- Email: [email protected]
Effects of soil properties, topography and landform on the understory biomass of a pine forest in a subtropical hilly region
Catena 176 (2019) 104–111
Contents lists available at ScienceDirect
Catena journal homepage: www.elsevier.com/locate/catena
Effects of soil properties, topography and landform on the understory biomass of a pine forest in a subtropical hilly region
T
⁎
Xiaodong Niea, Wang Guob, Bin Huanga, Muning Zhuoa, Dingqiang Lia,c, Zhongwu Lid,e, , ⁎⁎ Zaijian Yuana, a Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, PR China b The Second Surveying and Mapping Institute of Hunan Province, Changsha 410118, PR China c Guangdong Academy of Sciences, Guangzhou 510075, PR China d College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China e Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Pine forest Phytocoenosis Land degradation Soil erosion Vegetation biomass
Soil conservation will remain an essential subject for forest in slope land. However, the interactions between soil, vegetation and complex topography are not fully understood. To address this concern, field studies were performed in a typical small watershed in the subtropical hilly red soil region of China. This site covers an area of approximately 3.2 ha, and the mean slope steepness of the land is 15°. Soil properties (including available and total N, P, K, soil organic carbon (SOC), CEC, soil texture), understory vegetation biomass and diversity index on different slopes (different slope steepness, slope aspects, and slope positions) were measured. Meanwhile, interrelationships between understory vegetation biomass and soil properties, slope steepness and altitude were evaluated via redundancy analysis. The study results showed that the soil nutrients and SOC contents were greater in the down-slope and north and west slopes. And the soil properties displayed high spatial heterogeneity and had semi-variance structure, which were mainly derived from random factors. Due to obvious erosion characteristics and the negative correlations between soil properties and slope positions, we suspected that soil erosion was the primary random factor. In addition, the mean total understory vegetation biomass in the pine forest was 283.58 g m−2 (ranging from 66.60 to 573.79), and higher vegetation biomass was found in downslope and north and west slopes. Further analyses indicated that topography (slope steepness and altitude) and soil properties combined contributed to 58.7% of the variations in the understory biomass, and they individually had a contribution of 17.3% and 41.4%, respectively. Moreover, altitude (height of slope positions) alone explained 15.9% of the variation of the vegetation biomass. This study indicated that soil properties, which were highly affected by slope aspect and slope position, were the most important factor in influencing understory vegetation biomass in subtropical pine forest. Slope position also had a tremendous direct influence on soil vegetation. Controlling understory soil erosion is essential for conservation of pine forest land in subtropical China.
1. Introduction Soil conservation will remain an essential subject for forest land. Heavy rainfall events, forest fire, deforestation and landslide are great threat to land quality (Mekonnen et al., 2015). In forest, effects of these natural or human –driven disturbances on soil were often neglected due
to relatively high vegetation cover. Choosing and adopting appropriate protective measures are practical strategy for maintain soil sustainable development (Schwilch et al., 2014). While, to articulate clearly the relationship between soil and vegetation under complex topography is the basis for soil conservation. Soil and vegetation, which are the two key factors in controlling soil
Abbreviations: TN, total nitrogen; SOC, soil organic carbon; TP, total phosphorus; TK, total potassium; AN, available nitrogen; AP, available phosphorus; AK, available potassium; CEC, cation exchange capacity ⁎ Corresponding author at: College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China. ⁎⁎ Correspondence to: Z. Yuan, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, PR China. E-mail addresses: [email protected] (Z. Li), [email protected] (Z. Yuan). https://doi.org/10.1016/j.catena.2019.01.007 Received 28 March 2018; Received in revised form 28 November 2018; Accepted 8 January 2019 0341-8162/ © 2019 Elsevier B.V. All rights reserved.
Catena 176 (2019) 104–111
X. Nie et al.
Based on the above analyses, effects of topographical conditions on soil and vegetation should be considered when studying the relationships between soil and vegetation. The main objective of this study was to explore the influences of soil properties and topographical conditions on the variations in understory vegetation biomass in a pine forest. Combined with the distributions of plant biomass and soil properties in a pine forest in the hilly red soil region of southern China, relationships between the topographical conditions (slope aspect, slope steepness and slope position) and soil properties and vegetation were studied. The study results will help determine the characteristics of nutrient distribution in the soil-vegetation system in the hilly red soil region of China, and will provide a basis for formulating measures for soil conservation, thus improving the productivity of vegetation and the preservation of fertility.
conservation, have attracted increased attention worldwide. Vegetation is a useful parameter for predicting ecosystem status in hilly regions, it not only is strongly related to soil nutrients and also acts as protection against soil erosion and water loss (Wang et al., 2012; Keesstra et al., 2016). Many studies have reported on the interactions between soil and vegetation in soil-vegetation systems (Carroll et al., 2003; Naeth et al., 2011; Ceacero et al., 2012). Soil provides physical environment, water and mineral nutrition for growing plants, which not only affects the ontogenesis of the plants but also further determines the type, distribution, dynamics and yield of the phytocoenoses (Liao et al., 2006; Benbi and Chand, 2007; Xia and Shao, 2008). Soil type and depth have also been considered to be highly correlated to vegetation growth and vigour (Belcher et al., 1995; Fierer et al., 2003). Soil nutrients are the main limiting and direct factors of phytocoenosis biomass (Huang et al., 2007; Zheng et al., 2008), especially in soils with low water-holding capacity and low nutrient retention caused by severe water erosion and soil degradation. However, vegetation also exerts effects on soil ecosystems (White II et al., 2009), and the ecological effects of vegetation on soil nutrients in response to soil erosion have attracted the attention of a large number researchers (Feng et al., 2007; Bilodeau-Gauthier et al., 2011). According to previous studies (Li et al., 2005; Mohammad and Alseekh, 2013), these effects are derived mainly from plant absorption and fixation processes and the accumulation and decomposition of phytocoenosis biomass, which lead to variation in soil nutrients at temporal and spatial scales. Therefore, unexpectedly strong relationships exist between soil nutrients and characteristics of phytocoenoses, including phytocoenosis biomass, survival rates and turnover rates. The interactive effects between soil and plants are highly intricate, and have produced contrasting results. Jiang et al. (2010) reported that soil organic matter and total nitrogen (TN) were highly correlated with the understory biomass of plant communities, the variety of species and the density of the community. However, some studies have reported no significant correlation between soil nutrient contents and vegetation biomass (Ponette et al., 2001). These contracting results may result from different topographical conditions (including slope steepness, altitude, slope aspect and slope position), which are considered essential factors that affect aboveground biomass in subtropical area (Alves et al., 2010; Sato, 2010; Lin et al., 2012). Similar to the highly complex relationships between soil and vegetation, many complex problems in the soil-vegetation system remain to be solved. Specifically, within a single ecosystem, full understanding of the effects of topographical conditions on the distributions of plant and soil nutrients is essential for determining the interactive effects between soil and vegetation. Although studies addressing this understanding have been reported, some studies have considered the influence of different types of physiognomy on the characteristics of vegetation density and coverage, without considering the ecosystem spatial heterogeneity caused by differences in the tiny topographic and soil characteristics (Florinsky and Kuryakova, 1996). Other studies have investigated the influence of only slope steepness on vegetation (de Castilho et al., 2006). However, many other factors, such as slope aspect and slope position, have not been studied to the same extent as slope steepness. Both natural and planted pine forests are widely distributed within the humid subtropical areas of China (Yan et al., 2005; Huang et al., 2015). Compared with broadleaf forests, pine forests have lower soil organic carbon (SOC) contents, smaller labile carbon fractions, and lower amounts of SOC stocks (Nie et al., 2017). Furthermore, pine forests located in this region of China tend to experienced severe water erosion events, presenting soil loss rates of 4.15 ± 1.25 Mg ha−1 yr−1 (Ma et al., 2016). Therefore, pine forests are not the first choice for restoring degraded lands. However, considering the large planting area of pine forests and their low ecological function, this subject is under debate. Therefore, to fill in the gap in the relationships between soil, topographical conditions and vegetation will have important practical uses for the soil conservation in pine forest.
2. Material and methods 2.1. Site description The study site is located at the Institute of Soil and Water Conservation of Shaoyang city (111°22′E and 27°03′N), Hunan Province, South-Central China (Fig. 1). This area has a typical subtropical monsoon climate; the mean annual temperature is 17.1 °C, the frost-free period is approximately 272–304 days per year, and the mean annual precipitation is 1218.5–1473.5 mm, most of which occurs from April to June. The elevation varies between 231.18 m and 276.63 m. The soils are characterized as zonal red soils derived from Quaternary red clay, and the soil was classified as ultisols based on the US Soil Taxonomy (Soil Survey Staff, 2003). A closed watershed (approximately 3.2 ha) with a pine forest was selected and studied. According to previous study (Ma et al., 2016), the soil loss rates of this land varied from 0.18 to 9.18 Mg ha−1 yr−1, with a mean value of 4.15 Mg ha−1 yr−1. Based on the geomorphology, four slopes with different slope aspects were distinguished and marked as east slope, south slope, west slope, and north slope, respectively. Moreover, each slope was divided into three different landscape positions and marked as the upper, middle and down positions (Fig. 1). To survey and collect soil and vegetation samples, three large plots covering 5 × 5 m were established at the upper, middle and down slope positions, and five 1 × 1 m subplots were set up using a plum-shaped distribution method in each large plot. Therefore, 60 total subplots were established. The steepness of each large plot was measured with a clinometer, and the mean slope steepness for the east, west, south and north slope were 13.44°, 16.39°, 15.31°, 17.32°, respectively. The positions of each subplot were recorded using a GPS (Trimble GeoXT). The altitude of each subplot was also recorded, however, this parameter is just a quantitative expression of the plot position because of the lower values and small variation ranges. 2.2. Biomass and soil sampling At the November 2015, a field survey about the vegetation was performed. The vegetation survey found that the main species of macrophanerophytes was loblolly (Pinus taeda Linn); the constructive shrub species included white oak (Quercus fabri Hance), Eurya muricata (Eurya muricata Dunn), oil tea camellia (Camellia oleifera Abel), azure pigment (Ficus hookeriana), litsea (Litsea cubeba (Lour.) Pers), and fringe flower (Loropetalum chinensis (R. Br.) Oliv); the constructive species of herbs contained miscanthus (Miscanthus floridulu (Labnll.) Warb), chain fern (Woodwardia prolifera HOOK. et Arn), shield-fern (Dryopteris pacifica (Nakai) Tagawa), dicranopteris dichotoma (Dicranopteris dichotoma (Thunb.) Bernh), hedyotis auricularia (Paspalum conjugatum (Berg.)), and polystichum (Polystichum longipaleatum Christ). During the survey, the numbers of species and individuals of the shrubs and herbs within each subplot were counted and recorded. The aboveground deciduous shrubs, forbs and grasses were clipped, placed 105
Catena 176 (2019) 104–111
X. Nie et al.
Fig. 1. The location and set up of plots in the watershed.
