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Changes in soil physico-chemical properties following vegetation restoration mediate bacterial community composition and diversity in Changting, China
Ecological Engineering 138 (2019) 171–179
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Changes in soil physico-chemical properties following vegetation restoration mediate bacterial community composition and diversity in Changting, China
T
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Xiaolong Houa,c, Hang Hana, Mulualem Tigabua,d, Liping Caia,c, Fanrui Mengb, Aiqin Liua,c, , Xiangqing Maa,c a
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China Faculty of Forestry & Environmental Management, University of New Brunswick, Fredericton E3B5A3, Canada c Key Laboratory of State Forestry Administration on Soil and Water Conservation of Red Soil Region in Southern China, 350002, China d Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 230 53 Alnarp, Sweden b
A R T I C LE I N FO
A B S T R A C T
Keywords: Reforestation Soil properties Microbial communities 16S rDNA Soil bacteria
Changes in microbial communities and drivers of their composition in different restoration approaches are largely unexplored. The aim of this study was to evaluate the effects of different ecosystem restoration approaches on bacterial community diversity and whether changes in soil physico-chemical properties are driving bacterial community dynamics. Soil samples were collected from restored lands covered with grass, coniferous forest, young conifer-broadleaf forest, mature conifer-broadleaf forest, and natural broad-leaved forest and bare land. The bacterial community was determined by 16S rDNA sequencing while soil physico-chemical properties were determined using standard methodologies. We found that the physico-chemical properties of degraded red soil were improved following re-vegetation. Soil bulk density and soil pH decreased while soil moisture content increased in restored sites compared to bare land. Soil organic carbon, total P, K, N, and available P and K contents were the highest in conifer-broadleaved forest soil. Soil bacterial community diversity significantly increased following restoration of degraded landscape, with Chloroflexi, Proteobacteria, Firmicutes, Cyanobacteria, WPS-2, Acidobacteria, Verrucomicrobia, Actinobacteria, and Bacteroidetes being dominant. The major bacteria phyla were positively correlated with soil chemical properties, but negatively correlated with soil physical properties and pH. It can be concluded that favorable changes in soil physico-chemical properties following restoration mediate bacterial community diversity, depending on vegetation cover types used to restore the degraded land.
1. Introduction Restoration of degraded landscape has gained global recognition to restore the lost ecosystem services and goods as well as climate regulation (Jacobs et al., 2015). As a result, several restoration projects have been implemented; ranging from passive restoration (e.g. area exclosure) to active restoration involving planting of different species (Yirdaw et al., 2017). The restoration success is often evaluated based on recovery of the structural elements of the degraded ecosystem, such as species composition, diversity, density and cover as well as stand structure, which vary with the time elapsed since restoration began, disturbance type and landscape context (Crouzeilles et al., 2016). However, the functional recovery of degraded ecosystems following restoration, particularly soil microbial community dynamics, has just gained increasing attention with the advent of next generation
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sequencing technologies, which offers new opportunities for studying microbial composition at the species level (Zhang et al., 2016, 2018). Soil microbial communities play an important ecological role as they involve in a wide range of processes, including nutrient cycling through decomposition of litter, soil aggregate formation and promotion of plant growth (Feigl et al., 2017; Hiltbrunner et al., 2012). Changes in soil microbial communities are often associated with plant community structure and soil physico-chemical variability (Mahnert et al., 2015). The soil moisture, nutrition, pH, and other factors affect plant growth and vegetation diversity, whilst the plant community can also modify soil properties (Chen et al., 2008; Li et al., 2015; Klimek et al., 2015; Trivedi et al., 2017; Zhang et al., 2014). As soil physicochemical properties changes, the composition and diversity of soil microbial communities can shift following forest restoration, depending on vegetation cover type, soil depth and soil type as well as land use
Corresponding author. E-mail addresses: [email protected] (M. Tigabu), [email protected] (F. Meng), [email protected] (A. Liu).
https://doi.org/10.1016/j.ecoleng.2019.07.031 Received 16 May 2019; Received in revised form 12 July 2019; Accepted 26 July 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.
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Changting County is located in the west of Fujian Province, Southern China between 25°18′40″–26°02′05″N and 116°00′45″–116°39′20″E. It is underlain by granite geology and is characterized by complex geomorphic features, with hills and low mountains accounting for more than 70% of the total area. The dominant soil type in the area is red soil, rich in iron and aluminum oxides, but with low concentrations of potassium, sodium, calcium, and magnesium. The soil generally tends to be acidic and has high bulk density. The region has a typical subtropical monsoon climate. The annual mean temperature is 18.3 °C and annual precipitation is 1685 mm. The majority of precipitation occurs during the summer months (June–August) with high-intensity storms associated with typhoons during. Forests were re-established in Changting by aerial seeding, mainly of Pinus massoniana, on six different occasions between 1974 and 1993. After seeding, access to the area has been restricted, except for scientific research and inventory purposes. Six sites in the Changting erosion zone with different vegetation cover types, but at similar slope, slope direction, and altitude, were chosen for this research by a temporal–spatial substitution method (Yang et al., 2016). The vegetation cover types investigated in this study were grass land (GL), coniferous forest (CF), young conifer-broadleaf forest (YCBF), mature conifer-broadleaf forest (MCBF), and natural broad-leaved forest (NBF). In addition, bare land (BL) was included to compare the effectiveness of the restoration measures. BL is a severely degraded site with little to no vegetation for more than 50 years, closely reflecting the historical bare land conditions of the area. GL was in a similar condition to that of BL before establishment of exclosures in 1990, and the main species were Paspalum wettsteinii, Miscanthus floridulus. CF was established by aerial seeding of P. massoniana in 1990, and today about 11 species can be found in this vegetation cover type. YCBF was established by enrichment planting of CF in 2008 by a mixture of different broad-leaved species, including Liquidambar formosana, Schima superba, and Lespedeza bicolor. The MCBF was established in 1982 by planting broad-leaved species in CF site, the main species were Pinus massoniana, Schima superba, etc., about 24 species can be found. NBF is considered to be an old-growth natural forest because there are no historical records of disturbance by human activities for more than 100 years, and the main species were Schima superba, Adinandra millettii, etc., more than 30 species can be found. More details of each sampling site are provided in Table 1.
history and chemical characteristics (Kennedy et al., 2005; Noll and Wellinger, 2008; Deng et al., 2010; Hu et al., 2014; Gunina et al., 2017. Differences in the abundance of bacterial species between forest species have been observed with Acidobacteria, for instance, being dominant under birch while Firmicutes and Proteobacteria were more dominant under young pine forests (Nazaries et al., 2015). Thus, microbial communities might serve as a primary indicator of changes in soil properties and track trends in soil development following restoration of degraded landscape (Klimek et al., 2015; Winding et al., 2005). Despite advances in our understanding of soil microbial community dynamics during secondary succession, the effects of various restoration approaches on soil microbial communities were largely unexplored, although such studies may provide useful insights to evaluate the effectiveness of the ecological restoration. In the present study, four restoration approaches applied in the red soil region of Chanting restoration site, Southern China representing grass cover, coniferous forest, young and mature mixed conifer-broadleaved forests were selected to investigate the potential role of vegetation cover type in differentiating soil bacterial communities and whether changes in soil physico-chemical properties mediates changes in soil bacterial community in comparison with the nearby natural broad-leaved forest and bare land. The red soil region covers an area of 118 million hectare in 10 provinces in Southern China that had suffered degradation to the extent that it’s called “red desert”. The Changting restoration site is one of the red soil regions that have been most severely affected by soil erosion in China (Zheng et al., 2008; Lin et al., 2012; Chen et al., 2016) as a result of massive deforestation coupled with the acidic soil type, steep slopes, and high rain intensity associated with typhoons. The severity of soil erosion in Changting has led to soil loss, degradation of soil quality, and increasing sedimentation of rivers and lakes. In the past 50 years, a series of restoration measures have been implemented in Changting including exclosure from disturbance, grass seeding, planting conifer and broadleaved trees. The objectives of the study were to: (1) determine changes in soil physico-chemical properties in relation to vegetation cover types from Changting restoration site in comparison with bare land soil, (2) to analyze the soil bacterial diversity and community composition with respect to vegetation cover types; (3) examine the relationships between the physico-chemical characteristics of soil and the bacterial diversity within the surface soil (0–20 cm depth). We tested the hypothesis that vegetation cover type would select for distinct microbial communities beneath them, and such influences are presumably mediated by differences in soil physico-chemical properties, such as bulk density, soil moisture content, acidity, and nutrient status.
