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Surface-attached bacteria move towards antibiotics via twitching motilityWe used microfluidic devices and automated cell tracking to quantify the movement of P. aeruginosa cells as they are exposed to well-defined spatial gradients of antibiotics in developing biofilms (Fig. 1). We began with the antibiotic ciprofloxacin, which is widely used to treat P. aeruginosa infections27,28. To set a baseline, we first determined the minimum inhibitory concentration (hereafter MIC) of ciprofloxacin for P. aeruginosa (strain PAO1) in shaking cultures, which agrees with the published MIC of this strain (Fig. S129). We then exposed surface-attached cells to an antibiotic gradient in a microfluidic device where the antibiotic concentration ranged from zero to 10 times the MIC (Fig. 1A, B, Methods). After approximately 5 h of unbiased movement, we were surprised to see that twitching cells began to bias their movement towards increasing concentrations of ciprofloxacin (Fig. 1B, D, Movie 1). The movement bias, β, defined as the number of cells moving up the gradient divided by the number of cells moving down the gradient, peaks after approximately 10 h and then decays as the surface becomes crowded with cells (Movie 1) and tracking becomes difficult (Methods). The flow through the device also has a small influence on the direction of cell movement because it tends to pull cells in the downstream direction (Fig. 1B, C, E). However, this fluid flow is orthogonal to the direction of the antibiotic gradient, and so does not explain the movement towards antibiotics.Fig. 1: Twitching P. aeruginosa cells bias their motility towards increasing antibiotic concentrations.A A dual-inlet microfluidic device generates steady antibiotic gradients (e.g. ciprofloxacin, CMAX = 10X MIC) via molecular diffusion. Isocontours were calculated using mathematical modelling (Methods) and background shading shows approximate ciprofloxacin distribution visualised using fluorescein. B Red (blue) cell trajectories are moving towards (away from) increasing [ciprofloxacin]. Inset: A circular histogram of cell movement direction reveals movement bias towards increasing [ciprofloxacin]. A two-sided binomial test rejects the null hypothesis that trajectories are equally likely to be directed up or down the [ciprofloxacin] gradient (p  More

Field parameters and major ionsThe results of the hydrochemistry and environmental isotopes for the 40 lakes are presented spatially in Figs. S1–S11 and are located in Tables S1 and S2.The lake waters are oxic (8.6–12.6 mg l−1) and range from slightly acidic (pH 6.0) to slightly alkaline (pH 9.2). Lake water temperatures are generally highest for lakes along the west coast (greater than 10 °C, Table S2). Phosphate concentrations are below detection level (0.1 mg l−1) for all lakes and nitrate was low ranging from below detection limit ( More

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Indonesia’s bleeding toad (Leptophryne cruentata) is critically endangered.Credit: Pepew Fegley/Shutterstock

One-quarter of all plant and animal species are threatened with extinction owing to factors such as climate change and pollution. Starting this week, negotiators and ministers from more than 190 countries are meeting at a United Nations biodiversity summit called COP15 in Montreal, Canada, to address the emergency.
10 startling images of nature in crisis — and the struggle to save it
From 7 to 19 December, they will be trying to seal a new deal to save Earth’s biodiversity. The treaty, known as the post-2020 Global Biodiversity Framework, is intended to establish precise targets for countries to protect and restore nature, including conserving 30% of the planet by 2030 and cutting nutrient pollution, such as reducing nitrogen fertilizer loss from farmland.Time is running out. “We’re driving species to extinction at a rate about 1,000 times faster than they are created through evolution,” says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.As COP15 kicks off, researchers and policy experts are concerned that countries still disagree on too many issues to secure a deal that will protect species and ecosystems effectively. Here, Nature looks at the extent of the crisis, and what scientists say countries must do to succeed.Which species are most at risk, and what’s threatening them?Among the most at-risk groups are amphibians and reef-forming corals. A global assessment shows that more than 40% of amphibians are threatened with extinction1, including the critically endangered bleeding toad (Leptophryne cruentata), which lives in Mount Gede Pangrango National Park in Java, Indonesia.These toads were thought to be extinct until the year 2000, when some were spotted by a team led by Mirza Kusrini, a herpetologist at Bogor Agricultural University in Indonesia. But the researchers found that the amphibians were infected with chytrid (Chytridiomycota sp.), a fungus that has devastated global amphibian populations. Kusrini says that climate change is probably making life hard for the tiny toad, which got its common name from the crimson, splatter-like spots covering its body. Warm weather can stimulate fungal outbreaks and shift the timing of behaviours, such as the toads’ breeding season, making the amphibians vulnerable.

Source: Red List Index/IUCN

Global warming, which has been raising sea temperatures, is also responsible for harming coral reefs around the globe (see ‘Threat assessment’). Over a period of 9 years, up to 2018, 14% of the world’s coral died out — a massive problem, because today, coral reefs support one-quarter of all marine species.Research shows that climate change is quickly becoming a large threat to biodiversity2. But still, the most-destructive forces are the conversion of land and seas for agricultural uses and people exploiting natural resources through fishing, logging, hunting and the wildlife trade. About 75% of land and 66% of ocean areas have been significantly altered, usually for producing food.What might happen if species disappear?It’s difficult to predict, because doing so requires knowledge of which species are present in a particular ecosystem, such as a rainforest, and what functions they have, says Shahid Naeem, an ecologist at Columbia University in New York City. Much of that information is often unknown. However, scientists have shown3 that ecosystems with less biodiversity are not as good at capturing and converting resources into biomass, such as happens when plants capture nutrients or sunlight used for growth.
