Abstract

An Interconnected River System Network (IRSN) project could change the drainage pattern and influence the river's ecological health. However, the relevant research is still in a preliminary stage and needs to have a supplement. Considering environmental and ecological characteristics of the karst area, this paper analyzed the relationship between IRSN projects and environmental indicators, and proposed a multi-layer indicator system that has three first-class indicators (water environment, river–lake organism, connectivity) and 15 second-class indicators for assessment of the ecosystem. The weight of each level of indicators' can be determined using the analytic hierarchy process (AHP), and the change rate of indicators critical value is normalized, then five threshold levels are established within the range of 0–1. Refer to the established response mechanism and threshold level of the karst basin, The Wangerhe River project taken as an example can well reflect the IRSN status through this method. These results can provide scientific support for constructing an evaluation index in other karst areas.

HIGHLIGHTS

  • The relationship between the IRSN projects and environment in the karst area can be comprehensively analyzed by hydrological statistics of 20 years.

  • A network of environmental influencing factors in the karst area was established.

  • The threshold of the IRSN can estimate the influence of IRSN and apply it to other karst areas.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

Interconnected River System Network (IRSN) is an important method of ecological restoration and water environmental governance (Ostad-Ali-Askari & Shayannejad 2021). IRSN projects can provide a solution for improving resource allocation and enhancing the ability of the system to resist disasters (Luo et al.,2018b). Flow is a main variable in the hydraulics analysis and can give significant information to water management (Mohammadi et al. 2020, 2021). Its hydrological connectivity is the transfer efficiency of materials from the source area to the outlet (Ostad-Ali-Askari et al.,2019b; Golian et al. 2020), and the connectivity can have an influence on the ecological environment and water efficiency. Therefore, it becomes increasingly important to learn the characteristics of hydrological connectivity (Ostad-Ali-Askari et al. 2018; Derakhshannia et al. 2020), and IRSN projects should have a consideration for water system connectivity.

There are four main methods to study hydrological connectivity on a large scale: in-situ hydrological monitoring, hydrological modeling, connectivity function analysis, and graph theory (Kindlmann & Burel 2008; Poulter et al. 2008; Karim et al. 2012; McDonough et al. 2015). The assessment of large-scale hydrological connection is with complicated development. The method of in-situ monitoring recently focuses on the interaction of ice water, permafrost, and ground water in the plateau (Ma et al. 2017a; Yang et al.,2019b, and yet it is limited by the size of the study scale and the location of the region. The landscape model is a hydrological model, which is created to measure the connectivity (Laliberté & St-Laurent 2020). The connectivity function analysis can assess the connectivity based on hydrology (Luo et al. 2018a; Wang et al. 2019; Liu et al. 2020), while the calculation method varies with the water source. Because of the diversification of research, graph theory is popular, which is a theoretical model that uses an abstract method to assess the relations among research objects (Ishiyama et al. 2020; Feng et al. 2021). Besides, the indicator theory is widely used in the river connectivity at a macroscopic scale, and the theory can be defined as that some parameters that are related to hydrological connectivity are selected to have a comprehensive assessment (Leibowitz et al. 2018; Turnbull et al. 2018; Chen et al. 2020). Karst regions generally include the characteristics of diverse hydrological environments (Goldscheider et al. 2020). Therefore, it is essential to develop an evaluation system combing some of these methods to assess the hydrological connectivity in the karst area.

Most of the precedent studies are focused to evaluate the hydrological connectivity in the plain area, not the karst region. And the studies are operated in the graph theory or the comprehensive assessment, which cannot apply directly to the area including multiple ecological interactions between river and environment. To investigate the influence of IRSN projects on the karst area, this study was designed to investigate the flow pattern and ecological course using remote sensing and geographic information system (GIS). Then, an evaluation system of ecological environmental indicator and response mechanism for IRSN in karst area was established. Additionally, it can determine a complete set of eco-environmental model which can comprehensively evaluate the river–lake connectivity.

This paper collected the hydrological data of Huangguoshu Hydrological Station for 20 years, and the data were analyzed by GIS and remote sensing. We quantitatively analyzed the evaluation index system based on the theories that are AHP, single index method, and comprehensive index method, respectively. Referring to the influencing degree of each indicator and the relationship among indexes and environment, the response mechanism and threshold levels of karst regions can be concluded. Our results can improve the current understanding of the connectivity between IRSN projects and the ecosystem and provide key information for assessing other karst areas.