(pH 7) and analyzed via flame photometry.
in bags, and transported to the laboratory for drying. All the herbs and shrubs in the subplots were collected respectively, and all aboveground biomass was measured. The fresh biomass was dried in an oven at a temperature of 60 °C until stable. Moreover, three replicate soil samples (0–20 cm) were collected from each subplot to provide an adequate representation of the site. As such, 45 soil samples were collected within each slope. The soil samples were subsequently air-dried and analyzed for their physical and chemical properties. The soil particle size distribution was determined using a Malvern Mastersizer 2000 laser diffraction device (Malvern Instruments Ltd., UK). The cation exchange capacity (CEC) was determined by the BaCl2 displaced method (Hendershot et al., 1993), and the SOC and TN were determined with a CN analyzer (Vario MAX CN, Elementar, Hanau, Germany). Soil available nitrogen (AN) was considered to be the NO3−–N and NH4+–N, and the N content within both forms was measured via the colorimetric method, analyzed by automatic flow injection (Keeney and Nelson, 1982). Total phosphorus (TP) was measured with the method of molybdenum antimony blue colorimetry (Murphy and Riley, 1962), and the available phosphorus (AP) was determined by sodium bicarbonate extraction and subsequent colorimetric analysis (Olsen et al., 1954). Soil total potassium (TK) was measured with the method of alkali fusion and flame photometer, and the soil available potassium (AK) was extracted with 1 M NH4OAC
2.3. Statistical analysis Semivariogram of geostatistics, which is often used to measure the spatial variability of the soil nutrients (Wei et al., 2008; Zheng et al., 2009), was conducted to test the spatial autocorrelation within the data of soil properties. Different models of theoretical semivariance were used to fit the calculated values, and the model with the best-fitting value and the smallest nugget value was selected. The semivariogram parameters of the nugget value (C0), partial sill (C), sill (C0 + C), range value (A0), and nugget-sill-ratio (C0 / (C0 + C)) were summarized. C0 usually indicates the variation caused by experimental errors, which is less than the experimental sample scale; C represents the variation caused by system factors; and (C0 + C) indicates the total variation within the system. Different values of these parameters indicate different spatial distribution patterns (Cambouris et al., 2006; Hani et al., 2010). The larger the (C0 + C) is, the greater the spatial variation of the soil nutrients. Similarly, the smaller the proportion of the structure variance C in (C0 + C) is, the greater the impact of both random factors (non-area factors) and small-scale factors on soil nutrient contents. The smaller the C0 / (C0 + C) value is, the greater the impact of the natural factors (area factors). A0 indicates the corresponding distance when the 106
Catena 176 (2019) 104–111
X. Nie et al.
nutrients (TN, TP, TK) and silt among different slope positions. The AN content did not significantly differ between the upslope and downslope positions, but the AN content was significantly higher at the upslope and downslope positions than at the midslope position (P < 0.05). Compared with the total nutrients contents, the labile nutrients contents were more strongly related to slope position. Nonparametric correlations between the soil properties and the slope steepness and altitude are shown in Table 3. Most of the correlations between the soil properties and the slope steepness and altitude were negative but not significant. Slope steepness was negative correlated to SOC, CEC, TP, AP, and silt. Similarly, the altitude was negative correlated to SOC, TN, TP, TK, AN, AP, and AK. In general, increases in slope steepness and altitude had a negative effect on the soil physicochemical properties.
variation function reaches the partial sill. If the expressed distance of the observed value of a nutrient is larger than that value, they are independent; if the distance of the observed value of a nutrient is lower than this value, there is a correlation between them. C0 / (C0 + C) can reveal the extent of the spatial variation of the variables; if the ratio is lower than 0.25, there is a strong spatial correlation among the soil properties. If the ratio is between 0.25 and 0.75, the spatial correlation of the soil properties is at an intermediate level. If the ratio is > 0.75, the spatial correlation of the soil properties is weak. The data analysis in this paper used the average value of three replicated samples. Statistical analyses were performed with SPSS 20.0 (SPSS Inc., Chicago IL, USA), and significant differences at the 0.05 level (two-tailed) were identified by the tests. A redundancy analysis (RDA) was performed to study the relationships between the understory biomass and soil properties, slope steepness and altitude. RDA constraint sorting of the experimental data was performed with Canoco 4.5 (Microcomputer Power, Ithaca, NY, USA). The RDA analysis had two matrices, the species data and environmental data. The species data was the herb, shrub and total biomasses. The environmental variables were composed by soil nutrients, SOC, soil texture, altitude and slope steepness. Prior to sorting, all the different dimensional data were standardized. Therefore, in the ordination diagram (biplot), the length of the vector, which is represented by a red arrow for each environmental variable, represents the magnitude of those environmental factors with respect to explaining the vegetation biomass (blue dashed arrows). The angle between the two arrows represents the relationship between the environmental factors and the herb biomass. An angle between 0° and 90° indicates a positive relationship between the two variables. An angle of 90° indicates no significant correlation. An angle between 90° and 180° indicates a negative relationship. A nonparametric correlation analysis and partial correlation analysis were performed using SPSS 20.0, and GS+ software was used for geostatistical analysis. The species richness index and the ShannonWiener species diversity index parameters were calculated to characterize the species. The species richness index was calculated as follows:
R=
(S − 1) ln N
3.2. Variations in soil properties Distinct semivariance structures of spatial variability were observed for the soil nutrients and soil particle size distributions (Table 4). The C0 values of the SOC, CEC, AN, AP, AK, sand, silt, and clay were 1.99, 1.50, 92.20, 1.55, 13.20, 3.89, 1.10 and 7.73, respectively. These relatively large values indicate that small-scale processes cannot be ignored. Furthermore, relatively large (C0 + C) values for the CEC, AP, sand, silt, and clay were observed, and the highest and lowest values were found for the AN (256.60) and AP (3.12). These results illustrate that high spatial heterogeneity of the soil nutrients exists. The C0 / (C0 + C) values of the CEC, AP, sand, silt, and clay were lower than 25%, indicating strong spatial correlations exist among these soil properties. The C0 / (C0 + C) of the SOC, TN, TP, TK, AN, and AK varied from 25% to 75%, indicating that the spatial variations resulted mainly from the influences of random factors at a small scale. The C0 / (C0 + C) of the TK was 1, which indicates that the TK had constant variations in this area. In addition, the independent spacing A0 of the spatial variability of soil properties varied from 6.10 to 149.00, this range was lower range than that of the watershed scale in the study area, further demonstrating that the changes in the soil physical and chemical properties were strongly affected by random factors on a small scale.
(1)
3.3. Distributions in understory vegetation biomass
where R is the species richness index, S is the number of species, and N is the total number of individuals for all species. The Shannon-Wiener species diversity index was calculated as follows:
H ′ = − ∑ (pi × ln pi )
The mean total understory vegetation biomass in the pine forest was 283.58 g m−2 (ranging from 66.60 to 573.79). The understory vegetation biomass in the pine ecosystem varied with slope position and showed a clear increasing trend along the downward slope (Fig. 2a). The total understory biomass on the downslope was 127% and 119% of the upslope and midslope, respectively. The vegetation biomass on different slope aspects followed the order of west slope > north slope > east slope > south slope (Fig. 2b). The accumulated biomass on the west and north slopes was higher than that on the east and south slopes. On the whole, the herb biomass was less than the shrub biomass. In addition, little differences in herb biomass were observed among different slope aspects and among different slope positions; however, opposite results were observed in shrub biomass. Slop aspect and position not only are related to biomass but also are related the biological community structure (Table 5). For the west and north slopes, the difference in the number of individuals per unit area was not obvious among different slope positions. With respect to the east and south slopes, the number of individuals per unit area was higher at the downslope than midslope and upslope. The mean values of richness index for different slope aspects followed the order of south slope (0.325) > east slope (0.251) > north slope (0.219) > west slope (0.212). Furthermore, the mean values of vegetation diversity index on different slope aspects decreased in the order of west slope (2.400) > north slope (2.296) > east slope (1.745) > south slope (1.614). The variation trend of the richness index on different slope aspects was opposite that of the diversity index and understory biomass.
(2)
where H′ is the Shannon-Wiener species diversity index, and Pi is the ratio of the number of the species to the total number of individuals for all species. 3. Results 3.1. Distributions of soil properties in pine forest Owing to the tattered landform and complex topography in the hilly red soil region, different trends in the distributions of soil properties were observed among locations at different slope aspects and positions. The SOC, AN and clay contents in the soil were significantly higher on the west slopes than on the east slopes (Table 1). Moreover, the soil properties (excluding TN, TP, AP and clay) significantly differed between the south and north slopes. However, there were no significant differences in the soil physico-chemical properties between the south and east slope as well as between the north and west slope. The SOC, AP, AK, sand and clay contents were significantly higher on the upslope and midslope than on the downslope (P < 0.05) (Table 2). However, there was no significant difference in the CEC, total 107
Catena 176 (2019) 104–111
X. Nie et al.
Table 1 Mean and standard deviation of soil properties in each of the four slopes. Soil properties −1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
West slope
North slope
East slope
South slope
6.91 ± 1.49a 11.89 ± 1.48a 0.64 ± 0.06a 0.19 ± 0.01a 8.46 ± 0.34b 52.89 ± 6.30a 0.59 ± 0.50a 24.33 ± 1.76ab 38.20 ± 4.95b 20.84 ± 0.83b 40.96 ± 4.58a
6.59 ± 2.16a 8.99 ± 0.72b 0.66 ± 0.07a 0.19 ± 0.01a 9.93 ± 0.42a 51.22 ± 7.68a 0.43 ± 0.21b 27.22 ± 7.18a 38.79 ± 3.42b 26.18 ± 3.27a 35.02 ± 4.89b
5.32 ± 0.72b 9.77 ± 0.94ab 0.57 ± 0.03a 0.17 ± 0.01a 8.92 ± 0.44ab 41.33 ± 4.67b 0.52 ± 0.12ab 20.44 ± 1.83b 45.18 ± 5.44a 24.21 ± 2.69a 30.60 ± 4.41b
5.92 ± 0.86b 10.17 ± 2.38a 0.58 ± 0.06a 0.18 ± 0.02a 8.36 ± 0.24b 45.33 ± 9.82b 0.46 ± 0.02b 22.66 ± 3.51b 45.66 ± 8.80a 19.23 ± 1.40b 35.11 ± 7.88b
The data in table is mean value ± SD. Different lower case letters at the same row indicate significant difference in soil properties between different slope directions.
the variation, respectively. The studied soil properties differentially influenced the distribution gradient of the biomass (P < 0.05): the AK, AP, AN, slope, sand, TN, and TK were positively related to herb biomass; their respective correlation coefficients followed the same decreasing order. The clay, CEC, altitude, and TP were negatively related to the herb biomass, whereas silt, TK, TN, sand, and slope were positively related to the shrub biomass. Moreover, the AN, SOC, clay, CEC, and altitude were negatively related to the shrub biomass. There were no significant relationships between the TP, AP, and AK and the shrub biomass. The total biomass was negatively related to the SOC, CEC, clay, altitude and TP, but positively related to the sand, TN, TK, slope, AK, AP, and AN.
Table 2 Characteristics of the distributions of soil properties at different slope positions. Soil properties −1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
Upper slope
Middle slope
Down slope
5.85 ± 0.93b 10.49 ± 1.52a 0.61 ± 0.03a 0.19 ± 0.02a 8.91 ± 0.43a 48.42 ± 4.82a 0.37 ± 0.14b 22.49 ± 2.85b 37.03 ± 3.64b 23.49 ± 4.09a 39.48 ± 5.53a
5.70 ± 0.79b 10.23 ± 1.67a 0.61 ± 0.04a 0.18 ± 0.01a 9.03 ± 0.76a 43.67 ± 6.83b 0.42 ± 0.09b 22.83 ± 3.32b 41.13 ± 4.13b 22.82 ± 3.12a 36.05 ± 6.08a
7.02 ± 2.01a 9.90 ± 1.07a 0.65 ± 0.09a 0.18 ± 0.01a 8.82 ± 1.06a 51.00 ± 5.61a 0.72 ± 0.31a 25.67 ± 3.65a 47.72 ± 7.06a 21.54 ± 3.29a 30.74 ± 5.15b
4. Discussion
The data in table is mean value ± SD. Different lower case letters at the same row indicate significant difference in soil properties between different slope positions.
