Philippa Kaur

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Study areaThe study area was located in Northeastern Italy (Fig. 2). Specifically, it encompassed 13 municipalities in the Valbelluna (located in Belluno Province), Valsugana, and Cembra valleys (located in Trento Province). The study area has a sub-continental, temperate climate, with cold, often snowy winters and warm, mild summers. Human settlements consist mainly of small villages composed of country houses with private gardens and public parks, all surrounded by forested areas; among the sampled municipalities, only Belluno and Feltre had more than 10,000 inhabitants.Figure 2Study area. Points represent the sampling sites marked with the ID number as in Tables 2 and 3. Background satellite image from Sentinel-2 cloudless (https://s2maps.eu), and urban places from OpenStreetMap contributors (https://openstreetmap.org). Map created using QGIS 3.22.Full size imageHost surveyThe presence and abundance of domestic animal hosts in each site were estimated through a door-to-door census. As the flight range of Ae. koreicus is unknown, a field inspection was performed within a 200-m radius of the sampling site, corresponding to the average flight distance of Ae. albopictus recorded in a study conducted in Italy46. The survey was carried out once in 2020. Residents were asked if they owned animals (dogs, cats, farm animals) and how many they had or, where possible, they were counted directly by the study team (visual inspection). The presence of wild ungulates was estimated according to data provided by the Forestry and Fauna Service—Wildlife Office of the Autonomous Province of Trento. The wild ungulate census was carried out in spring by visual inspection along transects, and repeated three times by hunters and personnel of the wildlife management provincial office.47. The average number of roe deer, red deer, and chamois in 2020 was considered for the analyses. Collected information was used to qualitatively estimate potential host availability in the sampling areas. Human population density in the areas surrounding the sampling point was estimated using the Global Human Settlement Database (GHS Data)48.Collection of Aedes koreicus and blood meal analysisSampling was carried out from 2013 to 2020 (from May to October) with different frequencies in the various years; most collections were made in 2020 (20 collections) and just one in 2019. In total, 23 different sites were sampled where Ae. koreicus were known to be present: 14 in Trento and 9 in Belluno Province, respectively (Table 1 and Supplementary Table S1 online), with altitudes ranging from 234 to 775 m a.s.l.6,16. Engorged mosquitoes were collected in public and private houses, garden centers, cemeteries, and from periurban dry-stone walls using a home-built handheld aspirator (a modified handheld vacuum) (Fig. 3). Mosquitoes were aspirated from shady areas under vegetation, walls, and catch basins. In addition, all engorged females collected during routine invasive mosquito surveillance were used for the analyses. In this surveillance, BG-sentinel traps (Biogents AG, Regensburg, Germany) baited with a BG-Lure cartridge (Biogents) were activated for 24 h fortnightly. Immediately after collection, each sample was placed in a cooler, transported to the laboratory, and stored at − 80 °C until molecular analysis.Figure 3Home-built handheld aspirator (a modified handheld vacuum).Full size imageSampled mosquitoes were identified at species level according to Montarsi et al.21 and ECDC guidelines for invasive mosquito surveillance in Europe49. Blood-fed females were isolated from collected mosquitoes to identify the blood meal host.DNA of single blood-fed mosquito samples, collected from 2013 to 2016, was extracted using Microlab Starlet automated liquid-handling workstations (Hamilton), using a MagMAX Pathogen RNA/DNA kit (Applied Biosystems, USA), according to the manufacturer’s instructions. DNA of a single abdomen of blood-fed mosquitoes, collected from 2017 to 2020, was extracted using QIAamp DNA Investigator kit tissues (Qiagen, Germany), following the manufacturer’s protocol. All samples were analyzed using a nested PCR with a specific set of primers targeting the vertebrate mitochondrial cytochrome c oxidase subunit I (COI) gene, as previously described50. The first PCR reaction was carried out in a total volume of 50 μl, containing 2 units of AmpliTaq Gold DNA Polymerase (Applied Biosystem, USA), 5 μl of 10X Buffer, 2.5 mM of MgCl2, 0.2 mM of each dNTP, 2.5 μl of DMSO, 0.2 mM of primers M13BCV-FW (5’-TGT AAA ACG ACG GCC AGT HAA YCA YAA RGA YAT YGG-3’) and BCV-RV1 (5’-GCY CAN ACY ATN CCY ATR TA-3’), and 5 μl of extracted DNA. The second PCR reaction was carried out in a total volume of 50 μl containing 2 units of AmpliTaq Gold DNA Polymerase (Applied Biosystem, USA), 5 μl of 10X Buffer, 2.0 