1
Decreased soil cation exchange capacity across northern China’s
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grasslands over the last three decades
3 4 5 6
Kai Fang1,2, Dan Kou1,2, Guanqin Wang1,2, Leiyi Chen1, Jinzhi Ding1,2, Fei Li1,2, Guibiao Yang1,2, Shuqi Qin1,2, Li Liu1,2, Qiwen Zhang1,2 and Yuanhe Yang1,2*
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1
8
Chinese Academy of Sciences, Beijing 100093, China
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2
University of Chinese Academy of Sciences, Beijing 100049, China
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*
Corresponding author: Dr. Yuanhe Yang, tel.: + 86 10-6283 6638, fax: + 86
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10-6283 6632, E-mail:
[email protected]
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany,
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13 14 15 16
Revised manuscript submitted to Journal of Geophysical Research:
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Biogeosciences
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21th- Sep.-2017
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Manuscript information: 31 pages, 1 table, 5 figures and 1 supplementary dataset.
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Key Points:
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Decreases in topsoil cation exchange capacity were observed across northern
26 27 28 29 30
China’s grasslands over the last three decades. The decreases were detected on both the Tibetan Plateau and the Inner Mongolian Plateau, among various grassland types. The decreases were attributed to soil carbon loss, soil desertification, soil acidification, and extreme precipitation.
31 1
32
Abstract
33
Cation exchange capacity (CEC) helps soils hold nutrients and buffer pH, making it
34
vital for maintaining basic function of terrestrial ecosystems. However, little is known
35
about the temporal dynamics of CEC over broad geographical scales. In this study, we
36
used Random Forest method to compare historical CEC data from the 1980s with new
37
data from the 2010s across northern China’s grasslands. We found that topsoil CEC in
38
the 2010s was significantly lower than in the 1980s, with an overall decline of about
39
14%. Topsoil CEC decreased significantly in alpine meadow, alpine steppe, meadow
40
steppe, and typical steppe by 11%, 20%, 27% and 9% respectively. Desert steppe was
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the only ecosystem type which experienced no significant change. CEC was
42
positively related to soil carbon content, silt content, and mean annual precipitation,
43
suggesting that the decline was potentially associated with soil organic carbon loss,
44
soil degradation, soil acidification, and extreme precipitation across northern China’s
45
grasslands since the 1980s. Overall, our results demonstrate topsoil CEC loss due to
46
environmental changes, which may alter the vegetation community composition and
47
its productivity and thus trigger grassland dynamics under a changing environment.
48 49
Keywords: environmental change, geochemistry, grassland ecosystems, soil carbon
50
loss, soil desertification, soil acidification, extreme precipitation
2
51
1. Introduction
52
Cation exchange capacity (CEC) is usually expressed as the number of moles of
53
exchangeable cations adsorbed by electrostatic force per unit mass soil. CEC plays an
54
important role in holding soil nutrients [Brady and Weil, 2002] and buffering soil pH
55
changes [Xu et al., 2012; Luo et al., 2015], which makes it vital for maintaining the
56
structure and function of terrestrial ecosystems. CEC can affect ecosystem structure
57
and function by controlling the supply of exchangeable cations (i.e., Ca2+, Mg2+, K+,
58
Na+, Al3+, and Fe3+) in the soil [Lucas et al., 2011; Mueller et al., 2012]. Given that
59
different plant species require different concentrations and ratios of Ca2+, Mg2+, K+
60
and Na+, changes in these cations can alter plant biodiversity [Chen et al., 2013].
