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Article

Variations in Aquatic Vegetation Diversity Responses to Water Level Sequences during Drought in Lakes under Uncertain Conditions

by
Shan He
1,2,3,*,
Jijun Xu
1,3,
Yujun Yi
4 and
Enze Zhang
5
1
Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, No. 23 Huangpu Road, Wuhan 430010, China
2
State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
3
Hubei Key Laboratory of Water Resources & Eco-Environmental Sciences, Wuhan 430010, China
4
State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
5
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(13), 2395; https://doi.org/10.3390/w15132395
Submission received: 26 May 2023 / Revised: 19 June 2023 / Accepted: 27 June 2023 / Published: 28 June 2023
(This article belongs to the Section Ecohydrology)
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Abstract

:
Water level variability and temporal change are critical for shaping the structure of aquatic vegetation. Much research has examined the response of aquatic vegetation to hydrological metrics. However, the hydrological sequence is a fundamental driver of aquatic ecosystem structure and function. Given the aleatory uncertainty of future water levels under an unstable climate, how aquatic vegetation responds to changing dynamics in hydrological processes, especially shifting water level sequences, remains insufficiently explored. In this paper, we establish an evaluation framework to study the response of vegetation diversity to variation in water level sequences during a drought event. To do this, the uncertainty and variability of water level processes are both considered. Altering water level processes was achieved using two types of scenarios in order to explore the effects of differing water level sequences (i.e., changing the order of high vs. low water levels) on the probability distribution of four indexes of aquatic plant diversity (e.g., Margalef’s, Simpson’s, Shannon’s, and Pielou’s evenness index). Our results show that altering the order of water level state can lead to differences in the diversity of aquatic vegetation, with a pronounced impact on vegetation complexity. This suggests that the specific sequence of water level events is critical for shaping aquatic vegetation structure. In addition, we found that a uniform distribution of water level state is beneficial for enhancing a species’ dominance in aquatic vegetation. Our findings provide guidance for improving the future development of freshwater ecosystem protection and lake management.

1. Introduction

As primary producers, aquatic plants are one of the chief factors determining the structure of shallow lake ecosystems [1,2]. These plants provide essential food and shelter for many aquatic animals, and promote material circulation and energy flow in lake ecosystems. Aquatic vegetation plays an important role in maintaining both biodiversity and the ecosystem health of lakes. Under the combined influence of ongoing global climate change, environmental pollution, and intensified human activities, the frequency and intensity of extreme water level events are increasing, prolonging associated droughts and flooding events, and causing the loss of aquatic vegetation in lakes worldwide [3,4]. It has been estimated that 65.2% of the world’s lakes have incurred a significant reduction in the biodiversity of their aquatic vegetation [5,6]. For lakes with a water surface area > 50 km2, aquatic vegetation loss is accelerating and the problem is even more serious [6]. This leads to stark changes in the dominant species of lakes, and negatively affects the functioning of this freshwater ecosystem [7,8]. Hence, aquatic vegetation’s decline in lakes is an urgent problem that cannot be ignored. It is imperative to find and take proper measures to effectively protect and restore aquatic vegetation in lakes.
Water level is an important driving factor and a sensitive indicator of variation in aquatic vegetation [9,10,11]. Annual natural cycles in the water level of lakes can promote changes in the structure and function of their aquatic vegetation [12]. Rhythmic water level fluctuations can operate as an environmental filter for the establishment and population growth of plant species [13]. In particular, water level’s fluctuation can contribute to maintaining vegetation diversity. Many researchers have studied the relations between the vegetation and water level of lakes. For example, Riis and Hawes [14] studied how water level fluctuations influence the abundance and diversity of plant communities in a shallow lake in New Zealand. They found that vegetation diversity was shaped not only by the water level range, but also the frequency and duration of low-water-level events. In addition, species richness is much lower in shallow lakes with inter-annual water level fluctuations than those with intra-annual water level fluctuations. This suggests that it takes a relatively long time for species abundance to recover in species-rich lakes. In a later work, Grabas and Rokitnicki-Wojcik [15] studied the relationships between the plant community and the daily fluctuation intensity of water levels in the Lake Ontario coastal wetlands of Canada. They suggested the latter variable is a critical hydrological factor for vegetation establishment. Working in China, Zhang et al. [16] found that species richness is high in lakes that have a moderate water level fluctuation range, while lakes with similar water level fluctuation were the most similar in their plant species composition. Accordingly, those authors concluded that the water-level fluctuation’s magnitude is a prominent factor determining the distribution of lake vegetation, followed by relative altitude and submergence duration. Chen et al. [17] compared the responses of aquatic vegetation cover to the water level variation for different connected sub-lakes, and found that the aquatic vegetation cover of freely connected sub-lakes were more susceptible to water level variation than partially controlled sub-lakes. All these studies cited above were deterministic quantitative analyses of the relationships between water levels and vegetation based on field-sampled data.
Previous studies have mainly focused on hydrological metrics derived from historical hydrological sequences, such as the frequency and duration of water level changes, and the timing of extreme water level events [18,19]. These ecologically relevant metrics could reveal a surplus or shortage in the amount of lake water, and may be useful for assessing static ecosystem responses (e.g., static aquatic vegetation health) within the specific time range of those metrics. However, such metrics cannot characterize the process of temporal variation in aquatic vegetation. Environmental water management requires an adequate understanding of how aquatic vegetation responds to dynamic changes in hydrological processes, particularly the specific sequences of hydrological events. Specific hydrological sequences may be used to illustrate ecologically relevant hydrological events (e.g., extreme low or high water levels), which are key to shaping ecological outcomes [20]. It is long recognized that the hydrological sequence is a fundamental driver of aquatic ecosystem structure and function [21,22]. However, the effect of various possible hydrological sequences upon the diversity of aquatic vegetation, and how diversity changes temporally under a certain water level process in lakes, has yet to be rigorously investigated. This is because water level data are usually derived from historical long-term observations or modeled based on a known historical sequence [23]. Much contemporary research is largely based on the implicit assumption that the water level variability occurring under natural conditions adequately encompasses a variety of future situations [24]. More often than not, a single water level sequence is relied upon to express the water level process. Although the plausibly full range of variation in water levels may be considered, future extreme events are apt to arise in unknowable ways, and how future sequences actually unfold is impossible to forecast under a nonstationary climate [25,26]. Little attention has been paid to the uncertainty of long-term water level sequences in the field of environmental water management in lakes. Some studies have focused on the occasional short-term wet situation during long-term dry periods, finding they have the potential to buffer ecosystems to preclude their collapse during drought events [26,27].
Given this background, we designed the present study to examine the response of aquatic vegetation diversity to water level processes during drought in lakes under uncertain conditions. An evaluation framework with several scenarios—by changing or reordering water level sequences—was proposed to investigate the future uncertainty of water level sequences during drought, and identify the temporal variation in aquatic vegetation diversity. This study highlighted the importance of considering multiple possible sequences of water level events in lakes. The Baiyangdian Lake in China was used as a case study. By analyzing the probability distribution (PD) of its aquatic vegetation diversity under varying water level sequences, we studied how this lake’s diversity responds to differing water level processes.