mM of MgCl2, 0.2 mM of each dNTP, 2.5 μl of DMSO, 0.4.mM of primers M13 (5’-GTA AAA CGA CGG CCA GTG-3’) and BCV-RV2 (5’-ACY ATN CCY ATR TAN CCR AAN GG-3’), and 1 μl of the PCR products obtained during the first amplification step. The thermal profile of the first PCR consisted of activation at 95 °C for 10 min, followed by 40 cycles at 94 °C for 40 s, 45 °C for 40 s, and 72 °C for 1 min, with a final extension step of 7 min at 72 °C. The thermal profile of the second PCR consisted of activation for 10 min at 95 °C followed by 16 cycles of a touchdown protocol at 94 °C for 40 s, decreasing the annealing temperature from 60 °C to 45 °C for 40 s (1 °C/cycle), followed by 72 °C for 1 min. Then, 30 cycles at 94 °C for 40 s, 45 °C for 40 s, and 72 °C for 1 min, with a final extension step of 7 min at 72 °C. Negative controls were included during the extraction and amplification stages to confirm avoidance of contamination.The amplicons were sequenced in both directions using a 16-capillary ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems, USA). To identify the blood meal host species, nucleotide sequences were compared with representative sequences available in the GenBank database using the Basic Local Alignment Search Tool (BLAST). Positive identification was made when  > 97% identity was attained between the query and subject sequence.Statistical analysisAs most of the identified hosts were either humans or wild ungulates (see Results), we investigated how the probability of feeding on these two host groups was affected by different abiotic factors. Specifically, we considered two binary response variables indicating whether or not the blood meal was acquired from a human/wild ungulate host. We developed univariate (i.e., with only one explanatory variable) generalized linear models (GLMs) with a binomial-distributed error structure, considering in turn, for each response variable, the following four explanatory covariates: (i) the altitude of the sampling point; (ii) the human population density in the area surrounding the sampling point, defined as 250 m square units, as per the Global Human Settlement Database48; (iii) the percentage of non-artificial land cover within different buffers (100, 250 and 500 m radius from the sampling point), as per the Corine Land Cover dataset (defined as the sum of the fractions of agricultural and forested areas)51; the distance associated with the model with the lowest AIC value was then selected; (iv) the minimum distance of the sampling point from the nearest pixel labeled as forest, according to the Corine category. All analyses, including plot creation, was performed using R v4.0.252 and “tidyverse”, “ggplot2”, and “gridExtra” libraries.Map in Fig. 1 was generated by QGIS 3.22 using Sentinel-2 cloudless as background satellite image and urban places from OpenStreetMap database53,54,55. More

The theoretical modelThe model presented here builds on a recently developed metabolic theory based on biochemical kinetics. It describes a non-linear relationship between plant metabolic rate per unit of dry mass (Bs, nmol g−1 s−1), such as light-saturated photosynthetic rates and dark respiration rates, plant water content (S, g g−1), and temperature (in degrees K)19,20, i.e.$${B}_{{{{{{rm{s}}}}}}}={g}_{1}{e}^{{k}_{1}S/left({K}_{1}+Sright)}{e}^{-E/{kT}}$$
(1)
where g1 is a normalisation constant, k1 represents the maximum increase in specific metabolic rates due to changes in water content (i.e. from dehydrated to fully hydrated), K1 represents the water content when the mean reaction rate of cellular metabolism reaches one-half of its maximum, E is the activation energy, and k is Boltzmann’s constant. In this model S is defined on a dry mass basis (i.e. the ratio of plant water mass to plant dry mass) to broaden its range and better reflect the proportional changes in the amount of water in plant tissues20. Full details of the model’s assumptions can be found in the Methods and Huang et al.19,20. The model was tested and shown to hold true for a broad range of species and for whole plants and above- and belowground organs20. Here, we first applied the model to describe the quantitative effects of dry mass-based LWC (the ratio of leaf water mass to leaf dry mass) and temperature on the light-saturated leaf photosynthetic rate per unit of dry mass or leaf photosynthetic capacity (Ps, nmol CO2 g−1 s−1), which can be expressed as$${{{{{rm{ln}}}}}}left({P}_{{{mbox{s}}}}right)={{{{{rm{ln}}}}}}left(frac{{P}_{{{{{{rm{L}}}}}}}}{{M}_{{{{{{rm{L}}}}}}}}right)={{{{{rm{ln}}}}}}left({g}_{1}right)+frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}}-frac{E}{{kT}},$$
(2)
