Abad Viñas R, Caudullo G, Oliveira S, de Rigo D (2016) Pinus pinaster in Europe: distribution, habitat, usage and threats. In: San-Miguel-Ayanz J, de Rigo D, Caudullo G, Houston Durrant T, Mauri A (eds) European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, p e012d59+
Google Scholar
Aertsen W, Kint V, van Orshoven J, Ozkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221:1119–1130. https://doi.org/10.1016/j.ecolmodel.10.01.007
Article
Google Scholar
Albert M, Schmidt M (2010) Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). Forest Ecol Manag 259:739–749. https://doi.org/10.1016/j.foreco.2009.04.039
Article
Google Scholar
Alexander EB (1989) Bulk density equations for southern Alaska soils. Can J Soil Sci 69:177–180
Article
Google Scholar
Alía R, Martín S, De Miguel J, Galera R, Agúndez D, Gordo J, Salvador L, Catalán G, Gil A (1996) Regiones de procedencia Pinus pinaster Aiton. Dirección General de Conservación de la Naturaleza, Madrid
Google Scholar
Alía R, Moro J, Denis JB (1997) Performance of Pinus pinaster provenances in Spain: interpretation of the genotype by environment interaction. Can J For Res 27:1548–1559
Article
Google Scholar
Alía R, García Del Barrio JM, Iglesias S, Mancha JA, De Miguel J, Nicolás JL, Pérez F, Sánchez De Ron D (2009) Regiones de Procedencia de especies forestales en España. Ministerio de Medio Ambiente y Medio Rural y marino. Organismo Autónomo Parques nacionales, Madrid
Google Scholar
Álvarez-Álvarez P, Afif E, Cámara-Obregón A, Castedo-Dorado F, Barrio-Anta M (2011) Effects of foliar nutrients and environmental factors on site productivity in Pinus pinaster Ait. Stands in Asturias (NW Spain). Ann For Sci 68:497–509. https://doi.org/10.1007/s13595-011-0047-5
Article
Google Scholar
Álvarez-González JG, Ruiz González AD, Rodríguez Soalleiro R, Barrio Anta M (2005) Ecoregional site index models for Pinus pinaster in Galicia (northwestern Spain). Ann For Sci 62:115–127. https://doi.org/10.1051/forest:2005003
Article
Google Scholar
Arias-Rodil M, Barrio-Anta M, Diéguez-Aranda U (2016) Developing a dynamic growth model for maritime pine in Asturias (NW Spain): comparison with nearby regions. Ann For Sci 73(2):297–320. https://doi.org/10.1007/s13595-015-0501-x
Article
Google Scholar
Bará S, Toval G (1983) Calidad de estación del Pinus pinaster Ait. en Galicia. INIA. Serie: Recursos Naturales, 24. Madrid
Barrio Anta M, Diéguez-Aranda U (2005) Site quality of pedunculate oak (Quercus robur L.) stands in Galicia (northwest Spain). Eur J Forest Res 124:19–28. https://doi.org/10.1007/s10342-004-0045-3
Article
Google Scholar
Bede-Fazekas A, Levente Horváth KM (2014) Impact of climate change on the potential distribution of Mediterranean pines. Q J Hung Meteorol Serv 118:41–52
Google Scholar
Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
Article
PubMed
PubMed Central
Google Scholar
Bergès L, Chevalier R, Dumas Y, Franc A, Gilbert, JM (2005) Sessile oak (Quercus petraea Liebl.) site index variations in relation to climate, topography and soil in even-aged high-forest stands in northern France. Ann For Sci 62: 391–402. https://doi.org/10.1051/forest:2005035
Bjelanovic I, Comeau PG, White B (2018) High resolution site index prediction in boreal forests using topographic and wet areas mapping attribute. Forests 9:113. https://doi.org/10.3390/f9030113
Article
Google Scholar
Bontemps JD, Bouriaud O (2014) Predictive approaches to forest site productivity: recent trends, challenges and future perspectives. Forestry 87(1):109–128. https://doi.org/10.1093/forestry/cpt034