2. Study Area and Data

2.1. Site Description

Baiyangdian Lake (113°39′ E~116°12′ E, 38°3′ N~40°4′ N) is a shallow grassy lake in the hinterland of Beijing, Tianjin, and Baoding (Figure 1). This lake’s total surface area is 366.0 km2, and the annual average evaporation of the Baiyangdian Basin is 1500–2000 mm. The terrain of this basin is high in the west and low in the east, with mountains, hills, and plains in turn, of which the plain area accounts for 47%. The land use of this basin is diverse, mainly cultivated land, forest land, and construction land. As the largest freshwater lake on the North China Plain, Baiyangdian Lake has a suite of critical ecological functions, such as flood control, climate regulation, water storage, fishing, reed production, provision of a touristic landscape, and protection of biodiversity [2].
Before the 1960s, Baiyangdian Lake was a natural lake. After the 1960s, some reservoirs were built in the upper reaches of Baiyangdian Lake, and it changed into an artificial regulating lake. Through reservoir operation, ecological water replenishments were carried out many times in the historical period. The problems of water shortage and eutrophication have been solved accordingly.
Aquatic vegetation in the Baiyangdian Lake can be classified into submerged, emergent, floating leaf, and floating plants. The dominant emergent plants are Phragmites communis and Typha orintalis; the dominant submerged plants are Ceratophyllum demersum and Potamogeton pectinatus; the dominant floating plants are Salvinia natans and Spirodela polyrhiza; and the dominant floating leaf plants are Hydrocharis dubia and Nymphoides peltate. Submerged plants are the most abundant, whereas emergent plants have the highest population size [28].

2.2. Data Collection

Lake sediments harbor the pollen of aquatic vegetation, which based on paleolimnological records, and is an ideal tool for studying the historical evolution of the local ecological environment [29]. Here, we detected the long-term aquatic vegetation diversity through their pollen. Studying the processes of long-term change in aquatic vegetation in lakes in detail is helpful for elucidating its potential driving mechanism, which can be applied to slow the degradation of lakes [30]. Data on aquatic vegetation came from the macrophyte pollen data set based on the work of Yang et al. [28], in which a 95 cm deep sediment core was collected from a sampling site with less human activity in Baiyangdian Lake to ensure stable sedimentary environments of the samples and truly reflect the historical biological status of the lake. By analyzing its pollen content and distribution, the long-term presence and absence of plant species and their corresponding relative abundances were obtained. Both 210Pb and 137Cs dating techniques were used to measure the historical stages of the sediment core layer samples. Subsequently, the sporopollen specimens were identified, and the ecological and physicochemical indexes of each layer of sediment core samples were determined. In the present study, the time step is 1 year. Monthly water level data for Baiyangdian Lake, from 1950 to 2017, were provided by the Daqinghe Management Office in Baoding City, Hebei Province.

