Philippa Kaur

Long-term acoustic data collectionPassive acoustic monitoring was conducted from November 19, 2018 to October 3, 2020, with 683 days of recording effort overall (Supplementary Table 2), using a High-frequency Acoustic Recording Package (HARP)37. The HARP was deployed in Bahía Norte, Guadalupe Island, located approximately 150 miles offshore of México’s Baja California Peninsula (Fig. 1). The HARP was bottom-mounted and deployed to a depth of approximately 1100 m, with a calibrated hydrophone suspended ~30 m above the seafloor. The same hydrophone was used for both deployments to facilitate data comparison. The omnidirectional hydrophone sensor (ITC-1042, International Transducer Corporation, Santa Barbara, CA) had an approximately flat (±3 dB) hydrophone sensitivity from 10 Hz to 100 kHz of −200 dB re V/μPa. The sensor was connected to a custom-built preamplifier board and bandpass filter. The calibrated system response was corrected for during analysis. Data were sampled continuously at a 200 kHz sampling rate with 16-bit quantization, effectively monitoring a frequency range of 10 Hz–100 kHz.Automatic detection and manual classification of beaked whale echolocation clicksBeaked whales can be acoustically identified by their echolocation clicks38. These signals are frequency-modulated (FM) upswept pulses, which appear to be species-specific and are distinguishable by their spectral and temporal features. Cuvier’s beaked whale echolocation signals are well differentiated from the acoustic signals of other beaked whale species. They are polycyclic with a characteristic FM pulse upsweep, peak frequency around 40 kHz, and uniform inter-pulse interval of about 0.4–0.5 s39,40. Additionally, Cuvier’s beaked whale FM pulses have characteristic spectral peaks at approximately 17 and 23 kHz.Beaked whale FM pulses were detected in the HARP data with an automated method using the MATLAB-based (Mathworks, Natick, MA) custom software program Triton (https://github.com/MarineBioAcousticsRC/Triton) and other MATLAB custom routines. After all potential echolocation signals were identified with a Teager–Kaiser energy detector41,42, an expert system discriminated between delphinid clicks and beaked whale FM pulses. A decision about presence or absence of beaked whale signals was based on detections within a 75 s segment. Only segments with more than seven detections were used in further analysis. All echolocation signals with a peak and center frequency below 32 and 25 kHz, respectively, a duration less than 355 μs, and a sweep rate of More

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Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-019-0970-7, published online 2 September 2019.The Journal would like to note that the authors first made contact in September 2019 to raise the concerns that follow, and the Journal apologizes both for the delay in relaying these corrections publicly and for the changed instances that prevent making corrections to the original article itself. What follows is the Author correction.In the version of this article initially published, we made several errors in our R analysis code, and in the text and figures. First, the number of species with negative net dispersal velocity (net DV) were incorrectly calculated, resulting in slight changes in Fig. 2 and Supplementary Fig. 6, and in the text. The amended figures are provided below (Figs. 1–7). Changes to the text under the ‘DVs’ subsection of Results are: “When considering the mainstem pathway, we estimate that the mobile subpopulations of 134 (old version: 124) and 185 (old version: 174) (RCP 4.5 and 8.5, respectively) species will experience dispersal deficits in at least 50% of their southern Appalachian range, whereas these estimates increase to 229 (old version: 226) and 231 (old version: 232) species for the stationary subpopulation. Slow-climate-velocity tributaries reduce the number of species experiencing dispersal deficits by 99.3% (old version: 99.2%) and 90.3% (old version: 16.9%) (RCP 4.5 and 8.5, respectively) for the mobile component and 17.9% (old version: 90.8%) and 12.1% (old version: 12.9%) for the stationary component (Fig. 2a,b).” The two large discrepancies in dispersal deficit values (90.3% vs. 16.9%; 17.9% vs. 90.8%) were solely consequences of original text errors (16.9% and 90.8% values were erroneously switched), and not differences in calculations; therefore, the results did not change.Fig. 1Figure 2, original and corrected.Full size imageFig. 2Figure 3c,d, original and corrected.Full size imageFig. 3Figure 4, original and corrected.Full size imageFig. 4Figure 5, original and corrected.Full size imageFig. 