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Article

Quantitative Characteristics and Environmental Interpretation of Vegetation Restoration in Burned Areas of the Dry Valleys of Southwest China

by
Zhixue He
1,3,4,
Jun Luo
2,3,4,*,
Bin Zhang
1,3,4,
Lei Wang
1,3,4,
Hui Liu
1,3,4,
Xueyang Ma
1,3 and
Tianxiang Yue
2
1
Sichuan Provincial Engineering Laboratory of Monitoring and Control for Soil Erosion on Dry Valleys, China West Normal University, Nanchong 637009, China
2
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Liangshan Soil Erosion and Ecological Restoration in Dry Valleys Observation and Research Station, Xide 616753, China
4
School of Geographical Sciences, China West Normal University, Nanchong 637009, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(11), 2190; https://doi.org/10.3390/f14112190
Submission received: 14 September 2023 / Revised: 21 October 2023 / Accepted: 30 October 2023 / Published: 3 November 2023
(This article belongs to the Topic Forest Carbon Sequestration and Climate Change Mitigation)
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Abstract

:
Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method, adopted as an alternative to the time successional sequence method, we selected burned areas in different locations in the Anning River Basin, which encompasses typical dry valleys. Quadrat surveys and quantitative classification were used to identify and classify the vegetation and distribution pattern and to carry out environmental interpretation during the natural restoration process after a forest fire. The results showed the following: (1) in the early stage of natural recovery after a forest fire disturbance, the vegetation community could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) the vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most notably with altitude; and (3) a detrended correspondence analysis could be used to accurately classify vegetation community types, while a detrended canonical correspondence analysis could reveal the relationships between species and environmental factors. This study provides a scientific basis for guiding the restoration of ecosystem structural stability and biodiversity in burned areas.

1. Introduction

As an important part of the global terrestrial ecosystem, forests have important ecological functions such as preventing soil erosion and regulating climate [1]. There are many factors affecting forest ecosystems, among which fire has the most direct influence [2]. Fire can promote the secondary succession of vegetation communities while destroying the forest [2], and the renewal and restoration of vegetation after forest fire disturbance are important problems faced by environmental researchers and human society [3].
After vegetation communities are disturbed by forest fires, the greatest contributor to community changes is the growth of herbs and shrubs [4]. Shrub-grass vegetation can efficiently absorb soil nutrients and grow rapidly to cover the surface [4]. Shrub-grass vegetation communities develop rapidly in burned areas and then slowly succeed to woody plant communities. This causes the recovery rate of the normalized difference vegetation index (NDVI) in fire areas to be relatively slow [4,5,6,7]. The effects of fire on different vegetation communities vary, and there are significant differences in their degree of recovery. The similarity of the vegetation community shrub layer is higher than that of the herb layer in burned areas [8]. After forest fire disturbance, the similarity of the shrub layer of the vegetation community in short-term vegetation succession was found to be significantly higher than that in long-term vegetation succession [8,9]. The aboveground forest biomass is significantly affected by forest fire, which not only has profound impacts on the biomass of different tree species communities but also has different effects on communities in different succession periods [10]. In previous research, after forest fire disturbance, the vegetation community in the burned area was singular in the early recovery stage, and the vegetation community types differed minimally between quadrats [11,12]. The intensity level of forest fire disturbance is related to the characteristics of the understory plant community composition [13]. The fine-scale heterogeneity of the forest structure after fire is an important factor driving vegetation species diversity [13]. Forest fires promote the development of different dominant vegetation types through their positive effects on soil seed bank dynamics, which are of great significance in maintaining vegetation species richness [14].
Since the 1980s, the quantitative classification and ordination of vegetation have become a hot topic and indispensable methods of vegetation community ecological research [15,16]. In China, the research on quantitative vegetation classification and ordination can be summarized as a sequential process of introduction, learning through trial, application, and development [15,16]. Quantitative classification refers to the transformation of classification concepts from qualitative description to quantitative analysis using mathematical methods, which has great significance in promoting the development of biological taxonomy [17]. Compared with the quantitative classification method (determining the disjunctive distribution of the community), the quantitative ordination method can reveal continuous relationships in the community’s distribution [18]. Two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and detrended canonical correspondence analysis (DCCA) are the main methods used in quantitative vegetation classification and ordination. These methods can objectively and quantitatively reveal the distribution pattern of vegetation communities and their relationships with environmental factors [18,19,20,21].
Due to its unique dry valley climate conditions and human factors, the Anning River Basin has become a concentrated area of fire occurrence [22]. To date, research on the burned areas in the dry valleys of the Anning River Basin has focused on revealing the law of vegetation community restoration [22,23] and vegetation evaluation [24], while the quantitative classification and ordination of vegetation communities in burned areas remain unclear. The purpose of this study was to explore the classification and distribution pattern of vegetation communities and their influencing factors in the early stage of natural recovery after forest fire disturbance in dry valleys. The results are significant for the prevention of soil erosion and ecological civilization construction in dry valleys.

