Spatial heterogeneity of estuarine wetland ecosystem health ...

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An estuarine wetland ecosystem health. (EWEH) evaluation model was established. • The model covered all aspects of the natural and anthropogenic factors.
Science of the Total Environment 634 (2018) 1445–1462

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors Yuan Chi a,b, Wei Zheng a,b,⁎, Honghua Shi a,b, Jingkuan Sun c, Zhanyong Fu c a b c

The First Institute of Oceanography, State Oceanic Administration, Qingdao, Shandong Province 266061, PR China Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong Province 266061, PR China Shandong Provincial Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou University, Binzhou, Shandong Province 256603, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• An estuarine wetland ecosystem health (EWEH) evaluation model was established. • The model covered all aspects of the natural and anthropogenic factors. • Each factor in the model possesses its own spatial heterogeneity. • The spatial heterogeneity of EWEH in the Yellow River Delta was evaluated. • The model can be widely used to evaluate EWEH because of its high applicability.

a r t i c l e

i n f o

Article history: Received 9 February 2018 Received in revised form 11 March 2018 Accepted 6 April 2018 Available online xxxx Editor: Ouyang Wei Keywords: Estuarine wetland ecosystem health Spatial heterogeneity External factors Internal factors Ecological state Yellow River Delta

a b s t r a c t The evaluation of estuarine wetland ecosystem health (EWEH) is vital and difficult due to complex influencing factors and their spatial heterogeneities. An EWEH evaluation model was established in this study on the basis of the typical features of estuarine wetland ecosystems with focus on spatial heterogeneity. The index system comprises external factors, internal factors, and ecological state, and covers all aspects of the natural and anthropogenic factors, with each index possessing its own spatial heterogeneity. The Yellow River Delta, a typical estuarine wetland in China, was selected as the study area to demonstrate the model. Results indicated that the present EWEH in the entire study area was in good status with distinct spatial heterogeneity. Ecosystem productivity, seawater intrusion, human interference, and Yellow River input were the most relevant indexes of EWEH. The temporal variations of EWEH fluctuated from 1987 to 2016. The decrease in the Yellow River input and the increase in human activity intensity deteriorated EWEH, whereas the alongshore embankment and nature reserve construction improved EWEH in certain parts. The influence of natural factors continuously decreased, and human activity became the main driving factor of the EWEH spatial variation. Our model was proven to possess comprehensive reflections of estuarine wetland ecological characteristics, full exhibitions of spatial heterogeneity, and high applicability; therefore, it can be widely used to evaluate EWEH in different areas. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: The First Institute of Oceanography, State Oceanic Administration; Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, No.6, Xianxialing Road, Qingdao, Shandong Province 266061, PR China. E-mail address: zhengwei@fio.org.cn. (W. Zheng).

https://doi.org/10.1016/j.scitotenv.2018.04.085 0048-9697/© 2018 Elsevier B.V. All rights reserved.

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1. Introduction Ecosystem health and its evaluation have drawn considerable attention with the expansion and intensification of human activities and their increasing pressure on natural ecosystems (Olson et al., 1997; Halpern et al., 2008). In recent years, ecosystem health evaluations have been conducted in different regions and ecosystem types, such as urban and rural areas, forests, basins, wetlands, islands, coastal waters, and oceans, and good results were achieved, thereby providing important references for maintaining ecosystem health in specific areas (Styers et al., 2010; Halpern et al., 2012; Zhang et al., 2013; Li and Li, 2014; Sun et al., 2016; Zhang et al., 2017a; Cheng et al., 2018; Meng et al., 2018; Wu et al., 2018). Estuarine wetlands are important particular wetland ecosystems. They are important because of their large number, considerable size, various ecological functions, and distinct location advantages (Dai et al., 2013; Chi et al., 2018a). The particularity is represented by multiple natural and anthropogenic factors that influence ecosystem health. Strong land–sea interactions, including water and sediment inputs with changeable amounts (Kong et al., 2015), sea level rise (Blum and Roberts, 2009), continuous coastal erosion (Xing et al., 2016), and severe seawater intrusion (Zhang et al., 2011a), result in the complex and spatially heterogeneous natural conditions in estuarine wetlands. Meanwhile, the special locations and unique natural resources of the estuarine wetland attract considerable human activities with various exploitation types and increasing intensity (Wang and Yu, 2013; Chi et al., 2018a). Human activity threatens the estuarine wetlands in many ways and enhances the spatial heterogeneity of the land surface characteristics. The evaluation of estuarine wetland ecosystem health (EWEH) is therefore important in revealing the spatial variation of ecological characteristics and identifying the main influencing factors; it can also provide important references for maintaining ecological balance under multiple natural and anthropogenic factors. The complex influencing factors and their spatial heterogeneities are the key points of EWEH evaluation. Ecosystem health evaluations have been conducted in previous studies. Index selection is essential in the evaluation process. In some studies, targeted indexes with simple composition and based on specific ecological features, including physical, chemical, or biological indicators, were proposed (Dai et al., 2013; Gredilla et al., 2014; Tang et al., 2015; Bebianno et al., 2015; Mali et al., 2016; Li et al., 2017a; Cheng et al., 2018). Other studies established index system to represent ecosystem health under certain stress factors, such as seashore reclamation (Jin et al., 2016) and heavy rainfall (Wu et al., 2018). These indexes aimed to identify specific aspects of ecosystem health but could not represent its comprehensive characteristics and variation trend, as well as the relationships among different components in the ecosystem. Comprehensive evaluation models, such as pressure–state–response (PSR) (Sun et al., 2016) and natural–social–economic models (Li and Li, 2014; Meng et al., 2018), were also established. However, these studies were conducted in administrative divisions where the needed data were easily collected; but they were not applicable to estuarine wetlands, which are naturally formed areas. Moreover, the considerations of spatial heterogeneity in previous studies were insufficient; certain evaluations focused on the overall characteristics of ecosystem health without considering spatial heterogeneity (Li and Li, 2014; Jin et al., 2016; Niu et al., 2017); other spatial heterogeneity evaluations were conducted at the scales of sampling site (Zhang et al., 2013; Bebianno et al., 2015; Tang et al., 2015, 2018; Li et al., 2017a) or administrative division (Sun et al., 2016; Meng et al., 2018). Ecological characteristics and health conditions may considerably differ in different positions within estuarine wetlands. The spatial heterogeneity evaluation is critical for grasping the spatial characteristics of ecosystem health and providing effective countermeasures for maintaining the EWEH. Therefore, an EWEH evaluation model was established based on the typical features of estuarine wetlands with full considerations of the complex natural and anthropogenic factors and their spatial heterogeneities; the model used an index

