More stories

  • in

    Cross-biome antibiotic resistance decays after millions of years of soil development

    Van Goethem MW, Pierneef R, Bezuidt OKI, Van De Peer Y, Cowan DA, Makhalanyane TP. A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils. Microbiome. 2018;6:40.Article 

    Google Scholar 
    D’Costa VM, McGrann KM, Hughes DW, Wright GD. Sampling the antibiotic resistome. Science. 2006;311:374–7.Article 

    Google Scholar 
    Allen HK, Donato J, Wang HH, Cloud-Hansen KA, Davies J, Handelsman J. Call of the wild: antibiotic resistance genes in natural environments. Nat Rev Microbiol. 2010;8:251–9.CAS 
    Article 

    Google Scholar 
    Martinez JL, Coque TM, Baquero F. What is a resistance gene? Ranking risk in resistomes. Nat Rev Microbiol. 2015;13:116–23.CAS 
    Article 

    Google Scholar 
    Genilloud O. Actinomycetes: still a source of novel antibiotics. Nat Prod Rep. 2017;34:1203–32.CAS 
    Article 

    Google Scholar 
    Ochoa-Hueso R, Plaza C, Moreno-Jimenez E, Delgado-Baquerizo M. Soil element coupling is driven by ecological context and atomic mass. Ecol Lett. 2021;24:319–26.Article 

    Google Scholar 
    Wardle DA, Walker LR, Bardgett RD. Ecosystem properties and forest decline in contrasting long-term chronosequences. Science. 2004;305:509–13.CAS 
    Article 

    Google Scholar 
    Crews TE, Kitayama K, Fownes JH, Riley RH, Herbert DA, Mueller-Dombois D, et al. Changes in soil phosphorus fractions and ecosystem dynamics across a long chronosequence in Hawaii. Ecology. 1995;76:1407–24.Article 

    Google Scholar 
    Walker LR, Wardle DA, Bardgett RD, Clarkson BD. The use of chronosequences in studies of ecological succession and soil development. J Ecol. 2010;98:725–36.Article 

    Google Scholar 
    Delgado-Baquerizo M, Reich PB, Bardgett RD, Eldridge DJ, Lambers H, Wardle DA, et al. The influence of soil age on ecosystem structure and function across biomes. Nat Commun. 2020;11:4721.CAS 
    Article 

    Google Scholar 
    Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol. 2010;8:260–71.CAS 
    Article 

    Google Scholar 
    Zhu YG, Johnson TA, Su JQ, Qiao M, Guo GX, Stedtfeld RD, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci USA. 2013;110:3435–40.CAS 
    Article 

    Google Scholar 
    Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, Yu S, et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol. 2017;2:16270.CAS 
    Article 

    Google Scholar 
    Li J, Cao J, Zhu YG, Chen QL, Shen F, Wu Y, et al. Global survey of antibiotic resistance genes in air. Environ Sci Technol. 2018;52:10975–84.CAS 
    Article 

    Google Scholar 
    Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.CAS 
    Article 

    Google Scholar 
    Ortiz-Álvarez R, Fierer N, de Los Ríos A, Casamayor EO, Barberán A. Consistent changes in the taxonomic structure and functional attributes of bacterial communities during primary succession. ISME J. 2018;12:1658–67.Article 

    Google Scholar 
    Shen J, Li Z-M, Hu H, Zeng J, Zhang L-M, He J, et al. Distribution and succession feature of antibiotic resistance genes along a soil development chronosequence in Urumqi No. 1 Glacier of China. Front Microbiol. 2019;10:1569.Article 

    Google Scholar 
    Drenovsky RE, Vo D, Graham KJ, Scow KM. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Micro Ecol. 2004;48:424–30.CAS 
    Article 

    Google Scholar 
    Bastida F, Eldridge DJ, Garcia C, Kenny Png G, Bardgett RD, Delgado-Baquerizo M. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 2021;15:2081–91.CAS 
    Article 

    Google Scholar  More

  • in

    Phage-encoded ribosomal protein S21 expression is linked to late-stage phage replication

    Discovery of closely related phage sequences with the conserved genetic context of bS21Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region (Supplementary Table 1). Two related phages were included as outgroups for comparative analyses. The corresponding samples were collected from freshwater lakes or reservoirs (one from a wastewater treatment plant), and all but three were from the oxic layer (see Methods for details).General features of manually curated genomesAll the 49 phage sequences were manually curated to fill scaffolding gaps and fix the assembly errors, and nine of them (including one outgroup phage) were curated to completion (circular and no gaps or local assembly errors) (Supplementary Table 1). A total of 14 related phage genomes from IMG/VR were also included for further analyses. The eight bS21-encoding complete genomes had genome lengths of 293–331 kbp, GC contents of 31.0–33.7% and encoded 350–413 protein-coding genes (coding density, 91.1–94.9%), with 5–25 (average 17) tRNA genes. No alternative coding signal (i.e., stop codon reassignment) was detected in any genome. In comparison, the outgroup complete genome has a size of 308 kbp (450 protein-coding genes, 6 tRNAs, 94.7% coding density) and GC content of 27.3%.Genomic context of bS21 in phagesGenomic context analyses for bS21 genes showed a highly conserved gene architecture across phage genomes in proximity to the region encoding bS21 (see Fig. 1a for example). Specifically, we found that bS21 was consistently located in between two hypothetical protein families (positions 1 and –1 in Fig. 1b and Supplementary Table 2), with core structural proteins—including the TerL, PVP, prohead protease, and MCP—generally located within five genes in both the upstream and downstream DNA. Other hypothetical proteins were also consistently found in this region, although their positions were more variable upstream (positions –4 through –10, Fig. 1b). Importantly, the bS21 gene was consistently encoded in the reverse strand relative to the conserved hypothetical and structural protein genes (Fig. 1a and Supplementary Fig. 1).Fig. 1: Genetic context of the genes encoding bS21 in the phage genomes.a Examples of genetic context of phage genomes with and without bS21. The annotation of protein-coding genes is the same as indicated in b by different colors. Those in white are genes not shown in subfigure (b). b Summary of genetic context of all phage genomes encoding bS21. The relative position of genes near the bS21 gene is shown, and the size of circles indicates the number of phages with a gene belonging to a given protein family (annotation shown on right) at that relative position. Only the 12 most frequent families are shown. The details of the genetic context are shown in Supplementary Fig. 1.Full size imagePhylogeny of bS21-encoding phagesPhylogenetic analyses based on TerL suggested the phages belonging to several groups, we thus assigned them to clades a–e (Fig. 2 and Supplementary Table 1). Most of the phages belong to clades c, d, and e, and they have a broader environmental distribution than clades a and b. Interestingly, we found that some phages within a single clade were from distant sampling sites. Closer inspection indicated they also shared large genomic fragments with high similarity (82–98% for nucleotide sequences; Supplementary Fig. 2). Comparative genome-wide analyses of the complete genomes from the same site but sampled at different time points showed sequence variations in some genes (Supplementary Fig. 3).Fig. 2: The phylogeny of bS21 phages based on the large terminal (TerL) protein sequences.Two closely related phages without bS21 encoded were included as outgroups (shown at the top of the tree). The genomes are assigned to five clades (a, b, c, d, and e) based on the topology of the phylogenetic tree. The numbers in the brackets following the scaffold names indicate the total counts of the same scaffold detected from the corresponding sampling sites. The genomes that were manually curated to completion (circular and no gap) are indicated by squares, and the genome sizes are shown in brackets.Full size imageTerL phylogeny, constructed using sequences from this study and NCBI RefSeq sequences, indicated the most closely related classified phages belong to Caudovirales of either the Myoviridae or Ackermannviridae (Supplementary Fig. 4). A phage baseplate assembly protein was encoded in most curated genomes. This is an important building block for members of Siphoviridae and Myoviridae [8], so we concluded that the bS21-encoding phages are myoviruses.Predicted bacterial hosts of bS21-encoding phagesTo predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 Flavobacterium genome (affiliation: Bacteroidetes, Flavobacteria). We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [2] (Supplementary Table 3). The results indicated that all of the phages infect members of Bacteroidetes, which were detected in 43 out of 45 samples (Fig. 3 and Supplementary Table 4). The two metagenomic samples without Bacteroidetes identified were both collected via filtering through 0.2 μm and onto 0.1 μm pore size filters. Bacteroidetes were detected in both of the corresponding 0.2 μm fraction samples (Fig. 3).Fig. 3: The relative abundance of the Bacteroidetes classes in all the analyzed samples in this study.The microbial communities were profiled based on ribosomal protein S3 (rpS3) assigned to the Bacteroidetes classes. The sampling sites were indicated by colored names, and the filter sizes used during sampling are shown by circles. The three pairs of filter samples are indicated by colored stars.Full size imageWe profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections (Supplementary Fig. 5). However, this was uninformative because most samples contained more than one class. However, phages from clades a and b are unlikely to infect class Bacteroidia members, as they did not co-occur in any sample.Comparison of bacterial and phage-encoded bS21Phylogenetic analyses revealed that bS21 protein sequences from phages (this study) and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately (Supplementary Fig. 6). The bacterial bS21 sequences that are most similar to phage bS21 were from Bacteroidetes, mostly from the Flavobacteriia class (Supplementary Table 5). We aligned and compared the Bacteroidetes and phage bS21 sequences and mapped the divergent and non-divergent residues to the model of the ribosome of Flavobacterium johnsoniae (Fig. 4a). Multiple divergent positions are located at the beginning of the bS21 sequences and four residues (Arg21, Phe23, Asp25, and Thr28) were significantly divergent (Fig. 4b).Fig. 4: Conservation and differences between phage and bacterial bS21.a Location of bS21 (blue) within the 16S rRNA (green) and the ASD (magenta) of the F. johnsoniae ribosome (PDB ID: 7JIL) [9]. bS21 is in the neck region of the 16S rRNA, interacting closely with the 3’ end of the 16S rRNA, where the ASD is located. The 16S rRNA is shown from the subunit interface direction. b Zebra2 divergency results from an alignment of phage and bacterial bS21 sequences mapped on F. johnsoniae bS21. Divergent positions between phage and bacterial bS21 are shown with red. c Zebra2 conservation results from the same alignment as in (b) mapped on F. johnsoniae bS21 with conserved residues shown in yellow. The stacking interaction between Tyr54 and Adenine 1534 is indicated. d The sequence logo and consensus sequences of phage and bacterial bS21 alignments and the corresponding position of Tyr54 in F. johnsoniae bS21 in the alignment are highlighted. The C-terminal parts are highlighted with gray backgrounds.Full size imageBacteroidetes usually lack the SD sequences. It was recently reported that the bS21 Tyr54 (numbering in F. johnsoniae) is an important residue for blocking the ASD in the 16S rRNA within the ribosome [9]. Our analyses predict that all the analyzed bacterial and phage bS21 in this study have an amino acid with an aromatic ring (often Tyr54 but in a few cases His54, and in one case Phe54) at the position of Tyr54 in F. johnsoniae (Fig. 4c, d and Supplementary Fig. 6). This conservation of the aromatic property in phage bS21 should ensure stacking interaction with Adenine 1534 (numbering in F. johnsoniae 16S) from the ASD. In that way, phage bS21 mimics Bacteroidetes bS21 in the region where it binds the ribosome but differs from it in the region where the mRNA would bind.In contrast, the C-terminal regions of both the bacterial and phage bS21 sets were highly divergent (Fig. 4d). However, the phage C-terminal regions are generally conserved within the clades defined based on TerL phylogeny (Fig. 2 and Supplementary Fig. 7).Metabolic potentials of bS21-encoding phagesFunctional annotation of the predicted protein-coding genes revealed that in addition to bS21, these phages carry other genes related to protein production and stability (Supplementary Table 6). Examples include protein folding chaperones and Clp protease, suggesting the importance of controlling the proteostasis network of the cell. Interestingly, we also identified many genes involved in sugar-related chemistry and polysaccharide biosynthesis. Many of these genes were predicted to perform chemical transformations related to the biosynthesis of lipopolysaccharide, a major component of the Gram-negative bacterial outer membrane. We interpret this as a potential mechanism to remodel the cell surface and prevent superinfection by competitor phages, a strategy common to the phage lysogenic cycle. These phages lack detectable integration machinery (no gene for integrase or resolvase was detected), suggesting the possibility of a non-integrative long-term infection state such as pseudolysogeny [10].Clustering analyses of 22 phages with a minimum genome size of 100 kbp (including the two outgroup genomes) based on the presence/absence of protein families indicated they shared a total of 16 protein families (Supplementary Fig. 8 and Supplementary Table 7). Phosphate starvation-inducible protein PhoH (“fam582”) was the only predicted protein detected in all 22 phages (excluding the shared predicted proteins in the conserved rpS21-encoding region described above). Other common protein families include those related to DNA replication (e.g., DNA primase/helicase, DNA polymerase, HNH endonuclease, thymidylate synthase (EC:2.1.1.45), deoxyuridine 5’-triphosphate nucleotidohydrolase (EC:3.6.1.23)), those associated with virion assembly (e.g., a phage tail sheath protein, phage baseplate assembly protein W), and those for other functions (e.g., chaperone ATPase, alpha-amylase, DegT/DnrJ/EryC1/StrS aminotransferase).Temporal and spatial distribution and activity of bS21-encoding phages in Lake RotseeTo reveal the spatial and temporal distribution of the bS21-encoding phages, we focused on the Lake Rotsee data and profiled phage occurrence based on the sequencing coverage in the metagenomic datasets. The Lake Rotsee samples were collected from the oxic (7 samples) and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic (Fig. 5a).Fig. 5: The spatial and temporal distribution and activity of bS21 phages at Lake Rotsee.a The sequencing coverage of each phage genome in each metagenomic dataset is shown in the heatmaps. The phages are phylogenetically clustered based on their TerL protein sequences (bootstraps shown in numbers), the colored backgrounds are the same as shown in Fig. 2 for different clades. The sampling time points and depths are shown on the left, and the oxygen conditions are indicated by colored circles on the right. Two replicates were sequenced from the 15 m sample collected in 2018. b The percentage of mapped RNA reads to the phage genomes in the corresponding samples (rows labeled in (a)). The mapped RNA reads had a minimum similarity of 98% to the phage genomes. No RNA data were generated for the three samples collected on October 10, 2017. See the figure legend for each genome in the upper right, the circular genomes have names in bold font.Full size imageRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages (Fig. 5b). In general, the phages were likely to be most transcriptionally active in the oxic water columns. A total of 736 genes were transcribed in at least one sample (Supplementary Table 8), those for MCP, an AAA ATPase, tail sheath protein, bS21, FKBP-type peptidyl-prolyl cis-trans isomerase, and a methyltransferase FkbM domain protein are among the top 100 most highly transcribed. The high transcriptional activities of MCP in five phages indicated they were in the late stage of replication at the time of sampling.The transcriptional behavior of phage bS21 genesTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes (Fig. 6a). bS21 had very similar (but slightly lower) transcriptional activities as a neighboring gene (hereafter, bS21_CN gene) encoded on the opposite strand. The bS21_CN gene encodes a hypothetical protein (protein family: fam498) and was not detected in the two outgroup phages without bS21 (Supplementary Table 6). Interestingly, a comparison of the phylogenies of bS21 and bS21_CN showed a very similar evolutionary pattern (Supplementary Fig. 9), likely suggesting their potential functional relationship in the bS21-encoding phages.Fig. 6: The transcription levels of bS21 and core structural protein genes.a The normalized transcriptional level (NTL) of shared single-copy protein families of three phages (indicated by arrows in Fig. 5b) with ≥1000 RNA reads mapped. Two families (including MCP) are listed on a different scale due to their much higher transcription levels. Refer to Fig. 5 for shape symbols that designate phage genomes and samples. b Examples of RNA mapping profiles indicating the co-transcription of some genes neighboring bS21. Hypothetical protein genes are shown in white.Full size imageInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together (Fig. 6b). Given the observed RNA expression patterns, we conclude that the phage-encoded bS21 genes were actively transcribed during late-stage replication, along with other core structural proteins.Genomic context of bS21 genes in published phage genomesTo determine whether the phage bS21 genes are generally co-located with those for core structural proteins in diverse phages, we profiled the genomic context of bS21 in 900 published bS21-encoding phages [2, 11] (Supplementary Table 9). Functional annotations were performed for the upstream and downstream ten genes of the bS21 genes using pVOG (Supplementary Table 10). Of the 20 most abundant pVOGs, 6 were related to core structural assembly (Fig. 7a), i.e., prohead protease (n = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases (n = 50 and 47, respectively), and lysozyme (for lysis; n = 29). Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene (generally 2–4 genes; Fig. 7b), as in the bS21-encoding phage genomes analyzed in this study (2–6 genes away; Fig. 1 and Supplementary Fig. 1).Fig. 7: Neighboring genes within 10 genes of bS21 in published bS21-encoding phage genomes.a The annotation and corresponding functional category (if assigned) of the 20 most commonly detected pVOG genes and their predicted functions are shown on the left, the total number of genomes with the gene are shown on the right. b The distribution of the distance of each gene to bS21 in the genomes. The position of genes next to bS21 (thus distance = 1) is highlighted using a red dashed line. The average distance of each gene to bS21 is shown on the left. c The predicted hosts of bS21-encoding phages with the top 4 most abundant genes detected within 10 genes of bS21. The total count of hosts is shown on the right.Full size imageWe respectively predicted the hosts of the bS21-encoding phages with the four most dominant pVOGs within ten genes of bS21 (Fig. 7c and Supplementary Table 11). The bacterial hosts are diverse and include Proteobacteria, Bacteroidetes, and Firmicutes. More

