Evaluation on island ecological vulnerability and its spatial heterogeneity

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Evaluation on island ecological vulnerability and its spatial heterogeneity Yuan Chia,b, Honghua Shia,b,⁎,1, Yuanyuan Wangc, Zhen Guoa, Enkang Wanga 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 School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao, Shandong Province 266033, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Island ecosystem Island ecological vulnerability (IEV) Spatial heterogeneity Sensitivity analysis Different scales The southern islands of Miaodao Archipelago

The evaluation on island ecological vulnerability (IEV) can help reveal the comprehensive characteristics of the island ecosystem and provide reference for controlling human activities on islands. An IEV evaluation model which reflects the land–sea dual features, natural and anthropogenic attributes, and spatial heterogeneity of the island ecosystem was established, and the southern islands of Miaodao Archipelago in North China were taken as the study area. The IEV, its spatial heterogeneity, and its sensitivities to the evaluation elements were analyzed. Results indicated that the IEV was in status of mild vulnerability in the archipelago scale, and population pressure, ecosystem productivity, environmental quality, landscape pattern, and economic development were the sensitive elements. The IEV showed significant spatial heterogeneities both in land and surrounding waters sub-ecosystems. Construction scale control, optimization of development allocation, improvement of exploitation methods, and reasonable ecological construction are important measures to control the IEV.

1. Introduction Island is not only the storage pool of important ecological functions and the living carrier of human beings, but is also the platform of ocean conservation and exploitation (Jupiter et al., 2014; Chi et al., 2015a). In recent years, the increasing human activities on islands, such as urban construction, tourism, aquaculture, and shipping, have profoundly affected the island ecosystem, threatened island biodiversity, decreased the ecosystem productivity, deteriorated the environmental quality, and changed the landscape pattern (Dahlin et al., 2014; BenitezCapistros et al., 2014; Chi et al., 2015b; Chi et al., 2016). Thus, the island ecosystem and its vulnerability have aroused a significant concern. The island ecosystem is a special ecosystem that includes the land and surrounding waters sub-ecosystems, which are composed of interacting natural and anthropogenic factors (Shi et al., 2009; Chi et al., 2015a). Island ecological vulnerability (IEV) is the vulnerability to damage and the difficulty of restoration under unique conditions and various disturbances, and long-term, heterogeneity and controllability are the typical features of IEV (Chi et al., 2015a). Ecological vulnerability and ecological sensitivity are similar and both originated from the concept of ecotone (Dow, 1992). Ecotone emphasizes the regional particularity. Meanwhile, ecological sensitivity focuses on the features of susceptibility to damage. In comparison, ecological vulnerability

contains more connotations, which can reflect not only the features of land–sea interface, resources shortage, independence, and completeness, but also the degradation and restoration of the island ecosystem. Therefore, conducting evaluation on IEV, which helps reveal the comprehensive characteristics of the island ecosystem and provide reference for controlling human activities and protecting the island ecosystem, is of a great significance. The land–sea dual features of the island ecosystem should be the first consideration in the comprehensive evaluation of IEV. Land subecosystem has the common characteristics of a continental ecosystem and is similar with the mainland in terms of biological community and habitat (Lagerström et al., 2013; Nogué et al., 2013), yet has the uniqueness of limited area and isolated space (Chi et al., 2015a). Surrounding waters sub-ecosystem has the common characteristics of a marine ecosystem, but shows significant spatial heterogeneity because of the separation effect of islands (Shen et al., 2016). These two subecosystems have marked differences and close interrelations; therefore, balancing the unity and difference of their vulnerabilities is an important issue. Second, the island ecosystem simultaneously possesses natural and anthropogenic attributes and is now a natural–anthropogenic complex ecosystem because of the increasing island exploitation activities, especially in China (Ma and Wang, 1984). Thus, natural and anthropogenic factors and their relationships should be given thorough

⁎ Corresponding author at: The First Institute of Oceanography, State Oceanic Administration, PR China; Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, PR China. E-mail address: shihonghuafi[email protected] (H. Shi). 1 Address: No.6, Xianxialing Road, Qingdao, Shandong, China, 266,061, PR China.

http://dx.doi.org/10.1016/j.marpolbul.2017.08.028 Received 19 December 2016; Received in revised form 8 August 2017; Accepted 13 August 2017 0025-326X/ © 2017 Published by Elsevier Ltd.

Please cite this article as: Chi, Y., Marine Pollution Bulletin (2017), http://dx.doi.org/10.1016/j.marpolbul.2017.08.028

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

and public service is given as 0.6; the influence coefficient of farmland is given as 0.4; and the influence coefficient of garden and plantation is given as 0.2 (State Oceanic Administration PRC, 2015b). SA was calculated using the formula below:

consideration when evaluating IEV. In addition, islands are commonly clustered in the form of an archipelago, and all islands in an archipelago are related and interact with each other to form a unity. Meanwhile, significant differences exist in the basic features of the islands in an archipelago, such as areas, mutual distances, and terrain conditions (Vogiatzakis et al., 2008). Moreover, heterogeneous human activities, which account for the significant spatial heterogeneity of IEV, have been increasing. Many scholars conducted researches on the island ecosystem and its vulnerability to study their variation characteristics under the background of climate change and sea level rise or facing natural disturbances, such as typhoon, sea water intrusion, and biological invasion (Yamano, 2008; Baumberger et al., 2012; Morgan and Werner, 2014; Taramelli et al., 2015). The economic vulnerability of island countries was also given attention in several studies (Te'o, 2007; McGillivray et al., 2010; Guillaumont, 2010). However, these studies mostly considered vulnerability as the inherent feature of the island ecosystem and paid less attention to the vulnerability caused by human activities (Bonati, 2014). These studies have certain significances for island conservation, but lack applicability in regions with intensified human activities, especially in China, where the marine economy has been developing rapidly and island exploitation and conservation are now ascending. Meanwhile, current studies are unable to adequately reflect the land–sea dual features of the island ecosystem and their spatial heterogeneity. Therefore, the studies could not comprehensively reveal the IEV and its spatial characteristics. An IEV evaluation model, which reflects the land–sea dual features, natural and anthropogenic attributes, and their spatial heterogeneities, was established in this paper. The southern islands of Miaodao Archipelago in North China were taken as an example, and the IEV and its spatial heterogeneity were clarified to provide a basis for maintaining the island ecosystem and a new method for the evaluation of the island ecosystem.

n

SA =

∑i = 1 SAi × SRi PMO

(2)

where SAi is the area of sea area use type i, SRi is the influence coefficient of sea area use type i on the surrounding waters sub-ecosystem. The influence coefficient of infrastructure, urban construction, waste dumping, electric power industry, shipbuilding industry, chemical industry, steel industry, and aquatic products processing industry is given as 1.0; the influence coefficient of reclamation aquaculture, port, and saltern is given as 0.8; the influence coefficient of sewage discharge, cable and pipeline is given as 0.6; the influence coefficient of road, bridge, fairway, and anchorage is given as 0.4; and the influence coefficient of open aquaculture, solid mineral exploitation, and tourism is given as 0.2 (State Oceanic Administration PRC, 2015b). PMO is the marine space exploitation standards, which are calculated based on marine functional zoning (State Oceanic Administration PRC, 2012). 2.1.2.2. Terrain (B3). The terrain has one index, which is the steep region proportion (C4). The calculation method and evaluation standard are shown in Table 2. 2.1.2.3. Ecosystem productivity (B4). Ecosystem productivity is composed of two indices: land net primary productivity (C5) and surrounding waters primary productivity (C6). Land net primary productivity is calculated based on the CarnegieAmes-Stanford Approach (CASA) model (Potter et al., 1993), which requires remote sensing data, meteorological data and field investigation. The estimation method is as follows:

LNPP (x , t ) = APAR (x , t ) × ξ (x , t )

(3)

APAR (x , t ) = PAR (x , t ) × FPAR (x , t )

(4)

2.1. IEV evaluation model establishment

ξ (x , t ) = ft (t ) × fw (t ) × ξmax

(5)

2.1.1. Index system The index system of the IEV model was established based on “exposure–sensitivity–adaptability” framework (Wan et al., 2006; IPCC, 2007; Zhang et al., 2013), and the “objective–element–index layers” structure was adopted. The index system consisted of 1 objective, 3 subobjectives, 10 elements, and 18 indices. The objective layer took the IEV as the objective, including three sub-objectives: exposure (E), sensitivity (S), and adaptability (A). The elements were selected based on the comprehensive consideration of natural and anthropogenic factors. Indices were selected according to the land–sea dual features and their spatial heterogeneity (Table 1).