61
Besides being essential to plant growth [Likens et al., 1998; McLaughlin and Wimmer,
62
1999], the availability of these nutrients affects plant tolerance to drought, frost, and
63
pathogens [DeHayes et al., 1999; Demchik and Sharpe, 2000], therefore affecting
64
primary productivity and overall structure of terrestrial ecosystems. In contrast,
65
increased Al3+ and Fe3+ could limit vegetation productivity because of its toxicity
66
[Lieb et al., 2011]. CEC can also maintain the stability of terrestrial ecosystems by
67
buffering changes in soil acidity [Bowman et al., 2008], since soil pH decreases could
68
alter biological activity and cation supply and have negative impacts on terrestrial
69
ecosystems [Kirk et al., 2010]. Overall, in the context of environmental changes, a
70
deep understanding of the temporal dynamics of CEC is crucial for predicting the
71
responses of ecosystem structure and function, and in guiding policies that maintain
72
the stability of terrestrial ecosystems. 3
73 74
Given the importance of CEC, studies about the effects of environmental changes on
75
CEC have gradually accumulated in recent decades [Watmough and Dillon, 2003;
76
Högberg et al., 2006; Fissore et al., 2007; Ruiz Sinoga et al., 2012; Xu et al., 2012; Lu
77
et al., 2015]. It has been reported that CEC exhibited positive correlations with
78
2:1-type clay minerals [Xu et al., 2012] and organic matter content [Ruiz Sinoga et al.,
79
2012; Xu et al., 2012]. Due to the close relationships between soil organic matter and
80
climatic parameters, CEC has also been observed to be positively related with
81
precipitation [Ruiz Sinoga et al., 2012] but negatively related with mean annual
82
temperature [Fissore et al., 2007]. These studies have generated broad understanding
83
of spatial variation and environmental drivers of CEC over broad geographic scales,
84
but little is known about temporal CEC dynamics at large spatial scales. Long-term
85
site monitoring has demonstrated cation loss from soils due to soil acidification,
86
harvesting, and land-use change [Watmough and Dillon, 2003; Högberg et al., 2006;
87
Lu et al., 2015]. However, these observations are limited to the site scale and it is
88
unknown if they are representative of larger landscape or continental trends.
89
Considering that the driving factors for CEC dynamics (e.g., climatic factors, edaphic
90
factors, and human activity) exhibit substantial variations in space and time, a
91
comprehensive study about the temporal dynamics of CEC along broad environmental
92
gradients is needed.
93 94
China’s grasslands are distributed across large geographic scales, making them an 4
95
ideal ecosystem to explore the temporal dynamics of CEC for three reasons. First,
96
along with the geographic gradient, grassland types are diverse (including both alpine
97
and temperate grasslands) and environmental parameters such as precipitation and
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edaphic variables exhibit a wide range [Liao and Jia, 1996]. Second, since the 1980s,
99
northern China has experienced significant environmental changes, which mainly
100
include the following: (1) Overgrazing and cultivation [Bridges and Oldeman, 1999;
101
Yu et al., 2012] have led to soil desertification [Yang et al., 2007; Mu et al., 2013] and
102
pasture degradation [Dai et al., 2011], thereby decreasing grassland productivity
103
[Babel et al., 2014] and organic carbon storage [Xie et al., 2007; Dai et al., 2011]
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across the study area; (2) Elevated atmospheric acid deposition has induced soil
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acidification across northern China, with a decrease in soil pH of 0.63 units over the
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past two decades [Yang et al., 2012b]; (3) The frequency and intensity of extreme
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precipitation has increased across northern China [Zhai et al., 2005], specifically,
108
precipitation in the desert region of China has increased more than 40% since the
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beginning of the 21st century [Xu et al., 2010]. Third, soil physical and chemical
110
properties during the 1980s were well documented in historical soil inventory data
111
[Office of Soil Census of Inner Mongolia, 1994; Tibet Land Management Bureau,
112
1994; Office of Agriculture Resource Layouting of Qinghai, 1997]. Together, these
113
geographic conditions, environmental changes, and historical inventory make possible
114
an investigation of the temporal dynamics of CEC over the last three decades.
115 116
In this study, we investigated changes in CEC across northern China’s grasslands 5
117
from the 1980s to 2010s. We collected surface soil samples from 251 sites on the
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Tibetan Plateau and Inner Mongolian Plateau from 2013-2015, comparing these data
119
with 125 soil profiles measured during the 1980s, obtained from China’s Second
120
National Soil Inventory. We combined these two datasets of CEC with the related
121
environmental parameters, and used Random Forest method to explore the changes of
122
CEC between the two periods. We hypothesized that topsoil CEC across northern
123
China’s grasslands would show a significant decrease due to the substantial
124
environmental changes over the past three decades.
