Trophic mass balanced models and dynamic simulations of benthic communities from La Rinconada Marine...

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 20: 58–73 (2010)

Published online 3 June 2009 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/aqc.1047

Trophic mass balanced models and dynamic simulations of benthiccommunities from La Rinconada Marine Reserve off northern

Chile: network properties and multispecies harvestscenario assessments

MARCO ORTIZ�, MIGUEL AVENDANO, MARCELA CANTILLANEZ, FERNANDO BERRIOS

and LEONARDO CAMPOS

Modelacion de Sistemas Ecologicos Complejos, Instituto Antofagasta de Recursos Naturales Renovables (IARnR), Instituto de

Investigaciones Oceanologicas, Facultad de Recursos del Mar, Universidad de Antofagasta, P.O. Box 170, Antofagasta, Chile

ABSTRACT

1. Mass balanced trophic models and dynamic simulations of two benthic ecological systems from LaRinconada Marine Reserve (Antofagasta Bay, SE Pacific) were constructed.2. The scallop Argopecten purpuratus is the most important benthic resource in La Rinconada Marine Reserve,

followed by the carnivorous snail Thais chocolata, and the filter-feeder bivalves Tagelus dombeii and Transennellapannosa.3. Information on biomass, P/B ratios, catches, food spectrum, consumption, and dynamics of commercial and

non-commercial species was obtained and examined using Ecopath with an Ecosim software package.4. The bivalves A. purpuratus and T. dombeii represented the most abundant compartments in the studied

subsystems. Of the carnivores, the snail T. chocolata was dominant, followed by the crabs Cancer spp. and thefunctional group of large epifauna.5. The two subsystems presented similar values of system throughput. The mean trophic level of their fisheries

also reached similar magnitudes (2.0), showing that the harvests in each system concentrated on secondaryproducers. Likewise, both subsystems presented similar A/C ratios (29.9 and 30.3), suggesting that they wereimmature.6. The results obtained using mixed trophic impact (MTI) and Ecosim (increasing the fishing mortality Fi by

four times) showed that only four species propagated the highest direct and indirect effects. Coincidentally, thesespecies are the most economically important and the changes produced by the scallop A. purpuratus arenoteworthy.7. With regard to the system recovery time (SRT) estimates, only three species or functional groups presented

the highest magnitudes, from highest to lowest: the sea star Luidia magallanica, the scallop A. purpuratus, and thecrabs Cancer spp.8. The topological keystone indexes of Jordan and Libralato had divergent results. According to Jordan’s

index, the keystone species were L. magallanica, Cancer spp., and detritus; whereas Libralato’s index showedphytoplankton to be the keystone species.9. Based on the results obtained, it is concluded that trophic mass balanced models and simulated management

scenarios have considerable value for planning interventions and manipulations or for planning more sustainablemanagement strategies in La Rinconada Marine Reserve.Copyright r 2009 John Wiley & Sons, Ltd.

Received 1 August 2008; Revised 4 March 2009; Accepted 18 March 2009

KEY WORDS: La Rinconada Marine Reserve; Ecopath with Ecosim; benthic communities; trophic dynamics

*Correspondence to: M. Ortiz, Modelacion de Sistemas Ecologicos Complejos, Instituto Antofagasta de Recursos Naturales Renovables (IARnR),Instituto de Investigaciones Oceanologicas, Facultad de Recursos del Mar, Universidad de Antofagasta, P.O. Box 170, Antofagasta, Chile.E-mail: [email protected]

Copyright r 2009 John Wiley & Sons, Ltd.

INTRODUCTION

La Rinconada Marine Reserve, located at the northern end ofAntofagasta Bay (Mejillones Peninsula) is a coastal area of ca

270 ha where the main natural populations of the scallopArgopecten purpuratus (Lamarck, 1819) are distributed offnorthern Chile. This marine reserve was established by the

Chilean Government in 1997 in order to protect the scallopsfrom overfishing. Simultaneously, this measure has alsoreduced the exploitation of other species of commercialinterest living in the reserve such as the bivalves Aulacomya

ater (Molina, 1782) and Tagelus dombeii (Lamarck, 1818), andthe carnivorous snail Thais chocolata (Duclos, 1832). Althoughthe initial motivation for this protective measure was a noble

bio-ecological reason (the preservation of the scallop), it hasresulted in increased interest (pressure) on the part of variousartisanal fishing organizations to exploit the reserve’s different

resources. Unfortunately, this social pressure has resulted in anillegal scallop fishery, significantly reducing the scallopabundance and modifying its size structure (Avendano and

Cantillanez, 1996).Many research projects have been carried out in the reserve’s

coastal area, most notably, studies on oceanographic conditions(Rodrıguez et al., 1991; Marın et al., 1993; Escribano et al.,

1995), primary productivity (Rodrıguez et al., 1991), anddifferent aspects of A. purpuratus population dynamics(Avendano and Le Pennec, 1998; Avendano et al., 2001, 2007;

Avendano and Cantillanez, 2005; Cantillanez et al., 2005).In this respect, a recent study by Avendano et al. (2007) has

shown that the reserve contains areas that favour the artificial

collection of A. purpuratus spat, awakening the interest of scallopfarmers for use of the reserve as a natural spat supply that canthen be incorporated into their own culture systems. Althoughmuch has been learned regarding some properties of the reserve’s

coastal area, this knowledge is not enough if we want to respondto the multiple expectations of the different groups involved(artisanal fishermen, farmers, authorities) regarding the design,

implementation, and execution of ecologically sustainable,multispecies co-management. Given our limited knowledge, thedevelopment of an ecological and systemic (holistic) research

programme that permits the collection of biological andecological information on the more relevant species and/orfunctional groups inhabiting the benthic communities of the

