Environmental variability and its relationship to site index in Mediterranean maritine pine

Environmental variability and its relationship to site index in Mediterranean maritine pine A. Bravo-Oviedo1,2*, S. Roig1,3, F. Bravo1,4, G. Montero1,2 and M. del-Rio1,2 1 Sustainable Forest Management Research Institute. www.research4forestry.org 2 Departamento de Selvicultura y Gestión de Sistemas Forestales. CIFOR-INIA. Ctra. A Coruña, km 7,5. 28040 Madrid. Spain 3 Departamento de Silvopascicultura. ETS Ingenieros de Montes. Universidad Politécnica. Ciudad Universitaria, s/n. 28040 Madrid. Spain 4 Sustainable Forest Management Group. University of Valladolid. Avda. Madrid, 44. 34004 Palencia. Spain 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. Key words: Mediterranean region; Pinus pinaster; site index; soil-site. Resumen Variabilidad ambiental de las masas de pino negral y su relación con el índice de sitio La relación entre variabilidad ambiental y la productividad de estación, estimada mediante el índice de sitio, es clave en la toma de decisiones en la gestión forestal sostenible, ya que su conocimiento permite adecuar la práctica selvícola a la respuesta de la masa a dicha variabilidad ambiental. Los objetivos de este trabajo son estudiar la variabilidad climática y edáfica de Pinus pinaster en su distribución mediterránea en España y el uso práctico de dicha variabilidad en la determinación de la productividad de la estación mediante la estimación del índice de sitio. Para la descripción de la variabilidad ambiental se realizó un análisis de componentes principales y para la predicción del índice de sitio se optó por una regresión por mínimos cuadrados parciales, y de forma más parsimoniosa, mediante mínimos cuadrados ordinarios. Las variables climáticas, junto al material parental definieron regiones que comprendían estaciones que van de cálidas y húmedas hasta frías y secas. Los resultados mostraron cómo la temperatura media anual, la precipitación en otoño e invierno, junto con un gradiente longitudinal define calidades de estación extremas. Las mejores calidades se encuentran en estaciones cálidas y húmedas mientras que las peores están en estaciones frías y secas. Las variables edáficas explican poca variación del índice de sitio, aunque la inclusión del contenido en arcilla mejora notablemente el modelo. El clima es el precursor de la calidad de estación mientras que diferencias en el índice de sitio en zonas climáticamente homogéneas se asocian a variables edáficas. Palabras clave: índice de sitio; material parental; Pinus pinaster; región mediterránea. * Corresponding author: brav[email protected] Received: 15-04-10; Accepted: 07-01-11. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Forest Systems 2011 20(1), 50-64 Available online at www.inia.es/forestsystems ISSN: 1131-7965 eISSN: 2171-9845 Introduction Mediterranean environmental conditions, such as water stress, limit forest growth. However, there is a high environmental variability in the Mediterranean region and mesic forest ecosystems such as those found in Central Europe can also exist (Scarascia-Mugnozza et al., 2000). The great variability in climate, soil and physiographic conditions leads to a great variability of tree species, growth response and productivity, as it is the case of Maritime pine (Pinus pinaster Ait.). In this species the environmental variability has derived into two groups in the Iberian Peninsula with great differences in growth performance; the Atlantic Maritime pine (AMP), which is more productive and found mainly in areas with an Atlantic climate, and the Mediterranean Maritime pine (MMP), which grows under pure and mesic Mediterranean climate conditions with an irregular precipitation regime and diverse soil origin which, along with stand isolation, has lead to geographic differentiation