Advertisement

Modelling affected regions by the Iberian Quercus disease with proximity diagrams

  • 1 Accesses

Abstract

In this work, we propose a mathematical model to determine the potential propagation areas of the Mediterranean Quercus disease commonly referred to as “seca” (Tuser and Sánchez 2004) in specific areas of Extremadura. Although it is a syndrome of complex etiology caused by the action of the different biotic (insects and fungi) and abiotic factors (temperature, orography, soil, etc.), numerous studies suggest that the soil-borne pathogen cinnamomi represents the main responsible for the decay of the holm and cork oak. However, very little is known about the Phytophthora epidemic distribution patterns and its geographical dependence on other factors that favor its spread. With the aim to clarify this question, in this paper, we will use optimal computational geometry algorithms based on proximity diagrams that allow us to design a pathogen transmission map and to determine its correlation with different causing agents, specially with the presence of standing water or drainage lines water.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. Blanco E (2005) Los bosques ibéricos Una interpretación geobotánica. Planeta, Barcelona

  2. Brasier CM (1992) Oak tree mortality in Iberia. Nature, 360–539

  3. Borgefors G (1994) Applications using distance transformations. In: Arcelli CL, Corella P, di Boja GS (eds) Aspects of visua form processing. World Scientic, Singapore

  4. Borgefors G (1986) Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34:344–371

  5. Bosque Sendra J (1992) Sistemas de información geográfica. RIALP, Madrid

  6. Brassel K, Reif D (1979) Procedure to generate Thiessen polygon. Geogr Anal 11:289–303

  7. Cardillo E, Acedo A, Pérez C (2012) Patrones espaciales del decaimiento de encinas y alcornoques en Extremadura, España. Instituto del Corcho, la Madera y el Carbón (IPROCOR). http://iprocor.gobex.es/wp-content/uploads/2012/12/Patronesespaciales-decamientoespaC3B1ol1.pdf

  8. Chen J, Cui B (1997) Using Voronoi approach of developing topological functions in MapInfo. Journal of Wuhan Technical University of Surveying and Mapping 22:195–200

  9. Cubera E, Moreno G, Solla A (2009) Quercus ilex root growth in response to heterogeneous conditions of soil bulk density and soil NH4–N content. Soil Till Res https://doi.org/10.1016/j.still.2008.09.002

  10. De Berg M, Van Kreveld M, Overmars M, Schwarzkopf O (1997) Computational geometry, algorithms and applications. Springer, Berlin

  11. Del Pozo JL (2006) Prospección de la seca en Extremadura. Análisis de los resultados. En Gestión Ambiental y Económica del Ecosistema Dehesa en la Península Ibérica: 131-143. Junta de Extremadura. Consejería de Infraestructura y Desarrollo Tecnológico. Mérida

  12. Dunbar DM, Stephens GR (1975) Association of twolined chestnut borer and shoestring fungus with mortality of defoliated oak in Connecticut. For Sci 21:169–174

  13. ESMA SL (2003a) Desarrollo y metodología para la realización de un inventario piloto en zonas afectadas por la Seca de quercíneas. Madrid, p 46

  14. ESMA SL (2003b) Metodología para la prospección y seguimiento de masas de Quercus afectadas por la “Seca” en la provincia de cáceres. Madrid, p 130

  15. ESMA SL (2004) Metodología para la prospección y seguimiento de masas de Quercus afectadas por la “Seca” en la provincia de Badajoz. Madrid, p 102

  16. Fürher E (1998) Oak decline in Central Europe: a sinopsis of hypotheses. In: Mc Manus y M.L., Liebhold A.M. (eds) Proc. population dynamics, impacts and integrated management of forest defoliating insects. USDA Forest Service General Technical Report, pp 1–24

  17. García Rodríguez MP, Sanz Donaire JJ, Pérez González ME, Navarro Madrid A (2012) Guía práctica de teledetección y fotointerpretación. Universidad Complutense de Madrid. http://eprints.sim.ucm.es/17444/1/GUIA_PRACTICA_TELEDETECCION.pdf

  18. Geoportal (2019) Observación Dehesa Montado. visualización de información cartográfica IDE Extremadura. CICYTEX, Junta de Extremadura. http://geoportal.observatoriodehesamontado.gobex.es

  19. Gómez-Aparicio L, Ibanez B, Serrano M, De Vita P, Avila JM, Perez-Ramos J, García LV, Sanchez MA, Maranon T (2012) Spatial patterns of soil pathogens in declining Mediterranean forests: implications for tree species regeneration. New Phytologist 194:1014–1024

  20. Gottschalk KW, Wargo PM (1996) Oak decline around the world, Proceedings of the U.S. Department of Agriculture Interagency Gypsy Moth Research Forum

  21. Gold CM (1991) Problems with handling spatial data the Voronoi approach. CISM Journal 45:65–80

  22. Gold CM (1994) A review of potential applications of Voronoi methods in geomatics. Proceedings of the Canadian Conference on GIS, 4–10 June, Ottawa, 1647–1656

