Key food web technique and evaluation of nearshore marine ecological restoration of Bohai Bay

Ocean & Coastal Management 95 (2014) 1e10

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Key food web technique and evaluation of nearshore marine ecological restoration of Bohai Bay Ting Zheng, Xue-Yi You* School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China

a r t i c l e i n f o

a b s t r a c t

Article history: Available online

An ecological key food web restoration technique is proposed to restore the degraded nearshore marine ecosystem near the Bohai Bay, in which the key species are screened and the local key food web is built according to the trophic relationships of screening species to reference ecosystem. The trophic relationships of the key food web are quantified by the biomass of the screening species of each trophic level and the restoration input biomass is fixed. The restoration effect of the proposed technique on the restored ecosystem is predicted by the maturity and health with the Ecopath model and the ocean health index. The proposed technique is applied to the First Harbor of Tianjin Lin Gang Economic Zone. The calculated restoration effect shows that the restored (target) ecosystem is more mature, faster ecological balanced and healthier than the existing ecosystem. The ocean health index of the target ecosystem is 86.3, which is much higher than 51.8 of the existing ecosystem. It is concluded that the proposed technique is effective and applicable in the nearshore marine ecosystem restoration. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The marine ecosystems are very important to human. Over the world, the services of the ecosystems contribute to the economy at least US$33 trillion dollars annually, of which 63% come from those with the marine ecosystems (Costanza et al., 1997). However, the marine ecosystems have been degraded in many coastal areas where anthropogenic perturbations are most intense (Botsford et al., 1997; Pauly et al., 2002; Dulvy et al., 2004; Hilborn et al., 2004; Nyström et al., 2012). The degradation of nearshore ecosystems is caused by water pollution, greenhouse gas emissions and contaminated sludge etc. which are generated by the development of port and coastal infrastructure (Yap and Lam, 2013). In China, over-fishing (or other activities) has already led to the biodiversity loss, habitat degradation and marine environment damage, etc. (Mu et al., 2013). The nearshore ecosystem of the Bohai Bay is an example. The Bohai Bay is an important place for fishery species spawning and feeding (Xu et al., 2011). Among the cities within the Bay, Tianjin is the economic center. However, with the economic and social development, water of the Bohai Bay has become the most polluted in China (Zhou et al., 2012). The deterioration of water

* Corresponding author. E-mail address: [email protected] (X.-Y. You). http://dx.doi.org/10.1016/j.ocecoaman.2014.03.020 0964-5691/Ó 2014 Elsevier Ltd. All rights reserved.

quality and the reduction in biodiversity, biomass and species composition resulting from mariculture, land-based pollution, overfishing, eutrophication and oil spill, etc., degrade the Bohai Bay nearshore ecosystem significantly (Xu et al., 2011; Ning et al., 2010; Nie and Tao, 2009; Zhou et al., 2007; Jin, 2004; Tao, 2006; Wang et al., 2011). In the past, several species (local or non-indigenous species) were generally introduced to restore the nearshore marine ecosystem. Jones and Hanna (2004) applied an engineering restoration method where ecological, coastal, and civil engineering techniques were used to preserve the shoreline at Loyola Beach, Kleberg County, Texas. In this method, the native vegetation was introduced. After 5 years, a comprehensive evaluation on this restoration showed that the vegetation appeared to play a significant role in the stabilization of the shoreline (Jones et al., 2010). Terawaki et al. (2003) suggested that the periodic transplanting of Sargassum plants produces niches to allow faunal re-colonization effectively and thereby the Sargassum bed was restored. Spartina was introduced to Australia in the 1930s and China in the 1960s to protect coasts from eroding and it now becomes the invasive species (Kriwoken and Hedge, 2000; Wang et al., 2003). Borsje et al. (2011) utilized the pacific oyster as an invasive species to protect the coast and enhance the ecological functions. Obviously, the above restoration approaches need long time to achieve a new ecological balance even if the restoration is effective. Also, in the approach, the potential of the ecological risk on the alien species

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invasion are usually ignored. In order to restore the degraded nearshore marine ecosystem quickly and avoid the ecological risk of the biological invasion simultaneously, an ecological restoration technique based on the construction of the local key food web is proposed. The restoration effect of the proposed technique is predicted by the Ecopath model and the ocean health index. The prediction deals with the aspects of the maturity and health of the restored ecosystem. The Ecopath model was proposed by Polovina in 1984 to describe the coral reef ecosystem (Polovina, 1984a,b). It was further developed by Christensen and Pauly (1992a,b, 1995). In addition, the theoretical ecology concepts, mainly network models proposed by Ulanowicz (1986, 1995) and studied by Patten (1995), were combined with the Ecopath model by the researchers at the Fisheries Centre of University of British Columbia. The Ecopath data sets that are publically available provide a valuable resource of the comparative analysis of marine ecosystems (Steele and Ruzicka, 2011). The Ecopath model has been applied in Bohai Sea (Tong et al., 2000), Great South Bay (Nuttall et al., 2011), Sri Lanka (Haputhantri et al., 2008), and Bengal Bay (Ullah et al., 2012), etc., which predict the characteristics of the ecosystems quantitatively. Generally, health assessments for the marine ecosystem are qualitative and semi-quantitative (Wells, 2003; Haynes et al., 2007; Xu et al., 2004; Ye et al., 2007). Even for the quantitative assessments, most of the approaches just yet studied the marine life conservation without considering the human uses of oceans (Boesch, 2000). In addition, most of the marine health assessments only focused on the negative impacts of human on the ocean (Strain and Macdonald, 2002; Herrera-Silveira and Morales-Ojeda, 2009). In fact, some human activities, such as the closed fishing, have positive impacts on the ocean. The ocean health index proposed by Halpern et al. (2012) to evaluate the health of marine ecosystem not only in the marine life conservation, but also in the human uses of oceans (Anonymous, 2012). The index is a quantitative evaluation by considering the aspects of marine life conservation, human uses of ocean as well as the negative and positive impacts on the ocean. Furthermore, it can be implemented at any scale (Halpern et al., 2012). In this study, the First Harbor of Tianjin Lin Gang Economic Zone is taken as an example to show the application of the proposed technique. The restoration effect of the proposed technique is predicted in the aspects of the maturity and health of ecosystem by the Ecopath model and the ocean health index. 2. Study area The restored area is the First Harbor of Tianjin Lin Gang Economic Zone of China (38
340 e40
150 N, 116
430 e118
040 E). Tianjin Lin Gang Economic Zone is formed by land reclamation and its total planning area is 200 km2 . The Zone is the ecological economic zone of northern China. By 2020, one trillion RMB ($160 billion) annual industrial output is planned, to realize 100 million tons port throughput, and have over 0.5 million residents living in the ecological civilization demonstration area. The restoration of the degraded nearshore marine ecosystem of the First Harbor is the first step for realizing the ecological civilization demonstration area. The First Harbor of Tianjin Lin Gang Economic Zone has not been developed at present. The coast of this area is hard surface and the subtidal is riprap. There is no submerged plant and only small amount of wet plants along the land and sea boundary line. The average depth of the Harbor is about 7 m and the deepest is about 11 m with slow flowing water and small waves. The coastal ecosystem is degraded. The range of the restored area is about 4 000 m along the coastline and about 700 m away from the land and sea boundary

