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    Reply to: Conclusions of low extinction risk for most species of reef-building corals are premature

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    Conclusions of low extinction risk for most species of reef-building corals are premature

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    Decreasing rainfall frequency contributes to earlier leaf onset in northern ecosystems

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    Changes in rays’ swimming stability due to the phase difference between left and right pectoral fin movements

    Analytical targetsTwo species of undulation motion rays with different pectoral fin shapes bred in KAIYUKAN were analyzed: sharpnose stingray Dasyatis acutirostra and pitted stingray Dasyatis matsubarai (Fig. 1a,b). Blender 2.7925 was used to construct stingray models from pictures26,27 as accurately as possible; Blender is a free and open-source 3D creation suite used to make realistic characters for movies, etc. Detailed information on how to construct models using Blender is provided in our previous paper28. To focus on the effects of pectoral fin movements, we did not consider the body’s shape as in the previous studies12,29. The height and disk width (WD) of all models were set to 0.01 m and 0.44 m, respectively, considering the previous studies30,31. The disk length of each model was determined from WD, referring to the aspect ratio of the rays’ photographs26,27; the disk length (LD) of D. acutirostra and D. matsubarai are 0.348 m and 0.344 m, respectively.Figure 1Analytical targets and description of motion. (a) Analytical model of D. acutirostra. (b) Analytical model of D. matsubarai. (c) Description of motion, (d) the relationship between any two points on the surface before and after the deformation.Full size imageMotionThe motion was given to satisfy the following equations:$$z = left{ {begin{array}{*{20}l} {A;sin left( {omega left( {t – kTleft( {frac{{angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} }}{{All;angl{text{e}}}} – theta } right)} right)h_{1} h_{2} } right.} hfill & {left( {10^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 170^{{text{o}}} } right)} hfill \ {A;sin left( {omega left( {t – kTleft( {frac{{350^{{text{o}}} – left( {angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} } right)}}{{All;angl{text{e}}}}} right)} right)h_{1} h_{2} } right.} hfill & {left( {190^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 350^{{text{o}}} } right)} hfill \ end{array} } right.$$
    (1)
    $$begin{array}{c}{h}_{1}=a{r}_{i}^{3}+b{r}_{i}^{2}+c{r}_{i}end{array}$$
    (2)
    $$h_{2} = left{ {begin{array}{*{20}l} {dleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)^{2} + eleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} hfill & {left( {10^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 170^{ circ } } right)} hfill \ {dleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)^{2} + eleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)} hfill & {left( {190^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 350^{ circ } } right)} hfill \ end{array} } right.$$
    (3)
    $$begin{array}{c}{left({r}_{i}-{r}_{i-1}right)}^{2}+{left({z}_{i}-{z}_{i-1}right)}^{2}={left({r}_{i}^{mathrm{^{prime}}}-{r}_{i-1}^{mathrm{^{prime}}}right)}^{2}+{left({z}_{i}^{mathrm{^{prime}}}-{z}_{i-1}^{mathrm{^{prime}}}right)}^{2}end{array}$$
    (4)
    $$begin{array}{c}angleleft({x}_{i},{y}_{i}right)= angleleft({x}_{i}^{^{prime}},{y}_{i}^{^{prime}}right).end{array}$$
    (5)
    Equation (1) represents the amount of movement of the model surface in the z-axis direction, where (A) is the amplitude of the pectoral fin tip, (omega) is the angular velocity, (t) is time, (k) is the wavenumber, (T) is the period, angle(({x}_{i},{y}_{i})) is the angle made by the line connecting the center of rotation and any point (({x}_{i},{y}_{i})) on the model surface with the x-axis, Allangle is the range where the motion is given (160°), and (theta) is the phase difference between the movements of the right and left pectoral fins (Fig. 1c). ({h}_{1}) is the weighting from the center to the radial direction: it is necessary to set the amplitude at the ray’s center to zero