Cezmi ?zel · ?ükrü Teoman Güner · Mehmet Türkkan · Selda Akgül · ?zdemir ?entürk

Abstract The determination of site productivity in forest ecosystems plays a crucial role in resource management. This study was carried out to identify relationships between site characteristics and height growth of Corsican maritime pine ( Pinus pinaster Ait.) plantations in Turkey. Sixty-nine sample plots > 20 years of age were selected from locations with different inclinations, aspects, elevations, slope positions and site class. Soil samples were taken at various depths. Height and age were measured on a dominant tree after felling in each plot. Physical and chemical properties of the soil were determined. Relationships between site index (SI 25 ) and physiographic factors, climatic attributes as well as soil properties were evaluated using correlation analysis and multiple regression analysis. Site index was significantly related with annual precipitation, mean spring rainfall, rainfall June to September, rainfall of the driest month, length of the dry period, mean maximum temperature, mean temperature of the warmest month, stoniness of the soil, sand, silt, clay, pH, electrical conductivity, and available water capacity. Multiple regression accounted for 57.9% of variations in height growth. The models obtained can be used to determine the site index of potential areas in Turkey for maritime pine. It can be said that the productivity of maritime pine may decline in the future due to global climate change.

Keywords Pinus pinaster · Afforestation · Ecology · Site index

Introduction

Globally, and in Turkey, natural forests have growth rates of 1–2 m 3 ha?1a?1on average. In addition to these slow growth rates, the management time to harvest wood products may be 80–100 years or longer. With regards to industrial plantations, they generally are established on soils with high yield potential using intensive site preparation and maintenance methods. Furthermore, they often can be harvested in a short rotation period due to the use of genetically improved species. Annual growth rates may be at least 10 m3ha?1and the length of rotation may vary from 10 to 30 years. Industrial plantations should be encouraged and extended, as a viable solution to ensure raw material production and the protection of natural forests (Birler 2009).

There are a number of introduced and indigenous species in Turkey that are suitable for industrial plantations. Corsican or maritime pine ( Pinus pinaster Ait.) has been reported to be the most suitable introduced species for plantations in the Black Sea, Marmara, and parts of the Aegean regions (Birler 2009).

Maritime pine is native to southeast Europe, western Mediterranean and northwest Africa (Kandemir and Matarac? 2018). It was first used for sand dune fixation in Istanbul-Terkos in 1880 (?zcan 2003). Today, there are approximately 57,800 ha of maritime pine plantations in Turkey. Of this, some 48,100 ha (83%) are in the Marmara Region, 8730 ha (15%) in the Black Sea Region, and 1000 ha (2%) in the Aegean and Mediterranean regions (Güner et al. 2019).

It is important to determine site index areas for successful industrial plantations. There have been several studies on determining the potential productivity of various species (Bravo-Oviedo and Montero 2005; Bravo-Oviedo et al. 2011; Bueis et al. 2016; Moreno-Fernández et al. 2018) using different statistical techniques (Seynave et al. 2005; Bueis et al. 2016; Gülsoy and ??nar 2019) at local and regional scales (Bravo and Montero 2001; Diéguez-Aranda et al. 2005; Ercanl? et al. 2008; Bravo et al. 2011; álvarez-álvarez et al. 2011; Yener and Altun 2018). However, research at a country or regional scale is limited (Chen et al. 2002; Moreno-Fernández et al. 2018; Brandl et al. 2019; Eckhart et al. 2019). Several studies have investigated the relationships between the growth rate of maritime pine and ecological features in its natural distribution areas (álvarez-González et al. 2005; álvarez-álvarez et al. 2011; Bravo-Oviedo et al. 2011; Eimil-Fraga et al. 2014, 2015; Moreno-Fernández et al. 2018). There is research on this subject in Turkey that were conducted in the Black Sea Region (Sinop) (Güvendi 2005; Kahyao?lu 2005). However, this study is different as it was carried out in the Marmara and Black Sea regions that account for 98% of the country’s maritime pine plantations.

The objectives of this study were to: (1) identify the relationship between site index of maritime pine plantations and ecological features (landforms, climate and soil properties); and, (2) develop models to predict site index using ecological features as predictors. The results may be used to determine site index of areas to be afforested with maritime pine.