2.2. Soil sampling In each vegetation cover type, five 0.5 ha plots were delineated along an altitudinal gradient, and five quadrats (20 m × 20 m) were set up along an “S” shape within each plot at each site. Within each quadrat, five soil samples at a depth of 0–20 cm were collected randomly along an “S”-shaped transect in July 2015. A single composite soil sample from each site was prepared by mixing the five soil samples from quadrats. A total of 30 soil samples were collected from 6
2. Materials and methods 2.1. Site description The study was carried out in Changting model restoration site. The Table 1 Geographic and vegetation characteristics of study sites in the Changting. Vegetation cover types
Geographic coordinates
Elevation (m)
Aspect
Dominant species and number
Bare land (BL) Grassland (GL)
25°37′23.2″N 116°27′22.5″E 25°40′01.7″N 116°27′24.9″E
305 321
NW SW
Coniferous forest (CFL) Young conifer-broadleaved forest (FL-Y)
25°40′15.4″N 116°26′05.6″E 25°39′41.7″N 116°28′56.0″E
306 300
NW SW
Mature conifer-broadleaved forest (FLA)
25°40′16.9″N 116°26′07.4″E
297
SW
Natural broadleaved forest (BFL)
25°40′32.0″N 116°28′56.5″E
308
NW
Absent Paspalum wettsteinii, Miscanthus floridulus Pinus massoniana, Dicranopteris dichotoma, about 11 species Pinus massoniana, Liquidambar formosana, Dicranopteris dichotoma, about 12 species Pinus massoniana, Schima superba, Liquidambar formosana, Lespedeza bicolor, Dicranopteris dichotoma, about 24 species Schima superba, Adinandra millettii, Rhaphiolepis indica, Glochidion puberum, Machilus grijsii, etc., more than 30 species
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(b)
1.60
ab
3
Bulk density (g m )
1.50
a
225
ab
bc
1.45 1.40 1.35 1.30
ab
-1)
1.55
a
Moisture content (g kg
(a)
cd
1.25 1.20
d
200 bc 175
150
cd
e
de
1.15 BL
GL
CF
YCBF
MCBF
NBF
BL
Different vegetation cover stages
GL
CF
YCBF
MCBF
NBF
Different vegetation cover types
Fig. 1. Mean bulk density (a) and moisture content (b) of soils of different vegetation cover types (BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest). Values are means ± standard error (n = 5), and means followed by different letter (s) are significantly difference at 0.05 level.
40 min. The amplified products were detected and purified by electrophoresis and sent to Guangzhou Gene Denovo Biotechnology Co., Ltd. for sequencing by Illumina HiSeq sequencing platform (Illumina PE250). PCR products without primer dimer and contamination bands were selected for sequencing. Sequences with an average phred score less than 30, fuzzy bases, homopolymers running more than 15 bp, and more than two mismatches or sequence lengths less than 100 bp were discarded. Only sequences that were perfectly matched and overlapped by more than 10 bp were assembled. The sequences were further filtered and processed to remove chimaeras. The 16S rDNA gene sequences were aligned using PICRUSt against a template alignment of the Greengenes core set filtered at 97% similarity level, and a phylogenic tree was generated from the filtered alignment using FastTree. Each OTU sequence was used for subsequent classification and statistical analysis. Sequences were entered into the NCBI databases and compared using Mothur to calculate the abundance of each OTU.
vegetation cover types (including bare land). Visible roots, plant residues, and stones were removed from the composite samples, and the samples were divided into two parts for further analysis—one part was sieved through a 2 mm mesh, packed in individual sterile plastic bags and immediately stored in 4 °C cooler, and then transported to the laboratory and stored at −80 °C for DNA extraction later. The other part of the soil samples was air-dried at room temperature, sieved through a nylon net (with 0.25 mm aperture), and used for determining the physico-chemical properties of the soil. 2.3. Analysis of soil physico-chemical properties The soil bulk density and moisture were determined for each plot and site by the cutting ring method (Zeng et al., 2014) while the chemical properties were determined using the standard procedures (Bao, 1999). The soil samples were sieved through a 0.14 mm mesh and total nitrogen (TN), total phosphorus (TP), total potassium (TK), and organic carbon (OC) were measured as follows. TN was determined by dry combustion method using CN elemental analyzer (Vario MAX, Elementar, Germany). TP concentrations were measured using inductively coupled plasma atomic emission spectrometry (ICP-OES, PerkinElmer, USA) after wet digestion with H2SO4–HClO4 whereas TK was measured using a flame spectrophotometer. The available P (AP) was measured colorimetrically after extraction with ammonium fluoride–hydrochloric acid. The available K (AK) was measured using a flame spectrophotometer following extraction using a 1 mol/L ammonium acetate solution. Soil pH was determined with a glass electrode Thermo-Russel pH meter in a 1:2.5 (soil/water) suspension.
2.5. Statistical analysis The OTU richness index was calculated for each of the soil samples using the alpha diversity indices: Chao1, ACE, Shannon, and Simpson (Magurran, 2004). The data for soil physico-chemical properties and the bacterial alpha community diversity were subjected to One-way ANOVA to examine differences among vegetation cover types. Means that exhibited significant differences were compared by Tukey’s posthoc test (p < 0.05). All statistical analyses were performed using SPSS v.17.0 (SPSS Inc., Chicago, IL, USA). Redundancy analysis (RDA) was conducted to examine the relationship between the dominant bacteria phyla and soil physico-chemical properties using Canoco (4.5) software. To check the applicability of RDA analysis, we carried out a DCA, and the lengths of gradient was 1.291 which is shorter than 3.0. Thus, RDA is a better choice.
