculating beta dispersion for either leaf samples or belowground samples (combined root, O horizon soil, M horizon soil) within each site type (i.e., street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior). For beta diversity analysis, we performed batch correction on the unrarefied 0–15 cm depth soil, 15–30 cm depth soil, and root 16S and ITS ASV tables, respectively, and combined them into one belowground 16S ASV table and one belowground ITS ASV table to normalize for sequencing differences due to differences in extracting and amplifying the different sample types. We used the ComBat_seq function in the sva package in R (version 3.48.0) 120 , with sample type as the batch variable and tree age and tree site type (e.g. street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior) as the biological covariates to be preserved in the batch correction. We then calculated the Aitchison distance matrices of the leaf and belowground 16S and ITS ASV tables, with a pseudocount of “1” added to all values 121 . Principal components analysis (PCA) was used to visualize community structure differences among samples based on the Aitchison distance matrix using the prcomp function in the R stats package (version 4.3.1) 107 . We performed a multiple regression on distance matrices to test for the impact of tree site type and spatial distance on microbial community composition using the lm function in the R stats package (version 4.3.1) 107 . To test for microbial homogenization, we calculated the beta dispersion of the Aitchison distances against the centroids of the site types using the betadisper function of the vegan package in R (version 2.6-4) 117 , and then calculated confidence intervals on the difference in group means with the TukeyHSD function of the R stats package (version 4.3.1) 107 , with significance levels set at 5%. We also explored relationships between microbial functional groups and environmental factors with Pearson correlations using the cor.test function of the R stats package (version 4.3.1) 107 , with significance levels set at 5%. Declarations Data availability Raw amplicon sequence data will be deposited in the Sequence Read Archive at NCBI upon publication, and the accession number will be provided here. Code availability All data analysis scripts will be made publicly available on GitHub upon publication, and the link will be provided here. References Gilbert, J. A. et al. 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Additional Declarations There is NO Competing Interest. Supplementary Files supplementalinformationwithtablesdysbiosisintheurbantreemicrobiome.pdf Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5939048","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431240918,"identity":"c249b17d-bda0-4f74-bc2b-5100a69aeaba","order_by":0,"name":"Kathryn Atherton","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACCQYGNjCDXwJEshGlhRmkzIBBcgbJWgxuEKtFsv38sQcf9/yRM77dY8DwoewwYS3SPMnshjOeGRib3TljwDjjHBFa5BiS2aR5DhgkbruRY8DM20aMFv7HbNJ/gFo2zwBq+UuMFmkJoC0MQC0bJIBaGInRIjnjsZlkzwFjY4k7xwoO9pxLJ6xF4nziM4kfB+Tk+Gc3b3zwo8yasBYUcIBE9aNgFIyCUTAKcAEAB7Q26CQPxHMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2175-8331","institution":"Boston University","correspondingAuthor":true,"prefix":"","firstName":"Kathryn","middleName":"","lastName":"Atherton","suffix":""},{"id":431240919,"identity":"727f9a7a-22f0-40f5-bd9e-a076c68a7d22","order_by":1,"name":"Chikae Tatsumi","email":"","orcid":"https://orcid.org/0000-0001-7191-6049","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Chikae","middleName":"","lastName":"Tatsumi","suffix":""},{"id":431240920,"identity":"28908af6-9040-414d-968a-41d2a4469caa","order_by":2,"name":"Isabelle Frenette","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Frenette","suffix":""},{"id":431240921,"identity":"d40c435e-4a82-4060-98d6-16f208a1a54f","order_by":3,"name":"David Heaton","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Heaton","suffix":""},{"id":431240922,"identity":"e6bd7af1-90cb-44ee-9577-3d30d0a14937","order_by":4,"name":"Ian Smith","email":"","orcid":"https://orcid.org/0000-0002-7198-6183","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Smith","suffix":""},{"id":431240923,"identity":"cfd0a082-c3ae-498d-a260-3db26ad31dfd","order_by":5,"name":"Lucy Hutyra","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Hutyra","suffix":""},{"id":431240924,"identity":"455792db-2a19-47f8-a878-ed848abec7ce","order_by":6,"name":"Pamela Templer","email":"","orcid":"https://orcid.org/0000-0002-6570-3837","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Templer","suffix":""},{"id":431240925,"identity":"b78f8257-241b-44cb-b567-4d293063ac72","order_by":7,"name":"Jennifer Bhatnagar","email":"","orcid":"https://orcid.org/0000-0001-6424-4133","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Bhatnagar","suffix":""}],"badges":[],"createdAt":"2025-01-31 22:40:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5939048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5939048/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-025-00322-x","type":"published","date":"2025-10-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79160346,"identity":"97391098-f27e-40a6-8395-7c5633d38482","added_by":"auto","created_at":"2025-03-25 07:19:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":518450,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of urbanization on key functional groups within the tree microbiome. Relative abundances of ECM tree symbionts and leaf epiphytes (a) and wood decomposers (b) on different site types are shown, as well as the relationship between distance from Boston and plant pathogens in leaves (c). Pearson correlations between microbial functional groups and environmental factors are shown in (d). Error bars in (a) and (b) represent standard error around the mean and shading in (c) represents the standard deviation of the data (n = 87 trees in the fungal soil community analyses, n = 83 trees in the fungal leaf community analyses, n = 88 trees in the bacterial soil community analyses, n = 62 trees in the bacterial leaf community analyses). Letters in (a) and (b) denote Tukey groups (p \u0026lt; 0.05) and asterisks in (d) denote \u003cem\u003ep\u003c/em\u003e values (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/b29cd9723627be75eabb2273.png"},{"id":79160348,"identity":"cf21ace1-5053-4319-9764-c373edeb9add","added_by":"auto","created_at":"2025-03-25 07:19:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":338588,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of urbanization on animal and zoonotic pathogen loads within the tree microbiome. Fungal animal pathogens in leaves and soil and bacterial zoonotic pathogens of humans in soil are shown (a), as well as the impact of urbanization on bacterial zoonotic pathogens of humans in the leaf microbiome (b). Pearson correlations between microbial functional groups and environmental factors are shown in (c). Error bars in (a) represent standard error around the mean and shading in (b) represents the standard deviation of the data (n = 87 trees in the fungal soil community analyses, n = 83 trees in the fungal leaf community analyses, n = 88 trees in the bacterial soil community analyses, n = 62 trees in the bacterial leaf community analyses). Letters in (a) denote Tukey groups (p \u0026lt; 0.05) and asterisks in (c) denote \u003cem\u003ep\u003c/em\u003e values (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/c73edc10f4e991f38825d26f.png"},{"id":79161371,"identity":"5712af88-bcf9-4f5f-b5e5-b310a3a539e1","added_by":"auto","created_at":"2025-03-25 07:27:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193315,"visible":true,"origin":"","legend":"\u003cp\u003eUrbanization effects on soil NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e availability (a), N-cycling bacterial functional group abundances in soils (b), fungal decomposer abundances in soils (c), and methanotrophic bacterial abundances in leaves (d). Error bars in (a), (b), and (d) represent standard error around the mean (n = 88 trees in the inorganic N content analyses, n = 87 trees in the fungal soil community analyses, n = 88 trees in the bacterial soil community analyses, n = 62 trees in the bacterial leaf community analyses). Letters denote Tukey groups (p \u0026lt; 0.05), and asterisks in (c) denote \u003cem\u003ep\u003c/em\u003e values (** p \u0026lt; 0.01, *** p \u0026lt; 0.001) obtained from linear mixed model regression analyses.