Quantifying the relative contribution of natural and human factors to vegetation coverage variation in coastal wetlands in China

Catena 188 (2020) 104429

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Quantifying the relative contribution of natural and human factors to vegetation coverage variation in coastal wetlands in China Jing Hao, Guangyao Xu, Li Luo, Zhong Zhang, Haolu Yang, Hongyuan Li

T

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College of Environmental Science and Engineering, Nankai University, 38 Tong Yan Road, Jinnan District, Tianjin 300350, China Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, 38 Tong Yan Road, Jinnan District, Tianjin 300350, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Vegetation coverage Natural environment Human disturbance Coastal wetlands National scale

Vegetation coverage in coastal wetlands has been significantly altered in response to multiple disturbances over recent decades. However, the major driving factor of vegetation coverage in coastal wetlands remains unclear, with natural and human factors playing interactive roles at the national scale. To identify the major controls of vegetation coverage in coastal wetlands, structural equation modeling (SEM) was conducted to quantify the relative contribution of natural and human factors to vegetation coverage in coastal wetlands in China over a 16year period (2000–2015). The results showed that, in brief, vegetation coverage was slightly degraded (k = −0.0035) over the 2000–2015 period. The area with a probability of vegetation coverage degeneration ≥80% covered 16.6% of the study area. Most annual mean vegetation coverage was lower but improved near the sea, with inverse results obtained near land. In 2000, human1 (population and GDP, r = −0.31) and topography (r = 0.32) were the major controls of vegetation coverage. The vegetation coverage (2000) played the most important and positive role (r = 0.62, r = 0.66) in the vegetation coverage in 2015. Our findings highlight that the major drivers of vegetation coverage in coastal wetlands changed with time and selected variables.

1. Introduction

disturbance factors, which consist of the population, gross domestic product (GDP) (Ma et al., 2011; Han, 2007), land use (Ligate et al., 2018), agricultural activities (Angelini et al., 2016), natural resource exploitation, and government policies, among others, cause the local variations in vegetation coverage (Han, 2007). In the short term, human disturbance is the major factor affecting vegetation coverage (Wu et al., 2017). However, in the long term, vegetation coverage is controlled by both natural and human factors (Wang et al., 2019; Han, 2007). A growing number of studies on the driving factors that induce vegetation coverage changes in coastal wetlands have been conducted. These studies have mainly focused on a single effect factor, such as the climate (Yan et al., 2012; Gao et al., 2019), soil (Liu et al., 2018b; Chang et al., 2018; Li et al., 2018b; Zhao et al., 2018), topography (Chambers et al., 2019), or human disturbances (Maneas et al., 2019; Ma et al., 2011; Han, 2007) at the regional scale and have rarely focused on the interaction between the natural environment and human disturbance (Yang et al., 2019; Liang et al., 2015; Zhou et al., 2018) at the national scale. In eastern China, different types of coastal wetlands have been formed due to the differences in climatic and geographical conditions, the interaction between the river and sea, and the

Coastal wetlands are susceptible to loss in terms of both health and extent via stressors associated with natural environmental changes and anthropogenic disturbances (Chambers et al., 2019; Maneas et al., 2019; Limaye et al., 2017; Estoque et al., 2018). The structure, function, and species composition of a coastal wetland ecosystem are also dramatically affected (Meixler et al., 2018). However, as a sensitive indicator of the ecosystem’s environmental changes (Gao et al., 2019; Cheng et al., 2017; Li, 2003), the vegetation coverage of coastal wetlands has been significantly altered over recent decades, probably due to these multiple disturbances (Simpson et al., 2017; Chambers et al., 2019; Limaye et al., 2017; Maneas et al., 2019). The factors that affect the spatial-temporal dynamics of vegetation coverage in coastal wetlands mainly include the following two types: the natural environment and human disturbances (Zheng et al., 2019; Meixler et al., 2018). Natural environmental factors, which consist of the climate (Tian and Liang, 2016; Liang et al., 2015), topography (Liu et al., 2010), hydrology (Li et al., 2019), and soil (Chang et al., 2018; Li et al., 2018a; Liu et al., 2018b), among others, establish the general pattern of the spatial distribution of the vegetation coverage. Human

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Corresponding author at: College of Environmental Science and Engineering, Nankai University, 38 Tong Yan Road, Jinnan District, Tianjin 300350, China. E-mail address: [email protected] (H. Li).

https://doi.org/10.1016/j.catena.2019.104429 Received 1 August 2019; Received in revised form 23 November 2019; Accepted 18 December 2019 0341-8162/ © 2019 Elsevier B.V. All rights reserved.

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(approximately 29,2439 km2). The study area is attached to eight provinces, including Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong and Guangxi, and two municipalities directly under the Center Government, i.e., Tianjin and Shanghai (Fig. 1b). The main types of natural coastal wetlands include the following: shallow sea water, subtidal aquifers, coral reefs, rock coasts, intertidal silt sand beaches, intertidal beaches, intertidal salt marshes, mangrove swamps, coastal lagoons, coastal freshwater, estuarine water, and delta wetlands (Li and Liu, 2014; Mou et al., 2015). The area is located between the circum-Pacific tectonic belt and the Eurasian tectonic belt and is in the third ladder of the topography in China. The soil types are mainly sandy loam, light loam, light clay and clay. The soil salt content is high due to tidal and flooding effects. Bounded by Jiangsu province, the climate zone is divided into two parts, the north of which belongs to a temperate maritime monsoon climate and the south of which belongs to a subtropical maritime monsoon climate. Precipitation occurs more in the south and less in the north. The mean annual precipitation amounts were 427–2457 mm and 274–2061 mm in 2000 and 2015, respectively. The temperature is also higher in the south and lower in the north. The mean annual temperatures were 9.3–24.8℃ and 8.5–23.8℃ in 2000 and 2015, respectively. The elevation was approximately −3–638 m in 2015. The average population was 754 in 2000 and 983 in 2015 with a 1-km grid. The average GDP values were 1287 and 9228 CNY with a 1-km grid in 2000 and 2015, respectively.