system covering all aspects of external factors, internal factors, and ecological state of estuarine wetlands, with each index possessing its own spatial heterogeneity. We aimed to provide a new EWEH evaluation model with comprehensive reflections of natural and anthropogenic factors, full considerations of spatial heterogeneity, and high applicability in estuarine wetlands. A typical estuarine wetland in China, the Yellow River Delta, was selected as the study area to demonstrate the model. 2. Materials and methods 2.1. Study area The Yellow River Delta lies south to Bohai Bay and west to Laizhou Bay, which are in Bohai Sea in China (Fig. 1). It is the largest newly formed coastal wetland in North China and has a large amount of sediment input; it is covered with various wetland vegetation and provides an important habitat for rare and endangered bird species; but it exhibits evident vulnerabilities to disturbances (Kong et al., 2015). Furthermore, the delta is facing increasing ecological pressure due to complex natural and anthropogenic factors (Cui and Li, 2011; Chi et al., 2018a). Ecosystem health in the Yellow River Delta has been evaluated using different methods, including PSR model (Niu et al., 2017), AZTI marine biotic index based on macrobenthos (Li et al., 2017a), and seashore reclamation effect evaluation model (Jin et al., 2016). However, the spatial heterogeneity of ecosystem health has been rarely studied, and the natural and anthropogenic factors need to be further explored. The scope of the study area was determined using Yuwa Village as the zenith and Tiaohe Estuary and Hongguang fishing port as the endpoints on the basis of the modern Yellow River Delta, which was formed since 1934 (Jiang et al., 2011). The study area is in Dongying City, Shandong Province, China, and can be divided into three districts, namely, Kenli District (49%), Hekou District (44%), and Lijin County (7%). A national nature reserve with an area of 1530 km2 was established in 1992 for the conservation of estuarine wetland ecosystems. This reserve is distributed on both sides of the new and old estuaries and divided into core, buffer, and experimental zones. 2.2. Data sources 2.2.1. Field investigation Field investigations were conducted in February and August 2017. The sampling sites were set on the basis of distribution, representativeness, and accessibility, and 104 sampling sites were investigated (Fig. 1). The latitude and longitude of each sampling site were measured using a handheld GPS device, and land cover types and plant community were recorded. Surface soil samples (0–30 cm) were collected in February. Soil salinity, moisture content, organic matter, total nitrogen, available phosphorus, and available potassium were then measured in a laboratory. Plant data, including species, abundance, and height in tree, shrub, and herb layers, were recorded in August. 2.2.2. Remote sensing Remote sensing images acquired from satellites LANDSAT 5 in August 1987, September 1995, and July 2005 and LANDSAT 8 in August 2016, which have little or no clouds with a resolution of 30 m × 30 m, were adopted. Radiometric calibration and band fusion were conducted via ENVI 5.3 and ArcGIS 10.0. The links between the zenith and the endpoints were considered the landward boundary, and the coastlines were considered the seaward boundary. The outlines of the study area in different years were obtained (Fig. S1). The land cover types were classified as wetland vegetation, farmland, bare land, water area, saltern, building land, traffic land, and industrial land through visual interpretation of the remote sensing data and then modified by field investigation (Fig. S1). Wetland vegetation

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Fig. 1. Location and sampling sites of the study area.

comprises natural plants, with Phragmites australis as the dominant species, mixed with other species, such as Suaeda salsa, Tamarix chinensis, Apocynum venetum, Sonchus arvensis, and Glycine soja. Farmlands are mostly planted with rice, wheat, corn, and lotus. Bare lands consist of mudflats in alongshore areas and other forms of uncovered lands in inner study area. Water areas are composed of natural water areas, including the current and old courses of the Yellow River, and several artificial water areas, which are regarded as sources of drinking water with good ecological conditions. Salterns, which have regular shapes and large areas, are mainly distributed in alongshore areas. Building

lands are composed of urban and rural residential areas, including the towns of Huanghekou, Gudao, and Xianhe. Traffic lands comprise road and port. The former includes highway S7201 and provincial roads S312, S310, and S228, and the latter mainly refers to Dongying Port. Industrial lands comprise oil fields and refineries. Farmlands and salterns, together with building, traffic, and industrial lands, are typical human exploitation types in the study area, which put pressures on EWEH. The coastline types were divided into natural and artificial coastlines, of which the natural coastlines were unprotected and more influenced by coastal erosion.

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2.2.3. Hydrological and meteorological data The runoffs and sediments of the Yellow River into the sea in different years were collected from the water resources and sediment bulletins of the Yellow River (Yellow River Conservancy Commission of MWR, 2017a, 2017b). Meteorological data included total solar radiation, astronomical radiation, sunshine duration, temperature, rainfall, and humidity, which were derived from the long-term monitoring data of the weather stations in Dongying City and neighboring areas (Zhang et al., 2011b; Li et al., 2017b). 2.3. EWEH evaluation index system An EWEH evaluation index system was established on the basis of the typical features of estuarine wetland ecosystems with focus on spatial heterogeneity. The spatial heterogeneity indicates the unevenness and complexity of the value of a parameter shown in a specific area; it is presented by the differences of the value in different spatial locations, either in site or regional scale. In our study, it referred to the spatial differences of EWEH in the regional scale in the Yellow River Delta. The index system covered multiple natural and anthropogenic factors, and comprised all aspects of the typical features of the estuarine wetland (Fig. S2). The system was composed of three elements and 12 indexes (Table 1). The element layer referred to external factors, internal factors, and ecological state and each element consisted of several indexes. The index layer exhibited all kinds of indexes and each index has its own spatial heterogeneity (Table 1). The evaluation values of all indexes ranged from 0 to 1. 2.3.1. External factors 2.3.1.1. Yellow River input. The Yellow River and the sea are the most distinctive and important land features influencing the study area. The water–sediment input via the Yellow River is the essential driving factor of the formation and extension of the delta (Cui and Li, 2011; Yu et al., 2011). Moreover, the Yellow River provides an important water source and possesses considerable ecological functions for vegetation growth and soil quality improvement, and these functions decrease with the increase in distance to the river. The annual runoff in Lijin hydrological station, which is the last hydrological station along the Yellow River into the sea, was adopted to represent the Yellow River input. The average runoff from 1987 to 2016 was considered the evaluation standard, and the distance to the

Table 1 EWEH evaluation index system. Element

Index

External factors Yellow river input Coastal erosion Seawater intrusion Biological invasion Internal factors Human interference Ecological conservation Ecological state

Runoff Unprotected coastline Soil salinization Harmful alien species Human interference index Nature reserve construction Soil salinization control Landscape pattern Landscape pattern index Ecosystem productivity Net primary productivity Biodiversity Biodiversity index Soil property Fertility Moisture content

Index type E P E P E P P E E E E E

R − R I − I R + R + I R R I I I

The indexes could be divided into E and P Indexes according to their spatial distribution; E indexes cover the entire study area; P indexes cover parts of the study area and could be divided into P− and P+ indexes; P− indexes indicate the negative effects, and P+ indexes indicate the positive effects. The higher the values of E and P+ indexes are, the better the evaluation results are; the higher the values of P− indexes are, the worse the evaluation results are. According to the data source, the indexes could be divided into R and I indexes. R indexes are sourced from remote sensing data; I indexes are sourced from field investigation.

