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Here we assessed how species and functional diversity components of wild bee assemblages responded to increasing urbanization levels, using a large dataset encompassing recent surveys gathering 838 sampling sites located in natural, semi-natural and urban habitats of France, Belgium and Switzerland.We found a weak, but significant negative effect of the proportion of impervious surfaces in a 500 m radius around each site on local species richness of bee communities. Thus, sites with high soil sealing tended to host less species than those with low soil sealing. However, this trend was not observed when using human population density as an urbanization metric: sites with denser human populations hosted on average the same number of species as less densely populated sites.Concerning taxonomic homogenization of communities, we did not record any effects of urbanization, both in terms of impervious surfaces or human population density.Analyses of occurrence rates of bee functional traits revealed significant differences between poorly and highly urbanized communities, for both urbanization metrics. With higher human population density, probabilities of occurrence of above-ground nesters, generalist and small species increased, and a higher probability of occurrence of above-ground nesters, generalists and social bees were recorded in areas with high soil sealing.Therefore, we found overall consistent results linking urbanization and wild bees taxonomic as well as functional trait diversity, even though analyses stemmed from a combination of many independent studies covering a broad range of anthropized and natural aeras from western Europe. This further highlights the greater generalizability of those ecological trends throughout European temperate biomes compared to other studies typically focusing on a single city and its immediate vicinity.Two complementary metrics of urbanization intensityTo quantify urbanization, we used two variables: soil sealing12,16,19,36 in a 500 m radius, and the mean human population density, also in a 500 m radius, the latter variable being used only recently to assess pollinator responses to urban environments37,38. These two variables return different but complementary information concerning urban environments. Indeed, if soil sealing gives an idea as to how human activities impact land use, human population density helps distinguish between very dense urban areas and very impervious areas with lower densities of buildings. High human population density areas are usually associated with high levels of soil sealing, but the contrary is not true. Similarly, areas with low soil sealing are usually associated with low human population densities, but again, the opposite is not always true. Therefore, we found it informative to consider both variables when analyzing the response of wild bee assemblages to urbanization.Note that some specific habitat types, for example business districts, are exceptions to the rule. These places are indeed very densely urbanized, but with very low population density. However, no inventories have been carried out in these places, and thus will not be a problem for our study.Response of bee community species richness to urbanizationOne of our goals was to position this study in the context of the contrasting findings on pollinator communities and urbanization. Whereas no consistent trend is reported in literature15, our large dataset reveals that high soil sealing is detrimental to wild bee species richness. This offers a unified view of a trend that has been unequally evidenced from studies focusing on a single or few cities only. High proportions of soil sealing reduce the availability of nesting sites for ground-nesting bee species. This may in turn lower the species diversity of local assemblages, by filtering out ground-nesting bees, leaving mainly cavity-nesting bees. Furthermore, high levels of soil sealing can lead to depletion of floral resources, of extreme importance for bees, especially in highly disturbed environments such as cities39,40. Note that several previous studies report the opposite, with high local species richness of wild bees in urbanized habitats. However, these positive effects are often associated with intermediate levels of urbanization15,16, where private gardens and other green spaces may supply abundant floral resources, in conjunction with intermediate levels of soil sealing16,17,18,19,20,24.On the contrary, there was no significant relationship between local species richness and human population density. Recently, two