and increase the amplitude toward the pectoral fin tip (a = 119.786, b = -7.957, c = 0.498). ({h}_{2}) is the weighting in the circumferential direction: it is necessary to increase the amplitude from the anterior to the tip of the pectoral fin and decrease the amplitude from the tip of the pectoral fin to the posterior (d = − 1.563 × 10–4, e = 0.025). Equation (4) is the condition in which the distance between two neighboring points in the same radial direction is equal before and after the movement (Fig. 1d). (r) is the distance between the center of rotation and any point (({x}_{i},{y}_{i})), defined as (sqrt{{x}_{i}^{2}+{y}_{i}^{2}}). Equation (5) defines angle(({x}_{i},{y}_{i})) as being constant before and after the move (Fig. 1c). The variables after the move are marked with ‘. Variables used in the analysis are A = 0.089 m, T = 0.499, k = 1.270, and ω = 12.599 rad/s. Videos of the created motion from the front and the side are shown in the “Supplement” (Supplement Movies 3, 4).Analytical conditionsAnalysis cases were conducted with eight conditions: two types of pectoral fin shape (Fig. 1a,b) and four types of phase difference (0 (T), 0.25 (T), 0.5 (T), and 0.75 (T)). These conditions were set for investigating the effects of phase differences between left and right pectoral fin movements on swimming and how these effects vary with pectoral fin shape.Numerical methodsA CFD simulation of the ray models in the water flow was performed using OPENFOAM, an open-source finite volume method CFD toolbox32, to calculate the forces acting on the rays in each axial direction and the moment around each axis. The governing equations were the continuity equation and the three-dimensional incompressible Reynolds-averaged Navier–Stokes equation, expressed by:$$begin{array}{*{20}c} {nabla cdot u = 0} \ end{array}$$
    (6)
    $$begin{array}{*{20}c} {frac{partial u}{{partial {text{t}}}} + nabla cdot left( {uu} right) = – nabla p + nabla cdot left( {vnabla u} right) + nabla cdot left[ {nu left{ {left( {nabla u} right)^{T} – frac{1}{3}nabla cdot uI} right}} right], } \ end{array}$$
    (7)
    where (u) is the velocity vector, t is the time, p is the static pressure divided by the reference density, (nu) is the kinematic viscosity, and I is the unit tensor. The Reynolds number was defined regarding the previous studies3 as:$$begin{array}{c}{R}_{e}=frac{U{L}_{D}}{nu },end{array}$$
    (8)
    where (U) (/ms) is the given flow speed3 (1.5 × LD/ms), ({L}_{D}) (m) is the length of the ray models, and (nu) is the kinematic viscosity of water at 20 °C (1.0 × 10–6 m2/s). The Reynolds number in this study is 1.8 × 105; considering this, we used the k–ω shear stress turbulence model33,34. The k–ω shear stress turbulence model is a type of Reynolds-averaged Navier–Stokes equation (RANS) turbulence model that is widely used to calculate for the fish swimming flow35,36,37. The overset grid method was used in this study; it is a generic implementation of overset meshes. For both static and dynamic cases, cell-to-cell mapping between multiple, disconnected mesh regions is employed to generate a composite domain38,39. This method permits complex mesh motions and interactions without the penalties associated with deforming meshes. The process is described in detail by Noack40. The calculation volume was 5.4 WD in length, 5.4 WD in height, and 5.4 WD in width (Fig. 2a,b). A hexahedral volume mesh was created using the snappyHexMesh of OPENFOAM. The fluid region was divided into two parts: the overset region and the background region (Fig. 2a,b). The overset region moves and transforms to match the motion of the ray and was made with fine meshes around the analysis target and coarse meshes in the outlying areas; a one-layer boundary layer mesh was created around the analysis target. The overset region shape is an ellipsoid (Fig. 2a,b). The minimum mesh volume is 7.3 × 10–10 (m3), and the maximum mesh volume is 2.6 × 10–2 (m3). The total number of meshes was 9.0 × 105. At the outlet boundary, the average static relative pressure was set to 0 Pa. The surfaces of the fish model were formed into non-slip surfaces.Figure 2Meshes for CFD simulation and differences in force between different meshes. (a) Meshes at the coronal plane