  23. IBM Corp (2013) IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp

  24. IDEEX (2019) Infraestructura de datos espaciales de Extremadura. http://www.ideextremadura.com/Geoportal/

  25. Klein R (1988) Abstract voronoi diagrams and their applications. Lecture Notes in Computer Science, vol 333. Springer, Berlin

  26. Lee DT, Drysdale RL (1981) Generalization of Voronoi diagram in the plane. SIAM J Comput 10:73–87

  27. Li C, Chen J, Li Z (1999) A raster-based method for computing Voronoi diagrams of spatial objects using dynamic distance transformation. Int J Geographical Inform Sci 13(3):209–225

  28. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. University of California Press, pp 281–297

  29. Manzano MJ, Belvis G, Folgueiras R, Prieto JM (2016a) Comparativa del estado actual de los focos de Seca inventariados entre 2003 y 2004, en masas de Quercus de Extremadura. Cuad Soc Esp Cienc For 43:377–392

  30. Manzano MJ, Belvis G, Folgueiras R, Prieto JM (2016b) Evolución de la densidad arbolada de las masas de Quercus afectadas por seca en Extremadura desde 1957 hasta 2013. For. 66:52–57

  31. Miles RE, Maillardand RJ (1982) The basic structures of Voronoi et generalized Voronoi polygons. J Appl Probab 19A:97–111

  32. Ministerio de Fomento (2017) Ministerio de Fomento Secretaría de Estado de Infraestructuras, Transporte y Vivienda Dirección General de Arquitectura, Vivienda y Suelo. Documento descriptivo climas de referencia, Versión 2.0 Febrero. https://www.codigotecnico.org/index.php/menu-documentoscte/133-ct-documentos-cte/ahorro-de-energia.html

  33. Moreira AC, Martins JMS (2005) Influence of site factors on the impact of Phytophthora cinnamomi in cork oak stands in Portugal. For Pathol 35:145–162

  34. Preparata FP, Ian Shanmos M (1985) Computational geometry an introduction. Springer, New–York

  35. Rodríguez-Molina MC, Santiago Merino R, Blanco Santos A, Pozo Quintanilla JD, Colino Nevado MI, Palo Núnez EJ, Torres-Vila LM (2003) Detección de Phytophthora cinnamomi en dehesas de Extremadura afectadas por “seca” y su comportamiento in vitro. Bol San Veg Plagas 29:627–640

  36. Rodríguez-Molina MC, Palo E, Blanco A, Torres-Vila LM, Santiago R, Del Pozo J, Colino MI, Torres E, Suárez MA (2012) Phytophthora cinnamomi: un Oomycete implicado en la Seca de encinas y alcornoques. Revista Phytoma 236:36–40

  37. Steinhaus H (1957) Sur la division des corps materiels en parties. Bull Acad Polon Sci 4(12):801–804

  38. Sturrock RN, Frankel SJ, Brown AV, Hennon PE, Kliejunas JT, Lewis KJ, Worrall JJ, Woods AJ (2011) Climate change and forest diseases. Climate Change and Plant Diseases 60:133–149

  39. Tuser JJ, Sánchez G (2004) La Seca. El decaimiento de encinas, alcornoques y otros Quercus en Espana. Análisis de los resultados Ediciones Mundi Prensa. Madrid, Spain

  40. U.S. Department of Agriculture, Forest Service (2015) Sudden Oak Death. Major Forest Insect and Disease Conditions in the United States 2015:15–17

  41. Vivas M, Curbera E, Moreno G, Solla A (2009) The decline of Quercus ilex and Q. suber in the Iberian Peninsula, Agroforestry systems as a technique for sustainable land management, AECID Unicopia ediciones, Madrid, ISBN: 978-84-96351-59-2, 225–233

  42. Wargo MP, Houston DR, LaMadeleine LA (1983) Oak decline. Forest Insect and Disease Leaflet 165. U.S. Department of Agriculture Forest Service. http://www.na.fs.fed.us/spfo/pubs/fidls/oakdecline/oakdecline.htm

  43. Wolfram Research Inc (2016) Mathematica, Versión 10.4, Champaign, IL

Download references

Acknowledgments

This work has been partially supported through Ministerio de Economía y Competitividad [MTM2014-53309-P] of Spain and from the Junta de Extremadura through Research Group Grants [FQM-022]. We also thank the EGTEIC 2018 Conference give me the opportunity to present part of this work.

Author information

Correspondence to Carmen Calvo-Jurado.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Marcus Schulz

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Calvo-Jurado, C. Modelling affected regions by the Iberian Quercus disease with proximity diagrams. Environ Sci Pollut Res (2020) doi:10.1007/s11356-019-06871-8

Download citation

Keywords

  • Phytophthora cinnamomi
  • Proximity diagrams
  • Computational