Effects of beech and ash on small scale

J. Plant Nutr. Soil Sci. 2011, 174, 799–808

DOI: 10.1002/jpln.201000369

799

Effects of beech and ash on small-scale variation of soil acidity and
nutrient stocks in a mixed deciduous forest
Frédéric M. Holzwarth1,2*, Max Daenner1, and Heiner Flessa3,4
1

Department for Ecoinformatics, Biometrics, and Forest Growth, Büsgen Institute, Georg-August-Universität Göttingen, Büsgenweg 4,
37077 Göttingen, Germany
2 present address: Department for Systematic Botany and Functional Biodiversity Research, Institute of Biology, Universität Leipzig,
Johannisallee 21, 04103 Leipzig, Germany
3 Soil Science of Temperate and Boreal Ecosystems, Büsgen Institute, Georg-August-Universität Göttingen, Büsgenweg 2, 37077 Göttingen,
Germany
4 present address: Institute of Agricultural Climate Research, Johann Heinrich von Thünen-Institut, Federal Research Institute for Rural Areas,
Forestry and Fisheries, Bundesallee 50, 38116 Braunschweig, Germany

Abstract

Trees interact in a complex manner with soils: they recycle and redistribute nutrients via many ecological pathways. Nutrient distribution via leaf litter is assumed to be of major importance. Beech is
commonly known to have lower nutrient concentrations in its litter than other hardwood tree species
occurring in Central Europe. We examined the influences of distribution of beech (Fagus
sylvatica L.), ash (Fraxinus excelsior L.), lime (Tilia cordata Mill. and T. platyphyllos Scop.), maple
(Acer spp. L.), and clay content on small-scale variability of pH and exchangeable Ca and Mg stocks
in the mineral soil and of organic-C stocks in the forest floor in a near-natural, mature mixed
deciduous forest in Central Germany. The soil is a Luvisol developed in loess over limestone.
We found a positive effect of the proportion of beech on the organic-C stocks in the forest floor
and a negative effect on soil pH and exchangeable Ca and Mg in the upper mineral soil (0 to
10 cm). The proportion of ash had a similar effect in the opposite direction, the other species did
not show any such effect. The ecological impact of beech and ash on soil properties at a sample
point was explained best by their respective proportion within a radius of 9 to 11 m. The proportion of the species based on tree volume within this radius was the best proxy to explain species
effects. The clay content had a significant positive influence on soil pH and exchangeable Ca
and Mg with similar effect sizes.
Our results indicate that beech, in comparison to other co-occurring deciduous tree species,
mainly ash, increased acidification at our site. This effect occurred on a small spatial scale and
was probably driven by species-related differences in nutrient cycling via leaf litter. The distribution of beech and ash resulted not only in aboveground diversity of stand structures but also
induced a distinct belowground diversity of the soil habitat.
Key words: tree–soil interaction / species-specific effect / leaf litter / deciduous-tree species / forest floor /
exchangeable cations


Accepted March 29, 2011

1 Introduction
Tree species differ in their influence on the acidification and
the redistribution of nutrients in soils (see reviews by Binkley
and Giardina, 1998; Augusto et al., 2002). Many studies have
analyzed the differences between conifer and broadleaved
species (e.g., Sanborn, 2001; Rothe et al., 2002; Binkley,
2003; Rothe et al., 2003). A smaller number have investigated broadleaf species (e.g., Nordén, 1994a, b; Finzi et al.,
1998a, b; Neyrinck et al., 2000; Hagen-Thorn et al., 2004;
Oostra et al., 2006), and only very few studies have studied
species effects along a gradient of species mixture
(Klemmedson, 1991; Rothe, 1997; Sanborn, 2001; Rothe
et al., 2002).
One approach for treating small-scale heterogeneity and
establishing a gradient of species admixtures is garden

experiments (Challinor, 1968; Binkley and Valentine, 1991;
Scherer-Lorenzen et al., 2007). However, studying established forests may also reveal and describe mechanisms of

tree–soil interactions in detail. Although near-natural forests
might contain greater noise in the data, they are more representative of natural ecosystems and also cover long-term
effects (Binkley and Menyailo, 2005; Leuschner et al., 2009).
Although a multitude of pathways on how trees interact with
soils has been described, litterfall is commonly thought to be
of major importance (Ovington, 1953; Challinor, 1968; Finzi
et al., 1998a, b; Washburn and Arthur, 2003). Other factors,
which were found to contribute to differences in deciduous
tree species, are, e.g., canopy interception (throughfall),
canopy–precipitation interaction, stem flow, rooting patterns,