of the whole fluid region. (b) Frontal cross-section of the fluid region at the green line in (a). The red region is the overset region. (c,d) Comparison of the instantaneous drag coefficient and the moment coefficient around the y-axis of D. matsubarai between the coarse, fine, and dense mesh.Full size imageThe drag coefficient ({C}_{D}left(tright)), the lateral force coefficient ({C}_{l}left(tright)), the lift coefficient ({C}_{L}left(tright)), the moment coefficient around the x-axis ({C}_{mx}left(tright)), the moment coefficient around the y-axis ({C}_{my}left(tright)) and the moment coefficient around the z-axis ({C}_{mz}left(tright)) were calculated as:$$begin{array}{c}{C}_{D}left(tright)=frac{Dleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
    (9)
    $$begin{array}{c}{C}_{l}left(tright)=frac{lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
    (10)
    $$begin{array}{c}{C}_{L}left(tright)=frac{Lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
    (11)
    $$begin{array}{c}{C}_{mx}left(tright)=frac{{M}_{psi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
    (12)
    $$begin{array}{c}{C}_{my}left(tright)=frac{{M}_{phi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
    (13)
    $$begin{array}{c}{c}_{mz}left(tright)=frac{{M}_{theta }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}},end{array}$$
    (14)
    where (Dleft(tright)) is the calculated drag, (lleft(tright)) is the calculated lateral force, (Lleft(tright)) is the calculated lift, ({M}_{psi }left(tright)) is the calculated moment around the x-axis, ({M}_{phi }left(tright)) is the calculated moment around the y-axis, ({M}_{theta }left(tright)) is the calculated moment around the z-axis, and (rho) (kg/m3) is the density of water at 20 °C (998 kg/m3). As shown in a previous study41. the propulsive efficiency (eta) is defined as the ratio of output power ({P}_{o}) to input power ({P}_{e}) which can be written as:$$begin{array}{c}{P}_{o}left(tright)=frac{1}{T}{int }_{0}^{T}Dleft(tright)Udtend{array}$$
    (15)
    $$begin{array}{c}{P}_{e}left(tright)=frac{1}{T}{int }_{0}^{T}left[Dleft(tright)dot{x}left(tright)+lleft(tright)dot{y}left(tright)+Lleft(tright)dot{z}left(tright)right]dtend{array}$$
    (16)
    $$begin{array}{c}eta =frac{{P}_{o}}{{P}_{e}}.end{array}$$
    (17)
    An in-house program calculated the forces acting on rays in each axial direction and the moment around each axis. The numerical method’s validity and reliability were verified by comparing previous experimental and numerical analytical studies of heaving and pitching on airfoil naca001341. A high degree of similarity to previous studies was confirmed; the mean difference in the propulsive efficiency from the previous study of analysis was 5%, and the difference from the previous study of the experiment was 9%. Detailed information such as mesh, length, and velocity, of this analysis method’s verification is provided in the “Supplement”.A grid sensitivity study was conducted using three meshes: coarse, fine, and dense. The coarse mesh has 8.1 × 105 elements, the fine mesh has 9.0 × 105 elements, and the dense mesh has 9.9 × 105 elements. The analysis was conducted using a condition with no phase difference of D. matsubarai. As shown in Fig. 2c,d, the drag coefficient and the moment coefficient around the y-axis are almost the same when the mesh is fine and when the mesh is dense. The mean drag and propulsive efficiency error of fine and coarse meshes are 2.7% and 3.5%, respectively. The fine mesh was used in all simulation cases considering accuracy. We used the same meshes for all cases. More

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    Modelling the emergence dynamics of the western corn rootworm beetle (Diabrotica virgifera virgifera)

    Let (y_{itk}) denote the WCR count observed for trap i in week t in year k, and assume it to follow a Poisson distribution with parameter (mu _{itk})$$begin{aligned} y_{itk} | mu _{itk}, sim Poisson(mu _{itk}) end{aligned}$$
    (1)
    The intensity parameter (mu _{itk}) represents the rate of emergence for a given time period. Instead of allowing it to depend purely on time t, a phenological variable of growing degree days (GDD) is used, as warmer temperatures are required for WCR development25,26,27,28. GDDs reflect the heat accumulation and are defined as an integral of warmth above a base temperature after a given start date:$$begin{aligned} GDD = int (T(t)-T_{base})dt. end{aligned}$$