2. Materials and Methods

2.1. Study Area

The Anning River (27°05′–29°02′ N, 101°38′–102°43′ E) is a tributary of the Yalong River, which is a tributary of the Jinsha River (Figure 1). The main stream of the Anning River flows through Mianning, Xichang, and Dechang in Liangshan Prefecture and Miyi County in Pan Zhihua City, with a total length of 320 km and a drainage area of 11,150 km2 [25]. The north–south climate change and vertical difference in the basin are high. The precipitation is concentrated in the period from May to October, and the annual precipitation is more than 1000 mm [25]. The forests in this area are dominated by Pinus yunnanensis Franch. (Py)–Pinus massoniana Lamb. (Pm) mixed forests. The forest vegetation community mainly consists of shrubs and herbs such as Quercus guyavaefolia H. Leveille (Qg), Machilus pingii Cheng ex Yang (Mp), Duhaldea cappa (Buchanan-Hamilton ex D. Don) Pruski & Anderberg (Dc), Cymbopogon goeringii (Steud.) A. Camus Cymbopogon (Cg), and Paspalum paspaloides (Michx.) Scribn. (Pp). The results of an on-site investigation showed that after the forest fire disturbance in the sample area (Table 1), all the aboveground parts of the shrubs and grasses died, and only tree trunks with burned branches remained, while the soil structure and underground parts of the vegetation suffered less damage.

2.2. Methods

2.2.1. Quadrat Setting

The investigation quadrats were set up in burned areas during the early natural recovery period after a forest fire in the Anning River Basin (Figure 1 and Table 1). Surveys were conducted one year after the occurrence of the fires in each burned area, and a shrub (10 m × 10 m) quadrat was set at different slope positions (the bottom of the slope, downhill slope, middle slope, uphill slope, and top of the slope) of the shady slope and sunny slope of the burned area (Figure 2). Both the shady slope and sunny slope had 5 quadrats, and 10 quadrats were set up for each burned area. Then, grass quadrats (5 m × 5 m) were placed in the four corners of each shrub quadrat (Figure 2). A total of 6 burned areas had 300 quadrats: 60 shrub quadrats and 240 grass quadrats. The amount and type of understory vegetation were visually detected and recorded (compared with the Flora of China). The coverage of understory vegetation was measured with a vegetation cover measuring instrument (XST-PhotoNet-FVC). A tape was used to measure the height of the understory vegetation. An inclinometer and compass were used to measure the four environmental factors: elevation, slope, aspect, and slope position in each quadrat [26].

2.2.2. Importance Value Calculation

The importance value of a vegetation species refers to a comprehensive index of its relative importance in the community to which it belongs. The importance value calculation method selected in this study is as follows [27]:
Trees and shrubs      IVTS = (RD + RH + RC)/3
Herbs          IVH = (RH + RC)/2
In Formulas (1) and (2), IVTS is the importance value of trees and shrubs; IVH is the importance value of herbs; RD is the relative density; RH is the relative height; and RC is the relative coverage.

2.2.3. TWINSPAN Quantitative Classification

TWINSPAN, modified using an indicator species analysis, is capable of both quadrat and species classification, based on a reciprocal averaging axis [28]. In the present study, WinTWINS 2.3 software was used to perform TWINSPAN quantitative classification based on the importance value matrix and environmental factor matrix of vegetation species in typical quadrats, as well as the results of the 4th classification [29,30].

