A review on oak decline: The global situation, causative factors, and new research approaches
Abstract
Oak decline as a complex syndrome is one of the most relevant forest diseases worldwide. This disease has a complex and multifactorial nature, and this has caused conventional methods in plant pathology not to provide researchers with a correct and comprehensive analysis of oak decline. This issue entails the need for a multidisciplinary approach in examining and evaluating the disease, which will provide researchers with a more exhaustive understanding of the disease. The present review examines the concept of decline, the factors that contribute to the occurrence and development of the disease, its global distribution, and indexes used in the assessment of the disease. Furthermore, it draws attention to various research approaches that have been utilized to investigate oak decline.
Downloads
References
Aggarwal CC, 2018. Neural networks and deep learning. Springer Nature, Switzerland. https://doi.org/10.1007/978-3-319-94463-0
Ahmadi R, Kiadaliri H, Mataji A, Kafaki S, 2014. Oak forest decline zonation using AHP model and GIS technique in Zagros forests of Ilam Province. JBES 4: 141-150.
Ahmadi E, Kowsari M, Azadfar D, Salehi Jouzani G, 2019. Bacillus pumilus and Stenotrophomonas maltophilia as two potentially causative agents involved in Persian oak decline in Zagros forests (Iran). Forest Pathol 49: e12541. https://doi.org/10.1111/efp.12541
Attarod P, Sadeghi SMM, Pypker TG, Bayramzadeh V, 2017. Oak trees decline; a sign of climate variability impacts in the west of Iran. Casp J Environ Sci 15: 373-384.
Bahram M, Koljalg U, Courty PE, Diedhiou AG, Kjoller R, Polme S, et al., 2013. The distance decay of similarity in communities of ectomycorrhizal fungi in different ecosystems and scales. J Ecol 101: 1335-1344. https://doi.org/10.1111/1365-2745.12120
Bandte M, Rehanek M, Leder B, von Bargen S, Buttner C, 2020. Identification of an Emaravirus in a common oak (Quercus robur L.) conservation seed orchard in Germany: implications for oak health. Forests 11: 1174. https://doi.org/10.3390/f11111174
Barcenas-Moreno G, Garcia-Sanchez M, Ploetz RC, 2019. Microbial communities associated with laurel wilt-affected and unaffected Redbay trees, and the detection of Raffaelea lauricola in the phyllosphere. Plant Dis 103: 2627-2635.
Barrios-Masias FH, Jackson LE, Calderon FJ, 2019. Soil microbial communities and activities under intensive organic and conventional vegetable farming in Central Coast California. Sci Rep 9: 18715.
Bendixsen DP, Hallgren SW, Frazier AE, 2015. Stress factors associated with forest decline in xeric oak forests of south-central United States. For Ecol Manag 347: 40-48. https://doi.org/10.1016/j.foreco.2015.03.015
Berg G, Grube M, Schloter M, Smalla K, 2014. Unraveling the plant microbiome: looking back and future perspectives. Front Microbiol 5: 1-7. https://doi.org/10.3389/fmicb.2014.00148
Broberg M, Doonan J, Mundt F, Denman S, McDonald JE, 2018. Integrated multi-omic analysis of hostmicrobiota interactions in acute oak decline. Microbiome 6: 21. https://doi.org/10.1186/s40168-018-0408-5
Brown N, Inward DJ, Jeger M, Denman S, 2015. A review of Agrilus biguttatus in UK forests and its relationship with acute oak decline. Forestry 88: 53-63. https://doi.org/10.1093/forestry/cpu039