3. Methodology

Here, an evaluation framework for the response of vegetation diversity to water level processes is described and established. The framework consists of five steps, as described in the following subsections. Section 3.1 introduces how to evaluate aquatic vegetation diversity. Section 3.3 sets up a series of scenarios for differing water level processes based on the division of the water level state explained in Section 3.2. Next, a conditional state transition model is developed in Section 3.4 to simulate the dynamic response of aquatic vegetation diversity to water level processes. Finally, Section 3.5 summarizes the steps used to evaluate ecological states for the aquatic vegetation diversity’s response to changing water level sequences.

3.1. Evaluation of Aquatic Vegetation Diversity

The diversity of aquatic vegetation is a paramount indicator for evaluating the health status of shallow lakes [31]. When studying plant diversity, most ecologists use species richness as a measure of species diversity. But according to some studies, using a single diversity index to reflect plant community composition is likely to be unreliable [32]. In this paper, four indicators of plant diversity were chosen to comprehensively evaluate the alpha diversity of aquatic plant communities in Baiyangdian Lake from the perspectives of richness, dominance, complexity, and evenness.
(1) The Margalef index (dMa) quantifies the abundance of aquatic vegetation [33]:
dMa = (S − 1)/lnN
where S is the total number of species of plants in the sample, and N is the total number of individuals of all plants in the sample.
(2) The Simpson diversity index (D) conveys the degree of dominance of aquatic vegetation [34]:
D = 1 − ∑ i = 1 S n i ( n i − 1 ) N ( N − 1 )
where ni is the number of individuals of plant i.
(3) The Shannon–Wiener diversity index (Shannon index, H) is the most widely used method to evaluate species diversity. It conveys the complexity of aquatic plant communities by taking into account both species richness and the abundance of vegetation [35,36].
H = − ∑ i = 1 s n i N log 2 n i N
The sensitivity and insensitivity to the number of species in the aquatic plant community was characterized by the H and D index, respectively.
(4) Pielou’s evenness index (J) can be used to evaluate the evenness of species distribution [37,38].
J = H/log2S
where the value of J ranges from 0 to 1. A J value closer to 1 indicates that the number of individuals of each plant type in the sample is more similar, and thus the number of each type that constitutes this plant community is more even.
According to the historical conditions, the criteria in Table 1 were used here to evaluate aquatic vegetation diversity [33].

3.2. Division of the Water Level State

Baiyangdian Lake is substantially impacted by human activities, and its intra-annual and inter-annual variation in water level varies greatly. The historical trend for the water level of Baiyangdian Lake from 1950 to 2017 is shown in Figure 2. The average elevation of the lake’s bottom is 5.2 m. Since 1980, the water storage capacity of Baiyangdian Lake has been decreasing at a rate of 600,000 m3 per year, and historically, the annual water level displays an overall downward trend. The lake actually dried up for five consecutive years (1983–1988). In some months from 1984 to 1988 and also in 2003, the monthly water level reached its lowest in history, at just 5.2 m. In these years, dry lakes appeared in Baiyangdian Lake, severely damaging the wetland ecosystem. In Figure 2, the water level fluctuation from 1980 through 1989 is the most pronounced, and during this period, the lake’s water level was at its lowest. According to He et al. [39], the most severe and longest drought affecting Baiyangdian Lake occurred in this period. Thus, the 1980–1989 water level sequence was designated the historical drought water level sequence for use in this study.
According to the method of dividing runoff typically used in hydrology [40], we classified the water level in Baiyangdian lake based on historical data. Water level is divided into five states (k = 5), as shown in Table 2 (column 2): low water level (LWL), relatively low water level (RLWL), normal water level (NWL), relatively high water level (RHWL), and high water level (HWL), which correspond to different hydrological years. Applying the mean standard deviation method, the historical water level is then divided into five intervals corresponding to the five states. Thus, we can determine the state of each water level in the historical data.