Yellow River was used as the spatial heterogeneity rule. The equation is as follows: E1 ¼

Runoff x Runoff x DTY s −0:5   ; Runoff a Runoff a DTYmax

ð1Þ

where E1 is the evaluation value of Yellow River input; Runoffx and Runoffa are the runoff in year x and average runoff from 1987 to 2016, respectively (m3); and DTYs and DTYmax are the distance of position s and maximum distance to the Yellow River, respectively (km). The distance data were obtained through the Euclidean Distance tool in ArcGIS 10.0. 2.3.1.2. Coastal erosion. The area of the Yellow River Delta increased as a whole because of continuous sediment input (Chi et al., 2018a). However, the sediment input has experienced a sharp decrease with annual sediment load dropping from 19.1 × 103 t/a to 1.06 × 103 t/a during 2005–2016. The decrease was caused by the hydrological condition change and the dam and reservoir construction in the upper reaches. The decrease in sediment input, the rise of sea level, and increase in storm surges have resulted in serious erosion in parts of the coastlines (Xing et al., 2016), especially in the old estuary, where the coastline receded by 7 km from 1976 to 2004 (Zhang and Li, 2008). Meanwhile, the embankment construction along the coastline weakened the influence of coastal erosion and protected the alongshore areas to some extent (Jin et al., 2016). On the basis of the length and position of unprotected coastlines, the distance to the unprotected coastline was used as the spatial heterogeneity rule, and 7 km was considered the maximum distance influenced by the coastal erosion (Zhang and Li, 2008). The equation is as follows: 0 DTU s 1− 7

E2 ¼

DTU s N7km DTU s ≤7km

! ;

ð2Þ

where E2 is the evaluation value of coastal erosion and DTUs is the distance of position s to unprotected coastline (km). The distance data were obtained through the Euclidean Distance tool in ArcGIS 10.0. 2.3.1.3. Seawater intrusion. Seawater intrusion is also a serious threat to natural ecosystems (Tian et al., 2003). Soil salinization is a common ecological problem in coastal wetlands caused mainly by seawater intrusion and considerably threatens soil quality, biological community, and agricultural production (Zhang et al., 2011a; Yu et al., 2014; Gao et al., 2015). On the basis of the soil salinity data acquired by the field investigation, soil salinization was considered an indicator of seawater intrusion. A mass fraction of 0.2% was considered the evaluation standard of soil salinization (Wang et al., 1993), and spatial interpolation was used as the spatial heterogeneity rule. The equation is as follows: 0

1 1 Sas ≤0:2% @ A; E3 ¼ 0:2% Sas N0:2% Sas

ð3Þ

where E3 is the evaluation value of seawater intrusion and Sas is the soil salinity in position s (%). The spatial distribution of E3 was obtained through the Inverse Distance Weighted (IDW) interpolation tool in ArcGIS 10.0. 2.3.1.4. Biological invasion. Plant communities in estuarine wetlands comprise only a few species, which are simple in composition and thus vulnerable to biological invasion (Yan et al., 2014). On the basis of species data acquired by the field investigation, the Invasive Alien Species List in China was referenced and three alien species, namely, Spartina alterniflora, Aster subulatus, and Conyza canadensis, were found. The distance to the closest sampling site with alien species was used

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as the spatial heterogeneity rule, and 5 km was considered the maximum distance influenced by biological invasion (Ren et al., 2014). The equation is as follows: E4 ¼

0 DTAs 1− 5

DTAs N5km

!

DTAs ≤5km

;

ð4Þ

where E4 is the evaluation value of biological invasion and DTAs is the distance of position s to the closest site with alien species (km). The distance data were obtained through the Euclidean Distance tool in ArcGIS 10.0. 2.3.2. Internal factors 2.3.2.1. Human interference. Human activity in the Yellow River Delta is increasing in scope and intensity and exhibits noticeable spatial heterogeneity (Ren and Walker, 1998; Xu, 2005; Zhang et al., 2017b; Chi et al., 2018a). Human interferences, such as farming, urban construction, oil exploitation, and traffic, threaten the natural ecosystem by changing landform, splitting landscape, affecting community structure, and emitting pollutants (Bi et al., 2011; Li et al., 2011), and different exploitation types may have varying influences (Xu et al., 2015; Chi et al., 2018a). On the basis of different exploitation types and their spatial distributions, a human interference index (HII) was adopted to represent human activity intensity and its negative effects on natural ecosystem (Chi et al., 2018a). The equation is as follows: 0

5

∑i¼1 EAi  IC i =TA B HII ¼ @ ∑5i¼1 α  EAi  IC i =TA 5

∑i¼1 β  EAi  IC i =TA

1 Di ¼ 0m C 0mbDi ≤100m A; 100mbDi ≤200m

ð5Þ

where EAi is the area of exploitation type i, where i = 1, 2, and 3 denote construction land, saltern, and farmland, respectively; the construction land indicates the building, traffic, and industrial lands; ICi is the influence coefficient of exploitation type i, where IC1, IC2, and IC3 are 1, 0.8, and 0.4, respectively. Di is the distance to exploitation type i (m); and α and β are the attenuation coefficients when 0 b Di ≤ 100 m and 100 m b Di ≤ 200 m, respectively (Chi et al., 2018a). A high HII indicates a considerable human interference. The detailed calculation method was reported by Chi et al. (2018a). The evaluation value of human interference (E5) was calculated using the following equation: E5 ¼ 1‐HII;

ð6Þ

2.3.2.2. Nature reserve construction. Human activity can not only interfere natural ecosystem but also maintain ecological balance through ecological construction and management. Several ecological projects have been conducted since the 1990s. A national nature reserve was established in 1992, and its construction has been continuously enhanced (Wang et al., 2013). The evaluation value of nature reserve construction (E6) was determined by the established time, effort and investment, and spatial distributions of the core, buffer, and experimental zones. The nature reserve was established in 1992, thus the E6 was given as 0 in the entire area in 1987. In the 1990s, the construction was under exploration and the effect was limited. In the 2000s, the ecological construction in the new estuary was enhanced and the effect was improved while the old estuary was still given insufficient attention. Since 2010, the old estuary has received increasing attention and several ecological conservation measures have been conducted; the ecological effects on the new and old estuaries were both good (Chi et al., 2018b). The core zone is protected from human activity, and all individuals and organizations are forbidden from entering this zone. The buffer zone is around the core zone and can be accessed only by people conducting scientific investigations

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and research. The experimental zone is around the core and buffer zones and open to specific human activities, including scientific experiment, investigation, teaching, practice, visits, and tourism. Therefore, we obtained the spatial distribution of E6 in Table 2. 2.3.2.3. Soil salinization control. The soil salinization problem also received considerable attentions and was effectively controlled in parts of the Yellow River Delta (Li et al., 2003). The evaluation value of soil salinization control (E7) was represented by the variation in soil salinity between the base and present years. The soil salinity data in the present year (2016) were obtained through the field sampling, and those in the base year (2006) were obtained from the study of Fan et al. (2014). The spatial distributions of E7 were determined by the differences in soil salinity levels between the two years. 2.3.3. Ecological state 2.3.3.1. Landscape pattern. Landscape pattern reflects the large-scale land surface characteristics of estuarine wetlands; it refers to the joint effects of natural and anthropogenic factors on geographic space, and generally influences the structures, functions, and processes of the ecosystem (Strohbach and Haase, 2012; Ramalho et al., 2014). The Patch Analyst module in ArcGIS10.0 was used to calculate landscape pattern indexes on the basis of land cover type data. Various landscape pattern indexes are available in the module, and noticeable correlations are observed among them; thus, representative landscape pattern indexes, namely, patch density (PD) for landscape fragmentation and edge density (ED) for edge effect, were selected. The average values of PD and ED from four years were used as the standard values, and the evaluation value of landscape pattern (E8) was calculated using the following equation: 

