Vegetation Succession Dynamics in the Deglaciated Area of the Zepu Glacier, Southeastern Tibet

Abstract

Bare land exposed by glacier retreat provides new opportunities for ecosystem development. Investigating primary vegetation succession in deglaciated regions can provide significant insights for ecological restoration, particularly for future climate change scenarios. Nonetheless, research on this topic in the Qinghai–Tibet Plateau has been exceedingly limited. This study aimed to investigate vegetation succession in the deglaciated area of the Zepu glacier during the Little Ice Age in southeastern Tibet. Quadrat surveys were performed on arboreal communities, and trends in vegetation change were assessed utilizing multi-year (1986–2024) remote sensing data. The findings indicate that vegetation succession in the Zepu glacier deglaciated area typically adheres to a sequence of bare land–shrub–tree, divided into four stages: (1) shrub (species include Larix griffithii Mast., Hippophae rhamnoides subsp. yunnanensis Rousi, Betula utilis D. Don, and Populus pseudoglauca C. Wang & P. Y. Fu); (2) broadleaf forest primarily dominated by Hippophae rhamnoides subsp. yunnanensis Rousi; (3) mixed coniferous–broadleaf forest with Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu as the dominant species; and (4) mixed coniferous–broadleaf forest dominated by Picea likiangensis (Franch.) E. Pritz. Soil depth and NDVI both increase with succession. Species diversity is significantly higher in the third stage compared to other successional stages. In addition, soil moisture content is significantly greater in the broadleaf-dominated communities than in the conifer-dominated communities. An analysis of NDVI from 1986 to 2024 reveals an overall positive trend in vegetation recovery in the area, with 93% of the area showing significant vegetation increase. Temperature is the primary controlling factor for this recovery, showing a positive correlation with vegetation cover. The results indicate that Key ecological indicators—including species composition, diversity, NDVI, soil depth, and soil moisture content—exhibit stage-specific patterns, reflecting distinct phases of primary succession. These findings enhance our comprehension of vegetation succession in deglaciated areas and their influencing factors in deglaciated areas, providing theoretical support for vegetation restoration in climate change.

1. Introduction

In the context of global warming, glacier retreat has intensified, with an estimated global glacier mass loss of about 267 ± 16 (Gt yr^–1) [1]. Under different emission scenarios, the projected global deglaciated area by 2100 is estimated to be between 149,000 ± 55,000 km² and 339,000 ± 99,000 km² [2]. After glacier retreat, landforms will develop new ecosystems. The varying formation times of the deglaciated areas give rise to vegetation communities at different successional stages [3]. Employing the space-for-time substitution method [4], a complete vegetation succession can be observed within the same region, making deglaciated areas particularly suitable for studying primary succession [5,6]. Research in this field provides valuable insights for predicting the impacts of climate change on ecosystems and developing corresponding strategies (Figure 1).

Current research on vegetation succession in deglaciated areas has been conducted in polar [7] and alpine regions across Europe [8,9], the Americas [10], and Asia [11,12]. Among these studies, the most renowned is the work by Crocker and Major in the Glacier Bay deglaciated area of Alaska [13], where they investigated community structure and soil nitrogen content across various successional stages. In less than 100 years, the area has progressed to a mixed coniferous forest stage. In recent years, study themes have expanded from “community species composition” [14,15] to “vegetation biomass dynamics” [16], “plant and soil interaction” [17], and “soil microbial” [18]. Research theories have continuously evolved, advancing from the study of species composition and dynamics to the complex development of ecosystems. Current research focuses on regulating successional processes in deglaciated areas through interactions between soil, plants, and microbial [19,20,21]. While some studies on community succession have been conducted in debris flow-prone regions of southeastern Tibet [22], there is still a notable lack of published research on vegetation succession in deglaciated areas. Investigating these plant communities is crucial for understanding how climate change impacts growing conditions and community dynamics in deglaciated areas and provides scientific evidence for reconstructing the history of glacial movements.

Field investigations are widely employed in vegetation succession studies. They not only enable effective observation and collection of plant specimens, but also facilitate the accurate measurement of various on-site vegetation growth parameters. This approach provides a more reliable characterization of regional vegetation status [23,24,25]. Moreover, remote sensing serves as an essential tool for monitoring vegetation changes, providing continuous historical data. Vegetation indices are critical for quantifying vegetation growth and coverage, as they effectively capture the dynamic characteristics of vegetation over large spatial scales [26]. To date, researchers have developed more than 50 vegetation indices [27], with the Normalized Difference Vegetation Index (NDVI) being the most widely used [28]. Therefore, this study employs both quadrat sampling and the NDVI to analyze vegetation succession dynamics and vegetation change trends.

In this study, we investigated common vegetation indices (species composition, species importance value, NDVI, Margalef index, Simpson index, Shannon–Weiner index, and Pielou index) and soil depths (SD) and soil moisture content (SMC) along chronosequences by integrating field sampling and remote sensing images. We then analysed the variation characteristics of the common vegetation indices, SD, and SMC during primary succession. In addition, based on remote sensing imagery, we documented the long-term changes (1986–2024) in the deglaciated area and sampling sites, and further explored the potential driving factors by integrating meteorological data. The objectives of the present study were to elucidate changes in species diversity and composition within the tree layer of plant communities and classify the stages of vegetation succession in the Zepu deglaciated area. And we conducted a preliminary examination of the factors influencing vegetation succession. These findings will address the research gap in vegetation succession within southeastern Tibet’s deglaciated areas and contribute to a better understanding of the relationship between glacial retreat and vegetation dynamics.