125 126
2. Materials and Methods
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2.1. Study Area
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Our study area mainly contained alpine grasslands on the Tibetan Plateau and
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temperate grasslands on the Inner Mongolian Plateau (Figure 1), whose longitudes
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and latitudes are 80.10°-121.93° E and 28.07°-49.62° N, respectively. During the
131
2010s, the mean annual temperature (MAT) varied from -3.06 °C to 8.13 °C, and the
132
mean annual precipitation (MAP) varied from 111.51 mm to 719.28 mm. The
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dominant grassland types are alpine meadow (AM), alpine steppe (AS), meadow
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steppe (MS), typical steppe (TS) and desert steppe (DS) [Chinese Academy of
135
Sciences, 2001]. The dominant species are Kobresia pygmaea and K. tibetica in the
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AM, Stipa purpurea and Festuca ovina in the AS, S. baicalensis and Leymus
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chinensis in the MS, S. grandis and S. krylovii in the TS, and S. klemenzii, and S.
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breviflora in the DS [Ma et al., 2010]. According to the classification of the World 6
139
Reference Base for Soil Resources 2006, the soil types related to the five grassland
140
types were defined as Cambisols (in AM and AS), Chernozems (in MS), Kastanozems
141
(in TS) and Calcisols (in DS) [IUSS Working Group WRB, 2007].
142 143
2.2. Historical observations during the 1980s
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China’s Second National Soil Inventory during 1979-1989 recorded 74 and 51
145
representative grassland soil profiles on the Tibetan Plateau and Inner Mongolian
146
Plateau, respectively (Figure 1). From the 1980s data, we obtained CEC and other
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edaphic properties (e.g. SOC content, pH, and silt content) in the A horizon of 125
148
typical profiles, with a median depth of 15 cm [Office of Soil Census of Inner
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Mongolia, 1994; Tibet Land Management Bureau, 1994; Office of Agriculture
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Resource Layouting of Qinghai, 1997]. According to the historical records, CEC was
151
determined by hydrochloric acid neutralization titration, after extraction with a mixed
152
solution consisting of 1 M ammonium acetate and 0.005 M EDTA (adjusted to pH 7.0
153
for acidic soil and pH 8.5 for alkaline soil, respectively), and subsequent distillation of
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ammonium [Chang, 1979; Sparks et al., 1996]. SOC content was determined by
155
ferrous sulfate titration, after potassium dichromate oxidation [Nelson and Sommers,
156
1982]. Soil pH was measured by potentiometry, using a water soil ratio of 2.5:1
157
[Koncki et al., 1992]. Silt content (soil texture) was measured by the sieve-hydrometer
158
method, based on the speed of particle sedimentation at multiple time steps [Di
159
Stefano et al., 2010]. Grassland types were determined by the dominant species
160
related to the local soil types. Meanwhile, based on the available detailed 7
161
administrative address, longitude and latitude at these sites were determined from a
162
digital map developed by the National Administration of Surveying, Mapping, and
163
Geoinformation (http://map.tianditu.com/map/index.html). Historical MAT and MAP
164
at these sites were extracted using Kriging interpolation based on atmospheric
165
temperature and precipitation data from 120 meteorological stations on the Tibetan
166
Plateau and the Inner Mongolian Plateau from 1982-1989 (http://data.cma.cn/).
167 168
2.3. Field inventory during the 2010s
169
To characterize the current status of grassland soil CEC, we investigated 251 sites
170
(173 on the Tibetan Plateau [Ding et al., 2016] and 78 on the Inner Mongolian Plateau)
171
throughout northern China’s grasslands during July and August of 2013-2015. The
172
sampling sites during the 2010s covered extensive climatic gradients and major
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grassland types (Figure 1). Specifically, latitude and longitude were acquired using
174
GPS (M-GIS T20, China), and grassland types were determined by the dominant
175
species present. At each site, we set up five 1 m×1 m quadrats at every corner and in
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the center of a 10 m×10 m square. We further selected three quadrats along a
177
diagonal line of the square to take topsoil (0-10 cm) samples. Soil samples were
178
collected in plastic bags and sent to the laboratory.
