Mona

MIF

a

A

R

R

5

A

K

K

B

E

E

B

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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 6 ( 2 0 0 8 ) 31–46

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

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

ass balanced and dynamic simulations of trophic modelsf kelp ecosystems near the Mejillones Peninsula oforthern Chile (SE Pacific): Comparative network structurend assessment of harvest strategies

arco Ortiz ∗

nstituto Antofagasta de Recursos Naturales Renovables (IARnR), Instituto de Investigaciones Oceanologicas,acultad de Recursos del Mar, Universidad de Antofagasta, P.O. Box 170, Antofagasta, Chile

r t i c l e i n f o

rticle history:

eceived 23 May 2007

eceived in revised form

April 2008

ccepted 11 April 2008

eywords:

elp ecosystems

arren ground

copath

cosim

lade harvest

orthern Chile

a b s t r a c t

Mass balanced trophic models for kelp ecosystems which include subsystems dominated by

Macrocystis integrifolia, Lessonia trabeculata and areas of barren ground (BG) were constructed

for subtidal areas near the Mejillones Peninsula (SE Pacific), Chile. Information on biomass,

P/B ratios, catches, food spectrum, consumption and dynamics of commercial and non-

commercial populations was obtained and examined using Ecopath with Ecosim software

analyses. The biomass of blades of L. trabeculata and M. integrifolia represented the com-

partments most relevant to the subsystems studied. Within the herbivores, the sea urchin

Tetrapigus niger was dominant, followed by the snails Turritella sp. and Tegula sp. The fishes

Pinguipes chilensis and Cheilodactylus variegatus were the dominant predators, followed by

the asteroids Heliaster helianthus and Meyenaster gelatinosus. The highest system throughput

(72,512 g wet weight m−2 year−1) was calculated for the subsystem dominated by M. inte-

grifolia. The mean trophic level of the catch ranged from 1.1 (subsystem dominated by L.

trabeculata) to 1.3 (subsystem dominated by M. integrifolia) to 3.2 (barren ground subsystem),

showing that harvesting in each system was concentrated either on primary producers

(blades of kelp species) or top predator fishes. Although the values for the Relative Ascen-

dency (A/C) fluctuated from 36.5 to 45%, suggesting that all the systems were immature,

the subsystem dominated by M. integrifolia emerged as the least resistant to external distur-

bances (e.g. fisheries). This result agreed with the high value of the system recovery time

(SRT) for the M. integrifolia subsystem as a response to combined fisheries scenarios. The

results obtained using mixed trophic impact (MTI) and Ecosim [increasing the fishing mor-

tality Fi by 4×] showed that in most of the cases the predictions had the same qualitative

tendencies. One of the most important results obtained in this study was that exploitation

of kelp blades as an alternative strategy to harvesting the whole plants appeared to be eco-

logically sustainable, since harvesting the blades propagated only small effects on the entire

P. chilensis may be considered as a top predator species with a strong

subsystem. The fish

top-down control since an increase in its fishing mortality in the subsystem dominated

by M. integrifolia produced a high SRT value, and the FMSY was less than the originally

∗ Tel.: +56 55 637 866; fax: +56 55 637 804.E-mail address: mortiz@uantof.cl.

304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2008.04.006

32 e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 31–46

entered Fi in Ecopath. Based on the results obtained, it was concluded that the trophic

mass balanced models and simulated management scenarios offered good possibilities

for the planning of interventions and manipulations or the planning of more sustainable

ies in

management strateg

1. Introduction

The quantification of energy and material flows in dif-ferent, complex marine ecosystems has had importantimplications both in improving our understanding of theecological and dynamic processes underlying them, as wellas in the prediction of changes shown by ecosystems inresponse to anthropogenic disturbance (Jørgensen, 1992;Gaedke, 1995; Ortiz and Wolff, 2002a,b; Patrıcio and Marques,2006). Among the different types of human disturbance,fisheries activities impose a broad range of impacts on nat-ural systems, including both direct and indirect impacts(Pauly et al., 1998, 2002). Based on the preceding, quan-titative multi-species models that describe trophic websof ecosystems can be used for the design and evaluationof adaptive fisheries management by means of compar-ing alternative human intervention scenarios (Hilborn et al.,1995).

The majority of abstractions used for simulating andpredicting the behavior of populations exposed to fisheriesexploitation have been reductionist, and this has limitedthese approaches because: (1) the properties of the variablesexamined are local and isolated, and cannot be extrapolatedwhen examining them in covariation with others within theecosystem and (2) they have not been able to successfullypredict effects produced by human intervention (e.g. Levinsand Lewontin, 1985; Hilborn et al., 1995; Patten, 1997; Roberts,1997; Levins, 1998; Walters et al., 1999). For these reasons itis necessary to apply other complementary theoretical con-structs which permit the integration of a finite set of “core”variables representing and describing the dynamics and struc-ture of the ecosystems to which they belong (Robinson andFrid, 2003; Hawkins, 2004; Pikitch et al., 2004). The applica-tion of network theory has proven to be an efficient toolin the estimation of macrodescriptors of ecosystems. Thishas been used to evaluate and describe systems properties,dynamics, and overall health (Costanza and Mageau, 1999),as well as predict the propagation of direct and indirecteffects on system’s recovery time in response to human dis-turbance such as fisheries (e.g. Monaco and Ulanowicz, 1997;Jørgensen, 2000; Ortiz and Wolff, 2002a,b; Arias-Gonzalez etal., 2004; Pinneger and Polunin, 2004; Patrıcio and Marques,2006).

Some of the most degraded benthic ecological systemsalong the Chilean coast are those dominated by the largebrown macroalgae (kelp forests), since not only are themacroalgae harvested but also numerous species of theinvertebrates and coastal fishes that inhabit them. Kelp

beds made up of Macrocystis integrifolia and Lessonia trabec-ulata have been submitted to a high degree of harvesting,reaching more than 55,000 tonnes year−1 in 2005 in thenorth-central portion of Chile (SERNAPESCA, 2005). Present

highly disturbed natural systems.

© 2008 Elsevier B.V. All rights reserved.

legislation stipulates that only beached plants of these speciesmay be harvested; nevertheless, during the present researchwe observed the clandestine harvesting of living plants byharvesters who separated them from the substrate anddestroyed extensive beds of these kelps. These kelp speciestogether with other brown macroalgae have a high marketdemand both as raw materials for the chemical industryand in recent years as food for the different species ofabalone now being placed in culture along the coast ofChile.

Kelp forests around the world and along the Chileancoast have been the subject of numerous research projectsthat have researched biological and ecological aspects ofseveral species, contributing notable advances in the areasof reproduction, distribution, population dynamics, ecolog-ical functions, and changes occurring as consequences ofupwelling and the El Nino Southern Oscillation (ENSO) whichis an oceanographic phenomenon that periodically affectsSE Pacific coastal ecosystems (for review see Steneck et al.,2002; Tala and Edding, 2005; Vega et al., 2005). In spite ofthe knowledge available, there are few cases that lend them-selves to the construction and description of the networksoperating in these ecosystems. The studies of Vasquez et al.(1998) and Angel and Ojeda (2001) were the first attempts inconstruction of systems networks along the Chilean coast.These studies examined some interactions occurring withinthese ecological systems in general terms, but they did notinclude simulations and predictions of changes produced byfisheries.

