Ecology

Sample collection from sea turtlesIn total, 45 serum samples from 33 juvenile green turtles (C. mydas), including 6 sea turtles with tumors, 5 juvenile hawksbill turtles (Eretmochelys imbricate), and 7 olive ridley turtles (Lepidochelys olivacea) (juvenile = 5; sub-adult = 2). All turtles were sourced from: eastern Taiwan (n = 24), southern Taiwan (n = 14), central Taiwan (n = 6), and northern Taiwan (n = 1). Among the 45 sea turtle samples, 6 green turtles developed FP (n = 1 with tumor score 1; n = 1 with tumor score 2; n = 4 with tumor score 3)32, while 39 did not have FP. FP tumor tissues were collected from 6 green turtles (from shoulder/flippers/inguinal regions) with FP during surgical procedures. Regarding the collection of normal skin tissues, one normal skin tissue (from shoulder) was collected from one necropsied dead green turtles (stranding and discovered from southern Taiwan) confirmed without FP. All tissue samples were fixed in 10% neutral buffered formalin prior to further analysis. In this study, all sea turtles were discovered and rescued through the official reporting system of the Marine Animal Rescue Network (established by the Ocean Conservation Administration) and admitted to the National Museum of Marine Biology and Aquarium (NMMBA), between 2017 and 2020.Detection of ChHV5 DNA by polymerase chain reaction (PCR)Total DNA was extracted from blood of 45 sea turtles by DNeasy blood & tissue kit (Cat. No. 69504, Qiagen, Valencia, CA, USA) following manufacturer’s instructions. Subsequently, the ChHV5 infection status all 45 sea turtles was determined by PCR using primers targeting on UL18 (capsid protein gene), UL22 (glycoprotein H gene), and UL27 (glycoprotein B gene) regions4. The sequence of primer sets are: UL18F: 5′-CACCACGAGGGGGAAAATGA, UL18R:5′-TCAAATCCCCCGTTCACTCG; UL22F: 5′-ACGGCGTTGGCTAGTGAATC, UL22R: 5′-GCAGTTCGGTACACACCTCT; UL27F: 5′-TAACAAGAAAGAACCGCGCG; UL27R: 5′-ATTTTCCCGGTCAGTGCCAA. PCR amplifications were performed in a total volume of 50 μl. The reaction included 1 μl of the template DNA, 1 μl of each primer (10 μM), 22 μl of distilled water (DDW), and 25 μl of the AmpliTaq Gold® 360 Master Mix (Cat. No. 4398876, Life Technologies, Valencia, CA, USA). The thermocycle for amplification was: Initial denaturing at 95 °C for 10 min, followed by 40 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 60 s, and then a final extension at 72 °C for 7 min. Results were visualized by gel electrophoresis (2% agarose) with SYBR Safe DNA Gel Stain (Cat. No. S33102, Invitrogen, Carlsbad, CA, USA).Sequence optimization of the UL27 gene for expression of the ChHV5 glycoprotein protein using E. coli
To express large quantities of ChHV5 gB, we adopted the prokaryotic Escherichia coli (E. coli) expression system. The construct (namely UL27/pUC57) containing sequences of the full length UL27 fused with FLAG tag sequence (GenBank accession no. AF035003.3) was synthesized by Allbio Science Co., Ltd, Taiwan. The sequence information of the glycoprotein (gB) datasets used and analyzed for protein expression during the current study was obtained and available from the GenBank repository [https://www.ncbi.nlm.nih.gov/nuccore/AF035003.3]. Considering the difference in tRNA-codon usages between prokaryotes and eukaryotes would possibly affect subsequent protein expression, the optimized UL27 gene sequence, without altering the translated amino acid sequences, to fit the E. coli expression system was synthesized. The codon optimized UL27 gene was further used as the template for amplification of different gene fragments by Polymerase Chain Reaction (PCR).Construction of plasmids expressing partial fragments of ChHV5 gB proteinTo determine the relative antigenicity and also to increase the expression yield, plasmids expressing various regions of gB protein were constructed. Briefly, the five regions covering different fragments of the UL27 gene were amplified from plasmid UL27/pUC57 by PCR using specific primer sets with built-in restriction enzyme sequences shown as underlined in Table 1. The thermal cycling conditions were: 98 °C (5 min) followed by 35 cycles of denaturation (98 °C, 30 s), annealing (58 °C, 1 min), and extension (72 °C, 2 min), and finished with a final extension (72 °C, 10 min). PCR amplicons with expected sizes were isolated from gel and trimmed with the restriction enzymes followed by ligation with vectors either pET24a or pET32b (Novagen, Germany) linearized with the same restriction enzymes. The resulting plasmids with expected insert sizes as confirmed by restriction enzymes were sent for automated DNA sequencing (Mission Biotech, Taipei, Taiwan).Table 1 Information on the constructs expressing the UL27 fragments. The bold characters indicate sequences recognized by restriction enzymes for the ease of further cloning procedure.Full size tableExpression of recombinant gB fragments in E. coli