Campbell CL, Neher DA, 1994. Estimating disease severity and incidence. In: Epidemiology and management of root diseases; Campbell CL & Benson DM (Eds.), pp: 117-147. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-85063-9_5
Cardenas E, Kranabetter JM, Hope G, Maas KR, Hallam S, Mohn WW, 2015. Forest harvesting reduces the soil metagenomic potential for biomass decomposition. The ISME J 9: 2465-2476. https://doi.org/10.1038/ismej.2015.57
Chiang KS, Liu HI, Bock CH, 2017. A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy. Ann Appl Biol 171: 139-154. https://doi.org/10.1111/aab.12362
Costa D, Tavares RM, Baptista P, Lino-Neto T, 2020. Cork oak endophytic fungi as potential biocontrol agents against Biscogniauxia mediterranea and Diplodia corticola. J Fungi 6: 287. https://doi.org/10.3390/jof6040287
Costa D, Ramos V, Tavares RM, Baptista P, Lino-Neto T, 2022. Phylogenetic analysis and genetic diversity of the xylariaceous ascomycete Biscogniauxia mediterranea from cork oak forests in different bioclimates. Sci Rep 12: 2646. https://doi.org/10.1038/s41598-022-06303-7
Crampton BG, Plummer SJ, Kaczmarek M, McDonald JE, Denman S, 2020. A multiplex real-time PCR assay enables simultaneous rapid detection and quantification of bacteria associated with acute oak decline. Plant Pathol 69: 1301-1310. https://doi.org/10.1111/ppa.13203
Dantec CF, Ducasse H, Capdevielle X, Fabreguettes O, Delzon S, Desprez‐Loustau ML, 2015. Escape of spring frost and disease through phenological variations in oak populations along elevation gradients. J Ecol 103: 1044-1056. https://doi.org/10.1111/1365-2745.12403
Denman S, Webber J, 2009. Oak declines: New definitions and new episodes in Britain. Qr J For 103: 285-290.
Denman S, Brown N, Kirk S, Jeger M, Webber J, 2014. A description of the symptoms of acute oak decline in Britain and a comparative review on causes of similar disorders on oak in Europe. Int J For Res 87: 535-551. https://doi.org/10.1093/forestry/cpu010
Denman S, Barrett G, Kirk SA, McDonald JE, Coetzee MP, 2017. Identification of Armillaria species on declined oak in Britain: implications for oak health. Int J For Res 9: 148-161. https://doi.org/10.1093/forestry/cpw054
Denman S, Doonan J, Raffan A, Yeats N, Rutherford MA, 2018a. The use of metagenomics to investigate oak decline in the UK. Forest Pathol 48: e12420.
Denman S, Doonan J, Ransom-Jones E, Broberg M, Plummer S, Kirk S, et al., 2018b. Microbiome and infectivity studies reveal complex polyspecies tree disease in acute oak decline. ISME J 12: 386-99. https://doi.org/10.1038/ismej.2017.170
Denman S, Brown N, Vanguelova E, Crampton B, 2022. Temperate oak declines: Biotic and abiotic predisposition drivers. For Microbiol 2: 239-263. https://doi.org/10.1016/B978-0-323-85042-1.00020-3
Deveau A, Brule C, Palin B, Champmartin D, Rubini P, Garbaye J, et al., 2018. Role of fungal trehalose metabolism in the ectomycorrhizal symbiosis between Hessia abies and Piloderma croceum. Environ Microbiol 20: 1269-1286.
Dolezal J, Mazurek P, Klimesova J, 2010. Oak decline in southern Moravia: the association between climate change and early and late wood formation in oaks. Preslia 82: 289-306.
Dolezal J, Leheckova E, Sohar K, Altman J, 2016. Oak decline induced by mistletoe, competition and climate change: a case study from central Europe. Preslia 88: 323-346.