Here I have attempted to construct mass balanced trophicmodels of benthic kelp ecological subsystems dominated byM. integrifolia, Lessonia trabeculata and barren ground (BG) toinclude the effects of human disturbance in the form of kelpharvesting and fisheries. I introduce the use of the Ecopathwith Ecosim software package (Polovina, 1984; Christensenand Pauly, 1992; Walters et al., 1997; Christensen and Walters,2004) as a tool in this research. Using these models I estimatedthe macrodescriptors of each subsystem and tried to deter-mine: (1) the biomass distribution and biomass flow structurein each system type; (2) the principal benthic predators in eachsystem, and their consumption rates and prey items; (3) thepossibility for recognizing and quantifying redundancy, i.e. ifseveral species had similar trophic roles (Lawton, 1994) in thesystems; (4) which species or functional groups were mostlikely to be affected by different management scenarios andhow sustainable were potential different management strate-gies, (5) what was the resistance to disturbances and resiliencetime of each ecological subsystem as response to different

strategies of resource exploitation (harvest pressure) and (6)what was the fishing mortality representing the maximumsustainable yield (FMSY) for the most valuable and importantspecies in the systems (e.g. exploiting blades of M. integrofoliaand L. trabeculata).

n g

2

2

TaCbsbobselwfertSbuta12

2

T

B

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

. Materials and methods

.1. Study area

he ecological systems chosen for study were the kelp bedsdjacent to Santa Marıa Island off the Mejillones Peninsula,hile (Fig. 1). All the communities studied occupied rockyottoms formed of boulders and platforms with varying expo-ure to prevailing waves. The beds of M. integrifolia occurredetween 5 and 10 m depth, while those of L. trabeculataccurred between 10 and 15 m barren ground was observedetween 5 and 10 m. Fig. 1 shows the stations chosen for thetudy and for constructing the trophic models. Station 1, fullyxposed to the waves, contained dense beds of M. integrifo-ia (Mi) and L. trabeculata (Lt); Station 2, also fully exposed to

aves, consisted of only BG; Station 3, which was protectedrom waves, hosted only M. integrifolia; Station 4, which wasxposed to waves, had beds of L. trabeculata as well as bar-en ground; Stations 5 and 6, which were partially exposedo the waves, contained only stands of M. integrifolia; andtation 7, which was partially exposed to waves, containedoth M. integrifolia and L. trabeculata. There is an importantpwelling center in the sea near the Mejillones Peninsulahat supplies nutrients to the coastal ecosystem (Escribano etl., 2004). The temperature of surface water ranges between6 ◦C in winter and 20 ◦C in summer (Escribano et al.,004).

.2. Ecopath and Ecosim models

he basic equation of Ecopath can be represented as follows:

i

(P

B

)iEEi −

n∑j=1

Bj

(Q

B

)iDCji − Yi − BAi − Ei = 0 (1)

Fig. 1 – Study area of Mejillones Peninsula (SE Pacific), northe

2 1 6 ( 2 0 0 8 ) 31–46 33

where Bi and Bj are the prey i and predator j biomasses, respec-tively; P/Bi is the productivity (production/biomass ratio),which is equivalent to total mortality (Z) (Allen, 1971); EEi

is the ecotrophic efficiency, that is, the fraction of the totalproduction of a group used in the system; Yi is the yield of fish-eries per unit area and time (Y = fishing mortality × biomass);Q/Bj represents food consumption per unit biomass of j;DCji is the fraction of prey i in the average diet of preda-tor j; BAi is the biomass accumulation rate for i; and Ei

corresponds to the net migration of i (emigration less immi-gration). Under this theoretical framework, the energy inputand output of all living groups, by definition, must be bal-anced. Energy balance is ensured within each variable orcompartment group using the equation of Christensen et al.(2004):

Q = P + R + UAF (2)

where Q is consumption, P is production, R is respirationand UAF corresponds to unassimilated food of each variableor compartment in the system. The inclusion of biomassaccumulation and migration factors in Eq. (1) presents Eco-path models as an energy continuity approach rather than astrictly steady-state condition. This particular situation allowschanges in the variables or compartments when the mathe-matical function is expressed in dynamic form.

In order to employ Ecosim, an extension routine of Ecopathis included to define the consumption by compartment; Qij isrepresented by Eq. (3):

a v B B

Qij = ij ij i j

2vij + aijBj(3)

where aij represents the instantaneous mortality rate on preyi caused by a single unit of predator j biomass. Likewise, aij

rn Chile. The black points show the sampling stations.

l i n g

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

can be understood as the rate of effective search by predator

j for prey i. Each aij is estimated directly from the correspond-ing Ecopath models by aij = Qi/(BiBj) (Qi = total consumption ofi). The vij represents the transference rate between compart-ment i and j. This parameter determines if the flow control

Table 1 – Prey–predator and plant–grazer matrix (percentage ofEcopath with Ecosim program

Prey–predator 4 5 6 7 8

(a) Subsystem: Macrocystis integrifolia(1) M. integrifolia 5(2) Mesophylum sp.(3) Rhodophyta 2(4) Chlorophyta 2(5) H. helianthus 1 2(6) M. gelatinosus 2 1(7) Other seastar 1 1 1(8) Tegula sp. 10 5 10(9) Turritela sp. 10 5 10(10) Large epifauna 10 12 20(11) Small epifauna 62 70 55(12) P. chilensis(13) Ch. variegatus(14) Zooplankton(15) Phytoplankton(16) Detritus 4 4 4

Prey–predator 4 5 6 7

(b) Subsystem: Lessonia trabeculata(1) L. trabeculata 50(2) Mesophylum sp. 3(3) Rhodophyta 45(4) H. helianthus 1 2(5) M. gelatinosus 2 1(6) Other seastar 1 1 1(7) Tegula sp. 10 5 10(8) Turritela sp. 10 5 10(9) Large epifauna 10 12 20(10) Small epifauna 62 70 55(11) P. chilensis(12) Ch. variegatus(13) Zooplankton(14) Phytoplankton(15) Detritus 4 4 4 2

Prey–predator 4 5 6 7

(c) Subsystem: Barren ground(1) Mesophylum sp. 83(2) Rhodophyta 10(3) Chlorophyta 5(4) H. helianthus 1 2(5) M. gelatinosus 2 1(6) Other seastar 1 1 1(7) Tegula sp. 10 4 20(8) T. niger 32 70 40(9) Large epifauna 20 10 15(10) Small epifauna 30 8 20(11) Ch. variegatus(12) Zooplankton(13) Phytoplankton(14) Detritus 4 4 4 2

Sum 100 100 100 100

2 1 6 ( 2 0 0 8 ) 31–46

mechanism is top-down, bottom-up or mixed, ranging of 1.0

for bottom-up, to values �2.0 for top-down. A value of 2.0 rep-resents a mixed control mechanism. Details concerning theEcopath with Ecosim software package are given in Pauly etal. (2000).

wet biomass in stomach of predators) used for the

9 10 11 12 13 14

0 60 20 10 23 1 55 16 1 10 10 20 1 5 1

14 5 15 1533 6 15 10

1 38 3020 1 31 40

2525 95

2 2 9 8 1 1 5

8 9 10 11 12 13

60 20 10 22 1 5

36 1 15 2

15 5 15 1533 6 15 10

1 38 3020 1 31 40

2525 95

2 9 8 1 1 5

8 9 10 11 12

65 2010 13 715 10 7

25 8 1540 10

1 2025 1 40

2020 95

10 9 8 1 5

100 100 100 100 100

n g

(e(ttAT

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

Ecopath modelling combines the approach of Polovina1984) to estimate the biomass and food consumption of thecosystem variables or functional groups with Ulanowicz’s1986, 1997) network analysis of flows among variables of

he system for the calculation of ecosystem macrodescrip-ors. These descriptors are the total system throughput (T),scendency (A), Development Capacity (C), and A/C ratio.hroughput describes the vigor or size of a system and it

