Ecology

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

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

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

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

Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Biodiversity Synthesis. (World Resources Institute, 2005).Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 0023 (2017).
Google Scholar 
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).
Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science (80-.). 353, 288–291 (2016).ADS 
CAS 

Google Scholar 
Verones, F., Moran, D., Stadler, K., Kanemoto, K. & Wood, R. Resource footprints and their ecosystem consequences. Sci. Rep. 7, 40743 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
United Nations. Transforming our World: the 2030 Agenda for Sustainable Development. A/RES/70/1 (United Nations, 2015).Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science (80-.). 366, eaax3100 (2019).Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Hellweg, S. & Milà i Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science (80-.). 344, 1109–1113 (2014).ADS 
CAS 

Google Scholar 
Chaudhary, A. & Brooks, T. M. National Consumption and Global Trade Impacts on Biodiversity. World Dev. 121, 178–187 (2019).
Google Scholar 
Pereira, H. M., Ziv, G. & Miranda, M. Countryside Species-Area Relationship as a Valid Alternative to the Matrix-Calibrated Species-Area Model. Conserv. Biol. 28, 874–876 (2014).PubMed 
PubMed Central 

Google Scholar 
Lomolino, M. V & Heaney, L. R. Frontiers of Biogeography: New Directions in the Geography of Nature. (Sinauer Associates Inc. Publishers, 2004).World Wildlife Fund. WildFinder: Online database of species distributions. http://www.worldwildlife.org/WildFinder (2006).BirdLife International. IUCN Red List for birds. http://www.birdlife.org (2019).IUCN. The IUCN Red List of Threatened Species. Version 2021-1 https://www.iucnredlist.org (2021).Curran, M. et al. Toward Meaningful End Points of Biodiversity in Life Cycle Assessment. Environ. Sci. Technol. 45, 70–79 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Woods, J. S. et al. Ecosystem quality in LCIA: status quo, harmonization, and suggestions for the way forward. Int. J. Life Cycle Assess. 23, 1995–2006 (2018).PubMed 
PubMed Central 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop.). 36, 1058–1069 (2013).
Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (Cop.). 43, 1261–1277 (2020).
Google Scholar 
Brummitt, R. K., Pando, F., Hollis, S. & Brummitt, N. A. World Geographical Scheme for Recording Plant Distributions. International Working Group on Taxonomic Databases (TDWG) https://www.tdwg.org/standards/wgsrpd/ (2001).GBIF. The Global Biodiversity Information Facility: What is GBIF? https://www.gbif.org/what-is-gbif (2021).Phillips, S. J., Dudík, M. & Schapire, R. E. Maxent software for modeling species niches and distributions (Version 3.4.0). http://biodiversityinformatics.amnh.org/open_source/maxent/ (2016).Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. Proc. Twenty-first Int. Conf. Mach. Learn. 655–662 (2004).Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography (Cop.). 40, 887–893 (2017).
Google Scholar 
Reddy, S. & Dávalos, L. M. Geographical sampling bias and its implications for conservation priorities in Africa. J. Biogeogr. 30, 1719–1727 (2003).
Google Scholar 
Hortal, J., Jiménez-Valverde, A., Gómez, J. F., Lobo, J. M. & Baselga, A. Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117, 847–858 (2008).
Google Scholar 
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Linn. Soc. 115, 522–531 (2015).
Google Scholar 
Feeley, K. J. & Silman, M. R. Keep collecting: accurate species distribution modelling requires more collections than previously thought. Divers. Distrib. 17, 1132–1140 (2011).
Google Scholar 
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).
Google Scholar 
ter Steege, H. et al. Hyperdominance in the Amazonian Tree Flora. Science (80-.). 342, 1243092 (2013).
Google Scholar 
Kuipers, K. J. J., Hellweg, S. & Verones, F. Potential Consequences of Regional Species Loss for Global Species Richness: A Quantitative Approach for Estimating Global Extinction Probabilities. Environ. Sci. Technol. 53, 4728–4738 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gade, A. L., Hauschild, M. Z. & Laurent, A. Globally differentiated effect factors for characterising terrestrial acidification in life cycle impact assessment. Sci. Total Environ. 761, 143280 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Géron, C. et al. Urban alien plants in temperate oceanic regions of Europe originate from warmer native ranges. Biol. Invasions 23, 1765–1779 (2021).
Google Scholar 
Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).PubMed 

