trophic interactions and community structure in the upwelling

312 (2004) 349–366

www.elsevier.com/locate/jembe

Trophic interactions and community structure in the

upwelling system off Central Chile (33–398S)

Sergio Neira*, Hugo Arancibia

Seccion Pesquerıas, Departamento de Oceanografıa, Universidad de Concepcion,

PO Box 160-C, Concepcion, Chile

Received 1 November 2003; received in revised form 19 July 2004; accepted 28 July 2004

Abstract

Trophic interactions and community structure in the upwelling system off Central Chile (USCCh)

(33–398S) are analyzed using biological and ecological data concerning the main trophic groups and

the Ecopath with Ecosim software version 5.0 (EwE). The model encompasses the fisheries,

cetaceans, sea lion, marine birds, cephalopods, large-sized pelagic fish (sword fish), medium-sized

pelagic fish (horse mackerel, hoki), small-sized pelagic fish (anchovy, common sardine), demersal

fish (e.g. Chilean hake, black conger-eel), benthic invertebrates (red squat lobster, yellow squat

lobster) and other groups such as zooplankton, phytoplankton and detritus. Input data was gathered

from published and unpublished reports and our own estimates. Trophic interactions, system

indicators and food web attributes are calculated using network analysis routines included in EwE.

Results indicate that trophic groups are aligned around four trophic levels (TL) with phytoplankton

and detritus at the TL=1, while large-sized pelagic fish and cetaceans are top predators (TLN4.0). The

fishery is located at an intermediate to low trophic level (TL=2.97), removing about 15% of the

calculated system primary production. The pelagic realm dominates the system, with medium-sized

pelagic fish as the main fish component in biomass, while small-sized pelagic fish dominate total

landings. Chilean hake is by far the main demersal fish component in both, biomass and yield.

Predators consume the greater part of the production of the most important fishery resources,

particularly juvenile stages of Chilean hake. Consequently, mortality by predation is an important

component of total mortality. However, fishery also removes a large fraction of common sardine,

anchovy, horse mackerel, and Chilean hake. The analysis of direct and indirect trophic impacts

reveals that Chilean hake is a highly cannibalistic species. Chilean hake is also an important predator

0022-0981/$ -

doi:10.1016/j.

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Journal of Experimental Marine Biology and Ecology

see front matter D 2004 Elsevier B.V. All rights reserved.

jembe.2004.07.011

onding author. Tel.: +56 41 203532, +56 41 204382; fax: +56 41 256571.

ress: [email protected] (S. Neira).

S. Neira, H. Arancibia / J. Exp. Mar. Biol. Ecol. 312 (2004) 349–366350

on anchovy, common sardine, benthic invertebrates, and demersal fish. The fisheries heavily impact

on Chilean hake, common sardine, anchovy, and horse mackerel. Total system biomass (B=476 t

km�2 year�1) and throughput (T=89454 t km�2 year�1) estimated in the USCCh model are in

accordance with models of comparable systems. Considering system attributes derived from network

analysis, the USCCh can be characterized as an immature system, with short trophic chains and low

trophic transfer efficiency. Finally, we suggest that trophic interactions should be considered in stock

assessment and management programs in USCCh. In addition, future research programs should be

carried out in order to understand the ecosystem effects of fishing and trophic control in this highly

productive food web.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Central Chile; Community structure; Ecopath with Ecosim; Ecological network analysis; Trophic

interactions; Upwelling system

1. Introduction

Ecological and economic collapses of many important fisheries in the world (Garcia

and Grainger, 1997) have led scientist and managers of natural resources to call for more

holistic approaches that place fisheries in their ecosystem context (Christensen et al.,

1996). Considering that target and non-target species are embedded in complex trophic

webs, and their population dynamic can be influenced by interspecific relationships and

changes in the physical environment (i.e. Sinclair et al., 1997), it seems obvious that

information on the ecosystem from which target species are extracted can help to reduce

the uncertainty associated to the classical monospecific stock assessment and management

(Botsford et al., 1997).

Therefore, ecosystem models are a necessary and complementary approach to the

classical mono-specific models that have guide fisheries assessment and management,

since they include both the ecological relationships between target and non-target species,

and the environmental impact on fishery resources (Botsford et al., 1997; Walters et al.,

1997).