line. With the breakwater outside the First Harbor, the restored area can be assumed as an independent system. After ecological restoration, about 50% of the surface is non-rigid and the fishing and other extractive activities are banned. This area is mainly used for tourism and recreation. 3. Key food web ecological restoration technique 3.1. Reference ecosystem The Bohai ecosystem of 1982 (with little interference) is taken as the reference ecosystem of ecological restoration. The nearshore marine ecosystem before ecological restoration is called the existing ecosystem. The nearshore marine ecosystem after the ecological restoration is referred to the target ecosystem. 3.2. Key food web The food web depicts the feeding relationships among species in an ecological community. The key food web means that the major energy transfer pathway of the species is within the key food web. The food chain of the Bohai Bay is coastal continental shelf food chain and it is composed by four trophic levels, namely producer, grazers, carnivores and secondary carnivores (Shen and Shi, 2001; Su et al., 2002). In the Bohai Bay, the producer is mainly phytoplankton. Grazers are mainly zooplankton, crustaceans and shellfish. Carnivores are mainly carnivorous fishes. Secondary carnivores are mainly secondary carnivorous fishes (Su et al., 2002). Fish, shrimp, crabs, and shellfish are main aquatic resources and the species forming the key food web are mainly the above species. In the process of nearshore ecological restoration, the species are screened necessarily to form the key food web. The chosen screening species should satisfy the following requirements: (1) The screening species are the existing species. According to the survey and assessment of fisheries resources of the Bohai Bay released by Tianjin Fisheries Research Institute, the wild Paralichthys Olivaceus and Globefish are completely disappeared and these species are not used as the species for screening. The existence of species is found out in the survey data of 2009 (Li and Zhang, 2011). (2) The screening species are able to adapt to the environment of the restored area. The screening species should survive in the environment of the restored area. They must be chosen carefully with their ecological habit to avoid their maladjustment to the environment of the restored area. (3) The screening species are common species. The screening species are easy to survive, reproduce and maintain the community ecosystem. Scylla is not the species with large biomass (the annual biomass is about 100 t) in 1982e1983 (Cheng and Guo, 1998). Therefore, it is not recommended as the screening specie. The common species are referred to the related data in 1982/1983 (Su et al., 2002; Cheng and Guo, 1998). Based on the above principles, the screening species are determined and the key food web is built according to the trophic relationships of the screening species. 3.3. Trophic relationship The relationship of biomass proportion among the trophic levels is referred to that of the reference ecosystem (Su et al., 2002). The Bohai ecosystem survey (bottom trawl data) from April 1982 to May 1983 (Su et al., 2002) and the Bohai proliferation ecological

T. Zheng, X.-Y. You / Ocean & Coastal Management 95 (2014) 1e10 Table 1 The result of species screening. Trophic level

Main species and the percentage of each species at each trophic level

Producer Grazer

Phytoplankton: 100%; Portunus trituberculatus: 26.48%; Fenneropenaeus chinensis: 2.66%; Zooplankton: 69.7%; Scapharca subcrenata: 1.16% Oratosquilla oratoria: 16.34%; Mugil so-iuy: 3.16%; Pampus argenteus: 5.27%; Setipinna taty: 62.88%; Thryssa kammalensis: 4.07%; Harengula zunasi: 5.95%; Eupleurogrammus muticus: 0.75%; Clupanodon punctatus: 1.58% Lateolabrax japonicas: 39.81%; Johnius belangerii: 4.85%; Scomberomorus niphonius: 55.34%

Carnivore

Secondary carnivores

The biomass ratio of each trophic level is: Producer: grazers: carnivores: secondary carnivores ¼ 100: 40.5: 19.9: 8.0 (Su et al., 2002).

foundation survey from May 1982 to February 1983 (Cheng and Guo, 1998) provide the biomass of the screening species and the relationships of biomass proportion between the screening species of each trophic level. Therefore, the quantitative trophic relationships of the key food web can be determined.

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(5) Establish the key food web according to the trophic relationship of key species; (6) Determine quantitatively the trophic relationships with the reference ecosystem and the primary productivity of the existing ecosystem; (7) Realize the target ecosystem. Firstly, the actual biomass of the key species and primary productivity are measured in the restored area. Secondly, the expected biomass of the key species of target ecosystem is determined based on the quantitative trophic relationships of the key food web. Thirdly, the restoration input biomass of the key species is determined by the difference of the expected biomass of target ecosystem and the actual biomass of existing ecosystem. Finally, the above restoration input biomass of the key species is introduced in the restored area and the target ecosystem is formed. The target ecosystem is an expected ecosystem of the ecological restoration. 4. Assessment of ecological restoration

3.4. Primary productivity 4.1. Assessment of ecosystem maturity In the Bohai Bay, the producer contributing to the primary productivity is mainly phytoplankton. Therefore, the primary productivity of the target ecosystem is determined by the phytoplankton biomass in the existing ecosystem. The phytoplankton biomass is calculated by chlorophyll-a and the relationship of conversion is chlorophyll-a: phytoplankton wet weight ¼ 1:351.37 (Su et al., 2002). In the target ecosystem, the biomass of each screening species is calculated roughly according to the phytoplankton biomass and quantitative trophic relationships. 3.5. Restoration target ecosystem The result of species screening is shown in Table 1. According to the trophic relationships among the screening species (Su et al., 2002), the key food web is established (Fig. 1). The organisms of food web produce organic debris continually, which includes the excretion of waste as well as biological debris fragment after death (Shen et al., 2009). The organic debris is degraded by decomposing the organisms and it releases nutrients for producer absorption, or as the food of some animals. Then, it re-enters the food chain (web) and starts the next round of material recycling. Using the quantitative trophic relationships (in Section 3.3), the relationship of biomass proportion among trophic levels is obtained and the relationships of biomass proportion between the screening species of each trophic level are determined (Table 1). The target ecosystem is formed by the key food web with the quantitative trophic relationships. 3.6. Key food web ecological restoration technique The realization procedure of key food web ecological restoration technique is shown as follows (Fig. 2): (1) Choose a reference ecosystem; (2) Determinate the local existing species with the existing ecosystem; (3) Screen the adaptive local existing species which can adapt to the environment of the restored area according to their ecological habit; (4) Screen the key species with large biomass in the reference ecosystem from the local adaptive existing species;