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/831ff7ed06fbf0bf87495ecd.png"},{"id":79161372,"identity":"ac6c2f4f-45fe-4071-a899-6ec745269b8e","added_by":"auto","created_at":"2025-03-25 07:27:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":534156,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of urbanization on the diversity of fungal (a, c) and bacterial (b, d) communities in leaves (a, b) and belowground (c, d). Point shape represents sample type and shading around the trendline signifies the standard deviation of the data. Colors represent results for the entire fungal or bacterial community (light blue) or the non-pathogenic portion of the community (dark blue).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/fe748ea6a5a6f0fafd18a257.png"},{"id":92761463,"identity":"4ca9f190-aec1-4e96-ba46-22629c6ac4b3","added_by":"auto","created_at":"2025-10-04 07:05:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2488798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/0cd338b6-50a8-4a1e-8bb2-62c7898e4691.pdf"},{"id":79160362,"identity":"f31ee919-09bc-445f-8e9e-ea46757b6f26","added_by":"auto","created_at":"2025-03-25 07:19:05","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3879920,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"supplementalinformationwithtablesdysbiosisintheurbantreemicrobiome.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5939048/v1/f3c09eddde2b9b34c045f7d5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dysbiosis in the urban tree microbiome","fulltext":[{"header":"MAIN TEXT","content":"\u003cp\u003eSimilar to the human\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and animal microbiome\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, microbial communities associated with trees (i.e., the tree microbiome) play a critical role in tree health by regulating tree nutrition, metabolism, immunity, and stress tolerance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Cities around the world are investing in tree planting and forest conservation efforts to protect residents from urban heat, manage stormwater runoff, and improve human wellness, perception of place and safety, and property values\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, urbanization creates environmental conditions that threaten tree health, including forest edge effects\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, which increase air and soil temperatures\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and pollution levels\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Nevertheless, we have a poor understanding of how urbanization impacts the tree microbiome, leaving a critical knowledge gap about the sustainability and long-term efficacy of city greening and forest protection projects. Greening initiatives are ongoing in at least 100 cities around the world, with a global goal of including 1000 cities by 2030\u003csup\u003e15\u003c/sup\u003e, making urban tree health one of the most economically, socially, and environmentally important scientific topics of the next quarter century.\u003c/p\u003e \u003cp\u003eEvidence is accumulating that one of the main impacts of urbanization on the tree microbiome may be to induce whole-tree dysbiosis, or an imbalance in microbial equilibrium that leads to a loss of beneficial microbes and an increase in harmful ones\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Many trees engage in belowground symbiosis with ectomycorrhizal (ECM) fungi, which colonize live tree roots, aid in nutrient and water uptake from soils\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and protect the trees from pathogens\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Aboveground, microbes on leaves can also have symbiotic relationships with plants, aiding in stress response\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, protection from disease\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and overall tree fitness\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, urbanization creates stressful aboveground and belowground environments for trees and their associated microbes; for example, urbanization increases ambient pollutant concentrations and surface temperatures (i.e. known as the \u0026ldquo;heat island effect\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e). In soils of rural ecosystems, intense warming can shift fungal community composition away from ECM fungi\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and towards free-living decomposers\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and plant pathogens\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Recent observations support the concept of \u0026ldquo;systemic induced resistance\u0026rdquo; by mycorrhizal fungi\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, whereby ECM fungi can induce plant defenses against insects or pathogens in aboveground tissues. In cities, plant pathogens can cause catastrophic damage \u0026ndash; the spread of microbial plant pathogens can infect hundreds of thousands of trees in a single county, costing millions of dollars to remove and replant impacted trees\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. If urbanization disrupts the whole tree microbiome the way it disrupts the soil microbiome\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, the loss of ECM fungi may lead to widespread plant pathogen presence among urban trees.\u003c/p\u003e \u003cp\u003eTree microbiomes may also serve as bioindicators of Urban One Health, or the intersection and interconnection of human, animal, and ecosystem health in urban ecosystems\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. While the spread of zoonotic diseases in urban systems has historically been attributed to characteristics of human populations, infrastructure, public health systems, and pathogens in cities\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, trees may also influence zoonotic pathogen loads in urban areas. Past studies have utilized trees as passive biomonitors of human exposure to air pollutants\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and recent research shows that antibiotic-resistant bacteria that act as zoonotic pathogens of humans and animal pathogens can be transported on pollution particulates\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. If urban trees collect zoonotic pathogens of humans and animal pathogens, this may lead to cross-kingdom infections in humans, as has been found in agricultural and other plant systems like indoor plants\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Impacts of urbanization on total microbiome diversity hosted by trees may also