2.2.4. DCA Ordination and DCCA Ordination

DCA ordination and DCCA ordination are multivariate analysis techniques used to study the relationship between vegetation and the environment [29,31]. In the present study, the DCA ordination and DCCA ordination of the quadrats and environmental factors (elevation, slope, aspect, and slope position) were performed using CANOCO 4.5 software, and then, the ordination diagram was drawn using CanoDraw for Windows 4.5 [29].
The different slope positions (1, 2, 3, 4, and 5) included the bottom of the slope, downhill slope, middle slope, uphill slope, and top of the slope, respectively. The larger the number, the higher the slope position. The original value of the aspect could not directly represent the degree of sunshine exposure. Therefore, each aspect was assigned a numerical value, where 1 referred to the north slope (317.15°–22.15°), 2 referred to the northeast slope (22.15°–67.15°) and northwest slope (292.15°–317.15°), 3 referred to the east slope (67.15°–112.15°) and west slope (247.15°–292.15°), 4 referred to the southeast slope (112.15°–157.15°) and southwest slope (202.15°–247.15°), and 5 referred to the south slope (157.15°–202.15°). The higher the aspect number, the sunnier it was [26].

2.3. Statistical Analysis

To research the quantitative characteristics and environmental interpretation of vegetation restoration in burned areas, the WinTWINS 2.3 (Centre for Ecology & Hydrology and University of South Bohemia, České Budějovice) was used to classify vegetation communities, and CANOCO 4.5 (Microcomputer Power Corp., Ithaca, NY, USA) was used to sequence vegetation communities.

3. Results

3.1. Classification of the Vegetation Community

Based on the vegetation community classification methods and principles in China, the community types were named according to the survey results of the dominant species. Forty typical vegetation quadrats and eighty-six vegetation species were quantitatively classified using TWINSPAN. The fourth classification level was used as the classification result, which could be divided into the following seven community types (Figure 3 and Table 2):
Community I: The Vaccinium fragile Franch. (Vf)–Imperata cylindrica (Linn.) Beauv. (Ic) community included quadrats 20, 29, 30–32, 34–35, and 51–53. This community was mainly distributed on gentle, semi-sunny slopes. Except for the community indicator species, the shrub layer also included Qg, Rhododendron simsii Planch. (Rs), Mp, and Cotoneaster hissaricus Pojark. (Ch). The herb layer also included Pp, Cg, Uraria lagopodoides (L.) Desv. ex DC. (Ul), Leontopodium leontopodioides (Willd.) Beauv. (Ll), Ageratina adenophora (Sprengel) R. M. King & H. Robinson (Aad), and Tripogon chinensis (Franch.) Hack. (Tc).
Community II: The Dc–Leptochloa chinensis (L.) Nees (Lc) community included quadrats 12–13, 16, 18, 37, 54, and 55. This community was mainly distributed on steep semi-shady slopes or shady slopes. The major species included Qg, Hypericum patulum Thunb. ex Murray (Hp), Mp, Elsholtzia rugulosa Hemsl. (Er), Pp, Cg, Ll, Artemisia argyi Levl. et Van (Aar), and Hedyotis herbacea L. (Hh).
Community III: The Mp–Monogramma trichoidea J. Sm. (Mt) community included quadrats 9, 14, 15, 17, 19, 21, 24–26, 36, 38, and 56–59. This community was distributed on steep, sunny slopes with steep elevation drops. The main dominant species in the shrub layer was Mp, while Qg and Vf were relatively few in number. In addition to the main dominant species of Mt, the herbaceous layer was mainly composed of Er, Pp, Cg, Polygonatum verticillatum (L.) All. (Pv), Aa, and Cyperus cyperoides (L.) Kuntze (Cc).
Community IV: The Qg–Potentilla leuconota var. brachyphyllaria (Pl) community included quadrats 4, 22–23, 27–28, and 60. This community was distributed on steep, semi-shady slopes or semi-sunny slopes. In addition to community indicator species or dominant species, the shrub layer also mainly included Vf, Hp, and Mp, while the herb layer also included Er, Mt, Commelina diffusa N. L. Burm. (Cd), Desmodium microphyllum (Thunb.) DC. (Dm), Ll, and Aa.
Community V: The Qg–Pp community included quadrats 6–8, 10–11, 33, 41, 42, and 45–47. The community was mainly distributed on steep, shady slopes at high elevations. The major species included Mp, Leptodermis potanini Batalin (Lp), Campylotropis hirtella (Franch.) Schindl. (Ch), Cc, Er, Saussurea hieracioides Hook. f. (Sh), Lc, Ll, and Cg.
Community VI: The Qg–Monochasma savatieri Franch. ex Maxim. (Ms) community included quadrats 1, 2, 5, 43, 44, and 48−50. The community was mainly distributed on gentle, semi-shady slopes or semi-sunny slopes at high elevations. The main species in the shrub layer included Vf, Ch, Mp, and Lespedeza davidii (Ld). The herb layer included the major species Scutellaria baicalensis Georgi (Sb), Polygonum paleaceum Wall. ex HK. f. (Ppa), Pv, Er, Mt, and Sh.
Community VII: The Qg–Ll community included only quadrat 3. The vegetation coverage of this community reached 90%, and it was distributed on a gentle sunny slope at a high elevation. The major species included Vf, Ld, Mp, Lp, Cc, Ms, Rabdosia adenantha (Diels) Hara (Ra), Erigeron speciosus (Lindl.) DC. (Es), Anaphalis sinica Hance (As), and Iris tectorum Maxim. (It).