Materials and methods

Site description

The study was in maritime pine plantations in the Marmara and Black Sea Regions (Fig.1). The study sites are located over bedrocks of gneiss, mica schist, amphibolite, granite, granodiorite, quartz-diorite, diorite, gabbro, diabase, serpentine, basalt, dolerite, andesite, spillite, porphyritic, volcanic tuff , agglomerate, breccia and trachyte bedrocks in the Marmara Region, and on carboniferous, andesite, spillite and porphyritic bedrock in the Black Sea Region (GDMRE 2019).

Fig.1 Location of the study area and individual sites

The common soil types in the study areas are Leptosols, Arenesols, Cambisols and Luvisols (IUSS Working Group WRB 2015). The plantations were established on a wide variety of soils including clay, sand, sandy clay, sandy loam, clay loam, sandy clay loam, dusty clay loam and loam, and on non-calcareous soils.

In the Marmara Region, mean annual temperatures are 12.0–14.6 °C, the mean maximum temperature range 26–30 °C; average minimum temperatures are 0.5–3.0 °C, the average temperature range of the coldest month 3.9–6.6 °C and average temperature range of the warmest month 22–25 °C in the Marmara Region. The mean annual precipitation ranges from 538 mm (Lapseki) to 1007 mm (Kand?ra), the rainfall of the driest month varies from 6.9 mm (Lapseki) to 49 mm (Sakarya) and the total rainfall in June–September is 57.7 mm (Lapseki) and 235.8 mm (Kand?ra).

For sample plots in the Black Sea Region, average annual temperatures are 11.5–14.2 °C, mean maximum temperatures 24.0–28.2 °C, mean minimum temperatures -0.8–4.2 °C; the average temperature range of the coldest month is 3.1–6.6 °C and the average of the warmest month 20.4–23.0 °C. Mean annual precipitation ranges from 629 mm (Erfelek) to 1355 mm (Zonguldak), rainfall of the driest month varies from 14.6 mm (Erfelek) to 60.7 mm (Zonguldak), and total rainfall in June–September is between 98.3 mm (Erfelek) and 377.7 mm (Zonguldak) (GDM 2016).

Lower mountain slopes on the northern aspect in the Marmara Region are covered with pseudomaquis, sweet chestnut ( Castanea sativa Mill.) and oriental beech ( Fagus orientalis Lipsky) forests, whereas higher elevations have stands of Uluda? fir ( Abies nordmanniana (Steven) Spach). Maquis and brutian pine ( Pinus brutia Ten.) communities are present on valley bottoms and the southern aspects of mountains while higher elevations are covered with oak ( Quercus spp.) and black pine ( Pinus nigra Arn.) forests. Along the coast of the Black Sea, broad-leaved forests oforiental beech, linden ( Tilia rubra DC., Tilia tomentosa Moench), oak, alder ( Alnus barbata C.A.Mey., Alnus glutinosa (L.) Gaertn.), sweet chestnut, oriental hornbeam ( Carpinus orientalis Mill.) are dominant. The humid and temperate coastal zone of the eastern Black Sea Region is covered with primarily beech and alder, chestnut, linden and hornbeam forests. The important species in the beech forests include linden, chestnut, Norway maple ( Acer platanoides L.), elm ( Ulmus campestris L., Ulmus montana Stokes), sessile oak ( Quercus petraea (Matt.) Liebl.) and oriental hornbeam ( Carpinus orientalis Mill.). Fertile oriental beech forests are present in the central Black Sea Region. In the western Black Sea Region, brutian pine, hornbeam, bay laurel ( Laurus nobilis L.), sweet chestnut, linden, sessile oak, pubescent oak ( Quercus pubescens Willd.), Hungarian oak ( Quercus frainetto Ten.) are present. Oriental beech forests are located between 600 and 1200 m altitudes (Atalay 2002).

Sampling procedure

Samples were collected from 69 20 m × 20 m plots in plantations older than 20 years and of Corsican origin. Elevation, inclination, slope position and aspect for each plot were recorded. Aspect values were converted to the radiation index (RI) using Eq. ( 1) (Moisen and Frescino 2002; Aertsen et al. 2010).

where Q is the azimuth angle of the sample plot to the north.

Soil samples from soil pits in each plot were taken using volume cylinders at depth intervals of 0–10, 10–30, 30–60, and 60–100 cm. The top height was determined as average of total height of three dominant trees in each plot, and one felled for age and height measurement.