intensification of human disturbances. To our knowledge, almost all types of natural coastal wetlands classified in the Ramsar Convention can be found in China (Li and Liu, 2014; Mou et al., 2015; Zhang and Zhu, 2012). Particularly, the world’s largest continuous mudflat seashore, China's Migratory Bird Sanctuaries along the coast of the Yellow Sea-Bohai Gulf (Phase I), is located in this area (Ding, 2019). Moreover, the social economy of this area is the most developed in China. Therefore, areas with a typical coastal wetland composition and complexity of environmental conditions have become hot spots for international scientific communities. However, the major driving factors of vegetation coverage in coastal wetlands remain unclear, with all factors playing interactive roles in such areas. Here, based on database data obtained by remote-sensing images from 2000 to 2015, the relative contribution of natural-human variables to the vegetation coverage in coastal wetlands in China was analyzed with a structural equation model (SEM), which examines the networks of causal relationships among factors (Grace 2006). In the SEM, a set of causal hypotheses among variables are first constructed based on the theory and results of empirical studies. This conceptual model includes observed and latent variables. The observed variable is a variable that is measured directly. The latent variable is a variable that is not measured directly but estimated by several observed variables. The SEM can identify indirect measurements by integrating latent variables (Yang et al., 2019). The observed data are then analyzed using the conceptual model to test whether the hypothesized covariance structure differs from the observed covariance structure at an acceptable probabilistic level (Grace and Bollen, 2005; Grace et al. 2012). Finally, the relative contribution of each variable to the vegetation coverage can be determined (Xiong et al., 2018). Based on previous studies of coastal wetlands, we developed a series of hypotheses concerning multiple disturbance factors that we predicted might be responsible for the vegetation coverage in the coastal wetlands in China. Natural factors included the climate (precipitation and temperature), topography (elevation and slope), soil (soil moisture, SM; soil electrical conductivity, ECe; total nitrogen, TN; soil organic matter, SOM; and soil organic carbon, SOC), and vegetation coverage in coastal wetlands. Human disturbance factors included the population, gross domestic product (GDP), distance to a river, distance to an isobath (20 m), and distance to a road. We explain our reasons for choosing these variables in the Materials and methods section. To our knowledge, this is the first study to demonstrate the dynamics of the interaction effects of natural and human factors on vegetation coverage in coastal wetlands at the national scale. Based on these findings, we aimed to identify major controls of vegetation coverage over different times. These findings will provide an important scientific foundation for revealing the degradation mechanism of coastal wetlands at larger and even global scales.

2.2. Data sources Excluding the mean annual normalized differential vegetation index (NDVI) for 2000–2015, the study data mainly represents the following years: 2000 and 2015 (Table A1). The NDVI (http://www.resdc.cn/ DOI/doi.aspx?DOIid=49), mean annual precipitation (precipitation) (http://www.resdc.cn/data.aspx?DATAID=229), mean annual temperature (temperature) (http://www.resdc.cn/data.aspx?DATAID= 228), fixed digital elevation model (DEM) (http://www.resdc.cn/data. aspx?DATAID=123), population (http://www.resdc.cn/data.aspx? DATAID=251), GDP data (http://www.resdc.cn/data.aspx?DATAID= 252), river data (for 2000) (http://www.resdc.cn/DOI/doi.aspx? DOIid=44), and road data (for 1995 and 2016) (http://www.resdc. cn/data.aspx?DATAID=237) were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). The population and GDP were defined as the 1km grid population and GDP and were calculated by the multiple factor weight distribution method based on a comprehensive consideration of land use, night light brightness and residential density. The river data for 2000 were also used for 2015, as there were hardly any changes in the river network density. The road data were identified as secondary roads in urban areas because this type of road was denser than other types of roads in China’s road system. Considering that the road data for 2000 and 2015 were difficult to obtain, the road data for 1995 and 2016 replaced 2000 and 2015, respectively. The SM, SOC, and ECe data (from the China Soil Map-Based Harmonized World Soil Database version 1.1) (http://westdc.westgis.ac.cn/data/611f7d50-b419-4d14b4dd-4a944b141175), and the TN and SOM data (from A China Dataset of Soil Properties for Land Surface Modeling) (http://westdc. westgis.ac.cn/data/11573187-fd64-47b1-81a6-0c7c224112a0) in the 0–30 cm layer were provided by the Environmental and Ecological Science Data Center for West China, National Natural Science Foundation of China (http://westdc.westgis.ac.cn). The resolution of the above data is 1000 m. The first continuous 20-m isobath to seaside was obtained from the Isobaths Data of the Fours Seas (Bohai Sea, Huang Sea, East and South China Seas) in China (resolution: 100 m) (https:// download.csdn.net/download/u013151699/10920495) and referred to the ETPOP1 data. The ETOPO1 data (from ETOPO1 Global Relief Model) were provided by NOAA′s (National Oceanic and Atmospheric Administration) National Centers for Environmental Information

2. Materials and methods 2.1. Study site The study was conducted across the entire coastal wetlands of China, which are located on the east coast of China (Fig. 1a) and span three climatic zones: warm temperate zone, subtropical zone and tropical zone. Greater than 1500 rivers through this area flow into the Bohai sea, Yellow sea, East China sea and South China sea. The scope of the study site was defined according to the Brief Regulations for Comprehensive Survey of Coastal Zone and Coastal Area Resources in China. China’s coastline (land) was 18,983 km in 2013, which runs from the Yalujiang estuary to the Beilun estuary (Liu et al., 2015a). The study site takes the coastline as the center, a 10-km buffer zone to the landside as the landward boundary, and the first continuous 20-m isobath to the seaside as the seaward boundary (because the high resolution 15-m isobath was difficult to obtain.) (Liu, 2018; Cheng et al., 2017). The area between the land and sea boundaries is the study site 2

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Fig. 1. Location of the study area: study area location in China (a), eight provinces and two municipalities directly under the Center Government to which the study area is attached (b), design of the sampling sites in the study area (c).

these datasets were projected to the same coordinate system and resampled with a grid of 1000 m × 1000 m. There were 114,491 grids obtained from the study site every year. The sample sizes by random sampling for structural equation modeling (SEM) in 2000 and 2015 were 500 (Fig. 1c). These sampling sites were fixed from 2000 to 2015. After removing the outliers, 410 and 411 samples were available in the SEMs in 2000 and 2015, respectively.

(NCEI) (Amante and Eakins, 2009; https://www.ngdc.noaa.gov). The resolution of these data is 1 arc minute. The coastline data (from World Vector Shoreline (WVS)) were provided by the National GeospatialIntelligence Agency (NGA) (https://www.earthmodels.org). The accuracy of the coastline data is 250 m. The local adjustments of the coastline were made according to the national boundary line of the 1:4,000,000 National Fundamental Geographic Information System Data (2015) (https://download.csdn.net/download/xiukei/9421526). The distance to a river, distance to an isobath (20 m), and distance to a road were defined as the nearest distance from a point to a line. All of 3

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Human1

(a) Elevation

Human1

(b) Elevation

Slope

Precipitation/ Temperature

Slope

Precipitation/ Temperature

Human2

Human2

Topography

Topography

Coverage00 Soil1

Soil1

Soil2

Coverage00

Soil2

Coverage15

Fig. 2. Conceptual models of the hypothesized relationships showing the direct and indirect effects of natural and human variables on the vegetation coverage in 2000 (a) and 2015 (b). The two models were tested for natural and human variables and vegetation coverage parameters in 2000 and 2015. Topography is a latent variable and is reflected by two observed variables, i.e., elevation and slope. Soil1, soil2 and human1, human2, representing the two first axes of the PCA in soil and human in Fig. 7. Coverage00 and coverage15 denote the vegetation coverage in 2000 and 2015, respectively. The directional paths (single-headed arrows) indicate the causal relationships between two variables.