Marine Reserve is needed urgently.In recent years, an important interest in evaluating,

quantifying, and predicting the changes produced in theecosystem properties by the fishery has been observed (Hall,

1999a,b; Hawkins, 2004; Francis et al., 2007; Scotti et al., 2007).This has led to a change in the way studies are conducted,focusing on the different types of ecological interrelationships

occurring in the communities and ecosystems rather than on theprotection of isolated populations (Scotti et al., 2007).Multispecies or ecosystem models provide alternative

mechanisms to the classic population-reductionist models forthose cases in which the goals are to approach the holisticproperties of the ecosystems and assess the propagation ofhigher-order effects within complex subsystems (Levins, 1974,

1998a; Hawkins, 2004) and to estimate the topological keystonespecies in the networks (Jordan et al., 1999; Jordan, 2001;Libralato et al., 2006; Vasas et al., 2007). Supported by the

ecological network theory of Ulanovicz (1986, 1997), someexplorations have been performed with regard to the properties,

dynamics, and global health of ecosystems (Costanza andMageau, 1999). It is important to indicate that, along with theestimation of ecosystem macro-descriptors, estimates can be

made with respect to the system recovery time in response tohuman disturbances such as fisheries and aquaculture(Jørgensen, 1992, 2000; Wolff, 1994; Gaedke, 1995; Monaco

and Ulanowicz, 1997; Ortiz and Wolff, 2002a,b; Arias-Gonzalez et al., 2004; Pinneger and Polunin, 2004; Patrıcioand Marques, 2006).

Multispecies trophic models based on Ecopath II(Christensen and Pauly, 1992) and Ecosim (Walters et al.,1997) have become quite popular, especially for describingtrophic webs and predicting the effects produced by the

application of different exploitation scenarios in marineecosystems (Christensen and Pauly, 2004; Pikitch et al.,2004). The main objective of this work was to build

trophic models that represent interspecific relationships(prey–predator) taking place in the benthic subsystems of LaRinconada Marine Reserve, including the effects produced by

illegal A. purpuratus fisheries and the eventual implementationof multispecies management (exploitation of other species),using the Ecopath with Ecosim theory and software package

(Walters et al., 1997). In turn, and based on the trophic modelscreated, two methods designed to identify the topologicalkeystone species in networks were used, namely the Libralatoindex (Libralato et al., 2006) and the Jordan index (Jordan

et al., 1999). Supported by the aforementioned theoreticalframeworks the following were investigated: (1) biomassdistribution and flow structures in the existing ecological

subsystems; (2) main benthic predators, consumption rates,and prey items; (3) redundancy (i.e. the existence of specieswith similar trophic functions in the ecological systems (sensu

Lawton, 1994); (4) recognition of topological keystone species(i.e. species or functional groups with low biomass but withimportant effects on other species, sensu Paine, 1966, 1969;

Mills et al., 1993); (5) degree of resistance to disturbances andthe resilience time of ecological subsystems in response todifferent multispecies exploitation strategies; and (6) the mostaffected species or functional groups given the development of

different management scenarios. All the simulations wereexecuted using a mixed type flow control (both top-down andbottom-up), since this control mechanism is considered to be

more realistic (Levins, 1998b); its use was recentlydemonstrated to result in a high degree of certainty for thepredictions (Thompson et al., 2004; Ortiz, 2008b).

METHODS

Study area

The benthic system of La Rinconada Marine Reserve (231280S�701300W), located in the northern part of Antofagasta Bay(Mejillones Peninsula, Chile), was chosen for this study(Figure 1). Oceanographically, the reserve is influenced by

three different currents/water masses: subAntarctic water(SAW), subtropical water (STW), and equatorial subsurfacewater (ESSW) (Escribano et al., 1995). The bottom is

dominated by sand and gravel. Two ecological subsystemshosting different aggregations of species can be clearlyrecognized at 8–15m depth (Figure 1). It is important to

mention that near Mejillones Peninsula there is an important

TROPHIC MODEL OF MARINE RESERVE LA RINCONADA 59

Copyright r 2009 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 20: 58–73 (2010)

DOI: 10.1002/aqc

upwelling centre that supplies nutrients to the coastalecosystem (Escribano et al., 2004). The temperature ofsurface water ranges between 161C in winter and 201C in

summer (Escribano et al., 2004).

Ecopath and Ecosim theoretical frameworks

The balance of mass (energy or nutrients) for any species orfunctional group of the network can be represented as follows:

Bi �P

B

� �i

�EEi�Xnj¼1

Bj �Q

B

� �i

�DCji�Yi�BAi�Ei ¼ 0

ð1Þ

where Bi and Bj are the prey i and predator j biomasses,respectively; P/Bi is the productivity (production/biomassratio), which is equivalent to total mortality (Z) (Allen,

1971); EEi is the ecotrophic efficiency, that is, the fraction ofthe total production of a group used in the system; Yi is theyield of fisheries per unit area and time (Y5 fishing mortality

� biomass); Q/Bj represents food consumption per unitbiomass of j; DCji is the fraction of prey i in the average diet ofpredator j; BAi is the biomass accumulation rate for i; and

Ei corresponds to the net migration of i (emigration minusimmigration) (Christensen and Pauly, 1992; Walters et al.,1997; Christensen and Walters, 2004). Under this theoreticalframework, the energy input and output of all living groups,

Antofagasta bay

Rinconada Reserve

SS1SS2N

Study area

-70º5’

-23°5’

-23°6’

-23°7’ 2 km

N

2 km

SS1

20

40

60

80

100

SS2

-70º4’

%

CHlLE

Figure 1. Study area of La RinconadaMarine Reserve (SE Pacific), northern Chile. Both ecological subsystems (SS1 and SS2) are depicted. A speciesassemblage (cluster) is shown for both subsystems (SS1 and SS2).