* Correspondence: F. M. Holzwarth;
e-mail: frederic.holzwarth@uni-leipzig.de

 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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J. Plant Nutr. Soil Sci. 2011, 174, 799–808

Holzwarth, Daenner, Flessa

root litter (Falkengren-Grerup, 1989; Nordén, 1991; Dijkstra
and Smits, 2002; Dijkstra, 2003; Meinen, 2009). Horizontal
ranges of litter fall have been studied (Ferrari and Sugita,
1996; Staelens et al., 2003, 2004), but horizontal ranges of
tree effects on soils properties have been investigated less
(Zinke, 1962; Rothe, 1997; Rothe et al., 2002; Hojjati et al.,
2009).
Guckland et al. (2009) analyzed mixed deciduous forests on
a plot basis indicating differences in species-specific tree–soil
interactions, as well as clay content in the soil being an important covariable. The results suggested that beech affected
the soil nutrient status in a different manner than ash, lime,
maple, hornbeam, and elm. However, it was not possible to
quantify and separate effects of tree species and variations of
soil clay content. Nordén (1994c) and Jacob et al. (2009) confirmed that beech litter contains less Ca and Mg than litter of
several other deciduous European tree species and ash has
very low C : N ratios compared to other species especially

beech, which has rather high ratios.
We used some of the study sites described by Guckland et al.
(2009) for a more detailed analysis of the species effect on
small-scale variation of soil properties in these mixed deciduous stands. On the basis that beech furnishes more recalcitrant litter, which also contains less basic cations, and that litterfall shows spatial patterns, we hypothesize that higher proportions of beech result in (1) higher stocks of organic C in
the forest floor, lead to (2) a stronger acidification and to
smaller stocks of exchangeable base cations in the upper
mineral soil, and (3) to spatial patterns of the studied soil
parameters. The objectives of this study were (1) to determine the effects of tree species, especially beech, on the
stocks of organic C in the forest floor and the acidification of
the mineral soil (pH value, stocks of exchangeable Ca and
Mg cations), (2) to analyze the horizontal and vertical (soil
depth) extension of species effects, and (3) to separate
effects of tree species from effects related to small-scale variation of soil clay content.

2 Material and methods

ler-Manning, 2007). In December 1997, it became the core
zone of the Hainich National Park.
Four study plots with an extension of roughly 54 m × 54 m
(inclination < 5%) were established in the NE part of the

national park. Tree species on these plots were beech (Fagus
sylvatica L.), ash (Fraxinus excelsior L.), lime (Tilia cordata
Mill. and T. platyphyllos Scop.), hornbeam (Carpinus betulus
L.), maple (Acer pseudoplatanus L., A. platanoides L., and A.
campestre L.), and elm (Ulmus glabra Huds.). All plots contained a fair mixture of beech and the other species.
On each plot, all trees with a diameter at breast height (D) of
at least 7 cm were recorded in the spring of 2005. Measured
tree parameters were: coordinates of the trunk base, species,
D, height, and crown projection area (C) (Tab. 1).
Table 1: Inventory data of all trees (diameter at breast height, D >
7 cm) on the four studied plots (total area 12 395 m2), shown for each
genus and in total: number of stems, mean height, mean diameter at
breast height (D), basal area per hectare (BA), and proportion of
basal area. Species marked with an asterisk occur only on one of the
plots, respectively. All other species occur on all plots.
Genus
Acer
Carpinus*

Stems


Mean
height / m

Mean
D / cm

28

25.8

39.8

BA / m2
ha–1
3.2

BA / %
8


5

22.5

35.8

0.5

1

Fagus

432

22.0

26.0

23.6


59

Fraxinus

127

24.6

26.3

7.7

19

Tilia

138

19.7


21.1

4.6

12

2

27.1

48.4

0.3

1

732

22.2


25.8

39.8

100

Ulmus*
All species

2.2 Sampling and laboratory analysis
For soil sampling within plots, a grid of 12 m × 12 m (aligned
northward) was established within a radius of 20 m around
the plot center (12 sample points per plot). In addition, a soilprofile pit was dug adjacent to each plot.