Why deforestation and extinctions make pandemics more likely
Neither are less-diverse ecosystems as good at decomposing and recycling biological materials and nutrients. For example, studies show that dead organisms are broken down, and their nutrients recycled, more quickly when a high variety of plant litter covers the forest floor4. Ecosystems with low biodiversity also have low resilience — they are not as able to bounce back after a perturbation or shock, such as a fire, as more-diverse systems are, Naeem says.“If we lose parts of our system, it simply won’t function very efficiently, and it won’t be very robust,” he adds. “The science behind that is rock solid.”Ecosystems also provide clean water and can sometimes prevent diseases from spreading to humans. When species are lost, these services deteriorate, Kusrini says. For example, most amphibians eat insects, many of which are considered pests, such as cockroaches, termites and mosquitoes. Studies have shown a rise in cases of malaria — spread by mosquitoes — in areas in Central America where amphibian populations have collapsed5. “You know when they disappear”, Kusrini says, because insect numbers rise and people start using more pesticides to kill them.What solutions do researchers say are needed to protect biodiversity?Protecting and conserving habitats is central to saving species. This idea is captured in the framework being negotiated at COP15. The draft includes the goal of conserving at least 30% of the world’s land and sea by 2030. But for protections to be most effective, they must include regions that are rich in biodiversity, such as tropical forests, Pimm says. Despite an increase in protected areas worldwide over the past ten years, species numbers have still declined, because these safeguards were not in the right places, studies show6.

Delegates at COP15 in Montreal show their support for a new agreement among nations to protect Earth’s biodiversity.Credit: UN Convention on Biological Diversity (CC BY 2.0)

“What we’re going to be looking for at COP15 is more quality, not just more quantity,” Pimm says.Eradicating invasive species is another important conservation strategy, and the framework’s draft currently calls for cutting the introduction of such species in half. Some estimates suggest that invasive predators, such as cats and rats, are responsible for more than half of all extinctions of birds, mammals and reptiles7.It’s important that nations agree on a framework with at least some quantifiable targets, so that progress can be measured, and so that countries can be held accountable if they fail to meet their targets, researchers say. “I’m afraid what will happen is, they will produce a long list of ‘waffle’,” Pimm says. “We need quantification.”Will nations manage to agree on a new deal to protect nature?As COP15 begins, the outlook is not good. The text of the draft is still littered with unresolved issues. At a press conference on 6 December, Elizabeth Mrema, executive secretary of the Convention on Biological Diversity — the global treaty that underpins the new biodiversity deal — said that national negotiators had made insufficient progress in a final round of discussions before the start of the summit. She urged countries to compromise, otherwise they will fail to reach a deal. “The state of the planet is in crisis,” Mrema said. “This is our last chance to act.”
Troubled biodiversity plan gets billion-dollar funding boost
One key contentious issue is how to finance biodiversity conservation, particularly in low- and middle-income countries, which are home to much of the world’s biodiversity. These nations, including Brazil and Gabon, would like a new fund to be established with US$100 billion added per year in aid. So far, that proposal has not gained traction with wealthier countries. “They really need to have the financial commitments, because things don’t get done without the money,” Naeem says.Despite the pessimism, Naeem is certain that scientists and advocates will keep pushing for a deal. “There would be real change” if countries were able to achieve a universal decrease in biodiversity loss, he says. More

Chemotaxonomic characteristicsThe predominant respiratory quinone for all novel strains was ubiquinone 8 (Q-8), consistent with other Ramlibacter species. C16:0 and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c) were identified as the common major fatty acids ( > 10%) of the novel strains USB13T, AW1T, GTP1T, and HM2T. Other than the aforementioned fatty acids, strain USB13T had C10:0 3-OH additionally as its major fatty acid, whereas strains AW1T and HM2T shared C17:0 cyclo and summed feature 8 (consisting of C18:1 ω7c and/or C18: 1 ω6c) as its additional fatty acids. Detailed comparisons of the fatty acid profiles of the novel strains and their reference strains are summarized in Table S1.Strains USB13T, AW1T, GTP1T, and HM2T shared major polar lipids diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), and phosphatidylethanolamine (PE), which was consistent with the major polar lipids of the reference strains. Additionally, the polar lipid profile of USB13T consisted of one unidentified phosphoaminolipid, two unidentified phosphoglycoaminolipids, and six unidentified polar lipids while the polar lipid profile of AW1T had one unidentified lipid, one unidentified phosphoglycolipid, and three unidentified glycolipids in addition. The polar lipid profile of strain GTP1T additionally consisted of two unidentified phosphoaminolipids, and that of strain HM2T additionally had one unidentified phosphoaminolipid, one unidentified phosphoglycolipid, one unidentified phosphoglycoaminolipid, and two unidentified phospholipids. Polar lipid profiles of the novel strains USB13T, AW1T, GTP1T, and HM2T are shown in Figure S1.Physiological, morphological characteristics, and screening of cellulose-degrading strainsWhen grown on R2A agar, strain USB13T produced