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Brazilian Journal of Botany ISSN 0100-8404Volume 39Number 2 Braz. J. Bot (2016) 39:605-612DOI 10.1007/s40415-016-0273-z

Determinants of variation in heathvegetation structure on coastal dune fieldsin northeastern South America

Augusto C. Silva, José Luiz A. Silva &Alexandre F. Souza

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Determinants of variation in heath vegetation structure on coastaldune fields in northeastern South America

Augusto C. Silva1 • Jose Luiz A. Silva1 • Alexandre F. Souza1

Received: 5 June 2015 / Accepted: 16 March 2016 / Published online: 29 March 2016

� Botanical Society of Sao Paulo 2016

Abstract Despite its implications for carbon storage,

animal conservation, and plant regeneration, the variation

in the structure of heath vegetation in South America is still

poorly studied. In this study, we aimed at examining the

edaphic and topographic determinants of this variation

along 85 plots (5 9 5 m) randomly distributed in a restinga

heath vegetation occurring on coastal dune fields in

northeastern Brazil. We carried out a PCA analysis to

reduce eleven vegetation descriptors into a small number of

structural gradients, which were then assessed by a step-

wise standard least-squares multiple regression to reveal

the effects of the abiotic environment on structure. The

three following hypotheses were tested: (1) both soils and

topography are important to explain variation in vegetation

structure at local scale; (2) herbaceous plants, cactus, and

woody plants show differential responses to soil and

topographic variations; and (3) soil acidity and salinity are

more important determinants of herbaceous cover than

woody plant variation. PCA analysis revealed three major

structural gradients related to biomass, herbaceous cover,

and leaning plants, respectively. These gradients were only

related to calcium and nitrogen contents, which partially

supports our first hypothesis. Our results also suggest that

different groups of plants have different responses to abi-

otic gradients that are exposed. The effect of the soil

acidity and salinity did not appear to present an immediate

strong influence on the herbaceous community. It seems

that a reduced number of edaphic factors promote the

variation in vegetation structure in the restinga heath

vegetation.

Keywords Communities � PCA � Soil � Topography �Woody plants

Introduction

The detection of patterns in the determinants of vegetation

structure has been necessary mainly to understand plant

ecological communities (Werger and Sprangers 1982; Gao

et al. 2014). Recent advances in this area have been reg-

istered regarding biomass variation in forested ecosystems,

due to its implications for global carbon balance and

warming effects (Houghton 2005; Colgan et al. 2012;

Rosenfield and Souza 2014). However, other aspects of

vegetation structure like height, diameter distribution, and

lateral growth provide important descriptors about plant

growth strategies (McGill et al. 2006), and the complexity

of vegetation structure has ecological consequences for

birds (Dıaz 2006) and mammals (Moore et al. 2014) in

local communities. Vegetation structure variation is

directly linked to plant species diversity (Gao et al. 2014)

and productivity (Clark and Clark 2000; Slik et al. 2013). A

solid understanding of vegetation structure variation is thus

key to the ecological knowledge of natural plant commu-

nities (Petersen and Drewa 2009; Rosenfield and Souza

2014) as well as for sustainable management (Gao et al.

2014) and conservation plans (Oliveira Filho et al. 2013).

Despite its importance, the variation in the structure of

restinga heath vegetation in South America is still poorly

Electronic supplementary material The online version of thisarticle (doi:10.1007/s40415-016-0273-z) contains supplementarymaterial, which is available to authorized users.

& Augusto C. Silva

agst.25@gmail.com

1 Departamento de Ecologia, CB, Universidade Federal do Rio

Grande do Norte, Campus Universitario, Lagoa Nova, Natal,

RN 59072-970, Brazil

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Braz. J. Bot (2016) 39(2):605–612

DOI 10.1007/s40415-016-0273-z

Author's personal copy

studied. This understanding is particularly difficult due to

its high local variance along the coast plains (Magnago

et al. 2010). Brazilian restinga heath vegetation physiog-

nomy varies from herbaceous communities on mobile sand

dunes near the shore to a mosaic of open shrub-dominated

and short forest patches (Martins et al. 2008; Scarano 2009;

Oliveira Filho 2009). The heterogeneity of this vegetation

structure mosaic has been described in qualitative terms,

and its relationship with potentially determinant edaphic

factors remains unknown (Scarano 2002; Santos Filho et al.