where LNPP(x,t) is the net primary productivity of position x in month t; APAR(x,t) is the absorbed photosynthetic active radiation of position x in month t (MJ m− 2 month− 1); ξ(x,t) is the actual light utilization efficiency of position x in month t (g C MJ− 1); PAR(x,t) is the photosynthetic active radiation of position x in month t (MJ m− 2 month− 1), FPAR(x,t) is the fraction of photosynthetic active radiation of position x in month t (%); ft.(t) and fw(t) are the temperature and water stress factors in month t (%), respectively; and ξmax is the maximum light use efficiency of different vegetation (g C MJ− 1). The average annual value of LNPP is calculated based on the result of each month, and the detailed calculation method was reported by Chi et al. (2015b). Surrounding waters primary productivity is calculated based on the chlorophyll method, using the simplified formula proposed by Cadée and Hegeman (1974), which is as follows:

2. Materials and methods

2.1.2. Index calculation 2.1.2.1. Exploitation intensity (B2). Exploitation intensity includes two indices: land exploitation intensity (C2) and surrounding waters exploitation intensity (C3). Specific calculation methods and evaluation standards are shown in Table 2 (State Oceanic Administration PRC, 2015b). LA was calculated using the formula below:

SPP (x ) = Ps × E × D 2

where SPP(x) is the daily primary productivity in a season of position x (mg C m− 2 d− 1); Ps is the phytoplankton potential productivity in surface water (< 1 m); E is the euphotic depth, which is given as three times of transparency (m); and D is the daylight hours (h). Ps is calculated using the following formula:

n

LA =

∑ LAi × LRi i=1

(6)

(1)

Ps = Ca × Q

where LAi is the area of land use type i and LRi is the influence coefficient of land use type i on the land sub-ecosystem. The influence coefficient of industry, mining, warehousing and transportation is given as 1.0; the influence coefficient of water conservancy facility and aquaculture pond is given as 0.8; the influence coefficient of residence

(7)

where Ca is the chlorophyll a (Chl-a) content of the surface water (mg m− 3); Q is the assimilatory coefficient [mg C·(mg Chl-a)− 1 h− 1], an empirical value of 3.7 is adopted (Ryther, 1969). The surrounding waters annual primary productivity is calculated according to the daily primary productivity in different seasons. 2

3

B10 Energy consumption

B9 Environmental conservation

B7 Landscape pattern B8 Economic development

B6 Environmental quality

B5 Biodiversity

C18 Energy consumption per unit of GDP

C1 Population density C2 Island exploitation intensity C3 Surrounding water exploitation intensity C4 Steep region proportion C5 Land net primary productivity C6 Surrounding waters primary productivity C7 Island plant diversity C8 Surrounding waters phytoplankton diversity C9 Soil quality C10 Sea water quality C11 Marine sediment quality C12 Patch density C13 GDP per capita C14 GDP growth rate C15 Proportion of tertiary industries C16 Proportion of reserve areas C17 Urban sewage treatment rate

Index layer

H H H H H H U U U U U U

+ + − − − − + + + + + −

T S S T

T S

Details below Details below Details below Patch number/island area GDP/number of permanent population Annual average GDP growth rate in recent 3 years Tertiary industries added value/GDP Reserve areas/areas of land and surrounding waters Urban sewage treatment amount for meeting the standard/urban sewage production amount Energy consumption/GDP

Details below Details below

Details below Details below Details below

H H H

− + + T T S

Number of permanent population/island area Details below Details below

H H H

− − − T T S

Index calculation

Index type

87 ton standard coal/million yuan (SOA, PRC, 2015a)

Environmental quality standards Environmental quality standards Environmental quality standards Regional mean value Regional mean value Regional mean value Regional mean value 11% (SOA, PRC, 2015a) 90% (SOA, PRC, 2015a)

Regional mean value Regional mean value

Regional mean value Regional mean value

Regional mean value

Evaluation standard

Note: The indices could be divided into positive indices (+) and negative indices (−) according to their properties. The greater the positive indices are, the better the results are, i.e., the less the IEV is, while the negative indices are the opposite. According to the spatial distribution of indices, they could be divided into spatial homogeneity indices (U) and spatial heterogeneity indices (H). Spatial homogeneity indices used the same value in the entire study area at the same time while the values of spatial heterogeneity indices, which could be divided into land heterogeneity indices (T) and surrounding waters heterogeneity indices (S), vary in different positions.

Adaptability

Sensitivity

B3 Terrain B4 Ecosystem productivity

B1 Population pressure B2 Exploitation intensity

IEV

Exposure

Element layer

Objective layer

Table 1 Index system of IEV model.

Y. Chi et al.

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

index (IEVI) is calculated based on the formula below:

Table 2 Calculation methods and evaluation standards of exploitation intensity and terrain. Index

Calculation method

n

IEVI =

Evaluation standard

∑ RBi × Wi i=1

C2 C3 C4

Land exploitation area (LA)/Island area Surrounding waters exploitation area (SA)/Surrounding waters area Steep region (Slope ≥ 15°) area (LA)/ Island area

≤1 km2

1–5 km2

> 5 km2

0.2 0.15

0.3

0.4

0.2

0.3

0.4

where Wi is the weight of element i. Equal weight method is recommended to make the IEV evaluation results of different study areas comparable and the IEV evaluation model applicable, and was adopted in our study. The vulnerabilities of land and surrounding waters sub-ecosystems are evaluated using land heterogeneity indices (T) and surrounding waters heterogeneity indices (S), respectively, together with the common spatial homogeneity indices (U).

2.1.2.4. Biodiversity (B5). Biodiversity is composed of island plant diversity (C7) and surrounding waters phytoplankton diversity (C8), which stay stable in a specific period and exhibit spatial variations responding to the natural and human factors, thus can represent the spatial heterogeneity of the biodiversity. They are both calculated using the Shannon–Wiener (H′) and Pielou (E) indices, which are widely applied in ecology research; the former focuses on the complexity of species, while the latter emphasizes the evenness of species. The regional average values could be used as the evaluation standards, and biodiversity (B5) result used the average values of the H′ and E evaluation results. The calculation methods are as follows:

2.1.3.2. Island scale—different islands evaluation. The IEVI in island scale is determined based on the land heterogeneity indices (T) of each island and the common spatial homogeneity indices (U). 2.1.3.3. Grid scale—spatial heterogeneity evaluation. The study area is divided into land and surrounding waters grids of 100 m × 100 m area using the GIS method. Then, the IEVIs of the different grids are calculated using the above method. The evaluation results are presented in space, forming the spatial distribution map of IEV. The IEV levels are determined based on the IEVI standards. We consider the IEV levels as non vulnerability, critical vulnerability, mild vulnerability, moderate vulnerability and severe vulnerability when the IEVI are < 0.8, 0.8–1.0, 1.0–1.5, 1.5–2.0 and ≥ 2.0, respectively (State Oceanic Administration PRC, 2015b; Chi et al., 2017a).

n

Hs′ = − ∑ IVs, i LnIVs, i

(8)

i=1

(9)

Es = Hs Ln (Ns )

where H′s and Es are the Shannon–Wiener and Pielou indices 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 formula:

IVs, i =

Abs, i Abs

2.2. IEV evaluation of the southern islands of Miaodao Archipelago 2.2.1. Study area Miaodao Archipelago, typical islands in North China and location of Changdao National Nature Reserve, is located north of the Shandong Peninsula and at the juncture of the Yellow and Bohai Seas (Fig. 1). The southern islands of Miaodao Archipelago are an important part of the Miaodao Archipelago, which are close to the mainland with concentrated distribution. The southern islands of Miaodao Archipelago consist of five inhabited islands (Nanchangshan Island (NCSI), Beichangshan Island (BCSI), Miao Island (MI), Xiaoheishan Island (XHSI), and Daheishan Island (DHSI)) and several uninhabited islands. The study area is in the East Asian monsoon region, with an average annual temperature of 12.0 °C (average January temperature of −1.6 °C, and average July temperature of 24.5 °C) and an average annual rainfall of 537 mm (mostly concentrated between June and September). The terrain is undulating, with mountains lying in a roughly south–north direction; the highest point is approximately 190 m. The soils fall into three main categories, namely, brown, cinnamon, and fluvo-aquic soils, and the brown soil covers the largest area at a thickness of approximately 30 cm. The soil quality is poor with a significant amount of gravel (Shi et al., 2013). The native forests here are poor, and the current forests are dominantly planted with Pinus thunbergii, Robinia pseudoacacia, and Platycladus orientalis (Chi et al., 2016). The southern islands of Miaodao Archipelago are the demographic, economic, and cultural center of Changdao County, Shandong Province, China. In 2015, the gross domestic product (GDP) of the county was 6.243 billion Yuan (925.05 million Dollars) and the GDP per capita was 147,300 Yuan (21,826 Dollars) (People's Government of Changdao County, 2015), which was high in China. At the end of 2015, the total population was 42,183, of which the urban population was 21,658 (People's Government of Changdao County, 2015). The exploitation of the islands and their surrounding waters here was high in general and had an obvious spatial variation, that is, great differences in exploitation intensities existed in different regions of the islands and surrounding waters, which makes the southern islands of Miaodao Archipelago a natural laboratory for IEV and its spatial heterogeneity.