where PL is the light-saturated whole-leaf photosynthetic rate (nmol s−1). Equation 2 indicates that the log-transformed temperature-corrected Ps (i.e. Pscor) should increase with LWC, following Michaelis-Menten type hyperbolic response, i.e.$${{{{{rm{ln}}}}}}left({P}_{{{mbox{scor}}}}right)={{{{{rm{ln}}}}}}left({P}_{{{{{{rm{s}}}}}}}{e}^{E/{kT}}right)={{{{{rm{ln}}}}}}left({g}_{1}right)+frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}}.$$
(3)
Rearranging Eq. (2) shows that the temperature- and LWC-corrected whole-leaf photosynthetic rate (i.e. PLcor) should scale isometrically with ML, i.e.$${P}_{{{mbox{Lcor}}}}={P}_{{{{{{rm{L}}}}}}}{e}^{-{k}_{1}cdot {{{{{rm{LWC}}}}}}/left({K}_{1}+{{{{{rm{LWC}}}}}}right)}{e}^{E/{kT}}={g}_{1}{M}_{{{{{{rm{L}}}}}}}.$$
(4)
In this study, we refer to the temperature or water content correction as moving the temperature term ((frac{E}{{kT}})) or water content term ((frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}})) to the left-hand side of the model, an approach that has been commonly used in previous studies28.Following previous studies29,30,31, we assume that PL is proportional to AL, i.e. ({P}_{L}propto {A}_{L})because leaf area directly determines the light interception capacity1. Therefore, the quantitative relationship between SLA and LWC and temperature can be described as$${{{{{{mathrm{ln}}}}}}}left({{mbox{SLA}}}right)={{{{{{mathrm{ln}}}}}}}left(frac{{A}_{{{{{{rm{L}}}}}}}}{{M}_{{{{{{rm{L}}}}}}}}right)={{{{{{mathrm{ln}}}}}}}left({g}_{2}right)+frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}}-frac{E}{{kT}},$$
(5)
where g2 is another normalisation constant. Given that leaf area is a direct indicator of leaf photosynthetic capacity and that both traits reflect the long-term adaptation of plants to environmental change3,13, k1 in Eqs. (2) and (5) represent the maximum increase in mass-specific leaf photosynthetic capacity due to changes in water content. Since the temperature has a direct effect on the metabolic rates32 and productivity of ecosystems33, it is reasonable to assume that temperature can affect SLA globally34,35. Therefore, in this context T denotes the mean growing-season temperature (in degrees K), as SLA might be more responsive to long-term changes in temperature. Equation (5) predicts that SLA should increase with both LWC and the growing-season temperature. Rearranging Eq. (5) yields$${{{{{{mathrm{ln}}}}}}}left({{mbox{SL}}}{{{mbox{A}}}}_{{{mbox{cor}}}}right)={{{{{{mathrm{ln}}}}}}}left({{mbox{SLA}}}{e}^{E/{kT}}right)={{{{{{mathrm{ln}}}}}}}left({g}_{2}right)+frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}},$$
(6)
which predicts that the log-transformed temperature-corrected SLA (i.e. SLAcor) should increase with LWC following Michaelis-Menten dynamics. Likewise, by moving (frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}}) and (frac{E}{{kT}}) to the left-hand side and ML to the right-hand side of Eq. (5), we observe that the temperature- and LWC-corrected leaf area (i.e. ALcor) should scale isometrically with leaf mass, i.e.$${A}_{{{mbox{Lcor}}}}={A}_{{{{{{rm{L}}}}}}}{e}^{-{k}_{1}cdot {{{{{rm{LWC}}}}}}/left({K}_{1}+{{{{{rm{LWC}}}}}}right)}{e}^{E/{kT}}={g}_{2}{M}_{{{{{{rm{L}}}}}}}.$$
(7)
We note that the scaling of PLcor and ALcor with respect to ML will reveal how LWC mediates the scaling exponent of leaf trait relationships.Effects of LWC and temperature on leaf trait scalingThe numerical value of the exponent for the PL versus AL scaling relationship calculated from the empirical data was 0.99 (Supplementary Fig. 1; 95% CI = 0.95 and 1.02, r2 = 0.87), strongly supporting the model assumption that PL scales isometrically with AL. We then examined the effect of LWC on leaf trait scaling. The numerical value of the scaling exponent for the PL versus ML relationship was 0.95 (Fig. 2a; 95% CI = 0.92 and 0.99, r2 = 0.83). The non-linear relationship between Pscor and LWC described by Eq. (3) was supported by the empirical data (Fig. 2b; Supplementary Table 1). The non-linear model (Eq. 3) also had a lower Akaike’s Information Criterion score than the simple linear model between log-transformed Pscor and LWC (i.e. 793.5 versus 1988.8). After LWC and temperature were corrected (see Eq. 4), the numerical value of the scaling exponent became 0.97 (Fig. 2c; 95% CI = 0.94 and 1.01, r2 = 0.85), which was statistically indistinguishable from 1.0 (P  > 0.05), as predicted by the model. Likewise, the numerical value of the exponent (i.e. α) for the AL versus ML scaling relationship was 1.02 (Fig. 2d; 95% CI = 1.02 and 1.03, r2 = 0.92). Additional analyses using the pooled dataset showed that log-transformed SLA increased with LWC following Michaelis-Menten dynamics, as predicted by Eq. (6) (Fig. 2e; Supplementary Table 1). After the effects of LWC and temperature were accounted for (using Eq. 7), the numerical value of α became 1.01 (Fig. 2f; 95% CI = 1.00 and 1.01, r2 = 0.95). Thus, both of the scaling exponents numerically converged onto 1.0 once LWC and temperature were corrected. In addition, an inspection of the locally weighted smoothing (LOWESS) curves