of tree attributes such as tree height, stem straightness or productivity (Alía et al., 1997; Río et al., 2004; Bravo-Oviedo et al., 2007). Stand forest dynamics is related to site properties and the application of silvicultural systems must be based on the knowledge of current environmental conditions. These properties are often considered to be the foundations of silviculture (Toumey and Korstain, 1947). Autoecology, or the study of environmental factors and their effects on plants (SAF, 2008), has a long tradition in forestry studies in Spain. The first study on applied autoecology in Spain was conducted for Pinus pinaster within an important research program started in the early sixties. These studies first aimed to establish the autoecology of species of genus Pinus, as they were systematically used in restoration programs. Regarding Mediterranean Pinus pinaster (MMP) this study was carried out according to six natural regions (Nicolás and Gandullo, 1967) and the authors finally presented 5 ecotypes according to physical-soil properties. Recently, several works have increased the knowledge on tree-environment relation on forestry application for other species (Díaz-Maroto et al., 2007; SánchezPalomares et al., 2007, 2008; Alonso et al., 2010). Sustainable Forest Management must consider the environmental variability as an important factor in forest stand dynamics. The study of cause-effect relationships is essential in furthering scientific knowledge and understanding of biological processes. However, standard management requires simple tools to aid decision-making. One example of these are models which include indirect measures, like those used for evaluating forest quality and yield through site index (Curt et al., 2001). Forest site quality studies have the aim of describing, classifying and predicting the potential of a site to sustain biomass productivity. Forest site evaluation in even-aged forests is usually expressed as a function of intrinsic stand properties, such as tree height and age (Hägglund, 1981), i.e. site index. Forest site index, described as the dominant height attained at a reference age, is an indirect and partial measure of site quality. It is devoted to the tree bole production of aboveground biomass and it is related to mean annual volume increment, which is a basic unit of forest management. Consequently, a careful selection of appropriate dominant trees must be made. In some cases such trees are rare or even absent, e.g. marginal agricultural lands subjected to forestation, high graded or very sparse stands, etc. Where this situation exists, forest productivity can be assessed in one of two ways (Curt et al., 2001): the first is known as the synoptic approach and correlates site index to site attribute classes, such as regional classification according to a composite of ecological features (Wang and Klinka, 1996; Curt et al., 2001; Romanyà and Vallejo, 2004). The second method is analytical and consists of measuring site variables and relating site index to them (Chen et al., 2002; Klinka and Chen, 2003). The latter method is known as a soil-site study and has been widely used in forest productivity studies (Carmean, 1975; Monserud et al., 1990; Hollingsworth et al., 1996; Dunbar et al., 2002; Fontes et al., 2003; Nigh, 2006). In the course of a study on dominant height growth for MMP (Bravo-Oviedo et al., 2007), some regional and local differences in growth performance were detected using base-age invariant (BAI) equations, along with ecological regions defined by Costa et al. (2005). BAI is considered to be superior to base age specific equations, like previous existing curves for the species (Pita, 1968), in terms of site index model applicability and statistical validity (Krumland and Eng, 2004). The use of BAI species-specific equations may