(10)

where Abs,i is the abundance of species i in site s and Abs is the sum of species abundance in site s. 2.1.2.5. Environmental quality (B6). Environmental quality consists of soil quality (C9), sea water quality (C10), and marine sediment quality (C11), which are represented by the main environmental factors threatening the island ecosystem. The evaluation result of each index is calculated based on the Nemerow method using the formula below:

Pc =

⎡⎛ 1 ⎢ ⎣⎝ n

2

∑ Pi ⎞ ⎠

2 ⎤ + Pmax ⎥ ⎦

2 (11)

where Pc is the comprehensive index of environmental quality, n is the number of factors, Pi is the environmental quality index of factor i, and Pmax is the maximum environmental quality index of all factors. Pi is calculated using the following formula:

Pi = Ci Si

(12)

where Ci is the measured value of factor i and Si is the standard value of factor i based on the published environmental quality standards. 2.1.3. Evaluation methods of IEV 2.1.3.1. Archipelago scale—comprehensive evaluation. Each index is evaluated using the formula below:

Ci Si Ci is the negative index ⎞ RCi = ⎜⎛ ⎟ ⎝ Si Ci Ci is the positive index ⎠

(14)

(13)

where RCi, Ci, Si are the evaluation result, calculated value, and standard value of index i, respectively. According to the cask effect, the evaluation result of element i (RBi) is given as the highest value of the evaluation results of all the indices contained in the element i. Then, the island ecological vulnerability 4

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 1. Location and sampling plots of the southern islands of Miaodao Archipelago.

Ten islands, five aforementioned inhabited islands and five large uninhabited islands (Danglang Island (DLI), Tanglang Island (TLI), Yangtuozi Island (YTZI), Niutuozi Island (NTZI), and Nantuozi Island (NATZI) (area > 0.02 km2), were selected as the study objects. The range of the surrounding waters is difficult to determine due to its complexity. According to the influence scope of the human activities on the surrounding waters, 2 km was used as the radiation length. A rectangle was established based on the points to which 2 km were extended from the outer edges of the outest islands in the directions of 0°, 90°, 180°, and 270°, respectively, and was taken as the study area of surrounding waters. The area of the study region was 286.8 km2 in total, in which the areas of islands and surrounding waters were 31.72 and 255.08 km2, respectively (Fig.1).

cutting, radiometric calibration, and bands calculation. 2.2.2.2. Field investigation. The field investigation of land use types was carried out in July 2014, and the accurate island land use types were obtained. Then, land exploitation intensity (C2) and patch density (C12) were calculated, and mean patch density in the Miaodao Archipelago (the data was also calculated by the authors) was used as the evaluation standard of C12. Land net primary productivity (C5) was also calculated based on NDVI, meteorological data, and land use types, and the national mean value of the net primary productivity was taken as the evaluation standard (Zhu et al., 2007). Field sampling: Minimal industry pollution exists in the Changdao County and the adjacent mainland, and the pollution of heavy metals on soil, sea water, and sediment is avoided. Marine environmental quality bulletins have shown for years that all heavy metal contents satisfied the strictest standard. Therefore, heavy metal pollution was not the stress factor of the IEV in the study area. The southern islands of Miaodao Archipelago are all bedrock islands, with eroded hills as the main topographic feature. Thus, soil fertility is low and constrains plant growth. Therefore, the evaluation of soil quality focused on soil fertility. Aquaculture, domestic sewage, and other human activities have led to the pollution of chemical oxygen demand (COD), nitrogen, and phosphorus in the surrounding waters. In addition, frequent shipping has resulted in oil spill risk. Thus, oil is also an important influencing factor. Island plant community investigation and sampling were carried out in the summer of 2012. Fifty sampling plots with 20 m × 20 m area were set according to the spatial distribution, community types, and terrain (Fig. 1). The latitude, longitude, elevation, slope, and aspect of each sampling plot were measured using a handheld GPS device and an electronic compass. All tree species in each plot were recorded. The diameter at breast height (DBH), height, crown diameter of trees with DBH ≥ 3 cm were measured. All shrub species in each plot were recorded, and two 10 m × 10 m quadrats were set diagonally and, in which the basal diameter and height of all shrubs were measured. All herb species in each plot were recorded, and five 1 m × 1 m quadrats were set in the four corners and center of the plot in which the abundance, coverage, and height of all herbs were measured. The results of the investigation indicated that the number of woody plant species was small, of which most were artificial plants. However, a variety of widely distributed native herbaceous plants existed and were more sensitive to the environment (Chi et al., 2016). Thus, herbaceous plant diversity was used to represent the island plants diversity (C7) and the understory plants diversity in Kunyu Mountain, which is in the Eastern Shandong Peninsula and close to the study area (Liang et al., 2011). The soil samples collected from the three points in each plot were evenly mixed to represent the soil property of the plot. Organic matter, total nitrogen, available phosphorus, and available potassium were then

2.2.2. Data collection and analysis 2.2.2.1. Data collection. Population density (C1), GDP per capita (C13), GDP growth rate (C14), proportion of tertiaries industry (C15), proportion of reserve areas (C16), urban sewage treatment rate (C17), and energy consumption per unit of GDP (C18) were derived from the statistics bulletins and yearbooks of Changdao County in Shandong Province, China. The average values of the aforementioned indices in Shandong Province were taken as the evaluation standards. Sea area utilization and marine functional zoning data were provided by the Oceanic and Fishery Administration of Changdao County. Then, surrounding waters exploitation intensity (C3) was calculated. Rainfall, temperature, sunshine duration and relative humidity, which were required for the calculation of land net primary productivity (C5), were provided by a weather station in Changdao County. The total solar radiation adopted the annual mean value monitored in a weather station in the Fushan District of Yantai City in Shandong Province, China, which is close to the study area. The version two of Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (Aster GDEM) with a resolution of 30 m, which was produced based on the observed data from Terra satellite and published in 2011 with open access, was adopted. Steep region proportion (C4) was determined by the slope, which was extracted from the GDEM data through ArcGIS10.0. The panchromatic remote sensing image of WorldView-1 satellite in 2013 with a resolution of 0.45 m (Crespi et al., 2012) was collected, and island area, perimeter, and other basic information were obtained based on the island outline, which was derived through ArcGIS10.0. Then, land use types were derived by interpretation and divided into construction land, traffic land, other hardened grounds (square ground, sunning ground, and so on), plantation, farmland, and unused land (grassland and bare land). The normalized difference vegetation index (NDVI) of different seasons were extracted from the remote sensing images of LANDSAT 8 satellite in four phases (April 21, August 11, and November 15 in 2013 and January 2 in 2014) through the images 5

