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Pedodiversity and its controlling factors in mountain regions — A case study of Taihang Mountain, China
Geoderma 310 (2018) 230–237
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Pedodiversity and its controlling factors in mountain regions — A case study of Taihang Mountain, China
MARK
Tonggang Fua, Lipu Hana, Hui Gaoa,b, Hongzhu Lianga,b, Xiaorong Lia,b, Jintong Liua,⁎ a Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China b University of Chinese Academy of Sciences, Beijing 100049, China
A R T I C L E I N F O
A B S T R A C T
Handling Editor: A.B. McBratney
Knowledge on pedodiversity is crucial for the management and protection of soil resources. However, little is known about the controlling factors of pedodiversity, especially in mountain regions with a highly heterogeneous environment. This paper analyzed pedodiversity (soil taxa diversity) and the influencing factors in Taihang Mountain region in China. The richness (S), Menhinick's index (M), Shannon index (H), maximum Shannon index (Hmax) and evenness index (E) were used to explain pedodiversity in the study area. The influences of natural factors (including elevation, slope gradient, precipitation and temperature) and anthropogenic factors (including population density and percentage of farmland) were analyzed using partial correlation analysis and canonical correspondence analysis. The results showed that for the 101 counties in Taihang Mountain, average H was 1.69, which suggested a relatively low pedodiversity (normal H range is 1.5–3.5, and rarely exceeds 4.5) in the study area. Logarithmic normal distribution had the best fit for the abundance distribution model, indicating that intermediate abundance of soil type was most common. The best fit Logarithmic function was for the relationship between richness and area. Soil taxa richness increased significantly for areas less than 1000 km2, but increased gradually for areas larger than 1000 km2. This suggested that 1000 km2 was the breakthrough point on the richness-area curve, and that the effect of area on pedodiversity could be negligible for regions larger than 1000 km2. Canonical correspondence analysis showed that the influence of these factors on pedodiversity decreased in the order of: elevation > percent farmland > slope gradient > population density > precipitation. This suggested that elevation and farming had the highest effect on pedodiversity. The study provided further insight into pedodiversity in mountain regions, which is critical for the protection of soil resources and pedodiversity.
Keywords: Pedodiversity Heterogeneous environment Anthropogenic factor Shannon index Canonical correspondence analysis
1. Introduction Pedodiversity, or simply soil diversity, is used to explore, quantify and compare the complexity of soil patterns in different units and thereby provides the basis for biodiversity (Ibañez et al., 1995; Tennesen, 2014; Amundson et al., 2015). Pedodiversity could be divided into taxonomic pedodiversity, functional pedodiversity, genetic pedodiversity and soil property diversity (Saldaña and Ibáñez, 2004; Ibáñez et al., 2005; Kooch et al., 2015). The study of pedodiversity can be done at different scales, including polypedon, association, landscape, drainage basin and geographical scale of soil (Ibañez et al., 1995). A thorough understanding of pedodiversity is helpful in both soil management and soil protection, and can also be applied in the protection of biodiversity (Tennesen, 2014; Rannik et al., 2016). Compared with
biodiversity, however, research on pedodiversity is very limited, despite the stress for pedodiversity research in different regions (Guo et al., 2003; Saldaña and Ibáñez, 2004; Shangguan et al., 2014). The definition of pedodiversity first appeared in the 1990s (Mcbratney, 1992; Ibañez et al., 1995; Ibañez et al., 1998) and pedodiversity has since gradually gained interest in Spain, America, China and other countries (Amundson et al., 2003; Tan et al., 2003; Saldaña and Ibáñez, 2004; Toomanian et al., 2006; Minasny et al., 2010; Lo Papa et al., 2011; Ren and Zhang, 2015). Recently, books (Ibáñez and Bockheim, 2013) and special issues (Volume 135, Issues 3–4, Pages 213–352, 15 December 2011, Geomorphology) have been set up focusing mainly on pedodiversity. In Google Scholar, the search results of “pedodiversity/soil diversity” increased significantly from the 1990s (458) to the 2010s (3356). These existed researches on pedodiversity
⁎ Corresponding author at: Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 286 Huaizhong Road, 050021 Shijiazhuang, China. E-mail address: [email protected] (J. Liu).
http://dx.doi.org/10.1016/j.geoderma.2017.09.027 Received 9 January 2017; Received in revised form 18 September 2017; Accepted 21 September 2017 Available online 30 September 2017 0016-7061/ © 2017 Published by Elsevier B.V.
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province and Henan province. Taihang Mountain has a highly heterogeneous geological setting. Because of the orogeny, the western and eastern parts of the mountain have very different structural evolution, sedimentation and tectonic settings (Wang and Li, 2008). The average elevation of Taihang Mountain is 1000–1500 m, which decreases from the northwest to the southeast. The highest elevation (2882 m) is in the northern part of the mountain at Xiaowutai. The climate is the East Asian Monsoon type which is characterized by warm, rainy summer and cold, dry winter. The average annual (2005–2014) precipitation is 456.57 mm and the annual mean temperature is 11.36 °C. Both precipitation and temperature decrease from the southeast to the northwest. The mean precipitation for the southeast and northwest parts of the study area are 617.23 mm and 385.08 mm, with corresponding mean temperatures of 12.95 °C and 7.65 °C, respectively. Due to precipitation and temperature gradients, the heterogeneity of the vegetation decreases from the north to the south. In addition, the vegetation patterns are significantly affected by topography, elevation, slope and aspect as well as the interactions between these environmental factors (Zhang et al., 2006). Soils in Taihang Mountain developed mainly from limestone in the northern and southern parts, but from gneiss in the middle region. Moreover, the soils in the region are thin with abundant rock fragments. Based on data from an experimental site located in the middle region of the study area, the average soil thickness is 35 cm with above 0.1-cm rock fragment content of 21.4% (Cao, 2011). Taihang Mountain region contains 101 counties. The environmental and human-related conditions in this region are summarized in Table 1. In the table, mean elevation denotes the average elevation for each county. The range of elevation (i.e. elevation range in Table 1) for each county is calculated by subtracting the minimum from the maximum elevation. The means and ranges of other factors were calculated in the same way.
focused mainly on pedodiversity index, richness-area relationship and rare/endangered soil taxa at different scales. For example, Ibañez et al. (1995) proposed three ways to describe pedodiversity, including richness, abundance distribution models and indexes based on proportional abundance. Since then, these methods have been widely used (Saldaña and Ibáñez, 2004; Krasilnikov et al., 2009; Lo Papa et al., 2011; Costantini and L'Abate, 2016). Richness-area relationship has always been studied in conjunction with abundance models, both of which reflect the distribution of the individuals of different soil types (Ibáñez et al., 2005; Feoli et al., 2007). The loss of pedodiversity is also important phenomenon that has been studied. Spain, America, China and other countries are threatened by pedodiversity loss, and rare and endangered soils have been identified for these regions (Amundson et al., 2003; Guo et al., 2003; Shangguan et al., 2014; Tennesen, 2014). Until now, these aspects of pedodiversity have been well studied. However, what is not clear is the factors which control pedodiversity in a given region. Based on several studies, the main factors controlling pedodiversity include natural factors (e.g., geomorphology, meteorology, etc.) and anthropogenic factors (e.g., farming, urbanization, etc.). Geomorphology is an important factor, and mountain regions generally have higher pedodiversity than plain regions (Ibáñez and Feoli, 2013; Costantini and L'Abate, 2016). In addition, pedodiversity in transition zones (zones between different land use types or landforms) are higher than in non-transition zones (Bockheim and Schliemann, 2014). Meteorology also influences pedodiversity, and low pedodiversity is often associated with low or high rainfall/temperature (Minasny et al., 2010). Besides natural factors, agriculture and urbanization (which reflect human activity) are also important influencing factors of pedodiversity (Guo et al., 2003; Tennesen, 2014). Moreover, the controlling factors of pedodiversity can be different for different soil hierarchies. For example, soil diversity is mainly controlled by bioclimatic and hydrologic factors at high classification levels, but by parent material, topography and hydrological conditions at low levels (Shangguan et al., 2014). Although numerous factors were studied on pedodiversity, most are still described qualitatively. Quantitative analysis of the factors and ranking of their importance are critical for an in-depth understanding of pedodiversity for use in soil protection and management. Taihang Mountain in North China is a north-northeast range of mountain belt and is an important transition zone between the Loess Plateau and North China Plain (Wang and Li, 2008). Mountain regions, as noted by Costantini and L'Abate (2016), generally have high pedodiversity. Moreover, transition zones often have high pedodiversity (Bockheim and Schliemann, 2014). Considering these conditions, Taihang Mountain should have high pedodiversity. However, because of overexploitation of natural resources and excessive disturbance by local residents, the Taihang Mountain ecosystem has degenerated significantly (Li et al., 2004). Thus soil loss has become a severe issue, with more endangered soil types in this region than in other areas of China (Shangguan et al., 2014). This makes it more significant to understand pedodiversity and the controlling factors in the fragile Taihang Mountain ecosystem. Thus the objectives of this paper were to: 1) determine the spatial distribution of pedodiversity, 2) understand the richness-area relationship, and 3) quantify and rank the influencing factors of pedodiversity in Taihang Mountain region.