Google Scholar 
Bachman, S., Moat, J., Hill, A., de la Torre, J. & Scott, B. Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool. Zookeys 150, 117–126 (2011).
Google Scholar 
Cardoso, P. red – an R package to facilitate species red list assessments according to the IUCN criteria. Biodivers. Data J. 5, e20530 (2017).
Google Scholar 
Lee, C. K. F., Keith, D. A., Nicholson, E. & Murray, N. J. Redlistr: tools for the IUCN Red Lists of ecosystems and threatened species in R. Ecography (Cop.). 42, 1050–1055 (2019).
Google Scholar 
Bachman, S., Walker, B., Barrios, S., Copeland, A. & Moat, J. Rapid Least Concern: towards automating Red List assessments. Biodivers. Data J. 8 (2020).POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. http://www.plantsoftheworldonline.org/ (2021).Chamberlain, S. et al. taxize: Taxonomic information from around the web. R package version 0.9.98. https://github.com/ropensci/taxize (2020).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://www.r-project.org/ (2021).ITIS. Integrated Taxonomic Information System. https://www.itis.gov/ (2021).Wickham, H. rvest: Easily Harvest (Scrape) Web Pages. R package version 0.3.5. https://cran.r-project.org/package=rvest (2019).Desmet, P. & Page, R. WGSRPD. GitHub repository https://github.com/tdwg/wgsrpd (2018).Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0. https://cran.r-project.org/package=rgbif (2021).GBIF. GBIF Occurrence Download. https://doi.org/10.15468/dl.uvd56q (2021).Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E. & Knutti, R. Climate change now detectable from any single day of weather at global scale. Nat. Clim. Chang. 10, 35–41 (2020).ADS 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://cran.r-project.org/package=raster (2019).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography (Cop.). 29, 773–785 (2006).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2006).
Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography (Cop.). 31, 161–175 (2008).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Anderson, R. P. & Raza, A. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J. Biogeogr. 37, 1378–1393 (2010).
Google Scholar 
Själander, M., Jahre, M., Tufte, G. & Reissmann, N. EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure. arXiv 1–4 (2019).Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.1-4. https://cran.r-project.org/package=dismo (2017).Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 

Google Scholar 
Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad, Dataset https://doi.org/10.5061/dryad.kd1d4 (2018).ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. http://maps.elie.ucl.ac.be/CCI/viewer/download.php (2017).Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography (Cop.). 38, 541–545 (2015).
Google Scholar 
Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. in 2nd International Symposium on Information Theory (eds. Petrov, B. N. & Csaki, F.) 267–281 (Akademia Kiado, 1973).Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).MathSciNet 
MATH 

Google Scholar 
Sugiura, N. Further analysts of the data by akaike’ s information criterion and the finite corrections. Commun. Stat. – Theory Methods 7, 13–26 (1978).MATH 

Google Scholar 
Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ 5, e3093 (2017).PubMed 
PubMed Central 

Google Scholar 
Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).
Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 

Google Scholar 
Moran, P. A. P. Notes on Continuous Stochastic Phenomena. Biometrika 37, 17 (1950).MathSciNet 
CAS 
MATH 

Google Scholar 
Borgelt, J., Sicacha-Parada, J., Skarpaas, O. & Verones, F. Native range estimates for red-listed vascular plants. Dryad, Dataset https://doi.org/10.5061/dryad.qbzkh18h9 (2022).Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).CAS 
PubMed 

Google Scholar 
Grau, J., Grosse, I. & Keilwagen, J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31, 2595–2597 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression. The Statistician 45 (Wiley, 2013).Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).
Google Scholar 
Sofaer, H. R., Hoeting, J. A. & Jarnevich, C. S. The area under the precision‐recall curve as a performance metric for rare binary events. Methods Ecol. Evol. 10, 565–577 (2019).
Google Scholar 
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).PubMed 