4.1.1. Ecopath model It is assumed that the condition of steady living groups is held in the Ecopath model. This implies that the flows of input equals to those of output within a given time period (Pauly et al., 2000). In the Ecopath model, a set of mass balance linear equations for all groups (Walters et al., 1997; Christensen et al., 2000) are used to quantify the trophic flow among the trophic groups (Christensen and Pauly, 1992b, 1993). The simplified ecosystem model is expressed as:

Bi ðP=BÞi EEi ¼ Yi þ

X

Bj ðQ =BÞj DCij þ EXi

(1)

where, Bi is the biomass of function group i, (P/B)i is the production/ biomass rate, EEi is the ecotrophic efficiency, Yi is the yield of group i, Bj is the biomass of the predator group j, (Q/B)j is the relative food consumption ratio of j and DCij is the fraction of prey i in the diet of predator j. The Ecopath model needs for the balance of input and output. The balanced model should require that the value of EE is between 0 and 1 and the respiration of each functional group is positive. The uncertainties of the input parameters are specified under ‘pedigree’ in the Ecopath model. The ‘pedigree index’ is calculated to quantify the uncertainty related to the input values (Christensen et al., 2000; Christensen and Walters, 2004). A description of each input value consists of the data and its confidence level (samplebased, high or low precision, approximate or indirect method, or from other models and literature, etc.). Based on a set of qualitative choices of the origin of B, P/B, Q/B, the catch and diet input or model estimation value, percent ranges of uncertainty are determined in the model and they are resulted in an index value scaled from 0 (data not rooted in local) to 1 (data fully rooted in local data) for each input parameter. According to the individual pedigree index value, an overall ‘pedigree index’ P for a given Ecopath model is calculated as:

P ¼

n X X Iij n i¼1 j¼1

(2)

where, Iij is the pedigree index for functional group i and parameter j, n is the total number of functional groups (Christensen and Walters, 2004).

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Fig. 1. The established key food wed.

The maturity of the target and existing ecosystems is evaluated by the Ecopath model (EwE, 6.2 version). All models compose of 13 functional groups (Table 2) and the species of each functional group have similar habitats and diets. For the Ecopath model of the target ecosystem, the biomass of functional groups is mainly calculated by the quantitative trophic relationships (Table 1). The catches of functional groups are calculated by the following equation (Su et al., 2002):

B ¼ C=ðaqÞ

(3)

where, B is the biomass, C is the catch, a is the trawl sweeping area set to be unit area, q is the fishing coefficient fixed by 0.3 (Li and Zhang, 2011). For the Ecopath model of the existing ecosystem, the biomass and catches of functional groups are got from the Bohai bay ecological environmental quality survey in 2009 (Li and Zhang, 2011; Yang, 2012). For the above two Ecopath models, the biomass of detritus, P/B and Q/B are referred to those of the Ecopath models of Bohai in 1982 and 1992 (Tong et al., 2000; Lin et al., 2009). The food matrix is obtained by the stomach analysis of functional groups (Su et al., 2002; Yang, 2001; Wang et al., 1996). 4.1.2. Maturity metrics In the succession process of mature ecosystem, the structure tends to be complex and the function is perfect and stable. Odum (1969) selected 24 metrics from the community energetics, community structure, life history, nutrient cycling, selection pressure and overall homeostasis to summarize the trend of the structural and functional characteristics in the ecosystem development. Christensen (1995) investigated the quantitative explanatory power of Odum’s indices and established ‘the list of key attributes’. According to ‘the list of key attributes’, the Odum attributes are selected to be maturity metrics for assessing the maturity of ecosystem (Table 3). The Odum attributes of the reference ecosystem are adopted from the results of the Ecopath model of Bohai in 1982 (Lin et al., 2009).

Fig. 2. The key food web ecological restoration technique and the prediction of restoration effect.

4.1.3. Results and discussion The overall pedigree indices of the target and existing ecosystems are 0.5893 and 0.5714, respectively. Their values are large, in the range (0.16e0.68) of 150 Ecopath models (Morissette et al.,

T. Zheng, X.-Y. You / Ocean & Coastal Management 95 (2014) 1e10

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Table 2 Input values and estimated parameters (in bracket) for the Ecopath model of the target and existing ecosystems. Functional groups Target ecosystem Setipinna taty Scanberanorus niphonius Lateolabrax japonicus Other pelagic fishes Johnius belangerii Eupleurogrammus muticus Mugil so-iuy Oratosquilla oratoria Fenneropenaeus chinensis Portunus trituberculatus Zooplankon Phytoplankton Detritus Existing ecosystem Setipinna taty Scanberanorus niphonius Lateolabrax japonicus Other pelagic fishes Johnius belangerii Eupleurogrammus muticus Mugil so-iuy Oratosquilla oratoria Fenneropenaeus chinensis Portunus trituberculatus Zooplankton Phytoplankton Detritus

Catch (t km2)

B (t km2)

P/B (y1)

Q/B (y1)

EE

P/Q

0.5489 0.1942 0.1397 0.1472 0.0170 0.0065 0.0276 0.1427 0.0473 0.4704 e e e

1.8297 0.6474 0.4657 0.4908 0.0567 0.0218 0.0919 0.4755 0.1575 1.5681 4.1276 14.6220 43.0000

1.6971 0.6952 1.0580 2.3700 2.1000 2.1000 0.9139 8.2000 8.3000 3.5000 25.0000 71.2000 e

5.5000 5.8000 4.9450 7.9000 8.7000 8.7000 7.9000 28.9000 28.0000 11.6500 123.5500 e e

(0.7729) (0.4315) (0.2835) (0.9336) (0.1428) (0.1420) (0.3286) (0.9969) (0.0490) (0.3261) (0.1397) (0.5095) (0.0209)

(0.3086) (0.1199) (0.2140) (0.3000) (0.2414) (0.2414) (0.1157) (0.2837) (0.2964) (0.3004) (0.2023) e e

0.0230 0.0030 0.0026 0.1392 0.0019 0.0004 0.0002 1.0006 0.0215 0.0381 e e e

0.0767 0.0100 0.0086 0.4639 0.0065 0.0014 0.0007 3.3355 0.0717 (1.4900) 0.2080 14.6220 43.0000