3.3. Scenarios for Different Water Level Processes

To study the impact of differing water level processes (that is, changes in the sequence of low- and high- water level) on aquatic vegetation diversity, we set up a series of water level scenarios by changing or rearranging the water level sequences. To do this, we shuffled the water level data to simulate various water level processes. Each water level scenario includes a historical drought sequence and a water level sequence (spanning the same duration as the historical drought sequence) prior to that historical drought period (preceding water level sequence), as depicted in Figure 2. These scenarios are divided into two types: one retains the historical drought sequence as is, changing only the states of its preceding water level sequence; the other type keeps the preceding water level sequence as is, but reorders the state of the water level sequence during the historical drought. In this way, a total of 106 scenarios were set up (Table 3), for which the actual historical scenario (1historical) served as the baseline.
In the first scenario type, the historical drought sequence remains the same, and the preceding water level sequence is obtained via random sampling of the long historical water level data. Preceding water level sequences of different scenarios are constructed by calculating the 10-year moving average of historical water level data [20]. For example, periods with a ‘low’ state of the 10-year moving average are selected to represent the ‘2low’ scenario. For the baseline scenario (1historical), the preceding water level sequence was the same as its historical state.
In the second scenario type (7sh (i = 100)), the water level sequence during the historical drought is reordered on an annual step, with 100 stochastic replicates of water level sequences repeatedly generated to account for random uncertainties.

3.4. Uncertainty of Aquatic Vegetation Diversity and Water Level Processes

The nature of the dynamic process of aquatic vegetation diversity is stochastic, and therefore harbors uncertainty. We assumed that aquatic vegetation diversity has simple Markov characteristics; hence, its dynamics are represented here by a simple Markov process [41]. The Markov process is also widely used in the prediction of hydrological events such as runoff and water level [42,43,44]. The conditional state transition model based on the Markov process and Bayesian theory is then used to simulate the dynamic response of aquatic vegetation diversity to water level processes during a drought event. The transition of aquatic vegetation diversity between the different states can be calculated this way:
P ( V D t W L t , V D t − 1 ) = P ( W L t V D t ) P ( V D t V D t − 1 ) ∑ V D t P ( W L t V D t ) P ( V D t V D t − 1 )
where P(VDt|VDt−1) denotes the prior transition probability, which reflects the randomness of aquatic vegetation diversity, as represented by the probability of aquatic vegetation diversity VDt at time step t given the aquatic vegetation diversity VDt−1 present at time step t − 1. The term P(WLt|VDt) is the likelihood function; it represents the probability of water level WLt at time step t, given aquatic vegetation diversity VDt present at time step t. The posterior transition probability P(VDt|WLt, VDt−1) is the product of the likelihood and prior transition probability divided by its summation over all possible states of aquatic vegetation diversity.

3.5. Evaluation of Ecological States Based on the Response of Vegetation Diversity to Water Level Processes

Given the above water level sequence scenarios (Table 3), the impact of slight differences in water level processes upon the ecological state was evaluated next. The ecological state was expressed by four states of the diversity index for aquatic vegetation (Table 1). The current state of aquatic vegetation diversity depends on both the previous state of aquatic vegetation diversity and the current water level state. The ecological response model is a function of the antecedent ecological state (i.e., previous state of aquatic vegetation diversity) and water level state sequences as per Equation (5). It simulates the possible states and their probability of aquatic vegetation diversity under various water level scenarios. The probability distribution (PD) changing at each state for aquatic vegetation diversity was simulated. The steps are summarized as follows:
  • Establish a series of water level sequences using the set water level scenarios (Table 3).
  • According to water level state divisions in Section 3.2, the water level sequences are transformed into water level state sequences.
  • Applying the Table 1 criteria, the data for historical aquatic vegetation diversity are transformed into diversity state sequences. After running the Markov Chain test, the prior transition probability of aquatic vegetation diversity under each water level state is calculated using Equation (5).
  • Given the initial state of the distribution of aquatic vegetation diversity, the water level state sequences, and the prior transition probability of aquatic vegetation diversity, the state and corresponding probability of aquatic vegetation diversity at each time step are obtained.
Using the methods above, how aquatic vegetation diversity responds to changing water level sequences before or during drought periods was determined.

4. Results

4.1. Division of Water Level States for the Baiyangdian Lake

According to the 68-year (1950–2017) historical water level data set for Baiyangdian Lake, the mean value of its historical water level sequence is 7.59 m, and the standard deviation is 1.09 m. The mean standard deviation method was then applied to obtain the partitioned water level states for Baiyangdian Lake (see Table 2).