Table 2 – Parameter values entered (in bold) and estimated (stan

Compartments

Species/functional groups TL C

A. Macrocystis habitat(1) M. integrifolia 1.0 80.0(2) Mesophylum sp. 1.0(3) Rhodophyta 1.0(4) Chlorophyta 1.0(5) H. helianthus 3.3(6) M. gelatinosus 3.4(7) Other seastar 3.3(8) Tegula sp. 2.0(9) Turritela sp. 2.0(10) Large epifauna 2.8(11) Small epifauna 2.4(12) P. chilensis 3.4 5.0(13) Ch. variegatus 3.3 5.0(14) Zooplankton 2.0(15) Phytoplankton 1.0(16) Detritus 1.0

B. Lessonia habitat(1) L. trabeculata 1.0 160.0(2) Mesophylum sp. 1.0(3) Rhodophyta 1.0(4) H. helianthus 3.3(5) M. gelatinosus 3.4(6) Other seastar 3.3(7) Tegula sp. 2.0(8) Turritela sp. 2.0(9) Large epifauna 2.8(10) Small epifauna 2.4(11) P. chilensis 3.4 5.0(12) Ch. variegatus 3.3 5.0(13) Zooplankton 2.0(14) Phytoplankton 1.0(15) Detritus 1.0

C. Barren ground habitat(1) Mesophylum sp. 1.0(2) Rhodophyta 1.0(3) Chlorophyta 1.0(4) H. helianthus 3.3(5) M. gelatinosus 3.1(6) Other seastar 3.2(7) Tegula sp. 2.0(8) T. niger 2.0(9) Large epifauna 2.9(10) Small epifauna 2.3(11) Ch. variegatus 3.2 2.0(12) Zooplankton 2.0(13) Phytoplankton 1.0(14) Detritus 1.0

TL: trophic level; B: biomass (g wet weight m−2); C: catch; P/B: turnover rate

2 1 6 ( 2 0 0 8 ) 31–46 35

represents a measure of its metabolism. Ascendency inte-grates both size and organization of the systems. Organizationrefers to the number and diversity of interactions betweenits components. The Development Capacity quantifies the

upper limit to Ascendency and the A/C ratio describes thedegree of maximum specialization that is actually achievedin the system (maturity index) (e.g. Baird and Ulanowicz,1993; Costanza and Mageau, 1999). This ratio can also be used

dard) by Ecopath with Ecosim software

B P/B Q/B EE

1458.4 10.3 0.0810.0 15.0 0.57

1926.6 5.0 0.05105.8 20.0 0.17

0.9 1.4 2.3 0.222.5 0.6 5.0 0.115.9 1.5 3.0 0.04

61.9 4.0 20.0 0.72100.0 5.0 3.5 0.72100.0 1.5 9.5 0.26

50.0 8.0 12.5 0.613.1 2.1 4.5 0.76

10.6 2.1 6.0 0.2320.0 40.0 160 0.1930.0 250.0 0.43

0.01

2446.0 3.4 0.5330.0 15.0 0.58

250.0 13.0 0.874.9 1.4 2.3 0.063.1 0.6 5.0 0.217.3 1.5 3.0 0.04

100.0 4.5 20.0 0.951275.3 3.9 3.5 0.16200.0 1.5 9.5 0.44150.0 8.0 12.5 0.4534.3 2.1 4.5 0.0725.5 2.1 6.0 0.0920.0 40.0 160.0 0.5930.0 250.0 0.47

0.04

400.0 25.0 0.6880.0 15.0 0.89

100.0 20.0 0.71163.4 1.4 2.3 0.05

80.5 0.6 5.0 0.2410.0 1.5 3.0 0.5472.3 4.5 20.0 0.86

854.5 2.9 10.0 0.2870.0 2.5 9.5 0.8440.0 10.0 12.5 0.9117.0 2.1 6.0 0.0620.0 40.0 160.0 0.1330.0 250.0 0.42

0.08

; Q/B: annual food consumption; EE: ecotrophic efficiency.

l i n g

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

as the system’s ability to withstand disturbance (Ulanowicz,1986, 1997). All these macrodescriptors have been widelyused to describe and compare a variety of ecosystems ofdifferent spatial sizes, geographic locations and complex-ities (e.g. Monaco and Ulanowicz, 1997; Jarre-Teichmannand Christensen, 1998; Niquil et al., 1999; Heymans andBaird, 2000; Wolff et al., 2000; Ortiz and Wolff, 2002a; Arias-Gonzalez et al., 2004; Patrıcio and Marques, 2006; Patrıcio et al.,2006).

2.3. Selection of model compartments, samplingprograms and data sources

Studies were carried out in the field between 2004 and 2007at Santa Marıa Island (Fig. 1) for the selection of the vari-ables of each of the ecological systems, as well as for theestimations of biomass (B), catches (C), turnover rates (P/B),consumption rates (Q/B) and food items for the variablesselected. Appendix A shows the source data for each of thecompartments selected in the present paper. Although most ofthe model compartments represent individual species it wasnecessary to adapt functional groups which included differ-ent species. The compartments Rhodophyta and Chlorophytagroup different species of red algae (e.g. Chondrus canaliculatus)and green algae (e.g. Ulva sp.). The large epifauna (LE) com-partment includes the crabs Taliepus dentatus and Homalaspisplana; the small epifauna group (SE) is formed of the mollusksFissurella spp., Chiton spp., Nassarius spp., Mitrella spp. and thesea urchin Tetrapigus niger in the subsystems of M. integrifoliaand L. trabeculata. The seastar group includes the species Luidiamagellanica and Stichaster striatus. Food items of the seastarswere observed in the field. In the case of Tegula sp., Turritella sp.Taliepus sp., Ch. variegatus and Pinguipes chilensis samples werecollected in the field and analyzed in the laboratory. The diet

matrixes for each of the subsystems (Table 1) show that all thecompartments are trophically linked by detritus, primarily asmicrobial biofilm. Diverse studies have emphasized the impor-tance of the bacteria as food for various species of mollusks

Table 3 – Transfer efficiencies for each level per ecological subs

Source I II I

(A) Macrocystis subsystema

Producers – 15.9 1Detritus – 12.4 1All flows – 15.6 1

(B) Lessonia subsystemb

Producers – 18.0 1Detritus – 16.2 1All flows – 17.9 1

(C) Barren ground subsystemc

Producers – 9.8 1Detritus – 10.3 1All flows – 9.8 1

a Proportion of total flow originating from detritus = 0.44; average = 9.5.b Proportion of total flow originating from detritus = 0.35; average = 8.1.c Proportion of total flow originating from detritus = 0.31; average = 6.3.

2 1 6 ( 2 0 0 8 ) 31–46

(e.g. Grossmann and Reichardt, 1991; Plante and Mayer, 1994;Epstein, 1997; Plante and Shriver, 1998), zooplankton (Epstein,1997) and echinodermata (Findlay and White, 1983). Materialfrom bacterial films was also encountered in the stomachs offishes.

2.4. Balancing 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 <1.0 for all variables or compartments.If inconsistency was detected, the biomass values (annualaverages) were slightly changed within the confidence limits(standard deviation) obtained from field studies. However, forMesophyllum sp. biomass and turnover rate (P/B) values werecalculated by Ecopath. For Rhodophyta and Chlorophyta, P/Bvalues were also adjusted using Ecopath software. It was notnecessary to modify the diet matrixes when balancing themodels. As a second step, gross efficiency (GE) values werechecked for consistency by comparing them with data fromthe literature.