In the current study, the recombinant gB protein is a key reagent that served as antigen for seroprevalence of ChHV5 as well as for the generation of ChHV5 gB antibody (conducted by Yao-Hong Biotech Inc., Taiwan). The plasmids expressing individual gB fragment were transformed into E. coli host cells, strain BL21 (DE3), Rosetta. Expression of all the recombinant gB fragments was induced by 0.8 mM of isopropyl β-d-1-thiogalactopyranoside (IPTG) at 28 °C for 16 h. As all the gB fragments cloned into the pET series vectors were expressed as a fusion protein with a 6-histidine tag at C-terminus end, they could be further purified by Ni–NTA column chromatography using the chelating Sepharose Fast Flow (GE Healthcare) following the method described in one previous study33. The yield and purity of recombinant gB proteins were confirmed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Subsequently, 6 M urea and 0.4 M imidazole contained in the purified protein were depleted by step-wise dialysis against 1 × PBS buffer (0.02 M phosphate, 0.15 M NaCl) with gradually decreased concentrations of urea at 4 °C. The concentration of recombinant proteins were then estimated by National Institutes of Health ImageJ software (https://imagej.nih.gov/ij/, 1997–2018.) using the standard curve established by bovine serum albumin (BSA) with known concentrations42.Western blot analysisRecombinant gB fragments were separated by 12.5% or 15% SDS-PAGE and electrotransferred to PVDF membrane by using Mini Proten III apparatus (Cat. No. 165-3301, BioRad). The filters were blocked in PBS-T buffer (0.02 M phosphate, 0.15 M NaCl, 0.05% Tween-20) containing 5% skimmed milk and reacted with mouse anti-his tag antibody (1:5,000, Cat. No. GTX40628, GeneTex) at 4 °C for overnight. After six-time wash with PBS-T buffer, the PVDF filter was then incubated with the secondary antibody, 1:5000 diluted goat anti-mouse IgG conjugated with horseradish peroxidase (HRP), or 1:500 diluted Protein A/G-HRP (Cat. No. 32400, Thermo fisher scientific™, United States) for sea turtle antibody detection, at room temperature for 1 h followed by PBS-T wash to remove the unbound antibodies. Ultimately, the signal was detected by ECL reagents (Thermo Fisher Scientific, United States) and the image was acquired by ImageQuant LAS 4000 Mini (GE Healthcare).Immunohistochemical (IHC) analysisTo establish IHC protocol, normal skin tissue from PCR-negative sea turtles served as the negative control. In total, the FP on skin tissue from six individual sea turtles that were detected positive for ChHV5 DNA (positive tissue samples), and one normal tissue detected negative (the negative tissue) were included in the IHC analysis.IHC procedure was conducted as reported in our previous study34. In brief, sections of formalin-fixed and wax-embedded skin tissues of sea turtles were made using a rotary microtome (Leica RM2245, Leica Biosystems, Germany) and were further deparaffinized and rehydrated. Antigen retrieval was carried out by heat-induced epitope retrieval method: slides immersed into boiled sodium citrate buffer (0.01 M, pH 6.0), which was preheated up to 100 °C, for 20 min and cooled at room temperature for 20 min. Subsequently, the slides were incubated with peroxidase-blocking reagent (Cat. No. S200389, Dako, Denmark) for 30 min, and then treated with or without primary antibodies (the anti-gB serum prepared from this study). In each interval of the following procedures, sections were rinsed with a mixture of TBST buffer. Tissue sections were then reacted with secondary antibody (HRP anti-rabbit/mouse, DAKO, Denmark), followed by incubation of DAB and chromogen (dilution 1 μL in 100 μL) from a commercial ChemMate EnVision detection kit (Cat. No. K5007, Dako, Denmark). Ultimately, tissue sections were counterstained with Mayer’s hematoxylin reagents (Code S3309, Dako, Denmark) for 2 min followed by wash with DDW, and reacted with 37 mM ammonia water for 5 s and rinsed with DDW.Immunofluorescent assay (IFA)Human 293 T cells were transfected with plasmids expressing full-length ChHV5 gB protein fused with FLAG tag at its C-terminus. At 24 h post transfection, 293 T cells (CRL-3216, ATCC, USA) were fixed with 2% formaldehyde for 10 min, followed by permeabilization with 0.1% Triton X-100 for another 10 min. Subsequently, cells were incubated with anti-FLAG antibody (1:500) (F7425; Sigma-Aldrich), or antisera (F1, F2, F3, F2–3) at the dilution of 1:500 for 1 h at room temperature. After six times of washes with PBS containing1% bovine serum, goat anti-mouse IgG (1:2,000 fold diluted) (Cat. No. A28175, Alexa Fluor® 488, Invitrogen) was used as secondary antibody. After one-hour incubation, nuclei were stained with 4, 6-diamidino-2-phenylindole (DAPI, Cat. No. D9542, Sigma-Aldrich) for 10 min, followed by confocal microscopy (FV1000, Olympus, Tokyo, Japan) with Olympus FV10-ASW 1.3 viewer software.Statistical analysisTo evaluate the association between seropositivity and FP or viremia tested by PCR of UL27 gene, Fisher’s exact test was applied due to very limited number of sea turtles with FP. The statistical significance was determined by p  More