1.6971 0.6952 1.0580 2.3700 2.1000 2.1000 0.9139 8.2000 8.3000 3.5000 25.0000 71.2000 e

5.5000 5.8000 4.9450 7.9000 8.7000 8.7000 7.9000 28.9000 28.0000 11.6500 123.5500 e e

(0.4001) (0.4315) (0.2858) (0.1398) (0.1392) (0.1361) (0.3126) (0.1993) (0.0366) 0.9500 (0.8626) (0.0813) (0.0468)

(0.3086) (0.1199) (0.2140) (0.3000) (0.2414) (0.2414) (0.1157) (0.2837) (0.2964) (0.3004) (0.2023) e e

2006). This indicates that the input parameter quality of the Ecopath models of the target and existing ecosystems is reliable and the Ecopath models are acceptable. The input and output results of the Ecopath models of the target and existing ecosystems are shown in Table 2. The Odum attributes of the target, reference and existing ecosystems are shown in Table 3. It is found that only nine attributes can be used to evaluate the maturity of the reference ecosystem. Among them, four attributes (TPP/B, B/TST, B/P, B/(R þ EXP)) are lower and other five attributes (TPP/TR, SOI, H, R/B, I) are higher than those of the existing ecosystem. Compared with the reference ecosystem, only one attributes (SOI) of the target ecosystem is lower. Other eight attributes (TPP/TR, TPP/B, B/TST, H, B/P, B/(R þ EXP), R/B, I) are larger. Consequently, the maturity of the target ecosystem is the highest and that of the existing ecosystem is the lowest. SOI is used for describing food web structure and it reflects the complexity of feeding interactions. R/B is the direct stability measure of the system (Christensen, 1995). Compared with the existing ecosystem, the values of SOI and R/B are larger in the target ecosystem (Table 3). The results indicate that the target ecosystem has high complexity of feeding interaction and stability. Additionally, TPP/TR is an excellent functional index of relative maturity of the system and the energy fixed tends to be balanced when TPP/TR approaches to 1 (Odum, 1969). The TPP/TR of the target ecosystem is close to 1 comparing with that of the existing ecosystem (Table 3). This indicates that the energy fixed of the target ecosystem is more balanced. The target ecosystem is closer to the ecological balance and it can reach the ecological balance faster than the existing ecosystem. 4.2. Assessment of ecosystem health 4.2.1. Ocean health index The ocean health index is used to assess the health of the target and existing ecosystems. It is measured as a function of ten public goals which the marine ecosystem provides to people (Halpern

et al., 2012). The ten public goals are: food provision, artisanal fishing opportunity, natural products, carbon storage, coastal protection, coastal livelihoods & economies, tourism & recreation, sense of place, clean waters and biodiversity. The ocean health index is defined as (Halpern et al., 2012):

PN I ¼

i¼1

ai Ii

(4)

U

P where, ai is the appropriate weights for goal i, ai ¼ 1, the weight (ai) is assumed to be equal and the weight from the uncorrelated goals redistribute to the remaining goals equably. U is the sum of the maximum possible values for each goal indicator:

U ¼

N X i¼1

ai xmax i

(5)

where, xmax equals to the maximum attainable status given realistic i constraints. Ii is the score of goal i and it is an average of its present status xi and its likely near-term future status xi,F:

Ii ¼

xi þ xi;F 2

(6)

where, xi is present value (Xi) relative to a reference point (Xi,R) and it is rescaled in 0e100 as:

xi ¼

Xi Xi;R

(7)

xi,F is defined as:

xi;F ¼ ð1 þ dÞ

1

½1 þ bTi þ ð1  bÞðri  pi Þxi

(8)

d is the discount rate and it is 0 initially, b is the relative importance of trend versus resilience and pressure terms in determining the likely trajectory of the goal status of future,

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Table 3 Odum attributes of three ecosystems. Odum attributes

T

Unit

EE

RE

TE

EE VS RE in maturity

RE VS TE in maturity

Total primary production/Total respiration (TPP/TR) Total primary production/Total biomass (TPP/B) Biomass supported per unit energy flow (B/TST) Connectance (C) System omnivory index (SOl) Dominance of detritus (Dom.Det) Flow diversity (H) Average organism size (B/P) Finn’s cycling index (FCI) Path length (PL) Residence time (B/(R þ EXP)) Nutrient conservation (Oex) Schrodinger ratio (R/B) Information content of flows (I)

z1 e þ þ þ þ þ þ þ þ þ þ þ þ

e Year Year e e e e Year e e Year e e Bits

13.5283 51.2976 0.0092 0.1528 0.0291 0.4800 1.8611 0.0188 1.2000 2.1103 0.0195 89.3000 3.7919 1.0838

9.7450 127.4930 0.0040 e 0.5080 e 1.9573 0.0076 e e 0.0077 e 13.0831 1.1620

3.1088 42.3990 0.0105 0.1458 0.0379 0.3200 2.5775 0.0212 0.2500 2.2365 0.0236 52.0000 13.6383 1.2673

[ Y Y e [ e [ Y e e Y e [ [

[ [ [ e Y e [ [ e e [ e [ [

T ¼ Trend; EE ¼ Existing ecosystem; RE ¼ Reference ecosystem; TE ¼ Target ecosystem; VS ¼ Versus.

b ¼ 0.67, Ti is the recent trend 1  Ti  1, ri is social and ecological

resilience to negative pressure and it is scaled as 0  ri  1, pi is the cumulative pressures on the goal and it is classified as the ecological and social pressures scaled as 0  pi  1. Among the ten public goals, the food provision, artisanal fishing opportunity and natural products are commercially extractive activities, which are banned in the restored area after restoration. Therefore, the remaining 7 public goals are evaluated in the health assessment of target ecosystem. The sense of place, clean waters and biodiversity goals are evaluated in the health assessment of existing ecosystem because the restored area has no habitat and has not been developed before restoration. For the target ecosystem, (1) The goal of carbon storage is a measure of carbon storage provided by coastal habitat. Its status involves the habitat

Table 4 The list references for variables. Variables

Period (year)

References

WGI of China

2011e2011

The number of job in tourism of Tianjin, The total revenue in tourism of Tianjin

2005e2010

GCI of China

2010e2011

The number of tourist-days in Tianjin The total population size in Tianjin

2007e2010

http://info.worldbank.org/ governance/wgi/sc_country.asp Statistical Yearbook of China Tourism of 2006e2011 released by National Tourism Administration of the People’s Republic of China The Global Competitiveness Report 2011e2012 released by World Economic Forum Statistical Yearbook of China Tourism 2009, 2011 Sixth National Population Census Main Data Bulletin in 2010 released by National Bureau of Statistics of People’s Republic of China Travel & Tourism Competitiveness Report 2007 e2009, 2011 released by Word Economic Forum 2010 Tianjin Marine Environmental Quality Bulletin

