Ecology

Anderson, P. K. et al. Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19, 535–544 (2004).PubMed 

Google Scholar 
Brasier, C. M. The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).
Google Scholar 
Waage, J. K. & Mumford, J. D. Agricultural biosecurity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 863–876 (2008).CAS 
PubMed 

Google Scholar 
IPPC. Surveillance guide—A guide to understand the principal requirements of surveillance programmes for national plant protection organizations. Second edition. http://www.fao.org/documents/card/en/c/cb7139en (2021) https://doi.org/10.4060/cb7139en.Parnell, S., van den Bosch, F., Gottwald, T. & Gilligan, C. A. Surveillance to inform control of emerging plant diseases: An epidemiological perspective. Annu. Rev. Phytopathol. 55, 591–610 (2017).CAS 
PubMed 

Google Scholar 
Cunniffe, N. J., Cobb, R. C., Meentemeyer, R. K., Rizzo, D. M. & Gilligan, C. A. Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California. Proc. Natl. Acad. Sci. 113, 5640–5645 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gottwald, T. R., Dixon, W., Parnell, S. & Riley, T. Huanglongbing: The dragon arrives in the USA. In Huanglongbing-Greening International Workshop, July 14–21 13–14 (2006).Herms, D. A., Stone, A. K. & Chatfield, J. A. Emerald ash borer: The beginning of the end of ash in North America?. Ornam. Plants Annu. Rep. Res. Rev. 2003, 62–71 (2004).
Google Scholar 
Sansford, C. E. Pest Risk Analysis for Hymenoscyphus pseudoalbidus (anamorph Chalara fraxinea) for the UK and the Republic of Ireland. https://webarchive.nationalarchives.gov.uk/ukgwa/20140904094312mp_/http://www.fera.defra.gov.uk/plants/plantHealth/pestsDiseases/documents/hymenoscyphusPseudoalbidusPRA.pdf (2013).Alonso Chavez, V., Parnell, S. & van den Bosch, F. Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape. J. Theor. Biol. 407, 290–302 (2016).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Bourhis, Y., Gottwald, T. R., Lopez-Ruiz, F. J., Patarapuwadol, S. & van den Bosch, F. Sampling for disease absence-deriving informed monitoring from epidemic traits. J. Theor. Biol. 461, 8–16 (2019).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Mastin, A. J., van den Bosch, F., van den Berg, F. & Parnell, S. Quantifying the hidden costs of imperfect detection for early detection surveillance. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180261 (2019).
Google Scholar 
Mastin, A. J., van den Bosch, F., Gottwald, T. R., Alonso Chavez, V. & Parnell, S. R. A method of determining where to target surveillance efforts in heterogeneous epidemiological systems. PLoS Comput. Biol. 13, e1005712 (2017).PubMed 
PubMed Central 

Google Scholar 
Parnell, S., Gottwald, T. R., Gilks, W. R. & van den Bosch, F. Estimating the incidence of an epidemic when it is first discovered and the design of early detection monitoring. J. Theor. Biol. 305, 30–36 (2012).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Parnell, S., Gottwald, T. R., Cunniffe, N. J., Alonso Chavez, V. & van den Bosch, F. Early detection surveillance for an emerging plant pathogen: A rule of thumb to predict prevalence at first discovery. Proc. R. Soc. B Biol. Sci. 282, 20151478 (2015).
Google Scholar 
Silva, G. et al. Plant pest surveillance: From satellites to molecules. Emerg. Top. Life Sci. 5, 275–287 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mastin, A. J., Gottwald, T. R., van den Bosch, F., Cunniffe, N. J. & Parnell, S. Optimising risk-based surveillance for early detection of invasive plant pathogens. PLoS Biol. 18, e3000863 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martelli, G. P., Boscia, D., Porcelli, F. & Saponari, M. The olive quick decline syndrome in south-east Italy: A threatening phytosanitary emergency. Eur. J. Plant Pathol. 144, 235–243 (2015).
Google Scholar 
Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of DNA sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95, 668 (2013).
Google Scholar 
Ben Moussa, I. E. et al. Seasonal fluctuations of sap-feeding insect species infected by Xylella fastidiosa in Apulian olive groves of southern Italy. J. Econ. Entomol. 109, 1512–1518 (2016).PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa to grapevine by the meadow spittlebug. Phytopathology 106, 1285–1290 (2016).CAS 
PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa by naturally infected Philaenus spumarius (Hemiptera, Aphrophoridae) to different host plants. J. Appl. Entomol. 141, 80–87 (2017).
Google Scholar 
Saponari, M. et al. Infectivity and transmission of Xylella fastidiosa by Philaenus spumarius (Hemiptera: Aphrophoridae) in Apulia, Italy. J. Econ. Entomol. 107, 1316–1319 (2014).PubMed 

Google Scholar 
European Commission. Commission Implementing Regulation (EU) 2020/1201 of 14 August 2020 as regards measures to prevent the introduction into and the spread within the Union of Xylella fastidiosa (Wells et al.). (2021).EFSA et al. Guidelines for statistically sound and risk-based surveys of Xylella fastidiosa. EFSA. Support. Publ. 17, 1873 (2020).EFSA et al. General guidelines for statistically sound and risk-based surveys of plant pests. EFSA Support. Publ. 17, 1919E (2020).Bourhis, Y., Gottwald, T. & van den Bosch, F. Translating surveillance data into incidence estimates. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180262 (2019).CAS 

Google Scholar 
Cornara, D. et al. Spittlebugs as vectors of Xylella fastidiosa in olive orchards in Italy. J. Pest Sci. 90, 521–530 (2017).
Google Scholar 
Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: when an old acquaintance becomes a new threat to European agriculture. J. Pest Sci. 91, 957–972 (2018).
Google Scholar 
Almeida, R. P. P., Blua, M. J., Lopes, J. R. S. & Purcell, A. H. Vector transmission of Xylella fastidiosa: Applying fundamental knowledge to generate disease management strategies. Ann. Entomol. Soc. Am. 98, 775–786 (2005).
Google Scholar 
Purcell, A. H. & Finlay, A. H. Evidence for noncirculative transmission of Pierce’s disease bacterium by sharpshooter leafhoppers. Phytopathology 69, 393–395 (1979).
Google Scholar 
Hill, B. & Purcell, A. H. Acquisition and retention of Xylella fastidiosa by an efficient vector, Graphocephala atropunctata. Phytopathology 85, 209 (1995).
Google Scholar 
Hill, B. L. & Purcell, A. H. Multiplication and movement of Xylella fastidiosa within grapevine and four other plants. Phytopathology 85, 1368 (1995).
Google Scholar 
Huang, Q., Bentz, J. & Sherald, J. L. Fast, easy and efficient DNA extraction and one-step polymerase chain reaction for the detection of Xylella fastidiosa in potential insect vectors. J. Plant Pathol. 88, 77–81 (2006).CAS 

Google Scholar 
Harper, S. J., Ward, L. I. & Clover, G. R. G. Development of LAMP and real-time PCR methods for the rapid detection of Xylella fastidiosa for quarantine and field applications. Phytopathology 100, 1282–1288 (2010).CAS 
PubMed 

Google Scholar 
EFSA et al. Pest survey card on Xylella fastidiosa. EFSA Support. Publ. 16, (2019).Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 8723 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
EPPO. PM 7/24 (4) Xylella fastidiosa. EPPO Bull. 49, 175–227 (2019).Landa, B. B. et al. Emergence of a plant pathogen in Europe associated with multiple intercontinental introductions. Appl. Environ. Microbiol. 86, 1–15 (2019).
Google Scholar 
Castro, C., DiSalvo, B. & Roper, M. C. Xylella fastidiosa: A reemerging plant pathogen that threatens crops globally. PLoS Pathog. 17, e1009813 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in Apulia: Where we stand. Phytopathology 109, 175–186 (2019).CAS 
PubMed 

Google Scholar 
Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4, 432–439 (2018).CAS 
PubMed 

Google Scholar 
Gottwald, T. et al. Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control. Proc. Natl. Acad. Sci. 117, 3492–3501 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mendel, J., Furton, K. G. & Mills, D. An Evaluation of scent-discriminating canines for rapid response to agricultural diseases. HortTechnology 28, 102–108 (2018).
Google Scholar 
ECDC. Guidelines for the Surveillance of Invasive Mosquitoes in Europe. (2012).Kading, R. C., Golnar, A. J., Hamer, S. A. & Hamer, G. L. Advanced surveillance and preparedness to meet a new era of invasive vectors and emerging vector-borne diseases. PLoS Negl. Trop. Dis. 12, e0006761 (2018).PubMed 
PubMed Central 

Google Scholar 
Kumagai, L. B. et al. First report of Candidatus Liberibacter asiaticus associated with citrus huanglongbing in California. Plant Dis. 97, 283 (2013).CAS 
PubMed 

Google Scholar 
Ben Moussa, I. E. et al. Evaluation of “Spy Insect” approach for monitoring Xylella fastidiosa in symptomless olive orchards in the Salento peninsula (Southern Italy). IOBC WPRS Bull. 121, 77–84 (2017).
Google Scholar 
Cruaud, A. et al. Using insects to detect, monitor and predict the distribution of Xylella fastidiosa: A case study in Corsica. Sci. Rep. 8, 15628 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yaseen, T. et al. On-site detection of Xylella fastidiosa in host plants and in “spy insects” using the real-time loop-mediated isothermal amplification method. Phytopathol. Mediterr. https://doi.org/10.14601/Phytopathol_Mediterr-15250 (2015).Article 

