Next Article in Journal
New Flowering and Architecture Traits Mediated by Multiplex CRISPR-Cas9 Gene Editing in Hexaploid Camelina sativa
Next Article in Special Issue
Leaf Functional Traits and Relationships with Soil Properties of Zanthoxylum planispinum ‘dintanensis’ in Plantations of Different Ages
Previous Article in Journal
Soil Quality Index as Affected by Integrated Nutrient Management in the Himalayan Foothills
Previous Article in Special Issue
Stoichiometry of Soil, Microorganisms, and Extracellular Enzymes of Zanthoxylum planispinum var. dintanensis Plantations for Different Allocations
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparison of Aboveground Vegetation and Soil Seed Bank Composition among Three Typical Vegetation Types in the Karst Regions of Southwest China

1
Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China
2
College of Tourism and Landscape Architecture, Guilin University of Technology, Guilin 541006, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(8), 1871; https://doi.org/10.3390/agronomy12081871
Submission received: 31 March 2022 / Revised: 29 July 2022 / Accepted: 30 July 2022 / Published: 8 August 2022
(This article belongs to the Special Issue Emerging Research on Adaptive Plants in Karst Ecosystems)

Abstract

:
Rural agricultural activity generates cropland, secondary vegetation and straggling primary forest and can modify the soil seed bank (SSB), potentially impacting the restoration of preferred species. The interaction between vegetation and seed banks during the recovery process is dependent on management practices and recovery pathways. This study was carried out in Guilin of southwest China to assess the variation in plant diversity and species composition of both aboveground and soil seed banks across three typical vegetation types with different human interventions: orchard, bamboo shrub and primary forest. The results show that there were significant differences in the species composition and diversity of aboveground vegetation and SSB, as well as in soil properties among three typical vegetation types. The primary forest had the highest aboveground species diversity, while the orchard had the highest species diversity and seed density of SSB. In addition, principal component analysis (PCA) and canonical correspondence analyses (CCAs) showed that the species composition and plant life forms of the three typical vegetation types were significantly influenced by soil properties. Based on these findings, the characteristics of aboveground vegetation and the soil seed bank and their correlations with soil properties are expected to drastically change with human intervention. These results imply that unsustainable land use has greatly impacted soil properties, and consequently, the aboveground vegetation and SSB. Nevertheless, vegetation will recover quickly after farming is abandoned. The successful restoration of fragmented ecosystems requires the addition of seeds and seedlings of target species, especially perennial woody plants from the relevant natural ecosystems, to accelerate succession from bamboo shrub to forest.

1. Introduction

Southwest China is one of the largest continuous karsts in the world, and it is known for its unique landscapes and rich biodiversity. However, it has experienced rapid turnover within the past few decades due largely to intense human activity on the fragile karst ecosystems [1,2,3,4,5,6,7]. Farming leads to diverse land use patterns, with a range of specific types, such as farmlands, orchards, natural vegetation, and other, more complex landscapes. The 20th century intensification in grain and fruit production through agriculture resulted in severe vegetation cover loss and soil erosion. This unsustainable land use in the karst ecosystem could cause a catastrophic impact on ecosystem function and, therefore, the ecosystem may not recover on timescales relevant to land management decisions.
Since the end of the 1990s, the Chinese government at various levels has initiated ecological engineering projects to mitigate desertification and restore ecosystem services [1,8]. The karst environment has remarkably improved in the past 20 years [3]. Vegetation is one of the most important components in the karst ecosystem, and it has changed greatly over the years. Nowadays, orchards, secondary vegetation and a low percentage of primary forest are the three typical vegetation types in most karst areas. After 20 years of recovery, abandoned orchards were replaced by secondary vegetation, which was dominated by bamboo shrubs. However, it may take decades or centuries for a site to return to a state similar to primary forest, even if it is abandoned without any human disturbance. Ecologists have paid increasing attention to evaluating the improvement in the ecosystem services of karst degradation areas, mainly by remote sensing technology [3,9,10,11,12]. However, the changes in soil properties and vegetation variation in the process of vegetation restoration remain unclear.
As two major components of terrestrial ecosystems, vegetation and soil are strongly interlinked. Successional changes in vegetation are usually associated with the physical, chemical and biological alteration of soil properties. The effects of vegetation recovery on soil nutrients are still poorly understood and contradictory results have been reported in different studies [13]. This may be due to different management practices or recovery pathways. The usage of chemical fertilizers, pesticides, herbicides, and hormones in croplands has changed the physical, chemical and biological properties of soil [14]. The recovery of vegetation community structure may encourage the restoration of soil function after eliminating external interference [9]. Understanding how vegetation and soil properties are correlated and how their relationship develops in the process of vegetation restoration is key for effective forest restoration and management.
Soil seed banks (SSBs) play a key role in the post-disturbance recruitment of plant species. It provides a memory of past vegetation and an indication of the likely composition of future vegetation [15,16]. In addition, it has been suggested that SSBs could be one of the major sources that facilitate the recovery of vegetation after disturbance in degraded communities [17]. When assessing the restoration potential of degraded areas, viable SSBs may be useful for restoration after removing disturbances or stressors [18,19]. As a result, the evaluation of SSBs can therefore provide an index of the recovery potential of degraded plant communities. Knowing the relationships between SSB and aboveground vegetation may help to develop an effective conservation plan for restoring diversity, and for better understanding the resilience of plant communities after disturbances [16]. Vegetation dynamics, including the role of SSBs, in regeneration phases in karst areas are still poorly understood.
The influence of soil properties on the variation in the plant diversity and species composition of aboveground vegetation and the SSB of the three typical vegetation types are still unknown, despite their importance for understanding ecological patterns and processes, as well as for guiding management decisions for conservation or restoration measures. We therefore conducted a series of field surveys and specifically asked the following questions: (1) How do the soil properties differ among three typical vegetation types? (2) Do the characteristics of SSBs differ among three typical vegetation types? (3) How do the relationships between aboveground vegetation, SSB and soil properties change within different vegetation types? Finally, we discuss our results within the framework of future conservation management plans for karst ecosystems.