Duque-Zapata JD, Florez JE, Lopez-Alvarez D, 2023. Metagenomics approaches to understanding soil health in environmental research-a review. Soil Sci Anu 74: 163080. https://doi.org/10.37501/soilsa/163080
Eaton WD, Shokralla S, McGee KM, Hajibabaei M, 2017. Using metagenomics to show the efficacy of forest restoration in the New Jersey Pine Barrens. Genome 60: 825-836. https://doi.org/10.1139/gen-2015-0199
Fernandes C, Duarte L, Naves P, Sousa E, Cruz L, 2022. First report of Brenneria goodwinii causing acute oak decline on Quercus suber in Portugal. J Plant Pathol 104: 837-838. https://doi.org/10.1007/s42161-022-01046-w
Fernandez-Gonzalez AJ, Martinez-Hidalgo P, Cobo-Diaz JF, Villadas PJ, Martinez-Molina E, Toro N, et al., 2017. The rhizosphere microbiome of burned holm-oak: potential role of the genus Arthrobacter in the recovery of burned soils. Sci Rep 7: 6008. https://doi.org/10.1038/s41598-017-06112-3
Finch JP, Brown N, Beckmann M, Denman S, Draper J, 2021. Index measures for oak decline severity using phenotypic descriptors. For Ecol Manag 485: 118948. https://doi.org/10.1016/j.foreco.2021.118948
Forrestel AB, Ramage BS, Moody T, Moritz MA, Stephens SL, 2015. Disease fuels and potential fire behavior: impacts of sudden oak death in two coastal California forest types. For Ecol Manag 348: 23-30. https://doi.org/10.1016/j.foreco.2015.03.024
Gagen M, Matthews N, Denman S, Bridge M, Peace A, Pike R, et al., 2019. The tree ring growth histories of UK native oaks as a tool for investigating chronic oak decline: An example from the Forest of Dean. Dendrochronologia 55: 50-59. https://doi.org/10.1016/j.dendro.2019.03.001
Gathercole LAP, Nocchi G, Brown N, Coker TLR, Plumb WJ, Stocks JJ, et al., 2021. Evidence for the widespread occurrence of bacteria implicated in acute oak decline from incidental genetic sampling. Forests 12: 1683. https://doi.org/10.3390/f12121683
Gentilesca T, Camarero JJ, Colangelo M, Nole A, Ripullone F, 2017. Drought-induced oak decline in the western Mediterranean region: an overview on current evidences, mechanisms and management options to improve forest resilience. iForest-Biogeosc For 10: 796. https://doi.org/10.3832/ifor2317-010
Gil-Pelegrin E, Peguero-Pina JJ, Camarero JJ, Fernandez-Cancio A, Navarro-Cerrillo R, 2008. Drought and forest decline in the Iberian Peninsula: a simple explanation for a complex phenomenom? In: Sánchez JM (ed) Droughts: causes, effects and predictions. Nova Science Publ, New York, pp: 27-68.
Gottschalk KW, Wargo PM, 1997. Oak decline around the world. USDA, USA.
Grunwald NJ, Garbelotto M, Goss EM, Heungens K, Prospero S, 2012. Emergence of the sudden oak death pathogen Phytophthora ramorum. Trends Microb 20: 131-138. https://doi.org/10.1016/j.tim.2011.12.006
Guo Q, Li J, Guo J, 2019. Artificial neural network models for predicting oak decline in the Loess Plateau of China. PloS one 14: e0219200.
Gutierrez-Giron A, Sanchez-Salguero R, Camarero JJ, Zavala MA, Picon-Cochard C, 2019. Predicting the impact of climate change on oak declinein Mediterranean forests using artificial neural networks. Ecol Model 408: 108740.
Haavik LJ, Billings SA, Guldin JM, Stephen FM, 2015. Emergent insects, pathogens and drought shape changing patterns in oak decline in North America and Europe. For Ecol Manag 354: 190-205. https://doi.org/10.1016/j.foreco.2015.06.019
Hogan CM, 2011. Oak; Dawson A, Cleveland CJ (eds). Encyclopedia of Earth. National Council for Science and the Environment. Washington DC.
Hyun IH, Choi W, 2014. Phytophthora species, new threats to the plant health in Korea. Plant Pathol J 30: 331-342. https://doi.org/10.5423/PPJ.RW.07.2014.0068
Laakili A, Belkadi B, Gaboun F, Yatrib C, Makhloufi M, El Antry S, et al., 2016. Analysis of dendrometric diversity among natural populations of cork oak (Quercus suber L.) from Morocco. Turk J Agric For 40: 127-135. https://doi.org/10.3906/tar-1407-147
Lamichhane JR, Venturi V, 2015. Synergisms between microbial pathogens in plant disease complexes: a growing trend. Front Plant Sci 6: 385. https://doi.org/10.3389/fpls.2015.00385
Li J, Guo Q, Guo J, 2020. Multi-scale artificial neural network models for predicting oak decline in the Loess Plateau. Ecol Indic 113: 106215.
Liu J, Wang J, Huang Y, Chen J, 2020. Mapping oak decline in a mixed deciduous forest using multi-temporal Landsat imagery and random forest algorithm. Int J Appl Earth Obs Geoinf 91: 102149.