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

measured in the laboratory and used as the evaluation factors. Grade II standards of dry land of the 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 used as the evaluation standards of soil quality. Four voyages for investigation and sampling of the surrounding waters were carried out in November 2012, February 2013, May 2013, and August 2013. Twenty-one plots were set based on the principles of representativeness, uniformity, and accessibility (Fig. 1). The samples were collected according to the Specifications for Oceanographic Survey (GB/T 12763-2007), which is a national standard in China published in 2007. Surface seawater temperature (SST), pH, salinity, and transparency were measured in the field. Water samples were analyzed within 24 h after they were brought to the laboratory (the oil was within 10 h), and dissolved oxygen (DO), COD, dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), oil, and Chl-a were measured. Surrounding waters primary productivity (C6) was calculated based on Chl-a and transparency, and the average value of the primary productivity of Bohai Sea in 1982–1993, which was less affected by human activities and could represent the background value, was used as the evaluation standard (Lv et al., 1999). Sea water quality (C10) was calculated by DO, COD, DIN, DIP, and oil, with the first category of the Sea water Quality Standard (GB 3097-1997), which is a national standard in China published in 1997, as the evaluation standard. Phytoplankton samples were fixed with formaldehyde and stored in 0.5 L polyethylene (PE) bottles at 4 °C, away from light. Phytoplankton identification and counting were conducted by the method of Utermöhl (1958). Surrounding waters phytoplankton diversity (C8) was calculated based on cell abundance, and the average value of phytoplankton diversity in the Bohai and Yellow Seas was taken as the evaluation standard (Lu, 2012). Seven stations of the surface sediment in the surrounding waters were investigated in August 2015 (Fig. 1). Sediment samples were collected using a clamshell sediment sampler, and a plastic spoon was used to take samples from the central undisturbed 0–5 cm surface sediment into clean polyethylene bags. Then, the samples were placed in cold storage, sealed, and transported to the laboratory. Sediment samples were crushed after natural air drying at room temperature, and then screened with a 160-mesh sieve. Oil content was measured and used as the evaluation factor for marine sediment quality (C11), and the first category of the Marine Sediment Quality (GB18668-2002), which is a national standard in China published in 2002, was used as the evaluation standard.

Table 3 Evaluation results of IEV in the archipelago scale. Element layer

Land subecosystem

Surrounding waters subecosystem

B1 B2

1.237 0.691

1.237 0.691

– 0.332

B3 B4

0.495 1.635

0.495 0.979

– 1.635

B5

0.985

0.985

0.941

B6

1.935

1.935

0.786

B7 B8

1.184 1.293

1.184 1.293

– 1.293

B9

0.900

0.900

0.900

B10 IEVI

0.327 1.068

0.327 1.003

0.327 0.888

Evaluation results

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18

1.237 0.691 0.332 0.495 0.979 1.635 0.985 0.941 1.935 0.786 0.182 1.184 0.361 1.293 1.251 0.496 0.900 0.327

3. Results 3.1. Evaluation results of IEV 3.1.1. Archipelago scale The IEVI of southern islands of Miaodao Archipelago was 1.068, which indicated that the island ecosystem was in status of mild vulnerability. The land sub-ecosystem was in status of mild vulnerability with an IEVI of 1.003. Meanwhile, the surrounding waters sub-ecosystem was in status of critical vulnerability with an IEVI of 0.888 (Table 3). 3.1.2. Island scale The evaluation results in island scale are shown in Table 4. NSCI and MI were in status of mild vulnerability; BCSI, XHSI, DHSI, DLI, TLI, and NTZI were in status of critical vulnerability; and YTZI and NATZI were in status of non vulnerability. The differences of elements (B1–B7) among islands contributed to the spatial heterogeneity of IEV in the island scale.

2.2.4. Sensitivities of IEV to evaluation elements The sensitivity analysis of IEV to elements was conducted to determine the effect degree of each element on the IEV to explore the variation characteristics of IEV in the scenarios of element changes and to select the optimum parameters for model optimization in future works because numerous elements and indices are present in the IEV evaluation model (Martins et al., 2007; Zheng et al., 2012). Sensitivity is generally defined as the ratio of state variable change rate to the parameter change rate (Solidoro et al., 2003). This refers to the ratio of the IEV change rate to the elements change rate in our study. The method is as follows:

ΔIEVI IEVI ΔRBi RBi

Island ecosystem

Index calculation

element i was considered as a sensitive element; when Si < 0.1, element i was considered as an insensitive element (Shi et al., 2014). The sensitivities of IEV to elements in different scales were calculated, and the variation characteristics of IEV were simulated in different scenarios when the change rate of each element was given as 5%, 10%, 20%, −5%, −10% and −20%.

2.2.3. IEV and its spatial heterogeneity evaluation IEV in archipelago, island and grid scales were calculated using the methods in Section 2.1.3. Then, coefficient of variation (CV) was used to present the spatial heterogeneity degrees of the IEV and each element in the island and grid scales.

Si =

Evaluation results

3.1.3. Grid scale The evaluation results of elements (B1–B7) in the grid scale are shown in Fig. 2. All aforementioned elements, except B5, exhibited obvious spatial heterogeneity in the grid scale; B2, B3, B1, B4, B7, B6, and B5 were ordered in descending CV values, which were 265.86, 164.17, 74.37, 63.00, 62.32, 46.77, and 7.01, respectively. The evaluation results of IEV in the grid scale are shown in Fig. 3. The different vulnerability level zones for the island ecosystem and the two sub-ecosystems (except that the mild vulnerability zones covered the most area in the land sub-ecosystem) were critical vulnerability, mild vulnerability, non vulnerability, moderate vulnerability, and severe vulnerability zones in descending order of areas. IEV had a significant spatial heterogeneity, and the heterogeneity in the land sub-

(15)

where Si is the sensitivity of IEV to element i, and ΔIEVI and ΔRBi are the change rates of IEVI and element i, respectively. When Si ≥ 0.1, 6

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Table 4 Evaluation Results of IEV in the island scale. Element

NCSI

BCSI

MI

XHSI

DHSI

DLI

TLI

YTZI

NTZI

NATZI

CV

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 IEVI

2.283 0.846 0.361 1.104 0.969 2.100 1.000 1.293 0.900 0.327 1.118

0.589 0.620 0.638 0.891 1.036 2.217 1.103 1.293 0.900 0.327 0.961

0.495 0.657 0.692 0.930 1.103 1.486 2.296 1.293 0.900 0.327 1.018

0.348 0.705 0.690 1.154 0.943 1.454 1.283 1.293 0.900 0.327 0.910

0.325 0.516 0.530 0.870 0.937 1.662 1.373 1.293 0.900 0.327 0.873

– 0.499 0.203 1.472 – – 1.424 1.293 0.900 0.327 0.874

– 0.302 0.744 1.400 – – 0.681 1.293 0.900 0.327 0.807

– 0.694 0.083 0.892 – – 0.892 1.293 0.900 0.327 0.726

– 0.662 0.000 0.810 – – 1.700 1.293 0.900 0.327 0.813

– 0.247 0.000 1.403 – – 1.334 1.293 0.900 0.327 0.786

102.92 32.40 76.67 23.14 7.10 19.82 34.66 0 0 0 13.26

of IEV to an element was high where the evaluation value of the element was high. In the surrounding waters sub-ecosystem, the spatial distributions of the sensitivities to all elements were consistent with that of the IEV evaluation result, that is, the sensitivity values of IEV were high where IEVI was high. In the different scenarios of element change, the area exchanges among different vulnerability level zones caused by the change of each of element B1, B2, B3, and B7 were generally small, and their positive changes decreased the areas of non vulnerability and critical vulnerability zones, increased the area of mild vulnerability zones, and changed areas of moderate vulnerability and severe vulnerability zones slightly, except for B2. Meanwhile, their negative changes resulted in the opposite variation. The area of severe vulnerability had a marked increase when the change rate of B2 was given as 20%, and the area of moderate vulnerability had a marked decrease when the change rate of B2 was given as −20%. The area exchanges among the different vulnerability level zones were caused by the change in element B4, B5, B6, B8, B9, and B10, especially in the exchange area between non vulnerability and critical vulnerability zones; the positive changes decreased the area of non vulnerability zones, increased the area of critical vulnerability zones, and their negative changes resulted in the opposite variation, which was greater (Fig. 5). Note: NVA, area of non vulnerability; CVA, area of critical vulnerability; MVA, area of mild vulnerability; MoVA, area of moderate vulnerability; SVA, area of severe vulnerability.