The spatial distributions of soil properties were affected by many factors, such as slope aspect, slope steepness, and slope position. The study results showed that soil properties varied with slope aspect, and soil nutrients contents in north and west slopes were higher than in south and east slopes (Table 1). In general, slope aspect impact on soil nutrients mainly through indirect ways of altering litter decomposition and nutrient cycling (Hicks and Frank, 1984). Notably, vegetation, which plays an important role in controlling soil nutrients cycling, is a key medium of the interactions between slope aspect and soil nutrients (Yimer et al., 2006). Our results showed that north and west slopes had larger amounts of vegetation biomass than did south and east slopes (Fig. 2), which have proved the close relationships among slope aspect, vegetation and soil nutrients. In addition to slope aspect, slope steepness had been considered highly related to soil properties distributions. It has been reported that an increasing slope steepness can lead to increasing of erosion intensity and sediment transportation in slope land (Berger et al., 2010), and thus result in the decrease of soil nutrients. However, in the present study, most of the soil nutrients did not significantly decrease as the slope steepness increased (Table 3). We may attribute these phenomena to the presence of aboveground vegetation. Soil vegetation plays important roles not only in reducing soil erosion (Jordan et al., 2010) but also in affecting soil nutrients cycles. Complicated distributions of vegetation may change the negative relationships between slope steepness and soil nutrients. Furthermore, the present study showed that the increases in altitude negatively impacted soil nutrients contents, although the effect was not significant. Tripathi (1999) reported that, when the altitude was < 2000 m, the SOC had a higher accumulation rate and a lower decomposition rate as the altitude increased, and significant positive correlations between the altitude and SOC were observed. This is differed to our findings. We could attribute these differences to the low altitude values and small variation range in the study area (the highest altitude is just 268 m). Actually, the low altitude in our study just reflects the slope position (height) of sampling point in the pine forest, and the results indicated that soil nutrients contents decreased with the
Table 3 Nonparametric correlations between soil properties versus slope steepness and altitude. Soil properties
−1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%) ⁎
Slope steepness(°)
Altitude (m a.l.s.)
R
P
R
P
−0.152 −0.473⁎ 0.061 −0.426 0.394 0.308 −0.277 0.078 0.038 −0.239 0.084
0.493 0.033 0.784 0.061 0.075 0.168 0.215 0.730 0.608 0.454 0.794
−0.091 0.137 −0.303 −0.016 −0.455⁎ −0.062 −0.154 −0.357 −0.231 0.102 0.196
0.681 0.536 0.170 0.945 0.040 0.783 0.491 0.112 0.470 0.753 0.541
Significant difference at 0.05 level.
3.4. Relationships between understory biomass and soil properties and slope steepness and elevation To explore the impact of soil physico-chemical properties, slope steepness and altitude on the understory biomass of the phytocoenoses, RDA was performed to generate a RDA sequence diagram. The twodimensional sorting results show the regularities of the distribution of the understory biomass in response to different impact factors in the gradient. The results showed that the soil properties, slope steepness and altitude significantly affected the understory biomass (Fig. 3). Combined, the soil properties, altitude and slope steepness explained 58.7% of the variation in understory biomass. Specifically, soil properties explained 41.4% of the variance of understory vegetation biomass, while altitude and slope steepness explained 15.9% and 1.4% of 108
Catena 176 (2019) 104–111
X. Nie et al.
Table 4 Geostatistical summary of the content of soil properties. Soil properties
Model
C0
C0 + C
C0/(C0 + C)
A0
r2
SOC (g kg−1) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
Exponential Gaussian Exponential Gaussian Gaussian Gaussian Linear Gaussian Linear Gaussian Linear
1.99 1.50 0.87 × 10−2 0.24 × 10−3 0.14 92.20 1.55 13.20 3.89 1.10 7.73
4.01 5.74 2.02 × 10−2 0.48 × 10−3 0.14 256.60 3.12 47.04 79.36 11.20 56.07
0.50 0.09 0.43 0.50 1 0.36 0.18 0.28 0.05 0.10 0.14
6.10 11.60 149.00 43.00 68.26 9.90 17.80 11.40 68.26 23.80 68.26
0.10 0.27 0.11 0.29 0.12 0.32 0.21 0.27 0.68 0.73 0.64
C0 is the nugget value; C is partial sill; C0 + C is sill; A0 is the range value; r2 is the fit coefficient of semivariance models.
increasing slope position. This is in consistent with the distributions of soil nutrients in different slope positions (Table 2). The contents of the labile soil nutrients (AP, AK, SOC, etc.) at the upslope and midslope (eroding site) were significantly lower than those at the downslope (depositional site). This finding indicated clear erosion characteristics because the SOC and labile nutrients within light soil particles had been preferentially transported from eroded sites (upslope) to depositional sites (downslope) (Schiettecatte et al., 2008a, 2008b; Nie et al., 2015). Therefore, from the aspect of soil nutrients distributions, soil erosion is an unneglectable factor in the pine forest. Geostatistics showed that the distributions of soil properties in the pine forest were characterized by substantial spatial heterogeneity, which presented a semi-variance structure. This finding indicated that changes in the soil physico-chemical properties were strongly affected by random small-scale factors. Based on a comprehensive consideration of the above results and the relatively high erosion intensity in this area (mean erosion rate of 4.15 Mg ha−1 yr−1) (Li et al., 2015; Ma et al., 2016; Nie et al., 2018), the random factor was suggested to be soil erosion. Understory water erosion, which was highly depended on complex rainfall and underlying surface conditions (Huang et al., 2013), may transports and redistribute soil particles and nutrients randomly. Understory vegetation biomass, individual numbers of plants per unit area, Shannon-Wiener diversity index and species richness index, which were widely used in indicating distribution characteristics of species (Death and Winterbourn, 1995; Wu et al., 2010), were also measured in this study. The richness and diversity index of soil vegetation showed opposite trends. Similar to the distributions of soil nutrients, understory vegetation biomass and species diversity were higher on the north and west slopes and the down-slope when compared to other slope aspects and positions (Fig. 2 and Table 5). Higher
Table 5 Variations of vegetation communities under different topographic locations. Aspect
Slope position
Numer of species per m2
Numer of individuals in per m2
Index of species richness
ShannonWiener biodiversity index
West slope
Upper Middle Down Upper Middle Down Upper Middle Down Upper Middle Down
14 16 19 6 10 12 11 17 13 6 8 10
84 67 83 17 43 72 81 113 85 19 14 114
0.167 0.239 0.229 0.353 0.233 0.167 0.355 0.150 0.153 0.316 0.571 0.088
2.208 2.456 2.537 1.395 2.024 1.816 2.220 2.855 1.813 1.416 1.908 1.518
East slope
North slope
South slope
vegetation biomass were found on the north and west slopes, which is consistent with previous studies (Hicks and Frank, 1984; Liu et al., 2016). As south slope is exposed to more solar radiation and supports sparser biomass, whereas north slope retains more soil moisture and supports erosion-resistant denser vegetation cover (Yetemen et al., 2015). In addition, the enrichment of soil nutrients and soil water in down-slope will promote the accumulation of vegetation biomass. Meanwhile, the growth of plants will increase the plant litter return to soil and decrease its loss via water erosion. As a consequence, virtuous cycle formed and close correlations between soil nutrients and vegetation can be observed. Further analysis revealed that the soil properties, slope steepness and altitude explained 58.7% of the variation in the understory
Fig. 2. Distributions of vegetation biomasses at different (a) slope positions and (b) slope aspects. 109
Catena 176 (2019) 104–111
X. Nie et al.
vegetation biomass. 5. Conclusions The present study investigates the effects of soil properties and topography (slope aspect, slope steepness, and slope position) on the distributions of soil vegetation in a pine forest of the subtropical China. Similar distributions of soil nutrients and understory vegetation biomass were found, and slope aspect and slope position had a great impact on soil nutrients and soil vegetation. Soil properties, which explained 41.4% of the variation in the understory biomass, were the most important factors influencing understory vegetation. Slope position (altitude) directly explained 15.9% of the variation in the understory biomass. Above results suggested that for the understory vegetation biomass in pine forest, slope position and slope aspect were of the same importance as soil properties. Understory soil erosion should be considered seriously during adopting practical conservation practice.
Fig. 3. Overall redundancy analysis of the correlation among understory biomass, soil properties, slope gradient, and altitude, as determined by Canoco. The two axes illustrate the two principal components of the residuals of the multiple linear regression of X unto Y. The sampling points were indicated by circles with different numbers. AN, AP and AK represent the available nitrogen, phosphorus and potassium, respectively. TN, TP and TK represent the total nitrogen, phosphorus and potassium, respectively. The intersection angle between vector lines represents the significance of the correlation between their corresponding variables, in which a sharp angle represents positive correlation, an obtuse angle represents negative correlation, and a right angle represents no significant correlation.
Acknowledgments This paper was supported by the National Key Research and Development Program of China (2017YFC0505404), the National Natural Science Foundation of China (41807069), GDAS' Special Project of Science and Technology Development (2018GDASCX-1002, 2017GDASCX-0106), the High-level Leading Talent Introduction Program of GDAS (2016GDASRC-0103), the Scientific Platform and Innovation Capability Construction Program of GDAS (2016GDASPT0304).
aboveground biomass at our study site. Although a large proportion of the variation remains unexplained, the evidence suggested that small differences in soil properties, slope steepness and altitude could affect biomass accumulation. The soil properties individually explained 41.4% of the variation in the understory aboveground biomass, which indicated that soil factors are the most important factors influencing on the understory biomass (Paoli et al., 2008). In general, higher forest biomass is expected on high-fertility soils, independent of species composition, simply because there are more available resources for plant growth. In fact, significant correlations between understory biomass and soil nutrients have been reported (de Castilho et al., 2006; Jiang et al., 2010). The relationships between the herb biomass and the labile soil nutrients were stronger than those between the shrub biomass and the labile soil nutrients (Fig. 3), which indicated that the herbaceous species are relatively sensitive to soil fluctuations. It's worth noting that the shrub biomass was larger than the herb biomass, and the biomass accumulation effect may lead to weak correlations with soil nutrients. Moreover, the RDA analysis also showed that the herb, shrub, and total biomasses had no positive correlation with SOC, but was positively related to TN. Therefore, soil properties are important factors that affect the distributions of vegetation, the composition of plant functional groups and the diversity of community species. In addition, the altitude and slope steepness explained 15.9% and 1.4% of the variation in the understory vegetation biomass, respectively. As we discussed previously, the altitude in our study is a quantitative expression of the slope position. This indicated that the slope position (height) is the most important topographical factor in influencing vegetation. And negative correlations between soil vegetation biomass and slope position (height) were observed (Fig. 3). The movement of soil nutrients from the upslope to downslope caused by water erosion was considered a possible reason for this result (Zhang et al., 2013). However, slope steepness had a small impact on vegetation biomass. This finding is consistent with the results of da Silva et al. (2002), who studied the growth rates of trees along a topographic gradient in central Amazonia and failed to detect a topographic influence on growth rate. The results indicated that a biological response to soil nutrients occurred, and that the soil properties as well as slope aspect and slope position comprehensively affect the understory
References Alves, L.F., Vieira, S.A., Scaranello, M.A., Camargo, P.B., Santos, F.A., Joly, C.A., Martinelli, L.A., 2010. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For. Ecol. Manag. 260, 679–691. Belcher, J.W., Keddy, P.A., Twolan-Strutt, L., 1995. Root and shoot competition intensity along a soil depth gradient. J. Ecol. 673–682. Benbi, D., Chand, M., 2007. Quantifying the effect of soil organic matter on indigenous soil N supply and wheat productivity in semiarid sub-tropical India. Nutr. Cycl. Agroecosyst. 79, 103–112. Berger, C., Schulze, M., Rieke-Zapp, D., Schlunegger, F., 2010. Rill development and soil erosion: a laboratory study of slope and rainfall intensity. Earth Surf. Process. Landf. 35, 1456–1467. Bilodeau-Gauthier, S., Pare, D., Messier, C., Belanger, N., 2011. Juvenile growth of hybrid poplars on acidic boreal soil determined by environmental effects of soil preparation, vegetation control, and fertilization. For. Ecol. Manag. 261, 620–629. Cambouris, A.N., Nolin, M.C., Zebarth, B.J., Laverdière, M.R., 2006. Soil management zones delineated by electrical conductivity to characterize spatial and temporal variations in potato yield and in soil properties. Am. J. Potato Res. 83, 381–395. Carroll, J.A., Caporn, S.J.M., Johnson, D., Morecroft, M.D., Lee, J.A., 2003. The interactions between plant growth, vegetation structure and soil processes in semi-natural acidic and calcareous grasslands receiving long-term inputs of simulated pollutant nitrogen deposition. Environ. Pollut. 121, 363–376. Ceacero, C.J., Diaz-Hernandez, J.L., del Campo, A.D., Navarro-Cerrillo, R.M., 2012. Interactions between soil gravel content and neighboring vegetation control management in oak seedling establishment success in Mediterranean environments. For. Ecol. Manag. 271, 10–18. da Silva, R.P., dos Santos, J., Tribuzy, E.S., Chambers, J.Q., Nakamura, S., Higuchi, N., 2002. Diameter increment and growth patterns for individual tree growing in Central Amazon, Brazil. For. Ecol. Manag. 166, 295–301. de Castilho, C.V., Magnusson, W.E., de Araujo, R.N.O., Luizao, R.C.C., Lima, A.P., Higuchi, N., 2006. Variation in aboveground tree live biomass in a central Amazonian Forest: effects of soil and topography. For. Ecol. Manag. 234, 85–96. Death, R.G., Winterbourn, M.J., 1995. Diversity patterns in stream benthic invertebrate communities: the influence of habitat stability. Ecology 76, 1446–1460. Feng, D., Zongsuo, L., Xuexuan, X., Lun, S., Xingchang, Z., 2007. Community biomass of abandoned farmland and its effects on soil nutrition in the Loess hilly region of Northern Shaanxi, China. Acta Ecol. Sin. 27, 1673–1683. Fierer, N., Schimel, J.P., Holden, P.A., 2003. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176. Florinsky, I.V., Kuryakova, G.A., 1996. Influence of topography on some vegetation cover properties. Catena 27, 123–141. Hani, A., Pazira, E., Manshouri, M., Kafaky, S.B., Tali, M.G., 2010. Spatial distribution and mapping of risk elements pollution in agricultural soils of southern Tehran, Iran. Plant Soil Environ. 56, 288–296. Hendershot, W., Lalande, H., Duquette, M., 1993. Ion exchange and exchangeable