M. ORTIZ ET AL.60


DOI: 10.1002/aqc

by definition, must be balanced. The energy balance is ensuredwithin each variable or compartment group by using thefollowing equation of Christensen et al. (2004):

Q ¼ Pþ RþUAF ð2Þ

where Q is consumption, P is production, R is respiration, andUAF is the unassimilated food of each variable or

compartment in the system. The inclusion of biomassaccumulation and migration factors in Equation (1) presentsEcopath models as an energy continuity approach rather than

a strictly steady-state condition. This particular approachallows changes in the variables or compartments when themathematical function is expressed in its dynamic form.

In order to use Ecosim, an extension routine of Ecopath isincluded to define the consumption by compartment i; Qij isrepresented by the following equation:

Qij ¼aij � vij � Bi � Bj

ð2vij þ aij � BjÞð3Þ

where aij represents the instantaneous mortality rate on preyi caused by a single unit of predator j biomass. Likewise, aij canbe understood as the rate of effective search bypredator j for prey i. Each aij is estimated directly from

the corresponding Ecopath models as: aij ¼ Qi=ðBi � BjÞwhere (Qi 5 total consumption of i). The vij represents the

transference rate between compartments i and j. This para-meter determines whether the flow control mechanism istop-down, bottom-up, or mixed, ranging from 1.0 (bottom-up)

to c2.0 (top-down); a value of 2.0 represents a mixed controlmechanism. Details concerning the Ecopath with Ecosimsoftware package are given in Christensen and Pauly (1992),

Walters et al. (1997), and Pauly et al. (2000).The Ecopath theoretical framework combines the approach

of Polovina (1984) to estimate the biomass and food

consumption of the ecosystem variables or functional groupswith that of Ulanowicz (1986, 1997), a network analysis offlows among variables of the system, for calculating themacro-descriptors: total system throughput (T), ascendency (A),

development capacity (C), and relative ascendency (A/C ratio).Throughput describes the vigour or size of a system andprovides a measure of its metabolism. Ascendency integrates

both size and organization of the systems, with the latterreferring to the number and diversity of interactions betweenits components. The development capacity quantifies the upper

limit for ascendency, whereas the A/C ratio describes thedegree of maximum specialization that is actually achieved inthe system (maturity index) (Baird and Ulanowicz, 1993;

Costanza and Mageau, 1999). This ratio can also be used asthe system’s ability to withstand disturbance (Ulanowicz, 1986,1997). All these macro-descriptors have been widely used to

Table 1. Prey–predator and plant–grazer matrix (percentage of wet biomass in stomach of predators) for subsystem 1 (SS1) and subsystem 2 (SS2)used for the Ecopath with Ecosim software programme

Prey-predator 1 2 3 4 5 6 7 8 9 10 11 15

(A) Subsystem: SS1(1) A. purpuratus 10 10 10 5 5(2) sA. purpuratus(3) T. dombeii 5 4 3 15 10(4) T. pannosa 5 2 5 15 10(5) A. ater 10 10 10 5(6) T. chocolata 10 20 10 5(7) L. magallanica 1(8) Cancer spp. 2(9) SEH 8 30 20 10 20(10) SEC 7 20 14 1 10(11) LE 5 3 6 2(12) Chlorophyta 15(13) Rhodophyta 2(14) Phaeophyta 40(15) Zooplankton 5(16) Phytoplankton 85 85 70 85 85 3 95(17) Detritus 15 15 30 15 15 60 10 10 40 29 33 5

1 2 3 4 5 6 7 8 9 10 14

(B) Subsystem: SS2(1) A. purpuratus 5 5 8 5 5(2) sA. purpuratus(3) T. dombeii 15 58 15 25 15(4) T. pannosa 10 20 30 20 10(5) T. chocolata 10 15 10 10(6) L. magallanica 1(7) Cancer spp. 2(8) SEH 5 14 10 10 15(9) SEC 5 12 10 10(10) LE 5 2(11) Chlorophyta 15(12) Rhodophyta 2(13) Phaeophyta 40(14) Zooplankton 5(15) Phytoplankton 85 85 70 85 3 95(16) Detritus 15 15 30 15 55 10 7 40 24 33 5



DOI: 10.1002/aqc

describe and compare a variety of ecosystems of differentspatial sizes, geographic locations, and complexities (Monacoand Ulanowicz, 1997; Jarre-Teichmann and Christensen, 1998;

Niquil et al., 1999; Heymans and Baird, 2000; Wolff et al.,2000; Ortiz and Wolff, 2002a, 2004; Arias-Gonzalez et al.,2004; Patrıcio and Marques, 2006; Patrıcio et al., 2006).