2.5. System recovery time

Stability is the ability of a system to return to a state ofequilibrium after disturbance (Holling, 1973; Levins, 1974).Ulanowicz’s theory (Ulanowicz, 1986, 1997) states that ecosys-tem organization, in terms of Relative Ascendancy (A/C) andRedundancy (internal flow of overhead), may be the mostimportant attributes of system stability. Resilience has beenconceptualized as the speed at which the entire systemreturns to its original state after it has been displaced from itsoriginal state (Pimm, 1982). Resistance describes the capacityof the systems to withstand displacement (Begon et al., 1990).

Therefore, stability includes both resilience and resistance.In the present contribution we assume that system recoverytime (SRT) as obtained by our simulations as a measure of theinternal stability of the systems.

ystem

II IV V VI

3.0 5.3 11.1 0.72.3 5.4 11.13.0 5.4 11.1 0.7

3.0 4.5 3.1 0.31.3 4.5 3.12.9 4.5 3.1 0.3

7.4 5.5 1.1 0.11.7 4.0 1.0 0.16.9 5.5 1.0 0.1

n g

2

Trodstabgilputi

eatbaoptau

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

.6. Assessment of harvests

he mixed trophic impacts (MTI) (Ulanowicz and Puccia, 1990)outine of Ecopath was used to make a preliminary evaluationf the propagation of direct and indirect effects in response toisturbances affecting species of commercial interest. Ecosimimulations were used to evaluate the propagation of instan-aneous direct and indirect effects and SRT as a response to

4× increase in fisheries and mortality (Fi). This was doneetween the first and second year of simulation for M. inte-rifolia, L. trabeculata, Ch. variegatus and P. chilensis, exclusivelyn subsystems dominated by M. integrifolia and L. trabeculata,eaving out this analysis for the barren ground subsystem. Theropagation of instantaneous effects was determined by eval-ating the biomass of all the variables of both subsystems inhe third year of simulation, that is, 1 year after the increasen the fishery.

In the case of the kelp species we evaluated a new strat-gy of exploitation consisting of harvesting only the bladesnd leaving the rest of the plant attached to the substrate, sohat B and P/B for both brown macroalgae represent only thelades. It should be noted that the blades were cut at a pointbout 10 cm above their union with the stipe, leaving a sectionf tissue to allow subsequent recovery. For these reasons the

resent study represents a theoretical evaluation of the sus-ainability of this new exploitation strategy, which would notlter the structural function of the kelp beds. Finally, the val-es of FMSY were determined for the four species of interest. All

Table 4 – Summary statistics after mass-balance process by Eco

Macroc

(A) Summary statisticsSum of all consumption (g m−2 year−1) 6472Sum of all exports (g m−2 year−1) 31379Sum of all respiratory flows (g m−2 year−1) 3040Sum of all flows into detritus (g m−2 year−1) 31619Total system throughput (g m−2 year−1) 72512Sum of all production (g m−2 year−1) 36560Mean trophic level of the catch 1Gross efficiency of fisheries (catch/net pp, %) 0Total net primary production (g m−2 year−1) 34420Total primary production/total respiration 11Net system production (g m−2 year−1) 31379Total primary production/total biomass 8Total biomass/total throughput 0Total biomass (exc. Detritus) (g m−2 year−1) 3885Total catches (g m−2 year−1) 90

(B) Network flow indicesAscendency (total) flowbits 93462Overhead (total) flowbits 112548Capacity (total) flowbits 207777Pathway redundancy (of overhead) (%) 50A/C (%) 45Throughput cycled (exc. Detritus) (g m−2 year−1) 16Throughput cycled (inc. Detritus) (g m−2 year−1) 167Finn’s cycling index (FCI) (%) 0Average path length (APL) (dimensionless) 2Food web connectance (dimensionless) 0Omnivory index (OI) (dimensionless) 0

2 1 6 ( 2 0 0 8 ) 31–46 37

the dynamic simulations were carried out using a mixed flowcontrol mechanism (vij), since by using this control uniqueand stable values could be obtained for FMSY (Ortiz and Wolff,2002b) and also, as recently demonstrated (Ortiz, in press), theuse of mixed control permits obtaining the highest certaintyin predictions.

3. Results and discussion

3.1. Trophic flow structure and transfer efficiencies

L. trabeculata demonstrated the highest concentrations ofbiomass (53%) in the three systems studied. M. integrifolia wasresponsible for 37.5% of the biomass in subsystems whereit was dominant, and in the barren ground subsystem theherbivorous sea urchin Tetrapygus niger was dominant, rep-resenting 44% of the total biomass (Table 2) (Fig. 2). Amongthe predators, the fishes Ch. variegatus and P. chilensis dom-inated in abundance in the M. integrifolia and L. trabeculatasubsystems. On the barren ground the biomass of the seastarswas most important, particularly that of H. helianthus and M.gelatinosus. The biomass of fish predators may be explained bythe facts that the kelp offers a better habitat, greater quantity

of refuges, and more food (Bologna and Steneck, 1993; Levin,1994; Anderson et al., 1997).

The estimations of transfer efficiencies calculated by Eco-path for all the models fluctuated between 10 and 20%

path with Ecosim (A) and network flow indices (B)

Subsystem

ystis Lessonia Barren ground

.8 13794.6 15266.9

.8 16348.7 12991.9

.8 3167.7 7708.1

.1 16794.0 14166.5

.0 50105.0 50133.0

.0 27384.0 25205.0

.3 1.1 3.2

.0026 0.0087 0.0001

.5 19516.4 20700.0

.3 6.2 2.7

.7 16348.7 12991.8

.9 4.3 10.7

.05 0.09 0.04

.7 4576.4 1937.7

.0 170.0 2.0

.6 77613.5 72138.9

.0 117678.9 125232.0

.4 200609.4 197370.9

.5 54.6 52.0

.0 38.7 36.5

.1 38.3 20.7

.8 446.1 798.4

.2 0.9 1.6

.1 2.6 2.4

.3 0.3 0.3

.1 0.1 0.1

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

Fig. 2 – Trophic models for ecological subsystem. Verticalposition approximates trophic levels. The box isproportional to the square root of the compartment(populations and functional groups) biomass. Simplearrows represent the flow of matter among compartmentsand double arrows mean flow to fisheries.

2 1 6 ( 2 0 0 8 ) 31–46

(Table 3), which agreed in magnitude with other systemsdescribed in the literature (Odum, 1971; Barnes and Hughes,1988; Wolff, 1994; Monaco and Ulanowicz, 1997; Heymansand Baird, 2000; Ortiz and Wolff, 2002a; Arias-Gonzalez etal., 2004; Patrıcio and Marques, 2006). This result may beconsidered as an indicator of the quality of the modelsconstructed.