ecosystem was more significant than that in the surrounding waters sub-ecosystem. 3.2. Sensitivities of IEV to elements 3.2.1. Archipelago scale The sensitivities of IEV to the elements in the archipelago scale are shown in Table 5. B1, B4, B6, B7, and B8 were the sensitive elements in the island ecosystem; B1, B6, B7, and B8 were the sensitive elements in the land sub-ecosystem; and B4, B5, B6, B8, and B9 were the sensitive elements in the surrounding waters sub-ecosystem. In the different scenarios of element change, the vulnerability levels of the island ecosystem and surrounding waters sub-ecosystem remained the same, while the vulnerability level of the land sub-ecosystem decrease from mild vulnerability to critical vulnerability when the change rate of each of B3 and B10 was given as −10% or below, or the change rate of each of other elements was given as −5% or below. 3.2.2. Island scale The sensitivities of IEV to the elements in the island scale are shown in Table 6. B6 and B8 were the sensitive elements of IEV in all island; B5 and B7 were the sensitive elements of IEV in all island except NCSI; B4 was the sensitive element of IEV in XHSI, DHSI, and the five uninhabited islands; B9 was the sensitive element of IEV in the five uninhabited islands; B2 was the sensitive element of IEV in YTZI and NATZI; B1 was the sensitive element of IEV in NCSI; and B3 was the sensitive element of IEV in TLI. In the different scenarios of element change, the vulnerability levels of NCSI, XHSI, DHSI, DLI, and YTZI remained the same. The vulnerability level of BCSI increased from critical vulnerability to mild vulnerability when the change rate of B6 was given as 20%. The vulnerability level of MI decreased from mild vulnerability to critical vulnerability when the change rate of B7 was given as − 10% or below, or the change rate of each of B4, B5, B6, and B8 was given as − 20%. The vulnerability level of TLI decreased from critical vulnerability to non vulnerability when the change rate of each of B4 and B9 was given as − 5% or below, or the change rate of each of B3, B7 and B9 was given as − 10% or below, or the change rate of each of B2 and B10 was given as −20%. The vulnerability level of NTZI decreased from critical vulnerability to non vulnerability when the change rate of each of B7 and B8 was given as − 10% or below, or the change rate of each of B2, B5 and B9 was given as −20%. The vulnerability level of NATZI increased from non vulnerability to critical vulnerability when the change rate of each of B5, B7 and B8 was given as 10% or above, or the change rate of B9 was given as 20%.

4. Discussion 4.1. Evaluation model The evaluation model established in our study selected the evaluation elements covering natural and anthropogenic factors from aspects of exposure, sensitivity, and adaptability. Exposure was characterized by the population pressure, exploitation intensity, and terrain. Human activities are the main interference factors of the island ecosystem in most cases, especially in China (Li et al., 2015). Population growth is the major source of pressure to the island ecosystem (Toth and Szigeti, 2016), and the exploitations of the island and surrounding waters are direct manifestations of human activities. Bedrock islands, which occupy the majority of Chinese islands, are marked by complex terrains, and terrain is an important habit factor of island plants (Currie and Paquin, 1987) and greatly affects the spatial distribution of human activities (Chi et al., 2017b). Of all the elements in the sensitivity layer, ecosystem productivity directly represents the efficiency of the ecosystem and is an important factor for revealing the ecological processes and carbon source/sink characteristics (Field et al., 1998); diversity plays a fundamental role in maintaining and regulating the recycling of ecosystem materials, energy flow, and system stability (Tilman et al., 2006; Ma, 2013); environmental qualities, including soil, sea water, and marine sediment qualities, represent the appropriateness of environmental factors for the survival and development of organisms and

3.2.3. Grid scale The sensitivities of IEV to the elements in grid scale are shown in Fig. 4. The sensitivities of IEV to the elements also showed spatial variation. In the land sub-ecosystem, the spatial distribution of the sensitivity to an element was highly consistent with that of the evaluation result of the corresponding element, that is, the sensitivity value 7

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 2. Spatial heterogeneity of evaluation results of element B1–B7.

8

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 2. (continued)

9

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 2. (continued)

10

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 2. (continued)

11

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 3. Spatial heterogeneity of IEV in the grid scale.

Table 5 Sensitivity and variation of IEV to each element in the archipelago scale. Elements

Sensitivity Island ecosystem

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

0.116* 0.065 0.046 0.153* 0.092 0.181* 0.111* 0.121* 0.084 0.031

Variation Land sub-ecosystem

Surrounding waters sub-ecosystem

0.123* 0.069 0.049 0.098 0.098 0.193* 0.118* 0.129* 0.090 0.033

– 0.053 – 0.263* 0.151* 0.126* – 0.208* 0.145* 0.053

Island ecosystem S S S S S S S S S S

Land sub-ecosystem d,e,f

D Dd,e,f De,f Dd,e,f Dd,e,f Dd,e,f Dd,e,f Dd,e,f Dd,e,f De,f

Surrounding waters sub-ecosystem S S S S S S S S S S

Note: *, the sensitive element. S, the vulnerability level stays the same in different scenarios; D, the vulnerability level decrease in some scenarios; I, the vulnerability level increase in some scenarios; a, b, c, d, e, and f indicate different scenarios that change rate of the element was given as 5%, 10%, 20%, −5%, − 10% and −20%, respectively.

exploitation, complex terrain was used as the limiting factor; island area was used as the evaluation basis; financial base, environmental protection input, and energy saving condition were considered as the regulatory factors; and ecosystem productivity, biodiversity, environmental quality, and landscape pattern, which comprehensively reflect the island ecosystem, were analyzed. Meanwhile, the evaluation model adequately reflects the land-sea dual features and their spatial heterogeneities of island ecosystem. First, island ecosystem was divided to land sub-ecosystem and surrounding waters sub-ecosystem, and these two sub-ecosystems had clear boundary but interrelated closely with each other. Second, the evaluation elements and indices selected can represent the characteristics of land sub-ecosystem and surrounding waters sub-ecosystem, and reflect their spatial heterogeneity. Third, specific and targeted evaluation standards were adopted so that the unification, normalization and comparability of IEV of the two sub-ecosystems in space could be realized and the spatial heterogeneity of IEV was achieved. Besides that, this model pioneered IEV evaluation in archipelago scale, island scale and grid scale, with clear and concise calculation methods, strong applicability and comparability in evaluation results, and can be applied

humans because of the unique conditions of the island ecosystem or the pressure from pollutants emission; landscape pattern is the outcome of the comprehensive effects of natural and anthropogenic factors on geographical space and has a significant effect on the structure, function, and process of ecosystems (Strohbach and Haase, 2012; Ramalho et al., 2014). All these elements respond sensitively to human inference and have been increasingly influenced by it in the recent years (Chi et al., 2016; Chi et al., 2015b; Chen et al., 2013). Adaptability is the IEV regulatory capacity of humans in the aspects of economy development, environmental conservation, and energy consumption, which reflect the financial base, environmental protection input, and energy saving condition, respectively. In addition, the theory of island biogeography indicates that island area is the main restricting factor of biodiversity (MacArchur and Wilson, 1963, 1967); other studies also proved that island area is the basic factor of species extinction, human activities scale, and landscape pattern (Karels et al., 2008; Chi et al., 2017b). Therefore, island area was considered as an important basis for determining the evaluation standards, such as exploitation intensity and terrain, that is, the smaller the area of an island is, the lower the standard value is. In summary, under the pressure of population and 12

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Table 6 Sensitivities of IEV to each element in the island scale. Items Sensitivity

Variation

NCSI BCSI MI XHSI DHSI DLI TLI YTZI NTZI NATZI NCSI BCSI MI XHSI DHSI DLI TLI YTZI NTZI NATZI

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

0.204* 0.061 0.049 0.038 0.037 – – – – – S S S S S – – – – –

0.076 0.065 0.065 0.077 0.059 0.082 0.053 0.137* 0.116* 0.045 S S S S S S Df S Df S

0.032 0.066 0.068 0.076 0.061 0.033 0.132* 0.016 0 0 S S S S S S De,f S S S

0.099 0.093 0.091 0.127* 0.100* 0.241* 0.248* 0.176* 0.142* 0.255* S S Df S S S Dd,e,f S Df Ib,c

0.087 0.108* 0.108* 0.104* 0.107* – – – – – S S Df S S – – – – –

0.188* 0.231* 0.146* 0.160* 0.190* – – – – – S Ic Df S S – – – – –

0.089 0.115* 0.226* 0.141* 0.157* 0.233* 0.121* 0.176* 0.299* 0.242* S S De,f S S S De,f S De,f Ib,c

0.116* 0.135* 0.127* 0.142* 0.148* 0.211* 0.229* 0.254* 0.227* 0.235* S S Df S S S Dd,e,f S De,f Ib,c

0.081 0.094 0.088 0.099 0.103 0.147* 0.159* 0.177* 0.158* 0.164* S S S S S S De,f S Df Ic