showed that the curvature in both scaling relationships was reduced after the effects of LWC and temperature were corrected, as predicted by the model (Fig. 2).Fig. 2: The quantitative effects of dry mass-based leaf water content (LWC) on leaf photosynthesis and SLA.a Scaling of leaf photosynthetic rate (PL, nmol s−1) with leaf dry mass (ML, g). b Non-linear fit to the relationship between temperature-corrected mass-specific leaf photosynthetic rate (Pscor, nmol g−1 s−1) and LWC (g g−1) based on Eq. (3). c Scaling of temperature- and LWC-corrected leaf photosynthetic rate (PLcor, nmol s−1) with ML (g). d Scaling of leaf area (AL, cm2) with leaf dry mass (ML, g). e Non-linear fit to the relationship between temperature-corrected specific leaf area (SLAcor, cm2 g−1) and LWC based on Eq. (6). f Scaling of temperature- and LWC-corrected leaf area (ALcor, cm2) with leaf dry mass (ML, g). Data with LWC greater than 25 were not shown in panel e for a better visualisation. LOWESS curves (blue lines) and 95% confidence intervals are shown.Full size imageThe results presented here show that temperature and LWC quantitatively correlate with other leaf traits, such as Ps and SLA (or LMA), as predicted by the model. The increases in Ps and SLA attenuate with increasing LWC (Fig. 2b, e), indicating that leaf water availability sets a constraint on the maximum Ps and SLA that leaves can reach. It has long been recognised that SLA is closely correlated with leaf growth rate and metabolic activity14,36,37. Therefore, it is reasonable to also expect that SLA, as well as Ps, will be quantitatively affected by LWC, which can change as a function of developmental status (such as leaf maturation and the accumulation of lignified tissues) and transiently as a function of evapotranspiration. LWC is also a reflection of species-specific adaptation to environmental conditions in different biomes. Nevertheless, our model, as well as the empirical data used to test it, reveal a broad and statistically robust correlation between critical leaf functional traits and leaf tissue water content. However, the observed variations in Pscor (Fig. 2b) suggest that in addition to temperature and LWC, other factors (e.g. plant phylogeny and soil fertility) may also affect leaf photosynthetic capacity, which is not accounted for in our model and should be critically examined in future research.Leaf area-mass scaling among different groupsThe numerical value of α varied across different plant growth forms, ecosystems, and latitudinal zones (Fig. 3 and Supplementary Table 2). In particular, the leaf area versus mass scaling relationship showed a clear pattern along a latitudinal gradient (Fig. 3c and Supplementary Table 2). The numerical value of α decreased from 1.10 in boreal regions (95% CI = 1.08 and 1.12, r2 = 0.91) to 1.00 in temperate regions (95% CI = 0.99 and 1.01, r2 = 0.91), and to 0.94 in tropical regions (95% CI = 0.92 and 0.95, r2 = 0.91). However, after correcting for the effects of LWC and temperature, as predicted, α converged onto 1.0 across all different groupings, and the r2 values of the scaling relationships also increased (Fig. 3 and Supplementary Table 2).Fig. 3: The exponents of leaf area-mass scaling with and without temperature and LWC corrections among different groups.a Comparison of scaling exponents among plant growth forms (n = 1688, 491, 1097, and 832 for forbs, graminoids, shrubs, and trees, respectively). b Comparison of scaling exponents among ecosystem types (n = 97, 1367, 1285, 1271, and 114 for deserts, forests, grasslands, tundra, and wetlands, respectively). c Comparison of scaling exponents among different latitudinal zones (n = 1111, 2113, and 910 for tropical, temperate, and boreal zones, respectively). Error bars indicate 95% confidence intervals.Full size imageOur analyses show that LWC also affects the numerical values of the exponents of leaf trait scaling relationships, which helps to explain why different works sometimes report significant differences in the exponents governing these relationships8,10. In particular, the numerical values of the scaling exponent governing the leaf area versus mass scaling relationship differ among different plant growth forms, ecosystems, and latitudinal zones (Fig. 3 and Supplementary Table 2), indicating that no invariant “scaling exponent” (i.e. α) holds true for the leaf area-mass scaling relationship. For example, in our dataset, α is significantly smaller than 1.0 in tropical regions (i.e. in keeping with a “diminishing returns” relationship in leaf area with respect to increasing leaf mass), close to 1.0 in temperate regions (i.e. a break-even relationship), and significantly larger than 1.0 in boreal regions (i.e. an “increasing returns” relationship). This shift in α along a latitudinal gradient may be associated with the different strategies to cope with variations