indicate relationships among site conditions, i.e. climate and soil, and forest growth. Thus, a revision of soil-site relationships is needed for the species in order to estimate forest productivity. The main objective of this paper is to analyze the statistical variability in climate, soil and physiography Environmental variability and site index 51 and its relationship to site index in the distribution area of Mediterranean maritime pine in Spain. The specific objectives were to a) analyze the environmental variability and define homogeneous environmental regions, b) analyze the relationship between environmental variability and productivity values, estimated by means of site index, c) to develop a model for site index prediction from environmental variables. We hypothesized that climatic and edaphic variability explains differences in site productivity. Material and methods Stand selection and Site Index This study deals with most of the distribution of Mediterranean Maritime pine in Spain, which accounts for around 724,000 ha in Spain (DGCN, 1998). Within the institutional framework of the Sustainable Forest Management Research Institute (SFMRI; www. research4forestry.org), 191 plots were selected in the study area (Fig. 1). Ninety three of them belong to the CIFOR-INIA network of experimental plots installed in 1964 to study the growth and yield of Pinus pinaster, in which measurements have been taken periodically until 2004. In addition, 20 complementary plots were established in 2004 for stem analysis in order to complete the data from the first source. Finally, 78 plots belonging to the network installed by the University of Valladolid to study Pinus pinaster growth dynamics in the Iberian Mountain Range were incorporated into our database. In each plot, dominant height was calculated according to the mean value of the 100 thickest stems per hectare and the age was determined from cores of a sample (4 to 15) of dominant trees. Site index values were calculated according to the dominant height model developed by Bravo-Oviedo et al. (2007) using dominant height at the age of 70 years. We used a general model which is common to all regions, because we intend to find environmental variables that drive forest productive irrespective to regions. Average site index is 14.8 m (standard deviation 4.3 m), maximum site index is 26.1 m and the minimum is 4.7 m. Environmental attributes Climatic and physiographic data Climatic data for every plot were retrieved from the GENPT and COMPLET programs (Fernández-Cancio and Manrique, 2001; Manrique and Fernández-Cancio, 2005). Monthly climatic values for each plot were calculated according to regression models or mean values 52 A. Bravo-Oviedo et al. / Forest Systems (2011) 20(1), 50-64 Figure 1. Plot location in Pinus pinaster Ait. stands in Spain. from the ancillary nearest climatic stations. The program requirements are: longitude, latitude and altitude. The reference period 1961-1990 was used to calculate average climatic conditions for each plot. Physiographic data were obtained from a digital terrain model with a pixel size of 25 ×25 m. Climate-related variables such as drought length, intensity of drought, evapotranspiration, physiological drought, surplus, deficit, annual hydric index and surface drainage were also calculated (Table 1). Soil data Soil data were obtained in a subsample of 65 plots in the SFMRI database, according to elevation and soil parental material. A sample of every soil horizon was extracted for analysis in the laboratory. The soil attributes measured were: water-pH, conductivity, organic matter (oxidizable organic carbon via Walkley-Black’s method), total organic matter according to loss-onignition method, carbonates and active calcium (using Environmental variability and site index 53 Table 1. Description of