STUDY AREA AND METHODOLOGY

Overview of Dabang River

Dabang River is in the Pearl River basin and is a tributary of the Beipan River, and the dotted oval is the scope of this study (Figure 1). The basin has a subtropical humid monsoon climate because of its distinct climate characteristics (Ostad-Ali-Askari et al. 2018; Talebmorad et al. 2021). The researched karst area is one of the largest water sources in China and its average precipitation is greater than 1100 mm, so the underground runoff and groundwater storage are abundant (Liu et al. 2018). However, the karst surface area may exhibit a loss of surface water for its soil erosion, which can lower water utilization and worsen the ecological environment (Huang 2015). Generally, the soil layer is very thin since many karst areas are bare rock or have scarce vegetation, and the slow growth of vegetation makes it difficult for the environment to recover (Zhu et al. 2019). Water conservancy projects in karst areas are generally built in the mountains where is difficult to build large water conservancy facilities. Therefore, most small projects are built in these areas (Ren et al. 2020). The naturally mountainous terrain can create a water barrier for storing water, and it can provide a solution for farmland irrigation and raising the water usage rate (Lv et al. 2018).

Figure 1

Geographical location of study area. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2021.311.

Figure 1

Geographical location of study area. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2021.311.

Assessment of hydrological data

(1) Three-year moving average

First, 3 years are determined as a moving period, and the mean of this period is calculated, which can be used as a correction. Then, a new calculation period can be formed by sliding back a year, and the mean also can be obtained. The above steps are repeated until the last data and the formula is as following:
formula
(1)
where is the observed value, is the moving average of the jth year for m years, and .

(2) Single index method

The single index method is based on the background value of pollutants to evaluate the accumulation degree of pollutants, and the formula is as follows:
formula
(2)
where is the pollution concentration, is the standard concentration.

(3) Comprehensive index method

The comprehensive index method is as follows:
formula
(3)
where represents ith single pollution index, n is the total number of pollutants.

Construction principle of ecological indicators

The influence of IRSN projects is multiple. Constructing the evaluation index system should refer to the key point of the advanced evaluation system and combine with the actual development of China, and the construction principle as follows. (1) comprehensive principle: The evaluation system should reflect the basic characteristics of resources, economy, society, and environment. (2) scientific principle: The indicators should have representativeness and combine with actual problems. (3) the principle of hierarchy: Different types of impact factors have different effects, so the hierarchy of each influence factor should be presented. This study can be classified into three layers that are the target layer, criterion layer, index layer, respectively. (4) Regional principle: the system should reflect the environmental differences of different regions and refine the evaluation indexes reflecting regional characteristics.

Weight value of indicators

AHP was developed by Saaty & Kearns (1985), and it could assess the influencing factors and simplify the decision process. Thus, the evaluation index of the Dabang River was defined, and the calculation model based on AHP was built.

Building a judgment matrix

Saaty et al. proposed the consistent matrix method that is comparing all factors in pairs instead of being together. It can reduce the difficulty of comparing factors and improve its accuracy.

Sequencing by exponents and consistent test

According to the previous judgment matrix, the sequence can be defined as an order of importance among different criterion layers. Then, the consistency test is based on the eigenvalue of this matrix, and it is necessary to test the consistency to avoid the paradox of the sequence method. The non-zero eigenvalue is n and the max eigenvalue is , so the consistency step is:

  • (i)
    The consistency index :
    formula
    (4)
    where n is the matrix order.

    CI can quantify the deviation of the judgment matrix.

  • (ii)
    The Random consistence index :
    formula
    (5)

It can be concluded that the RI of 1 or 2 orders matrix is zero, and the RI from three orders to 10 orders can be calculated based on the previous equation as follows (Table 1).

  • (iii) The consistency ratio :
    formula
    (6)
    when the ratio is less than or equal to 0.1, it means that the judgment matrix reaches the qualified standard. Otherwise, the matrix is not consistent. The matrix cannot be revised and checked again until the consistency test passes.