Google Scholar 
Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Br. 12, 662–666 (2017).
Google Scholar 
Rivers, M. C. Laburnum anagyroides. The IUCN Red List of Threatened Species 2017: e.T79919483A79919650 https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T79919483A79919650.en (2017).Botanic Gardens Conservation International Group & IUCN SSC Global Tree Specialist. Terminalia macrostachya. The IUCN Red List of Threatened Species 2019: e.T150118895A150118897 https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T150118895A150118897.en (2019).Heil, K., Terry, M. & Corral-Díaz, R. Mammillaria grahamii (amended version of 2013 assessment). The IUCN Red List of Threatened Species 2017: e.T152723A121546147 https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T152723A121546147.en (2017).Brooker, M. & Kleinig, D. Field Guide to Eucalypts. (Bloomings Books, 2006).Koopman, M. M. A synopsis of the Malagasy endemic genus Megistostegium Hochr. (Hibisceae, Malvaceae). Adansonia 33, 101–113 (2011).
Google Scholar 
World Conservation Monitoring Centre. Memecylon elegantulum. The IUCN Red List of Threatened Species 1998: e.T32597A9713234 https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32597A9713234.en (1998).Landrum, L. R. A revision of the Psidium salutare complex (Myrtaceae). SIDA, Contrib. to Bot. 20, 1449–1469 (2003).
Google Scholar 
Tropical Plants Database. Ken Fern. tropical.theferns.info https://tropical.theferns.info/viewtropical.php?id=Psidium+salutare (2021).Bernal, R., Gradstein, S. R. & Celis, M. Siparuna conica S.S.Renner & Hausner. Catálogo de plantas y líquenes de Colombia http://catalogoplantasdecolombia.unal.edu.co (2015).Renner, S. S. & Hausner, G. New Species of Siparuna (Monimiaceae) II. Seven New Species from Ecuador and Colombia. Missouri Bot. Gard. Press 6, 103–116 (1996).
Google Scholar 
Melendo, M., Giménez, E., Cano, E., Mercado, F. G. & Valle, F. The endemic flora in the south of the Iberian Peninsula: taxonomic composition, biological spectrum, pollination, reproductive mode and dispersal. Flora – Morphol. Distrib. Funct. Ecol. Plants 198, 260–276 (2003).
Google Scholar 
Chari, L. D., Martin, G. D., Steenhuisen, S.-L., Adams, L. D. & Clark, V. R. Biology of Invasive Plants 1. Pyracantha angustifolia (Franch.) C.K. Schneid. Invasive Plant Sci. Manag. 13, 120–142 (2020).
Google Scholar 
Sasidharan, N. Amomum pterocarpum Thwaites. India Biodiversity Portal https://indiabiodiversity.org/species/show/258864#habitat-and-distribution (2013).Contu, S. Amomum pterocarpum. The IUCN Red List of Threatened Species 2013: e.T44393013A44450020 https://doi.org/10.2305/IUCN.UK.2013-1.RLTS.T44393013A44450020.en (2013).Babyrose Devi, N., Das, A. & Singh, P. Amomum Pterocarpum (Zingiberaceae): a new record in the flora of Manipur. Int. J. Adv. Res. 6, 546–549 (2018).
Google Scholar 
Jetz, W., Sekercioglu, C. H. & Watson, J. E. M. Ecological correlates and conservation implications of overestimating species geographic ranges. Conserv. Biol. 22, 110–9 (2008).PubMed 

Google Scholar 
Gibbs, D. & Khela, S. Magnolia pugana. The IUCN Red List of Threatened Species 2014: e.T194806A2363344 https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T194806A2363344.en (2014).Sayer, C. Vallesia glabra. The IUCN Red List of Threatened Species 2015: e.T62543A72668627 https://doi.org/10.2305/IUCN.UK.2015-2.RLTS.T62543A72668627.en (2015).Sánchez Gómez, P., Stevens, D., Fennane, M., Gardner, M. & Thomas, P. Tetraclinis articulata. The IUCN Red List of Threatened Species 2011: e.T30318A9534227 https://doi.org/10.2305/IUCN.UK.2011-2.RLTS.T30318A9534227.en (2011).Article 

Google Scholar 
Stritch, L., Roy, S., Shaw, K. & Wilson, B. Corylus cornuta (errata version published in 2017). The IUCN Red List of Threatened Species 2016: e.T194448A115337731 https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T194448A2336319.en (2016).Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience 51, 933–938 (2001).
Google Scholar 
Rivers, M. C. Cotoneaster cambricus. The IUCN Red List of Threatened Species 2017: e.T102827479A102827485 https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T102827479A102827485.en (2017).RStudio Team. RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/package=rgdal (2019).Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R package version 0.9-5. https://cran.r-project.org/package=maptools/ (2019).Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). R package version 0.5-1. https://cran.r-project.org/package=rgeos (2019).Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R. (Springer New York, 2013).Phillips, S. J. & Elith, J. POC plots: calibrating species distribution models with presence-only data. Ecology 91, 2476–2484 (2010).PubMed 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl. Acad. Sci. 104, 13384–13389 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).PubMed 

Google Scholar  More

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

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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