  • in

    Growth-stage-related shifts in diatom endometabolome composition set the stage for bacterial heterotrophy

    Co-culture dynamicsThis study was designed to enhance understanding of metabolite release and utilization across bloom stages in a simple community of phytoplankton and heterotrophic bacteria. The synthetic community was established with the diatom T. pseudonana and the bacterial strains R. pomeroyi DSS-3, Stenotrophomonas sp. SKA14, and P. dokdonensis MED152. These bacterial strains have high genetic similarity to isolates from phytoplankton cultures [14] and represent taxa that are common in phytoplankton blooms. Metabolites derived from the diatom were the sole source of carbon available for the bacteria, since no organic substrates were added. In addition, none of the bacteria can assimilate nitrate, and usable nitrogen was only available as diatom or bacterial extracellular products. The diatom had its highest specific growth rate of 1.65 d−1 during days 0–3, after which the rate declined (Fig. 1A). The total abundance of heterotrophic bacteria increased steadily but there was a succession that favored P. dokdonensis through day 15, and then R. pomeroyi by day 20; Stenotrophomonas disappeared from the model system by day 3 (Fig. 1B). The presence of bacteria did not affect the growth of diatoms based on comparisons of abundance in co-cultures versus axenic cultures at day 15 (Fig. 1A), as has been found previously [14, 26]. Inorganic nutrients were not limiting ( >5 μM at day 15; Table S1).Fig. 1: Time course of microbial abundances.A Cell abundance based on flow cytometric analysis for co-cultures (5 time points) and axenic cultures (day 15 only) (n = 3). The intensive sampling dates for the early and late bloom comparisons are marked with gray boxes. B Mean relative abundance of bacterial species is based on CFUs (n = 3). The day 0 samples were collected 8 h after inoculation.Full size imageDiatom endometabolite shiftsAnalyses focused on the day 3 (early bloom) and day 15 (late bloom) co-culture time points, for which a complete set of metabolomic and transcriptomic data were collected. Twenty-two diatom endometabolites that were annotated with high confidence by NMR analysis (Table S2) and quantified after normalizing to diatom cell number revealed that endometabolome composition differed substantially between bloom stages. Metabolites with significantly different cellular concentrations included nine compounds that were higher in intracellular concentration during the late bloom; these were arginine, valine, lysine, DHPS, glycerol-3-phosphate, phosphorylcholine, DMSP, glycine betaine, and homarine (T-test; P  More

  • in

    Viral diversity is linked to bacterial community composition in alpine stream biofilms

    Viral-like particle abundanceThe 10 sampling sites were equidistantly (average distance: 1.6 km) distributed between 1689 and 717 m above sea level in a 95.7 km2, pristine catchment and covered a flow-connected distance of 14.3 km (Fig. 1, Methods).Fig. 1: No evidence for a downstream accumulation of VLPs.Viral-like particles (VLP) were purified from 10 sites sampled during four seasons along an altitudinal gradient in an alpine stream (Vièze, Switzerland) (a). Neither VLP abundance (b) nor Virus-to-Prokaryote Ratios (VPR; (c)) showed pronounced spatial or temporal trends.Full size imageViral-like particle (VLP) counts normalized to areal coverage of the stream biofilm ranged from 2.8 × 109 to 3.4 × 1010 VLP m−2. On average, VLP abundance was highest in summer with 1.87 ± 0.75 × 1010 VLP m−2; however, there were no statistically significant seasonal differences in VLP abundance (repeated-measures ANOVA, F = 0.87, p = 0.47). VLP numbers did not exhibit a continuous spatial tendency, except during fall when VLP numbers increased significantly with downstream distance (r = 0.81, p 0.7 and/or pident >0.4). Indeed, 90 of the 203 putative viral depolymerases showed significant sequence similarity with 198 vOTU sequences (i.e., 6% of the overall vOTU diversity). We were able to obtain taxonomic classification for 80 of these 198 vOTUs, and found that all large Caudovirales families were represented (i.e., Myoviridae, n = 31, Siphoviridae, n = 17, Podoviridae, n = 15, Autographiviridae, n = 13, Ackermannviridae, n = 2, and Herelleviridae, n = 1). This suggests that depolymerase activity may be widespread among viruses infecting bacteria in stream biofilms. Although both the number of potential depolymerases included in our database and the number of classified vOTUs was limited, we observed that depolymerase-harboring Myoviridae vOTUs corresponded the expectation based on the overall relative abundance of Myoviridae, pointing toward the importance of dispersal for this important viral family. Siphoviridae, in contrast, were relatively underrepresented among depolymerase-harboring vOTUs. In combination with neutral model predictions, this may point towards a fundamental difference between Siphoviridae and Myoviridae in infecting stream biofilm bacteria. While Myoviridae may rather rely on efficiently spreading across distant biofilm patches facilitated by an ability to decompose the EPS matrix, many members of Siphoviridae seem to lack this ability.To investigate our second hypothesis, that lysogeny might be a successful viral life cycle strategy to spread locally within biofilm patches, we used BACPHLIP [36]. BACPHLIP predicted with high probability ( >75%) a lysogenic life cycle for 58 out of 256 complete viral genomes and a lytic life cycle for 177 viral genomes. For the remaining 21 complete viral genomes in our dataset, BACPHLIP did not result in sufficiently high prediction probability (i.e., More

  • in

    Genotype to ecotype in niche environments: adaptation of Arthrobacter to carbon availability and environmental conditions

    Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baesa Y, et al. Balance trees reveal microbial niche differentiation. MSystems. 2017;2:e00162–16.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salles JF, Poly F, Schmid B, Le Roux X. Community niche predicts the functioning of denitrifying bacterial assemblages. Ecology. 2009;90:3324–32.PubMed 

    Google Scholar 
    Ge X, Thorgersen MP, Poole FL, Deutschbauer AM, Chandonia J-M, Novichov PS, et al. Characterization of a metal-resistant bacillus strain with a high molybdate affinity ModA from contaminated sediments at the Oak Ridge Reservation. Front Microbiol. 2020;11:2543.
    Google Scholar 
    Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.CAS 
    PubMed 

    Google Scholar 
    Moon J-W, Paradis CJ, Joyner DC, von Netzer F, Majumder EL, Dixon ER, et al. Characterization of subsurface media from locations up- and down-gradient of a uranium-contaminated aquifer. Chemosphere. 2020;255:126951.CAS 
    PubMed 

    Google Scholar 
    Berkowitz B, Silliman SE, Dunn AM. Impact of the capillary fringe on local flow, chemical migration, and microbiology. Vadose Zo J. 2004;3:534–48.CAS 

    Google Scholar 
    Winter J, Ippisch O, Vogel H-J. Dynamic processes in capillary fringes. Vadose Zo J. 2015;14:1–2.Silliman SE, Berkowitz B, Simunek J, van Genuchten MT. Fluid flow and solute migration within the capillary fringe. Ground Water. 2002;40:76–84.CAS 
    PubMed 

    Google Scholar 
    Haberer CM, Rolle M, Liu S, Cirpka OA, Prathwohl P. A high-resolution non-invasive approach to quantify oxygen transport across the capillary fringe and within the underlying groundwater. J Contam Hydrol. 2011;122:26–39.CAS 
    PubMed 

    Google Scholar 
    Bouskill NJ, Conrad ME, Bill M, Brodie EL, Cheng Y, Hobson C, et al. Evidence for microbial mediated NO3− cycling within floodplain sediments during groundwater fluctuations. Front Earth Sci. 2019;7:189.
    Google Scholar 
    Rühle FA, von Netzer F, Lueders T, Stumpp C. Response of transport parameters and sediment microbiota to water table fluctuations in laboratory columns. Vadose Zo J. 2015;14:vzj2014.09.0116.Aigle A, Prosser JI, Gubry-Rangin C. The application of high-throughput sequencing technology to analysis of amoA phylogeny and environmental niche specialisation of terrestrial bacterial ammonia-oxidisers. Environ Microbiome. 2019;14:3.PubMed 
    PubMed Central 