Standardization of indicators

The indicators are divided into three types: positive indicator, neutral indicator, and negative indicator. First, the larger value of positive indicators is considered to be better. Second, neutral indicators that are too high or too low are considered improper. Third, lower negative indicators are considered to be better. The formula can be expressed as follows.
formula
(7)
formula
(8)
where A is the benchmark value, B is the predicted value, W is the normalized value.

The range of the normalized critical value was 0–1, and the threshold levels can be classified as excellent, good, medium, poor, and worst.

MODEL FORMULATION

Hydrological data from 1998 to 2017 and water quality data from 2015 to 2018 were obtained from Huangguoshu Hydrological Station (Supplementary information Table VI), and the statistics were analyzed. The vegetation coverage data for the last 20 years were collected through the geographic information system (GIS) (Figure 4(a)–4(e)). Thus, the ecological environment in the Dabang River area was studied by means of moving average, single index method, and comprehensive index method. Then, the IRSN network of the study area could be concluded as Supplementary information Figure I, and the threshold value was calculated based on the AHP method.

Assessment of the evaluation index

Wanger River originates in Linshao, Anshun City, Guizhou Province, and Wangerhe Reservoir is the water source of the Dabang River. The reservoir was planned in 1998 and completed in 2004, so the watershed area significantly increased after 2008 due to the reservoir operation (Figure 2), and the connectivity of the water system has undergone some changes over the last 20 years. This study extracted some GIS data to analyze the water surface area, and the ratios of the water area to the total area of the basin in 1998, 2003, 2008, 2013, and 2018 were 0.0134, 0.0153, 0.0258, 0.0249, and 0.0260, respectively.

Figure 2

Comparison of water area before and after 2004. (a) Water area in 2000 (b) Water area in 2008.

Figure 2

Comparison of water area before and after 2004. (a) Water area in 2000 (b) Water area in 2008.

Previous statistics showed that the water surface area did not increase significantly from 1998 to 2003. Yet the area significantly increased after 2008 due to the reservoir construction and the transfer project. Therefore, IRSN connectivity was greatly enhanced since then.

Climates of the basin area

Based on the annual average obtained from the Huangguoshu Hydrological Station from 1998 to 2017, the hydrological data were calculated by the three-year moving average (Guo 2016) including the precipitation, discharge, evaporation, water level, and temperature (Figure 3).

Figure 3

Three-year moving average of hydrological statistics.

Figure 3

Three-year moving average of hydrological statistics.

As showed in Figure 3, from 1998 to 2015, the three-year moving average of annual evaporation, precipitation, and discharge fluctuated in a large range, and the linear fitting curve of the water temperature showed an increasing trend. However, the water level remained stable due to the regulation of upstream reservoirs and water transfer projects. Therefore, evaporation, precipitation, discharge, and water temperature are important influencing factors. However, the water level is not greatly affected by the IRSN projects.

Vegetation coverage

The vegetation coverage is a crucial index for the ecological landscape, and it can reflect regional climate change (Ma et al.,2017b; Ostad-Ali-Askari et al. 2019a). In this study, Geographic Information System (GIS) was used to select the study basin, and the basic satellite remote sensing images from 1998, 2003, 2008, 2013, and 2018 were extracted to obtain the vegetation coverage maps (Figure 4(a)–4(e)), then, the normalized difference vegetation index (NDVI) can be summarized in Table 1.

Figure 4

Vegetation coverage of basin from 1998 to 2018. (a) Vegetation coverage in 1998, (b) Vegetation coverage in 2003, (c) Vegetation coverage in 2008, (d) Vegetation coverage in 2013, (e) Vegetation coverage in 2018.

Figure 4

Vegetation coverage of basin from 1998 to 2018. (a) Vegetation coverage in 1998, (b) Vegetation coverage in 2003, (c) Vegetation coverage in 2008, (d) Vegetation coverage in 2013, (e) Vegetation coverage in 2018.