In this context, the Ecopath with Ecosim software and model (Christensen and Pauly,

1992; Walters et al., 1997) has been widely applied to aquatic ecosystems (see

contributions in Christensen and Pauly, 1993a; www.ecopath.org), allowing quantitative

descriptions of ecosystems (Christensen and Pauly, 1993b) and generalities of marine food

webs (Pauly and Christensen, 1995), especially those in the major upwelling systems

(Jarre et al., 1989; Jarre-Teichman and Christensen, 1998).

The Humboldt Current off Central Chile (33–398S) is a typical eastern boundary

current system, or upwelling ecosystem. It is acknowledged that the high levels of

primary production reported for the upwelling system of Central Chile (USCCh), which

are one of the highest ever reported for the open ocean (Fossing et al., 1995; Daneri et

al., 2000), are influenced by the occurrence of wind-driven coastal upwelling events

(Strub et al., 1998). The primary productivity in this system sustains a remarkably high

fish biomass which, in turns, sustains one of the most productive fisheries worldwide

(FAO, 1995).

http://www.ecopath.org

S. Neira, H. Arancibia / J. Exp. Mar. Biol. Ecol. 312 (2004) 349–366 351

Industrial and artisan fishing activities, both pelagic and demersal, have been operating

off Central Chile since mid-1950s, reaching a historical landing of about 4.5 million

tonnes in 1994. However, total fishery landings in Central Chile have shown a decreasing

trend in last years, explained by the situation of the main fishery resources, which are

either in their maximum exploitable level, heavily overexploited or recovering from

previous overexploitation (Cubillos et al., 1998; SSP, 2003).

Unfortunately, the most of the ecological research conducted in the USCCh have been

directed to asses individual stock size and diet composition of fishery resources such as

anchovy (Engraulis ringens), common sardine (Strangomera bentincki), horse mackerel

(Trachurus symmetricus) and Chilean hake (Merluccius gayi). Consequently, multispecific

approaches have been only applied to the trophic structure of commercial fish species

(Neira et al., 2004).

The available information indicates that target species could play important ecological

roles as prey and/or predators in USCCh (Arancibia, 1987a, 1991, 1992; Arancibia and

Fuentealba, 1993; Quinones et al., 1997; Miranda et al., 1998; Neira et al., 2004).

However, since this information has not been systematized and summarized in quantitative

ecosystem models, trophic interactions among target and non-target species, matter flows,

community structure, and the ecosystem effects of fishing in this highly productive food

web are still poorly understood.

In this paper, we present results of an ecosystem model applied to the USCCh in the

year 1992, a period in which fish stocks were healthy (not fully exploited) and no mayor

environmental changes have occurred in the system. Consequently, the model is aimed to:

(1) describe community structure, quantifying biomass distribution and energy flows, and

(2) quantify trophic relationships among 22 functional groups.

2. Materials and methods

The study area corresponds to the marine zone off Central Chile (33–398S), and

extends up to 30 nautical miles offshore, covering a total area of 50 042 km2 (Fig. 1). The

defined area is the main fishing ground of both, the purse seine and the trawling industrial

fishing fleets. Our analysis covers a 1-year period, 1992.

The model encompasses 22 functional groups, including the main trophic components

of the system with emphasis on fish species, both target and non-target species. The

functional groups are: phytoplankton, zooplankton I (microzooplankton), zooplankton II

(mesozooplankton, represented by copepods), zooplankton III (macrozooplankton,

represented by euphausiids), jellies (salps and jellyfishes), macrobenthos (represented

by red squat lobster Pleuroncodes monodon; yellow squat lobster Cervimunida johni),

anchovy (E. ringens), common sardine (S. bentincki), mesopelagic fish, horse mackerel (T.

symmetricus), hake (M. gayi), pelagic fish I (medium-sized pelagic fish represented by

hoki Macrouronus magellanicus), demersal fish I (benthic feeders species), demersal fish

II (pelagic feeders species), condrichthyans (mainly skates), pelagic fish II (large-sized

pelagic fish represented by swordfish Xiphias gladius), cephalopods (squid Loligo gahi),

sea lion (Otaria flavescens), sea birds (penguins, pelicans, cormorants), cetaceans (killer

whale and dolphins), and detritus.

-32.00

-33.00

-34.00

-35.00

-36.00

-37.00

-38.00

-39.00

-40.00-75.00 -74.00 -73.00 -72.00 -71.00

Valparaiso

Concepcion

Fig. 1. Study area: the upwelling system of Central Chile (338S to 398S).


Considering that Chilean hake exhibits strong ontogenetic changes in diet (Arancibia,

1987b; Arancibia et al., 1998), we split this group into juvenile (small) and adult (large)

components. According to Arancibia (1987b), small hake includes age groups 0 to 3 years

old (b35 cm total length), while large hake are 4+ (N36 cm total length).

