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The influence of environmental variables and soil characteristics on

productivity of beech (Fagus sylvatica) ecosystems in Romanian forests

Introduction

Trees growth is significantly influenced by the soils physical and chemical properties as

specificities of sites and soils command the plants supply with water and nutrients, but as well

the conditions for roots aeration and their general stability. The most important properties of

soils that influence trees growth are the following: soil texture, pH, content of organic matter, N,

cation exchange capacity (CEC), content of other chemical elements (Ca, Mg, P, Na).Texture

(percent of clay + silt) correlates with site productivity (oak stands) in the same climatic

condition (McFadden et. al 1993). To these we should add as noteworthy the environmental

factors - rainfall regime and air temperature. Meteorological parameters and soil characteristics

influence biomass production (Bidini G. et. al 2010).

Productivity of a species in a particular site can be quantified by measuring the mean increment

of volume at a certain age or by assessing the volumetrical maximal growth. Thus, plants

characteristics in a given area may serve as site productivity indicators. However, as the

necessities for water and nutrients vary from one tree species to another, quantification of a

single tree species stands only for the relative productivity of certain sites. In the same time, the

ability of a particular site to provide adequate nutrition for trees depends on numerous factors

that may lead to a seasonal variation in growth. Soil characteristics and environmental factors

impact plant productivity especially water, wind and chemical erosion, soil water-logging, strong

acidification, compaction, contamination with heavy metals, pesticides, destruction of forest

litter or its excessive accumulation, the high density of stands, establishment, formation of

ortzands in soils, podzolization and gleyzation processes (Meislovas Vaiys and Jonas Mavila

2009). A regional scale study in Romanian forests related that soil stationery factors have a

strong influence on stand productivity in forests situated in the Dragomirna Plateau; a strong and

significant correlation (r=0.854) especially clay content in B-horizon (A% from B) and Idt are

strongly and distinctly significant correlated with average relative production class (Savin A. et

al 2011). Another important factor that influence stand productivity is species diversity that

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affect ecosystem productivity over a range of environmental conditions (M.Vil et. al.2005),

(Hector et al. 1999).

Beech stands (Fagus sylvatica) occupy 2.184.046 ha of land, being the most prevalent specie in

Romanian forests with 29 % coverage, from the total forest coverage (National Forest Inventory

database). Due to its large spread and economical and ecological value the aim of this study is to

evaluate the optimal environment conditions this species habitats and how specific variables

affects the productivity. The most important environmental factors associated with plant

composition in beech communities taken in consideration in this study were elevation (Altit),

slope, slope aspect (radiation), N, Na, K, Mg, Ca, pH, C, HS (hydrogen saturation), BS (base

saturation), soil texture, CEC (cation exchange capacity), BCSR (base-cation saturation ratio),

stone content, horizon depth, annual average temperature (Temp), annual average precipitation

(Rain), and biotic characteristics like height, DBH, trees number in the sampling area (trees nr.),

the age of the beech stands (Age).

A multivariate ordination was used to assess the intricate relation established between soil

properties and environmental conditions using techniques of PCA in various forestry sites from

Romania.

The research took in consideration a representative body of data, comprising more than 300

samples sites collected from specific sites across Romania where beech appears in a proportion

estimated as higher than 80% from the total volume of trees. Sites with a lesser amount were not

taken in consideration because their productivity might be influenced by interspecific relations

(e.g. Dorog 2011). In each of these sites a survey-pit was excavated and samples were collected

from every soil horizon cut in this way. The soil probes where taken to the laboratory and

analyzed (soil type identification and chemical analysis). References for climatic data sets like

precipitation and average annual temperature were taken from Romanian climatic maps.

Variables to trees productivity

The soil represents for plants the reservoir of nutrients necessary for their growth and

development. Soils physical and chemical properties become, thus, an essential factor in plants

development, by contributing to its fertility. Fertility is a fundamental quality of soil, a resultant

of its forming and evolution processes as of its physical, chemical and biological features. Soil

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fertility, when accompanied by a favorable climate, contributes to the production of biomass.

Fertility embodies the production potential of soil, being conditioned by ecological factors,

indispensable to plants life, like nutrients (macro and micro-elements), accessible water, air (O2,

N2, CO2), bio-stimulators, energy (solar radiation). Trees obtain their prerequisites dissolved in

the soil solution with the help of their roots.