2013). Coastal sand plains are subjected to intense edaphic

gradients (Scheel-Ybert 2000; Gomes et al. 2007) due to

salinity and acidity variation in a background of overall

low nutrient availability. In tropical areas, the coastal

vegetation receives high levels of solar incidence and

elevated temperatures (Scarano 2002; Oliveira et al. 2014).

Topography variation influences vegetation structure

through direct and indirect mechanisms along coastal areas

due to the formation of dune fields (Cordeiro 2005). Local

elevation and slope alter vegetation exposure to wind and

solar incidences, as well as produce changes in soil mois-

ture (Moeslund et al. 2013), producing varying degrees of

environmental stress to plants (Menezes and Araujo 2000).

Thus, both soil- and topographic-related variables are

expected to play their respective roles in the variation of

restinga heath vegetation (Lane et al. 2008; Assis et al.

2011; Fenu et al. 2012; Tissier et al. 2013).

Here we aimed at examining abiotic determinants of

variation in the structure of restinga heath vegetation

occurring on coastal dune fields in northeastern Brazil.

Specifically, we tested the following hypotheses: (1) both

soil and topographic factors are important to explain varia-

tion in vegetation structure at local scale. However, due to

the overall low nutrient availability, topographic factors are

more important than soil-related factors (Lane et al. 2008;

Assis et al. 2011; Fenu et al. 2012); (2) herbaceous plants,

cactus, and woody plants show differential responses to soil

and topographic variations (Petersen and Drewa 2009;

Moeslund et al. 2013; Tissier et al. 2013); and (3) soil acidity

and salinity are more important determinants of herbaceous

cover than woody plant variation due to the closer associa-

tion of the former with open, near shore areas and because it

is the short life cycle of the organism that may have affected

their development by such aspects (Guedes et al. 2006; Lane

et al. 2008; Petersen and Drewa 2009).

Materials and methods

Study area

The study was carried out in the Barreira do Inferno Lauch

Center, Parnamirim municipality, Rio Grande do Norte

state, northeastern Brazil (5�540S, 35�100W, Fig. 1). The

Launch Center belongs to the Brazilian Air Force, and

human activities are restricted to aerospace research since

1965 (CLBI 2014). The Launch Center’s 1900 ha area

accompanies the roughly North–South coastline along

which a set of tall (ca. 80 m high) sand dunes for up to

2.0 km, where it is replaced by relatively flat sandy plain

(ca. 40 m a.s.l.). Soils are nutrient-poor white sand Neosols

with patches of red or yellow Latosols (SUDENE/DNPEA

1971). Climate is tropical with a severe dry season (Aw,

Peel et al. 2007). Mean annual temperature is 26 �C and

mean annual precipitation is 1746 mm, concentrated

between March and August (INMET 2014). Vegetation is a

mosaic of herbaceous, scrub and restinga forest. Myrtaceae

is the most abundant family in the study area. See Silva

et al. (2015) for a complete description of the plant com-

munity and list of woody species in the studied area.

Data collection

Data were collected in 85 (5 9 5 m) plots randomly dis-

tributed along 17 (100 m long) transects (five plots per

transect). Transects were placed perpendicularly to preex-

isting trails scattered through the distinct physiognomies. All

woody plants with diameter at soil level C3.0 cm were

numbered and measured for diameter at soil level, total

height, number of ramets, and visually classified as Leaning

when leaning at an angle C45�. Litter depth was measured to

the nearest cm at the corners of each plot. Seedling density as

well as grass and forb cover were measured at 1 m2 smaller

plots. We also estimated canopy openness through three

nonoverlapping wide-angle digital photographs at each plot.

The photographs were taken using a 16-mm lens and a digital

still camera (Sony a57, Sony Corporation, Japan) during

uniformly overcast sky conditions.

Three topographic attributes were obtained for each plot:

elevation, terrain convexity, and slope. For the calculation of

elevation and convexity, 20 9 20 m virtual areas sur-

rounding each 5 9 5 m plot were used based on a Google

Earth 7.1.2.2014 satellite image. Elevation of a plot was

defined as the mean of the elevation values at its center and

four corners. Convexity was the elevation of the center of

the plot of interest minus the mean elevation of the four

corners (Legendre et al. 2009). Slope was measured in field

with a HEC Haglof digital clinometer perpendicular to the

elevation contour within each plot midline. Several edaphic

attributes were also collected. pH, Ca, Mg, Na, K, P, total N,

cation exchange capacity, organic matter, silt ? clay con-

tent, soil density, and moisture were obtained from three

1-kg soil surface samples (0–20-cm depth) collected using a

soil auger near the corners of each plot. The samples were

then bulked and subsampled to form 1-kg sample per plot.