We used the Ecopath with Ecosim software version 5.0 (EwE; Christensen and Pauly,

1992; Walters et al., 1997) to model the marine food web of USCCh. EwE model splits the

production of each group (i) in the system in the following components:

production ¼ catchesþ predation mortality þ biomass accumulation

þ net migrationþ other mortality

or, more formally,

Pi ¼ Yi þ BiTM2þ Ei þ BAi þ PiT 1� EEð Þ

where i is a model component or group, Pi is the total production rate of (i), Yi is total

fishery catch rate of (i), M2i is the total predation rate for the group (i), Bi the biomass of

the group, Ei the net migration rate (emigration–immigration), BAi is the biomass

accumulation rate for (i), M0i=Pi(1�EEi) is the other mortality rate for (i), while EEi is

the ecotrophic efficiency of (i), and represents the total fraction of the production that is

either eaten by predators or exported from the system.


These lead to the following linear equation:

BiTP=Bi þ EEi �Xj

BjTQ=BjTDCij

� �� EXi ¼ 0

where j indicates any of the predators of (i), P/Bi is the production of (i) per biomass unit

(equivalent to total mortality Z under steady-state conditions, sensu Allen, 1971), Q/Bi is

the consumption by (i) per biomass unit, DCij is the fraction of (i) in the diet of ( j) (in

mass units), EXi are the exports of i (by emigration or yields).

The mass balance of each component of the system is given by:

Q ¼ P þ Rþ U

where Q is prey consumption, both inside and outside the system (imports), P is

production (it must be eaten by predators, exported from the system or contributed to

detritus), R is respiration, and U is unassimilated food by predators.

This structure defines the input parameters needed to complete the model. Each group

requires estimates of B, P/B and Q/B ratios, DCij, EXi, assimilation and EEi.

Nevertheless, one of the parameters (B, P/B, Q/B or EE) can remain unknown for each

group, since it can be estimated (together with respiration) from the solutions of the system

of linear equations. Values of the gross efficiency of food conversion (GE), which

corresponds to the Production/Consumption ratio (P/Q), can be used as alternative inputs

to Q/B. For the phytoplankton group, it is not necessary to enter Q/B or P/Q values, since

this is an autotrophic group. Data source and estimation method used to estimate input

parameters are presented in Table 1. In absence of further information, we assumed steady

state conditions for each group (i) in 1992, i.e. BAi=0 and Ei=0.

Annual Bi of groups such as demersal fish I and demersal fish II, which are by-catch

species in the Chilean hake fishery, were estimated as follow:

Bi ¼ BhakeTCi

Chake

� �ð1Þ

where Bhake is the biomass of Chilean hake (Lillo et al., 1993), Ci is the yield of the

species i during a research cruise carried out to estimate the Chilean hake biomass in 1992,

Chake is the yield of Chilean hake during the same cruise. We assumed that all species had

the same response to the Chilean hake trawling fishing gear.

Annual Yi for the same groups, were estimated as follow:

Yi ¼ YhakeTCi

Chake

� �ð2Þ

where Yhake is the annual landing of Chilean hake (SERNAPesca, 1993).

The model was balanced by checking the values of EEi and of GEi. Obviously, EEi

must be between 0 and 1, while GEi has to be between 0.1 and 0.35. Exemptions are fast

growing groups which can have higher GEi (Christensen et al., 2000). For inconsistent

values of EEi or GEi, we make changes in input data Bi, Pi/Bi or DCij following criteria

presented in Christensen et al. (2000) until we obtained acceptable runs, i.e. EEib1 and

0.1bGEib0.35.

Table 1

Data source and method of parameter estimation for the ecosystem model representing the upwelling system of

central Chile, year 1992

Group/parameter Bi

(t km�2)

Pi/Bi

(year�1)

Qi/Bi

(year�1)

Yi(t km�2 year�1)