4.2. Analysis of Scenarios for Different Water Level Sequences

According to our analysis of the historical water level process in Baiyangdian Lake, the 10-year (1980–1989) water level sequence was deemed the historical drought sequence (1historical).
In the first scenario, the historical drought sequence was not altered, enabling us to study the effects of altering the 10-year (1970–1979) preceding water level sequence on the PDs of aquatic vegetation diversity, resulting in six scenarios of different water level processes. Figure 3 shows the water level processes from 1970 to 1989, these corresponding to the 1historical, 2low, 3rlow, 4normal, 5rhigh, and 6high scenarios in Table 3. The last five scenarios shift the state of the preceding water level sequence, and these changes are obvious (Figure 3). However, some years did not match their corresponding water level state. For instance, in the relatively low water level (3rlow) scenario, the water level in 1973 (5.2 m) belongs to the low water level group. Likewise, in the relatively high water level (5rhigh) scenario, the water level in 1977 (6.17 m) belongs to the low water level group. Conversely, in the low water level (2low) scenario, the water level in 1979 (8.45 m) belongs to the relatively high water level group.
In the second scenario type (7sh (i = 100)), we kept the preceding water levels at their historical level to study the effects of reordering the historical drought sequence (10 years) on the PDs of aquatic vegetation diversity. The second scenario type yielded 100 scenarios of differing water level processes. Figure 4 shows the variation in 20-year water levels from 1970 to 1989 under each scenario, corresponding to 7sh (i = 100) in Table 3. In that figure, the first column on the far left is the historical baseline scenario used for comparative analysis. The upper part of Figure 4 is the historical water level process during the first 10 years (1970–1979), during which the water level remained at the historical level.
Analyzing the water level sequences in the historical drought period (1980–1989) for Baiyangdian Lake, we find that the water level process of historical scenario is as follows: normal water level (1 year), relatively low water level (1 year), low water level (6 years), relatively low water level (1 year), relatively high water level (1 year). The results for annual water level states after reordering the historical drought sequence are presented in the bottom half of Figure 4. These 100 scenarios are sufficient to illustrate the various situations that could arise. Evidently, the low water level is the most concentrated in the baseline scenario, whereas the distribution of water level states for the 100 stochastic generated scenarios are either centralized or scattered. Unlike the historical baseline scenario, the water level processes of stochastically generated scenarios may directly change from a low water level to high water level, or may arise in any order at any time, and are more unpredictable.

4.3. Responses of Aquatic Vegetation Diversity to Shifts in the Preceding Water Level Sequence

Aquatic vegetation diversity is expressed using the dMa, D, H, and J indexes. The states of aquatic vegetation diversity were classified into four levels: unhealthy, sub-healthy, healthy, and very healthy (Table 1). We assumed that the initial diversity was healthy, in a state consistent with the historical one of aquatic vegetation diversity in 1969. The PDs of the four diversity indexes for the six scenarios under the first scenario type from 1970 to 1989 are shown in Figure 5; these correspond to the 1historical, 2low, 3rlow, 4normal, 5rhigh, and 6high scenarios in Table 3. It was verified that the state of aquatic vegetation diversity of the simulated historical scenario (1historical) is consistent with the actual historical scenario (1970–1989).
In most situations, the aquatic vegetation diversity is poor under the low water level scenario. For example, according to dMa, a very healthy status rarely arises in the 2low scenario. But as the water level rises, the number of years attaining a very healthy status increases. This demonstrates that water level greatly impacts vegetation diversity in lakes, and that a low water level is not conducive to the healthy development of aquatic vegetation. Reducing the water level leads to a decrease in the surface area of a lake. And in tandem, some plants lose their photosynthetic activity due to the lack of water. Accordingly, competition within and among plant species for space and water in lakes inevitably strengthens, which is harmful to the stable community development of aquatic vegetation.
However, some exceptions did occur when lake vegetation diversity was assessed using D. The number of years with a very healthy status is quite high, and the annual probability is large under the 2low, 3rlow, and 4normal scenarios. Yet, as the water level rises, the number of years distinguished by a very healthy status declines and the annual probability becomes smaller. The Simpson index is used to evaluate the degree of species dominance [34]. When the water level state improves, more of the aquatic vegetation can grow vigorously, and the dominance degree of various aquatic species is roughly equivalent under a certain water level. It makes aquatic vegetation suitable for high water levels have a relatively lower dominance degree. For D, H, and J, the probabilities of reaching a very healthy status do not always increase with rising water levels; rather, the probabilities of a sub-healthy status increase instead, especially so for the 5rhigh and 6high scenarios. This demonstrates that maintaining a higher water level does not necessarily lead to improved aquatic vegetation diversity. It follows that there likely exists a certain appropriate range of water level favorable for the overall growth of aquatic vegetation.
Figure 5 also sheds light on how aquatic vegetation diversity is affected by the six water level scenarios. Since the latter 10-year (1980–1989) water level processes remain at historical levels, the corresponding diversity indexes of aquatic vegetation changed little among differing water level processes, and over time, vis-à-vis the trends for 1970 to 1989. The PDs of aquatic vegetation diversity in 1979 and 1980 under the six scenarios do show a great difference, however. However, going from 1981 to 1989, the difference in the PDs among the six scenarios lessens such that eventually we find no difference among the six scenarios. This result is mainly caused by differential PDs in the previous year each scenario. It also suggests aquatic vegetation diversity is correlated with its status at the previous, most recent time step.