2. Materials and Methods

2.1. Study Area

The Zepu Glacier is located in the eastern Nyainqêntanglha Mountains, at the source of the Bodui Zangbo River, a tributary of the Parlung Zangbo River. It is the longest temperate glacier in the basin, with a terminus elevation of 3477 m and deglaciated area ranging from 3300 to 3500 m above sea level. The study area’s treeline lies approximately between 4300 and 4700 m, with the glacier tongue extending into the forest. According to datasets from the Tibetan Plateau Data Center (TPDC), the region experiences an average annual precipitation of 694 mm and a mean annual temperature of 3.56 °C from 1981 to 2023. Favorable topographic and hydrothermal conditions have facilitated the development of large-scale temperate glaciers in this area. Precipitation is primarily concentrated in the summer (April to September), and also governs the main accumulation and ablation processes of the glacier during this period [29].

2.2. Sample Collecting and Processing

2.2.1. Quadrat Survey

The sampling sites for this study are located in the Zepu Glacier Valley (30°16′38.88″ N, 95°14′58.62″ E) within Bomi County, southeastern Tibetan Plateau (Figure 2). The sampling period is 8–10 June 2024. Based on high-resolution remote sensing imagery, we planned the sampling route and divided the study area into sampling zones according to factors such as topography, tree distribution, and spacing intervals. Due to differential movement rates within the glacier, the glacier terminus exhibits a convex shape in the center, with lagging sides. Field observations and remote sensing image analysis indicate that this morphological difference is significant, and the glacier terminus position should be determined by its central part. Therefore, the sampling zones are primarily located on the glacier valley floor. The sampling area spans approximately 3 km horizontally, divided into 10 zones (Figure 2), including the inner and outer slopes of the terminal moraine. Within each of these zones, 10 m × 10 m quadrats were established for investigation, totaling 11 quadrats. The tree layer was surveyed, with a focus on tree species, location, height, crown width measurements, and tree core sampling. This allowed us to determine the species composition of the tree layer in each sampling zone.

In each quadrat, tree cores were collected at 1 m above ground using a 4.3 mm inner diameter increment borer (Mora, Sweden). Due to operational constraints, the oldest trees of each species within a quadrat were selected for sampling, while saplings with a diameter at 1 m above ground of less than 5 cm were excluded. Additionally, although standing live trees with decayed cores were sampled, these cores were discarded, as they were unreadable. However, the survey results still included information on these standing trees. The tree-ring samples were processed following standard dendrochronological procedures. The accuracy of cross-dating was verified using the COFECHA program [30]. The oldest sample from each quadrat was used to represent the quadrat’s age. Furthermore, three surface soil samples were randomly collected from each quadrat, with their locations and sampling soil depth (SD) recorded.

2.2.2. Estimation of Sample Pith Year

During field sampling, some samples failed to reach the pith due to constraints such as terrain, the radius of the increment borer, or core decay. For these samples, estimation methods proposed by Duncan (1989) [31] and Rozas (2003) [32] were applied. Based on the chord length of the innermost tree ring arc and its corresponding chord height, the distance from the innermost ring arc to the pith was calculated (Equation (1)). Finally, the number of missing rings from the innermost ring arc to the true pith was determined, by dividing the distance from the ring arc to the pith by the average width of the innermost five rings. This allowed us to estimate the pith year of the sample.

$$MSR=L^2/8h+h/2$$

(1)

where $L$ is the chord length of the innermost tree ring arc (mm); $h$ represents the corresponding chord height (mm).

2.2.3. Determination of Soil Moisture Content

The oven-drying method [33] is a standard and precise technique for determining soil moisture content (SMC). This method measures the mass loss of a soil sample after drying at a constant temperature (105 ± 2 °C), providing the gravimetric water content. The calculation formula is

$$w={\displaystyle \frac{m_w}{m_d}}={\displaystyle \frac{m_{wet}-m_{dry}}{m_{dry}}}\times 100\%$$

(2)

$w$ is gravimetric water content (g/g, %); $m_w$ is the mass of water lost (g); $m_{wet}$ is the mass of wet soil (g); and $m_{dry}$ is the mass of dry soil (g).

2.3. Community Succession Stage Characteristics in Deglaciated Areas

Tree species identification was performed in the laboratory by combining drone aerial imagery, local flora records, photographic documentation, and field quadrat survey data. Key indicators, such as the number of trees and cumulative basal area, were analyzed statistically, and the age distribution of trees in the sampling area was examined. Species composition and diversity were analyzed with a focus on two main aspects: importance value (IV) and species diversity. By calculating the IV [34], preliminary insights into changes in tree species across different successional stages were obtained.

2.3.1. IV Calculation

In quantitative vegetation ecology, IV is a comprehensive quantitative indicator that reflects the role and status of a species within a forest community. Initially proposed by Curtis et al. in their research on forest communities [35], the IV was later adopted by Lindsey [36], Ayyad, and Dix [37] in studies of grassland and forest communities, respectively. These researchers developed IV formulas incorporating varying numbers of relative value indicators. Due to its ability to quantify the importance of each species in a plant community simply and measurably, the IV is widely used in phytosociological research to analyze the concentration trends of dominant species and to conduct the quantitative classification of plant communities. The calculation formula is as follows:

IV_tree=(Dr + Pr + Hr)/3

(3)

$$D_r=D_i/D$$

(4)

$$P_r=P_i/P$$

(5)

$$H_r=H_i/H$$

(6)

${IV}_{tree}$ is the importance value of the tree species; $D_r$ is the relative density; $P_r$ is the relative prominence; and $H_r$ is the relative height. $D_i$ denotes the individual count of a target tree species, and $D$ represents the total number of all tree species within the quadrat; $P_i$ stands for the sum of basal areas at 1m above ground for a specific species, and $P$ indicates the cumulative basal area of all species in the quadrat; $H_i$ symbolizes the mean height of a given species, and $H$ corresponds to the sum of mean heights across all species.

2.3.2. Diversity Indices Calculation

The species diversity index is a quantitative metric used to assess the species richness and distribution evenness within an ecosystem, commonly applied in ecological, environmental science, and conservation biology research. To characterize plant community diversity in deglacialted areas, the following indices were employed: the Margalef Index (M) [38,39], which reflects species richness alone and is sensitive to sample size; the Simpson Index (D) [40,41], which emphasizes the influence of dominant species (with higher values indicating greater diversity); the Shannon–Wiener Index (H) [42], which integrates both richness and evenness (with higher values indicating greater diversity); and the Pielou Index (J) [43], which measures the equitability of species distribution (with values closer to 1 indicating greater evenness).

