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Ecological Modelling 220 (2009) 2802–2818

Contents lists available at ScienceDirect

Ecological Modelling

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

odeling changes in the coastal ecosystem of the Pearl Riverstuary from 1981 to 1998

.J. Duana, S.Y. Li a,∗, Y. Liua, J. Moreaub, V. Christensenc

School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, 510275, ChinaLaboratoire d’Ecologie fonctionnelle UMR 5245 e, INP/ENSAT, Avenue de l’Agrobiopole Auzeville Tolosane, BP 32607F, 31326 Castanet Tolosan Cedex, FranceSea Around Us Project, Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC, Canada, V6T 1Z4

r t i c l e i n f o

rticle history:eceived 24 December 2008eceived in revised form 13 June 2009ccepted 15 July 2009vailable online 25 August 2009

eywords:copathrophic modelsoastal ecosystemetwork analysisrophic structureearl River Estuary

a b s t r a c t

The coastal ecosystem of the Pearl River Estuary (PRE) has been overfished and received a high level ofcombined pollution since the 1980s. Ecopath with Ecosim was used to construct two ecosystem models(for 1981 and 1998) to characterize the food web structure and functioning of the ecosystem. Pedigreework and simple sensitivity analysis were carried out to evaluate the quality of data and the uncertaintyof the models. The two models seem reliable with regards to input data of good quality. Comparing thevariations of outputs of these two models aimed to facilitate assessment of changes of the ecosystemduring the past two decades.

The trophic structure of the ecosystem has changed with an increase in the biomass proportion oflower trophic level (TL) organisms and a decrease in top predator biomass proportion. All the indicesof ecosystem maturity examined show that the system was in a more mature condition in 1981 thanin 1998, although the system has been in a condition of stress due to anthropogenic disturbances, suchas environmental pollution and habitat destruction since 1981. The ecosystem was aggregated into sixand seven integral TLs in 1981 and 1998, respectively, using the trophic aggregation routine of Ecopath.Most of the total system biomass and catch took place at TL II and III in both years. But the distributionof the total system biomass and catch at different TLs changed with decreasing proportions in higher TLsin 1998. The mean transfer efficiency was 9.1% and 10.2% in 1981 and 1998, respectively.

Comparative network analysis allowed quantification of the importance of direct and indirect trophicinteractions among functional groups. Moreover, a method derived from the mixed trophic impact (MTI)analysis allowed estimating importance of groups in terms of “keystoneness” and identifying the keystonespecies in the two models over the past two decades. The results indicate that there were no clear keystonespecies in 1998 but two keystone species at medium trophic levels were identified in 1981. Moreover,organisms located at low trophic levels such as phytoplankton, zooplankton and benthic invertebrates

lativ

were identified to have re

. Introduction

It has been widely recognized that ecosystem structure andunction need to be taken into account with respect to ecosys-em’s sustainability of living aquatic resources, particularly therophic structure and flows of biomass through species interactionsChristensen and Pauly, 1995). Measurements of biomass transfermong functional groups and trophic efficiency provide informa-

ion on ecosystem structure and function, which can be used tovaluate the impact of change on some groups and the way its propagated through the whole ecosystem via the trophic webChristensen and Pauly, 1993; Christian et al., 1996).

∗ Corresponding author. Tel.: +86 20 84113620; fax: +86 20 84110692.E-mail addresses: eeslsy@mail.sysu.edu.cn, cholice shi@yahoo.com.cn (S.Y. Li).

304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.07.016

ely high keystoneness in the ecosystem.© 2009 Elsevier B.V. All rights reserved.

The Pearl River is the second largest river (2200 km) in China interms of water discharge. The annual variation in discharge is sig-nificant and depends on the amount of rainfall that the catchmentreceives. The Pearl River Estuary (PRE) is located in the southernprovince of Guangdong. Currently, the coastal region of the PRE is asignificantly and quickly developing economic zone. As a result ofrapid economic development in recent decades, the whole regionhas experienced rapid industrialization and urbanization. The highpopulation density and rapid development of industry and agricul-ture have resulted in severe stress to the aquatic environment. Agreat deal of waste, excessive shoal reclamation, overfishing and

frequent oil spills, etc., greatly influenced the water-related envi-ronmental quality in the PRE (Liang et al., 2005; Ke et al., 2007; Liand Huang, 2008). The sewage discharged into the PRE increasedfrom 1.62 × 109 t (in 1985) to 6.77 × 109 t (in 1998) (Cui, 2006).The water quality in the estuary has become progressively worse

L.J. Duan et al. / Ecological Modell

Fc

ieEa1Cbttawi1ht2eiahse(itaot

ooadhmtspotEwarobti

ig. 1. The landings and fishing effort history in the PRE coastal ecosystem. B indi-ates fishing effort, C indicates landings.

n the last 20 years of the twentieth century. The most seriousnvironmental issue in the Pearl River Estuary is eutrophication.utrophication has produced serious red tide events or harmfullgal blooms in the estuary and its adjacent coastal waters since the980s. Over 146 red tides events have been recorded in the Southhina Sea since 1981 (Qi et al., 1996). The frequency of harmful algallooms occurrences was high during late 1997 to early 1999 par-icularly in Hong Kong waters, which have posed a serious threato the aquaculture and fisheries industries (Tang et al., 2003). Inddition, the Pearl River Estuary is being reclaimed and estuarineaterways are getting narrower, 92% of shoal reclamation occurred

n the 1980s and 80% of waterway reclamation happened during the990s (Chen et al., 2005). The PRE coastal ecosystem has sustainedigh stress from fisheries since the 1980s and been proposed ashe first major human disturbance to coastal areas (Jackson et al.,001). The total landings and the fishing effort in the PRE experi-nced substantial increase since 1981 and reached the peak valuesn 1998 (Fig. 1). The landings and fishing effort in 1998 have beenlmost five times as high as in 1981. It appeared that the ecosystemad experienced large changes since 1981, switching from large-ize and high-value demersal fishes dominated ecosystem to ancosystem dominated by small-size and low-value pelagic speciesJia et al., 2005). Increasingly anthropogenic activities exert greatnfluence on the estuarine ecosystem. Therefore it is quite impor-ant to get knowledge of the development level of this ecosystemnd its state of maturity, which facilitates profound understandingf the structure and function of the whole ecosystem for analyzinghe impact of human influences.

With the development of an interdisciplinary research projectn exploitation in the coastal ecosystem of the PRE, a vast amountf physical, chemical and biological information has become avail-ble (Li et al., 2002; Zeng et al., 2005; Yao et al., 2006). But noetailed ecosystem analysis has been carried out to date and thereas been no attempt to summarize and integrate available infor-ation to obtain greater insights into ecosystem structure and

rophic relationships. Using ecosystem models, changes in thepecies within an ecosystem can be summarized and viewed asart of a whole. Both direct and indirect effects of species onthers within the ecosystem can be explored and the overall func-ioning of ecosystem can be compared (Heymans et al., 2004).copath with Ecosim (EwE) is an ecosystem-based analysis soft-are designed for straightforward construction, parameterization

nd analysis of mass-balance trophic models of aquatic and ter-estrial ecosystems (Christensen et al., 2005). It allows analysis

f different aspects of the resulting food web network. In mass-alanced models fisheries can be placed in the ecosystem contexto obtain more effective and feasible strategies by incorporat-ng knowledge of interspecific interactions of the groups, their

ing 220 (2009) 2802–2818 2803

physical environment and their habitat (Christensen et al., 1996).Furthermore, mass-balanced models enable comparisons betweendifferent ecosystems and between different periods of the sameecosystem. In this study, two mass-balanced models for the coastalecosystem of the PRE were constructed using EwE for two years,1981 and 1998. This paper aims to synthesize all information toquantify energy flows and to characterize some functional aspectsof the system. The changes in the trophic interactions, the commu-nity structure and function of the ecosystem during two decadeswere analyzed and evaluated to gain an insight into the status ofthe ecosystem development. Moreover, the interactions betweenfunctional species and the trophic role of different species in theecosystem were assessed as well as the possible impact of the fish-ing on overall performance of the whole ecosystem.

