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e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116

avai lab le at www.sc iencedi rec t .com

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

ood-web dynamics in the South Catalan Sea ecosystemNW Mediterranean) for 1978–2003

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

Institute of Marine Science (ICM-CSIC), Passeig Marítim de la Barceloneta 37-49, 08002 Barcelona, SpainDalhousie University, Department of Biology, Halifax, Nova Scotia, Canada B3H 4J1WWF Mediterranean Programme Office, Canuda 37, 08002 Barcelona, SpainDalhousie University, Department of Mathematics and Statistics, Halifax, Nova Scotia, Canada B3H 3J5

r t i c l e i n f o

rticle history:

eceived 10 November 2007

eceived in revised form

5 May 2008

ccepted 6 June 2008

ublished on line 18 July 2008

eywords:

ood-web modelling

copath with Ecosim

ishing impacts

nvironmental factors

cosystem indicators

rend analysis

atalan Sea

W Mediterranean

a b s t r a c t

An ecosystem model representing the continental shelf and upper slope of the South Catalan

Sea (NW Mediterranean) is calibrated and fitted to the available time series data from 1978

to 2003. We use a process-oriented model to explore the extent to which changes in marine

resources and the ecosystem were driven by trophic interactions, environmental factors

and fishing activities. Fishing effort and fishing mortality are used to drive the model, while

observed (absolute and relative) biomasses and catches are compared with the predicted

results. A reduction in the sum of the squared deviations of the observed and predicted

values of the biomass is used as a metric for calibrating and assessing the fit of the model.

A posteriori trophodynamic indicators are used to explore the ecosystem’s structural and

functional changes from 1978 to 2003, and a generalized least squares regression is used to

assess the significance of the predicted trends. In general, a high proportion of the variability

in the time series data is explained by the main trophic interactions (37–53%), fishing activ-

ities (14%), and indirectly by considering the environment (6–16%), as driving factors. The

model’s predictions match satisfactorily with the yearly data on the biomass for anglerfish,

adult hake, demersal sharks, anchovy and mackerel, which show a statistically significant

decrease over time, while the biomass of flatfish and seabirds are observed to increase.

Catch data show a significant decrease in anglerfish, demersal sharks, anchovy and sar-

dine, while there is an increase in red mullet, flatfish, juvenile hake and horse mackerel.

These changes in biomass are predicted to have direct and indirect impacts on the ecosys-

tem mediated by the trophic web, such as the proliferation of non-commercial species with

lower trophic levels (e.g., benthic invertebrates) or higher turnover rates (e.g., cephalopods

and benthopelagic fish). This is consistent with anecdotal information from the Mediter-

ranean and is likely caused by trophic cascades due to the removal of demersal and pelagic

higher trophic level organisms (predator release), and a decrease in small pelagic fish (com-

petitor release). Trophodynamic indicators suggest a degradation pattern over time: both

the mean trophic level of the community (mTLco, excluding primary producers and detri-

tus) and a modified version of Kempton’s index of biodiversity decrease with time, while

∗ Corresponding author at: Institute of Marine Science (ICM-CSIC), Passeig Marítim de la Barceloneta 37-49, 08002 Barcelona, Spain.el.: +34 93 230 95 43.

E-mail addresses: mcoll@icm.csic.es, martacoll@dal.ca (M. Coll).304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2008.06.013

96 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116

the total flow to detritus and the loss of production due to fishing increase from 1978 to

2003. Additionally, the demersal/pelagic ratio increases due to an overall decrease in the

pelag

abundance of small

1. Introduction

The development of an ecosystem approach to assessing andmanaging marine resources in the Mediterranean Sea requiresthe consideration of the broader ecological context, includingfishing activities (SCMEE, 2005). At present, fishing technology,overcapitalization and increasing market demand for marineproducts are placing intensive pressure on exploited resourcesand ecosystems in the Mediterranean Sea (Margalef, 1985;Aldebert and Recasens, 1996; Sardà, 1998; Papaconstantinouand Farrugio, 2000; Bas et al., 2003).

Mass balance ecosystem models recently developed to rep-resent the South Catalan Sea and the North and CentralAdriatic Sea marine ecosystems enabled the characterizationof structural and functional ecosystem properties. These mod-els also highlighted the large impact of fishing activities inthese ecosystems during the 1990s (Coll et al., 2006a, 2007).Recently, the application of a new index of fishing’s ecosys-tem effects (Tudela et al., 2005; Libralato et al., 2008) providedevidence of a high probability of ecosystem overfishing in theMediterranean Sea.

In addition, environmental factors strongly affect primaryand secondary production dynamics in the Mediterranean Sea(e.g., Estrada, 1996; Palomera, 1992; Sabatés and Olivar, 1996;Lloret et al., 2001, 2004; Santojanni et al., 2006). A wind mix-ing index was positively related to the recruitment of variousdemersal species, most likely due to enhanced fertilizationand local planktonic production in the NW Mediterranean(Lloret et al., 2001). This was also the case for sardine (Sardinapilchardus) landings from the Ebro River Delta area during thespawning season of this species (November–March) (Lloret etal., 2004). River runoffs from the Rhône and Ebro were posi-tively related to the recruitment of demersal species (Lloretet al., 2001), as well as with anchovy (Engraulis encrasicolus)landings during its spawning season (April–August) in theSouth Catalan Sea (Lloret et al., 2004) and in the Adriatic Sea(Santojanni et al., 2006). The progressive increase in watertemperature in the Mediterranean Sea (Salat and Pascual,2002) was related to an increase of sardinella (Sardinella aurita)landings and the species’ range expansion into the northernNW Mediterranean areas (Sabatés et al., 2006).

Thus fishing activities clearly take place within the ecosys-tem’s context, where target and non-target species displaycomplex interactions, and where environmental factors playan important role. Fishing will thus have direct and indirectimpacts on the ecosystem. These impacts can operate in syn-ergy with those induced by oceanographic features and otherdisturbances of anthropogenic and natural origin (Jacksonet al., 2001; Cury et al., 2003). The impact that fishing and

environmental factors have on marine ecosystems dependsgreatly on trophic flow controls, e.g., from bottom-up (or preycontrol), to top-down (or predator control). Abundant speciespositioned at intermediate trophic levels in the food web (like

ic fish in the ecosystem.

© 2008 Elsevier B.V. All rights reserved.

small pelagic fish in upwelling areas) have been described tobe involved in wasp-waist flow control situations, where theyinfluence the dynamics of both organisms with both lower andhigher trophic position, i.e., their prey and predators (Bakun,1996; Cury et al., 2000).

Ecological models, in particular Ecopath with Ecosim (EwE)models (Pauly et al., 2000; Christensen and Walters, 2004a),have demonstrated substantial progress in the incorporationof temporal dynamics into ecosystem analysis while account-ing for trophic interactions, exploitation and the environment(Walters et al., 1997). The temporal dynamic module Ecosimnow allows for the calibration and fitting of models to avail-able time series data, while exploring the contribution ofdifferent ecosystem drivers to marine resource dynamics (e.g.,Heymans, 2004; Shannon et al., 2004a; Heymans et al., 2005;Araújo et al., 2006). The ability to calibrate and fit ecologicalmodels increases confidence in model predictions and canbe used to analyze the ecosystem effects of fishing. Otherabilities of Ecosim include model fitting with production forc-ing, nutrient forcing, mediation effects, seasonality effects,and randomized climate forcing using historical PP trends,policy optimization, analysis of population variability andbehavioural ecology (Christensen et al., 2005).

In this study, the static mass balance ecosystem modelof the South Catalan Sea presented in Coll et al. (2006a) isextended to be time-dynamic, with calibration and fittingprovided for the available time series data. A posteriori tropho-dynamic indicators are then used to analyze the temporaldynamics of the food web in the South Catalan Sea from 1978to 2003. This constitutes the first example of trophodynamicmodelling with calibration and fitting to an available timeseries data in the Mediterranean Sea. The reconstruction ofhistorical food web changes in these long-exploited and bio-logically rich Mediterranean ecosystems, and the analysis ofthe roles played by ecosystem drivers and key species, bringswith it a unique opportunity to understand the response ofthe Mediterranean’s marine ecosystems in the face of humanpressures and climatic change. Consequently, the central aimsof this contribution are (a) to explore the dynamics of themarine resources of the South Catalan Sea from 1978 to 2003considering fishing and environmental factors as the mainexternal drivers, (b) to evaluate flow control patterns in theecosystem and (c) to explore the ecosystem’s structural andfunctional changes during this period using ecosystem indi-cators.