reddish white and flat colonies while strain AW1T produced orange, convex colonies, strain GTP1T produced white, convex colonies, and strain HM2T produced cream-colored, flat, transparent colonies. Under TEM, monotrichous flagella were observed only in strain HM2T, and when tested for motility, strain USB13T and AW1T showed gliding motility, whereas strain GTP1T was non-motile. Strains USB13T and HM2T showed positive results for both catalase and oxidase activities; strain AW1T showed positive results for catalase and negative results for oxidase activity, and strain GTP1T showed negative results for catalase and positive results for oxidase activity. All strains were identified to be strictly aerobic, while showing negative results for urea, gelatin, starch, chitin, and DNA hydrolysis and positive results for hydrolysis of Tween 80. In addition, strain USB13T was the only strain to produce iron-chelating siderophores. When tested for NaCl tolerance, growth of strain USB13T was observed in NaCl concentrations of 0–7% (w/v), possibly due to the fact the strain was isolated from a marine environment. A detailed comparison of physiological and morphological characteristics between the novel species and its closely related Ramlibacter strains is presented in Table 1, while TEM images of the novel strains are shown in Figure S2. Results of the reference strains in Table 1 coincided with the data from the original literature1,3,4,5,7,8.Table 1 Characteristics differentiating strains USB13T, AW1T, GTP1T, and HM2T from closely related strains of the genus Ramlibacter.Full size tableStrains: 1, USB13T; 2, AW1T; 3, GTP1T; 4, HM2T; R. monticola KACC 19175T; 6, R. alkalitolerans KACC 19305T; 7, R. ginsenosidimutans KACC 17527T; 8, R. humi KCTC 52922T; 9, R. henchirensis KACC 11925T; 10, R. tataouinensis KACC 11924T; 11, R. rhizophilus KCTC 52083T. All strains are positive for esterase lipase (C8), while all strains are negative for chitin hydrolysis. All data were obtained from this study unless indicated otherwise. + , Positive; w + , weakly positive; -, negative.R2A agar plates supplemented with 1% (w/v) CMC were stained with Congo red dye after 7 days of incubation. Clear zones only formed around colonies of strain USB13T, indicating that strain USB13T solely possessed CMC-hydrolyzing activity among the four novel strains. When inoculated in basal salt medium, filter paper from the USB13T sample underwent degradation, whereas samples containing strains AW1T, GTP1T, and HM2T did not show any signs of degradation.Phylogenetic and genomic analysesEzBioCloud search results and BLASTn searches revealed that the novel strains belonged to the family Comamonadaceae and genus Ramlibacter. Using BLASTn, 16S rRNA gene sequence similarities were determined where strain USB13T was closest to strain GTP1T (98.5%), followed by strain HM2T (98.1%) and strain AW1T (97.1%). Strain AW1T shared the highest similarity with strain GTP1T (97.3%), followed by strain HM2T (97.1%), while strain GTP1T shared a similarity of 98.2% with strain HM2T. Phylogenetic analysis based on the MP method (Fig. 1) showed the clustering of the novel strains USB13T, AW1T, GTP1T, and HM2T with strains such as R. monticola G-3-2T, R. ginsenosidimutans BXN5-27T, R. alkalitolerans CJ661T, and R. rhizophilus YS3.2.7T. Similar topologies were observed in trees reconstructed by ML (Figure S3) and MP methods. The UBCG phylogenomic tree (Fig. 2), which was reconstructed using whole genome sequences, also showed close clustering of the selected reference strains and novel strains.Figure 1Maximum-parsimony (MP) tree reconstructed based on 16S rRNA gene sequences, showing the relationship between strains USB13T, AW1T, GTP1T, and HM2T and other closely related type strains. Bootstrap values based on 1000 replications are listed as percentages at branching points. Only bootstrap values exceeding 50% are shown. Bar, 50 substitutions per nucleotide position.Full size imageFigure 2Phylogenomic tree of strains USB13T, AW1T, GTP1T, and HM2T and their closely related taxa was reconstructed based on core genomes using UBCG version 3.0 pipeline42. NCBI GenBank accession numbers are shown in parentheses. Bootstrap analysis was carried out using 1000 replications. Percentage bootstrap values ( > 50%) are given at branching points. Bar, 0.050 substitution per position.Full size imageDraft genome sequences of the novel strains USB13T, AW1T, GTP1T, and HM2T were deposited in the GenBank database under the accession numbers JACORT000000000, JAEQNA000000000, JACORU000000000, and JADDIV000000000, respectively. In addition, the draft genome sequences of R. monticola KACC 19175T, R. alkalitolerans KACC 19305T, and R. ginsenosidimutans KACC 17527T were also deposited in GenBenk under the accession numbers JAEQNE000000000, JAEQND000000000, and JAEPWM000000000, respectively. The assembled genome size of the novel strains USB13T, AW1T, GTP1T, and HM2T was 5.53 Mbp, 5.11 Mbp, 6.15 Mbp, 4.31 Mbp, respectively. G + C content ranged from 67.9% to 69.9%, which was similar to those of the reference strains. The genomic features of the novel strains and their closely related Ramlibacter strains are presented in Table S2. CheckM analysis showed the following estimations for each strain: USB13T, had a 99.84% completeness and 0.68% contamination; AWIT, had a 99.84% completeness and 0.86% contamination; GTP1T, had a 99.38% completeness and 1.32% contamination; HM2T, had a 97.51% completeness and 0.16% contamination. These results indicated that the draft genome results for all strains were reliable. ANI values between the novel strains and reference strains ranged from 76.5–83.4% while dDDH values ranged from 20.7–26.7%, and AAI values ranged from 65.7–80.4%. All values were below the threshold for delineation of a new species54. ANI values between the novel strains and their reference strains are presented in Fig. 3, while a detailed comparison of GGDC and