2.4. DNA extraction from soil and PCR amplification of 16S rDNA DNA was extracted from 0.25 g of field-moist soil using FastDNA® SPIN Kit for Soil (Bio 101, Vista, CA, USA) according to the manufacturer’s instructions, and then stored at −70 °C for subsequent analysis. The extracted DNA was subjected to PCR amplification with bacteria-specific primers (336F: 5′-GTACTCCTACGGGAGGCAGCA-3′; 806R; 5′-GTGGACTACHVGGGTWTCTAAT-3′) against the V3 + V4 region of the bacterial 16S rDNA gene. The PCR premix used was NEB Fusion PCR High-Fidelity Master Mix. PCR amplification was performed in a Genius Thermal Cycler in 50 µL reactions, containing approximately 30 ng of purified DNA. The temperature and cycling conditions were as follows: preheating at 98 °C for 3 min, followed by 30 cycles at 98 °C for 45 s, 55 °C for 45 s, and 72 °C for 45 s, and a final incubation at 72 °C for 7 min. The presence of PCR products and their concentrations was checked using electrophoresis on 1% agarose gel at 150 V for
3. Results 3.1. Soil physico-chemical properties The soil bulk density and soil moisture content varied significantly (p < 0.05) among the different vegetation cover types. Compared to the bare land, bulk density was low in soils of coniferous forest, mature conifer-broadleaf forest and the natural broad-leaved forest (Fig. 1a). However, the young conifer-broadleaved forest and the grassland had similar bulk density as the bare land. The soil moisture content gradually increased with complexity of the vegetation cover type (Fig. 1b) 173
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Table 2 Soil chemical properties across different vegetation cover types used to restore degraded land in Changting. Values are means ± standard error (n = 5) and means followed by different letter in the same column are significant difference at 0.05 level. Cover*
pH
BL GL CF YCBF MCBF NBF
5.15 4.82 4.57 4.66 4.34 5.30
± ± ± ± ± ±
0.16a 0.06b 0.08c 0.10c 0.11d 0.04a
Organic C (g kg−1)
Total N (g kg−1)
2.57 ± 0.12f 12.27 ± 0.40c 7.11 ± 0.92d 4.57 ± 0.12e 25.00 ± 0.20a 16.07 ± 0.65b
1.07 1.17 1.26 1.03 2.47 1.57
± ± ± ± ± ±
0.15c 0.12c 0.06c 0.12c 0.12a 0.23b
Total P (g kg−1) 0.12 0.21 0.14 0.18 0.27 0.26
± ± ± ± ± ±
0.02d 0.03b 0.01d 0.02c 0.02a 0.02a
Total K (g kg−1)
Available P (mg kg−1)
Available K (mg kg−1)
2.79 ± 0.12c 8.61 ± 0.28b 14.57 ± 3.72a 11.06 ± 0.67b 10.52 ± 1.00b 14.68 ± 1.30a
2.30 ± 0.41c 7.96 ± 0.21b 3.07 ± 0.16c 2.98 ± 0.31c 6.70 ± 0.67b 13.20 ± 1.80a
24.34 42.35 68.42 49.14 79.29 81.62
± ± ± ± ± ±
1.75d 1.60c 5.79b 2.01c 5.45a 6.60a
* The vegetation cover BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
(relative abundance > 5%) across the soil samples were Chloroflexi, Proteobacteria, Firmicutes, Cyanobacteria, WPS-2, Acidobacteria, Verrucomicrobia, Actinobacteria, and Bacteroidetes, accounting for more than 86% of the total sequences (Fig. 3). The bacterial phylum with the maximum relative abundance in the bare land soil was Chloroflexi (25.3%); its abundance gradually decreased from the grassland to the natural broadleaved forest soil. The relative abundance of Proteobacteria exceeded 20% for all vegetation cover types and showed a trend of increasing abundance from bare land to natural broadleaved forest soil. It accounted for more than 38% of the relative abundance in the mature conifer-broadleaved and natural broadleaved forest soils. The relative abundance of Firmicutes gradually decreased from bare land (17.4%) to young conifer-broadleaved soil (5.6%), and it was < 5% for mature conifer-broadleaved forest soil. Cyanobacteria were only present at > 5% relative abundance in the bare land (8.9%) and young conifer-broadleaved forest (6.9%) soils. The relative abundance of Acidobacteria increased significantly from the bare land (5.9%) to the grassland (21.8%) soils and other forest types except conifer-broadleaf young forest. At the order level, we observed at least one sample with relative abundance greater than 2%; the remaining species were classified into other categories, and those for which we were unable to comment to the level of sequences are classified as “unclassified category” (Fig. 4). Although Rhodospirillales, Rhizobiales, Acidobacteriales, and Solibacterales were present in all the sites, there were differences in relative abundance of bacterial order across the sites. iii1-15, Bacteroidales, Enterobacteriales, and Xanthomonadales were only present in natural broadleaved forest soil. Thermogemmatisporales (20%) and Bacillales (15%) were the main orders found in bare land soil. Acidobacteriales (9%), Ellin6513 (7%), Rhizobiales (6%), Rhodospirillales (6%), and Actinomycetales (5%) were the main orders found in grassland soils. Rhizobiales (9%), Acidobacteriales (8%), Rhodospirillales (7%), Burkholderiales (6%), Bacillales (5%), and Solibacterales (5%) were the main orders in coniferous forest soil. Bacillales (14%), Rhizobiales (7%), Acidobacteriales (5%), and Rhodospirillales (5%) were the main orders in young conifer-broadleaved mixed forest soil while Acidobacteriales (11%), Ellin6513 (9%), Rhizobiales (8%), Rhodospirillales (7%), and Xanthomonadales (6%) were the main orders found in soils of mature conifer-broadleaved forest soil. Rhizobiales (7%), Solibacterales (6%), Acidobacteriales (6%), and Rhodospirillales (5%) were the main orders found in soils of natural broadleaved forest.
and was significantly higher for all vegetation cover types, except for the grassland, than for bare land. The mean soil chemical properties also varied significantly (p < 0.05) across the different vegetation cover types (Table 2). Compared to the bare land, the pH was low for most of the vegetation cover types except the natural broadleaved forest soil that had a similar pH as the bare land. Soils of the mature confer-broadleaved forests and the natural broadleaved forests had higher organic carbon content, total N and Total P than both the bare land and the coniferous forest soils although the later had significantly more organic carbon than the former. The total K content was higher in soils of the conifer and the natural broadleaved forests than the other vegetation cover types, which in turn had higher total K content than soils of the bare land. Available P was the highest in the natural broadleaved forest soil while available K was the highest in mature conifer-broadleaved and the natural broadleaved forest soils compared to other vegetation cover types and the bare land soils. 3.2. Diversity of bacterial community A total of 2,487,353 sequences were obtained from 30 soil samples by analysing the V3-V4 region of bacterial 16S rRNA genes. The total number of effective sequences per sample ranged from 38 936 to 85,675 sequences. The unique tag sequences, selected through redundant processing by Mothur v1.34.0 software, resulted in a total of 867,624 OTUs (range: 14,282–35,187) with 97% similarity clustering level at a genetic distance of 3%. Shannon rarefaction analyses showed that the curve tended to plateau after 20,000 sequences at 97% similarity (Fig. 2). Thus, additional sequencing had little effect on species diversity. The alpha diversity indices of soil bacteria across different vegetation cover types are given in Table 3. The number of reads was significantly higher for conifer forest, mature conifer-broadleaved forest and natural broadleaved forest than the bare land. The number of OTUs was significantly greater for all vegetation cover types than the bare land except the grass land that had similar OTU number. The Chao and ACE richness indices showed that the diversity was highest for the coniferous forest, natural broadleaved forest young conifer-broadleaved forest mature conifer-broadleaved forest compared with grassland and bare land. The Shannon index indicated a gradual increase in diversity from the bare land to the natural broadleaved forest and significantly higher diversity in all vegetation cover types compared to bare land, whereas the Simpson index showed the opposite trend. As a whole, the bacterial community diversity increased with the progression of the revegetation stages.