    (2)
    The above integral can be approximated by$$begin{aligned} GDD = max left( frac{T_{max} – T_{min}}{2} – T_{base}, 0 right) . end{aligned}$$
    (3)
    Here (T_{min}) is the minimum daily temperature, (T_{max}) is the maximum daily temperature, and (T_{base}) is a set base temperature. In this study, the base temperature was set to (10,^{circ })C, and the starting date was the beginning of April, which marks the start of the growing season in Austria.The rate of cumulative emergence of the WCR beetle can be described by a Gompertz function. The Gompertz function is a sigmoidal function which describes growth as being slowest at the beginning and the end of a given period and is defined as$$begin{aligned} f(z_t) = alpha exp (-beta exp (-gamma z_t)). end{aligned}$$
    (4)
    where (alpha) is the upper asymptote, (beta) is a relative starting value, (gamma) is a growth rate coefficient which affects the slope, and (z_t) are the cumulative growing degree days. In this study, one can consider the asymptote as proxy to the saturation level of WCR population growth. Lower values of (beta) suggest an earlier first emergence in the season, while lower values of (gamma) indicate a longer emergence period. To investigate whether there is an association between climate variables and the emergence dynamics, the Gompertz curve parameters were assumed to linearly depend on climate covariates. In this regression modelling framework, a spatially correlated residual structure can be added in either (alpha), (beta), and/or (gamma) if there is evidence to do so.To reflect the nature of the emergence dynamics and to preserve the shape of the increasing Gompertz curve, the parameters of the model were restricted to positive values such that (alpha >0), (beta >0), and (gamma >0). The time at inflection or period of highest growth can be obtained by solving Eq. (4) for the value of t at which the concavity of the function changes. The time at inflection is described as:$$begin{aligned} T_z^* = frac{log (beta )}{gamma } end{aligned}$$
    (5)
    The Gompertz function describes cumulative emergence. Thus to describe the marginal emergence rate, the derivative of the Gompertz function can be used instead. Consequently, as the WCR trapping data consisted of weekly counts, the rate of emergence (mu _{itk}) is better described by the log of the derivative of the Gompertz function$$begin{aligned} log (mu _{itk}) = log (alpha _{ik}) + log (gamma _{ik}) + log (beta _{ik}) + gamma _i z_{itk} – beta _{ik} exp (-gamma z_{itk}). end{aligned}$$
    (6)
    The parameters (alpha _{ik}), (beta _{ik}) and (gamma _{ik}) are site and year specific such that:$$begin{aligned}&alpha _{ik} sim N(mu _{alpha _{ik}}, tau _{alpha }) end{aligned}$$
    (7)
    $$begin{aligned}&gamma _{ik} sim N(mu _{gamma _{ik}}, tau _{gamma }) end{aligned}$$
    (8)
    $$begin{aligned}&beta _{ik} sim N(mu _{beta _{ik}}, tau _{beta }). end{aligned}$$
    (9)
    Here, (tau _{alpha }), (tau _{beta }), and (tau _{gamma }) are the precision (inverse variance) parameters of the prior distributions for (alpha), (beta) and (gamma) respectively. Moreover, the means of the distributions (mu _{alpha _{ik}}), (mu _{beta _{ik}}), and (mu _{gamma _{ik}}) can be expressed as functions of known covariates:$$begin{aligned} mu _{alpha _{ik}}= & {} a_{0} + {mathbf {w}}^T X_{alpha _{ik}}, end{aligned}$$
    (10)
    $$begin{aligned} mu _{beta _{ik}}= & {} b_{0}, end{aligned}$$
    (11)
    $$begin{aligned} mu _{gamma _{ik}}= & {} g_{0} + {mathbf {u}}^T X_{gamma _{ik}}. end{aligned}$$
    (12)