2. Methods and materials

2.1. The study area

The PRE is located midway along the northern boundary of theSouth China Sea (SCS) and flows into the northern part of the SCS.Its shape is like an inverted funnel with the narrow neck in thenorth and wide mouth opening to the south. The annual aver-aged river discharge is 10 524 m−3 s−1, with 20% occurring duringthe dry season in October–March and 80% during the wet sea-son in April–September (Zhao, 1990; Yin et al., 2004b). On thewest side of the SCS adjacent to the PRE, a strong cyclonic west-ern boundary current system is driven by the combination of thewind and the seaward gradient force in the dry season. In thewet season, the southwest monsoon drives the water northeast-ward. Moreover, the coast has a NE–SW orientation (Xue et al.,2001). Due to the asymmetry of the fresh water source, salinityin the PRE increases from the northwest to the southeast withstrong seasonal variation (Hu et al., 2005). The bottom sedimentparticle sizes vary widely from 0.002 mm to 0.25 mm. Mediumsilts to fine sands (0.031–0.25 mm) are found in the westernpart and near the sea boundary of PRE. The bed sediments inthe other parts of PRE mainly make up of clay and silt particles(http://www.cse.polyu.edu.hk/lab/PRE/SEDIMENT.htm).

The coastal ecosystem of the PRE in our study, extended from112◦30′E to 115◦30′E, 21◦00′N to 23◦00′N, is a typical ecosystem ofChina’s coastal sea with an area of 72 600 km2 (Fig. 2). The coastalecosystem covers the shelf from the coast to approximately 100 mdepth with the characteristics of estuary coastal waters driven bygradients due to the combined influence of river flow and sea. ThePRE waters are subjected to the influence of three water sources:Pearl River discharge, oceanic waters from the South China Sea,and coastal waters from the South China Coastal Current (Yin et al.,2004a). The resultant nutrient-enriched waters provide high bio-logical productivity and sustain important commercial fisheries (Liet al., 2000; Wang and Lin, 2006). Annual mean of primary produc-tivity in the PRE is 55 mgC m−2 h−1. And, the yearly total landingsin the PRE were no less than 227 800 t since 1995 (Jia et al., 2005).The PRE plays a role as natural refuge and nursery area for hun-dreds of species, including some local and endangered species. It isalso an important fishing ground in the SCS. The whole system hasdiverse productivity, strong fishing activity, and complicated foodweb relationships (Zhang, 2004).

2.2. Ecopath approach

The trophic mass-balanced models for the coastal ecosystemof the PRE were constructed based on EwE software, version 6.0(Christensen et al., 2005; http://www.ecopath.org). Models repre-sent the annual average situation of the ecosystem in 1981 and

2804 L.J. Duan et al. / Ecological Modelling 220 (2009) 2802–2818

of th

1wPutEa

reogtete

P

wti(isiiTt

M

Fig. 2. The coastal ecosystem

998. The foundation of the EwE suite is the Ecopath model, whichas developed on the basis of an approach initially presented by

olovina (1984). To date, the software has been optimized for directse in fisheries management as well as for addressing environmen-al questions through the inclusion of the temporal dynamic model,cosim, and the spatial dynamic model, Ecospace (Christensen etl., 2005).

Ecopath creates a static mass-balanced snapshot of theesources in a given ecosystem and their interactions (Christensent al., 2005). Its core component consists of two master equations,ne to describe the production fate and one for metabolism of eachroup. For each of the living groups in the model it is assumed thathe ecosystem is in mass balance, which indicates that the inputquals to the output without biomass accumulation. The produc-ion fate equation describes how to split the production term forach group into components, which can be expressed as:

i = Yi + Bi × M2i + Ei + BAi + Pi × (1 − EEi) (1)

here i is the organism group, Pi is the total production rate of i, Bihe biomass of functional group i, Yi the total fishery catch rate of, M2i the total predation rate for group i, Ei the net migration rateemigration–immigration), BAi the biomass accumulation rate for, EEi is the ecotrophic efficiency (the fraction of production con-umed, fished or exported out the system). Here M0i = Pi × (1 − EEi)s the ‘other mortalities’ including all other mortalities not includedn the natural mortality, e.g., mortalities due to diseases or old age.he production term describing M2i, the predation mortality, serveso link predators and prey as:

2i =n∑

j=1

Qj × DCji (2)

e Pearl River Estuary (PRE).

The production fate equation can be represented as:

Bi ×(

P

B

)i× EEi = Yi +

n∑j=1

Bj ×(

Q

B

)

j

× DCji + Ei + BAi (3)

where Qj is the total consumption rate for predator group j, andDCji is the fraction of predator j’s diet contributed by prey i, (P/B)i isthe production/biomass ratio for functional group i, which equalsto the coefficient of total mortality Z under steady-state conditions(Allen, 1971); (Q/B)j is the ratio of the consumption to biomass forpredator j.

The metabolism balance of each component in the sys-tem is given by: consumption (Qi) = production (Pi) + respiration(Ri) + unassimilated food (Ui), which means the energy input andoutput of all living groups in the model must be balanced.

2.3. Model construction

There are many species in the PRE ecosystem, which makesfunctional group division difficult. The number of compartmentscan be reduced to an amount according to type of biomass pro-duction (producer/consumer), habitat (water column/sediment),size (micro-, meso- and macro-), type of food (herbivorous, car-nivorous, detritivorous, omnivorous), way of feeding (filter feeders,mixed feeders, predators) and taxonomy (mollusks, echinodermsand polychaetes), which can be handled with some ease while stillrepresenting typical features of the trophic network (Carrer andOpitz, 1999).

Considering above criteria and the availability of survey data

in the region as well as straightforward comparison between twoperiods, the two models for 1981 and 1998 were strictly con-structed in the same manner. The ecosystem was representedby 24 functional groups in each model and the fishery wasalso included. A functional group consists of species having a

L.J.Duan

etal./EcologicalM

odelling220 (2009) 2802–2818

2805

Table 1Model input and sources for groups in the coastal ecosystem of the PRE in 1981 and 1998.

Group name/parameters B P/B Q/B EE Y DC

1. Sousa chinensis Pitcher et al., 2002 Palomares and Pauly,1998

Given EE (1981:Cheung, 2007)

– He, 2005 (unpublished)

He, 2005 (unpublished) Hoenig, 1983 He, 2005 (unpublished) MO (1998)MO for 1981

2. Sharks Jia et al., 2005; FS1 Pitcher et al., 2002 Pitcher et al., 2002 MO (1981, 1998) – Zhang et al., 1994;Zhang, 2005; Zhangand Chen, 2005

3. Trachurus japonicus Jia et al., 2005; FS1 Chen and Qiu, 2004 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang et al., 1994

Chen and Qiu, 2004 Y = B × F (1998)F (Chen and Qiu, 2004)

4. Decapterus maruadsi Jia et al., 2005; FS1 Chen and Qiu, 2003 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang and Zhang,1999; Zhang et al.,1994; Zhang, 2005;Zhang and Chen, 2005

Chen and Qiu, 2004 Y = B × F (1998)F (Chen and Qiu, 2003)

5. Trichiurus haumela Jia et al., 2005; FS1 Zhu and Qiu, 2005 Pauly et al. (1990) MO (1981, 1998) FY (1981) Zhang, 2005Zhu and Qiu, 2005 Y = B × F (1998)

F (Zhu and Qiu, 2005)6. Saurida Jia et al., 2005; FS1 Shu and Qiu, 2004a Palomares and Pauly,

1998MO (1981, 1998) FY (1981) Zhang et al., 1994;

Zhang, 2005; Zhangand Chen, 2005

Shu and Qiu, 2004b Shu and Qiu, 2004a Y = B × F (1998)Shu and Qiu, 2004b F (Shu and Qiu, 2004a,b)

7. Psenopsis anomala Jia et al., 2005; FS1 Li, 1996 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang et al., 1994;Zhang, 2005

Li, 1996 Y = B × F (1998)F (Li, 1996)

8. Upeneus bensasi Jia et al., 2005; FS1 Ye et al., 1996 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang et al., 1994;Zhang, 2005; Zhangand Chen, 2005

Ye et al., 1996 Y = B × F (1998)F (Ye et al., 1996)

9. Nemipterus virgatus Jia et al., 2005; FS1 Wang et al., 2004 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang, 2005; Zhangand Chen, 2005

Wang et al., 2004 Y = B × F (1998)F (Wang et al., 2004)

10. Priacanthus macracanthus Jia et al., 2005; FS1 Sun and Qiu, 2004 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang, 2005; Zhangand Chen, 2005

Sun and Qiu, 2004 Y = B × F (1998)F (Sun and Qiu, 2004)