110
Catena 176 (2019) 104–111
X. Nie et al.
organic carbon in soil aggregates as affected by soil erosion and deposition. Soil Tillage Res. 175, 82–90. Olsen, S.R., Cole, C.V., Watambe, F.S., Dean, L.A., 1954. Estimation of Available Phosphorus in Soil by Extraction with NaHCO3, USDA Ciraza (Quoted from, Method of soil Analysis, CA Black 2nd ed.). 1165 Am. Soc. Agron., Inc., Medision Wisconsin, USA. Paoli, G.D., Curran, L.M., Slik, J., 2008. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 155, 287–299. Ponette, Q., Ranger, J., Ottorini, J.M., Ulrich, E., 2001. Aboveground biomass and nutrient content of five Douglas-fir stands in France. For. Ecol. Manag. 142, 109–127. Sato, T., 2010. Stocks of coarse woody debris in old-growth lucidophyllous forests in southwestern Japan. J. For. Res. 15 (6), 404–410. Schiettecatte, W., Gabriels, D., Cornelis, W.M., Hofman, G., 2008a. Enrichment of organic carbon in sediment transport by interrill and rill erosion processes. Soil Sci. Soc. Am. J. 72, 50–55. Schiettecatte, W., Gabriels, D., Cornelis, W.M., Hofman, G., 2008b. Impact of deposition on the enrichment of organic carbon in eroded sediment. Catena 72, 340–347. Schwilch, G., Liniger, H.P., Hurni, H., 2014. Sustainable land management (SLM) practices in drylands: how do they address desertification threats? Environ. Manag. 54, 983–1004. Soil Survey Staff, 2003. Keys to Soil Taxonomy. Department of Agriculture: Natural Resources Conservation Service. Tripathi, B., 1999. Review of Acid Soil and Its Management in Nepal. Lumle seminar paper no. 99/1NARC, Kathmandu, Nepalpp. 1–11. Wang, B., Xue, S., Liu, G.B., Zhang, G.H., Li, G., Ren, Z.P., 2012. Changes in soil nutrient and enzyme activities under different vegetations in the Loess Plateau area, Northwest China. Catena 92, 186–195. Wei, J.B., Xiao, D.N., Zeng, H., Fu, Y.K., 2008. Spatial variability of soil properties in relation to land use and topography in a typical small watershed of the black soil region, northeastern China. Environ. Geol. 53, 1663–1672. White II, D.A., Welty-Bernard, A., Rasmussen, C., Schwartz, E., 2009. Vegetation controls on soil organic carbon dynamics in an arid, hyperthermic ecosystem. Geoderma 150, 214–223. Wu, G.L., Liu, Z.H., Zhang, L., Hu, T.M., Chen, J.M., 2010. Effects of artificial grassland establishment on soil nutrients and carbon properties in a black-soil-type degraded grassland. Plant Soil 333, 469–479. Xia, Y.Q., Shao, M.A., 2008. Soil water carrying capacity for vegetation: a hydrologic and biogeochemical process model solution. Ecol. Model. 214, 112–124. Yan, Z., Sun, J., Don, O., Zhang, Z., 2005. The red turpentine beetle, Dendroctonus valens LeConte (Scolytidae): an exotic invasive pest of pine in China. Biodivers. Conserv. 14, 1735–1760. Yetemen, O., Istanbulluoglu, E., Flores Cervantes, J.H., Vivoni, E.R., Bras, R.L., 2015. Ecohydrologic role of solar radiation on landscape evolution. Water Resour. Res. 51, 1127–1157. Yimer, F., Ledin, S., Abdelkadir, A., 2006. Soil property variations in relation to topographic aspect and vegetation community in the south-eastern highlands of Ethiopia. For. Ecol. Manag. 232, 90–99. Zhang, X., Li, Z., Tang, Z., Zeng, G., Huang, J., Guo, W., Chen, X., Hirsh, A., 2013. Effects of water erosion on the redistribution of soil organic carbon in the hilly red soil region of southern China. Geomorphology 197, 137–144. Zheng, H., Chen, F.L., Ouyang, Z.Y., Tu, N.M., Xu, W.H., Wang, X.K., Miao, H., Li, X.Q., Tian, Y.X., 2008. Impacts of reforestation approaches on runoff control in the hilly red soil region of Southern China. J. Hydrol. 356, 174–184. Zheng, Z., Zhang, F., Ma, F., Chai, X., Zhu, Z., Shi, J., Zhang, S., 2009. Spatiotemporal changes in soil salinity in a drip-irrigated field. Geoderma 149, 243–248.
cations. In: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis for Canadian. Soc. Soil Sci. Boca. Ratonpp. 141–145. Hicks, R.R., Frank, P.S., 1984. Relationship of aspect to soil nutrients, species importance and biomass in a forested watershed in West-Virginia. For. Ecol. Manag. 8, 281–291. Huang, M., Ji, J.J., Li, K.R., Liu, Y.F., Yang, F.T., Tao, B., 2007. The ecosystem carbon accumulation after conversion of grasslands to pine plantations in subtropical red soil of south China. Tellus B 59, 439–448. Huang, J., Wu, P., Zhao, X., 2013. Effects of rainfall intensity, underlying surface and slope gradient on soil infiltration under simulated rainfall experiments. Catena 104, 93–102. Huang, Y.M., Kang, R.H., Mulder, J., Zhang, T., Duan, L., 2015. Nitrogen saturation, soil acidification, and ecological effects in a subtropical pine forest on acid soil in southwest China. J. Geophys. Res. Biogeo. 120, 2457–2472. Jiang, Y., Zhang, Y., Yang, Y., Xu, J., 2010. Impacts of grazing on the system coupling between vegetation and soil in the alpine and subalpine meadows of Wutai Mountain. Acta Ecol. Dermatol. Sin. 30, 837–846. Jordan, A., Zavala, L.M., Gil, J., 2010. Effects of mulching on soil physical properties and runoff under semi-arid conditions in southern Spain. Catena 81, 77–85. Keeney, D.R., Nelson, D.W., 1982. Nitrogen-inorganic forms. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd ed. Agronomy Monograph, vol. 9. ASA and SSSA, Madison, WI, pp. 643–698. Keesstra, S., Pereira, P., Novara, A., Brevik, E.C., Azorin-Molina, C., Parras-Alcántara, L., Jordán, A., Cerdà, A., 2016. Effects of soil management techniques on soil water erosion in apricot orchards. Sci. Total Environ. 551, 357–366. Li, H., Shi, K., Sun, Y., 2005. Content of organic carbon in shallow soil profile under 3 forest types. Chinese J. Ecol. 10, 023. Li, Z., Nie, X., Chen, X., Lu, Y., Jiang, W., Zeng, G., 2015. The effects of land use and landscape position on labile organic carbon and carbon management index in red soil hilly region, southern China. J. Mt. Sci. Eng. 12, 626–636. Liao, J.D., Boutton, T.W., Jastrow, J.D., 2006. Storage and dynamics of carbon and nitrogen in soil physical fractions following woody plant invasion of grassland. Soil Biol. Biochem. 38, 3184–3196. Lin, D., Lai, J., Muller-Landau, H.C., Mi, X., Ma, K., 2012. Topographic variation in aboveground biomass in a subtropical evergreen broad-leaved forest in China. PLoS One 7 (10), e48244. Liu, Q., Ouyang, Z., Li, A., Xu, W., 2016. Spatial distribution characteristics of biomass and carbon storage in forest vegetation in Chongqing based on RS and GIS. Nat. Environ. Pollut. Technol. 15, 1381–1388. Ma, W., Li, Z., Ding, K., Huang, B., Nie, X., Lu, Y., Xiao, H., 2016. Soil erosion, organic carbon and nitrogen dynamics in planted forests: a case study in a hilly catchment of Hunan Province, China. Soil Tillage Res. 155, 69–77. Mekonnen, M., Keesstra, S.D., Stroosnijder, L., Baartman, J.E., Maroulis, J., 2015. Soil conservation through sediment trapping: a review. Land Degrad. Dev. 26, 544–556. Mohammad, A.G., Alseekh, S.H., 2013. The effect of Sarcopoterium spinosum on soil and vegetation characteristics. Catena 100, 10–14. Murphy, J.A.M.E.S., Riley, J.P., 1962. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36. Naeth, M., Chanasyk, D., Burgers, T., 2011. Vegetation and soil water interactions on a tailings sand storage facility in the Athabasca oil sands region of Alberta Canada. Phys. Chem. Earth 36, 19–30. Nie, X., Li, Z., He, J., Huang, J., Zhang, Y., Huang, B., Ma, W., Lu, Y., Zeng, G., 2015. Enrichment of organic carbon in sediment under field simulated rainfall experiments. Environ. Earth Sci. 74, 5417–5425. Nie, X.D., Li, Z.W., Huang, J.Q., Huang, B., Xiao, H.B., Zeng, G.M., 2017. Soil organic carbon fractions and stocks respond to restoration measures in degraded lands by water erosion. Environ. Manag. 59, 816–825. Nie, X., Li, Z., Huang, J., Liu, L., Xiao, H., Liu, C., Zeng, G., 2018. Thermal stability of
111
Contents lists available at ScienceDirect
Catena journal homepage: www.elsevier.com/locate/catena
Effects of soil properties, topography and landform on the understory biomass of a pine forest in a subtropical hilly region
T
⁎
Xiaodong Niea, Wang Guob, Bin Huanga, Muning Zhuoa, Dingqiang Lia,c, Zhongwu Lid,e, , ⁎⁎ Zaijian Yuana, a Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, PR China b The Second Surveying and Mapping Institute of Hunan Province, Changsha 410118, PR China c Guangdong Academy of Sciences, Guangzhou 510075, PR China d College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China e Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha 410082, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Pine forest Phytocoenosis Land degradation Soil erosion Vegetation biomass
Soil conservation will remain an essential subject for forest in slope land. However, the interactions between soil, vegetation and complex topography are not fully understood. To address this concern, field studies were performed in a typical small watershed in the subtropical hilly red soil region of China. This site covers an area of approximately 3.2 ha, and the mean slope steepness of the land is 15°. Soil properties (including available and total N, P, K, soil organic carbon (SOC), CEC, soil texture), understory vegetation biomass and diversity index on different slopes (different slope steepness, slope aspects, and slope positions) were measured. Meanwhile, interrelationships between understory vegetation biomass and soil properties, slope steepness and altitude were evaluated via redundancy analysis. The study results showed that the soil nutrients and SOC contents were greater in the down-slope and north and west slopes. And the soil properties displayed high spatial heterogeneity and had semi-variance structure, which were mainly derived from random factors. Due to obvious erosion characteristics and the negative correlations between soil properties and slope positions, we suspected that soil erosion was the primary random factor. In addition, the mean total understory vegetation biomass in the pine forest was 283.58 g m−2 (ranging from 66.60 to 573.79), and higher vegetation biomass was found in downslope and north and west slopes. Further analyses indicated that topography (slope steepness and altitude) and soil properties combined contributed to 58.7% of the variations in the understory biomass, and they individually had a contribution of 17.3% and 41.4%, respectively. Moreover, altitude (height of slope positions) alone explained 15.9% of the variation of the vegetation biomass. This study indicated that soil properties, which were highly affected by slope aspect and slope position, were the most important factor in influencing understory vegetation biomass in subtropical pine forest. Slope position also had a tremendous direct influence on soil vegetation. Controlling understory soil erosion is essential for conservation of pine forest land in subtropical China.