0.029 0.034 0.032 0.036 0.037 0.053 0.058 0.064 0.057 0.059 S S S S S S Df S S S

Note: *, the sensitive element. S, the vulnerability level stays the same in different scenarios; D, the vulnerability level decrease in some scenarios; I, the vulnerability level increase in some scenarios; a, b, c, d, e, and f indicate different scenarios that change rate of the element was given as 5%, 10%, 20%, −5%, − 10% and −20%, respectively.

et al., 2015a, 2015c), and continuously influenced the island ecosystem by the discharge or leakage of pollutants (Li et al., 2015). Furthermore, counter measures, such as urban green space construction and ecological restoration, have been paid less attention in the current exploitation processes, so that the negative effects of exploitation had not been effectively controlled.

in IEV evaluation in different regions and different types of islands. 4.2. Evaluation results The IEV and its main driving factors in the southern islands of Miaodao Archipelago had their own characteristics in different scales. In the archipelago scale, high population density, low primary productivity of surrounding waters, poor soil quality, high landscape fragmentation, slowdown in economic growth, and low proportion of tertiary industries were the main causes of vulnerability. The vulnerability level of land sub-ecosystem decreased from mild vulnerability to critical vulnerability when each of most of the elements was improved, which indicated that the IEV in land sub-ecosystem was inclined to turn better, while the vulnerability level of the island ecosystem and the surrounding waters sub-ecosystem remained the same in each scenario, which meant that its IEV levels were stable. In the island scale, the IEVs of the islands and their main driving factors had obvious differences, which were mainly caused by the spatial variance in population pressure, terrain, landscape pattern, and exploitation intensity (CV > 30). In the different scenarios of element change, the IEVs of BCSI and NATZI were inclined to turn worse; the IEVs of MD, DLI, and NTZI were inclined to turn better; and the IEVs of NCSI, XHSI, DHSI, TLI, and YTZI were stable. In the grid scale, significant spatial heterogeneities in the evaluation results of elements B1–B7, IEV and the sensitivities of IEV to elements existed. Exploitation intensity, terrain, population pressure, landscape pattern, and ecosystem productivity (CV > 50) were the main causes of spatial heterogeneity. Large areas of critical vulnerability zones were transformed into non vulnerability zones when each of ecosystem productivity, biodiversity, environmental quality, economic development, environmental conservation, and energy consumption was improved, which indicated that the critical vulnerability zones were inclined to turn better. Population pressure was a driving factor for the IEV and its spatial heterogeneity. Exploitation intensity was an important factor of IEV in the island and grid scales, which indicated that human beings and their exploitation activities inevitably caused and aggravated IEV. Island exploitation included urban construction, transportation, aquaculture and fishing, farming, tourism, and so on (Chi et al., 2015a), which directly occupied the natural habitat, changed the topographic features and community structure, aggravated landscape fragmentation (Chi

4.3. Suggestions for island conservation and exploitation based on IEV The southern islands of Miaodao Archipelago were in status of mild vulnerability as a whole. The land sub-ecosystem was in status of mild vulnerability, indicating that evident damage existed. However, the IEV was inclined to recover to critical vulnerability under the conditions of decreased exploitation intensity, improved ecosystem quality, and promoted adaptability. The surrounding waters sub-ecosystem was in status of critical vulnerability, indicating that the damage on it was not evident, but it was under threat. The suggestions were as follows: population growth and its negative ecological effects should be regulated in general; exploitation scale should be controlled and allocation should be optimized; damaged habitats restoration and marine environmental monitoring should be enhanced; tertiary industries, such as tourism, should be vigorously developed and new breakthroughs for economy development should be identified to reduce the dependence on aquaculture. Current industries should be endowed with ecological ideas to minimize the negative effect of industry development on the island ecosystem. Evaluation results in the island scale indicated that NCSI and MI were in status of mild vulnerability. NCSI was the administrative center, where the population was concentrated and exploitation activities were particularly intensified. People here should control new constructions and land reclamations, build urban green space networks, and restore damaged soils and vegetations. MI is small in area, but is home to the oldest and most famous Mazu Temple, named Xianying Palace, in North China. Therefore, tourism activities were frequent and landscape fragmentation was obvious. People here should limit land use area, reduce the effect of tourism, including habitat destruction and pollutant emission, and improve the connection of vegetation landscape by means of forest reseeding and restoration to decrease its vulnerability level from mild vulnerability to critical vulnerability. BCSI, XHSI, DHSI, DLI, TLI, and NTZI were in status of critical vulnerability. In these 13

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. Spatial heterogeneity of sensitivity of IEVI to each element.

14

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. (continued)

15

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. (continued)

16

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. (continued)

17

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. (continued)

18

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 4. (continued)

conditions when necessary. As for the surrounding waters sub-ecosystem, the mild, moderate, and severe vulnerability zones were mainly located in the sea areas next to the coastline, Miaodao Bay, and other aquaculture areas; the non vulnerability zones were scattered in the surrounding waters and accounted for a small proportion; and the other areas were critical vulnerability zones. The spatial heterogeneity of IEV in the surrounding waters sub-ecosystem was more affected by the exploitation intensity (C2) than the land sub-ecosystem. Different sea area use types affect the marine ecosystem (Zhang et al., 2012). The difference in hydrodynamic condition is also an important factor of spatial distribution on marine ecological status (Zheng et al., 2016). The weak hydrodynamic conditions caused by the separation effect of the islands and the dam between NCSI and BCSI resulted in the high IEVI in Miaodao Bay. The new utilization in the sea areas of concentrated utilization next to the coastline should be controlled, and ecological measures, such as ecological aquaculture, should be promoted to minimize the negative effects on the marine ecosystem. In Miaodao Bay, the bridge between NCSI and BCSI, constructed for use in 2014, replaced the traffic functions of the dam built in the 1950s. The effect of the former on hydrodynamic conditions was much lower than the latter because of the water permeability of bridge. Thus, the demolition of the dam should be considered to improve the hydrodynamic conditions fundamentally and decrease the IEV of Miaodao Bay. In other areas, ecological restoration and construction, such as marine ranch, should be promoted steadily to improve ecosystem productivity and biodiversity and make the critical vulnerability zones transfer to non vulnerability zones. In fact, Changdao County carried out several coastline restoration projects, which have worked well in the recent years. BCSI was considered as an important developing island to undertake the exploitation pressure of NCSI, and the real property project named Changdao International Holiday Village, which covers an area of 7.8 hm2, was constructed (Chi et al., 2015a). Small docks were and will be established in uninhabited islands to improve the ecosystem conservation

islands, BCSI and DHSI are the largest in areas; the former is connected to the NCSI through a cross-sea bridge. Therefore, traffic here is convenient, and it is feasible for BCSI to participate in the exploitation activities of NCSI. At the same time, soil quality should be improved to avoid the increase of the vulnerability level. The latter should be exploited after determining the utilization direction in a step-by-step manner; meanwhile, the completeness and connectivity of natural habitat should be maintained. XHSI, DLI, TLI, and NTZI are small in areas; ecological recoveries, including ecosystem productivity promotion, and landscape pattern optimization, should be the major measures. YTZI and NATZI were in status of non vulnerability, and appropriate infrastructure construction can be developed in the premise of island ecosystem conservation to improve the protection capability of uninhabited islands and provide a basis for ecological tourism. Evaluation results in the grid scale fully exhibited the spatial heterogeneity of IEV. For the land sub-ecosystem, the severe and moderate vulnerability zones were mainly located in the areas around the dock in NCSI and BCSI; the mild vulnerability zones were spread all over NCSI and located in the urban construction areas, and steep areas of other islands. In the urban construction areas and dock areas, development should be optimized, land use should be more economical and effective, and green space establishment should be strengthened. In the steep areas, ecological recovery and construction, and the monitoring and prevention of geological hazards should be implemented. The critical vulnerability zones were distributed in BCSI, MI, XHSI, DHSI, and parts of uninhabited islands. The arbitrary occupation of critical vulnerability zones with human activities should be strictly controlled, and exploitation only occurs after reasonable argument. The non vulnerability zones were mainly distributed in areas with low exploitation, simple terrain, and good ecological status on each island, of which the plantation areas should be protected from occupation and destruction, and the stability of the plantation ecosystem can be improved through tree species structure optimization, pest control, and so on. The other areas in the non vulnerability zones can be exploited according to local 19

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

hm2 150

Fig. 5. Area variations of different vulnerability level zones in different scenarios.