AAI values are shown in Table 2.Figure 3Heatmap of strains USB13T, AW1T, GTP1T, and HM2T and other closely related strains within the genus Ramlibacter, generated with OrthoANI values calculated using OAT software45. Bacterial strains and accession numbers are indentical to those of Fig. 2.Full size imageTable 2 Average amino acid identity (AAI) and digital DNA-DNA hybridization (dDDH) value comparisons between the closely related Ramlibacter type species and the novel strains, USB13T, AW1T, GTP1T, and HM2T. AAI values were calculated by two-way AAI, while dDDH values were calculated based on formula 246.Full size tableBased on NCBI PGAP annotation and CAZyme prediction results, strain USB13T, which was the only strain to show cellulolytic activity, possessed a total of four protein CDs encoding CAZymes, namely, two GH15 proteins, one glycosyl hydrolase protein, and one GH99-like domain-containing protein. Despite not showing any cellulolytic activity, strain AW1T possessed eight CAZyme CDs; the most amount among the novel strains. The enzymes include, two GH2 proteins, one GH5 protein, three GH15 proteins, one glycoside hydrolase protein, and one cellulase family glycosyl hydrolase. Strain GTP1T possessed two CDs encoding one GH15 protein and one GH16 protein; strain HM2T possessed three CDs encoding one GH2, one GH15, and one GH18 protein. All strains possessed GH15, which is known for its glucoamylase activity in fungi55. A detailed summary of the novel strains CAZymes are presented in Table S3 and a comparison of CAZyme numbers between strains USB13T, AW1T, GTP1T, and HM2T is summarized in Table S4. The presence of these genes may suggest the cellulolytic activity of strain USB13T, while it is uncertain why GH families responsible for endoglucanase (GH 5–8, 12, 16, 44, 45, 48, 51, 64, 71, 74, 81, 87, 124, and 128), exoglucanase (GH 5–7, and 48), and β-glucosidase (GH 1, 3, 4, 17, 30, and 116) were not present in the genome11.COG predictions (Fig. 4) revealed that the majority of the core genes of the four novel strains accounted for genes belonging to the functional categories C (energy production and conversion), E (amino acid transport and metabolism), I (lipid transport and metabolism), T (signal transduction mechanisms), and K (transcription). Meanwhile, the number of core genes belonging in category G, carbohydrate transport and metabolism, was the highest for strain USB13T (258), followed by GTP1T (230), HM2T (212), and AW1T (181). The high number of genes in strain USB13T may be a contributing factor in the strain’s cellulolytic activity. A comparison of COG gene count distribution of the novel strains is presented in Table S5.Figure 4Comparison of total number of matched genes of strains USB13T, AW1T, GTP1T, and HM2T according to functional classes based on Cluster of Orthologous Groups of proteins (COG) predictions48.Full size imageAntiSMASH analysis results showed four gene clusters within the genome of strain USB13T: ribosomally synthesized and post-translationally modified peptides (RIPP)-like cluster (989,516–1,000,916 nt; JACORT010000001), terpene synthesis (8,622–30,347 nt; JACORT010000003), RIPP precursor peptide recognition element (RRE)-containing cluster (311,469–333,619 nt; JACORT010000004), and redox-cofactor (281,860–303,948 nt; JACORT010000007). Among the clusters, the RRE-containing cluster showed 11% similarity to streptobactin, a tricatechol-type siderophore isolated from Streptomyces sp. YM5-79956. Strain AW1T had a total of eight gene clusters which encoded for: arylpolyene (165,946–207,130 nt), terpene (618,322–640,854 nt), RIPP-like proteins (804,411–819,137 nt), non-ribosomal peptide synthetase cluster (NRPS)-like (61,798–104,764 nt), betalactone (323,399–348,739 nt), N-acetylglutaminylglutamine amide (NAGGN; 106,834–121,648 nt), type I polyketide synthase (T1PKS; 56,584–107,578 nt), and heterocyst glycolipid synthase-like polyketide synthase (hglE-KS; 75,419–113,566 nt). Strain GTP1T possessed four gene clusters that encoded for RRE-containing cluster (175,155–199,102 nt), homoserine lactone (110,293–130,892 nt), a signaling molecule known for its involvement in bacterial quorum sensing, the RIPP-like cluster (38,002–48,856 nt), and terpene synthesis (47,942–69,701 nt). Strain HM2T had two gene clusters that encoded for resorcinol (403,967–445,901 nt), an organic compound known for its antiseptic properties, and terpene (697,660–721,242 nt), which showed 100% similarity for carotenoid synthesis. BRIG analysis results showed that a majority of the regions within the four analyzed genomes were conserved with at least 70% similarity (Figure S4).Cellulolytic potential and FE-SEM analysis of strain USB13T
A USB13T-inoculated basal salt medium sample containing degraded filter paper was examined under FE-SEM to observe the morphological interactions between cellulose fibers and USB13T cells. Images in Fig. 5 show individual rod cells of strain USB13T surrounding filter paper fibers, indicating bacterial adherence.Figure 5Field emission-scanning electron microscopy (FE-SEM) images of adhesion of strain USB13T to degraded filter paper fibers. Arrows indicate filter paper fibers. (A) low magnification (5000(times)) and (B), high magnification (20,000(times)) images of strain USB13T surrounding filter paper fibers.Full size imageThe enzymatic assay results showed endoglucanase, exoglucanase, β-glucosidase, and filter paper cellulase (FPCase) activities of strain USB13T, wherein activities for endoglucanase was the highest and β-glucosidase was the lowest in all experiments. As seen in Fig. 6A, enzyme activity for all cellulolytic enzymes increased along with its cultivation time. In addition, enzyme activities showed the highest results when tested on buffer solutions of pH 6.0 (Fig. 6B), indicating the enzymes’ resistance to moderately acidic conditions. The pH of the buffer solution seemed to be an important factor in enzyme