2010e2010

TTCI of China

2007e2011

The concentration of Inorganic nitrogen, Phosphate, Organics and Petroleum The input of Inorganic nitrogen, Phosphate, Organics and Petroleum

2008e2010

2010e2010

2010 Tianjin Marine Environmental Quality Bulletin

WGI ¼ Worldwide Governance Indicators, GCI ¼ Global Competitive Index, TTCI ¼ Travel and Tourism Competitiveness Index.

area of existing and target ecosystems. There is no habitat to store carbon before restoration and there has seagrass habitat to store carbon after restoration. The trend is the slope of annual data on habitat area over the previous five years. The social pressures are 1-WGI (Worldwide Governance Indicators of China 2011, Table 4). The resilience includes regulations and social integrity. The social integrity is determined by WGI. (2) The goal of coastal protection aims to assess the protection amount of people valued coastal areas provided by marine and coastal habitats. Its evaluation object and method of goalspecific index is the same as that of the carbon storage goal. (3) The aim of coastal livelihoods & economics goal is to maintain the coastal and ocean-dependent livelihoods and productive coastal economies while keeping the maximum of livelihood quality. It includes the livelihoods and economics sub-goals. Tourism is only considered in this goal because the restored area is mainly used for tourism and recreation. For the livelihoods sub-goal, its status involves the number of direct and indirect jobs measured by the number of employees (Table 4) and the average wage of tourism. The best status of tourism is taken as the reference. It is the tourism of 2010 in Tianjin. The trend is the average slope of employee and wage during 2005e 2009. The social pressures and resilience are determined by sector evenness, WGI and Global Competitive Index (GCI). For the economics sub-goal, its status involves the total revenue of tourism (Table 4). The best status of tourism is taken as the reference. It is the tourism of 2010 in Tianjin. The trend is the slopes of the total revenue from 2005 to 2009. The ecological pressures are the same as that of livelihoods sub-goal. The social pressures and the resilience are determined by WGI and GCI. (4) The goal of tourism & recreation is a measure of how much people value the marine ecosystems. Its status involves the number of tourist-days, the total population size and sustainability factor for each year equated to the Travel and Tourism Competitiveness Index (TTCI) (Table 4). Without the data TTCI of China 2010, it is calculated by the average of the TTCI of China in 2009 and 2011. The trend is the slope of its status during 2007e2010. The variables of the pressures and resilience are the same as those of the carbon storage goal. (5) The goal of sense of place reflects the aspects of the coastal and marine system that people value as a part of their cultural identity. It includes the iconic species and lasting special places sub-goals.

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Table 5 The ocean health indices of the target and existing ecosystems. Goals

Sub-goals

Target ecosystem Carbon Storage Coastal Protection Coastal Livelihoods & Economies

e e Livelihoods Economies Tourism & Recreation e Sense of Place Iconic Species Lasting Special Places Clean Waters e Biodiversity Species Habitats The ocean health index of the target ecosystem Existing ecosystem Sense of Place Iconic Species Lasting Special Places Clean Waters e Biodiversity Species Habitats The Ocean health index of the existing ecosystem

xi

Ti

pi

ri

xi,F

Sub-goal Ii

Goal Ii

100.0 100.0 100.0 100.0 52.0 100.0 100.0 60.7 100.0 100.0

0 0 1.0000 1.0000 0.0603 0.3570 0 0.0420 0.3570 0

0.710 0.710 0.412 0.492 0.643 0.651 0.692 0.643 0.648 0.704

0.441 0.441 0.677 0.516 0.691 0.626 0.691 0.691 0.584 0.623

91.1 91.1 100.0 100.0 54.9 100.0 100.0 60.0 100.0 97.3

e e 100.0 100.0 e 100.0 100.0 e 100.0 98.7

95.6 95.6 100.0 53.5 100.0 60.4 99.3 86.3

100.0 0 60.7 100.0 0

0 0 0.0420 0 0

0.651 0 0.643 0.648 0

0.358 0 0.691 0.334 0

90.3 0 60.0 89.6 0

95.2 0 e 94.8 0

47.6 60.4 47.4 51.8

xi,F is set as 100 when its value is more than 100 (Halpern et al., 2012).

For the iconic species sub-goal, its status involves the iconic species. The iconic species of fish, shrimp, crab and shellfish are considered (Table 1) and they are classified as the least concern species because they are fishery resources in Tianjin. The trend is the average of the categorical trend for all the iconic species. The trend of species is 0.5 if the biomass of species in the target ecosystem is larger than that in the existing ecosystem and it is 0 for other cases. The variables of the pressures are the same as that of the carbon storage goal. The resilience is determined by regulatory, ecological integrity and WGI. For the lasting special places sub-goal, its status involves the protected coastal marine area and coastline. In the restored area, the coastal marine and coastline are not protected before restoration and they are protected after restoration. The trend is the change slope of status over the past five years. The variables of the pressures and the resilience are the same as that of the carbon storage goal. (6) For the clean waters goal, its status involves the inputs of inorganic nitrogen, phosphate, organics (expressed by COD) and petroleum (main pollutants in Tianjin marine) (Table 4). The trend is the average slope of the concentration of the above four pollutants during 2008e2010 in Tianjin marine. The variables of the pressures and the resilience are the same as that of the carbon storage goal. (7) The goal of biodiversity is identified to assess the conservation status of species. It includes the species and habitat subgoals.

and trend involve the iconic species of fish, shrimp, crab and shellfish (Table 1). The variables of the pressures and resilience are the same as those of the iconic species sub-goal in the target ecosystem. (2) For the clean waters goal, the score is the same as that of the target ecosystem because it is assumed that the condition of clean waters is the same. (3) For the biodiversity goal, the score of the habitat sub-goal is 0. For the species sub-goal, its status, trend and variables of the pressures and resilience are the same as those of the iconic species sub-goal. xmax is assumed to be 1. The details of calculation method of i goal-specific index can be found in supplementary information of Halpern et al. (2012).

