REPORT

Small-scale spatial variability of soil properties in a Koreanswamp

Tae Kyung Yoon • Nam Jin Noh • Saerom Han •

Hanbin Kwak • Woo-Kyun Lee • Yowhan Son

Received: 23 October 2012 / Revised: 4 September 2013 / Accepted: 23 October 2013

� International Consortium of Landscape and Ecological Engineering and Springer Japan 2013

Abstract Wetland soils have distinctive biogeochemical

processes and ecosystem functions. Therefore, knowledge

of wetland soils is important for conserving and rehabili-

tating wetland ecosystems. We investigated soil properties

and their spatial variability in a temperate swamp and

compared them with those of an adjacent upland within a

small-scale watershed in Korea. Soil water content and

carbon and nitrogen concentrations were two- to four-times

higher in wetland than in upland soils. Soil water content

and organic matter, which represented a large proportion of

the variability of wetland soil properties, could be con-

sidered primary soil quality indicators for wetland soils.

Wetland soils were characterized as having high spatial

variability and moderate to strong spatial autocorrelation

within a 30- to 50-m range. Nutrient availability was

mainly regulated by soil water content and organic matter,

not by pH, which had low variability and showed an

independent pattern. These findings imply that wetland

soils should be surveyed using an appropriate sampling

design to determine characteristics of spatial variability in

soil quality indicators in wetlands. Reference values of

wetland soil properties reported from this study are

expected to contribute to wetland conservation and

rehabilitation.

Keywords Factor analysis � Geostatistics �Reference wetland � Semivariogram � Soil quality

indicator � Soil survey

Introduction

Wetlands provide valuable ecosystem functions and ser-

vices, including providing resources, regulating hydrolog-

ical regimes and pollution, and supporting biodiversity and

nutrient cycles [Millennium Ecosystem Assessment (MEA)

2005; Mitsch and Gosselink 2007]. The Global Lakes and

Wetlands Database (Lehner and Doll 2004) estimates that

wetlands in Asia cover approximately 2.8 million km2,

which is 6.3 % of the land surface area. Despite a lack of

historical record, it is believed that Asian wetlands have

been intensively degraded and lost due to agriculture and

settlement since the preindustrial era (Finlayson and Spiers

1999; MEA 2005). Rice paddies represent a distinctive

pattern of wetland use in East Asian landscapes (Verho-

even and Setter 2010). With the rising awareness of the

importance of wetlands, significant efforts have been made

to conserve and rehabilitate these ecosystems in East Asia

(Zhao et al. 2006).

Soil properties have been widely investigated from

regional to national scales to determine the fundamental

land environment and fertility of various ecosystems

according to the diverse interests of land managers and

policy makers. In the last few decades, the importance of

surveying soil properties has been emphasized in terms of

soil quality, which is defined as the ‘‘capacity of soil to

function’’ (Karlen et al. 1997; Schoenholtz et al. 2000).

Several studies have sought to define appropriate soil-

quality indicators (SQIs) for assessing soil function and

health (e.g., Karlen et al. 2008; Schoenholtz et al. 2000).

T. K. Yoon � S. Han � H. Kwak � W.-K. Lee � Y. Son (&)

Department of Environmental Science and Ecological

Engineering, Graduate School, Korea University,

Seoul 136-713, Republic of Korea

e-mail: yson@korea.ac.kr

N. J. Noh � Y. Son

Institute for Basin Ecosystem Studies, Gifu University,

Gifu 501-1193, Japan

123

Landscape Ecol Eng

DOI 10.1007/s11355-013-0236-5

Further, conservation programs and practices are required

to monitor soil properties as well as other management

targets, including endangered species, biodiversity, and

habitat. However, knowledge of wetland soil properties is

limited, unlike agricultural or forest soil properties, which

have been widely studied. Understanding wetland soils is

essential to not only describe their status but also to analyze,

manage, and rehabilitate their distinctive ecosystem func-

tions. Moreover, wetland status references including soil

properties are required for establishing goals for wetland

rehabilitation activities in East Asia. Their unique biogeo-

chemical processes (e.g., methane emission and denitrifi-

cation) and ecosystem functions (e.g., carbon assimilation

and nutrient retention) differentiate wetlands from terres-

trial ecosystems. For example, Raymond et al. (2013)

reported that soil respiration was higher in a wetland than in

an upland, whereas Zak and Grigal (1991) found remark-

ably lower nitrogen mineralization in a wetland compared

with an upland. Although several studies have compared

soil properties between artificial and reference wetlands in

North America (e.g., Bishel-Machung et al. 1996; Stolt

et al. 2000), comprehensive data and understanding of

wetland soil properties in East Asia are limited due to a lack

of interest and available reference wetlands.