0 B E8 ¼ B @

 0:5 

1

SPD SED þ C PD;s C ED;s



 1 SPD SED N1 C þ C PD;s C ED;s  C; A SPD SED 0:5  ≤1 þ C PD;s C ED;s

0:5 

ð7Þ

where CPD,s and CED,s are the PD and ED values of position s, respectively, and SPD and SED are the evaluation standards of PD and ED, respectively. 2.3.3.2. Ecosystem productivity. Ecosystem productivity, which represents the efficiency and quality of an ecosystem, is fundamental for the survival and reproduction of each member and an important factor for identifying carbon source/sink characteristics (Field et al., 1998; Chi et al., 2018b). Vegetation net primary productivity (NPP) was used to reflect ecosystem productivity; it was estimated using the Carnegie–Ames– Stanford Approach (Potter et al., 1993), which required remote sensing images and meteorological data. The method is as follows: NPPðt Þ ¼ APARðt Þ  ξðt Þ;

ð8Þ

APARðt Þ ¼ PARðt Þ  FPARðt Þ;

ð9Þ

Table 2 Evaluation value of nature reserve construction (E6). Year

1987 1995 2005 2016

New estuary

Old estuary

Core zone

Buffer zone

Experimental zone

Core zone

Buffer zone

Experimental zone

0 0.3 0.6 0.6

0 0.2 0.4 0.4

0 0.1 0.2 0.2

0 0.3 0.3 0.6

0 0.2 0.2 0.4

0 0.1 0.1 0.2

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Fig. 2. Spatial distributions of present external factors: The colors from red to green indicate that the evaluation results are getting better. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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ξðt Þ ¼ ft ðt Þ  fwðt Þ  ξmax ;

ð10Þ

ecosystem productivity (E9) was calculated using the following equation: 0

−2

−1

where NPP(t) is the NPP in year t (g C m month ); APAR(t) is the absorbed photosynthetic active radiation in year t (MJ m−2 month−1); ξ(t) is the actual light utilization efficiency in year t (g C MJ−1); PAR(t) is the photosynthetic active radiation in year t (MJ m−2 month−1); FPAR(t) is the fraction of photosynthetic active radiation in year t (%); ft(t) and fw(t) are the temperature and water stress factors in year t, respectively (%); and ξmax is the maximum light use efficiency of each vegetation (g C MJ−1). The detailed calculation method was reported by Chi et al. (2018b). The average value of NPP at summer in China was used as the evaluation standard (Zhu et al., 2007), and the evaluation value of

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B 1 E9 ¼ B @ C NPP;s SNPP

1 C NPP;s N1 C SNPP C; A C NPP;s ≤1 SNPP

ð11Þ

where CNPP,s is the NPP value in position s and SNPP is the evaluation standard of NPP. 2.3.3.3. Biodiversity. Biodiversity reflects the stability of wetland ecosystems; it plays a fundamental role in maintaining and regulating the ecosystem material cycle and energy flow and exhibits enhanced importance in estuarine wetlands with simple species composition (Tilman et al., 2006; Ma, 2013). The field investigation results indicated that woody species were few, and most were artificial plants, whereas varieties of shrubs and herbs were widely distributed. Thus, shrub and herb diversity was

Fig. 3. Spatial distributions of present internal factors: Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4. Spatial distributions of present ecological state: Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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used to represent the biodiversity using the Shannon–Wiener (H′) and Pielou (E) indexes, which are widely applied in ecology research. The calculation methods are as follows: H0s ¼ −

n X

IV s;i LnIV s;i ;

ð12Þ

i¼1

Es ¼ H0s =LnðNs Þ;

ð13Þ

where H′s and Es are the Shannon–Wiener and Pielou indexes of site s, respectively; Ns is the species number in site s; and IVs,i is the importance value of species i in site s, which is calculated using the following equation:   Abs;i Cos;i Hes;i =3; ð14Þ þ þ IV s;i ¼ Abs Cos Hes where Abs,i, Cos,i, and Hes,i are the abundance, coverage, and height, respectively, of species i in site s, and Abs, Cos, and Hes are the total abundance, total coverage, and total height, respectively, in site s. On the basis of biodiversity data acquired by the field investigation, the average values of H′ and E in the entire study area were considered the evaluation standards of H′ and E, respectively, the spatial interpolation was used as the spatial heterogeneity rule, and the evaluation result of biodiversity (E10) was calculated using the following equation: 0 B B E10 ¼ B B @

0:5 

1 0:5 

C H0 ;s SH0

C E;s þ SE

! 0:5 

! 1 C E;s N1 C SH 0 SE C ! C; C C H0 ;s C E;s A ≤1 þ SH0 SE

C H0 ;s

þ

ð15Þ

where CH',s and CE,s are the H′ and E values in position s, respectively, and SH′ and SE are the evaluation standards of H′ and E, respectively. The spatial distribution of E10 was obtained through the IDW interpolation tool in ArcGIS 10.0. 2.3.3.4. Soil fertility. Soil is the base of ecosystems and highly significant in providing nutrients for plant growth (Schlesinger, 1991; Ouyang et al., 1999). On the basis of soil fertility data acquired by field investigation, the Grade II standards of soil fertility classification from the Green Food—Environmental Quality for Production Area (NY/T391-2013), which is an agricultural professional standard in China published in 2013, were considered the evaluation standards, and the spatial interpolation was used as the spatial heterogeneity rule. The equations are as follows:  E11 ¼

1 FIs

 FI s N1 ; FIs ≤1

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi # u" 2 u 1 2 t FI s ¼ ∑P i; s þ P min;s =2; n P i;s ¼ C i;s =Si ;

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content data acquired by field investigation, a mass fraction of 20% was considered the evaluation standard, and the spatial interpolation was used as the spatial heterogeneity rule. The equation is as follows: 0 1 E12 ¼ @ C MC;s SMC

C MC;s NSMC C MC;s ≤SMC

1 A;

ð19Þ

where E12 is the evaluation value of soil moisture content, CMC,s is the moisture content in position s, and SMC is the evaluation standard of moisture content. The spatial distribution of E12 was obtained through the IDW interpolation tool in ArcGIS 10.0. 2.4. EWEH spatial distribution evaluation model 2.4.1. Evaluation unit The size of the evaluation unit was determined based on pixel resolution, study area, and index calculation. The pixel resolution of the remote sensing data was 30 m × 30 m; the study area was approximately 2414 km2 in 2016; and the calculations of some indexes, such as landscape pattern and human interference, demanded a sufficient area of the evaluation unit. The unit size should satisfy the spatial heterogeneity demand, contain sufficient areas and pixel number, and avoid excessive data. If the size was too small, then the unit could not meet the area demand of index calculation and would create massive data, which was unnecessary; if the size was too large, then it could not exhibit the spatial heterogeneity. The 300 m × 300 m grids were generated using the Fishnet tool in ArcGIS 10.0, which could balance the aforementioned mentioned demands (Chi et al., 2018a). The evaluation units of the study area in different years were obtained, and there were 25,737, 25,681, 26,471, and 27,445 evaluation units in 1987, 1995, 2005, and 2016, respectively, because the study areas differed across years. 2.4.2. Evaluation method 2.4.2.1. Evaluation method of present EWEH. The evaluation value of an element was calculated on the basis of index types and evaluation values