impact human health in cities: the Old Friends Hypothesis posits that exposure to a broad diversity of microbes trains human immune systems to stop inflammatory responses to harmless allergens, microorganisms, or the human body itself, potentially reducing chronic disease burdens\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The percentage of people living in urban areas will increase to nearly 70% worldwide and almost 90% within the U.S. alone by the year 2050, such that a loss of microbiome diversity on urban trees could have widespread consequences for public health\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicroorganisms play a major role in controlling greenhouse gas fluxes from the land to the atmosphere, such that shifts in the tree microbiome with urbanization could alter the capacity of urban trees to mitigate climate change. For example, denitrifying microorganisms can accelerate climate change by increasing the release of nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), a potent greenhouse gas, to the atmosphere\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Cities often experience increased inorganic nitrogen deposition\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, which could promote the activity of these denitrifying bacteria\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In addition, urbanization could impact the ability of microorganisms to sequester carbon (C) from the atmosphere and store it in their biomass. Most tree-associated microorganisms use dead plant matter \u0026ndash; such as leaf litter \u0026ndash; as their primary C resource, using it for both biomass production and respiration of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) to the atmosphere. However, aboveground (e.g., leaf, branch, bark) litter is actively removed from city streets to prevent clogging of infrastructure like drains and pipes, creating an ecological opening for other types of C-cycling microbes (e.g., pathogens, methanogens) to be hosted by urban trees, with unclear consequences for the urban C cycle. Plant-associated microbes are now recognized to play significant roles in modes of C and N cycling that are atypical for trees, such as N and methane fixation\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, but their response to urbanization is unknown.\u003c/p\u003e \u003cp\u003eTo understand urbanization effects on the tree microbiome, we conducted a field study of trees, their associated microbial communities, and environmental conditions along an urban-to-rural gradient (Supplementary Table\u0026nbsp;1). We hypothesized that urbanization reduces tree-microbial mutualists and increases pathogen loads within the tree microbiome as a result of the unique, severely stressful environmental conditions in urban areas. We also expected that urbanization increases the abundance of zoonotic pathogens and decreases total microbial diversity across the tree microbiome, lowering the potential of tree microbiomes to mitigate climate change by selecting for populations of microbes that have greater capacity to release C and N from plant-soil systems to the atmosphere through processes such as plant pathogenesis and denitrification. To test these hypotheses, we characterized bacterial and fungal communities in soils, leaves, and roots of oak trees across the City of Boston and across the Urban New England (UNE) study\u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which includes eight fragmented temperate forest sites along an urban-to-rural gradient in the State of Massachusetts (MA). While previous work has investigated the impact of urbanization on a singular component of the tree microbiome (i.e. in leaves\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e or the soil\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e), our study characterized changes to the microbiome of multiple tree habitats \u0026ndash; leaves, roots, and soils \u0026ndash; of a singular host (oak trees) across the urban-to-rural gradient. This gradient includes trees from the edge (0\u0026ndash;15 meters from a forest edge) and interior (60\u0026ndash;90 meters from a forest edge) of both urban and rural forests (n\u0026thinsp;=\u0026thinsp;8 sites; 6 trees at each site), as conditions at a forest edge can exacerbate urban stressors, such as temperature and pollution deposition, as compared to the forest interior\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In our study, we also sampled trees planted along Boston streets in both sidewalk pits and grassy medians in nine different neighborhoods (3\u0026ndash;6 trees at each site). For each tree, we measured bacterial and fungal communities in roots, soils, and leaves using high-throughput amplicon sequencing, as well as tree size, root biomass, and soil properties (e.g., temperature, pH, bulk density, moisture, organic matter (SOM), available N; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e). We also assembled previously published data on atmospheric deposition rates and concentrations of nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), nitric oxide (NO), and tropospheric ozone (O\u003csub\u003e3\u003c/sub\u003e) from nearby collection sites\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. With these data, we tested the impact of urbanization and forest edge effects\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e on microbial diversity and abundances of key fungal and bacterial functional groups using linear regression models that accounted for spatial autocorrelation in community composition, as well as correlation analyses with environmental variables.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCity trees have low symbiont and high plant pathogen loads\u003c/h2\u003e \u003cp\u003eIn line with our hypothesis, we found that tree symbionts declined on city street trees compared to forest trees. City street trees had the lowest abundance of ectomycorrhizal (ECM) fungi in soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) of any site type, consistent with previous work showing lower root colonization rates in street trees compared to rural trees\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. ECM abundances were negatively correlated with soil temperature, pH, and available NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, but positively correlated with soil moisture and organic matter content (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), in line with previous observations that the abundance and activity of ECM fungi are reduced by inorganic nitrogen inputs\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Foliar epiphytes were also lowest on city street trees, correlating positively with the same soil properties as ECM fungi, as well as ammonium deposition rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Epiphytes are hypothesized to reduce plant oxidative stress by taking up ammonia in leaves\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, which could account for their positive relationship with ammonium deposition. However, we found that epiphytes were strongly negatively correlated with the deposition of nitrate, metals, and chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e), consistent with the idea that they can serve as a bioindicator of air quality\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These data suggest that anthropogenically-induced urban heat island effects, drought, and increased atmospheric inorganic N deposition are major inhibitors of fungal tree mutualists in urban environments, but that management