physiographic, climatic and climate-realted variables Variables typeaUnits Mean Std. Dev. Min. Max. Physiographic Longitude (XUTM) m (zone 30) 503,317 126,534 187,386.0 702,502.0 Latitude (YUTM) m (zone 30) 4,479,273 106,935 4,226,960.0 4,631,322.0 Elevation (ELV) m 1,021 224.8 377.0 1437.0 Slope (SLP) % 16.7 12.4 0 63.7 Aspect (ASP) º 70.9 111.9 0.0 356.8 Insolation 1 (INS) 1 0.2 0.4 1.3 Climatic Annual rainfall (P) mm 576 81 420.0 792.0 Spring rainfall (SGP) mm 164.9 18.2 118.6 204.5 Summer rainfall (SMP) mm 79.7 17.7 50.8 121.1 Autumn rainfall (AUP) mm 175.5 36.3 121.0 297.5 Winter rainfall (WP) mm 156.5 47.6 82.7 307.1 Mean annual Temperature (T) °C 11.4 1.6 8.9 15.9 Lowest Monthly mean temperature (TMF) °C 3.3 1.4 0.6 7.2 Highest Monthly mean temperature (TMC) °C 21.4 1.8 18.0 26.1 Mean value of minima temperature in the coldest month (TMMF) °C –1.3 1.5 –4.6 2.2 Mean value of maxima temperature in the warmest month (TMMC) °C 29.7 1.8 26.5 35.4 Climate-related Drought lenght 2 (DSQ) Months 2.6 0.7 1.6 4.5 Intensity of drought 3 (ISQ) 0.1 0.1 0.0 0.3 Annual Evapotanspiration (ET) mm 678.6 53.5 597.9 851.3 Winter ET mm 30.7 6.9 17.2 46.8 Spring ET mm 137.3 11.3 112.4 169.4 Summer ET mm 358.4 26.7 315.3 449.7 Autumn ET mm 153.2 12.8 135.3 187.2 Potential Evapotranspiration (PET) mm 444.7 33.3 358.7 538.0 Annual physiological drought 4 (APD) mm 234.0 72.1 78.3 426.3 Surplus 5 (SUP) mm 224.9 81.7 70.6 413.0 Deficit 6 (DEF) mm 326.9 62 225.7 500.6 Annual Hydric Index 7 (AHI) 4.3 11.2 –17.6 34.7 Drainage 8 (D) mm 131.9 93.1 0.0 380.1 aIn parenthesis acronym used in the analysis. 1According to Gandullo (1974). 2Number of months where precipitation curve is under temperature curve in the Walter-Lieth climodiagram. 3Quotient between dry area (temperature curve is above precipitation curve) and humid area (temperature curve is under precipitation curve) in the Walter-Lieth climodiagram. 4Sum of monthly values where evapotranspiration is higher than potential evapotranspiration. 5Sum of monthly values where precipitation is higher than evapotranspiration. 6Sum of monthly values where precipitation is lower than evapotranspiration. 7AHI =(100xS-60xD)/ET. 8Estimated soil drainage according to Thornthwaite (1957) and Gandullo (1985). Bernard’s calcimeter), phosphorus (according to Olsen’s Method), exchangeable calcium, magnesium, potasium and sodium (according to the ammonium acetate method), Cation-Exchange Capacity (determined according to Bascomb’s procedure), and nitrogen (according to Kjeldahl’s method). Physical properties and variables for nutrient availability were averaged for the whole pit. The topmost organic horizon A00 was discarded in the sampling because of little differentiation of needles that would not have any influence in past growth of the stand, the rest of A horizons (A0, A1and A2), if presented, were bulked together to form the first horizon sample. Nutrient variables, bulk density calculated according to the method proposed by Honeysett and Ratkowsky (1989) and the depth and percentage of roots were also recorded. Table 2 presents a description of soil attributes. Statistical analysis Multivariate analysis was performed in three ways: descriptive, explanatory and predictive. The first one was used to describe the environmental variability and to identify the most influential variables and homogeneous regions; the explanatory analysis was carried out to identify and select the environmental variables that better explain the site index variation. Finally, the predictive approach was done to develop a site index model depending on environmental variables. Descriptive multivariate techniques identify the tendencies and latent variables influencing the relationship of the explanatory variables under analysis and they serve to identify observations that share the same region in a