ð16Þ

ð17Þ ð18Þ

where E11 is the evaluation value of soil fertility; FIs is the fertility index in position s; and Pi,s, Ci,s, and Si,s are the fertility condition, measured value, and standard value of factor i, respectively, in position s, including four factors of organic matter, total nitrogen, available phosphorus, and available potassium. Pmin,s is the minimum value of Pi,s in position s. The spatial distribution of E11 was obtained through the IDW interpolation tool in ArcGIS 10.0. 2.3.3.5. Soil moisture content. Water is essential for wetlands, and wetland damage and degradation are closely related to water; soil water is the water source of vegetations and a key ecological factor (Zhou and Gong, 2007; Wang et al., 2011). On the basis of soil moisture

Fig. 5. Spatial distribution of present EWEHI: Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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contained in the element using the following equation: Eelement ¼

X X 1X EPþ − EP− ; EE þ n

ð20Þ

where Eelement is the evaluation value of an element; and EE, EP+, and EP− are the evaluation values of E, P+, and P− indexes, respectively. A higher Eelement indicated a better evaluation result. EWEH index (EWEHI) was proposed to identify the EWEH status. External and internal factors jointly influenced the ecological state; and the ecological state was the direct reflection of EWEH. Therefore, the EWEHI was calculated using the following equation: EWEHI ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ½αEex þ 1‐αEin   Eec ;

ð21Þ

where Eex, Ein, and Eec are the evaluation values of external factors, internal factors, and ecological state, respectively, and α indicates the weight of Eex relative to Ein and was set as 0.5 to avoid subjectivity of weight assignment and represent the equal importance of external and internal factors in our study area. The EWEHI of each evaluation unit was calculated using the aforementioned method. To reveal the overall characteristics, the EWEHIs of the entire area, different land cover types, districts, and nature reserves were analyzed. To reveal the spatial characteristics, the maps of indexes, elements, and EWEHI were exhibited. The EWEHI ranged from 0 to 1.0; a higher EWEHI indicated a better EWEH status. The EWEH levels were classified using an equal division method (Shen et al., 2016; Chi et al., 2018a); we set the EWEH levels as worst, bad, ordinary, good, and best status when EWEHIs were 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0, respectively. The evaluation results of the elements and indexes were also analyzed using the intervals. 2.4.2.2. Evaluation method for temporal variation of EWEH. The analysis of EWEH temporal variation is important for identifying the change characteristics and trend of ecosystem health. No continuous field investigation data were available; thus, the data for I indexes in historical years could not be obtained. Remote sensing provides a convenient and economic approach for calculating R indexes. In the EWEH index system, R indexes covered all the elements and could represent the EWEH to a certain extent. Therefore, R indexes were adopted to represent the EWEH temporal variation in the years of 1987, 1995, 2005, and 2016 through the aforementioned procedure; the elements and EWEHI were labeled by “(R)”, that is, external factors (R), internal factors (R), ecological state (R), and EWEHI (R). The EWEHI (R) in 2016 was used to compare with EWEHI in 2016, and the latter involved both R and I indexes. 2.5. Correlation analysis among indexes, elements, and EWEH To identify the main influencing factors of EWEH, correlation analyses were conducted through IBM SPSS 18. The evaluation values of indexes, elements, and EWEH in all evaluation units were taken as the input data. The correlations of EWEHI with the indexes and elements in 2016 and those of EWEHI (R) with the indexes and elements in 1987, 1995, 2005, and 2016 were analyzed, respectively.

areas. For coastal erosion, E2 was high near the unprotected coastlines and decreased with the distance to them; the overall influence scale was small. The influence scale of seawater intrusion was large; most alongshore areas, especially salterns, possessed low E3 and exhibited distinct soil salinization. Similar to coastal erosion, biological invasion influenced a small scale; high E4 areas were scattered in the study area. In general, the bad and worst status zones of external factors were distributed in alongshore areas and other areas influenced by biological invasion, the good status zones were in the central and west regions of the study area, other areas were in ordinary status, and nearly no best status zones existed. The spatial distributions of present internal factors are shown in Fig. 3. Human interference showed obvious spatial differences; the areas of urban construction, industry, port, and saltern were generally in bad or worst status; farmland was distributed most in ordinary status; and wetland vegetation, bare land, and water area were in good or best status. Nature reserve construction belonged to P+ indexes; it improved the EWEH in the nature reserve and differed across the core, buffer, and experimental zones. For soil salinization control, E7 was high in the alongshore vegetation areas and low in the newly reclaimed saltern and farmland areas. In general, the spatial heterogeneity of internal factors was distinct with urban construction, industry, and saltern areas in bad or worst status, a small part of farmland in ordinary status, and other areas, especially the nature reserve, in good or best status. The spatial distributions of present ecological state are shown in Fig. 4. For landscape pattern, E8 was low in the juncture of different landscape types or in small landscape patch areas and high in the homogeneous and large landscape patch areas. Ecosystem productivity noticeably differed in different land cover types; wetland vegetation and farmland were mostly in good or best status; and other types were in ordinary, bad, or worst status. For biodiversity, most areas possessed high E10 and were in best status; the bad and worst status zones were scattered in the study area. Soil fertility showed an overall distribution that the west was high and the east was low; most areas were in good or ordinary status. Soil moisture content was sufficient in nearly entire study area, and most areas were in best status. In general, the ecological state was fine, and best and good status zones occupied most areas. 3.1.2. Spatial distribution of present EWEH The EWEHI was 0.67 in the entire study area, which was in good status. Farmland, wetland vegetation, and water area possessed high EWEHI, followed by bare land, building land, and traffic land, and the EWEHIs of saltern and industrial land were lowest. In different districts, the EWEHIs followed this ascending order: Hekou District, Kenli District, and Lijin County. The EWEHIs were higher inside the nature reserve than that outside it, in experimental zone than that in core and buffer zones, in new estuary than that in old estuary (Fig. S3). The present EWEH exhibited distinct spatial heterogeneity. Urban, industry, and saltern areas were mostly in bad or ordinary status, the wetland vegetation and farmland in the central and west parts of the study area and along the current and old courses of the Yellow River were in best status, and other areas were in good status. The proportions of different EWEH status zones were in the following descending order: good (55.3%), ordinary (20.8%), best (18.5%), bad (5.5%), and worst (0) (Fig. 5).

3. Results 3.2. Temporal variations of EWEH 3.1. Present EWEH 3.1.1. Spatial distributions of indexes and elements The spatial distributions of present external factors are shown in Fig. 2. For Yellow River input, E1 gradually decreased with the increase in the distance to the current and old courses of the Yellow River. Since 2010, ecological water supplemental projects have resulted in an increase in ecological functions of the old course for the surrounding

3.2.1. Spatiotemporal characteristics of elements The spatiotemporal characteristics of external factors (R), internal factors (R), and ecological state (R) are shown in Figs. 6–8. The external factors (R) increased in 1987–2005 and decreased in 2005–2016, and it was worst in 2016. The influence of coastal erosion continuously decreased from 1987 to 2016 (Fig. 6). The internal factors (R) kept on decreasing from 1987 to 2016; the areas in best status were largest in 1987

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Fig. 6. Spatiotemporal characteristics of external factors (R): Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

and then drastically decreased; in 2016, the areas in ordinary status were the largest and the worst status zones occupied a considerable area (Fig. 7). The ecological state (R) also continuously decreased, and the spatial heterogeneity in 1995 and 2005 was more distinct than that in 1987 and 2016 (Fig. 8).