3.2. DCA Ordination of Vegetation Community Quadrats and Environmental Factors

In this study, the results of the TWINSPAN classification and DCA ordination revealed the distribution patterns of various vegetation communities, shown in the DCA ordination map (Figure 4). According to the correlation coefficient (r) of the environmental factor matrix of DCA ordination, the first axis of DCA reflected the change in altitude gradient (r = −0.49). The altitude decreased gradually along the direction of the first axis. The direction of the first axis was mainly associated with the temperature condition of the quadrats. The second axis of DCA reflected gradient changes in the slope (r = −0.23) and aspect (r = 0.13). The slope gradually decreased and the degree of sunshine gradually increased along the direction of the second axis. The direction of the second axis was mainly associated with changes in the hydrological conditions of the quadrats. According to their clustering conditions, the 40 typical vegetation quadrats in the DCA ordination diagram could be divided into the following five ecotopes:
Ecotope A: This ecotope was dominated by community I, with a large elevation drop (1825–2115 m), a gentle slope (10–25°), and high vegetation coverage (40%–85%). Py was the main tree species in this ecotope, and there were many kinds of shrub and grass vegetation. This ecotope was a savanna community.
Ecotope B: This ecotope mainly included community II and community III, with an elevation of 2090–2140 m, a slope of 30–50°, and vegetation coverage of 20%–60%. The trees included Py and Pm, and the shrubs mainly included Dc and Mp. This ecotope was a savanna community.
Ecotope C: This ecotope included communities II, III, IV, and V. It was mainly distributed between an elevation of 2100–2180 m, with a steep slope (30–40°) and 35%–70% vegetation coverage. There were various kinds of shrubs and grasses, mainly including Qg and Pp. This ecotope was a savanna community.
Ecotope D: Community III was dominant in this ecotope, and communities I, V, VI, and VII were also present. This ecotope is mainly located between an elevation of 2090–2245 m, with a steep slope of 25–45° and high vegetation coverage that reached 90%. In addition to indicator species or dominant species, this ecotype contained other vegetation species, such as Er, Pp, and Cg, and was a shrub-grass community.
Ecotope E: This ecotope included communities V and VI. The quadrats were located at a high elevation (2240–2390 m), with a slope of 10–45° and vegetation coverage of 50%–80%. The main vegetation was Qg. This ecotope was a shrub-grass community.

3.3. DCCA Ordination of Vegetation Community and Environmental Factors

In this study, DCCA ordination was used to analyze the relationships between the vegetation community and environmental factors in burned areas of the Anning River Basin (Figure 5). The arrow line in the figure represents each environmental factor, while the quadrant of the arrow represents the positive and negative relationships between the given environmental factor and the DCCA ordination axis. The slope between the arrow line and the ordination axis represents the correlation between the environmental factor and the ordination axis. The vertical distance from the quadrat to the arrow line represents the impact of the environmental factor on the quadrat. The first ordination axis mainly reflects the changes in elevation (r = −0.65) and slope position (r = −0.38). The elevation and slope position gradually decrease along the ordination axis. The second ordination axis mainly reflects the changes in slope (r = −0.46) and aspect (r = 0.48). The slope decreases along the direction of the ordination axis, and the aspect transitions from shady slope to sunny slope.
A comparison between the DCA and DCCA ordination diagrams (Figure 4 and Figure 5) showed that the quadrat distribution of the DCCA diagram was more concentrated than that of the DCA diagram, but the demarcations between quadrats were not clear. Therefore, DCCA ordination was not as intuitive as DCA ordination when classifying vegetation communities. The eigenvalues of the DCA ordination axis were significantly higher than those of the DCCA ordination axis (Table 3). The correlation coefficients of the species–environmental factors in the DCCA ordination axis were significantly larger than those of the DCA ordination axis (Table 3). These results indicated that the DCCA ordination focused on the relationships between vegetation species and environmental factors.