multivariate space. Factor analysis, according to the principal component extraction method (PCA), was used to identify likely environmental gradients. The analysis was performed firstly using climatic and physiographic variables (191 plots) and secondly adding soil data (65 plots). Those variables which were found to have a measure of sampling adequacy (Kaiser’s MSA) below 0.5 were rejected (Hair et al., 1999) and the analysis repeated until variables had an adequate Kaiser’s MSA (above 0.7). Principal components analysis will serve as a tool to delineate environmental classes or groups in order to define homogenous climatic regions that will be coded and treated as synoptic variables. Synoptic variables were introduced into a one-way analysis of variance to test differences in site index through climatic classes. Normality and independence of residuals within each group were also tested. TukeyKramer’s test for unequal sample size was applied to compare group means (SAS, 2004). As a second method, we used partial least squares regression, PLS (Abdi, 2003) to find a model capable of explaining site index from a large set of potential variables. The PLS regression is a powerful analysis tool and one of the least restrictive options in multivariate analysis. This technique is appropriate when the number of predictors is equal or higher than the number of observations or when there exists high correlation among predictors (Carrascal et al., 2009), which is the most common case in ecological based studies. It can be used as an exploratory analysis tool to select suitable predictor variables and to identify outliers before applying classical linear regression. It can be also used as a predictive analysis when predictors are many and collinear (Tobias, 1995). We applied the former case to analyse the relationship between site index and the matrix of environmental variables that loaded most heavily in the factor analysis for the sub-sampling containing soil and climate information. Multiple regression analysis was used to obtain parsimonious predictive models. This technique applies on large databases and it is usually performed with stepwise regression as selection method. Stepwise selection method often depends on the pool of variables that are included in the first stage, to the extent that by dropping one variable in the first stage the result could be different. Besides, the use of a sequential variable selection method may not be biologically sound (Fontes et al., 2003a) and there might exist a big uncertainty that the truly best model is not produced (Myers, 1990). Consequently, a direct selection of candidate variables was performed on the basis of the results found in the PLS analysis. Visual inspection of Q-Q plots and formal statistical tests were performed for normality assumption. Multicollinearity was assessed using the condition number index (Myers, 1990). Homogeneity of variance was evaluated according to visual inspection of ordinary and studentized residuals over predicted values. Model validation requires an independent data set that it is not used in the fitting phase. This requirement is often omitted as data gathering is expensive. In addition, splitting the sample may lead to differences in the results depending on the method chosen to split the sample. We select the one leave-one out approximation where one observation is deleted at a time and the model 54 A. Bravo-Oviedo et al. / Forest Systems (2011) 20(1), 50-64 is fitting to the remaining n-1 data (Vanclay, 1994). Finally, model performance was evaluated according to biological sense and statistical properties (Soares et al., 1995). The statistical validity of the model was evaluated computing bias and precision values (Huang et al., 2003). The selected prediction statistics for bias was the mean residual without current