The quantity and accessibility of these valuable nutrients depend on numerous variables like:

parental material - influencing mainly their quantity and proportion (Ca, P, Mg, Na, Fe and

others), relief, climatic and biological factors - influencing availability by: amount of

precipitation, temperature, slope, slope aspect, amount of solar radiation. Every one of the tree

species prospers in a certain optimal amount and mix of each of these essential life elements. If

the quantity is too small, the tree wont have a strong growth or may even dye. However, if the

quantity is too high, some nutrients may come to be toxic and diminish its growth altogether. In

reality, the relations between soil properties and trees productivity remain only partially revealed,

even if various studies on the topic were made (R. Jandl and E. Herzberger 2001).

Material and Methodology

The soil samples and data regarding the biomass were collected during the National Forestry

Inventory, undertaken in Romania between 2008 and 2012. The survey excavations were made

in forests after a grid with cells measuring 2x2 km in the plains and 4x4 km in forests sites

located in the hills or mountains. The lower sampling density was established according to the

smaller coverage in forestry vegetation of the considered areas. The surface, coverage and usage

determination for forestry sites is established according to a grid of cells measuring 500x500 m,

spreading evenly all over the territory of Romania.

For an increased efficiency, the measurements were made according to the following layout: in

the south-western corner of each cell measuring 4x4 km (2x2 km) a square test area was

established, measuring 250x250m. In every of its four corners a sampling excavation was done.

At the end of a 5 years project 24.000 samples of forestry and soil data were collected from

permanent test points and other 5000 from temporary locations. Temporary locations were as

well established on a systematic basis. From all data collected we extract stands where beech

appear more than 80 % of the total volume as presented in Fig 1.

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Fig 1 Distribution of sampled sites

Data sets on stand and soil were collected from each site by analyzing the surrounding forest on

three concentric circles:

- all the trees measuring 56 DBH 285 mm, were counted in a circle with a 7.98 m radius

- all the trees with DBH larger than 285 mm were measured in a circle with a 12.62 m radius

- data about type of forest, soils and forest border were collected in a circle with a 25 m radius.

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R=25mS=2000m

R=7,98mS=200m

R=12,62mS=500m

Figure 2 Configuration of a survey site where the data was collected

Soil samples were collected from one survey excavation site, out of the four corners of a

test area

In the circle of radius 25 m in the survey site was chosen an area that represents the

general configuration of the relief and there has been executed a soil profiles. Soil profiles have

been carried out with a spade.

Vertical soil profiles were performed on the entire morphological thickness of the soil

profile and soil samples were collected from the middle of the diagnostic horizons, except O

horizon which it is made of organic matter. Soil profiles were diagnosed in situ and classified

and 500 g of soil samples were collected from each soil horizon for the physical and chemical

analyzes in the laboratory.

Stand characteristics were collected using various tools and stored on a tablet in situ. Tree

height was measured using a vertex and tree age was determined by extracting and measuring the

annual rings in a specialized center Samples were taken up with Pressler drill. After recording

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preliminary evidence of growth data, they are mounted on wooden plaques made for this

purpose. Mounting was accomplished with an adhesive, and after drying, the samples were

polished in several stages. Sanding was done with different grit abrasive belts, from the coarse to

the fine. Annual ring width measurement was done using the software Coorecorder. Growth in

this sample is scanned with a high resolution scanner after being properly prepared. After has

been marked all the limits of annual ring growth present in the sample, the data obtained are

stored and get a file in which are recorded details of the bookmark and image name

corresponding sample that was measured.

Tree diameters were measured accurately (mm) at breast hight (DBH) using dendrometric clup

with the end of the clup pointing to the center of survey site. The trees that had nodes or

irregularities the DBH was measured above to express a correct estimation of growth.

Relative classes of production stands of even aged trees sampled were determined by the

relationship between average height and stand age, this represents the classification system that

shows relative productivity classes of stands in Romania (Giurgiu and Draghiciu, 2004). The

relative production class had been calculated after (Seceleanu 1998, Giurgiu 2004) using the

coefficients for beech stands (Table 1).

=0 + 1 1

2 + 2 2 3

3 + 3 3 5

5 + 4 4

22 + 4

3 + 64

Table 1 Coefficients used in the estimation of relative production class of beech stands (Prod)

a1 a2 a3 a4 a5 a6

440.718500 -72.010900 3.242600 -0.226200 0.175400 -0.017400

b0 b1 b2 b3 b4

7435.357426 255.469464 12.927024 0.150071 0.003429

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Climate data (average temperature and rainfall) for the sites selected for the study were obtained

from climatic maps of Romania, by digitizing them. Locating sample sites was made with

precision using GPS technology. Other factors that influence the production class of beech stands

such as relief elements (slope and exhibition) were determined respectively vertex and compass.

