Hidden Costs of Modelling Post-fire Plant Community Assembly Using Cellular Automata

  • Juan García-Duro
  • Luca Manzoni
  • Iria Arias
  • Mercedes Casal
  • Oscar Cruz
  • Xosé Manoel Pesqueira
  • Ana Muñoz
  • Rebeca Álvarez
  • Luca MariotEmail author
  • Stefania Bandini
  • Otilia Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11115)


Cellular Automata (CA) models have been applied to different fields of knowledge, from cryptography, arts, to the modelling and simulation of complex systems. In the latter area, however, sometimes the ability to properly represent complex interacting but distinct dynamics taking place within a given area is limited by the need of calibrating models in which the number of necessary parameters grows. Hidden costs related to the identification of specific values or plausible ranges for parameters can become overwhelming.

Here we model the assembly process of plant communities after fire. The number of elements of plant communities (plants of different species) and processes involved (seed dispersal, plant recruitment, competence, etc.) require a high degree of parameterization because all those processes have great relevance on the evolution of the system, for instance during post-fire recovery.

The fire, aside negative effects, releases a number of resources (space, nutrients, ...) making them easily available for plants, which promptly use those resources so they are no longer available to other plants after a period of time which usually ranges from months to years. In the meantime, the plasticity of species in relation to fire and environment and the interactions among species determine the direction of changes to occur.

In this work we present a novel approach to the assembly of plant communities after fire using CA. In particular we gather the preliminary results of their application and give a feasible way to optimize the parameterization of the model.