efforts to reverse these conditions could recover tree mutualists in urban areas\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast to tree mutualists, plant decomposers and pathogens thrived in both belowground and aboveground tree tissues in city streets. Wood decomposers, including fungal wood saprotrophs, cellulolytic bacteria, and lignolytic bacteria, significantly increased in the soil and roots of city trees compared to forest trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). A complete lack of aboveground tree litter in these street tree pits suggests that these microbial taxa may be decomposing street tree roots. Wood decomposers can thrive in the soil surrounding trees infected with insect pests\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, which our data indicate is happening in street trees. Street trees showed a sharp increase in foliar fungi (sooty molds) associated with insect damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec): these fungi consume honeydew produced by aphids\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, which can create outbreaks of insect damage in urban trees\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Street tree roots may also be more vulnerable to disease than forest tree roots if they have lost their ECM fungal symbionts that can confer disease resistance\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e: we found that some of the wood decomposers that emerge underneath street trees can also double as plant pathogens (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Environmental factors that correlated negatively with ECM fungi (e.g., soil temperature, pH, and N species) correlated positively with wood decomposer abundances (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), indicating that wood decomposers are effectively replacing mutualistic symbionts on city trees (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Bacterial plant pathogens also increased with urbanization in tree foliage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These plant pathogens were correlated with high temperatures, low soil moisture content, and atmospheric deposition of nitrate and alkali and alkaline-earth metals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), consistent with potential pathogen range expansion due to climate change-induced temperature increases and drought stress\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and increased tree susceptibility to pathogens from particulate matter (PM)- induced stomatal clogging\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Higher foliar pathogens in urban trees that suffer a loss of ECM fungi also supports the idea of \u0026ldquo;systemic induced resistance\u0026rdquo; by mycorrhizal fungi\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, such that re-introducing ECM fungi to urban trees could make trees less susceptible to aboveground disease\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCity trees collect zoonotic and animal pathogens\u003c/h3\u003e\n\u003cp\u003eWe found that, in addition to plant pathogens, zoonotic and animal pathogens increased in the tree microbiome with urbanization. Fungal animal pathogens in leaves and soil were highest on city street trees and lowest on rural forest trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In leaves, fungal animal pathogens were positively associated with atmospheric deposition of nitrate and metal ions, while in soils, fungal animal pathogens were positively associated with inorganic N and ozone concentrations, available soil NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, soil temperature, and soil pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These results suggest that reducing atmospheric ozone and nitrogen concentrations in the environment, via restrictions to vehicular emissions aboveground and fertilizer additions to soils, for example, may be an important first step for managing animal pathogen loads in cities. Bacterial zoonotic pathogens of humans also increased with urbanization in both leaves and soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and were positively associated with soil temperature, pH, and atmospheric deposition of nitrate and alkali and alkaline-earth metals, as well as soil bulk density (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). While plant-associated zoonotic pathogen outbreaks have been studied in an agricultural context\u003csup\u003e\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, our results suggest a similar potential in city streets. Because trees can collect airborne pathogens\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, we expect that elevated zoonotic pathogens in city trees reflects high zoonotic pathogen loads present generally in the city environment. Similar environmental factors correlated positively with animal, zoonotic, and plant pathogen abundances on trees (e.g. root decomposers and foliar plant pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed)) indicating that the environmental conditions leading to disease threat for trees also support proliferation of animal and zoonotic disease-causing microorganisms in cities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCity tree microbiomes have alternative carbon and nitrogen cycling strategies\u003c/h3\u003e\n\u003cp\u003e​Urbanization also increased microbial potential for ecosystem N and C loss through greenhouse gas emissions. Soil nitrate content was highest in urban street tree pits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), as were relative abundances of nitrifying and denitrifying bacteria, which possess the genomic capacity to emit N\u003csub\u003e2\u003c/sub\u003eO, a potent greenhouse gas\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. High levels of denitrifiers in city street trees is consistent with observed increases in N\u003csub\u003e2\u003c/sub\u003eO emissions within cities\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Evidence is emerging that ECM fungi have the capacity to suppress nitrifying and denitrifying bacterial growth in soil through competition for ammonium\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, such that the decline in ECM fungi in urban street tree soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) may create suitable conditions for denitrifiers to flourish. Additionally, we found that free-living soil decomposer communities shifted towards microbes that have higher potential for CO\u003csub\u003e2\u003c/sub\u003e release to the atmosphere in cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Wood, litter, and dung decomposers that dominate street tree pits use relatively labile C (instead of soil organic matter) as their primary C source, which could lead to increased soil respiration and reduced C-residence time, or sequestration, in soils underneath city trees\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. This is consistent with low soil organic matter content of city street tree pits (Fig. S2a) and would occur if trees reduce C allocation belowground in cities, either because of constricted root growth, low colonization by mycorrhizal fungi, increased herbivory, or other types of aboveground tree damage. In addition to sequestering CO\u003csub\u003e2\u003c/sub\u003e through photosynthesis and C allocation to soil, trees have the potential to directly take up methane (CH\u003csub\u003e4\u003c/sub\u003e) \u0026ndash; another potent greenhouse gas \u0026ndash; in various tissues\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This