Stand competition and slope increase the probability of occurrence of Gremmeniella abietina in forest stands in Spain

  • Carmen Romeralo Institute of Forest Sciences (ICIFOR), INIA-CSIC, 28040, Madrid, Spain https://orcid.org/0000-0002-8510-9915
  • Chantal Côtè Laurentian Forestry Centre, Natural Resources Canada, G1V 4C7, Quebec, QC, Canada
  • Gaston Laflamme Laurentian Forestry Centre, Natural Resources Canada, Quebec, QC, Canada. https://orcid.org/0000-0002-7598-3000
  • Vanessa Paredes Agricultural Technological Institute of Castilla and Leon (ITACyL), 47071, Valladolid, Spain https://orcid.org/0000-0002-7677-1923
  • Oscar Santamaría Sustainable Forest Management Research Institute (iuFOR), Department of Plant Production and Forest Resources, Higher Technical School of Agricultural Engineering of Palencia (ETSIIAA), University of Valladolid, 34004, Palencia, Spain https://orcid.org/0000-0001-5087-6519
  • Jorge Aldea Institute of Forest Sciences (ICIFOR), INIA-CSIC, 28040, Madrid, Spain https://orcid.org/0000-0003-2568-5192
  • Julio J. Diez Sustainable Forest Management Research Institute (iuFOR), Department of Plant Production and Forest Resources, Higher Technical School of Agricultural Engineering of Palencia (ETSIIAA), University of Valladolid, 34004, Palencia, Spain https://orcid.org/0000-0003-0558-8141
Keywords: Aleppo pine, forest health monitoring, NDVI (Normalized Difference Vegetation Index), random forest analysis, remote sensing

Abstract

Aim of study: This study aimed to determine the distribution of the fungal pathogen Gremmeniella abietina in forest stands close to the first detection zone in Spain. Additionally, we aimed to identify stand characteristics associated with the presence of the pathogen and evaluate the use of remote sensing methods, such as the Normalized Difference Vegetation Index (NDVI), for early disease detection.

Area of study: Province of Palencia, northern Spain.

Material and methods: We surveyed 36 forest stands to assess the distribution of G. abietina. Stand inventories, NDVI values derived from Deimos-1 satellite imagery and DNA amplification techniques were used to characterize each stand and determine the presence or absence of the pathogen. Finally, a combination of a random forest algorithm and a permutational multivariate analysis of variance (PERMANOVA) was applied to identify the most significant predictors of the pathogen presence.

Main results: The presence of G. abietina was confirmed in 13 plots and was found to be correlated with lower NDVI values during the summer months and with greater competition (i.e., high basal area) on steeper slopes. The algorithm had limited predictive power but it was sufficiently reliable for descriptive purposes. Infected stands also showed higher levels of defoliation, distortion of terminal twigs and dry needles.

Research highlights: The results indicate that although the pathogen has spread beyond the initial detection zone, it currently causes only moderate damage. The absence of epidemic outbreaks in the region may be due to environmental conditions that are not conducive to disease development. Nevertheless, the presence of G. abietina could negatively affect forest productivity, particularly in stands located on steep slopes and characterized by a high level of competition. Use of the NDVI may be a useful tool for detecting trees affected by the pathogen at an early stage, but confirmation through field diagnosis surveys and molecular diagnostics remains essential.

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Author Biography

Carmen Romeralo, Institute of Forest Sciences (ICIFOR), INIA-CSIC, 28040, Madrid, Spain

Estudiante de doctorado, Departamento de Producción Vegetal y Recursos Forestales, Unidad de Entomología y Patología Forestal

References

Agrios GN, 1997. Plant Pathology, 4th ed. Academic Press, San Diego, CA, USA. 635 pp.

Anglberger H, Halmschlager E, 2003. The severity of Sirococcus shoot blight in mature Norway spruce stands with regard to tree nutrition, topography and stand age. For Ecol Manage 177: 221–230. https://doi.org/10.1016/S0378-1127(02)00440-1

Bernier D, Lewis KJ, 1999. Site and soil characteristics related to the incidence of Inonotus tomentosus. For Ecol Manage 120: 131–142. https://doi.org/10.1016/S0378-1127(98)00534-9

Botella L, Santamaría O, Díez JJ, 2010a. Fungi associated with the decline of Pinus halepensis in Spain. Fungal Divers 40: 1–11. https://doi.org/10.1007/s13225-010-0025-5

Botella L, Tuomivirta TT, Kaitera J, Carrasco Navarro V, Díez JJ, Hantula J, 2010b. Spanish population of Gremmeniella abietina is genetically unique but related to type A in Europe. Fungal Biol 114: 778–789. https://doi.org/10.1016/j.funbio.2010.07.003

Butin HH, 1995. Tree diseases and disorders: causes, biology, and control in forest and amenity trees. Oxford Univ Press, Oxford, UK. 252 pp.