Google Scholar 
López-Mercadal, J. et al. Collection of data and information in Balearic Islands on biology of vectors and potential vectors of Xylella fastidiosa (GP/EFSA/ALPHA/017/01). EFSA Support. Publ. 18, 6925E (2021).
Google Scholar 
Cunty, A. Detection, identification and surveillance of Xylella fastidiosa on vectors in France https://zenodo.org/record/3551122#.XjGqBs77SUl. (2019) https://doi.org/10.5281/zenodo.3551122.Kottelenberg, D., Hemerik, L., Saponari, M. & van der Werf, W. Shape and rate of movement of the invasion front of Xylella fastidiosa spp. pauca in Puglia. Sci. Rep. 11, 1061 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Sampling, DNA preparation and sequencingStockholmSamples LOW002, LOW003, LOW006, LOW007, LOW008 and PON012 were processed at the Archaeological Research Laboratory at Stockholm University, Sweden, following methods previously described8. In brief, this involved extracting DNA by incubating the bone powder for 24 h at 37 °C in 1.5 ml of digestion buffer (0.45 M EDTA (pH 8.0) and 0.25 mg ml–1 proteinase K), concentrating supernatant on Amicon Ultra-4 (30-kDa molecular weight cut-off (MWCO)) filter columns (MerckMillipore) and purifying on Qiagen MinElute columns. Double-stranded Illumina libraries were prepared using the protocol outlined in ref. 48, with the inclusion of USER enzyme and the modifications described in ref. 49.Samples 367, PDM100, Taimyr-1 and Yana-1 were processed at the Swedish Museum of Natural History in Stockholm, Sweden, following previously described methods8. In brief, this involved extracting DNA using a silica-based method with concentration on Vivaspin filters (Sartorius), according to a protocol optimized for recovery of ancient DNA50. Double-stranded Illumina libraries were prepared using the protocol outlined in ref. 48, with the inclusion of USER enzyme.Samples ALAS_024, VAL_033, ALAS_016, VAL_008, HMNH_007, HMNH_011, VAL_050, VAL_005, DS04, VAL_037, VAL_012, VAL_011, VAL_18A, IN18_016 and IN18_005 were processed at the Swedish Museum of Natural History in Stockholm, Sweden, following previously described methods for permafrost bone and tooth samples51. In brief, this involved DNA extraction using the methodology of ref. 52 and double-stranded Illumina library preparation as described in ref. 48, with dual unique indexes and the inclusion of USER enzyme. Between eight and ten separate PCR reactions with unique indexes were carried out for each sample to maximize library complexity. The libraries were sequenced alongside samples HOV4, AL2242, AL2370, AL2893, AL3272 and AL3284 across three Illumina NovaSeq 6000 lanes with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.PotsdamSamples JAL48, JAL65, JAL69, JAL358, AH574, AH575 and AH577 were processed at the University of Potsdam. Pre-amplification steps (DNA extraction and library preparation) were conducted in separated laboratory rooms specially equipped for the processing of ancient DNA. Amplification and post-amplification steps were performed in different laboratory rooms. DNA was extracted from bone powder (29–54 mg) following a protocol specially adapted to recover short DNA fragments52. Single-stranded double-indexed libraries were built from 20 µl of DNA extract according to the protocol in ref. 53. The libraries were sequenced on an HiSeq X platform at SciLifeLab in Stockholm.Tübingen/JenaSamples JK2174, JK2175, JK2179, JK2181, JK2183, TU144, TU148, TU839 and TU840 were processed at the University of Tübingen, with DNA extraction and pre-amplification steps undertaken in clean room facilities and post-amplification steps performed in a separate DNA laboratory. Both laboratories fulfil standards for work with ancient DNA54,55. All surfaces of tooth and bone samples were initially UV irradiated for 30 min, to minimize the potential risk of modern DNA contamination. Subsequently, DNA was extracted by applying a well-established guanidine silica-based protocol for ancient samples52. Illumina sequencing libraries were prepared by using 20 µl of DNA extract per library48; afterwards, dual barcodes (indexes) were chemically added to the prime ends of the libraries56. For the samples from Auneau (TU839 and TU840), five sequencing libraries each were prepared; for all other samples processed in Tübingen, three sequencing libraries each were prepared. To detect potential contamination of the chemicals, negative controls were conducted for extraction and library preparation. After preparation of the sequencing libraries, DNA concentration was measured with qPCR (Roche LightCycler) using corresponding primers48. The DNA concentration was given by the copy number of the DNA fragments in 1 µl of the sample.Amplification of the indexed sequencing libraries was performed using Herculase II Fusion under the following conditions: 1× Herculase II buffer, 0.4 µM IS5 primer and 0.4 µM IS6 primer48, Herculase II Fusion DNA polymerase (Agilent Technologies), 0.25 mM dNTPs (100 mM; 25 mM each dNTP) and 0.5–4 µl barcoded library as template in a total reaction volume of 100 µl. The applied amplification thermal profile was processed as follows: initial denaturation for 2 min at 95 °C; denaturation for 30 s at 95 °C, annealing for 30 s at 60 °C and elongation for 30 s at 72 °C for 3 to 20 cycles; and a final elongation step for 5 min at 72 °C. Thereafter, the amplified DNA was purified using a MinElute purification step and DNA was eluted in 20 µl TET. The concentration of the amplified DNA sequencing libraries was measured using a Bioanalyzer (Agilent Technologies) and a DNA1000 lab chip from Agilent Technologies.The sequencing libraries were sequenced on an Illumina HiSeq 4000 platform at the Max Planck Institute for Science of Human History in Jena. The samples from Auneau (TU839 and TU840) were paired-end sequenced applying 2 × 50 + 8 + 8 cycles. All other libraries prepared in Tübingen were single-end sequenced using 75 + 8 + 8 cycles.OxfordSamples AL2657, AL2541, AL2741, AL2744, AL3185, AL2350, CH1109, AL2370, AL3272 and AL3284 were processed at the dedicated ancient DNA facility at the PalaeoBARN laboratory at the University of Oxford, following methods described previously8. In brief, double-stranded libraries were constructed following the protocol in ref. 48. These libraries were sequenced on a HiSeq 2500 (AL2657, AL2541, AL2741, AL2744) or a HiSeq 4000 (AL3185, AL2350, CH1109) instrument at the Danish National Sequencing Center or on a NextSeq 550 instrument (AL2741) at the Natural History Museum of London. For samples AL2370, AL3272 and AL3284, between six and eight separate PCR reactions with unique indexes were carried out on their libraries and they were sequenced alongside samples HOV4, VAL_18A and IN18_016 on an Illumina NovaSeq 6000 lane with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.CopenhagenSamples CGG13, CGG17, CGG19, CGG20, CGG21, CGG25, CGG26, CGG27, CGG28, CGG34, Tumat1 and IRK were processed at the GLOBE Institute, University of Copenhagen. All pre-PCR work was performed in ancient DNA facilities following ancient DNA guidelines57. The details of extraction, library construction and sequencing for the samples with CGG codes are described in ref. 21, in relation to the publication of mitochondrial data from these specimens. The Tumat1 sample was processed following the exact same protocol. In brief, DNA extraction was performed using a buffer containing urea, EDTA and proteinase K50, double-stranded libraries were prepared with NEBNext DNA Sample Prep Master MixSet 2 (E6070S, New England Biolabs) and Illumina-specific adaptors48, and sequencing was performed on an Illumina HiSeq 2500 platform using 100-bp single-read chemistry. For the IRK sample, DNA was extracted from three subsamples and purified as described in ref. 21. The three DNA extracts and the purified pre-digest of one subsample were incorporated into double-stranded libraries following the BEST protocol58, with the modifications described in ref. 59, and sequenced on a BGISEQ-500 platform using 100-bp single-read chemistry.Santa CruzSamples SC19.MCJ017, SC19.MCJ015, SC19.MCJ010 and SC19.MCJ014 were processed at the UCSC Paleogenomics Lab and were provided by the Yukon Government Paleontology program. All pre-PCR work was performed in a dedicated ancient DNA facility at the University of California, Santa Cruz, following standard ancient DNA methods60. Subsamples (250–350 mg) were sent to the UCI KECK AMS facility for radiocarbon dating, and the remaining amounts were powdered in a Retsch MM400 for extraction. For each sample, ~100 mg of powder was treated with a 0.5% sodium hypochlorite solution before extraction to remove surface contaminants61 and then combined with 1 ml lysis buffer for extraction, following the protocol in ref. 52. Samples were processed in parallel with a negative control. We quantified the extracts using a Qubit 1× dsDNA HS Assay kit (Q33231) before preparing libraries. We prepared single-stranded libraries following the protocol in ref. 62 and amplified the libraries for 9–16 cycles as informed by qPCR. After amplification, we cleaned the libraries using a 1.2× SPRI bead solution and pooled them to an equimolar ratio for in-house shallow quality-control sequencing on a NextSeq 550 paired-end 75-bp run. We then sent the libraries to Fulgent Genetics for deeper sequencing on two paired-end 150-bp lanes on a HiSeq X instrument.ViennaSample HOV4 was processed at the Department of Anthropology, University of Vienna. The sample is a canine tooth, which after sequencing was determined to derive from a dhole (Cuon alpinus). DNA was extracted from its cementum using the methods described in ref. 63 with a modified incubation time of ~18 h. The library was prepared according to the protocol in ref. 48 with the modifications from ref. 64. Five separate PCR reactions with unique indexes were carried out on the library and were sequenced alongside samples VAL_18A, IN18_016, AL2242, AL2370, AL2893, AL3272 and AL3284 on an Illumina NovaSeq 6000 lane with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.An overview of all samples and their associated metadata is available in Supplementary Data 1.Genome sequence data processingFor paired-end data, read pairs were merged and adaptors were trimmed using SeqPrep (https://github.com/jstjohn/SeqPrep), discarding reads that could not be successfully merged. Reads were mapped to the dog reference genome canFam3.1 using BWA aln (v.0.7.17)65 with permissive parameters, including a disabled seed (-l 16500 -n 0.01 -o 2). Duplicates were removed by keeping only one read from any set of reads that had the same orientation, length and start and end coordinates. For sample Taimyr-1, previously published data13 were merged with newly generated data. Data from samples processed in Copenhagen were processed as described previously66 except that they were also mapped to canFam3.1. Post-mortem damage was quantified using PMDtools (v0.60)67 with the ‘–first’ and ‘–CpG’ arguments.Genotyping and integration with previously published genomesTo construct a comparative dataset for population genetic analyses, we started from a published variant call set compiling 722 modern dog, wolf and other canid genomes from multiple previous studies (NCBI BioProject accession PRJNA448733)40. To this, we added additional modern whole genomes from other studies: 4 African golden wolves and 15 Nigerian village dogs (Genome Sequence Archive (http://gsa.big.ac.cn/), accession PRJCA000335)68, 12 Scandinavian wolves (European Nucleotide Archive accession PRJEB20635)69, 9 North American wolves and coyotes (European Nucleotide Archive accession PRJNA496590)25 and 8 other canids (African hunting dog, dhole, Ethiopian wolf, golden jackal, Middle Eastern grey wolves) (European Nucleotide Archive accession PRJNA494815)22. Reads from these genomes were mapped to the dog reference genome using bwa mem (version 0.7.15)70, marked for duplicates using Picard Tools (v2.21.4) (http://broadinstitute.github.io/picard), genotyped at the sites present in the above dataset using GATK HaplotypeCaller (v3.6)71 with the ‘-gt_mode GENOTYPE_GIVEN_ALLELES’ argument and then merged into the dataset using bcftools merge (http://www.htslib.org/). The following filters were then applied to sites and genotypes across the full dataset: sites with excess heterozygosity (bcftools fill-tags ‘ExcHet’ P value 5.8×. For divergence time analyses, haploid X chromosomes from two different male genomes were combined and the point at which the inferred effective population size for this ‘pseudodiploid’ chromosome increased sharply upwards was taken to correspond to a population divergence. Results were scaled using a mutation rate of 0.4 × 10−8 mutations per site per generation13,87 (with a 25% lower rate for X-chromosome analyses) and a mean generational interval of 3 years13. For effective population size inferences, transition variants were ignored and results were scaled using a transversions-only mutation rate inferred from results on modern genomes. For more details on the MSMC2 analyses, see Supplementary Information section 3.Selection analysesSelection analysis was performed using PLINK (v1.90b5.2)88. This analysis used the 72 ancient wolf genomes and 68 modern wolf genomes (with the latter including a historical Japanese wolf genome73 treated as ancient for analysis purposes, with its age set to 200 bp). A list of the genomes used for this analysis is available in Supplementary Data 2 (“Used for selection scan” column). All SNPs, not only transversions, were used for this analysis. The age of each wolf was set as the phenotype, with values of 0 for modern wolves, and the ‘–linear’ argument was used to test for an association between SNP genotypes and age, also applying the ‘–adjust’ argument to correct P values using genomic control. The application of genomic control34 here aimed to use the magnitude of temporal allele frequency variance observed across the genome to account for what was observed from genetic drift alone given wolf demographic history. Only results for the following sets of sites were retained and included in the Manhattan plot: sites where at least 40 ancient genomes had a genotype call, sites with a minor allele frequency among the ancient wolves of ≥5% and sites that had at least 7 neighbouring sites within a 50-kb window with a P value that was at least 90% as large (on a log10 scale) as the P value of the site itself. The last ‘neighbourhood filter’ aimed to reduce false positives by requiring similar evidence across multiple nearby sites. As a P-value significance cut-off to correct for the genome-wide testing, we used 5 × 10−8, which is commonly used in genome-wide association studies in humans and also in dogs89. We excluded 15 regions where only a single variant reached significance. A detailed table with the 24 detected regions is available in Supplementary Data 3. To test the robustness of this analysis to false positives arising from genetic drift alone, we applied the same analysis to data from neutral coalescent simulations generated using ms90 and found no false positives. For more details, see Supplementary Information section 4.Ancestry modelling with qpAdm and qpWaveWe used the qpAdm and qpWave methods43 from ADMIXTOOLS (v5.0)84 to test ancestry models for wolf and dog targets postdating 23 ka. For the primary analyses, we used the following set of candidate source populations (age estimate in brackets, years bp): Armenia_Hovk1.HOV4 (ancient dhole), Siberia_UlakhanSular.LOW008 (70,772), Germany_Aufhausener.AH575 (57,233), Siberia_BungeToll.CGG29 (48,210), Germany_HohleFels.JK2183 (32,366), Siberia_BelayaGora.IN18_016 (32,020), Yukon_QuartzCreek.SC19.MCJ010 (29,943), Altai_Razboinichya.AL2744 (28,345), Siberia_BelayaGora.IN18_005 (18,148) and Germany_HohleFels.JK2179 (13,229). We used a rotating approach in which, for each target, we tested all possible one-, two- and three-source models that could be enumerated from the above set. Individuals from the set that were not used as a source in a given model served as thereference set (or the ‘right’ population in the qpAdm framework). This means that, in every model, each of the above individuals was always either in the source list or in the reference list. We ranked models on the basis of their P values, but prioritized models with fewer sources using a P-value threshold of 0.01: if a simpler model (meaning a model with fewer sources) had a P value above this threshold, it ranked above a more complex model (meaning a model with more sources) regardless of the P value of the latter. We also failed models with inferred ancestry proportions larger than 1.1 or smaller than −0.1. For single-source models, qpWave was run instead of qpAdm. Both programs were run with the ‘allsnps: YES’ option (without this option, there was very little power to reject models). We describe ancestry assigned to the ancient dhole source (Armenia_Hovk1.HOV4) as ‘unsampled’ ancestry; note that this does not imply that such ancestry is of non-wolf origin, only that it is not represented by (that is, diverged early from and lacks shared genetic drift with) the ancient wolf genomes in the reference set.To test whether any post-23 ka or modern wolf genome available might be a good proxy for the western Eurasian wolf-related ancestry identified in Near Eastern and African dogs, we added the 9,500-year-old Zhokhov dog17 to the rotating set of candidate source populations. Chosen for its high coverage, early date and easterly location, this makes the assumption that the Zhokhov dog is a good representative for the eastern dog ancestry component. Using the African Basenji dog as a target, models involving the Zhokhov dog plus another given wolf thus allowed us to test whether that wolf was a good match for the additional component of ancestry. For more details on the qpAdm and qpWave analyses, see Supplementary Information sections 2 (wolf targets) and  5 (dog targets).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