3.2. Ecosystem structure

The subsystem dominated by M. integrifolia reached the high-est system throughput (T) (72,512 g wet weight m−2 year−1),followed by the barren ground and then the L. trabeculata(Table 4a) subsystems. All the T estimates were much higherthan those for benthic habitats of Tongoy Bay (Central-NorthChile) (Ortiz and Wolff, 2002a), perhaps because: (1) the modelsconstructed in the present study included flows to the pelagicsystem, (2) rocky bottoms dominated by brown macroalgaeand calcareous algae had greater heterogeneity of the habitat,permitting greater abundances of invertebrates and verte-brates, and (3) the marine macroalgae are highly productive.It should be noted that the magnitudes of T were also greaterwith respect to those obtained in other ecosystems such ascoral reefs and estuaries (Arias-Gonzalez et al., 2004; Monacoand Ulanowicz, 1997; Patrıcio and Marques, 2006; Patrıcio etal., 2006). The three subsystems presently studied showed thesame magnitudes of omnivory index (OI), which were lowerthan those described in other ecosystem models (Monaco andUlanowicz, 1997; Ortiz and Wolff, 2002a; Patrıcio and Marques,2006; Patrıcio et al., 2006), allowing inference that the trophicwebs of the three subsystems now studied had relatively lin-ear topology (Table 4b). The mean trophic level of the fisheryranged from 1.1 to 3.2 in our models. The lower values obtainedfor the L. trabeculata and M. integrofolia subsystems may be dueto the fact that in both systems the harvesting of biomass wasbasically concentrated on the macroalgae while on the barrenground subsystem only fish biomass was taken. An importantaspect is that in the present study the magnitudes of fishingmortality and landings of M. integrifolia and L. trabeculata rep-resent only the harvesting of blades between the years 2004and 2006, and it does not simulate the illegal exploitation ofplants that occurred in other coastal areas. In this sense thesubsystem dominated by L. trabeculata supported high levels ofexploitation which could be directly related to the high levelof biomass of blades of the species (Table 2). In the presentpaper we do not consider the total exploitation of the plantsfor two basic reasons: (1) the present study tried to evaluatea new and alternative strategy for exploiting the macroalgae(harvesting the blades) and (2) in the sectors studied, to date,there has been no complete removal of these plants.

The subsystem dominated by M. integrifolia shows thegreatest Development Capacity (C) (Table 4b). The M. integrifoliasubsystem ranked highest for Ascendancy (A) followed by theL. trabeculata and barren ground systems (Table 4b). The A val-ues estimated were higher than that calculated for the benthicsystem of Tongoy Bay (Ortiz and Wolff, 2002a). The Relative

Ascendency (A/C), which is considered as an index of matu-rity as well as the system’s ability to withstand disturbance(Ulanowicz, 1986, 1997), shows that the three subsystemsstudied are more mature in comparison with those described

n g

fsoIaaittts

rtdeott

Fv

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

or Tongoy Bay (Ortiz and Wolff, 2002a) and indicates that theubsystem dominated by M. integrifolia had the highest degreef maturity followed by L. trabeculata and the barren ground.n contrast, the barren ground subsystem was indicated to bes the most resistant to disturbance, followed by L. trabeculatand M. integrifolia. On the other hand, the pathway redundancyndicated that the L. trabeculata subsystem was the most resis-ant followed by the barren ground and M. integrifolia. Based onhe use of both ecosystemic macrodescriptors, it is concludedhat the barren ground would be more, and the M. integrifoliaubsystem less resistant to disturbance.

It should be noted that conclusions based on the A/Celation should be taken with a degree of caution, due tohe negative correlation between Ascendency and Maturityescribed by Christensen (1995). Ulanowicz (1997) proposed

stimating the Relative Ascendancy of each group as a wayf evaluating the contribution of each of the compartmentso the overall structure and function of the system. In allhe models the detritus accounted for ∼33%, followed by

ig. 3 – Mixed trophic impacts (direct and indirect) as response toariegatus. (a) Subsystem dominated by M. integrifolia and (b) dom

2 1 6 ( 2 0 0 8 ) 31–46 39

the group of macroalgae ∼ 25%, phyto-zooplankton ∼ 20%, her-bivores ∼ 16% and top predators ∼ 0.8%. These results areconsistent with the supposition of Duggins et al. (1989) thatthe kelp essentially concentrates the biomass and contributeof nutrients to coastal marine ecosystems through macroal-gal detritus. These results differ from those described for thebenthic systems of Tongoy Bay (Ortiz and Wolff, 2002a), whichwere basically dominated by the phyto- and zooplankton com-plex.

3.3. Assessment of management strategies anddynamic simulations

3.3.1. Mixed trophic impactsThe results obtained using the MTI for each of the eco-

logical subsystems studied are shown in Fig. 3a and b. M.integrifolia and L. trabeculata demonstrated a similar patternof impacts both on the quantitative and qualitative level,suggesting that each species had a similar function trophi-

impacting M. integrifolia, L. trabeculata, P. chilensis and Ch.inated by L. trabeculata.

40 e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 31–46

Table 5 – Summary of the system recovery time (SRT), fishing mortality (basal and at MSY), harvest at MSY and annualproduction for M. integrifolia, L. trabeculata, P. chilensis and Ch. variegates for each ecological subsystem using mixed flowcontrol mechanism (v = 2.0)

Ecological subsystem Mixed flow control (v = 2.0)

SRT (year) FEcopath FMSY HMSY (g m−2 year−1) Production (g m−2 year−1)

Macrocystis integrifoliaFishing

M. integrifolia ∼5 0.05 5.50 8021.20 15021.50P. chilensis �10 1.61 1.33 4.10 6.50Ch. variegates ∼8 0.47 0.86 9.10 22.30All �10

Lessonia trabeculataFishing

L. trabeculata ∼5.2 0.07 2.00 4892.00 8316.40P. chilensis ∼5 0.15 0.83 28.50 72.00Ch. variegates ∼5 0.20 0.80 20.40 53.60All ∼5.2

Fig. 4 – Dynamical responses of biomass behaviour for both ecological subsystem subject to 1 year of increased (4×) fishingmortality (between year 1 and 2 of simulation) under mixed flow controlling (v = 2.0) (Note: the dynamical response ofbiomass was obtained at 3rd year of simulation).

e c o l o g i c a l m o d e l l i n g 2 1 6 ( 2 0 0 8 ) 31–46 41

Fig. 5 – Dynamical changes of biomass at each ecological subsystem subject to 1 year of increased 4× fishing mortalityu

ccitt(ttftwntio

3Tiisrt

nder mixed flow controlling (v = 2.0).

ally. An increase in production of each kelp species wouldause the negative impacts on themselves and positive directmpacts on their herbivores (Tegula sp. and Turritela sp.) andhe fisheries. Also, a clear negative effect was observed onhe other groups of macroalgae, large epifauna, and fishesFig. 3a and b). The negative effects of the kelp species onhemselves and on other macroalgae can be explained byheir intense competition for light and space as describedor these and other species (Vasquez, 1995; Ortiz, 2003). Evenhough P. chilensis presented a qualitative pattern of impactshich were very similar in both subsystems, their mag-itudes were quite different. In the case of Ch. variegatushere was a similar pattern of quantitative and qualitativempacts in both subsystems, with a notable negative effectn LE.

.3.2. Dynamic simulations with Ecosimhe exploitation of blades of M. integrifolia (fishing mortal-

ty × 4) using Ecosim dynamic simulations, showed similar

mpacts on Tegula sp., Turritela sp., LE and other macroalgalpecies, coinciding with the results obtained using the MTIoutine (Fig. 4a). With exploitation of blades of L. trabeculatahere was a notable negative impact on Turritella sp. and no

impact on the kelp itself (b). On the other hand, exploitationof P. chilensis and Ch. variegatus propagated a similar patternin the two subsystems dominated by the kelp species (a andb), demonstrating a positive impact on LE and the negativeimpact on both species of fishes. The latter could indicate ahigher degree of competition between the fishes, visualizedin part by the high degree of dietary overlap of their fooditems. These results agreed only with those obtained in thesubsystem dominated by L. trabeculata when using the MTIroutine.