Table 1

NDVI parameter

 19982003200820132018
The highest vegetation 0.738 0.741 0.73 0.897 0.931 
The lowest vegetation 
The average vegetation 0.292 0.456 0.327 0.532 0.372 
The standard deviations 0.123 0.111 0.115 0.119 0.113 
Sum 52859 82770 59429 96676 67730 
 19982003200820132018
The highest vegetation 0.738 0.741 0.73 0.897 0.931 
The lowest vegetation 
The average vegetation 0.292 0.456 0.327 0.532 0.372 
The standard deviations 0.123 0.111 0.115 0.119 0.113 
Sum 52859 82770 59429 96676 67730 

According to Table 1 and Figure 4(a)–4(e), the vegetation cover rate remained at a medium level from 1998 to 2003, and the rate in 2018 was significantly higher than that in 1998, so its terrestrial environment changed greatly. During this period, the vegetation coverage rate fluctuated but increased steadily. The results indicate that the change of connectivity has a significant influence on the vegetation coverage, and the response degree is relatively high.

River water quality

Preliminary tests of water quality at the Huangguoshu Hydrographic Station from 2015 to 2018 (Supplementary information Table II) showed that main pollution factors such as dissolved oxygen (DO), ammonia nitrogen (AN), total phosphorus (TP), and 5 days' biochemical oxygen demand (BOD5) conform to criterion II, and the water qualities in the dry season are better than that in the rainy season. Scientific analysis was conducted with single and comprehensive index method. The eight pollution factors are the DO, BOD5, chemical oxygen demand (COD), potassium permanganate (PP), AN, TP, petroleum, and chrome (C), respectively, were selected based on the water quality of the Dabang River. According to the Environmental Quality Standards for Surface Water (GB 3838-2002) (Supplementary information Tables IV, V), the water quality of criterion III is used as the standard, and the evaluation can be displayed in Table 2.

Table 2

Comprehensive assessment of water quality

YearsPeriodP-valueWater quality
2015 Dry season 0.36 Better 
Rainy season 0.37 Better 
2016 Dry season 0.32 Better 
Rainy season 0.33 Better 
2017 Dry season 0.31 Better 
Rainy season 0.38 Better 
2018 Dry season 0.26 Better 
Rainy season 0.41 Slight pollution 
YearsPeriodP-valueWater quality
2015 Dry season 0.36 Better 
Rainy season 0.37 Better 
2016 Dry season 0.32 Better 
Rainy season 0.33 Better 
2017 Dry season 0.31 Better 
Rainy season 0.38 Better 
2018 Dry season 0.26 Better 
Rainy season 0.41 Slight pollution 

As showed in Table 2, water quality was good from 2015 to 2018. The value of P in the dry season is different from that in the rainy season. Water qualities were standard in the dry season and became a declining trend in the rainy season from 2015 to 2018, but most of them are not polluted. Thus, it indicates that the change of river–lake connectivity has an important influence on the water quality.

Response network of IRSN and identifying the Major Influencing Factors

Referring to the previous content, the influence rule of each factor is obtained, and the response law of each indicator is evaluated in combination with the principle of index construction and Figure I is obtained. The ecological environment in the karst area can be divided into three index layers, namely, water environment, river–lake organism, and connectivity of IRSN. Combining with construction principle and Figure I, the indicator can be determined. The response characteristics of the influencing factors of different layers for IRSN projects can be obtained.

Water environment

At present, the main monitoring indexes of water quality are water temperature, pH, DO, BOD5, TP, TN (total nitrogen), AN, heavy metal, and petroleum, respectively (Chen 2011). The analysis of each index is as follows. Chinese environmental standards have not clearly defined TN content of the river. Moreover, nitrogen and phosphorus have the same effect, so TN can be ignored. Karst areas mainly experienced chemical dissolution, so TP, BOD5, and AN, are the major evaluation indexes. Besides, DO and PH can reflect the living conditions of the aquatics organisms, so they also are crucial factors. Finally, the water temperature is an important factor referring to Figure 3.

River–lake organisms

Referred to assessment of the evaluation index section, the IRSN projects have an effect on the evaporation, precipitation, water level, discharge, water temperature, and vegetation coverage rate. Then, the temperature is an important factor in the terrestrial climate, directly affecting the evaporation, precipitation, and humidity, so humidity is an essential factor. Besides, there is a certain correlation between the discharge and flow rate, so the flow rate is also an important influencing factor.