11. Priacanthus tayenus Jia et al., 2005; FS1 Sun and Qiu, 2004 Palomares and Pauly,1998

MO (1981, 1998) FY (1981) Zhang and Chen, 2005

Sun and Qiu, 2004 Y = B × F (1998)F (Sun and Qiu, 2004)

12. Other pelagics Jia et al., 2005; FS2 Pitcher et al., 2002 Pitcher et al., 2002 MO (1981, 1998) FY (1981, 1998) www.fishbase.org13. Other demersals Jia et al., 2005; FS2 Pitcher et al., 2002 Pitcher et al., 2002 MO (1981, 1998) FY (1981, 1998) www.fishbase.org14. Other zoobenthos Jia et al., 2005 Jia et al., 2005; Brey, 1999 MO MO (1981, 1998) – Pitcher et al., 200215. Benthic crustaceans Jia et al., 2005 Jia et al., 2005; Brey, 1999 MO MO (1981, 1998) FY (1981, 1998) Huang, 2005; Pitcher et

al., 200216. Polychaetes Jia et al., 2005 Jia et al., 2005; Brey, 1999 MO MO (1981, 1998) – www.fishbase.org17. Mollusks Jia et al., 2005 Jia et al., 2005; Brey, 1999 MO MO (1981, 1998) FY (1981, 1998) www.fishbase.org18. Echinoderms Jia et al., 2005 Jia et al., 2005; Brey, 1999 MO MO (1981, 1998) FY (1981) www.fishbase.org19. Cephalopods Jia et al., 2005; FS1 Pitcher et al., 2002 Pitcher et al., 2002 MO (1981, 1998) FY (1981, 1998) Huang, 2005

2806 L.J. Duan et al. / Ecological Modell20

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ing 220 (2009) 2802–2818

similar trophic role. The main primary producers (i.e., phyto-plankton and benthic producers) were identified for modeling.Benthic invertebrates were divided into benthic crustaceans, poly-chaetes, mollusks, echinoderms and other zoobenthos accordingto availability of biomass data. Additionally, the other fisheriesresources were divided into the other pelagics and demersals inthe models. Only those of particular interest were kept as indi-vidual groups: commercially important species such as Trachurusjaponicus (Japanese jack mackerel), Decapterus maruadsi (Japanesescad), Trichiurus haumela (largehead hairtail), Saurida (lizzard fishincluding greater lizardfish and true lizardfish), Psenopsis anomala(melon seed), Upeneus bensasi (bensasi goatfish), Nemipterus vir-gatus (golden threadfin bream), Priacanthus macracanthus (redbigeye) and Priacanthus tayenus (purple-spotted bigeye), and eco-logically interesting species such as Sousa chinensis (Chinese whitedolphin) and sharks. Jellyfish and cephalopods were respectivelyseparated from zooplankton and nekton species as a single func-tional group for their especial biomass changes in the past twodecades.

2.4. Model database and parameterization

For each group, the key input parameters are Bi, (P/B)i, (Q/B)i, EEi.At least three of them must be known for each group and the modelwill estimate the fourth. In addition, the diet composition of all con-sumers DCji, the landings of each group Yi and the unassimilationrate (U/Q)i (i.e., GSi), the fraction of the food consumption that is notassimilated are also required (Walters et al., 1997; Christensen etal., 2005). BAi and Ei in Eq. (1) were assumed to be 0 in steady-statecondition due to absence of information (Coll et al., 2006). In theabsence of other information, the default value of 0.20 was used forthe unassimilated food for carnivorous fish groups and dolphin. Forherbivores, it is suggested to use a higher GSi value (Christensen etal., 2005). So for both zooplankton and P. anomala higher GS val-ues were used (up to 0.4) and it was assumed to be 0.35 for allzoobenthos compartments for balancing the model.

Input data for the models were derived either from published lit-erature, stock assessment reports, field survey data, or governmentstatistics for 1981 and 1998 (see Table 1). The original model inputdata and estimated parameters (bold) are summarized in Table 2.

2.4.1. Biomass (Bi) and Ecotrophic efficiency (EEi)As EE is difficult to estimate, it is usually unknown and estimated

by the model (Christensen et al., 2005). Biomass is the total massof each group per unit of area. Its unit in the model is wet weighttonnes per square kilometers (t km−2). Biomass data were eitherestimated by the model or obtained from stock assessment reports,field survey data, or other models (Table 1).

A regression equation was used to convert phytoplankton abun-dance (individual per square meter) to chlorophyll a concentration(Jia et al., 2005). Phytoplankton biomass (mg m−3) was estimatedthrough the conversion factor of 0.3 mg chlorophyll a per 100 mgphytoplankton (Liu et al., 2007). Then the corresponding phyto-plankton biomass (t km−2) was calculated using a mean depth of30 m. Biomass data of zooplankton and zoobenthos were obtaineddirectly from stock assessment literature (Jia et al., 2005). Forzooplankton, whose biomass data appeared in mg m−3, the cor-responding biomass values were presented in the form of t km−2

using a mean depth of 30 m.The biomass values of all fish compartments and cephalopods

were calculated with the swept area method (Pauly, 1984) using

bottom trawl survey data during the two time periods (1981 and1998), which were obtained from stock assessment reports (Jia etal., 2005). The swept area method takes into account the efficiencyof the net (i.e., the proportion of the fish in the path of the net whichis actually retained by it), which is commonly regarded as 0.5 in sur-

L.J. Duan et al. / Ecological Modelling 220 (2009) 2802–2818 2807

Table 2Input and estimated parameters (bold) for the Ecopath models of the coastal ecosystem of the PRE in 1981 and 1998.

Group name/parameters 1981 1998

B P/B Q/B EE P/Q Y ETL B P/B Q/B EE P/Q Y ETL

1. Sousa chinensis 0.0195 0.112 14.000 0.100 0.008 – 4.06 0.007 0.112 14.000 0.017 0.008 – 3.592. Sharks 0.032 0.400 6.830 0.341 0.059 – 4.21 0.002 0.400 6.830 0.341 0.059 – 3.823. Trachurus japonicus 0.446 1.887 10.388 0.927 0.182 0.028 3.28 0.012 2.150 10.471 0.964 0.205 0.015 3.114. Decapterus maruadsi 0.336 1.184 12.031 0.904 0.098 0.087 3.23 0.017 1.870 11.079 0.916 0.169 0.017 3.105. Trichiurus haumela 0.019 1.210 6.210 0.974 0.195 0.012 3.87 0.025 1.210 6.210 0.952 0.195 0.022 3.366. Saurida 0.087 1.200 6.655 0.948 0.180 0.010 3.90 0.062 1.420 7.993 0.934 0.178 0.049 3.367. Psenopsis anomala 0.0108 3.620 31.010 0.967 0.117 0.025 2.18 0.056 3.620 31.010 0.944 0.117 0.130 2.108. Upeneus bensasi 0.0252 1.013 11.870 0.942 0.085 0.010 3.56 0.019 1.013 11.352 0.936 0.089 0.013 3.329. Nemipterus virgatus 0.0179 1.440 8.347 0.956 0.173 0.020 3.64 0.013 2.070 7.230 0.929 0.286 0.019 3.3510. Priacanthus macracanthus 0.0367 2.310 11.534 0.952 0.200 0.046 3.54 0.009 1.970 9.685 0.938 0.203 0.011 3.2911. Priacanthus tayenus 0.0458 2.430 10.648 0.920 0.228 0.057 3.43 0.012 3.720 12.420 0.982 0.300 0.029 3.2812. Other pelagics 2.100 3.720 12.704 0.772 0.293 0.742 2.77 0.740 3.720 12.704 0.851 0.293 1.570 2.1313. Other demersals 3.990 3.468 12.885 0.400 0.269 0.463 2.71 0.679 3.468 12.885 0.656 0.269 0.442 2.3814. Other zoobenthos 1.331 6.553 26.212 0.878 0.250 – 2.28 1.690 6.553 26.212 0.440 0.250 – 2.1515. Benthic crustaceans 3.499 6.522 26.088 0.750 0.250 0.015 2.68 0.560 6.522 26.088 0.880 0.250 0.453 2.4316. Polychaetes 1.998 4.928 19.712 0.758 0.250 – 2.00 0.800 4.928 19.712 0.695 0.250 – 2.0017. Mollusks 18.472 4.800 19.200 0.250 0.250 0.013 2.00 0.700 4.800 19.200 0.514 0.250 0.382 2.0018. Echinoderms 4.329 8.629 34.516 0.542 0.250 0.0003 2.00 0.240 8.629 34.516 0.766 0.250 – 2.0019. Cephalopods 0.068 3.100 11.970 0.965 0.259 0.015 3.63 0.099 3.100 11.970 0.814 0.259 0.182 3.3120. Jellyfish 0.774 5.000 25.000 0.250 0.200 0.013 2.82 1.530 5.000 25.000 0.054 0.200 0.027 2.5221. Zooplankton 3.115 32.000 192.000 0.665 0.167 0.003 2.03 0.666 32.000 192.000 0.850 0.167 0.109 2.0322. Phytoplankton 22.069 71.500 – 0.322 – – 1.00 7.590 71.500 – 0.243 – – 1.0023. Benthic producers 8.710 11.885 – 0.669 – 0.006 1.00 17.400 11.885 – 0.111 – 0.016 1.00