Selection of model compartments, sampling programmes,

and data sources

Studies were carried out in the field between 2005 and 2007 atLa Rinconada Marine Reserve in order to select the variablesof each of the ecological systems and to estimate the biomass

(B), catches (C), turnover rates (P/B), consumption rates(Q/B), and food items for the variables selected. Appendix Ashows the source data for each of the compartments selected in

the current work. Although most of the model compartmentsrepresent individual species, as is the case of the scallopA. purpuratus, and for the bivalves Tagelus dombeii, Transennellapannosa and Aulacomya ater, the carnivorous snail Thais

chocolata, and the sea star Luidia magallanica, it was alsonecessary to adapt functional groups into compartments thatincluded different species. The Rhodophyta, Chlorophyta, and

Phaeophyta compartments included different species of red(e.g. Rhodymenia sp.), green (e.g. Ulva sp.), and brown(e.g. Glossophora kunthii) algae. The group Cancer spp.

includes the crabs Cancer coronatus, C. polyodon, C. edwardsiand C. porteri; the large epifauna (LE) compartment includesthe crabs Pagurus edwarsi and Eurypanopeus crenatus; the small

epifauna herbivorous group (SEH) is made up of the molluscsCalyptraea trochiformis, Tegula sp., Fissurella sp., Chiton sp.,Mitrella sp. and Colisella sp.; the small epifauna carnivorousgroup (SEC) includes the molluscs Aeneator fontanei, Nassarius

gayi, Cancellaria sp., Oliva sp., Crassilabrum sp., Priene sp.,Xanthochorus sp., Trigonostoma sp. and Nucula sp. In addition,the variable sArgopecten purpuratus (sAp) was incorporated

into the models, representing, for purposes of the simulationonly, the farming systems near the reserve.

The diet matrixes for both subsystems (Table 1) show that

all the compartments are trophically linked by detritus, primarilyas microbial biofilm. Diverse studies have emphasized theimportance of bacteria as food for various molluscs (Epstein,1997; Plante and Shriver, 1998), zooplankton (Epstein, 1997),

and echinodermata (Findlay and White, 1983). It is important tonote that the models were constructed to grasp the trophicrelationships for the most relevant species (commercial resources)

inhabiting benthic communities, leaving out the flows fromepiphytes, microphytobenthos, bacteria, and those leading tofishes, seals and birds due to insufficient scientific information.

Although this reduced the realism of the model configuration, wethink that the most relevant interdependencies and flows arereflected. Since the configuration of all models has followed the

same principle, this systematic error should not impede acomparative analysis with other ecological systems undersimilar constraints.

Balancing and calibration of the models

The first step in balancing the models was to determine if themodel outputs were realistic, that is, to check if the ecotrophicefficiency (EE) was o1.0 for all variables or compartments. If

an inconsistency was detected, the biomass values (annual

averages) were slightly changed within the confidence limits(standard deviation) obtained from field studies. Nevertheless,for the suspended scallop (sA. purpuratus), Chlorophyta,

Rhodophyta, and Phaeophyta, the turnover rate (P/B) valueswere calculated by Ecopath. It was not necessary to modify thediet matrixes when balancing the models. As a second step,

gross efficiency (GE) values were checked for consistency bycomparing them with data from the literature.

System recovery time

Stability is the ability of a system to return to a state ofequilibrium after a disturbance (Holling, 1973). Ulanowicz’s

theory (Ulanowicz, 1986, 1997) states that the ecosystemorganization, in terms of relative ascendancy (A/C) andredundancy (internal flow of overhead), may be the mostimportant attribute of system stability. Resilience has been

conceptualized as the speed at which the entire system returnsto its original state after it has been displaced from its originalstate (Pimm, 1982). Resistance describes the capacity of a

system to withstand displacement (Begon et al., 1990).Therefore, stability includes both resilience and resistance. Inthe present contribution, we assume that system recovery time

Table 2. Parameter values entered (in bold) and estimated (standard)for each ecological subsystem (SS1 and SS2) by Ecopath with Ecosimsoftware programme

Compartements

Species/Funcionalgroups

TC C B P/B Q/B EE

(A) Subsystem SS1(1) A. purpuratus 2.0 224.9 259.0 2.7 9.9 0.36(2) sA. purpuratus 2.0 2.5 50.0 2.0 9.9 0.03(3) T. dombeii 2.0 0.01 15.0 2.6 9.9 0.86(4) T. pannosa 2.0 0.01 14.0 2.8 9.9 0.88(5) A. ater 2.0 0.2 12.0 2.1 9.9 0.89(6) T. chocolata 2.5 0.16 14.2 2.7 7.2 0.72(7) L. magallanica 3.1 0.8 0.5 3.0 0.06(8) Cancer spp. 3.2 5.5 1.9 9.5 0.10(9) SEH 2.0 20.0 2.8 11.7 0.82(10) SEC 2.8 13.0 2.4 10.4 0.73(11) LE 2.8 7.1 1.9 9.2 0.72(12) Chlorophyta 1.0 10.0 5.0 0.70(13) Rhodophyta 1.0 83.5 5.0 0.01(14) Phaeophyta 1.0 20.0 5.0 0.94(15) Zooplankton 2.0 20.0 40.0 160.0 0.01(16) Phytoplankton 1.0 30.0 250.0 0.80(17) Detritus 1.0 100.0 0.19

(B) Subsystem SS2(1) A. purpuratus 2.0 46.1 53.6 2.7 9.9 0.57(2) sA. purpuratus 2.0 2.5 25.0 2.0 9.9 0.05(3) T. dombeii 2.0 0.2 314.2 2.0 9.9 0.18(4) T. pannosa 2.0 0.2 78.7 2.8 9.9 0.42(5) T. chocolata 2.5 0.5 48.9 2.7 7.2 0.26(6) L. magallanica 3.1 0.01 0.5 3.0 0.06(7) Cancer spp. 3.1 5.0 1.9 9.5 0.10(8) SEH 2.0 20.0 2.8 11.7 0.99(9) SEC 2.8 15.0 2.4 10.4 0.99(10) LE 2.8 12.5 1.9 9.2 0.84(11) Chlorophyta 1.0 10.0 5.0 0.70(12) Rhodophyta 1.0 255.0 5.0 0.004(13) Phaeophyta 1.0 20.0 5.0 0.94(14) Zooplankton 2.0 20.0 40.0 160.0 0.04(15) Phytoplankton 1.0 30.0 250.0 0.87(16) Detritus 1.0 100.0 0.34

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DOI: 10.1002/aqc

(SRT), as obtained by our simulations, is a measure of theinternal stability of the systems.