capacity would be particularly useful in cities, where atmospheric CH\u003csub\u003e4\u003c/sub\u003e concentrations can be extremely high\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. However, we found that CH\u003csub\u003e4\u003c/sub\u003e uptake capability of the foliar microbiome was significantly lower in urban street trees compared to forest trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), consistent with low measured rates of methane consumption in city vegetation\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Collectively, these data suggest that dysbiosis in the urban tree microbiome reduces its ability to mitigate climate change induced by three of the most potent greenhouse gases (N\u003csub\u003e2\u003c/sub\u003eO, CO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCity trees host lower microbial diversity than forests\u003c/h3\u003e\n\u003cp\u003eIn line with our expectations, urban tree microbiomes were significantly less diverse than rural tree microbiomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), especially for non-pathogenic microorganisms. Urbanization reshapes tree microbiomes, where microbial communities, despite showing strong spatial autocorrelation similar to soil and aquatic microbiomes\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e were distinct among site types (i.e., street trees, urban forest edge and interior, rural forest edge and interior) (Fig. S3). While urban land use has been shown to homogenize soil microbiomes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, we did not find evidence of microbiome convergence in urban trees (Fig. S4). Instead, urbanization lowered bacterial and fungal diversity in leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) and in the fungal soil community, especially for non-pathogenic fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). This loss in tree-associated microbial diversity with urbanization may be detrimental to trees, animals, and humans, as previous studies have shown that exposure to less microbial diversity is associated with a decline in immune system function and an increase in the prevalence of chronic inflammatory diseases, such as asthma and allergies\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCities are investing in greening initiatives, but the sustainability of these initiatives is unclear because the impact of urbanization on tree health is not fully resolved\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Borrowing from concepts in human microbiome science\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, we aimed to assess the impact of city living on the diverse tree microbiome and its potential to carry out mutualistic, pathogenic, and alternative metabolism that could impact trees and the larger urban ecosystem. We found that urban trees suffer dysbiosis of their microbiomes, losing aboveground and belowground microbial symbionts and collecting plant, zoonotic, and animal pathogens. This issue is important to address, because street trees determine pathogen loads in the air microbiome\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, making local, individual tree-based microbiomes major controls over both the biotic and abiotic components of the environment at city and regional scales. Furthermore, though trees are important biogeochemical cyclers, planted in part for their C sequestration abilities, our results suggest that street trees’ climate change mitigation functions may be offset by their accumulation of denitrifying bacteria and fungal decomposers that could accelerate the production of greenhouse gases. A concomitant loss of methanotrophic bacteria in city trees highlights the reality that, even in long-established urban areas, human-created biomes may not metabolize and sequester as much of our most harmful environmental pollutants as previously thought.\u003c/p\u003e \u003cp\u003eOur results have implications for managing and expanding urban street tree coverage to maximize the benefits of city greening and forest conservation efforts. Public health studies have called for the establishment of an airborne microbiome monitoring network to detect and characterize outbreaks of airborne diseases\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Street trees might provide such a pre-established biomonitoring system to surveil human exposure to pathogens, if future work can directly link pathogen loads on trees to disease outbreaks, similar to how COVID-19 outbreaks are surveilled by sampling wastewater\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. In addition, “rewilding” the urban microbiome, via either planting diverse, native vegetation in urban greenspaces\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e or transplanting native plant-associated microbiomes into soils\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, has the potential to restore microbial plant mutualist communities and improve plant and overall ecosystem health, as has been done in agricultural studies to promote nitrogen metabolism\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e and in forests to increase plant productivity\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Compost additions to urban trees can also increase soil moisture, lower pH, and increase soil C stocks within six years of treatment\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, which our data suggest could reduce pathogen loads and increase tree mutualist abundances. Aboveground management practices, such as applying fungicide to leaves, have also led to positive effects on diseased plants, including reverting belowground pathogen-induced microbiome changes\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Our results suggest policy measures to address greenhouse gas emissions, air pollution, and urban heat island effects more broadly must also be implemented to see the full benefits of investing in city greening projects.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"METHODS","content":"\u003ch2\u003eStudy system\u003c/h2\u003e\u003cp\u003eWe sampled oak trees (i.e., in the genus \u003cem\u003eQuercus\u003c/em\u003e) across a 120 km urban-to-rural gradient in the state of Massachusetts. The gradient includes street trees within nine neighborhoods in the City of Boston (described in Smith et al.\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e), as well as eight mid-successional mixed temperate forest sites that are part of the Urban New England (UNE) project: seven oak-dominated forest sites located along the urban-to-rural gradient from Boston into western Massachusetts (described in previous UNE studies\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e–\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e) and a well-characterized intact rural forest located at the Harvard Forest Environmental Measurement Station (EMS) in western Massachusetts (Petersham, MA, described in Smith et al.