2.1 Study site
The study was conducted in mature deciduous forest stands
(trees aged 1 to ≈ 250 y), which are located in the Hainich
National Park (51°06′ N, 10°31′ E), Thuringia, Germany, at
an elevation of ≈ 350 m asl (mean annual temperature 7.5°C;
mean annual precipitation 670 mm). The soil type at the study
plots is a Luvisol (FAO, 2006) developed from loess, which is
underlain by limestone. The loess cover is generally free of
carbonates, and its thickness varies between 60 and 120 cm.
However, the tree roots still extend into the calcareous subsoil.
The stands had been managed as coppice-with-standards
(timber trees above coppiced woodland, including all the
recent tree species) until ≈ 1900, after which they were gradually transformed into a beech-selection forest (no coppice).
Management and harvesting ceased by the year 1965 (But 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

In the winter of 2004/05, soil cores (∅ 6.4 cm) were taken
from the upper 30 cm of the mineral soil. They were divided
into three parts representing the soil depths of 0–10 cm,
10–20 cm, and 20–30 cm. Samples were dried at 40°C and
passed through a 2 mm sieve. Thickness of the loess cover
was determined using a soil auger. Samples of the forest floor
were collected at each sample point (300 cm2 surface).
Soil pH was measured in a suspension with 1M KCl (5 g of
soil, 15 mL KCl solution). Organic C (Corg) in the forest-floor
samples was determined by an automated analyzer (Heraeus
Elementar Vario EL, Hanau, Germany). Effective cationexchange capacity (CEC) was quantified by leaching soil
samples with 100 mL of 1M NH4Cl for 4 h. Cations in the
extract were quantified by AAS. We determined the soil texture using the sieving and pipette method (Schlichting et al.,
1995). Soil bulk density (after drying at 105°C) was calculated
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J. Plant Nutr. Soil Sci. 2011, 174, 799–808

Effects of beech and ash on soil acidity and nutrient stocks 801

gravimetrically from undisturbed soil cores taken from soilprofile pits adjacent to each plot (125 cm3, n = 3 each).
Stocks of exchangeable cations in the mineral soil were calculated with the analytical value (mmolc g–1), the bulk density
(g cm–3), and the thickness of the relevant layer (10 cm
each). The properties of the stands and soils were described
in detail by Guckland et al. (2009). Summary statistics of the
soil parameters used in this study are given in Tab. 2.

centage of the sum of the area of its crowns related to the
sum of the crowns of all trees within a 10 m radius. We used
a similar approach and additionally compared the suitability
of four different attributes (projected crown area [C], diameter
at breast height [D], basal area [BA], and tree volume [V]) for
all major tree species (beech, ash, lime, and maple) as proxy.
We calculated the proportion of a given species at a given
sample point as the percentage of the sum of a species’ attributes related to the sum of all trees attributes (Eq. 1):
m…i;r


2.3 Analysis of species effects
The soil-sample grid comprised 48 sample points (12 points
at each of the four sites), 3 samples were missing, one soil
sample (10 to 20 cm) missed only at the time of clay-content
measurement, but we could impute the clay content using
other measurements. We checked influence of stem flow,
which would add to any spatial signal, by plotting the soil
parameters against the distance to the nearest trunk, but did
not find any trend and, thus, used samples from all 45 sample
points in the analysis without further considering stem flow as
an influential factor. We also checked the influence of absolute stand density in order not to confound species effects
with mere density effects.
As dependent variables, we used the following soil parameters: in the forest floor the stock of Corg (Mg ha–1) and in
the upper mineral soil in both depth levels: the stocks of
exchangeable cations of Ca and Mg (kmolc ha–1), and pH
value (KCl).
Possible independent variables in this study were: the species proportion at a given point (Eq. 1), the clay content in the
upper mineral soil (soil depths: 0–10 cm, 10–20 cm) and the
absolute stand density (m2 ha–1) in different radii around the
sample point.
The potential influence of trees on soil properties at a given
point in a mixed stand can be estimated from several indicators, among these: species identity, tree distance, tree dimension, or other measurable proxies for the potential impact.
Rothe et al. (2002) proposed estimation of the influence of a
tree species on soil properties of a given point from the per-

proportion of a species =

Aij

j
n…r † m…i;r
X

i

(1)

;
Aij

j

where i is the tree species, A is one of a set of measured attributes of each individual tree, and r the radius of the circular
surrounding of the sample point ranging from 4.5 to 24 m with
intervals of 0.5 m. Furthermore, m(i, r) is the number of trees
of the species i with the trees j = 1 to m(i, r) in a circle of the
radius r, n(r) the number of species in the relevant circle with
the species i = 1 to n(r), and Aij the value of the attribute of
tree j of the species i. The attribute A of a tree (see above)
shall represent its ecological footprint with regard to its influence on the soil. For greater radii, parts of the circular surroundings would lie outside of the plot, where no tree data
were available. However, as will be shown later, greater radii
are not relevant for the analysis and, thus, a border correction
was not necessary.
The basal area (BA) was calculated as a × D2, while the volume (V) was approximated by V = a × D2.6, with a being a
species-specific parameter. The volume of a tree can be
approximated by V = BA × height × form factor, and the
height is typically estimated with height = a × Db. For the
sake of simplicity, we assumed the form factor and the allometric constants to be equal among species, which we deem
legitimate for our purpose and which is underpinned by height
regressions done with the present data set, where heights
were available for all trees (data not shown). While the factors
would drop out in Eq. 1, the estimated exponent with all studied species pooled together was 2.6.