Multispecies harvest assessment

Ecopath’s mixed trophic impact (MTI) routine (Ulanowicz

and Puccia, 1990) was used to make a preliminary evaluationof the propagation of direct and indirect effects in responseto disturbances affecting species of commercial interest.

Ecosim simulations were used to evaluate the propagation of

instantaneous direct and indirect effects and SRT as a responseto an increase in fishing mortality (Fi). This was calculatedfor the first and second year of simulation for the scallop

A. purpuratus; the bivalves T. dombeii, T. pannosa and A. ater;the carnivorous snail T. chocolata; the sea star L. magallanica;the group Cancer spp.; and the group Rhodophyta in the

subsystems. The propagation of instantaneous effects wasdetermined by evaluating the biomass of all the variables ofboth subsystems in the third year of simulation, that is, 1 year

after the increase in fishing mortality. Finally, the FMSY values

7

7

10

10

8

11

14

6

6

1 9

9

3

8 3 4

5

16 15 11 12 13

4 515 2

12

17(a)

(b)

16 12 13 14

Figure 2. Trophic models for ecological subsystem 1(a), and subsystem 2(b). Vertical position approximates trophic levels. The box is proportionalto the compartment biomass (g wet weight m�2). Simple arrows represent the flow of matter among compartments and double arrows means flow to

fisheries. The number in box corresponds to the species or functional groups (for details see Table 1).



DOI: 10.1002/aqc

were determined only for commercially important bivalvespecies. All the dynamic simulations were carried out using amixed flow control mechanism (vij) since the use of this control

resulted in unique and stable FMSY values (Ortiz and Wolff,2002b). Furthermore, Ortiz (2008b) recently demonstrated thatthe use of a mixed control gives the highest certainty in

predictions. When taken separately, top-down and bottom-upcontrols could be considered to be a false dichotomy inecology, distorting the capacity for understanding (sensu

Levins, 1998b; Thompson et al., 2004).

Topological keystone species index

The Libralato index (Libralato et al., 2006) is an extension ofthe mixed trophic impacts (MTI) (Ulanowicz and Puccia, 1990)since it uses the impact matrix. Since every impact can bequantitatively positive or negative, a new measure of overall

effect is needed for each species or functional group ei using thefollowing mathematical relationship:

ei ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiXnj 6¼i

m2ij

vuut ð4Þ

where mij corresponds to the elements of the MTI matrix andquantifies the direct and indirect impacts that each (impacting)

species or group i has on any (impacted) group j of the foodweb. However, the effect of the change in biomass on thegroup itself (i.e. mii) is not included. The contribution of

biomass from every species or functional group with respect to

the total biomass of the food web was estimated using thefollowing relationship:

pi ¼BiPni Bk

ð5Þ

where pi is the proportion of biomass of each species Bi

with respect to the sum of the total biomass Bk. Therefore,in order to balance the overall effect and biomass, we

established the topological keystone index (KS) for eachspecies or functional group, integrating Equations (4) and (5)as follows:

KSi ¼ log½eið1� piÞ� ð6Þ

The other topological keystone index used in this workwas developed by Jordan et al. (1999) and Jordan (2001). TheJordan index considers direct and indirect interaction in both

directions (i.e. bottom-up and top-down). The keystone indexof the xth species or functional group (Kx) is calculated asfollows:

Kx ¼Xnc¼1

1

dcð1þ KbcÞ þ

Xme¼1

1

feð1þ KteÞ ð7Þ

where n is the number of predators eating species x, dc is thenumber of prey of the cth predator, Kbc is the bottom-upkeystone index of the cth predator, and symmetrically we havem as the number of prey eaten by species x, fe as the number of

predators of its eth prey, and Kte as the top-down keystoneindex of the eth prey. Therefore, the keystone index (Kx)corresponds to the addition of bottom-up (Kbc) and top-

down (Kte) components. For more details of this method, see

Table 3. Summary statistics after mass-balance process by Ecopath with Ecosim (A) and network flow indices (B) for each ecological subsystem (SS1and SS2)

Subsystems

SS1 SS2

(A) Summary statisticsSum of all consumption (gm�2 year�1) 7256.8 8773.8Sum of all exports (gm�2 year�1) 4104.3 3417.1Sum of all respiratory flows (gm�2 year�1) 3963.2 5507.9Sum of all flows into detritus (gm�2 year�1) 4800.0 5062.6Total system through (gm�2 year�1) 20124.0 22761.0Sum of all production (gm�2 year�1) 9908.0 10436.0Mean trophic level of the catch 2.0 2.0Gross efficiency of fisheries (catch/net pp, %) 0.028 0.006Total net primary production (gm�2 year�1) 8067.5 8925.0Total primary production/Total respiration 2.0 1.6Net system production (gm�2 year�1) 4104.3 3417.1Total primary production (gm�2 year�1) 14.1 9.8Total biomass/total throughput 0.029 0.04Total biomass (exc. Detritus) (gm�2 year�1) 574.1 907.9Toal catches (gm�2 year�1) 227.8 49.5

(B) Netwok flow indicesAscendency (Total) Flowbits 24375.1 29002.2Overhead (Total) Flowbits 55945.9 67845.7Capacity (Total) Flowbits 80321.0 96847.8Pathway Redundancy (of Overhead) (%) 50.8 51.3A/C (%) 30.3 29.9Throughput cycled (exc. Detritus) (gm�2 year�1) 7.0 12.7Throughput cycled (inc. Detritus) (gm�2 year�1) 1.4 1.4Finn«s cycling index (FCI) (%) 2.4 4.1Average path length (APL) (dimensionless) 2.5 2.6Food web connectance (dimensionless) 0.2 0.2Omnivory index (OI) (dimensionless) 0.1 0.1

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Jordan et al. (1999), Jordan (2000), and Vasas et al. (2007). It isimportant to note that in the current work we have calculatedonly the global topological keystone index (Kx).