Within the finite spatial and temporal scale of a soil

survey, soil science is challenged by quantification and

prediction of variability in soil properties (Heuvelink and

Webster 2001; Mulla and McBrateny 2000). Understand-

ing spatial variability is critical to predictive soil mapping

and soil landscape modeling (Gessler et al. 1995; Hen-

derson et al. 2005; Ryan et al. 2000; Scull et al. 2003).

Descriptive statistics, such as the coefficient of variation

(CV) and standard deviation (SD), are classic measures of

spatial variability and geostatistics that provide the theo-

retical basis for spatial autocorrelation. Semivariogram, a

geostatistical technique, can quantify and model spatial

autocorrelation (spatial dependence), and the results of

semivariograms can be applied to interpolation methods

such as kriging.

This study aimed to elucidate soil properties and spatial

variability in a swamp by comparing those of an adjacent

upland in the same small-scale watershed. Specifically, our

objectives were to: (1) investigate soil properties in a well-

preserved swamp; (2) determine appropriate SQIs using

correlation and factor analyses; and (3) quantify spatial

variability in soil properties using geostatistical analysis.

To increase our knowledge of wetland soils, we tested the

following hypotheses: soil water content (SWC) and

organic matter would be representative properties of wet-

land soils; wetland soils would have high spatial variability

and strong autocorrelation; pH would regulate nutrient

availability. To achieve these objectives, differences in soil

properties and spatial variability between a swamp and an

adjacent upland were examined to reveal the distinctive

characteristics of wetland soils.

Materials and methods

Study area

This study was conducted in Heonilleung Ecosystem

Landscape Conservation Area (HELCA; 37�2705200N,

127�0405300E, 40–60 m a.s.l.) located in a small-scale

watershed (5.6 ha) on the southern slope of Mt. Daemo in

Seoul, Korea. This area, which surrounds two royal tombs

of the Joseon Dynasty (from the late fourteenth to late

nineteenth century), has been well preserved by the royal

court since the fifteenth century. In general, landscapes

surrounding the royal tombs are among the least-disturbed

ecosystems in Korea [e.g., Gwangneung Forest listed as

United Nations Educational, Scientific and Cultural Orga-

nization (UNESCO) Biosphere Reserve] and support many

ecological studies (e.g., studies in Gwangneung KoFlux

Supersite). Despite insufficient past records of HELCA,

historic photographs taken in the 1920s (Government-

General of Korea 1931) and aerial photographs taken

annually since the 1970s (http://gis.seoul.go.kr) certify the

well-preserved status of this area at least during the last

100 years. Recognizing the value of the long history of

preservation of this area, the Seoul Metropolitan Govern-

ment designated it as HELCA in 2005, and ecosystem-

monitoring programs have been conducted since then. A

Quercus aliena community and a partial Pinus densiflora

community dominate the upland area, whereas an

Alnus japonica community dominates the swamp area at

the bottom of the slope (Kim 2010). Structure and species

composition of HELCA represents a typical landscape

based on principles of Korean Bi-bo Feng-Shui (Whang and

Lee 2006). A. japonica is a typical species that is tolerant to

extremely hydric conditions in East Asian swamp forests

(Fujita and Fujimura 2008; Kim et al. 2005). Therefore, we

assumed that this study site could represent the reference

condition for soil properties in a typical swamp forest. Here,

‘‘reference condition’’ refers to the least-disturbed condi-

tion, which describes ‘‘the best available habitat conditions

given today’s state of the landscape,’’ rather than to other

definitions such as minimally disturbed, historical, or best-

attainable condition (see Stoddard et al. 2006). Mean annual

temperature at the site is 12.2 �C (monthly range -2.5� to

25.4 �C), and annual precipitation is 1,344 mm.