Table 2: Statistics (sample mean and standard deviation [SD] as well as minimum and maximum) of soil parameters measured in the analyzed
stands at two soil depths (0–10 cm, 10–20 cm).
Parameter
Corg in forest floor / Mg ha–1
Clay content / %

pH (KCl)

Ex. Mg / kmolc ha–1

Max.

0.7

5.9

24.7 (7)

14.4

41.6

10–20 cm

24.5 (7)

14.0

41.6

3.5

6.3

0–10 cm

4.4 (0.8)
4.7 (0.8)

3.4

6.4

0–10 cm

140 (86)

8.6

450

10–20 cm

150 (76)

3.2

330

0–10 cm

10 (4.7)

2.4

26

1.5

21

10–20 cm
a

Min.

3.2 (1.2)
0–10 cm

10–20 cm
Ex.a Ca / kmolc ha–1

Mean (SD)

8.9 (4.2)

Ex = exchangeable

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Holzwarth, Daenner, Flessa

2.4 Statistical analysis

3 Results

Visualization of the data suggested a linear relationship between the species proportions, the stand densities, as well as
the clay content on the one side and the studied soil parameters Corg, pH value, exchangeable Ca and Mg on the other
side. For all regressions, we used linear mixed-effect models
to account for the nested sampling design. Models were fit by
maximizing the likelihood (ML). Effects in the models were
chosen by comparing the models BIC (Bayesian information
criterion). We refer to the null model as the model with only
the random effects. Relating the model likelihood with the
likelihood of the null model, served us as goodness of fit
(Maddala pseudo-R 2).

3.1 Influence of covariables “clay content” and
“stand density”

For Corg in the forest floor, possible fixed effects were: clay
content (in the upper layer) and species proportion, and random effect: plot identity. For the other soil parameters (measurements in two depth layers), interaction of the species proportion with the depth layer was also possible and as random
effects: plot identity and sample-point identity nested within
plot identity. As the species proportion and the clay content
did not correlate, we used them as independent variables,
where applicable. To underline this, we also reported covariance of parameter estimates for these two variables in the
models. To check model assumptions, we analyzed the residuals with Tukey-Anscombe and QQ plots.
The species proportions were of course highly correlated, so
it is impossible to include all different proportions in a single
model. Moreover, we assume only a single gradient of species abundance to be responsible for the majority of variation
in the soil parameters caused by species identity. We did multivariate analysis (PCA) with the species proportions to identify the main axes of variation. The main axes as well as the
four species proportions from all available calculations
(depending on the species, the radius of the circular surrounding, and the attribute to represent the trees) were used
as explanatory variables. We compared the pseudo-R 2 of the
models between all possible proportions and the dependent
soil parameter to find the proportion that best explains a given
soil parameter. A similar approach (with the Pearson correlation coefficient) was described by Rothe (1997) and Rothe
et al. (2002).
We checked, whether distance signals in the models would
be artifacts of the rather dense and regular sample grid.
Therefore, we also calculated the models with subsets of the
points: in each plot we selected six points, which had the
furthest distance from each other (minimum distance not 12 m
but 17 m). With two possible subsets for each plot and four
plots there were 16 permutations, which we all calculated
independently and finally checked, whether, on average, any
previously found distance signal would change.
We used “R: A language and environment for statistical computing” (R Development Core Team, 2010) for all computations, including the package “lme4: Linear mixed-effects models using S4 classes” (Bates et al., 2011).
 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

The clay content in the upper 10 cm of the mineral soil as an
explaining factor accounted for a substantial degree of variance in stocks of exchangeable Ca and Mg (pseudo-R 2 =
0.38 and 0.36). In contrast, its influence on the pH value of
the mineral soil was quite low (pseudo-R 2 = 0.15) and nonexistent on Corg in the forest floor (Tab. 3). The clay content in
the underlying layer (10–20 cm) explained the variation of the
soil parameters (in the corresponding layer) even better with
pseudo-R 2 for pH, Ca, and Mg being 0.26, 0.58, and 0.63, respectively. Hence, clay content was an important predictor for
stocks of exchangeable Ca and Mg as well as the pH value in
the mineral soil and was thus used as covariable in further
models of these three parameters. Absolute stand densities
(m2 ha–1) in increasing radii around the sample points did not
show any explanatory effect nor any trend with the radius
(analogous: also no correlation with litter mass in litter traps
[15 traps per plot], data not shown) and thus allowed the use
of species proportions instead of absolute values (Fig. 2).
Table 3: Goodness of fit (pseudo-R 2) of mixed models with clay content in respective depth layer as fixed effect.