Misra, V. K. & Draper, D. E. On the role of magnesium ions in RNA stability. Biopolymers 48, 113–135 (1998).CAS 
PubMed 
Article 

Google Scholar 
Apell, H.-J., Hitzler, T. & Schreiber, G. Modulation of the Na, K-ATPase by magnesium ions. Biochemistry 56, 1005–1016 (2017).CAS 
PubMed 
Article 

Google Scholar 
Iseri, L. T. & French, J. H. Magnesium: Nature’s physiologic calcium blocker. Am. Heart J. 108, 188–193 (1984).CAS 
PubMed 
Article 

Google Scholar 
Rubin, H. Central role for magnesium in coordinate control of metabolism and growth in animal cells. Proc. Natl. Acad. Sci. USA 72, 3551–3555 (1975).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Baaij, J. H. F., Hoenderop, J. G. J. & Bindels, R. J. M. Magnesium in man: Implications for health and disease. Physiol. Rev. 95, 1–46 (2015).PubMed 
Article 
CAS 

Google Scholar 
Association, A. D. Diagnosis and classification of diabetes mellitus. Diabetes Care 37, S81–S90 (2014).Article 

Google Scholar 
Chatterjee, S., Khunti, K. & Davies, M. J. Type 2 diabetes. Lancet 389, 2239–2251 (2017).CAS 
PubMed 
Article 

Google Scholar 
Gommers, L. M. M., Hoenderop, J. G. J., Bindels, R. J. M. & de Baaij, J. H. F. Hypomagnesemia in type 2 diabetes: A vicious circle?. Diabetes 65, 3–13 (2016).CAS 
PubMed 
Article 

Google Scholar 
Pham, P.-C.T., Pham, P.-M.T., Pham, S. V., Miller, J. M. & Pham, P.-T.T. Hypomagnesemia in patients with type 2 diabetes. Clin. J. Am. Soc. Nephrol. 2, 366–373 (2007).CAS 
PubMed 
Article 

Google Scholar 
Mather, H. M. et al. Hypomagnesaemia in diabetes. Clin. Chim. Acta 95, 235–242 (1979).CAS 
PubMed 
Article 

Google Scholar 
Hubbard, S. R. Crystal structure of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog. EMBO J. 16, 5572–5581 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kurstjens, S. et al. Determinants of hypomagnesemia in patients with type 2 diabetes mellitus. Eur. J. Endocrinol. 176, 11–19 (2017).CAS 
PubMed 
Article 

Google Scholar 
Viering, D. H. H. M., de Baaij, J. H. F., Walsh, S. B., Kleta, R. & Bockenhauer, D. Genetic causes of hypomagnesemia, a clinical overview. Pediatr. Nephrol. 32, 1123–1135 (2017).PubMed 
Article 

Google Scholar 
Peacock, J. M. et al. Serum magnesium and risk of sudden cardiac death in the Atherosclerosis Risk in Communities (ARIC) Study. Am. Heart J. 160, 464–470 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Veronese, N. et al. Effect of magnesium supplementation on glucose metabolism in people with or at risk of diabetes: A systematic review and meta-analysis of double-blind randomized controlled trials. Eur. J. Clin. Nutr. 70, 1354–1359 (2016).CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Morán, M., Simental-Mendía, L. E., Gamboa-Gómez, C. I. & Guerrero-Romero, F. Oral magnesium supplementation and metabolic syndrome: A randomized double-blind placebo-controlled clinical trial. Adv. Chronic Kidney Dis. 25, 261–266 (2018).PubMed 
Article 

Google Scholar 
Grigoryan, R. et al. Multi-collector ICP-mass spectrometry reveals changes in the serum Mg isotopic composition in diabetes type I patients. J. Anal. At. Spectrom. 34, 1514–1521 (2019).CAS 
Article 

Google Scholar 
Bigeleisen, J. & Mayer, M. G. Calculation of equilibrium constants for isotopic exchange reactions. J. Chem. Phys. 15, 261–267 (1947).ADS 
CAS 
Article 

Google Scholar 
Bigeleisen, J. The relative reaction velocities of isotopic molecules. J. Chem. Phys. 17, 675–678 (1949).ADS 
CAS 
Article 

Google Scholar 
McKeegan, K. D. et al. Isotopic compositions of cometary matter returned by stardust. Science 314, 1724–1728 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jouzel, J. et al. Vostok ice core: A continuous isotope temperature record over the last climatic cycle (160,000 years). Nature 329, 403–408 (1987).ADS 
CAS 
Article 

Google Scholar 
Albarède, F., Télouk, P. & Balter, V. Medical applications of isotope metallomics. Rev. Mineral. Geochem. 82, 851–885 (2017).Article 
CAS 

Google Scholar 
Balter, V. et al. Natural variations of copper and sulfur stable isotopes in blood of hepatocellular carcinoma patients. PNAS 112, 982–985 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Télouk, P. et al. Copper isotope effect in serum of cancer patients. A pilot study. Metallomics 7, 299–308 (2015).PubMed 
Article 
CAS 

Google Scholar 
Lobo, L. et al. Elemental and isotopic analysis of oral squamous cell carcinoma tissues using sector-field and multi-collector ICP-mass spectrometry. Talanta 165, 92–97 (2017).CAS 
PubMed 
Article 

Google Scholar 
Costas-Rodríguez, M. et al. Body distribution of stable copper isotopes during the progression of cholestatic liver disease induced by common bile duct ligation in mice. Metallomics 11, 1093–1103 (2019).PubMed 
Article 
CAS 

Google Scholar 
Lamboux, A. et al. The blood copper isotopic composition is a prognostic indicator of the hepatic injury in Wilson disease. Metallomics 12, 1781–1790 (2020).CAS 
PubMed 
Article 

Google Scholar 
Moynier, F., Creech, J., Dallas, J. & Le Borgne, M. Serum and brain natural copper stable isotopes in a mouse model of Alzheimer’s disease. Sci. Rep. 9, 11894 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sauzéat, L. et al. Isotopic evidence for disrupted copper metabolism in amyotrophic lateral sclerosis. iScience 6, 264–271 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Krayenbuehl, P.-A., Walczyk, T., Schoenberg, R., von Blanckenburg, F. & Schulthess, G. Hereditary hemochromatosis is reflected in the iron isotope composition of blood. Blood 105, 3812–3816 (2005).CAS 
PubMed 
Article 

Google Scholar 
Anoshkina, Y. et al. Iron isotopic composition of blood serum in anemia of chronic kidney disease. Metallomics 9, 517–524 (2017).CAS 
PubMed 
Article 

Google Scholar 
Morgan, J. L. L. et al. Rapidly assessing changes in bone mineral balance using natural stable calcium isotopes. Proc. Natl. Acad. Sci. USA 109, 9989–9994 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eisenhauer, A. et al. Calcium isotope ratios in blood and urine: A new biomarker for the diagnosis of osteoporosis. Bone Rep. 10, 100200 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Isaji, Y. et al. Magnesium isotope fractionation during synthesis of chlorophyll a and bacteriochlorophyll a of benthic phototrophs in hypersaline environments. ACS Earth Space Chem. 3, 1073–1079 (2019).ADS 
CAS 
Article 

Google Scholar 
Pokharel, R. et al. Magnesium stable isotope fractionation on a cellular level explored by cyanobacteria and black fungi with implications for higher plants. Environ. Sci. Technol. 52, 12216–12224 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bolou-Bi, E. B., Poszwa, A., Leyval, C. & Vigier, N. Experimental determination of magnesium isotope fractionation during higher plant growth. Geochim. Cosmochim. Acta 74, 2523–2537 (2010).ADS 
CAS 
Article 

Google Scholar 
Wang, Y. et al. Magnesium isotope fractionation reflects plant response to magnesium deficiency in magnesium uptake and allocation: A greenhouse study with wheat. Plant Soil 455, 93–105 (2020).CAS 
Article 

Google Scholar 
Martin, J. E., Vance, D. & Balter, V. Natural variation of magnesium isotopes in mammal bones and teeth from two South African trophic chains. Geochim. Cosmochim. Acta 130, 12–20 (2014).ADS 
CAS 
Article 

Google Scholar 
Martin, J. E., Vance, D. & Balter, V. Magnesium stable isotope ecology using mammal tooth enamel. PNAS 112, 430–435 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
DeFronzo, R. A., Tobin, J. D. & Andres, R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am. J. Physiol.-Endocrinol. Metab. 237, E214 (1979).CAS 
Article 

Google Scholar 
Kim, J. K. Hyperinsulinemic-euglycemic clamp to assess insulin sensitivity in vivo. In Type 2 Diabetes: Methods and Protocols, Methods in Molecular Biology (ed. Stocker, C.) 221–238 (Humana Press, 2009).Chapter 

Google Scholar 
DeFronzo, R. A., Hendler, R. & Simonson, D. Insulin resistance is a prominent feature of insulin-dependent diabetes. Diabetes 31, 795–801 (1982).CAS 
PubMed 
Article 

Google Scholar 
Balter, V. et al. Contrasting Cu, Fe, and Zn isotopic patterns in organs and body fluids of mice and sheep, with emphasis on cellular fractionation. Metallomics 5, 1470–1482 (2013).CAS 
PubMed 
Article 

Google Scholar 
Zhang, B., Podolskiy, D. I., Mariotti, M., Seravalli, J. & Gladyshev, V. N. Systematic age-, organ-, and diet-associated ionome remodeling and the development of ionomic aging clocks. Aging Cell 19, e13119 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Morel, J.-D. et al. The mouse metallomic landscape of aging and metabolism. Nat. Commun. 13, 607 (2022).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grigoryan, R., Costas-Rodríguez, M., Vandenbroucke, R. E. & Vanhaecke, F. High-precision isotopic analysis of Mg and Ca in biological samples using multi-collector ICP-mass spectrometry after their sequential chromatographic isolation—Application to the characterization of the body distribution of Mg and Ca isotopes in mice. Anal. Chim. Acta 1130, 137–145 (2020).CAS 
PubMed 
Article 

Google Scholar 
Goff, S. L., Albalat, E., Dosseto, A., Godin, J.-P. & Balter, V. Determination of magnesium isotopic ratios of biological reference materials via multi-collector inductively coupled plasma mass spectrometry. Rapid Commun. Mass Spectrom. 35, e9074 (2021).PubMed 

Google Scholar 
DeRocher, K. A. et al. Chemical gradients in human enamel crystallites. Nature 583, 66–71 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johansen, T., Hansen, H. S., Richelsen, B. & Malmlöf, K. The obese Göttingen minipig as a model of the metabolic syndrome: dietary effects on obesity, insulin sensitivity, and growth hormone profile. Comp. Med. 51, 150–155 (2001).CAS 
PubMed 