2.2. Pedodiversity indexes The indexes used to describe pedodiversity can be divided into three main categories (Ibañez et al., 1995). The first is the richness index (S), which is the number of objects (i.e., soil types) within a region of interest. Richness index only focuses on the number of objects and largely ignores the number of individuals (N, i.e., the area of the soil types) in the region. Therefore, derived parameters based on richness are also used. Menhinick's index (M), for example, can be calculated from S and N as (Menhinick, 1964):
M=
S N
(1)
The second category used in pedodiversity analysis is abundance distribution model, which describes the distribution of the object abundance. The abundance model is a histogram in which the X-axis is the rank of the object abundance from maximum to minimum and the Y-axis is the logarithm of the abundance. Four models are widely used in this simulation model — geometric model (which suggests that a few objects are dominant), broken stick model (which gives an even distribution of the objectives), and logarithmic model and log normal model (both of which show that intermediate abundance objects are dominant). The detailed formulas for these models are given by Ibáñez et al. (2005). Each of the abundance models gives one type of richnessarea relationship and are widely used in both biodiversity and pedodiversity. For example, the broken-stick and lognormal models output power curves, but the geometric and logarithmic models output logarithmic curves for richness-area relationships. The third category of pedodiversity indexes is based on proportional abundance of objects. The Shannon index (H) belongs to this category and is given as:
2. Materials and methods 2.1. Site description Taihang Mountain (34°36′–40°47′N, 110°42′–116°34′E), which is located in North China (Fig. 1), is the natural boundary between the Loess Plateau and the North China Plain. It extends from the northeast to the southwest for an area of 120,000 km2 and stretches across five administrative regions, including Beijing, Hebei province, Shanxi 231
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Fig. 1. A map depicting the location of Taihang Mountain study area in China (left plate) and expansion on the study area depicting administrative counties. i=n
2.3. Data source
H = − ∑ pi × ln pi
(2)
i=1
The soil taxa data used were from Harmonized World Soil Database (FAO and Isric, 2009), where soils are divided into 28 categories. It is based on the FAO-90 soil classification system and can be considered equivalent (in terms of detail) to the order or suborder categories in the US Soil Taxonomy. The database is for the whole world, from which Taihang Mountain was clipped out for this study using ArcMap 9.3 (Environmental Systems Research Institute, Inc. (ESRI)). Topographic data (including elevation, slope gradient) were derived from ASTER GDEM (30 m resolution). This database was developed jointly by the Ministry of Economy, Trade and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA). Meteorological data (including precipitation and temperature) were obtained from 101 automatic weather stations in Taihang Mountain. The effect of humans was expressed in terms of population density and
where pi is the proportion of individual number of the i th object to the total number of individuals. Based on Margalef (1972), the range of H is 1.5–3.5 and it rarely exceeds 4.5. The maximum H (Hmax) occurs when all objects are evenly distributed and it is then calculated as: (3)
Hmax = lnS
The evenness index (E), derived from the Shannon index, is another important parameter used to describe pedodiversity and it is expressed as:
E=
H Hmax
(4)
The range of E is 0–1. When E approaches 1, all the objects get evenly distributed and when it approaches 0, the region is dominated by a few objects.
Table 1 Descriptive statistics of natural and anthropogenic factors in 101 counties in Taihang Mountain area, China. Note: p in p/km2 denotes persons. Factor
Min
Max
Mean
Standard deviation
Skewness
Kurtosis
Area (km2) Mean elevation (m) Elevation range (m) Mean slope (°) Slope range (°) Mean precipitation (mm) Range of precipitation (mm) Mean temperature (°C) Temperature range (°C) Population density (p/km2) (people/km2) Percent farmland (%)
126.00 77.47 98.00 3.38 36.74 350.49 4.12 5.46 0.14 42.52 3.60
3195.00 1504.73 2432.00 22.74 79.46 669.16 86.07 14.68 4.86 5766.06 76.63
1350.81 751.79 1321.85 12.44 68.30 456.57 34.89 11.63 1.40 494.31 41.41
704.07 415.95 514.38 4.28 7.70 49.64 15.59 2.10 0.94 747.45 16.97
0.45 − 0.03 0.01 0.05 − 1.41 0.32 0.32 − 0.79 1.47 4.73 − 0.06
− 0.51 − 1.31 − 0.44 − 0.77 2.80 − 0.49 0.24 0.41 3.09 27.70 − 0.75
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the north to the south. This indicated that although the trend of the richness was not significant, the number of individuals decreased from north to south, resulting in an increasing trend in Menhinick's index. The Menhinick's index was calculated from the richness and area of the counties and therefore depended both on area and richness. The increasing fragmentation from north to south was also the combined effect of area and richness. The Shannon index and the maximum Shannon index had similar trends, with the northern part higher than the middle and southern parts. This indicated that the northern part of Taihang Mountain had a relatively higher pedodiversity. However, the evenness index was quite different in spatial distribution, with the middle part higher than the northern and southern parts (Fig. 2). This indicated that soil taxa in the middle part was most evenly distributed.
the proportion of farmland in each county. The population density data (in the form of raster with a pixel size of 1 km) was obtained from the 2010 census data (Fu et al., 2014). The proportion of farmland was calculated in a raster map of land use type obtained from Landsat TM image for 2010 by visual interpretation method. All the calculations and analyses of the pedodiversity indexes were based on administrative boundaries (county boundary), rather than on ecological boundaries, as Amundson et al. (2003) pointed out that analysis based on political/ administrative boundaries can be applied in soil conservation planning and was good for public perception. 2.4. Data analysis The pedodiversity indexes (S, M, H, Hmax and E) were calculated for each county and the spatial distribution maps of these indexes were drawn in ArcMap 9.3. The abundance distribution model was simulated in Origin 8.0 (OriginLab) and Smirnov–Kolmogorov test of goodness of fit was used to determine the best model. The richness-area relationship was analyzed in Excel 2013 and regression analysis were used to obtain the best model, i.e., the model with the highest coefficient of determination. Partial correlation analysis between pedodiversity indexes and environmental factors for each county was done in SPSS 22.0 (International Business Machines Corporation, IBM). The influencing factors considered were the means and ranges of elevation, slope gradient, precipitation and temperature, and then population density and percent farmland. After correlation analysis, the factors with significant effect on pedodiversity were considered for canonical correspondence analysis (CCA). The CCA analysis was done in Canoco software version 5 (Plant Research International of Wageningen) and used to rank the importance of the influencing factors.
3.3. Abundance distribution model For the entire Taihang Mountain region, Cambisols was the most abundant and Podzoluvisols the least abundant (Fig. 3). The distribution of the soil types abundance was best described by the lognormal model, which indicated that soil types with intermediate abundance were most common (Ibañez et al., 1995). For the richness-area relationship, R2 was 0.23 (not shown) for the power curve and 0.46 for the logarithmic curve. This suggested that the richness-area relationship could be described by logarithmic model (Fig. 4), rather than power expression, although Ibáñez et al. (2005) noted that lognormal model of abundance distribution always output a power curve of richness-area relationship. Base on the richness-area curve, richness increased drastically with increasing area when area was less than 1000 km2, but increased gradually when area exceeded 1000 km2 (Fig. 4). It indicated that the influence of area on pedodiversity was negligible when the area was above 1000 km2.