Soil sampling and analysis

We selected 19 plots from the swamp area and 29 from the

upland area based on topography and vegetation (Figs. 1a,

Landscape Ecol Eng

123

b). In August 2008, three soil cores (5-cm diameter) were

sampled from the top 15 cm in each 10-m 9 10-m plot

(Fig. 1c). Mean distance between soil sampling in each

plot was approximately 6 m. After sampling, SWC was

immediately determined on a gravimetric basis. Next, soil

samples were air dried, crushed, and sieved (\2 mm). Soil

pH was determined using a 1:5 soil-to-water ratio. Total

carbon (C) and nitrogen (N) concentrations were deter-

mined by dry combustion with an elemental analyzer

(Vario Macro CN analyzer; Elementar Analysensysteme

GmbH, Germany). Available phosphorous (P) was extrac-

ted using the Bray No. 1 method (Bray and Kurtz 1945).

Cation exchange capacity (CEC) was measured by the

Brown method (Brown 1943), which is a pH-based mea-

surement of exchangeable hydrogen and base-cation

charge equivalents from extraction with 1 N ammonium

acetate (pH 7.0) and acetic acid (pH 2.31), respectively.

Exchangeable cations extracted by ammonium acetate

were determined using inductively coupled plasma-optical

emission spectrometry (Vista PRO, Varian, USA).

Statistical analysis

Descriptive statistics, correlation tests, factor analyses, and

geostatistical analyses were conducted using PROC

MEANS, PROC CORR, PROC FACTOR, and PROC

VARIOGRAM, respectively, of SAS 9.2 software (SAS

Institute Inc. 2009). Mean, SD, CV, median, minimum,

maximum, and skewness of soil properties were deter-

mined in each upland and swamp area. Soil properties that

were not normally distributed as determined by a Shapiro–

Wilk test were log-transformed. Soil pH values were

directly applied into statistical analysis without [H?]

transformation, which would result in erroneous analysis in

practical and statistical principles (see Boyd et al. 2011).

Pearson’s correlation coefficients were determined to ana-

lyze relationships among soil properties. Factor analysis

using varimax rotation was performed to identify principal

components with eigenvalues higher than approximately 1,

representing soil properties. Spatial variability and patterns

of soil properties were determined from CV, Moran’s I,

and semivariograms. Moran’s I coefficient is a measure of

spatial autocorrelation and ranges from -1 to ?1, indi-

cating negative to positive autocorrelation. A semivario-

gram shows the dissimilarity of values sampled at an

increasing lag distance from one another. The theory

behind semivariograms is well described in several papers

(e.g., Burrough et al. 1997; Ettema and Wardle 2002;

Heuvelink and Webster 2001; Mulla and McBrateny 2000),

and we briefly introduce the key approaches for semivari-

ograms here. A semivariogram is calculated using the

following equation:

c hð Þ ¼ 1

2N hð ÞXn

i¼1

zðxiÞ � zðxi þ hÞ½ �2

where c(h) is the semivariance at lag distance h; N(h) is the

number of pairs separated by lag distance h; z(xi) is the

observed value at location xi; and z(xi ? h) is the observed

value at a location at distance h from xi. In other words,

semivariogram provides a degree of variability between

observed values at the given lag distance. Semivariogram

plots semivariance values at different lag distances. The

spherical model is commonly applied to fit the

semivariance, as follows:

Fig. 1 Location of the study site (a, b) and sampling points (c)

Landscape Ecol Eng

123

c hð Þ ¼ C0 þ C 1:5h

a� 0:5

h

a

� �3" #

for 0� h� a;

and

c hð Þ ¼ C0 þ C; for h [ a

where C0 is the nugget; C is the partial sill; and a is the

range, which indicates the lag distance at which the

semivariance reaches an asymptote (sill: C0 ? C). The

nugget, sill, and range from the experimental semivario-

gram present information on the spatial distribution of soil

properties. The nugget, which is the semivariance value at

a zero lag distance, indicates the differences between

observed values at short distances due to errors in mea-

surement or small-scale variability, although the nugget

should theoretically be zero. Spatial autocorrelation is

defined as the range of lag distances when the semivariance

reaches the sill. Spatial autocorrelation dependency was

determined from the nugget-to-sill ratio and was classified

as weak ([0.75), medium (0.75–0.25), or strong (\0.25),

according to Cambardella et al. (1994).