Laboratory analysis

After air-drying 265 soil samples, clumps were crushed in a porcelain mortar with a pestle, sieved through a 2-mm screen, and weighed. The volume of coarse fragments (> 2 mm) remaining on the screen was measured volumetrically. Soil moisture was determined at 105 °C classified with a hydrometer according to particle size (Kroetsch and Wang 2008). Organic carbon was determined by the Walkley–Black wet oxidation method (TS 8336 1990), pH determined by an electrometric method in a solution of v/5v soil/water (TS ISO 10390 2013), and electrical conductivity determined by an electrometric method in a solution of m/5v soil/water (TS ISO 11265 1996b). Total CaCO3was found with a Scheibler calcimeter (TS 8335 ISO 10693 1996a) and available water with pressure plates (Soil Moisture Equipment Corp. Santa Barbara, Calif.).

Data analysis

The site index (SI25) of the plots at age 25 was calculated using the site index model developed by ?zcan ( 2003) for maritime pine plantations on the basis of age and height of trees that were cut in the plots. It was used as the dependent variable in the regression analysis.

Data from the closest meteorology station to the study area was used to determine climate properties of the plots. To interpolate this meteorological data, temperature was decreased by 0.5 °C for every 100 m and annual rainfall increased by 54 mm 100 m in altitude, which were then proportionally distributed by months (?zyuvac? 1999). To determine the bioclimate classes of the sample plots, Eq. ( 2) was used to determine the layers of the Emberger Mediterranean Climate Classification and the degree ofoverall drought (Akman 1990). It is more humid as the rainfalltemperature coeffi cient (Q) increases, whereas it is drier as it gets lower.

where Q is the rainfall-temperature coeffi cient, P the annual precipitation (mm), M the maximum average temperature of the warmest month (°C), and m is the minimum average temperature of the coldest month (°C).

Emberger also proposed Eq. ( 3) to determine the dry period (Akman 1990). A lower S coefficient shows the severity of summer drought, whereas a higher S coeffi cient indicates lower summer drought.

where PE is the total precipitation in June, July and August and ME the maximum average temperature of the same three months.

The relationships between SI25and ecological features were assessed by correlation analysis. Multiple regression analysis identified models that could provide the most suitable set of variables that had significant relationships with SI25. The MuMIn library in R was used to obtain the subsets of regression models and to select the best model based on the lowest AICc (Corrected Akaike Information Criterion) (Bartoń 2016; Ali and Yan 2017; Yuan et al. 2018). In this study, AICc is used instead of AIC (Akaike Information Criterion) due to n/K = 7.5 < 40. For all statistical analyses, the R 3.6.1 software was used (Team RDC 2019).

Results and discussion

The top height in stands aged 25 years varied from 5.3 m to 19.0 m (Table 1). According to the yield table developed by ?zcan ( 2003) for maritime pine plantations, the site class was I (SI25= 16.6–22.5 m) in nine sample plots (13.0%), II (SI25= 10.6–16.5 m) in 44 plots (63.8%) and III (SI25= 4.5–10.5 m) in 16 plots (23.2%).

The plots were located at elevations of 7.0–460 m, inclinations of 1–64% on sunny and shady aspects on upper and middle slopes (Table 1). The plots were concentrated at elevations up to 300 m; at elevations higher than 400 m, maritime pine plantations with site class I were not encountered. Similarly, plantations were proposed for coastal areas with elevations up to 400 m in the Marmara and Black Sea Regions (?im?ek et al. 1985). álvarez-álvarez et al. ( 2011) reported that maritime pine occupied sites up elevations of 1000 m but height growth decreased after approximately 513 m. Some 43% of the plots were located on flat areas and areas with low and moderate slopes (1–17%), 36% of the plots were on higher slopes (18–36%), 20% on steep and rugged slopes (> 37%). Plantations on site class-I were found in areas of high slopes (18–36%) and were not encountered on flat areas or lower, rugged slopes. Likewise, maritime pine plantations were not found on lower slopes and plains. While 40% of the plots were mainly on the upper slopes, 42% were on middle slopes. Three site classes were widely encountered on upper and middle slopes.

According to the Emberger climate classification, the sample plots could be categorized under three different bioclimate classes: hyper-humid, humid, and sub-humid. Approximately 55% of the plots in site class-I were in the humid class, while 45% were in the hyper-humid category. No sample plots in site class-I were found in the sub-humid class.