Prosopis juliflora leaves collection and processing for RibotypingProsopis juliflora species of the genus Prosopis, family of Fabaceae had its genetic variation in Doha evaluated. Seven samples of P. juliflora leaves were collected from six different locations in Doha, Qatar, during five field trips. Plant leaves were collected after proper permissions and all methods were carried out in accordance with relevant guidelines and regulations. Trees in all locations were naturally growing around urbanization areas in their normal arid habitat without artificial irrigation, samples were collected from fully mature trees. Table 1 shows the samples details. Figure 1 shows the location sites of where the samples were collected on the map of Qatar, Doha. Leaf samples were kept in sterile labeled bags until having reached the laboratory where few leaflets were washed with sterile distilled water and sterilized using 70% ethanol to be used for DNA extraction.Table 1 Location details of the collection sites of P. juliflora leaves.Full size tableFigure 1Location map of collection sites of P. juliflora leaf samples (ArcGIS software).Full size imageRibotyping analysisThe leaflets of each sample were transferred into a sterile mortar previously cooled at -20 ˚C and used for DNA extraction following the kit manufacturer instructions (DNeasy Plant Mini Kit-QIAGEN-USA).Extracted DNA of each sample were subject to PCR using ITS1 and ITS4 primers. PCR products obtained were purified using the Invitrogen Quick PCR Purification Kit (QIAGEN, Germany) as indicated by the manufacturer and sequenced using Sanger sequencer (3130/3130xl DNA Analyzers, Thermofisher Scientific, USA) as previously described22.Sanger sequencer raw data was read using BioEdit software. Basic Local Alignment Search Tool (BLAST) network services of the National Centre for Biotechnology Information (NCBI) database were used to compare the obtained sequences to the existing sequences. Sequences were submitted to NCBI for accession numbers. The various P. juliflora ribosomal sequences obtained were also uploaded on MEGA-X software and the phylogeny tree was generated using the neighbor-joining algorithm26.Minimum inhibitory concentrations of PJ-WS-LE extracts prepared using leaf samples collected from various locations against A. alternata and C. gloeosporioides
Preparation of PJ-WS-LE extractFresh, young full leaves of P. juliflora were collected from various locations as indicated in Fig. 1. Samples were washed, dried and ground into powder to be used in the preparation of PJ-WS-LE extract as previously described22. Briefly, every 20 g of the leaf powder were incubated in 200 mL of 70% ethanol for 48 h. The supernatant has its solvent evaporated, the extract was then re-dissolved in sterile distilled water. Only water-soluble phytochemicals were tested by centrifuging the final preparation tubes and excluding the pellet. Stock solution of 100 g L−1 was stored at 4 °C to be used for later experiments. PJ-WS-LE extract concentration used in treatments was 8 g L−1 which is double the highest minimum inhibitory concentration of the extract against spoiling microorganisms as previously determined22.Determination of minimum inhibitory concentrationThe MIC test was conducted in a sterile 96-well plate, with each well containing 100 μl of potato dextrose broth (PDB) (HIMEDIA-India). Every four wells made one replication, nine different concentrations of the crude extracts were tested (1:1 dilutions) ranging from 42 to 0.16 g L−1. Wells were then inoculated with one of the two tested microorganisms’ spore suspensions (A. alternata and C. gloeosporioides). The last three rows are control rows: no spores and no extract control wells, negative control with spores but no extract wells, and positive control with spores and 10 µl of the fungicidal Clatrimazole (1%) wells.Fungal spore suspensions were adjusted to the range of 104 spores L−1 using a 10 day old fungal plate and sterile distilled water, the spore concentration was calculated using a heamatocytometer.Fungal growth in each well was monitored using Resazurin (HIMEDIA-India) dye. Upon cells division, Resazurin changes its color from blue to pink and fluorescent27. Results were taken within 48 h of incubation at 25 °C. MIC was recorded as the last extract concentration that shows no change in the color of Resazurin within the incubation period.Curative and preventive effects of PJ-WS-LE extract against A. alternata and C. gloeosporioides induced infection in mangoesPathogensThe two fungal strains used C. gloeosporioides and A. alternata were obtained from our laboratory collection, Department of Biological and Environmental