    Google Scholar 
    Almeida EL, Carrillo Rincón AF, Jackson SA, Dobson ADW. Comparative genomics of marine sponge-derived Streptomyces spp. isolates SM17 and SM18 with their closest terrestrial relatives provides novel insights into environmental niche adaptations and secondary metabolite biosynthesis potential. Front Microbiol. 2019;10:1713.PubMed 
    PubMed Central 

    Google Scholar 
    Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T, et al. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:754.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bellanger X, Payot S, Leblond-Bourget N, Guédon G. Conjugative and mobilizable genomic islands in bacteria: evolution and diversity. FEMS Microbiol Rev. 2014;38:720–60.CAS 
    PubMed 

    Google Scholar 
    Harrison E, Brockhurst MA. Plasmid-mediated horizontal gene transfer is a coevolutionary process. Trends Microbiol. 2012;20:262–7.CAS 
    PubMed 

    Google Scholar 
    Wisniewski-Dyé F, Lozano L, Acosta-Cruz E, Borland S, Drogue B, Prigent-Combaret C, et al. Genome sequence of Azospirillum brasilense CBG497 and comparative analyses of Azospirillum core and accessory genomes provide insight into niche adaptation. Genes. 2012;3:576–602.Conn HJ, Dimmick I. Soil bacteria similar in morphology to Mycobacterium and Corynebacterium. J Bacteriol. 1947;54:291–303.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boivin-Jahns V, Bianchi A, Ruimy R, Garcin J, Daumas S, Cristen R, et al. Comparison of phenotypical and molecular methods for the identification of bacterial strains isolated from a deep subsurface environment. Appl Environ Microbiol. 1995;61:3400–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rusterholtz KJ, Mallory LM. Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb Ecol. 1994;28:79–99.CAS 
    PubMed 

    Google Scholar 
    Eschbach M, Möbitz H, Rompf A, Jahn D. Members of the genus Arthrobacter grow anaerobically using nitrate ammonification and fermentative processes: anaerobic adaptation of aerobic bacteria abundant in soil. FEMS Microbiol Lett. 2003;223:227–30.CAS 
    PubMed 

    Google Scholar 
    Banerjee S, Palit R, Sengupta C, Standing D. Stress induced phosphate solubilization by ’Arthrobacter’ Sp. and ’Bacillus’ sp. isolated from tomato rhizosphere. Aust J Crop Sci. 2010;4:378–83.CAS 

    Google Scholar 
    Keddie RM, Collins D, Jones D. Genus Arthrobacter. In: Sneath PHA, Mair NS, Sharpe ME, Holt JG, editors. Bergey’s manual of systematic bacteriology. Vol 2. Williams and Wilkins: New York, NY. 1986. p. 1288–301.Crocker FH, Fredrickson JK, White DC, Ringelberg DB, Balkwill DL. Phylogenetic and physiological diversity of Arthrobacter strains isolated from unconsolidated subsurface sediments. Microbiology. 2000;146:1295–310.CAS 
    PubMed 

    Google Scholar 
    Baran R, Brodie EL, Mayberry-Lewis J, Hummel E, Da Rocha UN, Chakraborty R, et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat Commun. 2015;6:8289.CAS 
    PubMed 

    Google Scholar 
    Wu X, Spencer S, Gushgari-Doyle S, Yee MO, Voriskova J, Li Y, et al. Culturing of “unculturable” subsurface microbes: natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front Microbiol. 2020;11:3171.
    Google Scholar 
    Watson DB, Kostka JE, Fields MW, Jardine PM. The Oak Ridge Field Research Center conceptual model. NABIR F. Res. Center: Oak Ridge, TN; 2004.Moon J, Roh Y, Phelps TJ, Phillips DH, Watson DB, Kim Y-J, et al. Physicochemical and mineralogical characterization of soil–saprolite cores from a field research site, Tennessee. J Environ Qual. 2006;35:1731–41.CAS 
    PubMed 

    Google Scholar 
    Wu X, Wu L, Liu Y, Zhang P, Li Q, Zhou J, et al. Microbial interactions with dissolved organic matter drive carbon dynamics and community succession. Front Microbiol. 2018;9:1234.PubMed 
    PubMed Central 

    Google Scholar 
    Chakraborty R, Woo H, Dehal P, Walker R, Zemla M, Auer M, et al. Complete genome sequence of Pseudomonas stutzeri strain RCH2 isolated from a Hexavalent Chromium [Cr(VI)] contaminated site. Stand Genomic Sci. 2017;12:23.PubMed 
    PubMed Central 

    Google Scholar 
    Guttenberger M, Hampp R. Ectomycorrhizins—symbiosis-specific or artifactual polypeptides from ectomycorrhizas? Planta. 1992;188:129–36.CAS 
    PubMed 

    Google Scholar 
    Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:e1005595.PubMed 
    PubMed Central 

    Google Scholar 
    Hyatt D, Chen G, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.PubMed 
    PubMed Central 

    Google Scholar 
    Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38:5825–9.Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics. 2013;14:60.PubMed 
    PubMed Central 

    Google Scholar 
    Meier-Kolthoff JP, Carbasse JS, Peinado-Olarte RL, Göker M. TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res. 2022;50:D801–D807.CAS 
    PubMed 

    Google Scholar 
    Price MN, Deutschbauer AM, Arkin AP. GapMind: automated annotation of amino acid biosynthesis. mSystems. 2020;5:e00291–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bertelli C, Laird MR, Wiliams KP, Lau BY, Hoad G, Winsor GL, et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 2017;45:W30–W35.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–W235.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Procter JB, Carstairs GM, Soares B, Mourão K, Ofoegbu TC, Barton D, et al. Alignment of biological sequences with Jalview. In: Katoh K Editor. Multiple sequence alignment. Springer, Humana Press: New York, NY. 2021. p. 203–24.Letunic I, Bork P. Interactive Tree of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6. https://doi.org/10.1093/nar/gkab301.Eren AM, Esen O, Quince C, Vines JH, Horrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ’omics data. PeerJ. 2015;3:e1319.PubMed 
    PubMed Central 

    Google Scholar 
    Qiong W, Garrity GM, Tiedge JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
    Google Scholar 
    Liao J, Guo X, Weller DL, Pollak S, Buckley DH, Wiedmann M, et al. Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution. Nat Microbiol. 2021;6:1021–30.CAS 
    PubMed 

    Google Scholar 
    Schwyn B, Neilands JB. Universal chemical assay for detection and determination of siderophores. Anal Biochem. 1987;160:47–56.CAS 
    PubMed 

    Google Scholar 
    Pérez-Miranda S, Cabirol N, George-Téllez R, Zamudio-Rivera LS, Fernandez FJ. O-CAS, a fast and universal method for siderophore detection. J Microbiol Methods. 2007;70:127–31.PubMed 

    Google Scholar 
    Nyyssönen M, Tran HM, Karaoz U, Weihe C, Hadi MZ, Martiny JBH, et al. Coupled high-throughput functional screening and next generation sequencing for identification of plant polymer decomposing enzymes in metagenomic libraries. Front Microbiol. 2013;4:282PubMed 
    PubMed Central 

    Google Scholar 
    Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Gregory Caporaso J, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.PubMed 

    Google Scholar 
    Oliveira PL, de, Duarte MCT, Ponezi AN, Durrant LR. Purification and partial characterization of manganese peroxidase from Bacillus pumilus and Paenibacillus sp. Braz J Microbiol. 2009;40:818–26.PubMed 
    PubMed Central 

    Google Scholar 
    Varrot A, Yip VLY, Li Y, Rajan SS, Yang X, Anderson WF, et al. NAD+ and metal-ion dependent hydrolysis by family 4 glycosidases: structural insight into specificity for phospho-β-D-glucosides. J Mol Biol.2005;346:423–35.CAS 
    PubMed 

    Google Scholar 
    Lambers H. Introduction: dryland salinity: a key environmental issue in southern Australia. Plant Soil. 2003;257:v–vii.Galinski EA, Trüper HG. Microbial behaviour in salt-stressed ecosystems. FEMS Microbiol Rev. 1994;15:95–108.CAS 

    Google Scholar 
    Korom SF. Natural denitrification in the saturated zone: a review. Water Resour Res. 1992;28:1657–68.CAS 

    Google Scholar 
    Niewerth H, Schuldes J, Parschat K, Kiefer P, Vorholt JA, Daniel R, et al. Complete genome sequence and metabolic potential of the quinaldine-degrading bacterium Arthrobacter sp. Rue61a. BMC Genomics. 2012;13:1–19.
    Google Scholar 
    See-Too W-S, Ee R, Lim Y-L, Convey P, Pearce DA, Mohidin TBM, et al. Complete genome of Arthrobacter alpinus strain R3. 8, bioremediation potential unraveled with genomic analysis. Stand Genomic Sci. 2017;12:1–7.
    Google Scholar 
    Bazhanov DP, Li C, Li H, Li J, Zhang X, Chen X, et al. Occurrence, diversity and community structure of culturable atrazine degraders in industrial and agricultural soils exposed to the herbicide in Shandong Province, PR China. BMC Microbiol. 2016;16:1–21.
    Google Scholar 
    Fan X, Nie MQ, Wang Y, Diwu ZJ, Liu L, Liu Y. Characteristics of the co-metabolism of 1-naphthol by Arthrobacter crystallopoietes NT16 and symbiotic Bacillus NG16. Acta Sci Circumstantiae. 2019;39:1482–8.CAS 

    Google Scholar 
    Nakatsu CH, Barabote R, Thompson S, Bruce D, Detter C, Brettin T, et al. Complete genome sequence of Arthrobacter sp. strain FB24. Stand Genomic Sci. 2013;9:106–16.PubMed 
    PubMed Central 

    Google Scholar 
    Shimasaki T, Masuda S, Garrido-Oter R, Kawasaki T, Aoki Y, Shibata A, et al. Tobacco root endophytic Arthrobacter harbors genomic features enabling the catabolism of host-specific plant specialized metabolites. MBio. 2021;12:e00846–21.CAS 
    PubMed Central 

    Google Scholar 
    Kumar R, Singh D, Swarnkar MK, Singh AK, Kumar S. Complete genome sequence of Arthrobacter alpinus ERGS4: 06, a yellow pigmented bacterium tolerant to cold and radiations isolated from Sikkim Himalaya. J Biotechnol. 2016;220:86–87.CAS 
    PubMed 

    Google Scholar 
    Russell DA, Hatfull GF. Complete genome sequence of Arthrobacter sp. ATCC 21022, a host for bacteriophage discovery. Genome Announc. 2016;4:e00168–16.PubMed 
    PubMed Central 

    Google Scholar 
    Fomenkov A, Akimov VN, Vasilyeva LV, Andersen DT, Vincze T, Roberts RJ, et al. Complete genome and methylome analysis of psychrotrophic bacterial isolates from Lake Untersee in Antarctica. Genome Announc. 2017;5:e01753–16.PubMed 
    PubMed Central 

    Google Scholar 
    Hiraoka S, Machiyama A, Ijichi M, Inoue K, Oshima K, Hattori M, et al. Genomic and metagenomic analysis of microbes in a soil environment affected by the 2011 Great East Japan Earthquake tsunami. BMC Genomics. 2016;17:1–13.
    Google Scholar 
    Han S-R, Kim B, Jang JH, Park H, Oh T-J. Complete genome sequence of Arthrobacter sp. PAMC25564 and its comparative genome analysis for elucidating the role of CAZymes in cold adaptation. BMC Genomics. 2021;22:1–14.
    Google Scholar 
    Koh H-W, Kang M, Lee K, Lee E, Kim H, Park SJ. Arthrobacter dokdonellae sp. nov., isolated from a plant of the genus Campanula. J Microbiol. 2019;57:732–7.CAS 
    PubMed 

    Google Scholar 
    Xu X, Xu M, Zhao Q, Xia Y, Chen C, Shen Z. Complete genome sequence of Cd (II)-resistant Arthrobacter sp. PGP41, a plant growth-promoting bacterium with potential in microbe-assisted phytoremediation. Curr Microbiol. 2018;75:1231–9.CAS 
    PubMed 