Google Scholar 
Coelho, P. G. et al. Effect of obesity or metabolic syndrome and diabetes on osseointegration of dental implants in a miniature swine model: A pilot study. J. Oral Maxillofac. Surg. 76, 1677–1687 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Saltiel, A. R. & Kahn, C. R. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Elin, R. J. Assessment of magnesium status. Clin. Chem. 33, 1965–1970 (1987).CAS 
PubMed 
Article 

Google Scholar 
Koopmans, S. J., van der Meulen, J., Dekker, R., Corbijn, H. & Mroz, Z. Diurnal variation in insulin-stimulated systemic glucose and amino acid utilization in pigs fed with identical meals at 12-hour intervals. Horm. Metab. Res. 38, 607–613 (2006).CAS 
PubMed 
Article 

Google Scholar 
Koopmans, S. J., Maassen, J. A., Radder, J. K. & Frölich, M. In vivo insulin responsiveness for glucose uptake and production at eu- and hyperglycemic levels in normal and diabetic rats. Biochimica et Biophysica Acta (BBA) General Subjects 1115, 230–238 (1992).CAS 
Article 

Google Scholar 
Koopmans, S. J. et al. Association of insulin resistance with hyperglycemia in streptozotocin-diabetic pigs: Effects of metformin at isoenergetic feeding in a type 2–like diabetic pig model. Metabolism 55, 960–971 (2006).CAS 
PubMed 
Article 

Google Scholar  More

Mack, R. M. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710. https://doi.org/10.1890/1051-0761(2000)010[0689:BICEGC]2.0.CO;2 (2000).Article 

Google Scholar 
Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communitiesand ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Change Biol. 18, 1725–1737. https://doi.org/10.1111/j.1365-2486.2011.02636.x (2012).ADS 
Article 

Google Scholar 
Wittenberg, R. & Cock, M. J. W. Invasive Alien Species: A Toolkit of Best Prevention and Management Practices (CAB International, 2001).Book 

Google Scholar 
DAISIE. Delivering Alien Invasive Species Inventories for Europe. http://www.europe-aliens.org/speciesFactsheet.do?speciesId=23539# (2018).Hejda, M., Pyšek, P. & Jarošík, V. Impact of invasive plants on the species richness, diversity and composition of invaded communities. J. Ecol. 97, 393–403. https://doi.org/10.1111/j.1365-2745.2009.01480.x (2009).Article 

Google Scholar 
Chmura, D. et al. The influence of invasive Fallopia taxa on resident plant species in two river valleys (southern Poland). Acta Soc. Bot. Pol. 84(1), 23–33. https://doi.org/10.5586/asbp.2015.008 (2015).Article 

Google Scholar 
Stefanowicz, A. M., Stanek, M., Nobis, M. & Zubek, S. Few effects of invasive plants Reynoutria japonica, Rudbeckia laciniata and Solidago gigantea on soil physical and chemical properties. Sci. Total Environ. 574, 938–946. https://doi.org/10.1016/j.scitotenv.2016.09.120 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stefanowicz, A. M., Stanek, M., Nobis, M. & Zubek, S. Species-specific effects of plant invasions on activity, biomass and composition of soil microbial communities. Biol. Fertil. Soils 52, 841–852. https://doi.org/10.1007/s00374-016-1122-8 (2016).CAS 
Article 

Google Scholar 
Zubek, S. et al. Invasive plants affect arbuscular mycorrhizal fungi abundance and species richness as well as the performance of native plants grown in invaded soils. Biol. Fertil. Soils 52, 879–893. https://doi.org/10.1007/s00374-016-1127-3 (2016).Article 

Google Scholar 
Krinke, L. et al. Seed bank of an invasive alien, Heracleum mantegazzianum, and its seasonal dynamics. Seed Sci. Res. 15, 239–248. https://doi.org/10.1079/SSR2005214 (2005).Article 

Google Scholar 
Gioria, M. & Osbourne, B. Similarities in the impact of three large invasive plant species on soil seed bank communities. Biol. Invasions 12, 1671–1683. https://doi.org/10.1007/s10530-009-9580-7 (2010).Article 

Google Scholar 
Kundel, D., van Kleunen, M. & Dawson, W. Invasion by Solidago species has limited impacts on soil seed bank communities. Basic Appl. Ecol. 15, 573–580. https://doi.org/10.1016/j.baae.2014.08.009 (2014).Article 

Google Scholar 
Dong, H., Liu, T., Liu, Z. & Song, Z. Fate of the soil seed bank of giant ragweed and its significance in preventing and controlling its invasion in grasslands. Ecol. Evol. https://doi.org/10.1002/ece3.6238 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Harper, J. L. Population Biology of Plants (Academic Press, 1977).
Google Scholar 
Gioria, M. & Pyšek, P. The legacy of plant invasions: Changes in the soil seed bank of invaded plant communities. Bioscience 66(1), 40–53. https://doi.org/10.1093/biosci/biv165 (2015).Article 

Google Scholar 
Gioria, M. & Osborne, B. Resource competition in plant invasions: Emerging patterns and research needs. Front. Plant Sci. 5, 501. https://doi.org/10.3389/fpls.2014.00501 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Holmes, P. M. & Cowling, R. M. Diversity, composition and guild structure relationships between soil-stored seed banks and mature vegetation in alien plant-invaded South African fynbos shrublands. Plant Ecol. 133, 107–122. https://doi.org/10.1023/A:1009734026612 (1997).Article 

Google Scholar 
Gioria, M., Pyšek, P. & Moravcová, L. Soil seed banks in plant invasions: Promoting species invasiveness and long-term impact on plant community dynamics. Preslia 84, 327–350 (2012).
Google Scholar 
Tokarska-Guzik, B. et al. Rośliny Obcego Pochodzenia w Polsce ze Szczególnym Uwzględnieniem Gatunków Inwazyjnych (Generalna Dyrekcja Ochrony Środowiska, 2012).
Google Scholar 
Thompson, K., Bakker, J. P. & Bekker, R. M. The Soil Seed Banks of North West Europe: Methodology, Density and Longevity (Cambridge University Press, 1997).
Google Scholar 
Gioria, M., Le Roux, J. J., Hirsch, H., Moravcová, L. & Pyšek, P. Characteristics of the soil seed bank of invasive and non-invasive plants in their native and alien distribution range. Biol. Invasions 21, 2313–2332 (2019).Article 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: Species traits, habitats, propagule pressure, residence time. Ecology 96(3), 762–774. https://doi.org/10.1890/14-1005.1 (2015).Article 
PubMed 

Google Scholar 
Hager, H. A., Rupert, R., Quinn, L. D. & Newman, J. A. Escaped Miscanthus sacchariflorus reduces the richness and diversity of vegetation and the soil seed bank. Biol. Invasions 17, 1833–1847. https://doi.org/10.1007/s10530-014-0839-2 (2015).Article 

Google Scholar 
Robertson, S. G. & Hickman, K. Aboveground plant community and seed bank composition along an invasion gradient. Plant Ecol. 213(9), 1461–1475. https://doi.org/10.1007/s11258-012-0104-7 (2012).Article 

Google Scholar 
Fumanal, B., Gaudot, I. & Bretagnolle, F. Seed-bank dynamics in the invasive plant, Ambrosia artemisiifolia L.. Seed Sci. Res. 18(2), 101–114 (2008).Article 

Google Scholar 
Funk, J. L. et al. Keys to enhancing the value of invasion ecology research for management. Biol. Invasions 22, 2431–2445. https://doi.org/10.1007/s10530-020-02267-9 (2020).Article 

Google Scholar 
Jalas, J. Problems concerning Rudbeckia laciniata (Asteraceae) in Europe Fragmenta Floristica et Geobotanica. Supplementum 2(1), 289–297 (1993).
Google Scholar 
Tokarska-Guzik, B. The Establishment and Spread of Alien Plant Species (Kenophytes) in the Flora of Poland (Prace Naukowe Uniwersytetu Śląskiego w Katowicach, 2005).
Google Scholar 
EPPO. Rudbeckia laciniata (Asteraceae). EPPO Reporting Service—Invsive Plants. European and Mediterranean Plant Protection Organization. https://www.eppo.int/INVASIVE_PLANTS/ias_lists.htm (2009).Zelnik, I. The presence of invasive alien plant species in different habitats: Case study from Slovenia. Acta Biol. Sloven. 55(2), 25–38 (2012).
Google Scholar 
Vojniković, S. Tall cone flower (Rudbeckia laciniata L.)—new invasive species in the flora of Bosnia and Herzegovina. Herbologia 15(1), 39–47. https://doi.org/10.5644/Herb.15.1.05 (2015).Article 

Google Scholar 
Auld, B., Morita, H., Nishida, T., Ito, M. & Michael, P. Shared exotica: Plant invasions of Japan and south eastern Australia. Cunninghamia 8, 147–152 (2003).
Google Scholar 
Akasaka, M., Osawa, T. & Ikegami, M. The role of roads and urban area in occurrence of an ornamental invasive weed: A case of Rudbeckia laciniata L.. Urban Ecosyst. 18, 1021–1030 (2015).Article 

Google Scholar 
GBIF. Global Biodiversity Information Facility. Checklist dataset. https://www.gbif.org/species/3114229 (2021).Francírková, T. Contribution of the invasive ecology of Rudbeckia laciniata in the Czech Republic. In Plant Invasions: Species Ecology and Ecosystem Management (eds Brundu, G. et al.) 89–98 (Backhuys Publishers, 2001).
Google Scholar 
Moravcová, L., Pyšek, P., Jarošík, V., Havlíčková, V. & Zákravský, P. Reproductive characteristics of neophytes in the Czech Republic: Traits of invasive and non-invasive species. Preslia 82, 365–390. https://doi.org/10.1371/journal.pone.0123634 (2010).CAS 
Article 

Google Scholar 
Kościńska-Pająk, M., Musiał, K. & Janiszewska, K. Embryological processes in ovules of Rudbeckia laciniata L. (Asteraceae) from Poland. Mod. Phytomorphol. 5, 19–23 (2014).
Google Scholar 
Urbatsch, L. E. & Cox, P. B. Rudbeckia laciniata in Flora of North America Editorial Committee. http://floranorthamerica.org/Rudbeckia_laciniata (2021).Jankowska-Błaszczuk, M. Zróżnicowanie banków nasion w naturalnych i antropogenicznie przekształconych zbiorowiskach leśnych. Monograph. Bot. 88, 25 (2000).
Google Scholar 
Osawa, T. & Akasaka, M. Management of the invasive perennial herb Rudbeckia laciniata L. (Compositae) using rhizome removal. Jpn. J. Conserv. Ecol. 14(1), 37–43. https://doi.org/10.18960/hozen.14.1_37 (2009).Article 

Google Scholar 
Gleason, H. A. & Cronquist, A. Manual of Vascular Plants of Northeastern United States and Adjacent Canada (The New York Botanical Garden, 1991).Book 

Google Scholar 
Gioria, M. & Osborne, B. The impact of Gunnera tinctoria (Molina) Mirbel invasions on soil seed bank communities. J. Plant Ecol. 2(3), 153–167. https://doi.org/10.1093/jpe/rtp013 (2009).Article 

Google Scholar 
Kleyer, et al. The LEDA Traitbase: A database of life-history traits of Northwest European flora. J. Ecol. 96, 1266–1274. https://doi.org/10.1111/j.1365-2745.2008.01430.x (2008).Article 

Google Scholar 
Ruprecht, E., Fenesi, A. & Nijs, I. Are plasticity in functional traits and constancy in performance traits linked with invasiveness? An experimental test comparing invasive and naturalized plant species. Biol. Invasions 16, 1359–1372. https://doi.org/10.1007/s10530-013-0574-0 (2014).Article 

Google Scholar 
Wróbel, M. Origin and spatial distribution of roadside vegetation within the forest and agricultural areas in Szczecin Lowland (West Poland). Pol. J. Ecol. 54(1), 137–143 (2001).
Google Scholar 
Dajdok, Z. & Pawlaczyk, P. Inwazyjne Gatunki Roślin Mokradłowych Polski (Wydawnictwo Klubu Przyrodnikow, 2009).
Google Scholar 
de Waal, L. C., Child, L. E., Wade, M. & Brock, J. H. Ecology and Management of Invasive Riverside Plants (Wiley, 1994).
Google Scholar 
Pyśek, P. & Prach, K. Plant invasions and the role of riparian habitats: A comparison of four species alien to central Europe. J. Biogeogr. 20, 413–420 (1993).Article 