Sciences, Qatar University, Qatar. Both fungal isolates were previously isolated from locally collected fruit samples. Isolates were molecularly identified by sequencing the Internal Transcribed Spacer (ITS) regions of fungal ribosomal DNA (rDNA) that was amplified by PCR. Identified fungal isolates were given the strains code of AaltQU17 for A. alternata and CgloQU17 for C. gloeosporioides22. Preserved cultures were sub-cultured on potato dextrose agar (PDA) plates and incubated at 25 °C for 10 days. Plates were then flooded with 10 mL of sterile distilled water each, to prepare the needed spores suspension solutions. The concentrations of spores suspensions were adjusted to 106 spores L−1 using a heamatocytometer18.FruitThe mango (Mangifera indica) type known as Neelam imported from India was used in the experiments. Fruit were bought from the whole sale market upon their arrival to the country. Only undamaged mature fruit were used in the experiment. Fruits chosen were ripen but not yet soft with firmness average of 20 ± 5.1 N, weight average of 177.61 ± 0.2 g and TSS average of 70 ± 5.3%. Fruit were first washed with sink water and sterilized twice with 70% ethanol to be then washed with sterile distilled water and left to air dry.Preventive and curative effects of PJ-WS-LE extractWounded mango fruit were used during the experiment, the wounds were made through three needle pricks (2 mm deep) in three different places for each plant using a sterile syringe. A completely randomized design was used and each treatment was made of a triplicate of 10 fruit each. The experiment was repeated twice.PJ-WS-LE extract of leaves collected from Qatar university field was first tested for its efficacy in preventing fungal contamination in wounded mango fruit (preventive effect). Therefore, the wounded zone of each fruit was sprayed with 8 g L−1 PJ-WS-LE extract and then left to air-dry. Once dry the fruit were sprayed again with the extract at the same concentration and left to dry. Control fruit were only treated with sterile distilled water without the plants extract. After two hours all wounds were inoculated with 20 μL of conidia aqueous solution (106 spores mL−1) of one of the tested fungi. The extract was then tested for its ability to cure fungal contamination in wounded fruit. Therefore, wounds were inoculated first with 20 μL of conidia aqueous solution (106 spores mL−1) and left to dry. Wounds were then sprayed twice with 8 g L−1 PJ-WS-LE extract.All mangoes were stored in sterilized plastic trays inside an incubator at 25 °C and 75% humidity. Fruit were observed every 24 h for 5 days for C. gloeosporioides inoculated fruit and for 10 days for A. alternata inoculated fruit. Three parameters were recorded at the end of the experiment: disease incidence (DI), disease severity (DS), and percent plant extract efficacy (%EE). To calculate disease severity, the diameter of the infected area of each fruit was measured in two perpendicular directions and mean diameter mycelial growth was calculated28,29.$$mathrm{DI}=frac{(mathrm{Number, of, rotten, fruit})times 100}{mathrm{Total, number, of, fruit}}$$$$mathrm{DS }=frac{(mathrm{Average, lesion, diameter, of, treated, plants})times 100}{mathrm{Average, Lesion, diameter, of, control, plants})}$$$$mathrm{%EE}=frac{(mathrm{Disease, incidence, in, Control, batch}-mathrm{Disease, incidence, in, treated, batch})times 100}{mathrm{Disease, incidence, in, Control, batch}}$$End of the trial samples firmnessAt the end of the trial, remaining mango fruit were tested for their flesh quality using a penetrometer (Agriculture Solutions, USA) to test the flesh firmness. Fruit were peeled, then the stainless steel probe of the instrument was inserted in three different points towards the equator of the fruit. Firmness in Newton was recorded and compared with standard fruit firmness to judge fruit quality18.Effectiveness of PJ-WS-LE extract as long-term coating material and the preservative value of its chitosan-embedded formCoating solutions preparationChitosan solution of 1% concentration was prepared by stirring chitosan powder (CAS 9012-76-4, Himedia, India) in 1% glacial acetic acid (IsoLab, Germany) overnight. The final chitosan solution pH was adjusted to 5.6 using 0.1 M NAOH (Sigma-Aldrich, Germany). To prepare PJ-WS-LE extract chitosan-embedded coating material, filter-sterilized PJ-WS-LE extract stock solution was added to 1% chitosan to achieve a final concentration of 8 g L−130.Samples preparationEighty-four mango samples chosen as described above, were divided into four groups of 18 samples each. Samples were divided into four treatment batches and treated as following:

Batch A: non-treated fruit.

Batch B: PJ-WS-LE extract at 8 g L–1 was used to spray the fruit.

Batch C: 1% chitosan was used to spray the fruit.

Batch D: 8 g L−1 PJ-WS-LE extract embedded in 1% chitosan was used to spray the fruit.

Every experimental replicate was made up of three mango samples that were stored together in one sterile bag at 4 °C. The number of replications per treatment was six. The experiment was repeated twice31.Evaluation of sensory qualityA five-points scale was used for the evaluation of the sensory quality of the samples for overall quality, smell, and color change. The attributes were evaluated weekly using the fruit of one experimental replicate. Scores were given using the following scale: 5 points indicate “extremely liked”, 4 points indicate “liked”, 3 points indicate “acceptable” 2 points indicate “disliked” and 1 point indicates “extremely disliked”. The weekly average score per batch was also calculated32.Estimation of weight lossUpon treatment at day zero, all mango samples were weighed and their weights were recorded as initial weights. Weights of all remaining samples were measured at the end of every week. The variation between the start weight and weekly weights is calculated as weekly weight loss. The average percent of weekly weight loss of each batch was calculated32.Determination of samples firmnessThe samples of each experimental replicate evaluated on a weekly basis had their firmness measured as previously described. The weekly average samples firmness (N) of every treatment batch was also calculated33.pH measurementMango fruit of each experimental replicate were blended weekly into juice, after filtration, a digital pH meter (Jenway, UK) was used to measure pH. The weekly average fruit pH of every treatment batch was also calculated. The pH meter was calibrated using a buffer solution of pH 734.Total soluble solids (TSS) measurementTotal soluble solids of the prepared mango juice samples were measured in percent brix using a refractometer (ANTAHI, New Zealand). The weekly average fruit TSS (%) for each treatment batch was also calculated. The refractometers was calibrated using distilled water35.DPPH radical scavenging assayA 1/10 mango juice dilution was prepared using sterile distilled water. 100 μL of each dilution was mixed with 1 mL of 2,2-diphenyl-1-picrylhydrazyl (DPPH) (100 mg L−1) to be incubated in the dark at 37 °C for 45 min. After incubation, samples were centrifuged and the pellet was discarded. The intensity of the change in color of the supernatant was measured by spectrophotometry at 517 nm using methanol as a blank. 100 μL of methanol in 1 mL DPPH was used as the control for the experiment. Percent radical scavenging activity was calculated as per the below formula:$$ % {text{ radical scavenging activity}}, = ,left( {{text{absorbance of the control solution}} – {text{ absorbance of the juice sample}}} right)*{1}00/{text{absorbance of the control solution}}. $$The weekly average % radical scavenging activity for each treatment batch was finally calculated31.Statistical analysisThe experimental design used was Completely Randomized Design (CRD). One-way ANOVA followed by Tukey Post-Hoc test was used to evaluate the significance of the weekly percent change in weight among treatment batches at P ≤ 0.05. The significances of pH and TSS variation within different treatment batches were evaluated using One-way ANOVA test at P ≤ 0.05. Data was presented as average ± standard error of the Means (SEM). SPSS (Ver. 27, SPSS Inc. Chicago, USA) was used to perform the statistical analysis tests. More

Printzen, C. & Lumbsch, H. T. Molecular evidence for the diversification of extant lichens in the late cretaceous and tertiary. Mol. Phylogenet. Evol. 17, 379–387 (2000).CAS 
PubMed 
Article 

Google Scholar 
Kraichak, E. et al. A Tale of two Hyper-diversities: Diversification dynamics of the two largest families of lichenized fungi. Sci. Rep. https://doi.org/10.1038/srep100288 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Leavitt, S. D., Lumbsch, H. T., Stenroos, S. & Clair, L. L. S. Pleistocene speciation in North American lichenized fungi and the the impact of alternative species circumscriptions and rates of molecular evolution on divergence estimates. PLoS ONE 8, e85240 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gaya, E. et al. The adaptive radiation of lichen-forming Teloschistaceae is associated with sunscreening pigments and bark-to-rock substrate shift. PNAS 112, 11600–11605 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schneider, K., Resl, P. & Spribille, T. Escape from the cryptic species trap: Lichen evolution on both sides of a cyanobacterial acquisition event. Mol. Ecol. 25, 3453–3468 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Widhelm, T. J. et al. Oligocene origin and drivers for diversification in the genus Sticta (Lobariaceae, Ascomycota). Mol. Phylogenetic Evol. 126, 58–73 (2018).Article 