Fig. 5 shows the possible dynamic responses on thebiomass in the subsystems dominated by M. integrofolia andL. trabeculata as a response to the combined effect of increasein mortality by fishing of both P. chilensis and Ch. variega-tus, obtaining the longest SRT in the M. integrifolia-dominatedsubsystem (Table 5). This suggests that the M. integrifolia sub-system would be less resilient to disturbance, coinciding withthe results obtained using the macrodescriptors A/C ratio andRedundancy. Another relevant aspect shown in Table 5 is that

the exploitation of P. chilensis in the subsystem dominatedby M. integrifolia provoked a high SRT and the FMSY was lessthan the basal Fi (Ecopath), which would be a consequenceof the important function carried out by this species in this

l i n g

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

subsystem. The results suggest that P. chilensis could be consid-ered as a top predator species with a strong top-down control(sensu Menge, 2000), nevertheless, further study is needed tobetter evaluate this hypothesis. The high estimates of FMSY

for the exploitation of blades of both kelp species (Table 5)shows that this new harvesting strategy seems to be ecolog-ically sustainable based on P/B and the annual productionof blades calculated for these species. Before promoting amassive application of this new exploitation strategy, futurestudies should be designed that not only contrast the estima-tions of FMSY with the maximum harvest of blades permissible,but also evaluate the effect of this harvesting on the struc-tural contribution of both species, as well as the effect on theircontribution to the detritus.

The present study represents the first attempt to modelthe trophic flows in the ecological subsystems associatedwith kelp beds in the SE Pacific upwelling system. Futuremanagement strategies for this ecosystem should be sup-ported by ecological models as illustrated in the present study,which examines the key ecological subsystems critical to thismanagement (Hunter and Price, 1992; Walters et al., 1999).Although in the present study it was possible to begin esti-mations of relevant properties of the trophic networks relatedto different exploitation strategies, future studies need to berefined by the inclusion of bacteria and dissolved organicmatter and particulate organic matter (DOM and POM) asnutrients for different organisms. This is a process in whichthe kelp species have been assumed to have an importantrole (Duggins et al., 1989; Tala and Edding, 2005). It is impor-tant to note that Ecosim evaluations contain some sourcesof uncertainty which limit and weaken its predictions, themore notable of which include: (1) the characteristics of theinitial conditions in the Ecopath model play an importantrole in the reliability of the period of time over which thesimulations are valid and (2) Ecosim considers only simpleassumptions such as diet relationships, and does not take intoaccount environmental variability (Walters et al., 1999). Butif we consider only short-term dynamics, as in the presentwork, Ecosim can be a useful tool for predicting changes inbiomass within a comparative analytical program for manage-ment strategies (Walters et al., 1997; Pauly et al., 2000; Ortiz,in press).

4. Conclusions

The brown macroalga L. trabeculata forms the patches havingthe highest blade biomass (g wet weight m−2); the sea urchinT. niger is the most abundant on the barren ground subsys-

2 1 6 ( 2 0 0 8 ) 31–46

tem. The system properties in terms of Relative Ascendency(A/C) showed that the subsystem dominated by M. integrifoliahad the highest degree of maturity, followed by L. trabeculata,and then by the barren ground subsystems. In contrast, thebarren ground subsystem emerged as the most resistant todisturbance, followed by the L. trabeculata and M. integrifoliasubsystems.

The results obtained in terms of MTI showed that M. integri-folia and L. trabeculata presented similar patterns under directand indirect impacts, coinciding with the outcomes obtainedfrom the Ecosim simulations. Based on SRT estimates, it wassuggested that the subsystem dominated by M. integrifolia wasless resistant to disturbance. This conclusion is similar tothose obtained using the macrodescriptor A/C ratio. Anothernotable result was the high SRT value in the subsystem dom-inated by M. integrifolia as a consequence of increasing thefishing pressure on the fish P. chilensis. This outcome may sug-gest that P. chilensis could be considered as a predator with astrong top-down control on this subsystem.

Based on the high magnitudes of FMSY for the exploita-tion of blades of both kelp species, we suggest that thisnew harvesting strategy appears to be ecologically sustain-able. Before establishment of this management plan, however,future studies need to be designed to assess the effect of bladeexploitation on the structural function, abundance, and diver-sity of other species such as the fishes, as well as its impacton detritus dynamics.

The present contribution represents the first attempt toquantitatively model and simulate the trophic flows of kelpforests in the SE Pacific upwelling system. New efforts shouldbe exerted to help clarify the relative importance of bacteria,DOM, and POM in the coastal trophic chains, particularly inregard to the kelp species responsible for making significantcontributions to pelagic and benthic detritus.

Finally, it is important to suggest that in spite of theinherent and well-known limitations and shortcomings of theEcopath and Ecosim theoretical frameworks, the models con-structed and the simulations executed in the present studyrepresent the phenomena underlying the systems studiedonly when considering their short-term dynamics.

Acknowledgements

This contribution was financed by the Chilean National Foun-

dation for Scientific and Technical Development (FONDECYT),Grant No. 1040293. The author thanks the anonymous refereesfor their valuable observations and suggestions for improve-ment of the MS.

n g 2 1 6 ( 2 0 0 8 ) 31–46 43

A

C Parameter

P/B3 Q/B4 Diet5 Literature source

10.3 1,2,3Field estimations for currentwork

3.4 1,2,3Field estimations for currentwork

15.0 All values correspond to Ecopathbalanced models

15.025.0

5.0 1Field estimations for current work13.0 3Estimated by Ecopath15.0

20.0 1Field estimations for current work3Estimated by Ecopath

20.0

1.4 2.3 1,3,5Field estimations for currentwork

1.4 2.3 4Ortiz and Wolff (2002a)1.4 2.3

0.6 5.0 1,3,5Field estimations for currentwork

0.6 5.0 4Ortiz and Wolff (2002a)0.6 5.0

1.5 3.0 1,3,5Field estimations for currentwork

1.5 3.0 4Ortiz and Wolff (2002a)1.5 3.0

4.0 20.0 1,3Field estimations for current work4.5 20.0 4,5Ortiz et al. (in prep.)4.5 20.0

1,3

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

ppendix A. Models data sources

ompartments species/functional groups

B1 C2

(1) Macrocystis integrifoliaMacrocystis subsystem 1458.4 80

Lessonia subsystemBarren ground subsystem

(2) Lessonia trabeculataMacrocystis subsystemLessonia subsystem 2446.0 160

Barren ground subsystem

(3) Mesophyllum sp.Macrocystis subsystem 10.0

Lessonia subsystem 30.0Barren ground subsystem 400.0

(4) RhodophytaMacrocystis subsystem 1926.6Lessonia subsystem 250.0Barren ground subsystem 80.0

(5) ChlorophytaMacrocystis subsystem 105.8Lessonia subsystemBarren ground subsystem 100.0

(6) Heliaster helianthusMacrocystis subsystem 0.9

Lessonia subsystem 4.9Barren ground subsystem 163.4

(7) Meyenaster gelatinosusMacrocystis subsystem 2.5

Lessonia subsystem 3.1Barren ground subsystem 80.5

(8) Other seastarMacrocystis subsystem 5.9

Lessonia subsystem 7.3Barren ground subsystem 10.0

(9) Tegula sp.Macrocystis subsystem 61.9Lessonia subsystem 100.0Barren ground subsystem 72.3

(10) Turritela sp.