RESULTS

The bivalve mollusc T. dombeii is the species with the highestbiomass (314.2 g wet weight (ww) m�2) in subsystem 2 (SS2).

On the other hand, the scallop A. purpuratus (259.0 gwwm�2)is the most abundant species in subsystem 1 (SS1). Thefunctional group Rhodophyta (255.0 gwwm�2) and the clam

T. pannosa (78.7 gwwm�2) also dominated in the SS2(Table 2). Regarding predators, T. chocolata (48.9 gwwm�2)and the large epifauna group stood out in SS2, followed by

the group of Cancer spp. (5.5 gwwm�2) in SS1 (Table 2)(Figure 2).

With regard to the ecosystem structure of the ecological

benthic systems, the SS2 had the highest magnitude of systemthroughput (T) (22761 gwwm�2 year�1) (Table 3). The twosubsystems studied presented a similar magnitude of food web

connectivity and omnivory index (OI), with comparable degreesof linearity in their topologic characteristics (Table 3). At thesame time, both subsystems reached a mean trophic level of

A. p

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Figure 3. Mixed trophic impacts (direct and indirect) as response to impacting A. purpuratus, T. dombeii, T. pannosa and A. ater (all commercialspecies). Ecological subsystem 1 (SS1) and subsystem 2 (SS2).



DOI: 10.1002/aqc

fisheries equal to 2.0, which proves that harvesting is donealmost exclusively over secondary producers, particularlythe scallop A. purpuratus. According to the magnitudes of

average path length (APL) and the omnivory index (OI),both subsystems presented a similar linearity of the trophicwebs. The SS2 presented the highest magnitude of developmentcapacity (C) and ascendency (A). However, with respect to

the level of maturity and resistance to disturbances based onrelative ascendency (A/C) and redundancy, both subsystemspresented similar characteristics (Table 3). With regard to

the contribution of each group to ascendency as a way ofevaluating the contribution of each of the compartments tothe overall structure and function of the system, in both

benthic models, the phyto-zooplankton reached levels ofmagnitude higher than 40%, followed by detritus (�28%),

the herbivore group (�16%), macroalgae (�10%) andcarnivores (�3%).

The propagation of direct and indirect effects estimated

using the mixed trophic impact (MTI) shows quantitativeand qualitative differences between both subsystems(Figure 3). The SS2 presented a higher magnitude of changeswith respect to SS1, highlighting the effects of the carnivorous

snail T. chocolata and bivalve filter-feeders T. dombeii andT. pannosa. On the contrary, the dynamic simulations usingEcosim showed a very different response pattern to that

obtained using MTI in which only the effects propagatedby the scallop A. purpuratus stand out in the two sub-systems (Figure 4).

Figure 5 show the possible responses in biomass and thehighest magnitudes of system recovery time (SRT) in both

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Figure 3. Continued.

M. ORTIZ ET AL.66


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subsystems analysed. The scallop A. purpuratus and the sea starL. magallanica presented the highest SRT magnitudes in SS1.

However, the propagation of direct and indirect effects wasclearly dissimilar. In SS2, on the contrary, the group of Cancerspp. and L. magallanica had the highest SRT values,propagating little effects in the remaining variables of the

system (Figure 5). Table 4 summarizes the SRT magnitudesobtained in the simulations carried out in both subsystems.It can be observed that a disturbance that is present

simultaneously in all the variables produces the highest SRTmagnitudes, which are similar to those obtained for the seastar L. magallanica.

As for the eventual presence of keystone species in SS1 andSS2, both indexes showed quite different results: according to

Jordan’s index, L. magallanica and Cancer spp. are thekeystone species in SS1, whereas Libralato’s index showsthe keystone species to be phytoplankton (Table 5). In SS2, therespective keystone species were detritus (Jordan index) and

phytoplankton (Libralato index) (Table 5).

DISCUSSION

The results obtained for the distribution of biomasses in La

Rinconada Marine Reserve clearly established that both

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Fishing on A. purpuratus

Fishing on Cancer spp.

Fishing on L. magallanica

Fishing on T. dombeii

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Fishing on RhodophytaFishing on A. aterz

Fishing on T. chocolata

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Figure 4. Dynamical responses of biomass behaviour for ecological subsystem SS1 (a) and SS2 (b) subject to one year of increased (4� ) fishingmortality (between year 1 and 2 of simulation) for A. purpuratus, T. dombeii, T. pannosa and A. ater. (Note: the dynamical response of biomass was

obtained at 3rd year of simulation using mixed controlling, u5 2.0.)



DOI: 10.1002/aqc

subsystems studied are dominated by commercially importantspecies: the scallop A. purpuratus in SS1 and the clam

T. dombeii in SS2. This natural distribution pattern allowsthe development of complementary non-spatially-superimposedfishing management, reducing disturbances associated with

human intervention in the global benthic system. It isimportant to mention that in SS2, along with the highabundance levels of T. dombeii; the clam T. pannosa and the

snail T. chocolata are also distinguished (both the latter speciesare economically valuable), thus the implementation of amultispecies management programme in the marine reserve is

required.