Google Scholar 
Vamosi, J. C. & Vamosi, S. M. Factors influencing diversification in angiosperms: At the crossroads of intrinsic and extrinsic traits. Am. J. Bot. 98, 460–471 (2011).PubMed 
Article 

Google Scholar 
Wagner, C. E., Harmon, L. J. & Seehausen, O. Ecological opportunity and sexual selection together predict adaptive radiation. Nature 487, 366–369 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Karunarathne, P. et al. Intraspecific ecological niche divergence and reproductive shifts foster cytotype displacement and provide ecological opportunity to polyploids. Ann. Bot. 121, 1183–1196 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Nakov, T., Beaulieu, J. & Alverson, A. Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta). New Phytol. 219, 462–473 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Tripp, E. A. Is asexual reproduction an evolutionary dead end in lichens?. Lichenologist 48, 559–580 (2016).Article 

Google Scholar 
Tripp, E. A. & Lendemer, J. C. Twenty-seven modes of reproduction in the obligate lichen symbiosis. Brittonia 70, 1–14 (2018).Article 

Google Scholar 
Bowler, P. A. & Rundell, P. W. Reproductive strategies in lichens. Bot. J. Linn. Soc. 70, 325–340 (1975).Article 

Google Scholar 
Honegger, R. Developmental biology of lichens. New Phytol. 125, 659–677 (1993).PubMed 
Article 

Google Scholar 
Buschbom, J. & Mueller, G. M. Testing “Species Pair” hypotheses: Evolutionary processes in the lichen-forming species complex Porpidia flavocoerulescens and Porpidia melinodes. Mol. Biol. Evol. 23, 574–586. https://doi.org/10.1093/molbev/msj063 (2006).CAS 
Article 
PubMed 

Google Scholar 
Sanders, W. B. Complete life cycle of the lichen fungus Calopadia puiggarii (Pilocarpaceae, Ascomycetes) documented in situ: Propagule dispersal, establishment of symbiosis, Thallus development, and formation of sexual and asexual reproductive structures. Am. J. Bot. 101, 1836–1848 (2014).PubMed 
Article 

Google Scholar 
Poelt, J. Flechtenflora und eiszeit in Europa. Phyton (Horn) 10, 206–214 (1963).
Google Scholar 
Stofer, S. et al. Species richness of lichen functional groups in relation to land use intensity. Lichenologist 38, 331–353 (2006).Article 

Google Scholar 
Ludwig, L. R., Summerfield, T. C., Lord, J. M. & Singh, G. Characterization of the mating-type locus (MAT) reveals a heterothallic mating system in Knightiella splachnirima. Lichenologist 49, 373–385 (2017).Article 

Google Scholar 
Czarnota, P. The lichen genus Micarea (Lecanorales, Ascomycota) in Poland. Pol. Bot. Stud. 23, 1–190 (2007).
Google Scholar 
Czarnota, P. & Guzow-Krzemińska, B. A phylogenetic study of the Micarea prasina group shows that Micarea micrococca includes three distinct lineages. Lichenologist 42, 7–21 (2010).Article 

Google Scholar 
Sérusiaux, E., Brand, A. M., Motiejūnaitè, J., Orange, A. & Coppins, B. J. Lecidea doliiformis belongs to Micarea, Catillaria alba to Biatora and Biatora lignimollis occurs in Western Europe. Bryologist 113, 333–344 (2010).Article 

Google Scholar 
van den Boom, P., Brand, A., Coppins, B. & Sérusiaux, E. Two new species in the Micarea prasina group from Western Europe. Lichenologist 49, 13–25 (2017).Article 

Google Scholar 
Guzow-Krzemińska, B., Czarnota, P., Łubek, A. & Kukwa, M. Micarea soralifera sp. nov., a new sorediate species in the M. prasina group. Lichenologist 48, 161–169 (2016).Article 

Google Scholar 
Guzow-Krzemińska, B. et al. Understanding the evolution of phenotypical characters in the Micarea prasina group (Pilocarpaceae) and descriptions of six new species within the group. MycoKeys 57, 1–30 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Kantvilas, G. & Coppins, B. J. Studies on Micarea in Australasia II. A synopsis of the genus in Tasmania, with the description of ten new species. Lichenologist 51, 431–481 (2019).Article 