Connectivity of IRSN

Firstly, the water surface area can not only reflect the degree of IRSN projects but it is also related to the organisms (Feng et al. 2015). Therefore, it is one of the major environmental factors. Secondly, the probability of the basic ecological flow is crucial, because it can provide a solution for dried-up rivers and water-deficient areas. Thirdly, the reasonableness of water allocation can be directly affected by the IRSN projects, so it is also a major factor (Ma et al. 2021). Finally, it is significant to consider the comfort degree of the scenery for Guizhou province, where is rich in tourism resources, because it can reflect the water surface flow and vegetation coverage of the area.

Calculating the weight value and standardization

In total, 20 questionnaires were created and handed out based on the framework of the response factors, and 16 questionnaires were recovered. Then, the AHP method could be used to construct the judgment matrix. MATLAB software was used to calculate the proportion of the index, and the weight of each influencing factor was allocated (Supplementary information Table I). Then, According to the definition of indicators, it can be classified that positive indicators such as DO and vegetation coverage, neutral indicators such as water temperature and temperature, and negative indicators such as COD and BOD5. Referring to the method proposed by Chu et al. (2014) and Qin et al. (2019), the change rates of the critical value could be calculated based on Equations (7) and (8). Then the rates were normalized and mapping from 0 to 1. At last, five levels of the threshold can be concluded that is worst, poor, medium, good, and excellent, respectively.

RESULTS AND DISCUSSION

According to the established karst evaluation index framework and previous calculation method, the threshold levels of three criterion layers that are water environment, river–lake organism, and connectivity of IRSN can be summarized in Table 3. Water environment and connectivity are less sensitive to IRSN projects than river–lake organisms between the good and excellent levels. The indicator of discharge varies greatly from rainy season to dry season, so its threshold levels are divided into two parts. The comprehensive threshold level can also obtain using the same method (Table 4). When the threshold level is greater than excellent, the threshold of IRSN is 0.8148 in both the wet season and the dry season. A comparison of the dry and rainy season between worst and medium was conducted, and it can be concluded that the threshold levels of the rainy season are all greater than the dry season especially in the worst level.

Table 3

Threshold levels of criterion layer

Criterion layerThreshold levels
WorstPoorMediumGoodExcellent
Water environment  <0.0907 0.0907–0.142 0.142–0.2052 0.2052–0.259 >0.259 
River–lake organism Dry season <0.0805 0.0805–0.1345 0.1345–0.2381 0.2381–0.3827 >0.3827 
Rainy season <0.0891 0.0891–0.1431 0.1431–0.2467 0.2467–0.3827 >0.3827 
Connectivity  <0.0758 0.0758–0.1131 0.1131–0.1431 0.1431–0.1731 >0.1731 
Criterion layerThreshold levels
WorstPoorMediumGoodExcellent
Water environment  <0.0907 0.0907–0.142 0.142–0.2052 0.2052–0.259 >0.259 
River–lake organism Dry season <0.0805 0.0805–0.1345 0.1345–0.2381 0.2381–0.3827 >0.3827 
Rainy season <0.0891 0.0891–0.1431 0.1431–0.2467 0.2467–0.3827 >0.3827 
Connectivity  <0.0758 0.0758–0.1131 0.1131–0.1431 0.1431–0.1731 >0.1731 
Table 4

Comprehensive threshold level

Comprehensive threshold levelWorstPoorMediumGoodExcellent
Dry season <0.247 0.247–0.3896 0.3896–0.5864 0.5864–0.8148 >0.8148 
Rainy season <0.2556 0.2556–0.3982 0.3982–0.595 0.595–0.8148 >0.8148 
Comprehensive threshold levelWorstPoorMediumGoodExcellent
Dry season <0.247 0.247–0.3896 0.3896–0.5864 0.5864–0.8148 >0.8148 
Rainy season <0.2556 0.2556–0.3982 0.3982–0.595 0.595–0.8148 >0.8148 

Above all, the boundary values that are from good to medium can be defined as the threshold of the ecological influence in karst areas combining with the actual development in Guizhou province that is the threshold is 0.5864 in the dry season and 0.5950 in the rainy season. The results indicate that the probability of the IRSN project's influence on the ecological environment is about 40%, and the percentage is higher than the other areas (Xu 2018). Therefore, the response of the karst area to the IRSN projects is high.

Case study

The threshold of the Wangerhe Reservoir can be analyzed by calculating previous statistics from the Huangguoshu Hydrological Station such as Figure 3, Table 5, and climates of the basin area section (the ratio of the water area and the total area). Then, the response results can be displayed in Table 6.