    Google Scholar 
    Lee GLY, Ahmad SA, Yasid NA, Zulkharnain A, Convey P, Johari WLW, et al. Biodegradation of phenol by cold-adapted bacteria from Antarctic soils. Polar Biol. 2018;41:553–62.
    Google Scholar 
    Stockdale A, Davison W, Zhang H. Micro-scale biogeochemical heterogeneity in sediments: a review of available technology and observed evidence. Earth-Science Rev. 2009;92:81–97.CAS 

    Google Scholar 
    Whiting AK, Boldt YR, Hendrich MP, Wackett LP, Que L. Manganese (II)-dependent extradiol-cleaving catechol dioxygenase from Arthrobacter globiformis CM-2. Biochemistry. 1996;35:160–70.CAS 
    PubMed 

    Google Scholar 
    Jeng W-Y, Wang M, Lin N, Lin C, Liaw Y, Cheng W, et al. Structural and functional analysis of three β-glucosidases from bacterium Clostridium cellulovorans, fungus Trichoderma reesei and termite Neotermes koshunensis. J Struct Biol. 2011;173:46–56.CAS 
    PubMed 

    Google Scholar 
    Stevenson IL. Utilization of aromatic hydrocarbons by Arthrobacter spp. Can J Microbiol. 1967;13:205–11.CAS 
    PubMed 

    Google Scholar 
    Dsouza M, Taylor MW, Turner SJ, Aislabie J. Genomic and phenotypic insights into the ecology of Arthrobacter from Antarctic soils. BMC Genomics. 2015;16:36.PubMed 
    PubMed Central 

    Google Scholar 
    Taylor R, Cronin A, Pedley S, Barker J, Atkinson T. The implications of groundwater velocity variations on microbial transport and wellhead protection–review of field evidence. FEMS Microbiol Ecol. 2004;49:17–26.CAS 
    PubMed 

    Google Scholar 
    Zhang X, Liu X, Yang F, Chen L. Pan-genome analysis links the hereditary variation of leptospirillum ferriphilum with its evolutionary adaptation. Front Microbiol. 2018;9:577.PubMed 
    PubMed Central 

    Google Scholar 
    Broadbent JR, Neeno-Eckwall EC, Stahl B, Tandee K, Cai H, Morovic W, et al. Analysis of the Lactobacillus casei supragenome and its influence in species evolution and lifestyle adaptation. BMC Genomics. 2012;13:533.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang Y, Sievert S. Pan-genome analyses identify lineage- and niche-specific markers of evolution and adaptation in Epsilonproteobacteria. Front Microbiol. 2014;5:110.PubMed 
    PubMed Central 

    Google Scholar 
    Aminov R. Horizontal gene exchange in environmental microbiota. Front Microbiol. 2011;2:158.PubMed 
    PubMed Central 

    Google Scholar 
    Kothari A, Wu Y, Chandonia J-M, Charrier M, Rajiv L, Rocha AM, et al. Large circular plasmids from groundwater plasmidomes span multiple incompatibility groups and are enriched in multimetal resistance genes. MBio. 2019;10:e02899–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Penn K, Jenkins C, Nett M, Udwary DW, Gontang EA, McGlinchey RP, et al. Genomic islands link secondary metabolism to functional adaptation in marine Actinobacteria. ISME J. 2009;3:1193–203.CAS 
    PubMed 

    Google Scholar 
    Wu X, Kazakov AE, Gushgari-Doyle S, Yu X, Trotter V, Stuart RK, et al. Comparative genomics reveals insights into induction of violacein biosynthesis and adaptive evolution in Janthinobacterium. Microbiol Spectr. 2022;9:e01414–e01421.
    Google Scholar 
    Jonkheer EM, Brankovics B, Houwers IM, van der Wolf JM, Bonants PJM, Vreeburg RAM, et al. The Pectobacterium pangenome, with a focus on Pectobacterium brasiliense, shows a robust core and extensive exchange of genes from a shared gene pool. BMC Genomics. 2021;22:265.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Abdel-Glil MY, Rischer U, Steinhagen D, McCarthy U, Neubauer H, Sprague LD. Phylogenetic relatedness and genome structure of Yersinia ruckeri revealed by whole genome sequencing and a comparative analysis. Front Microbiol. 2021;12:782415.González-Dominici LI, Saati-Santamaría Z, García-Fraile P. Genome analysis and genomic comparison of the novel species Arthrobacter ipsi reveal its potential protective role in its bark beetle host. Microb Ecol. 2021;81:471–82.PubMed 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
    PubMed 

    Google Scholar 
    Herrick JB, Stuart-Keil KG, Ghiorse WC, Madsen EL. Natural horizontal transfer of a naphthalene dioxygenase gene between bacteria native to a coal tar-contaminated field site. Appl Environ Microbiol. 1997;63:2330–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Griebler C, Lueders T. Microbial biodiversity in groundwater ecosystems. Freshw Biol. 2009;54:649–77.
    Google Scholar  More

  • in

    Gentamicin at sub-inhibitory concentrations selects for antibiotic resistance in the environment

    Kemper N. Veterinary antibiotics in the aquatic and terrestrial environment. Ecol Indic. 2008;8:1–13.CAS 
    Article 

    Google Scholar 
    Jechalke S, Heuer H, Siemens J, Amelung W, Smalla K. Fate and effects of veterinary antibiotics in soil. Trends Microbiol. 2014;22:536–45. Available from: https://doi.org/10.1016/j.tim.2014.05.005.CAS 
    Article 
    PubMed 

    Google Scholar 
    Kalasseril S, Paul R, J RK V, Pillai D. Investigating the impact of hospital antibiotic usage on aquatic environment and aquaculture systems: A molecular study of quinolone resistance in Escherichia coli. Sci Total Environ. 2020;748:141538. Available from: https://doi.org/10.1016/j.scitotenv.2020.141538.CAS 
    Article 

    Google Scholar 
    Ashbolt NJ. Human Health Risk Assessment (HHRA) for Environmental Development and Transfer of Antibiotic Resistance. Environ Health Perspect. 2013;121:993–1002.Article 

    Google Scholar 
    Bengtsson-Palme J, Kristiansson E, Larsson DGJ Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiol Rev. 2017;(October 2017):68–80. Available from: http://academic.oup.com/femsre/advance-article/doi/10.1093/femsre/fux053/4563583Manaia CM Assessing the Risk of Antibiotic Resistance Transmission from the Environment to Humans: Non-Direct Proportionality between Abundance and Risk. Vol. 25, Trends in Microbiology. 2017.Manaia CM, Macedo G, Fatta-Kassinos D, Nunes OC. Antibiotic resistance in urban aquatic environments: can it be controlled? Appl Microbiol Biotechnol. 2016;100:1543–57.CAS 
    Article 

    Google Scholar 
    Durso LM, Cook KL. Impacts of antibiotic use in agriculture: what are the benefits and risks? Curr Opin Microbiol. 2014;19:37–44. https://doi.org/10.1016/j.mib.2014.05.019. Available fromArticle 
    PubMed 

    Google Scholar 
    Almakki A, Jumas-Bilak E, Marchandin H, Licznar-Fajardo P. Antibiotic resistance in urban runoff. Sci Total Environ. 2019;667:64–76. https://linkinghub.elsevier.com/retrieve/pii/S0048969719306710.CAS 
    Article 

    Google Scholar 
    Andersson DI, Hughes D. Microbiological effects of sublethal levels of antibiotics. Nat Rev Microbiol. 2014;12:465–78. Available from: https://doi.org/10.1038/nrmicro3270.CAS 
    Article 
    PubMed 

    Google Scholar 
    Gullberg E, Cao S, Berg OG, Ilbäck C, Sandegren L, Hughes D, et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog. 2011;7:1–9.Article 

    Google Scholar 
    Murray AK, Zhang L, Yin X, Zhang T, Buckling A, Snape J, et al. Novel insights into selection for antibiotic resistance in complex microbial communities. MBio. 2018;9:1–12. http://mbio.asm.org/lookup/doi/10.1128/mBio.00969-18.CAS 
    Article 

    Google Scholar 
    Chow L, Waldron L, Gillings MR. Potential impacts of aquatic pollutants: sub-clinical antibiotic concentrations induce genome changes and promote antibiotic resistance. Front Microbiol. 2015;6:1–10.
    Google Scholar 
    Bruchmann J, Kirchen S, Schwartz T. Sub-inhibitory concentrations of antibiotics and wastewater influencing biofilm formation and gene expression of multi-resistant Pseudomonas aeruginosa wastewater isolates. Environ Sci Pollut Res. 2013;20:3539–49.CAS 
    Article 

    Google Scholar 
    Gullberg E, Albrecht LM, Karlsson C, Sandegren L, Andersson DI. Selection of a Multidrug Resistance Plasmid by Sublethal Levels of Antibiotics and Heavy Metals. mBio. 2014;5:19–23.Article 

    Google Scholar 
    Choung S, Yun Z, Kwon EE, Cho Y, Ha U-H, Oh J, et al. Transfer of antibiotic resistance plasmids in pure and activated sludge cultures in the presence of environmentally representative micro-contaminant concentrations. Sci Total Environ. 2014;468–469:813–20. https://doi.org/10.1016/j.scitotenv.2013.08.100.CAS 
    Article 
    PubMed 

    Google Scholar 
    Shun-Mei E, Zeng JM, Yuan H, Lu Y, Cai RX, Chen C. Sub-inhibitory concentrations of fluoroquinolones increase conjugation frequency. Microb Pathog. 2018;114:57–62.CAS 
    Article 

    Google Scholar 
    Jutkina J, Rutgersson C, Flach CF, Joakim Larsson DG. An assay for determining minimal concentrations of antibiotics that drive horizontal transfer of resistance. Sci Total Environ. 2016;548–549:131–8. https://doi.org/10.1016/j.scitotenv.2016.01.044.CAS 
    Article 
    PubMed 

    Google Scholar 
    Jutkina J, Marathe NP, Flach CF, Larsson DGJ. Antibiotics and common antibacterial biocides stimulate horizontal transfer of resistance at low concentrations. Sci Total Environ. 2018;616–617:172–8. https://doi.org/10.1016/j.scitotenv.2017.10.312.CAS 
    Article 
    PubMed 

    Google Scholar 
    Murray AK, Zhang L, Yin X, Zhang T, Buckling A, Snape J, et al. Novel insights into selection for antibiotic resistance in complex microbial communities. MBio. 2018;9:1–12.CAS 
    Article 

    Google Scholar 
    Le-minh N, Khan SJ, Drewes JE, Stuetz RM. Fate of antibiotics during municipal water recycling treatment processes. Water Res. 2010;44:4295–323. https://doi.org/10.1016/j.watres.2010.06.020.CAS 
    Article 
    PubMed 

    Google Scholar 
    George J, Halami PM. Sub-inhibitory concentrations of gentamicin triggers the expression of aac(6′)Ie-aph(2″)Ia, chaperons and biofilm related genes in Lactobacillus plantarum MCC 3011. Res Microbiol. 2017;168:722–31. https://doi.org/10.1016/j.resmic.2017.06.002.CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang AN, Li LG, Ma L, Gillings MR, Tiedje JM, Zhang T. Conserved phylogenetic distribution and limited antibiotic resistance of class 1 integrons revealed by assessing the bacterial genome and plasmid collection. Microbiome. 2018;6:1–14.Article 

    Google Scholar 
    Gillings MR. Integrons: Past, Present, and Future. Microbiol Mol Biol Rev. 2014;78:257–77.Article 

    Google Scholar 
    Guironnet A, Sanchez-Cid C, Vogel TM, Wiest L, Vulliet E Aminoglycosides analysis optimization using Ion pairing Liquid Chromatography coupled to tandem Mass Spectrometry and application on wastewater samples. J Chromatogr. 2021;1651.Muyzer G, Hottentrager S, Teske A, Wawer C Denaturing gradient gel electrophoresis of PCR-amplified 16S rDNA—a new molecular approach to analyse the genetic diversity of mixed microbial communities. In: Akkermans A, van Elsas J, de Bruijn F, editors. Molecular microbial ecology manual. Dordrecht, The Netherlands: Kluwer Academic Publishers; 1995. p. 1–23.Watanabe K, Kodama Y, Harayama S. Design and evaluation of PCR primers to amplify bacterial 16S ribosomal DNA fragments used for community fingerprinting. J Microbiol Methods. 2001;44:253–62.CAS 
    Article 

    Google Scholar 
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:1–11.Article 

    Google Scholar 
    Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics. 2012;13:31 http://www.biomedcentral.com/1471-2105/13/31.CAS 
    Article 

    Google Scholar 
    Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
    Article 

    Google Scholar 
    Holmes AJ, Gillings MR, Nield BS, Mabbutt BC, Nevalainen KMH, Stokes HW. The gene cassette metagenome is a basic resource for bacterial genome evolution. Environ Microbiol. 2003;5:383–94.CAS 
    Article 