3. Results 3.4. Effect of environmental and anthropogenic factors on pedodiversity 3.1. Descriptive analysis of pedodiversity indexes As mentioned earlier, the effect of area on pedodiversity was very small for areas larger than 1000 km2. In order to eliminate the effect of area, counties with area larger than 1000 km2 (65 counties in total) were selected for partial correlation analysis between the influencing factors and pedodiversity indexes. For the effect of environmental factors, richness index was significantly correlated with the ranges of elevation, precipitation and slope (Table 3). However, Menhinick's index was significantly correlated with mean precipitation and slope. While Shannon index was significantly influenced by the ranges of elevation and slope, and then percent farmland. The maximum Shannon index was influenced by precipitation range. Unlike the other indexes, evenness was only influenced by percent farmland. These results showed that for environmental factors, range had more effect on pedodiversity than mean. For anthropogenic factors, population density was only significantly correlated to Menhinick's index while percent farmland was significantly correlated with all the tested indexes, except the maximum Shannon index. This indicated that farming, rather than population density was a more important driving factor. Partial correlation analysis showed that the effect of elevation, precipitation and slope as well as population density and percent farmland on pedodiversity was stronger than the other factors. Therefore, the factors with stronger effects were selected for CCA (Fig. 5). These factors were grouped into two distinct categories. The first category mainly consisted of environmental factors (e.g., range of elevation, slope and precipitation) which were represented by the first axis. The second category was anthropogenic factors (e.g., percent farmland and population density), which was ranked on the second axis. The length of the arrow was longest for the range of elevation, indicating that it had a higher effect on pedodiversity than the other factors. The second important driving factor was farming, as the arrow for percent farmland was also very long. Compared with the other
The mean richness of soil taxa for the 101 counties in Taihang Mountain was 19, with the maximum value (26) 13 times higher than the minimum value (2). Although derived from richness index, the range of Menhinick's index for the counties was lower (with the maximum value about 8 times higher than the minimum). The range of Shannon indexes for the counties was 0.69–2.30, with a mean of 1.69. The mean evenness index was 0.58, with the maximum (1.00) occurring in Anyangshi county, indicating that the soil types were most evenly distributed in that area. The minimum value (0.29) was for Mengjin county, indicating that only few soil types were dominant in the region (mainly Cambisols soil). The range of the coefficient of variation of the indexes for the 101 counties was 0.10–0.26, which suggested a moderate variation of pedodiversity among the counties in Taihang Mountain region (Table 2). 3.2. Spatial distributions of selected pedodiversity indexes The soil taxa richness in the northern part was generally higher than in the southern and middle parts, but Menhinick's index increased from Table 2 Descriptive statistics of pedodiversity indexes for 101 counties in Taihang Mountain study area, China. Note: SD = standard deviation and CV = coefficient of variation. Index
Min
Max
Mean
SD
CV
Skewness
Kurtosis
Richness Menhinick's index Shannon's index The maximum Shannon's index Evenness
2 0.13 0.69 0.69
26 1.03 2.30 3.26
19 0.57 1.69 2.92
3.52 0.15 0.30 0.28
0.19 0.26 0.18 0.10
−1.04 0.74 −0.47 −5.23
4.34 1.57 0.57 40.72
0.29
1.00
0.58
0.09
0.16
0.46
3.43
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Fig. 2. Maps of spatial distribution of Richness (S), Menhinich's index (M), Shannon index (H), Maximum Shannon index (Hmax) and Evenness index (E) by county in Taihang Mountain study area, China.
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6
Log abundance
5 4 3 2 1
Cambisols Luvisols Fluvisols Regosols Leptosols Anthrosols Arenosols Kastanozems Gypsisols Gleysols Chernozems Calcisols Phaeozems Solonchaks Vertisols Andosols Greyzems Acrisols Alisols Lixisols Solonetz Ferralsols Histosols Planosols Nitisols Podzoluvisols
0
Fig. 3. Plot of abundance distribution model of soil taxa in Taihang Mountain study area, China. Fig. 5. Plot of canonical correspondence analysis (CCA) of the influencing factors on pedodiversity in Taihang Mountain study area, China. PF = percentage of farmland, PD = population density, RP = precipitation range, RS = slope gradient range and RE = elevation range.
correspondence analysis. 4. Discussions 4.1. Pedodiversity in Taihang Mountain Generally, pedodiversity in mountain areas is relatively high because complex environments in these terrains. Taihang Mountain has a high heterogeneity both in geological setting and vegetation pattern (Zhang et al., 2006; Wang and Li, 2008). The mean Shannon index for the investigated 101 counties in Taihang Mountain was 1.69 (Table 2), apparently lower than those reported by Ibañez et al. (1998) and Costantini and L'Abate (2016), which were respectively 2.30–2.92 (at continent scale) and 5.35 (for Italy). Smaller Shannon index also have been reported in studies by Scharenbroch and Bockheim (2007) and Kooch et al. (2015), with respectively values of 1.11 for Huron Mountains, Michiga and 0.94 for temperate forest in Mazandaran province in the north of Iran. Although the comparison of Shannon index for different studies could be unreasonable due to differences in scale,
Fig. 4. Plot of the relationship between soil taxa richness and county area in Taihang Mountain study area, China.
factors, the effect of population density and precipitation range on pedodiversity was relatively low (Fig. 5). The total variance interpreted by CCA was 33.3%, indicating that there were other factors with strong effect on pedodiversity, but not considered in the canonical
Table 3 Partial correlation analysis between pedodiversity indexes and the related influencing factors for Taihang Mountain study area, China. Note: * means the correlation was significant at 0.05 level, ** means the correlation was significant at 0.01 level. Factors
Parameters
Mean elevation
Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p
Elevation range Mean precipitation Precipitation range Mean temperature Temperature range Mean slope Slope range Population density Percent farmland
coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient
Richness
Menhinick's index
Shannon's index
Maximum Shannon's index
Evenness
−0.20 0.13 0.60⁎⁎ 0.00 0.24 0.06 0.33⁎⁎ 0.01 0.01 0.95 0.18 0.15 0.22 0.08 0.33⁎⁎ 0.01 −0.05 0.70 −0.24⁎ 0.05
−0.22 0.08 0.11 0.41 −0.30⁎ 0.02 −0.12 0.35 0.25⁎ 0.04 −0.12 0.35 −0.26⁎ 0.04 −0.17 0.18 0.25* 0.05 0.31⁎⁎ 0.02
− 0.24 0.44 0.25⁎ 0.05 0.19 0.12 0.14 0.26 − 0.01 0.92 0.02 0.89 − 0.14 0.25 0.44⁎⁎ 0.00 − 0.04 0.77 − 0.33⁎⁎ 0.01
− 0.10 0.06 0.22 0.09 0.22 0.07 0.32⁎⁎ 0.01 0.02 0.90 0.18 0.15 0.16 0.20 0.14 0.28 − 0.04 0.98 − 0.32 0.01
−0.20 0.13 0.03 0.81 0.13 0.31 0.04 0.73 −0.01 0.91 −0.04 0.76 0.10 0.42 0.04 0.76 0.22 0.87 −0.26⁎ 0.04
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results of this study confirmed this observation because elevation range had a stronger effect on pedodiversity than elevation mean (Table 3). This showed that pedodiversity in high relief/aptitude areas was higher than in plain areas — the higher the relief, the higher the pedodiversity. The same trend was noted for slope gradient and precipitation. This demonstrated that in mountain areas, the ranges of natural factors in each county (e.g., the range of elevation, slope gradient and precipitation) were a more important variable than their means. It was not difficult to understand because pedodiversity depends on soil forming conditions and processes (Caniego et al., 2007; Rannik et al., 2016). Pedodiversity can be high in regions with higher local variability of soil forming factors (Danek et al., 2016). Besides natural factors, anthropogenic activities significantly affect pedodiversity (Lo Papa et al., 2011). Farming can cause soil erosion and thereby limits pedodiversity (Costantini and Dazzi, 2013; Tennesen, 2014). This becomes more pronounced in mountain areas, especially areas with high slope gradient where percent farmland is negatively correlated with pedodiversity (Table 3). Interestingly, human population density had no effect on pedodiversity. This could be due to the fact that industries or tourist centers are developed in high population density areas, which elements have relatively little effect on pedodiversity. This suggested that anthropogenic activities limited pedodiversity mainly through farming. The total interpretation of the variance by CCA was relatively low (33.3%), suggesting that other factors influenced pedodiversity in the study area. However, the rank of environmental factors considered was very clear, with elevation having the most effect on pedodiversity and followed by percent farmland (Fig. 5). Other studies also noted that topography and anthropogenic activities influence pedodiversity (Phillips and Marion, 2005; Shangguan et al., 2014; Danek et al., 2016). This element was critical for the management of soil and the protection of pedodiversity. As already known, natural factors are not something we can control. Thus, the management of farmlands is the most feasible way of protecting pedodiversity.