\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All sites have a humid, continental climate with warm summers (mean monthly temperatures of 18.6 ºC to 21.7 ºC) and cold, snowy winters (-4.3 ºC to -0.1 ºC), and 1,100-1,300 mm of precipitation distributed evenly throughout the year\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe selected 93 oak trees from multiple age classes across the urban-to-rural gradient for sampling. Trees were sampled across age classes: 5–10 cm diameter at breast height (DBH, “young”, 21 trees), 10–30 cm DBH (“mid”, 32 trees), and 30–100 cm DBH (“old”, 40 trees). Some city trees were lining streets in concrete-enclosed pits, while others were surrounded by grass, but we maintained uniformity within a neighborhood by only sampling trees that were in the same growing conditions (pit vs. grass) within each neighborhood. Urban and rural site designations for forests were based on impervious surface area and population density, following previous analyses of UNE sites reported in our prior publications\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We sampled 37 trees from 9 Boston neighborhoods (3–6 trees per neighborhood), 14 trees from the Harvard Forest Inventory plots, and 42 trees from the forest edge (0–15 m from the edge of the forests\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, 21 trees) and interior (60–90 m from the edge of the forests\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, 21 trees) of the UNE forest sites. For each tree selected, we measured DBH to the nearest 0.1 cm and recorded the GPS coordinates of the tree with a Garmin GPS unit.\u003c/p\u003e\u003ch3\u003eSample collection and processing\u003c/h3\u003e\u003cp\u003eMicrobiome samples were collected during peak tree greenness (July and August) in 2021. At each tree, we collected three sample types: soil, roots, and leaves. For soil collection, we took three replicate 30 cm deep, 2.4 cm radius soil cores from within the drip line or the boundaries of the concrete tree pit. Each of the three cores was divided into two samples, the upper 15 cm (0–15 cm depth) and the lower 15 cm (15–30 cm depth), for a total of six soil samples per tree (549 soil samples in total, 93 trees x 3 cores per tree x 2 layers per core − 9 samples). Soil cores were separated by depth instead of horizon (organic vs. mineral) because street trees lacked a visible organic horizon. We recorded the depth of each core to calculate bulk density and we measured the temperature of each soil sample with a Hanna Instruments Thermistor Thermometer (± 0.4°C for one year; Waterproof Thermistor Thermometer: HI93510N, 2021). For roots, we subsampled 8–10 fine roots per soil sample, pooling roots from the same tree and sampling depth (24–30 root tips per pool) to generate enough tissue for DNA extraction. For leaves, we collected six replicates of mature, mid-canopy leaves in full sunlight from each tree using a 15’ pole pruner; seven trees were too tall to collect leaves with the pole pruner, resulting in 516 leaf samples collected in total (86 trees x 6 leaves per tree). Soil and leaf samples were kept on ice during same-day transport back to the laboratory. Upon returning to the laboratory, we weighed the soil samples for bulk density calculations, took a subsample of soil for microbial community analyses, and collected all roots from each soil sample. The soil and root subsamples, as well as the leaves, were stored at -80 ºC until DNA extraction. The remaining soil materials were stored at 4 ºC for nutrient analysis processing.\u003c/p\u003e\u003cp\u003eWithin 72 hours of sampling, we sieved the soils through a 2-mm sieve, pooling soils from the same depth and tree during sieving. What did not pass through the sieve, we stored at 4 ºC for total root biomass quantification (see Supplementary Methods). We took additional fresh soil subsamples from the pooled, sieved soils to measure soil physiochemical properties, including gravimetric moisture content, pH, organic matter concentration, and ammonium and nitrate content\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We ground the frozen leaves with a mortar and pestle under liquid nitrogen. A subsample of the leaves was stored at -80 ºC for DNA extraction. Additional soil and leaf subsamples were dried at 65 ºC and used for measuring total C and N content and total elemental abundance. Details for all biogeochemical assays can be found in the Supplementary Methods.\u003c/p\u003e\u003ch2\u003eDNA extractions\u003c/h2\u003e\u003cp\u003eDNA extraction procedures followed standard protocols, but were separately optimized for each sample type (upper soil, lower soil, leaf, root). To extract DNA from the upper soil (0–15 cm), we used the DNeasy PowerSoil Pro Kit (QIAGEN, Hilden, Germany) with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.25 g of soil of each sample, yielding 255 extracts in total (93 trees x 3 reps per tree − 24 samples which did not extract enough DNA). To extract DNA from the lower soil (15–30 cm), we used the DNeasy PowerLyzer PowerSoil Kit (QIAGEN, Hilden, Germany) with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.25 g of soil of each sample, yielding 253 extracts in total (93 trees x 3 reps per tree − 26 samples which did not extract enough DNA). To extract DNA from roots, we applied a cetyltrimethylammonium bromide (CTAB)/chloroform extraction and LiCl precipitation method\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e to 10–15 fine roots (approximately 0.2 g) per sample, resulting in 122 extracts in total (93 trees x 2 reps per tree − 64 samples which did not extract enough DNA) and stored the extracts at -80 \u003csup\u003eº\u003c/sup\u003eC. We cleaned the extracted root DNA with AMPure XP Bead-Based Reagent (Beckman-Coulter Life Sciences, Indianapolis, IN) with modifications (see Supplementary Methods). To extract DNA from leaves, we used the DNeasy Plant Pro Kit (QIAGEN, Hilden, Germany), with modifications (see Supplementary Methods). We extracted total DNA from approximately 0.03 g of leaf tissue of each sample, yielding 505 extracts in total (86 trees x 6 reps per tree − 11 samples which did not extract enough DNA). To accurately represent leaf microbiome diversity, we paired and pooled leaf DNA extracts from the same tree in equimolar concentrations into three replicates per tree, resulting in 259 pooled extracts representing the 86 trees in total.\u003c/p\u003e\u003ch2\u003eFungal and bacterial amplicon sequencing\u003c/h2\u003e\u003cp\u003eWe used modified versions of the fITS7 and ITS4 primer set to amplify the ITS2 region of fungal rDNA\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e and modified versions of the 515f and 806r primer set to target the v4 16S region of bacterial rDNA\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. All primers contained both Illumina adapters and unique sample indexes\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. See Supplementary Methods for PCR amplification details. Amplicon sequencing was carried out on 853 fungal DNA amplicon samples and 866 bacterial DNA amplicon samples (66 fungal samples and 56 bacterial samples did not amplify). We verified amplicons using agarose gel electrophoresis, cleaned them with the Just-a-Plate 96 PCR Purification and Normalization Kit (Charm Biotech, MO), and quantified them with the Qubit HS-dsDNA kit (Invitrogen, Carlsbad, CA). We then mixed 16S and ITS amplicons for each sample at equimolar concentrations, and combined up to 176 16S and ITS amplicons into a single library for sequencing, resulting in eight libraries. The TUFTS Genome Sequencing Core facility performed 250 base pair (bp) paired-end sequencing on the Illumina MiSeq for each library. Sequencing resulted in a total of 49,980,377 16S sequences and 33,193,586 ITS sequences. The average read number per sample and sample read standard deviation for each sample type throughout the data cleaning method are reported in Table S2.