Cui, L., Morris, A. & Ghedin, E. The human mycobiome in health and disease. Genome Med. 5(7), 63. https://doi.org/10.1186/gm467 (2013).
Google Scholar
Kariman, K., Barker, S. J. & Tibbett, M. Structural plasticity in root-fungal symbioses: Diverse interactions lead to improved plant fitness. PeerJ 6, e6030. https://doi.org/10.7717/peerj.6030 (2018).
Google Scholar
Zhang, Y. et al. Fungi-nematode interactions: Diversity, ecology, and biocontrol prospects in agriculture. J. Fungi 6(4), 206. https://doi.org/10.3390/jof6040206 (2020).
Google Scholar
Dighton, J. Fungi in Ecosystem Processes (CRC Press eBooks, 2018). https://doi.org/10.1201/9781315371528.
Google Scholar
Moënne-Loccoz, Y., Mavingui, P., Combes, C. & Steinberg, C. Microorganisms and Biotic Interactions. 395–444 (Springer eBooks, 2014). https://doi.org/10.1007/978-94-017-9118-2_11
Nilsson, R. H. et al. Mycobiome diversity: High-throughput sequencing and identification of fungi. Nat. Rev. Microbiol. 17(2), 95–109. https://doi.org/10.1038/s41579-018-0116-y (2018).
Google Scholar
Tiew, P. Y. et al. The mycobiome in health and disease: Emerging concepts, methodologies and challenges. Mycopathologia 5, 156. https://doi.org/10.1007/s11046-019-00413-z (2020).
Google Scholar
Porras-Alfaro, A. & Bayman, P. Hidden fungi, emergent properties: Endophytes and microbiomes. Annu. Rev. Phytopathol. 49(1), 291–315. https://doi.org/10.1146/annurev-phyto-080508-081831 (2011).
Google Scholar
Vignassa, M. et al. Pineapple mycobiome related to fruitlet core rot occurrence and the influence of fungal species dispersion patterns. J. Fungi 7(3), 175. https://doi.org/10.3390/jof7030175 (2021).
Google Scholar
Pozo, M. J., Zabalgogeazcoa, I., De Aldana, B. R. V. & Martinez-Medina, A. Untapping the potential of plant mycobiomes for applications in agriculture. Curr. Opin. Plant Biol. 60, 102034. https://doi.org/10.1016/j.pbi.2021.102034 (2021).
Google Scholar
Bösch, Y. et al. Dynamics of the apple fruit microbiome after harvest and implications for fruit quality. Microorganisms 9(2), 272. https://doi.org/10.3390/microorganisms9020272 (2021).
Google Scholar
Setati, M. E., Jacobson, D. & Bauer, F. F. Sequence-based analysis of the Vitis vinifera L. cv cabernet sauvignon grape must mycobiome in three South African vineyards employing distinct agronomic systems. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01358 (2015).
Google Scholar
Szymanski, S. et al. The blueberry fruit mycobiome varies by tissue type and fungicide treatment. Phytobiomes J. 7(2), 208–219. https://doi.org/10.1094/pbiomes-04-22-0028-fi (2023).
Google Scholar
Piombo, E. et al. Characterizing the fungal microbiome in date (Phoenix dactylifera) fruit pulp and peel from early development to harvest. Microorganisms 8(5), 641. https://doi.org/10.3390/microorganisms8050641 (2020).
Google Scholar
Bill, M., Viljoen, F., Chidamba, L., Gokul, J. & Korsten, L. Fungal microbiome shifts of avocado fruit from flowering to the ready-to-eat stage. Acta Hortic. 1363, 59–68. https://doi.org/10.17660/actahortic.2023.1363.9 (2023).
Google Scholar
TURKSTAT, Turkish Statistical Institute. Accessed 9 June 2024; https://www.tuik.gov.tr/. (2023).
Bayav, A. & Çetinbaş, M. Peach production and foreign trade of Turkey: Current situation, forecasting and analysis of competitiveness. Anadolu Ege Tarımsal Araştırma Enstitüsü Dergisi 31(2), 212–225. https://doi.org/10.18615/anadolu.1033597 (2021).
Google Scholar
Ozkilinc, H. et al. Species diversity, mating type assays and aggressiveness patterns of Monilinia pathogens causing brown rot of peach fruit in Turkey. Eur. J. Plant Pathol. 157(4), 799–814. https://doi.org/10.1007/s10658-020-02040-7 (2020).
Google Scholar
Jo, Y., Back, C. G., Choi, H. & Cho, W. K. Comparative microbiome study of mummified peach fruits by metagenomics and metatranscriptomics. Plants 9(8), 1052. https://doi.org/10.3390/plants9081052 (2020).