Soil parameter

Clay content
0–10 cm
Pseudo-R 2

10–20 cm

Corg forest floor / Mg ha–1

0.00



pH (KCl)

0.15

0.26

Ex.a Ca / kmolc ha–1

0.38

0.58

Ex. Mg / kmolc ha–1

0.36

0.63

a

Ex = exchangeable

3.2 Influence of species proportions and their
horizontal ranges
Figure 1 and Tab. 4 give the results of the PCA of the species
proportions as an example for one radius (10 m) and one
attribute (V). It reveals a rather strict dichotomy of beech vs.
ash on the first axis, covering 73% of the variation. The second axis spans beech and ash vs. lime and maple (16%), and

Table 4: Statistics of the first two axes of a PCA on species proportions calculated with tree volume (V) as attribute in a radius of 10 m.
Loadings of species proportions on respective axis and their proportion of explained variance.
Genus

PC 1

Beech

PC 2

–0.76

0.42

Ash

0.65

0.57

Lime

0.06

–0.65

Maple

0.03

–0.28

Proportion of variance

0.73

0.16

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J. Plant Nutr. Soil Sci. 2011, 174, 799–808

Effects of beech and ash on soil acidity and nutrient stocks 803
We show all pseudo-R 2 of models using the four species proportions and the first axis of the PCA in all different radii as
explanatory variable (Fig. 2). We only present results of models using species proportions based on the attribute tree volume (V), because projected crown area (C), diameter at
breast height (D), and basal area (BA) proved to convey less
explanatory power (with BA being significantly better than C
and D and only slightly worse than V), and we shall not
regard the latter anymore.

Figure 1: Bi-Plot of the first two axes of a PCA on species proportions
calculated with tree volume (V) as attribute in a radius of 10 m. Points
represent the observations in the reduced space, vectors represent
variable loadings on the two axes.

the third axis (not shown in Fig. 1) differentiates between lime
and maple (10%).

Models using species proportions of beech and ash as well
as the first PCA axis exhibited a significant explanatory power
in radii between 9 and 11 m for all modeled parameters,
except for Mg, where the proportion of ash did not perform
well. We observed a distinct change of the goodness of fit
with changing radius (steep increase of the model pseudo-R 2
with increasing radius and then smooth decline), which
clearly indicates a nonrandom correlation. Otherwise, a more
erratic development of the pseudo-R 2 with increasing radius
would have been observed. This trend was strongly pronounced for the models of pH, Ca, and Mg. Proportions of
lime and maple as well as the second and third axis of the
PCA explained far less variation of the soil parameters and
had no consistent trend. The spatial aspect of the sampling
grid had no influence on the estimated ranges. On average,
the models with 16 different subsets of points showed the

Figure 2: Goodness of fit
(pseudo-R 2) of mixed-effect
models regressing Corg in the
forest floor (A) as well as pH
values (B), exchangeable Ca
(C), and Mg (D) stocks in two
layers of the mineral soil on
species proportions (beech,
ash, lime, and maple) calculated
on the basis of estimated tree
volume (V) as well as on the first
PCA axis (PC1, Fig. 1) and on
stand densities (m2 ha–1) (basal
area, BA) with different circular
surroundings around the sample
points (radius on the x-axis).
Models in B to D also include
the clay content in the respective layer of the mineral soil, the
horizontal line marks the goodness of fit with only the clay
content as fixed effect. We
included smoothing lines to
guide the eye, they are in the
respective color (black or gray)
of the smoothed species and
dashed for PC1 and BA.

804

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Holzwarth, Daenner, Flessa

same distance trend. This corroborates our finding of a spatial signal in the species proportions and precludes that it is a
mere sampling artifact.

we did not deem it strong enough and thus necessary to
counter it with, e.g., a transformation of the dependent variable.

We selected the species proportions and the radii, that
entailed high pseudo-R 2 (beech for all soil parameters, ash
for Corg, pH, and Ca; in radii of 9 to 11 m) for candidate models, on which we had a closer look. The proportions were
averaged for the radii of 9 to 11 m, since the respective models had very similar pseudo-R 2 for these radii. We did not
choose to use both proportions in a model, because this
would lead to high intercorrelations and thus a great uncertainty in parameter estimations. We also did not choose to
use the first PCA axis, although it sometimes performed better than the mere species proportions. First, because the
PCA axis contained information from four species proportions
and thus, naturally has the potential to perform better than a
single proportion. And second, because the difference in
goodness of fit was not large enough to prefer it over simpler
and more intuitive predictors.