    Google Scholar 
    Gillings MR, Xuejun D, Hardwick SA, Holley MP, Stokes HW. Gene cassettes encoding resistance to quaternary ammonium compounds: a role in the origin of clinical class 1 integrons? ISME J. 2009;3:209–15.CAS 
    Article 

    Google Scholar 
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
    Article 

    Google Scholar 
    Minoche AE, Dohm JC, Himmelbauer H Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and Genome Analyzer systems. Genome Biol. 2011;12.Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:1–22.Article 

    Google Scholar 
    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. Available from: https://doi.org/10.1038/nmeth.1923.CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: An advanced analysis and visualization platformfor’omics data. PeerJ. 2015;2015:1–29.
    Google Scholar 
    Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, et al. CARD 2020: Antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020;48:D517–25.CAS 
    Article 

    Google Scholar 
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
    Article 

    Google Scholar 
    Menzel P, Ng KL, Krogh A Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7.Ramirez SM, Tolmasky EM. Aminoglycoside modifing enzymes. Drug Resist Updat. 2011;13:151–71. Available from: https://doi.org/10.1016/j.drup.2010.08.003.CAS 
    Article 

    Google Scholar 
    Ben Y, Fu C, Hu M, Liu L, Wong MH, Zheng C. Human Health Risk Assessment of Antibiotic Resistance Associated with Antibiotic Residues in the Environment: A Review. Environ Res. 2018;169:483–93. https://www.sciencedirect.com/science/article/pii/S0013935118304298.Article 

    Google Scholar 
    Bengtsson-Palme J, Larsson DGJ. Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environ Int. 2016;86:140–9. https://doi.org/10.1016/j.envint.2015.10.015.CAS 
    Article 
    PubMed 

    Google Scholar 
    Sultan I, Rahman S, Jan AT, Siddiqui MT, Mondal AH, Haq QMR Antibiotics, Resistome and Resistance Mechanisms: A Bacterial Perspective. Front Microbiol. 2018;9(September). Available from: https://www.frontiersin.org/article/10.3389/fmicb.2018.02066/fullCasin I, Bordon F, Bertin P, Coutrot A, Podglajen I, Brasseur R, et al. Aminoglycoside 6’-N-acetyltransferase variants of the Ib type with altered substrate profile in clinical isolates of Enterobacter cloacae and Citrobacter freundii. Antimicrob Agents Chemother. 1998;42:209–15.CAS 
    Article 

    Google Scholar 
    Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, et al. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol. 2015;13:310–7.CAS 
    Article 

    Google Scholar 
    Chow LKM, Ghaly TM, Gillings MR. A survey of sub-inhibitory concentrations of antibiotics in the environment. J Environ Sci (China). 2021;99:21–7. https://doi.org/10.1016/j.jes.2020.05.030.Article 

    Google Scholar 
    Gillings MR. Class 1 integrons as invasive species. Curr Opin Microbiol. 2017;38:10–5. https://doi.org/10.1016/j.mib.2017.03.002.CAS 
    Article 
    PubMed 

    Google Scholar 
    Ma L, Li AD, Yin XL, Zhang T. The prevalence of integrons as the carrier of antibiotic resistance genes in natural and man-made environments. Environ Sci Technol. 2017;51:5721–8.CAS 
    Article 

    Google Scholar 
    Gillings M, Boucher Y, Labbate M, Holmes A, Krishnan S, Holley M, et al. The evolution of class 1 integrons and the rise of antibiotic resistance. J Bacteriol. 2008;190:5095–100.CAS 
    Article 

    Google Scholar 
    Bürgmann H, Frigon D, Gaze WH, Manaia CM, Pruden A, Singer AC, et al. Water and sanitation: An essential battlefront in the war on antimicrobial resistance. FEMS Microbiol Ecol. 2018;94.Pena-Miller R, Laehnemann D, Jansen G, Fuentes-Hernandez A, Rosenstiel P, Schulenburg H, et al. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition. PLoS Biol. 2013;11:14–6.Article 

    Google Scholar  More

  • in

    Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit

    van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).
    Google Scholar 
    Peng, C. et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat. Clim. Chang. 1, 467–471 (2011).
    Google Scholar 
    Brienen, R. J. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).
    Google Scholar 
    Klein, T., Cahanovitc, R., Sprintsin, M., Herr, N. & Schiller, G. A nation-wide analysis of tree mortality under climate change: forest loss and its causes in Israel 1948–2017. For. Ecol. Manag. 432, 840–849 (2019).
    Google Scholar 
    Yu, K. et al. Pervasive decreases in living vegetation carbon turnover time across forest climate zones. Proc. Natl Acad. Sci. USA 116, 24662–24667 (2019).
    Google Scholar 
    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).
    Google Scholar 
    Kharuk, V. I. et al. Climate-driven conifer mortality in Siberia. Glob. Ecol. Biogeogr. 30, 543–556 (2021).
    Google Scholar 
    Breshears, D. D. et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl Acad. Sci. USA 102, 15144–15148 (2005).
    Google Scholar 
    Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. & Nepstad, D. The 2010 amazon drought. Science 331, 554 (2011).
    Google Scholar 
    Ruthrof, K. X. et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses. Sci. Rep. 8, 13094 (2018).
    Google Scholar 
    Senf, C. et al. Canopy mortality has doubled in Europe’s temperate forests over the last three decades. Nat. Commun. 9, 4978 (2018).
    Google Scholar 
    Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103 (2020).
    Google Scholar 
    Kannenberg, S. A., Driscoll, A. W., Malesky, D. & Anderegg, W. R. Rapid and surprising dieback of Utah juniper in the southwestern USA due to acute drought stress. For. Ecol. Manag. 480, 118639 (2021).
    Google Scholar 
    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).
    Google Scholar 
    Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Change Biol. 26, 3122–3133 (2020).
    Google Scholar 
    Werner, W. L. Canopy dieback in the upper montane rain forests of Sri Lanka. GeoJournal 17, 245–248 (1988).
    Google Scholar 
    Feldpausch, T. R. et al. Amazon forest response to repeated droughts. Glob. Biogeochem. Cycles 30, 964–982 (2016).
    Google Scholar 
    Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).
    Google Scholar 
    Werner, R. A. & Holsten, E. H. Mortality of white spruce during a spruce beetle outbreak on the Kenai Peninsula in Alaska. Can. J. For. Res. 13, 96–101 (1983).
    Google Scholar 
    Suarez, M. L., Ghermandi, L. & Kitzberger, T. Factors predisposing episodic drought-induced tree mortality in Nothofagus: site, climatic sensitivity and growth trends. J. Ecol. 92, 954–966 (2004).
    Google Scholar 
    Swemmer, A. M. Locally high, but regionally low: the impact of the 2014–2016 drought on the trees of semi-arid savannas, South Africa. Afr. J. Range Forage Sci. 37, 31–42 (2020).
    Google Scholar 
    Michaelian, M., Hogg, E. H., Hall, R. J. & Arsenault, E. Massive mortality of aspen following severe drought along the southern edge of the Canadian boreal forest. Glob. Chang Biol. 17, 2084–2094 (2011).
    Google Scholar 
    Kharuk, V. I. et al. Climate-induced mortality of Siberian pine and fir in the Lake Baikal Watershed, Siberia. For. Ecol. Manag. 384, 191–199 (2017).
    Google Scholar 
    Kharuk, V. I., Ranson, K. J., Oskorbin, P. A., Im, S. T. & Dvinskaya, M. L. Climate induced birch mortality in Trans-Baikal lake region, Siberia. For. Ecol. Manag. 289, 385–392 (2013).
    Google Scholar 
    Crouchet, S. E., Jensen, J., Schwartz, B. F. & Schwinning, S. Tree mortality after a hot drought: distinguishing density-dependent and -independent drivers and why it matters. Front. For. Glob. Change 2, 21 (2019).
    Google Scholar 
    Breshears, D. D. et al. The critical amplifying role of increasing atmospheric moisture demand on tree mortality and associated regional die-off. Front. Plant Sci. 4, 266 (2013).
    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
    Google Scholar 
    Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Chang. 4, 17–22 (2014).
    Google Scholar 
    Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).
    Google Scholar 
    Xu, C. et al. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nat. Clim. Chang. 9, 948–953 (2019).
    Google Scholar 
    Dore, M. H. Climate change and changes in global precipitation patterns: what do we know? Environ. Int. 31, 1167–1181 (2005).
    Google Scholar 
    Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 31, e2020GL087820 (2020).
    Google Scholar 
    Breshears, D. D. et al. Underappreciated plant vulnerabilities to heat waves. New Phytol. 231, 32–39 (2021).
    Google Scholar 
    Adams, H. D. et al. Temperature response surfaces for mortality risk of tree species with future drought. Environ. Res. Lett. 12, 115014 (2017).
    Google Scholar 
    McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Chang. 6, 295–300 (2016).
    Google Scholar 
    Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).
    Google Scholar 
    Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2413–2445 (2020).
    Google Scholar 
    Long, S. P. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).
    Google Scholar 
    Hickler, T. et al. CO2 fertilization in temperate FACE experiments not representative of boreal and tropical forests. Glob. Change Biol. 14, 1531–1542 (2008).
    Google Scholar 
    Baig, S., Medlyn, B. E., Mercado, L. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).
    Google Scholar 
    Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).
    Google Scholar 
    Belmecheri, S. et al. Precipitation alters the CO2 effect on water-use efficiency of temperate forests. Glob. Change Biol. 27, 1560–1571 (2021).
    Google Scholar 
    Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).
    Google Scholar 
    De Kauwe, M. G., Medlyn, B. E. & Tissue, D. T. To what extent can rising [CO2] ameliorate plant drought stress? New Phytol. 231, 2118–2124 (2021).
    Google Scholar 
    Martınez-Vilalta, J., Piñol, J. & Beven, K. A hydraulic model to predict drought-induced mortality in woody plants: an application to climate change in the Mediterranean. Ecol. Model. 155, 127–147 (2002).
    Google Scholar 
    McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).
    Google Scholar 
    McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).
    Google Scholar 
    Adams, H. D. et al. A multi-species synthesis of physiological mechanisms in drought-induced tree mortality. Nat. Ecol. Evol. 1, 1285–1291 (2017).
    Google Scholar 
    Fisher, R. et al. Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological scale limitations. New Phytol. 187, 666–681 (2010).
    Google Scholar 
    McDowell, N. G. et al. Evaluating theories of drought-induced vegetation mortality using a multimodel–experiment framework. New Phytol. 200, 304–321 (2013).
    Google Scholar 
    Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).
    Google Scholar 
    Christoffersen, B. O. et al. Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v. 1-Hydro). Geosci. Model Dev. 9, 4227–4255 (2016).
    Google Scholar 
    Kennedy, D. et al. Implementing plant hydraulics in the community land model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
    Google Scholar 
    Koven, C. D. et al. Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama. Biogeosciences 17, 3017–3044 (2020).
    Google Scholar 
    Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang. 3, 30–36 (2013).
    Google Scholar 
    Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 15–28 (2018).
    Google Scholar 
    Adams, H. D. et al. Ecohydrological consequences of drought- and infestation-triggered tree die-off: insights and hypotheses. Ecohydrology 5, 145–159 (2012).
    Google Scholar 
    Bearup, L. A., Maxwell, R. M., Clow, D. W. & McCray, J. E. Hydrological effects of forest transpiration loss in bark beetle-impacted watersheds. Nat. Clim. Chang. 4, 481–486 (2014).
    Google Scholar 
    Bennett, K. E. et al. Climate-driven disturbances in the San Juan River sub-basin of the Colorado River. Hydrol. Earth Syst. Sci. 22, 709–725 (2018).
    Google Scholar 
    Lutz, J. A. & Halpern, C. B. Tree mortality during early forest development: a long-term study of rates, causes, and consequences. Ecol. Monogr. 76, 257–275 (2006).
    Google Scholar 
    Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352 (2016).
    Google Scholar 
    McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).
    Google Scholar 
    Waring, K. M. et al. Modeling the impacts of two bark beetle species under a warming climate in the southwestern USA: ecological and economic consequences. Environ. Manag. 44, 824–835 (2009).
    Google Scholar 
    Barigah, T. S. et al. Water stress-induced xylem hydraulic failure is a causal factor of tree mortality in beech and poplar. Ann. Bot. 112, 1431–1437 (2013).
    Google Scholar 
    Guadagno, C. R. et al. Dead or alive? Using membrane failure and chlorophyll a fluorescence to predict plant mortality from drought. Plant Physiol. 175, 223–234 (2017).
    Google Scholar 
    Hammond, W. M. et al. Dead or dying? Quantifying the point of no return from hydraulic failure in drought-induced tree mortality. New Phytol. 223, 1834–1843 (2019).
    Google Scholar 
    Sapes, G. et al. Plant water content integrates hydraulics and carbon depletion to predict drought-induced seedling mortality. Tree Physiol. 39, 1300–1312 (2019).
    Google Scholar 
    Mantova, M., Menezes-Silva, P. E., Badel, E., Cochard, H. & Torres-Ruiz, J. M. The interplay of hydraulic failure and cell vitality explains tree capacity to recover from drought. Physiol. Plant. 172, 247–257 (2021).
    Google Scholar 
    Kono, Y. et al. Initial hydraulic failure followed by late-stage carbon starvation leads to drought-induced death in the tree Trema orientalis. Commun. Biol. 2, 8 (2019).
    Google Scholar 
    Preisler, Y. et al. Seeking the “point of no return” in the sequence of events leading to mortality of mature trees. Plant Cell Environ. 44, 1315–1328 (2020).
    Google Scholar 
    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).
    Google Scholar 
    Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).
    Google Scholar 
    McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Chang. 5, 669–672 (2015).
    Google Scholar 
    Stephenson, N. L. & van Mantgem, P. J. Forest turnover rates follow global and regional patterns of productivity. Ecol. Lett. 8, 524–531 (2005).
    Google Scholar 
    Zhu, K. C. et al. Dual impacts of climate change: forest migration and turnover through life history. Glob. Change Biol. 20, 251–264 (2014).
    Google Scholar 
    Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).
    Google Scholar 
    Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).
    Google Scholar 
    Hartmann, H. et al. Climate change risks to global forest health – emergence of unexpected events of elevated tree mortality world-wide. Annu. Rev. Plant Biol. https://doi.org/10.1146/annurev-arplant-102820-012804 (2022).Article 