classification standards, etc., it, however, can give a general sense of the trend in pedodiversity, which generally has a range of 1.5–3.5 and hardly exceeding 4.5 (Margalef, 1972; Ibañez et al., 1995). Therefore, pedodiversity in Taihang Mountain was not as high as expected for a mountain region. This could be due to the long-term human disturbances (e.g., farming and construction) which significantly impair pedodiversity. This was also noted by Shangguan et al. (2014), which study showed that there were more rare and endangered soils in Taihang Mountain than in other areas of China. Evenness index refers to the way in which each soil type is distributed. If two different regions have the same area and richness, the one with higher evenness index will have a higher pedodiversity because different types occupy relatively equal areas (Ibañez et al., 1995). The range of evenness index is 0–1, which can be very different for different regions (McBratney and Minasny, 2007; Lo Papa et al., 2011; Kooch et al., 2015; Costantini and L'Abate, 2016). Evenness index for Taihang Mountain was 0.58 (Table 2), which was a medium value compared with the values reported in other studies. The value in this study was reasonable because the environment in mountain areas always highly heterogeneous (Wang and Li, 2008; Fu et al., 2016), causing an uneven distribution of soil types. The study of pedodiversity using indexes such as Shannon index, evenness index, etc. is limited by different soil classification systems. This is because different classification standards give very different values of an index for the same region (Guo et al., 2003; Costantini and L'Abate, 2016). Another limitation in the study of pedodiversity is the lack of accurate data. There is a strong spatial variability of soil types, especially in mountain areas. More soil samples or soil surveys are needed to identify soil taxa. This always requires huge manpower and financial resources. Thus as stated by Tennesen (2014), much more should be done by national governments and the international community to increase the availability of data for research. 4.2. Richness-area relationship for Taihang Mountain
5. Conclusions
Just like biodiversity, pedodiversity increases with increasing area (Ibáñez and Feoli, 2013), although the rate of increase can be different for different regions. There are also differences in richness-area relationships, including different power and logarithmic functions (Feoli et al., 2007; Phillips and Marion, 2007; Saldaña and Ibáñez, 2007). The relationship between richness and area for Taihang Mountain was best described by logarithmic function (Fig. 4). Saldaña and Ibáñez (2004) also observed the same relationship for low and middle terraces, but a different one for high terraces. Several other studies have noted that the power function is most fit for richness-area relationship (Phillips, 2001; Ibáñez et al., 2005). In this study, R2 for richness-area correlation was 0.46 (Fig. 4), meaning that 46% of the variation in richness was explained by the area of the county. This value was much smaller than that of Saldaña and Ibáñez (2004) where R2 of 0.8 was observed, meaning 80% of the variation in richness was explained by the area. This difference in R2 is due to the fact that the complexity of driving factors in mountain areas causes high variations in pedodiversity. It is also possible that the interpreted variance by area is lower. Richness increased fastly when area was smaller than 1000 km2, but slowly when area was larger than 1000 km2 (Fig. 4). It suggested that 1000 km2 was the breakthrough point, above which the effects of area on pedodiversity was low. It was then concluded that the effect of area can be ignored and therefore statistical analysis used to analyze the effect of natural and anthropogenic factors on pedodiversity when area was larger than 1000 km2.
There was a moderate variation of pedodiversity in Taihang Mountain region, North China. The abundance distribution model output was lognormal, indicating that soils with medium abundance were most dominant in the study area. The richness-area relationship was best fitted by logarithmic function and the inflection point was at 1000 km2. It suggested that the effect of area on the calculated pedodiversity was negligible when the region of interest exceeded 1000 km2. Pedodiversity in Taihang Mountain was significantly driven by both natural and anthropogenic factors. The effect due to the range of each natural factors was more than that due to the mean of the factors. Elevation range was the natural factor with the highest effect and farming the anthropogenic factor with the highest effect on pedodiversity. The results of this study deepened the existing knowledge on pedodiversity, which is important for soil management and the protection of endangered soil types in mountain terrains. Acknowledgements This work was supported by the National Basic Research Program (973 Program) of China (No. 2015CB452705). References Amundson, R., Guo, Y., Gong, P., 2003. Soil diversity and land use in the United States. Ecosystems 6 (5), 470–482. Amundson, R., Berhe, A.A., Hopmans, J.W., Olson, C., Sztein, A.E., Sparks, D.L., 2015. Soil and human security in the 21st century. Science 348 (6235), 1261071. Bockheim, J.G., Schliemann, S.A., 2014. Soil richness and endemism across an environmental transition zone in Wisconsin, USA. Catena 113, 86–94. Caniego, F., Ibáñez, J., Martínez, F.S.J., 2007. Rényi dimensions and pedodiversity
4.3. Factors influencing pedodiversity in mountain areas Pedodiversity can be influenced by topography (Guo et al., 2003; Tan et al., 2003; Kooch et al., 2015) and studies show that mountain areas have a high pedorichness (Costantini and L'Abate, 2016). The 236
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diversity. Trans. Connecticut Acad. Arts Sci. 44, 211–235. Mcbratney, A., 1992. On variation, uncertainty and informatics in environmental soil management. Soil Res. 30 (6), 913–935. McBratney, A., Minasny, B., 2007. On measuring pedodiversity. Geoderma 141 (1–2), 149–154. Menhinick, E.F., 1964. A comparison of some species-individuals diversity indices applied to samples of field insects. Ecology 45 (4), 859–886. Minasny, B., McBratney, A.B., Hartemink, A.E., 2010. Global pedodiversity, taxonomic distance, and the World Reference Base. Geoderma 155 (3–4), 132–139. Phillips, J.D., 2001. The relative importance of intrinsic and extrinsic factors in pedodiversity. Ann. Assoc. Am. Geogr. 91 (4), 609–621. Phillips, J.D., Marion, D.A., 2005. Biomechanical effects, lithological variations, and local pedodiversity in some forest soils of Arkansas. Geoderma 124 (1–2), 73–89. Phillips, J.D., Marion, D.A., 2007. Soil geomorphic classification, soil taxonomy, and effects on soil richness assessments. Geoderma 141 (1), 89–97. Rannik, K., Kõlli, R., Kukk, L., Fullen, M.A., 2016. Pedodiversity of three experimental stations in Estonia. Geoderma Reg. 7 (3), 293–299. Ren, Y., Zhang, X., 2015. Study on pedodiversity: status quo and future chanllenges (in Chinese). Acta Pedol. Sin. 52 (1), 9–18. Saldaña, A., Ibáñez, J.J., 2004. Pedodiversity analysis at large scales: an example of three fluvial terraces of the Henares River (central Spain). Geomorphology 62 (1–2), 123–138. Saldaña, A., Ibáñez, J., 2007. Pedodiversity, connectance and spatial variability of soil properties, what is the relationship? Ecol. Mod. Theol. 208 (2), 342–352. Scharenbroch, B., Bockheim, J., 2007. Pedodiversity in an old-growth northern hardwood forest in the Huron Mountains, Upper Peninsula, Michigan. Can. J. For. Res. 37 (6), 1106–1117. Shangguan, W., Gong, P., Liang, L., Dai, Y., Zhang, K., 2014. Soil diversity as affected by land use in China: consequences for soil protection. Sci. World J. 2014. Tan, M., Zhang, X., Chen, J., Yan, W., Yan, Y., 2003. Pedodiversity: a case study based on 1:1 million scale SOTER of Shandong Province, China. Pedosphere 13 (3), 219–226. Tennesen, M., 2014. Rare earth. Science 346 (6210), 692–695. Toomanian, N., Jalalian, A., Khademi, H., Eghbal, M.K., Papritz, A., 2006. Pedodiversity and pedogenesis in Zayandeh-rud Valley, Central Iran. Geomorphology 81 (3–4), 376–393. Wang, Y., Li, H., 2008. Initial formation and Mesozoic tectonic exhumation of an intracontinental tectonic belt of the northern part of the Taihang Mountain belt, eastern Asia. J. Geol. 116 (2), 155–172. Zhang, J., Xi, Y., Li, J., 2006. The relationships between environment and plant communities in the middle part of Taihang Mountain Range, North China. Community Ecol. 7 (2), 155–163.