DCji EEi GEi

(1) Phytoplankton 8 – 0.300

(2) Zooplankton I 20 20 – G;20 0.999

(3) Zooplankton II 10 14; 20 – G;13 0.999

(4) Zooplankton III 12 – G;12 0.999 20

(5) Jellies 14 14 – 20 0.150 20

(6) Macrobenthos SA SA 4; 21 OR; 19 G; 21

(7) Anchovy SA SA OR; 19 GC; 3 11

(8) Common sardine SA SA OR; 19 GC; 3 11

(9) Mesopelagic fish 11 14 – 1 0.999

(10) Horse mackerel SA SA 13 OR; 19 GC

(11) Hake (small) SA 2 – GC; 15

(12) Hake (large) SA SA 2 OR; 19 GC; 15

(13) Pelagic fish I SA SA OR; 19 GC; 7

(14) Demersal fish I GE; Eq1 SA GE; Eq2 GC; 15

(15) Demersal fish II GE; Eq1 SA GE; Eq2 GC; 15

(16) Chondrichthyans GE; Eq1 SA GE; Eq2 GC; 15

(17) Pelagic fish II SA OR; 19 GC; 5

(18) Cephalopods 14 OR; 19 20; 16 14

(19) Sea lion GE on 9 13 GE on 13 – GC; 9

(20) Sea birds GE 13 13 – 13; 14

(21) Cetaceans GE 14; 18 6; 18 – 17;18

(Eq1)=equation 1 (see text); Eq2=equation 2 (see text); G=General knowledge of the same species/group;

GC=Gut content; GE=Guess estimated; OR=Official Report from the Chilean Fisheries Service; SA=Stock

assessment.

1=Amstrong et al. (1991); 2=Arancibia et al. (1998); 3=Arrizaga et al. (1993); 4=Arreguin-Sanchez et al. (1993);

5=Barbieri et al. (1998); 6=Browder (1993); 7=Cubillos et al. (1998); 8=Daneri et al., 2000; 9=Doppler (1997);

10=Escribano and McLaren (1999); 11=Hewitson and Crushak (1993); 12=Hutchings et al. (1991); 13=Jarre et al.

(1989); 14=Jarre-Teichman et al. (1998); 15=Lillo et al. (1993); 16=Lipinski (1992); 17=Majluf and Reyes

(1989); 18=Pauly et al. (1998); 19=SERNAPesca (1993); 20=Shannon and Jarre-Teichman (1999); 21=Wolff,

(1994).


After the model was balanced, network analysis routines (Ulanowicz, 1986;

Ulanowicz and Kay, 1991) incorporated in EwE, were used to calculate system

properties and flow indicators based on theoretical concepts of Odum (1969) and

Ulanowicz (1986). Later, a routine proposed by Ulanowicz (1995) was used to

aggregate the food web on discrete trophic levels (sensu Lindeman, 1942), then

assessing flow distributions and trophic transfer efficiency (TTE) among trophic levels.

In addition, the following trophic flows were quantified: total throughput (T), Finn’s

cycling index (F), which correspond to the fraction of T directed to matter cycling

(Finn, 1976), and the mean path length (MPL), which is a measure of the mean number

of transferences suffered by an energy unit since it enters the food web until it leaves.

Finally, the mixed trophic index (Ulanowicz and Puccia, 1990) was used to quantify

direct and indirect trophic interactions among groups in the system. In this analysis,

positive impact ( gij) of a prey i on a predator j corresponds to the fraction (in weight)


of each prey i in the stomach content of predator j, while negative impact ( fij) of a

predator j on its prey i corresponds to the fraction of predation caused by predator j on

total predation on prey i.

3. Results

Table 2 summarizes input parameters and some results obtained from the final balanced

run of the USCCh model in 1992. Table 3 shows the diet matrix for predators for the same

run.

The flow diagram for the USCCh model is shown in Fig. 2. Trophic groups are aligned

according with their trophic level (TL), which are not necessarily discrete (sensu

Lindeman, 1942) but fractional (Odum and Heald, 1975; Levine, 1980; Christensen and

Pauly, 1992). Phytoplankton and detritus are located at the base of the food web (TL=1)

while cetaceans and large pelagic fish represent top predators (Table 2, Fig. 2). The system

is dominated by the pelagic domain, which comprises over 90% of the system biomass

(excluding detritus) and the main energy flows (i.e. input flows), as reported for other

upwelling ecosystems (Jarre-Teichman, 1998). Plankton invertebrates (copepods and

euphausiids) are the main consumers in the system. Among fishes, medium-sized fish

(horse mackerel and hoki) are the main component in terms of biomass; these species are

also the main consumers of zooplankton (Table 3, Fig. 2). In turns, small-sized pelagic fish

(common sardine and anchovy) dominates total landings. In the demersal domain, Chilean

hake is by far the dominant group in terms of biomass, consumption, production and

landing (Table 2, Fig. 2).