The specific formulas for these diversity indices are as follows:

$$\mathrm{Margalef}\mathrm{index}:M=(S-1)/\ln N$$

(7)

$$\mathrm{Simpson}\mathrm{index}:D=1-{\sum }_{i=1}^S{P}_i^2$$

(8)

$$\mathrm{Shannon}–\mathrm{Weiner}\mathrm{index}:H=-{\sum }_1^S\left(P_{i}ln{P}_i\right)$$

(9)

$$\mathrm{Pielou}\mathrm{index}:J=H/\ln S$$

(10)

$S$ is the total number of species in the quadrat where species $i$ is located; $N$ is the total number of individuals of species $i$ in the quadrat; and $P_i$ is the proportion of the number of individuals of species $i$ relative to the total number of individuals.

2.4. Vegetation Indices

2.4.1. Data Sources and Calculations

Since the 1970s, the NDVI has been used to map vegetation from remote sensing images comparing reflectivity in the near-infrared and red bands [44]. The NDVI is calculated for each pixel of the selected Landsat data, according to Equation (11).

$$NDVI=(NIR-RED)/(NIR+RED)$$

(11)

This study utilized the Google Earth Engine (GEE) platform to synthesize NDVI images (1986 to 2024) based on the Landsat Collection 2, with a spatial resolution of 30 m. The maximum value composition (MVC) method was used to compute the yearly NDVI from the raw 32-day NDVI data. The method can further eliminate the influences of clouds, atmospheric water vapor, solar angle, etc. Additionally, NDVI values at points S1 to S11 were extracted to examine their interannual variation patterns. The MVC method is as follows:

$${NDVI}_m=Max\left({NDVI}_i\right)$$

(12)

${NDVI}_i$ is a 32-day NDVI, and ${NDVI}_m$ is composed of annual NDVI.

2.4.2. Ensemble Empirical Mode Decomposition

Ensemble Empirical Mode Decomposition (EEMD) can be used to analyze the nonlinear variation characteristics of NDVI, enabling the extraction of intrinsic mode functions (IMFs) (each IMF represents an intrinsic oscillatory pattern of a specific time scale (or frequency range) contained in the original signal) at different temporal scales, as well as a residual component that reflects the long-term trend of the time series data [45,46]. EEMD method decomposes nonlinear and non-stationary time series data into a set of Intrinsic Mode Functions (IMFs) with decreasing frequencies (IMF₁, IMF₂, …, IMFₙ) and a long-term trend component, using a “sifting” process that relies solely on local extrema [47]. To mitigate the problem of mode mixing and enhance the robustness of the decomposition, EEMD introduces a certain amount of white noise into the original data and then performs ensemble averaging.

The main steps of the EEMD model are as follows:

(1)

Add Gaussian white noise $w_1\left(t\right)$ with a certain amplitude to the original time series data $x\left(t\right)$

(2)

Interconnect all local maxima using cubic spline interpolation to form the upper envelope $e_u\left(t\right)$, and similarly connect all local minima to construct the lower envelope $e_i\left(t\right)$. The mean envelope $m_1\left(t\right)$ is then calculated as

$$m_1\left(t\right)=\left[e_u\left(t\right)+e_i\left(t\right)\right]/2$$

(13)

Subtract this mean envelope from the time series $x\left(t\right)$ to obtain

$$h_1\left(t\right)=x_1\left(t\right)-m_1\left(t\right)$$

(14)

(3)

Determine whether $m_1\left(t\right)$, the mean of the upper and lower envelopes is sufficiently close to zero at all points. If this condition is met, the sifting process stops. Otherwise, treat $h_1\left(t\right)$ as a new time series and repeat step 2 until the mean envelope of the k iteration satisfies the stopping criterion. The resulting function $h_K\left(t\right)$ is then designated as the first intrinsic mode function (IMF), denoted $C_1$

$$m_1\left(t\right)=\left[e_u\left(t\right)+e_i\left(t\right)\right]/2$$

(15)

(4)

Subtract $C_1$ from the original signal $x_1\left(t\right)$ to obtain the residual $R_1\left(t\right)$. If $R_1\left(t\right)$ still contains oscillatory components, it is treated as a new time series and subjected to the same sifting process (steps 2 and 3). This procedure continues until the final residual becomes a monotonic function or contains, at most one extremum. Ultimately, the original time series $x_1\left(t\right)$ is decomposed into n IMF components and a residual trend component.

$$x_1\left(t\right)={\sum }_{j=1}^n{C}_j\left(t\right)+R_n\left(t\right)$$

(16)

After extracting the EEMD trend component, it is subtracted from the original NDVI data to isolate nonlinear signals. Steps 1 through 4 are repeated iteratively, with equal-amplitude Gaussian white noise added in each iteration. The final result is obtained by taking the ensemble mean of both the IMF components and the residual trend component.

2.4.3. Theil–Sen Trend Analysis and Mann–Kendall Test

To clarify the trends of vegetation growth, Theil–Sen (SEN) trend analysis and the Mann–Kendall (MK) test [48,49] were applied to the yearly NDVI data. The SEN trend analysis and MK test are two non-parametric statistical methodologies widely employed for detecting trends in time series data. These approaches do not require the data to adhere to specific distributional assumptions, making them particularly advantageous when analyzing datasets characterized by non-normal distributions or containing outliers.

The SEN trend analysis, initially developed by Sen (1968) [50], is a non-parametric regression method widely utilized for quantifying trend slopes in time series data. This approach demonstrates notable robustness against outliers and non-normal distributions, with its computational procedure defined as follows:

$$Slope=Median\left({\displaystyle \frac{x_j-x_i}{j-i}}\right)\forall j>i$$

(17)

$x_j$ and $x_i$ are time series data; the $Slope$ reflects the sensitivity of vegetation change; $Slope$ > 0 indicates that the growth status of vegetation is becoming better; and $Slope$ < 0 indicates the vegetation is deteriorating.

