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PINEA in Catalonia

Status: Stable | Version: v1.0.0 (2026-08-28)

General description

  • Description: Individual-tree growth model for Pinus pinea in Catalonia (Spain)
  • Target Species: Pinus pinea (code: 23)
  • Application Region: Catalonia (Girona, Lleida, Barcelona, and Tarragona; Spain)

Technical characteristics

  • Model Level: Individual tree
  • Allows Projection: Yes
  • Projection Time Step: 5 years
  • Notes: PINEA model adapted for Catalonia.

To view the step-by-step sequence of initialization, projection, and harvest, see the individual-tree model calculation flow diagram.

Required variables

To run this model in SIMANFOR, the initial inventory must include the following minimum data:

Plot level (Plots sheet)

  • Inventory and plot IDs
  • age
  • dominant height
  • basal area

Tree level (Trees sheet)

  • Inventory, plot and tree IDs
  • species
  • expansion factor
  • dbh

Sample inventories

You can download the following sample inventories in Excel format to test this model:

Modules and equations

Breakdown of simulated biological processes and their methodological references:

  • Site index / Stand productivity: Calama et al. (2003)
  • Growth: Calama & Montero (2005)
  • Stand Density Index (SDI) reference value: Aguirre et al. (2017)
  • Height-diameter relationship / height estimation: Calama & Montero (2004)
  • Tree volume and taper: Calama & Montero (2006), Rodríguez (2009), MITECO (2025)
  • Biomass: Ruiz-Peinado et al. (2011)
  • Carbon content: Montero et al. (2005)
  • Non-wood forest products: Calama et al. (2008), Calama & Montero (2006)
  • Diversity indexes: Omoro et al. (2010), Simpson (1949), Ulanowicz (2001), Bray & Curtis (1957)
  • Structural and size indexes: Varga et al. (2005), Moreno-Fernández & others (2025), Barbeito (2009)

References and documentation

  • How to Cite this Model:

SIMANFOR, 2026. Individual-tree growth model for Pinus pinea in Catalonia (Spain) (v1.0.0). https://www.simanfor.es

  • SIMANFOR Citation:

Bravo, F., Ordóñez, C., Vázquez-Veloso, A., Michalakopoulos, S., 2025. SIMANFOR cloud Decision Support System: Structure, content, and applications. Ecological Modelling 499, 110912. https://doi.org/10.1016/j.ecolmodel.2024.110912

  • Technical Documentation: SIMANFOR Models Repository (GitHub)

  • Model Bibliography:

  • Aguirre, A., Condés, S., del Río, M. (2017). Variación de las líneas de máxima densidad de las principales especies de pino a lo largo del gradiente estacional de la Península Ibérica. https://7cfe.congresoforestal.es/sites/default/files/comunicaciones/38.pdf
  • Barbeito, I. (2009). Natural regeneration dynamics of Pinus sylvestris L. in Central Spain.
  • Bray, J. R., Curtis, J. T. (1957). An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecological Monographs.
  • Calama, Rafael, Montero, Gregorio (2006). Cone and seed production from stone pine (Pinus pinea L.) stands in Central Range (Spain). European Journal of Forest Research, 126(1), 23-35. 10.1007/s10342-005-0100-8
  • Calama, Rafael, Gordo, Fco. Javier, Mutke, Sven, Montero, Gregorio (2008). An empirical ecological-type model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau (Spain). Forest Ecology and Management, 255(3-4), 660-673. 10.1016/j.foreco.2007.09.079
  • Calama, Rafael, Montero, Gregorio (2004). Interregional nonlinear height-diameter model with random coefficients for stone pine in Spain. Canadian Journal of Forest Research, 34(1), 150-163. 10.1139/x03-199
  • Calama, Rafael, Montero, Gregorio (2005). Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea): a calibrating approach. Silva Fennica, 39(1). 10.14214/sf.394
  • Calama, Rafael, Cañadas, Nieves, Montero, Gregorio (2003). Inter-regional variability in site index models for even-aged stands of stone pine (Pinus pinea L.) in Spain. Annals of Forest Science, 60(3), 259-269. 10.1051/forest:2003017
  • Calama, R., Montero, G. (2006). Stand and tree-level variability on stem form and tree volume in Pinus pinea L.: A multilevel random components approach. Forest Systems, 15(1), 24-41. 10.5424/srf/2006151-00951
  • MITECO (2025). CUARTO INVENTARIO FORESTAL NACIONAL. Descripción de los códigos de la base de datos Sig. https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/temas/inventarios-nacionales/documentador_sig_tcm30-536622.pdf
  • Montero, G, Ruiz-Peinado, R, Muñoz (2005). Producción de biomasa y fijación de CO2 por los bosques españoles.. Monografías INIA: Serie Forestal no, 13. https://gregoriomontero.files.wordpress.com/2016/09/2005-01-monografc3ada-forestal-13-m-produccic3b3n-de-biomasa-y-fijacic3b3n-de-co2-por-los-bosques-espac3b1oles.pdf
  • Moreno-Fernández, D., others (2025). Structural diversity indices in managed forests.
  • Omoro, L. M. A., Starr, M., Pellikka, P. K. E. (2010). Tree biomass and soil carbon stocks in Taita Hills, Kenya. African Journal of Ecology.
  • Rodríguez, F. (2009). Cuantificación de productos forestales en la planificación forestal: Análisis de casos con cubiFOR. https://secforestales.org/publicaciones/index.php/congresos_forestales/article/view/17189/17024
  • Ruiz-Peinado, Ricardo, Del Rio, Miren, Montero, Gregorio (2011). New models for estimating the carbon sink capacity of Spanish softwood species. Forest Systems, 20(1), 176-188. 10.5424/fs/2011201-11643
  • Simpson, E. H. (1949). Measurement of Diversity. Nature, 163, 688.
  • Ulanowicz, R. E. (2001). Information theory in ecology. Computers & Chemistry.
  • Varga, P., Chen, H. Y., Klinka, K. (2005). Tree-size diversity in progression of stand development in Douglas-fir forests. Forest Ecology and Management.