In terms of the estimates of system throughput (T), SS2 hada slightly higher magnitude than that obtained in SS1, despite

the fact that both systems are dominated by different benthicspecies. The T magnitudes estimated for both subsystems arevery close to those calculated for the benthic ecological systems

in Tongoy Bay (Ortiz and Wolff, 2002a). It is also important toindicate that T estimations were also higher than thosedescribed for other ecosystem models such as estuaries and

mangroves (Wolff et al., 2000; Vega-Cendejas and Arreguin-Sanchez, 2001; Patricio and Marques, 2006; Patricio et al.,2006) but lower than those obtained in coral reefs (Arias-

Gonzalez et al., 2004). Trophic webs presented topological

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Fishing on A. purpuratus Fishing on Cancer spp.

Fishing on L. magallanicaFishing on T. dombeii

Fishing on T. pannosaFishing on Rhodophyta

Fishing on T. chocolata

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Figure 4. Continued.

M. ORTIZ ET AL.68


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characteristics very similar to those described for the TongoyBay benthic systems (Ortiz and Wolff, 2002a) and similar tothe trophic webs of kelp communities off Mejillones Peninsula

of northern Chile (Ortiz, 2008a).The magnitudes of capacity (C) that were calculated are

within the extreme values described for the Tongoy Bay

subsystems (Ortiz and Wolff, 2002a). However, these were

much lower than the values obtained for the kelp modelsystems of Mejillones Peninsula (Ortiz, 2008a). The relativeascendancy (A/C), considered to be an index of maturity as

well as of the system’s ability to withstand disturbances(Ulanowicz, 1986, 1997), shows that both subsystems havesimilar characteristics and are, in turn, very like those obtained

in the benthic systems of Tongoy Bay (Ortiz and Wolff, 2002a)and other ecological systems (Baird and Ulanowicz, 1993;Heymans and Baird, 2000; Wolff et al., 2000; Vega-Cendejas

and Arreguin-Sanchez, 2001). However, both subsystemswould be less resistant to the kelp benthic models ofMejillones Peninsula (Ortiz, 2008a). It is important toindicate that the conclusions based on A/C ratios should be

taken cautiously due to the negative relationship betweenascendency and maturity described by Christensen (1995).

Ulanowicz (1997) proposed estimating the relative

ascendency of each group as a way of evaluating thecontribution of each of the compartments to the overallstructure and function of the system. In this sense, the results

obtained are consistent with the oceanographic properties ofthe marine reserve, which is characterized by the permanentinfluence of nutrient-rich waters coming from the upwellingcentre located off Mejillones Peninsula (Escribano et al., 2004).

Similar results have been published for the benthic systemsof Tongoy Bay (central Chile) (Ortiz and Wolff, 2002a),also located very near an upwelling centre off Lengua Punta

de Vaca Peninsula (Daneri et al., 2000; Montecinos andQuiroz, 2000).

Unfortunately, the direct and indirect effects estimated

using MTI and Ecosim showed very different responsepatterns, with only four benthic species, all economically

Figure 5. Dynamical changes of biomass at each ecological subsystem subject to 1 year of increased (4� ) fishing mortality under mixed controlling(u5 2.0). The responses of the species with the largest system recovery time (SRT) are shown.

Table 4. Summary of the system recovery time (SRT) for eachecological subsystem (SS1 and SS2) using mixed flow controlmechanism (u5 2.0)

Mixed flow control (v5 2.0)

Ecological subsystem System recoverytime (year)

La Rinconada SS1FishingArgopecten purpuratus �15Tagelus dombeii �5Transanella pannosa �5Aulacomya ater �6Thais chocolata �6Cancer spp. �11L. magallanica �16Rhodophyta �7All �16

La Rinconada SS2FishingArgopecten purpuratus �10Tagelus dombeii �5Transanella pannosa �5Thais chocolata �6Cancer spp. �11L. magallanica �16Rhodophyta �8All �16



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important, standing out. Therefore, it is important to notethat, of all the species, the scallop A. purpuratus is the onepresenting the highest commercial value and it is illegally

exploited nowadays (Ortiz, pers. obs.). Regarding thesystem recovery time (SRT) magnitudes obtained, thecarnivorous sea star L. magallanica had the longest SRT in

both subsystems, coinciding with the keystone speciesbehaviour presented in SS1 (sensu Jordan’s index; Jordan,2001) (Table 5). It is worth pointing out that no con-

vergence was observed between both keystone indexes andonly the character of keystone species (or functional groups)for the group of phytoplankton coincided in both subsystemsusing Libralato’s index. The latter coincides with the highest

ascendency concentration in the phyto-zooplankton complextypical of upwelling-dominated ecosystems.

Thus the design and implementation of any exploitation

management strategy in La Rinconada Marine Reserve shouldtake into consideration at least the complexity detected in thiswork using different theoretical bodies of analysis, especially

considering the fact that the economically and commerciallyvaluable species (except the sea star L. magallanica) are thosethat propagate the greatest direct and indirect effects on thesubsystems.

The trophic models and the simulations carried out in thepresent work are the first attempt to describe the main flows of

matter and energy occurring in the principal benthic systems ofLa Rinconada Marine Reserve. Despite this, it is important tostress that new efforts should be made to extend the knowledge

obtained so far through the evaluation of the relativeimportance of bacteria, DOM, and POM in the benthictrophic webs. In this sense, it would also be relevant to know

the degree of influence on the benthic webs that theunpredictable aggregations of fish occurring in the reservecould cause. Finally, it is important to note that, despite the

known limitations and shortcomings of the Ecopath andEcosim theoretical frameworks (Christensen and Walters,2004), it is suggested that both models, constructed assimulations in the present contribution, represent the

underlying phenomena in the ecological systems studied onlywhen their short-term dynamics are considered.