5Supplementary Figure 6, original.Full size imageFig. 5Supplementary Figure 6, corrected.Full size imageFig. 6Supplementary Figure 8, original.Full size imageFig. 6Supplementary Figure 8, corrected.Full size imageFig. 7Supplementary Figure 9, original.Full size imageFig. 7Supplementary Figure 9, corrected.Full size imageSecond, we made errors when plotting Fig. 3c,d. Boxplots of mean change in habitat suitability were plotted instead of median change as specified in the caption; further, whiskers did not include the entire range of values. The amended figure is provided below. We would like to correct associated errors in text; specific changes are: “Our ENMs estimate a median 15.1% (old version: mean 14.1%) reduction (range −42.5% to +16.6% [old version: −51.6% to +2.4%] across 233 species) in habitat suitability associated with the tributary pathway compared with only a 3.8% (old version: 1.6%) reduction (−11.0% to +12.3% [old version: −7.9% to +1.8%]) for the mainstem pathway due to differing non-temperature habitat conditions (Fig. 3a,b).”Third, we made errors when plotting Fig. 4 and Supplementary Fig. 8. Specifically, net DV values were incorrectly rescaled; one extinct species was erroneously included in the plot; and the number of species in each quadrant was counted incorrectly. The amended figures are shown below. We would like to add a sentence (“The y-axes are inverse hyperbolic sine (asinh)-transformed”) to the caption of Fig. 4 to describe the y-axis scaling in the amended figures. There were two other text errors in the caption. The phrase “Mean net DV” should have been “Median net DV,” whereas the phrase “mean habitat suitability” should have read “median change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Species-level mismatch between net DV and upstream habitat suitability. a–d, Median net DV of mobile (a,b) and stationary subpopulations under the RCP 8.5 scenario plotted as a function of median change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point represents a species and is computed as the median response across all projected occupied reaches. Red and blue numbers correspond to the number of species in each of the four quadrants. The y-axes are inverse hyperbolic sine (asinh)-transformed. e–h, Four species highlighting the diversity in dispersal-based and habitat suitability-based vulnerability: streamline chub (e); brook trout (f); flathead catfish (g); blacknose dace (h). Credit: David Neely (e–h)”. These corrections did not change our inferences.Fourth, there were errors in rescaling and plotting net DV values and in the calculation of quadrant percentages in Fig. 5 and Supplementary Fig. 9. The amended figures are shown below. We would like to add two sentences at the end of Fig. 5 caption to provide greater detail on plotting methods: “The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” There was one other text error in the caption: the phrase “mean habitat suitability” should have read “mean change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Community-level mismatch between net DV and upstream habitat suitability. a–d, Mean net DV of mobile (a,b) and stationary (c,d) subpopulations under the RCP 8.5 scenario plotted as a function of mean change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point (scatterplot) and reach (map) is computed as the mean response for all species projected to occur within the reach. Quadrant numbers represent percentage of reaches in the quadrant. Colours associated with the upper-right quadrant correspond to ‘safe’ reaches where community members can keep pace with ISVs and habitat suitability increases. Colours associated with the lower-left quadrant correspond to ‘vulnerable’ reaches where community members cannot keep pace with ISVs and habitat suitability declines. The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” These corrections did not change our inferences.Fifth, there was an error in the last sentence of the “Calculating net DVs” subsection in Methods: “Last, we calculated the mean net DV for each species (species-specific DV) by averaging net DVs at all occupied reaches, as well as the community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.” This sentence should have read “Last, we calculated the median net DV for each species (species-specific DV) across all occupied reaches, as well as the mean community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.”Corrections of calculation errors yielded results that were similar to those in the original analysis whereas corrections of plotting and text errors did not affect our original inferences. Therefore, these errors did not change the overall results and conclusions of the article. We sincerely apologize for any misunderstanding and inconvenience caused by these errors. More