The exhibition was transposed statistics and by the amount of light in the northern hemisphere at

45 degrees depending on slope (Kumar et. al. 1997). Soil samples taken were classified horizons

and soil types by taxonomic Romanian system of soil classification (SRTS), which is consistent

with the European classification system. The methodology of sampling and analysis program

was conducted according to International Co-operative Programme on Assessment and

Monitoring of Air Pollution Effects on Forests (Manual on methods and criteria for harmonized

sampling, assessment, monitoring and analysis of the effects of air pollution on forests -

Sampling and Analysis of Soil).

Study of soil characteristics

Chemical analyses of soil samples are important in determining soil quality and chemical

properties of the soil directly influences the development of forest vegetation. Depending on the

variability of chemical properties can be determined by statistical analysis the affinity of beach

stands according to different chemical properties. Determination of chemical proprieties, soil

samples analyzed was conducted by the following methods:

Determination of soil organic carbon is made by the wet oxidation method and dosage

titration - Walkley and Black (1934) method of changing Doughnut. Organic carbon is oxidized

to the anhydride, dichromate (Cr2O72-) in excess sulfuric acid, oxidation facilitated by the heat

produced by diluting a volume of normal solution of potassium dichromate with 2 volumes of

concentrated sulfuric acid and maintaining the sample at 100 C for 20 minutes. The content of

organic carbon is calculated as the amount of dichromate consumed in the oxidation anhydride

thereof.

Soil pH was determined in water electrochemical readings accomplished with a Thermo

Orion pH meter 3.Carbonates were gazo-volumetric determined with Scheibler calcimeter. The

determination of hydrogen exchanged was carried out by percolating after Cernescu method

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(HS). Determination of the cation exchange capacity of the exchange complex and the base was

made by extraction with ammonium acetate solution of pH 7 (BS). Readings were performed in

flame spectrophotometer UNICAM.

Statistical analysis

Experimental data from the first horizon (A) were studied using the Principal Components

Analysis (PCA). Along data points (rows) a biplot algorithm show the projection of the original

variables (columns) plotted in the coordinate system given by the first two components (Fig. 2).

The first two components comprise 29.02% and, respectively, 25.35% of the variance of the

entire model. Statistical significance tests have revealed that these first two components are, in

fact, the only significant.

Table 2

Component coefficients for the three principal component functions

Y 1 Y 2 Y 3

Altit 0.24704 -0.13401 0.048328

ph -0.20712 0.15724 -0.12016

Humus 0.39153 0.15209 0.002044

C 0.39059 0.1527 0.001042

N 0.38062 0.16242 0.011531

HS 0.36495 0.004597 0.10744

BS -0.06196 0.45404 -0.11358

K 0.032945 0.26965 -0.01451

Na -0.03785 0.091295 0.056693

Mg -0.05631 0.2913 -0.08182

Ca -0.06159 0.41525 -0.11015

CEC 0.25032 0.33761 0.004181

BCSR -0.22597 0.29973 -0.16121

Texture 0.12675 -0.10837 -0.01428

Stone% 0.040669 -0.07176 -0.10853

< slope 0.021412 0.007276 -0.25309

H. Depth -0.20644 -0.16911 0.042248

Radiation -0.00786 -0.04644 0.214

Temp -0.20358 0.090086 -0.04194

Rain 0.25519 -0.15656 -0.0226

Height -0.07876 0.10763 0.48134

DBH -0.06324 0.1209 0.4669

Age -0.04319 0.13008 0.39008

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Trees nr. 0.067906 -0.11259 -0.34161

Prod -0.03176 0.006609 0.2568

The first principal component gives a clear view over the organic-mineral relationship in the soil

composition. Thus, the variables affecting the amount of organic compounds in soil (HT, C, Nt)

crowd on the left side of the first component axis while mineral factors (Ca, Mg, K, Na, total

carbonates) are on the right. Inherently, productivity occupies a position to the left on the first

component axis; however its position is not extreme.

Second component actually shows some of the analyzed elements ambivalence. Thus, the

organic and inorganic factors are in the same part of the second principal component (on the

upper side of the axis). The productivity occupies also a non-extreme position on the upper side

of the axis.

The entire system of the relationships illustrated by the PCA scatterplot is equally visible on the

first two components loadings (Fig. 4, Fig. 5).