1. Introduction Soil conservation will remain an essential subject for forest land. Heavy rainfall events, forest fire, deforestation and landslide are great threat to land quality (Mekonnen et al., 2015). In forest, effects of these natural or human –driven disturbances on soil were often neglected due
to relatively high vegetation cover. Choosing and adopting appropriate protective measures are practical strategy for maintain soil sustainable development (Schwilch et al., 2014). While, to articulate clearly the relationship between soil and vegetation under complex topography is the basis for soil conservation. Soil and vegetation, which are the two key factors in controlling soil
Abbreviations: TN, total nitrogen; SOC, soil organic carbon; TP, total phosphorus; TK, total potassium; AN, available nitrogen; AP, available phosphorus; AK, available potassium; CEC, cation exchange capacity ⁎ Corresponding author at: College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China. ⁎⁎ Correspondence to: Z. Yuan, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, PR China. E-mail addresses: [email protected] (Z. Li), [email protected] (Z. Yuan). https://doi.org/10.1016/j.catena.2019.01.007 Received 28 March 2018; Received in revised form 28 November 2018; Accepted 8 January 2019 0341-8162/ © 2019 Elsevier B.V. All rights reserved.
Catena 176 (2019) 104–111
X. Nie et al.
Based on the above analyses, effects of topographical conditions on soil and vegetation should be considered when studying the relationships between soil and vegetation. The main objective of this study was to explore the influences of soil properties and topographical conditions on the variations in understory vegetation biomass in a pine forest. Combined with the distributions of plant biomass and soil properties in a pine forest in the hilly red soil region of southern China, relationships between the topographical conditions (slope aspect, slope steepness and slope position) and soil properties and vegetation were studied. The study results will help determine the characteristics of nutrient distribution in the soil-vegetation system in the hilly red soil region of China, and will provide a basis for formulating measures for soil conservation, thus improving the productivity of vegetation and the preservation of fertility.
conservation, have attracted increased attention worldwide. Vegetation is a useful parameter for predicting ecosystem status in hilly regions, it not only is strongly related to soil nutrients and also acts as protection against soil erosion and water loss (Wang et al., 2012; Keesstra et al., 2016). Many studies have reported on the interactions between soil and vegetation in soil-vegetation systems (Carroll et al., 2003; Naeth et al., 2011; Ceacero et al., 2012). Soil provides physical environment, water and mineral nutrition for growing plants, which not only affects the ontogenesis of the plants but also further determines the type, distribution, dynamics and yield of the phytocoenoses (Liao et al., 2006; Benbi and Chand, 2007; Xia and Shao, 2008). Soil type and depth have also been considered to be highly correlated to vegetation growth and vigour (Belcher et al., 1995; Fierer et al., 2003). Soil nutrients are the main limiting and direct factors of phytocoenosis biomass (Huang et al., 2007; Zheng et al., 2008), especially in soils with low water-holding capacity and low nutrient retention caused by severe water erosion and soil degradation. However, vegetation also exerts effects on soil ecosystems (White II et al., 2009), and the ecological effects of vegetation on soil nutrients in response to soil erosion have attracted the attention of a large number researchers (Feng et al., 2007; Bilodeau-Gauthier et al., 2011). According to previous studies (Li et al., 2005; Mohammad and Alseekh, 2013), these effects are derived mainly from plant absorption and fixation processes and the accumulation and decomposition of phytocoenosis biomass, which lead to variation in soil nutrients at temporal and spatial scales. Therefore, unexpectedly strong relationships exist between soil nutrients and characteristics of phytocoenoses, including phytocoenosis biomass, survival rates and turnover rates. The interactive effects between soil and plants are highly intricate, and have produced contrasting results. Jiang et al. (2010) reported that soil organic matter and total nitrogen (TN) were highly correlated with the understory biomass of plant communities, the variety of species and the density of the community. However, some studies have reported no significant correlation between soil nutrient contents and vegetation biomass (Ponette et al., 2001). These contracting results may result from different topographical conditions (including slope steepness, altitude, slope aspect and slope position), which are considered essential factors that affect aboveground biomass in subtropical area (Alves et al., 2010; Sato, 2010; Lin et al., 2012). Similar to the highly complex relationships between soil and vegetation, many complex problems in the soil-vegetation system remain to be solved. Specifically, within a single ecosystem, full understanding of the effects of topographical conditions on the distributions of plant and soil nutrients is essential for determining the interactive effects between soil and vegetation. Although studies addressing this understanding have been reported, some studies have considered the influence of different types of physiognomy on the characteristics of vegetation density and coverage, without considering the ecosystem spatial heterogeneity caused by differences in the tiny topographic and soil characteristics (Florinsky and Kuryakova, 1996). Other studies have investigated the influence of only slope steepness on vegetation (de Castilho et al., 2006). However, many other factors, such as slope aspect and slope position, have not been studied to the same extent as slope steepness. Both natural and planted pine forests are widely distributed within the humid subtropical areas of China (Yan et al., 2005; Huang et al., 2015). Compared with broadleaf forests, pine forests have lower soil organic carbon (SOC) contents, smaller labile carbon fractions, and lower amounts of SOC stocks (Nie et al., 2017). Furthermore, pine forests located in this region of China tend to experienced severe water erosion events, presenting soil loss rates of 4.15 ± 1.25 Mg ha−1 yr−1 (Ma et al., 2016). Therefore, pine forests are not the first choice for restoring degraded lands. However, considering the large planting area of pine forests and their low ecological function, this subject is under debate. Therefore, to fill in the gap in the relationships between soil, topographical conditions and vegetation will have important practical uses for the soil conservation in pine forest.
2. Material and methods 2.1. Site description The study site is located at the Institute of Soil and Water Conservation of Shaoyang city (111°22′E and 27°03′N), Hunan Province, South-Central China (Fig. 1). This area has a typical subtropical monsoon climate; the mean annual temperature is 17.1 °C, the frost-free period is approximately 272–304 days per year, and the mean annual precipitation is 1218.5–1473.5 mm, most of which occurs from April to June. The elevation varies between 231.18 m and 276.63 m. The soils are characterized as zonal red soils derived from Quaternary red clay, and the soil was classified as ultisols based on the US Soil Taxonomy (Soil Survey Staff, 2003). A closed watershed (approximately 3.2 ha) with a pine forest was selected and studied. According to previous study (Ma et al., 2016), the soil loss rates of this land varied from 0.18 to 9.18 Mg ha−1 yr−1, with a mean value of 4.15 Mg ha−1 yr−1. Based on the geomorphology, four slopes with different slope aspects were distinguished and marked as east slope, south slope, west slope, and north slope, respectively. Moreover, each slope was divided into three different landscape positions and marked as the upper, middle and down positions (Fig. 1). To survey and collect soil and vegetation samples, three large plots covering 5 × 5 m were established at the upper, middle and down slope positions, and five 1 × 1 m subplots were set up using a plum-shaped distribution method in each large plot. Therefore, 60 total subplots were established. The steepness of each large plot was measured with a clinometer, and the mean slope steepness for the east, west, south and north slope were 13.44°, 16.39°, 15.31°, 17.32°, respectively. The positions of each subplot were recorded using a GPS (Trimble GeoXT). The altitude of each subplot was also recorded, however, this parameter is just a quantitative expression of the plot position because of the lower values and small variation ranges. 2.2. Biomass and soil sampling At the November 2015, a field survey about the vegetation was performed. The vegetation survey found that the main species of macrophanerophytes was loblolly (Pinus taeda Linn); the constructive shrub species included white oak (Quercus fabri Hance), Eurya muricata (Eurya muricata Dunn), oil tea camellia (Camellia oleifera Abel), azure pigment (Ficus hookeriana), litsea (Litsea cubeba (Lour.) Pers), and fringe flower (Loropetalum chinensis (R. Br.) Oliv); the constructive species of herbs contained miscanthus (Miscanthus floridulu (Labnll.) Warb), chain fern (Woodwardia prolifera HOOK. et Arn), shield-fern (Dryopteris pacifica (Nakai) Tagawa), dicranopteris dichotoma (Dicranopteris dichotoma (Thunb.) Bernh), hedyotis auricularia (Paspalum conjugatum (Berg.)), and polystichum (Polystichum longipaleatum Christ). During the survey, the numbers of species and individuals of the shrubs and herbs within each subplot were counted and recorded. The aboveground deciduous shrubs, forbs and grasses were clipped, placed 105
Catena 176 (2019) 104–111
X. Nie et al.
Fig. 1. The location and set up of plots in the watershed.