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Preliminary experiment using grapevine shoot samplesTo determine whether the profiles of endophytic bacterial microbiota vary widely between shoot samples collected from the same grapevine plant or between shoot samples collected from different grapevine plants of the same cultivar grown in the same vineyard, a preliminary experiment was performed. Microbiome analysis demonstrated that the profiles of endophytic bacterial microbiota were similar between two shoot samples collected from the same Chardonnay or Koshu grapevine plant (Fig. S2). In addition, the profiles of endophytic bacterial microbiota in shoot samples collected from different Chardonnay or Koshu grapevine plants cultivated in the same vineyard were also similar (Fig. S2). These results suggest that the profiles of endophytic bacterial microbiota in shoot samples collected from different grapevine plants of the same cultivar grown in the same vineyard were uniform. On the basis of this finding, we collected one shoot sample from a grapevine plant, at two different shoot growth stages (shoot elongation stage and véraison), of each cultivar grown in the eleven vineyards located in major grapevine-growing regions in Japan.Weather dataGDDs from April 1 to October 31, 2020 demonstrated that Minamisanriku and Ueda belonged to Region III on the Winkler Index and that Komoro, Shobara, and Saijo belonged to Region IV on the Winkler Index (Supplementary Table 2). Only Urausu belonged to Region II on the Winkler Index. Five vineyards including Kofu, Kai, Katsunuma, Izumo, and Omishima belonged to Region V on the Winkler Index, suggesting that Chardonnay, Pinot Noir, and Cabernet Sauvignon were cultivated under extremely high temperatures in those vineyards. Precipitation from April 1 to October 31, 2020 exceeded 1700 mm in Shobara, the highest among the vineyards (Supplementary Table 2).Amplicon sequences collected from grapevine shoot xylemsA total of 7,019,600 amplicon sequences from 52 samples were collected (Supplementary Table 3). We identified a total of 1305 OTUs on the basis of the conventional criterion of 99% sequence similarity. Irrespective of cultivar, grapevine-growing region, and shoot growth stage, Alphaproteobacteria, Gammaproteobacteria, and Oxyphotobacteria were predominant in shoot xylems (Fig. 1). Actinobacteria, Bacteroidia, Bacilli, and Clostridia were the endophytic bacteria detected in the shoot xylems.Figure 1Endophytic bacterial microbiota in shoot xylems of cultivars grown in the same vineyard. Endophytic bacterial microbiota in the shoot xylems of each cultivar collected from nine vineyards were identified and evaluated at the class level. Data are presented as relative abundance (%). KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir, SES shoot elongation stage, V véraison.Full size imageComparison of endophytic bacterial microbiota in grapevine shoot xylems of cultivars grown in the same vineyardShoot samples of two or more cultivars were collected from nine vineyards (Urausu, Katsunuma, Kofu, Kai, Komoro, Ueda, Izumo, Shobara, and Saijo) and evaluated (Fig. 1). Below are the detailed results for each vineyard.Urausu (Hokkaido Prefecture)At the shoot elongation stage, more than 90% of endophytic bacteria in Chardonnay and Pinot Noir shoot xylems belonged to class Gammaproteobacteria. Oxyphotobacteria was also detected in the shoot xylems albeit at a very low proportion (1% and 2% in Chardonnay and Pinot Noir, respectively). At véraison, the proportion of Oxyphotobacteria increased and reached 75% and 74% in Chardonnay and Pinot Noir shoot xylems, respectively. Overall, the profiles of endophytic bacterial microbiota were very similar between Chardonnay and Pinot Noir cultivated in Urausu at each shoot growth stage.Katsunuma (Yamanashi Prefecture)At the shoot elongation stage, Gammaproteobacteria was predominant in Koshu and Cabernet Sauvignon shoot xylems, although Oxyphotobacteria and Bacilli were detected as well. At véraison, the proportion of Oxyphotobacteria increased and reached 48% and 75% in Koshu and Cabernet Sauvignon shoot xylems, respectively. The proportion of Alphaproteobacteria also increased at véraison (37% and 15% in Koshu and Cabernet Sauvignon, respectively). Overall, the profiles of endophytic bacterial microbiota were similar between Koshu and Cabernet Sauvignon cultivated in Katsunuma at each shoot growth stage.Kofu (Yamanashi Prefecture)Shoot samples of Koshu, Chardonnay, Pinot Noir, and Cabernet Sauvignon were collected from Kofu. At the shoot elongation