2. Materials and methods

2.1. Area of study

The South Catalan Sea study area (Fig. 1) comprises the con-tinental shelf and upper slope associated with the Ebro RiverDelta. It has a depth range between 50 m and 400 m and covers

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116 97

F romw

aaoes

d11wiflf

FM

ig. 1 – Map of the South Catalan Sea study area (modified fww.icm.csic.es/geo/gma/MCB).

total area of soft bottom sediments of 4500 km2. Gener-lly this is an oligotrophic area, where nutrient enrichmentccurs due to regional environmental events related to windpisodes, the conditions of a temporal thermocline, a shelf-lope current and river discharges (Estrada, 1996).

Official landings from the Catalan Sea (Fig. 2) increasedramatically from the beginning of the 19th century to the950s when they stabilized. They then decreased until the970s, after which landings increased again. This trend

as mainly due to the expansion of the fishery and public

ncentives for the fishing sector. Landings showed markeductuations and they have undergone a progressive decrease

rom 1994 onward. However, nominal fishing effort has

ig. 2 – Historical reconstruction of official landings (t) from the Carine Science, CMIMA-CSIC, the Catalan Government, and from

Catalano-Balearic Sea—Bathymetric chart 2005,

progressively increased (Fig. 3a). Small pelagic fishes, such assardine (S. pilchardus) and anchovy (E. encrasicolus), constitutedthe principal component of the catches during the 1990s interms of biomass and were mainly caught by purse seinesand bottom trawlers. The demersal fishery targets mainlyjuveniles of the commercial species, e.g., hake (Merlucciusmerluccius), red mullet (Mullus barbatus and Mullus surmuletus),anglerfish (Lophius piscatorius and Lophius budegassa), bluewhiting (Micromesistius poutassou) and flatfishes (mainly Solea

vulgaris, Lepidorhombus spp., Citharus linguatula, Arnoglossusspp. and Symphurus spp.), are principally caught by thetrawling fleet. Large demersal fish (e.g., adult hake) and largepelagic fish (e.g., Atlantic bonito Sarda sarda, bluefin tuna

atalan region (1907–2004) (Databases of the Institute ofGarrido and Alegret 2006-HMAP historic database).

98 e c o l o g i c a l m o d e l l i n g

Fig. 3 – Time series of (a) nominal fishing effort(horsepower) by trawling, purse seining and long linefishing in the South Catalan Sea (1970–2003), (b) fishingmortality of anchovy and sardine in the South Catalan Sea(1978–2003), and (c) nominal trawling effort (horsepower)

i

and alternative scenarios tested (see Section 3.1.d, datasources indicated in the main text).

Thunnus thynnus and swordfish Xiphias gladius) are caught bylong line and troll bait fleets (Lleonart, 1993; Papaconstantinouand Farrugio, 2000; Bas et al., 2003).

2.2. Mass balance ecosystem modelling

Applying a “back to the future” approach (Pitcher, 2001), a mass

balance trophic model describing the annual average situa-tion in the South Catalan Sea (Fig. 1) (Coll et al., 2006a), wasadapted to represent the ecosystem in 1978–1979 by incor-porating available historical information. This provides the

2 1 7 ( 2 0 0 8 ) 95–116

initial conditions for the trophodynamic simulation describedbelow. This new ecological model is comprised of 40 functionalgroups, spanning the main trophic components of the ecosys-tem from primary producers to top predators and includestwo detritus groups (natural detritus and discards from fish-ing activities). The main fishing activities by operational fleetsoccurring in the area were also included in the analysis, i.e.,the trawling, purse seine, long line and troll bait fleet. Modifiedinput and output parameters of the model from 1978 to 1979are shown in Table 1.

European hake was the only species where enough datawas available to describe two age groups: namely juvenilehake (<2 years old or <25 cm) and adult hake (≥2 years old or≥25 cm). To ensure consistency between ontogenetic groups,the multiple stanza representation (Christensen and Walters,2004a) was used for modelling. The production rate (P/B) anddiet composition were provided for both groups (Bozzanoet al., 1997, 2005), while biomass (B) and consumption rate(Q/B) were introduced for the juvenile hake. Moreover, a fewnew ecological parameters from previously published modelswere introduced: k, the annual value from the von Berta-lanffy growth function, k = 0.15, and the ratio of weight atmaturity to the weight at infinity, Wmat/Winf = 0.064 (Recasensand Lleonart, 1999). Migratory patterns of large pelagic fishes(Atlantic bonito, Atlantic bluefin tuna and swordfish) weretaken into account within the model by describing a migra-tory flux of production in the area (ICCAT, 2003, 2004). Initialdiet information was taken from the model for 1994 (Coll etal., 2006a) based on the published literature. A new conver-sion factor to transform carbon units/m2 to organic matterwas used for production/biomass (P/B) value of phytoplankton(Dalsgaard and Pauly, 1997) based on previous parameteriza-tion (Coll et al., 2006a).

The EwE approach version 5.1 (Pauly et al., 2000;Christensen and Walters, 2004a) was used to ensure energybalance of the new model:

Pi =∑

j

BjM2ij + Yi + Ei + BAi + Pi(1 − EEi) (1)

EwE divides the production (P) of the ith component, or func-tional group, of the ecosystem into predation mortality (M2ij)caused by the biomass of the other predators (Bj); exports fromthe system both from fishing catches (Yi) and emigration (Ei);biomass accumulation in the ecosystem (BAi); and other mor-tality or mortality not captured by the model (1 − EEi), whereEEi is the ecotrophic efficiency of the group within the sys-tem, or the proportion of the production Pi that is exportedout of the ecosystem (i.e., by fishing activity) or consumed bypredators within it.

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

B(P/B)i =∑

j

Bj(Q/B)j DCij + Yi + Ei + BAj + Bi(P/B)i(1 − EEi) (2)

where (P/B) indicates the production of i per unit of biomass

and is equivalent to total mortality, or Z, under steady-stateconditions (Allen, 1971); (Q/B)i is the consumption of i perunit of biomass; and DCij indicates the proportion of i thatis in the diet of predator j in terms of volume or weight units.

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116 99

Table 1 – Input and output parameters for the ecosystem components used in the South Catalan Sea model (1978–1980)

Functional group B P/B Q/B U/Q Landings Discards TL EE

1. Phytoplankton 9.73 162.08 – – – – 1.00 0.132. Micro and mesozooplankton 6.05 20.87 48.85 0.40 – – 2.05 0.843. Macrozooplankton 0.48 20.41 50.94 0.20 – – 2.77 0.804. Jellyfish 0.33 13.87 50.48 0.20 – – 2.83 0.225. Suprabenthos 0.08 8.05 52.12 0.30 – – 2.11 0.936. Polychaetes 13.06 1.82 11.53 0.60 – – 2.00 0.327. Shrimps 0.04 3.08 7.20 0.20 0.001 0.0004 2.98 0.978. Crabs 0.15 2.10 4.73 0.20 0.053 0.016 2.89 0.979. Norway lobster 0.03 1.20 4.56 0.20 0.007 0.0003 2.82 0.9810. Benthic invertebrates 6.10 1.02 3.13 0.43 0.001 – 2.02 0.4311. Benthic cephalopods 0.12 2.34 5.30 0.13 0.110 0.005 3.10 0.9712. Benthopelagic cephalopods 0.10 2.06 26.47 0.40 0.032 0.002 3.66 0.9913. Mullets 0.24 2.29 6.90 0.20 0.009 0.0004 3.16 0.0514. Conger eel 0.02 1.40 3.50 0.20 0.014 0.0007 4.22 0.9715. Anglerfish 0.03 1.00 3.50 0.20 0.020 0.001 4.39 0.9816. Flatfishes 0.01 2.10 7.53 0.20 0.007 0.0004 3.20 0.9817. Poor cod 0.02 1.52 6.97 0.20 0.015 0.0008 3.31 0.9518. Juvenile hake 0.04 1.00 7.37 0.20 0.005 0.0002 3.45 0.4619. Adult hake 0.16 0.90 3.27 0.20 0.071 0.004 4.11 0.5120. Blue whiting 1.02 0.66 5.93 0.20 0.054 0.016 3.40 0.6321. Demersal fishes (1)* 0.43 1.16 6.85 0.20 0.157 0.047 3.08 0.9922. Demersal fishes (2)* 0.02 1.00 7.17 0.20 0.003 0.0008 3.01 0.8323. Demersal fishes (3)* 0.15 0.43 6.25 0.20 0.019 0.006 3.96 0.9724. Demersal small sharks 0.07 0.42 5.43 0.20 0.013 0.004 3.70 0.9025. Benthopelagic fishes 0.12 1.37 9.03 0.30 0.015 0.004 3.49 0.9826. European anchovy 2.14 1.33 13.91 0.30 0.934 0.047 3.05 0.7627. European pilchard 2.74 1.50 8.86 0.30 2.044 0.102 2.97 0.9728. Small pelagic fishes 0.93 0.52 7.39 0.30 0.061 0.018 3.00 0.9929. Horse mackerel 1.09 0.39 5.13 0.20 0.047 0.002 3.19 0.4430. Mackerel 0.43 0.46 4.88 0.20 0.004 0.0002 3.55 0.5131. Atlantic bonito 0.30 0.35 4.36 0.20 0.001 0.00006 4.06 0.0232. Large pelagic fishes 0.12 0.43 1.63 0.20 0.0004 – 4.19 0.0033. Loggerhead turtle 0.03 0.15 2.54 0.20 – 0.00004 2.54 0.0134. Audouin’s gull 0.00 4.64 70.00 0.20 – – 3.22 0.0035. Other sea birds 0.00 4.56 73.20 0.20 – 0.00001 2.19 0.2036. Dolphins 0.00 0.07 13.49 0.20 – 0.001 4.34 0.1437. Fin whale 0.37 0.04 4.11 0.30 – – 3.81 0.0038. Detritus 0.31 – – – – – 1.00 0.5139. Discards 0.00 – – – – – 1.00 0.0040. By-catch 70.00 – – – – – 1.00 0.17