    Here (a_{0}) is the intercept, ({mathbf {w}}) is a vector of the regression coefficients, and (X_{alpha _{ik}}) are the location and year specific covariates. The predictors used in the regression of (mu _{alpha _{ik}}) are the average winter temperature, the precipitation sum during winter, the year, the percentage of the agricultural area per Austrian municipality used for cultivating maize crops (maize), and the corresponding centred coordinates of the trap locations; x, y, and their functions (x^2), (y^2), and xy. The parameter (g_{0}) is the intercept for the regression of (mu _{gamma _{ik}}), and u is the corresponding regression coefficient. The predictor used for (mu _{gamma _ik}) is the average yearly spring temperature.The intercepts and regression coefficients ((mathbf {w}) and (mathbf {u})) were given non-informative normal priors N(0, 0.01). The precision parameters (tau _{alpha }), (tau _{beta }) and (tau _{gamma }) were assigned prior distributions Gamma(0.01, 0.01).The model was fitted using WinBUGS through the R2WinBUGS package in R29,30,31. The model was run for 20000 iterations, with a burn-in of 10000 iterations, and a thinning rate of five. Convergence was determined by visual assessments of trace plots and marginal posterior densities. More

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    Aged related human skin microbiome and mycobiome in Korean women

    Study subjects and measurement of skin physiological parametersWe analyzed skin microbiome and mycobiome from cheeks and foreheads of healthy younger (19–28 years old, Y-group) and older (60–63 years old, O-group) Korean women who were free from cutaneous disorders (Table 1 and Supplementary Table S1). All 61 subjects had been living in Seoul, Korea, for more than 3 years with normal skin conditions. We preferentially selected those who had sebum secretion greater than 30 arbitrary units and moisture greater than 50 arbitrary units in both groups. Among the measurements of moisture content, pH, sebum content, and transepidermal water loss (TEWL), only sebum and TEWL decreased significantly in the O-group compared to the Y-group in the cheeks (P = 2.25e−06, Wilcoxon rank-sum test; P = 0.019, Welch two-sample t test) and forehead (P = 1.33e−06, Wilcoxon rank-sum test; P = 0.003, Welch two-sample t test). Whereas no significant differences were found in the average values for moisture (cheeks: Y-group, 59.9; O-group, 56.6; forehead: Y-group, 61.1; O-group, 58.7) and pH (cheeks: Y-group, 6.0; O-group, 5.8; forehead: Y-group, 6.0; O-group, 5.6) between the two age groups.Table 1 Characteristics of subjects for aged related skin microbiome and mycobiome study.Full size tableComparisons in cheek and forehead microbiome and mycobiome between the two age groupsWe analyzed bacterial communities from 27 Y-group samples (cheeks, n = 13; forehead, n = 14) and 24 O-group samples (cheeks, n = 12; forehead, n = 12) and fungal communities from 28 Y-group samples (cheeks, n = 15; forehead, n = 13) and 32 O-group samples (cheeks, n = 16; forehead, n = 16), except for samples that were eliminated from the Illumina Mi-Seq sequencing due to low sequence reads (bacteria,  3. 0) (Fig. 4). Pathways belonging to the metabolism category were dominant in each age group. In the cheek of the Y-group, pathways involved in energy metabolism by bacteria, such as glycolysis/gluconeogenesis, citrate cycle, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-alanine metabolism, and thiamine metabolism, were predominant, whereas in the cheek of the O-group, degradation-related pathways, such as fatty acid degradation, synthesis and degradation of ketone bodies, benzoate degradation, and chloroalkane and chloroalkene degradation, were predominant. In the forehead of the Y-group, glycolysis/gluconeogenesis, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-glutamine and d-glutamate metabolism, d-alanine metabolism, and thiamine metabolism pathway were significantly more abundant, whereas in the forehead of the O-group, fatty acid degradation, synthesis and degradation of ketone bodies, valine/leucine and isoleucine degradation, and limonene/pinene degradation pathway were significantly more abundant.Figure 4Heat map for significantly different predicted functional pathways on (a) cheeks and (b) foreheads of Korean women by age based on LEfSe analysis (LDA score  > 3.0).Full size imageThe metabolism pathway for biotin, a water-soluble vitamin that is effective for skin health and essential for keratin production15, was more prevalent in the cheek and forehead of the Y-group. Interestingly, the metabolism pathway for lipoic acid, which is known to possess beneficial effects against skin aging and