recent studies have used this metric to analyze how urbanization impacts local diversity of bee, hoverfly37 or butterfly38 assemblages, and both studies report negative impacts of human population density. However, high levels of human population density do not necessarily correlate with low availability of floral resources or nesting sites for pollinating insects. Several studies show that densely-populated urban environments may be adequate habitats for pollinating insects, due to alternative management practices of urban green space41 and the year-round availability of ornamental flowers42,43. Here, the absence of a clear effect of human population density on local bee species richness masks a change in the species composition of the communities, as shown by the increasing proportion of cavity nesters, compared with ground nesters. Indeed, despite the lower availability of nesting resources for ground-nesters, cavity-nesters take over in high-density areas, where more concrete structures and buildings are present15, thus they may compensate for the loss of ground-nesting bee species.Wild bee community homogenization and urbanizationWe did not observe any relationship between mean pairwise β-diversity and the two metrics of urbanization. This result contrasts with those of Banaszak-Cibicka and Żmihorski (2020)44 who found more homogeneous wild bee communities in urban environments compared to non-urban ones. Similar results have been reported for bees, with homogenization of urban pollinator communities compared to rural ones28,45. Biotic homogenization in urban environments has also been reported for other taxa, for example birds46.In our study, when considering urbanization levels, either in terms of soil sealing or human population density, urban wild bee communities are not more or less taxonomically homogeneous than non-urban ones. It is important to note that this result does not imply that urban and non-urban wild bee communities are similar, but that the homogenization of wild bee communities is constant throughout the urbanization gradient. In other words, urban communities are as dissimilar as non-urban ones. Here, the β diversity values are quite high (ranging from 0.68 to 0.96), emphasizing that even urban areas have quite dissimilar communities when compared to each other. This high level of dissimilarity among wild bee communities in urban environments can be explained by the large range of biogeographical regions encompassed in our dataset (Fig. 5), as each of these regions harbors a specific wild bee fauna34.Local factors in cities might also explain these high levels of dissimilarity. We know for example that green space connectivity has effects on species richness, with more wild bee species and abundance in cities with more connected green spaces47. Another local explanation might come from contrasting green space management practices among cities. Not all cities have the same policies, and urban green space management is crucial to the establishment and sustainability of diverse pollinator communities14,15,48. Thus, we expect more dissimilar wild bee communities among cities with differing green space layout and management.Figure 5Grouped sampling sites (n = 532) in France, Belgium and Switzerland, with the biogeographical regions. In total, 238 sites belong to the Continental region, 178 to the Atlantic, 106 to de Mediterranean and 10 to the Alpine. This figure was generated using QGIS software, v3.10.13 (https://www.qgis.org/).Full size imageFunctional responses of bee communities to urbanizationSeveral studies have already shown trends on how urban areas filter wild bee communities based on their functional traits (see30 and49 for reviews). However, as for taxonomic diversity, it is often difficult to identify clear variation patterns50. Using our large dataset, we could identify typical wild bee functional traits that are favored in urban environments, thus informing on the average functional profiles of wild bee species that may thrive in cities. We found urban wild bees in general to be typically above-ground nesters and generalists, while different trends were established for their body size and sociality, depending on the considered urbanization metric (Fig. 6).Figure 6Summary picture of an urban bee community, compared to a non-urban one. This figure was generated using Inkscape v1.2 (https://inkscape.org/).Full size imageNesting habitsAbove-ground nesting species were more frequent with increasing urbanization than below-ground nesting ones, and this result was recorded with both urbanization metrics.This result is consistent