Discussion

Soil is an organic mineral complex where there are physical and chemical processes that

influence the development of biomass. Factors that condition the development of beech trees

have different nature and influence their development simultaneously with various scales of

intensities in different periods of time. The soil ecosystem has a productive potential for each

species, and trees are in direct competition for resources. The relationships between growth and

development of the factors analyzed and beech stands productivity are complex and not linear

(Table 2).

Altit ph Humus C N HS BS K Na Mg Ca DBH

Prod 0.004047 -0.022 -0.0372 -0.0379 -0.001 -0.01564 -0.02735 0.04008 0.065915 -0.0606 -0.021 0.17861

CEC BCSR Texture Stone% <

slope

H.

Depth Rad. Temp Rain Height Age

Trees

nr.

Prod -0.02989 -0.050 -0.0633 -0.0413 -0.224 0.065005 0.15583 -0.0047 -0.0827 0.64174 -0.092 0.000651

Table 2 Linear Pearson correlation

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To better highlight the influence of environmental factors on growth beech stands were selected

several variables that reflect the biomass production of trees such as the average height of trees

(Height), the average diameter of trees measured at the height of the breast (DBH) average age

of trees (Age), and the number of trees in the areas investigated.

To see if there is a difference between soil horizons on biotic components we grouped the dataset

based on soil horizon (All soil horizons, A horizon and B horizon) and analyzed them separated.

In the principal components analysis (PCA) the first three components have a major impact on

the distribution of the variables in all three sets of data (Fig. 2).

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ion

Fig. 2 The influence of principal components

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In analyzing the first Component (PC1) in the PCA plot of all horizons (Fig 3) the phenomenon

that influenced the distribution of data is given by the interaction of climatic factors (altitude,

rainfall, temperature) and soil chemical proprieties, mostly soil nutrient content (C, N), BCSR,

CEC, texture, pH and SH. The influence of other variables especially biotic factors is

marginalized by the weight of abiotic factors. On PC1 variables of biotic nature (Prod, Height,

DBH, stand age, trees number), interact in small extent and with abiotic and climatic factors.

The distribution of PC1 of variables show us the environment that beech stands grow. The group

of variables like altitude humus, C, N, SH, CEC, texture, stone content, precipitation are

inversely correlated with pH, BCSR, horizon depth and temperature. On the PC2 distribution

chemical component of the soil have a strong influence and group on variables like ph, Humus,

C, N, BS, K, Mg, Ca, CEC, BCSR and they are inversely correlated with altitude, texture,

horizon depth and precipitation. On the PC3 the distribution of variables is influenced by the

biotic factors and it groups productivity directly correlated with height, DBH, age, radiation, HS

and inversely correlated with pH, BCSR, stone content, slope and the number of trees. To extract

the influence of soil proprieties and environmental factors on productivity the best model will

emerge from combining PC2 with PC3 in the PCA model.

In the PCA analysis compose from PC1 and PC2 with all soil horizons (Fig. 4a) we noticed the

data sets are splitting by their chemistry composition and grouping depending on the horizon.

The ordination diagram PC1 and PC2 show some horizon types oriented to some environmental

variables such as A horizon oriented with humus, C, N, HS and CEC; B spodic horizon (Bs)

oriented to precipitation (Rain), altitude and texture; B cambic horizon (Bv) oriented with

horizon depth; B argic horizon (Bt) and C horizon oriented with pH, BCSR and temperature.

From this orientation grouping results the principal environmental factors or their effect that

influence soil proprieties and highlights the main soil processes that constitute the soil horizons.

The biotic factors including productivity are oriented at the intersection of horizons and that

means that productivity does not favor a particular soil horizon or a specific soil type.

Diagram orientation composed from PC1 and PC3 point out the chemical differences between

soil horizons grouping rich soil horizons in organic matter (A, Bs) oriented with C, N, Humus,

HS, Rain, altitude, slope, stone content and texture and rich soils in salts content (C, Bt and Bv)

that are oriented with horizon depth, BCSR and pH.

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Fig. 4(a, b c) Ordination diagram showing the result of PCA analysis of vegetation and

environment variables in the study areas

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This grouping is due the altitude difference, at low elevation are well developed soils in general

with a fine texture (loamy-clay) richer in salts and in contrast at high elevation were are low

temperatures, heavy precipitation, soils are leach in salts (big texture) and have an acidic pH.

In ordination diagram made from PC2 and PC3 (Fig. 4c) biotic parameters interact with soil and

environment variables. Biotic factors (Productivity, height, DBH, Age) orients with radiation,

altitude, horizon depth, and HS and Na and oppose the tree number, slope, stone content, BCSR,

pH, BS, Ca and Mg. The orientation diagram shows that the horizons are not grouping and the

factors that influence the productivity have different nature.