A comparative analysis of fishing and predatory impacts reveals that predation

mortality (M2) is an important component of total mortality (Z) on Chilean hake (small),

macrobenthos, anchovy, common sardine and cephalopods. Fishing mortality (F) is by far

the main cause of Z in horse mackerel, demersal fish I, and chondrichthyans. Some groups

are heavily impacted by fishing and predation, this is the case of Chilean hake (large)

(Table 2).

Although seven discrete trophic levels resulted from the trophic aggregation routine

(Table 4), the magnitude of flows and biomasses in trophic levels higher than TL III is

practically insignificant when compared with those of TLs I and II, and flows associated to

top predators represented only a small fraction of T (Table 4). In fact, 90% of total system

biomass (BT) is located in TLs I, II and III, while 97% of T is reached at TL II.

Consequently, TTE in USCCh is rather low in higher trophic levels (Table 4), which seems

to be a general rule in marine trophic food webs (Pauly and Christensen, 1995), especially

in upwelling ecosystems (Jarre-Teichman, 1998; Jarre-Teichman and Christensen, 1998).

BT (excluding detritus) corresponded to 476 t km�2, while T corresponded to 89,454 t km2

year�1, then locating USCCh in an intermediate position in terms of flows per area unit

when compared with data presented by Christensen and Pauly (1993b) and Jarre-Teichman

(1998).

Table 5 summarizes global attributes of USCCh. The low Primary Production/

Respiration ratio (PP/R=2.559) allows to characterize USCCh as an immature ecosystem

(sensu Odum, 1969), where more energy is fixed than respired. Accordingly, Primary

Table 2

Input parameters (bold) and results for the final run of the ecosystem model representing the upwelling system of central Chile, year 1992

Group name/

parameter

TLi Bi

(t km�2)

Pi/Bi

(year�1)

Qi/Bi

(year�1)

Yi(t km�2 year�1)

Fi

(year�1)

M2i(year�1)

M0i(year�1)

EEi GEi

(1) Phytoplankton 1.00 302.506 120.000 – – – 36.000 84.000 0.300 –

(2) Zooplankton I 2.25 11.623 482.000 1928.000 – – 481.518 0.482 0.999 0.250

(3) Zooplankton II 2.62 14.091 45.000 154.519 – – 44.955 0.045 0.999 0.291

(4) Zooplankton III 2.98 26.353 13.000 31.707 – – 12.987 0.013 0.999 0.410

(5) Jellies 2.81 44.707 0.584 1.420 – – 0.088 0.496 0.150 0.411

(6) Macrobenthos 2.00 2.008 3.569 14.104 0.228 0.114 2.589 0.866 0.757 0.253

(7) Anchovy 2.14 8.350 2.880 28.800 6.112 0.731 1.843 0.306 0.894 0.100

(8) Common sardine 2.14 11.590 2.450 24.500 8.952 0.772 1.280 0.397 0.838 0.100

(9) Mesopelagic fish 3.84 13.263 1.200 12.000 – – 1.199 0.001 0.999 0.100

(10) Horse mackerel 3.99 13.790 0.823 14.200 6.480 0.470 0.108 0.245 0.702 0.058

(11) Hake (large) 3.55 4.287 0.605 5.159 1.188 0.277 0.204 0.124 0.796 0.117

(12) Hake (small) 3.40 4.487 2.500 8.323 0.243 0.054 1.616 0.830 0.668 0.300

(13) Pelagic fish I 4.18 13.380 0.440 4.400 3.950 0.295 0.059 0.085 0.806 0.100

(14) Demersal fish I 3.89 1.683 0.700 3.500 0.196 0.116 0.044 0.540 0.229 0.200

(15) Demersal fish II 4.12 0.780 0.700 3.500 0.021 0.027 0.141 0.532 0.240 0.200

(16) Chondricththyans 3.00 0.436 0.362 2.413 0.134 0.307 – 0.055 0.849 0.150

(17) Pelagic fish II 5.08 0.318 0.500 5.000 0.106 0.333 – 0.167 0.667 0.100

(18) Cephalopods 3.78 1.636 3.500 10.606 0.001 0.001 3.496 0.003 0.999 0.330

(19) Sea lion 4.23 0.090 0.250 20.000 – – 0.250 – 0.999 0.012

(20) Sea birds 3.74 0.065 0.500 20.000 – – – 0.500 0.000 0.025

(21) Cetaceans 4.51 0.023 0.150 10.000 – – 0.01 0.140 0.067 0.015

S.Neira

,H.Arancib

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Exp.Mar.Biol.Ecol.312(2004)349–366