In this study, there was a positive correlation between site index and inclination (Fig.2). Similar results were reported by previous studies conducted on maritime pine (Güvendi 2005), black pine (Güner et al. 2016), Scots pine (Güner and Yücel 2015) and fir (Sara?o?lu 1989). Studies on oriental spruce (Günlü et al. 2006; Ercanl? et al. 2008; Yener and Altun 2018), beech (Y?lmaz 2005) and kasnak oak (Karata? et al. 2013), however, reported a negative correlation between site index and inclination. On the other hand, in a study on natural black pine forests, ?zkan et al. ( 2008) did not find any significant relationship between site index and inclination. Slope inclination not only has an impact on local climate but also affects the water and nutrient status of the soil; that’s why it is expected to find good site classes in areas with lower inclinations. Nevertheless, in our study, a positive correlation was found between the site index of maritime pine and silt and clay content, whereas a negative relationship was found with the sand content. In other words, maritime pine grows better on fine texture soils. Increased clay content in the soil results in an increased water retention and nutrient capacity in the soil and decreased air capacity. Decreased air capacity of the soil in turn inhibits root growth, thus leading to retardation in height growth. At this point, inclination plays an important role because increased inclination ensures that rainfall is drained from the soil, increasing the air capacity. Furthermore, high clay content in the soils with lower inclination increases the likelihood of crack formation in dry periods and thus water losses (Güner et al. 2016). The positive correlation we found between inclination and site index in our study can be explained by the possibility that the negative effect caused by the fine soil texture was compensated by inclination.

No significant relationship was found between site index of maritime pine and elevation, aspect or slope position. The importance ofelevation in ecology is due to its effect on climate. Increased elevation leads to increased precipitation,declining temperatures and reduction in evaporation, thus resulting in a more humid habitat. Research in areas of water deficit in Turkey reported positive relationships between elevation and site index (?zkan et al. 2008; Güner and Yücel 2015; Güner et al. 2016; Gülsoy and ??nar 2019). However, studies of maritime pine (álvarez-álvarez et al. 2011; Bravo-Oviedo et al. 2011; Eimil-Fraga et al. 2014, 2015), Scots pine (?epel et al. 1977), spruce (Ercanl? et al. 2008 ; Yener and Altun 2018), and radiata pine (Romanyà and Vallejo 2004) found negative relationships between elevation and site index. Height growth was negatively correlated with elevation in studies on oriental spruce and was suggested to be associated with declining temperatures and shorter growth periods due to increased elevation, leading to lesser height growth, although there was no water deficit. There was no significant relationship between site index and elevation due to the absence water deficit in the plantations in the Marmara and Black Sea Regions. They were established at elevations up to 400 m and did not create a wide climatic zone. In fact, all plots were located in the humid (hyper-humid, humid and sub-humid) bioclimate classes while those in the sub-humid class were in the cool bioclimate layer.

Table 1 Variables used in statistical analyses and their codes

Available water and nutrients increase on the lower slopes compared to the upper parts. Several studies report a positive relationship between the distance from the upper edge of slopes (slope position) and site index (?epel et al. 1977; ?zkan et al. 2008; Gülsoy et al. 2014). In this study, however, there was no significant relationship between slope position and site index. This might be explained by the fact that the slope effect was not so prominent because the maritime pine plantations in this study were mainly established on peneplain lands, areas more or less flat as the result of erosion.

Fig.2 Relationships among site index (SI 25 ), physiographic factors, and climatic attributes (** Correlation significant at 0.01 level; Mmat: mean maximum temperature, Mtwm: mean temperature of the warmest month, Mat: mean annual temperature, Sp: slope position, RI: radiation index, Mmit: mean minimum temperature, Elv: elevation, Mtcm: mean temperature of the coldest month, Inc: inclination, Rsp: rainfall in spring, Rspsm: rainfall in spring + summer, Ap: annual precipitation, Q: precipitation-temperature precedent, Rsm: rainfall in summer, S: dry period, Rjs: rainfall June to September, Rdm: rainfall of driest month)

Since aspect is an important factor that has a significant impact on temperature and humidity, it may influence site index values. However, aspect did not have an important effect on site index in this study. Güvendi ( 2005) also reported that aspect did not impact growth of maritime pine plantations in Sinop, Turkey. This is because it is challenging to include aspect as a parameter in the form of numerical values in statistical analysis. On the other hand, sample areas are usually located on relatively flat lands and the impact of aspect on local climate may not be as pronounced as to have an effect on site index.