–

B o (yeaP r−1) a

vodRoOw(s(

2

tFpot(tbett

2

medbsdicyea

24. Detritus 200.000 – – 0.372 –

is the biomass of the functional group (t km−2), P/B is the production/biomass rati/Q is the production/consumption ratio, Y is the total fishery catch rate (t km−2 yea

ey work for trawlers in Southeast Asia (Pauly, 1984). The biomassf S. chinensis in 1998 was estimated by multiplying the annualolphin number in the study area by their average body weight.elated information was absent for 1981. Therefore the biomassf S. chinensis in 1981 was estimated by model through given EE.ther groups (such as jellyfish and detritus) whose biomass dataere not available, reference was made to similar ecosystem model

Pitcher et al., 2002) in which the study site is included in ourtudied area or estimated by the model using the fixed EE valueTable 2).

.4.2. Production/biomass (P/B)i and consumption/biomass (Q/B)iThe (P/B)i value corresponds to the total instantaneous mor-

ality Z as estimated using the empirical equation (Brey, 1999).or those groups whose (Q/B)i and (P/B)i were not available thearameter values were obtained from similar ecosystem modelsr published literature (Table 1). The (Q/B)i values for all zooben-hos compartments were estimated by the model through givenP/Q)i (Morisssette and Pitcher, 2005). The (Q/B)i ratio representshe amount of food ingested by a group with respect to its owniomass in a given period. It was computed with the empiricalquation of Palomares and Pauly (1998), in which environmentalemperature, fish weight and size, and caudal fin morphology wereaken into account (Table 1).

.4.3. Diet composition (DCji) and yields (Yi)Diet compositions were derived from previous work and sum-

arized in Table 3. There are some fundamental problems with thestimation of dietary composition in several cases. Often, dietaryata are only available for part of the periods or area modeled,iasing the mean estimates. Taxonomic resolution is low in sometomach analyses. These are among the reasons for estimated pre-ation on some components exceeding their production. Therefore,

t is necessary to alter the contribution of some prey in the diets ofertain predators. Yields data for most functional groups in bothears were from local fisheries statistics yearbooks (unpublished)xcept for the nine economic fish compartments in 1998, whosennual yields were estimated based on Yi = Fi × Bi for data absence

1.00 200.000 – – 0.113 – – 1.00

r−1), Q/B is the consumption/biomass ratio (year−1), EE is the ecotrophic efficiency,nd ETL is the effective trophic level.

(see Table 1), where Fi is the instantaneous rate of fishing mortality(Neira et al., 2004).

2.5. Model balancing and analysis

The primary criterion used to balance the model is the value ofEEi which should be, obviously, between 0 and 1. Gross efficiency offood conversion (GEi), which is equal to (P/Q)i, should be between0.1 and 0.3 (Christensen et al., 2005). The model could be balancedby checking the values of EEi and the gross efficiency of food conver-sion (GEi). During the model balancing, only the diet composition(DCji) was manually adjusted by modifying the initial values to pro-duce small changes. Generally this approach (Morales-Zárate et al.,2004) is selected just because diet is the source of more uncertainty.However, large modifications resulting in changed feeding patternsof functional groups should be avoided.

A pedigree routine was implemented in Ecopath to categorizethe origin of a given input and specify the likely uncertainty associ-ated with this input. Based on it a pedigree index P (ranging from 0to 1), an overall index of model quality, can be obtained as the prod-uct of all pedigree parameter specific indices. A model constructedon the basis of locally and well-sampled high-precision data is ofhigher quality and consequently higher P value than a model inwhich input data were taken from other models or guessed. Inaddition, simple sensitivity analysis of the two models to inputEE values was conducted to evaluate the uncertainty of the out-put from the models. Some common analyses about trophic level,trophic aggregation and summary statistics were described alongwith the results below.

Relative impacts of each group on all other groups in the systemare quantified using the mixed trophic impact (MTI) analysis andare used to highlight those groups playing important roles in thesystem and those having only small effects on other system com-

ponents. Moreover, a method derived from the MTI analysis allowsestimating “keystoneness” of groups and identifying the keystonespecies in the ecosystem. The keystoneness value of a given speciesis decided as a function of its biomass and the impact on the differ-ent elements of an ecosystem resulting from a small change to its

2808L.J.D

uanet

al./EcologicalModelling

220 (2009) 2802–2818

Table 3Balanced diet composition of groups in the coastal ecosystem of the PRE in 1981 and 1998.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

1 0.0012 0.0203 0.049 (0.039) 0.040 0.070 0.026

0.019 (0.014) 0.020 0.010 0.0104 0.030 0.040 0.080 0.011 0.009

0.025 0.008 0.0105 0.015 0.010 0.020 0.0026 0.110 0.124 0.070 0.040

0.022 0.0357 0.050 0.012

0.090 0.0978 0.037 0.016

0.017 0.0059 0.010 0.005

0.01410 0.053 0.058 0.029

0.010 0.00711 0.085 0.082 0.030

0.012 0.02012 0.140 (0.144) 0.194 0.186 0.124 0.032 0.260 0.100 0.083 0.193 0.053 0.010 0.060 0.228

0.396 (0.392) (0.178) 0.132 (0.075) 0.020 0.17813 0.496 (0.502) 0.196 0.100 0.075 0.240 0.396 0.094 0.400 0.222 0.277 0.100 0.010 0.450

0.270 (0.279) (0.110) 0.271 0.230 0.50014 0.010 0.010 0.145 0.001 0.110 0.048 0.015 0.020 0.066

(0.013) 0.100 0.194 0.245 0.310 0.360 0.15015 0.080 0.040 0.060 0.010 0.103 0.273 0.410 0.214 0.230 0.250 0.010 0.032 0.154

(0.036) 0.092 (0.398) 0.065 0.03016 0.036 0.010 0.077 0.025 0.040 0.05017 0.064 0.070 0.080 0.173

0.020 0.01318 0.018 0.010 0.050 0.074 0.164

(0.017) 0.030 0.01219 0.160 0.173 0.010 0.010 0.028 0.042 0.006 0.029 0.010 0.01620 0.014 0.021 0.070 0.030 0.160 0.03021 0.600 0.700 0.050 0.120 0.166 0.100 0.232 0.382 0.437 0.040 0.150 0.150 0.800 0.030

0.050 0.066 (0.120) 0.032 0.070 0.060 0.020 0.300 (0.100)22 0.210 0.010 0.288 0.050 0.050 0.100 0.800

0.650 0.200 (0.700)23 0.640 0.010 0.349 0.170 0.300 0.050

0.710 0.400 0.29924 0.100 0.171 0.516 0.065 1.000 0.900 1.000 0.100 0.170

0.240 0.280 (0.200)Import 0.400

Predators are in columns, and the numbers refer to the group numbers (see Table 1).The contribution to diet in 1998 is tabulated below the 1981 contribution (italic). The data in the parentheses are those directly came from literature, which were altered for model balance.

odell

oee

ε

wnftIma

K

wb

3

3

tdgap0r

aebsfiomtab

FtrcZ

L.J. Duan et al. / Ecological M

wn biomass. It can be implemented by plotting the relative overallffect (εi) against the keystoneness (KSi). Here the overall effect ofach group is defined as (Libralato et al., 2006):

i =

√√√√n∑

j /= i

m2ij

(4)

here mij is obtained from the MTI analysis as the product of all theet impacts for all the possible pathways in the food web linking

unctional groups i and j. The relative overall effect (εi) is expressedo be relative to the maximum effect measured in the trophic web.n Eq. (4), the effect of the change in biomass on the group itself (i.e.,

ii) is excluded. The keystoneness (KSi) of each group is describeds (Libralato et al., 2006):

Si = log[εi × (1 − pi)] (5)

here pi is the contribution of the functional group to the totaliomass of the food web.

. Results

.1. Data quality and sensitivity analysis

Data pedigree and simple sensitivity analysis of the two modelso input EE values were conducted to evaluate the quality of inputata and the uncertainty of the output from the models. The pedi-ree indices of the two models in our study are respectively 0.562nd 0.573, which rank within the relatively high values when com-ared with 50 other models whose pedigree values ranged between.164 and 0.676 (Coll et al., 2006). Therefore, the models seemeliable with regards to input data of good quality.

It is possible to estimate biomass for some species or group inn Ecopath model through given EE values. However, it is consid-red as the last choice after all the approaches failed to provide aiomass estimate for a group because EE is often regarded as a veryensitive parameter. We gave EE values to S. chinensis and jelly-sh groups in 1981 to compensate for the absence of information

n biomass. The sensitivity analysis of EE values assumed in theodel was conducted by altering the input parameters of a func-

ional group and the results were provided (Fig. 3). The sensitivitynalysis shows that the largest impact of a given EE was on theiomass output of the same group and the trend lines describing

ig. 3. The sensitivity analysis applied to input EE values of the coastal ecosys-em of the PRE in 1981. Changes in estimated parameters were calculated as theatio of (estimated parameter − original parameter)/original parameter. (1) Sousahinensis; (5) Trichiurus haumela; (6) Saurida; (19) Cephalopods; (20) Jellyfish; (21)ooplankton.

ing 220 (2009) 2802–2818 2809

this kind of impact overlapped completely. The changes of param-eters of other groups resulting from altered input EE were small asalready documented by Coll et al. (2007).

3.2. Basic input and output

Table 2 summarizes the input parameters and the results of thebalanced trophic models for the coastal ecosystem of the PRE in1981 and 1998. Closer examination shows that the biomasses ofmostly groups decreased between 1981 and 1998 due to inten-sification of fishing activity and environmental degradation. Mostzoobenthos decreased in biomass, especially mollusks (from 18.4to 0.7 t km−2) and echinoderms (from 4.3 to 0.24 t km−2). This is theresult of uncontrolled expansion of fishing effort, mainly attributedto the bottom trawling which causes over exploitation as well asserious destruction of the benthic habitat. Simultaneously, trawl-ing also depletes habits (such as sponge bed and soft corals et al.)and food for benthic fishes, then redirect the trophic flow towardthe pelagic system (Pitcher and Pauly, 1998). Depletion of somegroups due to increased catch and predation rates may result inconsequential increase of biomass among other groups, such asT. haumela, P. anomala, cephalopods and jellyfish, which may bethe result of reduced predatory pressure from predators or weakencompetition for food with other groups. But that will eventuallylead to changes in trophic structure and threaten ecosystem stabil-ity and resources sustainability. As a result of such a cascade effect,the fishery resources were depleted and the food web constructionchanged since 1981.

As can be seen from Table 2, the EE values are high in bothyears for all commercial fish species because these species playedan important role in the trophic web and faced high predation risk.However, the primary producers and detritus were underexploited,especially in 1998. Phytoplankton with EE = 0.243 in 1998 indicatesthat most of the production perhaps dies off due to reduced pre-dation pressure from the herbivore zooplankton as a result of thedeclined biomass of the zooplankton. The low EE of phytoplank-ton may be partially due to excluding the bacteria in the presentstudy for lack of information related to microbial loop throughbacteria and Protozoa (Fetahi and Mengistou, 2007). The EE valuealso showed a decrease for the group of benthic producers in 1998(EE = 0.113), indicating that the group was preyed or grazed uponless, or subjected to lower fishing mortality from 1981 to 1998.The unconsumed matters produced by the primary producers ulti-mately flowed into the detritus and were buried in the sediment.The EE value for the detritus group decreased from the 1981 to1998, suggesting that the detritus were not better utilized in the1998 ecosystem with large decreases in stock biomass of zooben-thos.

3.3. Trophic levels (TLs) and aggregated flows

In Ecopath, the trophic levels are not necessarily integers as pro-posed by Lindeman (1942), but can be fractional as suggested byOdum and Heald (1975). A routine assigns definitional trophic levelof 1 to producers and detritus and a trophic level which add theweighted average of the preys’ trophic level to 1 for consumers,i.e., effective trophic level (ETL, Field et al., 1989; Christensen et al.,2005). There is another routine included in Ecopath which aggre-gates the entire system into discrete trophic level sensu Lindeman(1942). Based on an approach suggested by Ulanowicz (1995), thisroutine reverses the routine for calculation of fractional trophic

levels (Christensen et al., 2005). The results of the aggregation ofsystem biomass flows into TLs show the presence of seven levelsin 1981 and six levels in 1998. For each trophic group, the fractionsof biomass flows involved in different integer TLs are presentedin Table 4. Flows at TL II mainly involved zooplankton, zooben-

2810 L.J. Duan et al. / Ecological Modelling 220 (2009) 2802–2818

Table 4Trophic aggregation of the groups in the models of the coastal ecosystem of the PRE in 1981 and 1998: showing flowing by discrete trophic levels (I–VII).

Group Trophic level

I II III IV V VI VII

1981 1998 1981 1998 1981 1998 1981 1998 1981 1998 1981 1998 1981 1998

Sousa chinensis 0.338 0.551 0.418 0.367 0.207 0.074 0.034 0.008 0.003Sharks 0.234 0.383 0.507 0.5 0.213 0.106 0.043 0.011 0.003Trachurus japonicus 0.88 0.931 0.092 0.061 0.026 0.008 0.002Decapterus maruadsi 0.869 0.942 0.114 0.054 0.017 0.004Trichiurus haumela 0.42 0.748 0.408 0.222 0.149 0.028 0.022 0.002 0.001Saurida 0.403 0.756 0.461 0.213 0.125 0.029 0.011 0.002Psenopsis anomala 0.85 0.931 0.126 0.06 0.024 0.009Upeneus bensasi 0.02 0.021 0.509 0.73 0.43 0.232 0.037 0.016 0.004 0.001Nemipterus virgatus 0.515 0.739 0.404 0.242 0.079 0.019 0.002Priacanthus macracanthus 0.607 0.794 0.333 0.188 0.056 0.017 0.004 0.001Priacanthus tayenus 0.683 0.798 0.259 0.188 0.055 0.014 0.003Other pelagics 0.454 0.919 0.518 0.071 0.028 0.01Other demersals 0.554 0.694 0.272 0.271 0.17 0.034 0.004 0.001Other zoobenthos 0.759 0.911 0.241 0.089Benthic crustaceans 0.379 0.693 0.606 0.299 0.015 0.008Polychaetes 1 1Mollusks 1 1Echinoderms 1 1Cephalopods 0.57 0.78 0.34 0.199 0.087 0.021 0.003Jellyfish 0.2 0.5 0.8 0.5

tafiotstVtv

tTcaIt1TT(thmt

t

TP1

Zooplankton 1 1Phytoplankton 1 1Benthic producers 1 1Detritus 1 1

hos, P. anomala (herbivore), other pelagics and demersals, andsmall fraction of jellyfish. TL III involved the majority of jelly-

sh and cephalopods, the remaining biomasses from zoobenthos,ther pelagics and demersals, but also the biomasses from the nek-on groups. Top predators and carnivorous fish groups as well as amall fraction of cephalopods and benthic crustaceans dominatedhe biomass flows through TLs IV–VI, and formed a portion of TLII. This kind of biomass distribution leads to the values of effec-

ive trophic level ranging from 2.03 for zooplankton to the highestalue of 4.21 for sharks (see Table 2).

The total system biomass and catch in the coastal ecosystem ofhe PRE distributed among discrete aggregational TLs are listed inable 5. As the flows involved in TL VII in 1981 were very small, theyould be neglected. Thus, the ecosystem could be simplified to sixggregated TLs. The system biomass was concentrated in TL I andI (i.e., the producers and detritus) both in 1981 and 1998, similaro some coastal ecosystems (Silva et al., 1993; Vega-Cendejas et al.,993). But the proportions of the total system biomass at differentLs in the two years are distinct. The greatest changes happened atL I and II, where the biomass proportions decreased from 46.9%1981) to 19.2% (1998) at TL II and increased from 43.0% (1981)o 75.9% (1998) at TL I. Moreover, the proportions of biomass at

igher TLs (TL III–VI) decreased in 1998. Similarly, catches wereainly taken from TL II and III (Table 5) with a similar change of

he proportions at different TLs to system biomass.Fig. 4 shows the flow diagram organized by integer TLs in the

wo years in the form of Lindeman (1942) spines, in which primary

able 5roportional total system biomass (B, t km−2) and catch (C, t km−2 year−1) taken from dis998.

TL 1981

B B (%) C C (%)

I 30.779 43.028 0.006 0.383II 33.578 46.941 0.639 40.831III 6.090 8.514 0.733 46.837IV 1.008 1.409 0.170 10.863V 0.072 0.101 0.016 1.022VI 0.005 0.007 0.001 0.064

producers and detritus were separated for clarity. More than 99% ofthe flows concentrated in TL I–III in both years. There was a generaldecrease in all kinds of aggregated flows in the trophic networkfrom 1981 to 1998 (Fig. 4). This leads to a decrease in total systemthroughput for the ecosystem in 1998 compared with 1981 (Table 6described in the following Section 3.4). The transfer efficiency (TE)of each TL is calculated as the ratio between the summed exportsfrom a given TL plus the flow transferred from the TL to the nextand the throughput at the TL, which indicates how efficient transferis from one TL to the next. Generally, transfer efficiencies tend todecrease as ascending the trophic pyramid (Odum, 1971). This isalso the situation in the coastal ecosystem of the PRE in both years.However, there was an unexpected decrease in TE at TL IV in 1981and TL III in 1998, respectively. The mean transfer efficiencies were9.1% in 1981 and 10.2% in 1998. And TE values of flows at each TLexcept TL III in the 1998 model were higher than that of 1981.

3.4. Summary statistics and system flows

The summary statistics of the two Ecopath models analyzed hereare shown in Table 6, in which they are listed in terms of aggre-gated summary statistics, network flows and information indices.

The mean trophic level of the catch declined from 2.85 in 1981 to2.30 in 1998. Total biomass and other total flows (consumption,exports, respiratory flows, flows to detritus) through the studiedecosystem were larger in 1981 than in 1998 (Table 6). Thus the totalsystem throughput, which increases with the amount of material

crete trophic levels in the models of the coastal ecosystem of the PRE in 1981 and

1998

B B (%) C C (%)

24.990 75.893 0.016 0.4596.303 19.142 2.690 77.1881.538 4.671 0.669 19.1970.090 0.273 0.103 2.9560.007 0.021 0.007 0.2010.000 0.000 0.000 0.000

L.J. Duan et al. / Ecological Modelling 220 (2009) 2802–2818 2811

Fig. 4. The aggregational flows (t km−2 year−1) into a concatenated chain of transfers through trophic levels (TLs). Flows out to the bottom of the compartments representrespiration (Resp.) and flows out from the top represent export. Non-living material is recycled through compartment of detritus (D). TE: Transfer efficiency.

Table 6Summary of the indices for the coastal ecosystem of the PRE in 1981 and 1998 using the trophic network.

Parameter (unit) 1981 1998

Ecosystem theory indicesSum of all consumption (t km−2 year−1) 1377.53 285.22Sum of all exports (t km−2 year−1) 1094.11 636.30Sum of all respiratory flows (t km−2 year−1) 587.34 128.48Sum of all flows into detritus (t km−2 year−1) 1739.55 713.81Total system throughput (t km−2 year−1) 4799 1764Sum of all production (t km−2 year−1) 1977 808Mean trophic level of the catch 2.85 2.30Gross efficiency of catch (catch/net p.p.) 0.00093 0.00465Calculated total net primary production (t km−2 year−1) 1681.452 749.484Total primary production/total respiration 2.86 5.83Net system production (t km−2 year−1) 1094.11 621.01Total primary production/total biomass 23.51 22.76Total biomass/total throughput 0.015 0.019Total biomass (excluding detritus) (t km−2) 71.53 32.93Total catches (t km−2 year−1) 1.56 3.49Proportion of total flow originating from detritus 0.52 0.46Connectance index 0.276 0.274System omnivory index 0.167 0.127

Network flow indicesPredatory cycling index (% of throughput without detritus) 1.78 1.77Finn’s cycling index (% of total throughput) 9.21 2.72Finn’s mean path length 2.85 2.31Finn’s straight-through path length (without detritus) 2.20 2.50Finn’s straight-through path length (with detritus) 2.59 2.24

Information indicesAscendency (flowbits) 5417.0 2104.4Ascendency (% of capacity) 29.50 34.10Overhead (flowbits) 12972.6 3716.9Overhead (% of capacity) 70.50 65.90

2 odell

fldos(t

Fg

812 L.J. Duan et al. / Ecological M

owing through the ecosystem, was lower in 1998. The generalecrease of the flows through the system in 1998 gave an anal-

gous decrease on the information index, ascendency, which iscaled by system throughput and derived from information theoryUlanowicz, 1986; Ulanowicz and Norden, 1990). The absolute sys-em ascendency in the model of 1998 was 2104.4 flowbits, which

ig. 5. Mixed trophic impact (MTI) in the coastal ecosystem of the PRE (a) in 1981 and (roups are arranged down the vertical axis.

ing 220 (2009) 2802–2818

was 34.1% of the total capacity with an overhead of 65.9% of thetotal capacity.

The connectance index (CI) of a given food web is the proportionof connections in the ecosystem to the total possible connections.As an alternative to the connectance index, the system omnivoryindex (SOI) is defined as the average omnivory index of all con-

b) in 1998. Impacted groups are arranged along the horizontal axis and impacting

L.J. Duan et al. / Ecological Modelling 220 (2009) 2802–2818 2813

(Cont

s(idgur

Fig. 5.

umers weighted by the logarithm of each consumer’s food intakeChristensen et al., 2005). The SOI can be used for meaningful

ntersystem comparisons compared with the CI, which is stronglyependent on the level of taxonomic detail used to represent preyroups. The model for the studied ecosystem in 1981 presented val-es of 0.276 and 0.167 for the connectance and omnivory indices,espectively. The model in 1998 had similar CI (0.274) to that of the

inued ).

1981 model. But the SOI in 1998 (0.127) was low compared withthat in 1981.

The cycling as percentage of the total throughput expressed asPredatory cycling index (PCI) and Finn’s cycling index (FCI) werealso tabled. The PCI values (excluding detritus) in both years weresimilar. But the FCI value (including detritus) decreased consider-ably in 1998, evidencing that the detritus played a more important

2 odelling 220 (2009) 2802–2818

rmi

3i

oPsgthoaivtg

oPpwp“i“sAaScboihcs

ttnLtkski“totk

4

4

p1sw

Fig. 6. Keystoneness index (KSi) and relative overall effect (εi) for the functional

814 L.J. Duan et al. / Ecological M

ole in the 1981 ecosystem. Moreover, the changes in the indiceseasuring the path length (both with and without detritus) also

ndicate the importance of the detritus in 1981.

.5. Mixed trophic impact (MTI) and keystone speciesdentification

The MTI included in Ecopath was derived from economic the-ry and modified for ecological use (Hannon, 1973; Ulanowicz anduccia, 1990). The results of this routine for 1981 and 1998 are pre-ented in Fig. 3. The direct and indirect trophic interactions amongroups as well as the fisheries in the two time periods are shownhrough MTI with the values ranging from −1 to 1, which indicateow a small increase in the group biomass mentioned to the leftf the rows would impact on the biomass of the groups mentionedbove the columns. The MTI has a positive value when the impacts beneficial to the impacted group. Otherwise, it has a negativealue. Positive impacts are shown above the base line and nega-ive are below. The impacts are relative but comparable betweenroups.

The results show that all groups would have a negative impactn themselves due to within-group competition for resources.redators often have a negative impact on the system throughredation on other groups. Compartments at the base of the foodeb such as detritus, benthic producers, phytoplankton and zoo-lankton affected almost all groups positively. Most zoobenthos,other pelagics” and “other demersals” compartments had widempacts on numerous functional groups. Moreover, it is shown thatother pelagics” and “other demersals” compartments had oppo-ite impact on most groups due to their different habitats (Fig. 5).n increase in the biomass of carnivorous fish such as T. haumeland Saurida had a negative effect on almost all the other fish groups.ome fish groups such as P. anomala, U. bensasi, N. virgatus, P. macra-anthus, P. tayenus had almost no impacts on other groups, eitherecause of their very small population compared to the dominantnes in the models or because their main prey were not includedn the models due to lack of information (Neira et al., 2004). Fishingad larger and wider negative impacts on the groups (such as S.hinensis, sharks, T. japonicus, D. maruadsi, Saurida, U. bensasi ando on) in the ecosystem in 1998 than in 1981.

Keystone species are relatively low biomass species with a struc-uring role in the food web. These species have strong influence onhe abundance of other species and ecosystem dynamics in a man-er disproportionate to their own abundance (Power et al., 1996;ibralato et al., 2006). It is important to identify keystone specieso get further insight in the ecosystem structure. Fig. 6 shows theeystoneness of the functional groups in 1981 and 1998. The key-toneness index estimated allows ranking the groups by decreasingeystoneness. The keystone species are those groups with proposedndex values close to or greater than zero. “Other pelagics” andother demersals” were keystone species identified in 1981. Andhere were no clear keystone species in 1998 (Fig. 6). Zoobenthos,ther pelagics, zooplanktons, benthic crustaceans and phytoplank-on were among the first groups in terms of overall effects andeystoneness.

. Discussion

.1. Structural analysis of the models in two different years

The structure of the system, as characterized by the biomass pro-ortion at each TL with respect to total biomass, has changed since981, which can be viewed as expression of an alteration of thetructure of the system. The changes in the structure of the foodebs from the early eighties to the late nineties altered the diet

groups from the ecological models in 1981 and 1998. The species are numbered bydecreasing KSi , therefore the keystone functional groups are those ranking betweenthe first groups (values close or greater than zero).

composition, to a certain extent. Differences in availability of preyin 1981 and 1998 forced the top pelagic predators to consume largerquantities of prey at lower TL in the food web in 1998 (Table 3),leading to marginally reduced trophic levels in the 1998 model foralmost all trophic groups except for primary producers, zooplank-ton and three groups of benthic invertebrates (Table 2). This is alsoin agreement with the change of biomass fractions of each group atdifferent TLs. Catches in this ecosystem were mainly taken from TLII and III (Table 5). Moreover, the proportions of catches at higherTLs decreased in 1998 compared with that in 1981, which indicatesthe trend of “fishing down the food web” as documented by Paulyet al. (1998).

According to the theoretical ecology, transfer efficienciesdecrease in higher TLs (Odum, 1971). The general decreasing trendof TE values in both models is in line with the theoretical ecology. Ithas been reported that the mean TE between TLs is approximately10% in 50 models of estuarine and marine ecosystems (Christensenand Pauly, 1995). It is very close to this mean value for the 1981model. The geometric mean transfer efficiency for TL II–VII was9.1% in 1981, close to Daya bay Ecosystem (8.9%, a bay near thePRE, Wang et al., 2005) and much higher than that of two sim-ilar ecosystems of Danshuei Estuary (4.0%, Lin et al., 2007) andKuosheng Bay (6.5%, Lin et al., 2004). This indicates better utiliza-tion of mass flows in our studied ecosystem in 1981 than in similarsystems. In the 1981 model, fishery exploitation was concentratedat TL II and III corresponding to high transfer efficiencies at theseTLs (Fig. 4). However TE value at TL IV was lower than TL V and VI,which shows a low proportion of production consumed within thisTL. This is mainly related to cephalopods, most of which cannot beconsumed as food.

The higher transfer efficiencies at almost all trophic levels inthe 1998 model suggest better flows of the energy within this net-work, largely due to the high level of utilization of most groupseither by predation or fishery. But the increase in jellyfish biomass

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Table 2) could be a reason for the trophic efficiency decrease in TLII in 1998, because this group was not consumed widely. Therebyll energy that flowed to it would probably flow directly to detritusnd provide nutrients to the primary producers through detritusecomposition by microorganisms, corresponding to a high pro-ortion of production which is not consumed within the system.his shows some similarities to the Adriatic Sea (Coll et al., 2007).

.2. Comparative analysis of the overall system properties

Succession is assumed to be an orderly process of communityevelopment towards the mature stage and maturity is the lasttate in the process of succession (Odum, 1969). There are manyttributes indices related to the ecosystem’s maturity obtainedrom the model (Table 6), which follow the theories of Odum (1969)nd Christensen (1995) regarding the developmental stages thatcosystem undergoes. The ratio between primary production andotal system respiration (PP/R) is considered to be an importantescriptor of system maturity (Odum, 1971). It will approach to 1.0s an ecosystem develops toward “mature” stage (Odum, 1971). Its expected to be higher than 1 in the early developmental stage ofsystem and lower than 1 when an ecosystem receives large inputsf organic matter from the outside of itself (e.g., one suffering fromrganic pollution). The PP/R values were respectively 2.86 and 5.83t the two periods (Table 6), greater than 1, the value which wouldndicate a mature system. This suggests that the ecosystem in 1981

as more mature than that in 1998. Microbial loop through bacte-ia, fungi and protozoa was not included in this study (due to lack ofnformation that may give some highlight on the re-flow of energy).his may tend to underestimate respiration and hence overesti-ate PP/R in both years, which remains a scope for future research.

he primary production/biomass (PP/B) ratio is considered to benother important descriptor of system maturity (Odum, 1971). Itas high both in 1981 and in 1998 indicating a low level of biomass

ccumulation within the ecosystem compared with the primaryroduction, whereas values for South Catalan Sea, Northwesternediterranean (Coll et al., 2006) and the Northern and Central Adri-

tic Sea (Coll et al., 2007) were 6.55 and 8.82. The PP/R of 2.86 andP/B of 23.51 estimated from 1981 model indicate that the ecosys-em was still at an intermediate-low developmental stage even in981 (Coll et al., 2006; Coll et al., 2007). This is also in agreementith a character of immature ecosystem.

Gross efficiency of catch (GE), the ratio of fisheries catch to theotal primary production, varies within a wide range in differentcosystems (generally much lower than 1.0). It tends to have highalues for systems with a fishery harvesting at low trophic levelse.g., an upwelling fishery) and low values in systems where theshery is concentrated on top predators or whose stocks are under-xploited (e.g., oceanic tuna fisheries). In many aquatic ecosystems,here has been an increase in recent decades in the proportion ofatches taken from lower trophic levels, which is called “fishingown the food web” (Pauly et al., 1998). This trend has been per-eived to compromise the sustainability of fisheries in others’ workNeira et al., 2004; Villanueva et al., 2006; Lin et al., 2007; Jiangt al., 2008). As a coastal estuary ecosystem, the studied systemad relatively high gross efficiency values (0.00093 and 0.00465)

n both 1981 and 1998 compared with the average value of 0.0002or global data (Christensen et al., 2005). They are higher than otherimilar coastal ecosystems such as Danshuei Estuary (0.00007, Lint al., 2007), Kuosheng bay (0.0002, Lin et al., 2004) and lower thanapong Bay ecosystem (0.0080, Lin et al., 2006) and Chiku Lagoon

0.018, Lin et al., 1999). Furthermore, an evident increase of GEf catch can be detected in 1998, which indicates that the fisheryarvest has switched from a higher position in the food web to a

ower position since 1981. This may be attributed to the sequen-ial replacement of high-value upper trophic-level species by less

ing 220 (2009) 2802–2818 2815

valuable lower trophic-level species with the former depleted toeconomic extinction or the sequential addition of lower trophic-level fisheries within the ecosystem (Essington et al., 2006). Itmeans that the system has shown some indications of “fishingdown the food web” (Pauly et al., 1998) as described in Section4.1. This is consistent with the reduced mean trophic level of thecatch (2.85 in 1981 and 2.30 in 1998), an indicator reflecting thetrophic composition of catches.

CI and SOI are two indices reflecting the complexity of the innerlinkage within the ecosystem, which are also used to describe thematurity of an ecosystem and are expected to be higher in a maturesystem (Odum, 1971). The similar CI values (0.276 and 0.274) wereobtained from the models due to the similar diet composition usedin 1981 and 1998, which are very close to some coastal lagoon mod-els such as Alvaraso, Celestún and Huizache (0.27–0.30) in the studyof Cruz-Escalona et al. (2007). The SOI of the PRE coastal ecosystemin 1981 (0.167) is close to the coastal ecosystems of Celestún andTerminos (both 0.18), but the SOI in 1998 (0.127) has similar valueto Tamiahua system (0.13, Cruz-Escalona et al., 2007). The higherSOI value in 1981 indicates that the ecosystem was more complexand appeared more mature in 1981 than in 1998 (Christensen etal., 2005).

FCI has been shown to be correlated with system maturity,resilience and stability (Vasconcellos et al., 1997; Christensen et al.,2005). A high FCI is a feature of a mature ecosystem. The FCI of the1981 ecosystem was 9.21 and was more than three times higherthan that in the 1998 model (Table 6). PCI is computed exclud-ing cycles involving detritus, which may result from short and fastcycles, typical of disturbed systems, or long and slow ones, typi-cal of complex trophic structures (Odum, 1969; Kay et al., 1989;Christensen, 1995). The PCI values in the two years were similardue to similar system connectance. The higher FCI but similar PCIin the 1981 model compared to those in 1998 again show the highersystem maturity, complexity and higher dependence on detritus in1981. Finn’s mean path length (FML) is defined as the average num-ber of groups that a flow passes through (Finn, 1976). It is expectedto increase with the maturity of an ecosystem. The FML values were2.85 and 2.31, respectively, in the 1981 and 1998 model (Table 6),which suggests that the 1981 ecosystem was more complex andmature. But they were low compared with other modeled ecosys-tem such as 4.63 for the Ria Formosa model (Gamito and Erzini,2005), 3.17 and 3.28 for the southern Benguela model (in the 1980sand 1990s, Shannon et al., 2003).

It is suggested that overhead can be used as a possible mea-sure of ecosystem stability (Christensen, 1995), which reflects thepotential of a system to increase its ascendency when meetingunexpected perturbations. Relative overhead is dimensionless andtherefore suitable for comparison purposes. Our system had sim-ilar values of relative overhead (70.5% and 65.9%) to Ébrié lagoon(67.0%, Villanueva et al., 2006) and those obtained in the modelsof bays and estuaries of North America, Europe and South Africain the studies of Rybarczyk and Elkaïm (2003), which ranged from57.8% to 72%. The reduction of the relative overhead from 1981 to1998 indicates that the system was likely to be more susceptibleto stress-induced changes and needs longer time to recovery fromunexpected perturbations in 1998.

All these attributes of ecosystem maturity and stability indicateexplicitly that the coastal ecosystem of the PRE in 1981 was in arelatively mature condition compared with that in 1998. However,it was still at an intermediate-low developmental stage in 1981 interms of Odum’s theory of ecosystem development (Odum, 1969,

1971; Christensen, 1995; Coll et al., 2006, 2007). The results indi-cate that the system was still rather distant from reported valuesin other mature aquatic ecosystems even in 1981. The ecosystemhas deteriorated since 1981 and the situation was worsen in 1998.Also, the comparative analysis with other ecosystem was done

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ven though it was not as straightforward as what have been doneetween 1981 and 1998 in this study. This comparison was basedrimarily on energy budget models which were built by differentesearch groups and different aggregation levels and the resultsay be a little biased due to diverse construction. It is still consid-

red that this set of network metrics is valuable as an ecosystemomparison tool, but with the caveat that future work should focusn separating aggregation effects from ecosystem attributes in net-ork analysis where possible (Gaichas et al., 2009).

.3. The interactional analysis of trophic groups

The MTI routine can be used to quantify the direct and indi-ect impacts that one functional group has on all the other groupsn system after a short-term perturbation, which are ultimatelyxpressed as negative impacts and positive impacts. Negativempacts can be associated to prevailing top-down effects and pos-tive ones to bottom-up effects. It was indicated that there existedhree kinds of control mechanisms for the regime shift in our sys-em in the past two decades. A marginal top-down control of lowerL fish populations by predator groups such as S. chinensis, sharks, T.aumela and Saurida was identified. This kind of control mechanisman be attributed to the long-term and severe fishing stress and per-aps environmental interference that have reduced the biomassf top predators, which is similar to the situations in the North-rn and Central Adriatic Sea and the South Catalan Sea ecosystemsColl et al., 2006, 2007). The wide impacts of producers, zooplank-on and most zoobenthos on the other groups can be related toottom-up control of food web (Hunter and Price, 1992). “Otherelagics” and “other demersals” compartments were identified aseystone species in 1981 and estimated to have high keystone-ess in 1998 because of providing food for many other species,hich may be involved in wasp-waist control situation (Hunt andckinnell, 2006).The identity of keystone species is not straightforwardly pre-

ictable and can be surprising (Libralato et al., 2006). Generally,arine mammals ranked high (most often first) in most ecosystems

uch as Alaska gyre, Azores, Newfoundland, Norwegian Barents Seaodels. But in our models, S. chinensis had low rank and ranked

0 and 15, in the 1981 and 1998 model, respectively. Similarly,harks ranked low in both models (ranked 7 in 1981 and 6 in 1998).he lower part of the trophic web appeared to be important in theoastal ecosystem of the PRE, where phytoplankton, zooplanktonnd benthic invertebrates had high keystoneness index, in line withhe bottom-up control within the ecosystem that resulted fromhe MTI analysis. A prevalence of keystone group (other pelagics)n the intermediate position of the trophic web can be seen fromable 4, which indicates that this intermediate functional groupontributed to the typical control of wasp-waist in our ecosys-em. Therefore, the bottom-up and wasp-waist control dominatedhe whole ecosystem in the past two decades. Moreover, thereere obvious changes in rank of functional groups by keystone-ess over time in our models, which is evidence of the increase ofeystoneness of groups in the lower part of the food web and theoncomitant decrease of keystoneness of intermediate groups thusndicating an enhancement of bottom-up control over time.

. Conclusion

It is obvious that ecotrophic modeling is a useful approach

or study of aquatic ecosystem. Models can be used to improveur understanding of the predatory–prey interactions among thexploited and unexploited but potential fishery resources of theystem. Moreover, a synthesis of the ecological functioning can beiven on the basis of the energy flows of the trophic network.

ing 220 (2009) 2802–2818

Trophic models were constructed for two years (1981 and 1998)and a comparison between the trophic networks was used toquantify and analyze the trophic state and development stage ofthe ecosystem in the past two decades. Furthermore, comparisonbetween similar systems such as coastal ecosystems, estuaries andbays or between different periods for the same ecosystem is help-ful to get further and integrated knowledge of the system. The dietcomposition indicates most of the consumers fed lower in the foodweb in 1998 than before, corresponding to lower ETL for many func-tional groups in the 1998 model. Total biomass and many othertotal flows through the system were larger in the 1981 model thanthe 1998 model. The coastal ecosystem of the PRE was more heav-ily exploited in 1998 than in 1981 though system throughput andbiomass were lower in the 1998 model. Therefore, fishing had neg-ative impacts on more groups in 1998 than in 1981. The fisheryoperated at intermediate-low trophic levels during the 1980s andthere are some indications of ‘fishing down the food web’ in thisecosystem in 1998. The ecological indicators related to the theoryof ecosystem development indicate that the ecosystem was at adevelopment stage in 1981 and severely depleted in 1998, probablydue to both overfishing and environmental changes. The networkanalysis suggests structural and functional shift during the past twodecades. The keystone role of low and medium trophic levels of theecosystem and the change of keystoneness of groups also coincidewith the structural shift of the ecosystem.

The results of this study can be viewed as preliminary approxi-mation of the interactions occurring within the coastal ecosystemof the PRE. Given the variable accuracy and precision of the dataused to estimate input parameters for the models, and the con-straint of areas for which data were available, there is unavoidableuncertainty associated with the model outputs. Nevertheless, thepedigree indices indicate that the models are relatively robust withregards to input data, and an integrated picture of the system wasobtained with relatively high quality. For the first time, two modelsof the coastal ecosystem of the PRE with intensive fishery activi-ties were made available using the EwE software for comparisonwork. In general, the modeling conclusions are biologically reason-able and can be expected to provide an effective tool for analyzingecosystem structure and evolution and testing the trophic role ofdifferent species. Future work is required to collect more data seriesfor further dynamic simulation and fishery policy exploration.

Acknowledgements

This paper has been carried out with financial support fromthe Commission of the European Communities, specific RTD pro-gramme “International Research in Cooperation” (INCO-DEV),“Ecosystems, Societies, Consilience, Precautionary principle: devel-opment of an assessment method of the societal cost for best fishingpractices and efficient public policies” (ECOST). We are very grate-ful to M.C.S. Villanueva for assistance and discussions about modelconstruction.

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