    Google Scholar 
    Manion, P. D. Tree Disease Concepts (Prentice-Hall, 1981)Brodribb, T. J. Learning from a century of droughts. Nat. Ecol. Evol. 4, 1007–1008 (2020).
    Google Scholar 
    Anderegg, W. R. et al. Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674–683 (2015).
    Google Scholar 
    Huang, J. et al. Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling. New Phytol. 225, 26–36 (2019).
    Google Scholar 
    Martinez-Vilalta, J., Anderegg, W. R., Sapes, G. & Sala, A. Greater focus on water pools may improve our ability to understand and anticipate drought-induced mortality in plants. New Phytol. 223, 22–32 (2019).
    Google Scholar 
    Cuneo, I. F., Knipfer, T., Brodersen, C. R. & McElrone, A. J. Mechanical failure of fine root cortical cells initiates plant hydraulic decline during drought. Plant Physiol. 172, 1669–1678 (2016).
    Google Scholar 
    Johnson, D. M. et al. Co-occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought. Plant Cell Environ. 41, 576–588 (2018).
    Google Scholar 
    Cochard, H. A new mechanism for tree mortality due to drought and heatwaves. Peer Community J. 1, e36 (2021).
    Google Scholar 
    Duursma, R. A. et al. On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. New Phytol. 221, 693–705 (2019).
    Google Scholar 
    Beckett, R. P. Pressure–volume analysis of a range of poikilohydric plants implies the existence of negative turgor in vegetative cells. Ann. Bot. 79, 145–152 (1997).
    Google Scholar 
    Ding, Y., Zhang, Y., Zheng, Q. S. & Tyree, M. T. Pressure–volume curves: revisiting the impact of negative turgor during cell collapse by literature review and simulations of cell micromechanics. New Phytol. 203, 378–387 (2014).
    Google Scholar 
    Sperry, J. S., Adler, F. R., Campbell, G. S. & Comstock, J. P. Limitation of plant water use by rhizosphere and xylem conductance: results from a model. Plant Cell Environ. 21, 347–359 (1998).
    Google Scholar 
    Rodriguez-Dominguez, C. M. & Brodribb, T. J. Declining root water transport drives stomatal closure in olive under moderate water stress. New Phytol. 225, 126–134 (2020).
    Google Scholar 
    Carminati, A. & Javaux, M. Soil rather than xylem vulnerability controls stomatal response to drought. Trends Plant Sci. 25, 868–880 (2020).
    Google Scholar 
    Maseda, P. H. & Fernandez, R. J. Stay wet or else: three ways in which plants can adjust hydraulically to their environment. J. Exp. Bot. 57, 3963–3977 (2006).
    Google Scholar 
    Plaut, J. A. et al. Hydraulic limits preceding mortality in a piñon–juniper woodland under experimental drought. Plant Cell Environ. 35, 1601–1617 (2012).
    Google Scholar 
    Creek, D. et al. Xylem embolism in leaves does not occur with open stomata: evidence from direct observations using the optical visualization technique. J. Exp. Bot. 71, 1151–1159 (2020).
    Google Scholar 
    Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).
    Google Scholar 
    Hammond, W. M. & Adams, H. D. Dying on time: traits influencing the dynamics of tree mortality risk from drought. Tree Physiol. 39, 906–909 (2019).
    Google Scholar 
    Körner, C. No need for pipes when the well is dry — a comment on hydraulic failure in trees. Tree Physiol. 39, 695–700 (2019).
    Google Scholar 
    Machado, R. et al. Where do leaf water leaks come from? Trade-offs underlying the variability in minimum conductance across tropical savanna species with contrasting growth strategies. New Phytol. 229, 1415–1430 (2021).
    Google Scholar 
    Burghardt, M. & Riederer, M. in Biology of the Plant Cuticle (eds Riederer, M. & Müller, C.) 292–311 (Blackwell, 2006).Billon, L. M. et al. The DroughtBox: a new tool for phenotyping residual branch conductance and its temperature dependence during drought. Plant Cell Environ. 43, 1584–1594 (2020).
    Google Scholar 
    Wolfe, B. T. Bark water vapour conductance is associated with drought performance in tropical trees. Biol. Lett. 16, 20200263 (2020).
    Google Scholar 
    Martín-Gómez, P., Serrano, L. & Ferrio, J. P. Short-term dynamics of evaporative enrichment of xylem water in woody stems: implications for ecohydrology. Tree Physiol. 37, 511–522 (2017).
    Google Scholar 
    Arend, M. et al. Rapid hydraulic collapse as cause of drought-induced mortality in conifers. Proc. Natl Acad. Sci. USA 118, e2025251118 (2021).
    Google Scholar 
    Wang, W. et al. Mortality predispositions of conifers across western USA. New Phytol. 229, 831–844 (2020).
    Google Scholar 
    Christiansen, E., Waring, R. H. & Berryman, A. A. Resistance of conifers to bark beetle attack: searching for general relationships. For. Ecol. Manag. 22, 89–106 (1987).
    Google Scholar 
    Bigler, C., Bräker, O. U., Bugmann, H., Dobbertin, M. & Rigling, A. Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland. Ecosystems 9, 330–343 (2006).
    Google Scholar 
    Richardson, A. D. et al. Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees. New Phytol. 197, 850–861 (2013).
    Google Scholar 
    Meinzer, F. C. et al. Dynamics of water transport and storage in conifers studied with deuterium and heat tracing techniques. Plant Cell Environ. 29, 105–114 (2006).
    Google Scholar 
    McDowell, N. G., Allen, C. D. & Marshall, L. Growth, carbon-isotope discrimination, and drought-associated mortality across a Pinus ponderosa elevational transect. Glob. Change Biol. 16, 399–415 (2010).
    Google Scholar 
    Kane, J. M. & Kolb, T. E. Importance of resin ducts in reducing ponderosa pine mortality from bark beetle attack. Oecologia 164, 601–609 (2010).
    Google Scholar 
    Ferrenberg, S., Kane, J. M. & Mitton, J. B. Resin duct characteristics associated with tree resistance to bark beetles across lodgepole and limber pines. Oecologia 174, 1283–1292 (2014).
    Google Scholar 
    Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).
    Google Scholar 
    Muller, B., Pantin, F., Génard, M., Turc, O., Freixes, S., Piques, M. & Gibon, Y. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J. Exp. Bot. 62, 1715–1729 (2011).
    Google Scholar 
    Yu, S. Cellular and genetic responses of plants to sugar starvation. Plant Physiol. 121, 687–693 (1999).
    Google Scholar 
    Koster, K. L. & Leopold, A. C. Sugars and desiccation tolerance in seeds. Plant Physiol. 88, 829–832 (1988).
    Google Scholar 
    Sapes, G., Demaree, P., Lekberg, Y. & Sala, A. Plant carbohydrate depletion impairs water relations and spreads via ectomycorrhizal networks. New Phytol. 229, 3172–3183 (2021).
    Google Scholar 
    Hoekstra, F. A., Golovina, E. A. & Buitink, J. Mechanisms of plant desiccation tolerance. Trends Plant Sci. 6, 431–438 (2001).
    Google Scholar 
    Van den Ende, W. & Valluru, R. Sucrose, sucrosyl oligosaccharides, and oxidative stress: scavenging and salvaging? J. Exp. Bot. 60, 9–18 (2009).
    Google Scholar 
    Matros, A., Peshev, D., Peukert, M., Mock, H.-P. & Ende, W. Vden Sugars as hydroxyl radical scavengers: proof-of-concept by studying the fate of sucralose in Arabidopsis. Plant J. 82, 822–839 (2015).
    Google Scholar 
    Rolland, F., Baena-González, E. & Sheen, J. Sugar sensing and signaling in plants: conserved and novel mechanisms. Annu. Rev. Plant Biol. 57, 675–709 (2006).
    Google Scholar 
    Ramel, F., Sulmon, C., Bogard, M., Couée, I. & Gouesbet, G. Differential patterns of reactive oxygen species and antioxidative mechanisms during atrazine injury and sucrose-induced tolerance in Arabidopsis thaliana plantlets. BMC Plant Biol. 9, 28 (2009).
    Google Scholar 
    Fine, P. V. A. et al. The growth–defense trade-off and habitat specialization by plants in Amazonian forests. Ecology 87, S150–S162 (2006).
    Google Scholar 
    Huot, B., Yao, J., Montgomery, B. L. & He, S. Y. Growth–defense tradeoffs in plants: a balancing act to optimize fitness. Mol. Plant 7, 1267–1287 (2014).
    Google Scholar 
    Ouédraogo, D.-Y., Mortier, F., Gourlet-Fleury, S., Freycon, V. & Picard, N. Slow-growing species cope best with drought: evidence from long-term measurements in a tropical semi-deciduous moist forest of Central Africa. J. Ecol. 101, 1459–1470 (2013).
    Google Scholar 
    de la Mata, R., Hood, S. & Sala, A. Insect outbreak shifts the direction of selection from fast to slow growth rates in the long-lived conifer Pinus ponderosa. Proc. Natl Acad. Sci. USA 114, 7391–7396 (2017).
    Google Scholar 
    Roskilly, B., Keeling, E., Hood, S., Giuggiola, A. & Sala, A. Conflicting functional effects of xylem pit structure relate to the growth-longevity trade-off in a conifer species. Proc. Natl Acad. Sci. USA 116, 15282–15287 (2019).
    Google Scholar 
    Snyder, K. A. & Williams, D. G. Defoliation alters water uptake by deep and shallow roots of Prosopis velutina (Velvet Mesquite). Funct. Ecol. 17, 363–374 (2003).
    Google Scholar 
    Eyles, A., Pinkard, E. A. & Mohammed, C. Shifts in biomass and resource allocation patterns following defoliation in Eucalyptus globulus growing with varying water and nutrient supplies. Tree Physiol. 29, 753–764 (2009).
    Google Scholar 
    Hillabrand, R. M., Hacke, U. G. & Lieffers, V. J. Defoliation constrains xylem and phloem functionality. Tree Physiol. 39, 1099–1108 (2019).
    Google Scholar 
    Landhäusser, S. M. & Lieffers, V. J. Defoliation increases risk of carbon starvation in root systems of mature aspen. Trees 26, 653–661 (2012).
    Google Scholar 
    Poyatos, R., Aguadé, D., Galiano, L., Mencuccini, M. & Martínez-Vilalta, J. Drought-induced defoliation and long periods of near-zero gas exchange play a key role in accentuating metabolic decline of Scots pine. New Phytol. 200, 388–401 (2013).
    Google Scholar 
    Cardoso, A. A., Batz, T. A. & McAdam, S. A. Xylem embolism resistance determines leaf mortality during drought in Persea americana. Plant Physiol. 182, 547–554 (2020).
    Google Scholar 
    Mencuccini, M. et al. Leaf economics and plant hydraulics drive leaf:wood area ratios. New Phytol. 224, 1544–1556 (2019).
    Google Scholar 
    Cochard, H., Pimont, F., Ruffault, J. & Martin-St Paul, N. SurEau: a mechanistic model of plant water relations under extreme drought. Ann. Forest Sci. 78, 1–23 (2021).
    Google Scholar 
    Yin, M. C. & Blaxter, J. H. S. Temperature, salinity tolerance, and buoyancy during early development and starvation of Clyde and North Sea herring, cod, and flounder larvae. J. Exp. Mar. Biol. Ecol 107, 279–290 (1987).
    Google Scholar 
    Cahill, G. F. Jr. Fuel metabolism in starvation. Annu. Rev. Nutr. 26, 1–22 (2006).
    Google Scholar 
    Yandi, I. & Altinok, I. Irreversible starvation using RNA/DNA on lab-grown larval anchovy, Engraulis encrasicolus, and evaluating starvation in the field-caught larval cohort. Fish. Res. 201, 32–37 (2018).
    Google Scholar 
    Smith, A. M. & Stitt, M. Coordination of carbon supply and plant growth. Plant Cell Environ. 30, 1126–1149 (2007).
    Google Scholar 
    Schädel, C., Richter, A., Blöchl, A. & Hoch, G. Hemicellulose concentration and composition in plant cell walls under extreme carbon source–sink imbalances. Physiol. Plant. 139, 241–255 (2010).
    Google Scholar 
    Tsamir-Rimon, M. et al. Rapid starch degradation in the wood of olive trees under heat and drought is permitted by three stress-specific beta amylases. New Phytol. 229, 1398–1414 (2020).
    Google Scholar 
    McLoughlin, F. et al. Autophagy plays prominent roles in amino acid, nucleotide, and carbohydrate metabolism during fixed-carbon starvation in maize. Plant Cell 32, 2699–2724 (2020).
    Google Scholar 
    Quirk, J., McDowell, N. G., Leake, J. R., Hudson, P. J. & Beerling, D. J. Increased susceptibility to drought-induced mortality in Sequoia sempervirens (Cupressaceae) trees under Cenozoic atmospheric carbon dioxide starvation. Am. J. Bot. 100, 582–591 (2013).
    Google Scholar 
    Sevanto, S., Mcdowell, N. G., Dickman, L. T., Pangle, R. & Pockman, W. T. How do trees die? A test of the hydraulic failure and carbon starvation hypotheses. Plant Cell Environ. 37, 153–161 (2014).
    Google Scholar 
    Tomasella, M., Petrussa, E., Petruzzellis, F., Nardini, A. & Casolo, V. The possible role of non-structural carbohydrates in the regulation of tree hydraulics. Int. J. Mol. Sci. 21, 144 (2020).
    Google Scholar 
    Gaylord, M. L. et al. Drought predisposes piñon–juniper woodlands to insect attacks and mortality. New Phytol. 198, 567–578 (2013).
    Google Scholar 
    Dickman, L. T., McDowell, N. G., Sevanto, S., Pangle, R. E. & Pockman, W. T. Carbohydrate dynamics and mortality in a piñon-juniper woodland under three future precipitation scenarios. Plant Cell Environ. 38, 729–739 (2015).
    Google Scholar 
    Ruehr, N. K. et al. Drought effects on allocation of recent carbon: from beech leaves to soil CO2 efflux. New Phytol. 184, 950–961 (2009).
    Google Scholar 
    Mencuccini, M., Minunno, F., Salmon, Y., Martínez-Vilalta, J. & Hölttä, T. Coordination of physiological traits involved in drought-induced mortality of woody plants. New Phytol. 208, 396–409 (2015).
    Google Scholar 
    Hagedorn, F. et al. Recovery of trees from drought depends on belowground sink control. Nat. Plants 2, 16111 (2016).
    Google Scholar 
    Hesse, B. D., Goisser, M., Hartmann, H. & Grams, T. E. E. Repeated summer drought delays sugar export from the leaf and impairs phloem transport in mature beech. Tree Physiol. 39, 192–200 (2019).
    Google Scholar 
    Wiley, E., Hoch, G. & Landhäusser, S. M. Dying piece by piece: carbohydrate dynamics in aspen (Populus tremuloides) seedlings under severe carbon stress. J. Exp. Bot. 68, 5221–5232 (2017).
    Google Scholar 
    Weber, R. et al. Living on next to nothing: tree seedlings can survive weeks with very low carbohydrate concentrations. New Phytol. 218, 107–118 (2018).
    Google Scholar 