2. Materials and Methods

2.1. Study Area

The study was conducted in the Gongcheng Yao Autonomous County (hereinafter referred to as Gongcheng) (24°37′ N to 25°17′ N, 110°36′ E to 111°10′ E), Guangxi Zhuang Autonomous Region, in Southern China (Figure 1). Topographically, the area is a hilly area of karst terrain, with a valley, flat land, and hilly crisscrossing zone in the middle, among which mountains and hills account for more than 70% of the total land area. Typically, this area keeps a temperate climate throughout the year, belonging to the subtropical monsoon climate zone. The average annual temperatures and the average annual precipitation are 20.0 °C and 1517.5 mm, respectively, for the years 1971−2020. The annual accumulated temperature of measurements higher than 10 °C ranges from 5718.6 to 6382.6 °C.
The karst land area in Gongcheng is large. The rocky desertification area is mainly distributed across six townships (towns), Lianhua, Pingan, Gongcheng town, Xiling, Limu and Jiahui, with a total area of 25,300 hm2, accounting for 12% of the total land area of the county. Preliminary investigation showed that the vegetation has been completely removed in parts of the area by the intensive planting of fruit trees (e.g., peach, plum and persimmon), which caused the emergence of bare soil patches. The fruit industry has become the leading industry fueling local economic development. However, the area of rocky desertification caused by orchard planting accounted for 42.5% of the total area of rocky desertification, according to the 2019 technical reports from local authorities.
Bamboo shrubs (e.g., Phyllostachys sulphurea) are distributed either individually or in clumps in most of the karst mountainside. It became the main type of secondary vegetation after orchards were abandoned in this karst area. Vegetation species have drought-resistant characteristics because of their extensive exposure to the basement rocks and thin soil. The bamboo shrub is generally dominated by vines; small, almost leather-like tree species; and single and mini-type leaf plants. Long-term observations show that abandoned orchards turn into bamboo shrubs after 10 years of natural restoration, but it is difficult for them to turn into a shrub or forest that is dominated by tree species. The primary forest ecosystem characteristic of the mid-subtropics is dotted around a housing colony, which is preserved in the form of a geomantic forest. The local people claim that almost all primary forest was stripped in the “Great Leap Forward” from 1958 to 1961, after which the area was reclaimed repeatedly for farming or restored naturally to bamboo shrub (Figure 2).

2.2. Sampling of Vegetation and Site Conditions

We identified three typical vegetation types in our study area: orchard, bamboo shrub and primary forest. For each type, three 20 × 20 m plots were randomly selected (Table 1). At each plot, five 1 × 1 m quadrats were chosen in the corners and center. All woody stems with a diameter at breast height (DBH) of ≥ 1 cm in each plot were counted, measured and identified to the species level. The composition and abundance of vascular plant species in the herb layer (including seedlings of woody plants) were recorded in each quadrat. In addition, vegetation and litter height, percentage cover of bare rock, cover of bryophytes and litter, species composition and percentage cover of the woody overstorey were recorded. Plant specimens were identified according to Flora of China.
Parallel to the vegetation census, in August 2020, soil samples of 200 g each were taken at each quadrat at an average depth of 10 cm. Each soil sampling quadrat was marked with a PVC tube after sampling for later soil seed bank sampling. After air-drying and sieving to 2 mm to remove stones, roots and rhizomes, soil chemical properties (organic carbon (OCC), total nitrogen (TNC), total phosphorus (TPHC), total potassium (TPOC), available nitrogen (Alkeline-N), available phosphorus (Olsen-P), rapidly available potassium (Olsen-K), calcium (CAC) and magnesium (MAC)) were analyzed in October 2020, by the Shaanxi Biorace Biological Technologies Company using the procedures described by Bao [20].

2.3. Seed Bank Sampling

The SSB experiment was conducted in November 2020 prior to the germination of seeds in the field. Soil samples were manually collected using a 10 × 10 cm frame at each quadrat. Each sample was divided into three soil depths (0–5, 5–10 and 10–15 cm) (Figure S1). A total of 135 soil samples (3 vegetation types × 3 plots × 5 quadrats × 3 soil depths) were collected. Soil samples were stored at 4 °C for approximately one week. Soil samples were then sieved with a 2 mm screen to remove stones, roots and rhizomes. After sieving, each soil sample was manually homogenized and spread on 10 cm of sterilized sand in a 50 cm length × 20 cm width × 10 cm depth plastic tray with drainage holes in early January 2021. Trays were randomly arranged on benches lined with plastic in the greenhouse. Samples were generally kept at field moisture conditions by surface watering every 2 days. Newly emerged seedlings were removed from pots after identification to prevent crowding. Seed germination assays continued for six months after germination had ceased. The mean density of the total number of emerging seedlings per sample was expressed on a per m2 basis.