the following 5 ranks (from 5 to 1): degraded (k ≤ −0.004), slightly degraded (−0.004 < k < −0.001), stabilized (−0.001 ≤ k ≤ 0.001), slightly improved (0.001 < k < 0.004) and improved (k ≥ 0.004). The difference among ranks was significant according to the ANOVA test (p < 0.0001) (Fig. A1). The exponential model (having a better fit compared with the spherical model) was selected for the semi-variotropic function model. Based on the fitting parameters of the exponential model, the spatial estimation of indicator kriging interpolation was carried out for the whole study area. Given k ≤ −0.004 means degraded vegetation coverage, the threshold was selected as k ≤ −0.004, which is the probability of vegetation coverage degradation occurrence. The probability of degeneration was divided into the following 5 ranks: 0–20%, 20–40%, 40–60%, 60–80% and 80–100%.

2.3. Data analyses 2.3.1. Vegetation coverage The dimidiate pixel model is the simplest pixel decomposition model, which consists of the land covered and the land not covered by vegetation, in two parts. The NDVI is sensitive to the soil background and depends on the vegetation coverage and leaf area index (Liu et al., 2018a). The dimidiate pixel model with NDVI can be used to invert the vegetation coverage. Previous studies have identified that this method has higher accuracy; the correlation coefficient between the inverted and measured value of vegetation coverage is 0.89–0.99, and the covariance is 0.02–0.06 (Li, 2003; Li et al., 2004; Li, 2011).

VC =

NDVI − NDVIsoil × 100% NDVIveg − NDVIsoil

2.3.3. Principal component analysis (PCA) Considering that the soil properties and human disturbance factors were collinear in their internal variables by the collinearity test, we conducted a principal component analysis (PCA) to obtain the principal component of the soil and human disturbance. Only PCA axes with eigenvalues > 1 were retained to capture most of the variance and reduce the dimensions in soil and human disturbance. Before conducting the PCA, the variables were standardized using Z-scores.

VC is the vegetation coverage, NDVIsoil is the NDVI value of bare land, and NDVIveg is the NDVI value of the land completely covered with vegetation. To remove outliers, the cumulative frequencies of NDVI in each time-series image were calculated. Then, the NDVI value with the cumulative frequency of 5% was selected as the NDVIsoil value. The NDVI value with the cumulative frequency of 95% was selected as the NDVIveg value. In different time-series images, the NDVIsoil and NDVIveg values were different based on this method to minimize distractions (Li, 2003).

2.3.4. Structural equation modeling (SEM) Structural equation modeling (SEM), a methodology for developing and testing hypotheses about the relationships in a system, has its roots in the social sciences and has recently also been used in ecology (Angelini et al., 2016). Below we explain our reasons for choosing the natural and human variables for the SEMs (measurements obtained in 2000 and 2015, Fig. 2) to analyze the impacts of natural and human factors on the vegetation coverage in coastal wetlands. Precipitation and temperature are the two most important climatic factors affecting vegetation coverage in coastal wetlands (Liang et al., 2015). Precipitation shows a positive effect on vegetation coverage with a multi-time scale in the coastal zone (Xu et al., 2010; Liang et al., 2015). We hypothesized that precipitation is positively associated with vegetation coverage in coastal wetlands. The temperature can change the growth pattern of plants (Coldren et al., 2018) and further alter the vegetation coverage (Simpson et al., 2017) in coastal wetlands. For

2.3.2. K value The interannual variation trend of vegetation coverage is expressed by the slope of the linear regression equation of the minimum power of the multiyear value of each grid. If k > 0, the vegetation coverage increases during this period. Otherwise, the vegetation coverage decreases. n

k=

n

n

n ∑i = 1 (i × VCi ) − ∑i = 1 i × ∑i = 1 VCi n

n

n ∑i = 1 i 2 − ( ∑i = 1 i)2

In this equation, n is 16, and i is the sequence number of the year. VCi is the vegetation coverage value in year i. However, there is no uniform standard for dividing the k value (Zhuang et al., 2009). Generally, considering the vegetation coverage in coastal wetlands in China, the change in the trend of vegetation coverage was divided into 4

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vegetation coverage in coastal wetlands (Xu et al., 2010). We hypothesized that temperature would positively or negatively affect the vegetation coverage in coastal wetlands in China. Precipitation and temperature have significant effects on eco-topographic processes; thus, monitoring topographic dynamics has become a crucial aspect of studying the impacts of climate change in coastal zones (Al-Nasrawi et al., 2018). We hypothesized that precipitation and temperature would have significant effects on the topography in coastal wetlands. Elevation and slope are the most important topographic factors affecting the vegetation spatial distribution (Liu et al., 2010). There are positive correlations between the elevation, slope and vegetation coverage in coastal zones (Cheng et al., 2017; Liu et al., 2010). Based on these studies, we hypothesized that the topography (latent variable), including the elevation and slope (observed variables), would positively affect vegetation coverage in coastal wetlands. SM and salinity (shown by ECe) are key factors in the vegetation coverage evolution of coastal wetlands (Li et al., 2018b). They are positively correlated with vegetation coverage at the regional scale (Wang et al., 2011). Moreover, other soil nutrient factors also determine the spatial distribution of vegetation (Li et al., 2018b). The TN, SOM and SOC are strongly related to vegetation coverage (Liu et al., 2018b; Wijnen and Bakker, 1999). Soil1 and soil2 represented the two first components of the principal component analysis (PCA) conducted using the soil properties (SM, ECe, TN, SOM and SOC; Fig. 7a, b). We hypothesized that soil properties (soil1 and soil2) affect the vegetation coverage of coastal wetlands. Among the different environmental factors, soil plays a fundamental role in plant growth (Liu et al., 2018b). It is well-known that environmental factors usually affect soil properties through their influences on soil forming processes (Angelini et al., 2016; Ferraro and Ghersa, 2007). We hypothesized that soil properties would be significantly influenced by the climate, topography and human disturbance. The intensity of human disturbance has gradually increased over the past few decades (Li et al., 2019, Zhou et al., 2018). The coastal zones have become population and economic activity centers (Liu et al., 2015b). The transformation of coastal landscapes has accelerated dramatically (Li et al., 2017). The vegetation growth environment has been changed by the coastal reclamation, and the construction of river embankments and roads. At present, the main and common factors characterizing human disturbance include the population, GDP, distance to a river, distance to an isobath, and distance to a road. In coastal zones, vegetation coverage gradually decreases with an increasing population, GDP, the distance to a river (Han, 2007; Cheng et al., 2017). The distance to a road is positively related to vegetation coverage (Cheng et al., 2017; Ma et al., 2011). The correlation is nonsignificant between the distance to an isobath and vegetation coverage (Cheng et al., 2017). Human1 and human2 represented the two first components of the PCA conducted using the human disturbance variables (population, GDP, distance to a river, distance to an isobath, and distance to a road; Fig. 7c, d). We hypothesized that human disturbance (human1 and human2) would negatively affect the vegetation coverage in coastal wetlands. However, the population and economic development can also change the topography in coastal zones (Al-Nasrawi et al., 2018). We hypothesized that human disturbance would be negatively associated with the topography in coastal wetlands. Moreover, the vegetation plays an important role in the soil forming processes. Yang et al. (2019) identified that spatial variability in soils can be predicted from the vegetation variability in coastal wetlands. The growth pattern of coastal wetland plants can be changed by environmental conditions to alter their capacity to build land (Coldren et al., 2018). Moreover, the vegetation coverage change is autocorrelated (Hou et al., 2010). Based on these findings, we hypothesized that the vegetation coverage in 2000 affected the soil, topography and vegetation coverage in 2015. This hypothesis is only involved in the SEM in 2015. Before the analysis, the normality was tested for all variables based