Results

Mean SWC (1.32 mg mg-1) and C (52.37 g kg-1) and N

(5.23 g kg-1) concentrations of wetland soils were about 4,

2.5, and 2 times higher than those of upland soils,

respectively (P \ 0.0001; Table 1) and their ranges—

0.28–5.32 mg mg-1, 9.29–235.05 g kg-1, and 1.90–17.01

g kg-1, respectively—were wider than those in upland

soils. Mean pH was similar between wetland (5.29) and

upland (5.14) (P = 0.10), but range was wider for upland

(3.96–7.16) than for wetland (4.39–6.23). P concentration

was similar between wetland (49.73 mg kg-1) and upland

(54.72 mg kg-1); however, the variability of P concentra-

tion was higher in upland (CV: 109.61 %) than wetland

(CV: 42.75 %) soils. Cation concentrations, except for

Table 1 Descriptive analysis of topsoil (0- to 15-cm depth) properties of a wetland and an adjacent upland in a small-scale watershed in Korea

Number Mean Standard

deviation

CVa Median Min. Max. Skewness Moran’s

IbP valuec

pH Wetland 57 5.29 0.44 8.25 5.32 4.39 6.23 0.20 0.44 0.102

Upland 87 5.14 0.67 13.06 5.04 3.96 7.16 0.79 0.30

Soil water content

(mg mg-1)

Wetland 57 1.32 1.14 86.46 0.95 0.28 5.32 1.92d 0.73 \0.0001

Upland 87 0.35 0.15 43.03 0.30 0.12 0.83 1.53d 0.44

Cation exchange capacity

(Cmolc kg-1)

Wetland 57 15.50 5.41 34.89 14.96 7.04 28.60 0.48 0.45 \0.01

Upland 87 12.86 2.85 22.18 12.76 5.06 19.58 0.14 0.28

C (g kg-1) Wetland 57 52.37 45.27 86.43 38.18 9.29 235.05 1.84d 0.47 \0.0001

Upland 87 19.17 12.78 66.67 16.45 4.11 71.33 1.56d 0.33

N (g kg-1) Wetland 57 5.23 3.18 60.88 4.32 1.90 17.01 1.49d 0.44 \0.0001

Upland 87 2.45 0.83 34.03 2.26 1.25 5.32 1.12d 0.40

P (mg kg-1) Wetland 53 49.73 21.26 42.75 45.41 6.53 125.97 1.09d NS 0.026

Upland 82 54.72 59.98 109.61 45.41 0.34 418.37 3.66d 0.45

Ca (mg kg-1) Wetland 57 2,224 1,543 69.38 1,707 148 6,911 1.31d 0.64 \0.0001

Upland 87 1,067 1,072 100.50 705 50.16 6,402 2.82d NS

K (mg kg-1) Wetland 56 211.71 436.23 206.05 96.58 52.91 2863.08 4.88d NS 0.046

Upland 86 507.03 1,494.10 294.68 67.44 16.51 10,642 4.91d 0.50

Mg (mg kg-1) Wetland 57 164.08 106.56 64.94 140.19 23.10 516.44 1.58d 0.80 \0.01

Upland 86 124.15 81.50 65.65 102.97 13.65 379.92 1.10d NS

Na (mg kg-1) Wetland 57 153.69 92.57 60.23 127.78 49.19 433.33 1.20d 0.53 \0.0001

Upland 86 65.40 74.42 113.81 46.37 14.50 554.44 4.33d NS

C carbon, N nitrogen, P phosphorous, Ca calcium, K potassium, Mg magnesium, Na sodium, NS not significanta CV Coefficient of variation (%)b Moran’s I coefficients that were significant at P \ 0.05 are listedc P values for t tests between a wetland and an upland. Except for pH and cation exchange capacity, all soil properties were log-transformed

before analysisd Data not normally distributed (a = 0.05)

