Can wood-decaying urban macrofungi be identified by using fuzzy interference system? An example in Central European Ganoderma species
Language English Country England, Great Britain Media electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
34168175
PubMed Central
PMC8225830
DOI
10.1038/s41598-021-92237-5
PII: 10.1038/s41598-021-92237-5
Knihovny.cz E-resources
- MeSH
- Wood microbiology MeSH
- Ganoderma classification MeSH
- Fungi classification MeSH
- Fruiting Bodies, Fungal classification MeSH
- Spores, Fungal classification MeSH
- Trees microbiology MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Ganoderma is a cosmopolitan genus of wood-decaying basidiomycetous macrofungi that can rot the roots and/or lower trunk. Among the standing trees, their presence often indicates that a hazard assessment may be necessary. These bracket fungi are commonly known for the crust-like upper surfaces of their basidiocarps and formation of white rot. Six species occur in central European urban habitats. Several of them, such as Ganoderma adspersum, G. applanatum, G. resinaceum and G. pfeifferi, are most hazardous fungi causing extensive horizontal stem decay in urban trees. Therefore, their early identification is crucial for correct management of trees. In this paper, a fast technique is tested for the determination of phytopathologically important urban macrofungi using fuzzy interference system of Sugeno type based on 13 selected traits of 72 basidiocarps of six Ganoderma species and compared to the ITS sequence based determination. Basidiocarps features were processed for the following situations: At first, the FIS of Sugeno 2 type (without basidiospore sizes) was used and 57 Ganoderma basidiocarps (79.17%) were correctly determined. Determination success increased to 96.61% after selecting basidiocarps with critical values (15 basidiocarps). These undeterminable basidiocarps must be analyzed by molecular methods. In a case, that basidiospore sizes of some basidiocarps were known, a combination of Sugeno 1 (31 basidiocarps with known basidiospore size) and Sugeno 2 (41 basidiocarps with unknown basidiospore size) was used. 84.72% of Ganoderma basidiocarps were correctly identified. Determination success increased to 96.83% after selecting basidiocarps with critical values (11 basidiocarps).
See more in PubMed
Richter Ch, Wittstein K, Kirk PM, Stadler M. An assessment of the taxonomy and chemotaxonomy of Ganoderma. Fungal Divers. 2015;71(1):1–15. doi: 10.1007/s13225-014-0313-6. DOI
Bernicchia, A. Polyporaceae s.l. Fungi Europaei. (Massimo Candusso, 2005).
Breitenbach J, Kränzlin F. Fungi of Switzerland. Heterobasidiomycetes, Aphyllophorales, Gastromycetes. Mykologia Verlag; 1986.
Moncalvo JM. Systematics of Ganoderma. In: Flood J, Bridge PD, Holderness M, editors. Ganoderma Diseases of Perennial Crops. CAB International; 2000. pp. 23–45.
Ryvarden, L. Genera of Polypores. Nomenclature and Taxonomy. (Fungiflora, 1991).
Ryvarden L, Melo I. Poroid Fungi of Europe. Fungiflora; 2014.
Sokół, S. Ganodermataceae Polski: Taksonomia, ekologia i rozmieszczenie. (Wydawnictwo Uniwersytetu Śląskiego, 2000).
Gáperová S. Synantropné druhy v rode Ganoderma. Acta Fac. Ecol. 2001;8:93–98.
Schwarze FWMR, Ferner D. Ganoderma on trees—Differentiation of species and studies of invasiveness. Arboric. J. 2003;27(1):59–77. doi: 10.1080/03071375.2003.9747362. DOI
Tello ML, Tomalak M, Siwecki R, Gáper J, Motta E, Mateo-Sagasta M. Biotic urban growing conditions—Threats, pests and diseases. In: Konijnendijk CC, Nilsson K, Randrup T, Schipperijn J, editors. Urban Forests and Trees. Springer; 2005. pp. 325–365.