with what was previously reported in the literature16,49,51,52. Indeed, cities, with high proportions of impervious surfaces and buildings, offer fewer nesting habitats to ground-nesting species15, nesting sites becoming a limiting factor39. On the other hand, above-ground nesters can do well in cities with the presence of man-made structures, depending on their ability to use them and on their availability53.The presence of green areas in cities can help ground-nesting bee species by offering more nesting opportunities and resources17. Several studies highlight the importance of parks and gardens in supporting bee biodiversity in cities12,18,31,54, which otherwise are constraining environments due to soil sealing.DietGeneralist species were more frequent in more urbanized sites than specialist ones, and this was recorded for both urbanization metrics.This is in accordance with what was previously found in the literature32,50,51,52,54,55, as specialist bee species depend on the presence of their host plants to complete their life-cycle, which are often scarce due to the rarefaction of native flowering resources. As one can find many exotic flowers in cities, especially in residential gardens and urban parks56, we expect to detect less oligolectic bee species in densely urbanized habitats57.Notwithstanding, Banaszak-Cibicka et al. (2018)20 found more oligolectic species in urban parks of Poznań (Poland) compared to a national park. Thus, urban areas are not always depleted of specialist species, and well-managed parks with preserved native floral resources can obviously support specialist wild bee species in cities58.Additionally, it is important to emphasize that the presence of an exotic plant species may concomitantly support an associated specialist bee species. In Poland, for instance, the spread of Bryonia dioica in urban environments also brought the Andrena florea wild bee species, specialized on this plant59.Body sizeWe recorded contrasting effects of the two urbanization metrics on wild bee body size: small species were more frequent in relation to higher human population density compared to large species, but we found no difference with the proportion of impervious surfaces. Contrasting impacts of urbanization on bee body size are also reported in the literature, with some studies finding little to no effect32,50, and some finding that urbanization often favors smaller bee species12,30,60. Bee body size is of particular importance because it is related to the foraging range of individuals61,62. In fragmented habitats, such as dense urban environments, distances between suitable nesting and feeding habitats may select for smaller species that can remain on small green spaces and rarely need to commute across several green spaces. Furthermore, small bees may be favored given that they need fewer floral resources than large bees, even though large bees can fly further62.This might also explain the difference in the response of bee body size to the two urbanization metric results. In densely populated cities, it is harder to fly between suitable habitats, even for larger bees, as higher buildings and structures may act as barriers to their movement. Indeed, it has been recently shown that the 3D structure of cities impacts wild bee community composition63. Thus, being able to fly further might no longer be an advantage, and larger bees, requiring more floral resources than smaller ones, might be selected against. On the contrary, very impervious areas do not always host high building density (for example, as in the case of parking lots), thus making it easier for large wild bees to fly between bare soil areas.Densely populated areas might also exhibit warmer temperatures due to the urban heat island effect, and this could, in turn, result in the selection of smaller individuals, as we know that in cities, higher temperature results in smaller body sizes64.SocialityWe also recorded contrasting effects of the two urbanization metrics on sociality: social species were more frequent in relation to higher proportion of impervious surface compared to solitary ones, but no effect was recorded with human population density. This is in agreement with a recent literature review that reports on no consensus concerning the response of this trait to urbanization30.However, some urban habitats are shown to host more social species than rural habitats20,32, which may be linked to better reproductive success in cities compared to rural habitats such as agricultural environments65, an explanation that is consistent with our results on the soil sealing—sociality