Key message By combining inventory data and spatially-continuous environmental information, we were able to develop models for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain in order to predict suitable habitat and site index at a spatial resolution of 250 × 250 m.
Context Currently available, spatially continuous environmental information was used to make reliable predictions about suitable habitat and forest productivity.
Aims To develop raster-based distribution and productivity models for Atlantic populations of maritime pine in Spain to predict current and future suitable habitat and productivity.
Methods Occurrence data and site index values were obtained from the Third Spanish National Forest Inventory and research plots, respectively. After testing different algorithms, random forest were selected for modelling the relationships between maritime pine occurrence, site index and spatially continuous environmental variables.
Results The overall accuracy of the suitable habitat model was 73%, and climate (mainly thermal properties) and soil physical properties were the most important variables. The site index model explained 60% of the observed variability, and lithological properties were the most important variables. A slight increase in site index (0.46–0.51%) and a large increase in suitable habitat (50–66%) are expected for 2070 under the most pessimistic climate change scenario.
Conclusion The currently available spatial continuous information enables the development of accurate raster data models for predicting suitable habitat and site productivity without the need for fieldwork. Climate change is expected to increase the potentially suitable habitat of Atlantic maritime pine populations in Spain in the coming decades.
Pinus pinaster Aiton, Site index, Species distribution model, Environmental variables, Random forest, Climate change
Barrio-Anta, M., Castedo-Dorado, F., Cámara-Obregón, A. et al. Predicting current and future suitable habitat and productivity for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain. Annals of Forest Science 77, 41 (2020). https://doi.org/10.1007/s13595-020-00941-5
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The datasets generated and/or analysed during the current study are available in the Forest Inventory and Analysis Database. Ministerio de Agricultura, Pesca y Alimentación. Gobierno de España. https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/ifn3_bbdd_descargas.htm.aspx