    Hasanuzzaman, M. & Tanveer, M. (eds) Salt and Drought Stress Tolerance in Plants: Signaling Networks and Adaptive Mechanisms (Springer, 2020)O’Brien, M. J., Leuzinger, S., Philipson, C. D., Tay, J. & Hector, A. Drought survival of tropical tree seedlings enhanced by non-structural carbohydrate levels. Nat. Clim. Chang. 4, 710–714 (2014).
    Google Scholar 
    Nardini, A. et al. Rooting depth, water relations and non-structural carbohydrate dynamics in three woody angiosperms differentially affected by an extreme summer drought. Plant Cell Environ. 39, 618–627 (2016).
    Google Scholar 
    Zinselmeier, C., Westgate, M. E., Schussler, J. R. & Jones, R. J. Low water potential disrupts carbohydrate metabolism in maize (Zea mays L.) ovaries. Plant Physiol. 107, 385–391 (1995).
    Google Scholar 
    Desprez-Loustau, M.-L., Marçais, B., Nageleisen, L.-M., Piou, D. & Vannini, A. Interactive effects of drought and pathogens in forest trees. Ann. For. Sci. 63, 597–612 (2006).
    Google Scholar 
    Oliva, J., Stenlid, J. & Martínez-Vilalta, J. The effect of fungal pathogens on the water and carbon economy of trees: implications for drought-induced mortality. New Phytol. 203, 1028–1035 (2014).
    Google Scholar 
    Kolb, T. et al. Drought-mediated changes in tree physiological processes weaken tree defenses to bark beetle attack. J. Chem. Ecol. 45, 888–900 (2019).
    Google Scholar 
    Croize, L., Lieutier, F., Cochard, H. & Dreyer, E. Effects of drought stress and high density stem inoculations with Leptographium wingfieldii on hydraulic properties of young Scots pine trees. Tree Physiol. 21, 427–436 (2001).
    Google Scholar 
    Wullschleger, S. D., McLaughlin, S. B. & Ayres, M. P. High-resolution analysis of stem increment and sap flow for loblolly pine trees attacked by southern pine beetle. Can. J. For. Res. 34, 387–2393 (2004).
    Google Scholar 
    Hubbard, R. M., Rhoades, C. C., Elder, K. & Negron, J. Changes in transpiration and foliage growth in lodgepole pine trees following mountain pine beetle attack and mechanical girdling. For. Ecol. Manag. 289, 312–317 (2013).
    Google Scholar 
    Manter, D. K. & Kavanagh, K. L. Stomatal regulation in Douglas fir following a fungal-mediated chronic reduction in leaf area. Trees 17, 485–491 (2003).
    Google Scholar 
    Lahr, E. L. & Sala, A. Sapwood stored resources decline in whitebark and lodgepole pines attacked by mountain pine beetles (Coleoptera: Curculionidae). Environ. Entomol. 45, 1463–1475 (2016).
    Google Scholar 
    Marler, T. E. & Cascasan, A. N. Carbohydrate depletion during lethal infestation of Aulacaspis yasumatsui on Cycas revoluta. Int. J. Plant Sci. 179, 497–504 (2018).
    Google Scholar 
    Hood, S. & Sala, A. Ponderosa pine resin defenses and growth: metrics matter. Tree Physiol. 35, 1223–1235 (2015).
    Google Scholar 
    Roth, M., Hussain, A., Cale, J. A. & Erbilgin, N. Successful colonization of lodgepole pine trees by mountain pine beetle increased monoterpene production and exhausted carbohydrate reserves. J. Chem. Ecol. 44, 209–214 (2018).
    Google Scholar 
    Raffa, K. F. et al. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. Bioscience 58, 501–517 (2008).
    Google Scholar 
    Seidl, R., Schelhaas, M. J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Chang. 4, 806–810 (2014).
    Google Scholar 
    Ryan, M. G., Sapes, G., Sala, A. & Hood, S. M. Tree physiology and bark beetles. New Phytol. 205, 955–957 (2015).
    Google Scholar 
    Huang, J. et al. Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling. New Phytol. 225, 26–36 (2020).
    Google Scholar 
    Goodsman, D. W., Lusebrink, I., Landhäusser, S. M., Erbilgin, N. & Lieffers, V. J. Variation in carbon availability, defense chemistry and susceptibility to fungal invasion along the stems of mature trees. New Phytol. 197, 586–594 (2013).
    Google Scholar 
    Wiley, E., Rogers, B. J., Hodgkinson, R. & Landhäusser, S. M. Nonstructural carbohydrate dynamics of lodgepole pine dying from mountain pine beetle attack. New Phytol. 209, 550–562 (2016).
    Google Scholar 
    Netherer, S. et al. Do water-limiting conditions predispose Norway spruce to bark beetle attack? New Phytol. 205, 1128–1141 (2015).
    Google Scholar 
    Rissanen, K. et al. Drought effects on carbon allocation to resin defences and on resin dynamics in old-grown Scots pine. Environ. Exp. Bot. 185, 104410 (2021).
    Google Scholar 
    Gershenzon, J. Metabolic costs of terpenoid accumulation in higher plants. J. Chem. Ecol. 20, 1281–1328 (1994).
    Google Scholar 
    Navarro, L. et al. DELLAs control plant immune responses by modulating the balance of jasmonic acid and salicylic acid signaling. Curr. Biol. 1, 650–655 (2008).
    Google Scholar 
    Fox, H. et al. Transcriptome analysis of Pinus halepensis under drought stress and during recovery. Tree Physiol. 38, 423–441 (2018).
    Google Scholar 
    Caretto, S., Linsalata, V., Colella, G., Mita, G. & Lattanzio, V. Carbon fluxes between primary metabolism and phenolic pathway in plant tissues under stress. Int. J. Mol. Sci. 16, 26378–26394 (2015).
    Google Scholar 
    Franceschi, V. R., Krokene, P., Christiansen, E. & Krekling, T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. New Phytol. 167, 353–376 (2005).
    Google Scholar 
    Suárez-Vidal, E. et al. Drought stress modifies early effective resistance and induced chemical defences of Aleppo pine against a chewing insect herbivore. Environ. Exp. Bot. 162, 550–559 (2019).
    Google Scholar 
    Hood, S., Sala, A., Heyerdahl, E. K. & Boutin, M. Low-severity fire increases tree defense against bark beetle attacks. Ecology 96, 1846–1855 (2015).
    Google Scholar 
    Zhao, S. & Erbilgin, N. Larger resin ducts are linked to the survival of lodgepole pine trees during mountain pine beetle outbreak. Front. Plant Sci. 10, 1459 (2019).
    Google Scholar 
    Kichas, N. E., Hood, S. M., Pederson, G. T., Everett, R. G. & McWethy, D. B. Whitebark pine (Pinus albicaulis) growth and defense in response to mountain pine beetle outbreaks. For. Ecol. Manag. 457, 117736 (2020).
    Google Scholar 
    Gaylord, M. L., Kolb, T. E. & McDowell, N. G. Mechanisms of piñon pine mortality after severe drought: a retrospective study of mature trees. Tree Physiol. 35, 806–816 (2015).
    Google Scholar 
    Anderegg, W. et al. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8, 367–371 (2015).
    Google Scholar 
    De Kauwe, M. G. et al. Identifying areas at risk of drought-induced tree mortality across South-Eastern Australia. Glob. Change Biol. 26, 5716–5733 (2020).
    Google Scholar 
    Sperry, J. S. et al. The impact of rising CO2 and acclimation on the response of US forests to global warming. Proc. Natl Acad. Sci. USA 116, 25734–25744 (2019).
    Google Scholar 
    Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytol. 149, 247–264 (2001).
    Google Scholar 
    Klein, T. & Ramon, U. Stomatal sensitivity to CO2 diverges between angiosperm and gymnosperm tree species. Funct. Ecol. 33, 1411–1424 (2019).
    Google Scholar 
    Paudel, I. et al. Elevated CO2 compensates for drought effects in lemon saplings via stomatal downregulation, increased soil moisture, and increased wood carbon storage. Environ. Exp. Bot. 148, 117–127 (2018).
    Google Scholar 
    Bobich, E. G., Barron-Gafford, G. A., Rascher, K. G. & Murthy, R. Effects of drought and changes in vapour pressure deficit on water relations of Populus deltoides growing in ambient and elevated CO2. Tree Physiol. 30, 866–875 (2010).
    Google Scholar 
    Gimeno, T. E., McVicar, T. R., O’Grady, A. P., Tissue, D. T. & Ellsworth, D. S. Elevated CO2 did not affect the hydrological balance of a mature native Eucalyptus woodland. Glob. Change Biol. 24, 3010–3024 (2018).
    Google Scholar 
    Nowak, R. S. et al. Elevated atmospheric CO2 does not conserve soil water in the mojave desert. Ecology 85, 93–99 (2004).
    Google Scholar 
    Schäfer, K. V., Oren, R., Lai, C. T. & Katul, G. G. Hydrologic balance in an intact temperate forest ecosystem under ambient and elevated atmospheric CO2 concentration. Glob. Change Biol. 8, 895–911 (2002).
    Google Scholar 
    Novick, K. A., Katul, G. G., McCarthy, H. R. & Oren, R. Increased resin flow in mature pine trees growing under elevated CO2 and moderate soil fertility. Tree Physiol. 32, 752–763 (2012).
    Google Scholar 
    Li, X. M. et al. Temperature alters the response of hydraulic architecture to CO2 in cotton plants (Gossypium hirsutum). Environ. Exp. Bot. 172, 104004 (2020).
    Google Scholar 
    Li, W. et al. The sweet side of global change–dynamic responses of non-structural carbohydrates to drought, elevated CO2 and nitrogen fertilization in tree species. Tree Physiol. 38, 1706–1723 (2018).
    Google Scholar 
    Duan, H. et al. Elevated [CO2] does not ameliorate the negative effects of elevated temperature on drought-induced mortality in Eucalyptus radiata seedlings. Plant Cell Environ. 37, 1598–1613 (2014).
    Google Scholar 
    Duan, H. et al. CO2 and temperature effects on morphological and physiological traits affecting risk of drought-induced mortality. Tree Physiol. 38, 1138–1151 (2018).
    Google Scholar 
    Zavala, J. A., Nabity, P. D. & DeLucia, E. H. An emerging understanding of mechanisms governing insect herbivory under elevated CO2. Annu. Rev. Entomol. 58, 79–97 (2013).
    Google Scholar 
    Kazan, K. Plant-biotic interactions under elevated CO2: a molecular perspective. Environ. Exp. Bot. 153, 249–261 (2018).
    Google Scholar 
    Gessler, A., Schaub, M. & McDowell, N. G. The role of nutrients in drought-induced tree mortality and recovery. New Phytol. 214, 513–520 (2017).
    Google Scholar 
    Mackay, D. S. et al. Interdependence of chronic hydraulic dysfunction and canopy processes can improve integrated models of tree response to drought. Water Resour. Res. 51, 6156–6176 (2015).
    Google Scholar 
    Mackay, D. S. et al. Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics. New Phytol. 225, 679–692 (2020).
    Google Scholar 
    Tai, X. et al. Plant hydraulic stress explained tree mortality and tree size explained beetle attack in a mixed conifer forest. J. Geophys. Res. Biogeosci. 124, 3555–3568 (2019).
    Google Scholar 
    Sala, A., Piper, F. & Hoch, G. Physiological mechanisms of drought-induced tree mortality are far from being resolved. New Phytol. 186, 274–281 (2010).
    Google Scholar 
    Limousin, J. M. et al. Regulation and acclimation of leaf gas exchange in a piñon–juniper woodland exposed to three different precipitation regimes. Plant Cell Environ. 36, 1812–1825 (2013).
    Google Scholar 
    Sorek, Y. et al. An increase in xylem embolism resistance of grapevine leaves during the growing season is coordinated with stomatal regulation, turgor loss point and intervessel pit membranes. New Phytol. 229, 1955–1969 (2021).
    Google Scholar 
    Hudson, P. J. et al. Impacts of long-term precipitation manipulation on hydraulic architecture and xylem anatomy of piñon and juniper in Southwest USA. Plant Cell Environ. 41, 421–435 (2018).
    Google Scholar 
    Warren, J. M., Norby, R. J. & Wullschleger, S. D. Elevated CO2 enhances leaf senescence during extreme drought in a temperate forest. Tree Physiol. 31, 117–130 (2011).
    Google Scholar 
    Matusick, G. et al. Chronic historical drought legacy exacerbates tree mortality and crown dieback during acute heatwave-compounded drought. Environ. Res. Lett. 13, 095002 (2018).
    Google Scholar 
    Shirley, H. L. Lethal high temperatures for conifers, and the cooling effect of transpiration. J. Agric. Res. 53, 239–258 (1936).
    Google Scholar 
    Fisher, R. A. & Koven, C. D. Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. J. Adv. Model. Earth Syst. 12, e2018MS001453 (2020).
    Google Scholar 
    Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).
    Google Scholar 
    Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 4, 598–604 (2014).
    Google Scholar 
    Nakamura, T. et al. Tree hazards compounded by successive climate extremes after masting in a small endemic tree, Distylium lepidotum, on subtropical islands in Japan. Glob. Change Biol 27, 5094–5108 (2021).
    Google Scholar 
    Hummel, I. et al. Arabidopsis plants acclimate to water deficit at low cost through changes of carbon usage: an integrated perspective using growth, metabolite, enzyme, and gene expression analysis. Plant Physiol. 154, 357–372 (2010).
    Google Scholar 
    Jamieson, M. A., Trowbridge, A. M., Raffa, K. F. & Lindroth, R. L. Consequences of climate warming and altered precipitation patterns for plant-insect and multitrophic interactions. Plant Physiol. 160, 1719–1727 (2012).
    Google Scholar 
    Mithöfer, A. & Boland, W. Plant defense against herbivores: chemical aspects. Annu. Rev. Plant Biol. 63, 431–450 (2012).
    Google Scholar 
    Netherer, S. et al. Interactions among Norway spruce, the bark beetle Ips typographus and its fungal symbionts in times of drought. J. Pest Sci. 94, 591–614 (2021).
    Google Scholar 
    Love, D. M. et al. Dependence of aspen stands on a subsurface water subsidy: implications for climate change impacts. Water Resour. Res. 55, 1833–1848 (2019).
    Google Scholar 
    McDowell, N. G. et al. Mechanisms of a coniferous woodland persistence under drought and heat. Environ. Res. Lett. 14, 045014 (2019).
    Google Scholar 
    Rozendaal, D. M. et al. Competition influences tree growth, but not mortality, across environmental gradients in Amazonia and tropical Africa. Ecology 101, e03052 (2020).
    Google Scholar 
    Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
    Google Scholar 
    CH2018 Project Team. CH2018 — climate scenarios for Switzerland. NCCS https://doi.org/10.18751/Climate/Scenarios/CH2018/1.0 (2018).Article 