Macrocystis subsystem 100.0 5.0 3.5 Field estimations for current workLessonia subsystem 1275.3 3.9 3.5 4,5Ortiz et al. (in prep.)Barren ground subsystem 0

(11) Tetrapigus niger

l i n g 2 1 6 ( 2 0 0 8 ) 31–46

Parameter

B3 Q/B4 Diet5 Literature source

1,3,5Field estimations for current work4Estimated by Ecopath

.9 10.0

.5 9.5 1,3,5Field estimations for current work

.5 9.5 4Ortiz and Wolff (2002a)

.5 9.5

.0 12.5 1,3,5Field estimations for current work

.0 12.5 4Ortiz and Wolff (2002a)12.5

.1 4.5 1,2,3,5Field estimations for current work

.1 4.5 4Palomares and Pauly (1998)

.0 6.0 1,2,3,5Field estimations for current work

.0 6.0 4Palomares and Pauly (1998); 5Angeland Ojeda (2001)

.0 6.0 5Moreno and Flores (2002)

.0 160.0 1,3,4,5Ortiz and Wolff (2002a)

.0 160.0

.0 160.0

.0 1,3Ortiz and Wolff (2002a)

.0

.0

r

Christensen, V., Walters, C., Pauly, D., 2004. Ecopath with Ecosim:A User’s Guide. Fisheries Centre Research Reports, vol. 12.

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

Appendix A (Continued )

Compartments species/functional groups

B1 C2 P/

Macrocystis subsystemLessonia subsystemBarren ground subsystem 854.5 2

(12) Large epifaunaMacrocystis subsystem 100.0 1Lessonia subsystem 200.0 1Barren ground subsystem 70.0 2

(13) Small epifaunaMacrocystis subsystem 50.0 8Lessonia subsystem 150.0 8Barren ground subsystem 40.0 10

(14) Pinguipes chilensisMacrocystis subsystem 3.1 5 2Lessonia subsystem 34.3 5 2Barren ground subsystem

(15) Cheilodactylus variegatusMacrocystis subsystem 10.6 5 2Lessonia subsystem 25.5 5 2

Barren ground subsystem 17.0 2 2

(16) ZooplanktonMacrocystis subsystem 20.0 40Lessonia subsystem 20.0 40Barren ground subsystem 20.0 40

(17) PhytoplanktonMacrocystis subsystem 30.0 250Lessonia subsystem 30.0 250Barren ground subsystem 30.0 250

Note: B: biomass (g wet weight m−2), C: catches, P/B: turnover rates and Q/B:consumption rate.

e f e r e n c e s

Allen, K.R., 1971. Relation between production and biomass. J.Fish. Res. Bd. Can. 28, 1573–1581.

Anderson, R., Carrick, P., Levitt, G., Share, A., 1997. Holdfasts ofadult kelp Ecklonia maxima provide refuges from grazing forrecruitment of juvenile kelps. Mar. Ecol. Prog. Ser. 159,265–273.

Angel, A., Ojeda, P., 2001. Structure and trophic organization ofsubtidal fish assemblages on the northern Chilean coast:the effect of habitat complexity. Mar. Ecol. Prog. Ser. 217,81–91.

Arias-Gonzalez, J., Nunez-Lara, E., Gonzalez-Salas, C., Galzin, R.,2004. Trophic models for investigation of fishing effect oncoral reef ecosystems. Ecol. Model. 172, 197–212.

Baird, D., Ulanowicz, R., 1993. Comparative study on the trophicstructure, cycling and ecosystem properties of four tidal

estuaries. Mar. Ecol. Prog. Ser. 99, 221–237.

Barnes, R., Hughes, R., 1988. An Introduction to Marine Ecology,2nd ed. Blackwell, Oxford.

Begon, M., Harper, J.L., Townsend, C.R., 1990. Ecology: Individuals,Populations and Communities, 2nd ed. Blackwell ScientificPublications, Oxford.

Bologna, P.A., Steneck, R.S., 1993. Kelp beds as habitat forAmerican lobster Homarus americanus. Mar. Ecol. Prog. Ser.100, 127–134.

Christensen, V., 1995. Ecosystem maturity-towardsquantification. Ecol. Model. 77, 3–32.

Christensen, V., Pauly, D., 1992. Ecopath II: a software forbalancing steady-state ecosystem models andcalculating network characteristics. Ecol. Model. 61,169–185.

Christensen, V., Walters, C., 2004. Ecopath with Ecosim: methods,capabilities and limitations. Ecol. Model. 172, 109–139.

University of British Columbia, Vancouver, 154 p. (availableonline at http://www.ecopath.org andhttp://www.fisheries.ubc.ca).

n g

C

D

E

E

F

G

G

H

H

H

H

H

J

J

J

L

L

L

L

L

M

M

M

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

ostanza, R., Mageau, M., 1999. What is a healthy ecosystem?Aquat. Ecol. 33, 105–115.

uggins, D.O., Simenstad, C.A., Estes, J.A., 1989. Magnification ofsecondary production by kelp detritus in coastal marineecosystems. Science 245, 101–232.

pstein, S., 1997. Microbial food web in marine sediments. I.Trophic interactions and grazing rates in two tidal flatcommunities. Microb. Ecol. 34, 188–198.

scribano, R., Rosales, S., Blanco, J.L., 2004. Understandingupwelling circulation off Antofagasta (northern Chile): athree-dimensional numerical-modeling approach. Cont. ShelfRes. 24, 37–53.

indlay, R., White, D., 1983. The effects of feeding by the sanddollar Mellita quinquiesperforata (Leske) on the benthicmicrobial community. J. Exp. Mar. Biol. Ecol. 72, 25–41.

aedke, U., 1995. A comparison of whole-community andecosystem approaches (biomass size distributions, food webanalysis, network analysis, simulation models) to study thestructure, function and regulation of pelagic food webs. J.Plankton Res. 6, 1273–1305.

rossmann, S., Reichardt, W., 1991. Impact of Arenicola marina onbacteria in intertidal sediments. Mar. Ecol. Prog. Ser. 77, 85–93.

awkins, S., 2004. Scaling up the role of species and habitatpatches in functioning of coastal ecosystems. Aquat. Conserv.14, 217–219.

eymans, J., Baird, D., 2000. A carbon flow model and networkanalysis of the northern Benguela upwelling system, Namibia.Ecol. Model. 126, 9–32.

ilborn, R., Walters, C., Ludwig, D., 1995. Sustainableexploitation of renewable resources. Annu. Rev. Ecol. Syst. 26,45–67.

olling, C.S., 1973. Resilience and stability of ecological systems.Annu. Rev. Ecol. Syst. 4, 1–23.

unter, M.D., Price, P.W., 1992. Playing chutes and ladders:bottom-up and top-down forces in natural communities.Ecology 73, 724–732.

arre-Teichmann, A., Christensen, V., 1998. Comparativemodelling of trophic flows in four large upwelling ecosystems:global versus local effects. In: Durand, M.H., Cury, P.,Mendelssohn, R., Roy, C., Bakun, A., Pauly, D. (Eds.), Globalversus Local Changes in Upwelling Systems. ORSTOMEditions, Paris, pp. 425–443.

ørgensen, S.E., 1992. Integration of Ecosystem Theories: APattern. Kluwer, Dordrecht.