(pH 7) and analyzed via flame photometry.
in bags, and transported to the laboratory for drying. All the herbs and shrubs in the subplots were collected respectively, and all aboveground biomass was measured. The fresh biomass was dried in an oven at a temperature of 60 °C until stable. Moreover, three replicate soil samples (0–20 cm) were collected from each subplot to provide an adequate representation of the site. As such, 45 soil samples were collected within each slope. The soil samples were subsequently air-dried and analyzed for their physical and chemical properties. The soil particle size distribution was determined using a Malvern Mastersizer 2000 laser diffraction device (Malvern Instruments Ltd., UK). The cation exchange capacity (CEC) was determined by the BaCl2 displaced method (Hendershot et al., 1993), and the SOC and TN were determined with a CN analyzer (Vario MAX CN, Elementar, Hanau, Germany). Soil available nitrogen (AN) was considered to be the NO3−–N and NH4+–N, and the N content within both forms was measured via the colorimetric method, analyzed by automatic flow injection (Keeney and Nelson, 1982). Total phosphorus (TP) was measured with the method of molybdenum antimony blue colorimetry (Murphy and Riley, 1962), and the available phosphorus (AP) was determined by sodium bicarbonate extraction and subsequent colorimetric analysis (Olsen et al., 1954). Soil total potassium (TK) was measured with the method of alkali fusion and flame photometer, and the soil available potassium (AK) was extracted with 1 M NH4OAC
2.3. Statistical analysis Semivariogram of geostatistics, which is often used to measure the spatial variability of the soil nutrients (Wei et al., 2008; Zheng et al., 2009), was conducted to test the spatial autocorrelation within the data of soil properties. Different models of theoretical semivariance were used to fit the calculated values, and the model with the best-fitting value and the smallest nugget value was selected. The semivariogram parameters of the nugget value (C0), partial sill (C), sill (C0 + C), range value (A0), and nugget-sill-ratio (C0 / (C0 + C)) were summarized. C0 usually indicates the variation caused by experimental errors, which is less than the experimental sample scale; C represents the variation caused by system factors; and (C0 + C) indicates the total variation within the system. Different values of these parameters indicate different spatial distribution patterns (Cambouris et al., 2006; Hani et al., 2010). The larger the (C0 + C) is, the greater the spatial variation of the soil nutrients. Similarly, the smaller the proportion of the structure variance C in (C0 + C) is, the greater the impact of both random factors (non-area factors) and small-scale factors on soil nutrient contents. The smaller the C0 / (C0 + C) value is, the greater the impact of the natural factors (area factors). A0 indicates the corresponding distance when the 106
Catena 176 (2019) 104–111
X. Nie et al.
nutrients (TN, TP, TK) and silt among different slope positions. The AN content did not significantly differ between the upslope and downslope positions, but the AN content was significantly higher at the upslope and downslope positions than at the midslope position (P < 0.05). Compared with the total nutrients contents, the labile nutrients contents were more strongly related to slope position. Nonparametric correlations between the soil properties and the slope steepness and altitude are shown in Table 3. Most of the correlations between the soil properties and the slope steepness and altitude were negative but not significant. Slope steepness was negative correlated to SOC, CEC, TP, AP, and silt. Similarly, the altitude was negative correlated to SOC, TN, TP, TK, AN, AP, and AK. In general, increases in slope steepness and altitude had a negative effect on the soil physicochemical properties.
variation function reaches the partial sill. If the expressed distance of the observed value of a nutrient is larger than that value, they are independent; if the distance of the observed value of a nutrient is lower than this value, there is a correlation between them. C0 / (C0 + C) can reveal the extent of the spatial variation of the variables; if the ratio is lower than 0.25, there is a strong spatial correlation among the soil properties. If the ratio is between 0.25 and 0.75, the spatial correlation of the soil properties is at an intermediate level. If the ratio is > 0.75, the spatial correlation of the soil properties is weak. The data analysis in this paper used the average value of three replicated samples. Statistical analyses were performed with SPSS 20.0 (SPSS Inc., Chicago IL, USA), and significant differences at the 0.05 level (two-tailed) were identified by the tests. A redundancy analysis (RDA) was performed to study the relationships between the understory biomass and soil properties, slope steepness and altitude. RDA constraint sorting of the experimental data was performed with Canoco 4.5 (Microcomputer Power, Ithaca, NY, USA). The RDA analysis had two matrices, the species data and environmental data. The species data was the herb, shrub and total biomasses. The environmental variables were composed by soil nutrients, SOC, soil texture, altitude and slope steepness. Prior to sorting, all the different dimensional data were standardized. Therefore, in the ordination diagram (biplot), the length of the vector, which is represented by a red arrow for each environmental variable, represents the magnitude of those environmental factors with respect to explaining the vegetation biomass (blue dashed arrows). The angle between the two arrows represents the relationship between the environmental factors and the herb biomass. An angle between 0° and 90° indicates a positive relationship between the two variables. An angle of 90° indicates no significant correlation. An angle between 90° and 180° indicates a negative relationship. A nonparametric correlation analysis and partial correlation analysis were performed using SPSS 20.0, and GS+ software was used for geostatistical analysis. The species richness index and the ShannonWiener species diversity index parameters were calculated to characterize the species. The species richness index was calculated as follows:
R=
(S − 1) ln N
3.2. Variations in soil properties Distinct semivariance structures of spatial variability were observed for the soil nutrients and soil particle size distributions (Table 4). The C0 values of the SOC, CEC, AN, AP, AK, sand, silt, and clay were 1.99, 1.50, 92.20, 1.55, 13.20, 3.89, 1.10 and 7.73, respectively. These relatively large values indicate that small-scale processes cannot be ignored. Furthermore, relatively large (C0 + C) values for the CEC, AP, sand, silt, and clay were observed, and the highest and lowest values were found for the AN (256.60) and AP (3.12). These results illustrate that high spatial heterogeneity of the soil nutrients exists. The C0 / (C0 + C) values of the CEC, AP, sand, silt, and clay were lower than 25%, indicating strong spatial correlations exist among these soil properties. The C0 / (C0 + C) of the SOC, TN, TP, TK, AN, and AK varied from 25% to 75%, indicating that the spatial variations resulted mainly from the influences of random factors at a small scale. The C0 / (C0 + C) of the TK was 1, which indicates that the TK had constant variations in this area. In addition, the independent spacing A0 of the spatial variability of soil properties varied from 6.10 to 149.00, this range was lower range than that of the watershed scale in the study area, further demonstrating that the changes in the soil physical and chemical properties were strongly affected by random factors on a small scale.
(1)
3.3. Distributions in understory vegetation biomass
where R is the species richness index, S is the number of species, and N is the total number of individuals for all species. The Shannon-Wiener species diversity index was calculated as follows:
H ′ = − ∑ (pi × ln pi )
The mean total understory vegetation biomass in the pine forest was 283.58 g m−2 (ranging from 66.60 to 573.79). The understory vegetation biomass in the pine ecosystem varied with slope position and showed a clear increasing trend along the downward slope (Fig. 2a). The total understory biomass on the downslope was 127% and 119% of the upslope and midslope, respectively. The vegetation biomass on different slope aspects followed the order of west slope > north slope > east slope > south slope (Fig. 2b). The accumulated biomass on the west and north slopes was higher than that on the east and south slopes. On the whole, the herb biomass was less than the shrub biomass. In addition, little differences in herb biomass were observed among different slope aspects and among different slope positions; however, opposite results were observed in shrub biomass. Slop aspect and position not only are related to biomass but also are related the biological community structure (Table 5). For the west and north slopes, the difference in the number of individuals per unit area was not obvious among different slope positions. With respect to the east and south slopes, the number of individuals per unit area was higher at the downslope than midslope and upslope. The mean values of richness index for different slope aspects followed the order of south slope (0.325) > east slope (0.251) > north slope (0.219) > west slope (0.212). Furthermore, the mean values of vegetation diversity index on different slope aspects decreased in the order of west slope (2.400) > north slope (2.296) > east slope (1.745) > south slope (1.614). The variation trend of the richness index on different slope aspects was opposite that of the diversity index and understory biomass.
(2)
where H′ is the Shannon-Wiener species diversity index, and Pi is the ratio of the number of the species to the total number of individuals for all species. 3. Results 3.1. Distributions of soil properties in pine forest Owing to the tattered landform and complex topography in the hilly red soil region, different trends in the distributions of soil properties were observed among locations at different slope aspects and positions. The SOC, AN and clay contents in the soil were significantly higher on the west slopes than on the east slopes (Table 1). Moreover, the soil properties (excluding TN, TP, AP and clay) significantly differed between the south and north slopes. However, there were no significant differences in the soil physico-chemical properties between the south and east slope as well as between the north and west slope. The SOC, AP, AK, sand and clay contents were significantly higher on the upslope and midslope than on the downslope (P < 0.05) (Table 2). However, there was no significant difference in the CEC, total 107
Catena 176 (2019) 104–111
X. Nie et al.
Table 1 Mean and standard deviation of soil properties in each of the four slopes. Soil properties −1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
West slope
North slope
East slope
South slope
6.91 ± 1.49a 11.89 ± 1.48a 0.64 ± 0.06a 0.19 ± 0.01a 8.46 ± 0.34b 52.89 ± 6.30a 0.59 ± 0.50a 24.33 ± 1.76ab 38.20 ± 4.95b 20.84 ± 0.83b 40.96 ± 4.58a
6.59 ± 2.16a 8.99 ± 0.72b 0.66 ± 0.07a 0.19 ± 0.01a 9.93 ± 0.42a 51.22 ± 7.68a 0.43 ± 0.21b 27.22 ± 7.18a 38.79 ± 3.42b 26.18 ± 3.27a 35.02 ± 4.89b
5.32 ± 0.72b 9.77 ± 0.94ab 0.57 ± 0.03a 0.17 ± 0.01a 8.92 ± 0.44ab 41.33 ± 4.67b 0.52 ± 0.12ab 20.44 ± 1.83b 45.18 ± 5.44a 24.21 ± 2.69a 30.60 ± 4.41b
5.92 ± 0.86b 10.17 ± 2.38a 0.58 ± 0.06a 0.18 ± 0.02a 8.36 ± 0.24b 45.33 ± 9.82b 0.46 ± 0.02b 22.66 ± 3.51b 45.66 ± 8.80a 19.23 ± 1.40b 35.11 ± 7.88b
The data in table is mean value ± SD. Different lower case letters at the same row indicate significant difference in soil properties between different slope directions.
the variation, respectively. The studied soil properties differentially influenced the distribution gradient of the biomass (P < 0.05): the AK, AP, AN, slope, sand, TN, and TK were positively related to herb biomass; their respective correlation coefficients followed the same decreasing order. The clay, CEC, altitude, and TP were negatively related to the herb biomass, whereas silt, TK, TN, sand, and slope were positively related to the shrub biomass. Moreover, the AN, SOC, clay, CEC, and altitude were negatively related to the shrub biomass. There were no significant relationships between the TP, AP, and AK and the shrub biomass. The total biomass was negatively related to the SOC, CEC, clay, altitude and TP, but positively related to the sand, TN, TK, slope, AK, AP, and AN.
Table 2 Characteristics of the distributions of soil properties at different slope positions. Soil properties −1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
Upper slope
Middle slope
Down slope
5.85 ± 0.93b 10.49 ± 1.52a 0.61 ± 0.03a 0.19 ± 0.02a 8.91 ± 0.43a 48.42 ± 4.82a 0.37 ± 0.14b 22.49 ± 2.85b 37.03 ± 3.64b 23.49 ± 4.09a 39.48 ± 5.53a
5.70 ± 0.79b 10.23 ± 1.67a 0.61 ± 0.04a 0.18 ± 0.01a 9.03 ± 0.76a 43.67 ± 6.83b 0.42 ± 0.09b 22.83 ± 3.32b 41.13 ± 4.13b 22.82 ± 3.12a 36.05 ± 6.08a
7.02 ± 2.01a 9.90 ± 1.07a 0.65 ± 0.09a 0.18 ± 0.01a 8.82 ± 1.06a 51.00 ± 5.61a 0.72 ± 0.31a 25.67 ± 3.65a 47.72 ± 7.06a 21.54 ± 3.29a 30.74 ± 5.15b
4. Discussion
The data in table is mean value ± SD. Different lower case letters at the same row indicate significant difference in soil properties between different slope positions.