Landscape Ecol Eng

123

calcium (Ca), were higher in wetland than upland

(P \ 0.05). Ca, potassium (K), magnesium (Mg), and

sodium (Na) concentrations (mg kg-1) were 2,224, 211.71,

164.08, and 153.69 for wetland and 1,067, 507.03, 124.15,

and 65.40 for upland soils, respectively. CEC (Cmolc kg-1)

in wetland and upland soils was 15.50 and 12.86, respec-

tively (P \ 0.01). CV was largest for K concentration

(206.05 % for wetland and 294.68 % for upland) and

lowest for pH (8.25 % for wetland and 13.06 % for

upland). CVs for pH, P, Ca, K, and Na concentrations were

lower for wetland than for upland soils (Table 1). Moran’s

I coefficients of wetland soils were mostly higher than

those of upland soils. Among wetland soil properties, Mg

concentration (0.80), SWC (0.73), and Ca concentration

(0.64) exhibited strong positive autocorrelations (Table 1).

In wetland soils, significant correlations among SWC, C,

N, CEC, K, Mg, and Na were observed with generally

strong correlation coefficients (R = 0.67–0.99). In con-

trast, Ca and P were not correlated with any other soil

properties (Fig. 2a). In upland soils, significant correlations

among SWC, C, N, CEC, and Ca were observed but with

weaker correlation coefficients (R = 0.15–0.95) than those

of wetland soils (Fig. 2b). Soil pH was weakly correlated

with SWC, C, N, CEC, Ca, Mg, and Na in wetland soils

(R = 0.11–0.39; Fig. 2a) and with SWC, C, N, CEC, and

Ca in upland soils (R = 0.12–0.45; Fig. 2b).

Using factor analysis, we found that soil properties were

represented by three factors: SWC and organic matter

(factor 1), pH (factor 2), and P and K (factor 3) (Fig. 3).

The first three factors accounted for 85.6 % and 74.5 % of

the variance in wetland and upland soil properties,

respectively. In wetland soils, factor 1 explained 59.5 % of

the variance (eigenvalue 5.95), mainly related to SWC and

organic matter representing C, N, and CEC (Fig. 3a); factor

2, accounting for 16.3 % of the variance (eigenvalue 1.63),

represented pH (Fig. 3a); factor 3, accounting for 9.8 % of

the variance (eigenvalue 0.98), represented P and K

(Fig. 3b). The pattern of factor loading in upland soils was

similar to that in wetland soils; however, cations (Ca, Na,

and Mg) were grouped into factor 2 in upland soils

(Fig. 3c, d).

Wetland soil properties had higher spatial variability

than upland soils and moderate to strong spatial autocor-

relation within a 30- to 50-m range. Spatial autocorrelation

for SWC, C, and N was revealed in both wetland (Fig. 4a–c)

and upland (Fig. 4h–j); in the wetland only for CEC, Mg,

and Na (Fig. 4d–f); and in the upland only for pH and K

(Fig. 4g, k). Ranges for wetland soil properties, except Mg

(187 m), were relatively short (31–44 m), whereas those

for upland soil properties were greater ([80 m) (Fig. 4;

Table 2). In wetland soils, the nugget-to-sill ratio was

determined, and spatial classes for SWC (0.09) and Na

(0.20) were strong, whereas those for C (0.43), N (0.42),

CEC (0.33), and Mg (0.31) were moderate. In upland soils,

spatial classes for pH (0.51), SWC (0.41), and K (0.52)

were moderate (Table 2).

Discussion

General characteristics of wetland soil properties

in Korea

In general, the wetland soils at our study site were nutrient

rich with high organic matter (C and N) content and

moderate acidity compared with other wetlands in South

Korea (Cha et al. 2010; Koo 2001; Song et al. 2006). The

range of organic matter concentration in this study

(1.60–40.52 % based on an organic matter conversion

factor of 1.724; Table 1) was similar to that of a mountain

marsh in Mt. Jiri (18.60–43.04 %; Koo 2001), lower than

that of a swamp in Jangdo (20.60–72.40 %; Cha et al.