Since SEN trend analysis cannot determine the significance of trends in time series, the MK test is commonly introduced to assist in significance testing. This method was first proposed and applied by Mann (1945) [51], and later improved and refined by Kendall (1975) [52]. It effectively handles small outliers and missing value noise, and has been widely applied in time series analysis in climatology, hydrology, environmental science, and other related fields [53]. For a given time series x, the test statistic of the Mann–Kendall trend test is as follows:

$$S=\sum _{i=1}^{n-1}\sum _{j=i+1}^{n}sgn\left(x_j-x_i\right)$$

(18)

$x_j$ represents the j data value of the time series; $n$ denotes the length of the data sample; and $sgn$ is the sign function, defined as follows:

$$sgn\left(\theta \right)=\left\{\begin{array}{c}1\left(\mathsf{\theta }>0\right)\\ 0\left(\mathsf{\theta }=0\right)\\ -1\left(\mathsf{\theta }<0\right)\end{array}\right.$$

(19)

Mann (1945) and Kendall (1975) demonstrated that when $n$ ≥ 8, the test statistic $S$ approximately follows a normal distribution with a mean of 0 and a variance of

$$Var(S)={\displaystyle \frac{n(n-1)(2n+5)-{\sum }_{i=1}^n{t}_i(i-1)(2i+5)}{18}}$$

(20)

$t_i$ represents the number of data points in the $i$ group.

The standardized test statistic $Z_c$ is calculated as follows:

$$Z_c=\left\{\begin{array}{ll}{\displaystyle \frac{S-1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(S\right)}}},& ifS>0\\ 0,& ifS=0\\ {\displaystyle \frac{S-1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(S\right)}}},& ifS<0\end{array}\right.$$

(21)

The standardized test statistic $Z_c$ follows a standard normal distribution. The null hypothesis H₀ is rejected at significance level α when $Z_{\left(1-\alpha \right)/2}$, where $Z_{\left(1-\alpha \right)/2}$ represents the critical value of the standard normal distribution corresponding to the (1 − α/2) quantile.

2.4.4. Vegetation Response to Climate Change

Vegetation growth is largely dependent on the climate system, and vegetation dynamics result from climatic fluctuations and human activities. Thus, vegetation’s response to climatic and anthropogenic influences can be studied using correlation analysis. Multiple linear regression analysis was used to establish the relationship between NDVI, temperature, and precipitation.

This study utilized annual mean temperature and precipitation data from 1986 to 2023, sourced from the Tibetan Plateau Data Center (TPDC) with a resolution of 1 km. Given the relatively small extent of the study area, the meteorological data were downscaled to meet research needs, which resulted in a 30 m resolution dataset for annual mean temperature and precipitation. The metadata was generated through the Delta spatial downscaling method applied to the global 0.5° climate dataset released by the CRU and the high-resolution global climate dataset from WorldClim, downscaled specifically for China. Additionally, the dataset was validated using data from 496 independent meteorological observation stations, and the validation results were deemed reliable.

3. Results and Analyses

3.1. Succession Stage Characteristics

3.1.1. Tree Species, Quantity, and Ages

The research results indicate that vegetation in the deglaciated area exhibits continuous changes. In terms of species composition, the dominant species are deciduous broadleaf trees, including Betula albosinensis Burkill, Betula utilis D. Don, Salix paratradenia C. Wang & P. Y. Fu, Populus pseudoglauca C. Wang & P. Y. Fu, and Hippophae rhamnoides subsp. yunnanensis Rousi, as well as coniferous trees like Larix griffithii Mast., Abies spectabilis (D. Don) Mirb., and Picea likiangensis (Franch.) E. Pritz. Shrubs primarily include Berberis gyalaica Ahrendt and Ribes glaciale Wall. With increasing distance from the 2024 glacier terminus, the number of trees shows a decreasing trend. In the early stages of succession, seedlings and saplings are more abundant. As trees mature and compete for resources, non-dominant species decline, and tree numbers decrease. Subsequently, the number of trees fluctuates across different plots, reaching its lowest value by the late successional stage.

The cumulative basal area of trees in the quadrats is inversely proportional to the distance from the glacier terminus. While S1 to S9 exhibit slow growth, S10 and S11, representing the late successional stage, are dominated by older trees that occupy most of the resources for growth, resulting in a larger basal area (Figure 3a). Hippophae rhamnoides subsp. yunnanensis Rousi is distributed in all plots, indicating that Hippophae rhamnoides subsp. yunnanensis Rousi spans the entire process of vegetation succession in the retreat of the Zepu Glacier (Figure 3b). As a pioneer species, Populus pseudoglauca C. Wang & P. Y. Fu also spans much of the succession process, disappearing only at the late stage. However, since its age is generally younger than that of the Hippophae rhamnoides subsp. yunnanensis Rousi in the same area, we use the growth years of the oldest Hippophae rhamnoides subsp. yunnanensis Rousi to represent the age of the plots. The plot ages increase with the distance from the glacier terminus.

Moreover, within the same plot, the ages of conifers are noticeably younger than those of broadleaf trees, as Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu can better adapt to harsh environments in the early stages of succession. This age distribution aligns with observations made during our field investigation. These findings suggest that vegetation succession is interactively linked to glacier retreat.

3.1.2. Soil Moisture Content and Soil Depth Variations

Substrates in S1 and S2 are entirely moraine, with no developed soil layer. By the S3 stage, a thin soil layer of approximately 6 cm begins to form. From there, the SD gradually increases, reaching 18 cm at S9, which marks the transition to a mixed coniferous and broadleaf forest. By the late successional stage, the SD reaches 30 cm, with distinct soil horizons developing. However, due to sampling constraints, we were unable to collect samples from the deepest soil layers (Figure 3c).