Fig. 3. Trend of the vegetation coverage in coastal wetlands in 2000–2015 (the gray areas indicate 95% confidence intervals).

(a)

(b)

Fig. 4. Significant difference test of the vegetation coverage for 2000–2008 (a) and 2000 and 2009–2015 (b). Both ends of the black line show two years for the significant difference test. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns denotes a nonsignificant difference, p > 0.05.

instance, in some coastal wetlands, warming can facilitate changes in a plant community from marsh to mangrove (Simpson et al., 2017). When mangrove growth and coverage are enhanced, salt marsh coverage declines (Coldren et al., 2018). Some previous studies that lasted approximately 10a suggested that increasing the temperature positively affects vegetation coverage in a coastal zone (Ma et al., 2011, Liang et al., 2015, Yan et al., 2012); however, other studies with a duration of approximately 20a reported that increasing the temperature reduces

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Fig. 5. Spatial pattern of the annual mean vegetation coverage (a) and its k value (b) in the coastal wetlands in China from 2000 to 2015.

and ratio of chi-square to degrees of freedom (CMIN/DF) < 4 (Rosseel, 2012). Initially, we considered all plausible causal paths among the variables. Then, the nonsignificant (p > 0.05) paths were removed unless they increased the total variation in the variables explained by the combined independent variables, and the significantly (p < 0.05) and practical paths were added to obtain the modified models that were good fits (Grace et al., 2010). The path coefficients are standardized regression coefficients. More path coefficients indicate larger effects of a predictor variable on the response variable. The total standardized effects consist of direct and indirect standardized effects (Grace et al., 2016; Grace and Bollen, 2005).

on a Kolmogorov-Smirnov test. Vegetation coverage, precipitation, temperature, elevation and slope were Johnson-transformed to meet the assumptions of normality. The soil1, soil2, human1, and human2 variables obtained through PCA were also included in the SEM. In the SEM, “topography” is a latent variable and includes the following two highly correlated observed variables: elevation and slope. The other variables are observed variables. Considering that precipitation was highly correlated with temperature, and their different impacts on vegetation coverage, precipitation and temperature were taken as a single climate variable in the different SEMs, we present the standardized path coefficients for four SEMs as follows: (i) two 2000 models that represent multiple relations between vegetation coverage and climate, topography, soil, and human disturbance in 2000, (ii) two 2015 models that represent multiple relations between vegetation coverage and climate, topography, soil, and human disturbance after 16 years. In the two 2015 models, the vegetation coverage in 2000 was used to examine the autocorrelation of vegetation coverage. The model operation involves the process of estimating the model parameters. Maximum likelihood was used to estimate the model parameters and determine the goodness-of-fit for the model. The model is judged as a good fit if p > 0.05, which indicates that the difference between the hypothesized and the observed covariance matrices is not significant (Grace et al., 2010). The chi-square test is usually influenced by the sample size. By contrast, Akaike’s information criterion (AIC) is not influenced by the sample size. It is commonly accepted that the chi-square and AIC values should be as small as possible. Moreover, the following suggest a good model fit: goodness-of-fit index (GFI), comparative fit index (CFI), and TuckerLewis index (TLI) values > 0.95; root mean square error of approximation (RMSEA) < 0.06; root mean square residual (RMR) < 0.05;

2.4. Statistical analysis The relationship between vegetation coverage and topography (taking the elevation as an example) in 2000 was demonstrated through superposition analysis. The extracting elevation and slope from DEM, proximity analysis of the distance to a river, isobath and road, resampling, random sampling, indicator kriging interpolation and superposition between vegetation coverage and DEM, were conducted in ArcGIS 10. The interannual difference in vegetation coverage among years and the difference among k ranks were tested using the ggboxplot function within the ggpubr package in R software. Because the ggboxplot function only calculates 10 group variables at once, the significant difference test by ANOVA was computed two times. Only some significant p values of the test are shown in the figures. Based on R software, the collinearity test, cos2, normality test, and Johnson–transformation were calculated using the car package, factoextra package, ks.test function, and Johnson package, respectively. 6

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The SEM was conducted in AMOS 22 (IBM, Chicago).

3. Results 3.1. Temporal variation of vegetation coverage In general, vegetation coverage had a significantly declining trend in the 2000–2015 period (R2 = 0.735, p < 0.001) (Fig. 3). In 2000–2008, the vegetation coverage was above 70%. However, the vegetation coverage was reduced to 69.2% in 2009. The maximum vegetation coverage occurred in 2010 (71.5%). Since then, the vegetation coverage decreased, especially in 2014 and 2015, when it dramatically decreased to 63.7% and 64.1%, respectively. Overall, the vegetation coverage significantly varied from year to year (p < 0.0001). Take 2000 as the benchmark year, excluding 2008 (p > 0.05), the vegetation coverage in the other years (p < 0.05 or p < 0.0001) was significantly different from that in 2000. The difference in vegetation coverage between 2013 and 2014, 2015 (p < 0.0001) was more obvious than between 2014 and 2015 (p < 0.01) (Fig. 4).