Radiation is influential because it indirectly affects these key resources in an holistic way

through the amount of solar radiation received by the site, which in turn affects temperature, soil

moisture, humidity, vapor pressure deficits, transpiration, and other factors that limit the

survival, growth and distribution of plants (Sebastia, 2004; Mataji et al. 2009). Elevation is an

important factor for vegetation development and distribution in mountains (Zhangand Dong,

2010). In this study elevation have a large variation ranging from 170 m to more than 1600 m

altitude. From the frequency distribution we can conclude that beech covers a large spectrum of

altitude with higher frequency of altitudes were beech stands appear are between 500 m and

1100 m so this is the optimal niche in Romania were beech stands grow.

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In many studies soil depth play an important role in stand productivity because its influence tree

nutrition and growth (Sanchez et. al. 2002).

Badano et al. (2005) and Boll et al., 2005 also emphasized the role of slope in the pattern of plant

distribution across the landscape. Slope influences on plant distribution are related to a number

of factors that affect plants such as decreased soil depth, increased water drainage and salts

depletion. Plant establishment becomes increasingly difficult with increasing slope steepness due

to reduced soil depth, lack of nutrient sand moisture and also difficulty of constancy of seed

(Campo et al.,1999).

Soil pH is an important determinant of the productive capability of a site (Jobbagy and Jackson,

2003). Soil acidity has a strong influence on nutrient availability (Farley and Kelly, 2004).

The amount of salinity can have negative effects on species that are related to increase

environmental drought, increased osmotic pressure of the soil solution, and ion toxicity, which

limit the water and nutrients that can be absorbed by plant roots (Khresat and Qudah, 2006).

A study on climatic effects on beech stands (I. C. Meier and C. Leuschner 2013) especially

reduction in summer rainfall, as predicted for various parts of Europe in the course of climate

change, should primarily affect the carbon gain and/or hydraulic functioning of beech trees,

before drought-induced impairment of nutrient supply may harm beech vitality and growth.

To highlight the factors that influence productivity we divided the data set by main soil horizons

(A horizon and B horizon) to see how the environmental variables influence productivity

depending on soil horizon. The oriented diagram show the same distribution between

components as in all horizons data with a difference in orientation of variables. The orientation

diagram from PC2 and PC3 orients biotic factors with environment variables. In A horizon

distribution of ordination diagram (Fig 6.a) there are not major changes in distribution of

variables compared with all horizon dataset. From distribution PCA plot of B horizon (Fig 6.b)

compared with all horizons and A horizon ordination diagram, productivity orients strongly with

soil depth and it oppose slope in a larger degree. B horizon data in our opinion give a better

representation of soil characteristic because A horizon its very thin (around 10 cm depth) and the

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majority of radicular system spreads in B horizon, therefore B horizon physical and chemical

characteristics give a better view of soil importance related to productivity.

Fig. 6 Ordination diagram showing the result of PCA analysis of vegetation and environment

variables for A horizon (a) and B horizon (b)

In B horizon ordination diagram productivity orients with relief characteristics like slope aspect

(Radiation) and soil depth and in a smaller extent with altitude and hydrogen saturation (HS) and

oppose with slope, stand density (Tree nr.), and in a smaller extent with BCSR, stone content,

and base saturation.

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Conclusion

We assessed the impacts of environmental factors and soil characteristics on forests dominated

by Fagus sylvatica in Romania. We found that relief factors like slope, slope aspect (radiation)

and another consequence of the slope (soil depth) along with a biotic characteristic as stand

density (Trees nr) are the main factors that influence beech productivity. Trees number impacts

productivity because trees are in direct competition for light and resources (water and nutrients)

and a higher stand density correlates with a lower productivity (Magnurer M. et al 2012). Soil

physical and chemical characteristics have a lower impact on productivity, however higher

values of productivity being recorded in acidic soils with low concentrations of salts, mainly

BCSR, BS, Mg and Ca and with higher values of saturated Hydrogen (HS). Despite this

conditions favor a coarse texture, productivity correlated with finer texture because it have a

stronger water holding capacity. Stone contentment have a negative impact on productivity

decreasing the total space for root system and resources. Elevation and directed influenced

factors like temperature and precipitation dont have a strong impact on productivity with a

minor preference for high values of altitude and precipitation and lower temperatures.

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The influence of environmental variables and soil characteristics on productivity of beech (Fagus sylvatica) ecosystems in Romanian forests