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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 
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Our results show that the average diel activity patterns of Ae. aegypti populations in both Miami, FL and in Brownsville, TX were very similar; they both had two peaks, one in the early morning and the other in the evening, and the average host-seeking peaks are between 7:00 and 8:00 and between 19:00 and 20:00 (Fig. 4). Similar observations were previously reported by several investigators3,4,10,11,12 and the bimodal diel activity pattern is the most frequently reported for Ae. aegypti populations worldwide. However, variations between peak activity have been detected between populations. In East Africa, for instance, Trpis et al.3 reported peak activity at 7:00 and at 19:00, whereas McClelland10 reported peak activity two or three hours after sunrise (9:00 or 10:00) and one or two hours before sunset (17:00 or 16:00). Similarly, in the United States, Smith et al.7 observed a bimodal diel activity pattern for Ae. aegypti, but the evening peak was earlier, between 17:00 and 19:00. Despite these variations, the spacing of the peaks is similar in all these studies despite the fact that these studies were conducted in ecologically and climatically diverse locations.The activity patterns observed at site 3 in Brownsville (Fig. 2) and at site 1 in Miami (Fig. 1) were trimodal. In Brownsville, the trimodal activity peaks were between 6:30 and 7:30, 9:30 and 10:30, and 18:30 and 19:30 (Fig. 2), and in Miami the trimodal peaks were between 7:00 and 8:00, 9:00 and 10:00 and between 19:00 and 20:00 (Fig. 1). Interestingly, the timing of the third peak was similar in both Brownsville site 3 and Miami site 1 suggesting similar underlying factors despite geographic distance, different ecology, and different climate. Brownsville, Texas, is in the Lower Rio Grande Alluvial Floodplain ecoregion. The climate is humid subtropical and urbanization has removed most of the indigenous palm trees and floodplain forests vegetation (https://www.epa.gov/sites/default/files/2018-05/documents/brownsvilletx.pdf). Miami is in the Tropical Florida Ecoregion. Similar to Brownsville, Texas, urbanization and agriculture has replaced most of the indigenous Pine Rockland vegetation. Trimodal biting patterns for Ae. aegypti have been observed before in Trinidad by Chadee and Martinez4, but the middle peak was observed at 11:00 which is half an hour to an hour later than what we observed in Miami and Brownsville, respectively (Figs. 1 and 2). While the morning and evening peaks coincide with human outdoor activity, the middle peak occurs during high heat conditions and the factors that lead to this peak or its importance in the epidemiology of Ae. aegypti-borne arboviral diseases are currently not known. The studies by McClelland13 observed multiple activity peaks in an East African population of Ae. aegypti. The significance of the different activity patterns to the epidemiology of Ae. aegypti-borne arboviral diseases are currently unknown and we think they need more investigation especially since Ae. aegypti-borne arboviral infections have been rising in the recent past14,15.We observed that the host-seeking activity peaks were consistent between 5:45 and 7:30 and between 18:00 and 20:45 (Figs. 1 and 2). These observations are important in planning and conducting control operations directed at the adult Ae. aegypti female populations. During the 2016 Zika outbreak, there was no specific information on the host-seeking activity patterns of Ae. aegypti in Miami Dade County and the adulticide treatment implemented as part of an integrated approach targeted the morning activity16. The integrated approach effectively reduced the vector population and interrupted the transmission of the Zika virus; however, it highlighted the need for site-specific information on the diel activity patterns of Ae. aegypti in Miami Dade County in particular and the CONUS in general. There have been sporadic Ae. aegypti-borne arboviral disease outbreaks in Miami Dade County, FL and the city of Brownsville, TX17,18,19,20,21, in the future we will be better prepared to conduct effective adulticide applications with the current knowledge of the diel activity patterns of Ae. aegypti in these areas. Furthermore, we are now better equipped to educate the public on how to minimize exposure to Ae. aegypti-borne arboviral diseases by avoiding outdoor activities during peak biting activity periods.In our studies, we used BG-Sentinel 2 traps and monitored them every hour, twenty-four hours a day over 96 h, a method with some similarities to that used by Smith et al.7. In the past, diel biting activity studies were carried out using human landing catches following the methods primarily established by Haddow22. To our knowledge, only two studies have previously used sampling procedures not based on human landing catches to study the biting activity patterns of Ae. aegypti; the study by Ortega-Lopez et al.6 used mosquito electrocuting traps, and the study by Smith et al.7 used a mechanical rotator mosquito trap. In the present study, the use of BG-Sentinel II traps had the advantage that it was specifically designed to capture female host-seeking Ae. aegypti8,9. In addition, attached BG-Counter devices can keep track of the number of mosquitoes captured per specified unit time and environmental conditions, and store the information in a cloud server. However, the BG-Sentinel 2 traps collected a wide variety of mosquito species, (Table 1), and to keep track of specific species captured each hour, we had to monitor them every hour.Overall, we present data on the diel activity of Ae. aegypti populations in two cities in the southern United States. In both cities the activity patterns were bimodal; there were peaks of activity in the mornings and the evenings. The significance of these observations is that these peaks can be targeted to improve the effectiveness of adulticide treatments aimed at controlling Ae. aegypti adult populations. Using BG-Sentinel 2 traps eliminates individual variations associated with human landing catches and the associated danger of infections from wild mosquitoes especially during ongoing outbreaks. More