179 180
We analyzed soil CEC and other edaphic properties in the laboratory. First, composite
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soil samples (equally mixed subsamples from three samples from each site) were
182
air-dried and sieved (< 2 mm), and fine roots were manually removed. To avoid the 8
183
potential uncertainties caused by measuring method, soil CEC, SOC content, pH, and
184
silt content were all measured by the same methods as in the 1980s. MAT and MAP at
185
these sites were extracted in the same method but from meteorological data of
186
2010-2015.
187 188
2.4. Data Analyses
189
To examine the dynamics of soil CEC from 1980s to 2010s, it is necessary to have
190
time-paired data for each sampling site. This method has been used to examine soil
191
organic [Yu et al., 2009; Yang et al., 2014] and inorganic carbon [Yang et al., 2012a]
192
dynamics, as well as soil pH changes [Yang et al., 2012b]. Because the site locations
193
in the two sampling periods were inconsistent, we used the Random Forest model to
194
estimate CEC values for the missing points from each sampling period. Random
195
Forest extends Classification and Regression Tree analyses to improve prediction
196
accuracy without excessive computation [Breiman, 2001a, 2001b; Wiesmeier et al.,
197
2011; Ziegler and Konig, 2014; Were et al., 2015]. To reduce the uncertainties caused
198
by the uneven distribution of sampling sites during the two periods, we selected a
199
grassland transect (including 91 among the 125 sites in the 1980s and 189 among the
200
251 sites in the 2010s) from the central-southern Tibetan Plateau to the eastern Inner
201
Mongolian Plateau (Figure 1) to examine the CEC dynamics. Briefly, the dataset of
202
measured CEC was split into two parts: a training dataset used to construct the
203
Random Forest model (90% of the data points) and a test dataset used to assess the
204
performance of the model (10% of the data points). Based on the historical soil 9
205
inventory training dataset from the 1980s and related environmental variables (i.e.,
206
longitude, latitude, grassland type, MAT, MAP, SOC, pH, and silt content), we
207
constructed a Random Forest model to predict CEC for those sites without
208
measurement in the 1980s (189 sites that surveyed during the 2010s). By doing so, we
209
obtained a dataset consisting of 189 data points including measured soil CEC from the
210
2010s and corresponding estimated data for the 1980s (detailed procedure in Figure 2).
211
Similarly, using the training dataset from the current survey during the 2010s and the
212
corresponding environmental data, we created another Random Forest model to
213
simulate CEC during the 2010s for those sites without measurements from the 2010s
214
(91 sites measured in the 1980s). Thereafter, we obtained another dataset consisting of
215
91 data points including measured CEC from 1980s and estimated data from the
216
2010s (Figure 2). By combining these data sets, we finally generated a time-paired
217
dataset across the 280 sites with either measured or estimated data for the two
218
sampling periods for each sampling site. We used Pearson correlation coefficient (r)
219
and root-mean-squared error (RMSE) to test the accuracy of the model predictions.
220
We repeated the Random Forest training 1000 times, and the averages were treated as
221
the predicted CEC values. All the Random Forest Model computations were
222
conducted using R software [R Development Core Team, 2012]. Based on the
223
time-paired dataset obtained through the Random Forest method, we used a paired
224
t-test to explore the temporal dynamics of CEC. We also examined the relationships
225
between CEC dynamics and environmental variables using linear regression model.
226
All the statistical analyses were performed using SPSS 20.0 (IBM SPSS, Chicago, 10
227
Illinois, USA), with a significance level of 0.05.
228 229
3. Results
230
3.1. Reliable Estimation by the Random Forest Model
231
To test the accuracy of the model predictions, we examined the correlation between
232
the predicted and measured CEC values. Our results showed that during the 1980s, r
233
and RMSE between the testing data set and the measured values were 0.90 and 5.41
234
cmol kg-1, respectively (Figure 3a). Similarly, r and RMSE during the 2010s were
235
0.93 and 3.48 cmol kg-1, respectively (Figure 3b). These results showed high
236
correlation and low estimation error, indicating a reliable prediction ability for the
237
Random Forest model.
238 239
3.2. Temporal Dynamics of CEC across Northern China’s Grasslands
240
Topsoil CEC across northern China’s grasslands during the 2010s was significantly
241
lower than the 1980s (P < 0.001; Figure 4a). According to the results of 280 sampling
242
sites within the transect, topsoil CEC significantly declined by about 14% (Table 1).