3.2. Spatial variation of vegetation coverage From 2000 to 2015, the spatial pattern of the annual mean vegetation coverage in the coastal wetlands in China was heterogeneous (Fig. 5a). The area with an annual mean vegetation coverage of 80–100% mainly near land was the largest and covered approximately 49.6% of the coastal wetlands (Table A2), such as Liaoning, Shandong, the Yangtze Delta, Fujian and Guangxi. The area with an annual mean vegetation coverage of 60–80% covered approximately 23.6% of the coastal wetlands. The area with an annual mean vegetation coverage of 20–40% was the smallest (only 6.0%). The areas with the lowest annual mean vegetation coverage (0–20%) were mainly near the sea and were concentrated in the coastal wetlands in Tianjin, southeast Heibei, northern Shandong, the Yangtze Delta, and the Pearl River Delta in Guangdong. Overall, the vegetation coverage of the coastal wetlands in China was slightly degraded (mean k = −0.0035) in the 2000–2015 period. The vegetation coverage in areas near the sea and near the rivers were improved (k ≥ 0.004) in approximately 15.6% of the coastal wetlands in China (Table A3), such as in Liaoning, part of northeast Hebei, Tianjin, northern Shandong, the Yangtze Delta, Guangdong (except the Pearl River Delta and Zhanjiang city), and southeast Guangxi (Fig. 5b). The vegetation coverage was slightly improved (0.001 ＜ k ＜0.004) in approximately 12.5% of the coastal wetlands. In approximately 15.1% (such as part of the Yangtze Delta and northern Shandong) and 15.2% of the area, the vegetation coverage remained stable (−0.001 ≤ k ≤ 0.001) and slightly degraded (−0.004 ＜ k ＜ −0.001), respectively. However, the area of degraded vegetation coverage (k ≤ −0.004) very close to land was the largest, covering approximately 41.7% of the coastal wetlands, and it was mainly concentrated in Liaoning, Shandong, the Yangtze Delta, Fujian and the Pearl River Delta. It was these areas with a probability of vegetation coverage degeneration occurrence (k ≤ −0.004) ≥ 80% that covered 16.6% of the study area (Fig. 6, Table A4). The area with a degeneration probability of 60–80% covered approximately 9.3% of the study area. The area with a degeneration probability of 0–20% was the largest (36.9%) and mainly concentrated in the seaward zone. Interestingly, a higher annual mean vegetation coverage always had a lower k value, such as in the landward coastal wetlands in the Liaoning, Shandong, Jiangsu, Shanghai, Zhejiang and Fujian provinces. In fact, the annual mean vegetation coverage had a significantly negative correlation with the k value (p < 0.001).

Fig. 6. Probability of vegetation coverage degeneration occurrence (k ≤ −0.004) in the whole study area based on indicator kriging interpolation.

3.3. Trait analyses of soil and human disturbance In 2000, the percentage of explained variances in soil1 (first PCA axis, Fig. 7a) was 39.9%. The high scores for soil1 were mainly determined by the low SOM and TN. Soil2 was related to a high SOC as opposed to a low SM and ECe. In 2015, soil1 was defined by an increasing SOM and TN. Soil2 was related to a low SOC as opposed to a high SM and ECe (Fig. 7b). In 2000, human1 was largely determined by an increasing population and GDP, and human2 was defined by an increasing distance from an isobath but a decreasing distance to a river (Fig. 7c). In 2015, human1 was mainly determined by an increasing population and GDP, and human2 was defined by an increasing distance to a river and road but a decreasing distance to an isobath (Fig. 7d). 3.4. Quantification of interactive influencing factors For the 2000 SEM (pre) (Fig. 8a, Fig. A2a), taking precipitation as the climate factor, precipitation, human1 and human2 had no significantly direct effects on soil1 or soil2. Soil2 had no significantly direct effect on coverage00 (vegetation coverage in 2000), but the direct link of soil2 to coverage00 increased the total variation in coverage00, as explained by the combined independent variables. Therefore, excluding the direct link of soil2 to coverage00, these nonsignificant paths were consequently removed. Precipitation had a significant correlation with human1, and, in fact, the correlation indeed had practical significance. Therefore, the two-way path between precipitation and human1 was added. Similarly, for the 2000 SEM (tem) (Fig. 8b, Fig. A2b), taking temperature as the climate factor, the direct links of 7

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Fig. 7. Principal component analysis (PCA) of the soil properties in 2000 (a) and 2015 (b), and human disturbance in 2000 (c) and 2015 (d). Cos2 is a representation of the variables for the principal component. A high cos2 indicates a good representation of the variable for the principal component. In this case, the variable is positioned close to the circumference of the correlation circle. SM, soil moisture. ECe, soil electrical conductivity. SOM, soil organic matter. SOC, soil organic carbon. TN, total nitrogen. GDP, gross domestic product. Toriver, distance to a river. Toisobath, distance to an isobath (20 m). Toroad, distance to a road. The variance explained by each principal component is shown in brackets.

In the final 2000 SEM (pre) (Fig. 8a, Table A5), human1 had the largest influence on the vegetation coverage in the coastal wetlands in China, including negative direct (standardized path coefficient, r = −0.29) and indirect effects via the topography, soil1 and soil2 (r = −0.02). The negative total effect of human1 (r = −0.31) indicated that the higher the population and GDP, the lower was the vegetation coverage. Precipitation was the most important effect factor, with the exception of human1, with positive direct (r = 0.15) and indirect effects via topography, soil1 and soil2 (r = 0.08) on vegetation coverage. The positive total effect of precipitation (r = 0.23) indicated that the higher the precipitation, the greater was the vegetation coverage. Topography had positive direct (r = 0.17) and indirect effects via soil1 and soil2 (r = 0.05) on vegetation coverage. The positive total effects of topography (r = 0.22) indicated increasing vegetation coverage with increasing elevation and slope. Soil1 had a weaker effect (r = −0.16) on vegetation coverage. This result indicated that vegetation coverage increased with increases in SOM and TN. Human2 had positive direct (r = 0.13) and negative indirect effects via topography, soil1 and soil2 (r = −0.09) on vegetation coverage. Soil2 had weakly negative and no significant effect on vegetation coverage (r = −0.02). The total effect of human2 (r = 0.04) and soil2 indicated that the roles in the model of human2 and soil2 were very weak. In the final 2000 SEM (tem) (Fig. 8b, Table A5), topography had the largest influence on the vegetation coverage in the coastal wetlands in China. The positive total effect of topography (r = 0.32), including the