4.2.2. Results and discussion High ocean health index means good marine ecosystem health. The global ocean health index is 60 (maximum 100) (Halpern et al., 2012). The ocean health index of the target ecosystem is 86.3 (Table 5). It is much larger than 51.8 of the existing ecosystem. This result shows that the target ecosystem is healthier than the existing ecosystem. It is concluded that the health of the marine ecosystem is improved greatly after the proposed technique is applied in the restored area. The target ecosystem is more well-beings to people in the following aspects:

For the species sub-goal, its status and trend are the same as those of iconic species sub-goal. The variables of the pressures and the resilience are the same as that of the carbon storage goal. For the habitat sub-goal, its status involves two types of habitat developed in the restored area after restoration (i.e. the habitat of seagrass planted in non-rigid surface and the habitat in soft bottom with seagrass). The habitat area before restoration is taken as the reference habitat area and there is no habitat before restoration. The trend is the change rate of habitat area over the previous five years. The variables of the pressures and the resilience are the same as that of the iconic species sub-goal. For the existing ecosystem

(1) The developed habitat makes the restored area more carbon stored and species protected so that the scores of the carbon storage goal, the coastal protection goal and the habitat subgoal are large; (2) The restored area is mainly for tourism and recreation activities to develop the coastal livelihoods and economics; (3) The prohibition of fishing and other extractive activities protects the coastline and marine. It makes the high score of the lasting special places sub-goal. Additionally it also enhances the resilience of the iconic species and species subgoals (Table 5).

(1) For the sense of place goal, the score of the lasting special places sub-goal is 0. For the iconic species sub-goal, its status

The advantages and disadvantages of target ecosystem are as the following:

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(1) The high score of the carbon storage goal shows the target ecosystem can store much carbon; (2) The high score of the coastal protection goal shows that the protection provided by the target ecosystem is large; (3) The high score of the coastal livelihoods & economies goal shows that the target ecosystem can maintain the coastal and ocean-dependent livelihood and productive coastal economies while maximizing livelihood quality; (4) The high score of the sense of place goal shows that the people living near and far from the restored area derive a strong sense of identity or value from knowing particular places and species existence; (5) The high score of the biodiversity goal shows that the conservation status of species is good; (6) The low score of the clean waters goal (60.4) shows that the pollution is still heavy and attention is needed; (7) The score of the tourism & recreation goal is the lowest (only 53.5). It shows that many people have not paid much attention to this area’s marine ecosystem. This is because the average level of tourism & recreation of Tianjin in 2010 is used to calculate the goal. After restoration, the offshore sightseeing will be more attractive and the score of the tourism & recreation goal will increase. To further improve the health of target ecosystem, the fields of tourism & recreation and clean waters should be emphasized. 5. Discussions The proposed technique can avoid the ecological risk of the alien species invasion because all introduced species are local. The target ecosystem should satisfy the following two requirements to ensure a successful restoration: (1) The maturity of the target ecosystem must be higher than that of the existing ecosystem. This ensures the target ecosystem can achieve ecological balance faster than the existing ecosystem. (2) The ocean health index of the target ecosystem must be larger than that of the existing ecosystem and 60 of the global ocean ecosystem. The results of the Ecopath models show the target ecosystem is more mature than the existing ecosystem. The calculated ocean health index shows that the target ecosystem is healthier than the existing ecosystem. It is found that the target ecosystem satisfies the above two requirements. The proposed technique is therefore an effective way to conduct the nearshore ecological restoration. The study also suggests the implementation of the restoration should be carried out in summer for highest primary productivity (Wei et al., 2004) and most prosperous organisms in a year. Ecosystem-based management (EBM) has grown consistently over the last decades. It stresses the importance of ecosystem structures and functions which provide a range of services (Curtin and Prellezo, 2010). Marine EBM has been emphasized by the United States (National Ocean Council, 2012), Canada (O’Boyle and Jamieson, 2006), Australia (Scandol et al., 2005), Norwegian (Olsen et al., 2007) etc. In China, the marine EBM has been paid attention to and the attempt has been made in Xiamen that has achieved some success (Qiu et al., 2008). The proposed technique lays stress on the restoration effect on the ecosystem structures (expressed by maturity) and functions (expressed by ocean health index). It is a new realization way of EBM and it should be applied when the nearshore marine ecosystem degrades. The monitoring and control of the key species biomass should be conducted and the component

of the key species biomass should be checked by the quantitative trophic relationships of the key food web to know if the proposed restoration technique is required to be applied. Although the restoration effect of the proposed technique is good, some factors are still needed to be considered further: (1) The Ecopath model is used in the assessment of ecosystem maturity and its construction needs massive data. Some parameters (e.g. P/B, Q/B) are determined by the results of the Bohai Ecopath models in different time because of data incompleteness in the Bohai Bay. The Great South Bay (40
41.5520 N; 73
05.1440 W) has similar latitude with our study area. The Ecopath models of the Great South Bay in 1880s and 2000s were developed by Nuttall et al. (2011). The P/B of about 66.7% functional groups and the Q/B of about 95.2% functional groups are not changed in the Ecopath models of year 1880s and year 2000s. Therefore, the parameters of P/B and Q/B are fixed by those of the Ecopath models in 1982 and 1992. Because the existing and target ecosystems are in the same area, the biomass of detritus of them is the same. In order to get more accurate results, more research on determining the Ecopath parameters should be conducted in the coming studies. (2) In the ocean health index study, the decrease resilience of each goal leads to overoptimistic evaluation if the relevant environment management measures (e.g. closed fishing) are not implemented effectively after ecological restoration. Therefore, the effective implementation of relevant environment management should be paid much attention to after ecological restoration. In addition, the same weight for each goal is used in the ocean health index study. Halpern et al. (2012) calculated the overall index by using four different sets of weights to evaluate potential consequences of different weighting systems. They found the index score is about 60.1  3.5 SD. Their results indicate that the effect of different weight for each goal on the index is small. Therefore, the same weight for each goal adopted here is acceptable.

6. Conclusions Based on the key food web and quantitative trophic relationships, an ecological restoration technique is proposed. The restoration effect of the proposed technique is predicted in the aspects of the maturity and health of ecosystem by the Ecopath model and the ocean health index. The predicted results show that the target ecosystem is more mature and healthier than the existing ecosystem. The ecological balance of the target ecosystem is achieved faster than that of the existing ecosystem for its higher maturity. The target ecosystem has a higher ocean health index (86.3) over the existing ecosystem (51.8) and the global ocean ecosystem (60), and therefore is healthier. Additionally, the ecological risk of the alien species invasion is vanished for the proposed technique because all introduced species are local. EBM emphasizes the importance of ecosystem structures and functions providing a range of services. The proposed technique emphasizes the restoration effect on the ecosystem structures (expressed by maturity) and functions (expressed by ocean health index). The proposed technique is a new realization way of EBM. Overall, the proposed technique is an effective restoration method and a new realization way of EBM. The results of this work suggest that it is applicable in the restoration of nearshore marine ecosystem.