ption) defin

Eef(streiocS

2p

Tw

B: biomass (t km−2); P/B: production/biomass ratio (y−1); Q/B: consumexpressed in t km−2 y−1; TL: trophic level; EE: ecotrophic efficiency; (*

wE parameterizes the model by describing a system of linearquations for all the functional groups in the model, whereor each equation at least three of the basic parameters: Bi,P/B)i, (Q/B)i or EEi have to be known for each group i. The unas-imilated food rate (or the fraction of the food consumptionhat is not assimilated, U/Q) and the fate of detritus are alsoequired parameters. The energy balance within each group isnsured when consumption by group i equals production by

, respiration by i and food that is unassimilated by i. A reviewf Ecopath with Ecosim approach, capabilities and limitationsan be found in Christensen and Walters (2004a), Fulton andmith (2004) and Plagányi and Butterworth (2004).

.3. Temporal dynamic modelling and calibration

rocedure

he South Catalan Sea ecosystem data representing 1978–1979as then used to analyze the main temporal dynamics of the

/biomass ratio (y−1); U/Q: unassimilated food; landings and discardsed performing multivariate analysis (Coll et al., 2006a).

food web. With the application of the temporal dynamic mod-ule Ecosim (Walters et al., 1997) the model was calibrated andfitted for the period 1978–1979 to 2003.

Ecosim uses a system of time-dependent differential equa-tions from the baseline mass balance model where the growthrate of biomass is expressed as:

dBi

dt= (P/Q)i

∑Qji −

∑Qij + Ii − (Mi + Fi + ei)Bi (3)

where (P/Q)i is the gross efficiency; Mi is the non-predationnatural mortality rate; Fi is the fishing mortality rate; ei isthe emigration rate; I is the immigration rate; and B is the

i i

biomass of the functional group i. Calculations of consump-tion rates (Qij) are based upon the “foraging arena” theory(Walters and Juanes, 1993; Walters and Korman, 1999) wherethe biomass of prey i is divided between a vulnerable and a

i n g

100 e c o l o g i c a l m o d e l l

non-vulnerable fraction. This is represented as:

Qij = �ijaijBiBjTiTjSijMij/Dj

�ij + �′ijTiMij + aijMijBjSijTj/Dj

(4)

where �ij is the vulnerability and expresses the rate with whichprey move between being vulnerable and not vulnerable, aij

is the effective search rate for i by j, Ti and Tj are the rela-tive feeding time for prey and predator, Sij are the seasonal orlong term forcing effects, Mij are the mediation forcing effectsand Dj are the effects of handling time as a limit to consump-tion rate. Default values of vulnerabilities (�ij = 2) represent amixed flow control, whilst values of �ij = 1 describe bottom-up flow control (control of the predators by their prey) and�ij values �1 represent top-down control (control of prey bytheir predators) (Walters et al., 1997; Christensen and Walters,2004a). Vulnerabilities also express the maximum increase inpredation mortality under conditions of high predator/preyabundance so this parameter represents the fact that a largeincrease in predator biomass will cause an increase in pre-dation mortality for a given prey. Low vulnerability (close to1) means that an increase in a predator biomass will notcause any noticeable increase in the predation mortality ofthe given prey. A high vulnerability indicates that if the preda-tor biomass is doubled it will cause close to a doubling in thepredation mortality for a given prey (Christensen et al., 2005).Therefore, by calculating the consumption of each functionalgroup, Ecosim allows for dynamic simulations to consider dif-ferent mechanisms of flow control, the ecological behaviourof organisms and forcing elements like environmental factors(see Eq. (4)).

The following detailed procedure, modified from Shannonet al. (2004a), was applied to the Ecosim model to fit theSouth Catalan Sea model to the available data (see Table 2 for

acronyms and definitions). A reduction in the sum of squared(SS) deviations of observed log biomass from predicted logbiomass values was used as a metric for assessing the fit ofthe model (Christensen et al., 2005):

Table 2 – Definition of terms for the input and output measures

Acronym

B Mean annual biomass (t km−2) of trophic species (Table 1)C Mean annual catch (t km−2 y−1) of trophic species (Table 1)HP Sum of fishing fleet horse power used as a proxy of fishingCPUE Catch per unit of effort used as a proxy for relative biomasSS Sum of squared deviation of biomass observed values from

modelSSi Initial sum of squared deviation of biomass observed valu�ij Rate with which prey i move between being vulnerable an

SSEF Environmental anomaly function that impacts phytoplank

minimize SSD/P Total demersal to pelagic biomass ratio used as an indicatoQ′ index A modified version of the Kempton’s index of biodiversity,mTLco Mean trophic level of the community that reflects the tropmTLc Mean trophic level of the catch that reflects the strategy oL index Loss in production index that quantifies the theoretical deFD Total flow to detritus (t km−2 y−1) calculated as the amoun

2 1 7 ( 2 0 0 8 ) 95–116

(1) Input of data. The data sets were stored in the EwE database.Fishing effort and fishing mortality data (see Section 2.4)were used to drive the model from 1978 to 2003. Absoluteand relative biomass data were compared with predictedmodel results to assess the fit of the model. Catch datafrom commercial species was also utilized, but only forcomparison purposes due to the low quality of the data.Official landings data is notably biased due to discards andunreported catches (Coll et al., 2006a).

(2) Reset fishing data. Fishing effort and fishing mortalitieswere reset to the Ecopath baseline value of 1978 and abaseline goodness-of-fit of the model (SSi, i = initial) wascalculated.

(3) Search for vulnerabilities (15 interactions). Input time seriesdata were reintroduced into the EwE database and fishingrates were set again. Then the “fit to time-series” moduleof Ecosim was used to identify the prey–predator inter-actions that were most sensitive to changes in the �ij

value and would improve the fit of the model to data. Atotal of the 15 most sensitive �ij values were marked. Vul-nerabilities of anchovy, sardine, other small pelagic fish,horse mackerel and mackerel were marked for vulnerabil-ity searches as well to test the wasp-waist hypothesis. Avulnerability search was run to estimate the �ij values thatwould minimize the SSi.

(4) Search for environmental anomaly function (EF). By imple-menting a non-parametric routine incorporated in Ecosim,a search for an environmental anomaly (potentiallyimpacting the phytoplankton initial P/B value by addingan annual modifier to every year) was run to con-tinue minimizing the SS. An estimate of the F statisticSSreduced/SSbase using the null hypothesis of no produc-tivity anomaly was used to assess the anomaly reliabilityand to test for the probability that the decrease on SSvalues would be due to chance alone (Christensen et al.,

2005).

(5) Reverse order procedure. Procedures (3) and (4) were reversedto estimate the productivity pattern first and then the �ij

values. Moreover, both the vulnerability and environmen-

used in the model’s application

Definition

efforts of commercial species

model predictions used as a metric for assessing the fit of the

es from model predictionsd not vulnerable to a predator j used to fit the model by reducing the

ton initial P/B value calculated with a non-parametric routine to

r to explore processes benefiting demersal or pelagic compartmentsor biomass diversity indexhic structure of the ecosystemf a fisherypletion in secondary production due to fishingt of total trophic flows whose fate is the detritus box of the model

g 2

(

tcrvr(ltct

rdah

2

Tbrd

(

(

(

(

e c o l o g i c a l m o d e l l i n

tal anomaly searchs were run together with the �ij valuesand the productivity patterns calculated concurrently.