in water availability, e.g. the high evapotranspiration rates in tropical regions may constrain increases in leaf area with increasing leaf mass, therefore resulting in diminishing returns, whereas, in boreal regions, the reduced water stress may enable plants to maximise leaf area to achieve relatively high photosynthetic capacities. Despite the variability in the numerical values of scaling exponents across different groups, the degree of curvature in these scaling relationships is reduced, and exponents converge onto unity after the effects of LWC and temperature are accounted for (Fig. 3), as predicted by the model (Eq. 7). This finding indicates that the variations in the exponent can, at least partially, be ascribed to the effects of LWC on SLA and the relative rate of increase in leaf area versus leaf mass. It is noteworthy that the numerical value of the scaling exponent for the PL versus ML relationship is also very close to 1.0, indicating the relatively weak effects of temperature and LWC on leaf photosynthesis-mass scaling. This may be partially attributed to the relatively limited number of data with concurrent LWC measurements and the adaptation of leaf traits to long-term temperature changes (see below for detailed discussion). Nevertheless, more measurements on LWC and leaf photosynthetic rates are needed to further test how LWC mediates leaf photosynthesis-mass scaling.Given that LWC was weakly correlated with ML (Supplementary Fig. 2, log-log slope = −0.01, 95% CI = −0.02 and 0, r2  More

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In this episode:00:46 Inequalities in US faculty hiringIn the US, where a person gained their PhD can have an outsized influence on their future career. Now, using a decade worth of data, researchers have shown there are stark inequalities in the hiring process, with 80% of US faculty trained at just 20% of institutions.Research article: Wapman et al.09:01 Research HighlightsHow wildlife can influence chocolate production, and the large planets captured by huge stars.Research Highlight: A chocoholic’s best friends are the birds and the batsResearch Highlight: Giant stars turn to theft to snag jumbo planets11:42 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, what science says about grieving for a public figure, and why suburban Australians are sharing increasingly sophisticated measures to prevent cockatoos from opening wheelie bins.Nature News: Millions are mourning the Queen — what’s the science behind public grief?The Guardian: ‘Interspecies innovation arms race’: cockatoos and humans at war over wheelie bin raidsSubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed. 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

The shell of the endangered hawksbill sea turtle (pictured) is prized for trinkets and jewellery.Credit: Reinhard Dirscherl/SPL

Poaching is less of a threat to the survival of sea turtles than it once was, a new analysis suggests1. Illegal sea-turtle catch has dropped sharply since 2000, with most of the current exploitation occurring in areas where turtle populations are relatively healthy.This study is the first worldwide estimate of the number of adult sea turtles moved on the black market. According to the analysis, more than one million sea turtles were illegally harvested between 1990 and 2020. But the researchers also found that the illegal catch from 2010 to 2020 was nearly 30% lower than that in the previous decade.“The silver lining is that, despite the seemingly large illegal take, exploitation is not having a negative impact on sea-turtle populations on a global scale. This is really good news,” says co-author Jesse Senko, a marine conservation scientist at Arizona State University in Tempe. The research was published 7 September in Global Change Biology.Turtles for trinketsFor millennia, humans have used both adult sea turtles and their eggs as a food source and for cultural practices. In the past 200 years, however, many sea turtle populations declined steeply as hunting rose to meet a growing demand for turtle-based goods. In Europe, North America and Asia, sea-turtle shells were used to make combs, jewelry and furniture inlays. Turtles were also hunted for meat and for use in traditional medicine.The rise in turtle hunting meant that, by 2014, an estimated 42,000 sea turtles were legally harvested every year, and an unknown number of sea turtles were sold on the black market. Today, six of the seven sea-turtle species found around the globe are endangered owing to a deadly combination of habitat destruction, poaching and accidental entanglement in fishing gear.To pin down how many sea turtles were illegally harvested, Senko and his colleagues surveyed sea-turtle specialists and sifted through 150 documents, including reports from non-governmental organizations, papers in peer-reviewed journals and news articles.