243
However, substantial variations existed among the various grassland types (i.e., AM,
244
AS, MS, TS and DS). Topsoil CEC did not exhibit a significant change in DS perhaps
245
because of its lower initial values, but decreased by 11%, 20%, 27% and 9% in AM,
246
AS, MS and TS, respectively (Figure 4b; Table 1).
247 248
4. Discussion
249
There has been significant loss of topsoil CEC across northern China’s grasslands 11
250
over the last three decades. A decline in base cations over a longer time period was
251
also detected in North America [Watmough and Dillon, 2003], Europe [Högberg et al.,
252
2006], Oceania [Hartemink, 1998; Curtin et al., 2015], Africa [Jaiyeoba, 2003] and
253
Asia [Ali et al., 1997; Lu et al., 2015], indicating that this phenomenon is widespread.
254
The decrease of CEC observed in this study is within the range of previous studies,
255
varying from 8% to 50% [Ali et al., 1997; Hartemink, 1998; Jaiyeoba, 2003; Curtin et
256
al., 2015]. Such a pattern could be due to the following four phenomena. First, a
257
reduction in SOC content can lead to a decrease in CEC. Overgrazing and cultivation
258
in past decades [Bridges and Oldeman, 1999; Yang et al., 2007; Yu et al., 2012; Mu et
259
al., 2013] has increasingly degraded grasslands [Dai et al., 2011] and causing declines
260
in grassland productivity [Babel et al., 2014]. Consequently, the SOC stock of China’s
261
grasslands decreased by 3.56 Pg C (1 Pg = 1015 g), dropping from approximately
262
37.71 to 34.15 Pg C from the 1980s to the 2000s, with particularly marked changes in
263
Tibet, Qinghai, and Inner Mongolia [Xie et al., 2007]. The decrease in SOC has a
264
profound influence on CEC for two reasons. One is that the dissociation of some
265
functional groups (e.g., carboxyl and phenolic hydroxyl) decreases with the decrease
266
in SOC. As a result, the amount of negative charge possessed by humus is reduced,
267
weakening the complexation of the humus with exchangeable base cations [Brady and
268
Weil, 2002; Chapin et al., 2011]. The other is that cation-bridging (e.g., Ca2+, Fe3+,
269
Al3+) between clay minerals and humus can decline due to a reduction in SOC,
270
making humus-clay mineral complexes unstable [Brady and Weil, 2002; von Lutzow
271
et al., 2006; Mueller et al., 2012]. Consequently, the protection for SOM against 12
272
decomposition is weakened and causes the decrease in CEC. Consistent with this
273
inference, our results showed that CEC was positively correlated with SOC, and its
274
variations were mostly explained by SOC in both the 1980s (TP: r2 = 0.72, P < 0.001;
275
IM: r2 = 0.71, P < 0.001; Figure 5a) and 2010s (TP: r2 = 0.89, P < 0.001; IM: r2 =
276
0.84, P < 0.001; Figure 5e). Many studies also have found a positive relationship
277
between CEC and SOC [Tůma et al., 2011; Lu et al., 2014; Gruba and Mulder, 2015].
278 279
Second, the CEC reduction can also be caused by soil desertification. It has been
280
reported that soils in arid and semi-arid regions were more likely to be degraded
281
[Bridges and Oldeman, 1999]. Both the Tibetan Plateau and Inner Mongolian Plateau,
282
which are located in arid and semi-arid regions, have been suffering the effects of
283
climate warming, human disturbance, overgrazing, cultivation, and pikas, all of which
284
can result in soil degradation and desertification [Yang et al., 2007; Xue et al., 2009;
285
Xue et al., 2011; Yu et al., 2012; Qin et al., 2015]. It has been reported that lots of
286
Inner Mongolian grasslands have undergone degradation or desertification from 1985
287
to 2000 [Mu et al., 2013]. Once soil desertification occurs, wind erosion alters soil
288
texture, blowing away fine particles (e.g. clay and silt) and leaving coarse and barren
289
sand [Chepil, 1957; Hoffmann et al., 2008; Amundson et al., 2015]. Moreover, with
290
the decrease in particle contents, the surface areas provided for exchangeable cations
291
become smaller [Brady and Weil, 2002]. Furthermore, soil desertification causes a
292
decrease in soil productivity and SOC content [Larney et al., 1998; Yan et al., 2005].