3.4. Relationship between bacterial communities and soil physico-chemical properties Redundancy analysis was conducted to investigate the relationships between the eight main bacterial phyla and eight physico-chemical soil properties (Fig. 5). The composition of soil bacteria community varied considerably among vegetation cover types. All the bacterial phyla, except Firmicutes and Chloroflexi, were positively correlated with total K, total P, available K, available P, organic carbon, and total N, and
3.3. Composition of bacterial community All valid sequences from the soil sample libraries were classified into phylum and order, and a total of 32 phyla were found in all samples (see supplementary materials). The dominant bacterial phyla 174
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Fig. 2. Comparison of soil bacterial diversity across different vegetation cover types using multi samples Shannon–Wiener curves. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
4. Discussion
Table 3 Number of reads, operational taxonomic units (OTUs) and diversity indices of soil bacterial communities based on 16S sequencing data of samples collected from different vegetation cover types. Values are mean ± standard error (n = 5), and means followed by different letter (s) in across column are significantly difference at 0.05 level. Cover*
Reads
OTU
Chao
ACE
Shannon
Simpson
BL GL CF YCBF MCBF NBF
64623b 75611ab 81014a 70483ab 79704a 83616a
21301c 26206bc 33948a 28569ab 30399ab 33102a
102793c 134181bc 179166a 154492ab 145832ab 154894ab
241606c 330884bc 452860a 389533ab 357472ab 356714ab
6.99c 8.11b 8.61ab 8.14b 8.49b 9.13a
0.022a 0.005b 0.003b 0.007b 0.002b 0.001b
Generally reforestation results in an improvement in soil physicochemical properties (Gunina et al., 2017; Zeng et al., 2014). Soil bulk density plays an important role in soil nutrient storage, water-holding capacity and gas penetration (Wang et al., 2010), which is linked with soil development, parent material and land use pattern. For the studied sites, soil bulk density decreased by 6.5% in grassland, 18.6% in conifer forest, 8.7% in young conifer-broadleaved forest and 14.5% in mature conifer-broadleaved forest compared with the bare land soil. This suggests that reforestation loosens up the top-soil and increased soil porosity and water retention capacity as the vegetation established. This is further evidenced from increased soil moisture content with vegetation cover type (Fig. 1). The difference in soil bulk density among the different vegetation cover types could be attributed to amounts of easily-decomposable litter, resulting in thicker humus layers, and successional stages of the forest (Zeng et al., 2014). We also found that the bulk density of the YCBF were not only higher than the CF, but close to that of grass land. This result is contradicting to our expectation because the broad leaf planting into the conifer forest was designed to improve the ecological functions. We speculated the increase in bulk density in YCBF forest could be attributed to the compaction during the planting process. This hypothesis is supported by the fact that the bulk density of MCBF was also higher than that of CF, and lower than YCBF. The bulk density simply takes time to recover. In summary, forest cover will increase soil bulk density and increase soil moisture content. However, reducing compaction related to human activities, including silviculture operations is important
* The vegetation cover BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
negatively correlated with pH, bulk density and moisture content. Proteobacteria was most strongly associated with available P, Acidobacteria, Planctomycetes, and Verrucomicrobia were most strongly associated with organic carbon, and Bacteroidetes and Actinobacteria were most strongly associated with total K and total N. respectively. Whilst Firmicutes and Chloroflexi were strongly associated with pH and bulk density, Chloroflexi was negatively correlated with soil moisture content.
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Fig. 3. Phylogenetic relationships of bacterial communities and the relative abundance of bacterial phyla (> 5%) in the soils of different vegetation cover types. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
organic carbon and total N contents in the coniferous forest soil could be due to the lower litter input and the higher resistance to degradation (Chiti et al., 2012). While the total P content was 127.8% higher in mature conifer-broadleaved forest soil compared to the bare land soil, available P were 245.9% higher in grassland soil and 191.4% higher in mature conifer-broadleaved forest soils than the bare land soil. Total K was 422% higher in coniferous forest soil whereas available K was 225% higher in mature conifer-broadleaved forest soil than the bare land soil. Such big differences can be explained by the quality and quantity of litter accumulated on the soil surface and the increased activity and content of microbial biomass. As a whole, the results are consistent with previous studies that demonstrated improvements in soil nutrient condition with revegetation (Fan et al., 2014; Zhuet al., 2012). We noticed that organic C content in YCBF (4.6 g kg−1) was less than CF (7.1 g kg−1). Total N, Available P and Available K also showed the similar trend. The Available K in YCBF was significantly lower than CF without adding broadleaf species. This is again, contradicting to our expectations that a restoration effort resulted in poor performance. In compaction and increased exposure associated with planting broadleaf species in conifer forests did result in temporary degradation of soil physical and chemical properties. The soil physical and chemical properties will recover
to ensure the restoration of soil physical properties in degraded red soil. In the present study, the soil pH values decreased in the grass land, coniferous forest and conifer-broadleaved forest soils compared with the bare land and the natural broadleaved forest soils (Table 2). Our result concurs with those of previous studies, which showed that conifers can cause soil acidification (Kong et al., 2000; Kuang et al., 2008). The decrease in soil pH may also be attributed to the release of organic acids from tree roots, removal of exchangeable cations by plant uptake and/or soil microbial processes that transform organic matter and the release of organic acids (Wei et al., 2006). Microbial decomposition of litters of P. massoniana forest which contain less base ions, and more resin and tannin may also contribute to soil acidification. Reforestation improved soil fertility with marked variation among vegetation cover types used to restore the degraded land (Table 2). The organic carbon and total nitrogen contents were 873% and 130% more in the mature conifer-broadleaved forest soil than the bare land soil, respectively. Such huge changes could be related to accumulation of tree litter inputs over time which decomposes relatively quickly as the intrinsic microbial community is gradually adapted to this new substrate. It could also be related to lack of or minimal decomposition of the intrinsic soil organic carbon by microorganisms, thus resulting in lower C mineralization long after reforestation. Furthermore, the lower 176
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Fig. 4. Relative abundance of bacterial orders (> 2%) in soils of different vegetation cover types. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
development of soil, in the transformation of organic matter, and in maintaining a balanced ecosystem in the soil environment (Fierer and Jackson, 2006). Many studies have also demonstrated a direct relationship between soil bacteria and vegetation diversity (Faoro et al., 2010; Chen et al., 2015). For the studied sites, the number of OTUs was significantly greater for restored sites than the bare land, and those in the coniferous forest and natural broad leaved forest soils were greater than those in the grassland soil. This might be related to the time since restoration (25 years since the coniferous forest was established). The diversity of soil microorganisms was not always at the same pace as that of vegetation restoration, and varied with composition of the vegetation (Dan et al., 2011). Our study showed that the Chao and Ace indices tended to be highest for coniferous forest soil than the conifer-broadleaved forests (Table 3) probably because the soil microbial diversity lagged behind the vegetation change and, therefore, it took time to respond. As a whole, the alpha community diversity indices, such as the Shannon index, increased with the restoration of the vegetation (Table 3). Therefore, changes in the microbial diversity can reflect the process of change in the soil quality following restoration of degraded ecosystems. The dominant bacterial phyla in the bare land soil were Chloroflexi and Proteobacteria, which accounted for 46% of the bacterial sequences (Fig. 3), but the grassland soil was dominated by Acidobacteria with Proteobacteria, accounting for more than 40% of the bacterial sequences in all the soil samples. Proteobacteria was detected as one of the most abundant phyla in soil bacterial communities, which is related with Rhizobiales (a nitrogen-fixing organism), suggesting nitrogen fixation may play an important role in differentiating community structure. Similar result was obtained by He et al. (2008) who reported that 74% of the clones in the upland red soil of Yingtan, Jiangxi Province, China, were classified as Proteobacteria. The fact that
Fig. 5. Redundancy analysis of the relationships among the main bacterial phyla, OTU, and soil physico-chemical properties. BL: bare land (No. 1–5); GL: grass land (No. 6–10); CF: coniferous forest (No. 11–15); YCBF: young coniferbroadleaf forest (No. 16–20); MCBF: mature conifer-broadleaf forest (No. 21–25); NBF: natural broad-leaved forest (No. 26–30); BD: bulk density; MC: moisture content; TP: total phosphorus; AP: available phosphorus; TK: total potassium, AK: available K; OC: organic carbon; TN: total nitrogen.