indices of the earth pedotaxa distribution. Nonlinear Process. Geophys. 14 (4), 547–555. Cao, J., 2011. Research on Water Movement and High-Efficient Utilization on Slope Characterized by a Soil-Rock Binary Medium in Mountain Area of Taihang Mountain (in Chinese). Graduate University of Chiness Academy of Science. Costantini, E.A., Dazzi, C., 2013. The soils of Italy. Springer. Costantini, E.A.C., L'Abate, G., 2016. Beyond the concept of dominant soil: preserving pedodiversity in upscaling soil maps. Geoderma 271, 243–253. Danek, P., Samonil, P., Phillips, J.D., 2016. Geomorphic controls of soil spatial complexity in a primeval mountain forest in the Czech Republic. Geomorphology 273, 280–291. FAO, I., Isric, I., 2009. JRC: Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. Feoli, E., Pérez-Gómez, R., Oyonarte, C., Ibáñez, J.J., 2007. Using spatial data mining to analyze area-diversity patterns among soil, vegetation, and climate: a case study from Almería, Spain. Geoderma 287, 164–169. Fu, J., Jiang, D., Huang, Y., 2014. 1 km grid population dataset of China (2005, 2010) (in Chinese). Acta Geograph. Sin. 69, 41–44. Fu, T., Chen, H., Wang, K., 2016. Structure and water storage capacity of a small karst aquifer based on stream discharge in southwest China. J. Hydrol. 534, 50–62. Guo, Y., Gong, P., Amundson, R., 2003. Pedodiversity in the United States of America. Geoderma 117 (1), 99–115. Ibáñez, J.J., Bockheim, J.G., 2013. Pedodiversity. CRC Press. Ibáñez, J.J., Feoli, E., 2013. Global relationships of pedodiversity and biodiversity. Vadose Zone J. 12 (3). http://dx.doi.org/10.2136/vzj2012.0186. Ibañez, J.J., De-Albs, S., Bermúdez, F.F., García-Álvarez, A., 1995. Pedodiversity: concepts and measures. Catena 24 (3), 215–232. Ibañez, J.J., De-Alba, S., Lobo, A., Zucarello, V., 1998. Pedodiversity and global soil patterns at coarse scales (with discussion). Geoderma 83 (3), 171–192. Ibáñez, J.J., Caniego, J., San José, F., Carrera, C., 2005. Pedodiversity-area relationships for islands. Ecol. Model. 182 (3–4), 257–269. Kooch, Y., Hosseini, S.M., Scharenbroch, B.C., Hojjati, S.M., Mohammadi, J., 2015. Pedodiversity in the Caspian forests of Iran. Geoderma Reg. 5, 4–14. Krasilnikov, P., Calderon, N.G., Huerta, A.I., 2009. Pedodiversity in mountainous tropical semideciduous forests of Sierra Madre del Sur, Mexico. Eurasian Soil Sci. 42 (13), 1435–1442. Li, Y., Xu, Q., Wang, Z., 2004. Discussion on ecological restoration in Taihang mountain area of Hebei Province (in Chinese). In: Soil and Water Conservation in China. 11. pp. 10–11. Lo Papa, G., Palermo, V., Dazzi, C., 2011. Is land-use change a cause of loss of pedodiversity? The case of the Mazzarrone study area, Sicily. Geomorphology 135 (3–4), 332–342. Margalef, R., 1972. Homage to Evelyn Hutchinson, or why there is an upper limit to
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Pedodiversity and its controlling factors in mountain regions — A case study of Taihang Mountain, China
MARK
Tonggang Fua, Lipu Hana, Hui Gaoa,b, Hongzhu Lianga,b, Xiaorong Lia,b, Jintong Liua,⁎ a Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China b University of Chinese Academy of Sciences, Beijing 100049, China
A R T I C L E I N F O
A B S T R A C T
Handling Editor: A.B. McBratney
Knowledge on pedodiversity is crucial for the management and protection of soil resources. However, little is known about the controlling factors of pedodiversity, especially in mountain regions with a highly heterogeneous environment. This paper analyzed pedodiversity (soil taxa diversity) and the influencing factors in Taihang Mountain region in China. The richness (S), Menhinick's index (M), Shannon index (H), maximum Shannon index (Hmax) and evenness index (E) were used to explain pedodiversity in the study area. The influences of natural factors (including elevation, slope gradient, precipitation and temperature) and anthropogenic factors (including population density and percentage of farmland) were analyzed using partial correlation analysis and canonical correspondence analysis. The results showed that for the 101 counties in Taihang Mountain, average H was 1.69, which suggested a relatively low pedodiversity (normal H range is 1.5–3.5, and rarely exceeds 4.5) in the study area. Logarithmic normal distribution had the best fit for the abundance distribution model, indicating that intermediate abundance of soil type was most common. The best fit Logarithmic function was for the relationship between richness and area. Soil taxa richness increased significantly for areas less than 1000 km2, but increased gradually for areas larger than 1000 km2. This suggested that 1000 km2 was the breakthrough point on the richness-area curve, and that the effect of area on pedodiversity could be negligible for regions larger than 1000 km2. Canonical correspondence analysis showed that the influence of these factors on pedodiversity decreased in the order of: elevation > percent farmland > slope gradient > population density > precipitation. This suggested that elevation and farming had the highest effect on pedodiversity. The study provided further insight into pedodiversity in mountain regions, which is critical for the protection of soil resources and pedodiversity.
Keywords: Pedodiversity Heterogeneous environment Anthropogenic factor Shannon index Canonical correspondence analysis
1. Introduction Pedodiversity, or simply soil diversity, is used to explore, quantify and compare the complexity of soil patterns in different units and thereby provides the basis for biodiversity (Ibañez et al., 1995; Tennesen, 2014; Amundson et al., 2015). Pedodiversity could be divided into taxonomic pedodiversity, functional pedodiversity, genetic pedodiversity and soil property diversity (Saldaña and Ibáñez, 2004; Ibáñez et al., 2005; Kooch et al., 2015). The study of pedodiversity can be done at different scales, including polypedon, association, landscape, drainage basin and geographical scale of soil (Ibañez et al., 1995). A thorough understanding of pedodiversity is helpful in both soil management and soil protection, and can also be applied in the protection of biodiversity (Tennesen, 2014; Rannik et al., 2016). Compared with
biodiversity, however, research on pedodiversity is very limited, despite the stress for pedodiversity research in different regions (Guo et al., 2003; Saldaña and Ibáñez, 2004; Shangguan et al., 2014). The definition of pedodiversity first appeared in the 1990s (Mcbratney, 1992; Ibañez et al., 1995; Ibañez et al., 1998) and pedodiversity has since gradually gained interest in Spain, America, China and other countries (Amundson et al., 2003; Tan et al., 2003; Saldaña and Ibáñez, 2004; Toomanian et al., 2006; Minasny et al., 2010; Lo Papa et al., 2011; Ren and Zhang, 2015). Recently, books (Ibáñez and Bockheim, 2013) and special issues (Volume 135, Issues 3–4, Pages 213–352, 15 December 2011, Geomorphology) have been set up focusing mainly on pedodiversity. In Google Scholar, the search results of “pedodiversity/soil diversity” increased significantly from the 1990s (458) to the 2010s (3356). These existed researches on pedodiversity
⁎ Corresponding author at: Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 286 Huaizhong Road, 050021 Shijiazhuang, China. E-mail address: [email protected] (J. Liu).
http://dx.doi.org/10.1016/j.geoderma.2017.09.027 Received 9 January 2017; Received in revised form 18 September 2017; Accepted 21 September 2017 Available online 30 September 2017 0016-7061/ © 2017 Published by Elsevier B.V.