stage, Gammaproteobacteria was predominant (approximately 90%) in Koshu and Pinot Noir shoot xylems, whereas more than 80% of endophytic bacteria in Chardonnay and Cabernet Sauvignon shoot xylems belonged to class Oxyphotobacteria. At véraison, the profiles of endophytic bacterial microbiota were similar among the four cultivars grown in Kofu, and Oxyphotobacteria was predominant.Kai (Yamanashi Prefecture)Irrespective of the shoot growth stage, Gammaproteobacteria was predominant in Chardonnay and Cabernet Sauvignon shoot xylems. Although Gammaproteobacteria was also predominant in the Koshu shoot xylems at the shoot elongation stage, the proportions of Oxyphotobacteria and Alphaproteobacteria increased in Koshu shoot xylems at véraison (57% and 32%, respectively).Komoro (Nagano Prefecture)Irrespective of the cultivar (Chardonnay, Pinot Noir, and Cabernet Sauvignon), the profiles of endophytic bacterial microbiota in shoot xylems were very similar at each shoot growth stage, and Oxyphotobacteria was predominant. More than 80% of endophytic bacteria in the shoot xylems at véraison belonged to class Oxyphotobacteria.Ueda (Nagano Prefecture)The profiles of endophytic bacterial microbiota in shoot xylems at the shoot elongation stage were similar among Chardonnay, Pinot Noir, and Cabernet Sauvignon, whereas the profile in Chardonnay shoot xylems at véraison was different from those in Pinot Noir and Cabernet Sauvignon shoot xylems. Gammaproteobacteria (76%) was predominant in Chardonnay shoot xylem at véraison. In Pinot Noir and Cabernet Sauvignon shoot xylems at véraison, more than 70% of endophytic bacteria belonged to class Oxyphotobacteria.Izumo (Shimane Prefecture)Unlike other vineyards, there was no similarity of profiles between cultivars (Chardonnay and Cabernet Sauvignon) and between shoot growth stages. Gammaproteobacteria and Oxyphotobacteria were predominant in Chardonnay shoot xylems at the shoot elongation stage and véraison, respectively. In Cabernet Sauvignon shoot xylems, Gammaproteobacteria (36% and 52% at the shoot elongation stage and véraison, respectively) and Oxyphotobacteria (34% and 43% at the shoot elongation stage and véraison, respectively) were predominant irrespective of the shoot growth stage.Shobara (Hiroshima Prefecture)Similarly to Urausu and Katsunuma, Gammaproteobacteria was predominant in Chardonnay and Cabernet Sauvignon shoot xylems at the shoot elongation stage. The proportion of Oxyphotobacteria increased at véraison; more than 70% of endophytic bacteria in Chardonnay and Cabernet Sauvignon shoot xylems at véraison belonged to class Oxyphotobacteria. Overall, the profiles of endophytic bacterial microbiota were similar between Chardonnay and Cabernet Sauvignon cultivated in Shobara at each shoot growth stage.Saijo (Hiroshima Prefecture)Similarly to Urausu, Katsunuma, and Shobara, Gammaproteobacteria (89%, 89%, and 98% in Koshu, Pinot Noir, and Cabernet Sauvignon shoot xylems, respectively) was predominant at the shoot elongation stage and Oxyphotobacteria (60%, 56%, and 63% in Koshu, Pinot Noir, and Cabernet Sauvignon shoot xylems, respectively), at véraison. Overall, the profiles of endophytic bacterial microbiota were similar among Koshu, Pinot Noir, and Cabernet Sauvignon cultivated in Saijo at each shoot growth stage.Comparison of endophytic bacterial microbiota in grapevine shoot xylems of cultivars grown in different vineyardsThe profiles of endophytic bacterial microbiota in the shoot xylems of Koshu, Chardonnay, Pinot Noir, and Cabernet Sauvignon cultivated in different vineyards were evaluated (Fig. 2). In Koshu shoot xylems, the profiles of endophytic bacterial microbiota were similar at each shoot growth stage irrespective of the vineyard. Gammaproteobacteria (73–89%) was predominant in Koshu shoot xylems at the shoot elongation stage, whereas Oxyphotobacteria (48–63%) and Alphaproteobacteria (19–37%) were predominant at véraison. At the shoot elongation stage, Pinot Noir cultivated in Komoro showed different diversity of endophytic bacterial microbiota from Pinot Noir cultivated in the other vineyards. At véraison, the profiles of endophytic bacterial microbiota in Pinot Noir shoot xylems were similar irrespective of the vineyard. Gammaproteobacteria (76–98%) was predominant in Pinot Noir shoot xylems at the shoot elongation stage, whereas Oxyphotobacteria (56–81%) was predominant at véraison. In contrast to Koshu and Pinot Noir, the profiles of endophytic bacterial microbiota in Chardonnay and Cabernet Sauvignon shoot xylems showed diversity and complexity among vineyards. At the shoot elongation stage, Oxyphotobacteria was predominant in Chardonnay shoot xylems at Minamisanriku (70%) and