2.4. Statistical Analyses

This study employed the Shannon–Wiener diversity, Simpson diversity, species richness, Pielou evenness and Sørensen’s quotient of similarity indices to demonstrate the different cover of the distinguished vegetation types and the characteristics of changes in the species composition of the seed bank:
(1)
Diversity index (H), using the Shannon–Wiener index [21]:
H = i = 1 S P i ln P i
(2)
Dominance index (D), using the Simpson dominance index [22]:
D = 1 i = 1 S P i 2 = 1 i = 1 S N i n 2
(3)
Richness index (R):
R = S
(4)
Pielou index (E) [23]:
E = H ln S
(5)
Similarity index, using Sørensen’s quotient index (SQ) [24]:
SQ = [ 2 C A + B ] × 100 %
where N is the total number of plants, Ni is the number of individuals of species “i”, Pi is the relative abundance of species “i”, S is the total number of species, A is the number of species in the soil seed bank, B is the number of species in aboveground vegetation and C is the number of species common to the seed bank and aboveground vegetation.
One-way analysis of variance (ANOVA) was used to identify significant differences among three vegetation types for the different soil physiochemical properties. Principal component analysis (PCA) and canonical correlation analysis (CCA) were used to evaluate similarity in soil chemical properties, species composition of seed banks and aboveground community among three vegetation types. The species diversity, PCA and CCA were implemented using the vegen package [25] in R 4.1.2 [26].

3. Results

3.1. Soil Chemical Properties

There were significant differences in the soil chemical properties among different vegetation types (Figure 3). The TPHC, TPOC, Olsen-P and Olsen-K were higher in the orchard than bamboo shrub and primary forests. There were significantly higher contents in OCC, TNC and Alkeline-N in the bamboo shrub than the other two vegetation types, but CAC and MAC did not differ among the three vegetation types. According to the classification standards of the second national soil census, the TNC and Alkeline-N were at an extremely high level in the three vegetation types, while the TPHC and Olsen-P were at a depressed level in bamboo shrub and primary forest.
The first two principal components in PCA of the nine variables accounted for 68.63% of the total variation among sites (Table 2). Comp 1 (36.05% of variation) was positively correlated with eight of the nine variables, and negatively with MAC; Comp 2 (32.58% of variation) was highly correlated with eight of the nine variables but had no significant correlation with MAC. In addition, the PCA revealed a separation among different vegetation types, but with an obvious overlap between the bamboo shrub and primary forest (Figure 4a).

3.2. Species Composition of Aboveground Vegetation

A total of 80 species occurred in the 45 quadrats of the three vegetation types (Table S1). The species composition was quite different among the three vegetation types. It was dominated by annual herbs in the orchard. Perennial herbs and perennial woody vines (Scandent shrub) dominated the plant communities of the bamboo shrub, but perennial herbs and tree or shrub species dominated in the primary forest. The four indices of diversity, H, D, R and E, all indicated the highest species diversity in the primary forest and the lowest species diversity in the orchard (Figure 5). However, there was no significant difference between primary forest and bamboo shrub for the four diversity indices, while the orchard had a significantly lower diversity among the three vegetation types for H, D and E indices.

3.3. Species Composition of Soil Seed Bank

A total of 44 species occurred in the 135 soil samples of the three vegetation types (Table 3). The four indices of diversity, H, D, R and E, all indicated a higher species diversity in the orchard but a lower diversity in the primary forest. Although there was no significant difference between primary forest and bamboo shrub for the H, R and E indices of SSB, the SSB of the orchard had a significantly higher diversity among the three vegetation types, which is in contrast with aboveground vegetation (Figure 5). In addition, the mean Sørensen’s quotient of similarity between the SSB and aboveground vegetation was less than 10% in the three typical vegetation types, and almost all the similarity indices of communities were zero in bamboo shrub and primary forest (Figure 6).
The soil seed bank density was significantly different among the three vegetation types (Table 3). The average SSB density was 22,446.67 ± 14,315.18 seed/m2 in the orchard, 1086.6 ± 213.22 seed/m2 in the bamboo shrub and 1519.89 ± 383.65 seed/m2 in the primary forest. Besides the SSB density being higher in the orchard than in the bamboo shrub and primary forest, the standard deviation was also higher in the orchard than in the other vegetation types, indicating the high volatility of the SSB density in the orchard. The SSB density was gradually decreased with soil depth in the three typical vegetation types (Table 4).
The species composition and the dominant species of the SSB were significantly different among the three vegetation types (Table 3). In the orchard, a total of 31 plant species belonging to 20 families were observed in the SSB. The dominant species were Galium aparine (47.82%), Stellaria media (27.18%) and Oxalis corniculata (5.58%). In the bamboo shrub, only 19 species belonging to 12 families were observed in the soil bank. The dominant species were Conandron ramondioides (18.52%), Setaria viridis (15.74%) and Carex doniana (12.04%). In the primary forest, only 22 species belonging to 16 families were observed in the soil bank. The dominant species were Botrychium ternatum (34.87%), Woodsia ilvensis (18.42%) and Broussonetia papyifera (17.76%).
Annual herbs dominated the SSB in the orchard and included 18 species, accounting for more than 75% of life forms. Only one tree species, a native pioneer species, B. papyifera, was found in the orchard in two soil samples. Perennial plants dominated the species composition of both bamboo shrub and primary forest, and included 9 and 14 species, accounting for approximately 60% of life forms. However, the primary forest had a higher percentage of trees or shrubs than bamboo shrubs (Figure 7, Table 4).