2010), and higher than that of a mountain marsh in Busan

(2.38–16.70 %; Song et al. 2006). These were much higher

than the reference value for Korean upland forest soils

(4.49 %; Jeong et al. 2002). The wetlands could be rich in

organic matter due to anaerobic conditions that reduce

decomposition rates. Higher concentrations of cations such

as Ca, Mg, and Na in the wetland compared with the

upland might be related to the higher organic matter con-

centration and CEC. However, K was lower in the wetland

(211.71 mg kg-1) than in the upland (507.03 mg kg-1); it

is assumed that K was readily leached under hydric soil

conditions. In addition, mean pH (5.29) measured in this

study was considerably similar to that of other studies (5.26

for Song et al. 2006 and 5.41 for Koo 2001), although pH

ranges differed among studies.

SWC and organic matter as SQIs in wetlands

Our results support the first hypothesis, that wetland soil

properties are mainly represented by the combination of

SWC and organic matter. Factor 1 included SWC and

organic matter and contributed to approximately 60 % of

the total variability of wetland soil properties, whereas

factor 2 (pH) and factor 3 (P and K) contributed to a rela-

tively small portion of the total variability (Fig. 3a, b). In

addition, the contribution of factor 1 to the total variability

of soil properties of the wetland was about two times higher

than that of the upland (Fig. 3a, c). The factor separation of

this study corresponded to that from five regions in New

Zealand, which grouped factor 1 as organic matter, factor 2

as physical properties, factor 3 as P, and factor 4 as acidity

(Sparling and Schipper 2002)—with the exception of

physical properties, which were not investigated in this

study. Organic matter is generally considered to be one of

Landscape Ecol Eng

123

pH

0.11SWC

0.11 0.81C

0.12 0.78 0.99N

NA NA NA NAP

0.19 0.79 0.91 0.92 NACEC

0.39 0.68 0.72 0.69 NA 0.67Ca

NA NA NA NA NA NA NAK

0.31 0.77 0.78 0.76 NA 0.76 0.94 NAMg

0.24 0.76 0.82 0.79 NA 0.81 0.87 NA 0.90Na

–1.0 1.0 1.0 2.5 10 25 0.4 0.7 4.0 5.5

4.5

6.0

–1.0

1.0

2.5

4.5

1.0

2.5

2.0

4.0

1025

57

0.4

0.7

3.5

5.5

4.5 6.0

4.0

5.5

2.5 4.5 2.0 4.0 5 7 3.5 5.5

pH

0.20SWC

0.23 0.73C

0.21 0.75 0.95N

NA NA NA NAP

0.12 0.52 0.57 0.54 NACEC

0.45 0.15 0.16 0.21 NA 0.21Ca

NA NA NA NA NA NA NAK

NA NA NA NA NA NA NA NAMg

NA NA NA NA NA NA NA NA

–2.0 –0.5 0.5 1.5 5 15 0.0 0.6 3 5

4.0

6.0

–2.0

–0.5

1.5

3.5

0.5

1.5

–13

6

515

46

8

0.0

0.6

2.5

4.5

4.0 6.0

35

1.5 3.5 –1 3 6 4 6 8 2.5 4.5

NANa

a

b

Fig. 2 Soil properties of a

wetland (a) and an adjacent

upland (b). Solid curves on the

upper panels are locally

weighted scatterplot smoothing

(LOESS) smoothers. Numbers

on the lower panels are

correlation coefficients. The

diagonal cells show histograms

for each soil property. All soil

properties except pH and CEC

were log transformed for

normalization. SWC soil water

content, CEC cation exchange

capacity, NA correlation

coefficient not available

(P [ 0.05)

Landscape Ecol Eng

123

the important SQIs (Schoenholtz et al. 2000), and Larson

and Pierce (1994) suggested that organic matter should be

included in the minimum data set for soil quality assess-

ment. Shukla et al. (2006) reported that organic C was the

most dominant SQI at the North Appalachian Experimental

Watersheds in Ohio, USA. Although other studies did not

identify SWC as an SQI (e.g., Schoenholtz et al. 2000),

hydrological conditions represented by SWC are the main

regulators of biogeochemical processes, especially regard-

ing the accumulation of organic matter in wetland soils

(Vepraskas and Faulkner 2001). Indeed, the pattern of SWC

was highly correlated with those of C and N, and they were

coupled as factor 1 in this study. Thus, we conclude that

SWC and organic matter, which are dominantly affected by

SWC, could be primary SQIs in wetlands.