Source: Ref. 1

By combining this information, the researchers made a conservative estimate that around 1.1 million sea turtles were illegally caught between 1990 and 2020. Nearly 90% of these turtles were funneled into China and Japan, largely from a handful of middle- and low-income countries (see ‘Long-distance turtle transport’). Of the species that could be identified, the most frequently exploited were the endangered green turtles (Chelonia mydas), hunted for meat, and the critically endangered hawksbill turtles (Eretmochelys imbricata), prized for their beautiful shells.However, the data also showed that the number of illegally caught turtles decreased from around 61,000 each year between the start of 2000 and the end of 2009 to around 44,000 in the past decade (see ‘More sea turtles swim free’). And, although there were exceptions, most sea turtles were taken from relatively robust populations that were both large and genetically diverse.

Source: Ref. 1

Although sea turtles seem to be doing well globally, this doesn’t mean that threats to regional populations can be ignored, says Emily Miller, an ecologist at the Monterey Bay Aquarium Research Institute in California. The study pins down where — and for whom — sea turtles are being exploited, which could help conservationists to target communities for advocacy, she says.Overall, the numbers signal that conservation efforts could be working, says Senko. “Contrary to popular belief, most sea-turtle populations worldwide are doing quite well,” he says. “The number of turtles being exploited is a shocker, but the ocean is big, and there are a lot of turtles out there.” More

Study areaSoutheastern Chongqing, China (107° 14′–109° 19′ E, 28° 9′–30° 32′ N), has an area of about 19,800 km2 (Fig. 1). The study area has a subtropical monsoon climate. And the area has four distinct seasons, with an annual average temperature of 16.2 °C and abundant rainfall, with an average annual rainfall of 1209 mm. This region is located in the central part of the Wuling mountains, which is characterized by medium and low mountainous landforms, with an average altitude of greater than 1000 m. The water system (the Wujiang River system) in the study area is well developed, with a large drainage area and rich groundwater resources. The soil is dominated by yellow soil and limestone soil, and the sensitivity to soil erosion is high. The district exhibits the typical ecological fragility of karst areas, with barren soil, fragmented surfaces, a single community, and a low ecological carrying capacity. The area includes six counties: Qianjiang district, Shizhu Tujia Autonomous county, Xiushan Tujia and Miao Autonomous county, Youyang Tujia and Miao Autonomous county, Wulong district, and Pengshui Miao and Tujia Autonomous county. The coverage rates of the carbonatite layers in these counties are 42.11, 67.77, 25.70, 34.80, 59.70 and 88.46%, respectively38, and the average coverage of the carbonatite layers is 53.09%, making this a representative area of karst rocky desertification.Data and image pre-processingIn the study, the remote sensing data were obtained from the United States Geological Survey (USGS, https://earthexplorer.usgs.gov/), including landsat-5 thematic mapper (TM) images acquired in 2001, 2006 and 2011 and Landsat-8 operational land imager (OLI) images obtained in 2016 and 2021 (Table 1). The spatial resolution is 30 m. In order to ensure the comparability of spectral characteristics, the data collection was conducted from May to September when the vegetation grew better. In order to meet the usage requirements, the cloud cover of each image used is below 10%. For the images with poor quality, the adjacent years were selected for replacement. The difference in ecological quality between adjacent years in the same region was not particularly large. In order to represent the actual situation of the ecological environment quality in the target