3.4. Correlation between Vegetation, Soil Seed Banks and Soil Properties

The CCA identified the species composition groupings of the aboveground vegetation and SSBs among the three vegetation types (Figure 4b,c). The established vegetation and SSBs in the orchard were positively correlated with TPHC, TPOC, Olsen-P and Olsen-K, while the established vegetation and soil seed banks in the bamboo shrub had no significant correlations with any soil properties. Meanwhile, the CCA results indicate that there was a converse trend between the species composition of vegetation and SSB in the orchard and primary forest. The species composition of vegetation had a positive correlation with TPHC, TPOC, Olsen-P and Olsen-K, but the soil seed bank had a negative correlation with these variables. The SSBs in the primary forest had a positive correlation with MAC (Figure 4c).

4. Discussion

Our results show that the amounts of basic elements and available ingredients in the soil, such as TPHC, TPOC, Olsen-P and Olsen-K, were significantly higher in the orchard than in the other vegetation types. After 20 years of recovery, OCC, TNC and Alkeline-N showed a significant increase. This was mainly due to the well-developed fibrous roots of bamboo, which hold the soil in place, increasing the stability of soil matrixes as succession proceeds. The accumulated litter covers the soil and increases soil organic matter content and permeability, reduces runoff and extends runoff time [27]. The interlocked roots of bamboo and the dry micro-climate played a limiting role in litter decomposition. The PCA revealed a separation among different communities and a greater separation between the orchard and the other two vegetation types, and the typical vegetation types were influenced by different soil properties. These results indicate that aspects of intensive planting such as fertilizing, abusing herbicides and pesticides dramatically changed the soil chemical properties. In addition, the CCA also revealed a separation among different vegetation types but a modicum of overlap between bamboo shrub and primary forest in the SSBs. Almost all soil samples were gathered together, which indicated that the species composition of SSBs had incredible similarity in the orchard. In contrast, in bamboo shrub and primary forest, all soil samples were spread out, which indicated that the species composition of SSBs had a striking contrast.
Soil physiochemical properties are the most important factors influencing the vegetation restoration in karst regions [28,29]. As nutrients and organic matter accumulate with time, they increase species complexity along the following successional stages [30]. Available P, rather than N, in calcareous soils is an effective indicator of nutrient limitation in karst regions [31]. The secondary and primary forests are P-limited, while the shrub is constrained by N and P or by other nutrients [5]. The low availability of P is expected to be due to the very low solubility of Ca-phosphate minerals at a neutral to alkaline pH in karst soils [9]. There are extremely high levels of TNC and Alkaline-N but depressed levels of TPHC and Olsen-P in the bamboo shrub and primary forest in our study. Thus, the bamboo shrub and primary forest within our region may be limited by P rather than N. This could be an intrinsic factor making it difficult for an area to succeed from bamboo shrub to primary forest.
It was found that the total seed density in the seed banks of the orchard was much greater than that in the bamboo shrub and primary forest. The total seed density in the orchard was much greater than that in plant communities in which there was no bare rock. For example, a density of approximately 3500 seeds m−2 in SSBs at a depth of 0–10 cm was observed in wetlands of the Yellow River Delta [32]. However, the number of seeds produced by malignant weeds that adapted to herbicides was great, which resulted in very high seed bank densities. For example, the density of G. aparine and S. media reached 10,733.34 and 6099.99 seeds m−2, which accounted for approximately 80% of the total seed density in the orchard. A density of 1519.89 seeds m−2 was observed in SSBs in the primary forest of this study, which was much lower than that found in another tropical karst region of China [33]. The primary forest is located next to the village, and the understory herbaceous layer and litter layer might have therefore been disturbed by free-range poultry in this study. In addition, seed density declines with increasing soil depth, with the majority of seeds being distributed in the surface soil.
The biological characteristics of the species that present in the SSB influence its similarity to aboveground vegetation. There were significantly different proportions of plant life forms among the three typical vegetation types. Seeds of annual herbs constituted almost all SSBs in the orchard, while perennial plants constituted most of the SSBs in the bamboo shrub and primary forest in this study. These results are similar to those previously found regarding the level of life forms reported in secondary forests in karst areas of southwest China [34]. Of particular interest here is that the primary forest had a higher percentage of trees or shrubs, but the bamboo shrub had a higher proportion of perennial woody vines. In the intermediate succession stage (herein referred to as bamboo shrubs), there were few shrubs and trees, but perennial plants, especially woody vines, were still abundant, which caused some shade, although the habitats received abundant sunlight and had drought-resistant characteristics because of their extensive exposure to the basement rocks and thin soil. These conditions made them unfavorable for seed viability for some plant species. In addition, creeping rootstocks of bamboo were unfavorable for the maintenance of the seed viability of trees or shrubs, and climbing perennial woody vines are the main restricting factor affecting the vertical growth and development of trees.
The species diversity of the SSBs is much higher than that of the aboveground vegetation in the orchard, but the opposite patterns were found in the primary forest. The seed bank and aboveground vegetation assemblages differed substantially, with less than 10% similarity in species composition in the three vegetation types. In the current study, approximately 76.33% of the quadrats have no species common to the seed bank and aboveground vegetation. Some studies have demonstrated that SSBs consisting mainly of annual plants have a high degree of similarity in terms of species composition to the aboveground vegetation [32]. Seed banks that consist mainly of perennial plants often have a lower degree of similarity in terms of species composition to the aboveground vegetation [35]. Differences in the species composition of the seed bank and aboveground vegetation can be caused by many factors. A possible reason is that the aboveground vegetation utilizes a seasonal strategy in the orchard, but our vegetation census was conducted in August 2020, when some spring-appearing herbs had been removed by herbicides, while the seeds of some species delay germination and remain viable in the soil to await suitable conditions for germination in the bamboo shrub and primary forest. The perennial plant population usually produces a large number of fruits or seeds intermittently and synchronously in different years.