Lonsdale D, 2015. Review of oak mildew, with particular reference to mature and veteran trees in Britain. Arboric J 37: 61-84. https://doi.org/10.1080/03071375.2015.1039839
Machacova M, Nakladal O, Samek M, Bata D, Zumr V, Peskova V, 2022. Oak decline caused by biotic and abiotic factors in Central Europe: A case study from the Czech Republic. Forests 13(8): 1223. https://doi.org/10.3390/f13081223
Maleita C, Costa SR, Abrantes I, 2015. First report of Laimaphelenchus heidelbergi (Nematoda: Aphelenchoididae) in Europe. Forest Pathol 45: 76-81. https://doi.org/10.1111/efp.12120
Manion PD, 1981. Tree disease concepts. Prentice-Hall Inc, New Jersey.
Manion PD, Lachance D, 1992. Forest decline concepts. APS Press.
Martin-Benito D, Sanchez-Salguero R, Gonzalez-Doncel I, 2017. Artificial neural networks improve the accuracy of oak decline risk assessment in Mediterranean woodlands. Sci Total Enviro 579: 1024-1030.
McShea WJ, Healy WM, Devers P, Fearer T, Koch FH, Stauffer D, et al., 2007. Forestry matters: decline of oaks will impact wildlife in hardwood forests. J Wildl Manage 71: 1717-1728. https://doi.org/10.2193/2006-169
Meaden S, Metcalf CJE, Koskella B, 2016. The effects of host age and spatial location on bacterial community composition in the English oak tree (Quercus robur). Environ Microbiol Rep 8: 649-658. https://doi.org/10.1111/1758-2229.12418
Mendes R, Garbeva P, Raaijmakers JM, 2013. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol Rev 37: 634-663. https://doi.org/10.1111/1574-6976.12028
Moradi‐Amirabad Y, Rahimian H, Babaeizad V, Denman S, 2019. Brenneria spp. and Rahnella victoriana associated with acute oak decline symptoms on oak and hornbeam in Iran. For Pathol 49: e12535. https://doi.org/10.1111/efp.12535
Munir N, Hanif M, Abideen Z, Sohail M, El-Keblawy A, Radicetti E, et al., 2022. Mechanisms and strategies of plant microbiome interactions to mitigate abiotic stresses. Agronomy 12: 2069. https://doi.org/10.3390/agronomy12092069
Ostry ME, Venette RC, Juzwik J. 2011. Decline as a disease category: is it helpful? Phytopathology 101: 404-409. https://doi.org/10.1094/PHYTO-06-10-0153
Pedram M, Pourhashemi M, Hosseinzadeh J, Koolivand D, 2018. Comments on taxonomic status and host association of some Laimaphelenchus spp. (Rhabditida: Aphelenchoidea). Nematology 20: 483-489. https://doi.org/10.1163/15685411-00003153
Pernek M, Kovac M, Jukic A, Dubravac T, Lackovic N, Brady C, 2022. Acute oak decline (AOD) new complex disese on holm oak (Quercus ilex L.) and possibilities of spread on other oak species in Croatia. Sumarski list 146: 439-445. https://doi.org/10.31298/sl.146.9-10.5
Pinho D, Barroso C, Froufe H, Brown N, Vanguelova E, Egas C, et al., 2020. Linking tree health, rhizosphere physicochemical properties, and microbiome in acute oak decline. Forests 11: 1153. https://doi.org/10.3390/f11111153
Pontius J, Hallett R, 2014. Comprehensive methods for earlier detection and monitoring of forest decline. For Sci 60: 1156-1163. https://doi.org/10.5849/forsci.13-121
Pontius J, Schaberg P, Hanavan R, 2020. Remote sensing for early, detailed, and accurate detection of forest disturbance and decline for protection of biodiversity. Remote Sens Plant Biodivers 121-154. https://doi.org/10.1007/978-3-030-33157-3_6
Poudel R, Jumpponen A, Schlatter DC, Paulitz TC, Gardiner ES, Krom N, Six J, 2020. Microbial communities associated with declining oak trees. Front Microbiol 11: 1021.