356

Table 3

Diet composition of predators included in the ecosystem model representing the upwelling system of Central Chile, year 1992

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

(1) Phytoplankton 0.400 0.500 0.400 0.500 0.900 0.900

(2) Zooplankton I 0.200 0.500 0.050 0.050

(3) Zooplankton II 0.600 0.500 0.050 0.050 0.400

(4) Zooplankton III 0.600 0.960 0.053 0.201 0.755 0.426 0.500 0.103

(5) Jellies 0.020

(6) Macrobenthos 0.136 0.041 0.070 0.235

(7) Anchova 0.145 0.293 0.039 0.121 0.251 0.074

(8) Common sardine 0.142 0.267 0.054 0.166 0.347 0.102

(9) Mesopelagic fish 0.020 0.181 0.100 0.578 0.126 0.098

(10) Horse mackerel 0.017 0.480 0.177 0.114

(11) Hake (large) 0.014 0.004 0.277 0.020 0.075

(12) Hake (small) 0.165 0.074 0.042 0.020 0.195 0.172 0.063

(13) Pelagic fish I 0.500

(14) Demersal fish I 0.003 0.004

(15) Demersal fish II 0.061

(16) Chondricththyans

(17) Pelagic fish II

(18) Cephalopods 0.064 0.020 0.206 0.084 0.326

(19) Sea lion 0.098

(20) Sea birds

(21) Cetaceans 0.001

(22) Detritus 0.400 1.000

Import 0.325 0.124 0.458 0.400 0.765 0.049

Sum 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Numbers represent the fraction of the food intake in weight.

S.Neira

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357

Fig. 2. Flow diagram representing the food web in the upwelling system of Central Chile (338S to 398S), year 1992. Q =consumption (t km�2 year�1); P=production

(year�1).

S.Neira

,H.Arancib

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358

Table 4

Summary of total biomass (B), throughput (T ) and trophic transfer efficiency (TTE) among discrete trophic levels

in the ecosystem model representing the upwelling system of Central Chile, year 1992

TL B (t km�2 year�1) B (%) T (t km�2 year�1) T (%) TTE

VII 0.017 3.6�10�5 0.092 5�10�4 6.0

VI 0.826 0.2 4.538 5�10�3 7.4

V 12.468 3 115.582 0.13 6.7

IV 36.542 8 439.867 0.5 26.8

III 51.590 11 1614.490 1.8 27.4

II 71.517 15 19882.180 22 8.1

I 302.506 64 67397.500 75

Total 475.466 89454.240

In addition, biomass and throughput per discrete trophic level are expressed as percentage of total system biomass

and total system throughput.


Production/Biomass ratio (PP/B=76.348) is also high. FCI=8.97%, which implies a low

value for system maturity, is in accordance with data presented by Christensen and Pauly

(1993b) for upwelling ecosystems.

The MPL is low in USCCh (MPL=2.56) which is also a characteristic feature of

upwelling ecosystems (Jarre-Teichman and Christensen, 1998). The above results also

suggest an immature ecosystem with low conectance index (0.17), where energy seems

to be inefficiently retained. Finally, mean TTE (18%) suggest an intermediate to low

energy transfer efficiency, which is characteristic of immature ecosystems (Christensen

Table 5

Ecosystem indicators describing the upwelling system of Central Chile (33–398S), year 1992

Indicator Value Units

Sum of all consumption 26,543.420 t km�2 year�1

Sum of all respiration flows 14,185.680 t km�2 year�1

Sum of all flows to detritus 31,096.740 t km�2 year�1

Sum of all production 43,020.000 t km�2 year�1

Trophic level of fishery 2.97

Calculated total net primary production (TPP) 36,300.760 t km�2 year�1

Total primary production/total respiration 2.559

Net system production 22,115.080 t km�2 year�1

Primary production/total biomass 76.348

Total biomass/total throughput 0.005

Primary production required to sustain

fishery landing (PPR)

2385.500 t km�2 year�1

PPR as percentage of TPP 15.210

Finn’s cycling index 8.97 % of T

Finn’s mean path length 2.587

Conectance index 0.170

System omnivory index 0.337

Mean energy transfer efficiency among

discrete trophic levels

18.000

Total catches 27.601 t km�2 year�1


and Pauly, 1993b). The fishery had a trophic role equivalent to a predator with TL=2.97,

and the primary production required to sustain fishery landing (PPR) in USCCh, in

1992, was estimated in 2386 t km�2 year�1, or 15.2% of total primary production

calculated (Table 5).

