Simulating the effects of El Niño Southern Oscillation and ... Doccuments/2005/M/M0405.pdf · roles of the oceanographic variability and fishing pressure on the ecosystem are not

ICES CM 2005/M:04

Simulating the effects of El Niño Southern Oscillation and fishing effort on the central Chile marine ecosystem

Andrés C. Milessi1*, Hugo Arancibia2, Sergio Neira3

1Programa de Doctorado en Oceanografía, Universidad de Concepción, P.O. Box

160-C, 13

Concepción, Chile; Tel: (56-41) 207175; Fax: (56-41) 256571 2Departamento de Oceanografía, Universidad de Concepción, P.O. Box 160-C,

Concepción, Chile 3Zoology Department, University of Cape Town. Rondebosch 7701, Cape Town,

South Africa

*Corresponding author: [email protected]

Not to be cited without prior reference to the author Milessi et al.

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Abstract Community changes induced by bottom-up (El Niño Southern Oscillation, ENSO)

and top-down (fishing) forcing in the central Chile marine ecosystem (CChME) are

simulated using a 21-group ECOPATH model of the system in 1992 with the

ECOPATH with ECOSIM software. Three 10-year dynamic scenarios are

sketched: i) changes in phytoplankton biomass induced by sea surface

temperature anomalies (∆SST) at ENSO scale; ii) changes in mortality of fishing

resources; and, iii) the combined effects of i) and ii). ENSO induces changes in

phytoplankton biomass that are propagated in a cascade effect throughout the

entire food web as oscillations. The intensity and duration of the oscillations

decrease as the trophic level increases. Bottom-up forcing strongly affects the

abundance of fishery resources such as horse mackerel, anchovy, common

sardine, and Chilean hake. The fishing effort produces decreased anchovy and

common sardine biomass, while oscillations in horse mackerel, Chilean hake, and

sea lion biomass are observed. Although small, oscillations in phytoplankton

biomass under top-down forcing suggest that cascade effects derived from the

removal of target species by fishing could reach lower trophic levels. The interplay

between ENSO and fishing indicates that both forcings could also affect horse

mackerel, Chilean hake, and sea lion. Their effects on anchovy, common sardine,

and phytoplankton could be weakened. It is concluded that both the environment

(∆SST) and fishing can play significant structuring roles in the CChME. This should

be taken into account for fisheries management, since environmental forcing could

accelerate, hinder, or mask the effect of fishing.

Key Words: Ecosystem modeling; physical forcing; ENSO; fisheries; ECOPATH

with ECOSIM; central Chile


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INTRODUCTION

Ecosystems change can be driven by internal (trophic structure) and external

(environmental and exploitation) forcing. Therefore, the understanding of trophic

relationships, environmental variability and fishing pressure on ecosystems is

taking the attention of ecologists and resources managers around the world.

Environmental variability affects biological processes on different spatial-temporal

scales (Steele, 1985). One oceanographic process affecting marine organisms is

known as El Niño Southern Oscillation (ENSO). The intensity, phase (cold/warm),

and length of ENSO affects primary production through variations in the sea

surface temperature (SST) or reduced/intensified surface nutrient contributions

(Barber and Chávez, 1983; Carr, 2002). The deepening of the mixed layer due to

warm, nutrient-poor, equatorial waters suppresses fertilization in the surface layers

(Morales et al., 1999). Lower concentrations of surface phytoplankton biomass (Chl

a) are released (González et al., 2000; Rutllant and Montecinos, 2002), the flow of

material to the bottom decreases (Sellanes, 2002), and changes are observed in

the composition of the phytoplanktonic community (González et al., 1998; Ulloa et

al., 2001). In the central Chile marine ecosystem (CChME), the interannual

variability produced by ENSO is associated with the arrival of coastal-trapped

waves or equatorial Kelvin waves (Hormazábal et al., 2001; Pizarro et al., 2002,

Montecinos et al., 2003).