Pourhashemi M, Sadeghi MM, 2020. A review on ecological causes of oak decline phenomenon in forests of Iran. Ecol Iran Forest 8: 148-164. https://doi.org/10.52547/ifej.8.16.148
Puchałka R, Koprowski M, Gricar J, Przybylak R, 2017. Does tree-ring formation follow leaf phenology in pedunculate oak (Quercus robur L.)? Eur J For Res 136: 259-268. https://doi.org/10.1007/s10342-017-1026-7
Rizzo DM, Garbelotto M, Davidson JM, Slaughter GW, Koike ST, 2002. Phytophthora ramorum and sudden oak death in California: I. Host relationships. 5th Symp on California oak woodlands. USDA Forest Service, Gen Tech PSW-GTR-184: 733-740.
Rodriguez-Calcerrada J, Sancho-Knapik D, Martin-StPaul NK, Limousin JM, McDowell NG, Gil-Pelegrín E, 2017. Drought-induced oak decline-factors involved, physiological dysfunctions, and potential attenuation by forestry practices. In: Oaks physiological ecology. Exploring the functional diversity of genus Quercus L., Tree Physiology 7. Gil-Pelegrin et al. (Eds), pp: 419-451. Springer Int Publ. https://doi.org/10.1007/978-3-319-69099-5_13
Rosa JLG, 2013. Biologically plausible artificial neural network, In Artificial neural networks - Architectures and applications; Suzuki K (Ed), pp: 25-52. InTech, China.
Ruffner B, Schneider S, Meyer J, Queloz V, Rigling D, 2020. First report of acute oak decline disease of native and non-native oaks in Switzerland. New Dis Rep 41: 18. https://doi.org/10.5197/j.2044-0588.2020.041.018
Shigo AL, 1986. A new tree biology dictionary: terms, topics, and treatments for trees and their problems and proper care. Shigo and Trees Associates.132 pp.
Sinclair WA, 1965. Comparisons of recent declines of white ash, oaks, and sugar maple in northeastern woodlands. Cornell Plantations 20: 62-67.
Sinclair WA, 1967. Decline of hardwoods: possible causes. Int Shade Tree Conf 42: 17-32.
Sinclair WA, Hudler GW, 1988. Tree declines: four concepts of causality. J Arboric 14: 29-35. https://doi.org/10.48044/jauf.1988.009
Sinclair WA, Lyon HH, 2005. Diseases of trees and shrubs, 2nd ed. Cornell Univ Press, Ithaca, NY.
Sun J, Shi W, Wu Y, Ji J, Feng J, Zhao J, et al., 2021. Variations in acorn traits in two oak species: Quercus mongolica Fisch. Ex Ledeb. and Quercus variabilis Blume. Forests 12: 1755. https://doi.org/10.3390/f12121755
Thomas FM, 2008. Recent advances in cause-effect research on oak decline in Europe. CAB Rev 3: 1-12. https://doi.org/10.1079/PAVSNNR20083037
Thomas FM, Blank R, Hartmann G, 2002. Abiotic and biotic factors and their interactions as causes of oak decline in Central Europe. Forest Pathol 32: 277-307. https://doi.org/10.1046/j.1439-0329.2002.00291.x
Venice F, Vizzini A, Frascella A, Emiliani G, Danti R, Della Rocca G, et al., 2021. Localized reshaping of the fungal community in response to a forest fungal pathogen reveals resilience of Mediterranean mycobiota. Sci Total Environ 800: 149582. https://doi.org/10.1016/j.scitotenv.2021.149582
Zhang W, Liu Y, Zhang X, 2018. A hybrid model of artificial neural networks and multiple linear regression for predicting forest fire occurrence. Int J Wildland Fire 27: 679-690.
Zhao Y, Zhang Y, 2018. Modeling oak decline risk using artificial neural networks and logistic regression. For Ecol Manag 424: 1-8.
Copyright (c) 2023 Consejo Superior de Investigaciones Científicas (CSIC)
This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the printed and online versions of this Journal are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a “Creative Commons Attribution 4.0 International” (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the CC BY 4.0 License must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the published by the Editor, is not allowed.
