Environmental variability and its relationship to site index in Mediterranean maritime pine
Abstract
Environmental variability and site productivity relationships, estimated by means of soil-site equations, are considered a milestone in decision making of forest management. The adequacy of silvicultural systems is related to tree response to environmental conditions. The objectives of this paper are to study climatic and edaphic variability in Mediterranean Maritime pine (Pinus pinaster) forests in Spain, and the practical use of such variability in determining forest productivity by means of site index estimation. Principal component analysis was used to describe environmental conditions and patterns. Site index predictive models were fitted using partial least squares and parsimoniously by ordinary least square. Climatic variables along with parent material defined an ecological regionalization from warm and humid to cold and dry sites. Results showed that temperature and precipitation in autumn and winter, along with longitudinal gradient define extreme site qualities. The best qualities are located in warm and humid sites whereas the poorest ones are found in cold and dry regions. Site index values are poorly explained by soil properties. However, clay content in the first mineral horizon improved the soil-site model considerably. Climate is the main driver of productivity of Mediterranean Maritime pine in a broad scale. Site index differences within a homogenous climatic region are associated to soil properties.Downloads
References
Abdi H., 2003. Partial Least Squares (PLS) regression. In: Lewis-Beck M., Bryman A., Futing T., eds. Encyclopaedia of Social Sciences Research Methods, Sage, Thousand Oaks (CA).
Alía R., Moro J., Denis J.B., 1997. Performance of Pinus pinaster provenances in Spain: interpretation of the genotype by environment interaction, Can J For Res 27, 1548-1559. http://dx.doi.org/10.1139/X97-122
Alonso R., Sánchez-Palomares O., Roig S., López E., Gandullo J.M., 2010. Las estaciones ecológicas actuales y potenciales de los sabinares albares españoles. Ed Instituto Nacional de Investigación Agraria y Alimentaria. Ministerio de Ciencia e Innovación. Monografías INIA: Serie Forestal 19, 188 pp.
Bravo-Oviedo A., Río M., Montero G., 2007. Geographic variation and parameter assessment in generalized algebraic difference site index modelling. For Eco Manage 247, 107-119.
Bravo-Oviedo A., Tomé M., Bravo F., Montero G., RÍO M., 2008. Dominant height growth equations including site attributes in the generalized algebraic difference approach. Can J For Res 38, 2345-2358. http://dx.doi.org/10.1139/X08-077
Carmean W.H., 1975. Forest site quality evaluation in the United States. Adv Agron 27, 209-269. http://dx.doi.org/10.1016/S0065-2113(08)70011-7
Carrascal L.M., Galván I., GORDO O., 2009. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118, 681-690. http://dx.doi.org/10.1111/j.1600-0706.2008.16881.x
Costa M., Morla C., Sainz H. (eds), 2005. Los bosques Ibéricos. Una interpretación geobotánica. Ed Planeta, Madrid.
Curt T., Bouchaud M., Agrech G., 2001. Predicting site index of Douglas-Fir plantations from ecological variables in the Massif Central are of France. For Eco Manage 149, 61-74.
Chen H.Y.H., Krestov P.V., Klinka K., 2002. Trembling aspen site index in relation to environmental measures of site quality at two spatial scales. Can J For Res 32, 112-119. http://dx.doi.org/10.1139/x01-179
DGCN, 1998. El Segundo Invenatrio Forestal Nacional. España. MMA-DGCN, Madrid.
Díaz-Maroto I.J., Vila-Lameiro P., Guchu E., Díaz-Maroto M.C., 2007. A comparison of the autoecology of Quercus robur L. and Q. pyrenaicaWilld: present habitat in Galicia, NW Spain. Forestry 80, 223-229. http://dx.doi.org/10.1093/forestry/cpm019
Dunbar A., Dhubhain A.N., Bulfin M., 2002. The productivity of Douglas fir in Ireland. Forestry 75, 537-545. http://dx.doi.org/10.1093/forestry/75.5.537
Fernández-Cancio A., Manrique E., 2001. Programas GENPET para la reconstrucción de una estación meteorológica con resolución mensual en cualquier punto del territorio español, conocidas sus coordenadas y altitud. Software Registration 106,649, Spain.
Fontes L., Tome M., Thompson F., Yeomans A., Luis J.S., Savill P., 2003. Modelling the Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] site index from site factors in Portugal. Forestry 76, 491-507. http://dx.doi.org/10.1093/forestry/76.5.491
Fries A., Lindgren D., Ying C.C., Ruotsalainen S., Lindgren K., Elfving B., Karlmats U., 2000. The effect of temperature on site index in western Canada and Scandinavia estimated form IUFRO Pinus contorta provenance experiments. Can J For Res 30, 921-929.
Gandullo J.M., 1974. Ensayo de evaluación cuantitativa de la insolación en función de la orientación y de la pendiente del terreno. An INIA: Ser Recursos Naturales 1, 95-107.
Gandullo J.M., 1985. Ecología vegetal. Fundación Conde del Valle de Salazar, Madrid. 207 pp.
Gandullo J.M., Sánchez-Palomares O., 1994. Estaciones ecológicas de los pinares españoles. ICONAMinisterio de Agricultura, Pesca y Alimentación, Madrid. 188 pp.
Hägglund B., 1981. Evaluation of forest site productivity. Forestry Abstracts 42, 515-527.