Variability and spatial pattern of soil properties

Wetland SWC and organic matter (C and N) had high

variability (Table 1) and moderate (C and N) to strong

(SWC) spatial autocorrelation within a relatively narrow

range (\50 m) (Table 2), supporting our second hypothe-

sis. The high Moran’s I coefficients and the narrow auto-

correlation ranges for these soil properties indicate a highly

clustered pattern. The high variability observed in wetland

soil properties may be due to spatially complex interactions

among hydrology, flooding, nutrient retention, surface

runoff and erosion, and plant distribution (Bruland and

Richardson 2005; Stolt et al. 2001). The large variability in

organic matter content could be explained by the inher-

ently higher spatial variability of biologically controlled

0.2

0.4

0.6

0.8

Factor 1 (59.5%)

Facto

r3

(9.8 %)

pH

K

NaCaMg

CECSWC

NC

P

-0.6-0.8

-0.2

-0.4

-0.6

-0.8

0.2

0.4

0.6

0.8

Factor 1 (59.5%)

0.20.40.60.8

Fa cto

r2

(1 6.3%)

0.2

0.4

0.6

0.8

pH

K Na

CaMg

CECSWC

N CP--

-

-

-

-

0.2

0.4

0.6

0.8

Factor 1 (35.9%)

0.20.40.60.8

Fa cto

r2

(2 6.8%)

0.2

0.4

0.6

0.8

pH

K

NaCa

Mg

CECSWC

NC

P

--

-

-

-

-

0.2 0.4 0.6 0.80.2 0.4 0.6 0.8

0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8

0.2

0.4

0.6

0.8

Factor 1 (35.9%)

0.20.40.60.8

Fac to

r3

(11 .8%)

0.2

0.4

0.6

0.8

pH

K

Na

CaMg

CEC

SWC NC

P

--

-0.4 -0.2- -

- - - -

-

-

-

-

ba

dc

Fig. 3 Factor analysis for soil properties of a wetland (a, b) and an

adjacent upland (c, d) in a small-scale watershed in Korea. Numbers

in parentheses on each axis indicate the proportion of total variance

explained by each factor. All soil properties except pH and CEC were

log transformed for normalization. SWC soil water content, CEC

cation exchange capacity

Landscape Ecol Eng

123

elements than geologically controlled elements (Gallardo

2003). Autocorrelation ranges of SWC and organic matter

in wetlands may differ according to sampling scale and

hydrological conditions; for instance, those of SWC and

organic matter in a floodplain forest in Spain were

approximately 5–10 m (Gallardo 2003). On the other hand,

autocorrelation ranges of SWC for other soil types were

generally larger than those in wetlands; for instance,

autocorrelation of SWC was approximately 100 m in bare,

cultivated soils (Anctil et al. 2002) and upland forest soils

(this study; Table 2), and 100–200 m in pasture soils

(Western et al. 1998). The patterns of SWC and organic

matter were coupled (Fig. 4a–c), exhibiting high and

clustered spatial variability, and they were the most rep-

resentative properties as primary SQIs. Therefore, we must

consider the spatial variability of SWC and organic matter

and establish appropriate sampling designs for addressing

spatial variability when we survey soil properties or

investigate biogeochemical processes.

Relationship between pH and nutrient availability

CEC, Ca, Mg, and Na in wetland soils were weakly cor-

related with pH (Fig. 2a). Moreover, they were not grouped

with pH but, instead, with SWC and organic matter (factor

1, Fig. 3a). We assume that the weak relationship between

pH and nutrient availability was due to the following rea-

sons: First, pH had low variability (CV: 8.25 %) and no

significant spatial autocorrelation; therefore, the contribu-

tion of pH variability to changing ionic charge could be

limited. Low variability in soil pH has been previously

reported (Castrignano et al. 2000; Mulla and McBrateny

2000) and could be attributed to the high buffering capacity

of wetland soils, which are rich in organic matter. In

Lag distance (m)