temperature to soil1 and soil2, human1 to topography, soil1 and soil2, and human2 to soil1 and soil2 were removed. The direct link of soil2 to coverage00 was retained. For the 2015 SEM (pre) (Fig. 8c, Fig. A2c), the direct link of precipitation to soil2, the direct links of human1 to soil1, human2 to soil1, soil2 and coverage15 (vegetation coverage in 2015), topography to soil2, and coverage00 to soil2 were removed. The direct links of soil1 and soil2 to coverage15 were retained. The two-way paths between human1 and precipitation, between human2 and precipitation, between precipitation and coverage00, and between human1 and coverage00 were added. For the 2015 SEM (tem) (Fig. 8d, Fig. A2d), the direct effect paths of temperature to soil1 and soil2, human1 to topography and soil1, human2 to soil1, soil2 and coverage15, topography to soil2, and coverage00 to soil2 were also removed. The direct links of soil1 and soil2 to coverage15 were retained. The two-way path between human1 and coverage00 was added. The resultant SEMs yielded adequate fits for coverage00 (pre) (χ2 = 20.579, df = 14, p = 0.113, CMIN/DF = 1.470, RMR = 0.039, GFI = 0.988, CFI = 0.989, RMSEA = 0.034, AIC = 64.579), coverage00 (tem) (χ2 = 17.267, df = 16, p = 0.369, CMIN/DF = 1.079, RMR = 0.039, GFI = 0.990, CFI = 0.998, RMSEA = 0.014, AIC = 57.267), coverage15 (pre) (χ2 = 22.140, df = 16, p = 0.139, CMIN/DF = 1.384, RMR = 0.029, GFI = 0.988, CFI = 0.993, RMSEA = 0.031, AIC = 80.140) and coverage15 (tem) (χ2 = 31.257, df = 21, p = 0.069, CMIN/DF = 1.488, RMR = 0.046, GFI = 0.983, CFI = 0.988, RMSEA = 0.035, AIC = 79.257). 8

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Fig. 8. Final structural equation models showing the multivariate relationships in coastal wetlands in China among the quantified natural variables (climate, topography, soil1 and soil2) and human variables (human1 and human2) in 2000 (a and b), and the quantified natural variables (climate, topography, soil1 and soil2, coverage00) and human variables (human1 and human2) in 2015 (c and d). Topography is a latent variable and is reflected by the following two observed variables: elevation and slope. Soil1, soil2 and human1, human2 represent the first two axes of the PCA in soil and human shown in Fig. 7. Coverage00 and coverage15 denote the vegetation coverage in 2000 and 2015, respectively. The directional paths (single-headed arrows) indicate that the causal relationship is statistically significant or non-significant. The two-way arrows indicate statistically significant correlations. The thickness of the arrows is proportional to the standardized path coefficients shown on each arrow. Solid lines show statistically significant positive paths. Gray lines represent statistically significant negative paths. Dotted black lines show statistically non-significant positive paths. Dotted gray lines show statistically non-significant negative paths. Blue boxes depict natural variables, and purple red boxes depict human variables. The amount of variance explained for each dependent variable (R2) in the model is shown inside the respective box. * p < 0.05, ** p < 0.01, *** p < 0.001, ns denotes a nonsignificant path, p > 0.05. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

(r = 0.17) and negative indirect effects via topography, soil1 and soil2 (r = −0.14) on vegetation coverage. The total effect of human2 (r = 0.03) on vegetation coverage was still weak. However, human2 had a largely negative direct effect on topography in 2000 SEM (pre) (r = −0.42) and SEM (tem) (r = −0.43). It was found that an increasing elevation and slope occurred with increasing distance to a river but decreasing distance to an isobath. The role of soil2 was still the weakest in the model. In the final 2015 SEM (pre) (Fig. 8c, Table A5), coverage00 had the largest influence (r = 0.62) on coverage15, including a strongly positive effect (r = 0.56) and weakly positive effect via topography and soil1 (r = 0.06). This result indicated that the higher was coverage00,

positive direct (r = 0.27) and indirect effects via soil1 and soil2 (r = 0.05), indicated that the higher elevation and slope, the greater was the vegetation coverage (Fig. 9). Human1 only had a strong negative direct (r = −0.26) effect on the vegetation coverage in coastal wetlands. Soil1 had a negative direct (r = −0.17) effect on vegetation coverage. Temperature had negative total effects (r = −0.10) on vegetation coverage, including negative direct (r = −0.15) and positive indirect effects via topography, soil1 and soil2 (r = 0.05), which indicated that vegetation coverage was reduced with increasing temperature. Compared with the total effect of precipitation (r = 0.23) on vegetation coverage, the total effect of temperature on vegetation coverage was weaker and opposite. Human2 had positive direct 9

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Fig. 9. Superposition between topography (taking the elevation as an example) and vegetation coverage.

effect via topography and soil1 (r = 0.07). Moreover, coverage00 also significantly affected the topography (total effect, r = 0.30) and soil1 (total effect, r = 0.25). Topography, as the second major impact driver of coverage15, still had a strongly positive direct effect (r = 0.30) and weakly negative indirect effect via soil1 (r = −0.01) on coverage15. Compared with the temperature in 2000 (tem), it differed in that the temperature had a positive total effect (r = 0.16) on the vegetation coverage, including a positive direct effect (r = 0.13) and an indirect effect via topography and soil1 (r = 0.03). In 2015, the effects of precipitation and temperature on vegetation coverage were positive, but the total effect of temperature was slightly stronger than that of precipitation (r = 0.14). The effect of human1 (r = −0.10) became weaker compared with its effect in the other models. However, the positive total effect of human2 (r = 0.13) on vegetation coverage was stronger than in 2000 (tem) (r = 0.03). Compared with the negative effect of human2 on topography in 2000, human2 had significantly positive direct effects on topography in the 2015 SEM (pre) (r = 0.47) and SEM (tem) (r = 0.45). It was found that a higher elevation and slope were associated with being distant from a river and road but

the higher was coverage15. Moreover, topography (total effect, r = 0.26) and soil1 (total effect, r = 0.22) were also significantly affected by coverage00. This result indicated that a higher coverage00 could result in increases in elevation, slope, SOM and TN. Topography, as the second major impact driver of vegetation coverage, had a strongly positive direct effect (r = 0.32) and weakly negative indirect effect via soil1 (r = −0.02) on vegetation coverage. The negative total effect of human1 on vegetation coverage became weaker (r = −0.15) than the effect in the 2000 SEM (pre) (r = −0.31), including a negative direct effect (r = −0.11) and indirect effect via topography, soil1 and soil2 (r = −0.04). However, the positive total effect of human2 on coverage became stronger (r = 0.14) than the effect in 2000 (r = 0.04). Precipitation had positive direct (r = 0.11) and indirect effects via topography and soil1 (r = 0.03) on vegetation coverage. The total effect of precipitation (r = 0.14) on vegetation coverage became weaker than the effect in the 2000 SEM (pre) (r = 0.23). In the final 2015 SEM (tem) (Fig. 8d, Table A5), coverage00 still had the strongest influence (total effect, r = 0.66) on coverage15, including a strongly positive direct effect (r = 0.59) and weakly positive indirect 10