Dorworth CE, 1971. Disease of conifers incited by Scleroderris lagerbergii Gremmen: a review and analysis. Can For Serv Publ No. 1289, Ottawa, Canada.

Eichhorn J, Roskams P, Ferretti M, Mues V, Szepesi A, Durrant D, 2010. Manual Part IV. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests Programme Co-ordinating Centre, Hamburg. 49 pp.

Ennos RA, 2014. Resilience of forests to pathogens: an evolutionary ecology perspective. Forestry 88: 41–52. https://doi.org/10.1093/forestry/cpu048

Fallon B, Yang A, Lapadat C, Armour I, Juzwik J, Montgomery RA, Cavender-Bares J, 2020. Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes. Tree Physiol 40: 377–390. https://doi.org/10.1093/treephys/tpaa005

Forzieri G, Girardello M, Ceccherini G, Spinoni J, Feyen L, Hartmann H, Beck PSA, Camps-Valls G, Chirici G, Mauri A, Cescatti A, 2021. Emergent vulnerability to climate-driven disturbances in European forests. Nat Commun 12: 1081. https://doi.org/10.1038/s41467-021-21399-7

Gilabert MA, González-Piqueras J, García-Haro J, 1997. Acerca de los índices de vegetación. Rev Teledetección 8: 1–10.

Greenwell BM, 2017. pdp: An R package for constructing partial dependence plots. R J 9: 421–436. https://doi.org/10.32614/RJ-2017-016

Guo Q, Fei S, Potter KM, Liebhold AM, Wen J, 2019. Tree diversity regulates forest pest invasion. Proc Natl Acad Sci U S A 116: 7382–7386. https://doi.org/10.1073/pnas.1821039116

Hamelin RC, Bourassa M, Rail J, Dusabenyagasani M, Jacobi V, Laflamme G, 2000. PCR detection of Gremmeniella abietina, the causal agent of Scleroderris canker of pine. Mycol Res 104: 527–532. https://doi.org/10.1017/S0953756299002026

He Y, Chen G, Potter C, Meentemeyer RK, 2019. Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality. Remote Sens Environ 231: 111238. https://doi.org/10.1016/j.rse.2019.111238

Jactel H, Bauhus J, Boberg J, Bonal D, Castagneyrol B, Gardiner B, Gonzalez-Olabarria JR, Koricheva J, Meurisse N, Brockerhoff EG, 2017. Tree diversity drives forest stand resistance to natural disturbances. Curr For Rep 3: 223–243. https://doi.org/10.1007/s40725-017-0064-1

Jactel H, Moreira X, Castagneyrol B, 2021. Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Annu Rev Entomol 66: 277–296. https://doi.org/10.1146/annurev-ento-041720-075234

Kaitera J, Jalkanen R, 1992. Disease history of Gremmeniella abietina in a Pinus sylvestris stand. Eur J For Pathol 22: 371–378. https://doi.org/10.1111/j.1439-0329.1992.tb00309.x

Kamińska A, Lisiewicz M, Kraszewski B, Stereńczak K, 2020. Habitat and stand factors related to spatial dynamics of Norway spruce dieback driven by Ips typographus. For Ecol Manage 476: 118432. https://doi.org/10.1016/j.foreco.2020.118432

Kozanitas M, Osmundson TW, Linzer R, Garbelotto M, 2017. Interspecific interactions between the Sudden Oak Death pathogen Phytophthora ramorum and two sympatric Phytophthora species. Fungal Ecol 28: 86–96. https://doi.org/10.1016/j.funeco.2017.04.006

Kuhn M, 2008. Building predictive models in R using the caret package. J Stat Softw 28(5): 1–26. https://doi.org/10.18637/jss.v028.i05

Laflamme G, 1993. Pruning red pine to control Scleroderris canker: eight years of trials. In: Barklund P, Livsey S, Karlman M, Stephan R (eds). Shoot diseases of conifers. Proc Int Symp IUFRO S2.06.02, Uppsala, Sweden. pp: 131–133.