6) Analysis of results. Calculated vulnerabilities �ij were ana-lyzed by comparing the observed and predicted biomassand catch data trends. The environmental anomalyobtained from the calibration process was then correlatedwith the environmental data available (explained in Sec-tion 2.5).

To further analyze trophic flow control by functional group,he following procedure was applied: (1) After repeating pro-edures (1) and (2), explained above, initial data sets weree-stored in the EwE database; (2) For each functional group,ulnerabilities as prey and predators were changed to rep-esent bottom-up (�ij = 1), top-down (�ij = 10) or wasp-waist�ij = 1 as preys and �ij = 10) and the SS value was calcu-ated; (3) The percentage of decreasing SS under each ofhe three scenarios below was compared to explore theontribution of each group to different trophic flow controlypes.

During the calibration process, ecological parameterselated to the behaviour of species (see Eq. (4)) were set byefault (Christensen et al., 2005) with the exception of vulner-bilities (�ij) and the parameters linking the age stanzas forake.

.4. Time series

he available time series data for fishing effort and mortality,iomasses and catches were compiled for use with the tempo-al dynamic ecosystem model (Section 2.3) (see Table 3). Theseata and their sources are listed below:

1) Nominal fishing effort in trawling, purse seine and longline fishing are expressed in units of horse power (HP). Thenumber of boats, gross tonnage and numbers of days atsea were obtained from the Ministry of Agriculture andFisheries online database. The change in fishing effortexpressed in HP is shown as an example (Fig. 3a). We usedthis data as a proxy of fishing effort (Table 3).

2) Absolute biomass data for anchovy and sardine wereobtained by combining information from different pelagicsurveys conducted in the area (Oliver and Pastor, 1985;Pertierra and Castellón, 1985; López-Cazorla and Sánchez,1986; Pertierra, 1992; Pertierra and Palomera, 1993;Pertierra and Perrotta, 1993; García et al., 1994; Palomera,1995; Pertierra and Lleonart, 1995; Abad et al., 1998a,b;Quintanilla et al., 2004; Torres et al., 2004). Biomass andcatch data were used to calculate fishing mortality of sar-dine and anchovy (Fig. 3b). We used this data as a proxy offishing mortality (Table 3).

3) Relative biomasses expressed as catch per unit of effort(CPUE) of hake, red mullets, anglerfish, flat fishes, dem-ersal sharks (Scyliorhinus canicula, Galeus melastomus andEtmopterus spinax), horse mackerel (Trachurus spp.) andmackerel (Scomber spp.) were calculated from catch data

and fishing effort.

4) The abundance of seabirds was reconstructed from theavailable literature (Aguilar, 1991; Oro and Ruíz, 1997; Oro,1999, 2002; AAVV, 2002; D. Oro, pers. commun.) and con-

1 7 ( 2 0 0 8 ) 95–116 101

verted to total biomass using mean weight of individualfrom Del Hoyo et al. (1992). Data included Caspian gull(Larus cachinnans, adults and juveniles), Audouin’s gull(Larus audouinii), Slender-billed gull (Larus genei), LesserBlack-backed gull (Larus fuscus), Black-headed gull (Larusridibundus), Sandwich tern (Sterna sandvicensis) and com-mon tern (Sterna hirundo).

(5) Fishing mortality and absolute biomass data for bluefintuna and swordfish was obtained from ICCAT evalua-tions of the Mediterranean Sea and Atlantic Ocean stocks(ICCAT, 2003, 2004).

(6) The environmental data used were: (a) sea surface tem-perature from 1978 to 2003 (annual mean and wintermean); data was collected near Tarragona, 40◦N–2◦E(Smith and Reynolds, 2004); (b) Ebro River water runoff(annual mean, hm3 y−1) from 1978 to 2003 (Ebro Hydro-graphical Confederation, Tortosa station); (c) wind mix-ing index (annual mean, m3 s−3) from 1978 to 1996(National Institute of Meteorology, www.inm.es); and(d) North Atlantic Oscillation (NAO) Index from 1978to 2003 (http://www.cgd.ucar.edu/-cas/climind/nao.html).Both annual and winter values were included in the anal-ysis.

The environmental anomaly function resulting from thecalibration procedure was correlated with these availableenvironmental data. Linear correlations were performed byanalyzing the anomaly function and the available data withSpearman’s rank correlation. An analysis based on a multiplelinear regression was also implemented to see if the anomalycould be predicted by the combinations of different types ofenvironmental data.

2.5. Ecosystem indicators

After calibration, the trophodynamic indicators predictedfrom the model were examined to understand the main struc-tural and functional changes in the ecosystem during theperiod from 1978 to 2003. These indicators have been previ-ously defined and discussed (e.g., Rochet and Trenkel, 2003;Christensen and Walters, 2004b; Cury et al., 2005) (see Table 2for acronyms and definitions):

(1) The biomass predicted by the model were analyzed tofurther explore species dynamics in cases where only par-tial/anecdotal information was available.

(2) The total demersal versus pelagic biomass ratio (D/P ratio)was used as an indicator to explore processes benefit-ing the demersal or pelagic compartments. This ratio isexpected to increase with fishing (Rochet and Trenkel,2003; Cury et al., 2005).

(3) A modified version of Kempton’s index of biodiversity,the biomass diversity index (Q′), was calculated. Thisis a relative index of biomass diversity calculated fromKempton’s Q75 index developed for expressing speciesdiversity (Kempton and Taylor, 1976; Christensen and

Walters, 2004b; Ainsworth and Pitcher, 2006). It includesthose species or functional groups with a trophic level(TL) of three or higher. The index’s increase implies anincrease in the biomass of various high trophic level organ-

102e

co

lo

gic

al

mo

de

ll

ing

21

7(2

00

8)

95–116

Table 3 – Information on the time series data used to simulate the temporal dynamics in the South Catalan Sea (1978–2003)

Functional groups Time series of data Observations To drive the model To fit the model To compare results

Anchovy 26 Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

Corrected for known errors whileregistering catches

×

Absolute biomass (t km−2) from1979 to 2003

Standardized from acoustics,VPA-LCA and MPH methods

×

Fishing mortality Calculated from catches andfishing effort

×

Sardine 27 Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

Corrected for known errors whileregistering catches

×

Absolute biomass (t km−2) from1983 to 2003

Standardized from acoustics,VPA-LCA and MPH methods

×

Fishing mortality Calculated from catches andfishing effort

×

Adult hake 19 Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

×

Longline fishing effort from 1978to 2003

Horse power, no. of boats, grosstonnage, fishing days at sea

×

CPUEs from 1978 to 2003 Calculated from long lining fishingeffort and catches

×

Juvenile hake 18 Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

The division between juvenilehake and adult hake was doneconsidering detailed informationon catches from Tarragona andSant Carles harbours

×

Bottom trawl fishing effort from1978 to 2003 modified to accountfor underreporting

Horse power, no. of boats, grosstonnage, fishing days at sea

×

CPUEs from 1978 to 2003 Calculated from trawling fishingeffort and catches

×

Horse mackerel and mackerel 29,30

Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

×

Fishing effort from purse seinefrom 1978 to 2003

Horse power, no. of boats, grosstonnage, fishing days at sea

×

CPUEs from 1978 to 2003 Calculated from purse seinefishing effort and catches

×

Red mullets, anglerfish, flatfishes, demersal sharks

13,15,16,24

Total catches (t km−2 y−1) from1978 to 2003 (corrected forreporting errors)

×

Bottom trawl fishing effort from1978 to 2003

Horse power, no. of boats, grosstonnage, fishing days at sea

×

CPUEs from 1978 to 2003 Calculated from trawling fishingeffort and catches

×

e c o l o g i c a l m o d e l l i n g 2

Tabl

e3

(Con

tin

ued

)

Fun

ctio

nal

grou

ps

Tim

ese

ries

ofd

ata

Obs

erva

tion

sTo

dri

veth

em

odel

Tofi

tth

em

odel

Toco

mp

are

resu

lts

Larg

ep

elag

icfi

sh32

Cat

ches

(tkm

−2y−

1)f

rom

the

Med

iter

ran

ean

Sea

from

1980

/198

5to

2003

×

Tota

lbio

mas

ses

(tkm

−2)e

stim

ated

usi

ng

VPA

for

the

Med

iter

ran

ean

Sea

Bio

mas

san

dca

tch

dat

afo

rT

hunn

us

thyn

nus

from

1980

and

for

Xip

hias

glad

ius

from

1985

(IC

CA

T)

×

Fish

ing

mor

tali

ty×

Seab

ird

s34

,35

Bre

edin

gp

airs

ofse

abir

ds

from

1980

to20

03an

dw

inte

rin

gin

div

idu

als

from

1990

sin

the

Ebro

Riv

erD

elta

Nat

ion

alp

ark

and

surr

oun

din

gs

Abu

nd

ance

was

con

vert

edto

biom

ass

usi

ng

ind

ivid

ual

wei

ght

byD

elH

oyo

etal

.(19

92)

×

Lon

gli

ne

fish

ing

effo

rtfr

om19

78to

2003

Hor

sep

ower

,no.

ofbo

ats,

gros

sto

nn

age,

fish

ing

day

sat

sea

×

Info

rmat

ion

isp

rovi

ded

rega

rdin

gti

me

seri

esu

sed

tod

rive

and

fit

the

mod

el,a

nd

toco

mp

are

resu

lts.