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closer to an isobath. Compared with the SEMs in 2000, the SEMs with vegetation coverage (2000) in 2015 largely accounted for the vegetation variance. It was found that more attention should be paid to the autocorrelation of vegetation properties in revealing the major controls of vegetation variance. Our results showed that the major drivers of vegetation coverage changed with time and the variables selected in coastal wetlands. This finding was beneficial to correctly understand the dynamic contribution of natural and human disturbances to coastal wetland vegetation. Given the typicality of the coastal wetland composition and complexity of environmental conditions in China, the results obtained in our study could provide an important scientific foundation for degradation mechanism research on coastal wetlands at a larger even global scale.

near the sea in part of Liaoning, Tianjin, Hebei, northern Shandong, northeast Jiangsu, northeast and southeast Zhejiang and most of Guangdong had lower annual mean vegetation coverages and higher k values, as well as lower degeneration probabilities. The research of Han (2007) confirmed this finding of our study. Although the annual mean vegetation coverage was lower due to the influence of soil moisture and salinity, these areas are uninhabitable and have smaller populations, GDP values and construction land (Han, 2007). Moreover, coastal land reclamation has rapidly developed in recent years (Meng et al., 2017). The coastal wetland vegetation has gradually recovered to maintain or improve vegetation coverage in idle areas. In contrast, in Guangxi, the annual mean vegetation coverage has decreased while the probability of degeneration has increased from land to sea due to topography and human factor effects (Cheng et al., 2017).

4. Discussion

4.2. Major controls of vegetation coverage variation

4.1. Temporal-spatial variation of vegetation coverage

Generally, at the larger scale, the dynamics of vegetation coverage in coastal zones are greatly affected by climate (Chen et al., 2001). Some studies have considered the vegetation in coastal wetlands to be a zonal vegetation form, where the spatial distribution of the vegetation depends on the habitat conditions, such as the soil or topography (Hou et al., 2013). However, others have insisted that human disturbance is the major control factor (e.g., Fang et al., 2003). However, predominant control of vegetation coverage in coastal wetlands is well documented mostly from a single natural or human disturbance factor, and the relative contribution of natural and human disturbance factors is not well studied. Here, we accounted for vegetation coverage driver mechanisms at different temporal scales by comparing the relative importance of natural and human disturbance at a national spatial scale. Human1 (population and GDP) had the largest influence on vegetation coverage in the coastal wetlands in China in the final 2000 SEM (pre), in agreement with Morawitz et al. (2006). In the final 2000 SEM (tem), topography was the most important effect factor. It has been shown that the different relative importance levels of natural and human factors on vegetation coverage at national scale were influenced by different climate factors in 2000 (Liang et al., 2015). Our results showed that the vegetation coverage (2000) had the largest influence, but human disturbances had weaker effects on the vegetation coverage in the 2015 SEM (pre) and SEM (tem). The final 2000 SEM (pre) and SEM (tem) models only explained approximately 19.0% and 18.5% of the total variance in coverage00, while the 2015 SEM (pre) and SEM (tem) models including coverage00 could explain approximately 56.4% and 57.7% of the total variance in coverage15, respectively. These results clearly highlighted that vegetation coverage had a high autocorrelation to control the major development direction of vegetation coverage in coastal wetlands. One possibility is that prior colonization and dispersal events resulted in the high autocorrelation (Thomson et al. 1996). However, the spatial patterns of environmental conditions can control vegetation coverage processes (Mancera et al., 2005). In our study, topography and soil1 were influenced by coverage00 (a higher coverage00 resulted in higher elevation and slope; a higher coverage00 resulted in higher SOM and TN) and affected coverage15. Therefore, the spatial patterns of topography and soil1 were another cause of the autocorrelation of vegetation coverage. Although such an autocorrelation is a general property of ecological variables (Legendre, 1993), it is often neglected in revealing the major controls of vegetation coverage in coastal wetlands. During the 16 years, natural and human factors did not exert constant influences on the development of vegetation coverage in the coastal wetlands in China. Our study confirmed that the relative contribution of natural and human variables to vegetation coverage changed with time. In line with our hypothesis of precipitation, we identified a positive association of precipitation with vegetation coverage in coastal wetlands both in 2000 SEM (pre) and 2015 SEM (pre), consistent with previous studies (Ma et al., 2011; Yan et al., 2012). However, our study

Our results showed that from 2000 to 2015, the variation of vegetation coverage was quadratic curve due to the complex process in response to natural and human disturbances (Gao et al., 2019). Vegetation coverage significantly decreased over the 16-year period, particularly from 2014 to 2015, due to natural environmental changes and the intensification of human disturbances (Liang et al., 2015; Gao et al., 2019). However, Hou et al. (2010) found that vegetation coverage had a generally increasing trend. In their research, the boundary of the study site was provinces that were larger than ours, and the study period was 11 years, which are responsible for the difference. The significant differences of vegetation coverage among most years might be due to the reproductive characteristics of coastal plants and the intensification of environmental disturbances (Coldren et al., 2018). In the present study, the variations of the annual mean vegetation coverage, its k value and the probability of vegetation coverage degeneration (k ≤ −0.004) in coastal wetlands were spatially heterogeneous. Especially, in the landward area in Liaoning, Shandong, the Yangtze Delta, Fujian and the Pearl River Delta, the annual mean vegetation coverage was higher, but the k value was lower. A portion of the area with a patchy distribution had a probability of vegetation coverage degeneration ≥80%. Other studies have reported similar results. In a study performed by Zhang et al. (2014), the coastal wetlands in China were divided into 28 zones. Southern Liaoning, northern Shandong, northeast Zhejiang and the Pearl River Delta are zones of serious degradation. There are 14 areas belonging to the moderate degradation and 10 areas belonging to the slight degradation classifications. Hou et al. (2010) found that the vegetation in the Bohai Rim, Yangtze Delta and Pearl River Delta has been seriously degraded and will continue to degrade. The degradation is moderate and will continue around many small and medium-sized cities. The areas with slight and continuous degradation are the most widespread and dispersive. The local natural environment and economic development are responsible for the spatial heterogeneity (Zhang et al., 2014; Hou et al., 2010). Interestingly, our study indicated that for the annual mean vegetation coverage, the higher vegetation coverage near land was accompanied by lower vegetation coverage near the sea. In contrast, for the k value, the degraded vegetation coverage and probability of degeneration ≥80% mainly near land was accompanied by improved vegetation coverage near the sea. It was found that urbanization results in significant vegetation loss within 10-km buffer zones (Han, 2007). The areas with higher annual mean vegetation coverage always have higher urbanization levels, such as in the Bohai Rim, Yangtze Delta and Pearl River Delta, which destroys the microenvironment for vegetation growth and enhances damage to vegetation with an increasing population size and rapid economy development (Liu et al., 2010; Ma et al., 2011; Hou et al., 2010). Therefore, the vegetation coverage k value was lower and had a higher degeneration probability. However, the areas 11