year as much as possible, we tried to minimize the replaced part in each target year. A total of 20 images were collected in this study. The images downloaded were all L1T products, which had undergone systematic radiometric correction and geometric correction, so precise geometric correction was no longer performed. Before the subsequent processing, all 20 images were preprocessed by radiometric calibration, atmospheric correction, image mosaicking and cropping. Then these images were calculated to obtain NDVI, WET, NDBSI, LST and RI. And based on the preprocessed Landsat images, support vector machine classification was performed to obtain the land use (LU) status.Table 1 Information of images used in this study.Full size tableThe topographical data included the elevation (EV) and slope (SP) data. Among them, the elevation data was provided by the official website of the United States Geological Survey (USGS, https://earthexplorer.usgs.gov/). And the slope data was calculated from the elevation data. The meteorological data, including the monthly average temperature (MT), monthly mean precipitation (PR), monthly even relative humidity (RH), and monthly total sunshine hours (SH) from May to September of the target year, were got from the China Meteorological Data Network (http://data.cma.cn/). In addition, socioeconomic data, including the population density (PD) and gross domestic product (GDP), were obtained from the statistical yearbooks of each district and county in the study area. The nighttime light (NTL) data were obtained from the National Oceanic and Atmospheric Administration (NOAA, https://www.noaa.gov/). The above data and LU were used as the influencing factors of ecological quality to analyze the reasons for the change of local ecological environment quality. The statistical data and monitoring data of each evaluation index used to construct the EI come from the statistical yearbooks, water resources bulletin and soil and water conservation bulletin of each district and county.MethodologyStudy frameworkA framework was developed for evaluating the ecological quality in southeastern Chongqing from 2001 to 2021 in the study. And the framework included three parts: data preparation, construction of the MRSEI, and the analysis of the ecological status in the region. Figure 2 presents the detailed information about the framework. The operations of band calculation, normalization and PCA were all carried out using the ENVI 5.3 software (https://www.harrisgeospatial.com).Figure 2The study framework.Full size imageIndicators used in MRSEIThe greenness, humidity, heat, dryness, and degree of rocky desertification were used to construct the MRSEI. The NDVI39 was chosen to characterize the greenness. The humidity component acquired from the tasseled cap transformation (WET)40 was selected to represent the humidity. The LST41 was used to represent the heat, the normalized difference build-up soil index (NDBSI)42 was used to characterize the dryness. The RI was applied to characterize the degree of rocky desertification.The NDVI is an important indicator for monitoring the physical and chemical properties of vegetation, and it can be employed to calculate the vegetation coverage, leaf area index, and so on19. In addition, it eliminates some radiation errors and has a stronger response to surface vegetation. It has been widely used in vegetation remote sensing monitoring. The equation for calculating the NDVI is as follows39:$$ {text{NDVI}} = {{(uprho }}_{{{text{NIR}}}} – {uprho }_{{{text{Red}}}} {)}/{{(uprho }}_{{{text{NIR}}}} {{ + uprho }}_{{{text{Red}}}} ), $$
(1)