5. Implications for Ecological Restoration in Karst Rocky Desertification Regions

In the karst region of southwestern China, the indices of population density, the degree of gathering of low-income individuals and the size of the workforce are relatively lower. The growing population and low land productivity have triggered agricultural expansion to marginal cropland on slopes and ridges and have resulted in severe rocky desertification. If management practices are adjusted towards sustainable land use and the promotion of active restoration efforts, the karst in southwest China stands a chance to recover to its state of function in earlier centuries [4,12]. The karst environment has remarkably improved after more than 20 years of effort by the Chinese government at different levels.
With the development of the economy and social transition, many youths migrate to urban areas for better job opportunities, which helps to lessen regional land use pressure. However, at the same time, advanced tools have increased farmers’ production efficiency, and so bamboo shrub faces a greater risk of being reclaimed again in this region. In addition, the overuse of fertilizers, herbicides and pesticides has stripped the soil of nutrients, making it too acidic, and has resulted in the invasion of exotic noxious weeds, as well as severe agricultural diffuse pollution. Therefore, stereoscopic agriculture and/or complex ecological agriculture are sorely needed to increase the per-unit labor and consequently raise the per-unit profit. Further study is needed to explore the stereoscopic planting modes of interplanting in orchards, such as Chinese medicinal herbs and cash crops. How to comprehensively apply and evaluate stereoscopic agriculture and/or complex ecological agriculture is the most important aspect to consider when discussing ecosystem services in karst landscapes in the future.
Ecological restoration projects have greatly decreased the aerial cover of karst rocky desertification in southwestern China [1]. However, land use/land cover changes are only the first step toward ecological restoration, and further close-to-nature restoration methods are necessary to promote ecological functions, e.g., biodiversity and carbon stock. Bamboo shrub is a typical vegetation type with low species diversity and carbon stock, and it is difficult to turn this type of vegetation into shrub or forest dominated by tree species by natural succession. Close-to-nature restoration methods could accelerate the succession of the bamboo shrub into forest. Close-to-nature restoration adopts traditional artificial restoration approaches and relies on natural ecological processes to achieve sustainable ecological restoration. It focuses on a return to nature and realizes sustainable restoration through the self-regulating function of the natural ecosystem. Therefore, ecosystems that are restored through close-to-nature restoration may maintain higher biodiversity and productivity and provide more ecosystem functions and services. The addition of seeds and seedlings of target species from natural ecosystems, especially perennial woody plants, is needed to accelerate succession from bamboo shrub to forest.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12081871/s1, Figure S1: A sketch of the sampling method; Table S1: Abundance of each plant species in different vegetation types.