Google Scholar 
Launis, A. & Myllys, L. Micarea fennica, a new lignicolous lichen species from Finland. Phytotaxa 409, 179–188 (2019).Article 

Google Scholar 
Launis, A., Pykälä, J., van den Boom, P., Sérusiaux, E. & Myllys, L. Four new epiphytic species in the Micarea prasina group from Europe. Lichenologist 51, 7–25 (2019).Article 

Google Scholar 
Launis, A. et al. Sharpening species boundaries in the Micarea prasina group, with a new circumscription of the type species M. prasina. Mycologia 111, 574–592 (2019).CAS 
PubMed 
Article 

Google Scholar 
van den Boom, P., Guzow-Krzemińska, B. & Kukwa, M. Two new Micarea species (Pilocarpaceae) from Western Europe. Plant Fungal Syst. 65, 189–199. https://doi.org/10.35535/pfsyst-2020-0014 (2020).Article 

Google Scholar 
Kantelinen, A., Hyvärinen, M., Kirika, P. & Myllys, L. Four new Micarea species from the montane cloud forests of Taita Hills, Kenya. Lichenologist 53, 81–94. https://doi.org/10.1017/S0024282920000511 (2021).Article 

Google Scholar 
Coppins, B. J. A taxonomic study of the lichen genus Micarea in Europe. Bull. Br. Mus. (Nat. Hist.) Bot. 11, 17–214 (1983).
Google Scholar 
Launis, A. & Myllys, L. Micarea byssacea new to North America and Micarea hedlundii new to Maine, Michigan and Quebec. Opusc. Philolichenum 13, 84–90 (2014).
Google Scholar 
Myllys, L. & Launis, A. Additions to the diversity of lichens and lichenicolous fungi living on decaying wood in Finland. Graphis Scr. 30, 78–87 (2018).
Google Scholar 
Yahr, R., Florence, A., Škaloud, P. & Voytsekhovich, A. Molecular and morphological diversity in photobionts associated with Micarea s. str. (Lecanorales, Ascomycota). Lichenologist 47, 403–414 (2015).Article 

Google Scholar 
Spribille, T., Thor, G., Bunnell, F. L., Goward, T. & Björk, C. R. Lichens on dead wood: Species-substrate relationships in the epiphytic lichen floras of the Pacific Northwest and Fennoscandia. Ecography 31, 741–750 (2008).Article 

Google Scholar 
Resl, P., Fernańdez-Mendoza, F., Mayrhofer, H. & Spribille, T. The evolution of fungal substrate specificity in a widespread group of crustose lichens. Proc. R. Soc. B 285, 20180640. https://doi.org/10.1098/rspb.2018.0640 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood 412 (Cambridge University Press, Cambridge, 2012).Book 

Google Scholar 
Russell, M. B., Woodall, C. W., Fraver, S. & D’Amato, A. W. Estimates of downed woody debris decay class transitions for forests across the eastern United States. Ecol. Model. 251, 22–31 (2013).Article 

Google Scholar 
Russell, M. B. et al. Residence times and decay rates of downed woody debris biomass/carbon in eastern US Forests. Ecosystems 17, 765–777 (2014).CAS 
Article 

Google Scholar 
Zoller, S., Lutzoni, F. & Scheidegger, C. Genetic variation within and among populations of the threatened lichen Lobaria pulmonaria in Switzerland and implications for its conservation. Mol. Ecol. 8, 2049–2059 (1999).CAS 
PubMed 
Article 

Google Scholar 
Honegger, R., Zippler, U., Gansner, H. & Scherrer, S. Mating systems in the genus Xanthoria (lichen forming Ascomycetes). Mycol. Res. 108, 480–488 (2004).CAS 
PubMed 
Article 

Google Scholar 
Honegger, R. & Zippler, U. Mating systems in representatives of the Parmeliaceae, Ramalinaceae and Physciaceae (Lecanoromycetes, lichen-forming ascomycetes). Mycol. Res. 11, 424–432 (2007).Article 
CAS 

Google Scholar 
Ament-Velásquez, S. L. et al. The plot thickens: Haploid and triploid-like thalli, hybridization, and biased mating Type Ratios in Letharia. Front. Fungal Biol. 2, 656386. https://doi.org/10.3389/ffunb.2021.656386 (2021).Article 