where ({uprho }_{{{text{NIR}}}}) is the reflectance of the near-infrared band and ({uprho }_{{{text{Red}}}}) refers to the reflectance of the red band corresponding to each image.The WET can effectively reflect the humidity conditions of the surface vegetation, water, and soil, and can reveal the changes in the ecological environment, such as soil degradation. Therefore, it is commonly used in ecological environment monitoring43. The WET can be expressed as40,43:$$ {text{WET}}_{{{text{TM}}}} { = 0}{{.3102uprho }}_{{{text{Red}}}} { + 0}{{.2021uprho }}_{{{text{Green}}}} { + 0}{{.0315uprho }}_{{{text{Blue}}}} { + 0}{{.1594uprho }}_{{{text{NIR}}}} – {0}{{.6806uprho }}_{{{text{SWIR1}}}} – {0}{{.6109uprho }}_{{{text{SWIR2}}}} , $$
(2)
$$ {text{WET}}_{{{text{OLI}}}} { = 0}{{.3283uprho }}_{{{text{Red}}}} { + 0}{{.1972uprho }}_{{{text{Green}}}} { + 0}{{.1511uprho }}_{{{text{Blue}}}} { + 0}{{.3407uprho }}_{{{text{NIR}}}} – {0}{{.7117uprho }}_{{{text{SWIR1}}}} – {0}{{.4559uprho }}_{{{text{SWIR2}}}} , $$
(3)
where ({uprho }_{{text{i}}} ,) is the reflectance of band i.The NDBSI is expressed as the average of two indicators, the bare soil index (SI)44 and the index-based built-up index (IBI)45. It can be applied to characterize the dryness. The calculation formulas are44,45:$$ {text{IBI }} = {text{ }}left[ {2uprho _{{{text{SWIR1}}}} /left( {uprho _{{{text{SWIR1}}}} + {text{ }}uprho _{{{text{NIR}}}} } right) – uprho _{{{text{NIR}}}} /(uprho _{{{text{NIR}}}} + {text{ }}uprho _{{{text{Red}}}} } right) – uprho _{{{text{Green}}}} /(uprho _{{{text{Green}}}} + {text{ }}uprho _{{{text{SWIR1}}}} )]/[2uprho _{{{text{SWIR1}}}} /left( {uprho _{{{text{SWIR1}}}} + {text{ }}uprho _{{{text{NIR}}}} } right) + {text{ }}uprho _{{{text{NIR}}}} /(uprho _{{{text{NIR}}}} + {text{ }}uprho _{{{text{Red}}}} ) + {text{ }}uprho _{{{text{Green}}}} /(uprho _{{{text{Green}}}} + {text{ }}uprho _{{{text{SWIR1}}}} )], $$
(4)
$$ {text{SI = }}left[ {{uprho }_{{{text{SWIR1}}}} {{ + uprho }}_{{{text{red}}}} – left( {{uprho }_{{{text{Blue}}}} {{ + uprho }}_{{{text{NIR}}}} } right)} right]/left[ {{uprho }_{{{text{SWIR1}}}} {{ + uprho }}_{{{text{red}}}} { + }left( {{uprho }_{{{text{Blue}}}} {{ + uprho }}_{{{text{NIR}}}} } right)} right], $$
(5)
$$ {text{NDBSI = (IBI + SI)/2,}} $$
(6)
where ({uprho }_{{text{i}}} ,) is the reflectance of band i.The LST is closely related to natural processes and human phenomena such as crop yield, vegetation growth and distribution, surface water cycle, etc. It can well reflect the state of the surface ecological environment. The atmospheric correction method is used to invert the LST here46,47, it can be expressed as:$$ {text{L = gain}} times {text{DN + bias,}} $$
(7)
$$ {text{T = K}}_{{2}} /{text{ln}}left( {frac{{{text{K}}_{{1}} }}{{text{L}}}{ + 1}} right){,} $$
(8)
$$ {text{LST = T}}/left[ {{1 + }left( {frac{{{lambda T}}}{{upalpha }}} right){{lnvarepsilon }}} right]{,} $$
(9)
where L is the radiation value in the thermal infrared band, DN is the gray value, gain and bias is the gain value and offset value of the L-band, which was got from the image header file. And T is the temperature value at the sensor; K1 and K2 are calibration parameters respectively (for TM, K1 = 607.76 W/(m2 sr μm), K2 = 1260.56 K; for TIRS, K1 = 774.89 W/(m2 sr μm), K2 = 1321.08 K); λ is the central wavelength of thermal infrared band; α = 1.438 × 10−2 m K. ε is the surface emissivity and the value is estimated by the vegetation index mixture model48,49. It is calculated as follows:$$ {text{VFC = }}frac{{{text{NDVI}} – {text{NDVI}}_{{{text{Soil}}}} }}{{{text{NDVI}}_{{{text{Veg}}}} – {text{NDVI}}_{{{text{Soil}}}} }}, $$
(10)
$$ {text{d}}_{{upvarepsilon }} { = }left( {{1} – {upvarepsilon }_{{text{s}}} } right){{ times (1}} – {text{VFC) }}times text{F} times upvarepsilon _{{text{v}}} , $$
(11)
$$ {{upvarepsilon = upvarepsilon }}_{{text{v}}} times {text{ VFC}} + varepsilon _{{text{s}}} {{ times }}left( {{1} – {text{FVC}}} right){text{ + d}}_{{upvarepsilon }} , $$
(12)
where VFC is the vegetation fractional cover, ({text{NDVI}}_{{{text{Veg}}}}) is the NDVI of the pixel covered by full vegetation and the pixels with NDVI  > 0.72 are regarded as pure vegetation pixels; ({text{NDVI}}_{{{text{Soil}}}}) is the NDVI of the bare pixel and the pixels with NDVI  More