Kofu (85%), whereas Gammaproteobacteria was predominant in the other vineyards. At véraison, more than 95% of endophytic bacteria in shoot xylems of Chardonnay cultivated in Minamisanriku and Omishima belonged to class Gammaproteobacteria. In the case of Cabernet Sauvignon, although Oxyphotobacteria and Gammaproteobacteria were predominant in shoot xylems at both shoot elongation stage and véraison, their proportions drastically varied among vineyards.Figure 2Endophytic bacterial microbiota in shoot xylems of cultivars grown in the different vineyards. Endophytic bacterial microbiota in the shoot xylems of each cultivar collected from different vineyards were identified and evaluated at the class level. Data are presented as relative abundance (%). KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir, SES shoot elongation stage, V véraison, UR Urausu, MS Minamisanriku, KF Kofu, KA Kai, KN Katsunuma, KM Komoro, UE Ueda, SH Shobara, IZ Izumo, SA Saijo, OM Omishima.Full size imageComparison of endophytic bacterial microbiota in grapevine shoot xylems between shoot elongation stage and véraisonThe profiles of endophytic bacterial microbiota in the shoot xylems, regardless of the cultivar, at each shoot growth stage were evaluated (Fig. 3). The profiles of endophytic bacterial microbiota in grapevine shoot xylems at the shoot elongation stage were diverse and complex. Although Oxyphotobacteria and Gammaproteobacteria were predominant in the shoot xylems at the shoot elongation stage, various endophytic bacteria including those belonging to classes Actinobacteria, Bacteroidia, Bacilli, Clostridia, and Alphaproteobacteria existed in the shoot xylems as well. In contrast, the profiles of endophytic bacterial microbiota in grapevine shoot xylems at véraison showed far less variation than those at the shoot elongation stage. Oxyphotobacteria, Alphaproteobacteria, and Gammaproteobacteria accounted for more than 95% of endophytic bacteria in the shoot xylems at véraison.Figure 3Endophytic bacterial microbiota in grapevine shoot xylems at shoot elongation stage and véraison. Endophytic bacterial microbiota in the shoot xylems collected at the shoot elongation stage and véraison were identified and evaluated at the class level. Data are presented as relative abundance (%). UR Urausu, MS Minamisanriku, KF Kofu, KA Kai, KN Katsunuma, KM Komoro, UE Ueda, SH Shobara, IZ Izumo, SA Saijo, OM Omishima, KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir.Full size imageAlpha diversity of endophytic bacterial microbiota in grapevine shoot xylemsOTUs, Chao1 index, and Shannon index were used as indexes of alpha diversity of endophytic bacterial microbiota among cultivars, shoot growth stages, and vineyards (Fig. 4). The medians of OTUs were similar among the four cultivars (60.5 for Koshu and Pinot Noir, and 62.5 for Chardonnay and Cabernet Sauvignon). The medians of the Chao1 index were also comparable among the four cultivars (60 for Koshu and Pinot Noir, 63 for Chardonnay, and 65 for Cabernet Sauvignon). The median of the Shannon index (2.8) was highest for Koshu, whereas those for Pinot Noir, Chardonnay, and Cabernet Sauvignon were similar (2.0, 1.9, and 2.1, respectively). These results suggest that Koshu shoot xylems had a higher diversity of endophytic bacterial microbiota than Pinot Noir, Chardonnay, and Cabernet Sauvignon shoot xylems.Figure 4Alpha diversity of endophytic bacterial microbiota in grapevine shoot xylems. Alpha diversity analyses of cultivars, shoot growth stages, and vineyards were performed. Upper panels, OTUs; middle panels, Chao1 index; lower panels, Shannon index. Cross (×) indicates the average for each sample. KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir, SES shoot elongation stage, V véraison, UR Urausu, MS Minamisanriku, KF Kofu, KA Kai, KN Katsunuma, KM Komoro, UE Ueda, SH Shobara, IZ Izumo, SA Saijo, OM Omishima.Full size imageThe medians of OTUs and Chao1 index at the shoot elongation stage were comparable to those at véraison. The median of the Shannon index at the shoot elongation stage (3.0) was higher than that at véraison (1.7), indicating that grapevine shoot xylems at the shoot elongation stage had a higher diversity of endophytic bacterial microbiota than those at véraison.The medians of OTUs and Chao1 index were the highest for Ueda (74.5 and 75, respectively), whereas those were the lowest for Komoro (49.5 and 50, respectively). The medians of the Shannon index were lowest and highest for Minamisanriku (1.2) and Ueda (3.9), respectively. These results suggest that a large number of endophytic bacterial species existed in the shoot xylems of grapevine cultivated in Ueda, and that Ueda