Google Scholar 
Kucharczyk, M. & Krawczyk, R. Kenophytes as river corridor plants in the vistula and the san river valleys. Teka Komisji Ochrony Kształtowania Środowiska Przyrodniczego 1, 110–115 (2004).
Google Scholar 
Walck, J. L. et al. Defining transient and persistent seed banks in species with pronounced seasonal dormancy and germination patterns. Seed Sci. Res. 15(3), 189–196. https://doi.org/10.1079/SSR2005209 (2005).ADS 
Article 

Google Scholar 
Gioria, M. & Pyšek, P. Early bird catches the worm: Germination as a critical step in plant invasion. Biol. Invasions 19, 1055–1080. https://doi.org/10.1007/s10530-016-1349-1 (2017).Article 

Google Scholar 
Gioria, M., Pyšek, P. & Osborne, B. Timing is everything: Does early and late germination favor invasions by herbaceous alien plants?. J. Plant Ecol. 11(1), 4–16. https://doi.org/10.1093/jpe/rtw105 (2018).Article 

Google Scholar 
Perglová, I. et al. Differences in germination and seedling establishment of alien and native Impatiens species. Preslia 81, 357–375 (2009).
Google Scholar 
Haines, D. F., Larson, D. L. & Larson, J. L. Leafy spurge (Euphorbia esula) affects vegetation more than seed banks in mixed-grass prairies of the Northern Great Plains. Invas. Plant Sci. Manage. 6, 416–432. https://doi.org/10.1614/IPSM-D-12-00076.1 (2013).Article 

Google Scholar 
Gioria, M., Jarosík, V. & Pyšek, P. Impact of invasions by alien plants on soil seed bank communities: Emerging patterns. Perspect. Plant Ecol. Evol. Syst. 16, 132–142. https://doi.org/10.1016/j.ppees.2014.03.003 (2014).Article 

Google Scholar 
Gioria, M. & Osbourne, B. Assessing the impact of plant invasions on soli seed bank communities: Use of univariate and multivariate statistical approaches. J. Veg. Sci. 20, 547–556. https://doi.org/10.1111/j.1654-1103.2009.01054.x (2009).Article 

Google Scholar 
Tokarska-Guzik, B., Bzdega, K., Knapik, D. & Jenczała, G. Changes in plant species richeness in some riparian plant communities as a result of their colonisation by taxa of Reynoutria (Fallopia). Biodivers. Res. Conserv. 1–2, 122–130 (2006).
Google Scholar 
Dölle, M. & Wolfgang, S. The relationship between soil seed bank, above-ground vegetation and disturbance intensity on old-field successional permanent plots. Appl. Veg. Sci. 12, 415–428 (2009).Article 

Google Scholar 
Thompson, K. & Grime, J. P. Seasonal variation in the seed banks of herbaceous species in ten contrasting habitats. J. Ecol. 67, 893–921. https://doi.org/10.2307/2259220 (1979).Article 

Google Scholar 
Czarnecka, J. Microspatial structure of the seed bank of xerothermic grassland—intracommunity differentiation. Acta Soc. Bot. Pol. 73(2), 155–164. https://doi.org/10.5586/asbp.2004.022 (2004).Article 

Google Scholar 
Kalamees, R., Püssa, K., Zobel, K. & Zobel, M. Restoration potential of the persistent soil seed bank in successional calcareous (alvar) grasslands in Estonia. Appl. Veg. Sci. 15, 208–218 (2012).Article 

Google Scholar 
Skowronek, S. et al. Regeneration potential of floodplain forests under the influence of nonnative tree species: Soil seed bank analysis in Northern Italy. Restor. Ecol. 22(1), 22–30. https://doi.org/10.1111/rec.12027 (2014).Article 

Google Scholar  More

Korup, O. Landslides in the Fluvial System. Treatise on Geomorphology Vol. 9 (Elsevier Ltd., 2013).
Google Scholar 
Kuo, C. W. & Brierley, G. The influence of landscape connectivity and landslide dynamics upon channel adjustments and sediment flux in the Liwu Basin, Taiwan. Earth Surf. Process. Landf. 39, 2038–2055 (2014).ADS 
Article 

Google Scholar 
Tunnicliffe, J. F., Leenman, A. & Reeve, M. The influence of large, chronic landslides on the fluvial system AGU Fall Meeting Abstracts, EP33A-3620 (2014).
Fox, G. A., Purvis, R. A. & Penn, C. J. Streambanks: A net source of sediment and phosphorus to streams and rivers. J. Environ. Manag. 181, 602–614 (2016).CAS 
Article 

Google Scholar 
Biswas, S. P. Restoration of riverine health. Handb. Ecol. Ecosyst. Eng. https://doi.org/10.1002/9781119678595.ch14 (2021).Article 

Google Scholar 
Lutgen, A. et al. Nutrients and heavy metals in legacy sediments: Concentrations, comparisons with upland soils, and implications for water quality. J. Am. Water Resour. Assoc. 56, 669–691 (2020).ADS 
CAS 
Article 

Google Scholar 
Emenike, P. C. et al. An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria. Environ. Pollut. 265, 114795 (2020).CAS 
PubMed 
Article 

Google Scholar 
Fox, G. A. & Wilson, G. V. The role of subsurface flow in hillslope and stream bank erosion: A review. Soil Sci. Soc. Am. J. 74, 717–733 (2010).ADS 
CAS 
Article 

Google Scholar 
Duró, G., Crosato, A., Kleinhans, M. G., Roelvink, D. & Uijttewaal, W. S. J. Bank erosion processes in regulated navigable rivers. J. Geophys. Res. Earth Surf. 125, 1–26 (2020).Article 

Google Scholar 
Keesstra, S. D. et al. Evolution of the morphology of the river Dragonja (SW Slovenia) due to land-use changes. Geomorphology 69, 191–207 (2005).ADS 
Article 

Google Scholar 
Pizzuto, J. & O’Neal, M. Increased mid-twentieth century riverbank erosion rates related to the demise of mill dams, South River, Virginia. Geology 37, 19–22 (2009).ADS 
Article 

Google Scholar 
Abam, T. K. S. Factors affecting distribution of instability of river banks in the Niger delta. Eng. Geol. 35, 123–133 (1993).Article 

Google Scholar 
Jordan, C. et al. Sand mining in the Mekong Delta revisited—current scales of local sediment deficits. Sci. Rep. 9, 1–14 (2019).Article 
CAS 

Google Scholar 
Hackney, C. R. et al. River bank instability from unsustainable sand mining in the lower Mekong River. Nat. Sustain. 3, 217–225 (2020).Article 

Google Scholar 
Yang, S. L., Milliman, J. D., Li, P. & Xu, K. 50,000 dams later: Erosion of the Yangtze River and its delta. Glob. Planet. Change 75, 14–20 (2011).ADS 
Article 

Google Scholar 
Royall, D. Land-use impacts on the hydrogeomorphology of small watersheds. Ref. Modul. Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-818234-5.00010-9 (2021).Article 

Google Scholar 
Johnson, P. & Royall, D. Evaluating the effects of urbanization age on the morphology of low-order urban streams in the U.S. southern Piedmont. Phys. Geogr. 40, 1–27 (2019).Article 

Google Scholar 
Zaimes, G., Tamparopoulos, A. E., Tufekcioglu, M. & Schultz, R. C. Understanding stream bank erosion and deposition in Iowa, USA: A seven year study along streams in different regions with different riparian land-uses. J. Environ. Manag. 287, 112352 (2021).Article 

Google Scholar 
Zaimes, G. N. & Schultz, R. C. Riparian land-use impacts on bank erosion and deposition of an incised stream in north-central Iowa, USA. CATENA 125, 61–73 (2015).Article 

Google Scholar 
Simon, A., Curini, A., Darby, S. E. & Langendoen, E. J. Bank and near-bank processes in an incised channel. Geomorphology 35, 193–217 (2000).ADS 
Article 

Google Scholar 
Rinaldi, M. & Casagli, N. Stability of streambanks formed in partially saturated soils and effects of negative pore water pressures: The Sieve River (Italy). Geomorphology 26, 253–277 (1999).ADS 
Article 

Google Scholar 
Wynn, T. & Mostaghimi, S. The effects of vegetation and soil type on streambank erosion, Southwestern Virginia, USA. J. Am. Water Resour. Assoc. 42, 69–82 (2006).ADS 
Article 

Google Scholar 
Hecker, G. A., Meehan, M. A. & Norland, J. E. Plant community influences on intermittent stream stability in the great plains. Rangel. Ecol. Manag. 72, 112–119 (2019).Article 

Google Scholar 
Konsoer, K. M. et al. Spatial variability in bank resistance to erosion on a large meandering, mixed bedrock-alluvial river. Geomorphology 252, 80–97 (2016).ADS 
Article 

Google Scholar 
Abernethy, B. & Rutherfurd, I. D. Does the weight of riparian trees destabilize riverbanks?. River Res. Appl. 16, 565–576 (2000).
Google Scholar 
Collison, A. J. C. The distribution and strength of riparian tree roots in relation to riverbank reinforcement. Hydrol. Process. 15, 63–79 (2001).Article 

Google Scholar 
Simon, A. & Collison, A. J. C. Quantifying the mechanical and hydrologic effects of riparian vegetation on streambank stability. Earth Surf. Process. Landf. 27, 527–546 (2002).ADS 
Article 

Google Scholar 
Krzeminska, D., Kerkhof, T., Skaalsveen, K. & Stolte, J. Effect of riparian vegetation on stream bank stability in small agricultural catchments. CATENA 172, 87–96 (2019).Article 

Google Scholar 
Yu, G. A. et al. Effects of riparian plant roots on the unconsolidated bank stability of meandering channels in the Tarim River, China. Geomorphology 351, 106958 (2020).Article 

Google Scholar 
Halder, A. & Mowla Chowdhury, R. Evaluation of the river Padma morphological transition in the central Bangladesh using GIS and remote sensing techniques. Int. J. River Basin Manag. 1–15 (2021).
Bernier, J. F., Chassiot, L. & Lajeunesse, P. Assessing bank erosion hazards along large rivers in the Anthropocene: A geospatial framework from the St. Lawrence fluvial system. Geomat. Nat. Hazards Risk 12, 1584–1615 (2021).Article 

Google Scholar 
Lawler, D. M., Grove, J. R., Couperthwaite, J. S. & Leeks, G. J. L. Downstream change in river bank erosion rates in the Swale-Ouse system, northern England. Hydrol. Process. 13, 977–992 (1999).ADS 
Article 

Google Scholar 
Gholami, V., Sahour, H. & Hadian Amri, M. A. Soil erosion modeling using erosion pins and artificial neural networks. CATENA 196, 104902 (2021).Article 

Google Scholar 
Simon, A., Pollen-Bankhead, N. & Thomas, R. E. Development and application of a deterministic bank stability and toe erosion model for stream restoration. Geophys. Monogr. Ser. 194, 453–474 (2011).ADS 

Google Scholar 
Klavon, K. et al. Evaluating a process-based model for use in streambank stabilization: Insights on the Bank Stability and Toe Erosion Model (BSTEM). Earth Surf. Process. Landf. 42, 191–213 (2017).ADS 
Article 

Google Scholar 
Partheniades, E. Erosion and deposition of cohesive soils. J. Hydraul. Div. 91, 105–139 (1965).Article 

Google Scholar 
Fredlund, D. G., Morgenstern, N. R. & Widger, R. A. Shear strength of unsaturated soils. Can. Geotech. J. 15, 313–321 (1978).Article 

Google Scholar 
Myers, D. T., Rediske, R. R. & McNair, J. N. Measuring streambank erosion: A comparison of erosion pins, total station, and terrestrial laser scanner. Water (Switzerland) 11, 1846 (2019).
Google Scholar 
Casagli, N., Rinaldi, M., Gargini, A. & Curini, A. Pore water pressure and streambank stability: Results from a monitoring site on the Sieve River, Italy. Earth Surf. Process. Landf. 24, 1095–1114 (1999).ADS 
Article 