over time; however, the degradation will take more than 8 years or even longer to recover in our study sites. Soil microorganisms play an important role in the formation and 177
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References
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5. Conclusions Based on the findings, the following conclusions can be drawn: (1) the physical and chemical properties of soil were improved with revegetation of degraded land; particularly conifer-broadleaved mixed forest yield substantial improvement in soil quality; (2) the diversity and composition of soil bacterial community vary with the vegetation cover type used to restore the degraded land, suggesting selecting for distinct microbial communities beneath them; (3) diversity and structure of bacterial community are driven by changes in soil physicochemical properties, which in turn are dependent on the vegetation cover types. Thus, composition and diversity of bacterial community may serve as indicator of functional recovery of restored ecosystem. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the Key Project of Fujian Province Science and Technology Department (2017Y0001), and the National Key Technology R&D Program (2014BAD15B02). We also thank Dr Kate Heal from School of GeoSciences, The University of Edinburgh, for offering comments which have substantially improved the manuscript. 178
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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Changes in soil physico-chemical properties following vegetation restoration mediate bacterial community composition and diversity in Changting, China
T
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Xiaolong Houa,c, Hang Hana, Mulualem Tigabua,d, Liping Caia,c, Fanrui Mengb, Aiqin Liua,c, , Xiangqing Maa,c a
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China Faculty of Forestry & Environmental Management, University of New Brunswick, Fredericton E3B5A3, Canada c Key Laboratory of State Forestry Administration on Soil and Water Conservation of Red Soil Region in Southern China, 350002, China d Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 230 53 Alnarp, Sweden b
A R T I C LE I N FO
A B S T R A C T
Keywords: Reforestation Soil properties Microbial communities 16S rDNA Soil bacteria
Changes in microbial communities and drivers of their composition in different restoration approaches are largely unexplored. The aim of this study was to evaluate the effects of different ecosystem restoration approaches on bacterial community diversity and whether changes in soil physico-chemical properties are driving bacterial community dynamics. Soil samples were collected from restored lands covered with grass, coniferous forest, young conifer-broadleaf forest, mature conifer-broadleaf forest, and natural broad-leaved forest and bare land. The bacterial community was determined by 16S rDNA sequencing while soil physico-chemical properties were determined using standard methodologies. We found that the physico-chemical properties of degraded red soil were improved following re-vegetation. Soil bulk density and soil pH decreased while soil moisture content increased in restored sites compared to bare land. Soil organic carbon, total P, K, N, and available P and K contents were the highest in conifer-broadleaved forest soil. Soil bacterial community diversity significantly increased following restoration of degraded landscape, with Chloroflexi, Proteobacteria, Firmicutes, Cyanobacteria, WPS-2, Acidobacteria, Verrucomicrobia, Actinobacteria, and Bacteroidetes being dominant. The major bacteria phyla were positively correlated with soil chemical properties, but negatively correlated with soil physical properties and pH. It can be concluded that favorable changes in soil physico-chemical properties following restoration mediate bacterial community diversity, depending on vegetation cover types used to restore the degraded land.
1. Introduction Restoration of degraded landscape has gained global recognition to restore the lost ecosystem services and goods as well as climate regulation (Jacobs et al., 2015). As a result, several restoration projects have been implemented; ranging from passive restoration (e.g. area exclosure) to active restoration involving planting of different species (Yirdaw et al., 2017). The restoration success is often evaluated based on recovery of the structural elements of the degraded ecosystem, such as species composition, diversity, density and cover as well as stand structure, which vary with the time elapsed since restoration began, disturbance type and landscape context (Crouzeilles et al., 2016). However, the functional recovery of degraded ecosystems following restoration, particularly soil microbial community dynamics, has just gained increasing attention with the advent of next generation
⁎
sequencing technologies, which offers new opportunities for studying microbial composition at the species level (Zhang et al., 2016, 2018). Soil microbial communities play an important ecological role as they involve in a wide range of processes, including nutrient cycling through decomposition of litter, soil aggregate formation and promotion of plant growth (Feigl et al., 2017; Hiltbrunner et al., 2012). Changes in soil microbial communities are often associated with plant community structure and soil physico-chemical variability (Mahnert et al., 2015). The soil moisture, nutrition, pH, and other factors affect plant growth and vegetation diversity, whilst the plant community can also modify soil properties (Chen et al., 2008; Li et al., 2015; Klimek et al., 2015; Trivedi et al., 2017; Zhang et al., 2014). As soil physicochemical properties changes, the composition and diversity of soil microbial communities can shift following forest restoration, depending on vegetation cover type, soil depth and soil type as well as land use
Corresponding author. E-mail addresses: [email protected] (M. Tigabu), [email protected] (F. Meng), [email protected] (A. Liu).
https://doi.org/10.1016/j.ecoleng.2019.07.031 Received 16 May 2019; Received in revised form 12 July 2019; Accepted 26 July 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.
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Changting County is located in the west of Fujian Province, Southern China between 25°18′40″–26°02′05″N and 116°00′45″–116°39′20″E. It is underlain by granite geology and is characterized by complex geomorphic features, with hills and low mountains accounting for more than 70% of the total area. The dominant soil type in the area is red soil, rich in iron and aluminum oxides, but with low concentrations of potassium, sodium, calcium, and magnesium. The soil generally tends to be acidic and has high bulk density. The region has a typical subtropical monsoon climate. The annual mean temperature is 18.3 °C and annual precipitation is 1685 mm. The majority of precipitation occurs during the summer months (June–August) with high-intensity storms associated with typhoons during. Forests were re-established in Changting by aerial seeding, mainly of Pinus massoniana, on six different occasions between 1974 and 1993. After seeding, access to the area has been restricted, except for scientific research and inventory purposes. Six sites in the Changting erosion zone with different vegetation cover types, but at similar slope, slope direction, and altitude, were chosen for this research by a temporal–spatial substitution method (Yang et al., 2016). The vegetation cover types investigated in this study were grass land (GL), coniferous forest (CF), young conifer-broadleaf forest (YCBF), mature conifer-broadleaf forest (MCBF), and natural broad-leaved forest (NBF). In addition, bare land (BL) was included to compare the effectiveness of the restoration measures. BL is a severely degraded site with little to no vegetation for more than 50 years, closely reflecting the historical bare land conditions of the area. GL was in a similar condition to that of BL before establishment of exclosures in 1990, and the main species were Paspalum wettsteinii, Miscanthus floridulus. CF was established by aerial seeding of P. massoniana in 1990, and today about 11 species can be found in this vegetation cover type. YCBF was established by enrichment planting of CF in 2008 by a mixture of different broad-leaved species, including Liquidambar formosana, Schima superba, and Lespedeza bicolor. The MCBF was established in 1982 by planting broad-leaved species in CF site, the main species were Pinus massoniana, Schima superba, etc., about 24 species can be found. NBF is considered to be an old-growth natural forest because there are no historical records of disturbance by human activities for more than 100 years, and the main species were Schima superba, Adinandra millettii, etc., more than 30 species can be found. More details of each sampling site are provided in Table 1.
history and chemical characteristics (Kennedy et al., 2005; Noll and Wellinger, 2008; Deng et al., 2010; Hu et al., 2014; Gunina et al., 2017. Differences in the abundance of bacterial species between forest species have been observed with Acidobacteria, for instance, being dominant under birch while Firmicutes and Proteobacteria were more dominant under young pine forests (Nazaries et al., 2015). Thus, microbial communities might serve as a primary indicator of changes in soil properties and track trends in soil development following restoration of degraded landscape (Klimek et al., 2015; Winding et al., 2005). Despite advances in our understanding of soil microbial community dynamics during secondary succession, the effects of various restoration approaches on soil microbial communities were largely unexplored, although such studies may provide useful insights to evaluate the effectiveness of the ecological restoration. In the present study, four restoration approaches applied in the red soil region of Chanting restoration site, Southern China representing grass cover, coniferous forest, young and mature mixed conifer-broadleaved forests were selected to investigate the potential role of vegetation cover type in differentiating soil bacterial communities and whether changes in soil physico-chemical properties mediates changes in soil bacterial community in comparison with the nearby natural broad-leaved forest and bare land. The red soil region covers an area of 118 million hectare in 10 provinces in Southern China that had suffered degradation to the extent that it’s called “red desert”. The Changting restoration site is one of the red soil regions that have been most severely affected by soil erosion in China (Zheng et al., 2008; Lin et al., 2012; Chen et al., 2016) as a result of massive deforestation coupled with the acidic soil type, steep slopes, and high rain intensity associated with typhoons. The severity of soil erosion in Changting has led to soil loss, degradation of soil quality, and increasing sedimentation of rivers and lakes. In the past 50 years, a series of restoration measures have been implemented in Changting including exclosure from disturbance, grass seeding, planting conifer and broadleaved trees. The objectives of the study were to: (1) determine changes in soil physico-chemical properties in relation to vegetation cover types from Changting restoration site in comparison with bare land soil, (2) to analyze the soil bacterial diversity and community composition with respect to vegetation cover types; (3) examine the relationships between the physico-chemical characteristics of soil and the bacterial diversity within the surface soil (0–20 cm depth). We tested the hypothesis that vegetation cover type would select for distinct microbial communities beneath them, and such influences are presumably mediated by differences in soil physico-chemical properties, such as bulk density, soil moisture content, acidity, and nutrient status.