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province and Henan province. Taihang Mountain has a highly heterogeneous geological setting. Because of the orogeny, the western and eastern parts of the mountain have very different structural evolution, sedimentation and tectonic settings (Wang and Li, 2008). The average elevation of Taihang Mountain is 1000–1500 m, which decreases from the northwest to the southeast. The highest elevation (2882 m) is in the northern part of the mountain at Xiaowutai. The climate is the East Asian Monsoon type which is characterized by warm, rainy summer and cold, dry winter. The average annual (2005–2014) precipitation is 456.57 mm and the annual mean temperature is 11.36 °C. Both precipitation and temperature decrease from the southeast to the northwest. The mean precipitation for the southeast and northwest parts of the study area are 617.23 mm and 385.08 mm, with corresponding mean temperatures of 12.95 °C and 7.65 °C, respectively. Due to precipitation and temperature gradients, the heterogeneity of the vegetation decreases from the north to the south. In addition, the vegetation patterns are significantly affected by topography, elevation, slope and aspect as well as the interactions between these environmental factors (Zhang et al., 2006). Soils in Taihang Mountain developed mainly from limestone in the northern and southern parts, but from gneiss in the middle region. Moreover, the soils in the region are thin with abundant rock fragments. Based on data from an experimental site located in the middle region of the study area, the average soil thickness is 35 cm with above 0.1-cm rock fragment content of 21.4% (Cao, 2011). Taihang Mountain region contains 101 counties. The environmental and human-related conditions in this region are summarized in Table 1. In the table, mean elevation denotes the average elevation for each county. The range of elevation (i.e. elevation range in Table 1) for each county is calculated by subtracting the minimum from the maximum elevation. The means and ranges of other factors were calculated in the same way.
focused mainly on pedodiversity index, richness-area relationship and rare/endangered soil taxa at different scales. For example, Ibañez et al. (1995) proposed three ways to describe pedodiversity, including richness, abundance distribution models and indexes based on proportional abundance. Since then, these methods have been widely used (Saldaña and Ibáñez, 2004; Krasilnikov et al., 2009; Lo Papa et al., 2011; Costantini and L'Abate, 2016). Richness-area relationship has always been studied in conjunction with abundance models, both of which reflect the distribution of the individuals of different soil types (Ibáñez et al., 2005; Feoli et al., 2007). The loss of pedodiversity is also important phenomenon that has been studied. Spain, America, China and other countries are threatened by pedodiversity loss, and rare and endangered soils have been identified for these regions (Amundson et al., 2003; Guo et al., 2003; Shangguan et al., 2014; Tennesen, 2014). Until now, these aspects of pedodiversity have been well studied. However, what is not clear is the factors which control pedodiversity in a given region. Based on several studies, the main factors controlling pedodiversity include natural factors (e.g., geomorphology, meteorology, etc.) and anthropogenic factors (e.g., farming, urbanization, etc.). Geomorphology is an important factor, and mountain regions generally have higher pedodiversity than plain regions (Ibáñez and Feoli, 2013; Costantini and L'Abate, 2016). In addition, pedodiversity in transition zones (zones between different land use types or landforms) are higher than in non-transition zones (Bockheim and Schliemann, 2014). Meteorology also influences pedodiversity, and low pedodiversity is often associated with low or high rainfall/temperature (Minasny et al., 2010). Besides natural factors, agriculture and urbanization (which reflect human activity) are also important influencing factors of pedodiversity (Guo et al., 2003; Tennesen, 2014). Moreover, the controlling factors of pedodiversity can be different for different soil hierarchies. For example, soil diversity is mainly controlled by bioclimatic and hydrologic factors at high classification levels, but by parent material, topography and hydrological conditions at low levels (Shangguan et al., 2014). Although numerous factors were studied on pedodiversity, most are still described qualitatively. Quantitative analysis of the factors and ranking of their importance are critical for an in-depth understanding of pedodiversity for use in soil protection and management. Taihang Mountain in North China is a north-northeast range of mountain belt and is an important transition zone between the Loess Plateau and North China Plain (Wang and Li, 2008). Mountain regions, as noted by Costantini and L'Abate (2016), generally have high pedodiversity. Moreover, transition zones often have high pedodiversity (Bockheim and Schliemann, 2014). Considering these conditions, Taihang Mountain should have high pedodiversity. However, because of overexploitation of natural resources and excessive disturbance by local residents, the Taihang Mountain ecosystem has degenerated significantly (Li et al., 2004). Thus soil loss has become a severe issue, with more endangered soil types in this region than in other areas of China (Shangguan et al., 2014). This makes it more significant to understand pedodiversity and the controlling factors in the fragile Taihang Mountain ecosystem. Thus the objectives of this paper were to: 1) determine the spatial distribution of pedodiversity, 2) understand the richness-area relationship, and 3) quantify and rank the influencing factors of pedodiversity in Taihang Mountain region.
2.2. Pedodiversity indexes The indexes used to describe pedodiversity can be divided into three main categories (Ibañez et al., 1995). The first is the richness index (S), which is the number of objects (i.e., soil types) within a region of interest. Richness index only focuses on the number of objects and largely ignores the number of individuals (N, i.e., the area of the soil types) in the region. Therefore, derived parameters based on richness are also used. Menhinick's index (M), for example, can be calculated from S and N as (Menhinick, 1964):
M=
S N
(1)
The second category used in pedodiversity analysis is abundance distribution model, which describes the distribution of the object abundance. The abundance model is a histogram in which the X-axis is the rank of the object abundance from maximum to minimum and the Y-axis is the logarithm of the abundance. Four models are widely used in this simulation model — geometric model (which suggests that a few objects are dominant), broken stick model (which gives an even distribution of the objectives), and logarithmic model and log normal model (both of which show that intermediate abundance objects are dominant). The detailed formulas for these models are given by Ibáñez et al. (2005). Each of the abundance models gives one type of richnessarea relationship and are widely used in both biodiversity and pedodiversity. For example, the broken-stick and lognormal models output power curves, but the geometric and logarithmic models output logarithmic curves for richness-area relationships. The third category of pedodiversity indexes is based on proportional abundance of objects. The Shannon index (H) belongs to this category and is given as:
2. Materials and methods 2.1. Site description Taihang Mountain (34°36′–40°47′N, 110°42′–116°34′E), which is located in North China (Fig. 1), is the natural boundary between the Loess Plateau and the North China Plain. It extends from the northeast to the southwest for an area of 120,000 km2 and stretches across five administrative regions, including Beijing, Hebei province, Shanxi 231
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Fig. 1. A map depicting the location of Taihang Mountain study area in China (left plate) and expansion on the study area depicting administrative counties. i=n
2.3. Data source
H = − ∑ pi × ln pi
(2)
i=1
The soil taxa data used were from Harmonized World Soil Database (FAO and Isric, 2009), where soils are divided into 28 categories. It is based on the FAO-90 soil classification system and can be considered equivalent (in terms of detail) to the order or suborder categories in the US Soil Taxonomy. The database is for the whole world, from which Taihang Mountain was clipped out for this study using ArcMap 9.3 (Environmental Systems Research Institute, Inc. (ESRI)). Topographic data (including elevation, slope gradient) were derived from ASTER GDEM (30 m resolution). This database was developed jointly by the Ministry of Economy, Trade and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA). Meteorological data (including precipitation and temperature) were obtained from 101 automatic weather stations in Taihang Mountain. The effect of humans was expressed in terms of population density and
where pi is the proportion of individual number of the i th object to the total number of individuals. Based on Margalef (1972), the range of H is 1.5–3.5 and it rarely exceeds 4.5. The maximum H (Hmax) occurs when all objects are evenly distributed and it is then calculated as: (3)
Hmax = lnS
The evenness index (E), derived from the Shannon index, is another important parameter used to describe pedodiversity and it is expressed as:
E=
H Hmax
(4)
The range of E is 0–1. When E approaches 1, all the objects get evenly distributed and when it approaches 0, the region is dominated by a few objects.