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Acknowledgment This study was supported by Tianjin Oceanic Administration through the marine promotion project for science and technology (KJXH2011-15). References Anonymous, 2012. Ocean health index shows room for improvement. Science 337, 784. Boesch, D.F., 2000. Measuring the health of the Chesapeake Bay: toward integration and prediction. Environ. Res. Sect. A 82, 134e142. Borsje, B.W., Wesenbeeck, B.K.V., Dekker, F., Paalvast, P., Bouma, T.J., Katwijk, M.M.V., Vries, M.B.D., 2011. How ecological engineering can serve in coastal protection. Ecol. Eng. 37 (2), 113e122. Botsford, L.W., Castilla, J.C., Peterson, C.H., 1997. The management of fisheries and marine ecosystems. Science 277, 509e515. Cheng, J.S., Guo, X.W., 1998. Distribution and dynamic variations of species and quality of benthos in the Bohai Sea. Mar. Fish. Res. 19 (1), 31e42 (in Chinese). Christensen, V., 1995. Ecosystem maturity-towards quantification. Ecol. Model. 77, 3e32. Christensen, V., Pauly, D., 1992a. A guide to the ecopath II software system (Version 2.1). ICLARM Software 6, 72. Christensen, V., Pauly, D., 1992b. Ecopath IIda software for balancing steady-state ecosystem models and calculating network characteristics. Ecol. Model. 61, 169e185. Christensen, V., Pauly, D., 1993. Trophic Models of Aquatic Ecosystems, vol. 26, p. 390. ICLARM Conference Proceedings. Christensen, V., Pauly, D., 1995. Fish production, catches and the carrying capacity of the world oceans. NAGA, ICLARM Quart. 18, 34e40. Christensen, V., Walters, C.J., Pauly, D., 2000. Ecopath with Ecosim: a User’s Guide. Fisheries Center, University of British Columbia, Vancouver and ICLARM, Malaysia. Christensen, V., Walters, C.J., 2004. Ecopath with Ecosim: methods, capabilities, and limitation. Ecol. Model. 172, 109e139. Costanza, R., D’Arge, R., Groot, R.De., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., Belt, M.V.D., 1997. The value of the world’s ecosystem services and natural capital. Nature 387, 253e260. Curtin, R., Prellezo, R., 2010. Understanding marine ecosystem based management: a literature review. Mar. Policy 34, 821e830. Dulvy, N.K., Ellis, J.R., Goodwin, N.B., Grant, A., Reynolds, J.D., Jennings, S., 2004. Methods of assessing extinction risk in marine fishes. Fish. Fish. 5, 255e276. Halpern, B.S., et al., 2012. An index to assess the health and benefits of global ocean. Nature 488, 615e620. Haputhantri, S.S.K., Villanueva, M.C.S., Moreau, J., 2008. Trophic interactions in the coastal ecosystem of Sri Lanka: an ECOPATH preliminary approach. Estuar. Coast. Shelf Sci. 76, 304e318. Haynes, D., Brodie, J., Waterhouse, J., Bainbridge, Z., Bass, D., Hart, B., 2007. Assessment of the water quality and ecosystem health of the Great Barrier Reef (Australia): conceptual models. Environ. Manag. 40, 993e1003. Herrera-Silveira, J.A., Morales-Ojeda, S.M., 2009. Evaluation of the health status of a coastal ecosystem in southeast Mexico: assessment of water quality, phytoplankton and submerged aquatic vegetation. Mar. Pollut. Bull. 59, 72e86. Hilborn, R., Branch, T.A., Ernst, B., Magnusson, A., Minte-Vera, C.V., Scheuerell, M.D., Valero, J.L., 2004. State of the world’s fisheries. Annu. Rev. Environ. Resour. 28, 359e399. Jin, X.S., 2004. Long-term changes in fish community structure in the Bohai Sea, China. Estuar. Coast. Shelf Sci. 59, 163e171. Jones, K., Hanna, E., 2004. Design and implementation of an ecological engineering approach to coastal restoration at Loyola Beach, Kleberg County, Texas. Ecol. Eng. 22 (4e5), 249e261. Jones, K., Pan, X.B., Garza, A., Lloyd-Reilley, J., 2010. Multi-level assessment of ecological coastal restoration in South Texas. Ecol. Eng. 36, 435e440. Kriwoken, L.K., Hedge, P., 2000. Exotic species and estuaries: managing Spartina anglica in Tasmania, Australia. Ocean Coast. Manag. 43, 573e584. Li, X., Zhang, L., 2011. Tianjin Mazu Economic and Trade Park Perimeter Seawallmarine Environment Impact Report. The Ministry of Transport and Tianjin Port & Waterway Engineering Research Institute, Tianjin. Report 6-2011-50. (in Chinese). Lin, Q., Jin, X.S., Zhang, B., Guo, X.W., 2009. Comparative study on the changes of the Bohai Sea ecosystem structure based on Ecopath model between ten years. Acta Ecol. Sin. 29 (7), 3613e3620 (in Chinese ). Morissette, L., Hammill, M.O., Savenkoff, C., 2006. The trophic role of marine mammals in the northern Gulf of St Lawrence. Mar. Mammal. Sci. 22, 74e103. Mu, R., Zhang, L., Fang, Q., 2013. Ocean-related zoning and planning in China: a review. Ocean Coast. Manag. 82, 64e70. National Ocean Council, 2012. Draft National Ocean Policy Implementation Plan. Nie, H.T., Tao, J.H., 2009. Eco-environment status of the Bohai Bay and the impact of coastal exploitation. Mar. Sci. Bull. 11 (2), 81e96. Ning, X.R., Lin, C.L., Su, J.L., Liu, C.G., Hao, Q., Le, F.F., Tang, Q.S., 2010. Long-term environmental changes and the responses of the ecosystems in the Bohai Sea during 1960e1996. Deep-Sea Res. II 57, 1079e1091.