Predators consume the most of the production of fishing resources such as Chilean hake

(small), macrobenthos, common sardine and anchovy. On the other hand, fishery removes

the most of the production of Chilean hake (large), horse mackerel and pelagic fish I (hoki)

(Table 6).

The analysis of mixed trophic impacts shows negative impacts of predators on prey

and positive impacts of prey on predators (Fig. 3). Chilean hake exhibits a highly

cannibalistic behavior as shown by the large negative impact of large hake on small

hake. Large hake also impacts on macrobenthos through predation, while small hake

impacts on common sardine and anchovy. However, predators have indirect impacts on

the community, e.g. the negative impact of horse mackerel on pelagic fish I. This is

explained because both groups share zooplankton III (euphausiids) as an important prey

item (Table 3).

On the other hand, preys have positive impacts on their predators, e.g. phytoplankton

has a strong positive impact on zooplankton (I, II, and III), common sardine, and anchovy

(Fig. 3).

Demersal fish (I and II) and chondrichthyans have a light or null impact on other groups

in the system. This result is predictable since these groups exhibit low biomass levels and

their prey are considered mostly as imports (Table 3). However, it must be pointed out that

biomasses of these groups could be underestimated.

The fisheries impact negatively on target species such as Chilean hake (large),

common sardine, anchovy, horse mackerel, pelagic fish I, demersal fish I, chon-

drichthyans and pelagic fish II. However, the fisheries show positive impacts on

euphausiids, macrobenthos, mesopelagic fish, demersal fish II, and cephalopods, which

could be explained as a top-down or cascade effect caused by fishery removal of

predators such as Chilean hake, small-sized pelagic fish, and horse mackerel (Tables 2

and 3).

Table 6

Utilization of the main fishery resources production in the ecosystem model representing the upwelling system of

Central Chile, year 1992. P=production; Y=catch; QP=predators consumption; BL=biomass loss not due to

predation

Group P (t km�2 year�1) Y (t km�2 year�1) QP (t km�2 year�1) BL (t km�2 year�1)

Hake I (small) 11.22 0.243 7.25 3.727

Hake II (large) 2.59 1.188 0.876 0.522

Common sardine 28.40 8.952 14.84 4.608

Anchovy 24.04 6.102 15.387 2.555

Macrobenthos 7.17 0.228 5.198 1.744

Horse mackerel 11.35 6.480 1.484 3.386

Pelagic fish I 5.88 3.950 0.795 1.113

Pelagic fish II 0.159 0.106 0 0.053

Total 90.809 27.199 45.83 17.708

Fig. 3. Mixed trophic impacts in the upwelling system of Central Chile (338S to 398S), year 1992. The figure shows direct and indirect impacts caused by each group in the

system (impacting group; bYQ exe) on the other living groups (impacted groups; bXQ exe). Positive impacts are shown above the base line, while negative below. The

impacts are relative but comparable between groups.

S.Neira

,H.Arancib

ia/J.


361


4. Discussion

Although recent reports have analyzed trophic relationships of commercial fish species

in Central Chile (Neira et al., 2004), this is the first ecosystem model applied to the

USCCh. In this model, we assumed steady-state and mass-balance conditions for the

system during 1992. In this respect, we consider that a 1-year period is as a proper time

scale, i.e. it is long enough to collect all the necessary data input, and short enough to

avoid the impacts of mayor environmental changes in the system, as those related to

regimen shift and/or El Nino Southern Oscillation events (Yanez et al., 1992; Gutierrez et

al., 2001; Arcos et al., 2001). However, intra-annual variability in the system (Strub et al.,

1998) was not included in the modeling due to monthly and/or seasonal estimates for the

required input data are lacking. Therefore, resulting rates and flows must be analyzed

under the above restriction.

The USCCh can be characterized as an immature system, in terms of structure and

flows (sensu Odum, 1969), with low trophic transfer efficiency, short food chains and low

matter cycling (Table 5), which is consistent with previous reports for upwelling

ecosystems (Jarre-Teichman and Christensen, 1998). In this analysis we assumed that, in

the USCCh, energy is predominantly trespassed through the classic food web. It means,

short food chains where big-sized phytoplankton cells are efficiently grazed by

zooplankton, which, in terms, is predated by fishes. However, recent evidence indicates

that an important fraction of primary production in USCCh could be deviated to the

microbial food web (Pacheco and Troncoso, 1998). Therefore, future research effort

should be directed to incorporate this important group into the modeling of matter and

energy flows in USCCh.