Google Scholar 
Tufekcioglu, M. et al. Stream bank erosion as a source of sediment and phosphorus in grazed pastures of the Rathbun Lake Watershed in southern Iowa, United States. J. Soil Water Conserv. 67, 545–555 (2012).Article 

Google Scholar 
Palmer, J. A., Schilling, K. E., Isenhart, T. M., Schultz, R. C. & Tomer, M. D. Streambank erosion rates and loads within a single watershed: Bridging the gap between temporal and spatial scales. Geomorphology 209, 66–78 (2014).ADS 
Article 

Google Scholar 
Pollen, N. & Simon, A. Estimating the mechanical effects of riparian vegetation on stream bank stability using a fiber bundle model. Water Resour. Res. 41, 1–11 (2005).Article 

Google Scholar 
Pollen-Bankhead, N. & Simon, A. Sensitivity of post-hurricane beach. Earth Surf. Process. Landf. 34, 471–480 (2009).ADS 
Article 

Google Scholar 
Wasige, J. E. et al. A land use and land cover classification system for use with remote sensor data. Prof. Pap. 100, 753–764 (1976).
Google Scholar 
Al-Doski, J., Mansor, S. B., Ng, H., San, P. & Khuzaimah, Z. Land cover mapping using remote sensing data. Am. J. Geogr. Inf. Syst. 2020, 33–45 (2020).
Google Scholar 
Okeke, C. A. U., Ede, A. N. & Kogure, T. Monitoring of riverbank stability and seepage undercutting mechanisms on the Iju (Atuwara) River, Southwest Nigeria. IOP Conf. Ser. Mater. Sci. Eng. 640, 012105 (2019).Article 

Google Scholar 
Abam, T. K. S. Aspects of alluvial river bank recession: Some examples from the Niger delta. Environ. Geol. 31, 211–220 (1997).Article 

Google Scholar 
Okeke, C. A. U., Azuh, D., Ogbuagu, F. U. & Kogure, T. Assessment of land use impact and seepage erosion contributions to seasonal variations in riverbank stability: The Iju River, SW Nigeria. Groundw. Sustain. Dev. 11, 100448 (2020).Article 

Google Scholar 
Voltz, T. et al. Riparian hydraulic gradient and stream-groundwater exchange dynamics in steep headwater valleys. J. Geophys. Res. Earth Surf. 118, 953–969 (2013).ADS 
Article 

Google Scholar 
Thomas, J., Kumar, S. & Sudheer, K. P. Channel stability assessment in the lower reaches of the Krishna River (India) using multi-temporal satellite data during 1973–2015. Remote Sens. Appl. Soc. Environ. 17, 100274 (2020).
Google Scholar 
Ran, Y. et al. A higher river sinuosity increased riparian soil structural stability on the downstream of a dammed river. Sci. Total Environ. 802, 149886 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Midgley, T. L., Fox, G. A. & Heeren, D. M. Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks. Geomorphology 145–146, 107–114 (2012).ADS 
Article 

Google Scholar 
Daly, E. R., Miller, R. B. & Fox, G. A. Modeling streambank erosion and failure along protected and unprotected composite streambanks. Adv. Water Resour. 81, 114–127 (2015).ADS 
Article 

Google Scholar 
Saleem, A. et al. Spatial and temporal variations of erosion and accretion: A case of a large tropical river. Earth Syst. Environ. 4, 167–181 (2020).ADS 
Article 

Google Scholar 
Biswas, R. N., Islam, M. N., Islam, M. N. & Shawon, S. S. Modeling on approximation of fluvial landform change impact on morphodynamics at Madhumati River Basin in Bangladesh. Model. Earth Syst. Environ. 7, 71–93 (2021).Article 

Google Scholar 
Li, J., Tooth, S., Zhang, K. & Zhao, Y. Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change. J. Environ. Manag. 278, 111559 (2021).Article 

Google Scholar 
Graziano, M. P., Deguire, A. K. & Surasinghe, T. D. Riparian buffers as a critical landscape feature : Insights for riverscape conservation and policy renovations. Diversity 14, 172 (2022).Article 

Google Scholar 
Rauch, H. P., von der Thannen, M., Raymond, P., Mira, E. & Evette, A. Ecological challenges* for the use of soil and water bioengineering techniques in river and coastal engineering projects. Ecol. Eng. 176, 106539 (2022).Article 

Google Scholar 
East, A. E. et al. Channel-planform evolution in four rivers of Olympic National Park, Washington, USA: The roles of physical drivers and trophic cascades. Earth Surf. Process. Landf. 42, 1011–1032 (2017).ADS 
Article 

Google Scholar 
Kumar, P. et al. Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations. Sci. Total Environ. 784, 147058 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laubel, A., Kronvang, B., Hald, A. B. & Jensen, C. Hydromorphological and biological factors influencing sediment and phosphorus loss via bank erosion in small lowland rural streams in Denmark. Hydrol. Process. 17, 3443–3463 (2003).ADS 
Article 

Google Scholar 
Veihe, A., Jensen, N. H., Schiøtz, I. G. & Nielsen, S. L. Magnitude and processes of bank erosion at a small stream in Denmark. Hydrol. Process. 25, 1597–1613 (2011).ADS 
Article 

Google Scholar 
Kronvang, B., Andersen, H. E., Larsen, S. E. & Audet, J. Importance of bank erosion for sediment input, storage and export at the catchment scale. J. Soils Sediments 13, 230–241 (2013).Article 

Google Scholar 
Rajakumari, S., Meenambikai, M., Divya, V., Sarunjith, K. J. & Ramesh, R. Morphological changes in alluvial and coastal plains of Kandaleru river, Andhra Pradesh using RS and GIS, Egypt. J. Remote Sens. Space Sci. 24, 1071–1081 (2021).
Google Scholar 
Zegeye, A. D., Langendoen, E. J., Steenhuis, T. S., Mekuria, W. & Tilahun, S. A. Bank stability and toe erosion model as a decision tool for gully bank stabilization in sub humid Ethiopian highlands. Ecohydrol. Hydrobiol. 20, 301–311 (2020).Article 

Google Scholar 
Shields, F. D. J., Morin, N. & Cooper, C. M. Design of large woody debris structures for channel rehabilitation. In Seventh Federal Interagency Sedimentation Conference, Vol. 8 (2001).C A U, Okeke A N, Ede (2019) Mechanisms of riverbank failure and channel instability on the Nkisi River Southeast Nigeria. IOP Conference Series: Materials Science and Engineering 640(1), 012104. https://doi.org/10.1088/1757-899X/640/1/012104Article 

Google Scholar  More

Chen T, Nomura K, Wang X, Sohrabi R, Xu J, Yao L, et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature.2020;580:653–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaparro JM, Badri DV, Vivanco JM. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 2014;8:790–803.CAS 
PubMed 
Article 

Google Scholar 
Manching HC, Carlson K, Kosowsky S, Smitherman CT, Stapleton AE. Maize phyllosphere microbial community niche development across stages of host leaf growth. F1000Research. 2017;6:1698.PubMed 
Article 

Google Scholar 
Wagner MR, Lundberg DS, Del Rio TG, Tringe SG, Dangl JL, Mitchell-Olds T. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun. 2016;7:12151.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 2016;14:e1002352.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Durán P, Thiergart T, Garrido-Oter R, Agler M, Kemen E, Schulze-Lefert P, et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell. 2018;175:973–83. e14PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Carrión VJ, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science. 2019;366:606–12.PubMed 
Article 
CAS 

Google Scholar 
Karasov TL, Almario J, Friedemann C, Ding W, Giolai M, Heavens D, et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe. 2018;24:168–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coleman-Derr D, Desgarennes D, Fonseca-Garcia C, Gross S, Clingenpeel S, Woyke T, et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. N. Phytol. 2016;209:798–811.CAS 
Article 

Google Scholar 
Xiong C, Zhu YG, Wang JT, Singh B, Han LL, Shen JP, et al. Host selection shapes crop microbiome assembly and network complexity. N. Phytol. 2021;229:1091–104.CAS 
Article 

Google Scholar 
Lemonnier P, Gaillard C, Veillet F, Verbeke J, Lemoine R, Coutos-Thévenot P, et al. Expression of Arabidopsis sugar transport protein STP13 differentially affects glucose transport activity and basal resistance to Botrytis cinerea. Plant Mol Biol. 2014;85:473–84.CAS 
PubMed 
Article 

Google Scholar 
Nobori T, Cao Y, Entila F, Dahms E, Tsuda Y, Garrido-Oter R, et al. Dissecting the co-transcriptome landscape of plants and microbiota members. bioRxiv; 2022. p. 2021.04.25.440543.Yamada K, Saijo Y, Nakagami H, Takano Y. Regulation of sugar transporter activity for antibacterial defense in Arabidopsis. Science. 2016;354:1427–30.CAS 
PubMed 
Article 

Google Scholar 
Baker RF, Leach KA, Braun DM. SWEET as sugar: new sucrose effluxers in plants. Mol Plant. 2012;5:766–8.PubMed 
Article 

Google Scholar 
Tegeder M, Hammes UZ. The way out and in: phloem loading and unloading of amino acids. Curr Opin Plant Biol. 2018;43:16–21.CAS 
PubMed 
Article 

Google Scholar 
O’Leary BM, Neale HC, Geilfus CM, Jackson RW, Arnold DL, Preston GM. Early changes in apoplast composition associated with defence and disease in interactions between Phaseolus vulgaris and the halo blight pathogen Pseudomonas syringae Pv. phaseolicola. Plant Cell Environ. 2016;39:2172–84.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rico A, Preston GM. Pseudomonas syringae pv. tomato DC3000 uses constitutive and apoplast-induced nutrient assimilation pathways to catabolize nutrients that are abundant in the tomato apoplast. Mol Plant-Microbe Interact. MPMI. 2008;21:269–82.CAS 
PubMed 
Article 

Google Scholar 
Yu X, Lund SP, Scott RA, Greenwald JW, Records AH, Nettleton D, et al. Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. Proc Natl Acad Sci USA. 2013;110:E425.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Winter H, Riens B, Heldt HW. Further studies of the phloem loading process in leaves of barley and spinach. The comparison of metabolite concentrations in the apoplastic compartment with those in the cytosolic compartment and in the sieve tubes. Bot Acta. 1995;108:270–5.CAS 
Article 

Google Scholar 
Chen LQ, Hou BH, Lalonde S, Takanaga H, Hartung ML, Qu XQ, et al. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature. 2010;468:527–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xin XF, Nomura K, Aung K, Velásquez AC, Yao J, Boutrot F, et al. Bacteria establish an aqueous living space in plants crucial for virulence. Nature. 2016;539:524–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paulsen IT, Press CM, Ravel J, Kobayashi DY, Myers GSA, Mavrodi DV, et al. Complete genome sequence of the plant commensal Pseudomonas fluorescens Pf-5. Nat Biotechnol. 2005;23:873–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 
Article 

Google Scholar 
Hoek TA, Axelrod K, Biancalani T, Yurtsev EA, Liu J, Gore J. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLOS Biol. 2016;14:e1002540.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zimmermann J, Obeng N, Yang W, Pees B, Petersen C, Waschina S, et al. The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans. ISME J. 2020;14:26–38.CAS 
PubMed 
Article 

Google Scholar 
Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol. 2021;5:195–203.PubMed 
PubMed Central 
Article 

Google Scholar 
Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:1–17.Article 

Google Scholar 
Gerlich SC, Walker BJ, Krueger S, Kopriva S. Sulfate metabolism in C4 Flaveria species is controlled by the root and connected to serine biosynthesis. Plant Physiol. 2018;178:565–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gowik U, Bräutigam A, Weber KL, Weber APM, Westhoff P. Evolution of C4 photosynthesis in the genus Flaveria: How many and which genes does it take to make C4? Plant Cell. 2011;23:2087–105.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKown AD, Dengler NG. Vein patterning and evolution in C4 plants. Botany. 2010;88:775–86.CAS 
Article 