Author Contributions

Data curation: Y.G. and Y.L.; Investigation: J.L. (Jianxing Li), J.L. (Jiaqi Li), S.W., F.H., S.L., D.L. and W.X.; Methodology: Y.G. and S.W.; Project administration: Y.G. and X.L.; Resources: Y.G.; Software: Y.G., B.W. and W.H.; Supervision: Y.G. and X.L.; Visualization: Y.G. and W.H.; Writing—original draft: Y.G.; Writing—review & editing: Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by “National key Research and development program, grant number 2019YFC0507503-05” and “National Natural Science Foundation of China, grant number 32071540, 31760141”.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, K.; Zhang, C.; Chen, H.; Yue, Y.; Zhang, W.; Zhang, M.; Qi, X.; Fu, Z. Karst landscapes of China: Patterns, ecosystem processes and services. Landsc. Ecol. 2019, 34, 2743–2763. [Google Scholar] [CrossRef] [Green Version]
  2. Yue, Y.; Wang, K.; Zhang, B.; Jiao, Q.; Liu, B.; Zhang, M. Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment. Procedia Environ. Sci. 2012, 13, 847–853. [Google Scholar] [CrossRef] [Green Version]
  3. Hu, Z.; Wang, S.; Bai, X.; Luo, G.; Li, Q.; Wu, L.; Yang, Y.; Tian, S.; Li, C.; Deng, Y. Changes in ecosystem service values in karst areas of China. Agric. Ecosyst. Environ. 2020, 301, 107026. [Google Scholar] [CrossRef]
  4. Liao, C.; Yue, Y.; Wang, K.; Fensholt, R.; Tong, X.; Brandt, M. Ecological restoration enhances ecosystem health in the karst regions of southwest China. Ecol. Indic. 2018, 90, 416–425. [Google Scholar] [CrossRef]
  5. Zhang, W.; Zhao, J.; Pan, F.; Li, D.; Chen, H.; Wang, K. Changes in nitrogen and phosphorus limitation during secondary succession in a karst region in southwest China. Plant Soil 2015, 391, 77–91. [Google Scholar] [CrossRef]
  6. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef] [Green Version]
  7. Winbourne, J.B.; Brewer, S.W.; Houlton, B.Z. Iron controls over di-nitrogen fixation in karst tropical forest. Ecology 2017, 98, 773–781. [Google Scholar] [CrossRef] [PubMed]
  8. Ouyang, Z.; Zheng, H.; Xiao, Y.; Polasky, S.; Liu, J.; Xu, W.; Wang, Q.; Zhang, L.; Xiao, Y.; Rao, E.; et al. Improvements in ecosystem services from investments in natural capital. Science 2016, 352, 1455–1459. [Google Scholar] [CrossRef] [PubMed]
  9. Green, S.M.; Dungait, J.A.; Tu, C.; Buss, H.L.; Sanderson, N.; Hawkes, S.J.; Xing, K.; Yue, F.; Hussey, V.L.; Peng, J.; et al. Soil functions and ecosystem services research in the Chinese karst Critical Zone. Chem. Geol. 2019, 527, 119107. [Google Scholar] [CrossRef] [Green Version]
  10. Quine, T.; Guo, D.; Green, S.M.; Tu, C.; Hartley, I.; Zhang, X.; Dungait, J.; Wen, X.; Song, Z.; Liu, H.; et al. Ecosystem service delivery in Karst landscapes: Anthropogenic perturbation and recovery. Acta Geochim. 2017, 36, 416–420. [Google Scholar] [CrossRef] [Green Version]
  11. Jiang, C.; Zhang, H.; Zhang, Z. Spatially explicit assessment of ecosystem services in China’s Loess Plateau: Patterns, interactions, drivers, and implications. Glob. Planet. Chang. 2018, 161, 41–52. [Google Scholar] [CrossRef]
  12. Tong, X.; Brandt, M.; Yue, Y.; Horion, S.; Wang, K.; De Keersmaecker, W.; Tian, F.; Schurgers, G.; Xiao, X.; Luo, Y.; et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 2018, 1, 44–50. [Google Scholar] [CrossRef]
  13. Huang, F.; Zhang, W.; Gan, X.; Huang, Y.; Guo, Y.; Wen, X. Changes in vegetation and soil properties during recovery of a subtropical forest in South China. J. Mt. Sci. 2018, 15, 46–58. [Google Scholar] [CrossRef]
  14. Matson, P.A.; Parton, W.J.; Power, A.G.; Swift, M.J. Agricultural Intensification and Ecosystem Properties. Science 1997, 277, 504–509. [Google Scholar] [CrossRef] [Green Version]
  15. Fisher, J.; Loneragan, W.A.; Dixon, K.; Veneklaas, E.J. Soil seed bank compositional change constrains biodiversity in an invaded species-rich woodland. Biol. Conserv. 2009, 142, 256–269. [Google Scholar] [CrossRef]
  16. Hopfensperger, K.N. A review of similarity between seed bank and standing vegetation across ecosystems. Oikos 2007, 116, 1438–1448. [Google Scholar] [CrossRef]
  17. Shang, Z.; Yang, S.; Wang, Y.; Shi, J.; Ding, L.; Long, R. Soil seed bank and its relation with above-ground vegetation along the degraded gradients of alpine meadow. Ecol. Eng. 2016, 90, 268–277. [Google Scholar] [CrossRef]
  18. Sanou, L.; Savadogo, P.; Zida, D.; Thiombiano, A. Contrasting land use systems influence soil seed bank composition and density in a rural landscape mosaic in West Africa. Flora 2018, 250, 79–90. [Google Scholar] [CrossRef]
  19. Niknam, P.; Erfanzadeh, R.; Ghelichnia, H.; Cerdà, A.; Erfanzadah, R.; Ghelichhnia, H. Spatial Variation of Soil Seed Bank under Cushion Plants in a Subalpine Degraded Grassland. Land Degrad. Dev. 2017, 29, 4–14. [Google Scholar] [CrossRef]
  20. Bao, S. Soil Agrochemical Analysis, 3rd ed.; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  21. Peet, R.K. The Measurement of Species Diversity. Annu. Rev. Ecol. Syst. 1974, 5, 285–307. [Google Scholar] [CrossRef]
  22. Simpson, E.H. Measurement of diversity. Nature 1949, 163, 668. [Google Scholar] [CrossRef]
  23. Pielou, E.C. An Introduction to Mathematical Ecology; John Wiley: New York, NY, USA, 1969. [Google Scholar]
  24. Ikeda, A.; Matsuoka, S.; Masuya, H.; Mori, A.; Hirose, D.; Osono, T. Comparison of the diversity, composition, and host recurrence of xylariaceous endophytes in subtropical, cool temperate, and subboreal regions in Japan. Popul. Ecol. 2013, 56, 289–300. [Google Scholar] [CrossRef]