Google Scholar
Bien, S. & Damm, U. Prunus trees in Germany—A hideout of unknown fungi?. Mycol. Progress 19(7), 667–690. https://doi.org/10.1007/s11557-020-01586-4 (2020).
Google Scholar
Ren, N., Dong, N. & Yan, N. Organs, cultivars, soil, and fruit properties affect structure of endophytic mycobiota of pinggu peach trees. Microorganisms 7(9), 322. https://doi.org/10.3390/microorganisms7090322 (2019).
Google Scholar
Newberger, D. R., Minas, I. S., Manter, D. K. & Vivanco, J. M. A microbiological approach to alleviate soil replant syndrome in peaches. Microorganisms 11(6), 1448. https://doi.org/10.3390/microorganisms11061448 (2023).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
Google Scholar
Hoist-Jensen, A., Vaage, M. & Schumacher, T. An approximation to the phylogeny of Sclerotinia and related genera. Nord. J. Bot. 18(6), 705–719. https://doi.org/10.1111/j.1756-1051.1998.tb01553.x (1998).
Google Scholar
Gdanetz, K. & Trail, F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiomes J. 1(3), 158–168. https://doi.org/10.1094/pbiomes-05-17-0023-r (2017).
Google Scholar
Paiva, D. S. et al. Uncovering the fungal diversity colonizing limestone walls of a forgotten monument in the central region of Portugal by high-throughput sequencing and culture-based methods. Appl. Sci. 12(20), 10650. https://doi.org/10.3390/app122010650 (2022).
Google Scholar
Purahong, W. et al. Characterization of unexplored deadwood mycobiome in highly diverse subtropical forests using culture-independent molecular technique. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.00574 (2017).
Google Scholar
Wijayawardene, N. N. et al. Current insight into culture-dependent and culture-independent methods in discovering ascomycetous taxa. J. Fungi 7(9), 703. https://doi.org/10.3390/jof7090703 (2021).
Google Scholar
Durak, M. R., Arslan, K., Silan, E., Yildiz, G. & Ozkilinc, H. A novel approach for in vitro fungicide screening and the sensitivity of Monilinia populations from peach orchards in Turkey to respiratory inhibitor fungicides. Crop Prot. 147, 105688. https://doi.org/10.1016/j.cropro.2021.105688 (2021).
Google Scholar
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37(5), 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
Google Scholar
Poniatowska, A., Michalecka, M. & Puławska, J. Genetic diversity and pathogenicity of Monilinia polystroma—The new pathogen of cherries. Plant Pathol. 65(5), 723–733. https://doi.org/10.1111/ppa.12463 (2015).
Google Scholar
Zhu, X. Q. & Guo, L. Y. First report of brown rot on plum caused by Monilinia polystroma in China. Plant Disease 94(4), 478. https://doi.org/10.1094/pdis-94-4-0478a (2010).
Google Scholar
Vasić, M., Duduk, N. & Ivanović, M. S. First report of brown rot caused by Monilia polystroma on apple in Serbia. Plant Disease 97(1), 145. https://doi.org/10.1094/pdis-07-12-0670-pdn (2013).
Google Scholar
Deltedesco, E., Oettl, S. & Spitaler, U. First report of brown rot caused by Monilinia polystroma on sweet cherry and almond in Italy. Plant Disease 107(7), 2252. https://doi.org/10.1094/pdis-10-22-2482-pdn (2023).
Google Scholar
Spitaler, U., Oettl, S. & Deltedesco, E. First report of brown rot caused by Monilinia polystroma on quince in Italy. Plant Disease 107(1), 229. https://doi.org/10.1094/pdis-06-22-1442-pdn (2023).
Google Scholar
Gautam, A. K. et al. Current insight into traditional and modern methods in fungal diversity estimates. J. Fungi 8(3), 226. https://doi.org/10.3390/jof8030226 (2022).
Google Scholar
Polonio, L., Seoane, P., Claros, M. G. & Pérez-García, A. The haustorial transcriptome of the cucurbit pathogen Podosphaera xanthii reveals new insights into the biotrophy and pathogenesis of powdery mildew fungi. BMC Genom. https://doi.org/10.1186/s12864-019-5938-0 (2019).
Google Scholar
Prusky, D. & Romanazzi, G. Induced resistance in fruit and vegetables: A host physiological response limiting postharvest disease development. Annu. Rev. Phytopathol. 61(1), 279–300. https://doi.org/10.1146/annurev-phyto-021722-035135 (2023).
Google Scholar
Bahram, M. & Netherway, T. Fungi as mediators linking organisms and ecosystems. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuab058 (2021).
Google Scholar
Liu, X. & Zhang, Z. A double-edged sword: reactive oxygen species (ROS) during the rice blast fungus and host interaction. FEBS J. 289(18), 5505–5515. https://doi.org/10.1111/febs.16171 (2021).
Google Scholar
Hanson, M., Petch, G., Ottosen, T. & Skjøth, C. Climate change impact on fungi in the atmospheric microbiome. Sci. Total Environ. 830, 154491. https://doi.org/10.1016/j.scitotenv.2022.154491 (2022).
Google Scholar
Liu, Z. et al. Elevated CO2 and temperature increase arbuscular mycorrhizal fungal diversity, but decrease root colonization, in maize and wheat. Sci. Total Environ. 873, 162321. https://doi.org/10.1016/j.scitotenv.2023.162321 (2023).
Google Scholar
Han, L. et al. Deciphering the diversity, composition, function, and network complexity of the soil microbial community after repeated exposure to a fungicide boscalid. Environ. Pollut. 312, 120060. https://doi.org/10.1016/j.envpol.2022.120060 (2022).
Google Scholar
Sumbula, V., Kurian, P. S., Girija, D. & Cherian, K. A. Impact of foliar application of fungicides on tomato leaf fungal community structure revealed by metagenomic analysis. Folia Microbiologica 67(1), 103–108. https://doi.org/10.1007/s12223-021-00920-x (2021).
Google Scholar
Verde, I. et al. The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat. Genet. 45(5), 487–494. https://doi.org/10.1038/ng.2586 (2013).
Google Scholar
Geneious 9.1.8. https://www.geneious.com
White, T., Bruns, T., Lee, S., & Taylor, J. Amplıfıcatıon and dırect sequencıng of fungal rıbosomal RNA genes for phylogenetıcs. 315–322 (Elsevier eBooks, 1990b). https://doi.org/10.1016/b978-0-12-372180-8.50042-1
Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes—application to the identification of mycorrhizae and rusts. Mol. Ecol. 2(2), 113–118. https://doi.org/10.1111/j.1365-294x.1993.tb00005.x (1993).
Google Scholar
Jo, T., Takao, K. & Minamoto, T. Linking the state of environmental DNA to its application for biomonitoring and stock assessment: Targeting mitochondrial/nuclear genes, and different DNA fragment lengths and particle sizes. Environ. DNA 4(2), 271–283. https://doi.org/10.1002/edn3.253 (2021).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17(1), 10. https://doi.org/10.14806/ej.17.1.200 (2011).
Google Scholar
Abarenkov, K. et al. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa, and classifications reconsidered. Nucleic Acids Res. 52(D1), D791–D797. https://doi.org/10.1093/nar/gkad1039 (2023).
Google Scholar
Robeson, M. S. et al. RESCRIPt: Reproducible sequence taxonomy reference database management. PLOS Comput. Biol./PLoS Comput. Biol. 17(11), e1009581. https://doi.org/10.1371/journal.pcbi.1009581 (2021).
Google Scholar
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x (1948).
Google Scholar
Berger, W. H. & Parker, F. L. Diversity of planktonic foraminifera in deep-sea sediments. Science 168(3937), 1345–1347. https://doi.org/10.1126/science.168.3937.1345 (1970).
Google Scholar
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2021). https://www.R-project.org/
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016). https://ggplot2.tidyverse.org
RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020). http://www.rstudio.com/
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26(3), 297–302. https://doi.org/10.2307/1932409 (1945).
Google Scholar
Jaccard, P. Nouvellesrecherches sur la distribution florale. Bull. Soc. V Sci. Nat. 44, 223–270 (1908).
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: A tool for visualizing high-throughput microbial community data. Gigascience https://doi.org/10.1186/2047-217x-2-16 (2013).
Google Scholar
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evolut. 30(4), 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
Google Scholar
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30(9), 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).
Google Scholar
Nylander, J. A. A. MrModeltest v2. Program distributed by the author. Evolutionary Biology Centre, Uppsala University (2004).
Han, M. V. & Zmasek, C. M. phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinform. https://doi.org/10.1186/1471-2105-10-356 (2009).
Google Scholar
Friedman, J. & Alm, E. J. inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8(9), e1002687. https://doi.org/10.1371/journal.pcbi.1002687 (2012).
Google Scholar
Shaffer, M., Thurimella, K., Sterrett, J. D. & Lozupone, C. A. SCNIC: Sparse correlation network investigation for compositional data. Mol. Ecol. Resour. 23(1), 312–325. https://doi.org/10.1111/1755-0998.13704 (2022).
Google Scholar
Shaffer, M., Thurimella, K., Sterrett, J. D. & Lozupone, C. A. SCNIC: Sparse correlation network investigation for compositional data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13704 (2020).
Google Scholar
Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
Google Scholar
Google Maps [View of the target orchard in Umurbey/Çanakkale], accessed 27 July 2024; https://goo.gl/maps