B1

100 5%

50

10% 20%

0

-5% -10%

-50

-20% -100 -150 -200 NVA

CVA

MVA

hm2 250

MoVA

SVA

B2

200 150

5% 10%

100

20%

50

-5% -10%

0

-20%

-50 -100 -150 -200 NVA

CVA

MVA

MoVA

SVA

20

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 5. (continued)

hm2 100

B3

80 60 5% 40

10%

20

20%

0

-5% -10%

-20

-20%

-40 -60 -80 -100 NVA

CVA

hm2 10000

MVA

MoVA

SVA

B4

8000 6000 5% 4000

10%

2000

20%

0

-5% -10%

-2000

-20%

-4000 -6000 -8000 -10000 NVA

CVA

MVA

MoVA

SVA

21

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 5. (continued)

hm2 5000

B5

4000 3000 5% 2000

10%

1000

20%

0

-5% -10%

-1000

-20%

-2000 -3000 -4000 -5000 NVA

CVA

hm2 4000

MVA

MoVA

SVA

B6

3000 5%

2000

10% 1000

20% -5%

0

-10% -1000

-20%

-2000 -3000 -4000 NVA

CVA

MVA

MoVA

SVA

22

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 5. (continued)

hm2 200

B7

150 5%

100

10% 20%

50

-5% -10%

0

-20% -50 -100 -150 NVA

CVA

hm2 8000

MVA

MoVA

SVA

B8

6000 5%

4000

10% 2000

20% -5%

0

-10% -2000

-20%

-4000 -6000 -8000 NVA

CVA

MVA

MoVA

SVA

23

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Fig. 5. (continued)

hm2 5000

B9

4000 3000 5% 2000

10%

1000

20%

0

-5% -10%

-1000

-20%

-2000 -3000 -4000 -5000 NVA

CVA

MVA

hm2 1000

MoVA

SVA

B10

800 600

5% 10%

400

20%

200

-5% -10%

0

-20%

-200 -400 -600 -800 NVA

CVA

MVA

MoVA

SVA

24

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

island of Madeira. Proc. Econ. Fin. 2014 (18), 513–520. Cadée, G.C., Hegeman, J., 1974. Primary production of phytoplankton in the Dutch Wadden Sea. Neth. J. Sea Res. 8 (s2–3), 240–259. Chen, L.D., Sun, R.H., Liu, H.L., 2013. Eco-environmental effects of urban landscape pattern changes: progresses, problems and perspectives. Acta Ecol. Sin. 33 (4), 1042–1050 (in Chinese). Chi, Y., Shi, H.H., Guo, Z., Ding, D.W., 2015a. Connotation, features and causes of island ecological vulnerability. Acta Oceanol. Sin. 37 (12), 93–105 (in Chinese). Chi, Y., Shi, H.H., Wang, X.L., Li, J., Feng, A.P., 2015b. The spatial distribution and impact factors of net primary productivity in island ecosystem of five Southern Islands of Miaodao Archipelago. Acta Ecol. Sin. 35 (24), 8094–8106 (in Chinese). Chi, Y., Shi, H.H., Feng, A.P., 2015c. Typical island landscape ecological network establishment — a case study of Chongming Island. Mar. Environ. Sci. 34 (3), 433–440 (in Chinese). Chi, Y., Shi, H.H., Wang, X.L., Qin, X.B., Zheng, W., Peng, S.T., 2016. Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China. Chin. J. Oceanol. Limnol. 34 (5), 937–951. Chi, Y., Shi, H.H., Wang, Y.Y., Guo, Z., Ma, D.M., 2017a. Evaluation on spatial heterogeneity of island ecosystem carrying capacity — a case study of southern Miaodao Archipelago. China Environ. Sci. 37 (3), 1188–1200 (in Chinese). Chi, Y., Shi, H.H., Wang, E.K., Guo, Z., Feng, A.P., Ma, D.M., 2017b. The landscape pattern characteristics and its ecological effects on five Northern Islands of Miaodao Archipelago. Acta Ecol. Sin. 37 (4), 1270–1285 (in Chinese). Crespi, M., Fratarcangeli, F., Nascetti, A., Pieralice, F., Capaldo, P., 2012. DSM generation from high resolution imagery: applications with WorldView-1 and GeoEye-1. Eur. J. Remote Sens. 44 (1), 41–53. Currie, D.J., Paquin, V., 1987. Large-scale biogeographical patterns of species richness of trees. Nature 329 (6137), 326–327. Dahlin, K.M., Asner, G.P., Field, C.B., 2014. Linking vegetation patterns to environmental gradients and human impacts in a Mediterranean-type island. Landsc. Ecol. 29, 1571–1585. Dow, K., 1992. Exploring differences in our common future: the meaning of vulnerability to global environmental change. Geoforum 23, 417–436. 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. Guillaumont, P., 2010. Assessing the economic vulnerability of small island developing states and the least developed countries. J. Dev. Stud. 46 (5), 828–854. IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. Jupiter, S., Mangubhai, S., Kingsford, R.T., 2014. Conservation of biodiversity in the Pacific islands of Oceania: challenges and opportunities Pacific. Pac. Conserv. Biol. 20 (2), 206–220. Karels, T.J., Dobson, F.S., Trevino, H.S., Skibiel, A.L., 2008. The biogeography of avian extinctions on oceanic islands. J. Biogeogr. 35 (6), 1106–1111. Lagerström, A., Nilsson, M.C., Wardle, D.A., 2013. Decoupled responses of tree and shrub leaf and litter trait values to ecosystem retrogression across an island area gradient. Plant Soil 367 (1–2), 183–197. Li, X.M., Zhang, J., Cao, J.F., Ma, Y., 2015. Ecological risk assessment of exploitation and utilization in Chuanshan archipelago, Guangdong province, China. Acta Ecol. Sin. 35 (7), 2265–2276 (in Chinese). Liang, J., Sun, Z.Q., Zhu, Y.P., Zhang, X.Y., Yu, S.D., Zhang, Y.J., Yang, X.Y., Tang, X.J., 2011. 13-years succession dynamic of Kunyushan natural forest: change of diversity, species turnover and herbivorous insect's short-term disturbance. J. Cent. S. Univ. Forest. Technol. 31 (1), 9–17 (in Chinese). Lu, L., 2012. Species Diversity of Phytoplankton in Yellow Sea and Bohai Sea and Molecular Identification of Microalgal Isolates. Ocean University of China (in Chinese). Lv, R.H., Xia, B., Li, B.H., Fei, Z.L., 1999. The fluctuations of primary productivity in Bohai Sea waters over ten years. Adv. Mar. Sci. 3, 80–86 (in Chinese). Ma, K.P., 2013. Studies on biodiversity and ecosystem function via manipulation experiments. Biodivers. Sci. 21 (3), 247–248 (in Chinese). Ma, S.J., Wang, R.S., 1984. The social-economic-natural complex ecosystem. Acta Ecol. Sin. 4 (1), 1–9 (in Chinese). MacArchur, R.H., Wilson, E.O., 1963. An equilibrium theory of insular zoogeography. Evolution 37, 373–387. MacArchur, R.H., Wilson, E.O., 1967. The Theory of Island Biogeography. Princeton University Press, Princeton. Martins, I., Lopes, R.J., Lillebø, A.I., Neto, J.M., Pardal, M.A., Ferreira, J.G., Marques, J.C., 2007. Significant variations in the productivity of green macroalgae in a mesotidal estuary: implications to the nutrient loading of the system and the adjacent coastal area. Mar. Pollut. Bull. 54 (6), 678–690. McGillivray, M., Naudé, W., Santos-Paulino, A.U., 2010. Vulnerability, trade, financial flows and state failure in small island developing states. J. Dev. Stud. 46 (5), 815–827. Morgan, L.K., Werner, A.D., 2014. Seawater intrusion vulnerability indicators for freshwater lenses in strip islands. J. Hydrol. 508, 322–327. Nogué, S., Nascimento, D.L., Fernández-Palacios, J.M., Whittaker, R.J., Willis, K.J., 2013. The ancient forests of La Gomera, Canary Islands, and their sensitivity to environmental change. J. Ecol. 101 (2), 368–377. People's Government of Changdao County, 2015. Statistics Bulletin of the National Economic and Social Development of Changdao County in 2015. http://www. changdao.gov.cn/. Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A.,