4.4. Responses of Aquatic Vegetation Diversity to the Reordering of the Historical Drought Sequence

Figure 6 shows the PDs of the four diversity indexes in the last year (1989) for 100 different water level processes under the second scenario type. The first scenario on the far right is the historical baseline scenario (1histor). Generally, D has the best evaluation, while dMa has the worst evaluation of aquatic vegetation diversity among these four indexes. The results of 100 water level scenarios of four diversity indexes are not alike, especially when evaluated by dMa and H. This indicates that altering the historical drought sequence had an effect on vegetation diversity. Aquatic vegetation responds sensitively to variations in lake water levels, and its ecological status either deteriorates or recovers accordingly.
Reordering the historical drought sequence changes the relative occurrence of low water levels, as well as its frequency and duration, which affects the ecological states during drought. Compared with the baseline scenario, the distributions of water level states under the 11th, 20th, and 89th scenarios are as concentrated as for the historical scenario, which leads to a low frequency and long duration of low water levels. According to the dMa and J indexes, the diversity arising under these three scenarios is better than that of the historical scenario, but worse when evaluated by D. This indicates that a uniform distribution of the water level state increases the frequency of low water levels, but shortens its duration, which is conducive to augmenting the species dominance of aquatic vegetation, but is harmful for the abundance and evenness of the aquatic vegetation community in the lake. Hence, we may infer that a specific sequence of ecologically consequential events (the relative timing of low and high water level events) is critical for shaping the freshwater lake ecosystem.
The results for vegetation diversity status depended on the sequence and distribution of water level states. For example, when gauged by H, the historical baseline scenario performed better than the 47th, 80th, and 99th scenarios, whereas the 26th, 35th, and 43th scenarios performed better than the baseline scenario. Comparing the water level sequences revealed that the 1st, 26th, 35th, 43rd, 47th, 58th, 63rd, 80th, 99th, and the baseline scenario all featured a relatively high water level in 1989. However, in 1988, the historical scenario had a relatively low water level, while the 26th, 35th, and 43rd scenarios had a normal water level, and the 47th, 80th, and 99th scenarios had a low water level. These differences indicate better results for the historical scenario than the 47th, 80th, and 99th scenarios. For the 1st, 58th, and 63rd scenarios, their water level in 1988 and 1989 was the same as in the historical scenario. However, the probabilities of a sub-healthy status for vegetation diversity under the 1st, 58th, and 63rd scenarios are 0.3543, 0.2679, and 0.3725, respectively, which are all lower than the 0.4118 under the historical scenario. Hence, compared with the historical scenario, the prospects for vegetation diversity for these three scenarios are better, mainly because their water level states in 1987 or 1986 were better. On this basis, we may infer that altering the order of water level state in a certain year can drive differences in the diversity of aquatic vegetation, greatly impacting that vegetation’s complexity. This suggests that aquatic vegetation can change considerably in response to even minor changes in the water level processes of lakes.

5. Discussion

5.1. Comparison of the Response of Different Diversity Indexes to Water Level Sequences

It is evident that the PDs of very healthy status for D are the greatest (Figure 5b), while the PDs of an unhealthy status for dMa are the greatest (Figure 5a) overall among the four diversity indexes. Hence, D has a better evaluation while dMa has a worse evaluation of aquatic vegetation diversity. The responses of dMa and J to the water level process are thus different, a finding consistent with the work of Filstrup et al. [45], showing that an ecosystem may respond differently to species richness and evenness. The reason for the discrepancy found is that the Margalef index focuses on evaluating the richness of aquatic vegetation, while Pielou’s index focuses on evaluating the uniformity of aquatic vegetation’s distribution in the ecosystem. Comparing 100 stochastically generated scenarios with the 1histor scenario in Figure 6, when evaluated according to the dMa, D, and H indexes, the historical scenario has less vegetation diversity, and vice versa for the J index. Collectively, these results suggest that aquatic vegetation diversity responds differently to water level processes, which are also consistent with the work of Filstrup et al. [45].