activity, as activity of endoglucanase, exoglucanase, and FPCase drastically decreased when the pH was altered from pH 6.0 to pH 7.0. Meanwhile, β-glucosidase activity was relatively resistant to pH change as its activity decreased less than 50%. On day 7, enzyme activities were measured as 1.91 IU/mL for endoglucanase, 1.77 IU/mL for exoglucanase, 0.76 IU/mL for β-glucosidase, and 1.12 IU/mL for FPCase at pH 6.0. When measured at pH 8.0, where enzyme activity was the lowest, enzyme activities were measured as 0.51 IU/mL for endoglucanase, 0.25 IU/mL for exoglucanase, 0.45 IU/mL for β-glucosidase, and 0.23 IU/mL for FPCase; all values were less than half of the measured activity at pH 6.0. The results of strain USB13T are comparable to FPCase results of other species such as Mucilaginibacter polytrichastri RG4-7T (0.98 U/mL) isolated from the moss Polytrichastrum formosum14, Paenibacillus lautus BHU3 (2.9 U/mL) isolated from a landfill site57, and Serratia rubidaea DBT4 (0.5 U/mL) isolated from the gastrointestinal tract of a black Bengal goat58.Figure 6Cellulolytic enzyme activity of strain USB13T. Enzyme activity was defined in international units (IU); one unit of enzymatic activity was defined as the amount of enzyme that releases 1 μmol of glucose per mL per 1 min of reaction. (A) cellulase activity results under different cultivation time; (B) cellulase activity under different buffer solution pH. Values in the figure are mean values of triplicate data with standard deviation.Full size imageDespite the absence of the main three cellulolytic enzymes, endoglucanase, exoglucanase, and β-glucosidase, the cellulolytic activity of strain USB13T was confirmed through SEM images, CMC agar screening, and enzymatic assay results. However, because PGAP annotation results showed that other non-cellulolytic strains also possessed CAZymes, in some cases more than strain USB13T, further research is necessary to understand the mechanics of how CAZymes and other cellulases interact to degrade cellulose, and how these genes are expressed under certain conditions. Furthermore, the cellulolytic activity of strain USB13T can be further optimized for commercial use by adjusting growth conditions such as pH, temperature, and growth media.While cellulolytic bacteria are known to inhabit animal intestinal tracts, the rumen, and soil, they can be found almost everywhere, such as ocean floors, municipal landfills, and even extreme environments such as hot springs59. In these habitats, cellulolytic bacteria utilize cellulose while cohabiting with non-cellulolytic bacteria. There have been many studies suggesting the synergistic role non-cellulolytic bacteria play in cellulose degradation, where non-cellulolytic bacteria aid cellulose degradation by neutralizing pH or removing harmful metabolites60,61,62.Bacterial cellulases have shown immense value in various industries such as animal feed processing, food and brewery production, and agriculture, not to mention biofuel synthesis through biomass utilization11. Due to the versatile uses of bacterial cellulases, the cellulolytic strain USB13T has the potential to become an invaluable resource. However, further research of the novel strain’s cellulose-degradation mechanisms is necessary to develop and commercially make use of its bacterial cellulases in the future. In addition, research regarding co-culturing non-cellulolytic bacteria and strain USB13T may also help in developing effective methods to use an otherwise underutilized bioresource.Taxonomy of novel Ramlibacter speciesWhile phylogenetic analyses indicated that the novel strains USB13T, AW1T, GTP1T, and HM2T should be assigned to the genus Ramlibacter, differences in fatty acid compositions, polar lipid profiles, and physiological characteristics suggested that the four novel strains are noticeably distinct from other validly published species of the genus. Additionally, genomic characteristics such as ANI, dDDH, and AAI values further supported the novel strains’ position as a distinct species within the genus Ramlibacter. Therefore, we propose that the strains USB13T, AW1T, GTP1T, and HM2T represent novel species within the genus Ramlibacter.Description of the novel Ramlibacter speciesThe descriptions of the novel species are given according to the standards of the Judicial Commission of the International Committee on Systematic Bacteriology63.Description of Ramlibacter cellulosilyticus sp. nov
Ramlibacter cellulosilyticus (cel.lu.lo.si.ly’ti.cus. N.L. n. cellulosum, cellulose; N.L. adj. lyticus from Gr. lytikos, dissolving; N.L. masc. adj. cellulosilyticus, cellulose-dissolving).Cells of strain USB13T are Gram-negative, rod-shaped, non-flagellated and motile by gliding. The strain is positive for both oxidase and catalase activity, while cells have a width of 0.3–0.5 μm and length of 2.0–2.4 μm. When observed on R2A agar, colonies are reddish white, flat with entire margins, and have a diameter of 1–2 mm. Growth of strain USB13T is observed at 7–50 °C (optimum, 28–30 °C), at pH 5.0–10.0 (optimum, pH 6.0), and at NaCl concentrations of 0–7% (optimum, 0–3%). The strain is unable to grow in anaerobic conditions. Produces siderophores and hydrolyzes Tween 20, Tween 80, CMC, and esculin. According to the API ZYM results, the strain showed positive results for alkaline phosphatase, esterase lipase (C8), leucine arylamidase, acid phosphatase, β-galactosidase, α-glucosidase, and β-glucosidase. In the API 20NE assay, strain USB13T showed positive results only for β-galactosidase. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C10:0 3-OH, and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoaminolipid, two unidentified phosphoglycoaminolipids, and six unidentified polar lipids. The G + C content is 69.7%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain USB13T are MN603953 and JACORT000000000, respectively.The type strain USB13T (= KACC 21656T = NBRC 114839T) was isolated from shallow coastal water at Haeundae Beach, Busan, Republic of Korea.Description of Ramlibacter aurantiacus sp. nov