Overall, SMC gradually decreases as vegetation succession progresses. Vegetation type influences soil moisture, with stages dominated by coniferous trees showing significantly lower moisture levels compared to those dominated by broadleaf trees. The average SMC during the S3–S7 stages is 70%, much higher than the 36% average in later successional stages. Notably, moisture content significantly decreases from S8 to S10 but increases at S11, which may be due to our sampling depth surpassing the surface soil layer.

3.1.3. Species IV Changes

The IV of Hippophae rhamnoides subsp. yunnanensis Rousi varies across different quadrats, showing an overall trend of initially increasing and then decreasing with increasing distance from the modern glacier terminus (Figure 4).

S1 is located on the supraglacial moraine at the terminus of the glacier tongue, where no soil development has occurred. Mosses grow on the glacial boulders, and shrubs (young trees) with heights below 1.5 m are found in the crevices. The dominant tree species include Larix griffithii Mast., Hippophae rhamnoides subsp. yunnanensis Rousi, Betula utilis D. Don, and Populus pseudoglauca C. Wang & P. Y. Fu, with IVs of 29%, 28%, 25%, and 18%, respectively. The oldest tree in the quadrat is Hippophae rhamnoides subsp. yunnanensis Rousi, which was established in 2015; S2 is situated on the inner side of the first moraine ridge, with a substrate primarily composed of glacial boulders and similarly lacking soil development. Young trees, all below 5 m in height, grow in the crevices. The dominant species is Hippophae rhamnoides subsp. yunnanensis Rousi (74%IV), followed by Populus pseudoglauca C. Wang & P. Y. Fu and Salix paratradenia C. Wang & P. Y. Fu (both with 9%), and Larix griffithii Mast. (8%). The oldest tree in this quadrat dates back to 2003.

S3 and S4 are located on the outer sides of two modern moraine ridges, respectively. The substrate consists of glacial boulders with thin soil development, and herbaceous plants begin to appear. The dominant species is Hippophae rhamnoides subsp. yunnanensis Rousi, with an IV of approximately 70%. Populus pseudoglauca C. Wang & P. Y. Fu and broadleaf trees such as Betula utilis D. Don and Betula albosinensis Burkill are sparsely distributed. In S3, a single Hippophae rhamnoides subsp. yunnanensis Rousi tree has begun to decay, and several Betula utilis D. Don trees in S4 have died. The year of the S3 is 1987, and for S4 it is 1981.

S5, S6, and S7 exhibit a decline in the IV of Hippophae rhamnoides subsp. yunnanensis Rousi and an increase in the IV of Populus pseudoglauca C. Wang & P. Y. Fu. The combined IVs of Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu account for approximately 70%, with Hippophae rhamnoides subsp. yunnanensis Rousi still dominating as the primary species, and Populus pseudoglauca C. Wang & P. Y. Fu as a co-dominant species. Additionally, coniferous trees such as Abies spectabilis (D. Don) Mirb. and Picea likiangensis (Franch.) E. Pritz. begin to appear. In S5, the coniferous trees are represented by young individuals with heights below 3 m, and some Hippophae rhamnoides subsp. yunnanensis Rousi individuals have died. In S6, the coniferous are all below 10 m, and by S7, they exceed 15 m in height, with several Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu individuals have died out entirely. The years of the S5 and S6 are both 1973, while for S7 it is 1963.

S8 and S9 represent a transitional phase from broadleaf forest to mixed coniferous–broadleaf forest. During this stage, Populus pseudoglauca C. Wang & P. Y. Fu has the highest proportion and serves as the dominant species, with Hippophae rhamnoides subsp. yunnanensis Rousi acting as a co-dominant species. Dead Hippophae rhamnoides subsp. yunnanensis Rousi trees are present in both quadrats. S10 and S11 represent the late successional stage of mixed coniferous–broadleaf forest. In these communities, the IV of Picea likiangensis (Franch.) E. Pritz. exceeds 60%, making it the dominant species during this successional stage. Shrubs are virtually absent in the understory. S8, S9, S10, and S11 date back to 1951, 1944, 1925, and 1892, respectively.

3.1.4. Community Species Diversity Patterns

Along the eleven sampling sites, plant species diversity showed a fluctuation trend from the debris-covered surfaces to the oldest exposed sites. Specifically, species richness, the Pielou index, the Shannon–Weiner index, and the Simpson index increased from a lack of biological life at the current glacier terminus to the highest diversity (1.67, 0.96, 1.55, and 0.85, respectively) at S9 (approximately 80 years of site exposure), with minimal differences between S7 (approximately 61 years of site exposure) and S9. The highest average values for each index are in stages from S5 (approximately 51 years of site exposure) to S9. During S1–S4, the Simpson index, Pielou index, and Shannon–Weiner index exhibited consistent patterns. The S1 value is highest (0.76,0.91,1.26), and S2–S4 values increase incrementally. The Margalef index and Shannon–Weiner index showed similar trends in S5–S9 values, ranging from 1.02 to 1.67 and 0.96 to 1.55. The Simpson index and Pielou index exhibited similar trends (Figure 5a). Species richness, species diversity, and species evenness peaked in the mid-successional stage (S5–S9).

All indices showed a decline at S8 (approximately 73 years of site exposure), although to varying degrees, with values ranging from 0.60 to 1.03. This decline may be due to the transitional phase between coniferous and broadleaf mixed forests at S8, where community evenness showed minor differences, but diversity decreased significantly.

3.1.5. Succession Stages Division

The deglaciated area has undergone a succession process from shrubs to trees over approximately 132 years. According to the clustered heatmap, which is calculated by communities’ IVs, this process can be broadly divided into four stages: shrub community; broad-leaved forest with Hippophae rhamnoides subsp. yunnanensis Rousi as the dominant species; mixed coniferous and broad-leaved forest with Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu as dominant species; and mixed coniferous and broad-leaved forest with Picea likiangensis (Franch.) E. Pritz. as the dominant species (S1\S2–S4\S5–S9\S10–S11). The Shrub Stage persisted for approximately 9 years post-retreat. The Hippophae Stage occurred 21–34 years after glacier retreat, followed by the Hippophae–Populus Stage at 51–80 years after glacier retreat. The Picea Stage commenced 99 years post-retreat (Figure 5b).