observation [e¯–1, eq. 1] and its percentage error [e¯%, eq. 2]. The mean squared error of prediction without current observation [RMSEP, eq. 3] ant the relative error in prediction [RE%, eq. 4] was computed to evaluate the precision of the multiple linear model. Environmental variability and site index 55 Table 2. Average soil attributes description Variables type Units Mean Std. Dev. Min. Max. Physical properties for the whole pit Fine fraction (%F) % 60.3 26.7 16.9 100 Coarse fraction (%C) % 39.7 26.7 0 83.1 Sand % 65 21.2 20.5 93.3 Clay % 11.5 10.8 2 49.7 Silt % 23.5 14.2 1.6 61.9 CCC 0.2 0.4 0 2.5 CIL 0.1 0.1 0 0.5 Permeability 4 1.3 1 5 Chemical properties for the whole pit Water Holding Capacity (WHC) mm 104.3 74.7 12.8 367.8 Equivalent humidity (EH) mm 16.7 7.6 7 35.4 pH 6.6 0.9 5.1 8.7 Organic matter (OM) % 1.3 1.3 0.1 8.5 Total organic matter (TOM) % 1.8 1.7 0.2 11.3 Calcium (Ca++) ppm 1,071.7 1,231.3 100.1 5,496 Sodium (Na+) ppm 22.5 16.3 2.4 64.2 Potasium (K+) ppm 84.7 99.1 15.9 498.9 Phosporus (P) ppm 0.8 2.7 0 14.6 Magnesium (Mg++) ppm 253.3 561.7 14.1 3,201.4 Nitrogen (N) % 0.1 0 0 0.3 Carbonates (CB) % 4.1 8.8 0 38.5 Active carbonates (ACB) % 0.4 1.1 0 6.3 Conductivity (CVY) mmhos cm–1 0.1 0.1 0 0.4 Cationic Exchange Capacity (CEC) meq 10 g–1 10.8 5.8 3.9 32 Base sum (S) meq 100 g–1 7.8 9.8 1 43.6 Saturation Rate (TSAT) % 57.6 38.8 6.9 186.3 Chemical and physical properties for the first horizon Water Holding Capacity (WHC1h) mm 97.2 66.9 11.4 383 Organic matter (OM1h) % oxidable 1.9 2 0.1 10.5 Calcium (Ca1h) meq 100 g–1 5.6 7.1 0.5 27.6 Sodium (Na1h) meq 100 g–1 0.1 0.1 0 0.6 Potassium (K1h) meq 100 g–1 0.3 0.4 0 2 Magnesium (Mg1h) meq 100 g–1 2.3 5.1 0.1 26.4 Nitrogen (N1h) % 0.1 0.1 0 0.4 Carbonates (CB1h) meq 100 g–1 3.7 9.2 0 46.4 Cationic Exchange Capacity (CEC1h) meq 100 g–1 11.6 6.8 4.1 30.6 Saturation Rate (TSAT1h) % 56.9 42.3 6.8 198.4 Carbon-Nitrogen rate (CN1h) % 30.6 16 3.3 73.5 Bulk density (BD1h) g cm–3 1.4 0.4 0.6 2.3 Depth (D1h) cm 25.2 10.7 10 70 Roots (R1h) % 84.7 15.8 10 100 In parenthesis, the abbreviation used in the text. CCC: compactness capacity coefficient. CIL: silt impermeability coefficient. [1] [2] [3] RE%= 100 ×RMSEP /y¯ [4] where yiis the ith site index observation, yˆi,–i prediction without current ith observation, k is the number of observations and pis the number of parameters. Results Descriptive analysis Principal components analysis The variance explained by the two first climatic factors in PCA is 82% (Table 3). Climatic factor analysis showed that the temperature regime, drought length, elevation and evapotranspiration accounted for the maximum amount of variance in the first factor (58%). The second factor can be labelled as precipitation regime, as long as annual precipitation, seasonal rainfall in autumn and winter, water surplus and annual hydric index loaded most in this factor. The inclusion of site index in the analysis as supplementary variable did not show any discernable pattern in the PCA. However, the incorporation of rock type and elevation in the climatic PCA, showed a pattern of aggregation. Figure 2 depicts regions with common rock type origin when axis 1 and 2 of climatic PCA are displayed, while Table 4 shows the main physiographic, climatic variables and parental material type, as well as site index values of each group. Those groups were incorporated into a one-way analysis of variance to detect statistical differences in mean site index. Groups were only entered into the analysis if at least 5 observations were available. The first region (A in Fig. 2) holds acidic conglomerate rock under humid and cold conditions. The plots belonging to this region are included in Soria-Burgos Mountains and are located in the northern part of the study area. Dolomite origin stands are located in two latitude bands and differ in temperature values. The warmest band (mean annual temperature of 13.1°C, Table 4) is located in Segura-Alcaraz area (Factorial region B). The second group is formed by three sampled stands located in the colder Iberian Mountains. This last group is underrepresented in our study and it was not used in the following analysis. The acidic warm sites, which include slate and schist origin, are located next to each other in the western part of the study area (D and E group, respectively). Granite origin is separated into two open subgroups according to temperature and elevation. The stands below 900 m of altitude (group C) grow within the Tiétar river basin (the same area where stands on schist origin grow, group E) with mean annual temperature above 12°C and annual rainfall above 640 mm. The stands growing on granite over 900 m of altitude (group F) are located within the Tagus river basin and the mean temperature is under 12°C. Soils developed on gravels (G group) and sand drifts origin (H group) are located in the same Castilian Plateau. However, gravels are found in the eastern part of the region, whereas sand drifts are spread within the whole region following an elevation gradient. RMSE = Σk i=1 (yi–yˆi,–i) k i=1 (yi–yˆi,–i) k–1–p e% = 100 × ¯e–1 ¯ y¯ e–1 = Σk i=1 (yi–yˆi,–i) ¯k k i=1 (yi–yˆi,–i) k 56 A. Bravo-Oviedo et al. / Forest Systems (2011) 20(1), 50-64 Table 3. PCA’s factor loadings for climate attributes Variable Factor 1 Factor 2 Factor 3 Factor 4 YUTM –0.28 –0.20 0.87 0.27 ELV –0.87 –0.03 –0.40 –0.11 DSQ 0.86 0.19 0.07 –0.20 P 0.13 0.98 –0.13 –0.01 T0.95 0.17 –0.22 –0.11 TMF 0.94 0.21 –0.19 –0.10 TMC 0.85 0.18 –0.41 –0.20 TMMF 0.90 0.23 –0.18 –0.12 TMMC 0.87 0.15 –0.29 –0.16 WP 0.47 0.85 –0.02 –0.08 AUP 0.35 0.87 –0.12 –0.09 Winter ET 0.91 0.21 –0.05 –0.07 Spring ET 0.97 0.04 0.09 –0.01 Summer ET 0.86 0.19 –0.38 –0.15 Autumn ET 0.89 0.30 –0.24 –0.08 ET 0.95 0.18 –0.20 –0.10 PET –0.19 –0.10 0.21 0.95 APD 0.79 0.18 –0.25 –0.51 SUP 0.22 0.96 –0.09 –0.10 AHI –0.29 0.95 –0.01 0.00 ISQ 0.84 –0.15 0.02 –0.26 Eigenvalue 11.72 4.78 1.69 1.52 % Variance 0.58 0.24 0.08 0.08 Cumulative % variance explained 0.58 0.82 0.90 0.98 Bold indicate loadings greater than 0.7. Stands located on Buntsandstein’s sandstone and quartizite bedrock are embedded in a broad factorial domain (Group I). Finally, the stands located in cretaceous’ sandstone (J region) in the eastern part of the study area are characterized by cold temperature and low precipitation. When climatic and edaphic variables are analysed together in the PCA, 91% of variance is explained by four axes (Table 5). The first factor is related to the temperature regime and elevation, the second to soil reaction, the third to nutrient status and the fourth to soil texture type, whereas precipitation is relegated to Environmental variability and site index 57 Climatic PCA and parental material –2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5 2 2.5 –2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5 2 2.5 3 Factor 2 (precipitation surrogate) Factor 1 (temperature surrogate) ×× × ×× ×× ×× ×× ××× ×××× × × ×× ×× ×× × ××× × ×× × ××× ++ +++ + ++ D C E B F J I H G A Conglomerate Dolomite ×Granite Slates Schists Gravels × Sand drifts Bunt’s sandstone and quartzites Createceous sandstone + Figure 2. Principal component analysis and groups defined using parental material information. Table 4. Main climatic and physiographic features and site index values found in the regions defined in PCA Factorial Parenteral Longitude Latitude (N) Elevation Annual Annual Site index domain material Ntemperature precipitation Mean Min/Max Mean Min/Max Mean Min/Max Mean Min/Max Mean Min/Max Mean Min/Max A Conglomerate 11 –2° 57.7' –2° 57'/–2° 58' 41° 49.2' 41° 49'/41° 50' 1,204 1,181/1,226 9.0 8.9/9.2 704 690/714 15.9 13.9/18.5 B Dolomite 25 –2° 31.9' –2° 