Table 1 Descriptive statistics of natural and anthropogenic factors in 101 counties in Taihang Mountain area, China. Note: p in p/km2 denotes persons. Factor
Min
Max
Mean
Standard deviation
Skewness
Kurtosis
Area (km2) Mean elevation (m) Elevation range (m) Mean slope (°) Slope range (°) Mean precipitation (mm) Range of precipitation (mm) Mean temperature (°C) Temperature range (°C) Population density (p/km2) (people/km2) Percent farmland (%)
126.00 77.47 98.00 3.38 36.74 350.49 4.12 5.46 0.14 42.52 3.60
3195.00 1504.73 2432.00 22.74 79.46 669.16 86.07 14.68 4.86 5766.06 76.63
1350.81 751.79 1321.85 12.44 68.30 456.57 34.89 11.63 1.40 494.31 41.41
704.07 415.95 514.38 4.28 7.70 49.64 15.59 2.10 0.94 747.45 16.97
0.45 − 0.03 0.01 0.05 − 1.41 0.32 0.32 − 0.79 1.47 4.73 − 0.06
− 0.51 − 1.31 − 0.44 − 0.77 2.80 − 0.49 0.24 0.41 3.09 27.70 − 0.75
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the north to the south. This indicated that although the trend of the richness was not significant, the number of individuals decreased from north to south, resulting in an increasing trend in Menhinick's index. The Menhinick's index was calculated from the richness and area of the counties and therefore depended both on area and richness. The increasing fragmentation from north to south was also the combined effect of area and richness. The Shannon index and the maximum Shannon index had similar trends, with the northern part higher than the middle and southern parts. This indicated that the northern part of Taihang Mountain had a relatively higher pedodiversity. However, the evenness index was quite different in spatial distribution, with the middle part higher than the northern and southern parts (Fig. 2). This indicated that soil taxa in the middle part was most evenly distributed.
the proportion of farmland in each county. The population density data (in the form of raster with a pixel size of 1 km) was obtained from the 2010 census data (Fu et al., 2014). The proportion of farmland was calculated in a raster map of land use type obtained from Landsat TM image for 2010 by visual interpretation method. All the calculations and analyses of the pedodiversity indexes were based on administrative boundaries (county boundary), rather than on ecological boundaries, as Amundson et al. (2003) pointed out that analysis based on political/ administrative boundaries can be applied in soil conservation planning and was good for public perception. 2.4. Data analysis The pedodiversity indexes (S, M, H, Hmax and E) were calculated for each county and the spatial distribution maps of these indexes were drawn in ArcMap 9.3. The abundance distribution model was simulated in Origin 8.0 (OriginLab) and Smirnov–Kolmogorov test of goodness of fit was used to determine the best model. The richness-area relationship was analyzed in Excel 2013 and regression analysis were used to obtain the best model, i.e., the model with the highest coefficient of determination. Partial correlation analysis between pedodiversity indexes and environmental factors for each county was done in SPSS 22.0 (International Business Machines Corporation, IBM). The influencing factors considered were the means and ranges of elevation, slope gradient, precipitation and temperature, and then population density and percent farmland. After correlation analysis, the factors with significant effect on pedodiversity were considered for canonical correspondence analysis (CCA). The CCA analysis was done in Canoco software version 5 (Plant Research International of Wageningen) and used to rank the importance of the influencing factors.
3.3. Abundance distribution model For the entire Taihang Mountain region, Cambisols was the most abundant and Podzoluvisols the least abundant (Fig. 3). The distribution of the soil types abundance was best described by the lognormal model, which indicated that soil types with intermediate abundance were most common (Ibañez et al., 1995). For the richness-area relationship, R2 was 0.23 (not shown) for the power curve and 0.46 for the logarithmic curve. This suggested that the richness-area relationship could be described by logarithmic model (Fig. 4), rather than power expression, although Ibáñez et al. (2005) noted that lognormal model of abundance distribution always output a power curve of richness-area relationship. Base on the richness-area curve, richness increased drastically with increasing area when area was less than 1000 km2, but increased gradually when area exceeded 1000 km2 (Fig. 4). It indicated that the influence of area on pedodiversity was negligible when the area was above 1000 km2.
3. Results 3.4. Effect of environmental and anthropogenic factors on pedodiversity 3.1. Descriptive analysis of pedodiversity indexes As mentioned earlier, the effect of area on pedodiversity was very small for areas larger than 1000 km2. In order to eliminate the effect of area, counties with area larger than 1000 km2 (65 counties in total) were selected for partial correlation analysis between the influencing factors and pedodiversity indexes. For the effect of environmental factors, richness index was significantly correlated with the ranges of elevation, precipitation and slope (Table 3). However, Menhinick's index was significantly correlated with mean precipitation and slope. While Shannon index was significantly influenced by the ranges of elevation and slope, and then percent farmland. The maximum Shannon index was influenced by precipitation range. Unlike the other indexes, evenness was only influenced by percent farmland. These results showed that for environmental factors, range had more effect on pedodiversity than mean. For anthropogenic factors, population density was only significantly correlated to Menhinick's index while percent farmland was significantly correlated with all the tested indexes, except the maximum Shannon index. This indicated that farming, rather than population density was a more important driving factor. Partial correlation analysis showed that the effect of elevation, precipitation and slope as well as population density and percent farmland on pedodiversity was stronger than the other factors. Therefore, the factors with stronger effects were selected for CCA (Fig. 5). These factors were grouped into two distinct categories. The first category mainly consisted of environmental factors (e.g., range of elevation, slope and precipitation) which were represented by the first axis. The second category was anthropogenic factors (e.g., percent farmland and population density), which was ranked on the second axis. The length of the arrow was longest for the range of elevation, indicating that it had a higher effect on pedodiversity than the other factors. The second important driving factor was farming, as the arrow for percent farmland was also very long. Compared with the other
The mean richness of soil taxa for the 101 counties in Taihang Mountain was 19, with the maximum value (26) 13 times higher than the minimum value (2). Although derived from richness index, the range of Menhinick's index for the counties was lower (with the maximum value about 8 times higher than the minimum). The range of Shannon indexes for the counties was 0.69–2.30, with a mean of 1.69. The mean evenness index was 0.58, with the maximum (1.00) occurring in Anyangshi county, indicating that the soil types were most evenly distributed in that area. The minimum value (0.29) was for Mengjin county, indicating that only few soil types were dominant in the region (mainly Cambisols soil). The range of the coefficient of variation of the indexes for the 101 counties was 0.10–0.26, which suggested a moderate variation of pedodiversity among the counties in Taihang Mountain region (Table 2). 3.2. Spatial distributions of selected pedodiversity indexes The soil taxa richness in the northern part was generally higher than in the southern and middle parts, but Menhinick's index increased from Table 2 Descriptive statistics of pedodiversity indexes for 101 counties in Taihang Mountain study area, China. Note: SD = standard deviation and CV = coefficient of variation. Index
Min
Max
Mean
SD
CV
Skewness
Kurtosis
Richness Menhinick's index Shannon's index The maximum Shannon's index Evenness
2 0.13 0.69 0.69
26 1.03 2.30 3.26
19 0.57 1.69 2.92
3.52 0.15 0.30 0.28
0.19 0.26 0.18 0.10
−1.04 0.74 −0.47 −5.23
4.34 1.57 0.57 40.72
0.29
1.00
0.58
0.09
0.16
0.46
3.43
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Fig. 2. Maps of spatial distribution of Richness (S), Menhinich's index (M), Shannon index (H), Maximum Shannon index (Hmax) and Evenness index (E) by county in Taihang Mountain study area, China.
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6
Log abundance
5 4 3 2 1
Cambisols Luvisols Fluvisols Regosols Leptosols Anthrosols Arenosols Kastanozems Gypsisols Gleysols Chernozems Calcisols Phaeozems Solonchaks Vertisols Andosols Greyzems Acrisols Alisols Lixisols Solonetz Ferralsols Histosols Planosols Nitisols Podzoluvisols
0
Fig. 3. Plot of abundance distribution model of soil taxa in Taihang Mountain study area, China. Fig. 5. Plot of canonical correspondence analysis (CCA) of the influencing factors on pedodiversity in Taihang Mountain study area, China. PF = percentage of farmland, PD = population density, RP = precipitation range, RS = slope gradient range and RE = elevation range.
correspondence analysis. 4. Discussions 4.1. Pedodiversity in Taihang Mountain Generally, pedodiversity in mountain areas is relatively high because complex environments in these terrains. Taihang Mountain has a high heterogeneity both in geological setting and vegetation pattern (Zhang et al., 2006; Wang and Li, 2008). The mean Shannon index for the investigated 101 counties in Taihang Mountain was 1.69 (Table 2), apparently lower than those reported by Ibañez et al. (1998) and Costantini and L'Abate (2016), which were respectively 2.30–2.92 (at continent scale) and 5.35 (for Italy). Smaller Shannon index also have been reported in studies by Scharenbroch and Bockheim (2007) and Kooch et al. (2015), with respectively values of 1.11 for Huron Mountains, Michiga and 0.94 for temperate forest in Mazandaran province in the north of Iran. Although the comparison of Shannon index for different studies could be unreasonable due to differences in scale,
Fig. 4. Plot of the relationship between soil taxa richness and county area in Taihang Mountain study area, China.
