an ecological model of the northern and central adriatic sea: analysis of ecosystem structure and...

s 67 (2007) 119–154www.elsevier.com/locate/jmarsys

Journal of Marine System

An ecological model of the Northern and Central Adriatic Sea:Analysis of ecosystem structure and fishing impacts

Marta Coll a,⁎, Alberto Santojanni b, Isabel Palomera a, Sergi Tudela c, Enrico Arneri b

a Institut de Ciències del Mar (ICM–CSIC), Passeig Marítim de la Barceloneta, 37-49-08003 Barcelona, Spainb Istituto di Scienze Marine (CNR), Sede di Ancona, Largo Fiera della pesca, 2-60125 Ancona, Italy

c WWF Mediterranean Programme Office, Canuda, 37, 08002, Barcelona, Spain

Received 14 June 2006; received in revised form 25 September 2006; accepted 4 October 2006Available online 20 November 2006

Abstract

A trophic mass-balance model was developed to characterise the food web structure and functioning of the Northern andCentral Adriatic Sea and to quantify the ecosystem impacts of fishing during the 1990s. Forty functional groups were described,including target and non-target fish and invertebrate groups, and three detritus groups (natural detritus, discards and by-catch ofcetaceans and marine turtles). Results highlighted that there was an important coupling between pelagic–benthic production ofplankton, benthic invertebrates and detritus. Organisms located at low and medium trophic levels, (i.e. benthic invertebrates,zooplankton and anchovy), as well as dolphins, were identified as keystone groups of the ecosystem. Jellyfish were an importantelement in terms of consumption and production of trophic flows within the ecosystem. The analysis of trophic flows ofzooplankton and detritus groups indirectly underlined the importance of the microbial food web in the Adriatic Sea.

Fishing activities inflicted notable impacts on the ecosystem during the 1990s, with a high gross efficiency of the fishery, a highconsumption of fishable production, high exploitation rates for various target and non target species, a low trophic level of the catchand medium values of primary production required to sustain the fishery. Moreover, the analysis of Odum's ecological indicatorshighlighted that the ecosystem was in a low-medium developmental stage. Bottom trawling (Strascico), mid-water trawling(Volante) and beam trawling (Rapido) fleets had the highest impacts on both target and non target ecological groups. On thecontrary, purse seining (Lampara) showed medium to low impacts on the ecosystem; cetaceans, marine turtles and sea birds werenot significantly involved in competition with fishing activity.© 2006 Elsevier B.V. All rights reserved.

Keywords: Adriatic Sea; Food web model; Ecopath; Network analysis; Keystone species; Fishing impact

1. Introduction

Fishing activities inflict direct and indirect impactson the ecosystem, where target and non-target species

⁎ Corresponding author.E-mail addresses: [email protected] (M. Coll),

[email protected] (A. Santojanni), [email protected](I. Palomera), [email protected] (S. Tudela), [email protected](E. Arneri).

0924-7963/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.jmarsys.2006.10.002

interact establishing complex relationships (e.g. Jen-nings and Kaiser, 1998; Jackson et al., 2001). The needfor the adoption of integrative approaches to understandhow fishing activities impact complex food webs is thusevident under an ecosystem approach to fisheries (FAO,2003; Walters et al., 2005). At present, ecologicalmodelling, in part due to the application of the Ecopathwith Ecosim approach (EwE, Christensen and Walters,2004), is a worldwide used tool that can be applied forthe description of ecosystem structure and functioning.

mailto:[email protected]





http://dx.doi.org/10.1016/j.jmarsys.2006.10.002

120 M. Coll et al. / Journal of Marine Systems 67 (2007) 119–154

Fishing activity can be placed within the ecosystemcontext enabling an integrated study of ecosystemimpacts of fishing.

The Mediterranean region has been inhabited formillennia and ecosystems have thus been altered inmany ways (Bianchi and Morri, 2000). Fishing activitieshave been proposed as the first major human distur-bance in coastal areas (Jackson et al., 2001), and evi-dence of such activity, going back to ancient times, canbe found all around the Mediterranean Sea (Margalef,1985). Moreover, although artisanal gear is still im-portant, many fleets have developed towards an in-dustrial type of activity. These fleets fully exploitcontinental shelves and upper slopes of the basin and arecomposed of bottom and mid water trawlers and purseseiners (Bas, 2002). Many demersal stocks are fullyexploited or overexploited, and some pelagic stocks alsoshow overexploitation trends (Papaconstantinou andFarrugio, 2000; Lleonart and Maynou, 2003).

However, few applications of ecological modellinghave been developed to date in the Mediterranean Seaand these were mainly applied to coastal and shallowareas (e.g. Libralato et al., 2002; Pinnegar and Polunin,2004). Exceptions are found in the Black and SouthCatalan seas (Daskalov, 2002; Coll et al., 2006a), whereecosystem modelling has been extended to shelf areas.

The Northern and Central Adriatic Sea constitutes thewidest continental shelf in theMediterranean Sea (Pinardi

Fig. 1. The Northern and Central Adriatic

et al., in press) and is of great value for fishing within theItalian and the European context (Bombace, 1992;Mannini and Massa, 2000) (Fig. 1). Important changesin landings have been registered in this area (Fig. 2), witha dramatic increase from the mid 1970s to the mid 1980s,mainly due to the increase of small pelagic fish in thecatch. This was followed by marked fluctuations in land-ings until catches progressively declined from late 1980sto the present; primarily because of the decrease in smallpelagic fish, especially of European anchovy (Engraulisencrasicolus) and sardine (Sardina pilchardus) (Cingo-lani et al., 1996; Azzali et al., 2002; Santojanni et al.,2003, 2005). Moreover, various target demersal specieshave been reported as overexploited (e.g. Papaconstanti-nou and Farrugio, 2000; Jukic-Peladic et al., 2001; Vrgocet al., 2004) and important amounts of discards areproduced (Wieczorek et al., 1999; Pranovi et al., 2000,2001; Tudela, 2004). Total official landings from2000 arelower than those reached in the late 1970s (Fig. 2).Existing data show a significant decrease in fish landingswith time (non-parametric Spearman's correlation coef-ficient: piscivorous fish: rs=−0.428, p=0.015; plankti-vorous fish: rs=−0.768, p=0.000; non piscivorousdemersal fish: rs=−0.811, p=0.000), coupled with anon significant increase of invertebrate landings(rs=0.329, p=0.051).

This work presents an ecological model of theNorthern and Central Adriatic Sea with the aim of: (a)

Sea. The study area is highlighted.

Fig. 3. Mean landings composition (N2%) of main species or groupsfrom the 1990s (CNR and ISTAT).

Fig. 2. Total landings (t) from the Northern and Central Adriatic Sea(CNR and ISTAT) (1976–2000).

121M. Coll et al. / Journal of Marine Systems 67 (2007) 119–154

describing the structure and functioning of this exploitedshelf ecosystem in terms of trophic flows andbiomasses; (b) analysing the ecological role of theprincipal species; (c) quantifying the ecosystem impactsof fishing and analysing the role of fishing activity.

2. Materials and methods

2.1. The Northern and Central Adriatic Sea

Owing to ecological and fishing similarities (Sarà,1983; Bombace, 1992), the northern and central areas ofthe Adriatic Sea were chosen to develop a mass balancemodel of an average annual situation of the continentalshelf. The model represents the ecosystem in the 1990s,after the collapse of the anchovy stock and the decreaseof other small pelagic fish species in the area (Cingolaniet al., 1996; Azzali et al., 2002; Santojanni et al., 2003,2005). It covers a total area of 55,500 km2, with a meandepth of 75 m (Fig. 1). The area within 3 nm from thecoast, or down to 10 m depth, where the artisanal fleetsmainly operate and trawling activity is banned, isexcluded from the western part; whilst the area within12 nm from the coast, where the rocky archipelagosoccur, is excluded from the eastern part (Bombace,1992).

Three main water types have been identified to occurin the Adriatic Sea (Artegiani et al., 1997a): the SurfaceWater (SW), the Deep Water (DW) and the ModifiedLevantine Intermediate Waters (MLIW), and the generalcirculation is baroclinic (Artegiani et al., 1997b). Owingto river runoff and oceanographic conditions, the region

exhibits a decreasing trend of nutrient concentration andproduction from north to south and from west to east(Fonda Umani, 1996; Zavatarelli et al., 1998). Thesubstrate is characterised by muddy to sandy bottoms(Brambati et al., 1983).

The area presents a high diversity of environmentalconditions that is translated into a high biodiversity (Ott,1992). Numerous studies describe the distribution andabundance of marine fauna and flora of the Adriatic Sea(e.g. Riedl, 1986; Zupanovic and Jardas, 1989; Jukic-Peladic et al., 2001). Moreover, it is also a strategic areafor marine vertebrate conservation, sheltering importantseabird populations (Zotier et al., 1999; Baccetti et al.,2002). The area also includes important populations ofendangered marine mammals and turtles (Delaugerre,1987; Groombridge, 1990; Manoukian et al., 2001;Bearzi et al., 2004).

The modelled area comprises the fishing harboursfrom Trieste to Vieste. Small pelagic fish, mainly sar-dine and anchovy, constituted the principal componentof the catches in terms of biomass in the 1990s and aremainly caught by purse seines and mid water trawlers(Fig. 3) (Arneri, 1996; Mannini and Massa, 2000). Thedemersal fishery mainly comprises juveniles of severaltarget species, e.g. hake (Merluccius merluccius) andred mullet (Mullus barbatus), caught principally by thetrawling fleet. Invertebrates (cephalopods and crabs)also constitute an important proportion of the catch.

2.2. Mass balance modelling approach

The Ecopath and Ecosim (EwE) modelling approachversion 5 (Christensen andWalters, 2004; www.ecopath.org) was used to ensure energy balance of the model.EwE divides the production (P) of each component orfunctional group (i) of the ecosystem into predationmortality (M2ij) caused by the biomass of the otherpredators (Bj); exports from the system both from fishing

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activity (Yi) and other exports (Ei); biomass accumula-tion in the ecosystem (BAi); and baseline mortality orother mortality (1−EEi), where EE is the ecotrophicefficiency of the group within the system, or theproportion of the production of (i) that is exported outof the ecosystem (i.e. by fishing activity) or consumed bypredators within it.

Pi ¼Xj

BjdM2ij þ Yi þ Ei þ BAi þ Pid ð1−EEiÞ ð1Þ

Eq. (1) can be re-expressed as:

BdPB

� �i

¼Xj

BjdQB

� �j

d DCij þ Yi þ Ei þ BAj

þ BidPB

� �i

d ð1−EEiÞ ð2Þ

where (P /B)i indicates the production of (i) per unit ofbiomass and is equivalent to total mortality, or Z, understeady-state conditions (Allen, 1971); (Q /B)i is theconsumption of (i) per unit of biomass; and DCij indi-cates the proportion of (i) that is in the diet of predator (j)in terms of volume or weight units. EwE parameterizesthe model by describing a system of linear equations forall the functional groups of the model, where for eachequation three of the basic parameters: Bi, (P /B)i, (Q /B)ior EEi have to be known for each group (i). The energybalance within each group is ensured when consumptionby group (i) equals production by (i), respiration by (i)and food that is unassimilated by (i). When the systemequations are solved they provide a snapshot of thetrophic flow within the system.

Units of the model are expressed in t·km−2·yr−1 wetweight organic matter for flows and t·km−2 for bio-masses. To ensure consistency between ontogeneticgroups, the multiple stanza representation (Christensenand Walters, 2004) was used for modelling Europeanhake, a highly commercial species for which dietaryinformation is available for different population frac-tions. Two groups were defined considering the vulner-ability of this species to fishing in the area (mainly due tobottom trawling), namely vulnerable hake or group“hake (1)” (b40 cm) and non vulnerable hake or group“hake (2)” (≥40 cm). (P /B)i and diet composition wereprovided for both groups, while Bi and (Q /B)i wereintroduced for the leading stanza group only, group“hake (1)” (Abella et al., 1997; Caddy and Abella, 1999;Vrgoc et al., 2004).

The Automatic Mass Balance Procedure (Kavanaghet al., 2004) was used after having modified data with

higher uncertainty in terms of diet composition andbiomass for those groups whose initial biomass wasassessed by the swept area method applied to experi-mental trawl survey data or with information pertainingto areas different from the study area (Coll, 2006). Theautomatic procedure modified the diet matrix, and to alesser extent biomass data, as they were the parameterswith a higher associated uncertainty. The model wasconsidered balanced when: (1) realistic estimates of themissing parameters were calculated (EEb1); (2) valuesof production/consumption ratios (P/Q, or gross effi-ciency of food conversion) for functional groups werebetween 0.1 and 0.35 with the exception of fast growinggroups with higher values and top predators with lowervalues; (3) values of respiration/biomass ratios (R/B)were consistent with the group's activities with highvalues for small organisms and top predators; (4) valuesof respiration/food assimilation ratios (R/A) were b1 andvalues for top predators were higher; (5) values of netefficiency of food conversion were b1 for all the func-tional groups (Christensen et al., 2004).

The Pedigree routine was used to describe the originand quality of the data and the model, and a sensitivityanalysis routine was implemented to explore the impactof the uncertainty of the initial parameters on modelresults (Christensen and Walters, 2004; Christensenet al., 2004).

2.3. Parameterization and functional groups

Input parameters were mainly compiled from avail-able published and unpublished information of the Isti-tuto di Scienze Marine — Sede di Ancona (CNR–ISMAR, Italy) and they are summarized in Table 1(Appendix). Biomass values (Bi) were obtained fromdata collection using the swept area method, sedimentcores, bottom dredge sampling, acoustic surveys andinformation available in the literature. Production/biomass ratios (P/B)i and consumption/biomass ratios(Q/B)i were taken from the literature or obtained fromthe application of empirical equations using length,weight and growth data (Nilsson and Nilsson, 1976;Pauly, 1980; Innes et al., 1987; Pauly et al., 1990;Christensen et al., 2004). Diet composition (DCij) (seeTable 1, Appendix) and assimilation rates (Coll et al.,2006a) where compiled from published information.Migratory patterns of Atlantic bonito, large pelagic fishand seabirds were taken into account by modelling aproportion of the diet composition of these groups asimports to the ecosystem (De La Serna and Alot, 1990;Block et al., 2001; Sabatés and Recasens, 2001). Themicrobial food web was partially included in the model

Table 1Input data of the Northern and Central Adriatic Sea model by functional group

Functional group Bi EE P/B Q/B Landings Discards

1 Phytoplankton 16.658 – 69.03 – – –2 Micro and mesozooplankton 9.512 – 30.43 49.87 – –3 Macrozooplankton 0.540 – 21.28 53.14 – –4 Jellyfish 4.0 – 14.60 50.48 – –5 Suprabenthos 1.01 – 8.40 54.36 – –6 Polychaetes 9.984 – 1.90 11.53 – –7 Commercial bivalves and gastrop. 0.043 – 1.06 3.13 0.035 –8 Benthic invertebrates 79.763 – 1.06 3.13 – 0.3289 Shrimps – 0.95 3.21 7.20 0.016 0.01710 Norway lobster 0.018 – 1.25 4.56 0.037 –11 Mantis shrimp 0.015 – 1.50 4.56 0.072 –12 Crabs 0.009 – 2.44 4.73 0.002 0.17713 Benthic cephalopods 0.068 – 2.96 5.30 0.154 0.00214 Benthopelagic cephalopods 0.02 – 3.11 26.47 0.041 –15 Hake (1) 0.06 – 1.00 4.24 0.113 0.00716 Hake (2) – – 0.50 – – –17 Other gadiformes 0.029 – 1.59 4.37 0.025 0.08318 Red mullets 0.025 – 1.90 8.02 0.112 –19 Conger eel 0.005 – 1.92 6.45 – 0.00820 Anglerfish 0.006 – 1.04 4.58 0.007 –21 Flatfish 0.009 – 1.43 9.83 0.040 –22 Turbot and brill – 0.95 1.43 5.34 0.016 –23 Demersal sharks 0.018 – 0.63 4.47 0.008 –24 Demersal skates 0.003 – 1.11 7.08 0.002 –25 Demersal fish (1) 0.056 – 2.40 7.68 0.055 0.05126 Demersal fish (2) – 0.95 2.40 5.68 0.016 0.00127 Benthopelagic fish – 0.95 1.07 7.99 0.002 –28 European anchovy 1.019–6.611 – 0.87 11.02 0.496 0.00529 European pilchard 2.985–7.803 – 0.75 9.19 0.364 0.04230 Other small pelagic fish 0.413–1.517 – 1.10 11.29 0.012 0.00131 Horse mackerel 0.659–2.455 – 0.99 7.57 0.020 0.00232 Mackerel 0.452–1.683 – 0.99 6.09 0.017 0.00833 Atlantic bonito 0.3 – 0.39 4.54 0.018 –34 Large pelagic fish 0.138 – 0.37 1.99 0.026 –35 Dolphins 0.012 – 0.08 11.01 – 0.000136 Loggerhead turtle 0.032 – 0.17 2.54 – 0.00437 Sea birds 0.001 – 4.61 69.34 – –38 Discards 0.733 – – – – –39 By-catch 0.004 – – – – –40 Detritus 200.0 – – – – –

Table 1 (Appendix) lists main data sources and estimation methods.Bi = Initial biomass (t·km−2); EE=Ecotrophic efficiency; P/B=Production/biomass ratio (yr−1); Q/B=Consumption/biomass ratio (yr−1); Landingsand discards (t·km−2·yr−1).


in two different ways taking into account availabledata (Fonda Umani, 1996; Fonda Umani and Beran,2003): (a) through zooplankton consumption by con-sidering detritus as a proportion of zooplankton diet,(b) through zooplankton feeding as imported to thesystem.

Principal fishing activities in the area were included inthe analysis (Bombace, 1992): bottom trawling (namedStrascico), beam trawling (Rapido), mid-water trawling(Volante), purse seine (Lampara) and tuna fisheries (in-cluding purse seines, troll bait and recreational fleets).

Official landings statistics from the 1990s were obtainedfrom the governmental statistical institute (ISTAT). Thesedata were corrected by considering discard informationdrawn from the literature, including by-catch of cetaceansand turtles (Wieczorek et al., 1999; Cingolani et al., 2000;Cooper et al., 2000; Affronte and Scaravelli, 2001; Bearzi,2002; Casale et al., 2004; Tudela, 2004; Santojanni et al.,2005) and estimates of illegal, unregulated or unreportedlandings (Mattei and Pellizzato, 1996; Santojanni et al.,2001a,b, 2005; Cingolani et al., 2002a,b; Arneri, Frogliaand Piccinetti, unpublished data).

Fig. 4. Results from Factorial Correspondence Analysis of trophic data of 41 fish species with first and second axes represented (species codes arelisted in Table 2 Appendix).


The definition of functional groups included in themodel was based on similarities in their ecological andbiological features (e.g. feeding, habitat, growth) and onthe importance of the species in terms of the fisheries.Moreover, dealing with the multispecificity of Mediter-ranean catches implied the application of FactorialCorrespondence Analysis (FCA) and HierarchicalCluster Analysis on available ecological information.They were applied to stomach-content data for 41 fishspecies to establish new mixed groups of benthic, de-mersal and benthopelagic fish species. When defined,these new groups were added to the other functionalgroups and input data were integrated taking intoaccount species composition and biomass proportionswithin each group.

2.4. Model analysis and indices

Ecological indices were used to analyse ecosystemstructure and ecosystem impacts of fishing based ontrophic flows analysis, thermodynamic concepts, infor-mation theory and trophodynamic indicators (Christen-sen et al., 2004; Christensen and Walters, 2004; Curyet al., 2005).

Total trophic flows within the ecosystem in terms ofconsumption, production, respiration, exports andimports and flow to detritus (t·km−2·yr−1) were quan-tified. The sum of all these flows, the Total SystemThroughput (TST), can be seen as an indirect indicatorof the size of the ecosystem (Christensen and Pauly,1993).

The Trophic Levels (TL) of the functional groups werealso calculated. The TL was first defined as an integeridentifying the position of organisms within food webs(Lindeman, 1942) and it was later modified to be frac-tional (Odum and Heald, 1975). Following an establishedconvention, a TL of I is attributed to primary producersand detritus, a TL of II to herbivores, a TL of III to firstorder carnivores and a TL of IV to second order car-nivores. Thus, the TL can be formulated as follows:

TLj ¼ 1þXnj¼1

DCjid TLi ð3Þ

where j is the predator of prey i, DCji is the fraction ofprey i in the diet of predator j and TLi is the trophic levelof prey i. Trophic flows and TL can be represented interms of a flow diagram by functional group.

Fig. 5. Cluster analysis representing the similarity between 41 fish species analyzed with the Factorial Correspondence Analysis (species codes arelisted in Table 2 Appendix).


From trophic flows and TLs, the Transfer Efficiency(TE) can be calculated, which summarizes all the in-efficiencies of the food web (due to respiration, excretion,

egestion and other natural mortality) present at each step ofthe trophic chain (Lindeman, 1942). The TE is obtained bycalculating the ratio between the production of a given TL

Table 2Modified input parameters and output parameters for the Northern and Central Adriatic Sea model

Functional group TL Bf P/Q EE F M2 M0 F/Z OI NE R/A FD Q

1 Phytoplankton 1.00 16.66 – 0.34 0.00 23.55 45.48 – 0.00 – – 757.64 –2 Micro- and mesozoop. 2.05 9.51 0.39 0.97 0.00 23.19 0.81 – 0.05 0.65 0.35 237.45 580.233 Macrozoop. 3.05 0.54 0.40 0.67 0.00 14.17 7.11 – 0.21 0.48 0.52 8.43 28.704 Jellyfish 2.66 4.00 0.29 0.17 0.00 2.52 12.08 – 0.31 0.32 0.68 68.50 201.935 Suprabenthos 2.11 1.01 0.15 0.51 0.00 4.29 4.11 – 0.10 0.21 0.79 18.98 54.906 Polychaetes 2.00 9.98 0.16 0.10 0.00 0.18 1.72 – 0.00 0.39 0.61 83.90 115.127 Comm. bivalves and gastrop. 2.00 0.04 0.34 0.81 0.84 0.02 0.20 0.98 0.00 0.60 0.40 0.07 0.138 Benthic invertebrates 2.00 79.76 0.34 0.03 0.004 0.03 1.02 0.11 0.00 0.60 0.40 189.25 249.669 Shrimps 3.02 0.17 0.45 0.95 0.19 2.86 0.16 0.06 0.02 0.56 0.44 0.28 1.2510 Norway lobster 3.76 0.05 0.27 0.98 0.69 0.54 0.02 0.48 0.22 0.34 0.66 0.05 0.2411 Mantis shrimp 3.30 0.05 0.33 0.98 1.46 0.01 0.03 0.86 0.22 0.41 0.59 0.05 0.2212 Crabs 2.99 0.55 0.49 0.99 0.33 2.11 0.003 0.12 0.35 0.61 0.39 0.55 2.7413 Benthic cephalop. 3.30 0.12 0.49 0.94 1.27 1.50 0.19 0.41 0.29 0.57 0.43 0.12 0.7414 Benthop. cephalop. 4.14 0.03 0.12 0.95 1.28 1.67 0.15 0.43 0.04 0.18 0.82 0.30 0.8515 Hake (1) 4.00 0.12 0.24 0.98 0.98 0.00 0.02 0.88 0.14 0.29 0.71 0.11 0.5216 Hake (2) 4.12 0.14 0.27 0.0002 0.00 0.0001 0.50 0.00 0.03 0.34 0.66 0.12 0.2617 Other gadiformes 3.36 0.07 0.36 0.98 1.51 0.05 0.03 0.86 0.20 0.45 0.55 0.06 0.3118 Red mullets 3.19 0.06 0.24 0.98 1.75 0.11 0.04 0.87 0.15 0.30 0.70 0.11 0.5119 Conger eel 4.15 0.01 0.30 0.86 1.28 0.37 0.26 0.74 0.08 0.37 0.63 0.01 0.0420 Anglerfish 4.55 0.01 0.23 0.98 1.00 0.01 0.02 0.91 0.09 0.28 0.72 0.01 0.0321 Flatfish 3.88 0.03 0.15 0.98 1.28 0.13 0.02 0.69 0.45 0.18 0.82 0.06 0.3122 Turbot and brill 4.16 0.01 0.27 0.95 1.22 0.13 0.07 0.87 0.04 0.33 0.67 0.01 0.0723 Dem. sharks 4.09 0.02 0.14 0.73 0.45 0.01 0.17 0.62 0.27 0.18 0.82 0.02 0.0824 Demersal skates 4.16 0.003 0.16 0.69 0.68 0.09 0.35 0.67 0.25 0.20 0.80 0.01 0.0225 Demersal fish (1) 3.32 0.13 0.31 0.98 0.79 1.57 0.04 0.30 0.25 0.39 0.61 0.21 1.0226 Demersal fish (2) 3.64 0.06 0.42 0.95 0.33 1.96 0.12 0.12 0.49 0.53 0.47 0.07 0.3127 Benthopelagic fish 3.72 0.30 0.13 0.95 0.01 1.01 0.05 0.01 0.24 0.19 0.81 0.74 2.4228 European anchovy 3.05 1.72 0.08 0.90 0.29 0.49 0.09 0.37 0.00 0.11 0.89 5.82 18.9129 European pilchard 2.97 2.99 0.08 0.78 0.14 0.45 0.17 0.21 0.08 0.12 0.88 8.73 27.4430 Other small pel. 3.25 0.59 0.10 0.53 0.02 0.57 0.51 0.03 0.16 0.14 0.86 2.28 6.6231 Horse mackerel 3.49 0.65 0.13 0.29 0.03 0.26 0.70 0.06 0.24 0.16 0.84 1.43 4.8932 Mackerel 3.32 0.44 0.16 0.52 0.06 0.46 0.48 0.08 0.22 0.20 0.80 0.75 2.7033 Atlantic bonito 4.05 0.29 0.09 0.001 0.06 0.0003 0.39 0.06 0.90 0.11 0.89 0.38 1.3434 Large pelagic fish 4.38 0.14 0.18 0.02 0.19 0.00 0.36 0.13 1.23 0.23 0.77 0.10 0.2735 Dolphins 4.31 0.01 0.01 0.27 0.02 0.00 0.05 0.21 0.08 0.01 0.99 0.03 0.1336 Loggerhead turtle 3.01 0.03 0.07 0.76 0.13 0.00 0.04 0.93 0.01 0.08 0.92 0.02 0.0837 Seabirds 3.89 0.001 0.07 0.00 0.00 0.00 4.61 0.00 0.56 0.08 0.92 0.02 0.0738 Discards 1.00 0.73 – 0.90 – – – – 0.00 – – 0.08 –39 By-catch 1.00 0.004 – 0.00 – – – – 0.00 – – 0.002 –40 Detritus 1.00 200.0 – 0.48 – – – – 0.37 – – – –

TL=Trophic level; Bf = final biomass (t·km−2); P/Q=production/consumption ratio or Gross efficiency; EE=Ecotrophic efficiency; F=Fishingmortality (yr−1); M2=Predation mortality (yr−1); M0=Other natural mortality (yr−1); F/Z=Exploitation rate; OI=Omnivory index, NE=Netefficiency; R/A=respiration/assimilation ratio; FD=Flow to detritus (t·km−2·yr−1); Q=Consumption (t·km−2·yr−1).


and the preceding TL (Lalli and Parsons, 1993; Pauly andChristensen, 1995). Flows, TLs and TE were visualized inthe form of a Lindeman Spine (Lindeman, 1942;Ulanowicz, 1986;Wulff et al., 1989; Libralato et al., 2002).

The Mixed Trophic Impact (MTI) analysis, derivedfrom economic theory (Leontief, 1951; Ulanowicz andPuccia, 1990), allowed the quantification of direct andindirect trophic interactions among functional groups.This analysis provides a quantification of the positive ornegative impact that a hypothetical increase in the bio-mass of a group would produce on the other groups in the

ecosystem, including the fishery.Moreover, theMTI wasused to calculate the Total Mixed Trophic Impact (TMTI)that one functional group would have on the other groupsby adding all its impacts weighted by the inverse of thebiomass of the impacted groups, as proposed by Pranoviet al. (2003) and Libralato et al. (2004). This gives anindication of the total effect that a unit change in thebiomass of one group has on the predicted biomasses ofthe other groups.

A method for identifying keystone species (or groupsof species) derived from the MTI analysis and proposed

Table 3Diet composition matrix for the functional groups of the model

Predator groups are placed down the vertical axis and prey groups are along the horizontal axis. Data is shown when values are N1%; grey cells indicate lower values.⁎The proportion of zooplankton diet from the microbial food web has been established from detritus by default.

127M.Coll

etal.

/Journal

ofMarine

Systems67

(2007)119–154

Fig. 6. Example of results from the sensitivity analysis applied to inputparameters of the Northern and Central Adriatic Sea model. The figureshows the impact of 10% changes in the input parameters for anchovy(functional group 28) on anchovy and zooplankton (fg2) outputs.

Table 4Ecological indicators related with community energetics, communitystructure, cycling of nutrients and information theory

Statistics and flows

Sum of all consumptions 1305.04 t·km−2·yr−1

Sum of all exports 730.15 t·km−2·yr−1

Sum of all respiratory flows 421.09 t·km−2·yr−1

Sum of all flows into detritus 1387.46 t·km−2·yr−1

Total system throughput 3844 t·km−2·yr−1

Sum of all production 1566 t·km−2·yr−1

Calculated total net primary production 1149.85 t·km−2·yr−1

Total primary production/total respiration 2.73Net system production 728.76 t·km−2·yr−1

Total primary production/total biomass 8.82Total biomass/total throughput 0.03Total biomass (excluding detritus) 130.30 t·km−21

Total transfer efficiency 10%Total catches 2.44 t·km−2·yr−1

Mean trophic level of the catch 3.07Primary production required to sustainthe fishery (considering pp)

6.59%

Primary production required to sustainthe fishery (considering pp+det)

15.0%

Gross efficiency of the fishery (catch/net pp) 0.002Ecopath Pedigree index (0–1) 0.66

Network flow indices

Throughput cycled (excluding detritus) 42.43 t·km−2·yr−1

Predatory cycling index (of throughputw/o detritus)

3.97%

Throughput cycled (including detritus) 7.51 t·km−2·yr−1

Finn's cycling index (of total throughput) 14.70%Finn's mean path length 3.34System Omnivory Index 0.19

Information indices

Ascendency 27.0%Overhead 73.0%Capacity (total) 15,406.7 flowbits

pp=primary production; det: detritus.


by Libralato et al. (2006) was also applied. Keystonespecies are those that show relatively low biomass buthave a structuring role in the ecosystem (Power et al.,1996). Therefore, they can be identified by plotting therelative overall effect (εi), calculated from the MTI (mij),against the keystoneness (KSi). The overall effect (εi) isdescribed as:

ei ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiXnj¼1

m2ij

vuut ð4Þ

where mij is calculated from the MTI analysis as theproduct of all net impacts for all the possible pathwaysin the food web linking prey, i, and predators, j. The

keystoneness (KSi) of a functional group is calculatedas:

KSi ¼ log½eið1−piÞ� ð5Þwhere pi is the contribution of the functional group to thetotal biomass of the food web. This index is high whenfunctional groups (species or groups of species) have bothlow biomass proportions within the ecosystem and highoverall effects, in linewith the keystone species definition.

The Primary Production Required (PPR) to sustain thefishery, the average Trophic Level of the Catch (TLc),partitioning of mortalities, the Gross Efficiency of thefishery (GEf=catch/primary production) and the relativeconsumption of total production, (considering total pro-duction, total production excluding plankton and benthicinvertebrates and fishable production) were analysed toplace the fisheries into the ecosystem context. The PPRfrom the primary production and detritus (flows fromTL=1), typically measured as t·km−2·yr−1, is obtained byback calculating the flows, expressed in primary productionand detritus equivalents, for all pathways from the caughtspecies down to the primary producers and detritus (Paulyand Christensen, 1995). It is formulated as:

PPR ¼ 19dXi

Yid1TE

� �TLi−1" #

ð6Þ

where Yi is the catch of a given group (i), TE is the meantransfer efficiency, TLi is the trophic level of group (i) andfactor 1/9 is taken as the average conversion coefficient

Fig. 7. Schematic representation of trophic flows from the Northern and Central Adriatic Sea ecosystem organized by integer trophic levels (TL) in theform of Lindeman Spines. Results are computed by (A) considering zooplankton feeding on the microbial food web as part of the detritus; and (B)considering zooplankton feeding on the microbial food web as imported to the system.


from wet weight to gC. This index can also be expressedper unit of catch relative to the primary production anddetritus of the ecosystem (%PPR). The TLc reflects theoverall strategy of a fishery and is calculated by weightingthe proportions of each type of organism from the catch andits TL (Pauly et al., 1998a).

Finally, various Odum's ecological indicators relatedto the ecosystem development theory (Margalef, 1968;Odum, 1969; Christensen, 1995) were analysed and putinto context by comparing results with other previouslymodelled ecosystems (e.g. from tropical areas: Christen-sen and Pauly, 1993; upwelling systems: Jarre-Teich-mann, 1998; Moloney et al., 2005; and temperateecosystems: Guénette et al., 2001; Pinnegar and Polunin,2004; Sánchez and Olaso, 2004; Coll et al., 2006a). Theseindicators included: (a) various coefficients of flows and

biomasses (total primary production/ total respiration,total biomass / total production and total biomass / totalsystem throughputs); (b) the Finn's cycling index andpredatory cycling index; (c) the System Omnivory Index(SOI); and (d) the Ascendency, which is related with theaverage mutual information in a system scaled by the TST(Finn, 1976; Ulanowicz, 1986).

3. Results

3.1. Definition of functional groups

The Northern and Central Adriatic Sea model isdefined with 40 ecological groups (Table 1), spanningthe main trophic components of the ecosystem and in-cluding target and non-target fish and invertebrate

Fig. 8. Flow diagram of the Northern and Central Adriatic Sea organised by functional groups and fractionated trophic levels (TL) and dividedbetween demersal and pelagic habitats. Trophic links higher than 10% of total flow within each group are shown.


groups, and three detritus groups (natural detritus, dis-cards and by-catch of vulnerable species of cetaceans,seabirds and marine turtles).

Results of Factorial Correspondence Analysis appliedto ecological data from 41 fish species show that fourfactors accumulated 57.84% of the total variance. Factors1 and 2 (representing the first and second axes) explain19.58% and 13.99% of variance, respectively (Fig. 4).Prey groups of micro-, meso- and macrozooplankton hadpositive values along the first axis, while cephalopods,fish species and benthic invertebrates have negativevalues. Along the second axis, benthic invertebrates (withthe exception of benthic cephalopods) and benthopelagicfish have positive values. Cephalopods and the remainingfish species show negative values. From this analysis, atotal of 4 groups have been defined from the FCA and areillustrated in the cluster analysis (Fig. 5):

– Class 1 is composed of demersal fish species withmixed trophic habits (main prey being benthic inver-

tebrates, suprabenthos, crustaceans and small bentho-pelagic fish). This class was included in the model asa functional group named “demersal fish (1)”;

– Class 2 is composed of demersal fish with trophichabits based on small demersal and pelagic fish asmain prey species and was named “demersal fish (2)”;

– Class 3 and Class 4 are composed of benthopelagicfish species with trophic habits based on micro-,meso- and macrozooplankton. These two classeswere joined together in a single functional groupnamed “benthopelagic fish”.

3.2. Input parameters and quality of data

Input data of the model by functional group are listedin Table 1. Main data sources and estimation methodsare compiled in Table 1 (Appendix). Modified input andoutput parameters of the model are shown in Table 2,while the diet matrix of the last run of the model isshown in Table 3.

Fig. 9. Main partitioning of (A) total consumption of production; (B)total consumption of production excluding plankton and benthicinvertebrates; and (C) total consumption of fished groups' production(consumption values represented are ≥2%). TE=Transfer efficiency.


With the exception of top predators, benthic inverte-brates, jellyfish, phytoplankton and detritus, ecotrophicefficiencies (EE) show that a high proportion of pro-duction from most of these groups is either consumedwithin the ecosystem or exported from it (e.g. in terms ofcatches) (Table 2).

The pedigree index of the model (0.657, Table 4) issimilar to that obtained for the South Catalan Sea (Collet al., 2006a) and ranks within the highest values whencompared with other 50 previously constructed modelsfor which pedigree values ranged between 0.164 and0.676 (Lyne Morisette, personal communication; Fish-eries Centre — UBC). The sensitivity analysis showed

that, by altering the input parameters of a functionalgroup, the largest impacts were seen on the outputparameters of the same group. As an example, Fig. 6represents the results of the sensitivity analysis carriedout by modifying input parameters related to anchovy(functional group 28) on anchovy and micro- andmesozooplankton (functional group 2).

3.3. Summary statistics and transfer efficiency

Results from the ecological model in terms ofaggregated summary statistics, network flows andinformation indices are shown in Table 4. Total con-sumption dominated the Total System Throughput (TST,t·km−2·yr−1) with 53.4% of the total flows, followed byflow to detritus (28.1%) and total respiration (17.2%).

Fig. 7 schematically represents the Northern and Cen-tral Adriatic Sea ecosystem flow diagram organized byinteger trophic levels (TLs) in the form of Lindemanspines, where primary producers and detritus are separatedto clarify the representation (both with TL=I, t·km−2·yr−1). Main flows are included within TL I, II and III. TL Igenerated 66.8%of theTST, followed byTL IIwith 27.7%of the flows (Fig. 7A). This figure also shows the importantlink between detritus and TL II, where TL II (mainlycomposed of plankton and benthic invertebrates) consumeand generate a high proportion of detritus. However, ifzooplankton feeding on the microbial food web isconsidered as imported to the system (Fig. 7B), theconsumption of TL II on detritus decreases.

The average transfer efficiency of the ecosystem is10%–10.2% and is, on average, lower for the food webrelated with detritus compared to that related withprimary producers (Fig. 7A and B). Values of TE forflows through TL II and TL III are high. Nevertheless,TE values decrease from TL II to TL III; this is followedby an increase from TL III to higher TLs.

3.4. Trophic flows and trophic levels by functional groups

Fig. 8 gives a schematic representation of the func-tional groups of the model, organised by their fractionalTL and habitat (demersal or pelagic). Ecological groupsare organized from TL 1 to TL 4.55, the highest valuescorresponding to anglerfish, large pelagic fish, dolphins,demersal skates, turbot and brill, conger eel, bentho-pelagic cephalopods, hake (2), demersal sharks andAtlantic bonito (TLN4, Table 2). The remainingfunctional groups are classified between 4.00 and 3.05for fish species (with the exception of sardine whoseTL=2.97 due to it partially feeding on phytoplankton),and between 3.76 and 2.00 for invertebrates.

Fig. 10. Mixed Trophic Impact (MTI) analysis. Impacted groups are placed along the horizontal axis and impacting groups are placed along thevertical axis. The bars indicate relative impact (between 0 and 1) where positive impacts are above the zero line and negative impacts are below.


Total flows by functional group excluding detritus showthat 65.1% of the TST is related to the pelagic domain, butimportant flows are also related to benthic invertebrates.

Important contributions by functional groups are affordedby micro- and mesozooplankton (38.9% of the total TST),benthic invertebrates (20.6%), jellyfish (16.3%),


polychaetes (9.5%), suprabenthos (4.4%), phytoplankton(2.4%), sardine (2.2%), macrozooplankton (2.1%) andanchovy (1.5%). Moreover, in terms of biomass (t·km−2)(Table 2), detritus, non-crustacean benthic invertebrates,phytoplankton, zooplankton and jellyfish are the dominantgroups, followed by sardine and anchovy and theremaining small and medium-sized pelagic fish. Ninety-eight percent of the total production (t·km−2·yr−1) of thesystem is due to phytoplankton, zooplankton, non-crustacean benthic invertebrates and jellyfish.

The analysis of consumption (≥2%) of ecosystemproduction is represented in Fig. 9. Leaving detritusaside, zooplankton, macrobenthos and jellyfish domi-nate the system (Fig. 9A). By excluding the zooplankton,macrobenthos and detritus, the importance of jellyfish,sardine and anchovy (7%) is emphasized (Fig. 9B). Theanalysis of consumption (≥2%) of fishable organismsby main predators highlights the high impact of crus-taceans and fishing activity, followed by cephalopodsand various demersal and pelagic fish species (Fig. 9C).The results of mortality partitioning within functionalgroups (Table 2) are in accordance with the range ofvalues obtained from stock assessment in the region(Vrgoc et al., 2004), where most of the groups show highpredatory mortality values (M2).

3.5. Mixed trophic impact analysis and keystone index

Direct and indirect interactions within the ecosystem,analysed using the Mixed Trophic Impact (MTI) rou-

Fig. 11. Total Mixed Trophic Impacts (TMTI) of impacting fu

tine, are shown in Fig. 10. Indirect impacts betweengroups due to prey availability can be detected, as forexample the negative effect of an increase in sardine oranchovy biomass on other small pelagic fish due topartial niche overlapping. Other indirect impacts in theform of trophic cascades can also be identified, e.g. anincrease in dolphins would have an indirect positiveimpact on anchovy and sardine due to the decrease inpredators (e.g. hake) and competitors (e.g. other smallpelagic fish).

Numerous functional groups in the model would beimpacted by groups at the base of the food web such asdetritus, phytoplankton, zooplankton, suprabenthos,benthic invertebrates, shrimps and crabs. Anchovy, andalso sardine, horse mackerel and mackerel with lowerimportance, show a wide impact on numerous functionalgroups of higher and lower TLs.

The coupled benthic–pelagic interaction of plank-tonic invertebrates, benthic invertebrates and detritus isalso observed from the MTI. Other important demer-sal–pelagic relationships are identified from the MTIanalysis, e.g. between demersal predators (e.g. adulthake or conger eel) and forage fish (e.g. anchovy andsardine).

Fig. 11 shows the results of the total MTI analysisby functional group. Hake (1), mackerel, suprabenthosand jellyfish groups show the highest total negativeimpacts of varying the biomass of the group on thewhole ecosystem when excluding fishing activity. Onthe contrary, sardine, zooplankton, anchovy and phyto-

nctional groups on the remaining groups in the model.

Fig. 12. Keystoneness index (KSi) and overall effect (εi) of each functional group from the ecological model of the Adriatic Sea. Keystone groups arethose with higher εi and higher KSi (value close or grater than zero).


plankton rank among the groups with the highestpositive impacts.

Estimated keystoneness of the functional groups isshown in Fig. 12. Benthic invertebrates, micro- andmesozooplankton, dolphins, suprabenthos and anchovyare among the first groups in terms of total effects andkeystoneness.

3.6. Fishing activity and impact

Total landings and discards calculated from thestudied area are shown in Table 1. The primary pro-duction required to sustain the fishing activity during the1990s (%PPR) is 6.6%, when taking into account theprimary producers, and 15.0% when considering boththe primary producers and detritus (Table 4). In addition,TLc is low (3.07) and GEf high (0.002). Exploitationrates (F/Z) (Table 2) show high values for variousdemersal and pelagic target and non target fish andinvertebrates, whilst marine turtles also show very highvalues of F/Z. These results are in line with previousvalues of consumption of fishable production by fishing(Fig. 9C).

The MTI analysis highlights the direct and indirectimpacts that an increase in fishing activity by fleetwould have on the other groups (Fig. 10). An increase ofthe Strascico activity would have the widest-ranging

impact on all ecosystem compartments and the largestimpacts on some demersal groups. This fleet wouldnegatively impact various benthic and demersal groups,as well as dolphins and marine turtles, mainly due to thedecrease of their main prey and the direct mortalityassociated with by-catch. However, it would positivelyimpact hake (2) and various demersal fish (2) likely dueto top-down effects or trophic cascades caused byremoval of predators (Christensen et al., 2004).Increases in Rapido and tuna fleets would have largenegative impacts on their main target species, while anincrease in Volante would imply large negative impactson non target species (e.g. dolphins). Negative impactsof Strascico, Volante and Rapido are also highlightedby the total MTI analysis (Fig. 11), where they rankedamong the most negatively impacting groups in theecosystem. On the contrary, Lampara appears to causethe lowest impacts on the ecosystem in terms of fishingactivity. Dolphins and marine turtles do not significantlycompete with fishing activity.

4. Discussion

4.1. Structure and functioning of the ecosystem

Results regarding trophic flows are in agreement withprimary production dynamics in the Mediterranean


basin (Bosc et al., 2004), showing higher values in theAdriatic and in coastal areas (Revellata Bay: Pinnegarand Polunin, 2004) than in the South Catalan Sea (Collet al., 2006a), but lower values than in other moreproductive areas from upwelling, tropical and temperateregions (e.g. Christensen and Pauly, 1993; Jarre-Teichmann, 1998; Guénette et al., 2001; Pinnegar andPolunin, 2004; Sánchez and Olaso, 2004; Moloneyet al., 2005). In general terms, trophic flows (consump-tion, production, respiration and flow to detritus) andbiomasses show that the Adriatic ecosystem wasdominated by the pelagic compartment during the1990s, mainly in terms of plankton and small pelagicfish. This is in line with results from the South CatalanSea (Coll et al., 2006a).

Values of TE are within the range of values reportedin the literature (Lalli and Parsons, 1993; Pauly andChristensen, 1995). They highlight a good couplingbetween benthic and pelagic invertebrates and theirpredators (Baird et al., 1991). Moreover, higher valuesof mean TE related with the primary producer food webin comparison with values from detritus would suggest ahigher limitation of the ecosystem in terms of primaryproductivity. In addition, the lower value of TE betweenTL III and TL IV shows that there is a high proportion ofproduction which is not consumed within the system,mainly related to jellyfish and macrozooplanktongroups.

The decreasing trend of TE values from TL II to TLIV is in line with theoretical ecology (Lalli and Parsons,1993), where a decrease in TE with higher values of TLhas been described and explained through food chaininefficiencies. However, the increasing trend from TLIV to higher TLs is in contrast with the ecologicaltheory. A comparison performed between two standard-ized models representing the former exploited ecosys-tem of the Adriatic Sea and a non exploited ecosystemfrom Miramare marine protected area suggested thatfishing could be the cause of the anomaly in the energytransfer within the exploited food web (Libralato et al.,2005).

Results from the model also highlight an importantcoupling between the pelagic and demersal compart-ments mainly due to the link between detritus andbenthic and pelagic invertebrates. This is likely ex-plained by the shallow waters and the oceanographicfeatures of the area which create special ecologicalconditions related with water exchanges and recircula-tion of nutrients (Ott, 1992). For example, zooplanktonproduction that is directed to detritus is high and has animpact on benthic production, underlining the impor-tance of pelagic production for the maintenance of

benthic production within the ecosystem (Ölscher andFedra, 1977; Ott, 1992). Moreover, benthic inverte-brates are partially fished but there is an overall low useof their production, which is also converted intodetritus, favouring zooplankton dynamics further.This also indicates the importance of detritus fromthe benthic compartment in favour of planktonicproduction (Giordani et al., 1992). In this context,when parametrizing the model, zooplankton group hasbeen indicated to partially feed on detritus to indirectlysimulate its diet based in the microbial food web(Fonda Umani and Beran, 2003; Calbet and Saiz,2005). However, if the diet of zooplankton is importedinto the system instead of being referred to as detritus,the importance of detritus decreases. This can be anindirect and quantitative measure of the importance ofthe microbial food web in zooplankton dynamicswithin the Adriatic Sea. Results from the model aresimilar to the ones previously quantified in the AdriaticSea (Vichi et al., 1998; Zavatarelli et al., 2000; FondaUmani and Beran, 2003).

Shallow waters and oceanographic features aside,the link between detritus and invertebrates could bepartially enhanced directly or indirectly by fishingactivity. Fishing could generate important amounts ofdiscards that may be converted to benthic detritus; orcould enhance re-suspended organic matter, favouringdemersal filter feeders with a wide trophic spectrum (ashas been reported in the Venice lagoon with Tapesphilippinarum, Libralato et al., 2002). Furthermore, animportant bentho-pelagic coupling has also beendescribed as being partially characteristic of highlyexploited ecosystems. For example, this was found tobe an important feature of the Namibian upwellingecosystem after depletion of its stocks, particularly itssmall pelagic fish stocks, due to industrial fishing andenvironmental events, an important feature that appearsto set this system apart from other upwellingecosystems (Moloney et al., 2005), showing somesimilarities with the South Catalan Sea (Coll et al.,2006b). In the Mediterranean Sea this could beindicative of the fishing pressure to which the areahas been subjected from ancient times (Margalef,1985).

4.2. Trophic flows and trophic levels by functionalgroup

Trophic levels of fish species and groups are inagreement with previous results for the MediterraneanSea (Stergiou and Karpouzi, 2002) and are lower thanvalues from other exploited temperate areas (e.g.


Sánchez and Olaso, 2004). TLs obtained for dolphins,seabirds and demersal sharks are similar to thosepreviously recorded (Hobson et al., 1994; Pauly et al.,1998b; Cortés, 1999).

Various functional groups in the model areimpacted by the groups at the base of the food websuch as phytoplankton, zooplankton and benthicinvertebrates. This could be related to possiblebottom-up flow control situations occurring in theecosystem (Hunter and Price, 1992). The wide impactthat anchovy, and also sardine and medium-sizedpelagic fish with lower importance, have on numerousfunctional groups of higher and lower TLs is mostlikely highlighting the importance of these groups inthe ecosystem. This would possibly be related withbottom-up and wasp-waist flow control situations(Rice, 1995; Cury et al., 2000). A marginal top-down control of forage fish by predator populations(dolphins and large pelagic fish) within the system isidentified. This is in agreement with the long historyof fishing activity in the region that will have stronglyreduced the biomass of top predators (Bearzi et al.,2004), resulting in the fishing fleets acting as toppredators.

Although results showed that no clear keystonespecies would be identified from the model (nonspecies have keystoneness value close or grater thanzero, Libralato et al., 2006), results also underline theimportant role of low trophic level organisms andsmall pelagic fish in the system. These results arecoherent with MTI results and similar to the onesobtained for the South Catalan Sea (Palomera et al.,accepted for publication), but with sardine being moreimportant than anchovy. Moreover, when comparingthese results with previously published ones (Libralatoet al., 2006), it has to be highlighted that zooplankton,anchovy and benthic invertebrates have been identifiedas keystone groups in the Northern Benguela upwell-ing (Jarre-Teichmann and Christensen, 1998), and invarious other upwelling regions. Zooplankton is also akeystone group of coastal and semi-closed marineenvironments (e.g. Chesapeake Bay, Georgia Strait,Boliano reef). The Interaction Strength Indicatorproposed by Shannon and Cury (2003) also showeda high importance of small pelagic fish in the SouthernBenguela ecosystem, supporting previous results forupwelling regions. These results highlight the impor-tance of the medium-low trophic levels in thefunctioning of these systems, due to their essentialrole in capturing energy and making it available to thehigher trophic levels. Both the Adriatic and the SouthCatalan Sea are limited by primary production (Bosc

et al., 2004) and they are under the influence of localenrichments (e.g. due to river runoff or wind episodes)that produce temporal upwelling areas, associatedwith higher productivity areas (Estrada, 1996; Salat,1996; Agostini and Bakun, 2002; Lloret et al., 2004;Santojanni et al., 2006).

On the other hand, the identification of dolphins as akeystone group of the Northern and Central Adriatic Seais related to the fact that this group is a non-exploited (ifby-catch is excluded) top predator within the system.Cetaceans are also keystone groups within Californiaupwelling, and in temperate ecosystems such as theAzores, the Gulf of Biscay, Newfoundland and theNorwegian Barents Sea. The importance of cetaceans askeystone groups has decreased in different periods oftime in various systems (e.g. California upwelling,Libralato et al., 2006). Thus, this group could be used asan ecological indicator to monitor fishing impact in theAdriatic Sea.

Generally, keystoneness results, the dominance of thepelagic compartment in the system in terms of trophicflows, the abundance of small pelagic fish in terms ofbiomass and catches and the possible wasp-waistpredator–prey interactions are features that usuallycharacterize upwelling ecosystems (Jarre-Teichmann,1998; Cury et al., 2000; Shannon et al., 2003, Heymanset al., 2004; Moloney et al., 2005). However, theNorthern and Central Adriatic Sea is limited by pro-duction (high TE and EE values) and both primaryproduction and detritus are more intensively used withinthe system than in typical upwelling regions.

Other important results emphasize the relevance ofgelatinous zooplankton in terms of consumption,production and flow to detritus within the modelledecosystem. The relevance of gelatinous plankton in theconsumption of production is also an important featureof the Namibian upwelling (Moloney et al., 2005). In theNamibian system, the proliferation of jellyfish appearedafter the collapse of sardine fisheries in the 1960–1970s.The proliferation of some jellyfish species in theAdriatic Sea since the 1980s has been also described(e.g. Rottini-Sandrini and Stravisi, 1981; Zavodnik,1991; Arai, 2001; Mills, 2001), in parallel with thedecrease of small pelagic fish. In the Black Sea, over-fishing and eutrophication have been related withmassive development of jellyfish during the 1970s and1980s (Daskalov, 2002).

4.3. Fishing activity and impact

The recorded mean trophic level of the catch(TLc) is low and similar to that obtained from the


North and South Catalan Sea in 1994 (TL 3.04–3.12:Tudela, 2000; Coll et al., 2006a), lower than thatcomputed for the Western Mediterranean in 1998(TL=3.25, Pinnegar et al., 2003), and slightly higherthan that obtained for the whole Mediterranean basinin 1994 (TL=3.0, Pauly et al., 1998a). This indicatorreflects the composition of catches (Fig. 3) whichmainly comprise small pelagic fish and invertebrates(Fig. 2).

An increase in discards is an indicator of a decreasein the sustainability of a fishing activity (Lleonart,2000). In the case of the Northern and CentralAdriatic Sea, discards represent 30% of total catches(Section 2.3). This value is high and in agreementwith other values previously calculated from variousexploited Mediterranean ecosystems (Sánchez et al.,2004). The primary production required to sustainthe fishery (PPR%) taking into account only theprimary producers is within the range of values fromtemperate shelves recorded by Pauly and Christensen(1995). When considering the primary producers andthe detritus, the PPR(%) value is higher pointing atthe importance of detritivorous organisms within thecatch.

The gross efficiency of the fishery (GEf) from theAdriatic Sea is high if compared with other modelledsystems and the mean of 0.0002 reported for globaldata (Christensen et al., 2004). Moreover, theecotrophic efficiencies and mortality rates suggestthat the ecosystem is highly constrained by predators(i.e. natural predators and the fishery). In this context,although predation mortality is high for most groupsof the model as has been proven to occur in marineecosystems even under heavy fishing regimes (Jarre-Teichmann, 1998), fishing mortality is very high forsome groups. The exploitation rates in the case ofanglerfish, hake, turbot and brill, red mullets and othergadiformes are higher than the recommended value of0.8 for ground fish stocks (Mertz and Myers, 1998).Values of F/Z for anchovy are high but lower than themaximum recommended of 0.5 for pelagic fish species(Patterson, 1992). This is in agreement with resultsfrom the partitioning of consumption of fishableorganisms and the overfished status of various ex-ploited resources.

Fishing impacts per fleet indicate large impacts onthe principal components of the ecosystem. Strascicois the least selective gear, has the widest-rangingimpact on the different functional groups of theecosystem and the largest impacts on some demersaltarget and non-target groups. The ecosystem impactsof Rapido and Volante are also high. Fishing fleets

also have a high impact on vulnerable species, mainlymarine turtles and cetaceans. On the contrary, neitherthe consumption of fished production nor the mixedtrophic impact analysis suggest significant competitionbetween vulnerable species (cetaceans, seabirds andturtles) and fishing activity, in agreement with resultsfrom the South Catalan Sea (Coll et al., 2006a).

Ecological indicators related to the theory of eco-system development (Margalef, 1968; Odum, 1969,Christensen, 1995), and the comparison of values withother previously modelled ecosystems, would suggestthat the Northern and Central Adriatic Sea ecosystem isat an intermediate-low developmental stage (Table 4).For example, the total primary production/respirationratio (Pp/R=2.73) indicates that there is more energyproduced than respired within the system and the totalprimary production/total biomass ratio (Pp/B=8.82) isalso high indicating a low level of biomass accumula-tion compared with primary production. The SystemOmnivory Index (SOI=0.19), which is related withecosystem complexity, shows low values indicating alineal tendency in the food web (Christensen, 1995). Inaddition, the ecosystem shows moderate rates of totalcycling in terms of Fin's cycling index (FCI=14.70%),low values of predatory cycling index (PCI=3.97%)and low values of Ascendency (A=27.0%). Theseresults could be, at least partially, related to the highfishing intensity in the ecosystem, and are in accor-dance with results obtained from the application of anew index of ecosystem impact of fishing based on theloss in secondary production due to fishing (Libralato etal., 2005). This indicator highlighted that the situationof the ecosystem in the 1990s signalled a medium tohigh risk of ecosystem overfishing sensu Murawski(2000).

5. Conclusions

The ecological model of the Northern and CentralAdriatic Sea represents an important effort to integratethe available biological data from the area into acoherent format and it creates the baseline uponwhich new data can be added to improve model re-sults. Further efforts to better characterize key ele-ments of the ecosystem, such as the trophic niche ofsardine and medium-sized pelagic fish and datarelated with benthopelagic fish, macrozooplankton,suprabenthos and gelatinous zooplankton could be animportant step forward towards the characterization ofthe ecosystem.

This contribution constitutes the first attempt atmodelling the wide continental shelf food web of


the Northern and Central Adriatic Sea, consideringlow to high trophic levels, and at quantifying theecosystem effects of fishing. The model highlightsimportant features in terms of the structure andfunctioning of the ecosystem, such as the highproportion of production used within the system, thetight link between detritus and invertebrates, thekeystone role of low and medium trophic levels ofthe ecosystem and of dolphins and the importance ofjellyfish.

Global results in terms of fishing activity indicatethat the ecosystem was subjected to a wide impacton its principal components (with TL≥ II) during the1990s. This factor may have certainly contributed tomodifying the structure and functioning of thisecosystem, in addition to the changes induced byoceanographic features and other kinds of distur-bance. Especially important is the fact that smallpelagic fish are intensively fished and are key ele-ments in the Northern and Central Adriatic Sea foodweb.

Acknowledgements

This work has been possible under a bilateral Co-operative-Agreement between CNR (Italy) and CSIC(Spain). During the work, M.C. was supported finan-cially by a FPI fellowship from theMYCTof the SpanishGovernment. The authors wish to acknowledge allthose colleagues that provided data and technical advicefor the development of this work, in particular colleaguesfrom the Istituto di Scienze Marine (CNR), Sede diAncona: A. Artegiani, M. Azzali, N. Cingolani, G. Fabi,C. Froglia, M.E. Gramitto; and A. Russo (UniversitàPolitecnica delle Marche), A. Di Natale (Aquastudio), S.Fonda Umani (Università di Trieste), D. Holcer (NaturalHistory Museum of Zagreb), S. Libralato (IstitutoNazionale di Oceanografia e di Geofisica Sperimentale,Trieste), C. Piccinetti (Università di Bologna), R.Santolini (Università di Urbino) and M. Zavatarelli(Università di Bologna). Moreover, the authors wish tothanks E.B. Morello and L.J. Shannon for usefulcomments on the manuscript.

Appendix A

Table 1Input data and references by functional group for the Northern and Central Adriatic Sea model

Functional group O
riginal value S ource Observations
1. Phytoplankton
Biomass 3 2.66 mg de Cl-a·m−2
(16.66 t·km−2)Z
avatarelli et al. (1998) ConversionfactorusedtotransformunitsofCl-a·m−2to
carbon units·m−2 and organic matter units·km−2 fromJorgensenet al. (1991)andDalsgaardandPauly (1997)

P/B 1
15–330 gC·m−2
(11.61–33.02 yr−1)HN
eilmann and Richardson, 1996;ew Jersey University database
Conversion factor used to transform carbonunits·m−2 to organic matter units·km−2 fromDalsgaard and Pauly (1997)

2. Micro- and mesozooplankton
Biomass M icro: 0.55 gC·m−2;
meso: 0.55 gDW·m−2

(9.512 t·km−2)

RB
evelante and Gilmartin, 1983;enovic et al., 1984; Benovic, 2000
Used conversion factor to transform units ofmgC/l to g·m−2 of 1.1 (M. Alcaraz, personalcommunication). Conversion factor from dry weightto wet weight: 13.48% (Jorgensen et al., 1991)

P/B 3
9.02 gWW·m−2·yr−1
(20.87 yr−1)B
enovic (2000) Conversion factor from dry weight to wet weight:
13.48% (Jorgensen et al., 1991)
Q/B 4 9.87 yr−1 P innegar (2000) Data modified to consider area's temperature with
an empirical equation from Opitz, 1996
Trophic data S olic et al., 1998; Fonda Umani
and Beran, 2003
Indirect consideration of food intake of micro- andmesozooplankton from the microbial food web
3. Macrozooplankton E
uphausids, mysids Biomass 0 .54 t·km−2 H oenigman, 1963; Sipos, 1977;
Giacco, 1995
Dimensions of organisms N5 mm.Data from 1992 to 1993.
P/B 2
1.28 yr−1 L abat and Couzin-Roudy (1996) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Q/B 5
3.14 yr−1 B aamstedt and Karlson (1998) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Trophic data C
asanova, 1974; Baamstedtand Karlson, 1998


(continued)Appendix A Table 1 (continued )

Functional group O
4. Jellyfish P
elagia noctiluca Biomass 0 .15–0.42 ind·m−2
(1.9–6.04 g·m−2)PB
iccinetti and Piccinetti Manfrin, 1984;enovic and Bender, 1987; Benovic
and Lucic, 1996

Mean body weight of Pelagia noctiluca of 14.48 g(Malej, 1989)

P/B 1
4.60 yr−1 M alej (1989) Q/B 5 0.48 yr−1 M alej (1989) Trophic data Z avodnik (1991)
5. Suprabenthos A
mphipods, cumaceans, isopods Biomass 1 .01 g·m−2 T angorra, 1984; Giacco, 1995 From 50 to 200 m depth P/B 8 .40 yr−1 C artes and Maynou (1998) Data modified to consider area's temperature with
Q/B 5 4.36 yr−1 C artes and Maynou (2001) Data modified to consider area's temperature with
Trophic data C artes et al. (2001)
6. Polychaetes
Biomass 9 .984 t·km−2 A AVV, 1994; Moodley et al., 1998 Used conversion factor from dry weight to wet
weight of 15% (Rumohr et al., 1987)
P/B 1 .90 yr−1 M oodley et al. (1998) Used conversion factor from dry weight to wet
Q/B 1 1.53 yr−1 A rreguín-Sánchez et al. (1993) Data from the Florida shelf corrected to consider
differences of temperature between areas with theequation of Opitz, 1996

Trophic data F
auchald and Jumars (1979)
7. Commercial benthic invertebrates C
hlamys opercularis, Pecten jacobaeus, Cassidaria echinophora Biomass 0 .043 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
1999) in the Adriatic Sea (ISMAR–CNR, Italy)
P/B 1 .06 yr−1 M oodley et al. (1998) Used conversion factor from dry weight to wet
Q/B 3 .13 yr−1 P auly et al., 1993; Mackinson et al.,
2000
Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Trophic data R
iedl (1986)
8. Benthic invertebrates E
chinodermata, Mollusca, Crustacea Biomass 7 9.73 g·m−2 A AVV, 1994; Giacco, 1995;
Simunovic, 1997; Moodley et al.,1998

Used conversion factor from dry weight to wetweight of 3.40% for molluscs, 1.90% forechinoderms and 4.30% for crustaceans and otherinvertebrates (D. Escobar, unpublished data)

P/B 1
.06 yr−1 M oodley et al. (1998) Used conversion factor from dry weight to wetweight of 15% (Rumohr et al., 1987)
Q/B 3
.13 yr−1 P auly et al., 1993; Mackinson et al.,2000

Trophic data M
illar, 1971; Rodríguez, 1972; Perronand Turner, 1978; Fauchald and Jumars,1979;Wurzian, 1984; Riedl, 1986;TaylorandMiller,1988;LalliandGilmer,1989; Coulon and Jangoux, 1993
9. Shrimps A
lpheus glaber,Melicertus kerathurus, Parapenaeus longirostris, Sergestes arcticus, Solenocera membranacea Biomass 0 .001 t·km−2 S imunovic, 1997; ISMAR–CNR
database
Biomass estimates from trawling surveys (1990–1999) in the Adriatic Sea (ISMAR–CNR, Italy)
P/B 3
.21 yr−1 F roglia, 1975; Froglia and Gramitto,1987; Marano et al., 1998; AAVV, 2003
Q/B 7
.20 yr−1 M aynou and Cartes (1998) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Trophic data C
artes, 1991, 1993
(continued on next page)



Functional group O
10. Norway lobster N
ephrops norvegicus Biomass 0 .018 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 1 .25 yr−1 F roglia and Gramitto, 1981a; Froglia
and Gramitto, 1988; Marrs et al., 2000
Q/B 4 .56 yr−1 S ardà and Valladares (1990) Data modified to consider area's temperature with
Trophic data W ieczorek et al. (1999)
11. Mantis shrimp S
quilla mantis Biomass 0 .015 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 1 .50 yr−1 I RPEM–CNR (1993a) Q/B 4 .56 yr−1 S ardà and Valladares (1990) Data modified to consider area's temperature with
Trophic data F roglia and Giannini (1989)
12. Crabs C
alocaris macandreae, Goneplax rhomboides, Homarus gammarus, Jaxea nocturna, Liocarcinusdepurator, Macropipus tuberculatus, Macropodia longipes, Maja squinado, Monodaeus couchii,Munida intermedia, Pagurus excavatus, Pagurus prideaux, Rissoides desmaresti
Biomass 0
.009 t·km−2 S imunovic, 1997; ISMAR–CNRdatabase
Biomass estimates from trawling surveys (1990–1999) in the Adriatic Sea (ISMAR–CNR, Italy)

P/B 2
.44 yr−1 F roglia, 1975; Gramitto and Froglia,1998
Q/B 4
.73 yr−1 M aynou and Cartes (1998) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Trophic data C
artes, 1991, 1993; Freire, 1996; Pinnet al., 1998; Wieczorek et al., 1999
13. Octopuses E
ledone cirrosa, E. moschata, Octopus vulgaris, Scaeurgus unicirrhus, Sepia elegans, S. officinalis, S.orbignyana, Sepietta oweniana, Sepiolidae
Biomass 0
.068 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–1999) in the Adriatic Sea (ISMAR–CNR, Italy)
P/B 2
.96 yr−1 M ackinson et al. (2000) Data from the Florida shelf model corrected toconsider differences of temperature between areaswith the equation of Opitz, 1996
Q/B 5
.30 yr−1 G uerra, 1979; Amaratunga, 1983;Mendoza, 1993;

Trophic data S
ánchez, 1981; Castro and Guerra,1990; Quetglas et al., 1998; Bello andPiscitelli, 2000
14. Squids I
llex coindetii, Loligo vulgaris, Alloteuthis media Biomass 0 .020 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 3 .11 yr−1 M ackinson et al. (2000) Data from the Florida shelf model corrected to
consider differences of temperature between areaswith the equation of Opitz, 1996

Q/B 2
6.47 yr−1 G uerra, 1979; Amaratunga, 1983;Mendoza, 1993;

Trophic data S
ánchez, 1982; Pierce et al., 1994;Rasero et al., 1996
15. Hake (1) M
erluccius merluccius Vulnerable to fishing gears from the area Biomass 0 .0603 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 1 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Zupanovic and Jardas, 1986; Vrgoc et al., 2004 Q/B 4 .24 yr−1 E mpiricalequationfromPaulyetal.,1990 Trophic data F roglia (1973)



Functional group O
16. Hake (2) M
erluccius merluccius Non vulnerable to fishing gears from the area Biomass E stimated by themodel P/B 0 .5 yr−1 Z =F+M;M=empirical equation from Pauly, 1980; Zupanovic and Jardas, 1986; Caddy and Abella, 1999 Trophic data F roglia, 1973; Monti, 1979
17. Other gadids A
ntonogadus megalokynodo, Gadiculus argenteus, Gaidropsarus mediterraneus, Merlangius merlangus,Micromesistius poutassou,Phycis blennoides, Trisopterus minutus
Biomass 0
P/B 1
.59 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Giovanardi and Rizzoli, 1984; Froglia and Gramitto,1981b; Gramitto, 1985; Giannetti and Gramitto, 1993; Merella et al., 1997; Vrgoc et al., 2004; AAVV,2003; Sotiropolou, 1999
Q/B 4
.37 yr−1 E mpirical equation fromPauly et al., 1990
Trophic data M
acpherson, 1981; Gramitto, 1985;Lodi, 1989; Gramitto, 1999;Wieczorek et al., 1999
18. Mullets M
ullus barbatus, M. surmuletus Biomass 0 .025 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 1 .90 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Andaloro and Giarritta, 1985;
AAVV, 2003; Vrgoc et al., 2004
Q/B 8 .02 yr−1 E mpirical equation from
Pauly et al., 1990
Trophic data J ukic-Peladic, 1972; Aguirre, 2000
19. Conger eel C
onger conger Biomass 0 .005 t·km−2 I SMAR–CNR database Biomass
estimates from trawling surveys(1990–1999) in the Adriatic Sea(ISMAR–CNR, Italy)

P/B 1
.92 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Merella et al., 1997 Q/B 6 .45 yr−1 E mpirical equation from
Pauly et al., 1990
Trophic data M acpherson (1981)
20. Anglerfish L
ophius budegassa, L. piscatorius Biomass 0 .006 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 1 .04 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Vrgoc et al., 2004 Q/B 4 .58 yr−1 E mpirical equation from
Pauly et al., 1990
Trophic data Z upanovic and Jardas (1989)
21. Flatfishes A
rnoglossus kessleri, A. laterna, A. thori, Buglossidium luteum, Citharus linguatula, Lepidorhombusboscii, L. whiffiagonis, Microchirus variegatus, Monochirus hispidus, Pegusa kleini, Phrynorhombusregius, Platichthys flesus, Solea lascaris, S. vulgaris, Symphurus ligulatus, S. nigrescens,Synapturichthys kleinii
Biomass 0
.009 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys(1990–1999) in the Adriatic Sea(ISMAR–CNR, Italy)
P/B 1
.43 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Giovanardi and Piccinetti, 1981; Piccinetti andGiovanardi, 1983; Giovanardi and Piccinetti, 1984a,b; Bello and Rizzi, 1988; Castellarin et al., 1995;Dulcic and Kraljevic, 1996; Merella et al., 1997
Q/B 9
.83 yr−1 E mpirical equation fromPauly et al., 1990



Functional group O
21. Flatfishes A
rnoglossus kessleri, A. laterna, A. thori, Buglossidium luteum, Citharus linguatula, Lepidorhombus boscii,L. whiffiagonis, Microchirus variegatus, Monochirus hispidus, Pegusa kleini, Phrynorhombus regius,Platichthys flesus, Solea lascaris, S. vulgaris, Symphurus ligulatus, S. nigrescens, Synapturichthys kleinii
Trophic data M
acpherson, 1981; Giovanardi andPiccinetti, 1981, 1984a,b; Jardas,1984; Tito de Morais, 1986; Pellegriniand Barghigiani, 1989; Zupanovic andJardas, 1989; Morte et al., 1999a
22. Turbot and brill P
setta maxima, Scophthalmus rhombus Biomass 0 .001 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
1999) in the Adriatic Sea (ISMAR-CNR, Italy)
P/B 1 .43 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Arneri et al., 2001 Q/B 5 .43 yr−1 E mpirical equation from Pauly et al.,
1990
Trophic data I RPEM–CNR (1993b)
23. Demersal sharks M
ustelus asterias, M. mustelus, M. punctulatus, Scyliorhinus canícula, S. stellaris, Squalus acanthias Biomass 0 .018 t·km−2 I SMAR–CNR database Biomass estimates from trawling surveys (1990–
P/B 0 .63 yr−1 Z =F+M;M=empirical equation from Zupanovic, 1961; Jardas, 1979; Pauly, 1980; Dulcic and Kraljevic,
1996; Merella et al., 1997
Q/B 4 .47 yr−1 E mpirical equation from Pauly et al.,
1990
Trophic data J ardas, 1972a,b; Costantini et al., 2000
24. Demersal rays D
asyatis pastinaca, D. violacea, Myilobatis aquila, Raja asterias, R. clavata, R. miraletus,Torpedo marmorata
Biomass 0
P/B 1
.11 yr−1 Z =F+M; M=empirical equation from Zupanovic, 1961; Pauly, 1980; Merella et al., 1997 Q/B 7 .08 yr−1 E mpirical equation from Pauly et al.,
1990
Trophic data J ardas, 1972a; Capapé and Quignard, 1977;
Zupanovic and Jardas, 1989; Abdel-Aziz, 1994

25. Demersal fishes (1) A
cantholabrus palloni, Argentina sphyraena, Aspitrigla cuculus, Bellottia apoda, Blennius ocellaris,Callionymus maculatus, Carapus acus, Cepola rubescens, Diplodus annularis, D. vulgaris, Echiodondentatus, Eutrigla gurnardus, Gnathophis mystax, Gobius niger, Helicolenus dactylopterus, Lepidopuscaudatus, Lepidotrigla cavillone, Lepidotrigla diezeudei, Lesueurigobius friesi, Lithognathus mormyrus,Ophidion barbatum, Pagellus acarne, P. bogaraveo, P. erythrinus, Pagrus pagrus, Peristedion cataphractum,Sciaena umbra, Scorpaena notata, S. porcus, Serranus hepatus, Sparus aurata, Symphodus sp., Trachinusdraco, Trachinus radiatus, Trigla lyra, T. lucerna, Trigloporus lastoviza, Umbrina cirrosa
Biomass 0
P/B 2
.40 yr−1 Z =F+M; M=empirical equation fromFroglia, 1976a; Pauly, 1980; Andaloro,1983; Fabi and Froglia, 1984; Kraljevicet al., 1995, 1996; Dulcic andKraljevic, 1996; Merella et al., 1997;Marsan et al., 1998; Vrgoc et al., 2004
Q/B 7
.68 yr−1 E mpirical equation from Pauly et al., 1990 Trophic data V ives et al., 1959; Jukic-Peladic, 1972; Froglia, 1976a,b; Gibson and
Ezzi, 1979; Macpherson, 1981; Bell and Harmelin-Vivien, 1983; Jardasand Zupanovic, 1983; Moreno and Matallanas, 1983; Patzner, 1983;Rosecchi, 1983; Harmelin-Vivien et al., 1989; Papaconstantinou et al.,1989; Pallaoro and Jardas, 1991; Moreno-Amich, 1994; Froglia andGramitto, 1998; Labropoulou and Papadopoulou-Smith, 1999; Maranoet al., 1999; Morte et al., 1999b; Terrats et al., 2000; Morato et al., 2001



Functional group O
26. Demersal fishes (2) D
eltentosteus quadrimaculatus, Dentex dentex, Dicentrarchus labrax, Maurolicus muelleri,Pomatoschistus sp., Scorpaena scrofa, Serranus cabrilla, Uranoscopus scaber, Zeus faber
Biomass 0
P/B 2
.40 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Dulcic and Kraljevic, 1996; Merella et al., 1997 Q/B 5 .68 yr−1 E mpirical equation from Pauly et al.,
1990
Trophic data J ardas, 1973; Scotto di Carlo et al.,
1982; Zander, 1982; Froglia andGramitto, 1998; Labropoulou andPapadopoulou-Smith, 1999

27. Benthopelagic fishes A
therina boyeri, Capros aper, Chlorophthalmus agassizi, Hoplostetus mediterraneus,Macroramphosus scolopax, Oblada melanura, Spicara flexuosa, Spicara maena, Spicara smaris
Biomass 0
P/B 1
.07 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Dulcic et al., 2000; Duli et al., 2003 Q/B 7 .99 yr−1 E mpirical equation from Pauly et al.,
1990; Merella et al., 1997; Dulcic etal., 2000; Duli et al., 2003

Trophic data M
acpherson, 1981; Scotto di Carloet al., 1982; Khoury, 1984
28. European anchovy E
ngraulis encrasicolus Biomass 1 .02–6.611 t·km−2 C ingolani et al., 2002a; Azzali et al.,
2002; Santojanni et al., 2003
Biomass estimated from acoustic surveys andvirtual population analysis
P/B 0
.87 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Sinovcic, 2003; Santojanni et al., 2003 Q/B 1 1.02 yr−1 T udela and Palomera (1995) Trophic data T udela and Palomera (1997)
29. European pilchard S
ardina pilchardus Biomass 2 .985–7.804 t·km−2 C ingolani et al., 2002a,b; Azzali et al.,
2002; Santojanni et al., 2003
P/B 0
.75 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Sinovcic, 1984; Santojanni et al., 2001b Q/B 9 .19 yr−1 E mpirical equation from Pauly et al.,
1990
Trophic data D emirhindi (1961)
30. Small pelagic fishes A
losa fallax, Boops boops, Sardinella aurita, Sphyraena sphyraena, Sprattus sprattus Biomass 0 .413–1.517 t·km−2 A zzali, 2002; Azzali et al., 2002; A.
Santojanni, unpublished data
Biomass estimated from acoustic surveys andvirtual population analysis. Complemented withbiomass estimates from trawling surveys (1990–1999) in the Adriatic Sea (ISMAR–CNR, Italy)
P/B 1
.10 yr−1 Z =F+M; M=empirical equation fromPauly, 1980; Alegria Hernández, 1989; Merella et al., 1997; Sinovcic et al., 2004a,b
Q/B 1
1.29 yr−1 E mpirical equation from Pauly et al.,1990
Trophic data A
ndreu and Rodriguez-Roda, 1951;Ticina et al., 2000
31. Horse mackerel T
rachurus mediterraneus, T. picturatus, T. trachurus Biomass 0 .659–2.455 t·km−2 A rneri andTangerini, 1984;Azzali et al.,
2002; A. Santojanni, unpublished data
P/B 0
.99 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Arneri and Tangerini, 1984; Alegria Hernández,1984; Merella et al., 1997; Šantic et al., 2003;Jardas et al., 2003
Q/B 7
.57 yr−1 E mpirical equation from Pauly et al.,1990



Functional group O
31. Horse mackerel T
rachurus mediterraneus, T. picturatus, T. trachurus Trophic data A legria Hernández, 1984; Ben Salem,
1988;Kyrtatos,1998a,b;Šanticetal.,2003

32. Mackerel S
comber japonicus, S. scombrus Biomass 0 .452–1.683 t·km−2 A zzali et al., 2002; A. Santojanni,
unpublished data
P/B 0
.99 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Sinovcic, 2001; Sinovcic et al., 2004b Q/B 6 .09 yr−1 E mpiricalequationfromPaulyetal.,1990 Trophic data M oreno and Castro (1995)
33. Atlantic bonito S
arda sarda Biomass 0 .3 t·km−2 L leonart (1990) VPA assessment from the Catalan Sea P/B 0 .39 yr−1 Z =F+M; M=empirical equation from Pauly, 1980; Lleonart, 1990; Sinovcic et al., 2004b Q/B 4 .54 yr−1 E mpiricalequationfromPaulyetal.,1990 Trophic data L leonart (1990)
34. Large pelagic fishes T
hunnus thynnus, Xiphias gladius Biomass 0 .138 t·km−2 I CCAT, 2003, 2004 Xiphias gladius and Thunnus thynnus,
global stocks of the Mediterranean Sea
P/B 0 .37 yr−1 Z =F+M; M=empirical equation from Scaccini, 1965; Pauly, 1980; Campillo, 1992; Tserpes and
Tsimenides, 1995; Sinovcic et al., 2004b
Q/B 1 .99 yr−1 E mpirical equation from
Pauly et al., 1990
Trophic data M orovic, 1961; Orsi Relini et al., 1995
35. Dolphins T
ursiops truncatus Biomass 0 .029 ind·km−2 V irno Lamberti, 1992; Azzali,
unpublished data
Data from visual surveys around the area. Units ofindividuals·km−2 has been transformed to t·km−2
with the mean body weight per species(Carwardine and Camm, 1998)

P/B 0
.08 yr−1 M ackinson et al. (2000) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Q/B 1
1.01 yr−1 I nnes et al., 1987; Trites et al., 1997 Trophic data M iokovic et al., 1999;Blanco et al., 2001
36. Loggerhead turtle C
aretta caretta Biomass 0 .664 ind·km−2 L azar and Tvrtkovic, 1995; Gómez de
Segura et al., 2003
with the mean body weight per species (Merchánand Martínez, 1999)

P/B 0
.17 yr−1 M ackinson et al. (2000) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Q/B 2
.54 yr−1 P olovina (1984) Data modified to consider area's temperature withan empirical equation from Opitz, 1996
Trophic data G
odley et al., 1997; Tomas et al., 2001
37. Sea birds P
halacrocorax carbo, P. pygmeus, Gelochelidon nilotica, Sterna sandvicensis, S. hirundo, S. albifrons,Larus melanocephalus, L. ridibundus, L. genei, L. cachinnans, L. argentatus, L. canus, L. fuscus,Puffinus yelkouan, Calonectris diomedea
Biomass 0
.001 t·km−2 B arbieri, 1986; Fasola et al., 1989;Fasola and Canova, 1992; Thibault,1993; Boldreghini et al., 1997a;Stipcevic et al., 1998; Baccetti et al.,2002; Serra and e Brichetti, 2002;Brichetti and Fracasso, 2003; R.Santolini, personal communication

with the mean body weight per species (Del Hoyoet al., 1992)

P/B 4
.61 yr−1 P innegar (2000) Data modified to consider area's temperature withan empirical equation from Opitz, 1996



Functional group O
37. Sea birds P
halacrocorax carbo, P. pygmeus, Gelochelidon nilotica, Sterna sandvicensis, S. hirundo, S. albifrons,Larus melanocephalus, L. ridibundus, L. genei, L. cachinnans, L. argentatus, L. canus, L. fuscus,Puffinus yelkouan, Calonectris diomedea
Q/B 6
9.34 yr−1 N ilsson and Nilsson (1976) Trophic data F asola et al., 1989; Bogliani et al., 1992;
Grieco, 1994; Oro, 1996; Boldreghiniet al., 1997b; Granadeiro et al., 1998

38. Detritus
Biomass 0 .001 t·km−2 M oodley et al., 1998 Used conversion factor to transform units of carbon
units to organic matter (Parsons et al., 1977)

Table 2Codes and names of fish species used during the FactorialCorrespondence Analysis (Figs. 4 and 5)

Code
Scientific name
Aspi_cuc
Aspitrigla cuculus Call_mac Callionymus maculatus Capr_ape Capros aper Cepo_rub Cepola rubescens Trig_luc Trigla lucerna Chlo_aga Chlorophthalmus agassizi Delt_qua Deltentosteus quadrimaculatus Dent_den Dentex dentex Dipl_ann Diplodus annularis Dipl_vul Diplodus vulgaris Eutr_gur Eutrigla gurnardus Gnat_mys Gnathophis mystax Gobi_nig Gobius niger Heli_dac Helicolenus dactylopterus Lepi_cau Lepidopus caudatus Lepi_cav Lepidotrigla cavillone Lesu_fri Lesueurigobius friesii Macr_sco Macroramphosus scolopax Maur_mue Maurolicus muelleri Obla_mel Oblada melanura Ophi_bar Ophidion barbatum Page_aca Pagellus acarne Page_bog Pagellus bogaraveo Page_ery Pagellus erythrinus Pagr_pag Pagrus pagrus Poma_spp Pomatoschistus spp. Scia_umb Sciaena umbra Scor_not Scorpaena notata Scor_por Scorpaena porcus Scor_scr Scorpaena scrofa Serr_cab Serranus cabrilla Serr_hep Serranus hepatus Spar_spp Sparus spp. Spic_spp Spicara spp. Syph_spp Symphodus spp. Trac_dra Trachinus draco Trig_las Trigloporus lastoviza Trig_lyr Trigla lyra Umbr_cir Umbrina cirrosa Uran_sca Uranoscopus scaber Zeus_fab Zeus faber
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