had the highest diversity of endophytic bacterial microbiota among the vineyards tested.Beta diversity of endophytic bacterial microbiota in grapevine shoot xylemsPCoA demonstrated that the plots of Koshu and Pinot Noir were relatively close to each other at the shoot elongation stage and very close to each other at véraison irrespective of the vineyard (Fig. 5), suggesting that the profiles of endophytic bacterial microbiota in Koshu and Pinot Noir shoot xylems were similar irrespective of both shoot growth stage and vineyard. Although the plots of Chardonnay and Cabernet Sauvignon in each vineyard were widely scattered at the shoot elongation stage, they were very close to each other at véraison. These results suggest that the profiles of endophytic bacterial microbiota in grapevine shoot xylems at véraison were uniform irrespective of the vineyard.Figure 5Principal coordinate analysis of endophytic bacterial microbiota in grapevine shoot xylems. Circles (○) and squares (□) indicate endophytic bacterial microbiota at the shoot elongation stage and véraison, respectively. KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir.Full size imagePERMANOVA demonstrated that the p-values for all combinations of cultivars exceeded 0.05 (Supplementary Table 4). In contrast, there was a significant difference (p = 0.001) between the shoot elongation stage and véraison. Although three of fifty-five combinations of vineyards showed significant differences (p = 0.04 for Komoro and Izumo, p = 0.007 for Komoro and Kai, and p = 0.034 for Kai and Kofu), there was no significant difference between most of the combinations. These results suggest that the variations of endophytic bacterial microbiota in grapevine shoot xylems greatly depended on the shoot growth stage.Cluster analysis of endophytic bacterial microbiota in grapevine shoot xylemsCluster analysis of endophytic bacterial microbiota in grapevine shoot xylems in various cultivars, shoot growth stages, and vineyards was performed by MDS (Figs. 6 and 7). Cladistic analysis was also conducted using a group average method. Except for Kai and Komoro, nine vineyards were very close to each other in the position map and eight vineyards formed a cluster in the cladogram (Fig. 6A). The four cultivars in the vineyards tested were widely scattered in the position map (Fig. 6B). On the other hand, Koshu and Pinot Noir at the shoot elongation stage, cultivated in Kofu, were close to each other in the position map and formed a cluster in the cladogram (Fig. 7A). Chardonnay and Cabernet Sauvignon at the shoot elongation stage, cultivated in Kofu, were close to each other but apart from Koshu and Pinot Noir, and formed a cluster in the cladogram. Interestingly, at véraison, the four cultivars were very close to each other in the position map (Fig. 7B).Figure 6Multidimensional scaling analysis of endophytic bacterial microbiota in grapevine shoot xylems among vineyards or cultivars. (A) Vineyards. (B) Cultivars. Left, position map. Right, cladogram. UR Urausu, MS Minamisanriku, KF Kofu, KA Kai, KN Katsunuma, KM Komoro, UE Ueda, SH Shobara, IZ Izumo, SA Saijo, OM Omishima, KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir.Full size imageFigure 7Multidimensional scaling analysis of endophytic bacterial microbiota in grapevine shoot xylems among cultivars grown in Kofu vineyard. (A) Shoot elongation stage. (B) Véraison. Left, position map. Right, cladogram. KO Koshu, CH Chardonnay, CS Cabernet Sauvignon, PN Pinot Noir.Full size imageNext, MDS and cladistic analysis of each cultivar in the vineyards were performed (Fig. 8). The distances among vineyards cultivating Koshu were small irrespective of the shoot growth stage (Fig. 8A). The distances among vineyards cultivating Pinot Noir were also small at the shoot elongation stage, and were further decreased at véraison (Fig. 8B). In contrast, the distances among vineyards cultivating Chardonnay and Cabernet Sauvignon were large at the shoot elongation stage (Fig. 8C,D). Although the distances among some vineyards (Urausu, Kofu, Kai, Izumo, and Shobara for Chardonnay, and Ueda, Kofu, Katsunuma, Komoro, and Shobara for Cabernet Sauvignon) decreased at véraison, they were large compared with Koshu and Pinot Noir.Figure 8Multidimensional scaling analysis of endophytic bacterial microbiota in grapevine shoot xylems among vineyards cultivating each cultivar. (A) Koshu. (B) Pinot Noir. (C) Chardonnay. (D) Cabernet Sauvignon. Left, position map. Right, cladogram. UR Urausu, MS Minamisanriku, KF Kofu, KA Kai, KN Katsunuma, KM Komoro, UE Ueda, SH Shobara, IZ Izumo, SA Saijo, OM Omishima.Full size image More

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