In general, the hypothesis that primary productivity decreases during warm El Niño

events (positive ∆SST) and increases during cold La Niña events (negative ∆SST)

is accepted (González et al., 1998; González et al., 2000; Carr, 2002; Rutllant and

Montecinos, 2002). This bottom-up, ENSO-derived effect is transmitted through

higher trophic levels, affecting even top predators (Barber and Chávez, 1983; Arntz

and Fahrbach, 1996). Several studies show ENSO effects on the CChME at

different trophic levels (see Escribano et al., 2004 for a review). For example,

changes in the composition of benthic communities (Gutiérrez et al., 2000;


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Sellanes, 2002) and modifications in feeding, reproductive behavoiur and

recruitment of small- and medium-sized pelagic fish such as horse mackerel

(Trachurus symmetricus), common sardine (Strangomera bentincki) and anchovy

(Engraulis ringens) (Yáñez et al., 1992; Arcos et al., 2001; Cubillos and Arcos,

2002; Alheit and Ñiquen, 2004; Arcos et al., 2004; Cubillos et al., 2004).

However, living and non-living components of the ecosystem are also impacted by

fishing. This top-down effect has been described and widely quantified (Jennings

and Kaiser, 1998; Hall, 1999; ICES, 2000; Daskalov, 2002). Fishing removal of

target and by-catch species impacts the whole community by altering trophic

relationships between the latter species and their predators, prey, and competitors

(Goñi, 1998; Hollingworth et al., 2000; Shannon et al., 2000; Harvey et al., 2003). It

is also well known that fishing activities directly affect target species’ population

structure, growth, reproduction, and distribution and have indirect effects on non-

target species and their habitats. In addition, the ecosystem structure in response

to fishing, produce changes on the structural aspects and have indirect effects via

predation processes. The extractions of elements from the intricate food web

promote in the community structure serious impacts (Zabel et al., 2003). In fact,

one example of these ecosystem-effects of fishing is the process called “fishing

down marine food webs” (sensu Pauly et al., 1998), which has also been described

for the CChME (Arancibia and Neira, 2005).

Although trophic relationships and community structure in the CChME has been

recently modelled (Neira et al., 2004; Neira and Arancibia, 2004), the structuring

roles of the oceanographic variability and fishing pressure on the ecosystem are

not fully understood. Arancibia and Neira (2005), analyzed the trend in the mean

trophic level of the catch showing that fisheries have been fishing down the food

web as the result of fishery-induced changes more than environmental variability,

since ENSO events do not seem to have induced a significant effect in this trend.


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Considering the global call for an ecosystem-approach to fisheries (FAO, 2001),

the evaluation of oceanographic (specifically ∆SST) and fishing forcing on trophic

links, specially those of targeted species, could provide valuable information for

fisheries management by reducing the uncertainty in some fishing predictors, and

then assuring species and ecosystem sustainability (Mercer, 1982; Shannon et al.,

1988; Shelton, 1992; Botsford et al., 1997).

Therefore, the objectives of this paper are: i) to analyze the likely effects of bottom-

up forcing on the CChME induced by changes in phytoplankton biomass in

response to ∆SST (ENSO event scale), with emphasis in fishery resources located

on middle and upper trophic levels (Neira and Arancibia, 2004; Neira et al., 2004);

and ii) to evaluate the top-down effects of CChME fisheries induces by changes in

mortality of fishing resources; and, iii) the combined effects of i) and ii).

MATERIALS AND METHODS

Study area The study area corresponded to the marine zone off central Chile: 33º S to 39º S

and from the coast to 30 nm, i.e., approximately 50000 km2 (Fig. 1). This is the

main fishing ground for the purse-seine and trawling industrial fishing fleets

(Arancibia et al., 2003; Neira et al., 2004). According to Camus (2001), because of

its oceanographic and bio-zoo-geographic characteristics, the area can be

considered as an independent and intermediate unit – the Mediterranean District –

between the Peruvian and Magallánica Province, respectively.

Effect of SST on phytoplankton biomass Environmental data The monthly SST data (precision 1º latitude x 1º longitude) from January 1982 to

December 2003 were taken from http://ingrid.idgo.columbia.edu/sources/igoss

(Reynolds and Smith, 1994). Then the annual average SST anomalies (∆SST)


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were determined for the entire study area based on the long-term monthly average

(1982 to 2003).

Physical forcing on phytoplankton biomass The primary assumption of this work is that ∆SST produces variations in the

phytoplankton biomass (Chl a) within the CChME, which is consistent with results

of Thomas et al. (2001) and Carr (2002), who found a positive and significant

relationship between ∆SST and surface Chl a on a large scale in the Humboldt

Current ecosystem.