Hair J.F., Anderson R.E., Tatham R.L., Black W.C., 1999. Análisis multivariante. Prentice Hall Iberia, Madrid. 832 pp. PMCid:2326768
Hollingsworth I.D., Boardman R., Fitzpatrick R.W., 1996. A soil-site evaluation index of productivity in intensively managed Pinus radiata (D Don) plantations in south Australia. Environmental Monitoring and Assessment 39, 531-541. http://dx.doi.org/10.1007/BF00396166
Huang S., Yang Y., Wang Y., 2003. A Critical look at procedures for validating growth and yield models. In: Amaro R., ed. Modelling Forest Systems, CAB.
Klinka K., Chen H.Y.H., 2003. Potential productivity of three interior subalpine forest tree species in British Columbia. For Eco Manage 175, 521-530.
Krumland B., Eng H., 2005. Site index systems for major young-growth forest and woodland species in northern California. The Rources Agency Dpt Forestry & Fire Protection. p. 219.
Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S., 1996. Application of neural networks to modelling nonlinear relationships in ecology. Eco Model 90, 39-52. http://dx.doi.org/10.1016/0304-3800(95)00142-5
Manrique E., Fernández-Cancio A., 2005. Sistema informático para la generación de datos climáticos y fitoclimáticos. In: For SEC, ed. IV Congreso Forestal Español, Zaragoza, Spain. p. 7.
Monserud R.A., Moody U., Breuer D.W., 1990. A soil-site study for inland douglas-fir. Can J For Res 20, 686-695. http://dx.doi.org/10.1139/x90-092
Myers R.H., 1990. Classical and modern regression with applications. Wadsworth Publishing Company, Pacific Grove, CA. 488 pp.
Nicolás A., Gandullo J.M., 1967. Ecología de los pinares Españoles I. Pinus pinaster Ait. Ministerio de Agricultura, Dirección General de Montes, Caza y Pesca Fluvial, IFIE, Madrid. 311 pp.
Nigh G., 2006. Impact of climate, moisture regime, and nutrient regime on the productivity of douglas-fir in coastal British Columbia, Canada. Climatic Change 76, 321-337. http://dx.doi.org/10.1007/s10584-005-9041-y
Pita P.A., 1968. Clasificación provisional de las calidades de la estación en las masas de P. pinaster Sol (continental) y P. uncinata Ram. en la península Ibérica. Anales del IFIE, pp. 125-139.
Río M., Bravo F., Pando V., Sanz G., Sierra De Grado R., 2004. Influence of individual tree and stand attributes in stem straightness in Pinus pinaster Ait. stands. Ann For Sci 61, 141-148. http://dx.doi.org/10.1051/forest:2004005
Romanyà J., Vallejo V.R., 2004. Productivity of Pinus radiata plantations in Spain in response to climate and soil. For Eco Manage 195, 177-189.
Society Of American Foresters (SAF), 2008. The dictionary of Forestry. Avalaible in www.dictionayofforestry.org [10 November, 2010].
Sánchez-Rodríguez F., Rodríguez-Soalleiro R., Español E., López C.A., Merino A., 2002. Influence of edaphic factors and tree nutritive status on the productivity of Pinus radiata D. Don plantations in northwestern Spain. For Eco Manage 171, 181-189.
Sánchez-Palomares O., Jovellar L.C., Sarmiento L.A., Rubio A., Gandullo J.M., 2007. Las estaciones ecológicas de los alcornocales españoles. Ed Instituto Nacional de Investigación Agraria y Alimentaria. Ministerio de Educación y Ciencia. Monografías INIA: Serie Forestal 14, 232 pp.
Sánchez-Palomares O., Roig S., Río M., Rubio A., Gandullo J.M., 2008. Las estaciones ecológicas actuales y potenciales de los rebollares españoles. Ed Instituto Nacional de Investigación Agraria y Alimentaria. Ministerio de Ciencia e Innovación. Monografías INIA: Serie Forestal 17, 343 pp.
SAS I.I., 2004. SAS/STAT(R) 9.1 User's guide, SAS Inst Inc, Cary, NC, X, 5121.
Scarascia-Mugnozza G., Oswald H., Piussi P., Radoglou K.M., 2000. Forests of the Mediterranean region: gaps in knowledge and research needs. For Eco Manage 132, 97-109.
Seynave I., Gegout J.C., Herve J.C., Dhote J.F., Drapier J., Bruno E., Dume 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, 1669-1678. http://dx.doi.org/10.1139/x05-088
Soares P., Tomé M., Skovsgaard J.P., Vanclay J.K., 1995. Evaluating a growth model for forest management using continuous forest inventory data. For Ecol Manage 71, 251-265.
Tobias R., 1995. An introduction to partial least squares regression. Proceedings of the 20th Annual SAS Users Group International Conference, Cary, NC, SAS INSTITUTE INC.
Toumey J.W., Korstian C.F., 1947. Foundations of silviculture upon an ecological basis. Ed John Wiley & Sons, NY. 468 pp. PMid:20242267
Thornthwaite C.W., 1957. Instruction and tables for computing potential Evapotranspirataion and the Water Balances. Centerton, New Jersey.
Vanclay, 1994. Modelling forest growth and yield. CAB Internatioal, Wallingford.
Wang G.G., Klinka K., 1996. Use of synoptic variables in predicting white spruce site index. For Eco Manage 80, 95-105.
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