Google Scholar 
van den Boom, P. & Coppins, B. J. Micarea viridileprosa sp. nov., an overlooked lichen species from Western Europe. Lichenologist 33, 87–91 (2001).Article 

Google Scholar 
Simon, J.-C., Rispe, C. & Sunnucks, P. Ecology and evolution of sex in aphids. Trends Ecol. Evol. 17, 34–39 (2002).Article 

Google Scholar 
Silvertown, J. The evolutionary maintenance of sexual reproduction: Evidence from the ecological distribution of asexual reproduction in clonal plants. Int. J. Plant Sci. 169, 157–168 (2008).Article 

Google Scholar 
Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700. https://doi.org/10.1111/J.1558-5646.2012.01715.X (2012).Article 
PubMed 

Google Scholar 
Dańko, A., Schaible, R. & Dańko, M. J. Salinity effects on survival and reproduction of hydrozoan Eleutheria dichotoma. Estuaries Coasts 43, 360–374. https://doi.org/10.1007/s12237-019-00675-2 (2020).CAS 
Article 

Google Scholar 
Coppins, B. J. & Tønsberg, T. A new xanthone-containing Micarea from Northwest Europe and the Pacific Northwest of North America. Lichenologist 33, 93–96 (2001).Article 

Google Scholar 
Konoreva, L., Chesnokov, S., Kuznetsova, E. & Stepanchikova, I. Remarkable records of Micarea from the Russian Far East and significant extension of Micarea laeta and M. microareolata range. Botanica 25, 186–201 (2019).Article 

Google Scholar 
Weber, L., Printzen, C., Bässler, C. & Kantelinen, A. Seven Micarea (Pilocarpaceae) species new to Germany and notes on deficiently known species in the Bavarian forest. Herzogia 34, 5–17 (2021).Article 

Google Scholar 
van den Boom, P. Some interesting records of lichens and lichenicolous fungi from The Netherlands VI. Osten. Z. Pilzk. 12 (2003).Orange, A., James, P. W. & White, F. J. Microchemical Methods for the Identification of Lichens 101 (British Lichen Society, London, 2010).
Google Scholar 
Meyer, B. & Printzen, C. Proposal for a standardized nomenclature and characterization of insoluble lichen pigments. Lichenologist 32, 571–583 (2000).Article 

Google Scholar 
Culberson, C. F. & Kristinsson, H. D. A standardized method for the identification of lichen products. J. Chromatocraphy A 46, 85–93 (1970).CAS 
Article 

Google Scholar 
Myllys, L. et al. Phylogeny of the genus Bryoria. Lichenologist 43, 617–638 (2011).Article 

Google Scholar 
Myllys, L., Lohtander, K., Källersjö, M. & Tehler, A. Sequence insertion and ITS data provide congruent information in Roccella canariensis and R. tuberculata (Arthoniales, Euascomycetes) phylogeny. Mol. Phylogenetics Evol. 12, 295–309 (1999).CAS 
Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to the Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 
Zoller, S., Scheidegger, C. & Sperisen, C. PCR primers for the amplification of mitochondrial small subunit ribosomal DNA of lichen-forming ascomycetes. Lichenologist 31, 511–516 (1999).Article 

Google Scholar 
Leavitt, S. D., Johnson, L. A., Goward, T. & Clair, L. L. S. Species delimitation in taxonomically difficult lichen-forming fungi: an example from morphologically and chemically diverse Xanthoparmelia (Parmeliaceae) in North America. Mol. Phylogenetics Evol. 60(3), 317–332 (2011).Article 

Google Scholar 
Schmitt, I. et al. New primers for promising single-copy genes in fungal phylogenetics and systematics. Persoonia 23, 35–40 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kauff, F. & Lutzoni, F. Phylogeny of the Gyalectales and Ostropales (Ascomycota, Fungi): Among and within order relationships based on nuclear ribosomal RNA small and large subunits. Mol. Phylogenet. Evol. 25, 138–156 (2002).CAS 
PubMed 
Article 

Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
Article 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huelsenbeck, J. P., Larget, B. & Alfaro, M. E. Bayesian phylogenetic model selection using reversible jump markov chain monte carlo. Mol. Biol. Evol. 21, 1123–1133. https://doi.org/10.1093/molbev/msh123 (2004).CAS 
Article 
PubMed 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior Summarization in bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maddison, D. R. & Maddison, W. P. Mesquite: a modular system for evolutionary analysis. Version 3.40 http://mesquiteproject.org (2018).Pagel, M. Detecting correlated evolution on phylogenies: A general method for the comparative analysis of discrete characters. Proc. R. Soc. B. 255, 37–45 (1994).ADS 
Article 

Google Scholar  More

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