SI25had a positive correlation with annual precipitation, annual mean spring, summer, and spring + summer rainfall, rainfall of the driest month, rainfall from June to September and S value. It was negatively correlated with the average maximum temperature and average temperature of the warmest month. After evaluation of all these climate features, it was concluded that maritime pine had better growth in areas with high precipitation, a short dry period, low annual average maximum temperatures and low average temperatures in the warmest month. Likewise, Bravo-Oviedo et al. ( 2011) reported that climate had a crucial effect on the productivity of maritime pine, with good performance on warm, moist sites but relatively poor growth on cool or hot sites. Similarly, site index of black pine was reported to have a positive relationship with annual precipitation and rainfall of the driest month (Güner et al. 2016). In a study carried out on Douglas- fir plantations in Italy, site index increased in association with annual precipitation and water surplus (Corona et al. 1998). Spring and summer rainfall were the most important factors that restricted horizontal and vertical growth (Akgül 2010). On the other hand, there was a negative relationship between average temperatures in summer and productivity in a study in Douglas-fir stands in central Europe (Eckhart et al. 2019). The fact that increased annual mean maximum temperatures and mean temperatures of the warmest month have a negative impact on height growth of maritime pine suggests that its productivity may decrease due to projected climate change.

Fig.3 Relationship between site index and soil properties (* Correlation is significant at the 0.05 level, ** Correlation is significant at the 0.01 level; Fne: fine earth (? < 2 mm), Ston: stoniness, Corg: organic carbon, pH: soil reaction, EC: electrical conductivity, Aw: available water)

Site index of maritime pine had negative relationship with stoniness, sand, pH and EC, whereas it had positive relationships with silt and clay levels and available soil water. No significant relationship was found between SI 25 and fine earth (? < 2 mm) and organic carbon (Fig.3). In a study of maritime pine plantations (Sinop), Kahyao?lu ( 2005) also concluded that increased clay content resulted in increased height growth, whereas increased sand content resulted in a decline in height growth. These findings demonstrate that maritime pine grows well on humid soils with fine texture; however, it shows a poor growth pattern on stony soils with coarse texture, high pH and EC content. Therefore, maritime pine develops better in habitats with fine textured soil, and relatively high inclination and precipitation. Two of the sample plots in this study were on calcareous soils, while 67 were on non-calcareous soils. For this reason, the relationship between SI25and soil lime content was not evaluated. However, Akgül ( 2010) reported that maritime pine plantations in the same bioclimate class had poor growth on alkaline and calcareous soils.

Multiple regression analysis was performed in the MuMln library using the variables that have the highest correlation between the percent values of site index and physiographic factors, climate features, and soil properties at different depths. Among all the models obtained, the one with the lowest AICc value was rainfall of the driest month, pH at 60–100 cm and stoniness at 10–30 cm (Table 2). All other subsets of regression models with AIC and delta values less than two units are given Table 3. These three variables explained 57.9% of the variation in the height growth of maritime pine. Precipitation had a significant effect on the site index value of maritime pine. Increased site index with decreasing soil stoniness shows that maritime pine grows much better on stone-free soils. In a study carried out in northwestern Spain (Asturia) by álvarez-álvarez et al. ( 2011), 45% of the variation in site index of maritime pine was explained by mean summer temperatures and soil depth. In another study in Galicia, Eimil-Fraga et al. ( 2014) reported that maritime pine showed high growth rates at low elevations with relatively high temperatures and deep soil. They explained 52% of the variation in site indexby potassium and calcium concentrations of needles, soil depth, and annual mean temperature by stepwise regression analysis.

Table 2 Multiple linear regression models developed based on ecological variables to predict the site index

Table 3 Models with delta values less than two units

Conclusion

The results of this study show that maritime pine grows better on humid soils with fine texture, whereas it has poor growth on coarse-textured stony soils, with high pH and electrical conductivity. The species will grow better on inclined lands rather than plains. In particular, establishment of maritime pine plantations on fine-textured soils at inclinations of 20–40% will probably increase the rate of success. In this study, areas with site class-I were not found at elevations above 400 m, and maritime pine plantations should not be considered above this elevation. Maritime pine may grow successfully in areas of high precipitation, a brief dry period, lower annual average maximum temperatures and lower mean temperatures of the warmest month. In the light of these findings, it may be concluded that productivity of maritime pine stands will be negatively affected by projected climate changes. The models developed by multiple regression analysis can be used to determine the site index of bare lands where maritime pine plantations are being considered to be established.

AcknowledgementsIt was presented orally as “Relationships between growth of maritime pine ( Pinus pinaster Ait.) plantation and site characteristics”, and its abstract was published in the Proceedings of the “10th International Soil Congress 2019” in Ankara, Turkey, 17–19 June 2019. We thank the editors for their valuable suggestions which improved the paper.

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