Google Scholar 
Gentzel I, Giese L, Zhao W, Alonso AP, Mackey D. A simple method for measuring apoplast hydration and collecting apoplast contents. Plant Physiol. 2019;179:1265–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mayer T, Mari A, Almario J, Murillo-Roos M, Syed M, Abdullah H, et al. Obtaining deeper insights into microbiome diversity using a simple method to block host and nontargets in amplicon sequencing. Mol Ecol Resour. 2021;21:1952–65.PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.R-project.org/.Callahan B, McMurdie PJ, Rosen M, Han A, Johnson A, Holmes S. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:61217.Article 
CAS 

Google Scholar 
Oksanen J, Blanchet GF, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2020. Available from: https://CRAN.R-project.org/package=vegan.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schlechter RO, Jun H, Bernach M, Oso S, Boyd E, Muñoz-Lintz DA, et al. Chromatic bacteria – A broad host-range plasmid and chromosomal insertion toolbox for fluorescent protein expression in bacteria. Front Microbiol. 2018;9:3052.PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Pennewiss K, Sattelmacher B, Hussmann M, Hermann Muehling K. Is the infiltration-centrifugation technique appropriate for the isolation of apoplastic fluid? A critical evaluation with different plant species. Physiol Plant. 2001;111:457–65.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dal Bello M, Lee H, Goyal A, Gore J. Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat Ecol Evol. 2021;5:1424–34.PubMed 
Article 

Google Scholar 
Sattelmacher B. The apoplast and its significance for plant mineral nutrition. N. Phytol. 2001;149:167–92.CAS 
Article 

Google Scholar 
Regalado J, Lundberg DS, Deusch O, Kersten S, Karasov T, Poersch K, et al. Combining whole-genome shotgun sequencing and rRNA gene amplicon analyses to improve detection of microbe–microbe interaction networks in plant leaves. ISME J. 2020;14:2116–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Weng FCH, Joubert PM, Metcalf CJE, Lindow S, Koskella B. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc Natl Acad Sci USA. 2020;117:1148–59.CAS 
PubMed 
Article 

Google Scholar 
Remus-Emsermann MNP, Lücker S, Müller DB, Potthoff E, Daims H, Vorholt JA. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ Microbiol. 2014;16:2329–40.CAS 
PubMed 
Article 

Google Scholar 
Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
Herren CM. Disruption of cross-feeding interactions by invading taxa can cause invasional meltdown in microbial communities. Proc R Soc B Biol Sci. 2020;287:20192945.Article 

Google Scholar 
Rahme LG, Mindrinos MN, Panopoulos NJ. Plant and environmental sensory signals control the expression of hrp genes in Pseudomonas syringae pv. phaseolicola. J Bacteriol. 1992;174:3499–507.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Zhang X, Koskella B. Tomato seed-associated bacteria confer protection of seedlings against foliar disease caused by Pseudomonas syringae. Phytobiomes J. 2019;3:177–90.Article 

Google Scholar 
Cha JY, Han S, Hong HJ, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29.CAS 
PubMed 
Article 

Google Scholar 
Lundberg DS, Jové R de P, Ayutthaya PPN, Karasov TL, Shalev O, Poersch K, et al. Contrasting patterns of microbial dominance in the Arabidopsis thaliana phyllosphere. bioRxiv. 2021;2021.04.06.438366.Ikawa Y, Tsuge S. The quantitative regulation of the hrp regulator HrpX is involved in sugar-source-dependent hrp gene expression in Xanthomonas oryzae pv. oryzae. FEMS Microbiol Lett. 2016;363:fnw071.Wei ZM, Sneath BJ, Beer SV. Expression of Erwinia amylovora hrp genes in response to environmental stimuli. J Bacteriol. 1992;174:1875–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Akashi H, Gojobori T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci USA. 2002;99:3695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oña L, Kost C. Cooperation increases robustness to ecological disturbance in microbial cross-feeding networks. Ecol Lett. 2022;25:1410–20.Cadot S, Guan H, Bigalke M, Walser JC, Jander G, Erb M, et al. Specific and conserved patterns of microbiota-structuring by maize benzoxazinoids in the field. Microbiome. 2021;9:103.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Voges MJEEE, Bai Y, Schulze-Lefert P, Sattely ES. Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome. Proc Natl Acad Sci USA. 2019;116:12558–65.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Aulakh MS, Wassmann R, Bueno C, Kreuzwieser J, Rennenberg H. Characterization of root exudates at different growth stages of ten rice (Oryza sativa L.) cultivars. Plant Biol. 2001;3:139–48.CAS 
Article 

Google Scholar 
Dietz S, Herz K, Gorzolka K, Jandt U, Bruelheide H, Scheel D. Root exudate composition of grass and forb species in natural grasslands. Sci Rep. 2020;10:10691.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Sharko, F. S. et al. Steller’s sea cow genome suggests this species began going extinct before the arrival of Paleolithic humans. Nat. Commun. 12, 2215 (2021).ADS 
CAS 
Article 

Google Scholar 
Crerar, L. D., Crerar, A. P., Domning, D. P. & Parsons, E. C. Rewriting the history of an extinction-was a population of Steller’s sea cows (Hydrodamalis gigas) at St Lawrence Island also driven to extinction? Biol. Lett. 10, 20140878 (2014).Article 

Google Scholar 
Domning, D. P., Thomason, J. & Corbett, D. G. Steller’s sea cow in the Aleutian Islands. Mar. Mamm. Sci. 23, 976–983 (2007).Article 

Google Scholar 
Savinetsky, A. B., Kiseleva, N. K. & Khassanov, B. F. Dynamics of sea mammal and bird populations of the Bering Sea region over the last several millennia. Palaeogeogra. Palaeoclimatol. Palaeoecol. 20, 335–352 (2004).ADS 
Article 

Google Scholar 
Whitmore, F. C. & Gard, L. M. J. Steller’s sea cow (Hydrodamalis gigas) of late Pleistocene age from Amchitka, Aleutian Islands, Alaska. US Geol. Surv. Prof. Pap. 1036, 1–19 (1977).
Google Scholar 
Sheppard, J. K. et al. Movement heterogeneity of dugongs, Dugong dugon (Muller), over large spatial scales. J. Exp. Mar. Biol. Ecol. 334, 64–83 (2006).Article 

Google Scholar 
Deutsch C. J., et al. Seasonal movements, migratory behavior, and site fidelity of West Indian manatees along the Atlantic Coast of the United States. Wildlife Monogr., 151, 1–77 (2003).Reed, R. K. Transport of the Alaskan Stream. Nature 220, 681–682 (1968).ADS 
Article 

Google Scholar 
Detlef, H. et al. Sea ice dynamics across the Mid-Pleistocene transition in the Bering Sea. Nat. Commun. 9, 941 (2018).ADS 
CAS 
Article 

Google Scholar 
Ragen, T. J., Antonelis, G. A. & Kiyota, M. Early migration of northern fur-seal pups from St-Paul Island, Alaska. J. Mammal. 76, 1137–1148 (1995).Article 

Google Scholar 
Estes, J. A., Burdin, A. & Doak, D. F. Sea otters, kelp forests, and the extinction of Steller’s sea cow. Proc. Natl Acad. Sci. USA 113, 880–885 (2016).ADS 
CAS 
Article 

Google Scholar 
Larson, S., Jameson, R., Etnier, M., Jones, T. & Hall, R. Genetic diversity and population parameters of sea otters, Enhydra lutris, before fur trade extirpation from 1741–1911. PLoS ONE 7, e32205 (2012).ADS 
CAS 
Article 

Google Scholar 
Bullen, C. D., Campos, A. A., Gregr, E. J., McKechnie, I. & Chan, K. M. A. The ghost of a giant – Six hypotheses for how an extinct megaherbivore structured kelp forests across the North Pacific Rim. Glob. Ecol. Biogeogr. 30, 2101–2118 (2021).Article 

Google Scholar 
Plon, S., Thakur, V., Parr, L. & Lavery, S. D. Phylogeography of the dugong (Dugong dugon) based on historical samples identifies vulnerable Indian Ocean populations. PLoS ONE 14, e0219350 (2019).CAS 
Article 

Google Scholar 
Seddon, J. M. et al. Fine scale population structure of dugongs (Dugong dugon) implies low gene flow along the southern Queensland coastline. Conserv. Genet. 15, 1381–1392 (2014).Article 

Google Scholar 
Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).ADS 
CAS 
Article 

Google Scholar  More

Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32.CAS 
PubMed 

Google Scholar 
Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007;107:486–513.CAS 
PubMed 

Google Scholar 
Timmers PHA, Welte CU, Koehorst JJ, Plugge CM, Jetten MSM, Stams AJM. Reverse methanogenesis and respiration in methanotrophic Archaea. Archaea. 2017;2017:1–22.
Google Scholar 
Hallam SJ, Putnam N, Preston CM, Detter JC, Rokhsar D, Richardson PM, et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science. 2004;305:1457–62.CAS 
PubMed 

Google Scholar 
Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34.CAS 
PubMed 

Google Scholar 
Vanwonterghem I, Evans PN, Parks DH, Jensen PD, Woodcroft BJ, Hugenholtz P, et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat Microbiol. 2016;1:16170.CAS 
PubMed 

Google Scholar 
McKay LJ, Dlakić M, Fields MW, Delmont TO, Eren AM, Jay ZJ, et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat Microbiol. 2019;4:614–22.CAS 
PubMed 

Google Scholar 
Wang Y, Wegener G, Hou J, Wang F, Xiao X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat Microbiol. 2019;4:595–602.CAS 
PubMed 

Google Scholar 
Wang Y, Wegener G, Ruff SE, Wang F. Methyl/alkyl‐coenzyme M reductase‐based anaerobic alkane oxidation in archaea. Environ Microbiol. 2021;23:530–41.CAS 
PubMed 

Google Scholar 
Bertram S, Blumenberg M, Michaelis W, Siegert M, Krüger M, Seifert R. Methanogenic capabilities of ANME‐archaea deduced from 13C‐labelling approaches. Environ Microbiol. 2013;15:2384–93.CAS 
PubMed 

Google Scholar 
Sousa DZ, Smidt H, Alves MM, Stams AJM. Syntrophomonas zehnderi sp. nov., an anaerobe that degrades long-chain fatty acids in co-culture with Methanobacterium formicicum. Int J Syst Evol Micr. 2007;57:609–15.CAS 

Google Scholar 
Yamada T, Sekiguchi Y, Hanada S, Imachi H, Ohashi A, Harada H, et al. Anaerolinea thermolimosa sp. nov., Levilinea saccharolytica gen. nov., sp. nov. and Leptolinea tardivitalis gen. nov., sp. nov., novel filamentous anaerobes, and description of the new classes Anaerolineae classis nov. and Caldilineae classis nov. in the bacterial phylum Chloroflexi. Int J Syst Evol Micr. 2006;56:1331–40.CAS 

Google Scholar 
Yamada T, Sekiguchi Y, Imachi H, Kamagata Y, Ohashi A, Harada H. Diversity, localization, and physiological properties of filamentous microbes belonging to Chloroflexi subphylum I in mesophilic and thermophilic methanogenic sludge granules. Appl Environ Microb. 2005;71:7493–503.CAS 

Google Scholar 
Manzoor S, Schnürer A, Bongcam-Rudloff E, Müller B. Complete genome sequence of Methanoculleus bourgensis strain MAB1, the syntrophic partner of mesophilic acetate-oxidising bacteria (SAOB). Stand Genomic Sci. 2016;11:80.PubMed 
PubMed Central 

Google Scholar 
Engelhardt T, Sahlberg M, Cypionka H, Engelen B. Biogeography of Rhizobium radiobacter and distribution of associated temperate phages in deep subseafloor sediments. ISME J. 2013;7:199–209.CAS 
PubMed 

Google Scholar 
Nölling J, Groffen A, de Vos WM. φ F1 and φF3, two novel virulent, archaeal phages infecting different thermophilic strains of the genus. Methanobacterium Microbiol. 1993;139:2511–6.
Google Scholar 
Meile L, Jenal U, Studer D, Jordan M, Leisinger T. Characterization of ψM1, a virulent phage of Methanobacterium thermoautotrophicum Marburg. Arch Microbiol. 1989;152:105–10.CAS 

Google Scholar 
Weidenbach K, Nickel L, Neve H, Alkhnbashi OS, Künzel S, Kupczok A, et al. Methanosarcina spherical virus, a novel archaeal lytic virus targeting Methanosarcina strains. J Virol. 2017;91:e00955–17.PubMed 
PubMed Central 