\u003c/p\u003e\u003ch2\u003eBioinformatics\u003c/h2\u003e\u003cp\u003eThe 16S and ITS sequencing data were processed using BU16S (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Boston-University-Microbiome-Initiative/BU16s\u003c/span\u003e\u003cspan address=\"https://github.com/Boston-University-Microbiome-Initiative/BU16s\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a customized QIIME2\u003csup\u003e101\u003c/sup\u003e pipeline designed to operate on Boston University’s Shared Computing Cluster\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. In summary, BU16S identifies amplicon sequence variants (ASVs) that are 99% similar in rDNA sequence using DADA2\u003csup\u003e103\u003c/sup\u003e and then classifies ASVs with 95% or greater sequence identity to a database, SILVA99 (v138.1)\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e for bacteria and UNITE dynamic database (v9.0)\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e for fungi, using VSEARCH\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e (see Supplementary Methods). Additional bioinformatics analyses were conducted in the R software environment (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. To assign taxa to functional groups, fungal genera were matched to the FungalTraits database\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e,\u003c/sup\u003e and bacterial genera were compared against an in-house functional database developed based on the presence of genes encoding enzymes involved in specific biochemical pathways, including copiotroph, oligotroph, cellulolytic, ligninolytic, methanotroph, chitinolytic, assimilatory nitrite-reducing, dissimilatory-nitrite reducing, assimilatory nitrate-reducing, dissimilatory nitrate-reducing pathways\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e (see Supplementary Methods). Fungal plant pathogens were assigned to genera identified by the FungalTraits database with any plant pathogenic capacity description, while fungal animal pathogens were assigned to genera identified by the FungalTraits database with an animal biotrophic capacity description (% identity of ASVs to fungal pathogens in each sample type are reported in Tables S3-S6). To assign pathogen status to bacterial ASVs, representative sequences for ASVs were blasted against the Multiple Bacterial Pathogen Detection database\u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e and classified with 95% or greater sequence identity (% identity of ASVs to bacterial pathogens in each sample type are reported in Tables S7-S10).\u003c/p\u003e\u003ch2\u003eAdditional tree and environmental factors\u003c/h2\u003e\u003cp\u003eTo calculate the proximity of each tree to the City of Boston, we used the distHaversine function in the geosphere R package (v.1.5–18)\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e to find the distance between the latitude and longitude of the tree and Boston downtown, as defined by the coordinates of Park Street Massachusetts Bay Transit Authority (MBTA) subway station. Tree growth rate was calculated for street trees and trees in the Harvard Forest Biomass plots using the difference between the recorded DBH and a previously-recorded DBH from at least one year prior and annualized by dividing by the number of years since the last recorded DBH.\u003c/p\u003e\u003cp\u003eData from Smith et al. (2019) was used for DBH from 2014 for the street trees\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e and the Harvard Forest Data Archive was used for annual DBH for the Harvard Forest Biomass plot trees\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. Foliar N concentration (%), foliar C to N ratio (C:N), and soil soluble salt content (µS/m) data collected in 2018–2019 for Caron et al. (2023) were averaged across oak tree samples taken from the edge (0 m from forest edge) and interior (90 m from forest edge) of each forest\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and assigned to the UNE trees at the edge and interior of that forest, respectively. Atmospheric gas concentration (O\u003csub\u003e3\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, and total nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e) parts per billion (ppb)) and annual inorganic ion throughfall deposition (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e−\u003c/sup\u003e, and total inorganic nitrogen deposition) data collected in 2019 for Rindy et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e was averaged between replicates at the edge and interior of a forest, separately, and assigned to the UNE trees at the edge and interior of that forest, respectively. Annual rates of atmospheric wet deposition (kg ha\u003csup\u003e− 1\u003c/sup\u003e yr\u003csup\u003e− 1\u003c/sup\u003e) of hydrogen (H\u003csup\u003e+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e), chlorine (Cl\u003csup\u003e−\u003c/sup\u003e), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), sodium (Na\u003csup\u003e+\u003c/sup\u003e), sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2−\u003c/sup\u003e), NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e−\u003c/sup\u003e, and total inorganic N deposition were previously collected at National Atmospheric Deposition Program (NADP) sites at Boston University (site MA22) and the Arnold Arboretum (MA98), reported in Conrad-Rooney et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, and assigned to any trees within 15 km of the collection sites, as a 15 km radius around the NADP sites was used to categorize the NADP sites as urban, suburban, or rural\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Any trees within 15 km of both collection sites were assigned the data of the closer NADP site. Similarly, atmospheric gas concentration data and inorganic ion throughfall deposition data from Rindy et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e from the edge of the Arnold Arboretum forest was also assigned to the Boston street trees within 15 km of the Arnold Arboretum forest edge, and the data from the interior of the closest Harvard Forest UNE study site (Harvard Forest UNE-1) was also assigned to the Harvard Forest Biomass plot trees, as they were within 15 km of the Harvard Forest UNE-1 site.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eBefore conducting statistical analyses, contaminating sequences were removed from the amplicon sequence data based on their prevalence in sequenced negative controls using the decontam package in R (version 1.20.0)\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e. To normalize ASV counts across samples for each sample type, we conducted rarefaction with the rarefy_even_depth function in the phyloseq package in R (version 1.44.0)\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. This normalization was done to reduce the false discovery rate of ASVs, following our previous analysis of urban forest microbiomes\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, as samples varied more than 10-fold in sequencing depth\u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. We rarefied ASV counts for each sample type using random subsampling based on a read count that minimized dropped samples and maximized number of reads per sample: a 5,769 read cutoff was used for fungal leaf communities, a 3,395 read cutoff for fungal 0–15 cm depth soil communities, a 3,307 read cutoff for fungal 15–30 cm depth soil communities, and a 5,204 read cutoff for fungal root communities. A 200 read cutoff was used for bacterial leaf communities, a 6,508 read cutoff for bacterial 0–15 cm depth soil communities, a 6,194 read cutoff for bacterial 15–30 cm depth soil communities, and a 1,974 read cutoff for bacterial root communities.