The stocks of organic C in the forest floor (Corg) (Mg ha–1)
could be explained by the proportion of beech (pseudo-R 2
0.32) equally well as by the proportion of ash (0.30), and no
influence of the clay content was found (Tab. 5). A change
from 0% to 100% beech proportion resulted in an estimated
increase of the Corg stocks in the forest floor of more than
100% (1.9 to 4.1 Mg ha–1). Alternatively, a decrease from
100% ash proportion to 0% predicted a similar increase (1.3
to 3.7 Mg ha–1). The uncertainties of the parameter estimates
were considerable (0.5 Mg ha–1) (Tab. 5).

The candidate models are summarized in Tab. 5. The fixed
effect parameter “clay content” was normalized to an interval
of [0, 1], to better comprehend and compare the effect sizes.
In the models, where both fixed effects were included (pH,
Ca, and Mg), the parameter estimates for the species proportion and the clay content were not or only very weakly correlated. No major deviations from the model assumptions
(homoscedasticity, normally distributed residuals) were found
in any of the models. There was a slight trend of higher variance of the residuals with increasing estimates for Ca, but

The variation of the pH value in the upper 20 cm of the
mineral soil could be explained to a certain degree by a
model comprising the proportion of beech (pseudo-R 2 0.43)
or ash (0.45) and the clay content (Tab. 5). Species-proportion effects were strong in the upper layer (beech: –1.7, ash:
2.1 pH units) and noticeably weaker in the lower layer (beech:
–0.8, ash: 1.3 pH units). The effect of the clay content was
estimated to be of similar magnitude (1.5 and 1.2 pH units,
respectively).
Variation of exchangeable Ca (kmolc ha–1) (0 to 20 cm) was
explained to an even greater extent with a pseudo-R 2 of 0.55
(beech) or 0.56 (ash) and clay content. Of greatest importance was clay content (230 kmolc ha–1 in both models), followed by the species proportion with an effect of –140
(beech) or 170 (ash) in the upper 10 cm of the mineral soil

Table 5: Candidate models. Model properties: sample size (N), attribute and radius of the proportion of beech used, goodness of fit in relation to
null model (Maddala pseudo-R 2), and correlation of parameter estimates for clay content and species proportion (Clay ∼ prop. hi; in the upper
layer [hi]; values for the lower layer [lo] are very similar). Fixed effects: parameter estimates (± SE) for lower depth layer (10–20 cm), clay
content (normalized), and species proportion. Parameters for species proportion differ in the two depth layers (upper = 0–10 cm, lower =
10–20 cm). The parameter values also resemble the respective effect size for the variation from minimum to maximum values of each variable.
Random effects: variance attributed to random effects and residual variance, given as standard deviations.
Soil parameters
Model properties

Corg / Mg ha–1

pH (KCl)

Ex.a Ca / kmolc ha–1

Ex. Mg / kmolc ha–1

N

45

90

90

90

Species

prop.b

beech

Pseudo-R 2
Clay ∼ prop. hi

0.32


ash
0.30


beech

ash

0.43

0.45

beech
0.55

ash
0.56

beech
0.65

0.04

0.01

0.08

–0.12

0.04

4.8 (.3)

3.5 (.2)

Fixed effects
Intercept
Layer lo

1.9 (.4)


Prop. hi

2.2 (.5)

3.7 (.3)

120 (24)

8 (17)

9.8 (1.5)



–0.3 (.1)

0.4 (.1)

–33 (11)

29 (7)

–4.5 (0.6)

–2.5 (.6)

–1.7 (.3)

2.1 (.3)

–140 (25)

170 (26)

–9.0 (1.2)

Prop. lo





–0.8 (.3)

1.3 (.3)

–57 (25)

100 (26)

–3.3 (1.2)

Clay content





1.5 (.4)

1.2 (.3)

230 (30)

230 (28)

14.3 (1.6)

0.2

0.1

Random effects
Plot
Sample

0.6




Residual

0.8

a

b

Ex = exchangeable;

0.5

0.9

24

23

2.1

0.5

0.4

38

32

1.7

0.3

0.3

23

25

1.3

prop. = proportion

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J. Plant Nutr. Soil Sci. 2011, 174, 799–808
and mere –57 (beech) or 100 (ash) one layer deeper (Tab. 5).
The species effect weakened considerably with increasing
soil depth.
Stocks of exchangeable Mg (kmolc ha–1) in the upper mineral
soil could be modeled by clay content and proportion of
beech with a high pseudo-R 2 of 0.65 (Tab. 5). Again, clay
content had the strongest influence, leading to an estimated
shift of up to 14.3 kmolc ha–1. Changes of –9.0 in the upper
layer and just –3.3 in the lower layer were attributed to an
increase of beech proportion from 0% to 100%.