17'/–2° 47' 38° 24.3' 38° 11'/38° 38' 1,099 844/1,286 13.1 9.9/14.2 651 603/739 14.8 7.3/19.8 C Granite < 900 m 11 –4.9° 13.7' –6° 0'/–4° 40' 40° 14.4' 40° 10'/41° 32' 735 605/895 13.2 11.9/15.9 610 439/699 19.4 13.4/23.6 D Slate 8 –6° 23.6' –6° 8'/–6° 40' 40° 16.6' 40° 12'/40° 24' 483 377/607 15.0 14.6/15.4 756 688.9/792 19.8 16.6/21.8 E Schists 8 –5° 6.8' –5° 5'/–5° 8' 40° 11' 40° 11'/40° 11' 652 465/927 13.8 12.3/15.1 648 594/692 22.9 19.7/26.1 F Granite > 900 m 14 –4.1° 15.9' –5° 0'/–4° 25' 40° 32.9' 40° 17'/40° 39' 1,138 914/1,339 11.2 10.4/12.9 594 500/690 15.2 12/19.1 G Gravel 12 –2° 41.6' –2° 33'/–2° 56' 41° 32.8' 41° 29'/41° 36' 1,010 949/1,100 10.2 9.8/10.6 585 556/607 15.6 11.5/19.7 H Miocene’s sand drifts 38 –2.9° 39.4' –4° 1'/–2° 59' 41° 26.4' 41° 10'/41° 36' 873 697/1,024 11.1 10.1/12.1 501 420/589 16.4 8.2/22.4 I Bunt’s sandstone and quartzite 58 –1.3° 19.7' –2° 0'/–1° 59' 39.6° 41.9' 39° 00'/41° 59' 1,193 945/1,437 10.4 9/12.2 545 501/606 11.5 4.7/19.1 J Createceous sandstone 8 0° 42.3' 0° 37'/0° 46' 40° 12.8' 40° 10'/40° 16' 1,066 976/1,127 11.4 10.9/12 489 471/496 9.1 6.5/10.6 the fifth axis. Again, when plotting factorial axis using site index as a supplementary variable no clear distinction is detected in the pattern of dispersion. Dolomite stands are clearly grouped together according to the second axis. Little information is gained with this analysis comparing when only climatic variables are consi58 A. Bravo-Oviedo et al. / Forest Systems (2011) 20(1), 50-64 Table 5. PCA’s factor loadings for climate and soil attributes Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Elevation –0.74 0.26 0.33 0.10 0.16 Annual rainfall 0.19 0.24 0.36 0.15 0.85 Summer rainfall –0.72 –0.31 0.13 –0.09 –0.32 Autumn rainfall 0.47 0.24 0.28 0.14 0.77 Winter rainfall 0.49 0.28 0.19 0.13 0.78 Mean annual temperature 0.96 0.14 0.10 0.10 0.12 Lowest monthly mean temperature 0.94 0.21 0.06 0.07 0.10 Highest monthly mean temperature 0.85 0.17 0.27 0.17 0.17 Mean value of minima temperature in the coldest month 0.86 0.35 0.04 0.04 0.16 Mean value of maxima temperatrue in the warmest month 0.90 0.08 0.16 0.16 0.18 Drought length 0.88 0.23 –0.12 0.07 0.20 Intensiy of drought 0.85 0.18 –0.22 0.03 –0.12 Annual evapotranspiration 0.96 0.10 0.10 0.10 0.14 Spring evapotranspiration 0.95 –0.02 –0.15 –0.01 –0.01 Summer evapotranspiration 0.86 0.09 0.24 0.15 0.22 Autumn evapotranspiration 0.88 0.22 0.09 0.18 0.22 Winter evapotranspiration 0.85 0.28 –0.02 0.00 0.01 Annual physiological drought 0.78 0.19 0.31 –0.14 0.18 Surplus 0.30 0.29 0.27 0.15 0.85 Fine fraction 0.17 –0.01 –0.61 –0.23 0.00 Sand –0.11 –0.13 –0.36 –0.85 –0.14 Clay 0.09 0.40 0.02 0.87 0.05 CCC 0.06 0.38 0.09 0.77 0.13 Permeability –0.07 –0.22 –0.09 –0.93 –0.05 Equivalent humidity 0.12 0.24 0.37 0.85 0.13 Organic matter 0.06 0.26 0.91 0.08 0.12 Total organic matter 0.04 0.32 0.90 0.09 0.11 pH 0.25 0.81 –0.04 0.01 0.10 Calcium 0.12 0.79 0.29 0.42 0.04 Potasium 0.21 0.74 0.25 0.34 0.20 Conductivity 0.21 0.80 0.25 0.23 0.20 Carbonates 0.22 0.70 0.01 0.34 0.21 Active carbonates 0.16 0.63 0.01 0.40 0.22 Nitrogen 0.16 0.41 0.76 0.27 0.13 Magnesium 0.21 0.78 0.11 0.04 0.09 Cationic exchange capacity 0.23 0.73 0.35 0.29 0.01 Saturation rate 0.11 0.79 0.24 0.34 0.01 Base sum 1st horizon 0.13 0.83 0.41 0.25 0.15 Saturation rate 1st horizon –0.02 0.67 0.39 0.36 0.15 Bulk density 1st horizon 0.05 –0.14 –0.85 –0.08 –0.25 Organic matter 1st horizon 0.03 0.16 0.91 –0.03 0.13 Calcium 1st horizon 0.04 0.66 0.47 0.36 0.13 Magnesium 1st horizon 0.20 0.78 0.21 0.03 0.11 Cationic exchange capacity 1st horizon 0.25 0.67 0.53 0.14 0.10 Nitrogen 1st horizon 0.13 0.30 0.82 0.04 0.15 Clay horizon B 0.07 0.33 –0.08 0.88 0.01 Eigenvalue 12.45 9.68 7.18 6.01 3.55 % Variance explained 0.32 0.25 0.18 0.15 0.09 Cumulative % variance explained 0.32 0.57 0.75 0.91 1.00