Article
Google Scholar
Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) Bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxENT studies. Diversity Distrib 20:1–9
Article
Google Scholar
Brandl S, Falk W, Klemmt HJ, Stricker G, Bender A, Rötzer T, Pretzsch H (2014) Possibilities and limitations of spatially explicit site index modelling for spruce based on National Forest Inventory Data and digital maps of soil and climate in Bavaria (SE Germany). Forests 5(11):2626–2646. https://doi.org/10.3390/f5112626
Article
Google Scholar
Bravo-Oviedo A, Roig S, Bravo F, Montero G, Del-Río M (2011) Environmental variability and its relationship to site index in Mediterranean maritime pine. Forest Syst 20(1):50–64. https://doi.org/10.5424/fs/2011201-9106
Article
Google Scholar
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Article
Google Scholar
Burkhart HE, Tomé M (2012) Modelling forest trees and stands. Springer, Berlin
Book
Google Scholar
Chaudhari PR, Ahire DV, Ahire VD, Chkravarty M, Maity S (2013) Soil bulk density as related to soil texture, organic matter content and available total nutrients of Coimbatore soil. Int J Sci Res 3(2):1–8
CAS
Google Scholar
Clutter J, Fortson J, Pienaar L, Brister H, Bayley R (1983) Timber management: a quantitative approach. Wiley, New York
Google Scholar
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Whichmann V, Böhner J (2015) System for automated geoscientific analyses (SAGA) v. 3.0.0. Geosci Model Dev 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Article
Google Scholar
Cosenza DN, Leite HG, Marcatti GE, Binoti DHB, Alcántara AEM, de Rode R (2015) Classificação da capacidade produtiva de sítios florestais utilizando máquina de vetor de suporte e rede neural artificial. Scientia Forestalis 43:955–963. https://doi.org/10.18671/scifor.v43n108.19
Article
Google Scholar
Davis ME, Shaw RG, Etterson JR (2005) Evolutionary responses to climate change. Ecology 86:1704–1714. https://doi.org/10.1890/03-0788
Article
Google Scholar
De Uña Álvarez E (2001) El clima, in: Atlas de Galicia. Tomo 1: Medio Natural, edited by: Precedo Ledo, A and Sancho Comíns, J, Sociedade para o Desenvolvemento Comarcal de Galicia, Xunta de Galicia
Deschamps B, McNairn H, Shang J, Jiao X (2012) Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier. Can J Remote Sens 38:60–68. https://doi.org/10.5589/m12-012
Article
Google Scholar
DGCN (2006) III Inventario Forestal Nacional (1997–2006). Principado de Asturias, Galicia y León. Dirección General de Conservación de la Naturaleza, Secretaría General de Medio Ambiente, Ministerio de Medio Ambiente. Madrid
Diéguez-Aranda U, Rojo Alboreca A, Castedo-Dorado F, Álvarez-Gonzalez JG, Barrio-Anta M, Crecente-Campo F, González-González JM, Pérez-Cruzado C, Rodríguez-Soalleiro R, López-Sánchez CA, Balboa-Murias MA, Gorgoso Varela, JJ, Sánchez-Rodríguez F (2009) Herramientas selvícolas para la gestión forestal sostenible en Galicia. Xunta de Galicia
Drummond ST, Sudduth KA, Joshi A, Birrell SJ, Kitchen NR (2003) Statistical and neural methods for site-specific yield prediction. Trans ASAE 46:5–14. https://doi.org/10.13031/2013.12541
Article
Google Scholar
Dyderski MK, Paz S, Frelich LE, Jagodzinski AM (2017) How much does climate change threaten European forest tree species distributions? Glob Change Biol 24:1150–1163. https://doi.org/10.1111/gcb.13925
Article
Google Scholar
EEA (2017) Climate change, impacts and vulnerability in Europe 2016. An indicator-based report, European Environment Agency Report N° 1/2017