3.2.2. Spatiotemporal characteristics of EWEH The EWEHI (R) in the entire study area in 1987, 1995, 2005, and 2016 was 0.78, 0.75, 0.80, and 0.64, respectively, showing a fluctuating change. The EWEH was in best status in 2005 and in good status in 1987, 1995, and 2016. Different districts demonstrated the same change characteristics as the entire study area; among the districts, the EWEHI (R) in Linjin County was the lowest in 1995 but the highest in the other years. Regarding different land cover types, the EWEHI (R) in saltern initially increased and then decreased, and the other types were consistent with the change characteristics of the entire study area. The EWEHI (R) outside the nature reserve showed a larger fluctuation than that inside it; that in the core and buffer zones continuously decreased; and

that in the new estuary was higher than that in the old estuary, but the differences between them decreased from 1987 to 2016 (Fig. S4). The spatial distributions of EWEHI (R) in different years are shown in Fig. 9. In 1987, most areas were in best or good status, the good status zone was mainly distributed in the alongshore areas, the best status zone was in the central and west parts, the other status zones occupied a small proportion, and the spatial heterogeneity was not obvious. In 1995, a considerable area in best status changed to good status and the spatial heterogeneity increased. In 2005, the best status zone increased and the good status zone decreased, whereas the spatial heterogeneity slightly decreased. In 2016, the good status zone occupied the largest area; the ordinary and bad status zones greatly increased, and the spatial heterogeneity also increased. The proportions of different EWEH status areas revealed that the best status zone first decreased, then increased, and then decreased; the good status zone showed the opposite characteristics; the conversion between the best and good status zones was the main cause of the spatiotemporal variation of EWEH; the ordinary and bad status zones continuously increased; and the worst status zone occupied little proportion in all years (Fig. S5).

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Fig. 7. Spatiotemporal characteristics of internal factors (R): Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.3. Correlations among indexes, elements, and EWEH The correlation results of EWEHI with the indexes and elements in 2016 were shown in Table 3. Ecosystem productivity, seawater intrusion, human interference, and Yellow River input possessed high correlation coefficients, which decreased in the aforementioned order. Coastal erosion, nature reserve construction, biological invasion, landscape pattern, and soil moisture content possessed low correlation coefficients, which decreased in the aforementioned order. The correlation coefficients of the three elements were in the following descending order: ecological state, internal factors, and external factors. The correlation results of EWEHI (R) with the indexes and elements in 1987, 1995, 2005, and 2016 were shown in Table 4. Yellow River input possessed higher correlation coefficients in 1987 and 2016 than in 1995 and 2005. The correlation coefficient of coastal erosion was high in 1987 and then decreased until it reached the lowest in 2016. Human interference and nature reserve construction overall showed an increase in correlation coefficient. Landscape pattern possessed

lower correlation coefficients in 1987 and 2016 than in 1995 and 2005. The correlation coefficient of ecosystem productivity was high and continuously increased from 1987 to 2016. In different elements, the correlation coefficients of external and internal factors continuously decreased and increased, respectively; and that of ecological state was high and increased from 1987 to 2016. 4. Discussion 4.1. Feature and applicability of the EWEH evaluation model The EWEH evaluation index system consisted of external factors, internal factors, and ecological state, which involved various natural and anthropogenic factors. The external factors were mainly driven by natural conditions and increasingly influenced by human activity. Water flow–sediment regulation and man-made course diversion have changed the amount and direction of the Yellow River input (Fan et al., 2006; Kong et al., 2015). The embankment construction has

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Fig. 8. Spatiotemporal characteristics of ecological state (R): Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

improved the resistance to coastal erosion (Jin et al., 2016; Xing et al., 2016). Groundwater extraction has aggravated soil salinization, for which control measurements were also implemented (Liu and Huang, 2013). Certain alien species, such as Spartina alterniflora, have been introduced by humans (Ren et al., 2014). In the internal factors, human interference and regulation simultaneously influenced the estuarine wetland; they are both anthropogenic factors and influenced by natural conditions (Chi et al., 2018a). For instance, saltern was reclaimed in alongshore areas; farmland was reclaimed in areas with low salinity; and the nature reserve was established in areas with considerable ecological functions and vulnerability to disturbances. Ecological state, which is the direct reflection of EWEH, involved the landscape fragmentation and edge effect (landscape pattern), vegetation growth condition and ecosystem vitality (ecosystem productivity), species composition and ecosystem stability (biodiversity), and water–nutrient supply and ecosystem base (soil property). They reflected EWEH from different perspectives under the multiple influences of natural condition and human activity.

In the index system, some indexes were frequently used in previous studies, such as river input, landscape pattern index, NPP, and biodiversity (Sun et al., 2016; Zhang et al., 2017a; Chi et al., 2018c). Some indexes were first integrated in evaluating ecosystem health based on the features of estuarine wetlands, such as seawater intrusion, biological invasion, soil salinization control, and nature reserve construction. Moreover, the full exhibitions of spatial heterogeneity were also the features of our model. All the 12 indexes differed spatially and were divided into E and P indexes according to their spatial characteristics. The geographic information system method was used to realize the spatial exhibitions of the indexes, elements, and EWEH. For the applicability of the evaluation model, half of the 12 indexes were R indexes and the other half were I indexes. The data required for the R indexes could be derived from remote sensing images, such as LANDSAT series images, with convenient access, low cost, stable quality, and continuous data. The data required for the I indexes were obtained through conventional approaches of investigation, sampling, and measurement. The evaluations could be conducted through ArcGIS

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Fig. 9. Spatiotemporal characteristics of EWEHI (R): Different colors indicate different evaluation results as for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

and ENVI with clear processes and simple operations. The accessibility of the needed data and operability of the evaluation method allowed the model to have high applicability. Because of comprehensive reflections of estuarine wetland ecological characteristics, full exhibitions of spatial heterogeneity, and high applicability, the EWEH evaluation model can be widely used in evaluating EWEH in different areas.

A simplified version of the EWEH evaluation model was proposed on the basis of the R indexes to evaluate EWEH temporal variations. The simplified version decreased the comprehensiveness but increased the applicability of the model due to the decrease in index number and the avoidance of field investigation. The correlations among different indexes revealed the significant correlations between the R and I indexes

Table 3 Correlation coefficients of EWEHI with indexes and elements in 2016. Item

CC

Item

CC

Item

CC

Item

CC

Item

CC

E1 E2 E3

0.538⁎⁎ −0.178⁎⁎ 0.672⁎⁎

E4 E5 E6

−0.087⁎⁎ 0.620⁎⁎ 0.118⁎⁎

E7 E8 E9

0.259⁎⁎ −0.027⁎⁎ 0.732⁎⁎

E10 E11 E12

0.441⁎⁎ 0.358⁎⁎ 0.021⁎⁎

Eex Ein Eec

0.589⁎⁎ 0.739⁎⁎ 0.758⁎⁎

CC: correlation coefficient. E1:Yellow River input (R); E2:coastal erosion (R); E3: seawater intrusion (I); E4: biological invasion (I); E5: human interference (R); E6: nature reserve construction (R); E7: soil salinization control (I); E8: landscape pattern (R); E9: ecosystem productivity (R); E10: biodiversity (I); E11: soil fertility (I); E12: soil moisture content (I). Eex: external factors; Ein: internal factors; Eec: ecological state. R and I indicate the R and I indexes, respectively. ⁎⁎ P b 0.01.