relationship.Conclusion, limits & future directionsOverall, our findings suggest that urban environment filters wild bee communities based on their functional traits. Our results also underscore different impacts of urbanization metrics on local species diversity, with a significant negative impact of soil sealing. On the contrary, both soil sealing and human population densities create strong functional filtering of trait assemblages.These results are particularly relevant since they arise from a range of independent studies, thus providing a general view on the wild bee communities in urban environments from western Europe. Since this study covers different biogeographical zones, it further underlines its applicability to other temperate countries. We therefore expect similar patterns to shape wild bee communities in urbanized areas from other temperate regions, but further confirmatory studies would be welcome.Our study also delivers a clear message concerning wild bee communities in urban environments. Urban environments cannot compare with non-urban ones in terms of species richness and trait diversities of bee communities. However, simple management practices of urban green spaces, such as differentiated management, or simply low management66, may help in maintaining this diversity. Indeed, not all green spaces are equally valuable in supporting wild bees, and pollinator assemblages in general49. For example, it has been shown that pollinator richness was positively influenced by green space size, but also by management measures such as mowing67. Increasing the quantity of floral resources and their spatio-temporal availability and diversity40,68 could also help conserving pollinator communities and pollination function in cities69, as long as these resources are native or attractive to pollinators.We can then hypothesize that changes in managing practices could help increase functional diversity of bees in cities, with specialist and ground-nesting species being found more frequently in these low-managed urban areas.Finally, if managing urban green space is of great importance to protect biodiversity in cities, it is crucial to involve all stakeholders, especially residents70 to achieve efficient and socially-accepted measures.In the future, it will be important to consider intra-city landscape variation, and see how urban characteristics might influence taxonomic and trait diversity. This will surely allow us to better understand the dynamics shaping wild bee communities in urban environments. More

Study sites and data collectionDuring 2017 and 2018, we carried out 14 experiments in rivers located in temperate, tropical, and subarctic biomes to capture a gradient of river productivity and climatic characteristics (Table 1, Fig. 1). Apart from the Mekong and Sekong rivers in Cambodia that were impacted by plantations, rice cultivation, grassland, and urban areas (56% impacted land cover in the Mekong and 38% in the Sekong), the selected rivers were predominantly in pristine areas (impacted land-use ≤ 8%), although two rivers in Mongolia were affected by livestock grazing (with 26% of land cover at the Khovd and 59% in the two Zavkhan rivers).We conducted traditional O2 concentration metabolic assessments, assessments of isotopic fractionation, and 24 h characterization of δ18O2 at each site. We measured changes in dissolved O2 concentrations and temperature every 10 min over at least 24 h with at least one MiniDOT logger (PME, Vista, California, USA). We calibrated for drift using the average measurement values made in 100% saturated water for at least 30 min before and after each deployment to allow adjustment to temperature and placed sensors in the river for at least 30 min prior to using data to allow equilibration to temperature (following methods detailed in ref. 52).We collected δ18O2 samples by hand every 2 h during the same 24-h period of the O2 concentration measurements in pre-evacuated 100 mL vials loaded with 50 µl HgCl2 as a preservative and sealed with septum stoppers (Bellco Glass Inc., Supelco, Vineland NJ). We analyzed samples for δ18O2 at the Nevada Stable Isotope Lab of the University of Nevada, Reno with a Micromass Isoprime (Middlewich, UK) stable isotope ratio mass spectrometer. We followed the method described by ref. 17 and injected 1.0–2.5 mL of headspace gas taken from the serum bottles using a gastight syringe (SGE, Australia) into a Eurovector (Pavia, Italy) elemental analyzer equipped with a septum injector port, and a 1.5 m long molecular sieve gas chromatography column. Water-δ18O was also collected at each site every 2 