9

Nuttall, M.A., Jordaan, A., Cerrato, R.M., Frisk, M.G., 2011. Identifying 120 years of decline in ecosystem structure and maturity of Great South Bay, New York using the Ecopath modelling approach. Ecol. Model. 222, 3335e3345. Nyström, M., Norström, A.V., Blenckner, T., Torre-Castro, M.D.L., Eklöf, J.S., Folke, C., Österblom, H., Steneck, R.S., Thyresson, M., Troell, M., 2012. Confronting feedbacks of degraded marine ecosystems. Ecosystems 15, 695e710. O’Boyle, R., Jamieson, G., 2006. Observations on the implementation of ecosystembased management: experiences on Canada’s east and west coasts. Fish. Res. 79, 1e12. Odum, E.P., 1969. The strategy of ecosystem development. Science 164 (3877), 262e 270. Olsen, E., Gjøsæter, H., Røttingen, I., Dommasnes, A., Fossum, P., Sandberg, P., 2007. The Norwegian ecosystem-based management plan for the Barents Sea. ICES J. Mar. Sci. 64, 599e602. Patten, B.C., 1995. Network integration of ecological extremal principles: exergy, emergy, power, ascendency, and indirect effects. Ecol. Model. 79 (1e3), 75e84. Pauly, D., Christensen, V., Guénette, S., Pitcher, T.J., Sumaila, U.R., Walters, C.J., Watson, R., Zeller, D., 2002. Towards sustainability in world fisheries. Nature 418, 689e695. Pauly, D., Christensen, V., Walters, C., 2000. Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci. 57, 697e 706. Polovina, J.J., 1984a. Model of a coral reef ecosystems I. The ECOPATH model and its application to French Frigate Shoals. Coral Reels 3, 1e11. Polovina, J.J., 1984b. An overview of the Ecopath model. Fishbyte 2 (2), 5e7. Qiu, j., Zhao, J.Z., Deng, H.B., Lin, M.J., 2008. Ecosystem-based marine management: principles, practices and suggestions. Mar. Environ. Sci. 27 (1), 74e78 (in Chinese). Scandol, J.P., Holloway, M.G., Gibbs, P.J., Astles, K.L., 2005. Ecosystem-based fisheries management: an Australian perspective. Aquat. Living Resour. 18, 261e 273. Shen, G.Y., et al., 2009. Marine Ecology. Science Press, Beijing, p. 174 (in Chinese). Shen, G.Y., Shi, B.Z., 2001. Marine Ecology. Science Press, Beijing, pp. 230e231 (in Chinese). Steele, J.H., Ruzicka, J.J., 2011. Constructing end-to-end models using ECOPATH data. J. Mar. Syst. 87, 227e238. Strain, P.M., Macdonald, R.W., 2002. Design and implementation of a program to monitor ocean health. Ocean Coast. Manag. 45, 325e355. Su, J.L., et al., 2002. Ocean Ecosystem Dynamic Research of China II Ecosystem Dynamic Process in Bohai Sea. Science Press, Beijing, pp. 216e217 (in Chinese). Tao, J.H., 2006. Numerical simulation of aquatic eco-environment of Bohai bay. J. Hydrodynam. Ser.B 18 (Suppl. 3), 34e42. Terawaki, T., Yoshikawa, K., Yoshida, G., Uchimura, M., Iseki, K., 2003. Ecology and restoration techniques for Sargassum beds in the Seto Inland Sea, Japan. Mar. Pollut. Bull. 47 (1e6), 198e201. Tong, L., Tang, Q.S., Pauly, D., 2000. A preliminary approach on mass-balance ecopath model of the Bohai Sea. Chin. J. Appl. Ecol. 11 (3), 435e440. Ulanowicz, R.E., 1986. Growth and Development: Ecosystem Phenomenology. Springer-Verlag, New York. Ulanowicz, R.E., 1995. Ecosystem trophic foundations: Lindeman Exonerata. In: Patten, B.C., Jørgensen, S.E. (Eds.), Complex Ecology: the Partewhole Relation in Ecosystems. Prentice-Hall, Englewood Cliffs, New Jersey, pp. 549e560. Ullah, M.D., Rashed-Un-Nabi, M., Al-Mamun, M.A., 2012. Trophic model of the coastal ecosystem of the Bay of Bengal using mass balance Ecopath model. Ecol. Model. 225, 82e94. Wang, B., Yu, G., Huang, J., Wang, T., Hu, H.Y., 2011. Probabilistic ecological risk assessment of DDTs in the Bohai Bay based on a food web bioaccumulation model. Sci. Total Environ. 409, 495e502. Wang, C.L., Xu, S.L., Mei, W.X., Zhang, Y.H., Wang, D.G., 1996. A biological basic character of Squilla oratoria. J. Zhejiang Coll. Fish. 15 (1), 60e62 (in Chinese). Wang, W., Zhang, K., Ru, S.G., 2003. The status of speartina spp. invasions in the world and the advances controlling over them. Mar. Sci. 27 (7), 38e42 (in Chinese). Walters, C., Christensen, V., Pauly, D., 1997. Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Rev. Fish. Biol. Fish. 7, 139e 172. Wei, H., Sun, J., Moll, A., Zhao, L., 2004. Phytoplankton dynamics in the Bohai Seadobservations and modeling. J. Mar. Syst. 44, 233e251. Wells, P.G., 2003. Assessing health of the Bay of Fundyeeconcepts and framework. Mar. Pollut. Bull. 46, 1059e1077. Xu, F.L., Lam, K.C., Zhao, Z.Y., Zhan, W., Chen, Y.D., Tao, S., 2004. Marine coastal ecosystem health assessment: a case study of the Tolo Harbour, Hong Kong, China. Ecol. Model. 173, 355e370. Xu, S.S., Song, J.M., Yuan, H.M., Li, X.G., Li, N., Duan, L.Q., Yu, Y., 2011. Petroleum hydrocarbons and their effects on fishery species in the Bohai Sea, North China. J. Environ. Sci. 23 (4), 553e559. Yang, H.L., 2012. The impact of reclamation engineering on surrounding zooplankton community in Tianjin Lin Gang Industry Zone. In: Chinese Society for Environmental Sciences Annual Conference Proceedings, vol. 2, pp. 1279e 1286. Yang, J.M., 2001. A study on food and trophic levels of Baohai Sea fish. Mod. Fish. Inf. 16 (10), 10e19.

10

T. Zheng, X.-Y. You / Ocean & Coastal Management 95 (2014) 1e10

Yap, W.Y., Lam, J.S.L., 2013. 80 Million-TEU Container Port? Sustainability Issues in port and coastal development. Ocean Coast. Manag. 71, 13e25. Ye, S.F., Liu, X., Ding, D.W., 2007. Ecosystem health assessment of the Changjiang River Estuary: indicator system and its primarily assessment. Acta Oceanol. Sin. 29 (4), 128e136 (in Chinese).

Zhou, H., Zhang, Z.N., Liu, X.S., Hua, E., 2012. Decadal change in sublittoral macrofaunal biodiversity in the Bohai Sea, China. Mar. Pollut. Bull. 64 (11), 2364e2373. Zhou, H., Zhang, Z.N., Liu, X.S., Tu, L.H., Yu, Z.S., 2007. Changes in the shelf macrobenthic community over large temporal and spatial scales in the Bohai Sea, China. J. Mar. Syst. 67, 312e321.