Google Scholar 
Molnár J, Magyar B, Schneider G, Laczi K, Valappil SK, Kovács ÁL, et al. Identification of a novel archaea virus, detected in hydrocarbon polluted Hungarian and Canadian samples. PLOS ONE. 2020;15:e0231864.PubMed 
PubMed Central 

Google Scholar 
Paul BG, Bagby SC, Czornyj E, Arambula D, Handa S, Sczyrba A, et al. Targeted diversity generation by intraterrestrial archaea and archaeal viruses. Nat Commun. 2015;6:6585.CAS 
PubMed 

Google Scholar 
Pourcel C, Touchon M, Villeriot N, Vernadet J-P, Couvin D, Toffano-Nioche C, et al. CRISPRCasdb a successor of CRISPRdb containing CRISPR arrays and cas genes from complete genome sequences, and tools to download and query lists of repeats and spacers. Nucleic Acids Res. 2019;48:D535–D544.PubMed Central 

Google Scholar 
Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife. 2015;4:e08490.PubMed Central 

Google Scholar 
Lever MA, Teske AP. Diversity of methane-cycling Archaea in hydrothermal sediment investigated by general and group-specific PCR primers. Appl Environ Microb. 2015;81:1426–41.
Google Scholar 
Jian H, Yi Y, Wang J, Hao Y, Zhang M, Wang S, et al. Diversity and distribution of viruses inhabiting the deepest ocean on Earth. ISME J. 2021;15:3094–110.Paez-Espino D, Pavlopoulos GA, Ivanova NN, Kyrpides NC. Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nature Protoc. 2017;12:1673–82.CAS 

Google Scholar 
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
Ren J, Song K, Deng C, Ahlgren NA, Fuhrman JA, Li Y, et al. Identifying viruses from metagenomic data using deep learning. Quant Biol. 2020;8:64–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Páez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2020;49:D764–D775.PubMed Central 

Google Scholar 
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol. 2021;39:578–85.CAS 
PubMed 

Google Scholar 
Sandaa R, Gómez‐Consarnau L, Pinhassi J, Riemann L, Malits A, Weinbauer MG, et al. Viral control of bacterial biodiversity – evidence from a nutrient‐enriched marine mesocosm experiment. Environ Microbiol. 2009;11:2585–97.CAS 
PubMed 

Google Scholar 
Howard-Varona C, Hargreaves KR, Abedon ST, Sullivan MB. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 2017;11:1511–20.PubMed 
PubMed Central 

Google Scholar 
Li Z, Pan D, Wei G, Pi W, Zhang C, Wang J-H, et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. ISME J. 2021;15:2366–78.CAS 
PubMed 
PubMed Central 

Google Scholar 
Krupovič M, Forterre P, Bamford DH. Comparative analysis of the mosaic genomes of tailed archaeal viruses and proviruses suggests common themes for virion architecture and assembly with tailed viruses of bacteria. J Mol Biol. 2010;397:144–60.PubMed 

Google Scholar 
Thiroux S, Dupont S, Nesbø CL, Bienvenu N, Krupovic M, L’Haridon S, et al. The first head‐tailed virus, MFTV1, infecting hyperthermophilic methanogenic deep‐sea archaea. Environ Microbiol. 2021;23:3614–26.CAS 
PubMed 

Google Scholar 
Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.
Google Scholar 
Hao L, Bize A, Conteau D, Chapleur O, Courtois S, Kroff P, et al. New insights into the key microbial phylotypes of anaerobic sludge digesters under different operational conditions. Water Res. 2016;102:158–69.CAS 
PubMed 

Google Scholar 
Bedoya K, Hoyos O, Zurek E, Cabarcas F, Alzate JF. Annual microbial community dynamics in a full-scale anaerobic sludge digester from a wastewater treatment plant in Colombia. Sci Total Environ. 2020;726:138479.CAS 
PubMed 

Google Scholar 
Murphy KC, Fenton AC, Poteete AR. Sequence of the bacteriophage P22 Anti-RecBCD (abc) genes and properties of P22 abc region deletion mutants. Virology. 1987;160:456–64.CAS 
PubMed 

Google Scholar 
Millman A, Bernheim A, Stokar-Avihail A, Fedorenko T, Voichek M, Leavitt A, et al. Bacterial retrons function in anti-phage defense. Cell. 2020;183:1551–61.CAS 
PubMed 

Google Scholar 
Pawluk A, Davidson AR, Maxwell KL. Anti-CRISPR: discovery, mechanism and function. Nat Rev Microbiol. 2018;16:12–7.CAS 
PubMed 

Google Scholar 
Jonge PA, de, Nobrega FL, Brouns SJJ, Dutilh BE. Molecular and evolutionary determinants of bacteriophage host range. Trends Microbiol. 2018;27:51–63.PubMed 

Google Scholar 
Daly RA, Roux S, Borton MA, Morgan DM, Johnston MD, Booker AE, et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat Microbiol. 2019;4:352–61.CAS 
PubMed 

Google Scholar 
Salmond GPC, Fineran PC. A century of the phage: past, present and future. Nat Rev Microbiol. 2015;13:777–86.CAS 
PubMed 

Google Scholar 
Rastogi S, Liberles DA. Subfunctionalization of duplicated genes as a transition state to neofunctionalization. BMC Evol Biol. 2005;5:28.PubMed 
PubMed Central 

Google Scholar 
Petitjean C, Makarova KS, Wolf YI, Koonin EV. Extreme deviations from expected evolutionary rates in archaeal protein families. Genome Biol Evol. 2017;9:2791–811.CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson CL, Sullivan MB, Fernando SC. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome. 2017;5:155.PubMed 
PubMed Central 

Google Scholar 
Gao S-M, Schippers A, Chen N, Yuan Y, Zhang M-M, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.PubMed 
PubMed Central 

Google Scholar 
Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfennig N, Widdel F, Trüper HG. The prokaryotes, A handbook on habitats, isolation, and identification of bacteria. Springer-Verlag, Berlin, Germany. 1981.Moran MA, Durham BP. Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol. 2019;17:665–78.CAS 
PubMed 

Google Scholar 
Kumar S, Cheng X, Klimasauskas S, Sha M, Posfai J, Roberts RJ, et al. The DNA (cytosine-5) methyltransferases. Nucleic Acids Res. 1994;22:1–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashcroft AE, Lago H, Macedo JMB, Horn WT, Stonehouse NJ, Stockley PG. Engineering thermal stability in RNA phage capsids via disulphide bonds. J Nanosci Nanotechno. 2005;5:2034–41.CAS 

Google Scholar 
Walter M, Fiedler C, Grassl R, Biebl M, Rachel R, Hermo-Parrado XL, et al. Structure of the receptor-binding protein of bacteriophage Det7: a podoviral tail spike in a Myovirus. J Virol. 2008;82:2265–73.CAS 
PubMed 

Google Scholar 
Shai Y. Mode of action of membrane active antimicrobial peptides. Peptide Sci. 2002;66:236–48.CAS 

Google Scholar 
Thevissen K, Ferket KKA, François IEJA, Cammue BPA. Interactions of antifungal plant defensins with fungal membrane components. Peptides. 2003;24:1705–12.CAS 
PubMed 

Google Scholar 
Broderick JB, Duffus BR, Duschene KS, Shepard EM. Radical S-adenosylmethionine enzymes. Chem Rev. 2014;114:4229–317.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wildschutte H, Preheim SP, Hernandez Y, Polz MF. O‐antigen diversity and lateral transfer of the wbe region among Vibrio splendidus isolates. Environ Microbiol. 2010;12:2977–87.CAS 
PubMed 

Google Scholar 
Samuel G, Reeves P. Biosynthesis of O-antigens: genes and pathways involved in nucleotide sugar precursor synthesis and O-antigen assembly. Carbohyd Res. 2003;338:2503–19.CAS 

Google Scholar 
Polz MF, Alm EJ, Hanage WP. Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet. 2013;29:170–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Markine-Goriaynoff N, Gillet L, Etten JLV, Korres H, Verma N, Vanderplasschen A. Glycosyltransferases encoded by viruses. J Gen Virol. 2004;85:2741–54.CAS 
PubMed 

Google Scholar 
Clifford JC, Rapicavoli JN, Roper MC. A rhamnose-rich O-antigen mediates adhesion, virulence, and host colonization for the xylem-limited phytopathogen Xylella fastidiosa. Mol Plant-microbe Interac. 2013;26:676–85.CAS 

Google Scholar 
Trueba G, Zapata S, Madrid K, Cullen P, Haake D. Cell aggregation: a mechanism of pathogenic Leptospira to survive in fresh water. Int Microbiol Official J Span Soc Microbiol. 2004;7:35–40.
Google Scholar 
Trunk T, Khalil HS, Leo JC. Norway BCSG Section for Genetics and Evolutionary Biology, Department of Biosciences, University of Oslo, Oslb,. Bacterial autoaggregation. Aims Microbiol. 2018;4:140–164.CAS 
PubMed 
PubMed Central 

Google Scholar 
Guan S, Bastin DA, Verma NK. Functional analysis of the O antigen glucosylation gene cluster of Shigella flexneri bacteriophage SfX. Microbiology. 1999;145:1263–73.CAS 
PubMed 

Google Scholar 
Rakhuba DV, Kolomiets EI, Dey ES, Novik GI. Bacteriophage receptors, mechanisms of phage adsorption and penetration into host cell. Pol J Microbiol. 2010;59:145–55.CAS 
PubMed 

Google Scholar 
Silva JB, Storms Z, Sauvageau D. Host receptors for bacteriophage adsorption. FEMS Microbiol Lett. 2016;363:fnw002.
Google Scholar 
Tsuzuki K, Kimura K, Fujii N, Yokosawa N, Oguma K. The complete nucleotide sequence of the gene coding for the nontoxic-nonhemagglutinin component of Clostridium botulinum type C progenitor toxin. Biochem Bioph Res Co. 1992;183:1273–9.CAS 

Google Scholar 
Enav H, Mandel-Gutfreund Y, Béjà O. Comparative metagenomic analyses reveal viral-induced shifts of host metabolism towards nucleotide biosynthesis. Microbiome. 2014;2:9.PubMed 
PubMed Central 

Google Scholar 
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.PubMed 
PubMed Central 

Google Scholar 
Anderson RE, Reveillaud J, Reddington E, Delmont TO, Eren AM, McDermott JM, et al. Genomic variation in microbial populations inhabiting the marine subseafloor at deep-sea hydrothermal vents. Nat Commun. 2017;8:1114.PubMed 
PubMed Central 

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

Google Scholar 
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 

Google Scholar 
Lu J, Salzberg SL. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome. 2020;8:124.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. Peerj Comput Sci. 2017;3:e104.
Google Scholar 
Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021;10:e65088.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.PubMed 
PubMed Central 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. Peerj. 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;3:1043–55.
Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;6:1925–7.
Google Scholar 
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.PubMed 
PubMed Central 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.CAS 
PubMed 

Google Scholar 
Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–W251.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lowe TM, Eddy SR. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucleic Acids Res. 1997;25:955–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar 
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2019;36:2251–52.PubMed Central 

Google Scholar 
Mistry J, Bateman A, Finn RD. Predicting active site residue annotations in the Pfam database. BMC Bioinform. 2007;8:298.
Google Scholar 
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pratama AA, Bolduc B, Zayed AA, Zhong Z-P, Guo J, Vik DR, et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. Peerj. 2021;9:e11447.PubMed 
PubMed Central 

Google Scholar 
Zimmermann L, Stephens A, Nam S-Z, Rau D, Kübler J, Lozajic M, et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol. 2018;430:2237–43.CAS 
PubMed 

Google Scholar 
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MJ, Petty NK, Beatson SA. Easyfig: a genome comparison visualizer. Bioinform Oxf Engl. 2011;27:1009–10.CAS 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, Haeseler Avon, et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:gkab301-.
Google Scholar 
Dick GJ, Andersson AF, Baker BJ, Simmons SL, Thomas BC, Yelton AP, et al. Community-wide analysis of microbial genome sequence signatures. Genome Biol. 2009;10:R85–R85.PubMed 
PubMed Central 

Google Scholar  More