2.2. Soil sampling In each vegetation cover type, five 0.5 ha plots were delineated along an altitudinal gradient, and five quadrats (20 m × 20 m) were set up along an “S” shape within each plot at each site. Within each quadrat, five soil samples at a depth of 0–20 cm were collected randomly along an “S”-shaped transect in July 2015. A single composite soil sample from each site was prepared by mixing the five soil samples from quadrats. A total of 30 soil samples were collected from 6
2. Materials and methods 2.1. Site description The study was carried out in Changting model restoration site. The Table 1 Geographic and vegetation characteristics of study sites in the Changting. Vegetation cover types
Geographic coordinates
Elevation (m)
Aspect
Dominant species and number
Bare land (BL) Grassland (GL)
25°37′23.2″N 116°27′22.5″E 25°40′01.7″N 116°27′24.9″E
305 321
NW SW
Coniferous forest (CFL) Young conifer-broadleaved forest (FL-Y)
25°40′15.4″N 116°26′05.6″E 25°39′41.7″N 116°28′56.0″E
306 300
NW SW
Mature conifer-broadleaved forest (FLA)
25°40′16.9″N 116°26′07.4″E
297
SW
Natural broadleaved forest (BFL)
25°40′32.0″N 116°28′56.5″E
308
NW
Absent Paspalum wettsteinii, Miscanthus floridulus Pinus massoniana, Dicranopteris dichotoma, about 11 species Pinus massoniana, Liquidambar formosana, Dicranopteris dichotoma, about 12 species Pinus massoniana, Schima superba, Liquidambar formosana, Lespedeza bicolor, Dicranopteris dichotoma, about 24 species Schima superba, Adinandra millettii, Rhaphiolepis indica, Glochidion puberum, Machilus grijsii, etc., more than 30 species
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(b)
1.60
ab
3
Bulk density (g m )
1.50
a
225
ab
bc
1.45 1.40 1.35 1.30
ab
-1)
1.55
a
Moisture content (g kg
(a)
cd
1.25 1.20
d
200 bc 175
150
cd
e
de
1.15 BL
GL
CF
YCBF
MCBF
NBF
BL
Different vegetation cover stages
GL
CF
YCBF
MCBF
NBF
Different vegetation cover types
Fig. 1. Mean bulk density (a) and moisture content (b) of soils of different vegetation cover types (BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest). Values are means ± standard error (n = 5), and means followed by different letter (s) are significantly difference at 0.05 level.
40 min. The amplified products were detected and purified by electrophoresis and sent to Guangzhou Gene Denovo Biotechnology Co., Ltd. for sequencing by Illumina HiSeq sequencing platform (Illumina PE250). PCR products without primer dimer and contamination bands were selected for sequencing. Sequences with an average phred score less than 30, fuzzy bases, homopolymers running more than 15 bp, and more than two mismatches or sequence lengths less than 100 bp were discarded. Only sequences that were perfectly matched and overlapped by more than 10 bp were assembled. The sequences were further filtered and processed to remove chimaeras. The 16S rDNA gene sequences were aligned using PICRUSt against a template alignment of the Greengenes core set filtered at 97% similarity level, and a phylogenic tree was generated from the filtered alignment using FastTree. Each OTU sequence was used for subsequent classification and statistical analysis. Sequences were entered into the NCBI databases and compared using Mothur to calculate the abundance of each OTU.
vegetation cover types (including bare land). Visible roots, plant residues, and stones were removed from the composite samples, and the samples were divided into two parts for further analysis—one part was sieved through a 2 mm mesh, packed in individual sterile plastic bags and immediately stored in 4 °C cooler, and then transported to the laboratory and stored at −80 °C for DNA extraction later. The other part of the soil samples was air-dried at room temperature, sieved through a nylon net (with 0.25 mm aperture), and used for determining the physico-chemical properties of the soil. 2.3. Analysis of soil physico-chemical properties The soil bulk density and moisture were determined for each plot and site by the cutting ring method (Zeng et al., 2014) while the chemical properties were determined using the standard procedures (Bao, 1999). The soil samples were sieved through a 0.14 mm mesh and total nitrogen (TN), total phosphorus (TP), total potassium (TK), and organic carbon (OC) were measured as follows. TN was determined by dry combustion method using CN elemental analyzer (Vario MAX, Elementar, Germany). TP concentrations were measured using inductively coupled plasma atomic emission spectrometry (ICP-OES, PerkinElmer, USA) after wet digestion with H2SO4–HClO4 whereas TK was measured using a flame spectrophotometer. The available P (AP) was measured colorimetrically after extraction with ammonium fluoride–hydrochloric acid. The available K (AK) was measured using a flame spectrophotometer following extraction using a 1 mol/L ammonium acetate solution. Soil pH was determined with a glass electrode Thermo-Russel pH meter in a 1:2.5 (soil/water) suspension.
2.5. Statistical analysis The OTU richness index was calculated for each of the soil samples using the alpha diversity indices: Chao1, ACE, Shannon, and Simpson (Magurran, 2004). The data for soil physico-chemical properties and the bacterial alpha community diversity were subjected to One-way ANOVA to examine differences among vegetation cover types. Means that exhibited significant differences were compared by Tukey’s posthoc test (p < 0.05). All statistical analyses were performed using SPSS v.17.0 (SPSS Inc., Chicago, IL, USA). Redundancy analysis (RDA) was conducted to examine the relationship between the dominant bacteria phyla and soil physico-chemical properties using Canoco (4.5) software. To check the applicability of RDA analysis, we carried out a DCA, and the lengths of gradient was 1.291 which is shorter than 3.0. Thus, RDA is a better choice.