\u003c/p\u003e\u003cp\u003eTo quantify the impact of urbanization on the tree microbiome, we conducted several different analyses using two different metrics of urbanization intensity as predictor variables. In the first set of analyses, microbiomes in different sample types (root, soil, leaves) were analyzed separately. Shannon’s alpha diversity for each fungal and bacterial community was computed using the vegan package in R (version 2.6-4)\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e. Initially, we tested for urbanization effects on the relative abundance of microbial taxonomic groups, functional groups, and Shannon’s diversity using distance from Boston and distance from the forest edge, as well as their interaction, as the fixed independent variables in regression analyses, following previous analyses of forest microbiomes along the UNE gradient\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Regressions were run using the maximized log-likelihood method of the lme function in the nlme package (version 3.1–166) in R\u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. City street trees were categorized as the most urban and most edge (0 m to edge) trees. To account for replicate samples taken from individual trees at individual locations, tree ID nested within sampling site (either forest site at UNE or neighborhood in the City of Boston) was included as a random variable in each model. If we found a significant edge effect or urbanization x edge effect on microbiome composition, we then tested the effect of different site types (i.e., street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior) using the maximized log-likelihood method of the lme function in the nlme package (version 3.1–166)\u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. ANOVA for LMMs was performed on standardized values using the scale function in R, with the lmerTest (version 3.1-3) packages\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e, and confidence intervals on the difference in group means were calculated with the TukeyHSD function of the R stats package (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e, with significance levels set at 5%.\u003c/p\u003e\u003cp\u003eIn a second set of analyses, we assessed urbanization and edge effects on microbiome homogenization across sample types by calculating beta dispersion for either leaf samples or belowground samples (combined root, O horizon soil, M horizon soil) within each site type (i.e., street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior). For beta diversity analysis, we performed batch correction on the unrarefied 0–15 cm depth soil, 15–30 cm depth soil, and root 16S and ITS ASV tables, respectively, and combined them into one belowground 16S ASV table and one belowground ITS ASV table to normalize for sequencing differences due to differences in extracting and amplifying the different sample types. We used the ComBat_seq function in the sva package in R (version 3.48.0)\u003csup\u003e\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e, with sample type as the batch variable and tree age and tree site type (e.g. street tree, urban forest edge, urban forest interior, rural forest edge, and rural forest interior) as the biological covariates to be preserved in the batch correction. We then calculated the Aitchison distance matrices of the leaf and belowground 16S and ITS ASV tables, with a pseudocount of “1” added to all values\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e. Principal components analysis (PCA) was used to visualize community structure differences among samples based on the Aitchison distance matrix using the prcomp function in the R stats package (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. We performed a multiple regression on distance matrices to test for the impact of tree site type and spatial distance on microbial community composition using the lm function in the R stats package (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. To test for microbial homogenization, we calculated the beta dispersion of the Aitchison distances against the centroids of the site types using the betadisper function of the vegan package in R (version 2.6-4)\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e, and then calculated confidence intervals on the difference in group means with the TukeyHSD function of the R stats package (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e, with significance levels set at 5%. We also explored relationships between microbial functional groups and environmental factors with Pearson correlations using the cor.test function of the R stats package (version 4.3.1)\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e, with significance levels set at 5%.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw amplicon sequence data will be deposited in the Sequence Read Archive at NCBI upon publication, and the accession number will be provided here.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCode availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysis scripts will be made publicly available on GitHub upon publication, and the link will be provided here.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGilbert, J. 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Microbiol. \u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5939048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5939048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe tree microbiome is a critical determinant of tree and ecosystem functioning, but human disturbances can disrupt natural microbe-tree relationships. Here, we show that urban trees exhibit microbial dysbiosis along a model urbanization gradient, with declines in mutualistic root and leaf symbionts, but increases in decomposers and pathogens, including those relevant to plant, animal, and human health. These shifts correlate with urban stressors such as heat, drought, and atmospheric deposition. Urban tree microbiomes also show altered biogeochemical cycling capabilities, with high potential for nitrogen loss through greenhouse gas (N2O) production and reduced capacity for methane consumption relative to rural trees. Additionally, urbanization reduces overall tree microbiome diversity, particularly among non-pathogenic microbes, potentially diminishing the ecological and health benefits of diverse environmental microbiomes in cities. These findings underscore the need to consider the microbiome in urban forestry management practices to maximize the ecological and health benefits of city greening and forest conservation efforts.\u003c/p\u003e","manuscriptTitle":"Dysbiosis in the urban tree microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 07:19:00","doi":"10.21203/rs.3.rs-5939048/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ab0e9e6a-4619-4816-9411-0464e1589121","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45933638,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":45933639,"name":"Biological sciences/Ecology/Urban ecology"}],"tags":[],"updatedAt":"2025-10-04T07:05:12+00:00","versionOfRecord":{"articleIdentity":"rs-5939048","link":"https://doi.org/10.1038/s44284-025-00322-x","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2025-10-03 04:00:00","publishedOnDateReadable":"October 3rd, 2025"},"versionCreatedAt":"2025-03-25 07:19:00","video":"","vorDoi":"10.1038/s44284-025-00322-x","vorDoiUrl":"https://doi.org/10.1038/s44284-025-00322-x","workflowStages":[]},"version":"v1","identity":"rs-5939048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5939048","identity":"rs-5939048","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}