3.2. Vegetation Succession and Climate Change

3.2.1. Annual Variation in NDVI

Figure 6 presents the decomposition of the NDVI time series using the EEMD method. The dataset spans from approximately 1986 to 2024, with NDVI values ranging between 0.3 and 0.8. The original NDVI time series exhibits interannual fluctuations and an overall increasing trend, likely influenced by climatic variability.

Through analysis, the NDVI data is decomposed into multiple IMFs representing oscillatory components at different temporal scales, along with a residual component that captures the long-term trend. The residual indicates a persistent upward trajectory, aligning with the overall increasing trend observed in the original NDVI series. This suggests a long-term greening trend, consistent with broader patterns of vegetation growth in response to climate change.

3.2.2. Spatiotemporal Variation in NDVI

According to the MK–Sen test results (Figure 7), the p-value for 83% of the area is less than 0.01, indicating that the trend analysis results are highly reliable. Vegetation growth in the deglaciated area shows a significant increasing trend, with 93% of the area exhibiting substantial vegetation increases, the most pronounced changes occurring near the glacier terminus. The slope value is 0.03, and the Z-value is 7.08, both of which are the highest in this region, suggesting that the rate of community succession is fastest near the glacier terminus. This area corresponds to the locations of S1, S2, S3, and S4, with the age of S4 dating back to 1981. The strong correlation between these findings further confirms the reliability of the vegetation quadrat survey results.

3.2.3. Quadrat NDVI Variation

Although fluctuations in vegetation growth exist at individual sampling points, the overall trend demonstrates steady improvement over time (Figure 8). The multi-year average NDVI values at different points are 0.05, 0.27, 0.48, 0.60, 0.71, 0.73, 0.78, 0.77, 0.81, 0.88, and 0.93, respectively. It is evident that the NDVI values of vegetation from S1 to S11 progressively increase, suggesting that the farther the distance from the glacier terminus, the higher the NDVI value and, consequently, the greater the vegetation coverage. This pattern closely aligns with the vegetation succession trends observed in this region.

3.2.4. Vegetation Changes and Climate

The results of multiple linear regression analysis indicate a positive correlation between temperature and NDVI, suggesting that rising temperatures promote vegetation growth, leading to an increase in NDVI values (Figure 9). This finding is consistent with the established role of temperature in enhancing photosynthesis and plant growth. Conversely, a negative correlation is observed between precipitation and NDVI, indicating that increased precipitation does not significantly promote vegetation growth and may even have a detrimental effect in some areas. This phenomenon could be attributed to factors such as waterlogging, soil salinization, or other environmental stresses caused by excessive precipitation, which may hinder healthy vegetation growth.

In the significance analysis of regression coefficients, 64% of the area passed the significance test at p < 0.05, indicating that the influence of temperature on NDVI is significant and has a positive effect. However, the impact of precipitation on NDVI is relatively minor and negative, suggesting that precipitation is not a primary driver of NDVI changes.

The R² results indicate that the model has limited explanatory power for NDVI variation, explaining at most 50% of the variation. Although temperature and precipitation are the primary factors influencing NDVI changes, other variables not included in the model, such as soil type and light intensity, may also play significant roles. Additionally, since there are no meteorological observation stations in the region, the use of grid data may introduce some errors. Therefore, further refinement of the model is needed. Given the small regional scope and the high precision required for data analysis, incorporating additional high-resolution environmental variables could enhance the model’s explanatory power.

The restricted geographical coverage necessitates exceptionally stringent requirements for analytical data accuracy. To compensate for spatial limitations and enhance model interpretability, strategic incorporation of supplementary high-fidelity environmental parameters is, therefore, critically required.

4. Discussion

4.1. Factors Influencing the Community Succession Process in Deglaciated Areas

Typically, xerarch succession follows the sequence of lichen–moss–herb–shrub–tree. However, at the terminus of the Zepu Glacier, the succession over approximately 132 years has manifested as a shrub–tree process. In the first stages, soil development was absent, and only in the second stage did thin soil layers begin to form, allowing the emergence of herbaceous plants and low ferns. In contrast, the Hailuogou Glacier on Gongga Mountain and Baishui No. 1 Glacier on Yulong Snow Mountain [54] typically followed the sequence of herb–shrub–tree. The differences in vegetation succession among these deglaciated areas may be attributed to factors such as altitude and latitude. By comparing the succession patterns across various deglaciated areas, we can gain a better understanding of the impacts of glacier retreat on ecosystems and the processes of ecosystem recovery and adaptation.

On the modern glacial terminus moraine of Zepu Glacier, shrubs such as Hippophae rhamnoides subsp. yunnanensis Rousi and seedlings of trees like Populus pseudoglauca C. Wang & P. Y. Fu and Betula utilis D. Don have already been established. This suggests that once the glacier terminus stabilizes, pioneer species rapidly invade and colonize. Studies reveal that the colonization of pioneer species (Populus pseudoglauca C. Wang & P. Y. Fu, Larix griffithii Mast.) in other deglaciated areas of southeastern Tibet typically takes 3–8 years [55,56], 4 years for the Hailuogou Glacier (Hippophae tibetana) [56], and 5–6 years for the Baishui No. 1 Glacier on Yulong Snow Mountain (Meconopsis racemosa) [57]. In contrast, the colonization period for pioneer species on the Zepu Glacier is almost negligible. This phenomenon may be attributed to the intensified warming since the mid-20th century and the increased warmth and humidity in southeastern Tibet [58], which favor the establishment and growth of light-demanding broadleaf species such as Hippophae rhamnoides subsp. yunnanensis Rousi and Populus pseudoglauca C. Wang & P. Y. Fu. Additionally, Hippophae rhamnoides subsp. yunnanensis Rousi, a typical clonal plant, produces seeds that serve as food for birds and animals, facilitating rapid and widespread dispersal [59]. The surrounding slopes, rich in vegetation, also provide a seed source for the growth of plants in the deglaciated area, significantly reducing the colonization time. In the Bodui Zangbo River Valley, vegetation (mostly Hippophae) is also found on the terminal moraines of Guanxing, Baitong, Baiyu, and Zhuxigou Glaciers. This is likely due to the location of these glaciers on the windward slopes of the Nyainqêntanglha Mountains, making them more susceptible to the warm and moist air currents brought by the Indian monsoon. The abundant precipitation in the basin provides favorable conditions for vegetation growth. In contrast, the Zepu Glacier, compared to temperate glaciers such as the Xinpu Glacier, Gongpu Glacier, and Gyalaper Glacier, has a lower terminus elevation, gentler slopes, and a larger volume. The relatively stable movement of the glacier terminus is more conducive to vegetation colonization, which may be the reason for the differences in colonization periods.