293
All of these processes will reduce CEC, along with soil desertification. Our data 13
294
support the above conclusions, with CEC positively correlated with silt content
295
(1980s TP: r2 = 0.12, P < 0.01; IM: r2 = 0.71, P < 0.001; Figure 5b; 2010s TP: r2 =
296
0.25, P < 0.001; IM: r2 = 0.68, P < 0.001; Figure 5f), but negatively associated with
297
sand content (1980s TP: r2 = 0.12, P < 0.01; IM: r2 = 0.62, P < 0.001; Figure 5c;
298
2010s TP: r2 = 0.14, P < 0.001; IM: r2 = 0.67, P < 0.001; Figure 5g).
299 300
Third, the CEC reduction can still be induced by soil acidification. It has been
301
reported that enhanced atmospheric nitrogen deposition [Liu et al., 2013] and sulfur
302
deposition [Zhao et al., 2009] resulted in soil acidification across China’s grasslands
303
[Yang et al., 2012b]. Sustained acidification leads to a lower soil pH, which can be
304
buffered by carbonate and base cations. It has been hypothesized that when the soil
305
pH is over 7.5, the change is mainly buffered by carbonate [Bowman et al., 2008;
306
Yang et al., 2012a, b], while the exchangeable base cations play a lesser role. When
307
the soil pH is in the range of 4.5-7.5, it is primarily the exchangeable cations that
308
buffer pH change [Bowman et al., 2008]. Exchangeable ions including Ca2+, Mg2+, K+
309
and Na+ are adsorbed on the surface of the soil colloid, making CEC value higher.
310
With the increases in acidity, H+ gradually occupies the exchangeable sites. At the
311
same time, base cations disassociate and are more likely to be leached by rainfall,
312
resulting in decreased CEC [Watmough and Dillon, 2003; Lapenis et al., 2004; Lieb et
313
al., 2011; Chen et al., 2013; Lu et al., 2014]. This classic hypothesis is supported by
314
experimental studies on long-term dynamics of CEC and base cations at the local
315
scale [Watmough and Dillon, 2003; Högberg et al., 2006; Lu et al., 2015]. To be 14
316
specific, results from a manipulative experiment also showed that soil pH and base
317
saturation significantly increased when acid addition stopped [Högberg et al., 2006].
318
Therefore, we argue that acidification mainly affects CEC when the pH was low (e.g.
319
in AM and MS).
320 321
Fourth, the decrease in CEC is possibly related to extreme precipitation. In recent
322
decades, extreme precipitation has been observed more frequently [IPCC, 2007;
323
Moldan et al., 2012]. Recent studies indicated that the frequency and intensity of
324
extreme precipitation were gradually increasing across western China and the Tibetan
325
Plateau [Zhai et al., 2005; Yang et al., 2008; Xu et al., 2010], making soil base cations
326
leached. Normally, precipitation is positively correlated with SOC [Ruiz Sinoga et al.,
327
2012], which is strongly associated with CEC [Gruba and Mulder, 2015]. Normally,
328
precipitation is positively correlated with SOC [Ruiz Sinoga et al., 2012], which is
329
strongly associated with CEC [Gruba and Mulder, 2015]. In both periods, our results
330
showed that CEC increased along the precipitation gradient (1980s TP: r2 = 0.12, P
Mg2+ > K+ > Na+, and so, if CEC is reduced, base cations
347
will be lost in different degrees. As a result, species that rely on a certain cation may
348
decrease or even become extinct on long time scales. In addition, in alpine meadow
349
and meadow steppe environments with a low pH, soil acidification caused by nitrogen
350
and sulfur deposition leads to the decrease in CEC, eventually releasing Al3+ and Fe3+
351
into the soil [Bowman et al., 2008]. Consequently, species that are sensitive to Al3+ or
352
Fe3+ may decrease. Second, ecosystem function across northern China’s grasslands
353
may be weakened when CEC is reduced. The decrease in CEC and the increase in
354
Al3+ and Fe3+ weaken the abilities of the nutrient (i.e., Ca2+, Mg2+, K+ and Na+) supply
355
and plant tolerance to drought, frost or pathogen attacks [DeHayes et al., 1999;
356
Demchik and Sharpe, 2000]. Thus, to some degree, it may potentially decrease
357
vegetation productivity [Likens et al., 1998; McLaughlin and Wimmer, 1999]. To
358
avoid the ecological consequences mentioned above, it is vital to reduce the
359
interference of human activities in these ecosystems and strictly control the intensity 16
360
of grazing and cultivation [Mu et al., 2013]. Reducing the emissions of SO2 and NOx
361
is also essential to relieve soil acidification and environment aggravation caused by
362
continuous CEC loss [Lucas et al., 2011].