The organic matter layer was removed before sampling. The

606 A. C. Silva et al.

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laboratory methods used for soil analyses are detailed in

EMBRAPA/CNPS (1997). See Silva et al. (2015) for a

complete description of the abiotic setting.

Data analysis

To analyze the effect of environmental variables on veg-

etation structure, we conducted a principal components

analysis (PCA, function ‘principal’ from package ‘psych’

in software R 2.15.3) on a correlation matrix using vege-

tation structure descriptors (woody plant density, basal

area, average height, inclined plant, ramifications, forbs,

grassy, canopy openness, seedlings, cactus, and leaf litter

depth).Variables were log- or square root transformed

when necessary to achieve normality and standardized

prior to analysis. Significance of component loadings was

obtained from Hair et al. (1998) based on sample size

needed to attain significance based on a 0.05 significance

level, a power level of 80 % and standard errors assumed to

be twice those of conventional correlation coefficients.

Each PCA axis values were plotted considering longitude

and latitude as x-axis and y-axis in scatterplots to display

spatial variations in vegetation structure among sites, using

Rstudio Program v. 2.15.3.

We used stepwise standard least-squares multiple

regression to assess the effects of edaphic and topographic

variables on principal components. Variable selection was

performed using the Akaike Informaton Criterion through

function stepAIC of MASS library using direction ‘‘both,’’

and they were considered only for the final model of the

variables with a significance level of\5 % (R Core Team

2013). When more than one explanatory variable was

included in the model, both partial and standardized partial

regression coefficients are shown, since the latter can be

compared directly to show the relative standardized

strengths of the effects of several independent variables on

the same dependent variable.

Results

The soil and topographic environment

The soils of the study area have high sodium content, are

acidic, have low fertility, and have a mainly sandy texture

(Supplementary Material Table 1). Higher plots showed

looser soils that were also richer in aluminum and had

higher mineral nutrients as showed by positive correlations

between elevation and cation exchange capacity and sum

of bases. Organic matter was weakly related with elevation

but seemed to play an important role in nutrient availability

due to its positive correlation with most of mineral ion

concentrations, as well as with cation exchange capacity

and calcium in these environments. Soils with greater

Fig. 1 Geographical location of the study area in northeastern Brazil and the spatial distribution of the study transects in coastal vegetation areas.

Shades of gray correspond to mobile dunes (light gray), scrub (medium gray) and forest (dark gray) physiognomies

Determinants of variation in heath vegetation structure on coastal dune fields in… 607

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silt/clay content were found in the lower plots (silt/clay

fraction was negatively correlated with elevation, Table 1).

Soil nutrients were, however, not related to silt/clay con-

tent, but were found mostly on dune slopes, as depicted by

the correlations of cation exchange capacity and phos-

phorus with elevation and slope, and the indirect relation-

ship between sum of bases and elevation through its

correlation with cation exchange capacity (Table 1). Soil

moisture increased with elevation, as well as sodium con-

tent. Soil nutrients (sum of bases and cation exchange

capacity) were lower in plots with higher density soils.

Vegetation structure

Vegetation descriptors revealed an overall short

(3.04 ± 1.50 m) and considerably open canopy

(36 % ± 0.32 canopy openness) (Table 1). The number of

stems was larger (20.17 ± 15.99 ramets/plot) than the

number of individuals (12.70 ± 8.88 ind./plot), and stem

leaning was also frequent among individuals

(0.32 ± 0.27 % ind./plot). The herbaceous covers were

quite variable, respectively, forbs (0.05 ± 0.17 % cover)

and grassy (0.03 ± 0.10 % cover). The soil was covered by

an average cm deep leaf litter (3.28 ± 2.51 cm).