The spatial distributions of soil properties were affected by many factors, such as slope aspect, slope steepness, and slope position. The study results showed that soil properties varied with slope aspect, and soil nutrients contents in north and west slopes were higher than in south and east slopes (Table 1). In general, slope aspect impact on soil nutrients mainly through indirect ways of altering litter decomposition and nutrient cycling (Hicks and Frank, 1984). Notably, vegetation, which plays an important role in controlling soil nutrients cycling, is a key medium of the interactions between slope aspect and soil nutrients (Yimer et al., 2006). Our results showed that north and west slopes had larger amounts of vegetation biomass than did south and east slopes (Fig. 2), which have proved the close relationships among slope aspect, vegetation and soil nutrients. In addition to slope aspect, slope steepness had been considered highly related to soil properties distributions. It has been reported that an increasing slope steepness can lead to increasing of erosion intensity and sediment transportation in slope land (Berger et al., 2010), and thus result in the decrease of soil nutrients. However, in the present study, most of the soil nutrients did not significantly decrease as the slope steepness increased (Table 3). We may attribute these phenomena to the presence of aboveground vegetation. Soil vegetation plays important roles not only in reducing soil erosion (Jordan et al., 2010) but also in affecting soil nutrients cycles. Complicated distributions of vegetation may change the negative relationships between slope steepness and soil nutrients. Furthermore, the present study showed that the increases in altitude negatively impacted soil nutrients contents, although the effect was not significant. Tripathi (1999) reported that, when the altitude was < 2000 m, the SOC had a higher accumulation rate and a lower decomposition rate as the altitude increased, and significant positive correlations between the altitude and SOC were observed. This is differed to our findings. We could attribute these differences to the low altitude values and small variation range in the study area (the highest altitude is just 268 m). Actually, the low altitude in our study just reflects the slope position (height) of sampling point in the pine forest, and the results indicated that soil nutrients contents decreased with the
Table 3 Nonparametric correlations between soil properties versus slope steepness and altitude. Soil properties
−1
SOC (g kg ) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%) ⁎
Slope steepness(°)
Altitude (m a.l.s.)
R
P
R
P
−0.152 −0.473⁎ 0.061 −0.426 0.394 0.308 −0.277 0.078 0.038 −0.239 0.084
0.493 0.033 0.784 0.061 0.075 0.168 0.215 0.730 0.608 0.454 0.794
−0.091 0.137 −0.303 −0.016 −0.455⁎ −0.062 −0.154 −0.357 −0.231 0.102 0.196
0.681 0.536 0.170 0.945 0.040 0.783 0.491 0.112 0.470 0.753 0.541
Significant difference at 0.05 level.
3.4. Relationships between understory biomass and soil properties and slope steepness and elevation To explore the impact of soil physico-chemical properties, slope steepness and altitude on the understory biomass of the phytocoenoses, RDA was performed to generate a RDA sequence diagram. The twodimensional sorting results show the regularities of the distribution of the understory biomass in response to different impact factors in the gradient. The results showed that the soil properties, slope steepness and altitude significantly affected the understory biomass (Fig. 3). Combined, the soil properties, altitude and slope steepness explained 58.7% of the variation in understory biomass. Specifically, soil properties explained 41.4% of the variance of understory vegetation biomass, while altitude and slope steepness explained 15.9% and 1.4% of 108
Catena 176 (2019) 104–111
X. Nie et al.
Table 4 Geostatistical summary of the content of soil properties. Soil properties
Model
C0
C0 + C
C0/(C0 + C)
A0
r2
SOC (g kg−1) CEC (cmol(+) kg−1) TN (g kg−1) TP (g kg−1) TK (g kg−1) AN (mg kg−1) AP (mg kg−1) AK (mg kg−1) Sand (%) Silt (%) Clay (%)
Exponential Gaussian Exponential Gaussian Gaussian Gaussian Linear Gaussian Linear Gaussian Linear
1.99 1.50 0.87 × 10−2 0.24 × 10−3 0.14 92.20 1.55 13.20 3.89 1.10 7.73
4.01 5.74 2.02 × 10−2 0.48 × 10−3 0.14 256.60 3.12 47.04 79.36 11.20 56.07
0.50 0.09 0.43 0.50 1 0.36 0.18 0.28 0.05 0.10 0.14
6.10 11.60 149.00 43.00 68.26 9.90 17.80 11.40 68.26 23.80 68.26
0.10 0.27 0.11 0.29 0.12 0.32 0.21 0.27 0.68 0.73 0.64
C0 is the nugget value; C is partial sill; C0 + C is sill; A0 is the range value; r2 is the fit coefficient of semivariance models.
increasing slope position. This is in consistent with the distributions of soil nutrients in different slope positions (Table 2). The contents of the labile soil nutrients (AP, AK, SOC, etc.) at the upslope and midslope (eroding site) were significantly lower than those at the downslope (depositional site). This finding indicated clear erosion characteristics because the SOC and labile nutrients within light soil particles had been preferentially transported from eroded sites (upslope) to depositional sites (downslope) (Schiettecatte et al., 2008a, 2008b; Nie et al., 2015). Therefore, from the aspect of soil nutrients distributions, soil erosion is an unneglectable factor in the pine forest. Geostatistics showed that the distributions of soil properties in the pine forest were characterized by substantial spatial heterogeneity, which presented a semi-variance structure. This finding indicated that changes in the soil physico-chemical properties were strongly affected by random small-scale factors. Based on a comprehensive consideration of the above results and the relatively high erosion intensity in this area (mean erosion rate of 4.15 Mg ha−1 yr−1) (Li et al., 2015; Ma et al., 2016; Nie et al., 2018), the random factor was suggested to be soil erosion. Understory water erosion, which was highly depended on complex rainfall and underlying surface conditions (Huang et al., 2013), may transports and redistribute soil particles and nutrients randomly. Understory vegetation biomass, individual numbers of plants per unit area, Shannon-Wiener diversity index and species richness index, which were widely used in indicating distribution characteristics of species (Death and Winterbourn, 1995; Wu et al., 2010), were also measured in this study. The richness and diversity index of soil vegetation showed opposite trends. Similar to the distributions of soil nutrients, understory vegetation biomass and species diversity were higher on the north and west slopes and the down-slope when compared to other slope aspects and positions (Fig. 2 and Table 5). Higher
Table 5 Variations of vegetation communities under different topographic locations. Aspect
Slope position
Numer of species per m2
Numer of individuals in per m2
Index of species richness
ShannonWiener biodiversity index
West slope
Upper Middle Down Upper Middle Down Upper Middle Down Upper Middle Down
14 16 19 6 10 12 11 17 13 6 8 10
84 67 83 17 43 72 81 113 85 19 14 114
0.167 0.239 0.229 0.353 0.233 0.167 0.355 0.150 0.153 0.316 0.571 0.088
2.208 2.456 2.537 1.395 2.024 1.816 2.220 2.855 1.813 1.416 1.908 1.518
East slope
North slope
South slope
vegetation biomass were found on the north and west slopes, which is consistent with previous studies (Hicks and Frank, 1984; Liu et al., 2016). As south slope is exposed to more solar radiation and supports sparser biomass, whereas north slope retains more soil moisture and supports erosion-resistant denser vegetation cover (Yetemen et al., 2015). In addition, the enrichment of soil nutrients and soil water in down-slope will promote the accumulation of vegetation biomass. Meanwhile, the growth of plants will increase the plant litter return to soil and decrease its loss via water erosion. As a consequence, virtuous cycle formed and close correlations between soil nutrients and vegetation can be observed. Further analysis revealed that the soil properties, slope steepness and altitude explained 58.7% of the variation in the understory
Fig. 2. Distributions of vegetation biomasses at different (a) slope positions and (b) slope aspects. 109
Catena 176 (2019) 104–111
X. Nie et al.
vegetation biomass. 5. Conclusions The present study investigates the effects of soil properties and topography (slope aspect, slope steepness, and slope position) on the distributions of soil vegetation in a pine forest of the subtropical China. Similar distributions of soil nutrients and understory vegetation biomass were found, and slope aspect and slope position had a great impact on soil nutrients and soil vegetation. Soil properties, which explained 41.4% of the variation in the understory biomass, were the most important factors influencing understory vegetation. Slope position (altitude) directly explained 15.9% of the variation in the understory biomass. Above results suggested that for the understory vegetation biomass in pine forest, slope position and slope aspect were of the same importance as soil properties. Understory soil erosion should be considered seriously during adopting practical conservation practice.
Fig. 3. Overall redundancy analysis of the correlation among understory biomass, soil properties, slope gradient, and altitude, as determined by Canoco. The two axes illustrate the two principal components of the residuals of the multiple linear regression of X unto Y. The sampling points were indicated by circles with different numbers. AN, AP and AK represent the available nitrogen, phosphorus and potassium, respectively. TN, TP and TK represent the total nitrogen, phosphorus and potassium, respectively. The intersection angle between vector lines represents the significance of the correlation between their corresponding variables, in which a sharp angle represents positive correlation, an obtuse angle represents negative correlation, and a right angle represents no significant correlation.
Acknowledgments This paper was supported by the National Key Research and Development Program of China (2017YFC0505404), the National Natural Science Foundation of China (41807069), GDAS' Special Project of Science and Technology Development (2018GDASCX-1002, 2017GDASCX-0106), the High-level Leading Talent Introduction Program of GDAS (2016GDASRC-0103), the Scientific Platform and Innovation Capability Construction Program of GDAS (2016GDASPT0304).
aboveground biomass at our study site. Although a large proportion of the variation remains unexplained, the evidence suggested that small differences in soil properties, slope steepness and altitude could affect biomass accumulation. The soil properties individually explained 41.4% of the variation in the understory aboveground biomass, which indicated that soil factors are the most important factors influencing on the understory biomass (Paoli et al., 2008). In general, higher forest biomass is expected on high-fertility soils, independent of species composition, simply because there are more available resources for plant growth. In fact, significant correlations between understory biomass and soil nutrients have been reported (de Castilho et al., 2006; Jiang et al., 2010). The relationships between the herb biomass and the labile soil nutrients were stronger than those between the shrub biomass and the labile soil nutrients (Fig. 3), which indicated that the herbaceous species are relatively sensitive to soil fluctuations. It's worth noting that the shrub biomass was larger than the herb biomass, and the biomass accumulation effect may lead to weak correlations with soil nutrients. Moreover, the RDA analysis also showed that the herb, shrub, and total biomasses had no positive correlation with SOC, but was positively related to TN. Therefore, soil properties are important factors that affect the distributions of vegetation, the composition of plant functional groups and the diversity of community species. In addition, the altitude and slope steepness explained 15.9% and 1.4% of the variation in the understory vegetation biomass, respectively. As we discussed previously, the altitude in our study is a quantitative expression of the slope position. This indicated that the slope position (height) is the most important topographical factor in influencing vegetation. And negative correlations between soil vegetation biomass and slope position (height) were observed (Fig. 3). The movement of soil nutrients from the upslope to downslope caused by water erosion was considered a possible reason for this result (Zhang et al., 2013). However, slope steepness had a small impact on vegetation biomass. This finding is consistent with the results of da Silva et al. (2002), who studied the growth rates of trees along a topographic gradient in central Amazonia and failed to detect a topographic influence on growth rate. The results indicated that a biological response to soil nutrients occurred, and that the soil properties as well as slope aspect and slope position comprehensively affect the understory
References Alves, L.F., Vieira, S.A., Scaranello, M.A., Camargo, P.B., Santos, F.A., Joly, C.A., Martinelli, L.A., 2010. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For. Ecol. Manag. 260, 679–691. Belcher, J.W., Keddy, P.A., Twolan-Strutt, L., 1995. Root and shoot competition intensity along a soil depth gradient. J. Ecol. 673–682. Benbi, D., Chand, M., 2007. Quantifying the effect of soil organic matter on indigenous soil N supply and wheat productivity in semiarid sub-tropical India. Nutr. Cycl. Agroecosyst. 79, 103–112. Berger, C., Schulze, M., Rieke-Zapp, D., Schlunegger, F., 2010. Rill development and soil erosion: a laboratory study of slope and rainfall intensity. Earth Surf. Process. Landf. 35, 1456–1467. Bilodeau-Gauthier, S., Pare, D., Messier, C., Belanger, N., 2011. Juvenile growth of hybrid poplars on acidic boreal soil determined by environmental effects of soil preparation, vegetation control, and fertilization. For. Ecol. Manag. 261, 620–629. Cambouris, A.N., Nolin, M.C., Zebarth, B.J., Laverdière, M.R., 2006. Soil management zones delineated by electrical conductivity to characterize spatial and temporal variations in potato yield and in soil properties. Am. J. Potato Res. 83, 381–395. Carroll, J.A., Caporn, S.J.M., Johnson, D., Morecroft, M.D., Lee, J.A., 2003. The interactions between plant growth, vegetation structure and soil processes in semi-natural acidic and calcareous grasslands receiving long-term inputs of simulated pollutant nitrogen deposition. Environ. Pollut. 121, 363–376. Ceacero, C.J., Diaz-Hernandez, J.L., del Campo, A.D., Navarro-Cerrillo, R.M., 2012. Interactions between soil gravel content and neighboring vegetation control management in oak seedling establishment success in Mediterranean environments. For. Ecol. Manag. 271, 10–18. da Silva, R.P., dos Santos, J., Tribuzy, E.S., Chambers, J.Q., Nakamura, S., Higuchi, N., 2002. Diameter increment and growth patterns for individual tree growing in Central Amazon, Brazil. For. Ecol. Manag. 166, 295–301. de Castilho, C.V., Magnusson, W.E., de Araujo, R.N.O., Luizao, R.C.C., Lima, A.P., Higuchi, N., 2006. Variation in aboveground tree live biomass in a central Amazonian Forest: effects of soil and topography. For. Ecol. Manag. 234, 85–96. Death, R.G., Winterbourn, M.J., 1995. Diversity patterns in stream benthic invertebrate communities: the influence of habitat stability. Ecology 76, 1446–1460. Feng, D., Zongsuo, L., Xuexuan, X., Lun, S., Xingchang, Z., 2007. Community biomass of abandoned farmland and its effects on soil nutrition in the Loess hilly region of Northern Shaanxi, China. Acta Ecol. Sin. 27, 1673–1683. Fierer, N., Schimel, J.P., Holden, P.A., 2003. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176. Florinsky, I.V., Kuryakova, G.A., 1996. Influence of topography on some vegetation cover properties. Catena 27, 123–141. Hani, A., Pazira, E., Manshouri, M., Kafaky, S.B., Tali, M.G., 2010. Spatial distribution and mapping of risk elements pollution in agricultural soils of southern Tehran, Iran. Plant Soil Environ. 56, 288–296. Hendershot, W., Lalande, H., Duquette, M., 1993. Ion exchange and exchangeable