  25. Jari, O.F.; Guillaume, B.; Michael, F.; Roeland, K.; Pierre, L.; Dan, M.; Peter, R.M.; O’Hara, R.B.; Gavin, L.S.; Peter, S.; et al. Vegan: Community Ecology Package. R package version 2.5-7. 2020. Available online: https://CRAN.R-project.org/package=vegan (accessed on 15 December 2021).
  26. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2020. Available online: https://www.R-project.org/ (accessed on 11 August 2021).
  27. Wang, Y.; Chu, L.; Daryanto, S.; Lü, L.; Ala, M.; Wang, L. Sand dune stabilization changes the vegetation characteristics and soil seed bank and their correlations with environmental factors. Sci. Total Environ. 2018, 648, 500–507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Hofmeister, J.; Mihaljevič, M.; Hošek, J.; Sádlo, J. Eutrophication of deciduous forests in the Bohemian Karst (Czech Republic): The role of nitrogen and phosphorus. For. Ecol. Manag. 2002, 169, 213–230. [Google Scholar] [CrossRef]
  29. Rivera, L.W.; Aide, T. Forest recovery in the karst region of Puerto Rico. For. Ecol. Manag. 1998, 108, 63–75. [Google Scholar] [CrossRef]
  30. Tackett, N.W.; Craft, C.B. Ecosystem Development on a Coastal Barrier Island Dune Chronosequence. J. Coast. Res. 2010, 264, 736–742. [Google Scholar] [CrossRef]
  31. Wen, L.; Li, D.; Yang, L.; Luo, P.; Chen, H.; Xiao, K.; Song, T.; Zhang, W.; He, X.; Chen, H.; et al. Rapid recuperation of soil nitrogen following agricultural abandonment in a karst area, southwest China. Biogeochemistry 2016, 129, 341–354. [Google Scholar] [CrossRef]
  32. Guan, B.; Chen, M.; Elsey-Quirk, T.; Yang, S.; Shang, W.; Li, Y.; Tian, X.; Han, G. Soil seed bank and vegetation differences following channel diversion in the Yellow River Delta. Sci. Total Environ. 2019, 693, 133600. [Google Scholar] [CrossRef]
  33. Shen, Y.; Liu, W.; Cao, M.; Li, Y. Seasonal variation in density and species richness of soil seed-banks in karst forests and degraded vegetation in central Yunnan, SW China. Seed Sci. Res. 2007, 17, 99–107. [Google Scholar] [CrossRef]
  34. He, X.; Yuan, L.; Wang, Z.; Zhou, Z.; Wan, L. A study of soil seed banks across one complete chronosequence of secondary succession in a karst landscape. PeerJ 2020, 8, e10226. [Google Scholar] [CrossRef]
  35. Touzard, B.; Amiaud, B.; Langlois, E.; Lemauviel, S.; Clément, B. The relationships between soil seed bank, aboveground vegetation and disturbances in an eutrophic alluvial wetland of Western France. Flora 2002, 197, 175–185. [Google Scholar] [CrossRef]
Figure 1. Sampling points distribution of plant communities.
Figure 1. Sampling points distribution of plant communities.
Agronomy 12 01871 g001
Figure 2. The characteristics of the three typical vegetation types and the interrelations between them.
Figure 2. The characteristics of the three typical vegetation types and the interrelations between them.
Agronomy 12 01871 g002
Figure 3. Difference analysis of soil physiochemical properties in each vegetation type (mean ± standard error, n = 15). Different capital letters indicate significant differences (ANOVA, LSD test, p < 0.05).
Figure 3. Difference analysis of soil physiochemical properties in each vegetation type (mean ± standard error, n = 15). Different capital letters indicate significant differences (ANOVA, LSD test, p < 0.05).
Agronomy 12 01871 g003
Figure 4. Principal component analysis (PCA, (a)) and canonical correlation analysis (CCA, (b,c)) of the correlation between aboveground vegetation, soil seed bank and soil chemical properties.
Figure 4. Principal component analysis (PCA, (a)) and canonical correlation analysis (CCA, (b,c)) of the correlation between aboveground vegetation, soil seed bank and soil chemical properties.
Agronomy 12 01871 g004aAgronomy 12 01871 g004b
Figure 5. Species diversity index of vegetation and soil seed bank in each vegetation type (mean ± standard error, n = 15). Different capital letters indicate significant differences (ANOVA, LSD test, p < 0.05): lowercase letters for aboveground vegetation and uppercase letters for soil seed bank.
Figure 5. Species diversity index of vegetation and soil seed bank in each vegetation type (mean ± standard error, n = 15). Different capital letters indicate significant differences (ANOVA, LSD test, p < 0.05): lowercase letters for aboveground vegetation and uppercase letters for soil seed bank.
Agronomy 12 01871 g005
Figure 6. The Sørensen’s quotient of similarity between the SSB and aboveground vegetation in each vegetation type (mean ± standard error, n = 15).
Figure 6. The Sørensen’s quotient of similarity between the SSB and aboveground vegetation in each vegetation type (mean ± standard error, n = 15).
Agronomy 12 01871 g006
Figure 7. Soil seed bank life form spectrum of sampling points among different vegetation types. O, P and B represent the orchard, primary forest and bamboo shrub, respectively. The black column indicates annual herbs, the grey column indicates perennial plants (herbs and woody vines), and the white column indicates trees or shrubs.
Figure 7. Soil seed bank life form spectrum of sampling points among different vegetation types. O, P and B represent the orchard, primary forest and bamboo shrub, respectively. The black column indicates annual herbs, the grey column indicates perennial plants (herbs and woody vines), and the white column indicates trees or shrubs.
Agronomy 12 01871 g007
Table 1. Sample plot survey.
Table 1. Sample plot survey.