Laflamme G, 1999. Traitement réussi d’une plantation de pins rouges affectée par Gremmeniella abietina, race européenne. Phytoprotection 80: 55–64. https://doi.org/10.7202/706180ar

Liaw A, Wiener M, 2002. Classification and regression by randomForest. R News 2: 18–22.

Martín Albertos S, Díaz-Fernández PM, De Miguel y del Ángel J, 1998. Regiones de procedencia de las especies forestales españolas. Organismo Autónomo Parques Nacionales, Madrid, Spain.

Martínez J, 1933. Una grave micosis del pino observada por primera vez en España. Bol Soc Esp Hist Nat 33: 25–29.

Masaitis G, Mozgeris G, Augustaitis A, 2013. Spectral reflectance properties of healthy and stressed coniferous trees. iForest 6: 30–36. https://doi.org/10.3832/ifor0709-006

McCartney HA, Fitt BDL, 1998. Dispersal of foliar fungal plant pathogens: mechanisms, gradients and spatial patterns. In: Jones DG (ed). The epidemiology of plant diseases. Springer, Dordrecht. pp: 159–176. https://doi.org/10.1007/978-94-017-3302-1_7

Meigs GW, Kennedy RE, Cohen WB, 2011. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens Environ 115: 3707–3718. https://doi.org/10.1016/j.rse.2011.09.009

Mulder O, Sleith R, Mulder K, Coe NR, 2020. A Bayesian analysis of topographic influences on the presence and severity of beech bark disease. For Ecol Manage 472: 118198. https://doi.org/10.1016/j.foreco.2020.118198

Murfitt J, He Y, Yang J, Mui A, De Mille K, 2016. Ash decline assessment in emerald ash borer infested natural forests using high spatial resolution images. Remote Sens 8: 256. https://doi.org/10.3390/rs8030256

Niemelä P, Lindgren M, Uotila A, 1992. The effect of stand density on the susceptibility of Pinus sylvestris to Gremmeniella abietina. Scand J For Res 7: 129–133. https://doi.org/10.1080/02827589209382705

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, et al., 2020. vegan: Community ecology package. R package version 2.5-7.

Oliva J, 2021. Forest disease affecting pines in the Mediterranean Basin. In: Ne’eman G, Osem Y (eds). Pines and their mixed forest ecosystems in the Mediterranean Basin. Springer, Cham. pp: 183–198. https://doi.org/10.1007/978-3-030-63625-8_10

Petäistö R, Heinonen J, 2003. Conidial dispersal of Gremmeniella abietina: climatic and microclimatic factors. For Pathol 33: 363–373. https://doi.org/10.1111/j.1439-0329.2003.00335.x

Petäistö R, Kurkela T, Heinonen J, 2000. Climatic factors and phases of Gremmeniella abietina conidial dispersal. For Pathol 30: 57–60.

Ramsfield TD, Bentz BJ, Faccoli M, Jactel H, Brockerhoff EG, 2016. Forest health in a changing world: effects of globalization and climate change on forest insect and pathogen impacts. Forestry 89: 245–252. https://doi.org/10.1093/forestry/cpw018

Romeralo C, Botella L, Santamaría O, Díez J, 2012. Effect of putative mitoviruses on in vitro growth of Gremmeniella abietina isolates under different laboratory conditions. For Syst 21(3): 515–525. https://doi.org/10.5424/fs/2012213-02266

Romeralo C, Botella L, Santamaría O, Díez JJ, Laflamme G, 2023. Gremmeniella abietina: a loser in the warmer world or still a threat to forestry? Curr For Rep. https://doi.org/10.1007/s40725-023-00193-2

Sangüesa-Barreda G, Camarero JJ, García-Martín A, Hernández R, de la Riva J, 2014. Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. For Ecol Manage 320: 171–181. https://doi.org/10.1016/j.foreco.2014.03.008

Santamaría O, Alves-Santos FM, Díez JJ, 2005. Genetic characterization of Gremmeniella abietina var. abietina isolates from Spain. Plant Pathol 54: 331–338. https://doi.org/10.1111/j.1365-3059.2005.01184.x

Santamaría O, Botella L, Díez JJ, 2007. Gremmeniella abietina in north-western Spain: distribution and associated mycoflora. Acta Silv Lign Hung: 137–145. https://doi.org/10.37045/aslh-2007-0024

Santamaría O, Pajares JA, Díez JJ, 2004. Physiological and morphological variation of Gremmeniella abietina from Spain. For Pathol 34: 395–405. https://doi.org/10.1111/j.1439-0329.2004.00380.x