Terho M, Hantula J, Hallaksela AM. Occurrence and decay patterns of common wood-decay fungi in hazardous trees felled in the Helsinki City. For. Pathol. 2007;37(6):420–432. doi: 10.1111/j.1439-0329.2007.00518.x. DOI
Dunster JA, Smiley ET, Matheny N, Lilly S. Tree risk assessment manual. Arboricult. J. 2014;36:179–180.
Guglielmo F, Bergemann SE, Gonthier P, Nicolotti G, Garbelotto M. A multiplex PCR-based method for the detection and early identification of wood rotting fungi in standing trees. J. Appl. Microbiol. 2007;103(5):1490–1507. doi: 10.1111/j.1365-2672.2007.03378.x. PubMed DOI
Guglielmo, F., Gonthier, P., Garbelotto, M., Nicolotti, G. A PCR-based method for the identification of important wood rotting fungal taxa within Ganoderma, Inonotus s.l. and Phellinus s.l. FEMS Microbiol. Lett.282(2), 228–237. 10.1111/j.1574-6968.2008.01132.x (2008). PubMed
Guglielmo F, Gonthier P, Garbelotto M, Nicolotti G. Optimization of sampling procedures for DNA-based diagnosis of wood decay fungi in standing trees. Lett. Appl. Microbiol. 2010;51:90–97. doi: 10.1111/j.1472-765X.2010.02860.x. PubMed DOI
Jargalmaa, S., Eimes, J.A., Park, M.S., Park, J.Y., Oh, S.Y., Lim, Y.W. Taxonomic evaluation of selected Ganoderma species and database sequence validation. PeerJ5, e3596. 10.7717/peerj.3596 (2017). PubMed PMC
Schoch, C.L., Seifert, K.A., Huhndorf, S., Robert, V., Spouge, J.L., Levesque, C.A., Chen, W., Fungal Barcoding Consortium. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. PNAS109(16), 6241–6246. 10.1073/pnas.1117018109 (2012). PubMed PMC
Schmidt O, Gaiser O, Dujesiefken D. Molecular identification of decay fungi in the wood of urban trees. Eur. J. For. Res. 2012;131(3):885–891. doi: 10.1007/s10342-011-0562-9. DOI
Kozel TR, Wickes B. Fungal diagnostics. Cold Spring Harb. Perspect. Med. 2014;4(4):a019299. doi: 10.1101/cshperspect.a019299. PubMed DOI PMC
Fraley Ch, Raftery AE. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J. 1998;41(8):578–588. doi: 10.1093/comjnl/41.8.578. DOI
Gülağız FK, Şahin S. Comparison of hierarchical and non-hierarchical clustering algorithms. Int. J. Comput. Eng. Inf. Technol. 2017;9(1):6–14.
Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J. Intell. Inf. Syst. 2001;17(2–3):107–145. doi: 10.1023/A:1012801612483. DOI
Romesburg, Ch. Cluster Analysis for Researchers, 1 edn. ISBN 978-1411606173. (Lulu Press, 2004).
Bezdek JC, Ehrlich R, Full W. The fuzzy c-means clustering algorithm. Comput. Geosci. 1984;10(2–3):191–203. doi: 10.1016/0098-3004(84)90020-7. DOI
Roy, S., Sadhu, S., Bandyopadhyay, S.K., Bhattacharyya, D., Kim, T.H. Brain tumor classification using adaptive neuro-fuzzy inference system from MRI. Int. J. Bio-Sci. Bio-Technol.8(3), 203–218. 10.14257/ijbsbt.2016.8.3.21 (2016).
Abikoye OC, Popoola EO, Aro TO, Popoola VO. Adaptive neuro-fuzzy inference system for HIV/AIDS diagnosis, clinical staging and regimen prescription. Comput. Sci. Telecommun. 2017;51(1):62–76.