4. Discussion

4.1. Vegetation Restoration Features of Burned Areas in Dry Valleys

Forest fires occur frequently in the Anning River Basin, leading to forest resources being widely destroyed, with the burned land showing different vegetation communities in different recovery years. Therefore, the TWINSPAN classification method was used to classify the vegetation community in different recovery stages. In the early stage of vegetation regeneration and succession to seven community types, the community was dominated by savanna types. A Qg community was widely distributed in the region. The rapid growth of herb vegetation usually leads to intensified interspecific competition. The succession of the vegetation to a climax community was limited by the dominance of a single or a few herb species [32]. Based on the climax pattern hypothesis, the seven vegetation communities in the study area have not reached the climax level, and the stability of the community structure and spatial distribution pattern is poor. The vegetation community competes fiercely for nutrient resources in the early stage of vegetation regeneration after forest fire disturbance. The unique arid valley climate and topographical features of the Anning River Basin led to a serious deterioration in its environmental conditions (such as a dry climate and poor soil) in the early stage after forest fire disturbance. The secondary succession process of the vegetation communities was slow, and only vegetation species with strong tolerance (such as Qg, Mt, and Pp) could grow and cover the surface rapidly. Therefore, many community types, with Qg as an indicator species, formed in the Anning River Basin. The community distribution range is large, but the species diversity and the community stability are low [19,33], resulting in a fragile ecosystem structure that is vulnerable to natural disasters such as debris flows. When conducting artificial restorations of vegetation in burned areas, it is necessary to consider soil and water conservation and vegetation diversity in order to accelerate this process [11]. The recovery of shrubs may be particularly important for mitigating the competitive advantage of herbs and achieving ecological balance [5]. Species that are more tolerant and able to grow in this area, such as Qg, Dc, and Vf, should be sown. In addition, it is necessary to consider the influence of environmental factors such as altitude and slope in order to design a reasonable vegetation configuration and thus to improve the species diversity and stability of the ecosystem and to achieve the best restoration effect.

4.2. TWINSPAN Quantity Classification and DCA Ordination Features

TWINSPAN quantitative classification is based on indicator species. Generally, five main dominant species are selected as important indicator species to objectively classify vegetation communities using TWINSPAN [28,31]. The method of DCA ordination was proposed by Hill and Gauch after research based on a correspondence analysis [34]. This method effectively eliminates the bow effect of a correspondence analysis and is therefore widely used in vegetation community analyses [35,36]. For different research sites, scales, and objects, the ecological interpretation of the DCA ordination axis will yield different research results [35]. In this study, TWINSPAN quantitative classification was combined with the DCA ordination method, and the DCA ordination diagram was reclassified on the basis of TWINSPAN quantitative classification. In the DCA ordination diagram, the burned areas were divided into five ecotopes based on the distribution of the quadrats.
Elevation was significantly correlated with the classification and distribution pattern of community types in this area. Previous studies have shown that elevation and slope are the main influential factors [37]. With the increase in elevation and slope, the drainage conditions of the soil improve, the soil layers become thinner, and the root fixation ability of the vegetation becomes weaker, all of which can easily cause water and soil erosion to occur [38]. With the decrease in elevation, the dominance of the main species, Qg, decreased continuously, and the community type changed from shrub-grass to savanna. The method clearly and accurately revealed the quantitative classification and distribution pattern of the vegetation communities in the study area, which is of great significance for the efficient implementation of artificial restoration projects in burned areas. Due to the limited amount of quadrat data, only the classification results of the fourth-level TWINSPAN quantitative classification were used in this study; thus, the classification of the vegetation community types was not determined in sufficient detail. In subsequent studies, the amount of quadrat data should be increased to achieve a more detailed classification of the vegetation community in the study area.