Ramlibacter aurantiacus (au.ran.ti’a.cus. L. masc. adj. aurantiacus, orange-colored, referring to the orange colonies of the strain).Cells of strain AW1T are Gram-negative, coccoid to short rod-shaped, non-flagellated, and motile by gliding. The strain is negative for oxidase activity, and positive for catalase activity. When observed on R2A agar, colonies are orange, convex, with entire margins, and 0.5–1.0 mm in diameter. Under TEM cells have and approximate width of 0.3–0.5 μm and length of 0.6–0.8 μm. Growth of strain AW1T can be observed at 7–45 °C (optimum, 30 °C), at pH 7.0–10.0 (optimum, 7.0–8.0), and at NaCl concentrations of 0–3% (optimum, 0–1%). The strain does not grow under anaerobic conditions but is able to hydrolyze Tween 80. In addition, AW1T is not able to produce siderophores. In the API ZYM assay, positive for alkaline phosphatase, esterase (C4), esterase lipase (C8), leucine arylamidase, and β-glucosidase. In the API 20NE assay, positive for esculin hydrolysis. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C17:0 cyclo, summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c), and summed feature 8 (consisting of C18:1 ω7c and/or C18:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoglycolipid, one unidentified lipid, and three unidentified glycolipids. The G + C content is 68.6%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain AW1T are MN498045 and JAEQNA000000000, respectively.The type strain AW1T (= KACC 21544T = NBRC 114862T) was isolated from soil at Aewol, Jeju Island, Republic of Korea.Description of Ramlibacter albus sp. nov
Ramlibacter albus (al’bus. L. masc. adj. albus, white, referring to the white colonies of the strain).Strain GTP1T is non-motile, Gram-negative, strictly aerobic, positive for oxidase activity, and negative for catalase activity. When observed on R2A, colonies are white, convex, with entire margins, and 1–2 mm in diameter. Under TEM, cells lack flagella, are rod-shaped, and have a width of 0.7–0.8 μm and length of 1.6–1.9 μm. Growth of strain GTP1T can be observed at 10–45 °C (optimum, 30 °C), at pH 5.0–8.0 (optimum, pH 7.0), and at NaCl concentrations of 0–2% (optimum, 0%). The strain shows positive results for Tween 20 and Tween 80 hydrolysis. GTP1T does not produce siderophores when tested on CAS-blue agar. According to API ZYM results, strain GTP1T is positive for alkaline phosphatase, esterase (C4), esterase lipase (C8), and leucine arylamidase, while the API 20NE assay results show negative results for all substrates. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0 and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), and two unidentified phosphoaminolipids. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C17:0 cyclo, summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c), and summed feature 8 (consisting of C18:1 ω7c and/or C18:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoaminolipid, one unidentified phosphoglycolipid, one unidentified phosphoglycoaminolipid, and two unidentified polar lipids. The G + C content is 67.9%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain GTP1T are MN498046 and JACORU000000000, respectively.The type strain GTP1T (= KACC 21702T = NBRC 114488T) was isolated from soil at Seogwipo, Jeju Island, Republic of Korea.Description of Ramlibacter pallidus sp. nov
Ramlibacter pallidus (pal’li.dus. L. masc. adj. pallidus, pale, referring to the color of the colonies).Cells of strain HM2T are Gram-negative, and positive for both oxidase and catalase activities. When observed on R2A agar, colonies are cream-colored, transparent, 1.0–2.5 mm in diameter, and flat with entire margins. Under TEM, monotrichous flagella are observed, and cells are rod-shaped with a width of 0.4–0.78 μm and length of 1.7–1.8 μm. The strain shows the fastest growth at a temperature range of 25–35 °C and at pH values between 8.0 and 9.0. When NaCl is present, growth is observed at concentrations of 0–3% (w/v), with optimal growth was observed at concentrations of 0–1% (w/v). The strain is not able to tolerate anaerobic conditions. Strain HM2T hydrolyzes Tween 80 and weakly hydrolyzes casein. However, siderophore production cannot be observed when tested on CAS-blue agar. According to API ZYM tests, strain HM2T shows positive results for alkaline phosphatase, esterase (C4), esterase lipase (C8), leucine arylamidase, valine arylamidase, acid phosphatase, and naphthol-AS-BI-phosphohydrolase. In addition, API 20NE tests show positive results for nitrate (NO3) to nitrite (NO2-) reduction and esculin hydrolysis. The G + C content is 69.9%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain HM2T are MN498047 and JADDIV000000000, respectively.The type strain HM2T (= KCTC 82557T = NBRC 114489T) was isolated from soil at Seopjikoji, Jeju Island, Republic of Korea. More

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Study areaPenang is situated in the northern part of Peninsular Malaysia and lies within the latitudes 5°12’N to 5°30′ N and longitudes 100°09’E to 100°26’E (Fig. 7). Penang with a land area of 295 Km2, has an estimated population of 720,000 and is regarded as the most populated island in Malaysia. Penang shares the same border on the north and east with Kedah State and the south with Perak State. There are two main parts of Penang State: Penang Island and the mainland which is also regarded as Seberang Perai. These two parts of the State are connected by the two bridges. The eastern part of Penang Island is the most urbanized area comprising industries, commercial centres and residential buildings. However, the western part is less developed comprising mainly hilly terrain and forests22. This study is focused on the Island part of Penang. This island is endowed with a yearly equatorial climate (hot and humid). It has a mean