5.2. Response of Aquatic Vegetation Diversity to Each Water Level State

The variation in aquatic vegetation diversity responses to water level sequences under uncertain conditions was studied above. Here, we discussed the determined response of aquatic vegetation diversity to each water level state under certainty in a historical situation. The vegetation diversity index results were divided into five corresponding states. Then, we implemented trinomial curve fitting to simulate the vegetation diversity, as shown in Figure 7. For dMa, D, H, and J indexes, when water levels are in the HWL state, the fit between the observed and simulated values is best, with R2 values of 0.665, 0.816, 0.756, and 0.896, respectively. Although these fitting results are clearly good, the vegetation diversity corresponding to each water level state should actually span a certain range and harbor uncertainty. This is because vegetation diversity is related to both the lake water level and the diversity at the last time step, as well as water quality along with some other environmental factors.
For dMa, when water levels are in the RLWL state, the range of vegetation diversity is largest. The difference between the largest and smallest value for vegetation diversity is 0.8822. For D, H, and J, vegetation diversity ranged most when water levels are in the NWL state. Interestingly, for dMa and D, the smallest range in vegetation diversity occurs when water levels are in the LWL state; by contrast, for H and J, this occurs in the HWL state. This indicates that lake vegetation diversity differentially responds to water level states when different diversity indexes are used.

5.3. Extension Opportunities

Water level changes contribute to shaping aquatic vegetation diversity. Through our research, we found that a uniform distribution of water level state—which shortens a lake’s duration in a low water level state—is beneficial for enhancing the species dominance of aquatic vegetation (Figure 6). This conclusion is also confirmed to some extent by the research of Riis and Hawes [14]. According to their works, increasing the duration of low water level events within one month appeared to increase plant diversity, while events longer than 2 months reduced it. It’s obvious that shortening duration in a low water level state is of importance for vegetation diversity. Long-term drought (low water level events) may alter the composition and structure of vegetation communities, and even cause reverse succession of aquatic vegetation in lakes [46]. The study of Peng et al. [47] has shown that drought is unfavorable to emergent plants, but beneficial to floating and submerged plants in Changhu Lake in China. This study suggests that different aquatic vegetation responds differently to drought environments. As such, vegetation diversity responds differently to low water level events.
This paper adopted pollen analysis to detect the variation in aquatic vegetation diversity during a historical period. Some aquatic plants normally propagate vegetatively, and do not produce any pollen. For this reason, it is likely that the “true” biodiversity of aquatic plants in the lake was underestimated. However, pollen analysis in this paper could detect the long-term (more than 50 years) variation in vegetation diversity to some extent, which cannot be determined by many other methods. In addition, macrophyte pollen proved to be an effective ecological indicator for reflecting the abundance and composition of macrophytes in lakes [28,48]. Four indicators of plant diversity were used to evaluate vegetation diversity. The obvious effect of water level changes on aquatic vegetation are often changes in abundance and species composition, particularly in shallow lakes where species may survive in pools during periods of drought. While the Margalef index quantified the abundance of aquatic vegetation in this paper, the species composition should be detected in the next study. In this way, some information with respect to which species contributed most to the observed changes in vegetation diversity could be studied.
The response of plant diversity to water level was described by Equation (5), which took uncertainty into account. Based on this relationship, the probability distribution of vegetation diversity under different water level processes were simulated. Previous studies have relied on observed water levels to forecast the future, but this is unlikely to capture future extreme events [26]. Here, we defined the water level process by changing the water level state (reordering water level sequences). Through different scenarios used in this paper, we may reliably simulate aquatic vegetation under extreme events.

6. Conclusions

The overarching aim of this paper was to explore the response of aquatic vegetation diversity to altered water level processes by establishing different scenarios. Variation in water level processes of lakes was generated using two categories of scenarios.
(1) In the first type of scenario, only the water state of the preceding water level sequence is altered. In comparing the four indexes of aquatic plant diversity, we found that aquatic vegetation diversity responds differently to water level processes. With a higher water level, the overall status of the four indexes does not always increase in tandem, but generally tends to fluctuate. This demonstrates that a certain range of water level is the most suitable for promoting aquatic vegetation growth.
(2) In the second type of scenario, only the water level sequence of the historical drought scenario is reordered. To incorporate uncertainty and stochasticity, 100 different sequences were randomly set. The results suggest that altering the water level sequence can significantly affect aquatic vegetation diversity. Importantly, we find that the specific sequence of water level events is critical to shaping aquatic vegetation diversity. In addition, an evenly distributed water level state characterized by a high frequency and long duration of low water levels is beneficial for increasing the species dominance of aquatic vegetation.
As such, this study provides guidance for subsequent water management associated with the ecological water demand of lakes. By fully considering the dynamic interactions between hydrology and ecology in lakes, the importance of ranking water level processes is examined. This paper provides a timely reference that could assist in the conservation of aquatic vegetation biodiversity in lakes.