363 364
Acknowledgments
365
Associated data are available in supporting information. We are grateful to the
366
members of the IBCAS Sampling Campaign Teams for their assistance in field
367
investigations. This work was supported by the National Natural Science Foundation
368
of China (31670482 and 41371213), Key Research Program of Frontier Sciences,
369
Chinese Academy of Sciences (QYZDB-SSW-SMC049), Chinese Academy of
370
Sciences-Peking University Pioneer Cooperation Team, and the Thousand Young
371
Talents Program.
372 373
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Table 1. Means of CEC and its changes among five grassland types within a grassland
609
transect across northern China from the 1980s-2010s. The values in
610
parenthesis show the 95% confidence interval calculated using the Random
611
Forest Model. Notes: n, sample size; AM, alpine meadow; AS, alpine steppe;
612
MS, meadow steppe; TS, typical steppe; DS, desert steppe. CEC during the 1980s
CEC during the 2010s
Change in CEC
(cmol kg-1)
(cmol kg-1)
(cmol kg-1)
Grassland type
n
Total
280
17.06 [16.04, 18.08] 14.69 [13.76, 15.61]
-2.37 [-2.97, -1.78]
AM
97
22.57 [20.92, 24.23] 20.13 [18.35, 21.91]
-2.45 [-3.71, -1.18]
AS
90
13.84 [12.30, 15.39]
11.07 [9.85, 12.29]
-2.77 [-3.60, -1.94]
MS
16
26.41 [22.14, 30.67] 19.39 [17.24, 21.55]
-7.01 [-10.32, -3.70]
TS
52
13.44 [12.19, 14.69] 12.22 [11.26, 13.18]
-1.22 [-2.17, -0.26]
DS
25
8.83 [7.99, 9.66]
8.72 [7.26, 10.18]
-0.11 [-1.54, 1.32]
613
29
614
Figure Legends
615
Figure 1. The distribution of sampling sites during the 1980s and the 2010s. The
616
background colors show grassland extent and type across northern China
617
[Chinese Academy of Sciences, 2001]. The rectangle represents the
618
grassland transect selected for exploring the temporal dynamics of CEC.
619
Blue points represent the sites during the 1980s and green points represent
620
the 2010s.
621 622 623
Figure 2. Procedure of constructing Random Forest (RF) models to predict data for those sampling sites during the 2000s (a) and during the 1980s (b).
624 625 626
Figure 3. Comparison between predicted and measured CEC for the period of (a) the 1980s and (b) the 2010s.
627 628
Figure 4. Changes in CEC (a) along a grassland transect across northern China’s
629
grasslands and (b) among various grassland types between the 1980s and
630
the 2010s. The inset in panel (a) shows the comparison of CEC between
631
the two periods, with a 1:1 line for reference. Different letters above
632
box-and-whisker plots express significant differences between the two
633
periods. The gap and square, lower and upper edges and bars in the boxes
634
represent median and mean values, 25th and 75th percentiles, and standard
635
deviations of all data, respectively. AM: alpine meadow, AS: alpine steppe, 30
636
MS: meadow steppe, TS: typical steppe, DS: desert steppe.
637 638
Figure 5. Relationships of CEC with (a, e) SOC, (b, f) silt content, (c, g) sand content,
639
and (d, h) MAP. Panels (a-d) and (e-h) illustrate the relationships between
640
CEC and environmental factors during the 1980s and the 2010s,
641
respectively. Data and trends on the Tibetan Plateau are represented by red
642
points and lines, while those on the Inner Mongolian Plateau are shown by
643
blue points and lines.
31
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