The ecological succession process in the deglaciated area of the Zepu Glacier began with the colonization of pioneer species, when the moraine surface had not yet developed soil. Under these conditions, the shrub stage lasted approximately 9 years. Subsequently, broadleaf trees became the dominant species, and community species diversity increased. This stage persisted for about 60 years, eventually developing into a mixed coniferous–broadleaf forest dominated by Picea likiangensis (Franch.) E. Pritz. In contrast, the herbaceous stage in the deglaciated area of the Hailuogou Glacier was 7–13 years after deglaciation [60], followed by a broadleaf tree stage that lasted approximately 32 years, and a mixed coniferous–broadleaf forest stage that lasted 28 years, ultimately forming a pure Abies forest [61]. At the Baishui No. 1 Glacier on Yulong Snow Mountain, the herbaceous stage lasted approximately 20 to 30 years, ultimately forming a pure Abies georgei forest. Among the three temperate glaciers, the deglaciated area of the Hailuogou Glacier, with the lowest elevation, experienced the shortest succession time of 125 years, while the Zepu Glacier had succession times of 132 years. The Baishui No. 1 Glacier, with higher elevations, had succession times of 250 years [57]. Notably, successional trajectories and chronosequences of the Hailuogou Glacier and Zepu Glacier exhibit remarkable similarities, likely attributable to their comparable mean annual temperatures, which provide favorable thermal conditions for vegetation colonization. The first three stages of succession at the Baishui No. 1 Glacier were dominated by herbaceous vegetation, primarily due to the steep slopes of its deglaciated area, an elevation difference of 500 m, and its location spanning the subarctic humid mountain climate zone and the tundra climate zone. These factors resulted in significant variations in hydrothermal conditions, leading to the formation of a unique succession pattern (

Table 1. Comparison of Natural Geographical Characteristics of Deglaciated Areas in the Zepu Glacier, Hailuogou Glacier, and Baishui No. 1 Glacier on Yulong Snow Mountain.

	Zepu	Hailuogou [61,62]	Baishui No. 1 [63]
Succession Time (yr)	132	125	250
Elevation of Deglaciated Area (m asl)	3300–3500	2700–2952	3800–4300
Elevation Difference (m)	200	150	500
Mean Annual Temperature (°C)	3.6	3.8	12.8
Total Annual Precipitation (mm)	694	1960	967
Glacier Area (km2) [64]	76.1	24.5	1.3

).

In summary, while these three glaciers exhibit similar successional patterns, their succession rates and final community structures diverge, due to factors including climatic conditions, succession duration, elevation, glacier volume, ice-tongue slope, and elevation differences within the deglaciated areas. Among these variables, elevation serves as the main factor determining differences in succession patterns between regions, whereas temperature regulates succession pace between regions. Given that climax communities in comparable glacier deglaciated areas consistently develop into pure coniferous forests, we hypothesize that vegetation at the terminus of the Zepu Glacier will continue progressing toward a pure coniferous forest state, with coniferous species progressively expanding their dominance.

4.2. Community Succession Process

In the context of glacial retreat, ecological indicators such as species composition, species diversity, SD, and SMC in the deglaciated area of the Zepu glacier exhibit distinct stage-specific variations. These variations reflect the different phases of primary vegetation succession. Within the chronosequence of primary succession in the Zepu Glacier deglaciated area, pioneer species play a critical role in ameliorating the initially barren soil conditions through various ecological mechanisms, thereby facilitating the establishment and progression of subsequent plant communities.

In the early stage of succession (S1), nitrogen-fixing species dominate the plant community, including Hippophae rhamnoides subsp. yunnanensis Rousi, Betula utilis D. Don, and Populus pseudoglauca C. Wang & P. Y. Fu. These pioneer species significantly enhance organic carbon and nitrogen levels in the substrate through biological nitrogen fixation, thereby promoting soil development and nutrient accumulation. This process lays a critical foundation for the establishment of the tree layer in later successional stages (S2–S4). Despite the shallow soil and limited moisture and nutrient availability during this early phase, Hippophae rhamnoides subsp. yunnanensis Rousi maintains dominance, due to its strong environmental adaptability. However, as vegetation cover increases and interspecific competition intensifies, Betula utilis D. Don becomes less competitive, and is gradually excluded from the community.

During the mid-successional stages (S5–S9), Populus pseudoglauca C. Wang & P. Y. Fu gradually strengthens its competitive advantage within the community through niche expansion, with both intraspecific and interspecific thinning becoming increasingly evident. As canopy closure intensifies, Hippophae rhamnoides subsp. yunnanensis Rousi experiences suppressed growth due to limited light availability and intensified nutrient competition, gradually transitioning into a declining population. Populus pseudoglauca C. Wang & P. Y. Fu occupies the dominant position in the forest canopy. Meanwhile, coniferous species begin to establish in the understory, signaling a structural transition toward a mixed conifer–broadleaf forest. Species diversity reaches its peak during this stage of succession.