Three PCA axes summarized the 11 vegetation structure

descriptors, explaining together 69 % of the variation in the

data (Table 2). The first axis alone explained 41 % of the

total variation and was correlated with most variables

(Fig. 2): number of ramets (0.91), wood plant density (0.90),

basal area (0.82), tree height (0.78), litter depth (0.68), and

canopy openness (-0.89) (Table 2). The second axis

described a gradient in herbaceous cover, with positive

correlationswith grass (0.80) and forb (0.79) cover. The third

axis was positively correlated with the number of leaning

stems (0.76) and cactus basal area (0.55), and negatively

correlated with the number of woody plant regeneration

(-0.60). When PCA axes were plotted to display spatial

variation in vegetation structure among sites, high similari-

ties in restinga physiognomies at short scales were shown

(distances among plots or transects) that tended to decrease

at larger scales (distances among areas), as well as nonlinear

vegetation gradients along shore to inland (Fig. 3).

Relationship between abiotic factors and vegetation

structure

A large portion of the variation (57.3 %) in the main

vegetation structure gradient (PCA1) was explained by

variation in soil pH, Ca, total nitrogen, moisture, and with a

minimum contribution by elevation (F = 23.5;

P = 2.2 9 10-14) (Table 3). The vegetation complexity

and biomass depicted by this axis was explained by more

acidic soils (-0.51), richer in the macronutrients Ca (0.94)

and total N (0.48), and with higher soil moisture (0.27).

Even with a statistical significance of elevation to increase

the woody layer, it was not a biological significance (0.01).

Variation in herbaceous cover (PCA2) was weakly

(16.5 %) explained by the combined soil texture fractions

of silt and clay (-0.01), while the variation of cacti and

plants in the third inclined shaft PCA was weakly (6 %)

elevation explained by (-0.02).

Discussion

A growing body of research has shown that topographic

and soil conditions frequently act together not only in the

assembly of plant communities but also in the determina-

tion of vegetation structure (Lane et al. 2008; Colgan et al.

Table 1 Averages and standard deviations of measured topographic,

soil, and vegetation structure variables

Variable Value

Vegetation structure

Woody plant basal area (m) 0.06 ± 0.06

Cactus basal area (m) 0.001 ± 0.003

Wood plant density (ind./plot) 12.70 ± 8.88

Seedling density (ind. m2) 21.55 ± 32.81

Leaning plants (% ind./plot) 0.32 ± 0.27

Ramet density (ramets/plot) 20.17 ± 15.99

Height (m) 3.04 ± 1.50

Grass cover (% cover) 0.03 ± 0.10

Forb cover (% cover) 0.05 ± 0.17

Leaf litter depth (cm) 3.28 ± 2.51

Canopy openness (% pixels) 0.36 ± 0.32

Soil and topography

pH 5.64 ± 0.49

Humidity (%) 4.07 ± 2.32

Density (kg dm-3) 1.34 ± 0.08

Ca (cmolc dm3) 0.38 ± 0.21

Mg (cmolc dm3) 0.33 ± 0.24

Na (cmolc dm3) 0.13 ± 0.08

K (cmolc dm3) 0.09 ± 0.06

P (cmolc dm3) 16.59 ± 14.36

Total N (cmolc dm3) 0.67 ± 0.45

Sum of bases (cmolc dm3) 0.93 ± 0.39

Cation exchange capacity (cmolc dm3) 4.48 ± 2.21

Silt ? clay content (g kg-1) 40.88 ± 22.38

Organic matter (g kg-1) 23.80 ± 12.38

Slope (�) 9.38 ± 8.97

Elevation (m) 43.20 ± 16.11

Convexity (m) 0.41 ± 1.82

The table lists the variables collected accompanied by his unit of

measure and their respective means and standard deviations

608 A. C. Silva et al.

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2012; Lin et al. 2012; Moeslund et al. 2013; Rosenfield and

Souza 2014). Contrary to results from other restingas, in

which both topography and soil characteristics showed to

be determinants of vegetation variation (Scarano 2002;

Guedes et al. 2006; Assis et al. 2011; Santos Filho et al.

2013; Silva et al. 2015), vegetation structure in the study

area was shown to be only affected by a reduced number of

soil factors. This result contradicts our first hypothesis,

which stated that both soil and topographic factors are

important to explain variation in vegetation structure at

local scale. It is likely that variations in vegetation structure

are composed of the differential responses of particular

species or species groups to edaphic variation (Petersen

and Drewa 2009), as the abundance of species of distinct

sizes and architectures respond in different ways to varia-

tions in the abiotic setting. Furthermore, the reduced

heterogeneity of topographic and edaphic characteristics in

the study area relative to the woody vegetation are

attributable to the young age of the sand dunes we studied

(Gomes et al. 2007; Assis et al. 2011; Lima et al. 2011).