h and analyses were performed using a Picarro L2130-i cavity ringdown spectrometer at the Nevada Stable Isotope Lab of the University of Nevada, Reno. δ18O2 values are reported in the usual δ notation vs. VSMOW in units of ‰, with an analytical uncertainty of ±0.2‰ for δ18O2, or an analytical uncertainty of ±0.1‰ for water-δ18O.We characterized physical characteristics at each site to provide parameters to estimate whole-system metabolism. We measured conductivity, slope, and flow velocity and depth at ten transects using a flow meter when wadeable or with an Acoustic Doppler Velocimeter (Sontek, Xylem, San Diego, CA) when rivers were not wadeable. At each site, we measured light as photosynthetically active radiation (PAR) every 10 min, using Odyssey PAR loggers (Data Flow Systems, Christchurch, New Zealand) calibrated with a Li-Cor PAR sensor (Lincoln, Nebraska, USA).At each site, we also directly measured biofilm ash-free dry mass (AFDM) from 8 to 12 rocks (53). The material was scrubbed from the rocks, agitated, filtered (Whatman glass microfiber GF/F filters). Rock area was estimated with calibrated pictures processed with the ImageJ processing program (National Institutes of Health and the Laboratory for Optical and Computational Instrumentation LOCI, University of Wisconsin). For AFDM analyses, samples were dried, and weighed before and after combustion.Additionally, we collected data on the percentage of impacted land use in the watershed above each sampling site: for the Mekong and the Sekong we used Landsat satellite imagery from ref. 54, for the US and Mongolian sites land use characteristics were derived from the National Land Cover Database55 and for Patagonia we used the Chilean national land use inventory maps from ref. 56.δ18O2 stable isotope fractionation during respiration in sealed recirculating chambersModels based on oxygen isotopes are sensitive to the oxygen isotope fractionation factor (αR) during respiration used; αR can vary widely among sites and is influenced by temperature and water velocity30. We used in our models the range of αR values measured by30 using sealed Plexiglas recirculating chambers as in ref. 57. These measurements were done at the same time as the 24 h δ18O2 sample collections in the rivers of this study. We placed rocks, sediment, macrophytes (macrophytes dominated in the Zavkhan 1 site) inside the chambers, depending on the site’s dominant substrata (see ref. 30 for more details on chamber measurements). We collected water samples in the chambers for δ18O2 analyses before and after the incubations and the O2 isotope fractionation factor was calculated using Eq. (2).$$delta =(delta i+1000){F}^{left(alpha -1right)}-1000$$
(2)
where δ is the O2 isotopic composition of dissolved oxygen at the end of the dark incubation, δi is the O2 isotopic composition of dissolved oxygen at the beginning of the dark incubation, F the fractional abundance of O2 concentration remaining at the end of the dark incubation, and α is the isotopic fractionation factor during respiration.Ecosystem metabolism O2 single station modelingWe modeled metabolism as a function of GPP, ER, and reaeration with the atmosphere, using the single-station open-channel metabolism method4 using the same approach as15, given in Eq. (3).$${O}_{{2}_{(t)}}={O}_{{2}_{(t-1)}}+left(left(frac{{GPP}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+frac{{ER}}{z}+{K}_{{O}_{2}}left({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)right)triangle t$$
(3)
where GPP is gross primary production in g O2 m−2 d−1, ER is ecosystem respiration in g O2 m−2 d−1, ({K}_{{O}_{2}}) is the reaeration coefficient (d−1). PPFD is photosynthetic photon flux density (µmol m−2 s−1), z is mean stream depth (m), and ∆t is time increment between logging intervals (d). We used Bayesian inverse modeling approach to estimate the probability distribution of parameters GPP and ER that produce the best model fit between observed and modeled O2 data. We fixed site-specific ({K}_{{O}_{2}}) estimates using K600 (d−1) (normalized beyond gas-specific Schmidt number conversions among gases58) based on prior work characterizing K using BASE59, and converted these prior estimates of K600 to ({K}_{{O}_{2}})using appropriate temperature corrections. We estimated daily GPP and ER from diel O2 data only (Eq. (3)) to be used as prior estimates of daily GPPO2 and ERO2 in the coupled O2 and δ18O2 model (Eqs. (4a) and (4b))15, where the mean and SD of GPP and ER from the O2 _only method were used as prior estimates of GPPO2 and ERO2 in the dual O2 and δ18O2 model described below.Ecosystem metabolism: Diel δ18O2 modelingWe also modeled metabolism using an updated version of the model developed by ref. 15 coupling high-frequency O2 concentration data with δ18O2 collected every 2 h throughout the same 24 h period of the O2 concentration measurements. With this model, daily rates of ecosystem metabolism are derived from diel changes in δ18O2 and O2, where values of δ18O2 are converted to g 18O m−3 (18O2 in Eq. 4b) and modeled as a function of water isotope values, isotope fractionation, reaeration with the atmosphere, ER, and GPP. As with Eq. 3, the ratio of light at the previous logging time (({{PPFD}}_{left(t-1right)})) relative to the sum of light over 24 h (({sum {PPFD}}_{24h})) is used to characterize times when GPP is zero and only ER is taking place (Eqs. (4a) and (4b)):$${O}_{{2}_{left(tright)}}= , {O}_{{2}_{left(t-1right)}}+left(frac{{{GPP}}_{O2}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+left(frac{{{ER}}_{O2},xtriangle t}{z}right)\ +left({K}_{{O}_{2}}xleft({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)xtriangle tright)$$
(4a)
$${18O}_{{2}_{(t)}}=, {18O}_{{2}_{(t-1)}}+left(frac{left({{GPP}}_{O2}+{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{{sum {PPFD}}_{24h}}x,{alpha }_{P},x,{{AF}}_{W}right)\ +left(frac{{{ER}}_{O2},xtriangle t}{z}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left(frac{left(-{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left({K}_{{O}_{2}}x,{alpha }_{g}xtriangle t,xleft(left({O}_{{2}_{{sat}left(t-1right)}}x,{alpha }_{g},x,{{AF}}_{{atm}}right)-{18O}_{{2}_{(t-1)}}right)right)$$
(4b)
Where GPPO2 and ERO2 (g O2 m−2 d−1) refer to the values obtained from diel O2 only, dielMET (g O2 m−2 d−1) is the diel metabolism term that allows for the estimation of diel ER and GPP from 18O2, KO2 is the O2 gas exchange rate (d−1), z is mean stream depth (m), PPFD is photosynthetic photon flux density (µmol m−2 s−1), Δt is time step between measurements (d), 18O2 is the concentration of 18O in dissolved O2 (g 18O m−3), AFDO is atomic fraction of dissolved O2 (mol18O:mol O2, measured), AFw is atomic fraction of H2O (mol 18O:mol O2, measured), AFatm is atomic fraction of atmospheric air (mol18O:mol O2, literature), αg is the fractionation factor during air–water gas exchange (0.9972, from ref. 60), αR is the fractionation factor during respiration measured in the chambers (varied by site30; Fig. 1), αp is the fractionation factor during photosynthesis (1.0000 from ref. 60).The inverse modeling approach finds the best estimates of parameters to match measured and modeled dissolved O2. The model assumes that the measured changes in O2 concentration represent the actual net diel changes in O2 concentration and uses an additional parameter, dielMET, that is a function of the isotopic enrichment occurring during respiration, derived from diel 18O2. This parameter increases daily ERO2 and GPPO2 of the same amount, adding and subtracting dielMET, to obtain daily δ18O2-ER and δ18O2-GPP, respectively.We estimated the posterior distributions of unknown parameters (ERO2, GPPO2, and dielMET) using a Bayesian inverse modeling approach15 and Markov chain Monte Carlo sampling with the R metrop function in the mcmc package61,62. Each model was run for at least 200,000 iterations using nominally informative priors based on the range of ERO2 and GPPO2. For dielMET, we used a minimally informative uniform prior distribution (0–100 g O2 m−2 d−1). We removed the first 10,000 iterations of model burn-in and assessed quality of model fit. Model runs using the minimum, average, and maximum αR values measured in the field recirculating chambers were also compared, and we selected the αR and report associated model metabolism estimates that generated the lowest sum of squared differences between the observed and modeled O2 and 18O2 diel values.Temperature-normalized comparisonsTo test the effect of temperature from the daily δ18O2-ER and δ18O2-GPP rates and account for daily variations in temperature, we normalized estimates from models to 20 °C (and report them as 20δ18O2-ER and 20δ18O2-GPP) for comparison with O2-derived metabolism estimates following33 with Eq. (5):$${rate},{at},20,{}^circ C=frac{{2.523* e}^{(0.0552* 20)}}{{2.523* e}^{(0.0552* {t}_{1})},* {rate},{at},{t}_{1}}$$
(5)
Where t1 is site temperature and rate is the measured rate (i.e., GPP or ER) at t1.Statistical analysesWe used multiple linear regression to find the best predictor of the magnitude of diel 20δ18O2-ER and differences between sites. To select the best model, we performed a stepwise variable selection and selected the best model based on the lowest AIC. Tested variables included percentage of impacted land use (%), 20δ18O2-GPP (g O2 m−2 d−1), conductivity (µS/cm), ash-free dry mass (AFDM, g), slope (%), water depth (m), and flow velocity (m/s) measured in the field. We used ANOVA to test the relative contribution of each variable selected with the AIC to total variance. Analyses were run with the R software61.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Soil sampling and determination of soil physical properties and synoptic dataSoil samples were taken from two coastal deserts in the north and south of Iran. Details of their geographic distribution and eco-physiological characterization were shown in Table 1. A total of 2 kg of soil samples were collected from 2 distinct sampling locations ranging in depth from 0 to 30 cm, and the samples were dried for 3 days at room temperature and in the dark before sifting. The soil samples were sieved using a 2 mm sieve to remove stones and other inert material before being stored in zip-top bags. Table 1 lists the soil samples’ physical characteristics, including soil texture (sand 2–0.02 mm; silt 0.02–0.002 mm; clay 0.002 mm), pH, and the proportions of clay, silt, and sand. Synoptic data from the past 10 years (2009–2019), including the average annual temperature, maximum temperature, minimum temperature, average rainfall, average annual wind speed, and maximum wind speed, were obtained from the I.R.OF Iran Meteor (http://www.irimo.ir/far/index.php).Bacterial isolation and effect of manure-based medium on their growthAccording to Chen et al. 2005, the soil-borne bacteria were isolated using direct-spreading method. For this essence soil samples were treated through a series of dilutions. The mixture of 1 g of soil sample was vortexed for 1 min after being suspended in 2 ml of sterile physiological saline (0.9% w/v NaCl). The mixture was then diluted serially (typically 10–1 to 10–7), and level 100 μl of the diluted soil samples were scattered on the surface of solidified plates using glass spreaders. The samples were then incubated for 1 to 3 days at 30 °C in an inverted posture without light. For bacterial isolation, we used eleven culture media including Nutrient Agar (NA), Nutrient Agar plus MnSO4 (NA + MnSO4), LB, Moller Hinton Agar (MHA), Acidithiobacillus (APH) medium, Violet Red Bile Lactose (VRB) agar medium, GYM Streptomyces medium, DPM medium, Azospirillum medium, Azotobacter medium and Manure based medium (MB).To prepare MB medium, dry animal manure and distilled water (1:6 w/v) were combined to create MB medium, which was then let to sit at room temperature for 16 h. The resulting mixture was then centrifuged at 5000 rcf for 30 min after being filtered twice. The next stage involved adding Hoagland salts (10% w/v) to the final extract, adjusting the medium’s pH to 5.8 ± 0.02, and autoclaving it for 20 min at 121 °C and 1.5 kPa. Before sterilization, bacteriological agar (1.5 w/v) was employed as a gelling agent to solidify the medium.After bacterial isolation on NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospibrillum media, the growth of all isolates was evaluated on an MB medium. To investigate isolates biomass in the same condition, we elected MB medium. First, the bacteria were grown in the liquid form of NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospirillum and Azotobacter media at 30 °C for 48 h, then 103 cells of each isolate were transferred to 48 wells plates containing MB medium, and plates were incubated at 30 °C for 10 h. Then, the growth of bacteria was read at an optical density (OD) of 630 nm 10 h after inoculation, the experiment was performed with three replicates. In the following step, CFU/ml equivalent to each OD was obtained by inoculating the uniform amount of liquid culture of the isolates on the solid form of MB medium at 30 °C for 16 h.Phenotypic characterization and biochemical identification of bacterial isolatesThe morphological analysis of the cell shape, colony (i.e., shape, color, and size), and biochemical tests were used to identify the bacterial isolates. Biochemical characterization was carried out By using gram staining, KOH27, oxidase, and catalase tests. For this essence, following Bartholomew’s method28, gram staining of bacteria was studied 48 h after inoculation on MHA, and the non-staining KOH method was used to confirm the results. Using 0.5 ml of a 10% hydrogen peroxide solution, a catalase test was conducted, and the generation of gas bubbles was monitored. Using biochemical oxidase discs, the oxidative activity of 27 isolates was investigated.Effect of abiotic stresses on bacterial