In upwelling ecosystems, both euphausiids and small-sized pelagic fish play

important roles as biological components, distributing primary production towards

intermediate and high trophic levels, where carnivorous fish, birds, mammals and the

fisheries are located (Pillar et al., 1992; Cury et al., 2000). In the USCCh, an important

fraction of the primary production is transferred through common sardine, anchovy and

euphausiids toward higher trophic levels to predators such as Chilean hake and horse

mackerel, which are important fishery resources (Fig. 2; Table 2). Although the biomass

of mesopelagic fish and jellies was estimated by the model, it is likely that those groups

exhibit high biomass levels in the system. Unfortunately, basic knowledge on these

groups and their role in this food web is poorly understood in USCCh, and it should be

studied further.

In marine ecosystems, it is expected that changes in fish biomass regulate, by cascade

effect, both production and consumption on lower trophic levels (Carpenter and Kitchell,

1988). According to Cury et al. (2000), in upwelling systems there is a negative

relationship between zooplankton abundance (prey) and small-sized pelagic fish

(predators). However, in the USCCh this kind of top-down control has not been proved

and it is unlikely to occur due to common sardine and anchovy seem to feed almost

exclusively on phytoplankton (Arrizaga, 1983; Arrizaga et al., 1993).

However, predation of horse mackerel on zooplankton can be considered an example of

top-down control in the USCCh (Cury et al., 2000). In fact, Quinones et al. (1997)

reported a strong local impact of horse mackerel associations on euphausiids abundance.


Another indirect evidence of top-down control in the USCCh could be the positive impacts

of fishery on zooplankton, macrobenthos, mesopelagic fish, demersal fish II, and

cephalopods, due to fishing removal of predators such as Chilean hake and horse

mackerel (Tables 2 and 3).

Although this is the first ecosystem model constructed for the USCCh, this kind of

analysis have been widely applied to upwelling ecosystems, allowing to local descriptions

(Jarre et al., 1991; Jarre-Teichman et al., 1998) and general patterns (Jarre-Teichman,

1998; Jarre-Teichman and Christensen, 1998). Unlike previously analyzed upwelling

ecosystems characteristically dominated by one species of clupeids (Jarre-Teichman and

Christensen, 1998; Cury et al., 2000), the USCCh is dominated by medium sized pelagic

fish (horse mackerel and hoki) in terms of biomass. The importance of horse mackerel and

hoki in the USCCh in 1992, was reflected in the fact that they supported 6% of BT

(without including detritus) and 38% of total landing.

Predation is the main component of total mortality in the USCCh (Table 2). In marine

ecosystems, predatory mortality is higher than fishing mortality, even in heavily

exploited ecosystems such as the Peruvian upwelling ecosystem (Jarre et al., 1991), and

also the upwelling ecosystems of Namibia, California, Norwest Africa (Jarre-Teichman,

1998), and the North Sea (Bax, 1991). In the USCCh, fishing resources are heavily

affected by predation. This is not the case of horse mackerel, for which the purse-seine

fleet seems to be the main predator, as pointed out by Quinones et al. (1997) and Neira

et al. (2004).

According with our results, the fishery removes a large fraction of the production

of target and non-target species (Table 2), which correspond to 15% of total primary

production in USCCh. These results are in agreement with previous reports for

comparable ecosystems (Jarre-Teichman, 1998). However, PPR in USCCh in 1992

was lower than the global estimated inferred by Pauly and Christensen (1995) for

upwelling ecosystems (25%). This result could be explained by the high levels of

primary production informed for the study area, some of them representing the

highest values informed for the open ocean (Fossing et al., 1995; Daneri et al.,

2000).

Finally, we suggest that trophic interactions should be considered in stock assessment

and management programs in USCCh, since predation could modulate population

dynamic of the most important fishery resources. In addition, future research programs

should be carried out in order to understand the ecosystem effects of fishing and trophic

control in this highly productive food web.

Acknowledgements

We are grateful to the EU INCO-DC project bPlacing fisheries in their ecosystem

contextQ and a grant from IRD IDYLE project, through which funding was provided for a

workshop in Brazil (December 1998) and South Africa (November 2002), where authors

were able to improve the model, and interpret results. We also thank Dr. Villy Christensen,

Dr. Francisco Arreguın-Sanchez and an anonymous referee for their valuable comments on

an earlier version of this paper. [RW]


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trophic interactions and community structure in the upwelling

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