ACKNOWLEDGEMENTS

This contribution was financed by CORFO (Chile), Grant No.04CR7IPM-01.

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Table 5. Network keystone indices (Jordan, 2001 and Libralato et al.,2006) for each ecological subsystem (SS1 and SS2)

(Subsystem/species Jordan’s index Libralato’s indexKx KSi

(A) SS1(1) A. purpuratus 1.082 (�) 0.36(2) sA. purpuratus 0.226 (�) 2.14(3) T. dombeii 1.082 (�) 0.73(4) T. pannosa 1.08 (�) 0.73(5) A. ater 0.77 (�) 0.84(6) T. chocolata 2.21 (�) 0.29(7) L. magallanica 6.89 (�) 0.30(8) Cancer spp. 6.89 (�) 0.25(9) SHE 4.08 (�) 0.65(10) SEC 4.24 (1) 0.07(11) LE 4.48 (�) 0.38(12) Chlorophyta 0.37 (�) 0.85(13) Rhodophyta 0.37 (�) 1.77(14) Phaeophyta 0.37 (�) 0.43(15) Zooplankton 0.40 (�) 1.16(16) Phytoplankton 5.02 (1) 0.15(17) Detritus 5.87 (�) 0.27

(B) SS2(1) A. purpuratus 1.217 (�) 0.19(2) sA. purpuratus 0.258 (�) 1.45(3) T. dombeii 1.217 (�) 0.68(4) T. pannosa 1.217 (�) 0.60(5) T. chocolata 2.330 (1) 0.09(6) L. magallanica 3.914 (�) 0.30(7) Cancer spp. 3.914 (�) 0.28(8) SHE 4.217 (�) 0.75(9) SEC 4.057 (1) 0.06(10) LE 3.914 (�) 0.37(11) Chlorophyta 0.392 (�) 0.85(12) Rhodophyta 0.392 (�) 1.84(13) Phaeophyta 0.392 (�) 0.42(14) Zooplankton 0.392 (�) 1.14(15) Phytoplankton 4.433 (1) 0.13(16) Detritus 5.392 (�) 0.25

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APPENDIX A: MODELS DATA SOURCES

CompartmentsSpecies/Functional groups

Parameter

C1 B2 P/B3 Q/B4 Diet5 Literature source

(1) A. purpuratusSS1 subsystem 224.9 259.0 2.7 9.9 12,3Field estimations for current workSS2 subsystems 46.1 53.6 2.7 9.9 4,5Ortiz and Wolff (2002a)

(2) sA. purpuratusSS1 subsystem 2.5 50.0 2.0 9.9 12Field estimations for cuurent workSS2 subsystems 2.5 25.0 2.0 9.9 3Estimated by Ecopath, 4,5Ortiz and Wolff (2002a)

(3) T. dombeiiSS1 subsystem 0.01 15.0 2.6 9.9 1,3,5Field estimations for current workSS2 subsystems 0.2 314.2 2.0 9.9 4Ortiz and Wolff (2002a)

(4) T. pannosaSS1 subsystem 0.01 14.0 2.8 9.9 1,3,5Field estimations for current workSS2 subsystems 0.2 78.7 2.8 9.9 4Ortiz and Wolff (2002a)

(5) A. aterSS1 subsystem 0.2 12.0 2.1 9.9 1,3,5Field estimations for current workSS2 subsystems 4Ortiz and Wolff (2002a)

(6) T. chocolataSS1 subsystem 0.16 14.2 2.7 7.2 1,3,4,5Field estimations for current workSS2 subsystems 0.5 48.9 2.7 7.2

(7) L. magallanicaSS1 subsystem 0.8 0.5 3.0 1,3,5Field estimations for current workSS2 subsystems 0.0 0.5 3.0 4Ortiz and Wolff (2002a)

(8) Cancer spp.SS1 subsystem 5.5 1.9 9.5 1,3,4,5Field estimations for current workSS2 subsystems 5.0 1.9 9.5

(9) SEHSS1 subsystem 20.0 2.8 11.7 1,3,5Field estimations for current workSS2 subsystems 20.0 2.8 11.7 4Ortiz and Wolff (2002a)

(10) SECSS1 subsystem 13.0 2.4 10.4 1,3,5Field estimations for current workSS2 subsystems 15.0 2.4 10.4 4Ortiz and Wolff (2002a)

(11) LESS1 subsystem 7.1 1.9 9.2 1,3,5Field estimations for current workSS2 subsystems 12.5 1.9 9.2 4Ortiz and Wolff (2002a)

(12) ChlorophytaSS1 subsystem 10.0 5.0 1Field estimations for current workSS2 subsystems 10.0 5.0 3Estimated by Ecopath

M. ORTIZ ET AL.72


DOI: 10.1002/aqc

(13) RhodophytaSS1 subsystem 83.5 5.0 1Field estimations for current workSS2 subsystems 255.0 5.0 3Estimated by Ecopath

(14) PhaeophytaSS1 subsystem 20.0 5.0 1Field estimations for current workSS2 subsystems 20.0 5.0 3Estimated by Ecopath

(15) ZooplanktonSS1 subsystem 20.0 40.0 160.0 1,3,4,5Ortiz and Wolff (2002a)SS2 subsystems 20.0 40.0 160.0

(16) PhytoplanktonSS1 subsystem 30.0 250.0 1,3,4,5Ortiz and Wolff (2002a)SS2 subsystems 30.0 250.0



DOI: 10.1002/aqc

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