1 7 ( 2 0 0 8 ) 95–116 103

isms. Consequently the index’s value would be expectedto decrease with increased impact from fishing.

(4) The mean trophic level of the community (mTLco) andmean trophic level of the catch (mTLc) were also included.mTLc reflects the strategy of a fishery in terms of selectedfood-web components and is calculated as the weightedaverage of the TL of harvested species (Pauly et al., 1998).mTLco reflects the structure of the community and is cal-culated as the weighted average of the TL of all the specieswithin the ecosystem (Jennings et al., 2002; Rochet andTrenkel, 2003). Both indicators decrease with an increaseof fishing impact due to a reduced numbers of large preda-tors in the ecosystems, and lower trophic level organismsprevailing (Pauly et al., 1998; Jennings et al., 2002).

(5) The loss in production index (L index) quantifies the theo-retical depletion in secondary production in an exploitedecosystem due to fishing. This index was proposed as aproxy for the quantification of fishing’s ecosystem effectsand for estimating the probability that the ecosystem isbeing sustainably fished (Libralato et al., 2008). It is basedon previous work by Tudela et al. (2005). The index takesinto account both the ecosystem properties (the primaryproduction, PP, and mean transfer efficiency of the energy,TE) as well as features of fishing activities (mTLc and theprimary production required to sustain the fishery, PPR)(Lindeman, 1942; Pauly and Christensen, 1995; Pauly etal., 1998). We obtained mTLc, TE, PPR and PP from thecalibrated model. PPR was calculated taking into account(1) production from primary producers and (2) produc-tion from primary producers and detritus. The L indexincreases with fishing impact (Libralato et al., 2008).

(6) Total flow to detritus (FD) was calculated as the amountof the total trophic flows whose fate is to end up in thedetritus box of the model (t km−2 y−1). As fishing impactincreases this indicator is expected to increase due to dis-ruption of energy paths in the food web (Walsh, 1981;Shannon et al., in press).

2.6. Trend analysis of model output

A trend analysis was undertaken to assess the significanceof the predicted increases or decreases in the model derivedtime series for biomass, catch data and trophodynamic indi-cators. These are relatively short time series (n = 26), andfrequently characterized by strong autocorrelation, imposedas a consequence of the ecosystem dynamics and inputdata. A linear trend model was fit to each of the pre-dicted time series using a generalized least squares regressionframework, which models the temporal correlations in theerror using a two-stage estimation procedure. Details onthe methodology are described in Appendix B. The signifi-cance of the estimated trend (whether the predicted slopeis significantly different from zero) was then assessed. Thep-values and the coefficient of variation (R2) from this anal-

ysis are reported, and remarks are made for each case onpotential violations of the regression assumptions in termsof non-stationarity and non-linearity. In this manner, sta-tistically significant changes in ecosystem attributes wereidentified.

i n g 2 1 7 ( 2 0 0 8 ) 95–116

Table 4 – Results from fitting the South Catalan Seamodel to the empirical time series data

SS values Contribution(%)

(A) 1st vulnerabilities, 2nd EFFirst run 212.22Fishing 181.98 14.25Trophic interactions 70.32 52.62Primary production

anomaly (p-value = 0.03)57.64 5.97

(B) 1st EF, 2nd vulnerabilitiesFirst run 212.22Fishing 181.98 14.25Trophic interactions 103.78 36.85Primary production

anomaly (p-value = 0.001)70.38 15.74

(C) Both vulnerabilities and EFFirst run 212.22Fishing 181.98 14.25Trophic interactions 75.15 50.34Primary production

anomaly (p-value = 0.006)55.69 9.17

SS, the sum of the squared deviation of the log of the observedvalues from the log of the predicted value (Christensen et al., 2005).

104 e c o l o g i c a l m o d e l l

3. Results

3.1. Model setup and initial conditions

After the mass balance model representing the South CatalanSea during 1994 (Coll et al., 2006a) was modified to representthe ecosystem in 1978–1979, a few adjustments had to be madeto the initial parameters to successfully apply the temporaldynamic module:

(a) The Ebro River Delta area was protected in 1980 enablingseabirds to increase their breeding area and increasetheir population. However, the model alone could notreproduce these dynamics. Hence, initial biomass ofAudouin’s gull (functional group, f.g. 34 Table 1) wasslightly increased (B = 0.0001 t km−2) and initial by-catchfor the other sea birds group (f.g. 35, Table 1) was decreasedfrom 0.00004 t km−2 y−1 to 0.00001 t km−2 y−1.

(b) The initially overestimated value of production/biomassratio (P/B = 1.4) for anglerfish was corrected to a lower valueP/B = 1.0 to better reproduce the population dynamics ofthis species.

(c) To correctly reproduce the dynamics of adult and juve-nile hake the recruitment power set by default to 1 inthe multi stanza model was modified to 0.2, making theassumption that juvenile hake population’s fraction is lessdependent on the adult hake stock (Christensen et al.,2005).

(d) As different nominal fishing effort data was available(horse power, number of boats, gross tonnage or daysat sea) different simulations were performed to evaluatewhich data was better able to reproduce our time series.Fishing effort expressed in terms of horsepower gavethe most satisfactory results, so it was used for furtheranalyses. Because the real trajectory of bottom trawlinghorsepower from the mid 1990s might have been increas-ing instead of decreasing, but not registered in official datadue to the fact that maximum allowed power is 500 hpper boat for the trawling fleet (F. Sardà, unpublished data),we also described alternative scenarios of horse powerfor trawling (constant or increasing) from 1990 to 2003,modifying the baseline trajectory in Fig. 3a (see Fig. 3c).The best results from the fitting were given when the HPfrom trawling as a proxy of fishing effort was hypoth-esized to continue increasing from the 1990s as it wasobserved previously during the 1970s and 1980s (Fig. 3c,scenario 2).

After these modifications were completed, the model was fit-ted to data following the procedure presented in Section 2.3.Results are presented below.

3.2. Fitting the model to data

Using the reduction in the SS deviations of log observed

biomass from log predicted biomass values as a metricfor assessing the fit of the model, considering trophic webconfiguration, and including fishing data and the environ-mental function in the fitting procedure, the model correctly

Reduction in the SS is shown when sequentially adding explanatoryquantities to the model analysis.

reproduced 67–74% of the variability found in time series(Table 4). The initial goodness-of-fit of the model (SSi = 212.22)was greatly improved when environmental data, trophicweb configuration and fishing parameters were includedin the searching routine together (74%), rather than firstsearching for vulnerability parameters and then for envi-ronmental anomaly and vice versa. If one considers theimprovements in SS, our results show that trophic inter-actions are the main factor that describe the dynamics ofmarine resources (37–53% of the variability) from 1978 to2003, followed by fishing (14%) and the environment (6–16%).The environmental function defined by the model is sig-nificant in all three cases (p-values = 0.03, 0.001 and 0.006)considering respectively first the vulnerabilities and then theenvironmental function, and both factors together during thefitting.

The time series of biomass trends estimated by Ecosim(line), when compared with independent data (dots), showedan overall quite satisfactory match between predicted andavailable data (Fig. 4). The trend analysis showed that thepredicted time series has a statistically significant decreasingpattern for the biomass of anglerfish, adult hake, demersalsharks, anchovy and mackerel from 1978 to 2003 (Fig. 4b,e–gand j). On the contrary, flatfishes, Audouin’s gull and theother seabirds group showed clear increasing patterns (Fig. 4a,l and m). Sardine showed an overall marginally significantincrease in biomass during 1978–2003, although it initiallyshowed an important increase in biomass, but after themid 1990s a progressive decrease was registered. The model

showed a time lag in reproducing the peak of sardine biomassobserved during the early 1990s. Most of the time series ana-lyzed showed strong autocorrelation, as indicated in Fig. 4,and in few cases the normality and linearity assumptions

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116 105

Fig. 4 – Time series of biomass trends (t km−2) estimated by Ecosim (line) and from the available data (dots) for the period1978–2003. Relative values (CPUEs) are scaled from initial value of the baseline model. The estimated trend is shown (thinline) with the value of the slope, the p-value and the coefficient of variation (R2) for the regression model. The followingaspects of the trend analysis are also indicated: A, data was corrected for autocorrelation and a generalized least squaresmodel was applied for the regression; N, normality assumption.