Eimil-Fraga C, Rodríguez-Soalleiro R, Sánchez-Rodríguez F, Pérez-Cruzado C, Álvarez-Rodríguez E (2014) Significance of bedrock as a site factor determining nutritional status and growth of maritime pine. Forest Ecol Manag 331:19–24. https://doi.org/10.1016/j.foreco.2014.07.024
Article
Google Scholar
Fontes L, Tomé M, Thompson F, Yeomans A, Sales LJ, Savill P (2003) Modelling the Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) site index from site factors in Portugal. Forestry 76:491–507. https://doi.org/10.1093/forestry/76.5.491
Article
Google Scholar
Gadow K, Bredemkamp B (1992) Forest management. Academica, Pretoria
Google Scholar
Gandullo JM, Sánchez Palomares O (1994) Estaciones ecológicas de los pinares españoles. MAPA-ICONA, Madrid
Google Scholar
Grigal DF, Vance ED (2000) Influence of soil organic matter on forest productivity. NZ J Forestry Sc 30(1/2):169–205
CAS
Google Scholar
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Article
Google Scholar
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–18. https://doi.org/10.1145/1656274.1656278
Article
Google Scholar
Harris RMB, Grose MR, Lee G, Bindoff NL, Porfirio LL, Fox-Hughes P (2014) Climate projections for ecologists. WIREs Clim Change 5:621–637. https://doi.org/10.1002/wcc.291
Article
Google Scholar
Hasenauer H, Monserud RA (1997) Biased predictions for tree height increment models developed from smoothed ‘data’. Ecol Model 98:13–22. https://doi.org/10.1016/S0304-3800(96)01933-3
Article
Google Scholar
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotic A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12(2):e0169748. https://doi.org/10.1371/journal.pone.0169748
Article
CAS
PubMed
PubMed Central
Google Scholar
Hijmans RJ, Cameron SE, Parra JL, Jones P, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276
Article
Google Scholar
Huang S, Ramírez C, Conway S, Kennedy K, Kholer T, Liu J (2017) Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California. USA Can J For Res 47:113–124. https://doi.org/10.1139/cjfr-2016-0209
Article
Google Scholar
IGME (2015a) Mapa Geológico de España a escala 1:200.000. Instituto Geológico y Minero de España, Ministerio de Ciencia, Innovación y Universidades Madrid
IGME (2015b) Mapa Geológico de la Península Ibérica, Baleares y Canarias a escala 1:1.000.000. Instituto Geológico y Minero de España, Ministerio de Ciencia, Innovación y Universidades Madrid
IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel of climate change. Cambridge University Press, Cambridge
Google Scholar
Jiang H, Radtke PJ, Weiskittel AR, Coulston JW, Guertin PJ (2015) Climate- and soil-based models of site productivity in eastern US tree species. Can J For Res 45:325–342. https://doi.org/10.1139/cjfr-2014-0054
Article
Google Scholar
Juez L, González-Martínez SC, Nanos N, de-Lucas AI, Ordóñez C, del Peso C, Bravo F (2014) Can seed production and restricted dispersal limit recruitment in Pinus pinaster Aiton from the Spanish northern plateau? Forest Ecol Manag 313:329–339. https://doi.org/10.1016/j.foreco.2013.10.033
Article
Google Scholar
Latta G, Temesgen H, Barrett TM (2009) Mapping and imputing potential productivity of Pacific northwest forests using climate variables. Can J For Res 39:1197–1207. https://doi.org/10.1139/X09-046
Article
Google Scholar
MAPA (2019) Anuario de Estadística Forestal 2019. Ministerio de Agricultura, Pesca y Alimentación. Madrid
Maugé J (1987) Le pin maritime. Premier résineux de France. IDF, Paris, 192 pages
McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol 31:1161–1175. https://doi.org/10.1007/s10980-016-0374-x
Article
Google Scholar
McKenney DW, Pedlar JH (2003) Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecol Manag 175:497–507. https://doi.org/10.1016/S0378-1127(02)00186-X
Article
Google Scholar
Monzón J, Moyer-Horner L, Palamar MB (2011) Climate change and species range dynamics in protected areas. BioScience 61:752–761. https://doi.org/10.1525/bio.2011.61.10.5
Article
Google Scholar
Nicolás A, Gandullo JM (1967) Ecología de los pinares españoles. I Pinus pinaster Ait. IFIE, Madrid, 311 p
Novo-Fernández A, Barrio-Anta M, Recondo C, Cámara-Obregón A, López-Sánchez CA (2019) Integration of National Forest Inventory and nationwide airborne laser scanning data to improve forest yield predictions in North-Western Spain. Remote Sens 11(14):1693. https://doi.org/10.3390/rs11141693
Article
Google Scholar
Oliveira AC, Pereira JS, Correia AV (2000). A silvicultura do pinheiro bravo. Centro Pinus, 102 pp. Portugal
Pacheco Marques C (1991) Evaluating site quality of even-aged maritime pine stands in northern Portugal using direct and indirect methods. For Ecol Manag 41:193–204. https://doi.org/10.1016/0378-1127(91)90103-3
Article
Google Scholar
Parresol BR, Scott DA, Zarnoch SJ, Edwards LA, Blake JI (2017) Modelling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA. For Ecol Manag 406:196–207. https://doi.org/10.1016/j.foreco.2017.10.006
Article
Google Scholar
Périé C, Ouimet R (2008) Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can J Soil Sci 88(3):315–325
Article
Google Scholar
Prasad A, Iverson L, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199. https://doi.org/10.1007/s10021-005-0054-1
Article
Google Scholar
Roces-Díaz JV, Jiménez-Alfaro B, Álvarez-Álvarez P, Álvarez-García MA (2015) Environmental niche and distribution of six deciduous tree species in the Spanish Atlantic region. iForest 8:224–231. https://doi.org/10.3832/ifor1183-008
Article
Google Scholar
Rodríguez Soalleiro R, Madrigal Collazo, A (2008) Selvicultura de Pinus pinaster Ait. subsp. atlantica H. de Vill. In: Serrada R, Montero G, Reque JA (eds) Compendio de selvicultura aplicada en España. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Ministerio de Educación y Ciencia, Madrid, pp 367–398
Romanyà J, Vallejo VR (2004) Productivity of Pinus radiata plantation in Spain in response to climate and soil. For Ecol Manag 195:177–189. https://doi.org/10.1016/j.foreco.2004.02.045
Article
Google Scholar
Ruíz Zorrilla P (1980) Notas para una historia del pino en Galicia. Ministerio de Cultura, Dirección General del Patrimonio Artístico, S.G. de Archivos, Madrid
Ryan TP (1997) Modern regression methods. John Wiley & Sons, New York
Google Scholar
Sabatia CH, Burkhart HE (2014) Predicting site index of plantation loblolly pine from biophysical variables. For Ecol Manag 326:142–156. https://doi.org/10.1016/j.foreco.2014.04.019
Article
Google Scholar
Sakin E (2012) Organic carbon organic matter and bulk density relationships in arid-semi arid soils in Southeast Anatolia region. Afr J Biotechnol 11(6):1373–1377. https://doi.org/10.5897/AJB11.2297
Article
CAS
Google Scholar
Sánchez-Rodríguez F, Rodríguez-Soalleiro R, Español E, López CA, Merino A (2002) Influence of edaphic factors and tree nutritive status on the productivity of Pinus radiata D. Don plantations in Northwestern Spain. For Ecol Manag 171(1–2):181–189. https://doi.org/10.1016/S0378-1127(02)00471-1
Article
Google Scholar