4.3. DCA Ordination and DCCA Ordination Features

The objects to which DCA ordination and DCCA ordination are applicable are different [35,39]. It can be seen from the DCA ordination diagram that, due to the clear boundaries between communities, DCA ordination is suitable for classifying vegetation communities and exploring relationships between communities [35]. DCCA ordination is based on DCA ordination, and it is used to perform a multiple linear regression (environmental constraint) between the quadrat environment matrix value and the quadrat ordination value, which reflects not only the similarity in species composition among the quadrats but also the similarity of the environmental factors influencing each quadrat [22]. It can be seen from the DCCA ordination diagram that the first ordination axis mainly reflects the changes in elevation and slope position, which gradually decrease along the first ordination axis. The second ordination axis mainly reflects the changes in slope and aspect. This aspect decreases along the second ordination axis direction, and the aspect transitions from shady slopes to sunny slopes. DCCA ordination can simultaneously display the three parameters quadrat, species, and environmental factors on the coordinate plane of the sorting axis. The interaction of these three parameters leads to a blurring of the boundaries between communities in the ordination diagram [40]. Therefore, DCCA ordination is mainly suitable for revealing the relationships between vegetation species and environmental factors [40]. Only a small number of environmental factors were investigated in this study. The results of this vegetation community boundary study were subject to a certain degree of chance, and the environmental factors had a low interpretation rate for community distribution variation. Additional environmental factors, such as soil physicochemical properties and climate factors, should be further explored and comprehensively analyzed in subsequent research.

5. Summary

In this study, spatial sequencing rather than temporal sequencing was used to classify and analyze vegetation change over time in the early natural recovery period after forest fire in the dry valleys. The main conclusions follow. (1) after fire, there are seven community types evident in the early stage of succession, with the dominant community type being savanna, with the Qg community the most widely distributed. (2) Ordination results separated burned areas into five ecotopes based. Elevation is significantly correlation with the classification and distribution of community types in burned areas. With decreasing elevation, the community type changes from shrub-grass to savanna. (3) Results show that DCA ordination is suitable for classifying vegetation communities and exploring relationships among them, while DCCA ordination is most suitable for revealing relationships among vegetation species and environmental factors.

Author Contributions

Methodology, X.M.; Software, H.L.; Resources, B.Z.; Data curation, L.W.; Writing—original draft, Z.H.; Writing—review & editing, J.L.; Funding acquisition, T.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number (41930647).

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to the anonymous reviewers and the associate editor for their comments and suggestions, which helped to improve this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

TWINSPAN: two-way indicator species analysis; DCA: detrended correspondence analysis; DCCA: detrended canonical correspondence analysis; Qg: Quercus guyavaefolia H. Leveille; Py: Pinus yunnanensis Franch.; Pm: Pinus massoniana Lamb.; Mp: Machilus pingii Cheng ex Yang; Dc: Duhaldea cappa (Buchanan-Hamilton ex D. Don) Pruski & Anderberg; Cg: Cymbopogon goeringii (Steud.) A. Camus Cymbopogon; Pp: Paspalum paspaloides (Michx.) Scribn.; Vf: Vaccinium fragile Franch.; Ic: Imperata cylindrica (Linn.) Beauv.; Rs: Rhododendron simsii Planch.; Ch: Cotoneaster hissaricus Pojark.; Ul: Uraria lagopodoides (L.) Desv. ex DC.; Ll: Leontopodium leontopodioides (Willd.) Beauv.; Aad: Ageratina adenophora (Sprengel) R. M. King & H. Robinson; Tc: Tripogon chinensis (Franch.) Hack.; Lc: Leptochloa chinensis (L.) Nees; Hp: Hypericum patulum Thunb. ex Murray; Er: Elsholtzia rugulosa Hemsl.; Aa: Artemisia argyi Levl. et Van; Hh: Hedyotis herbacea L.; Mt: Monogramma trichoidea J. Sm.; Pv: Polygonatum verticillatum (L.) All.; Cc: Cyperus cyperoides (L.) Kuntze; Pl: Potentilla leuconota var. brachyphyllaria; Cd: Commelina diffusa N. L. Burm.; Dm: Desmodium microphyllum (Thunb.) DC.; Lp: Leptodermis potanini Batalin; Ch: Campylotropis hirtella (Franch.) Schindl.; Sh: Saussurea hieracioides Hook. f.; Ms: Monochasma savatieri Franch. ex Maxim.; Ld: Lespedeza davidii; Sb: Scutellaria baicalensis Georgi; Ppa: Polygonum paleaceum Wall. ex HK. f.; Ra: Rabdosia adenantha (Diels) Hara; Es: Erigeron speciosus (Lindl.) DC.; As: Anaphalis sinica Hance; It: Iris tectorum Maxim.