106 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116

Fig. 5 – Time series of catch trends (t km−2 y−1) estimated by Ecosim (line) and from available data (dots) for the period1978–2003. The estimated trend is shown (thin line) with the value of the slope, the p-value and the coefficient of variation(R2) for the regression model. As in Fig. 4, the following are noted: A, data was corrected for autocorrelation and a

ion;

generalized least squares model was applied for the regress

were violated (e.g., adult hake, Fig. 4e, and seabirds, Fig. 4land m) (autocorrelation, normality and linearity statisticalresults are not shown, but results are indicated in each fig-ure).

The model underestimated the catches from demersalorganisms in many cases (Fig. 5). Predicted catch trends fromthe model showed a statistically significant decrease over timeof anglerfish, demersal sharks, and anchovy from 1978 to 2003

N, normality assumption.

(Fig. 5b, f and g), while a significant increase was shown forred mullets, flatfish, juvenile hake, horse mackerel and largepelagic fish (Fig. 5a, c, d, i and k). No clear patterns wereseen for adult hake (increasing and stabilizing). Predictions

for mackerel showed no trend, while available catches fromthe area abruptly increase then decrease with time. Strongautocorrelations were also observed in most of the time seriesanalyzed.

g 2

3c

V

(

(

(

Fsi

e c o l o g i c a l m o d e l l i n

.3. Searching for vulnerabilities and trophic flowontrol analyses

ulnerabilities �ij resulting from the model indicated:

a) Bottom-up settings (�ij = 1) for suprabenthos (prey)–flatfish(predator), polychaetes–flatfish, polychaetes–demersalfish (1), benthic invertebrates–flatfishes, benthopelagiccephalopods–large pelagic fish and demersal fish(1)–flatfish.

b) Top-down settings (�ij � 1) for macrozooplankton(prey)–benthopelagic fish (predator), flatfish–anglerfish,flatfish–demersal fish (1), anglerfish–demersal sharks,detritus–benthic invertebrates and discards (1)–Audouin’sgull.

c) For sardine, �ij values on phytoplankton and zooplanktonwere high indicating top-down settings (�ij � 1), while �ij

values on its predators were low (�ij = 1), highlighting itsimportance as prey in the ecosystem (described as a wasp-

ig. 6 – Partial contribution of each functional group of the modeituation from mixed to bottom-up, top-down or wasp-waist. Nendicate an improvement from the initial conditions.

1 7 ( 2 0 0 8 ) 95–116 107

waist flow control scenario, Cury et al., 2000; Shannon etal., 2004a,b). For anchovy and other pelagic fish, �ij valueson phytoplankton and zooplankton were low indicatingbottom-up settings, as well their �ij values as prey werelow, highlighting their importance as prey in the ecosys-tem and their role as bottom-up species.

In addition, Fig. 6 shows the partial contribution that eachgroup would have on improving the fit of the model (i.e.,decreasing the SSi) by changing the trophic flow controlfrom mixed to bottom-up, top-down or wasp-waist. Here,negative results indicate bad fitting, while positive resultsindicate an improvement. Sardine, demersal fish (1), shrimpsand macro zooplankton lead to the highest improvementon the model under wasp-waist control scenarios. Ben-

thopelagic fish, demersal fish (3), benthic cephalopods, crabs,suprabenthos, micro- and mesozooplankton, phytoplanktonand anchovy also showed improvements under bottom-upflow control scenarios. Top-down control situations were

l to improve the fit by changing the trophic flow controlgative results indicate bad fitting, while positive results

108 e c o l o g i c a l m o d e l l i n g

Fig. 7 – Environmental anomaly resulting from thecalibration process that expresses the relativephytoplankton biomass estimated by the trophic model forthe period 1978–2003. The estimated trend is shown (thin

line) with the value of the slope and the p-value for theregression model.

only established by anglerfish and jellyfish. Mixed flowcontrol (�ij = 2) was dominant in many groups (e.g., Nor-way lobster, benthopelagic cephalopods, mullets, poor cod,hake).

3.4. Environmental factors and ecosystem indicators

The three EFs derived from the model further explain 6–16% ofthe variability in the data, and were significant (Table 4). Theywere very similar, so only the one used in the calculationsthat was obtained when searching for both vulnerabilitiesand the anomaly is shown (Fig. 7). The EF showed a slightbut no significant decrease with time that was not linearlycorrelated with any of the environmental series available.Multiple regression models did not satisfactorily predict theanomaly function by combining the environmental dataavailable.

The predicted biomass trends of other functional groupsfor which no empirical data was available showed a signif-icant decrease with time, such as Norway lobster (42%) andpoor cod (41%) (Fig. 8a and h). To the contrary, benthic inverte-brates (269%), benthic cephalopods (400%) (and benthopelagiccephalopods that are not shown in this figure), shrimps (178%)(as well as crabs that are not shown), and benthopelagic fishes(333%) showed a significant increase with time (Fig. 8b, c, e andi). Additionally, jellyfish showed an increase with time (32%),although it was not significant.

The demersal/pelagic biomass ratio and the mTLco (includ-ing all trophic levels) showed a significant increase during1978–2003, while the flow to detritus increased, although notsignificantly (Fig. 9a, c and e). However, the modified versionof Kempton’s index of biodiversity and the mTLco (excludingTL = 1) showed a significant decreasing trend (Fig. 9b and d).The mTLc showed an increase and later a decrease with time

(Fig. 9f). There was a non-significant increase in the loss insecondary production index (L index) with time both consid-ering (1) production from primary producers (black line) and(2) production from primary producers and detritus (grey line)

2 1 7 ( 2 0 0 8 ) 95–116

(Fig. 9g). The probability of the ecosystem being sustainablefished was lower than 75% for most of the time period, and itwas lower than 50% during most of the 1990s and early 2000.

4. Discussion

4.1. Model adjustments, calibration and assumptions

The calibration and fitting of the South Catalan Sea ecologicalmodel to data has enabled us to refine the baseline 1978–1979model’s input initial conditions, such as the overestimationof P/B ratio for anglerfish and the overestimation of by-catchmortality rates for seabirds. In general, the biomass trendsof commercial and non-commercial species have been wellpredicted by the time-dependent trophodynamic model forthe period 1978–2003. This validates the ecological model andincreases its credibility, thereby setting it as a baseline fromwhich to perform fishing management scenarios.

By forcing the model to accept different types of fishingeffort data we tested different hypotheses on the time evolu-tion of fishing effort. The results highlighted a likely increaseof fishing effort from trawling, as has been previously sug-gested (Bas et al., 2003; F. Sardà, unpublished data). The modelreproduced correctly catches from the pelagic fraction, whilethe demersal fraction tended to be underestimated. This islikely due to data quality and assumptions made about thepartitioning of coastal and shelf catches and IUU estimations(Coll et al., 2006a). This validates the decision to not includecatch data in the model’s calibration, but only to compareresults (as explained in Sections 2.3 and 2.4).

When interpreting results and predictions, we are awarethat assumptions underlying EwE have to be carefully consid-ered. For instance, Plagányi and Butterworth (2004) discussedthe implications of the foraging arena hypothesis. However, byfitting the model with a time series and searching for the mostsensitive values of the vulnerabilities, and then comparing thebiological consequences of these results, our confidence inthe model’s predictions has increased. Aydin (2004) exploredthe implications of the “fixed growth efficiency” function inEcosim represented by P/Q rates, implying that the model doesnot account adequately for changes in population energeticsas a population’s size and structure changes (e.g., due to heavyfishing or release of fishing pressure). This can lead Ecosimto underestimate the prey biomass that can be supported bythe ecosystem if predation pressure is released by removalof top predators through fishing. As a result, some of our pre-dicted increases in species could be even larger than predicted.Moreover, some migratory species are poorly represented inEwE (Martell, 2004) so their dynamics could be only partiallycaptured.

4.2. Trophic interactions, fishing and environmentaldrivers

Our model explained a large proportion of the variability

(67–74%) in the available time series by combining fishing,trophic interactions and environmental factors, for the periodfrom 1978 to 2003 (this is measured by their contribution to thereduction in the sum of the squared deviation of the biomass’s

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116 109

Fig. 8 – Time series of biomass trends (t km−2) estimated from the calibrated model for the period 1978–2003. Biomass timeseries were not available for these species. The estimated trend is shown (thin line) with the value of the slope, the p-valueand the coefficient of variation (R2) for the regression model. A: data was corrected for autocorrelation and a generalizedl assu

oeooa

rfir(ieibdtptt

east squares model applied for the regression; N: normality

bserved value from its predicted value). Trophic interactionsxplain the highest variability, and highlights the importancef the trophic interactions on food-web dynamics as previ-usly noted (Shannon et al., 2004a; Heymans, 2004; Walters etl., 2005; Araújo et al., 2006).