In the late successional stage (S10–S11), shade-tolerant coniferous species such as Picea likiangensis (Franch.) E. Pritz. exhibit accelerated growth, and gradually replace Populus pseudoglauca C. Wang & P. Y. Fu as the dominant species in the canopy layer. As a result of insufficient light availability, Populus pseudoglauca C. Wang & P. Y. Fu experiences growth suppression, and gradually disappears from the community. During this stage, the community structure tends to stabilize, transitioning into a conifer-dominated mixed conifer–broadleaf forest.

Soil properties play a critical role in driving vegetation succession [63,64,65]. Increased SD is typically strongly correlated with greater vegetation cover, as deeper soil horizons provide more sufficient moisture and nutrient availability, thereby improving conditions for plant growth. SMC directly regulates plant water availability, and is a key determinant of vegetation growth rates and community structural dynamics [65]. In the early stages, soils are poorly developed, characterized by shallow profiles and low organic-matter content, which severely constrains plant establishment. As succession advances, soils gradually accumulate organic materials, leading to increased water retention and nutrient levels that create favorable conditions for higher-successional species. However, during the later stages of succession, despite the enhanced water-holding capacity resulting from organic-matter accumulation, intensified water uptake and transpiration by densely distributed tree roots reduce surface soil moisture. This is reflected in significantly lower SMC in conifer-dominated forest types compared to those dominated by broadleaved species.

Typically, community diversity increases gradually with succession. However, since only the tree layer was surveyed, the diversity of shrub and herbaceous vegetation was not considered. In mid-to-late successional stages, tree species dominate, and intense intraspecific competition reduces mutual facilitation, leading to decreased diversity and a peak in mid-successional diversity.

In summary, the primary vegetation succession in the deglaciated area of the Zepu Glacier exhibits a clear pattern of vegetation–soil co-development. The succession of plant community structure is not only influenced by soil conditions, but also further drives the amelioration of soil environments and successional progression through plant-mediated feedback mechanisms. These findings align with the conclusions of Wang et al. [66], validating the close plant–soil coupling relationship during ecosystem recovery in deglaciated areas.

4.3. Factors Affecting Vegetation Growth

To investigate the role of climate change in this process, we integrated remote sensing data and utilized NDVI analysis. The results reveal that between 1986 and 2024, the most significant vegetation increase occurred at the glacier terminus, suggesting that temperate glaciers are highly sensitive, and responsive to climate change. Quadrat surveys show that vegetation began to grow at the glacier terminus in 1981, while NDVI data indicate significant vegetation changes since 1986. This highlights the utility of dendrochronological methods in determining the age of moraine ridges.

Based on recent meteorological data, temperature is identified as the primary driver of vegetation changes in the region, while precipitation plays a relatively limited role. Regression models incorporating both temperature and precipitation suggest that these climatic factors can explain, at most, approximately 50% of the variation in vegetation, indicating that other factors may also be influencing vegetation dynamics. Other studies show that NDVI in southeast Tibet is primarily influenced by soil type [67], precipitation [68,69], and temperature [70], with precipitation and temperature effects varying by vegetation type and region. Human activities and their interaction with natural factors also play a role, sometimes enhancing and sometimes degrading vegetation cover [71,72]. Therefore, comprehensive monitoring of various environmental characteristics in this region is essential, to gain a deeper understanding of the influencing factors in this process.

4.4. Limits and Future Works

As glaciers retreat, improved habitat suitability, increased biological interaction complexity, and extended colonization periods all contribute to enhanced biodiversity [73]. This process has profound impacts on community composition. Changes in plant communities are closely associated with the diversity of other biological groups, and play a critical role in the formation and development of ecosystems [74]. Due to the region’s remoteness and limited accessibility, detailed investigations of soil chemical properties, vegetation biomass, and microbial community composition were not conducted, nor were microclimate observations systematically obtained. Consequently, our analysis of the driving factors across successional stages remains superficial, lacking integration of stage-specific interactions between plant, environment (e.g., temperature, solar radiation, and humidity), soil properties, and microbial dynamics. This data gap has constrained mechanistic exploration and contributed to the low explanatory power of the regression models. Addressing these unresolved issues will be a central focus of our future research.

5. Conclusions

In this study, we integrated vegetation quadrat surveys with vegetation index to investigate the successional trajectories and temporal trends of vegetation in the deglaciated forelands of the Zepu Glacier. The results showed that as the Zepu glacier retreats, vegetation succession follows a directional trajectory, manifesting overall as a shrub–tree transition. Indicators, including species composition, diversity, soil depth, and moisture content in deglaciated areas, exhibit stage-specific variations, reflecting distinct phases of succession.

In the Zepu Glacier deglaciated area, the composition of tree-layer species varies significantly across succession stages, progressing from shrub-dominated communities in the early stage (S1), to broadleaved forests (S2–S4), then to mixed coniferous–broadleaved forests (S5–S9), and, ultimately, to conifer-dominated communities in the later stages (S10–S11). The mixed coniferous–broadleaved forest stage exhibits significantly higher species diversity compared to other successional stages.

As succession progresses, SD gradually increases, accompanied by a corresponding rise in vegetation cover. Notably, during the conifer-dominated stage, soil moisture content is significantly lower than that observed during the broadleaved-dominated stage.

Comparative analyses of vegetation succession across different glacier deglaciated areas suggest that elevation is the primary factor driving regional differences in successional patterns, while temperature mainly regulates the rate of succession among regions. Analysis of multi-year temperature, precipitation, and NDVI data indicates that temperature is the primary controlling factor of vegetation change in the region, showing a positive correlation with vegetation cover.

The integration of remote sensing data, field surveys, and environmental monitoring establishes a robust framework for analyzing ecological succession dynamics. This approach is transferable to other deglaciated areas, providing not only mechanistic insights into vegetation succession in high-altitude deglaciated areas, but also contributing to a broader understanding of global ecosystem responses to climate change in analogous environments—thereby enhancing predictive capabilities for ecosystem recovery and adaptation under climatic change.