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Forests 14 02190 g001
Figure 1. Study sites along the Anning River.
Figure 1. Study sites along the Anning River.
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Figure 2. Schematic diagram of setting the quadrat in the burned area.
Figure 2. Schematic diagram of setting the quadrat in the burned area.
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Figure 3. Two-way indicator species analysis (TWINSPAN) quantitative classification of vegetation samples after fire in the Anning River Basin (D: classification level; N: the total number of quadrats; the numbers in the rectangular boxes are the serial numbers of the samples).
Figure 3. Two-way indicator species analysis (TWINSPAN) quantitative classification of vegetation samples after fire in the Anning River Basin (D: classification level; N: the total number of quadrats; the numbers in the rectangular boxes are the serial numbers of the samples).
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Figure 4. Detrended correspondence analysis (DCA) ordination of vegetation quadrats and the environment in burned areas along the Anning River (1–60: the serial number of vegetation quadrats; I–VII: vegetation community (Table 2); A–E: ecotope).
Figure 4. Detrended correspondence analysis (DCA) ordination of vegetation quadrats and the environment in burned areas along the Anning River (1–60: the serial number of vegetation quadrats; I–VII: vegetation community (Table 2); A–E: ecotope).
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Figure 5. Detrended canonical correspondence analysis (DCCA) ordination of vegetation quadrats and environmental factors in burned areas along the Anning River (1–60: the serial number of vegetation quadrats; I–VII: vegetation community (Table 2)).
Figure 5. Detrended canonical correspondence analysis (DCCA) ordination of vegetation quadrats and environmental factors in burned areas along the Anning River (1–60: the serial number of vegetation quadrats; I–VII: vegetation community (Table 2)).
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Table 1. Information on the selected areas.
Table 1. Information on the selected areas.
Quadrat NameFire Time/YearFire Area/hm2Elevation/mSlope/°
1Maan Village, Mianning County20211001991–211012–20
2Lushan, Xichang City202010001825–198410–30
3Zhongba Village, Xide County20202002063–239020–60
4Shanzha Village, Mianning County20191002064–212725–50
5Gantuo Village, Xide County2019802070–221220–42
6Lizi Village, Mianning County2020301985–210510–25
Table 2. Different community types and environmental characteristics of burned areas in the Anning River Basin.
Table 2. Different community types and environmental characteristics of burned areas in the Anning River Basin.
Community TypeCommunity NameEigenvalueElevation/mSlope/°AspectVegetation Coverage/%
IVf-Ic0.5241825–211510–252–540–85
IIDc-Lc0.6522110–214030–501–320–60
IIIMp-Mt0.5222090–224525–453–520–85
IVQg-Pl0.3222120–238025–352–435–40
VQg-Pp0.5522240–238530–451–440–65
VIQg-Ms0.4542360–239010–202–455–80
VIIQg-Ll0.49223805490
Note: Aspects 2 (slope position)–5 (aspect).
Table 3. Characteristic values and correlation coefficients between species–environmental factors in the DCA ordination and DCCA ordination axes.
Table 3. Characteristic values and correlation coefficients between species–environmental factors in the DCA ordination and DCCA ordination axes.
Correlation CoefficientEigenvalueCorrelation Coefficient between Species and Environmental Factors
DCCADCADCCADCA
First axis0.2440.6180.7120.505
Second axis0.1460.4060.7760.325
Third axis0.0380.3040.5810.402
Fourth axis0.0160.1860.5140.275
Note: DCCA is detrended canonical correspondence analysis; DCA is detrended correspondence analysis.
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He, Z.; Luo, J.; Zhang, B.; Wang, L.; Liu, H.; Ma, X.; Yue, T. Quantitative Characteristics and Environmental Interpretation of Vegetation Restoration in Burned Areas of the Dry Valleys of Southwest China. Forests 2023, 14, 2190. https://doi.org/10.3390/f14112190

AMA Style

He Z, Luo J, Zhang B, Wang L, Liu H, Ma X, Yue T. Quantitative Characteristics and Environmental Interpretation of Vegetation Restoration in Burned Areas of the Dry Valleys of Southwest China. Forests. 2023; 14(11):2190. https://doi.org/10.3390/f14112190

Chicago/Turabian Style

He, Zhixue, Jun Luo, Bin Zhang, Lei Wang, Hui Liu, Xueyang Ma, and Tianxiang Yue. 2023. "Quantitative Characteristics and Environmental Interpretation of Vegetation Restoration in Burned Areas of the Dry Valleys of Southwest China" Forests 14, no. 11: 2190. https://doi.org/10.3390/f14112190

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