ørgensen, S.E., 2000. Application of exergy and specific exergy asecological indicators of coastal areas. Aquat. Ecosyst. HealthManage. 3 (3), 419–430.

awton, J., 1994. What do species do in ecosystems? Oikos 71,367–374.

evin, P.S., 1994. Fine-scale temporal variation in recruitment of atemperate demersal fish: the importance of settlement versuspost-settlement loss. Oecologia 97, 124–133.

evins, R., 1974. The qualitative analysis of partially specifiedsystems. Ann. N.Y. Acad. Sci. 231, 123–138.

evins, R., 1998. The internal and external explanatory theories.Sci. Cult. 7 (4), 557–582.

evins, R., Lewontin, R., 1985. The Dialectical Biologist. HarvardUniversity Press, Cambridge, MA.

enge, B.B., 2000. Top-down and bottom-up communityregulation in marine rocky intertidal habitats. J. Exp. Mar. Biol.Ecol. 250, 257–289.

onaco, M., Ulanowicz, R., 1997. Comparative ecosystem trophicstructure of three U.S. mid-Atlantic estuarios. Mar. Ecol. Prog.Ser. 161, 239–254.

oreno, M., Flores, H., 2002. Stomach contents of Cheilodactylusvariegatus Valenciennes 1833, Pinguipes chilensis Valenciennes1833 and Prolatilus jugularis Valenciennes 1833 in HerraduraBay, Coquimbo, During Spring 2001. Gayana 66(2), 213–217.

2 1 6 ( 2 0 0 8 ) 31–46 45

Niquil, N., Arias-Gonzalez, J., Delesalle, B., Ulanowicz, R., 1999.Characterization of the planktonic food web of TarapotoAtoll lagoon, using network analisis. Oecologia 118,232–241.

Odum, E., 1971. Fundamentals of Ecology. Saunders, Philadelphia.Ortiz, M., 2003. Qualitative modelling of the kelp forest of Lessonia

nigrescens Bory (Laminariales: Phaeophyta) in eulittoralmarine ecosystems of the south-east Pacific: an approachto management plan assessment. Aquaculture 220, 423–436.

Ortiz, M., in press. The effect of a crab predator (Cancer porteri) onsecondary producers versus ecological model predictions inTongoy Bay (SE Pacific coast): implications to managementand fisheries.

Ortiz, M., Wolff, M., 2002a. Trophic models of four benthiccommunities in Tongot Bay (Chile): comparative analisis andpreliminary assessment of management strategies. J. Exp.Mar. Biol. Ecol. 268, 205–235.

Ortiz, M., Wolff, M., 2002b. Dynamical simulation ofmass-balance trophic models for benthic communities ofnorth-central Chile: assessment of resilience time underalternative management scenarios. Ecol. Model. 148,277–291.

Palomares, M.L., Pauly, D., 1998. Predicting food consumption offish population as functions of mortality, food type,morphometrics, temperature and salinity. Mar. FreshwaterRes. 49, 447–453.

Patrıcio, J., Marques, J.C., 2006. Mass balanced models of the foodweb in three areas along a gradient of eutrophicationsymptoms in the south arm of the Mondego estuary(Portugal). Ecol. Model. 197, 21–34.

Patrıcio, J., Ulanowicz, R., Pardal, M., Marques, J., 2006.Ascendency as ecological indicator for environmental qualityassessment at the ecosystem level: a case study.Hydrobiologia 555, 19–30.

Patten, B., 1997. Synthesis of chaos and sustainability innonstationary linear dynamic model of the AdirondackMountains of New York. Ecol. Model. 100, 11–42.

Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., Torres Jr., F.,1998. Fishing down marine food webs. Science 279, 860–863.

Pauly, D., Christensen, V., Walters, C., 2000. Ecopath, Ecosim, andEcospace as tools for evaluating ecosystem impact offisheries. ICES J. Mar. Sci. 57, 697–706.

Pauly, D., Christensen, V., Guenette, S., Pitcher, J.T., Sumalia, R.U.,Walters, J.C., Watson, R., Zeller, D., 2002. Towardssustainability in world fisheries. Nature 418, 689–695.

Pikitch, E., Santora, C., Babcock, E., Bakum, A., Bonfil, R., Conover,D., Dayton, P., Doukakis, P., Fluharty, D., Heneman, B., Houde,E., Link, J., Livingston, P., Mangel, M., McAllister, M., Pope, J.,Sainsbury, K., 2004. Ecosystem-based fishery management.Science 305, 346–347.

Pimm, S.L., 1982. Food Webs. Chapman and Hall, New York.Pinneger, J.K., Polunin, N.V., 2004. Predicting indirect effects of

fishing in Mediterranean rocky littoral communities using adynamic simulation model. Ecol. Model. 172, 249–267.

Plante, C., Mayer, L., 1994. Distribution and efficiency ofbacteriolysis in the gut of Arenicola marina and threeadditional deposit feeders. Mar. Ecol. Prog. Ser. 109, 183–194.

Plante, C., Shriver, A., 1998. Patterns of differential digestion ofbacteria in deposit feeders: a test of resource partitioning.Mar. Ecol. Prog. Ser. 163, 253–258.

Polovina, J., 1984. Model of a coral reef ecosystem. I. ECOPATHmodel and its application to French Frigate Shoals. CoralReefs 3, 1–11.

Roberts, C., 1997. Ecological advice for the global fisheries crisis.

Trend Ecol. Evol. 12, 35–38.

Robinson, L., Frid, C., 2003. Dynamic ecosystem models and theevaluation of ecosystem effects of fishing: can we makemeaningful predictions? Aquat. Conserv. 13, 5–20.

l i n g

Mar. Biol. Ecol. 182, 149–168.Wolff, M., Koch, V., Isaac, V., 2000. A trophic flow model of the

Caete mangrove estuary (North Brazil) with considerations for

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

SERNAPESCA, 2004. Anuario Estadıstico de Pesca 2004.Departamento de Informacion y Estadıstica Pesquera,Ministro de Economıa, Fomento y Reconstruccion, Chile.

Steneck, R., Graham, M., Bourque, B., Corbett, D., Erlandson, J.,Estes, J., Tegner, M., 2002. Kelp ecosystems: biodiversity,stability, resilience and future. Environ. Conserv. 29 (4), 436,459.

Tala, F., Edding, M., 2005. Growth and loss of distal tissue inblades of Lessonia nigrescens and Lessonia trabeculata(Laminariales). Aquat. Bot. 82, 39–54.

Ulanowicz, R., 1986. Growth and Development: EcosystemsPhenomenology. Springer, New York.

Ulanowicz, R., 1997. Ecology, the Ascendant Perspective.Complexity in Ecological Systems Series. Columbia UniversityPress, New York.

Ulanowicz, R., Puccia, Ch., 1990. Mixed trophic impacts inecosystems. Ceonoces 5, 7–16.

Vasquez, J., 1995. Ecological effects of brown seaweed harvesting.Bot. Mar. 38, 251–257.

Vasquez, J., Camus, P., Ojeda, P., 1998. Diversidad, estructura yfuncionamiento de ecosistemas costeros rocosos del norte deChile. Rev. Chil. Hist. Nat. 71, 479–499.

2 1 6 ( 2 0 0 8 ) 31–46

Vega, A., Vasquez, J., Buchmann, A., 2005. Population biology ofthe subtidal kelps Macrocystis integrifolia and Lessoniatrabeculata (Laminariales, Phaeophyceae) in an upwellingecosystem of northern Chile: interannual variability and ElNino 1997–1998. Rev. Chil. Hist. Nat. 78, 33–50.

Walters, C., Christensen, V., Pauly, D., 1997. Structuring dynamicmodels of exploited ecosystems from trophic mass-balanceassessments. Rev. Fish. Biol. Fish. 7, 139–172.

Walters, C., Pauly, D., Christensen, V., 1999. Ecospace: predictionof mesoscale spatial patterns in trophic relationships ofexploited ecosystems, with emphasis on the impacts ofmarine protected areas. Ecosystems 2, 539–554.

Wolff, M., 1994. A trophic model for Tongoy Bay—a systemexposed to suspended scallop culture (Northern Chile). J. Exp.

the sustainable use of its resources. Estuar. Coast. Shelf Sci.50, 789–803.