Santamaría O, Pajares JAJ, Díez JJ, 2003. First report of Gremmeniella abietina on Pinus halepensis in Spain. Plant Pathol 52: 425. https://doi.org/10.1046/j.1365-3059.2003.00847.x

Sanz-Ros AV, Pajares JA, Díez JJ, 2017. Influence of climatic variables on crown condition in pine forests of northern Spain. In: Bravo F, LeMay V, Jandl R (eds). Managing forest ecosystems: the challenge of climate change. Springer Int Publishing, Switzerland. pp: 103–117. https://doi.org/10.1007/978-3-319-28250-3_6

Sohn JA, Saha S, Bauhus J, 2016. Potential of forest thinning to mitigate drought stress: a meta-analysis. For Ecol Manage 380: 261–273. https://doi.org/10.1016/j.foreco.2016.07.046

Stemmelen A, Castagneyrol B, Ponette Q, Prospero S, Martin GS, Schneider S, Jactel H, 2023. Tree diversity reduces co-infestation of Douglas fir by two exotic pests and pathogens. NeoBiota 84: 397–413. https://doi.org/10.3897/neobiota.84.94109

Uotila A, 1983. Physiological and morphological variation among Finnish Gremmeniella abietina isolates. Commun Inst For Fenn 119.

Uotila A, 1988. The effect of climatic factors on the occurrence of scleroderris canker. Folia For.

Uotila A, Petäistö R, 2007. How do the epidemics of Gremmeniella abietina start. Acta Silv Lign Hung Spec Ed: 147–151. https://doi.org/10.37045/aslh-2007-0025

Veuillen L, Prévosto B, Alfaro-Sánchez R, Badeau V, Battipaglia G, Beguería S, Bravo F, Boivin T, Camarero JJ, Čufar K, et al., 2023. Pre- and post-drought conditions drive resilience of Pinus halepensis across its distribution range. Agric For Meteorol 339: 109577. https://doi.org/10.1016/j.agrformet.2023.109577

Vogelmann JE, Tolk B, Zhu Z, 2009. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sens Environ 113: 1739–1748. https://doi.org/10.1016/j.rse.2009.04.014

Wang X, Stenström E, Boberg J, Ols C, Drobyshev I, 2017. Outbreaks of Gremmeniella abietina cause considerable decline in stem growth of surviving Scots pine trees. Dendrochronologia 44: 39–47. https://doi.org/10.1016/j.dendro.2017.03.006

White TJ, Bruns TD, Lee SB, Taylor JW, 1990. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ (eds). PCR protocols: a guide to methods and applications. Academic Press, New York. pp: 315–322.

Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL, 2006. Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manage 221: 27–41. https://doi.org/10.1016/j.foreco.2005.09.021

Wulff S, Hansson P, Witzell J, 2006. The applicability of national forest inventories for estimating forest damage outbreaks – experiences from a Gremmeniella outbreak in Sweden. Can J For Res 36: 2605–2613. https://doi.org/10.1139/x06-148

Wulff S, Walheim M, 2003. Gremmeniella abietina: uppträdande i Sverige. Resultat från Riksskogstaxeringen och skogsskadeinventeringen 2002. Umeå, Sweden.

Ylimartimo A, Laflamme G, Simard M, Rioux D, 1997. Ultrastructure and cytochemistry of early stages of colonization by Gremmeniella abietina in Pinus resinosa seedlings. Can J Bot 75: 1119–1132. https://doi.org/10.1139/b97-123

Zeng QY, Hansson P, Wang XR, 2005. Specific and sensitive detection of the conifer pathogen Gremmeniella abietina by nested PCR. BMC Microbiol 5: 65. https://doi.org/10.1186/1471-2180-5-65

Published
2026-01-16
How to Cite
Romeralo, C., Côtè, C., Laflamme, G., Paredes, V., Santamaría, O., Aldea, J., & Diez, J. J. (2026). Stand competition and slope increase the probability of occurrence of Gremmeniella abietina in forest stands in Spain. Forest Systems, 34(3), 20973. https://doi.org/10.5424/fs/2025343-20973
Section
Special Issue. Adaptation of forests in an unpredictable future

Funding data

Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente
Grant numbers AGL2008-03622

Ministerio de Ciencia e Innovación
Grant numbers RYC2021-033031

Universidad de Valladolid
Grant numbers AYUDAS PARA LA ASISTENCIA A CURSOS, CONGRESOS Y JORNADAS RELEVANTES PARA EL DESARROLLO DE TESIS DOCTORALES. Convocatoria 2013