4 Discussion
As we hypothesized at the outset, the occurrence of beech in
the investigated mixed deciduous forest resulted in higher C
stocks in the forest floor and lower stocks of exchangeable
Ca and Mg as well as lower pH values in the upper mineral
soil. However, the occurrence of ash was negatively correlated with the occurrence of beech and the proportion of ash
had a similar explanatory power with opposite directions for
the first three parameters. We could hardly separate the
effect of these two species by our methods alone. We argue
that in the studied stands both species act antagonistically
and the relative importance of these two species varied with
the soil parameter. While for stocks of exchangeable Mg,
beech stood against all other species and ash did not show a
remarkably different behavior than lime and maple, for stocks
of Corg, exchangeable Ca, and the pH value, beech and ash
formed a dichotomy, where lime and maple stood in between.
The effect of species identity interfered with the influence of
clay content, which was of opposite direction (except for the
forest-floor C stock, which was not influenced by clay content). Our results support the conclusion by Guckland et al.
(2009) that soil acidification and soil nutrient distribution in
these stands are affected by the abundance of beech and
add to it that ash stands out of the other deciduous species
and spans the other end of the gradient. Their conclusion
was constrained by the interfering effects of species abundance and soil clay content, which could not be separated, as
well as the coarse resolution of the species distribution on
just the whole plot size. Our methods allowed a clear quantitative separation of these effects and a small-scale resolution
of the species distribution. Our results showed that in the
analyzed stands the proportions of beech and ash had a similar effect magnitude on soil pH and exchangeable Ca and
Mg in the upper 10 cm of the mineral soil as the soil clay content.
The influence of tree species diminished with soil depth. Similar results were described for beech and spruce (Rothe et al.,
2002), and some genera also used in this study (Acer sp.,
Fagus sp., Fraxinus sp. among others, Finzi et al., 1998a).
The weakening of the effect with soil depth indicates that the
analyzed soil properties were probably influenced by an
aboveground process, most probably leaf litterfall. Analyses
of leaf litter from all occurring species clearly showed a difference in cation content between beech, on the one hand, and
the remaining species on the other (Nordén, 1994c). Jacob
et al. (2009) analyzed the leaf-litter composition of the investi 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Effects of beech and ash on soil acidity and nutrient stocks 805
gated stands: beech litter had lower contents of Ca and Mg
than the nonbeech litter (16.8 vs. 21.5 to 30.2, 1.2 vs. 2.1 to
3.2 [mg (g dry mass)–1]). This resulted in an increased annual
deposition of Ca, Mg, and alkalinity via leaf litter with decreasing abundance of beech (Guckland et al., 2009).
Our results support the observations of other studies that
species-related differences in nutrient recycling via litter can
have significant implications for the pattern of soil fertility and
soil acidity in mixed stands (Nordén, 1994b; Finzi et al.,
1998a; Rothe and Binkley, 2001; Sayer, 2006). Augusto et al.
(2002) summarized effects of tree species in European temperate forests on soil fertility. They concluded that beech and
oak species were the deciduous species, which exerted the
greatest acidifying effect on soils. At our site, the redistribution of Ca and Mg in the soil profile via nutrient uptake and litter deposition had a highly beneficial effect since it led to the
translocation of base cations and alkalinity from the alkaline
subsoil (limestone) to the surface parent material (loess),
which has rather low buffer capacity and tends to form
strongly acidic forest soils (Guckland et al., 2009). Differences of cation contents in fine-root litter were not observed
(C. Meinen, pers. communication), which excludes fine-root
litter as the cause of the observed differences.
The C stock in the forest floor was found to be independent of
the underlying clay content but depended on the species proportion. The accumulation of litter on the mineral soil is mainly
a function of decay rates, not of litter production (Guckland
et al., 2009). Increasing litter accumulation with increasing
abundance of beech can be explained by the relatively high
recalcitrance of beech litter, which has lower nutrient concentrations and a higher C : N ratio than litter of Acer and Fraxinus species (Mellilo et al., 1982; Finzi et al., 1998b). Jacob
et al. (2009) reported that beech leaf litter at our sites had
higher C : N ratios than all other occurring species (means:
56 vs. 28 to 40), while ash marked the lower end. In addition,
beech litter may affect the activity of decomposers due to its
influence on soil pH and soil-nutrient availability (Reich et al.,
2005; Jacob et al., 2009). Our observations on beech effects
on the forest floor agree with the conclusions of Neirynck
et al. (2000) that beech belongs to the mullmoder-forming
species, whereas maple, lime, and ash are mull-forming
trees.
Soil properties at a given point in this mixed stand were influenced by stand structure and species composition within a
radius of 9 to 11 m. This is in the near range of the radius
around litter traps, in which the proportion of beech was correlated to the proportion of beech litter in the traps (13 to 16 m;
Holzwarth, 2008). Similar radii (11 to 18 m) for litter dispersal
of deciduous trees in mature stands were found by Staelens
et al. (2004), and Ferrari and Sugita (1996) modeled that
50% of the leaf litter would fall down in distances of 5 to 13 m
from the stems. Nevertheless, litter dispersal of different
deciduous tree species depends on the canopy structure, the
form of falling leaves and the wind regime (Hirabuki, 1991;
Staelens et al., 2003). Also redistribution after litterfall due to
wind and slope might blur dispersal patterns (Welbourn et al.,
1981; Wilke et al., 1993). However, litter redistribution was
likely to be small because of lack of slopes (this study),
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Holzwarth, Daenner, Flessa