Fishing explained 14% of the variability while the envi-onment explained 6–16%. These results indicate that bothshing and the environment are important drivers of marineesources in the South Catalan Sea. Partial fittings of the modelnot shown in the paper) highlighted that fishing has a highermpact in driving the dynamics of demersal species, while thenvironment more strongly affected the pelagic system. Thiss consistent with the biological features of pelagic specieseing more influenced by climatic and environmental factorsuring their life cycle (Chambers and Trippel, 1997) and with

he fact that the pelagic food web is more closely linked torimary production, while the demersal food web is linkedo detritus production (Coll et al., 2006a). An exception washe anchovy, which is a main target species of the fishery

mption violated.

(Palomera et al., 2007). Overall, the importance of fishing asa resource driver is higher in the Catalan Sea that in otherareas previously analyzed with calibrated models, such as theSouthern Benguela ecosystem from 1978 to 2002 (where fish-ing explained 2–3% of the variability) (Shannon et al., 2004a).This is in agreement with previous comparative analysis andthe high impact of fishing during the 1990s described in theCatalan Sea (Coll et al., 2006a,b). Results from fishing scenar-ios showed a partial recovery of the highly exploited specieswhen fishing mortality is slightly reduced (Coll et al., 2008) andnotable recovery when theoretical scenarios of no fishing areexplored (Coll, 2006).

Our results regarding the ecological role of small pelagicfishes and of organisms with a low trophic level (involvedin wasp-waist and bottom-up control) are in agreement with

dynamics of true upwelling systems (Bakun, 1996; Cury et al.,2000; Shannon et al., 2004a). The wasp-waist flow control ofsardines highlights that this species would play a fundamentalrole in the Catalan Sea and that changes in its biomass would

110 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 95–116

g 2

hgwac(Crtliilt

4

4WteafiafP

awsHatld1bjftw1w(iddtcbf

FKflidva

e c o l o g i c a l m o d e l l i n

ave important consequences for the rest of the functionalroups on both higher and lower trophic levels, in agreementith previous results from the mixed trophic impact analysis

nd network flows (Coll et al., 2006a, 2008). This also con-urs with results from applying a new index of keystonenessLibralato et al., 2006) to analyze the food web of the Southatalan Sea (Palomera et al., 2007; Coll, 2006). In contrast, ouresults suggest that anchovy has a lower contribution in con-rolling its preys’ dynamics, but it is important to upper trophicevel organisms and controls its predators dynamics (involvedn bottom-up flow control interactions). On the contrary, themportance of top-down control scenarios was low in the Cata-an food web, in agreement with the high fishing impact andhe low trophic levels of the community (Coll et al., 2006a,b).

.3. Biomass and catch trends

.3.1. Demersal predatorse have shown significant trends of decreasing biomass of

he important commercial species and top predators of thecosystem, such as adult hake and anglerfish (p-value 0.003nd 0.000, respectively). These are mainly due to increasingshing mortality. These are similar results to those from stockssessments, which suggest that several demersal stocks areully exploited or overexploited (Aldebert and Recasens, 1996;apaconstantinou and Farrugio, 2000).

The decrease of adult hake biomass is mainly due ton increase in long line fishing effort, which is consistentith increasing concerns over recruitment overfishing of this

pecies (Recasens et al., 1998; Recasens and Lleonart, 1999).owever, to satisfactorily reproduce the dynamics of adultnd juvenile hake in our model, the recruitment power rela-ionship needed to be modified to a lower value, indicating aower dependence of the juvenile stock on the adult stock, asescribed for numerous exploited species (Myers and Cadigan,993). But the apparently low density dependence could alsoe an indication of a partial source of egg production, larvae or

uveniles from outside the area that are transported into it. Inact, the eggs and larvae of hake are found in higher concen-rations on the edge of the continental shelf and upper slope,here the adults congregate to reproduce (Recasens et al.,

998; Olivar et al., 2003), while the size of individuals increasesith depth when moving from the coast to the open ocean

Maynou et al., 2003). Another explanation partially observedn the model (results not shown) is that the steady and intenseecline of anglerfish and other demersal predators would haveecreased the predation mortality on juvenile hake. As well,

hese decreases in the biomass of predatory fish might haveaused an increase in the biomass of other organisms such asenthic invertebrates and benthopelagic fish that are also preyor juvenile hake (Bozzano et al., 1997, 2005). These hypothe-

ig. 9 – Ecosystem indicators calculated from the calibrated modempton’s index of biodiversity (Q’s index), (c) mean trophic leveow to detritus (t km−2 y−1), (f) mean trophic level of the catch (m

ndex) considering (1) production from primary producers (grey letritus (black line). The estimated trend is shown (thin line) witariation (R2) for the regression model. A: data was corrected forpplied for the regression; N: normality assumption violated.

1 7 ( 2 0 0 8 ) 95–116 111

ses can partially explain the biomass trends of juvenile hake,which match with results from trawl survey data covering1994–1999 that were not included in this study (Orsi-Relini etal., 2002).

Demersal sharks are non-target species but they alsoshowed marked declines due to the fact that they are accom-panying or by-catch species of trawl fishing so they showedan increase in fishing mortality (results not shown). Thisis consistent with data from the north of the Catalan Sea,where decreasing catch and relative biomass trends were alsorecorded for demersal sharks from 1950 (Bas et al., 2003) andfrom the Gulf of Lions, where the development of bottomtrawling had a strong impact on these groups from 1960 to1990 (Aldebert, 1997).

4.3.2. Other demersal fishAlthough juveniles of red mullets are mainly located in coastalareas and adults are mainly found at around 120–150 m depth(Tserpes et al., 2002), our results in terms of observed andpredicted biomass trends did not show a clear pattern in theSouth Catalan Sea, although the stock is described to be fullyexploited (FAO, 2005). This matches trawl survey data covering1994–1999 (Tserpes et al., 2002), but is not accounted for in thisstudy. A decline of predator mortality from demersal preda-tors on red mullet (predicted by the model, but not shown)could partially explain these results, although fishing mortal-ity has increased with time. Fishing mortality has increasedfor flatfishes as well, although they showed a clear increasein biomass probably due to a decrease in predation mortalityfrom other demersal fishes and adult hake (predicted by themodel, but results not shown).

4.3.3. Small pelagic fishesThe observed and predicted anchovy biomass overall signif-icantly decreased from 1978 to 2003 (p-value = 0.023). This ismainly due to increased fishing mortality, in line with theconcern over recruitment overfishing affecting stocks of smallpelagic fish in the Mediterranean Sea (Palomera et al., 2007).

Interestingly, the observed sardine biomass first showed aclear increase from 1978 to the mid 1990s and then a steadydecrease towards 2003. The model reproduced the biomasspattern of sardine, but with a time delay. Model predictionsshowed that even if fishing activity played a role in driv-ing the dynamics of this species, environmental factors hada greater influence in explaining sardine biomass. Here wehypothesize that the significant increase in water tempera-ture recorded in the area (Salat and Pascual, 2002) could have

played a role, as there is a negative correlation between thenumber of sardine observed as well as the predicted biomassin the South Catalan Sea area and the mean SST during thespawning months (Fig. 10). Cold waters (12–14 ◦C) are preferred

el (1978–2003): (a) the demersal/pelagic biomass ratio, (b)l of the community (mTLco), (d) mTLco excluding TL = 1, (e)TLc), and (g) the loss in secondary production index (Line) and (2) production from primary producers andh the value of the slope, the p-value and the coefficient ofautocorrelation and a generalized least squares model

112 e c o l o g i c a l m o d e l l i n g

Fig. 10 – Time series of sardine biomass (t km−2) andwinter mean SST (sea surface temperature, ◦C) duringsardine spawning months in the South Catalan Sea area

(modified from Palomera et al., 2007).

by sardine for spawning (Palomera et al., 2007), although thespawning starts when the water temperature is below 19 ◦C.The higher sea water temperatures during winter from the1970s to the 1990s could have had a negative impact on sardinespawning and recruitment. Further field data and analyses areneeded to validate this hypothesis and the biological mecha-nisms involved should be investigated. An increase in sardineabundance has been previously related with warm periods inthe Pacific Ocean (Chavez et al., 2003) and in the Benguelaregion (Shannon et al., 2004b). These works suggested thatthe plankton composition changes with warmer temperaturesand the sardines benefit from increases in small zooplanktonand phytoplankton fractions, as the sardines prefer filter feed-ing (Stergiou and Karpouzi, 2002). However in our ecosystem,the positive impacts of warming trends on sardine abundancedue to a change in plankton composition could be overshad-owed after the early 1990s by the negative effect that thereduction of the annual cold period could have on sardinespawning.