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population, GDP and construction land and develops into a halophytic vegetation cover area before infrastructure objects are established (Han, 2007). These areas also contribute further evidence for the particularly important role of human2 (distance to a river, distance to a road, and distance to an isobath) in vegetation coverage in 2015. The effect of human2 on the topography changed from negative in 2000 to positive in 2015, which was inconsistent with our hypothesis. Urban development was potentially concentrated in regions with flat topographies, far away from the sea in 2000. However, in later years with rapid urbanization, urban land became scarce. Coastal reclamation rapidly expanded to increase the elevation and slope in coastal zones (Zhang et al., 2014).

suggested that temperature negatively affected coverage00 in 2000 and positively affected coverage15 in 2015. Our hypothesis of temperature was reasonable. The effect of temperature on vegetation coverage varies not only with changes at the temporal scale and the climate itself but also with changes of the topography and soil (Liang et al., 2015). The hypothesis that precipitation and temperature have significant effects on topography in coastal wetlands was supported in our study. AlNasrawi et al. (2018) also confirmed that precipitation and temperature have significant effects on eco-topographic processes, with slight impacts on the production and transportation rates of sediment (AlNasrawi et al., 2016). Interestingly, our results showed that the effects of precipitation and temperature on vegetation coverage had different strengths. In 2000, the positive effect strength of precipitation was higher than the negative effect strength of temperature and was different from that described in previous studies (Yan et al., 2012; Tian and Liang, 2016) due to the different temporal-spatial scales. However, in 2015, the positive effect strength of temperature was higher than the positive effect strength of precipitation in our study. Yan et al. (2012) and Tian and Liang (2016) showed that temperature plays a larger role than precipitation. Here, vegetation coverage was enhanced by increasing the elevation and slope, which supported our hypothesis regarding topography. An explanation for this finding might be that flat areas are more conducive to urban construction, and hilly and sediment areas change the flood time of the vegetation growth, resulting in spatial heterogeneity of the vegetation community distribution (Ma et al., 2011; Li et al., 2018b). Our results showed that, generally, vegetation coverage increased with increases in SOM, TN, SM and ECe and decreases in SOC, consistent with the hypothesis regarding soil. This result indicates that the coastal wetlands in China are still N and SOM limitation areas, and rich TN and SOM can promote vegetation growth (Wijnen and Bakker, 1999). Moreover, Li et al. (2018a) identified that rises in SM and ECe promote the increase in vegetation coverage. In contrast, increasing SOC limits vegetation growth, in contrast to the studies of Chang et al. (2018). The major explanation for these findings was the difference in ecological level of the target plants. In other studies, the ecological level of the target plants was a community, while in our research, the ecological level of the target plants was a coastal wetland vegetation form. Our study suggested that soil2 (SOC, SM and ECe) in 2000 and soil1 (SOM and TN) and soil2 (SOC, SM and ECe) in 2015 had nonsignificant or even different effects on vegetation coverage due to the relative hysteresis of the soil development (Mascaro and Vivoni, 2016) and the limitation of the sampling time. However, human disturbance had an indirect effect on soil at a national scale in 2000. In 2015, human disturbance had not only a direct but also an indirect effect on the soil. Moreover, the climate, topography and coverage00 also significantly influenced the soil properties. Coverage00 had positive direct and indirect effects via topography and soil on coverage15. The corresponding hypothesis was supported in the present research. Indeed human1 (population and GDP) had a negative effect on vegetation coverage, which was in line with our hypothesis and identified by Xu et al. (2010) and Morawitz et al. (2006). However, our hypothesis of human disturbance was inconsistent with human2. In 2000, human2 (distance to an isobath and distance to a river) had a positive effect on vegetation coverage. Because being near an isobath means a higher density of salt and a higher water level, the soil quality is so low that vegetation colonization and growth is impeded (Xian et al., 2019). By contrast, a suitable SM and a lower density of salt near a river provided good growth conditions for aquatic and hygrophyte vegetation. However, in the present study in 2015, increasing the distance to a river and road and decreasing the distance to an isobath resulted in increased vegetation coverage because, during the 16 year-period, the development of estuaries and road network was enhanced (Cheng et al., 2017). The growing environment of plants near rivers and roads was fragmentized and destroyed (Zhang, 2013). The expanded coastal reclamation that gradually approaches the isobath has a smaller

4.3. Main limitations of the study In our study, taking 2000 as a benchmark year, the major controls of the vegetation coverage in the coastal wetlands in China over a 16-year period (2000–2015) were assessed using SEM. However, the soil, DEM and river data were limited by the period. In the future, if updated soil, DEM and river data, as well as other natural and human factors data are available, research on the driving mechanism of vegetation properties in coastal wetlands will be more interesting. Given the anabatic natural environmental changes and anthropogenic disturbances, as well as the typicality of the coastal wetland composition, this study provides an important scientific foundation for revealing the degradation mechanism of coastal wetlands vegetation at the global scale. Although coastal wetlands in China are diverse, the types of coastal wetlands show some differences at a larger scale. Additionally, natural and human factors are different, and the major drivers of vegetation coverage in coastal wetlands may vary to some extent. Changes in the major drivers of vegetation coverage in coastal wetlands at a larger scale will be our next research objective.

5. Conclusions In summary, from 2000 to 2015, vegetation coverage was slightly degraded (k = −0.0035) in the coastal wetlands in China. The area with a probability of vegetation coverage degeneration ≥80% covered 16.6% of the study area. The annual mean vegetation coverage had a significantly negative correlation with its interannual variation trend (k value) (p < 0.001). For the 2000 SEM (pre) and SEM (tem), human1 (population and GDP, r = −0.31) and topography (r = 0.32) had the largest influence on vegetation coverage, respectively. For the 2015 SEM (pre) and SEM (tem), the vegetation coverage (2000) played the most important role (r = 0.62, r = 0.66) in the vegetation coverage in 2005. Compared with the SEM in 2000, the SEM with vegetation coverage (2000) in 2015 largely accounted for the vegetation variance. Thus, more attention should be paid to the autocorrelation of vegetation properties in revealing the major controls of vegetation variance. Our findings indicate that the major drivers of vegetation coverage changed with time and the variables selected in the coastal wetlands. This study provides an important scientific foundation for revealing the degradation mechanism of coastal wetlands at larger and even global scales.

Declaration of Competing Interest The authors declared that there is no conflict of interest.

Acknowledgments This work was supported by the Project of Tianjin Science and Technology Planning of China (grant no. 18ZXSZSF00200) 12

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Appendix A. Supplementary material

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