We found that forested physiognomies in the heath

vegetation, as depicted by higher values in the first PCA

axis, was favored by more acidic soils richer in calcium

and total nitrogen. This is attributable to organic matter

accumulation in the forested areas, with the consequent

production of organic acids and biomass build up (Queiroz

et al. 2012). The fact that these same factors were not

relevant for the herbaceous cover in general confirms our

second hypothesis, namely, herbaceous plants, cactus, and

woody plants show differential responses to soil and

topographic variations (Moeslund et al. 2013; Santos Filho

et al. 2013; Tissier et al. 2013). These results indicate that

distinct plant groups and vegetation layers respond in very

different ways to the same edaphic gradients found in the

coastal sand plains, and thus that distinct mechanistic

explanations should be developed for each of these groups

(Petersen and Drewa 2009).

Woody plants on steep fields are exposed to varying

degrees of wind exposure, solar incidence, and water

availability that are to a large extent explained by undu-

lated topography (Moeslund et al. 2013). Windward slopes

and dune tops on coastal fields tend to lose water, nutrients,

and organic matter faster than more protected microsites

like leeward slopes of valleys between adjacent dunes

(Scarano 2009; Giaretta et al. 2013). Furthermore, low

wind erosion and stress in protected areas reduce physical

damage to established plants as well as promotes natural

regeneration (Moeslund et al. 2013). An increase in woody

plant biomass has been found to accompany increased soil

organic matter in plots further inland in other restingas

(Cordeiro 2005). In our study area, our results suggest two

possible explanatory scenarios: (1) topography influences

variation in soil properties which in turn shapes vegetation

structure; (2) edaphic variation results from a reinforce-

ment mechanism produced by the vegetation itself with

topographic effects reduced to its effect on vegetation due

to wind and moisture variations.

Fig. 2 Principal components ordination of restinga heath vegetation

in Parnamirim, northeastern South America. Dots correspond to plots

and arrows correspond to environmental variables

Table 2 Main results of the Principal Components Analysis on

vegetation structure variables

Axis 1 Axis 2 Axis 3

Eigenvalue 4.52 1.68 1.40

Explained variance (%) 41 15 13

Component loadings

Plant basal area 0.82 -0.22 -0.06

Cactus basal area -0.05 0.02 0.55

Plant density 0.90 0.04 -0.13

Seedlings density 0.50 0.14 -0.60

Leaning plants 0.20 0.24 0.76

Ramets density 0.91 -0.04 -0.07

Height 0.78 -0.35 -0.05

Grass cover -0.15 0.80 0.02

Forb cover -0.20 0.79 0.14

Leaf litter 0.68 -0.34 0.32

Canopy openness -0.89 0.25 -0.07

Eigenvalues and percentage of the variance explained by each axis of

the PCA are shown, as well as loading components (eigenvectors) for

each variable associated to each axis

Bold figures represent significant component loadings

Determinants of variation in heath vegetation structure on coastal dune fields in… 609

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Soil acidity and salinity have been shown to play an

important role in the distribution of coastal herbaceous

plants (Magnago et al. 2010). Although herbaceous plants

in coastal regions frequently present strategies to deal with

increased soil salinity, too salty soil patches are frequently

found to reduce their abundance and occurrence, due to

root development impairment and consequent reduced

access to soil resources (Guedes et al. 2006). Acidic soils

reduce the availability of Ca, Mg, K, and P while

increasing the availability of compounds potentially toxic

to plants (Brady and Weil 2013). The lack of response of

the herbaceous cover to the topographic and soil factors we

measured suggests the existence of unmeasured gradients

in the study area, such as light and temperature gradients

(Pires et al. 2006; Guedes et al. 2006; Giaretta et al. 2013).

This result contradicts our third hypothesis, which stated

that soil acidity and salinity are more important determi-

nants of herbaceous cover than woody plant variation due

to the closer association of the former with open, near-

shore areas, and possible nutritional reduction and damage

that these the soil components can cause to the root system

these of plants (Guedes et al. 2006; Lane et al. 2008;

Petersen and Drewa 2009).