capacity and develop ecological tourism. In the surrounding waters, Zostera marina restoration was implemented in the sea areas around XHSI (Wang, 2015) and several marine ranches were established. Moreover, the monitoring of marine ecosystem was implemented continuously and efficiently. All the measures were based on the comprehensive consideration of IEV and aimed to make the Miaodao Archipelago the ecologically sustainable islands in China. 5. Conclusions The features and innovations of our study are as follows. (1) The IEV evaluation model, which comprehensively reflected the dual characteristics of the land and surrounding waters sub-ecosystems and their spatial heterogeneities, was established, and the unification of the IEV spatial distribution in the two sub-ecosystems was realized. (2) IEV and its sensitivity to each element in archipelago, island, and grid scales were firstly evaluated and analyzed, respectively. (3) Counter measures for island conservation and exploitation based on IEV and its spatial heterogeneity were proposed. The results of the IEV evaluation of the southern islands of Miaodao Archipelago indicated the followings. (1) In the archipelago scale, the IEV was in status of mild vulnerability, with the land sub-ecosystem in status of mild vulnerability and the surrounding waters sub-ecosystem in status of critical vulnerability. Population growth and exploitation scale should be controlled and optimized; damaged habitats restoration and marine environmental monitoring should be enhanced; tertiary industries, such as tourism, should be vigorously developed; and current industries should be endowed with ecological ideas. (2) In the island scale, NCSI and MI were in status of mild vulnerability, BCSI, XHSI, DHSI, DLI, TLI, and NTZI were in status of critical vulnerability, and YTZI and NATZI were in status of non vulnerability, which suggested that different exploitation methods should be carried out in different islands. (3) In the grid scale, the IEV showed a significant spatial heterogeneity. The different vulnerability level zones were critical vulnerability, mild vulnerability, non vulnerability, moderate vulnerability, and severe vulnerability zones in descending order of areas. In the land sub-ecosystem, the severe and moderate vulnerability zones were mainly located in areas around the dock in NCSI and BCSI; the mild vulnerability zones were spread all over NCSI and located in the urban construction areas and steep areas on other islands; the critical vulnerability zones were distributed in BCSI, MI, XHSI, DHSI, and parts of uninhabited islands; and the non vulnerability zones were mainly distributed in areas with low exploitation, simple terrain, and good ecological status on each island. In the surrounding waters subecosystem, the mild, moderate, and severe vulnerability zones were mainly located in sea areas next to the coastline, Miaodao Bay, and other aquaculture areas; the non vulnerability zones were scattered in the surrounding waters and accounted for a small proportion; and the other areas were critical vulnerability zones. Acknowledgments This work is supported by the Public Science and Technology Research Funds Projects of Ocean of China (Nos. 201505012), the East Asia Marine Cooperation Platform of China-ASEAN Maritime Cooperation Fund (YZ0416003), and the Basic Scientific Fund for National Public Research Institutes of China (Nos. 2015G13). References Baumberger, T., Affre, L., Torre, F., Vidal, E., Dumas, P.J., Tatoni, T., 2012. Plant community changes as ecological indicator of seabird colonies' impacts on Mediterranean Islands. Ecol. Indic. 15 (1), 76–84. Benitez-Capistros, F., Hugé, J., Koedam, N., 2014. Environmental impacts on the Galapagos Islands: identification of interactions, perceptions and steps ahead. Ecol. Indic. 38 (3), 113–123. Bonati, S., 2014. Resilientscapes: perception and resilience to reduce vulnerability in the

25

Marine Pollution Bulletin xxx (xxxx) xxx–xxx

Y. Chi et al.

Tilman, D., Reich, P.B., Knops, J.M.H., 2006. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632. Toth, G., Szigeti, C., 2016. The historical ecological footprint: from over-population to over-consumption. Ecol. Indic. 60, 283–291. Utermöhl, H., 1958. Zur Vervollkommung der quantitativen phytoplankton-methodik. Limnology 9, 263–272. Vogiatzakis, I.N., Pungetti, G., Mannion, A.M., 2008. Mediterranean Island Landscapes: Natural and Cultural Approaches. Springer Publishing, New York City. Wan, H.X., Sun, Z.D., Wang, R., 2006. Study on the evaluation of ecological frangibility of the wetlands in the Bosten Lake region. Arid Land Geogr. 29 (2), 248–254 (in Chinese). Wang, M., 2015. Zostera marina (Eelgrass) Ecosystem Restoration in Miaodao Islands: A Case Study in Xiaoheishan Island. Ocean University of China (in Chinese). Yamano, H., 2008. Islands without maps: integrating geographic information on atoll reef islands and its application to their vulnerability assessment and adaptation to global warming. J. Geogr. 117 (2), 412–423 (in Japanese). Zhang, Y., Zhang, Y.J., Jing, X.D., Song, D.R., Zhao, J.H., Wang, Q.M., Miao, F.M., 2012. Basic situation in gulf waters of China. Mar. Environ. Sci. 31 (5), 755–757 (in Chinese). Zhang, R., Liu, X., Heathman, G.C., Yao, X., Hu, X., Zhang, G., 2013. Assessment of soil erosion sensitivity and analysis of sensitivity factors in the Tongbai-Dabie mountainous area of China. Catena 101 (2), 92–98. Zheng, W., Shi, H.H., Fang, G.H., Hu, L., Peng, S.T., Zhu, M.Y., 2012. Global sensitivity analysis of a marine ecosystem dynamic model of the Sanggou Bay. Ecol. Model. 24, 83–94. Zheng, W., Li, F., Shi, H.H., Huo, Y.Z., Li, Y., Chi, Y., Guo, Z., Wang, Y.Y., Shen, C.C., Liu, J., Qiao, M.Y., 2016. Spatiotemporal heterogeneity of phytoplankton diversity and its relation to water environmental factors in the southern waters of Miaodao Archipelago, China. Acta Oceanol. Sin. 35 (2), 46–55. Zhu, W.Q., Pan, Y.Z., Zhang, J.S., 2007. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. J. Plant Ecol. 31 (3), 413–424 (in Chinese).

Klooster, S.A., 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob. Biogeochem. Cycles 7 (4), 811–841. Ramalho, C.E., Laliberté, E., Poot, P., Hobbs, R.J., 2014. Complex effects of fragmentation on remnant woodland plant communities of a rapidly urbanizing biodiversity hotspot. Ecology 95 (9), 2466–2478. Ryther, J.H., 1969. Photosynthesis and fish production in the sea. Science 166, 72–76. Shen, C.C., Shi, H.H., Zheng, W., Ding, D.W., 2016. Spatial heterogeneity of ecosystem health and its sensitivity to pressure in the waters of nearshore archipelago. Ecol. Indic. 61, 822–832. Shi, H.H., Zheng, W., Ding, D.W., Lv, J.B., 2009. Valuation and ecosystem services of typical island. Mar. Environ. Sci. 28 (6), 743–748 (in Chinese). Shi, H.H., Wang, X.L., Wang, A., Liu, Z.Y., Ma, D.M., 2013. Storage and drivers of forests carbon on the Beichangshan Island of Miaodao Archipelago. Acta Ecol. Sin. 33 (19), 6363–6372 (in Chinese). Shi, H.H., Shen, C.C., Li, F., Wang, Y.Z., 2014. Parameter sensitivity analysis of a coupled biological physical model in Jiaozhou Bay. Acta Ecol. Sin. 34 (1), 41–49 (in Chinese). Solidoro, C., Crise, A., Crispi, G., Pastres, R., 2003. An a priori approach to assimilation of ecological data in marine ecosystem models. J. Mar. Syst. 40–41, 79–97. State Oceanic Administration PRC, 2012. National Marine Functional Zoning in China (2011 − 2020). (in Chinese). State Oceanic Administration PRC, 2015a. Index System of Marine Ecological Civilization Demonstration Area (Revised Edition). (in Chinese). State Oceanic Administration PRC, 2015b. The Guideline of Index System and Technical Methods for Monitoring and Early-warning of Marine Resources and Environment Carrying Capacity. (in Chinese). Strohbach, M.W., Haase, D., 2012. Above-ground carbon storage by urban trees in Leipzig, Germany. Landsc. Urban Plan. 104 (1), 95–104. Taramelli, A., Valentini, E., Sterlacchini, S., 2015. A GIS-based approach for hurricane hazard and vulnerability assessment in the Cayman Islands. Ocean Coast. Manag. 108, 116–130. Te'o, I.J.F., 2007. Economic vulnerability and resilience of small island states. Isl. Stud. J. 2 (1), 133–140.

26