110
Catena 176 (2019) 104–111
X. Nie et al.
organic carbon in soil aggregates as affected by soil erosion and deposition. Soil Tillage Res. 175, 82–90. Olsen, S.R., Cole, C.V., Watambe, F.S., Dean, L.A., 1954. Estimation of Available Phosphorus in Soil by Extraction with NaHCO3, USDA Ciraza (Quoted from, Method of soil Analysis, CA Black 2nd ed.). 1165 Am. Soc. Agron., Inc., Medision Wisconsin, USA. Paoli, G.D., Curran, L.M., Slik, J., 2008. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 155, 287–299. Ponette, Q., Ranger, J., Ottorini, J.M., Ulrich, E., 2001. Aboveground biomass and nutrient content of five Douglas-fir stands in France. For. Ecol. Manag. 142, 109–127. Sato, T., 2010. Stocks of coarse woody debris in old-growth lucidophyllous forests in southwestern Japan. J. For. Res. 15 (6), 404–410. Schiettecatte, W., Gabriels, D., Cornelis, W.M., Hofman, G., 2008a. Enrichment of organic carbon in sediment transport by interrill and rill erosion processes. Soil Sci. Soc. Am. J. 72, 50–55. Schiettecatte, W., Gabriels, D., Cornelis, W.M., Hofman, G., 2008b. Impact of deposition on the enrichment of organic carbon in eroded sediment. Catena 72, 340–347. Schwilch, G., Liniger, H.P., Hurni, H., 2014. Sustainable land management (SLM) practices in drylands: how do they address desertification threats? Environ. Manag. 54, 983–1004. Soil Survey Staff, 2003. Keys to Soil Taxonomy. Department of Agriculture: Natural Resources Conservation Service. Tripathi, B., 1999. Review of Acid Soil and Its Management in Nepal. Lumle seminar paper no. 99/1NARC, Kathmandu, Nepalpp. 1–11. Wang, B., Xue, S., Liu, G.B., Zhang, G.H., Li, G., Ren, Z.P., 2012. Changes in soil nutrient and enzyme activities under different vegetations in the Loess Plateau area, Northwest China. Catena 92, 186–195. Wei, J.B., Xiao, D.N., Zeng, H., Fu, Y.K., 2008. Spatial variability of soil properties in relation to land use and topography in a typical small watershed of the black soil region, northeastern China. Environ. Geol. 53, 1663–1672. White II, D.A., Welty-Bernard, A., Rasmussen, C., Schwartz, E., 2009. Vegetation controls on soil organic carbon dynamics in an arid, hyperthermic ecosystem. Geoderma 150, 214–223. Wu, G.L., Liu, Z.H., Zhang, L., Hu, T.M., Chen, J.M., 2010. Effects of artificial grassland establishment on soil nutrients and carbon properties in a black-soil-type degraded grassland. Plant Soil 333, 469–479. Xia, Y.Q., Shao, M.A., 2008. Soil water carrying capacity for vegetation: a hydrologic and biogeochemical process model solution. Ecol. Model. 214, 112–124. Yan, Z., Sun, J., Don, O., Zhang, Z., 2005. The red turpentine beetle, Dendroctonus valens LeConte (Scolytidae): an exotic invasive pest of pine in China. Biodivers. Conserv. 14, 1735–1760. Yetemen, O., Istanbulluoglu, E., Flores Cervantes, J.H., Vivoni, E.R., Bras, R.L., 2015. Ecohydrologic role of solar radiation on landscape evolution. Water Resour. Res. 51, 1127–1157. Yimer, F., Ledin, S., Abdelkadir, A., 2006. Soil property variations in relation to topographic aspect and vegetation community in the south-eastern highlands of Ethiopia. For. Ecol. Manag. 232, 90–99. Zhang, X., Li, Z., Tang, Z., Zeng, G., Huang, J., Guo, W., Chen, X., Hirsh, A., 2013. Effects of water erosion on the redistribution of soil organic carbon in the hilly red soil region of southern China. Geomorphology 197, 137–144. Zheng, H., Chen, F.L., Ouyang, Z.Y., Tu, N.M., Xu, W.H., Wang, X.K., Miao, H., Li, X.Q., Tian, Y.X., 2008. Impacts of reforestation approaches on runoff control in the hilly red soil region of Southern China. J. Hydrol. 356, 174–184. Zheng, Z., Zhang, F., Ma, F., Chai, X., Zhu, Z., Shi, J., Zhang, S., 2009. Spatiotemporal changes in soil salinity in a drip-irrigated field. Geoderma 149, 243–248.
cations. In: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis for Canadian. Soc. Soil Sci. Boca. Ratonpp. 141–145. Hicks, R.R., Frank, P.S., 1984. Relationship of aspect to soil nutrients, species importance and biomass in a forested watershed in West-Virginia. For. Ecol. Manag. 8, 281–291. Huang, M., Ji, J.J., Li, K.R., Liu, Y.F., Yang, F.T., Tao, B., 2007. The ecosystem carbon accumulation after conversion of grasslands to pine plantations in subtropical red soil of south China. Tellus B 59, 439–448. Huang, J., Wu, P., Zhao, X., 2013. Effects of rainfall intensity, underlying surface and slope gradient on soil infiltration under simulated rainfall experiments. Catena 104, 93–102. Huang, Y.M., Kang, R.H., Mulder, J., Zhang, T., Duan, L., 2015. Nitrogen saturation, soil acidification, and ecological effects in a subtropical pine forest on acid soil in southwest China. J. Geophys. Res. Biogeo. 120, 2457–2472. Jiang, Y., Zhang, Y., Yang, Y., Xu, J., 2010. Impacts of grazing on the system coupling between vegetation and soil in the alpine and subalpine meadows of Wutai Mountain. Acta Ecol. Dermatol. Sin. 30, 837–846. Jordan, A., Zavala, L.M., Gil, J., 2010. Effects of mulching on soil physical properties and runoff under semi-arid conditions in southern Spain. Catena 81, 77–85. Keeney, D.R., Nelson, D.W., 1982. Nitrogen-inorganic forms. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd ed. Agronomy Monograph, vol. 9. ASA and SSSA, Madison, WI, pp. 643–698. Keesstra, S., Pereira, P., Novara, A., Brevik, E.C., Azorin-Molina, C., Parras-Alcántara, L., Jordán, A., Cerdà, A., 2016. Effects of soil management techniques on soil water erosion in apricot orchards. Sci. Total Environ. 551, 357–366. Li, H., Shi, K., Sun, Y., 2005. Content of organic carbon in shallow soil profile under 3 forest types. Chinese J. Ecol. 10, 023. Li, Z., Nie, X., Chen, X., Lu, Y., Jiang, W., Zeng, G., 2015. The effects of land use and landscape position on labile organic carbon and carbon management index in red soil hilly region, southern China. J. Mt. Sci. Eng. 12, 626–636. Liao, J.D., Boutton, T.W., Jastrow, J.D., 2006. Storage and dynamics of carbon and nitrogen in soil physical fractions following woody plant invasion of grassland. Soil Biol. Biochem. 38, 3184–3196. Lin, D., Lai, J., Muller-Landau, H.C., Mi, X., Ma, K., 2012. Topographic variation in aboveground biomass in a subtropical evergreen broad-leaved forest in China. PLoS One 7 (10), e48244. Liu, Q., Ouyang, Z., Li, A., Xu, W., 2016. Spatial distribution characteristics of biomass and carbon storage in forest vegetation in Chongqing based on RS and GIS. Nat. Environ. Pollut. Technol. 15, 1381–1388. Ma, W., Li, Z., Ding, K., Huang, B., Nie, X., Lu, Y., Xiao, H., 2016. Soil erosion, organic carbon and nitrogen dynamics in planted forests: a case study in a hilly catchment of Hunan Province, China. Soil Tillage Res. 155, 69–77. Mekonnen, M., Keesstra, S.D., Stroosnijder, L., Baartman, J.E., Maroulis, J., 2015. Soil conservation through sediment trapping: a review. Land Degrad. Dev. 26, 544–556. Mohammad, A.G., Alseekh, S.H., 2013. The effect of Sarcopoterium spinosum on soil and vegetation characteristics. Catena 100, 10–14. Murphy, J.A.M.E.S., Riley, J.P., 1962. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36. Naeth, M., Chanasyk, D., Burgers, T., 2011. Vegetation and soil water interactions on a tailings sand storage facility in the Athabasca oil sands region of Alberta Canada. Phys. Chem. Earth 36, 19–30. Nie, X., Li, Z., He, J., Huang, J., Zhang, Y., Huang, B., Ma, W., Lu, Y., Zeng, G., 2015. Enrichment of organic carbon in sediment under field simulated rainfall experiments. Environ. Earth Sci. 74, 5417–5425. Nie, X.D., Li, Z.W., Huang, J.Q., Huang, B., Xiao, H.B., Zeng, G.M., 2017. Soil organic carbon fractions and stocks respond to restoration measures in degraded lands by water erosion. Environ. Manag. 59, 816–825. Nie, X., Li, Z., Huang, J., Liu, L., Xiao, H., Liu, C., Zeng, G., 2018. Thermal stability of
111