isolatesTo determine the effect of abiotic stresses on isolates alkaline (MH medium with pH  10), salinity (MH medium supplemented with the final concentration of 100 mM NaCl), osmotic [MH medium supplemented with 25% polyethylene glycol (PEG) Mn6000], and thermal stresses (MH medium incubated at 15 °C for cold stress and 60 °C for heat stress) were screened. For all experiments, the incubation period was 15 h, and plates were kept in a dark condition.MALDI-TOF MS identification of isolatesSoil bacterial isolates were subcultured twice on MHA and incubated at 30 °C for 24 h before MALDI-TOF MS measurement. Then ∼0.1 µg of cell material was directly transferred from a bacterial colony or smear of colonies to a MALDI target spot. After drying at laboratory temperature, sample spots were overlaid with 1 μl of matrix solution (10 mg/mL a-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2.5% trifluoroacetic acid) and each measurement was carried out in triplicate (technical replicates). MS analysis was performed on an Autoflex MALDI-TOF mass spectrometer (Bruker Daltonics, Germany) using Flex Control 3.4 software (Bruker Daltonics, Germany). Calibration was carried out with the use of the Bacterial Test Standard (Bruker Daltonics, Germany). Soil isolates with a valid MALDI-TOF MS score of 2 were undoubtedly assigned to the genus/species level. For bacterial classification and identification, BioTyper 3.1 software (Bruker Daltonics, Germany) equipped with MBT 6903 MPS Library (released in April 2016), the MALDI Biotyper Preprocessing Standard Method, and the MALDI Biotyper MSP Identification Standard Method adjusted by the manufacturer (Bruker Daltonics, Germany) were used. Only the highest score value of all mass spectra belonging to individual cultures (biological and technical replicates) was recorded25. The score between 2.3 and 3.00 shows highly probable species-level identification and between 2.0 and 2.29 represents genus-level identification and probable species level of identification. A score between 1.7 and 1.99 indicates probable genus-level identification29.Effects of bacterial isolates on plants growthThe Seed and Plant Improvement Institute of Karaj (Karaj, Iran; http://www.spii.ir/homepage.aspx?site=DouranPortal&tabid=1&lang=faIR) provided the maize, canola, and wheat seeds (Zea mays. Var Kosha; Brassica napus Var Nima; Triticum aestivum Var Kalate). In greenhouse trials, 2 × 103 cells/seed of soil-borne isolates cultured in a manure-based medium were inoculated to maize, canola, and wheat plants. During the studies, sand that had been acid washed and autoclaved was used for planting. For three weeks, seedlings were kept under a 16/8 h day/night photoperiod with a 25 °C temperature. Three replications of a complete randomized block design were used for the colonization experiment’s treatments. Under the bacterial treatments, measurements were made of the plant growth parameters including shoot dry biomass (mg), root dry biomass (mg), shoot length (cm), root length (cm), shoot density (mg/cm), root density (mg/cm), and shoot/root weight (mg). Samples were dried at 60 °C for three days to measure dry biomass.Statistical analysisStatistical analysis was done by R software (version 4.1.3). One-way analysis of variance (ANOVA) was used to determine the significance of the experiment, and Fisher’s protected Least Significant Difference (LSD) test with a P-value of 0.01 was performed to separate the means. Furthermore, PCA analysis has been carried out based on the Clustvis package and the SVD imputation approach.Ethics approval and consent to participateAll authors agree to the ethics and consent to participate in this article and declare that this submission follows the policies of Scientific Reports. Accordingly, the material is the author’s original work, which has not been previously published elsewhere. The paper is not being considered for publication elsewhere. All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.Ethics for research involving plantsAll authors confirmed that experimental research and field studies on plants, including receiving the seeds from the Seed and Plant Improvement Institute of Karaj, complied with relevant institutional, national, and international guidelines and legislation. Furthermore, methods were conducted according to the relevant guidelines and regulations. More

Measurement of colony strength traits and Varroa destructor loadHives located in the natural area (henceforth natural hives or colonies) weighed the most (hive weight, Kruskal–Wallis test, p  More

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