annual temperature ranging between 27 and 30 °C while the mean annual relative humidity ranges between 70 and 90%. Also, the mean annual rainfall is about 267–624 cm.Figure 7The map of Penang State showing the Penang Island (created by the authors using ArcMap 10.8 software).Full size imageData acquisitionThe flow chart of the methodology is presented in Fig. 8. Landsat satellite images were used for the assessment of changes in land use covering a period of 2010–2021 (11 years).Figure 8The flow chart of the methodology.Full size imageThese images were gotten from the website of the United State Geological Survey (https://earthexplorer.usgs.gov). The Landsat images include the Landsat 5 TM (thematic mapper) and Landsat 8 OLI / TIRS (operational land imager / thermal infrared sensor). These were downloaded from the Landsat level 1 dataset (Table 6) with additional criteria which reduced the.Table 6 The characteristics of the satellite data used.Full size tableDetermination of LST and NDVI for Landsat 5 and 8Band 6 of Landsat 5 and band 10 of Landsat 8 were used for the determination of the land surface temperature (LST). The LST and normalized difference vegetation index were determined using the following steps:Conversion of top of atmosphere (TOA) radianceUsing the radiance rescaling factor, thermal infra-red digital numbers were converted to TOA spectral radiance using the equation below29: (frac{Red – NIR}{{Red + NIR}}) (frac{Red – NIR}{{Red + NIR}}) For Landsat 8,$$ {text{L}}lambda = left( {{text{ML}} times {text{ Qcal}}} right) + left( {{text{AL}} – {text{Oi}}} right) $$
(1)
For Landsat 5,$$ {text{L}}lambda = left( {{ }frac{{{text{LMax}}lambda – {text{LMin}}lambda }}{{{text{QcalMax}} – {text{QcalMin}}}}} right) times left( {left( {{text{Qcal }} – {text{QcalMin}}} right) + {text{LMin}}lambda } right) $$
(2)
where Lλ is TOA spectral radiance, ML is radiance multiplicative band Number, AL is radiance add band number, Qcal is quantized and calibrated standard product pixel values (DN for band 6 or band 10), Oi is the correction value for the respective bands, LMaxλ is spectral radiance scaled to QcalMax, LMinλ is spectral radiance scaled to QcalMin, QcalMax is maximum quantized calibrated pixel value, and QcalMin is minimum quantized calibrated pixel value.Conversion to TOA brightness temperature (BT)Spectral radiance data were converted to TOA brightness temperature using the thermal constant values in the Metadata file29.Kelvin (K) to Celcius (°C) degrees$$ BT = {raise0.5exhbox{$scriptstyle {K2}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {ln left( {frac{K1}{{{text{L}}lambda { } + { }1}}} right)}$}} – 273.15 $$
(3)
where BT is the Top of atmosphere brightness temperature (°C), Lλ is TOA spectral radiance (W.m−2 .sr−1 .µm−1)), K1 is the K1 constant band number, and K2 is the K2 constant band number. For Landsat 5, K1 is 607.76, and K2 is 1260.56.Normalized difference vegetation index (NDVI)The Normalized Difference Vegetation Index (NDVI) is a standardized vegetation index which reveals the intensity of greenness and surface radiant temperature of the area30,31. The index value of NDVI usually ranges from − 1 to 1. The higher NDVI value indicates that the vegetation of the area is denser and healthier. This shows that the NDVI values of normal healthy vegetation range from 0.1– 0.75, while it is almost zero for rock and soil, and negative value for water bodies24. The NDVI is calculated using the followings:$$ {text{NDVI }} = frac{{left( {{text{NIR }}{-}{text{ RED}}} right){ }}}{{left( {{text{NIR }} + {text{ RED}}} right)}} $$
(4)
In Landsat 4–7$$ {text{NDVI }} = , left( {{text{Band 4 }}{-}{text{ Band 3}}} right) , / , left( {{text{Band 4 }} + {text{ Band 3}}} right) $$In Landsat 8$$ {text{NDVI }} = , left( {{text{Band 5 }}{-}{text{ Band 4}}} right) , / , left( {{text{Band 5 }} + {text{ Band 4}}} right) $$where: RED = DN values from the RED band, and NIR = DN values from the Near Infra-red band.Land Surface Emissivity (LSE)Land Surface Emissivity is the average emissivity of an element on the surface of the earth calculated from NDVI values.$$ {text{PV }} = left{ {frac{{left( {{text{NDVI }} – {text{ NDVImin}}} right)}}{{left( {{text{NDVImax }} – {text{ NDVImin}}} right)}}} right}^{2} $$
(5)
where PV is the Proportion of vegetation, NDVI is the DN value from the NDVI image, NDVImin is the minimum DN value from the NDVI image, and NDVImax is the maximum DN value from the NDVI image.$$ {text{E }} = left( {0.004{ } times {text{PV}}} right) + 0.986 $$
(6)
where E is land surface emissivity, PV is the Proportion of vegetation, 0.986 corresponds to a correction value of the equation.Land Surface Temperature (LST)Land Surface Temperature (LST) is the radiative temperature which is calculated using top of atmosphere brightness temperature, the wavelength of emitted radiance and land surface emissivity.$$ {text{LST}} = {raise0.5exhbox{$scriptstyle {BT}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {left( {1 + left( {{lambda } times { }frac{{{text{BT}}}}{{{text{c}}2}}} right) times {text{ln}}left( {text{E}} right)} right)}$}} $$
(7)