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Table 4 Correlation coefficients of EWEHI (R) with indexes and elements from 1987 to 2016. Year

E1

E2

E5

E6

E8

E9

Eex(R)

Ein(R)

Eec(R)

1987 1995 2005 2016

0.387⁎⁎ 0.163⁎⁎ 0.320⁎⁎ 0.400⁎⁎

−0.565⁎⁎ −0.317⁎⁎ −0.348⁎⁎ −0.127⁎⁎

0.437⁎⁎ 0.596⁎⁎ 0.585⁎⁎ 0.698⁎⁎

– 0.030⁎⁎ −0.040⁎⁎ 0.125⁎⁎

0.172⁎⁎ 0.386⁎⁎ 0.330⁎⁎ 0.152⁎⁎

0.706⁎⁎ 0.728⁎⁎ 0.815⁎⁎ 0.849⁎⁎

0.608⁎⁎ 0.335⁎⁎ 0.365⁎⁎ 0.219⁎⁎

0.437⁎⁎ 0.601⁎⁎ 0.606⁎⁎ 0.723⁎⁎

0.711⁎⁎ 0.769⁎⁎ 0.858⁎⁎ 0.878⁎⁎

Abbreviations for indexes are the same as for Table 3. Eex(R): external factors (R); Ein(R): internal factors (R); Eec(R): ecological state (R). ⁎⁎ P b 0.01.

(Table S1). It indicated that the R indexes, which were derived from the remote sensing data, possessed considerable ecological significance and were in accordance with the field data. The EWEHI and EWEHI (R) in the entire study area in 2016 were 0.67 and 0.64, respectively, which were both in good status and close to each other; their spatial distributions demonstrated similar characteristics (Figs. 5 and 9). Therefore, EWEHI (R) can also represent the spatial characteristics of EWEH and be used in the evaluation of EWEH temporal variation. However, because of the uncertainty of remote sensing data, field data in the present

year should be obtained to validate the ecological significance of and their correlations with the remote sensing data in other areas. 4.2. Influencing factors of EWEH in the Yellow River Delta EWEH is influenced by complex factors. All indexes and elements were significantly correlated with EWEHI in 2016 (Table 3). The high correlation coefficients of ecosystem productivity, seawater intrusion, human interference, and Yellow River input indicated their large

Fig. 10. Spatial distributions of △EWEHI (R): △EWEHI (R) indicates the difference of EWEHI (R) between two years, e.g., △EWEHI (R) of 1987–1995 is the value of EWEHI (R) in 1995 minus EWEHI (R) in 1987.

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contributions to the EWEHI spatial heterogeneity. River input was the fundamental factor of the estuarine ecosystem, and seawater intrusion was a common problem in the coastal areas (Metternicht and Zinck, 2003; Kong et al., 2015; Piroddi et al., 2016); the two belonged to external factors and mainly represented the influences of natural conditions. Human interference indicated the human exploitation types, distributions, and their ecological effects (Chi et al., 2018a); it was an internal factor and represented the influences of human activity. Ecosystem productivity reflected vegetation condition, ecosystem vitality, and blue carbon potential (Field et al., 1998; Chi et al., 2018b); it belonged to ecological state and played an important role in influencing the EWEHI spatial distribution. The low correlation coefficients of coastal erosion, nature reserve construction, biological invasion, landscape pattern, and soil moisture content indicated their small contributions to the EWEHI spatial heterogeneity. Coastal erosion, nature reserve construction, and biological invasion were P indexes and affected parts of the study area. Landscape pattern and soil moisture content showed fine evaluation results with low spatial heterogeneity. The internal factors controlled by human activity contributed more to the EWEH spatial heterogeneity than the external factors driven by natural conditions did. Regarding EWEHI (R) in different years, the annual Yellow River runoff in 1987 and 2016 was low, thereby becoming the restriction factor of vegetation growth and consequently influencing the EWEHI (R) spatial distribution more than in 1995 and 2005 (Chi et al., 2018b). The correlation coefficient of coastal erosion decreased because the influence extent of coastal erosion decreased with the continuous alongshore embankment constructions. Human activity had expanded and intensified in decades, leading to the overall increase in the correlation coefficient of human interference; this finding agreed with the studies of Sun et al. (2017) and Chen et al. (2017). Nature reserve construction began in the 1990s, and the correlation coefficient continuously increased with the increase in the effort and investment. The correlation coefficients of landscape pattern were lower in 1987 and 2016 than in 1995 and 2005. In 1987, human activity was in the preliminary stage and the landscape pattern was simple. With the increase in human activity, exploitation type patches were scattered with small areas and irregular shapes, thereby increasing the landscape fragmentation and edge effect and consequently leading to the high correlation coefficient of landscape pattern in 1995 and 2005. The human activities were further intensified and expanded since 2010, some small patches were merged and distributed continuously, thus decreasing the correlation coefficient (Chi et al., 2018a). Ecosystem productivity possessed a high and increasing correlation coefficient. This index responded sensitively to external and internal factors and can therefore represent EWEH to a certain extent (Chi et al., 2018b). With the increases in exploitation types, scope, and intensity, the influence of natural factors decreased and human activity has been the main driving factor of EWEH spatial variation, which was in accordance with the findings of Niu et al. (2017). The spatial distributions of ΔEWEHI (R) between two years are shown in Fig. 10. In 1987–1995, farmland reclamation along the current and old courses of the Yellow River led to the decrease in EWEHI (R) in these areas. The increase in Yellow River input improved the vegetation growth condition in alongshore areas, and the alongshore embankment construction increased the EWEHI (R) near the coastline. In 1995–2005, the farmland and saltern reclamations resulted in the decrease in EWEHI (R) in parts of the study area. Meanwhile, further improvements in Yellow River input, alongshore embankment, and nature reserve construction increased EWEHI (R) in most areas. In 2005–2016, the considerable increase in saltern areas greatly decreased the EWEHI (R), and the sharp decrease in Yellow River input also decreased the EWEHI (R). Small parts with positive ΔEWEHI (R) were caused by the enhancement of alongshore embankment and nature reserve construction. In 1987–2016, the decrease in Yellow River input and increase in human activity intensity, which were external and internal factors, respectively, threatened the ecological state and led to the overall decrease in EWEHI