2.4. DNA extraction from soil and PCR amplification of 16S rDNA DNA was extracted from 0.25 g of field-moist soil using FastDNA® SPIN Kit for Soil (Bio 101, Vista, CA, USA) according to the manufacturer’s instructions, and then stored at −70 °C for subsequent analysis. The extracted DNA was subjected to PCR amplification with bacteria-specific primers (336F: 5′-GTACTCCTACGGGAGGCAGCA-3′; 806R; 5′-GTGGACTACHVGGGTWTCTAAT-3′) against the V3 + V4 region of the bacterial 16S rDNA gene. The PCR premix used was NEB Fusion PCR High-Fidelity Master Mix. PCR amplification was performed in a Genius Thermal Cycler in 50 µL reactions, containing approximately 30 ng of purified DNA. The temperature and cycling conditions were as follows: preheating at 98 °C for 3 min, followed by 30 cycles at 98 °C for 45 s, 55 °C for 45 s, and 72 °C for 45 s, and a final incubation at 72 °C for 7 min. The presence of PCR products and their concentrations was checked using electrophoresis on 1% agarose gel at 150 V for
3. Results 3.1. Soil physico-chemical properties The soil bulk density and soil moisture content varied significantly (p < 0.05) among the different vegetation cover types. Compared to the bare land, bulk density was low in soils of coniferous forest, mature conifer-broadleaf forest and the natural broad-leaved forest (Fig. 1a). However, the young conifer-broadleaved forest and the grassland had similar bulk density as the bare land. The soil moisture content gradually increased with complexity of the vegetation cover type (Fig. 1b) 173
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Table 2 Soil chemical properties across different vegetation cover types used to restore degraded land in Changting. Values are means ± standard error (n = 5) and means followed by different letter in the same column are significant difference at 0.05 level. Cover*
pH
BL GL CF YCBF MCBF NBF
5.15 4.82 4.57 4.66 4.34 5.30
± ± ± ± ± ±
0.16a 0.06b 0.08c 0.10c 0.11d 0.04a
Organic C (g kg−1)
Total N (g kg−1)
2.57 ± 0.12f 12.27 ± 0.40c 7.11 ± 0.92d 4.57 ± 0.12e 25.00 ± 0.20a 16.07 ± 0.65b
1.07 1.17 1.26 1.03 2.47 1.57
± ± ± ± ± ±
0.15c 0.12c 0.06c 0.12c 0.12a 0.23b
Total P (g kg−1) 0.12 0.21 0.14 0.18 0.27 0.26
± ± ± ± ± ±
0.02d 0.03b 0.01d 0.02c 0.02a 0.02a
Total K (g kg−1)
Available P (mg kg−1)
Available K (mg kg−1)
2.79 ± 0.12c 8.61 ± 0.28b 14.57 ± 3.72a 11.06 ± 0.67b 10.52 ± 1.00b 14.68 ± 1.30a
2.30 ± 0.41c 7.96 ± 0.21b 3.07 ± 0.16c 2.98 ± 0.31c 6.70 ± 0.67b 13.20 ± 1.80a
24.34 42.35 68.42 49.14 79.29 81.62
± ± ± ± ± ±
1.75d 1.60c 5.79b 2.01c 5.45a 6.60a
* The vegetation cover BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
(relative abundance > 5%) across the soil samples were Chloroflexi, Proteobacteria, Firmicutes, Cyanobacteria, WPS-2, Acidobacteria, Verrucomicrobia, Actinobacteria, and Bacteroidetes, accounting for more than 86% of the total sequences (Fig. 3). The bacterial phylum with the maximum relative abundance in the bare land soil was Chloroflexi (25.3%); its abundance gradually decreased from the grassland to the natural broadleaved forest soil. The relative abundance of Proteobacteria exceeded 20% for all vegetation cover types and showed a trend of increasing abundance from bare land to natural broadleaved forest soil. It accounted for more than 38% of the relative abundance in the mature conifer-broadleaved and natural broadleaved forest soils. The relative abundance of Firmicutes gradually decreased from bare land (17.4%) to young conifer-broadleaved soil (5.6%), and it was < 5% for mature conifer-broadleaved forest soil. Cyanobacteria were only present at > 5% relative abundance in the bare land (8.9%) and young conifer-broadleaved forest (6.9%) soils. The relative abundance of Acidobacteria increased significantly from the bare land (5.9%) to the grassland (21.8%) soils and other forest types except conifer-broadleaf young forest. At the order level, we observed at least one sample with relative abundance greater than 2%; the remaining species were classified into other categories, and those for which we were unable to comment to the level of sequences are classified as “unclassified category” (Fig. 4). Although Rhodospirillales, Rhizobiales, Acidobacteriales, and Solibacterales were present in all the sites, there were differences in relative abundance of bacterial order across the sites. iii1-15, Bacteroidales, Enterobacteriales, and Xanthomonadales were only present in natural broadleaved forest soil. Thermogemmatisporales (20%) and Bacillales (15%) were the main orders found in bare land soil. Acidobacteriales (9%), Ellin6513 (7%), Rhizobiales (6%), Rhodospirillales (6%), and Actinomycetales (5%) were the main orders found in grassland soils. Rhizobiales (9%), Acidobacteriales (8%), Rhodospirillales (7%), Burkholderiales (6%), Bacillales (5%), and Solibacterales (5%) were the main orders in coniferous forest soil. Bacillales (14%), Rhizobiales (7%), Acidobacteriales (5%), and Rhodospirillales (5%) were the main orders in young conifer-broadleaved mixed forest soil while Acidobacteriales (11%), Ellin6513 (9%), Rhizobiales (8%), Rhodospirillales (7%), and Xanthomonadales (6%) were the main orders found in soils of mature conifer-broadleaved forest soil. Rhizobiales (7%), Solibacterales (6%), Acidobacteriales (6%), and Rhodospirillales (5%) were the main orders found in soils of natural broadleaved forest.
and was significantly higher for all vegetation cover types, except for the grassland, than for bare land. The mean soil chemical properties also varied significantly (p < 0.05) across the different vegetation cover types (Table 2). Compared to the bare land, the pH was low for most of the vegetation cover types except the natural broadleaved forest soil that had a similar pH as the bare land. Soils of the mature confer-broadleaved forests and the natural broadleaved forests had higher organic carbon content, total N and Total P than both the bare land and the coniferous forest soils although the later had significantly more organic carbon than the former. The total K content was higher in soils of the conifer and the natural broadleaved forests than the other vegetation cover types, which in turn had higher total K content than soils of the bare land. Available P was the highest in the natural broadleaved forest soil while available K was the highest in mature conifer-broadleaved and the natural broadleaved forest soils compared to other vegetation cover types and the bare land soils. 3.2. Diversity of bacterial community A total of 2,487,353 sequences were obtained from 30 soil samples by analysing the V3-V4 region of bacterial 16S rRNA genes. The total number of effective sequences per sample ranged from 38 936 to 85,675 sequences. The unique tag sequences, selected through redundant processing by Mothur v1.34.0 software, resulted in a total of 867,624 OTUs (range: 14,282–35,187) with 97% similarity clustering level at a genetic distance of 3%. Shannon rarefaction analyses showed that the curve tended to plateau after 20,000 sequences at 97% similarity (Fig. 2). Thus, additional sequencing had little effect on species diversity. The alpha diversity indices of soil bacteria across different vegetation cover types are given in Table 3. The number of reads was significantly higher for conifer forest, mature conifer-broadleaved forest and natural broadleaved forest than the bare land. The number of OTUs was significantly greater for all vegetation cover types than the bare land except the grass land that had similar OTU number. The Chao and ACE richness indices showed that the diversity was highest for the coniferous forest, natural broadleaved forest young conifer-broadleaved forest mature conifer-broadleaved forest compared with grassland and bare land. The Shannon index indicated a gradual increase in diversity from the bare land to the natural broadleaved forest and significantly higher diversity in all vegetation cover types compared to bare land, whereas the Simpson index showed the opposite trend. As a whole, the bacterial community diversity increased with the progression of the revegetation stages.