Here c2 is 14388. The value of λ for Landsat 5 (Band 6) is 11.5 µm and Landsat 8 (Band 10) is 10.8 µm.Where BT is the top of atmosphere brightness temperature, λ is the wavelength of emitted radiance, and E is land surface emissivity.c2 = h*c/s (1.4388*10–2 mK = 14388 mK), h is Planck’s constant (6.626*1034 Js), s is Boltzmann constant (1.38*1023 JK), c is velocity of light (2.998*108 m/s).Determination of land use and land cover (LULC) of the study areaThe Landsat images were pre-screened and subjected to clipping and classification32. The boundary shape file of Penang was used to clip out the area of study.Image classificationThe unsupervised method involving a random assignment of sample training points and supervised methods of satellite image classification was employed in this study for determining the LULC types. This mixture of image classification methods has been reported as vital in achieving a high accuracy level33. Bands 5, 4 and 3 were used to classify Landsat 8 while bands 4, 3 and 2 were used for classifying Landsat 5. We used the extraction by mask in the spatial analyst tool of ArcMap 10.2.1 software to extract the study area from the selected bands of the Landsat satellite images. A widely used supervised image classification method was adopted for classifying the Landsat bands in this study32,34. The principle of operation of this method involves the identification of known sample training points which are then used to classify other unknown points with related spectral signatures35. The three monochromatic satellite bands were combined to produce the false colour composite (FCC) using the data management tool36. This involves drawing polygons on the LULC type to select the training points. The LULC types adopted for this study include urbanized areas, agricultural land, rocks, forests, bare surfaces, and water bodies. These were modified LULC types from IPOC Good Practice Guidance37. To achieve this, a minimum of 40 sample points were selected randomly for each category of LULC type36. Having prior knowledge of the study area assisted in the selection of the training points38.The multivariate maximum likelihood classification (MLC) technique was used for transforming the images. Other image transformation techniques have been used by researchers. These include the fuzzy set classifier, neural networks (NN) classifier, extraction and classification of homogenous objects (ECHO) classifier, per-field classifier, sub-pixel classifier, decision trees (DTs), support vector machines (SVMs), minimum distance classifier (MDC) and so-on39. The adoption of any of these techniques is dependent on the knowledge of the area of study, band selection, accessibility of data, the complexity of the landscape, the classification algorithm, and the proficiency of the analyst39. We preferred MLC to other techniques in this study due to its reported high level of accuracy in tropical regions32,34. Another reason for choosing MLC is that it is readily incorporated in many widely used GIS software packages. This MLC algorithm operates based on assigning pixels to the highest probability class and establishing the class ownership of such pixels. It is also regarded as a parametric classifier whose data follows almost a normal distribution39. We ensured the accuracy of this classifier by assigning a large number of training sample points using our prior knowledge of the study area.Description of the LULC categoriesThe urbanized area is the developed part of the study area. This includes houses, roads, railways, and industries. This is also known to be a settlement in other literature40. Agricultural land is the part of the study area dominated by agricultural activities and herbaceous plants and grasses. Agricultural land is generally a product of deforestation36. Rocks are part of the study area comprising solid mineral materials (rocks). Bare land is the bare soil which is either made open by natural or human activities.Forests are parts of the study area dominated by trees. They can be primary or secondary forests depending on the rate of disturbances. According to41, forest land is an area having more than 0.5 ha of flora comprising trees (height is above 5 m) with a canopy greater than 10%. The forests in Penang are generally both primary and secondary42. Water bodies are parts of the study area covered by water seasonally or permanently. These include seas, rivers, lakes, ponds, streams, or reservoirs40.Determination of change in the LULCThe rate and extent of change in the LULC of Penang within the periods under consideration were determined following the formula below43:$$ {text{Changed area }}left( {{text{C}}_{{text{a}}} } right) , = {text{ T}}_{{text{a}}} left( {text{year 2}} right) , {-}{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
(8)
$$ {text{Changed extent }}left( {{text{C}}_{{text{e}}} } right) , = {text{ C}}_{{text{a}}} /{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
(9)
$$ {text{Percentage of change }} = {text{ C}}_{{text{e}}} {text{x 1}}00 $$
(10)
where Ta means the total area.Determination of relationship between LST and NDVIThe values of LST and NDVI at 20 random points of each LULC class were used. The relationship between the LST and NDVI across all the LULC classes in each year was determined using the bivariate linear regression analysis. This was done in Paleontological Statistical (PAST) package 3.0.Classification accuracy assessmentThe classification accuracy was assessed by taking ground truth coordinate data of the LULC of the study area using a geographical positioning system (GPS) device (Garmin Etrex 10). These data were compared with the LULC classified in this study32. Consequently, an error matrix was generated. This normally uses ground truth data to explain the accuracy of the classified LULC. The error matrix comprises the user’s accuracy, the producer’s accuracy, overall accuracy and the Kappa index32.The producer’s accuracy (omission error) represents the probability of the correctly classified reference pixel and it is determined using this formula below:$${text{Producer’s accuracy }}left( % right) , = { 1}00% , – {text{ error of omission}} $$
(11)
Also, the user’s accuracy (commission error) represents the probability that the classified pixel matches the one on the ground36 and it is determined using the formula below:$$ {text{User’s accuracy }}left( % right) , = { 1}00% , – {text{ error of commission}} $$
(12)
The statistical accuracy of the matrix was determined using the Kappa coefficient44. This Kappa coefficient ranges from − 1 to + 145. Therefore, the overall accuracy of the classification was determined by dividing the total number of correctly classified pixels by the total number of sampled ground truth data40. More