  1. 1.
    Alexandridis, A., Vakalis, D., Siettos, C.I., Bafas, G.V.: A cellular automata model for forest fire spread prediction: the case of the wildfire that swept through spetses island in 1990. Appl. Math. Comput. 204(1), 191–201 (2008)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Allen, K.A., Harris, M.P., Marrs, R.H.: Matrix modelling of prescribed burning in calluna vulgaris-dominated moorland: short burning rotations minimize carbon loss at increased wildfire frequencies. J. Appl. Ecol. 50(3), 614–624 (2013)CrossRefGoogle Scholar
  3. 3.
    Altartouri, A., Nurminen, L., Jolma, A.: Spatial neighborhood effect and scale issues in the calibration and validation of a dynamic model of phragmites australis distribution-a cellular automata and machine learning approach. Environ. Model. Softw. 71, 15–29 (2015)CrossRefGoogle Scholar
  4. 4.
    Alvarez, R., Munoz, A., Pesqueira, X., Garcia-Duro, J., Reyes, O., Casal, M.: Spatial and temporal patterns in structure and diversity of mediterranean forest of quercus pyrenaica in relation to fire. For. Ecol. Manag. 257(7), 1596–1602 (2009)CrossRefGoogle Scholar
  5. 5.
    Anderson, T., Dragicevic, S.: A geosimulation approach for data scarce environments: modeling dynamics of forest insect infestation across different landscapes. ISPRS Int. J. Geo-Inf. 5(2), 9 (2016)CrossRefGoogle Scholar
  6. 6.
    Baetens, J.M., De Baets, B.: A Spatial sensitivity analysis of a spatially explicit model for myxomatosis in Belgium. In: El Yacoubi, S., Wąs, J., Bandini, S. (eds.) ACRI 2016. LNCS, vol. 9863, pp. 91–100. Springer, Cham (2016). Scholar
  7. 7.
    Baltzer, H., Braun, P., Köhler, W.: Modeling population dynamics with cellular automata. United States Department of Agriculture Forest Service, General Technical report RM, pp. 703–712 (1996)Google Scholar
  8. 8.
    Bandini, S., Manzoni, S., Redaelli, S., Vanneschi, L.: Automatic detection of go–based patterns in CA model of vegetable populations: experiments on Geta pattern recognition. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 427–435. Springer, Heidelberg (2006). Scholar
  9. 9.
    Bandini, S., Pavesi, G.: Simulation of vegetable populations dynamics based on cellular automata. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) ACRI 2002. LNCS, vol. 2493, pp. 202–209. Springer, Heidelberg (2002). Scholar
  10. 10.
    Bandini, S., Pavesi, G.: A model based on cellular automata for the simulation of the dynamics of plant populations. In: International Congress on Environmental Modelling and Software, vol. 160 (2004)Google Scholar
  11. 11.
    Beck, J., Holloway, J.D., Schwanghart, W.: Undersampling and the measurement of beta diversity. Methods Ecol. Evol. 4(4), 370–382 (2013)CrossRefGoogle Scholar
  12. 12.
    Bellingham, P.J., Sparrow, A.D.: Resprouting as a life history strategy in woody plant communities. Oikos 89(2), 409–416 (2000)CrossRefGoogle Scholar
  13. 13.
    Bond, W.J., Keeley, J.E.: Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20(7), 387–394 (2005)CrossRefGoogle Scholar
  14. 14.
    Colasanti, R., Hunt, R., Watrud, L.: A simple cellular automaton model for high-level vegetation dynamics. Ecol. Model. 203(3–4), 363–374 (2007)CrossRefGoogle Scholar
  15. 15.
    Davies, G.M., Gray, A., Rein, G., Legg, C.J.: Peat consumption and carbon loss due to smouldering wildfire in a temperate peatland. For. Ecol. Manag. 308, 169–177 (2013)CrossRefGoogle Scholar
  16. 16.
    García-Duro, J., Álvarez, R., Basanta, M., Casal, M.: Aplicación de redes bayesianas ás relacións entre especies vexetais despois de incendio forestal e a súa sensibilidade ó tamaño das unidades de mostraxe. In: BIOapps2016. Encontro Galaico-Portugués de Biometría, Con Aplicación Ás Ciencias Da Saúde, Á Ecoloxía E Ás Ciencias Do Medio AmbienteD (2016)Google Scholar
  17. 17.
    Hogeweg, P.: Cellular automata as a paradigm for ecological modeling. Appl. Math. Comput. 27(1), 81–100 (1988)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Huntley, B., Baxter, R.: Vegetation ecology and global change. In: Vegetation Ecology, pp. 357–372 (2005)Google Scholar
  19. 19.
    Kowalewski, L.K., Chizinski, C.J., Powell, L.A., Pope, K.L., Pegg, M.A.: Accuracy or precision: implications of sample design and methodology on abundance estimation. Ecol. Model. 316, 185–190 (2015)CrossRefGoogle Scholar
  20. 20.
    Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)CrossRefGoogle Scholar
  21. 21.
    Lengyel, A., Csiky, J., Botta-Dukát, Z.: How do locally infrequent species influence numerical classification? A simulation study. Community Ecol. 13(1), 64–71 (2012)CrossRefGoogle Scholar
  22. 22.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT press, Cambridge (1998)zbMATHGoogle Scholar
  23. 23.
    Muñoz, A., García-Duro, J., Álvarez, R., Pesqueira, X., Reyes, O., Casal, M.: Structure and diversity of Erica ciliaris and Erica tetralix heathlands at different successional stages after cutting. J. Env. Manag. 94(1), 34–40 (2012)CrossRefGoogle Scholar
  24. 24.
    Pesqueira, X.M., del Viejo, A.M., Álvarez, R., Duro, J.G., Reyes, O.: Estudio ecológico del matorral atlántico de interés para conservación. Respuesta estructural a usos tradicionales en galicia. Rev. Real Acad. Galega de Cienc. 24, 41–60 (2005)Google Scholar
  25. 25.
    Proença, V., Pereira, H.M., Vicente, L.: Resistance to wildfire and early regeneration in natural broadleaved forest and pine plantation. Acta Oecol. 36(6), 626–633 (2010)CrossRefGoogle Scholar
  26. 26.
    Reyes, O., Casal, M.: Regeneration models and plant regenerative types related to the intensity of fire in atlantic shrubland and woodland species. J. Veg. Sci. 19(4), 575–583 (2008)CrossRefGoogle Scholar
  27. 27.
    Reyes, O., Casal, M., Rego, F.C.: Resprouting ability of six atlantic shrub species. Folia Geobot. 44(1), 19–29 (2009)CrossRefGoogle Scholar
  28. 28.
    Reyes, O., García-Duro, J., Salgado, J.: Fire affects soil organic matter and the emergence of pinus radiata seedlings. Ann. For. Sci. 72(2), 267–275 (2015)CrossRefGoogle Scholar
  29. 29.
    Sree, P.K., Babu, I.R., et al.: Cellular automata and its applications in bioinformatics: a review. Glob. Perspect. Artif. Intell. 2, 16–22 (2014)Google Scholar
  30. 30.
    Stier, A.C., Bolker, B.M., Osenberg, C.W.: Using rarefaction to isolate the effects of patch size and sampling effort on beta diversity. Ecosphere 7(12) (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Juan García-Duro
    • 1
  • Luca Manzoni
    • 2
  • Iria Arias
    • 1
  • Mercedes Casal
    • 1
  • Oscar Cruz
    • 1
  • Xosé Manoel Pesqueira
    • 1
  • Ana Muñoz
    • 1
  • Rebeca Álvarez
    • 1
  • Luca Mariot
    • 2
    Email author
  • Stefania Bandini
    • 2
  • Otilia Reyes
    • 1
  1. 1.Área de Ecoloxía, Departamento de Bioloxía Funcional, Facultade de BioloxíaUniversidade de Santiago de CompostelaSantiago de CompostelaSpain
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanoItaly

Personalised recommendations