3.4. Relationship between bacterial communities and soil physico-chemical properties Redundancy analysis was conducted to investigate the relationships between the eight main bacterial phyla and eight physico-chemical soil properties (Fig. 5). The composition of soil bacteria community varied considerably among vegetation cover types. All the bacterial phyla, except Firmicutes and Chloroflexi, were positively correlated with total K, total P, available K, available P, organic carbon, and total N, and
3.3. Composition of bacterial community All valid sequences from the soil sample libraries were classified into phylum and order, and a total of 32 phyla were found in all samples (see supplementary materials). The dominant bacterial phyla 174
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Fig. 2. Comparison of soil bacterial diversity across different vegetation cover types using multi samples Shannon–Wiener curves. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
4. Discussion
Table 3 Number of reads, operational taxonomic units (OTUs) and diversity indices of soil bacterial communities based on 16S sequencing data of samples collected from different vegetation cover types. Values are mean ± standard error (n = 5), and means followed by different letter (s) in across column are significantly difference at 0.05 level. Cover*
Reads
OTU
Chao
ACE
Shannon
Simpson
BL GL CF YCBF MCBF NBF
64623b 75611ab 81014a 70483ab 79704a 83616a
21301c 26206bc 33948a 28569ab 30399ab 33102a
102793c 134181bc 179166a 154492ab 145832ab 154894ab
241606c 330884bc 452860a 389533ab 357472ab 356714ab
6.99c 8.11b 8.61ab 8.14b 8.49b 9.13a
0.022a 0.005b 0.003b 0.007b 0.002b 0.001b
Generally reforestation results in an improvement in soil physicochemical properties (Gunina et al., 2017; Zeng et al., 2014). Soil bulk density plays an important role in soil nutrient storage, water-holding capacity and gas penetration (Wang et al., 2010), which is linked with soil development, parent material and land use pattern. For the studied sites, soil bulk density decreased by 6.5% in grassland, 18.6% in conifer forest, 8.7% in young conifer-broadleaved forest and 14.5% in mature conifer-broadleaved forest compared with the bare land soil. This suggests that reforestation loosens up the top-soil and increased soil porosity and water retention capacity as the vegetation established. This is further evidenced from increased soil moisture content with vegetation cover type (Fig. 1). The difference in soil bulk density among the different vegetation cover types could be attributed to amounts of easily-decomposable litter, resulting in thicker humus layers, and successional stages of the forest (Zeng et al., 2014). We also found that the bulk density of the YCBF were not only higher than the CF, but close to that of grass land. This result is contradicting to our expectation because the broad leaf planting into the conifer forest was designed to improve the ecological functions. We speculated the increase in bulk density in YCBF forest could be attributed to the compaction during the planting process. This hypothesis is supported by the fact that the bulk density of MCBF was also higher than that of CF, and lower than YCBF. The bulk density simply takes time to recover. In summary, forest cover will increase soil bulk density and increase soil moisture content. However, reducing compaction related to human activities, including silviculture operations is important
* The vegetation cover BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
negatively correlated with pH, bulk density and moisture content. Proteobacteria was most strongly associated with available P, Acidobacteria, Planctomycetes, and Verrucomicrobia were most strongly associated with organic carbon, and Bacteroidetes and Actinobacteria were most strongly associated with total K and total N. respectively. Whilst Firmicutes and Chloroflexi were strongly associated with pH and bulk density, Chloroflexi was negatively correlated with soil moisture content.
175
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Fig. 3. Phylogenetic relationships of bacterial communities and the relative abundance of bacterial phyla (> 5%) in the soils of different vegetation cover types. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
organic carbon and total N contents in the coniferous forest soil could be due to the lower litter input and the higher resistance to degradation (Chiti et al., 2012). While the total P content was 127.8% higher in mature conifer-broadleaved forest soil compared to the bare land soil, available P were 245.9% higher in grassland soil and 191.4% higher in mature conifer-broadleaved forest soils than the bare land soil. Total K was 422% higher in coniferous forest soil whereas available K was 225% higher in mature conifer-broadleaved forest soil than the bare land soil. Such big differences can be explained by the quality and quantity of litter accumulated on the soil surface and the increased activity and content of microbial biomass. As a whole, the results are consistent with previous studies that demonstrated improvements in soil nutrient condition with revegetation (Fan et al., 2014; Zhuet al., 2012). We noticed that organic C content in YCBF (4.6 g kg−1) was less than CF (7.1 g kg−1). Total N, Available P and Available K also showed the similar trend. The Available K in YCBF was significantly lower than CF without adding broadleaf species. This is again, contradicting to our expectations that a restoration effort resulted in poor performance. In compaction and increased exposure associated with planting broadleaf species in conifer forests did result in temporary degradation of soil physical and chemical properties. The soil physical and chemical properties will recover
to ensure the restoration of soil physical properties in degraded red soil. In the present study, the soil pH values decreased in the grass land, coniferous forest and conifer-broadleaved forest soils compared with the bare land and the natural broadleaved forest soils (Table 2). Our result concurs with those of previous studies, which showed that conifers can cause soil acidification (Kong et al., 2000; Kuang et al., 2008). The decrease in soil pH may also be attributed to the release of organic acids from tree roots, removal of exchangeable cations by plant uptake and/or soil microbial processes that transform organic matter and the release of organic acids (Wei et al., 2006). Microbial decomposition of litters of P. massoniana forest which contain less base ions, and more resin and tannin may also contribute to soil acidification. Reforestation improved soil fertility with marked variation among vegetation cover types used to restore the degraded land (Table 2). The organic carbon and total nitrogen contents were 873% and 130% more in the mature conifer-broadleaved forest soil than the bare land soil, respectively. Such huge changes could be related to accumulation of tree litter inputs over time which decomposes relatively quickly as the intrinsic microbial community is gradually adapted to this new substrate. It could also be related to lack of or minimal decomposition of the intrinsic soil organic carbon by microorganisms, thus resulting in lower C mineralization long after reforestation. Furthermore, the lower 176
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Fig. 4. Relative abundance of bacterial orders (> 2%) in soils of different vegetation cover types. BL: bare land; GL: grass land; CF: coniferous forest; YCBF: young conifer-broadleaf forest; MCBF: mature conifer-broadleaf forest; NBF: natural broad-leaved forest.
development of soil, in the transformation of organic matter, and in maintaining a balanced ecosystem in the soil environment (Fierer and Jackson, 2006). Many studies have also demonstrated a direct relationship between soil bacteria and vegetation diversity (Faoro et al., 2010; Chen et al., 2015). For the studied sites, the number of OTUs was significantly greater for restored sites than the bare land, and those in the coniferous forest and natural broad leaved forest soils were greater than those in the grassland soil. This might be related to the time since restoration (25 years since the coniferous forest was established). The diversity of soil microorganisms was not always at the same pace as that of vegetation restoration, and varied with composition of the vegetation (Dan et al., 2011). Our study showed that the Chao and Ace indices tended to be highest for coniferous forest soil than the conifer-broadleaved forests (Table 3) probably because the soil microbial diversity lagged behind the vegetation change and, therefore, it took time to respond. As a whole, the alpha community diversity indices, such as the Shannon index, increased with the restoration of the vegetation (Table 3). Therefore, changes in the microbial diversity can reflect the process of change in the soil quality following restoration of degraded ecosystems. The dominant bacterial phyla in the bare land soil were Chloroflexi and Proteobacteria, which accounted for 46% of the bacterial sequences (Fig. 3), but the grassland soil was dominated by Acidobacteria with Proteobacteria, accounting for more than 40% of the bacterial sequences in all the soil samples. Proteobacteria was detected as one of the most abundant phyla in soil bacterial communities, which is related with Rhizobiales (a nitrogen-fixing organism), suggesting nitrogen fixation may play an important role in differentiating community structure. Similar result was obtained by He et al. (2008) who reported that 74% of the clones in the upland red soil of Yingtan, Jiangxi Province, China, were classified as Proteobacteria. The fact that
Fig. 5. Redundancy analysis of the relationships among the main bacterial phyla, OTU, and soil physico-chemical properties. BL: bare land (No. 1–5); GL: grass land (No. 6–10); CF: coniferous forest (No. 11–15); YCBF: young coniferbroadleaf forest (No. 16–20); MCBF: mature conifer-broadleaf forest (No. 21–25); NBF: natural broad-leaved forest (No. 26–30); BD: bulk density; MC: moisture content; TP: total phosphorus; AP: available phosphorus; TK: total potassium, AK: available K; OC: organic carbon; TN: total nitrogen.
over time; however, the degradation will take more than 8 years or even longer to recover in our study sites. Soil microorganisms play an important role in the formation and 177
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References
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5. Conclusions Based on the findings, the following conclusions can be drawn: (1) the physical and chemical properties of soil were improved with revegetation of degraded land; particularly conifer-broadleaved mixed forest yield substantial improvement in soil quality; (2) the diversity and composition of soil bacterial community vary with the vegetation cover type used to restore the degraded land, suggesting selecting for distinct microbial communities beneath them; (3) diversity and structure of bacterial community are driven by changes in soil physicochemical properties, which in turn are dependent on the vegetation cover types. Thus, composition and diversity of bacterial community may serve as indicator of functional recovery of restored ecosystem. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the Key Project of Fujian Province Science and Technology Department (2017Y0001), and the National Key Technology R&D Program (2014BAD15B02). We also thank Dr Kate Heal from School of GeoSciences, The University of Edinburgh, for offering comments which have substantially improved the manuscript. 178
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