Ibrahim S, Chowriappa P, Dua S, Acharya UR, Noronha K, Bhandary S, Mugasa H. Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Med. Biol. Eng. Comput. 2015;53(12):1345–1360. doi: 10.1007/s11517-015-1329-0. PubMed DOI
Marzuki A, Tee SY, Aminifar S. Study of fuzzy systems with Sugeno and Mamdani type fuzzy inference systems for determination of heartbeat cases on electrocardiogram (ECG) signals. Int. J. Bio Eng. Technol. 2014;14(3):243–276. doi: 10.1504/IJBET.2014.059673. DOI
Rawat J, Singh A, Bhadauria HS, Virmani J, Devgrun JS. Leukocyte classification using adaptive neuro-fuzzy inference system in microscopic blood images. Arab. J. Sci. Eng. 2018;43:7041–7058. doi: 10.1007/s13369-017-2959-3. DOI
Sabrol, H., Kumar, S. Fuzzy and neural network based tomato plant disease classification using natural outdoor images. Indian J. Sci. Technol. 9(44), 1–8. 10.17485/ijst/2016/v9i44/92825 (2016).
Saw AK, Raj G, Das M, Talukdar NCh, Tripathy BCh, Nandi S. Alignment-free method for DNA sequence clustering using Fuzzy integral similarity. Sci. Rep. 2019;9(1):3753. doi: 10.1038/s41598-019-40452-6. PubMed DOI PMC
Sugeno M, Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1993;1(1):7–31. doi: 10.1109/TFUZZ.1993.390281. DOI
Takagi, T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. B Cybern. SMC-15(1), 116–132 (1985).
Fuzzy Logic Toolbox, R2018b. MathWorks. https://www.mathworks.com/help/fuzzy/ Accessed 10 Oct 2019 (2019).
The Plant List, 2013. Version 1.1. http://www.theplantlist.org. Accessed 22 Nov 2019 (2019).
MATLAB Runtime, Version R2018b, Windows 64-bits, 2019. MathWorks. https://www.mathworks.com/products/compiler/matlab-runtime.html . Accessed 10 Oct 2019 (2019).
Holec J, Bielich A, Beran M. Přehled Hub Střední Evropy. Academia; 2012.
RAL Color Chart. https://www.ralcolor.com/. Accessed 10 Oct 2019 (2019).
Beck T, Gáperová S, Gáper J, Náplavová K, Šebesta M, Kisková J, Pristaš P. Genetic (non)-homogeneity of the bracket fungi of the genus Ganoderma (Basidiomycota) in Central Europe. Mycosphere. 2020;11(1):225–238. doi: 10.5943/mycosphere/11/1/3. DOI
Cooper, J. & Kirk, P. CABI Bioscience Database, Landscape Research, Index Fungorum Database, http://www.speciesfungorum.org/names/names.asp, Index Fungorum ID (P1391) (2020).
Selosse MA, Vincenot L, Öpik M. Data processing can mask biology: towards better reporting of fungal barcoding data? New Phytol. 2016;210(4):1159–1164. doi: 10.1111/nph.13851. PubMed DOI
Hofstetter V, Buyck B, Eyssartier G, Schnee S, Gindro K. The unbearable lightness of sequenced-based identification. Fungal Divers. 2019;96(1):243–284. doi: 10.1007/s13225-019-00428-3. DOI
Niemelä T, Miettinen O. The identity of Ganoderma applanatum (Basidiomycota) Taxon. 2008;57(3):963–966. doi: 10.1002/tax.573024. DOI
Xing, J.H., Song, J., Decock, C., Cui, B.K. Morphological characters and phylogenetic analysis reveal a new species within the Ganoderma lucidum complex from South Africa. Phytotaxa266(2), 115–124. 10.11646/phytotaxa.266.2.5 (2016).
Kotlaba, F., Pouzar, Z. Ganoderma adspersum (S. Schulz) Donk.-lesklokorka tmavá, dvojník lesklokorky ploské-G. applanatum (Pers. ex S. F. Gray) Pat. Česká Mykol. 25(2), 88–102 (1971).