Author Contributions

Conceptualization, S.H.; methodology, S.H.; writing—original draft, S.H.; funding acquisition, S.H. and J.X.; supervision, J.X. and Y.Y.; writing—review, E.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The research is financially supported by the Nature Science Foundation of Hubei Province (Grant No. 2022CFB573), the China Postdoctoral Science Foundation (Grant No. 2022M710491), the Fundamental Research Funds for the Central Public Welfare Research Institutes (Grant No. CKSF2021432), and the National Natural Science Foundation of China (Grant No. 52009004).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The case study area of Baiyangdian Lake.
Figure 1. The case study area of Baiyangdian Lake.
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Figure 2. The water level process from 1950 through 2017 for Baiyangdian Lake.
Figure 2. The water level process from 1950 through 2017 for Baiyangdian Lake.
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Figure 3. Variation in 10-year water levels for preceding water level sequences in each scenario under the first scenario type.
Figure 3. Variation in 10-year water levels for preceding water level sequences in each scenario under the first scenario type.
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Figure 4. Variation in 20-year water levels derived from 100 stochastic generated scenarios under the second scenario type.
Figure 4. Variation in 20-year water levels derived from 100 stochastic generated scenarios under the second scenario type.
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Figure 5. Probability distributions (PDs) of four diversity indexes for six scenarios under the first scenario type. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J.
Figure 5. Probability distributions (PDs) of four diversity indexes for six scenarios under the first scenario type. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J.
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Figure 6. Probability distributions of four diversity indexes in 1989 for 100 different water level processes under the second scenario type. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J.
Figure 6. Probability distributions of four diversity indexes in 1989 for 100 different water level processes under the second scenario type. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J.
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Figure 7. Responses of the four diversity indexes to each water level state. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J. (Circles represent the observed values; lines represent the simulated values.).
Figure 7. Responses of the four diversity indexes to each water level state. (a) the Margalef index, dMa; (b) Simpson index, D; (c) Shannon–Wiener index, H; (d) Pielou’s evenness index, J. (Circles represent the observed values; lines represent the simulated values.).
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Table
Table 1. Evaluation criteria for the status of aquatic vegetation diversity.
Table 1. Evaluation criteria for the status of aquatic vegetation diversity.
IndexUnhealthySubhealthyHealthyVery Healthy
(1)(2)(3)(4)
dMa<11~1.31.3~1.6>1.6
D<0.40.4~0.50.5~0.60.6~1
H<1.31.3~1.61.6~1.9>1.9
J<0.40.4~0.50.5~0.70.7~1
Table
Table 2. Classification of water level periods and states for Baiyangdian Lake.
Table 2. Classification of water level periods and states for Baiyangdian Lake.
NumberWater Level StateClassification StandardRange (m)
1low (LWL)x < x ¯ – 1.0S(5.20, 6.50)
2relatively low (RLWL) x ¯ – 1.0S ≤ x < x ¯ – 0.5S[6.50, 7.05)
3normal (NWL) x ¯ – 0.5S ≤ x < x ¯ + 0.5S[7.05, 8.14)
4relatively high (RHWL) x ¯ + 0.5S ≤ x < x ¯ + 1.0S[8.14, 8.68)
5high (HWL) x ¯ + 1.0S ≤ x ¯ [8.68, 11.15]
Table
Table 3. Summary of water level sequence scenarios that consisted of a preceding sequence and a drought sequence (both lasting 10 years).
Table 3. Summary of water level sequence scenarios that consisted of a preceding sequence and a drought sequence (both lasting 10 years).
Scenario NameScenario Description
1historicalHistorical preceding water level sequence + historical drought water level sequence
2lowLow preceding water level sequence + historical drought water level sequence
3rlowRelatively low preceding water level sequence + historical drought water level sequence
4normalNormal preceding water level sequence + historical drought water level sequence
5rhighRelatively high preceding water level sequence + historical drought water level sequence
6highHigh preceding water level sequence + historical drought water level sequence
7sh (i = 100)Historical preceding water level sequence + reordering the historical drought water level sequence
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He, S.; Xu, J.; Yi, Y.; Zhang, E. Variations in Aquatic Vegetation Diversity Responses to Water Level Sequences during Drought in Lakes under Uncertain Conditions. Water 2023, 15, 2395. https://doi.org/10.3390/w15132395

AMA Style

He S, Xu J, Yi Y, Zhang E. Variations in Aquatic Vegetation Diversity Responses to Water Level Sequences during Drought in Lakes under Uncertain Conditions. Water. 2023; 15(13):2395. https://doi.org/10.3390/w15132395

Chicago/Turabian Style

He, Shan, Jijun Xu, Yujun Yi, and Enze Zhang. 2023. "Variations in Aquatic Vegetation Diversity Responses to Water Level Sequences during Drought in Lakes under Uncertain Conditions" Water 15, no. 13: 2395. https://doi.org/10.3390/w15132395

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