is used widely in cosmetic and dermatological products16,17, was significantly higher in the foreheads of the Y-group. We tracked the specific ASVs possessing these pathways, in both biotin metabolism and lipoic acid metabolism, Cutibacterium sp. (ASV2136 and ASV2130) and Staphylococcus sp. (ASV3008) were predicted to have the top three relative abundances in KOs. The relative abundances in biotin metabolism and lipoic acid metabolism of Cutibacterium sp. (ASV2136) were 24.9% and 26.1%, respectively. The relative abundances for each pathway for Staphylococcus sp. (ASV3008) were 10.2% and 18.7%, and for Cutibacterium sp. (ASV2130), they were 9.3% and 10.0%, respectively. We confirmed these two pathways in the genome of skin bacteria, C. acnes (Supplementary Fig. S2). These additional analyses support the reliability of the function in the skin environment of Cutibacterium. Interestingly, from the LEfSe result, Cutibacterium sp. (ASV2136) had a significantly higher abundance in the cheek and forehead microbiome of the Y-group. The pathway of biosynthesis of lipopolysaccharide, also known as bacterial endotoxins, showed higher abundance in the cheek and forehead microbiome of the O-group. The ASVs that contribute to inferring the LPS biosynthesis pathway were identified as Paraburkholderia sp. (ASV5030) and B. vesicularis (ASV4155). Also, pathways related to antibiotic biosynthesis (biosynthesis of vancomycin group antibiotics) and bacterial motility (bacterial chemotaxis and flagellar assembly; both belonging to the cellular processes category) were prominent in the cheek and forehead of the O-group. PICRUSt2 analysis implied that, regardless of skin site differences, the potential functions of the microbial community that compose the skin microbiome were similar according to age.Network analysis on cheek and forehead microbiome and mycobiomeWe performed SParse InversE Covariance estimation for Ecological Association Inference (SPIEC-EASI) analysis to evaluate the overall network of the skin microbes. The results of network density (D) on 81 cheek and 87 forehead ASVs, calculated using the ratio of the number of edges, showed higher network density in the skin microbiome of the Y-group (D = 0.015 and D = 0.001, in cheek and forehead, respectively) than the O-group (D = 0.007 and D = 0.007, respectively) (Fig. 5). To examine network correlation between bacteria and fungi, network density for Bacteria–Fungi (DBF) was calculated by the actual number of edges and a potential number of edges in a correlation ([bacterial nodes × fungal nodes]/2). We confirmed higher network density in the cheek of the Y-group (DBF = 0.008) than the O-group (DBF = 0) and edges of the major bacterial and fungal taxa, such as Staphylococcus sp. (ASV3008)—M. sympodialis (ASV500) and Roseomonas sp. (ASV4088)—M. restricta (ASV482), were observed in the cheek of the Y-group. In the forehead, edges of Methylobacterium sp. (ASV4314)—M. globosa (ASV454), Methylobacterium sp. (ASV4314)—Zygosaccharomyces rouxii (ASV208), and Venionella sp. (ASV3575)—M. sympodialis (ASV500) were observed in the Y-group, and edges of Cutibacterium sp. (ASV2107)—M. globosa (ASV461), Staphylococcus sp. (ASV3024)—M. arunalokei (ASV446), and Methylobacterium (ASV4314)—M. dermatis (ASV448) were observed in the O-group (DBF = 0.004). We found a network between bacteria and fungi with different kingdom levels in the skin microbiome, and especially, we confirmed that different genus or species level microbe was involved in the microbial network according to skin location and Y-, O-group.Figure 5Network analysis of the ASVs on (a) cheeks and (b) forehead of Korean women. Each node represents the ASV and the size of the node is based on relative abundance of each ASV. Color markings indicate the major taxa except for unidentified bacteria or fungi. Shapes represent the level of kingdom, Bacteria (bold) and Fungi (dotted line). The ASVs were selected for bacterial ASVs found in more than half of all samples on the cheeks and forehead, respectively, and for the fungal ASVs with a relative abundance of more than 0.1% in each of the cheeks and forehead samples. The D value is network density calculated using the ratio of the number of edges.Full size image More