Rule-based vs parametric approaches for developing climate-sensitive site index models: a case study for Scots pine stands in northwestern Spain

Aertsen W, Kint V, van Orshoven J, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model. https://doi.org/10.1016/j.ecolmodel.2010.01.007

Barrio-Anta M, Castedo-Dorado F, Cámara-Obregón A, López-Sánchez CA (2020) Predicting current and future suitable habitat and productivity for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain. Ann For Sci. 77(2):41. https://doi.org/10.1007/s13595-020-00941-5, https://link.springer.com/10.1007/s13595-020-00941-5

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/10.1023/A:1010933404324

Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://link.springer.com/article/10.1023/A:1010933404324, https://doi.org/10.1023/A:1010933404324

Canty A, Ripley BD (2017) boot: Bootstrap R (S-Plus) Functions

Carmean W (1972) Site Index Curves for Upland Oaks in the Central States. For Sci. https://doi.org/10.1093/forestscience/18.2.109

Codilan AL, Nakjima T, Tatsuhara S, Shiraisi N (2015) Estimating site index from ecological factors for industrial tree plantation species in Mindanao, Philippines. Bull. Univ. of Tokyo For 133:19–41

Crookston NL, Rehfeldt GE, Dixon GE, Weiskittel AR (2010) Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. For Ecol Manag 260(7):1198–1211. https://doi.org/10.1016/j.foreco.2010.07.013

Diéguez-Aranda U, Álvarez González JG, Barrio Anta M, Rojo Alboreca A (2005a) Site quality equations for Pinus sylvestris L. plantations in Galicia (northwestern Spain). Ann For Sci. https://doi.org/10.1051/forest:2005006

Diéguez-Aranda U, Burkhart HE, Rodríguez-Soalleiro R (2005b) Modeling dominant height growth of radiata pine (Pinus radiata D. Don) plantations in north-western Spain. For Ecol Manag. https://doi.org/10.1016/j.foreco.2005.05.015

Diéguez-Aranda U, Castedo Dorado F, Álvarez González JG, Rojo Alboreca A (2006) Dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain). Ecol Model. https://doi.org/10.1016/j.ecolmodel.2005.04.026

Efron B, Tibshirani R (1997) Improvements on cross-validation: The .632+ bootstrap method. J Am Stat Assoc. https://doi.org/10.1080/01621459.1997.10474007, arXiv:1011.1669v3

Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. https://doi.org/10.1002/joc.5086

Fontes L, Bontemps JD, Bugmann H, Van Oijen M, Gracia C, Kramer K, Lindner M, Rotzer T, Skovsgaard JP (2010) Models for supporting forest management in a changing environment. Forest Systems 19:8–29. https://doi.org/10.5424/fs/201019S-9315

Gompertz B (1825) On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Phil Transac Roy Soci Londo 115:513–585. https://doi.org/10.1098/rspl.1815.0271

González-Rodríguez M, Diéguez-Aranda U (2020) Exploring the use of learning techniques for relating the site index of radiata pine stands with climate, soil and physiography. For Ecol Manag. p 458. https://doi.org/10.1016/j.foreco.2019.117803

González-Rodríguez M, Dieguez-Aranda U (2021) Height growth paramters of Scots pine plantations in the north-west of Spain from plot measurements and stem analysis [Data set]. https://doi.org/10.5281/zenodo.4535243

Greenwell B, Boehmke B, Cunningham J, Developers GBM (2019) gbm: Generalized Boosted Regression Models. https://cran.r-project.org/package=gbm

Hamel B, Bélanger N, Paré D (2004) Productivity of black spruce and Jack pine stands in Quebec as related to climate, site biological features and soil properties. For Ecol Manag. https://doi.org/10.1016/j.foreco.2003.12.004

Hossfeld J (1822) Mathematika für Forstmänner, Ö konomen und Cameralisten

Kirilenko AP, Sedjo RA (2007) Climate change impacts on forestry. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.0701424104

Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World Map of Köppen-Geiger Climate Classification - (updated with CRU TS 2.1 temperature and VASClimO v1.1 precipitation data 1951 to 2000). Meteorologische Zeitschrift

Kuhn M, Quinlan R (2018) Cubist: Rule- And Instance-Based Regression Modeling. https://cran.r-project.org/package=Cubist

Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2(3):18–22. https://cran.r-project.org/doc/Rnews/