factors, the effect of population density and precipitation range on pedodiversity was relatively low (Fig. 5). The total variance interpreted by CCA was 33.3%, indicating that there were other factors with strong effect on pedodiversity, but not considered in the canonical
Table 3 Partial correlation analysis between pedodiversity indexes and the related influencing factors for Taihang Mountain study area, China. Note: * means the correlation was significant at 0.05 level, ** means the correlation was significant at 0.01 level. Factors
Parameters
Mean elevation
Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p Correlation p
Elevation range Mean precipitation Precipitation range Mean temperature Temperature range Mean slope Slope range Population density Percent farmland
coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient
Richness
Menhinick's index
Shannon's index
Maximum Shannon's index
Evenness
−0.20 0.13 0.60⁎⁎ 0.00 0.24 0.06 0.33⁎⁎ 0.01 0.01 0.95 0.18 0.15 0.22 0.08 0.33⁎⁎ 0.01 −0.05 0.70 −0.24⁎ 0.05
−0.22 0.08 0.11 0.41 −0.30⁎ 0.02 −0.12 0.35 0.25⁎ 0.04 −0.12 0.35 −0.26⁎ 0.04 −0.17 0.18 0.25* 0.05 0.31⁎⁎ 0.02
− 0.24 0.44 0.25⁎ 0.05 0.19 0.12 0.14 0.26 − 0.01 0.92 0.02 0.89 − 0.14 0.25 0.44⁎⁎ 0.00 − 0.04 0.77 − 0.33⁎⁎ 0.01
− 0.10 0.06 0.22 0.09 0.22 0.07 0.32⁎⁎ 0.01 0.02 0.90 0.18 0.15 0.16 0.20 0.14 0.28 − 0.04 0.98 − 0.32 0.01
−0.20 0.13 0.03 0.81 0.13 0.31 0.04 0.73 −0.01 0.91 −0.04 0.76 0.10 0.42 0.04 0.76 0.22 0.87 −0.26⁎ 0.04
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results of this study confirmed this observation because elevation range had a stronger effect on pedodiversity than elevation mean (Table 3). This showed that pedodiversity in high relief/aptitude areas was higher than in plain areas — the higher the relief, the higher the pedodiversity. The same trend was noted for slope gradient and precipitation. This demonstrated that in mountain areas, the ranges of natural factors in each county (e.g., the range of elevation, slope gradient and precipitation) were a more important variable than their means. It was not difficult to understand because pedodiversity depends on soil forming conditions and processes (Caniego et al., 2007; Rannik et al., 2016). Pedodiversity can be high in regions with higher local variability of soil forming factors (Danek et al., 2016). Besides natural factors, anthropogenic activities significantly affect pedodiversity (Lo Papa et al., 2011). Farming can cause soil erosion and thereby limits pedodiversity (Costantini and Dazzi, 2013; Tennesen, 2014). This becomes more pronounced in mountain areas, especially areas with high slope gradient where percent farmland is negatively correlated with pedodiversity (Table 3). Interestingly, human population density had no effect on pedodiversity. This could be due to the fact that industries or tourist centers are developed in high population density areas, which elements have relatively little effect on pedodiversity. This suggested that anthropogenic activities limited pedodiversity mainly through farming. The total interpretation of the variance by CCA was relatively low (33.3%), suggesting that other factors influenced pedodiversity in the study area. However, the rank of environmental factors considered was very clear, with elevation having the most effect on pedodiversity and followed by percent farmland (Fig. 5). Other studies also noted that topography and anthropogenic activities influence pedodiversity (Phillips and Marion, 2005; Shangguan et al., 2014; Danek et al., 2016). This element was critical for the management of soil and the protection of pedodiversity. As already known, natural factors are not something we can control. Thus, the management of farmlands is the most feasible way of protecting pedodiversity.
classification standards, etc., it, however, can give a general sense of the trend in pedodiversity, which generally has a range of 1.5–3.5 and hardly exceeding 4.5 (Margalef, 1972; Ibañez et al., 1995). Therefore, pedodiversity in Taihang Mountain was not as high as expected for a mountain region. This could be due to the long-term human disturbances (e.g., farming and construction) which significantly impair pedodiversity. This was also noted by Shangguan et al. (2014), which study showed that there were more rare and endangered soils in Taihang Mountain than in other areas of China. Evenness index refers to the way in which each soil type is distributed. If two different regions have the same area and richness, the one with higher evenness index will have a higher pedodiversity because different types occupy relatively equal areas (Ibañez et al., 1995). The range of evenness index is 0–1, which can be very different for different regions (McBratney and Minasny, 2007; Lo Papa et al., 2011; Kooch et al., 2015; Costantini and L'Abate, 2016). Evenness index for Taihang Mountain was 0.58 (Table 2), which was a medium value compared with the values reported in other studies. The value in this study was reasonable because the environment in mountain areas always highly heterogeneous (Wang and Li, 2008; Fu et al., 2016), causing an uneven distribution of soil types. The study of pedodiversity using indexes such as Shannon index, evenness index, etc. is limited by different soil classification systems. This is because different classification standards give very different values of an index for the same region (Guo et al., 2003; Costantini and L'Abate, 2016). Another limitation in the study of pedodiversity is the lack of accurate data. There is a strong spatial variability of soil types, especially in mountain areas. More soil samples or soil surveys are needed to identify soil taxa. This always requires huge manpower and financial resources. Thus as stated by Tennesen (2014), much more should be done by national governments and the international community to increase the availability of data for research. 4.2. Richness-area relationship for Taihang Mountain
5. Conclusions
Just like biodiversity, pedodiversity increases with increasing area (Ibáñez and Feoli, 2013), although the rate of increase can be different for different regions. There are also differences in richness-area relationships, including different power and logarithmic functions (Feoli et al., 2007; Phillips and Marion, 2007; Saldaña and Ibáñez, 2007). The relationship between richness and area for Taihang Mountain was best described by logarithmic function (Fig. 4). Saldaña and Ibáñez (2004) also observed the same relationship for low and middle terraces, but a different one for high terraces. Several other studies have noted that the power function is most fit for richness-area relationship (Phillips, 2001; Ibáñez et al., 2005). In this study, R2 for richness-area correlation was 0.46 (Fig. 4), meaning that 46% of the variation in richness was explained by the area of the county. This value was much smaller than that of Saldaña and Ibáñez (2004) where R2 of 0.8 was observed, meaning 80% of the variation in richness was explained by the area. This difference in R2 is due to the fact that the complexity of driving factors in mountain areas causes high variations in pedodiversity. It is also possible that the interpreted variance by area is lower. Richness increased fastly when area was smaller than 1000 km2, but slowly when area was larger than 1000 km2 (Fig. 4). It suggested that 1000 km2 was the breakthrough point, above which the effects of area on pedodiversity was low. It was then concluded that the effect of area can be ignored and therefore statistical analysis used to analyze the effect of natural and anthropogenic factors on pedodiversity when area was larger than 1000 km2.
There was a moderate variation of pedodiversity in Taihang Mountain region, North China. The abundance distribution model output was lognormal, indicating that soils with medium abundance were most dominant in the study area. The richness-area relationship was best fitted by logarithmic function and the inflection point was at 1000 km2. It suggested that the effect of area on the calculated pedodiversity was negligible when the region of interest exceeded 1000 km2. Pedodiversity in Taihang Mountain was significantly driven by both natural and anthropogenic factors. The effect due to the range of each natural factors was more than that due to the mean of the factors. Elevation range was the natural factor with the highest effect and farming the anthropogenic factor with the highest effect on pedodiversity. The results of this study deepened the existing knowledge on pedodiversity, which is important for soil management and the protection of endangered soil types in mountain terrains. Acknowledgements This work was supported by the National Basic Research Program (973 Program) of China (No. 2015CB452705). References Amundson, R., Guo, Y., Gong, P., 2003. Soil diversity and land use in the United States. Ecosystems 6 (5), 470–482. Amundson, R., Berhe, A.A., Hopmans, J.W., Olson, C., Sztein, A.E., Sparks, D.L., 2015. Soil and human security in the 21st century. Science 348 (6235), 1261071. Bockheim, J.G., Schliemann, S.A., 2014. Soil richness and endemism across an environmental transition zone in Wisconsin, USA. Catena 113, 86–94. Caniego, F., Ibáñez, J., Martínez, F.S.J., 2007. Rényi dimensions and pedodiversity
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