Besides being associated with abiotic variables, vege-

tation structure has been shown to be associated with sig-

nificant compositional (Sundarapadian and Swamy 1999;

Peterson and Reich 2008; Gao et al. 2014) and productivity

gradients (Clark and Clark 2000). Biomass variation has

important implications for the structure of trophic webs

(Uetz 1991) and for the provisioning of distinct habitat

types for the fauna (Tews et al. 2004; Dıaz 2006; Moore

et al. 2014). These effects seem to be stronger in olig-

otrophic ecosystems like heath vegetation (Oliveira Filho

et al. 2013). Vegetation complexity usually accompanies

variation in vegetation biomass, and modulates the

ecosystem services provided by the vegetation like the

fixation of sand dunes (Menezes and Araujo 2000; Tabalipa

and Fiori 2008). All the above-mentioned effects make

vegetation structure an important plant community

descriptor, mainly when considering the reduced sampling

effort needed to study vegetation structure relative to spe-

cies composition (Werger and Sprangers 1982).

Our results suggest that the combined effect of topog-

raphy and soil nutrients, which has been shown to produce

structuring effects on the heath vegetation floristic gradi-

ents, is not important in the shaping of vegetation structure.

Fig. 3 Spatial distributions of

a sample plots. Ellipses indicate

clusters of transects used as

small scales; b magnitude of

variation in the structure

according to the size of the

circles related to PCA axis 1,

and the woody, respectively;

c variation structural relative to

the axis 2, concerning

herbaceous plants; d variations

related to axis 3, cactus, and

leaning plants in the Parnamirim

restinga

610 A. C. Silva et al.

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Calcium and nitrogen showed to be limiting factors for the

development of vegetation structure and biomass build up

in the restinga. Woody and herbaceous plants show dif-

ferential responses to the same abiotic gradients found in

the restinga. Herbaceous plants seem to respond to envi-

ronmental gradients other than those related to topographic

and soil nutrient ones. It seems that a reduced number of

edaphic factors promote the variation in vegetation struc-

ture in the restinga heath vegetation.

Acknowledgments Financial support was provided by Fundacao de

Apoio a Pesquisa do Estado do Rio Grande do Norte (FAPERN)

through the Grant Edital N8005/2011 – Programa Primeiros Projetos

IV, by CAPES through a master scholarship to JLAS, and by CNPq

through a scientific initiation scholarship to ACS. The authors thank

the Brazilian Air Force, Aviator Cel. Luiz Guilherme S. Medeiros,

and Glauberto Leilson for facilitation of access to the Barreira do

Inferno Launch Center. The authors are thankful to Amarilys D.

Bezerra, Atila D.E. Melo, Angelica A. Souza, and Morvan Franca for

their invaluable help in the field. Comments by Adriano C. F. Silva

and Luiz A. Cestaro greatly helped to improve an earlier version of

this manuscript.

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Table 3 Multiple regression models between vegetation structure principal components and topographic and soil variables

Coefficients Estimate std. Error t value P([|t|)

PCA1 (initial model AIC: 185.79; final model AIC: AIC: 176.76)

(Intercept) 2.38 1.14 2.08 0.039*

Elevation (m) 0.01 (0.02) 0.00 3.12 0.002**

pH -0.51 (-0.79) 0.16 -3.13 0.002**

Ca (cmolc dm3) 0.94 (0.98) 0.36 2.60 0.01*

Humidity (%) 0.27 (0.24) 0.08 3.15 0.002**

Total N (cmolc dm3) 0.48 (0.47) 0.11 4.25 0.000***

R2 adjusted = 0.57; F = 23.5; P = 2.2 9 10-14

PCA2 (initial model AIC: 241.49; final model AIC: 229.89)

(Intercept) 0.75 0.20 3.67 0.000***

Silt ? clay (g kg-1) -0.01 (-0.01) 0.00 -4.19 0.000***

R2 adjusted = 0.16; F = 17.57; P = 6.8 9 10-5

PCA3 (initial model AIC: 250.71; final model AIC: 239.42)

(Intercept) 0.74 0.30 2.46 0.01*

Elevation (m) -0.02 (-0,02) 0.00 -2.62 0.01*

R2 adjusted = 0.06; F = 6.90; P = 0.010

Standardized regression coefficients and nonstandard (in parentheses) are shown, with significant values detached by asterisks

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