Lindner M, Garcia-Gonzalo J, Kolström M, Green T, Reguera R, Maroschek M, Seidl R, Lexer MJ, Netherer S, Schopf A, Kremer A, Delzon S, Barbati A, Marchetti M, Corona P (2008) Impacts of climate change on european forests and options for adaptation. Report to the European Commission Directorate-General for Agriculture and Rural Development

Monserud RA, Huang S, Yang Y (2006) Predicting lodgepole pine site index from climatic parameters in Alberta. For Chron 82(4):562–571. https://doi.org/10.5558/tfc82562-4

Newberry JD (1991) A note on Carmean’s estimate of height from stem analysis data. https://doi.org/10.1093/forestscience/37.1.368

Oliver JE (ed) (2005) Encyclopedia of world Climatology, 1st edn. Springer, Netherlands, Dordrecht

Øyen BH, Blom HH, Gjerde I, Myking T, Sætersdal M, Thunes KH (2006) Ecology, history and silviculture of Scots pine (Pinus sylvestris L.) in western Norway - A literature review. Forestry 79(3):319–329. https://doi.org/10.1093/forestry/cpl019

Pâques LE (2013) Forest tree breeding in Europe: Current state-of-the-art and perspectives. https://doi.org/10.1007/978-94-007-6146-9\_9

Quinlan JR (1992) Learning with continuous classes. Fifth Austrailian Joint Conference of Artifical Intelligence 92:343–348

R Core Team (2018) R: A Language and Environment for Statistical Computing. https://www.r-project.org/

Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot. https://doi.org/10.1093/jxb/10.2.290

Sabatia CO, 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

Savill PS (2013) The silviculture of trees used in British forestry, 2nd edn. CABI Publishing, Oxfordshire

Seynave I, Gégout JC, Hervé JC, Dhôte JF, Drapier J, Bruno É, Dumé G (2005) Picea abies site index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases. Can J For Res 35(7):1669–1678. https://doi.org/10.1139/x05-088

Skovsgaard JP, Vanclay JK (2008) Forest site productivity: A review of the evolution of dendrometric concepts for even-aged stands. Forestry 81(1):13–31. https://doi.org/10.1093/forestry/cpm041

Smith WK, Roy J, Hinckley TM (1995) Ecophysiology of Coniferous Forests. Elsevier, https://doi.org/10.1016/C2009-0-02453-2, https://linkinghub.elsevier.com/retrieve/pii/C20090024532

Swenson JJ, Waring RH, Fan W, Coops N (2005) Predicting site index with a physiologically based growth model across Oregon, USA. Can J For Res. 35(7):1697–1707. https://doi.org/10.1139/x05-089, http://www.nrcresearchpress.com/doi/10.1139/x05-089

Tange T, Ge F (2020) Topographic factors and tree heights of aged Cryptomeria japonica plantations in the Boso Peninsula, Japan. Forests 11(7). https://doi.org/10.3390/F11070771

Valiant LG (1984) A theory of the learnable. In: Proceedings of the sixteenth annual ACM symposium on Theory of computing - STOC ’84. https://doi.org/10.1145/800057.808710

Wang GG, Huang S, Monserud RA, Klos RJ (2004) Lodgepole pine site index in relation to synoptic measures of climate, soil moisture and soil nutrients. For Chron 80(6):678–686. https://doi.org/10.5558/tfc80678-6

Watt MS, 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 airborne laser scanning. For Ecol Manag. https://doi.org/10.1016/j.foreco.2015.08.001

Watt MS, Dash JP, Watt P, Bhandari S (2016) Multi-sensor modelling of a forest productivity index for radiata pine plantations. N Z J For Sci. 46(1). https://doi.org/10.1186/s40490-016-0065-z

Watt MS, Palmer DJ, Leonardo EMC, Bombrun M (2021) Use of advanced modelling methods to estimate radiata pine productivity indices. For Ecol Manag. 479:118557. https://doi.org/10.1016/j.foreco.2020.118557, https://linkinghub.elsevier.com/retrieve/pii/S0378112720313268

Weiskittel AR, Crookston NL, Radtke PJ (2011) 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, http://www.nrcresearchpress.com/doi/abs/10.1139/x11-086

Zhu G, Hu S, Chhin S, Zhang X, He P (2019) Modelling site index of Chinese fir plantations using a random effects model across regional site types in Hunan province, China. For Ecol Manag 446:143–150. https://doi.org/10.1016/j.foreco.2019.05.039