Table 5

RI value of the matrix order from 1 to 9

Order123456789
RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45 
Order123456789
RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45 
Table 6

Comprehensive level of Wangerhe Reservoir

Month123456
Index 0.821 0.7945 0.8086 0.7709 0.8323 0.8127 
Month789101112
Index 0.7905 0.8152 0.7550 0.7974 0.7776 0.8172 
Month123456
Index 0.821 0.7945 0.8086 0.7709 0.8323 0.8127 
Month789101112
Index 0.7905 0.8152 0.7550 0.7974 0.7776 0.8172 

The evaluation value of Wangerhe Reservoir can be obtained from the average of Table 6. Referring to the reservoir operation regulation, the calculation should be concentrated on the dry season, so its environmental status can be evaluated at a good level because 0.797 is greater than the standard threshold. In other words, the IRSN project in the Wangerhe River project can provide an improvement in this area.

DISCUSSION

Our results indicate that the established response mechanism can reveal the interaction between ecology and IRSN projects. As shown in Table 4, the threshold level of the study object can be normalized to 5 degrees. It can be further summarized as that when the threshold value is higher than the excellent level, the IRSN project is absolutely beneficial to the environment. Then, the project has a minor impact when the threshold between good and medium. The rest of the range means that the IRSN project is harmful to the environment, which is up to 40% (Table 4). The projects can also improve the connectivity of the water system by about 30% (Chen 2019) and greatly lower the pollutants such as AN, TP, etc. (Yang et al. 2019a). Hence, the IRSN could play an unfavorable role in the natural flow patterns, and it means that the response mechanism of IRSN projects to river ecology is very obvious. Additionally, our findings on the evaluation index system agree with those reported that the evaluation system should be compose of ecology and social network science (Turnbull et al. 2018). Two first-class indicators that are water environment and river–lake organism embody the ecological characteristic, and the connectivity is close to social network science. The second-class indicators of water environment and river–lake organism including vegetation rate, discharge, temperature, BOD5, etc. are similar to the indicators proposed by Wang et al. (2020) and Guo (2016). Additionally, another important first-class indicator that is connectivity reflects the characteristics of karst areas in Guizhou. The major strength of this study was evaluating the influence of the IRSN project in different karst areas. Our study had several limitations. It is necessary to test the threshold in more regions, then the value is more general-purpose. Besides, it is appropriate to add some indicators with the characteristics of karst areas.

CONCLUSIONS

IRSN projects provide a solution for ecological restoration and water environmental governance. Based on the characteristics of the karst basin, this paper tried to put forward a multi-layer indicator system that has 3 first-class indicators (water environment, river–lake organism, connectivity) and 15 second-class indicators for assessment of the ecosystem. The judgment matrix can be calculated with the questionnaire data and the AHP, and the index weight was obtained by MATLAB, then the change rate of the index critical value is normalized. Referring to the relevant research results, the threshold is divided into five levels that are excellent, good, medium, poor, and worst, respectively. It can be concluded that the threshold in the karst areas was defined as the boundary data for medium and good conditions just as 0.5864 in the dry season and 0.5950 in the rainy season. Combining the established ecological response with the threshold level, Wangerhe Reservoir was selected as an example for evaluation. The monthly average of ecological influence degree of this project can be concluded that its weight value is 0.797 and the threshold level is good, which could be the threshold of the ecological environment in Guizhou. The results of this study provide theoretical support for the framework of an evaluation index, and the characteristics of the watershed environment such as the water environment, river–lake ecosystem, and connectivity in karst areas, can be studied further using this method. In the next few phases of the study, it is necessary to apply this value to various karst areas.

ACKNOWLEDGEMENTS

This research was supported by Young talents project of Guizhou Provincial Department of Education KY[2017]120.

ETHICAL STANDARDS

This study did not involve animal or human testing, so does not need to be approved by the Human Ethics Committee.

CONSENT TO PARTICIPATE

Written informed consent was obtained from the individuals.

CONSENT TO PUBLISH

All of the data generated during this study are included in this published article.

COMPETING INTERESTS

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The processed data required to reproduce these findings cannot be shared at this time because the data are part of an ongoing study.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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