Sem

ivar

ian

ce

0.0

0.2

0.4

0.6

0.8

R2 = 0.55, P < 0.001

0 20 40 60 80 100 120 140

aWetland soil water content

Sem

ivar

ian

ce

0.0

0.2

0.4

0.6

0.8

R2 = 0.61, P < 0.01

Lag distance (m)0 20 40 60 80 100 120 140

b Wetland C

Sem

ivar

ian

ce

0.0

0.1

0.2

0.3

0.4

R2 = 0.70, P < 0.001

Lag distance (m)0 20 40 60 80 100 120 140

c Wetland N

Lag distance (m)S

emiv

aria

nce

0.0

0.1

0.2

0.3

0.4

0.5

R2 = 0.66, P < 0.001

0 20 40 60 80 100 120 140 160

e Wetland Mg

Sem

ivar

ian

ce

0.0

0.1

0.2

0.3

0.4

R2 = 0.63, P < 0.001

Lag distance (m)0 20 40 60 80 100 120 140

fWetland Na

Sem

ivar

ian

ce

0.0

0.2

0.4

0.6

0.8

R2 = 0.63, P < 0.01

Lag distance (m)0 20 40 60 80 100 120

gUpland pH

Sem

ivar

ian

ce

0.00

0.05

0.10

0.15

0.20

0.25

0.30

R2 = 0.31, P < 0.001

Lag distance (m)0 20 40 60 80 100 120

h Upland soil water content

Lag distance (m)

Sem

ivar

ian

ce

0.0

0.2

0.4

0.6

0.8

R2 = 0.61, P < 0.001

0 50 100 150 200 250

iUpland C

Lag distance (m)

Sem

ivar

ian

ce

0.00

0.05

0.10

0.15

0.20

R2 = 0.57, P < 0.001

0 50 100 150 200 250

jUpland N

Sem

ivar

ian

ce

0

10

20

30

40

50

R2 = 0.66, P < 0.001

Lag distance (m)0 20 40 60 80 100 120

dWetland CEC

Lag distance (m)0 20 40 60 80 100 120 140 160

Sem

ivar

ian

ce

0.00

0.01

0.02

0.03

0.04

0.05

0.06

R2 = 0.58, P < 0.001

kUpland K

Fig. 4 Semivariogram for soil properties of a wetland (a–f) and an adjacent upland (g–k) in a small-scale watershed in Korea. All soil properties

except pH and CEC were log transformed for normalization. CEC cation exchange capacity

Landscape Ecol Eng

123

addition, pH was not correlated with and did not represent

any other soil properties in the wetland. Second, strong

CEC due to high organic matter concentration would

tightly bind cations eroded from the upland, leading to their

retention in wetland soils. In contrast, pH and cations were

grouped together as factor 2 in upland soils (Fig. 3c),

supporting the observation that pH is generally the domi-

nant regulator of nutrient availability (Camberato and Pan

2000; Ross et al. 2008). Therefore, Ca, Mg, and Na in

wetland soils were coupled with SWC and organic matter

in factor 1, and their patterns were independent from that of

pH. These results cause us to reject our third hypothesis,

that pH regulates nutrient availability in wetland soils;

therefore, for wetlands, it may not be necessary to focus on

variability in pH, which is a major SQI for forest and

agricultural lands (Schoenholtz et al. 2000). Measures of

central tendency (mean and median) and dispersion (SD

and CV) from a minimal number of sampling points could

be representative for wetland pH.

In summary, we investigated wetland soil properties and

spatial patterns and contrasted them with those of an

adjacent upland in Korea. Wetland soils were rich in

organic matter under high SWC. SWC and organic matter,

which were coupled with each other, accounted for the

majority of the variability in wetland soils. Their spatial

variability was high, with a narrow range of autocorrela-

tion. On the other hand, the pH of wetland soils had low

variability and was not correlated with any other soil

property. SWC and organic matter can be considered as

SQIs for wetland soils under an appropriate sampling

design due to their spatial variability. These findings con-

tribute to our knowledge of soil properties of reference

wetlands and are expected to aid in wetland conservation

and rehabilitation.

Acknowledgments This study was supported by the Seoul Metro-

politan Government (No. 2008-221), National Research Foundation

of Korea (2010-0020227), and the Korea University Grant (2013). We

appreciate the cooperation of Heolleung Office, Culture Heritage

Administration of Korea for conserving this precious ecosystem and

allowing us to access HELCA. We also appreciate Ah Reum Lee, Sue

Kyoung Lee, and Joomi Kim for their assistance in the laboratory and

field. This study complies with the current laws of Republic of Korea.

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