autumnal rainfall and moisture surplus during winter months,
decreased wind velocities in a closed forest, due to the
understory vegetation (Guckland et al., 2009), and fungal
hyphae binding fresh litter to the forest floor (Ferguson, 1985;
Lodge and Asbury, 1988; Hirabuki, 1991).
These factors may thus affect the horizontal extension of
tree-species effects on soil chemical properties. The results
of studies on litter dispersal (Staelens et al., 2004; Ferrari
and Sugita, 1996; Rothe, 1997; Staelens et al., 2003), on the
range of species-specific effects on soil properties (Rothe,
1997; Rothe et al., 2002; this study), on litter chemistry
(Jacob et al., 2009), as well as on effects of litter on soil
chemistry (Ferguson, 1985; Binkley and Valentine, 1991;
Sayer, 2006) show that distribution of tree litter is an important factor, which causes small-scale variability of chemical
soil properties in mixed stands. The negative correlation of
the occurrence of beech and ash found on the plots might be
the reason for their similar explanatory power in the models,
while underneath there is still a dichotomy between beech
and the rest of the species lumped together, as indicated by
litter chemistry (see above and Jacob et al., 2009). More and
larger plots with different species admixtures could have
facilitated a clear separation of species effects.
We also stress the need to consider confounding soil factors
(e.g., texture) in this research area. In our study, the species
effect was nearly as strong as or slightly stronger than the
effect of clay content, thus species identities can be as important as the parent material for soil development. This underlines the responsibility that forest management has not only
for a sustainable yield of wood and other ecosystem services,
but also for sustainable soil fertility. Our results also indicate
that small-scale variability of chemical soil properties is not
only driven by species mixture and identity, but also by the
spatial pattern of individual trees. On the one hand, this can
be applied in forestry planting to help regulating soil chemistry, but on the other hand, it needs to be carefully considered within biodiversity studies.
Even though we were able to explain a considerable part of
the small-scale variability of C accumulation in the forest floor
and soil acidity in the upper mineral soil, our models left substantial parts of the variation in these properties unexplained.
This variation might be explained by other covariables, e.g.,
multidimensional representation of species proportions,
intraspecific variation of litter quality, interspecific or interindividual variation of horizontal extension of litterfall (Staelens
et al., 2003), management history (Hüttl and Schaaf, 1995;
Johnson and Curtis, 2001), or rooting patterns (Rothe and
Binkley, 2001). Yet, it is also common to encounter smallscale heterogeneity of random nature in near-natural ecosystems (Jackson and Caldwell, 1993; Ettema and Wardle,
2002) and a number of unknown or uncontrolled sources of
influence (Ehrenfeld et al., 2005). Unfortunately, no garden
experiments of similar extent, age, and species composition
are available thus far (Leuschner et al., 2009), which calls for
continued research in near-natural ecosystems (Ehrenfeld
et al., 2005; Leuschner et al., 2009).
 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

The published results on litter quality in the studied stands
(Jacob et al., 2009) and the vertical and horizontal ranges of
the effects of the proportion of beech found in this study indicate that the main cause of species-specific effects on soil
parameters mentioned here are differences in leaf-litter
chemistry. In addition, our results show that the distribution of
beech and ash resulted not only in aboveground diversity of
stand structures but also caused a distinct small-scale belowground diversity of the soil habitat.

Acknowledgments
Funding by the Deutsche Forschungsgemeinschaft (DFG;
Graduiertenkolleg 1086) is gratefully acknowledged. We want
to thank Anja Guckland and Mascha Jacob for sharing their
data with us. The authors are grateful to an anonymous
referee for many useful suggestions.

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