Vegetation TypesSample NumberLongitudeLatitudeAltitude
(m)
Rock Exposed RateSlope
(°)
Edificator
Orchard O-1110°47′16″ E24°55′00″ N208755Prunus salicina
O-2110°47′13″ E24°55′05″ N235802
O-3110°47′15″ E24°55′06″ N234703
Bamboo shrub B-1110°47′20″ E24°55′16″ N198856Phyllostachys sulphurea
B-2110°47′11″ E24°55′07″ N229859
B-3110°47′23″ E24°55′17″ N192808
Primary forest S-1110°51′50″ E24°45′49″ N1736510Cyclobalanopsis glauca, Pterospermum heterophyllum
S-2110°51′49″ E24°45′46″ N1557517
S-3110°52′07″ E24°45′18″ N150705
Table 2. Coefficients of the linear combinations of variables making up the principal components of nine soil properties.
Table 2. Coefficients of the linear combinations of variables making up the principal components of nine soil properties.
VariableComp 1 (36.05%)Comp 2 (32.58%)
Organic carbon (OCC) 0.3690.387
Total nitrogen (TNC) 0.4420.341
Total phosphorus (TPHC) 0.392−0.379
Total potassium (TPOC) 0.14−0.399
Available nitrogen (Alkeline-N) 0.4170.361
Available phosphorus (Olsen-P) 0.353−0.356
Rapidly available potassium (Olsen-K) 0.379−0.388
Calcium (CAC) 0.1910.145
Magnesium (MAC) −0.132
Table 3. Average soil seed bank density (m2) of each plant species in different vegetation types.
Table 3. Average soil seed bank density (m2) of each plant species in different vegetation types.
Latin NameFamilyLife FormVegetation Type
OrchardBamboo ShrubPrimary Forest
1Galium aparineRubiaceaeAnnual herb10,733.34--
2Stellaria mediaCaryophyllaceaeAnnual herb6099.999.99-
3 Oxalis corniculataOxalidaceaeAnnual herb1253.3420.01-
4 Clinopodium chinenseLabiataePerennial herb860.01--
5 Gnaphalium affineCompositaeAnnual herb826.68--
6 Youngia japonicaCompositaeAnnual herb346.6869.99-
7 Paspalum thunbergiiGramineaePerennial herb246.66--
8 Cardamine hirsutaBrassicaceaeHerbs annual146.67--
9 Capsella bursa-pastorisBrassicaceaeAnnual or biennial herb133.32--
10 Talinum paniculatumPortulacaceaePerennial herb60--
11 Thyrocarpus sampsoniiBoraginaceaeAnnual herb33.33--
12 Lygodium japonicumLygodiaceaePerennial herbaceous vines26.676.6-
13 Agastache rugosaLabiataePerennial herb13.32--
14 Centella asiaticaUmbelliferaePerennial herb13.32--
15 Eclipta prostrataCompositaeAnnual herb13.32--
16 Dendranthema indicumCompositaePerennial herb13.32110.01-
17 Solanum lyratumSolanaceaePerennial herbaceous vines6.6639.99-
18 Parathelypteris glanduligeraThelypteridaceaePerennial herb6.66--
19 Cichorium endiviaCompositaeAnnual herb6.66--
20 Clematis floridaRanunculaceaePerennial herbaceous vines6.66--
21 Selaginella uncinataSelaginellaceaePerennial herb-20.01-
22 Conandron ramondioidesGesneriaceaePerennial herb-200.01-
23 Botrychium ternatumBotrychiaceaePerennial herb279.99-530.01
24 Woodsia ilvensisWoodsiaceaePerennial herb33.3330279.99
25 Broussonetia papyiferaMoraceaeTrees or shrubs33.3350.01270
26 Solanum nigrumSolanaceaeAnnual herb53.3480.0160
27 Carex donianaCyperaceaeAnnual herb6.66129.9950.01
28 Oplismenus compositusGramineaeAnnual herb--50.01
29 Stephania tetrandraMenispermaceaePerennial herbaceous vines-39.9939.99
30 Arthraxon hispidusGramineaeAnnual herb--39.99
31 Achyranthes bidentataAmaranthaceaePerennial herb--39.99
32 Setaria viridisGramineaeAnnual herb20.01170.0130
33 Erigeron acerCompositaeBiennial herb13.3239.9920.01
34 Commelina bengalensisCommelinaceaePerennial herb1113.339.999.99
35 Pilea notateUrticaceaePerennial herb26.679.999.99
36 Artemisia carvifoliaCompositaeAnnual or biennial herb13.329.999.99
37 Fatoua villosaMoraceaeAnnual herb6.66-9.99
38 Mallotus paniculatusEuphorbiaceaeTrees or shrubs--9.99
39 Vernonia esculentaCompositaeTrees or shrubs-20.019.99
40 Pistacia chinensisAnacardiaceaeTrees or shrubs--9.99
41 Praxelis clematideaCompositaeAnnual-20.019.99
42 Celtis sinensisUlmaceaeTrees or shrubs--9.99
43 Cyclobalanopsis glaucaFagaceaeTrees or shrubs--9.99
44 Cayratia japonicaVitaceaePerennial herbaceous vines--9.99
Table 4. Average soil seed bank density (m2; mean ± standard deviation) of each vegetation type in different soil depths.
Table 4. Average soil seed bank density (m2; mean ± standard deviation) of each vegetation type in different soil depths.
Soil DepthOrchardBamboo ShrubPrimary Forest
0–5 cm12,906.67 ± 8995.6406.67 ± 55.87826.67 ± 197.66
5–10 cm6033.33 ± 5357.29353.33 ± 68.24493.33 ± 106.77
10–15 cm3526.67 ± 1398.33326.6 ± 67.21200 ± 43.38
Total22,446.67 ± 14,315.181086.6 ± 213.221519.89 ± 383.65
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Guo, Y.; Li, Y.; Li, J.; Li, J.; Wen, S.; Huang, F.; He, W.; Wang, B.; Lu, S.; Li, D.; et al. Comparison of Aboveground Vegetation and Soil Seed Bank Composition among Three Typical Vegetation Types in the Karst Regions of Southwest China. Agronomy 2022, 12, 1871. https://doi.org/10.3390/agronomy12081871

AMA Style

Guo Y, Li Y, Li J, Li J, Wen S, Huang F, He W, Wang B, Lu S, Li D, et al. Comparison of Aboveground Vegetation and Soil Seed Bank Composition among Three Typical Vegetation Types in the Karst Regions of Southwest China. Agronomy. 2022; 12(8):1871. https://doi.org/10.3390/agronomy12081871

Chicago/Turabian Style

Guo, Yili, Yufei Li, Jianxing Li, Jiaqi Li, Shujun Wen, Fuzhao Huang, Wen He, Bin Wang, Shuhua Lu, Dongxing Li, and et al. 2022. "Comparison of Aboveground Vegetation and Soil Seed Bank Composition among Three Typical Vegetation Types in the Karst Regions of Southwest China" Agronomy 12, no. 8: 1871. https://doi.org/10.3390/agronomy12081871

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop