De

Ma

b

c

a

ARRAA

KFETEEKTW

1

ietapefbl

O

0d

Ecological Modelling 220 (2009) 2088–2102

Contents lists available at ScienceDirect

Ecological Modelling

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

ecadal changes in a NW Mediterranean Sea food web in relation to fishingxploitation

arta Coll a,b,∗, Isabel Palomeraa, Sergi Tudelac

Institute of Marine Science (ICM-CSIC), Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainDalhousie University, Department of Biology, 1355 Oxford Street, Halifax, Nova Scotia B3H 4J1, CanadaWWF Mediterranean Programme Office, c/Canuda, 37, 08002 Barcelona, Spain

r t i c l e i n f o

rticle history:eceived 17 March 2008eceived in revised form 15 March 2009ccepted 24 April 2009vailable online 17 June 2009

eywords:ood-web modelcopath with Ecosimrophodynamic indicatorscosystem structurecosystem functioningeystone speciesransfer efficiencyestern Mediterranean

a b s t r a c t

We analysed changes in the ecological roles of species, trophic structure and ecosystem functioning usingfour standardized mass-balance models of the South Catalan Sea (North-western Mediterranean). Modelsrepresented the ecosystem during the late 1970s, mid 1990s, early 2000s, and a simulated no-fishing sce-nario. The underlying hypothesis was that ecosystem models should quantitatively capture the increasingexploitation in the ecosystem from the 1970s to 2000s, as well as differences between the exploited andnon-exploited scenarios. Biomass showed a general decrease, while there was an increase in biomass atlower trophic levels (TL) from the 1970s to 2000s. The efficiency of energy transfer (TE) from lower tohigher TLs significantly increased with time. The ecosystem during the 1990s showed higher biomassand flows than during the 1970s and 2000s due to an increase in small pelagic fish biomass (especiallysardines). Exploited food webs also showed similarities in terms of general structure and functioning dueto high intensity of fishing already in the 1970s. This intensity was highlighted with low trophic levelsin the catch, high consumption of production by fisheries, medium to high primary production requiredto sustain the catches and high losses in secondary production due to fishing. Significant differences onecosystem structure and functioning were highlighted between the exploited and no-fishing scenarios.Biomass of higher TLs increased under the no-fishing scenario and the mean trophic level of the com-

munity and the fish/invertebrate biomass ratios were substantially lower in exploited food webs. Theefficiency of energy transfer (TE) from lower to higher TLs was lower under the no-fishing scenario, andit showed a continuous decrease with increasing TL. Marine mammals, large hake, anglerfish and largepelagic fish were identified as keystone species of the ecosystem when there was no fishing, while theirecological importance notably decreased under the exploited periods. On the contrary, the importance ofsmall-sized organisms such as benthic invertebrates and small pelagic fish was higher in exploited food webs.

. Introduction

The Mediterranean is a region of outstanding importancen terms of biodiversity: it harbours important populations ofndemic species, as well as endangered species such as tur-les and cetaceans, and provides unique critical habitats (Bianchind Morri, 2000). However, the Mediterranean Sea has sup-orted human civilizations for millennia, and evidence of the

xploitation of marine resources from ancient times can beound all around the basin (Margalef, 1985). Ecosystems haveeen altered in many ways due to the overexploitation of bio-

ogical resources, direct habitat modification, introduction of

∗ Corresponding author at: Dalhousie University, Department of Biology, 1355xford Street, Halifax, Nova Scotia B3H 4J1, Canada.

E-mail addresses: martacoll@yahoo.com, mcoll@icm.csic.es (M. Coll).

304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.04.049

© 2009 Elsevier B.V. All rights reserved.

exotic species, pollution and climate change (Bianchi and Morri,2000).

In particular, the development of fishing technologies, theexcess of fishing effort, and an increasing demand for marineresources is placing intensive pressure on Mediterranean marineecosystems. General assessments suggest that most commerciallyimportant demersal species are fully exploited or overexploited,while some commercially important pelagic species show over-exploitation trends (Farrugio et al., 1993; Aldebert and Recasens,1996; Sardà, 1998; Papaconstantinou and Farrugio, 2000; Bas etal., 2003; Palomera et al., 2007). The reconstruction of ecologicalchanges in the long-exploited and still biologically rich Mediter-

ranean Sea and the quantification of the ecological roles played bykey species and humans bring a unique opportunity to understandthe response of marine ecosystems facing human pressures.

The development of an ecosystem approach to marine resourcesin the Mediterranean Sea requires a broad ecological analysis con-

odelling 220 (2009) 2088–2102 2089

samtipti(tdptmtwfi

aelamaWvmittssd1aCttlr

Smiotaumatfirbbtbutidaolff

M. Coll et al. / Ecological M

idering anthropogenic activities and environmental factors withinn ecosystem context (SCMEE, 2005). Mass-balance ecosystemodels (Christensen and Walters, 2004) facilitate the characteriza-

ion of structural and functional ecosystem properties, while takingnto account fishing activities and the environment in the modellingrocess. In this context, the South Catalan Sea, an exploited ecosys-em in the NW Mediterranean Sea, is one of the first pilot studiesn the Mediterranean to implement mass-balance modelling toolsColl et al., 2006a,b). These applications represented the ecosys-em during the mid 1990s and showed that this ecosystem wasominated by the pelagic fraction, where sardines and anchoviesrevailed in terms of fish biomass, trophic flows and catches. Notehat the microbial food web was only indirectly included in the

odel. Detritus and detritivorous groups were also important inhe ecosystem and strongly coupled pelagic-demersal interactionsere described. The model also highlighted the high intensity ofshing.

The previous ecosystem model was calibrated and fitted to avail-ble time series of data from the 1978 to 2003 to explore to whatxtent historical changes in marine resources of the South Cata-an Sea were driven by trophic interactions, environmental factorsnd fishing activities (Coll et al., 2008a). The fitting procedureakes use of the best time series of data available to calibrate

nd validate mass-balance models (Christensen and Walters, 2004;alters and Martell, 2004). In general, a high proportion of the

ariability in the available time series of data was explained byain trophic interactions (37–53%), fishing activities (14%), and

ndirectly by considering the environment (6–16%) as driving fac-ors. Model predictions satisfactorily matched the independentime series of biomass for commercial species, which showed aignificant decrease with time, while the biomass of flatfish andeabirds increased. Predicted and empirical biomass data for sar-ines showed an increasing trend from the late 1970s to the early990s and then a steady decrease until the present due to fishingnd environmental factors acting together (Palomera et al., 2007;oll et al., 2008a). These overall changes in biomass were predictedo have direct and indirect impacts on other organisms mediated byhe food web such as the proliferation of non-commercial species inower trophic levels (e.g., benthic invertebrates) or higher turnoverates (e.g., cephalopods and benthopelagic fish).

Thus, previous process-oriented modelling performed on theouth Catalan Sea showed important changes in abundance ofarine resources during the last three decades, as well as changes

n the food web. These changes are analysed here in detail in termsf the ecological roles of species and ecosystem structure and func-ioning traits for different time periods using standardized modelsnd species-level to ecosystem-level trophodynamic indicators. Wese a comparative approach of model results using the availableodels during the late 1970s (Coll et al., 2008a), mid 1990s (Coll et

l., 2006a), and early 2000s (Coll et al., 2008b). Moreover, and dueo the long history of exploitation in the area, a hypothetical no-shing scenario was included in the comparison to put previousesults into a broader ecological context and provide an ecologicalaseline from were the structure and functioning of the system cane characterise without the fishing factor. This scenario attemptso represent a situation where fishing has been stopped and theiomass of commercial species has been left to recover so it enabless to further quantify the ecological role of fishing in the ecosys-em. Our hypothesis is that model results should reflect changesn abundance of marine resources during the last three decades,ue to increasing exploitation as described in Coll et al. (2008a),

s well as between the exploited and no-exploited scenarios (asbserved when a partial closure of a neighbouring areas was estab-ished during the 1960s, IMEDES, 1999). Therefore, we comparedour mass-balance models representing the ecosystem under dif-erent situations and we identified the most suitable model-derived

Fig. 1. South Catalan Sea study area (modified from Catalano-Balearic Sea-Bathymetric chart 2005, ICM).

indicators to pick up differences and similarities between foodwebs.

The comparative approach using models and indicators havebeen previously successfully applied to learn about the structureand functioning of marine ecosystems around the world, bothusing food webs representing different ecosystems subjected todifferent environmental and anthropogenic forcing (cross-systemcomparison), or representing different periods (temporal com-parison) (e.g., Heymans et al., 2004; Moloney et al., 2005; Collet al., 2006b; Mackinson et al., in press). Cross-system com-parisons are informative tools, as well, to calculate emergentproperties from marine ecosystems (Christensen, 1995; Pauly andChristensen, 1995; Libralato et al., 2008). In the Mediterranean Sea,the comparison of food-web models representing Mediterraneanand non-Mediterranean marine ecosystems (Coll et al., 2006b,2008c) or between protected and exploited areas (Libralato et al.,submitted for publication), evidenced the higher fishing impactsin the Mediterranean basin, and highlighted changes in the struc-ture and functioning in exploited food webs such as biomass andproduction allocation. In this context, this work further exploresthese changes representing the first comparisons in the Mediter-ranean Sea of an exploited food web in four different periods oftime, including a no-fishing scenario as an ecological baseline.

2. Materials and methods

2.1. Study area

The South Catalan Sea study area in the NW Mediterranean(Fig. 1) comprises the continental shelf and upper slope associ-ated with the Ebro River Delta, ranges in depth between 50 and400 m and covers a total area of soft bottom sediments of 4500 km2.This is mainly an oligotrophic area, where enrichment occurs dueto regional environmental events related to wind conditions, a tem-poral thermocline and a shelf-slope current with river discharges(Estrada, 1996). These episodes greatly influence the productivity

and fishing activity in the area and it is especially important forthe reproduction of small pelagic fishes (such as European anchovyEngraulis encrasicolus and sardine Sardina pilchardus, Palomera etal., 2007).

2 odelling 220 (2009) 2088–2102

itp2upcjm(

2

tco

dm1i1mpmnam

ofilet(w(

2

ca

(

Table 1Input (B, P/B, Q/B) by functional group of the South Catalan Sea ecosystem modelrepresenting the no-fishing scenario. B: Initial biomass (t km−2); P/B: produc-tion/biomass ratio (yr−1); Q/B: consumption/biomass ratio (yr−1).

Functional group B P/B Q/B

1 Phytoplankton 10.38 153.26 –2 Zooplankton 9.43 21.10 49.383 Macrozooplankton 0.50 20.34 50.784 Jellyfish 0.35 13.84 50.375 Suprabenthos 0.04 8.42 54.536 Polychaetes 15.84 1.83 11.567 Shrimps 0.04 3.02 7.048 Crabs 0.20 2.00 4.509 Norway lobster 0.04 1.23 4.66

10 Benthic invertebrates 8.54 1.03 3.1611 Benthic cephalopods 0.30 2.16 4.8912 Benthop. Cephalopods 0.32 2.08 26.7113 Mullets 0.17 1.38 4.1514 Conger eel 0.08 1.36 3.4015 Anglerfish 1.07 0.17 0.4416 Flatfishes 0.08 1.58 5.6417 Poor cod 0.05 1.44 6.6118 Juvenile hake 0.01 1.74 2.2519 Adult hake 6.92 0.02 0.4520 Blue whiting 1.15 0.65 5.9021 Demersal fishes (1)a 0.46 1.17 6.9322 Demersal fishes (2)a 0.03 0.98 6.9823 Demersal fishes (3)a 0.14 0.48 6.9024 Demersal sharks 0.09 0.41 5.3125 Benthopelagic fishes 0.18 1.38 9.0926 Anchovy 2.82 1.29 13.4327 Sardine 4.98 1.38 8.1728 Other small pelagics 0.58 0.54 7.6729 Horse mackerel 1.25 0.39 5.1830 Mackerel 0.66 0.45 4.7931 Atlantic bonito 0.47 0.34 4.2632 Large pelagics 0.79 0.24 0.9833 Marine turtles 0.02 0.16 2.4834 Audouins gull 0.002 4.63 69.9535 Other sea birds 0.001 6.03 69.9036 Dolphins 0.002 0.06 13.5837 Fin whale 0.34 0.04 4.0738 Discards (1) – – –

090 M. Coll et al. / Ecological M

Official landings from the study area increased extraordinar-ly from the beginning of the 19th century to the 1960s and fromhen to the 1990s, mainly due to the expansion of the fishery andublic incentives to the fishing sector (Garrido, 2006; Coll et al.,008a). Marked fluctuations in landings occurred from the 1970sntil they underwent a progressive decrease from the 1994. Smallelagic fishes such as sardine and anchovy constitute the principalomponent of the catch. The demersal fishery comprises mainlyuveniles of several species such as hake (Merluccius merluccius), red

ullet (Mullus barbatus) and Norway lobster (Nephrops norvegicus)Lleonart, 1993; Bas et al., 2003).

.2. Ecological modelling

Ecopath with Ecosim version 5 (EwE) open-source modellingool (Christensen and Walters, www.ecopath.org) was used for theonstruction of the four ecosystem models and the quantificationf indicators.

The mass-balance model for the late 1970s (details are fullyescribed in Coll et al., 2008a) was based on the model of theid 1990s (details in Coll et al., 2006a). This model from the mid

990s was calibrated with time series data using independent fish-ng effort and fishing mortality, biomass and catch data from the978 to 2003 (details are fully described in Coll et al., 2008a). Theodel of the early 2000s was obtained as a result of the calibration

rocess with Ecosim (details are provided in Coll et al., 2008b). Theodel of a hypothetical no-fishing scenario was obtained by run-

ing a simulation with Ecosim using the 1990s model and excludingll fishing activities during 25 years. Input parameters of this newodel are provided in Table 1.All the models comprised primary producers, the main species

f benthic, demersal and pelagic invertebrates, fishes and non-sh vertebrates and three detritus groups. Trawling, purse seine,

ong-line and troll bait fishing fleets were included in the param-terization of exploited food webs. Models were standardized sohey shared a similar structure organized into 40 functional groupsTable 2). Functional groups comprised species, groups of speciesith similar ecological traits or population fractions of species

such as juveniles and adults).

.3. Quantification of structure and dynamics by indicators

Trophic indicators were calculated from each model and thenompared. They included species to ecosystem-level indicators andre divided into:

(a) Ratios of biomass, production and catch by species groups: Wequantified the total biomass/total production (Bt/Pt), totalcatch/total biomass (Ct/Bt), total catch of non-commercialspecies/total catch (Cnc/Ct), biomass, production and catchof piscivorous species/total biomass, production and catch(Bpp/Bt, Ppp/Pt, Cpp/Ct), biomass, production and catch offorage species/total biomass, production and catch (Bpl/Bt,Ppl/Pt, Cpl/Ct), and total pelagic biomass, production andcatch/demersal biomass, production and catch (Bp/Bd, Pp/Pd,Cp/Cd).

b) The keystone species indicator: We applied a method for identi-fying keystone species derived from the mixed trophic impact(MTI) analysis and proposed by Libralato et al. (2006). Keystonespecies are those that show relatively low biomass but havea structuring role in the ecosystem (Power et al., 1996). The

Mixed Trophic Impact (MTI) analysis derived from economictheory (Leontief, 1951; Ulanowicz and Puccia, 1990) allows thequantification of direct and indirect trophic interactions amongfunctional groups for each model. This analysis provides a quan-tification of the positive or negative impact that a hypothetical

39 Discards (2) – – –40 Detritus 71.27 – –

a These groups contain demersal fishes and were defined in Coll et al. (2006a).

increase in the biomass of a group would produce on the othergroups in the ecosystem, including the fishery. Therefore, key-stone species can be identified by plotting the relative overalleffect (εi), calculated from the MTI, against the keystoneness(KSi). The overall effect (εi) is described as:

εi =

√√√√n∑

j /= i

m2ij

(1)

where mij is calculated from the MTI analysis as the productof all net impacts for all the possible pathways in the food-web linking prey, i, and predators, j. The keystoneness (KSi) of afunctional group is calculated as:

KSi = log[εi(I − pi)] (2)

where pi is the contribution of the functional group to the totalbiomass of the food web. This index is high when functionalgroups (species or groups of species) have both low biomassproportions within the ecosystem and high overall effects, inline with the keystone species definition.

(c) Trophic levels (TLi), mean trophic level of the community (mTLco)and mean trophic level of the catch (mTLc): The trophic level(TL) was first defined as an integer identifying the position oforganisms within food webs (Lindeman, 1942) and it was latermodified to be fractional (Odum and Heald, 1975). Following an

M.Collet

al./EcologicalModelling

220(2009)

2088–21022091

Table 2Outputs by functional group of the ecosystem models of the South Catalan Sea from the late 1970s, mid 1990s, early 2000s and under a no-fishing scenario.

FG Group name Late 1970s Mid 1990s Early 2000s No fishing

TL EE M2 F TL EE M2 F TL EE M2 F TL EE M2

1 Phytoplankton 1.00 0.13 20.50 – 1.00 0.20 31.56 – 1.00 0.13 20.46 – 1.00 0.20 30.212 Zooplankton 2.05 0.84 17.51 – 2.05 0.66 13.75 – 2.06 0.84 16.82 – 2.05 0.68 14.303 Macrozooplankton 2.77 0.80 16.42 – 2.77 0.91 18.48 – 2.77 0.97 20.25 – 2.75 0.91 18.594 Jellyfish 2.83 0.22 3.00 – 2.83 0.22 3.00 – 2.84 0.21 2.83 – 2.80 0.21 2.875 Supra benthos 2.11 0.93 7.46 – 2.11 0.93 7.49 – 2.11 0.96 7.44 – 2.10 0.92 7.736 Polychaetes 2.00 0.32 0.59 – 2.00 0.32 0.59 – 2.00 0.31 0.59 – 2.00 0.33 0.617 Shrimps 2.98 0.97 2.95 0.04 2.98 0.96 1.92 1.03 2.98 0.97 2.83 0.07 2.99 0.91 2.758 Crabs 2.89 0.97 1.59 0.45 2.89 0.98 1.11 0.96 2.88 0.97 1.37 0.78 2.92 0.87 1.749 Norway lobster 2.82 0.98 0.90 0.27 2.82 0.98 0.92 0.25 2.75 0.98 0.99 0.46 2.83 0.99 1.21

10 Benthic invertebrates 2.02 0.43 0.43 <0.01 2.02 0.43 0.44 <0.01 2.03 0.44 0.50 <0.01 2.02 0.44 0.4511 Benthic cephalop. 3.10 0.97 1.30 0.97 3.10 0.94 1.10 1.10 3.09 0.97 0.73 1.77 3.15 0.90 1.9412 Benthop. cephalop. 3.66 1.00 1.71 0.34 3.67 0.71 1.27 0.20 3.65 0.90 1.46 0.60 3.76 0.75 1.5713 Mullets 3.16 0.05 0.07 0.04 3.16 0.97 0.40 1.83 3.22 0.08 0.10 0.09 3.23 0.91 1.2614 Conger eel 4.22 0.97 0.63 0.73 4.22 0.94 0.56 0.75 4.36 0.99 0.19 1.40 4.17 0.93 1.2615 Anglerfish 4.39 0.98 0.42 0.96 4.39 0.97 0.02 1.33 4.40 0.92 0.18 0.94 4.42 0.46 0.0816 Flatfishes 3.20 0.98 1.20 0.86 3.20 0.98 0.50 1.56 3.16 0.98 0.38 2.34 3.21 0.94 1.4817 Poor cod 3.31 0.95 0.69 0.75 3.31 0.95 0.89 0.55 3.55 0.96 0.49 1.29 3.42 0.93 1.3318 Juvenile hake 3.45 0.46 0.34 0.12 3.45 0.75 0.58 0.39 3.56 0.32 0.18 0.24 3.45 0.45 0.7719 Adult hake 4.11 0.51 0.00 0.46 4.10 0.98 <0.01 0.59 4.13 0.70 <0.01 0.42 4.07 0.00 0.0020 Blue whiting 3.40 0.63 0.34 0.07 3.40 0.90 0.45 0.15 3.28 0.67 0.39 0.12 3.37 0.90 0.5921 Demersal fishes (1)a 3.08 0.99 0.65 0.50 3.08 0.97 0.80 0.33 3.08 0.99 0.51 0.88 3.10 0.96 1.1322 Demersal fishes (2)a 3.01 0.83 0.64 0.19 3.01 0.82 0.57 0.25 3.02 0.72 0.37 0.37 3.02 0.31 0.3123 Demersal fishes (3)a 3.96 0.97 0.25 0.17 3.96 1.00 0.42 0.01 3.87 0.97 0.24 0.27 3.99 0.83 0.4024 Demersal sharks 3.70 0.90 0.12 0.26 3.68 0.90 0.28 0.10 3.63 0.96 0.06 0.64 3.81 0.90 0.3725 Benthopelagic fishes 3.49 0.98 1.18 0.16 3.49 0.93 0.98 0.30 3.56 0.98 0.87 0.21 3.47 0.76 1.0526 Anchovy 3.05 0.76 0.55 0.46 3.05 0.94 0.90 0.36 3.06 0.79 0.56 0.36 3.05 0.94 1.2127 Sardine 2.97 0.97 0.67 0.79 2.97 0.97 0.67 0.79 2.99 1.00 0.93 0.77 2.96 0.98 1.3528 Other small pelagics 3.00 1.00 0.43 0.08 3.00 0.85 0.43 0.02 3.01 0.98 0.60 0.13 2.99 0.83 0.4529 Horse mackerel 3.19 0.44 0.12 0.05 3.19 0.30 0.10 0.01 3.12 0.59 0.17 0.10 3.19 0.34 0.1330 Mackerel 3.55 0.51 0.22 0.01 3.55 0.51 0.14 0.09 3.47 0.65 0.40 0.02 3.55 0.57 0.2631 Atlantic bonito 4.06 0.02 <0.01 0.01 4.06 0.13 <0.01 0.04 4.04 0.25 <0.01 0.12 4.04 0.00 <0.0132 Large pelagics 4.19 0.00 0.00 <0.01 4.19 0.72 0.00 0.31 4.23 0.36 0.00 0.15 4.26 0.00 0.0033 Marine turtles 2.54 0.01 0.00 <0.01 2.54 0.07 0.00 0.01 2.64 0.25 0.00 0.04 3.06 0.00 0.0034 Audouins gull 3.22 0.00 0.00 <0.01 3.22 0.00 0.00 0.02 2.31 0.00 0.00 <0.01 3.98 0.00 0.0035 Other sea birds 2.19 0.20 0.73 0.18 2.19 0.33 1.47 0.02 2.09 0.19 0.92 0.01 4.14 0.00 0.0036 Dolphins 4.34 0.14 0.00 0.01 4.33 0.14 0.00 0.01 4.36 0.22 0.00 0.02 4.52 0.00 0.0037 Fin whale 3.81 0.00 0.00 0.00 3.81 0.00 0.00 0.00 3.84 0.00 0.00 0.00 3.80 0.00 0.0038 Discards (1) 1.00 0.51 – – 1.00 0.51 – – 1.00 0.57 – – – – –39 Discards (2) 1.00 0.00 – – 1.00 0.00 – – 1.00 0.00 – – – – –40 Detritus 1.00 0.17 – – 1.00 0.22 – – 1.00 0.17 – – 1.00 0.22 –

FG: Functional groups, TL: trophic level, EE: ecotrophic efficiency, M2: predation mortality (yr−1), F: fishing mortality (yr−1).a These groups contain demersal fishes and were defined in Coll et al. (2006a).

2092 M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102

Table 3ANOSIM (Global R and p-value) and SIMPER results when comparing the four models representing exploited and no-fishing situations.

Test description ANOSIM SIMPER

R p Average square distance Properties contributing todistances (%)

Test 1 Trophic levels by functional groups (Table 1) 1.00 0.25 0.45 F.g. 35 (70.87%), 34 (22.54%)Test 2 EE by functional groups (Table 1) 1.00 0.04 2.90 F.g. 19 (25.14%), 38 (18.26%), 32

(12.40%), 13 (11.31%)Test 3 M2 by functional groups (Table 1) 1.00 0.04 3.73 F.g. 35 (27.91%), 1 (16.99%), 13

(14.55%), 14 (6.44%)Test 4 Consumption of production overtime (Fig. 2) 1.00 0.04 24.27 Fisheries (26.44%), F.g. 19

(10.16%)Test 5 Keytoneness of functional groups (Fig. 3) 1.00 0.04 121.01 F.g. 39 (40.49%), 33 (20.59%), 35

(14.55%), 36 (13.82%)Test 6 Biomass, production and catch ratios (Fig. 4a) 1.00 0.04 5.64 Cp/Cd (79.78%), Cpl/Ct (13.51%)Test 7 Biomass and production ratios (Fig. 4b) 0.33 0.07 0.07 Bp/Bd (54.03%), Pp/Pd (20.47%),

Bpp/Bt (11.56%)Test 8 Biomass by groups (Fig. 5a) 1.00 0.01 12.17 TL > IV (38.82%), Demfish

(23.90%), Fish (16.49%), Pelfish(15.38%)

Test 9 Ratios and mean TLco by groups (Fig. 5b) 1.00 0.04 0.24 mTLco (45.43%), Pred/prey(37.21%)

Test 10 Biomass spectra (Fig. 6a) 0.17 0.25 1.87 TL 2 (14.56%), TL 2.1 (10.89), TL4 (8.01%), TL 4.1 (7.40%), TL 2.4(7.09%), TL 3.9 (6.37%)

Test 11 Mean transfer efficiency (all data, Fig. 7) 0.52 0.01 13.33 TL VI (31.61%), TL V (20.65%%),TL II (20.53%)

Test 12 Statistics and flows (Table 5) 0.67 0.04 44.92 NSP (33.81%), All Q (22.32%),

T

F mTLc

(

(

est 13 Network flow and information theory (Table 5) 0.33

.G. = functional groups, EE = ecotrophic efficiency, M2 = predation mortaility (yr−1),

established convention, a TL of I is given to primary producersand detritus, the TL can be formulated as follows

TLj = 1 +n∑

j=1

DCji · TLi (3)

where j is the predator of prey i, DCji is the fraction of prey i inthe diet of predator j and TLi is the trophic level of prey i.

The mTLco reflects the structure of the community and is cal-culated as the weighted average of the TL of all species withinthe ecosystem (Jennings et al., 2002; Rochet and Trenkel, 2003).The mTLc reflects the strategy of a fishery in terms of selectedfood-web components and is calculated as the weighted aver-age of the TL of harvested species (Pauly et al., 1998). Bothindicators are expected to decrease with an increase in fishingimpact due to the reduction of large predators in ecosystems,with lower trophic level organisms prevailing.

d) Trophic flows and trophic spectra: We quantified total trophicflows within the ecosystem in terms of consumption, pro-duction, respiration, exports, imports and flow to detritus(t km−2 yr−1). The sum of all these flows is the total systemthroughput (TST), an indirect indicator of the “size” of theecosystem (Christensen and Pauly, 1993). By combining totalbiomass, total catch and trophic levels we calculated the trophicspectra for biomass, catch and the catch:biomass ratio based onthe methodology presented by Gascuel et al. (2005).

e) Transfer efficiency (TE): We calculated the transfer efficiency (TE)from trophic flows and TLs, which summarized all inefficien-cies of the food web due to respiration, excretion, egestion andother natural mortality present at each step of the trophic chain(Lindeman, 1942). The TE values were obtained by calculatingthe ratio between the production of a given TL and the preced-

ing TL (Lalli and Parsons, 1993; Pauly and Christensen, 1995).We used a Lindeman Spine (Lindeman, 1942; Wulff et al., 1989)to visualize flows, TLs and TE.

(f) Network analysis, community energetics and information theoryindicators: We analysed various ecological indicators related

All R (12.89%), TST (10.67%)0.07 73.55 C (99.77%)

o = mean trophic level of the community.

to the ecosystem development theory (Margalef, 1968; Odum,1969, 1985; Ulanowicz, 1986; Wulff et al., 1989; Christensen,1995). These indicators included: (1) coefficients of flow andbiomass (total primary production/total respiration, Pp/R, totalprimary production/total biomass, Pp/B, total biomass/totalproduction, B/P, total respiration/total biomass, R/B, and totalbiomass/total system throughputs, B/TST). (2) Ecotrophic effi-ciencies (EE) by functional group (that expresses the proportionof production of each group that is consumed within the ecosys-tem or exported out of it, Christensen and Walters, 2004). (3)The Finn’s cycling index (FCI) and mean path length (FPL). (4)The System Omnivory Index (SOI); and (5) the ascendency andcapacity, which are related to the average mutual informationin a system scaled by the TST (Ulanowicz, 1986).

(g) Indicators of fishing intensity: To quantify fishing intensity weanalysed the relative consumption of production of commer-cial species, fishing mortality (F), the Gross Efficiency of thefishery (GEf = catch/primary production), the Primary Produc-tion Required (PPR) to sustain the fishery (%PPR), and the lossin secondary production due to fishing (L index).

The PPR from primary production and detritus (flows fromTL = 1, typically measured as t km−2 yr−1) was obtained by back-calculating the flows, expressed in primary production and detritusequivalents, for all pathways from the caught species down to theprimary producers and detritus (Pauly and Christensen, 1995):

PPR = 19

·∑

i

[Yi ·

(1

TE

)TLi−1](4)

where Yi is the catch of a given group (i), TE is the mean transferefficiency, TLi is the trophic level of a group (i) and factor 1/9 istaken as the average conversion coefficient from wet weight to gC.

This index can be expressed per unit of catch relative to primaryproduction and detritus of the ecosystem (%PPR).

The loss in production index (L index) proposed by Libralato et al.(2008) and inspired by Tudela et al. (2005) quantifies the theoreticaldepletion in secondary production in an exploited ecosystem due

M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102 2093

Table 4ANOSIM (Global R and p-value) and SIMPER results when comparing the three exploited food-web models with time (late 1970s, mid 1990s and early 2000s).

Test description ANOSIM SIMPER

R p Average square distance Properties contributing to distances(FGa)

Test 1 Trophic levels by functional groups (Table 1) 1.00 0.07 1978 & 1994 (0.00), 1978 & 2003(0.0.09), 1994 & 2003 (0.09)

1978 & 1994 (24: 59.20%), 1978 & 2003(34: 85.63%), 1994 & 2003 (34: 85.71%)

Test 2 EE by functional groups (Table 1) 1.00 0.07 1978 & 1994(1.61), 1978 & 2003 (0.73),1994 & 2003 (0.90)

1978 & 1994(32: 44.81%; 13: 36.07%),1978 & 2003 (32: 49.42%; 33: 21.96%),1994 & 2003 (13: 54.79%; 18: 10.03%)

Test 3 M2 by functional groups (Table 1) 1.00 0.07 1978 & 1994 (2.52), 1978 & 2003 (0.93),1994 & 2003 (2.38)

1978 & 1994 (1: 47.10%; 15: 10.17%),1978 & 2003 (16: 24.76%; 3: 21.64%; 14:13.82%), 1994 & 2003 (1: 50.24%; 2:6.48%; 18: 4.77%)

Test 4 Consumption of production over time 1.00 0.07 1978 & 1994 (0.35), 1978 & 2003(10.59), 1994 & 2003 (12.65)

1978 & 1994 (fisheries: 60.44%), 1994 &2003 (10: 32.29%, 11: 16.05%; fisheries:10.19%), 1994 & 2003 (10: 31.12%,fishery: 19.49%, 11: 14.72%)

Test 5 Keytoneness of functional groups 1.00 0.07 1978 & 1994 (5.08), 1978 & 2003(11.20), 1994 & 2003 (17.58)

1978 & 1994 (31: 30.26%; 35: 25.25%),1978 & 2003 (24: 45.14%; 35: 16.42%),1994 & 2003 (35: 35.24%; 32: 17.22%)

Test 6 Biomass, production and catch ratios (Fig. 4a) 1.00 0.07 1978 & 1994 (0.20), 1978 & 2003 (0.35),1994 & 2003 (0.11)

1978 & 1994 (Cp/Cd: 91.45%), 1978 &2003 (Cp/Cd: 65.74%), 1994 & 2003(Bp/Bd: 62.87%)

Test 7 Biomass and production ratios (Fig. 4b) 1.00 0.07 1978 & 1994 (0.00), 1978 & 2003 (0.09),1994 & 2003 (0.08)

1978 & 1994 (Bt/Pt: 52.24%), 1978 &2003 (Bp/Bd: 83.44%), 1994 & 2003(Bp/Bd: 88.74%)

Test 8 Biomass by groups (Fig. 5a) 1.00 0.07 1978 & 1994(1.84), 1978 & 2003 (0.98),1994 & 2003 (0.96)

1978 & 1994 (Inve: 40.09%; Pel.inve:24.77%; Dem.inve: 18.85%), 1978 &2003 (Dem.inve: 57.57%; Inve: 40.37%),1994 & 2003 (Pel.inve: 57.31%; Fish:16.91%)

Test 9 Ratios and mean TLco by groups (Fig. 5b) 1.00 0.07 1978 & 1994 (0.00), 1978 & 2003 (0.01),1994 & 2003 (0.00)

1978 & 1994 (mTLco: 56.18%), 1978 &2003 (Fish/Inve: 92.82%), 1994 & 2003(Fish/Inve: 92.15%)

Test 10 Biomass spectra (Fig. 6a) 1.00 0.07 1978 & 1994 (0.45), 1978 & 2003 (2.42),1994 & 2003 (4.81)

1978 & 1994 (TL 2: 29.48%; TL 2.1:25.02%), 1978 & 2003 (TL 2: 27.96%; TL2.1: 21.34%), 1994 & 2003 (TL 2:29.29%; TL 2.1: 23.11%)

Test 11 Landings spectra (Fig. 6b) 1.00 0.07 1978 & 1994 (0.15), 1978 & 2003 (0.10),1994 & 2003 (0.27)

1978 & 1994 (4.2: 9.07%; 4.1: 8.42%; 3:7.53%; 3.5: 6.99), 1978 & 2003 (2.5:13.40%; 3.2: 11.94%; 3: 10.86%), 1994 &2003 (3.1: 16.44%; 3: 16.26%; 3.2:11.99; 2.9: 11.64)

Test 12 Landings/biomass spectra (Fig. 6c) 1.00 0.07 1978 & 1994(1.64), 1978 & 2003 (3.28),1994 & 2003 (2.31)

1978 & 1994(3.3: 22.31%; 3.2: 20.84%;3.1: 15.23%), 1978 & 2003 (2.7: 11.23%;2.6: 9.60; 2.8: 8.23%), 1994 & 2003(2.6: 14.80%; 2.5: 14.11%; 2.7: 12.11)

Test 13 Mean transfer efficiency (all data, Fig. 7) 0.75 0.00 1978 & 1994 (3.37), 1978 & 2003 (6.06),1994 & 2003 (2.20)

1978 & 1994 (TL II: 34.30%; TL VI:33.89%), 1978 & 2003 (TL V: 38.37%; TLIV: 21.39%), 1994 & 2003 (TL II: 42.74%;TL IV: 26.66%)

Test 14 Statistics and flows (Table 5) 1.00 0.07 1978 & 1994(41.04), 1978 & 2003(2.84), 1994 & 2003 (45.50)

1978 & 1994 (All Q: 54.82%), 1978 &2003 (TST: 24.51%, NSP: 15.66%, All E:15.57%), 1994 & 2003 (All Q: 52.12%; AllR: 19.82%)

Test 15 Network flow and information theory (Table 5) 1.00 0.07 1978 & 1994(101.49), 1978 & 2003(0.27), 1994 & 2003 (111.98)

1978 & 1994 (C: 99.64%), 1978 & 2003(C: 97.96%), 1994 & 2003 (C: 99.74%)

Test 16 Fishing mortality by functional groups (Table 1) 1.00 0.07 1978 & 1994 (3.05), 1978 & 2003 (1.37),1994 & 2003 (3.07)

1978 & 1994(13: 43.56%, 7: 21.77%),1978 & 2003 (16: 26.39%; 32: 10.91%;11: 8.68%; 14: 7.86%), 1994 & 2003 (13:36.13%; 7: 18.35%)

Test 17 Fishing intensity (Table 5) 1.00 0.05 1978 & 1994 (4.82), 1978 & 2003 (3.13), 1978 & 1994 (Psust: 86.49%), 1978 &

1), mT

t(a1

L

Lf

a FG = functional groups, EE = ecotrophic efficiency, M2 = predation mortaility (yr−

o fishing. This index takes into account both ecosystem propertiesthe primary production, P1, and TE) as well as features of fishingctivities (mTLc and PPR) (Lindeman, 1942; Pauly and Christensen,995; Pauly et al., 1998):

∼= −PPR · TEmTLc−1

P1 · lnTE(5)

index increases with fishing impact and was proposed as a proxyor quantifying ecosystem effects of fishing. It can be used to esti-

1994 & 2003 (0.30) 2003 (Psust: 94.60%), 1994 & 2003 (PPR:65.31%)

Lco = mean trophic level of the community.

mate the probability that the ecosystem is being sustainably fished(Psust, Libralato et al., 2008; Coll et al., 2008d).

2.4. Statistical analysis

For an overall interpretation of the ratios of biomass, pro-duction and catch by species groups, we performed a principalcomponent analysis on the correlation matrix (PCA, Jongman et

2094 M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102

F explo( ≥2%.

aWuSututeswec

3

3

wdnguTcanvef(

metd

ig. 2. Main partitioning of total consumption of production from species potentiallyc) early 2000s and (d) a no-fishing situation. Consumption values represented are

l., 1999) using PRIMER (http://www.primer-e.com, Clarke andarwick, 2001). Similarities between food webs were explored

sing the non-parametric statistical procedures of ANOSIM andIMPER. The ANOSIM procedure (Clarke and Green, 1988) wassed to test for significant differences between food-web struc-ure and functioning. The SIMPER procedure (Clarke, 1993) wassed to quantify the similarity of ecosystems and analyse the rela-ive contribution of each indicator to differences observed amongcosystems. Data were normalized and transformed when neces-ary prior to comparisons to focus attention on patterns within thehole ecosystem, and to take into account contributions from prop-

rties with different scales. The Euclidean distances were used toompute dissimilarity matrices.

. Results

.1. Ecological roles

Trophic levels (TLs) were similar between ecosystem models,ith the exceptions for large pelagics, marine turtles, seabirds, andolphins that showed an increase of TL from the late 1970s to theo-fishing situation (Table 2). This was especially relevant for bothroups of seabirds and for marine turtles since in the fishing sit-ations the fraction of the diet that comes from discards (with aL = 1 by convention) was high and in the no-fishing situation dis-ards were not present in the model (Table 3, test 1). Crabs, mullets,nglerfish and demersal fish group (1) also showed higher TLs in theo-fishing scenario. The model in the early 2000s showed loweralues of TL for Norway lobster, flatfishes, blue whiting, the dem-rsal fish group (2) and other small pelagic fish, and higher valuesor conger eel, poor cod and adult hake, although not significantTable 4, test 1).

The ecotrophic efficiencies (EE) showed similarities betweenodels, and in general the organisms with the lowest and the high-

st TLs showed lower values than intermediate organisms (Table 2),hus they were less consumed in the ecosystem. There were someifferences between models: phytoplankton, detritus and micro-

ited by fishing in the South Catalan Sea models during (a) late 1970s, (b) mid 1990s,

and meso-zooplankton showed slightly higher EE during the mid1990s and in the no-fishing situation. Overall differences betweenexploited and non-exploited models were significant due to val-ues for mullets, adult hake, large pelagics and discards (Table 3,test 2). EEs for some previously exploited species such as benthiccephalopods, anglerfish, flatfish and adult hake decreased in theno-fishing scenario, while the EE of low trophic level species suchas polychaetes, mullet, blue whiting and anchovy increased. Hake,mullets, blue whiting, anchovy, Atlantic bonito and large pelagicfish showed increasing trends of EE from the late 1970s to the 1990sdue to an increase in fishing mortality. Differences between modelsof the late 1970s, mid 1990s and early 2000s were not significant(Table 4, test 2). Similar values for M2 were found between all mod-els examined (Table 2), but we registered increases for several lowand intermediate TL organisms when fishing were excluded andsignificant differences were found between overall results (Table 3,test 3).

The analysis of the partitioning of consumption of productionfrom commercial species (i.e., that are potentially targeted by fish-ing) showed similar results during the late 1970s and 1990s (Fig. 2).Fisheries, benthopelagic cephalopods, gadiformes and the demersalfish group (1) were the most important groups regarding consump-tion in all time periods. Gadoids and the demersal fish group (1)maintained their consumption values during the three decades.Marine mammals represented a consumption of 2% during the late1970s and 1990s but their consumption was lower in 2000s. Theconsumption from large pelagic fishes also diminished with time,and that of the demersal fish group (2) increased. The no-fishingscenario showed the highest values for the demersal fish group (2),followed by the demersal and pelagic fish and invertebrate groups.Differences between exploited and no-exploited models were sig-nificant mainly due to fishing and adult hake consumptions (Table 3,

test 4).

There were no species identified by the model as real key-stone species when fishing was present (with values closer toor greater than zero, Libralato et al., 2006), with the exceptionof benthic invertebrates (Fig. 3). We observed small changes in

M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102 2095

F m thee and h

kdtbwTAdodtnw(

3

dslstbppiaaT

ig. 3. Keystoneness (KSi) and relative overall effect (εi) of each functional group froarly 2000, and (d) a no-fishing situation. Keystone groups are those with higher εi

eystone species from the late 1970s to the early 2000s. Notableifferences were seen between the fishing and no-fishing situa-ions (Fig. 3). Marine mammals, Audouins gull, adult hake, detritus,enthopelagic cephalopods, Atlantic bonito, sardine and anglerfishere identified as keystone species in the no-fishing ecosystem.

he importance of the higher trophic levels (dolphins, adult hake,tlantic bonito and anglerfish) was lower in fished scenarios andecreased from the 1970s to the 2000s. In parallel, lower TLrganisms (such as benthic invertebrates, polychaetes, zooplanktonetritus and sardine), became more important thereby increasinghe dependence on organisms at the bottom of the trophic web. Sig-ificant differences between exploited and no-exploited situationsere mainly due to marine turtles, seabirds, dolphins and discards

Table 3, test 5).

.2. Ecosystem structure

A principal component analysis (PCA) including biomass, pro-uction and catch ratios highlighted similarities in food-webtructure in the late 1970s, mid 1990s and early 2000s, andarger significant differences in comparison with the no-fishingcenario (Fig. 4a, Table 3, test 6). Total biomass/total produc-ion (Bt/Pt), biomass and production of piscivorous species/totaliomass and production (Bpp/Bt, Ppp/Pt), total pelagic biomass androduction/demersal biomass and production (Bp/Bd, Pp/Pd) and

roduction of forage species/total production (Ppl/Pt) were higher

n the no-fishing situation, followed by the 1970s model, the 1990snd the 2000s, consecutively. On the contrary, the biomass of for-ge species/total biomass (Bpl/Bt) was the highest in the 2000s.otal catch/total biomass (Ct/Bt) was also the highest in the 2000s,

ecological model of the South Catalan Sea during (a) late 1970s, (b) mid 1990s, (c)igher KSi (value close to or greater than zero).

followed by the 1990s and 1970s, respectively, while total catchof non-commercial species/total catch (Cnc/Ct) was the highest inthe 2000s but was followed by the late 1970s. Catch of piscivorousspecies/total catch (Cpp/Ct) increased from the 1970s to the 1990sand later decreased in the 2000s, while catch of forage species/totalcatch (Cpl/Ct) and total pelagic/total demersal catch decreased fromthe 1970s to the 2000s. Differences between exploited and no-exploited models were due to changes in catch ration (Table 3,test 6), and differences between models with time were drivenby changes in catch ratios and pelagic biomass/demersal biomass(Bp/Bd), although were not significant (Table 4, test 6). When catcheswere excluded from the analysis, the food web in the 1970s wasslightly closer to the no-fishing scenario (Fig. 4b, and there were nosignificant differences between models, Tables 3 and 4, test 7).

Biomass allocation by organism type (fish, invertebrates andhigher TLs) showed changes from the 1970s to the 2000s (Fig. 5a).Biomass of demersal invertebrates increased with time, whilepelagic invertebrates increased from the 1970s to the 1990s anddecreased again toward the 2000s. Pelagic fishes were also higherin the 1990s than in the 1970s and 2000s, while demersal fishdid not show important variations with time from the 1970s tothe 2000s. Biomass of demersal and pelagic fish and top predatorsincreased significantly under the no-fishing scenario, and it practi-cally doubled the biomass of all exploited situations (Table 3, test8). Invertebrates, on the contrary, showed similar levels as in fished

situations, although demersal invertebrates did slightly decreasefrom the 2000s in the no-fishing scenario and pelagic invertebratesshowed a slight increase. When previous data was aggregated, thefish/invertebrate biomass ratio decreased from the 1970s to the2000s and increased in the no-fishing situation (Fig. 5b). Preda-

2096 M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102

F m thep

ttaew(

dinwc(tToah

3

tte

ig. 4. PCA applied to indicators from ecosystem models of the South Catalan Sea froroduction and catch ratios and (b) excluding catch ratios.

or (TL > III)/prey (TL < II) (Pred/prey) biomass ratio and the meanrophic level of the community was similar during fishing scenariosnd increased substantially under the no-fishing scenario. Differ-nces between models of exploited and non-exploited situationsere due to changes in mTLco and Pred/prey biomass ratio mainly

Table 3, test 9).The trophic spectra of biomass (Fig. 6a) showed a general

ecrease of biomass from the 1970s to the 2000s and an increasen biomass at higher trophic levels (TL) under a no-fishing sce-ario. The biomass of TL around III was higher during the 1990sith respect to the 1970s and decreased again in the 2000s. These

hanges were not significant (Tables 3 and 4, test 10). Catch spectraFig. 6b) showed an increase in catch in all TLs from the 1970s tohe 1990s and a decrease in the 2000s centered in TL around III.he catch/biomass spectra ratio (Fig. 6c) showed a higher removalf intermediate trophic levels during the 1990s (TLs around III) anddecrease during the 2000s, where a broader impact on lower andigher TLs was seen due to overall lower biomass (Fig. 6a).

.3. Ecosystem functioning

Changes in biomass, production and catch in the ecosystem withime happened in parallel with changes in the efficiency of energyransfer from lower to higher TLs (Fig. 7). The mean total transferfficiency increased significantly from the late 1970s to the early

late 1970s, mid 1990s, early 2000s and a no-fishing scenario including (a) biomass,

2000s, and was lowest under the no-fishing scenario (Table 5).The transfer efficiency (TE) decreased from lower to higher trophiclevels in the no-fishing scenario (Fig. 7a) with the exception ofincreasing TE from integer TL V to VI in the primary producers-associated food web (Fig. 7b). On the contrary, TE showed increasingtrends in fishing scenarios from TL IV in the case of the late 1970sand mid 1990s and from TL III in the case of the 2000s. This increasewas seen both when looking at the TE associated with the primaryproducer food web (Fig. 7b) and with the detritus-associated foodweb (Fig. 7c). Differences between models of exploited and non-exploited situations were significant due to changes in the TE of TLVI, TL V and TL II mainly (Table 3, test 11). Differences between mod-els of the late 1970s, mid 1990s and early 2000s were significant dueto changes in TE from TL II and TL V mainly (Table 4, test 3). As fish-ing was an important factor contributing to increases in exports outof the ecosystem from the 1970s to the 2000s, we excluded fishingfrom models of the 1970s, 1990s and 2000s, and we calculated theTE values again (Fig. 7d): all four models showed similar trends ofTE with a general decrease of TL with higher TLs.

Previous results in terms of flow, TLs and TE were visualized in

the form of a Lindeman Spine (Fig. 8). This analysis revealed fur-ther differences between the ecosystem models. The model in the1990s showed higher biomass and flow for low and intermediatetrophic levels than in the 1970s and 2000s, but lower values thanin the no-fishing scenario (Fig. 8a–c). The 2000s model showed the

M. Coll et al. / Ecological Modelli

Fig. 5. (a) Biomass by groups, and (b) ratios and mean trophic level of the community(mTLco) from ecosystem models of the South Catalan Sea during the late 1970s, mid1990s, early 2000s, and a no-fishing situation.

Table 5Ecological indicators related to community energetics, community structure, cycling of nSea during the late 1970s, mid 1990s, early 2000s and under the no-fishing scenario.

Indicators Late 1970s

Statistics and flowsSum of all consumptions (Q) 597.40Sum of all exports (E) 1348.09Sum of all respiratory flows (R) 230.58Sum of all flows into detritus (FD) 1610.06Total system throughputs (TST) 3786.0Sum of all production (P) 1757.0Net system production (NSP) 1346.42Total primary production/total respiration (Pp/R) 6.84Total primary production/total biomass (Pp/B) 33.96Total biomass/total production (B/P) 0.03Total respiration/total biomass (R/B) 4.97Total biomass/total throughput (B/TST) 0.01Total biomass (excluding detritus) (B) 46.44Mean transfer efficiency (TE) 11.50

Network flowFinn’s cycling index (of total throughput) (FCI) 4.98Finn’s mean path length (FPL) 2.40System Omnivory Index (SOI) 0.22

Information theoryAscendency (A) 41.70Capacity (total, flowbits) (C) 10570.90

Fishing intensityTotal catches 3.97Mean trophic level of the catch (mTLc) 3.07Primary production required to sustain the catch (%PPR) 6.35Gross efficiency of the fishery (catch/net pp) 0.003Loss in production index (L index) 0.033Probability of fishing sustainability (Psust) 65.88

ng 220 (2009) 2088–2102 2097

lowest levels of biomass and flow for several compartments. Flowsand biomass associated with higher trophic levels increased in themodel of the no-fishing situation and were higher in the 1970s incomparison with the 1990s and 2000s (Fig. 8d). These results werealso seen when analysing total flows by the ecosystem (Table 5):total production, total respiration and total system throughputs(TST) were higher in the 1990s and under the no-fishing scenarioand lower in the 1970s and 2000s. Exports were higher in the 1970s,while net system production was higher in the no-fishing situationfollowed by the 1970s scenario. Total ecosystem biomass was higherin the no-fishing situation followed by the 1990s, 2000s and 1970s,respectively. Significant differences were found between exploitedand non-exploited situations due to changes in net system produc-tion, total consumption, total respiration and TST (Table 3, test 12),but not between models with time (Table 4, test 14).

Network analysis, community energetics and information the-ory indicators (Table 5) showed an increase in “maturity” orcomplexity of the ecosystem from the exploited to the no-fishing situation: the total primary production/total biomass(Pp/B) decreased, while total biomass/total production (B/P), totalbiomass/total system throughputs (B/TST) and the Finn’s cyclingindex (FCI) increased. The total primary production/total respira-tion, the Finn’s mean path length (FPL), ascencency and capacitywere similar in the no-fishing scenario and in the 1990s, and lowerthan the other models representing the 1970s and 2000s. Thesystem omnivory index (SOI) showed similar values in the fourmodels. Differences between exploited and non-exploited modelsand between models with time were not significant (Tables 3 and 4,tests 13 and 5, respectively).

3.4. Fishing intensity

Fishing mortality (F) for several groups notably increased fromthe late 1970s to the mid 1990s and decreases in the early 2000s

utrients and information theory from the ecosystem models of the South Catalan

Mid 1990s Early 2000s No fishing Units

851.77 591.25 847.75 t km−2 yr−1

1251.87 1299.70 1265.85 t km−2 yr−1

326.89 227.37 326.81 t km−2 yr−1

1607.50 1565.50 1624.91 t km−2 yr−1

4038.0 3684.0 4065.0 t km−2 yr−1

1848.0 1702.0 1858.0 t km−2 yr−1

1250.11 1297.92 1590.774.82 6.71 4.87

26.74 28.98 23.290.03 0.03 0.045.54 4.32 4.780.01 0.01 0.02

58.98 52.64 68.31 t km−2

12.20 13.30 10.40

6.77 5.22 6.63 %2.56 2.41 2.550.22 0.20 0.22

35.80 41.10 36.70 %12739.80 10465.70 12779.60 Flowbits

5.36 5.17 – t km−2 yr−1

3.12 3.11 –10.61 7.92 – %0.003 0.003 –0.058 0.056 –

37.44 40.81 – %

2098 M. Coll et al. / Ecological Modelli

FSs

(pifidmht(Dwfl(

Lalli and Parsons, 1993), but in line with what was described com-

ig. 6. Trophic spectra of (a) biomass, (b) catches, and (c) catch:biomass ratios for theouth Catalan Sea during the late 1970s, mid 1990s, early 2000s and in a no-fishingcenario.

e.g., mullet, anglerfish, juvenile and adult hake, blue whiting, largeelagic fishes) (Table 2). In other cases though, F continued to

ncrease until the 2000s (e.g., benthic cephalopods, conger eel, flat-shes, demersal fish group (2), Atlantic bonito, marine turtles, andolphins). For anchovy and sardine, the F-value was practicallyaintained during the three decades. Fishing fleets consumed a

igh proportion of fishable production (Fig. 2). The importance ofhe fishery was high in all ecosystems but decreased from the 1970s23%) to the 1990s (17%), and increased again in the 2000s (33%).

ifferences between the late 1970s, mid 1990s and early 2000sere mainly due to changes in fishing mortality to shrimps, mullets,atfishes and large pelagic fish, although they were not significantTable 4, test 14).

ng 220 (2009) 2088–2102

Integrative indicators of fishing intensity highlighted the highpressure of fishing during the three decades analysed (Table 4, test16, and Table 5). The gross efficiency of the fishery (GEf) duringthe 1970s, 1990s and 2000s was three orders of magnitude higherwhen compared with other modelled ecosystems and when com-pared to the mean reported for global data (0.0002) (Christensenet al., 2005). The primary production required for sustaining thecatch (PPR%) increased from the 1970s to the 1990s and decreasedagain, and it was around 6–11% of total primary productivity of thesystem. The mean trophic level of the catch was low in all periodsanalysed but slightly increased from the 1970s to the 1990s and2000s due to the decrease in small pelagic fish catch in line withthe fishing in balance indicator (Coll et al., 2006a). The Loss in pro-duction index (L index) increased from the 1970s to the 1990s, andit was similar in the 1990s and 2000s (Table 5). The probability ofthe ecosystem being sustainably fished related to these values ofthe L index decreased with time from the 1970s to the 1990s andit was below 50% in both the 1990s and 2000s scenarios (37–41%),although it was also low in the 1970s (66%).

4. Discussion

Results showed two clear patterns: smaller but still in few occa-sions noticeable changes of the food web from the 1970s to the2000s due to increasing fishing intensity, and a clear differenceon the food-web structure and functioning under fishing and no-fishing situations.

4.1. Food web changes from the late 1970s to the early 2000s

Comparison of modelling results showed that changes in struc-ture and functioning of marine food webs have occurred from the1970s to the 2000s. These changes are due to (1) a high exploitationof fishing on the organisms with higher trophic positions (therebydecreasing their biomass), (2) an increasing abundance of inverte-brates and small pelagic fish during the 1990s (and thus an increaseon their catch), and (3) complex direct and indirect food-web con-sequences of these changes in biomasses and trophic flows. Theseresults are in line with those from the fitting of the model todata (Coll et al., 2008a; Shannon et al., 2009). Indicators that cor-rectly captured these changes are biomass by organism types (totalfish biomass decreases with time, invertebrate biomass increaseswith time), the keystone species indicator, and the fish/invertebratebiomass, total biomass/total production and total biomass of pisciv-orous fish/total biomass, all decreased with time. However, smallpelagic fish biomass decreased from the mid 1990s (mainly due tosardine decline, because anchovies showed a decrease in biomasssince the 1970s, Palomera et al., 2007). This change during the1990s was captured by the total pelagic biomass/total demersalbiomass (with similar values during the 1970s and 1990s), and totalpelagic production/total demersal production. We could observedthat changes in small pelagic fish had important and wide ecologi-cal impacts on food webs as previously discussed to occur in severalmarine ecosystems (Cury et al., 2000; Shannon et al., in press).

Changes in ecosystem functioning from the 1970s to the 2000swere illustrated by the increase in efficiency of energy transferfrom lower to higher trophic levels. This suggests that the food webbecame more efficient with time, and may be due to less biomassand production in the ecosystem. This increasing trend is in con-trast with the theoretical description of food webs (Strayer, 1991;

paring a protected a non-protected area of the Adriatic Sea (CentralMediterranean, Libralato et al., submitted for publication). Simi-lar results were also obtained from two ecosystem models of theVenice lagoon representing 1988 and 1998 under an increasing fish-

M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102 2099

F talan Ss eb, (cw

imbtaelseAmBSai

2sabfimwC2tilew

iootti

ig. 7. Transfer efficiency values (TE) by integer trophic levels (TL > II) in the South Cacenario: (a) mean values, (b) values from the primary producers-associated food where fishing was excluded.

ng effort scenario (Libralato et al., 2004). Higher efficiency mayean that changes in production of lower and intermediate TLs will

e more rapidly channelled to the higher TLs, and thus the ecosys-em may become more vulnerable to the dynamics of organismst the base of the food web, thereby more sensitive to changes innvironmental factors. The higher vulnerability to lower trophicevel organisms is in agreement with results from the keystonepecies indicator that showed how low-positioned organisms arecologically more important in the exploited ecosystem with time.comparison of trends in time series indicators predicted fromodels fitted to time series data for the South Catalan Sea, Southern

enguela and Southern Humboldt revealed that the Mediterraneanea may be in fact becoming less responsive to the effects of fishingnd more vulnerable to environmental variability (Shannon et al.,n press).

These changes in the South Catalan Sea from the 1970s to the000s were, at least partially related to increasing fishing inten-ity in the ecosystem, as well as with environmental factors (Coll etl., 2008a). This was highlighted with the TE of exploited ecosystemeing similar to values obtained from the no-fishing situation whenshing was eliminated from the models. This was also in agree-ent with the probability of overexploitation increasing with timehen applying the L index (Tudela et al., 2005; Libralato et al., 2008;oll et al., 2008d) and being lower than 50% during the 1990s and000s. The probability of the ecosystem overfishing was lower inhe 1970s but still notable. Thus, the loss in secondary productionndex (L index) was a good indicator for capturing the overall eco-ogical impacts of fishing due to depletion of production in exploitedcosystems: it was sensitive when comparing highly exploited foodebs such as the South Catalan Sea during the 1970–2000s.

Our results also highlighted that there were overall similar-ties in the food web in the 1970s, 1990s and 2000s, and we

bserved similar consumption of production, mean trophic levelf the catch and of the community, biomass of demersal fishes andop predators, predator/prey biomass, total biomass/total produc-ion, total biomass/total system throughput and system omnivoryndex. These results coincides line with Coll et al. (2008c) that high-

ea ecosystem models during the late 1970s, mid 1990s, early 2000s and a no-fishing) values from the detritus-associated food web, and (d) mean values from models

lighted high similarities between the topology of Mediterraneanmarine food webs. Results from keystone species indicators dur-ing the 1970s, 1990s and 2000s were similar as well, but therewas a generally decreasing importance of higher trophic levels withtime as it was seen from other ecosystems from other ecosystemspreviously modelled (Libralato et al., 2006).

4.2. Differences between exploited and non-exploited food webs

When previous results were set in a broader ecological contextand a no-fishing scenario was included in the comparison, impor-tant and significant differences were described under the no-fishingsituation. This was mainly due to the fact that the exploited foodweb analysed during the 1970s, 1990s and 2000s showed depletedbiomass and flow at higher trophic levels and a higher importanceof lower and intermediate consumers. Although food webs showedincreasing fishing intensity and impacts with time, results high-lighted that the food web was already intensively exploited in the1970s. These findings agreed with results when comparing foodwebs of Mediterranean ecosystems with other non-Mediterraneansystems (Coll et al., 2008c).

Indicators that showed main differences between exploited andnon-exploited food webs were: (1) the keystone species indicator,showing a higher importance of high TLs, (2) the transfer effi-ciency showing a decreasing trend, (3) biomass of group types(fish biomass showing increasing trends, invertebrates showingdecreasing or maintained trends), (4) the mean trophic level ofthe community, showing an increasing trend, and (5) biomassrations of fish/invertebrate, predator/prey, pelagic/demersal organ-isms and total biomass/total production, biomass and productionof piscivorous fishes/total biomass and production, pelagic produc-tion/demersal production and net system production, all showing

an increasing trend. Size spectra of biomass enabled us to visualizechanges in biomass with TL.

The no-fishing scenario enabled us to set an ecological baselineuseful to compare it with the structure and functioning of exploitedfood webs. This is a theoretical scenario that may be reinforced

2100 M. Coll et al. / Ecological Modelling 220 (2009) 2088–2102

alan S

iianp

Fig. 8. Schematic representation of trophic flows from the South Cat

n the future with the comparison of real implementation of non-ntake zones such as new marine protected areas to enhance fishingctivities or areas restricted to fishing. These management tools areot currently available so reality cannot be compared with modelredictions. But, interestingly, results from a historical experience

ea ecosystem through time organized by integer trophic levels (TL).

where an exploited area was closed to trawl fishing just south ofthe study site produced results with interesting similarities to ourstudy. In this real experience, known as “El Plà Castelló”, the areawas closed temporarily during 5 years (1961–1966) to trawl fishingand results showed an increase in biomass of species that were

odelli

sionrat

tbrettwes(fi2cdpmp(

edttfitfpeuismlss2oe

4l

dwHa2mes

iaed(t

M. Coll et al. / Ecological M

ubjected to higher fishing and with high TLs, thus an increasen potential landings (IMEDES, 1999). The notable difference thatur results highlighted between the exploited food webs and theo-fishing food web shows to what degree fishing activity is cur-ently conditioning the structure and functioning of this ecosystem,nd when fishing is eliminated from the ecosystem this is stronglyransformed.

The no-fishing scenario, however, does not represent the ecosys-em in its pristine state, and these results must be interpreted whileearing in mind the long history of exploitation of the Mediter-anean Sea. The organisms positioned at the highest levels of thecosystem are no longer present in the current situation due tohey were mainly removed many centuries before the industrializa-ion of fisheries. For example, the monk seal (Monachus monachus)as already intensively hunted by the Romans and was ecologically

xtinct from the ecosystem since 1800–1900, while large pelagicharks were also depleted or rare in the Mediterranean by the 1900sLotze et al., 2006). Dolphins and large pelagic fish were regularlyshed at least from the 14th century in the Catalan Sea (Garrido,006). These depletions most probably caused early food webhanges by releasing prey from predation such as medium–largeemersal and pelagic fishes, medium and small sharks and rays. Arevious study estimated that trophic links related to marine mam-als, sharks and turtles on a coastal Mediterranean food web in a

ristine stage could have accounted for 15% of total trophic linksSala, 2004).

After large organisms were exploited and became scarce, fish-rmen moved on to exploit smaller organisms such as smalleremersal and pelagic fishes, and extended their fishing range fromhe coastal areas to deep water, continuously improving fishingechnology and development (Garrido, 2006). These expansions ofshing to lower trophic level organisms and deeper areas have con-inued until the present in the Mediterranean Sea. Our study timerame, three decades from the 1970s to the 2000s, corresponds to aeriod where the depletion of organisms with the highest TLs in thecosystem is well advanced and where we are witnessing a contin-ous decline in biomass of the following higher TL organisms and

ncreasing biomass in intermediate and lower trophic levels. At theame time, these organisms at intermediate trophic positions areore exploited by fisheries. Recent declines in small pelagic fishes,

ow biomass of demersal fish species, and proliferation of smallerized species (such as shrimp and crabs) or higher turnover ratepecies (such as cephalopods and benthopelagic fishes) (Coll et al.,008a) brings the ecosystem to the next episode of this exploitationdyssey where invertebrates are the next profitable trophic level toxploit.

.3. Comparison of models and indicators: capabilities andimitations

This modelling application illustrates the usefulness of stan-ardized models to analyse temporal changes on marine foodebs, as previously evidenced in other areas (Shannon et al., 2003;eymans et al., 2004; Cury et al., 2005). These comparisons canlso be useful for analysing different ecosystems (Moloney et al.,005; Coll et al., 2006b; Shannon et al., 2009, in press). Ecologicalodels integrate much of the knowledge we posses from marine

cosystems and can be useful tools to quantify indicators from thepecies to the ecosystem-level.

This comparison also highlights the usefulness of model-derivedndicators to pick up differences and similarities between food webs

long time and of exploited and non-exploited situations. Consid-ring our overall results, indicators that performed best on thisual task were (1) biomass by organism type (fish, invertebrates),2) ratios of fish/invertebrate biomass, total biomass/total produc-ion, total pelagic biomass/total demersal biomass, total pelagic

ng 220 (2009) 2088–2102 2101

production/total demersal production, total biomass of piscivorousfish/total ecosystem biomass, (3) the transfer efficiency of energyfrom lower to higher trophic levels, and (4) the keystone speciesindicator.

Thus, simple indicators such as biomass by organism type orratios of biomass and production can be useful indicators for pick-ing up important structural changes in marine ecosystems. Morecomplex indicators such as transfer efficiency or keystone speciesindicators can be very informative for ecosystem function changes.When changes in food webs are predicted to be important, the meantrophic level of the community seemed to be a good indicator forquantifying these structural changes as well (Rochet and Trenkel,2003).

A limitation of this study is the low number of food-web repli-cates, translated into constrains on the uncertainly analysis to apply.The lack of information on uncertainty for model inputs preventedus from performing Monte Carlo simulations (Christensen andWalters, 2004). These simulations are based on the initial parame-ters of the model and the uncertainties of the inputs provided by thecritical evaluation of the inputs and the knowledge of the expert inthe form of coefficients of variations. They can be applied to mass-balanced models and, although it is not a true empirical estimate ofthe uncertainty associated with the data, it is a step toward explor-ing the effects of uncertainty on model estimates (Bundy, 2005;Bundy and Fanning, 2005). Therefore, non-parametrical analysisimplementing PCA, ANOSIM and SIMPER enabled us to provide onlya basic estimation of significance between results.

EwE ecosystem models have limitations that have been crit-ically reviewed in Christensen and Walters (2004), Christensenet al. (2005), Fulton and Smith (2004), Plagányi and Butterworth(2004) and Plagányi (2007). These limitations must be taken intoaccount when performing comparisons of model results and arein many cases associated with data availability and data qual-ity. Results from ecosystem models should be compared withmodel-independent data to be reinforced. In some cases, suchas with ecosystem-level indicators like the transfer efficiency orthe keystoneness, such comparisons between model results andindependent data are difficult due to limitations of direct quan-tification of functional ecosystem properties. Comparing indicatorsfrom modelling-independent data with modelling results (mainlyspecies to community-level indicators such as biomass and pro-duction of species), and then complementing these results withecosystem indicators only provided by models, seems a suitable andreasonable methodological approach. This can contribute toward abetter understanding of structural and functioning processes thatcharacterise dynamics of marine ecosystems.

Acknowledgements

The authors acknowledge colleagues from the Institute ofMarine Science, the Centre for Advanced Studies of Blanes, theMediterranean Institute for Advanced Studies, the Spanish Instituteof Oceanography and the Autonomous University of Barcelona, whoprovided essential data and technical advice for the development ofthis work. We thank scientific researchers from the Fisheries Centre(University of British Columbia) who kindly supported the work andadvised on ecological modelling, and specially Dr. V. Christensen.We thank Dr. S. Libralato for assistance of the application of the key-stone species indicator, and two anonymous reviewers for usefulcomments. M.C. was supported financially by a research fellowshipfrom the Spanish Ministry of Science and Technology.

References

Aldebert, Y., Recasens, L., 1996. Comparison of methods for stock assessment ofEuropean hake Merluccius merluccius in the gulf of Lion (Northwestern Mediter-ranean). Aquat. Liv. Res. 9, 13–22.

2 odelli

B

B

B

B

C

C

C

C

C

C

C

C

C

C

C

C

C

C

C

E

F

F

G

G

H

I

J

J

L

L

102 M. Coll et al. / Ecological M

as, C., Maynou, F., Sardà, F., Lleonart, J., 2003. Variacions demogràfiques a les pobla-cions d’espècies demersals explotades: els darrers quaranta anys a Blanes iBarcelona. Institut d’Estudis Catalans. Sec. Ciènc. Biol. 135, 202.

ianchi, C.N., Morri, C., 2000. Marine biodiversity of the Mediterranean Sea: sit-uation, problems and prospects for future research. Mar. Pollut. Bull. 40 (5),367–376.

undy, A., 2005. Structure and functioning of the eastern Scotian Shelf ecosystembefore and after the collapse of groundfish stocks in the early 1990s. Can. J. Fish.Aquat. Sci. 62, 1453–1473.

undy, A., Fanning, L.P., 2005. Can Atlantic cod (Gadus morhua) recover? Exploringtrophic explanations for the non-recovery of the cod stock on the eastern ScotianShelf, Canada. Can. J. Fish. Aquat. Sci. 62, 1474–1489.

hristensen, V., 1995. Ecosystem maturity—towards quantification. Ecol. Model. 77,3–32.

hristensen, V., Pauly, D., 1993. Trophic Models of Aquatic Ecosystems. ICLARM,Manila, Philippines, p. 390.

hristensen, V., Walters, C., 2004. Ecopath with Ecosim: methods, capabilities andlimitations. Ecol. Model. 172 (2–4), 109–139.

hristensen, V., Walters, C., Pauly, D., 2005. Ecopath with Ecosim: A User’s Guide.Fisheries Centre of University of British Columbia, Vancouver, Canada, p. 154.

larke, K.R., 1993. Non-parametric multivariate analyses of changes in communitystructure. Aust. J. Ecol. 18, 117–143.

larke, K.R., Warwick, R.M., 2001. Changes in Marine Communities: An Approach toStatistical Analysis and Interpretation, second ed. PRIMER-E, Plymouth.

larke, K.R., Green, R.H., 1988. Statistical design and analysis for a “biological effects”study. Mar. Ecol. Prog. Ser. 92, 205–219.

oll, M., Palomera, I., Tudela, S., Sardà, F., 2006a. Trophic flows, ecosystem structureand fishing impacts in the South Catalan Sea Northwestern Mediterranean. J.Mar. Syst. 59, 63–96.

oll, M., Shannon, L.J., Moloney, C.L., Palomera, I., Tudela, S., 2006b. Comparingtrophic flows and fishing impacts of a NW Mediterranean ecosystem with coastalupwellings by means of standardized ecological models and indicators. Ecol.Model. 198, 53–70.

oll, M., Palomera, I., Tudela, S., Dowd, M., 2008a. Food web dynamics of marineresources in the South Catalan Sea (NW Mediterranean) for the period1978–2003. Ecol. Model. 217, 95–116.

oll, M., Bahamon, N., Sardà, F., Palomera, I., Tudela, S., Suuronen, P., 2008b.Ecosystem effects of improved trawl selectivity in the South Catalan Sea (NWMediterranean). Mar. Ecol. Prog. Ser. 355, 131–147.

oll, M., Lotze, H., Romanuk, T., 2008c. Structural degradation in MediterraneanSea food webs: testing ecological hypotheses using stochastic and mass-balancemodelling. Ecosystems 11, 939–960.

oll, M., Libralato, S., Tudela, S., Palomera, I., Pranovi, F., 2008d. Ecosystem Overfish-ing in the Ocean. PLos ONE 3 (12), e3881.

ury, P., Bakun, A., Crawford, R.J.M., Jarre, A., Quinones, R.A., Shannon, L.J., Verh-eye, H.M., 2000. Small pelagic in upwelling systems: patterns of interaction andstructural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618.

ury, P., Shannon, L.J., Roux, J.-P., Daskalov, G., Jarre, A., Pauly, D., Moloney, C.L., 2005.Trophodynamic indicators for an ecosystem approach to fisheries. ICES J. Mar.Sci. 62, 430–442.

strada, M., 1996. Primary production in the Northwestern Mediterranean. Sci. Mar.60 (Suppl. 2), 55–64.

arrugio, H., Oliver, P., Biagi, F., 1993. An overview of the history, knowledge, recentand future research trends in the Mediterranean fisheries. Sci. Mar. 57 (2–3),105–119.

ulton, E.A., Smith, A.D.M., 2004. Lessons learnt from a comparison of three ecosys-tem models for Port Phillip Bay, Australia. In: Shannon, L.J., Cochrane, K.L., Pillar,S.C. (Eds.), An Ecosystem Approach to Fisheries in the Southern Benguela. Afr. J.Mar. Sci., vol. 26, pp. 219–243.

arrido, A. 2006. Evolution of fishery technologies and uses of territory: a long termpoint of view. (HMAP) History of Marine Animal Population – The Mediter-ranean and Black Sea Second workshop – human–environment interactionsin the Mediterranean Sea since the Roman period until the 19th century: anhistorical and ecological perspective on fishing activities. Chioggia, Venice, the27th–29th of September 2006.

ascuel, D., Bozec, Y.-M., Chassot, E., Colomb, A., Laurans, M., 2005. The trophicspectrum: theory and practical applications. ICES J. Mar. Sci. 62, 443–452.

eymans, J.J., Shannon, L.J., Jarre, A., 2004. Changes in the northern Benguelaecosystem over three decades: 1970s 1980s and 1990s. Ecol. Model. 172, 175–195.

MEDES, 1999. The Castellón trawling project (1961–1966): teachings for a sustain-able fisheries management. IMEDES, COPEMED and FAO special publication, 173pp.

ennings, S., Greenstreet, S.P.R., Hill, L., Piet, G.J., Pinnegar, J.K., Warr, K.L., 2002. Long-term trends in the trophic structure of the North Sea fish community: evidencefrom stable-isotope analysis, size-spectra and community metrics. Mar. Biol. 141,1085–1097.

ongman, R.H.G., ter Braak, C.J.F., van Tongeren, O.F.R., 1999. Data Analysis in Com-munity and Landscape Ecology. Cambridge Univ. Press, Cambridge.

alli, C.M., Parsons, T.R., 1993. Biological Oceanography: An Introduction. PergamonPress, Oxford, p. 296.

eontief, W.W., 1951. The Structure of the US Economy, second ed. Oxford UniversityPress, New York.

ng 220 (2009) 2088–2102

Libralato, S., Pranovi, F., Raicevich, S., Giovanardi, O., 2004. Mixed trophic impact andtransfer efficiency as indicators of species’ role and ecosystem state. Biol. Mar.Med. A 11 (2), 255–264.

Libralato, S., Christensen, V., Pauly, D., 2006. A method for identifying keystonespecies in food web models. Ecol. Model. 195, 153–171.

Libralato, S., Coll, M., Tudela, S., Palomera, I., Pranovi, F., 2008. A new index to quantifyecosystem impacts of fisheries as the removal of secondary production. Mar. Ecol.Prog. Ser. 355, 107–129.

Libralato, S., Coll, M., Tempesta, M., Santojanni, A., Spoto, M., Palomera, I., Arner, E.,Solidoro, C. Food-web traits of protected and exploited areas of the Adriatic Sea.Biol. Conserv., submitted for publication.

Lindeman, R.L., 1942. The trophic-dynamic aspect of ecology. Ecology 23, 399–418.Lleonart, J. (Ed.), 1993. Northwestern Mediterranean Fisheries. Sci. Mar. 57 (2–3),

1993.Lotze, H.K., Lenihan, H.S., et al., 2006. Depletion, degradation and recovery potential

of estuaries and Coastal Seas. Science 312, 1806–1809.Mackinson, S., Daskalov, G., Heymans, J.J., Neira, S. Arancibia, H., Zetina-Rejón, M.,

Jiang, H., Cheng, H.Q., Coll, M., Arreguin-Sanchez, F., Keeble, K., Shannon, L. Whichforcing factors fit? Using ecosystem models to investigate the relative influ-ence of fishing and changes in primary productivity on the dynamics of marineecosystems. Ecol. Model., in press, doi:10.1016/j.ecolmodel.2008.10.021.

Margalef, R., 1968. Perspectives in Theoretical Ecology. The University of ChicagoPress, Chicago, London, p. 111.

Margalef, R., 1985. Introduction to the Mediterranean. In: Margalef, R. (Ed.), KeyEnvironments: Western Mediterranean. Pergamon Press, Oxford, pp. 1–16.

Moloney, C.L., Jarre, A., Arancibia, H., Bozec, Y.-M., Neira, S., Roux, J.-P., Shannon, L.J.,2005. Comparing the Benguela and Humboldt marine upwelling ecosystemswith indicators derived from inter-calibrated models. ICES J. Mar. Sci. 62 (3),493–502.

Odum, E.P., 1969. The strategy of ecosystem development. Science 104, 262–270.Odum, E.P., 1985. Trends expected in stressed ecosystems. BioScience 35 (7),

419–422.Odum, W.E., Heald, E.J., 1975. The detritus-based food web of an estuarine mangrove

community. In: Cronin, L.E. (Ed.), Estuarine Research, vol. 1. Academic Press, NewYork.

Palomera, I., Olivar, M.P., Salat, J., Sabatés, A., Coll, M., García, A., Morales-Nin, B.,2007. Small pelagic fish in the NW Mediterranean Sea: an ecological review.Prog. Oceanogr. 74, 377–396.

Papaconstantinou, C., Farrugio, H., 2000. Fisheries in the Mediterranean. Medit. Mar.Sci. 1 (1), 5–18.

Pauly, D., Christensen, V., 1995. Primary production required to sustain global fish-eries. Nature 374, 255–257.

Pauly, D., Christensen, V., Dalsgaard, A., Froese, R., Torres, J., 1998. Fishing downmarine food webs. Science 279 (5352), 860–863.

Plagányi, É.E., 2007. Models for an ecosystem approach to fisheries. FAO FisheriesTechnical Paper, No. 477. FAO, Rome, p. 108.

Plagányi, É.E., Butterworth, D.S., 2004. A critical look at the potential of Ecopathwith Ecosim to assist in practical fisheries management. Afr. J. Mar. Sci. 26,261–288.

Power, M.E., Tilman, D., Ester, J.A., Menge, B.A., Bond, W.A., Mills, L.S., Daily, G.,Castilla, J.C., Lubchenco, J., Paine, R.T., 1996. Challenge in the question for key-stones. Bioscience 46 (8), 609–620.

Rochet, M.-J., Trenkel, V.M., 2003. Which community indicators can measurethe impact of fishing? A review and proposals. Can. J. Fish. Aquat. Sci. 60,86–99.

Sala, E., 2004. The past and present topology and structure of Mediterranean subtidalrocky-shore food webs. Ecosystems 7 (4), 333–340.

Sardà, F., 1998. Symptoms of overexploitation in the stock of the Norway lobster(Nephrops norvegicus) On the “Serola Bank” (Western Mediterranean Sea offBarcelona). Sci. Mar. 62 (3), 295–299.

SCMEE, 2005. Transversal workshop on ecosystem approach to fisheries. GFCM SACand SCMEE. FAO Salammbo, Tunisia, 7/9 September 2005, 19 pp.

Shannon, L.J., Moloney, C., Jarre-Teichmann, A., Field, J.G., 2003. Trophic flows in thesouthern Benguela during the 1980s and 1990s. J. Mar. Syst. 39, 83–116.

Shannon, L.J., Coll, M., Neira, S., 2009. Exploring the dynamics of ecological indicatorsusing food web models fitted to time series of abundance and catch data. Ecol.Ind. 9, 1078–1095.

Shannon, L.J., Coll, M., Cury, P., Neira, S., Roux, J.-P. Role of small pelagic fish on theecosystem. In: Checkley, D.M., Roy, C., Alheit, J., Oozeki, Y. (Eds.), Climate Changeand Small Pelagic Fish, in press.

Strayer, D., 1991. Notes on Lindeman’s progressive efficiency. Ecology 72 (1),348–350.

Tudela, S., Coll, M., Palomera, I., 2005. Developing an operational reference frame-work for fisheries management on the basis of a two-dimensional index ofecosystem overfishing. ICES J. Mar. Sci. 62 (3), 585–591.

Ulanowicz, R.E., Puccia, C.J., 1990. Mixed trophic impacts in ecosystems. Coenoses 5,7–16.

Ulanowicz, R.E., 1986. Growth and Development: Ecosystems Phenomenology.Springer, New York, p. 203.

Walters, C.J., Martell, S., 2004. Harvest Management for Aquatic Ecosystems. Prince-ton University Press, p. 420.

Wulff, F., Field, J.G., Mann, K.H. (Eds.), 1989. Coastal and Estuarine Studies, vol. 32.Springer–Verlag, New York, p. 134.