Serra-Varela MJ, Alía R, Ruiz Daniels R, Zimmermann NE, Gonzalo-Jiménez J, Grivet D (2017) Assessing vulnerability of two Mediterranean conifers to support genetic conservation management in the face of climate change. Divers Distrib 23:507–516. https://doi.org/10.1111/ddi.12544
Article
Google Scholar
Sharma RP, Brunner A, Eid T (2012) Prediction from site and climate variables for Norway spruce and Scots pine in Norway. Scand J For Res 27:619–636. https://doi.org/10.1080/02827581.2012.685749
Article
Google Scholar
Shirk AJ, Cushman SA, Waring KM, Wehenkel CA, Leal-Sáenz A, Toney C, Lopez-Sanchez CA (2018) Southwestern white pine (Pinus strobiformis) species distribution models project a large range shift and contraction due to regional climatic changes. Forest Ecol Manag 411:176–186. https://doi.org/10.1016/j.foreco.2018.01.025
Article
Google Scholar
Skovsgaard JP, Vanclay JK (2008) Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands. Forestry 81:13–31. https://doi.org/10.1093/forestry/cpm041
Article
Google Scholar
Thuiller W, Georges D, Engler R, Breiner F (2016) ‘biomod2’: ensemble platform for species distribution modelling
van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. https://doi.org/10.1007/s10584-011-0148-z
Article
Google Scholar
Vanclay JK (1994) Modelling forest growth and yield: applications to mixed tropical forests. Wallingford CAB International
Vennetier M, Vilà B, Liang E, Guibal F, Taahbet A, Gadbin-Henry C (2007) Impact of climate change on pines forest productivity and on the shift of a bioclimatic limit in Mediterranean area. In: Leone V, Lovreglio R (eds), proceedings of the international workshop MEDPINE 3: conservation, regeneration and restoration of Mediterranean pines and their ecosystems. Options Méditerranéennes: Série a. Séminaires Méditerranéens n.75
Wang Y, Raulier F, Ung CH (2005) Evaluation of spatial predictions of site index obtained by parametric and nonparametric methods-a case study of lodgepole pine productivity. Forest Ecol Manag 214:201–211. https://doi.org/10.1016/j.foreco.2005.04.025
Article
Google Scholar
Wang L, Zhou X, Zhu X, Dong X, Guo W (2016) Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J 4:212–219. https://doi.org/10.1016/j.cj.2016.01.008
Article
Google Scholar
Waring RH, Milner KS, Jolly WM, Phillips L, McWethy D (2006) Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U.S.A. with a MODIS satellite-derived vegetation index. Forest Ecol Manag 228:285–291. https://doi.org/10.1016/j.foreco.2006.03.019
Article
Google Scholar
Watt SW, Dash JP, Bhandari S, Watt P (2015) Comparing parametric and non-parametric methods of predicting site index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airbone laser scanning. Forest Ecol Manag 357:1–9. https://doi.org/10.1016/j.foreco.2015.08.001
Article
Google Scholar
Weiskittel AR, Hann DW, Kershaw JA Jr, Vanclay JK (2011a) Forest growth and yield modeling. Wiley-Blackwell, Oxford
Book
Google Scholar
Weiskittel AR, Crookston NL, Radtke PJ (2011b) Linking climate, gross primary productivity, and site index across forests of the western United States. Can J For Res 41(8):1710–1721. https://doi.org/10.1139/x11-086
Article
Google Scholar
Williams JN, Seo C, Thorne J, Nelson JK, Erwin S, O’Brien JN, Schwartz MW (2009) Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15:565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x
Article
Google Scholar
Zhiwei X, Xinghua W (2010) Research for information extraction based on wrapper model algorithm. 2010 Second international conference on computer research and development. Kuala Lumpur, Malaysia, pp. 652–655