4.3.4. Medium-sized and large pelagic fishesObserved data from medium-sized pelagic fish showed ahigher biomass of mackerel at the beginning of the time seriesand an increase in horse mackerel toward the end, which isconsistent with what has been previously described in otherregions, such as Blanes and Barcelona (Bas et al., 2003). Themodel partially reproduces these trends, although the qualityof the biomass data for these groups is low, and catch data isquestionable. These results could be also linked with increas-ing water temperature in the area, and the fact that horsemackerel species reproduce in the summer, while mackerel(mainly Scomber scomber) spawns during the winter (Sabatés,1996). This is only partially captured by the environmentalfunction in our analysis.

Predicted catch trends of large pelagic fish in the areashowed a significant increase with time (p-value = 0.000).Catch data used here correspond to the stock assessment ofbluefin tuna in the Mediterranean Sea. These data are highly

2 1 7 ( 2 0 0 8 ) 95–116

underestimated due to non-reporting (WWF, 2006), so ourresults may not be realistic.

4.3.5. SeabirdsSeabird populations in the Ebro Delta River, like Audouin’sgull, increased notably from the early 1980s due mainly to: (a)breeding grounds and population protection, and (b) discardavailability in the area being high from the local bottom trawl-ing and purse seining (e.g., Oro, 1999, 2002; Oro and Ruíz, 1997;Arcos and Oro, 2002). The model satisfactorily reproduced theincrease of seabird populations in the area of the Ebro Riverfrom the early 1980s after the correction of initial levels ofbiomass and by-catch. However, the increase in the mixedgroup of seabirds was not fully captured by the model. This ismost probably due to the non-equilibrium situation of the EbroDelta, where there is still a migratory flux from neighbouringareas into the delta since protection, spatial dynamics andprocesses including dispersion and migration between localpopulations seem to be important (D. Oro, pers. commun.).

4.3.6. Ecological surprisesThe model predicts the proliferation of certain species in thelower trophic levels (e.g., shrimps and benthic invertebrates)or higher turnover rates (e.g., cephalopods and benthopelagicfish) (Fig. 8). Although no time series of observed data is avail-able from the Catalan Sea to validate these results, this notionof proliferation matches with anecdotal information for theMediterranean and non-Mediterranean areas. It is related tothe concept of trophic cascades (Pace et al., 1999), due to theremoval of higher trophic level organisms from the food web(i.e., the predator mortality decreases), and due to the removalof small pelagic fish (i.e., the competition for planktonic preydecreases).

For example, the Alboran Sea, SW Mediterranean, hasshown a proliferation of boarfishes Capros aper after thecollapse of small pelagic fisheries, which resulted fromhigh fishing intensity and environmental changes (Abad andGiraldez, 1990). The increase of cephalopod species due toincreasing fishing impact has been previously described inother Mediterranean areas (e.g., Caddy, 1997; Pipitone et al.,2000; Pinnegar and Polunin, 2004). Likewise, in the Black Sea,indirect effect of fishing and eutrophication have been relatedto the massive jellyfish explosions during the 1970s and 1980s(Daskalov et al., 2007). The proliferation of jellyfish in the West-ern Mediterranean was described during the 1980s (Buecher,1999) and there has been increasing concern over the jellyfishpresence in the coastal areas of the Catalan Sea (Pagès, 2003).

In non-Mediterranean areas, the proliferation of non-commercial species in the Northern Benguela area (Namibia)appeared after the collapse of pelagic fisheries in the1960–1970s, and jellies have become more abundant with thepelagic goby (Sufflogobius bibarbatus) and horse mackerel (Tra-churus trachurus capensis) (Heymans et al., 2004). There hasbeen proliferation of shrimp in the North Atlantic Ocean aftera collapse of the cod (Gadus morhua) population (Worms andMyers, 2003), and also an increase in shrimp and crab in the

Eastern Scotian Shelf after the collapse of ground-fish stocks(Frank et al., 2005). In the Gulf of Thailand, a proliferation ofshrimps occurred after the introduction of bottom trawlingfisheries (Christensen, 1998).

g 2

41

D2tWltndsttoltswfoeadfstCc

glStatfieqtot2

5

IagtaitacAsos

r

e c o l o g i c a l m o d e l l i n

.4. Structural and functional ecosystem changes from978 to 2003

ue to these changes in diversity and biomass from 1978 to003, trophodynamic indicators predict changes in the struc-ure and functioning of the South Catalan Sea ecosystem.

hen TL = 1 is excluded from the analysis, the mean trophicevel of the community, mTLco, decreases consistently withhe decrease of fish biomass and the increase of the domi-ance of low trophic level organisms. This coincides with theecreasing trends in the diversity Q index that includes thosepecies or groups with TL ≥ 3, and highlights the decrease inhe biomass of various high TL organisms. On the other hand,he mean trophic level of the catch, mTLc, reflects the declinef small pelagic fish biomass that has occurred during the

ast two decades. As a result, the ecosystem has progressedowards being more demersal-dominated. These results areimilar to what has been reported in Northern Benguela,here there is evidence for a shift from a pelagic food web to a

ood web dominated by demersal pathways due to overfishingf the pelagic component (Heymans et al., 2004; Van der Lingent al., 2006; Shannon et al., in press). However, these resultsre in contrast with results from Pinnegar et al. (2003) where aecrease in mTLc was described in the Western Mediterraneanrom 1972 to 1998, but a marginally significant increase waseen in mTLc when excluding small pelagic fish landings. Theotal demersal versus pelagic biomass ratio decreased in theatalan Sea due to a decrease in the small pelagic fish biomassaused by overexploitation and environmental impacts.

The increase of flows to detritus has been previously sug-ested as an indicator of disruption in energy transfer fromower to higher trophic levels (Walsh, 1981; Odum, 1985;hannon et al., in press). In the Catalan Sea, flows to detri-us increased, although not significantly, from 1978 to 2003,nd indicates that the transfer of flow from lower to higherrophic levels may have decreased due to ecosystem simpli-cation, which results in a less efficient use of the system’snergy, and overexploitation of the food web. As a conse-uence, there may have been an increase of trophic flowsowards detritus or horizontal transport. The loss in the sec-ndary production index indicates a decreasing probability ofhe fishing activities’ sustainability with time (Libralato et al.,008).

. Conclusions

n summary, our results, obtained through the application oftrophodynamic process-oriented model with Ecosim, sug-

est that intense fishing activity during the period from 1978o 2003 in the South Catalan Sea (NW Mediterranean) wasn important driver of the structural and functional changesn the marine ecosystem, in conjunction with environmen-al factors. Important changes in the ecosystem included

change in the biomass of several commercial and non-ommercial species (observed and predicted by the model).

decrease in the biomass of higher trophic level fish andmall pelagic fish was observed in parallel with predictionsf an increase in lower trophic level or higher turnover ratepecies. This is mostly due to a decrease of predator mortal-

1 7 ( 2 0 0 8 ) 95–116 113

ity and trophic cascades as well as a decrease in competitionfrom small pelagic fish. The proliferation of non-commercialspecies, the decrease of the mean trophic level of the commu-nity and of biomass diversity, and the increase of total flow todetritus and the demersal/pelagic ratio are signs of the ecosys-tem changes in the South Catalan Sea from 1978 to 2003. Theseresults are consistent with the available information, and theypoint to a pattern of ecosystem degradation mainly due to highfishing activity. In addition, there is a decreasing probability ofsustainability with time. An improved understanding of thedirect and indirect impact of fishing mediated by the trophicweb, in the context of a changing environment, is of specialimportance for ecosystem management.

Acknowledgements

This research was funded by the Spanish research projectCICYT.REN 2000-0878/MAR. During the work M.C. was sup-ported financially by a FPI fellowship from the MYCT ofthe Spanish Government. We wish to acknowledge all ourcolleagues from the Institute of Marine Science (ICM), theUniversity of Girona (UdG), the Centre for Advanced Studiesof Blanes (CEAB), the Mediterranean Institute for AdvancedStudies (IMEDEA) and the Spanish Institute of Oceanography(IEO) that provided essential data and technical advice for thedevelopment of this work. We especially thank Dr. FrancescSardà for providing data and useful comments to improve themanuscript. We also thank the scientific researchers from theFisheries Centre (University of British Columbia) that kindlysupported the work and advised us on ecological modellingprocedures. Two anonymous reviewers are also thanked fortheir comments to improve the manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.ecolmodel.2008.06.013.

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