(R) in the entire study area. The alongshore embankment and nature reserve construction increased EWEHI (R) in a small part of the study area. In summary, natural and anthropogenic factors jointly influenced the EWEH in the study area. The natural conditions were increasingly affected by human activity, and the latter has been the main driving factor of EWEH spatiotemporal variation. Previous studies always focused on the negative effects of human activity on the natural ecosystem (Bebianno et al., 2015; Tang et al., 2015; Sun et al., 2016; Uusitalo et al., 2016; Zhang et al., 2017a; Chi et al., 2018a; Wu et al., 2018; Cheng et al., 2018). Our results revealed not only the negative effects of human exploitation but also the positive effects of human regulation, such as alongshore embankment construction, nature reserve construction, and soil salinization control. The findings indicated that exploitation and conservation in estuarine wetlands can be coordinated and balanced through reasonable exploitation regulation and ecological construction. EWEH-based recommendations were proposed. The Yellow River input was controlled by natural conditions and influenced by the water flow–sediment regulation in the upper reaches. Although the water flow–sediment regulation involved various aspects, the runoff in the lower reaches should be considered. The alongshore embankment constructions decreased the influence of coastal erosion and should be enhanced in the future. The soil salinization problem should be continuously controlled, and targeted measures, including agricultural techniques, water conservancy projects, chemical modification, and biological improvement, should be conducted according to actual conditions. Human activity inevitably threatened the natural ecosystem; the exploitation scale should be controlled, the landscape pattern should be optimized, and the utilization methods should be improved to reduce the negative effects of human activity. Nature reserve construction helped maintain and improve EWEH and should therefore be enhanced. The ecological state was the largest contributor to the EWEH and should thus be improved by conducting reasonable ecological restoration and construction. 5. Conclusions We established an EWEH evaluation model on the basis of the typical features of estuarine wetland ecosystem with focus on spatial heterogeneity. The index system comprised external factors, internal factors, and ecological state and covered all aspects of the natural and anthropogenic factors, with each index possessing its own spatial heterogeneity. The EWEH evaluation model was demonstrated in the Yellow River Delta. The results indicated that in the present year (2016), the EWEH was in good status in the entire study area with distinct spatial heterogeneity. The proportions of different EWEH status zones were in the following descending order: good (55.3%), ordinary (20.8%), best (18.5%), bad (5.5%), and worst (0). From 1987 to 2016, the EWEH fluctuated temporally; it was in best status in 2005 and in good status in 1987, 1995, and 2016. The best status zone first decreased, then increased, and then decreased; the good status zone showed the opposite change; the ordinary and bad status zones continuously increased; and the worst status zone occupied little proportion in all years. The EWEH was influenced by complex factors. The influence of natural factors continuously decreased, and human activity has been the main driving factor of the EWEH spatial variation. Our model was proven to possess comprehensive reflections of estuarine wetland ecological characteristics, full exhibitions of spatial heterogeneity, and high applicability. Therefore, it can be widely used to evaluate EWEH and guide management and conservation in different areas. The accuracy and applicability are the key points of EWEH evaluation. The remote sensing method is effective in conducting regional ecological evaluation. The EWEHI (R) possesses high applicability because the needed data are all derived from remote sensing. However, only the EWEHI (R) cannot totally ensure the accuracy of the results due to the uncertainty of remote sensing data. The data acquired by

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field investigation can be considered as the data with little uncertainty, thus the EWEHI using both R and I indexes can achieve high accuracy. Therefore, the combination of EWEHI and EWEHI (R) could meet the demand of accuracy and applicability, and represent the present EWEH and EWEH temporal variations. In different areas, the relationships between remote sensing and field data are uncertain because of the uncertainty of the former. Though the EWEHI and EWEHI (R) results were similar in the Yellow River Delta, the field data in the present year are necessary to validate the ecological significance of and their correlations with the remote sensing data when using the EWEH evaluation model in other areas. Acknowledgments This research was funded by the National Natural Science Foundation of China (Nos. 51779048; 41701214), the Basic Scientific Fund for National Public Research Institutes of China (2018Q07), the East Asia Marine Cooperation Platform of China-ASEAN Maritime Cooperation Fund (YZ0416003), and the Open Research Fund Program of Shandong Provincial Key Laboratory of Eco-Environmental Science for Yellow River Delta (2017KFJJ01). We thank the editor and anonymous reviewers for their valuable comments which have greatly improved the quality of the manuscript. We are indebted to Dr. Fan Yang and Dr. Shuting Yu who assisted in the field and laboratory work. We also thank USGS for the open source LANDSAT data (https://landsat.usgs.gov/). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.04.085. References Bebianno, M.J., Pereira, C.G., Rey, F., Cravo, A., Duarte, D., D'Errico, G., Regoli, F., 2015. Integrated approach to assess ecosystem health in harbor areas. Sci. Total Environ. 514, 92–107. Bi, X., Wang, B., Lu, Q., 2011. Fragmentation effects of oil wells and roads on the Yellow River Delta, North China. Ocean Coast. Manag. 54 (3), 256–264. Blum, M.D., Roberts, H.H., 2009. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nat. Geosci. 2 (7), 488–491. Chen, L., Ren, C.Y., Wang, C., Yao, Y.C., Song, K.S., 2017. Dynamic of coastal wetlands of the Yellow River Delta for 6 periods. Wetl. Sci. 15 (2), 179–186. Cheng, X., Chen, L., Sun, R., Kong, P., 2018. Land use changes and socio-economic development strongly deteriorate river ecosystem health in one of the largest basins in China. Sci. Total Environ. 616–617, 376–385. Chi, Y., Shi, H.H., Zheng, W., Sun, J.K., Fu, Z.Y., 2018a. Spatiotemporal characteristics and ecological effects of the human interference index of the Yellow River Delta in the last 30 years. Ecol. Indic. 89, 880–892. Chi, Y., Shi, H.H., Sun, J.K., Li, J., Yang, F., Fu, Z.Y., 2018b. The temporal and spatial characteristics and main influencing factors of vegetation net primary productivity of the Yellow River Delta in recent 30 years. Acta Ecol. Sin. https://doi.org/10.5846/ stxb201705301000. Chi, Y., Shi, H.H., Zheng, W., Wang, E.K., 2018c. Archipelagic landscape patterns and their ecological effects in multiple scales. Ocean Coast. Manag. 152, 120–134. Cui, B.L., Li, X.Y., 2011. Coastline change of the Yellow River estuary and its response to the sediment and runoff (1976–2005). Geomorphology 127 (1), 32–40. Dai, X., Ma, J., Zhang, H., Xu, W., 2013. Evaluation of ecosystem health for the coastal wetlands at the Yangtze Estuary, Shanghai. Wetl. Ecol. Manag. 21 (6), 433–445. Fan, H., Huang, H.J., Zeng, T.Q., Wang, K.R., 2006. River mouth bar formation, riverbed aggradation and channel migration in the modern Huanghe (Yellow) River delta, China. Geomorphology 74 (1), 124–136. Fan, X.M., Liu, G.H., Liu, H.G., 2014. Evaluating the spatial distribution of soil salinity in the Yellow River Delta based on Kriging and Cokriging methods. Resour. Sci. 36 (2), 321–327. Field, C.B., Behrenfeld, M.J., Randerson, J.T., Falkowski, P., 1998. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281 (5374), 237–240. Gao, Y.C., Wang, J.N., Guo, S.H., Hu, Y.L., Li, T.T., Mao, R., Zeng, H.H., 2015. Effects of salinization and crude oil contamination on soil bacterial community structure in the Yellow River Delta region, China. Appl. Soil Ecol. 86, 165–173. Gredilla, A., Fdez-Ortiz de Vallejuelo, S., de Diego, A., Arana, G., Madariaga, J.M., 2014. A new index to sort estuarine sediments according to their contaminant content. Ecol. Indic. 45 (45), 364–370. Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D'Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.,

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