    Google Scholar 
    McMaster, G. S. & Wilhelm, W. W. Growing degree-days: one equation, two interpretations. Agric. For. Meteorol. 87, 291–300 (1997).
    Google Scholar 
    McDowell, N. G. Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol. 155, 1051–1059 (2011).
    Google Scholar  More

  • in

    Dozens of unidentified bat species likely live in Asia — and could host new viruses

    NEWS
    29 March 2022

    Dozens of unidentified bat species likely live in Asia — and could host new viruses

    Study suggests some 40% of horseshoe bats in the region have yet to be formally described.

    Smriti Mallapaty

    Smriti Mallapaty

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    There could be more species of horseshoe bat than previously thought.Credit: Chien Lee/Nature Picture Library

    A genomic analysis suggests that there are probably dozens of unknown species of horseshoe bats in southeast Asia1. Horseshoe bats (Rhinolophidae) are considered the reservoir of many zoonotic viruses — which jump from animals to people — including the close relatives of the viruses that caused severe acute respiratory syndrome and COVID-19. Identifying bat species correctly might help pinpoint geographical hotspots with a high risk of zoonotic disease, says Shi Zhengli, a virologist at the Wuhan Institute of Virology in China. “This work is important,” she says. The study was published in Frontiers in Ecology and Evolution on 29 March.Better identification of unknown bat species could also support the search for the origins of SARS-CoV-2 by narrowing down where to look for bats that may harbour close relatives of the virus, says study co-author Alice Hughes, a conservation biologist at the University of Hong Kong. The closest known relatives of SARS-CoV-2 have been found in Rhinolophus affinis bats in Yunnan province, in southwestern China2, and in three species of horseshoe bat in Laos3.Cryptic speciesHughes wanted to better understand the diversity of bats in southeast Asia and find standardized ways of identifying them. So she and her colleagues captured bats in southern China and southeast Asia between 2015 and 2020. They took measurements and photographs of the bats’ wings and noseleaf — “the funky set of tissue around their nose”, as Hughes describes it — and recorded their echolocation calls. They also collected a tiny bit of tissue from the bats’ wings to extract genetic data.To map the bats’ genetic diversity, the team used mitochondrial DNA sequences from 205 of their captured animals, and another 655 sequences from online databases — representing a total of 11 species of Rhinolophidae. As a general rule, the greater the difference between two bats’ genomes, the more likely the animals represent genetically distinct groups, and therefore different species.The researchers found that each of the 11 species were probably actually multiple species, possibly including dozens of hidden species across the whole sample. Hidden, or ‘cryptic’, species are animals that seem to belong to the same species but are actually genetically distinct. For example, the genetic diversity of Rhinolophus sinicus suggests that the group could be six separate species. Overall, they estimated that some 40% of the species in Asia have not been formally described.“It’s a sobering number, but not terribly surprising,” says Nancy Simmons, a curator at the American Museum of Natural History in New York City. Rhinolophid bats are a complex group and there has been only a limited sampling of the animals, she says.However, relying on mitochondrial DNA could mean that the number of hidden species is an overestimate. That is because mitochondrial DNA is inherited only from the mother, so could be missing important genetic information, says Simmons. Still, the study could lead to a burst of research into naming new bat species in the region, she says.Further evidenceThe findings corroborate other genetic research suggesting that there are many cryptic species in southeast Asia, says Charles Francis, a biologist at the Canadian Wildlife Service, Environment and Climate Change Canada, in Ottawa, who studies bats in the region. But, he says, the estimates are based on a small number of samples.Hughes’ team used the morphological and acoustic data to do a more detailed analysis of 190 bats found in southern China and Vietnam and found that it supported their finding that many species had not been identified in those regions. The study makes a strong argument for “the use of multiple lines of evidence when delineating species”, says Simmons.Hughes says her team also found that the flap of tissue just above the bats’ nostrils, called the sella, could be used to identify species without the need for genetic data. Gábor Csorba, a taxonomist at the Hungarian Natural History Museum in Budapest, says this means that hidden species could be identified without doing intrusive morphology studies or expensive DNA analyses.

    doi: https://doi.org/10.1038/d41586-022-00776-2

    ReferencesChornelia, A., Jianmei, L. & Hughes, A. C. Front. Ecol. Evol. 10, 854509 (2022).Article 

    Google Scholar 
    Zhou, P. et al. Nature 579, 270–273 (2020).PubMed 
    Article 

    Google Scholar 
    Temmam, S. et al. Nature https://doi.org/10.1038/s41586-022-04532-4 (2022).PubMed 
    Article 

    Google Scholar 
    Download references

    Subjects

    SARS-CoV-2

    Virology

    Ecology

    Latest on:

    SARS-CoV-2

    Time is running out for COVID vaccine patent waivers
    Editorial 29 MAR 22

    Global vaccination must be swifter
    Comment 28 MAR 22

    A TMPRSS2 inhibitor acts as a pan-SARS-CoV-2 prophylactic and therapeutic
    Article 28 MAR 22

    Virology

    Time is running out for COVID vaccine patent waivers
    Editorial 29 MAR 22

    A TMPRSS2 inhibitor acts as a pan-SARS-CoV-2 prophylactic and therapeutic
    Article 28 MAR 22

    Global vaccination must be swifter
    Comment 28 MAR 22

    Ecology

    The marine biologist whose photography pastime became a profession
    Career Column 25 MAR 22

    Subaqueous foraging among carnivorous dinosaurs
    Article 23 MAR 22

    Where are Earth’s oldest trees? Far from prying eyes
    Research Highlight 22 MAR 22

    Jobs

    Co-Leader, Cancer Biology and Evolution Program

    H. Lee Moffitt Cancer Center & Research Institute
    Tampa, FL, United States

    Postdoctoral Position

    Schepens Eye Research Institute, MEEI
    Boston, MA, United States

    Assistant Professor in Medical Science

    Karolinska Institutet (KI)
    Stokholm, Sweden

    Postdoctoral fellowship in RNA biology and transcription in the Gregersen Group at Department of Cellular and Molecular Medicine (ICMM)

    University of Copenhagen (UCPH)
    Copenhagen, Denmark More