Satellite sea colour information (sensor SeaWiFS) was used as an indicator of

biomass of phytoplankton in the study area, specifically images of Chl a

concentration. Images of weekly Chl a averages were used for establishing

monthly averages. The data were provided by the Programa Regional de

Oceanografía Física y Clima (PROFC), Universidad de Concepción.

A Chl a series was constructed compared using the annual ∆SST from the CChME

and the empirical model developed by Watters et al. (2003). The relationship

predicts that, on average, pigment concentrations (mg/m3) change in the North

Pacific Ocean by around -0.183 per each ºC of ΔSST as follows:

(1)

where: Bt is the phytoplankton biomass over time t; Bt=0 is the phytoplankton

biomass at time t=0 (September 1997), Chl a is the average value of the change in

Chl a obtained in the 1997 to 2003 series for the study area (Chl a = -0.183), ΔSST

is the SST anomaly off CChME in the same period.


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With the model of Watters et al., (2003), predicted values of phytoplankton

biomass over time t in the area of study were compared with observed values of

Chl a obtained from SeaWiFS by using lineal regression analysis.

Simulations Dynamic scenarios we constructed using an Ecopath model built by Neira et al.,

(2004) and the ECOPATH with ECOSIM software version 5.0 (EwE, Walters et al.,

1997). In EwE the change in biomass per unit of time for each model group

occupying a trophic level higher than 1 is determined by the following equation:

(2)

where:

dt/dBi is the biomass change in group i during time interval t; gi is the net efficiency

of group i; cij(Bi, Bj) is the function that predicts the consumption of prey i by

predator j, which is influenced by environmental variations; Mi is other mortalities;

Fi is mortality by fishing in group i.

A forcing function is defined as the effect of an environmental factor (physical

forcing) or a human factor (fishing effort) over an ecotrophic group. The forcing

function can modify the ecosystem structure through trophic interactions

(Christensen et al., 2004). In the present study, changes in phytoplankton biomass

are produced by ΔSST, and changes in fishing resources are produced by F.

According with the trophic control, the effects in a trophic group can be trespassed

to other groups in the system.

In ECOSIM, each prey pool is viewed as a composite of biomass that is either

available or unavailable to each consumer at any moment in time. The rate at

which biomass changes state from unavailable to available to predators is a


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function of a vulnerability ratio (v= from 0 to 1), which is controlled by the modeller

(see Walters et al., 1997, for details). Setting a low value of v (i.e. 0) leads to

bottom-up control of flow rates from prey to predators, while a high value (v= 1)

leads to top-down control and intermediate values (v= 0.3) a mixed trophic control

(Christensen & Walters, 2004).

Scenario 1: Physical forcing A physical forcing function was built with Equation 1 using the annual values of

ΔSST in the CChME from 1992-2002. This was then applied to the phytoplankton

biomass to evaluate the possible impact of warm (El Niño) and cold (La Niña)

events on the phytoplankton biomass in the study area. The objective of this

simulation is to analyze whether these effects are trespassed to groups located in

higher trophic levels. In this scenario we assumed that the system is bottom-up

controlled (i.e., v=0 for each group), since under this type of control, impacts on

middle and higher tropic levels are considered to be indirect (Watters et al., 2003).

Scenario 2: fishing force The effect of fishing was simulated using the coefficient of fishing mortality (F) as

the forcing function for the biomass of fishery resources and by-catch species in

the CChME from 1992 to 2002 (Table 2). F’s values were taken from Arancibia and

Neira (2003) and Arancibia et al. (2003) and correspond to the official F’s during

the same period. The purpose of this simulation is to evaluate the response of

model groups under changes in F. The changes were analyzed under top-down

control (v=1 for each group), that is, the direct effects are observed in the fishery

resources and the indirect effects in prey and competitor organisms (Watters et al.,

2003).

Scenario 3: Interplay between physical forcing and fishing force This scenario combines scenarios 1 and 2. Changes in relative biomass were

analyzed considering bottom-up control in phytoplankton and top-down control in

fishery resources (sardine, anchovy, horse mackerel, and Chilean hake). For the


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remaining components, the control was considered to be mixed (sensu Watters et

al., 2003). The purpose of this mixed scenario is to reveal which of the two forcings

has a greater effect on model groups and whether these are strengthened or

weakened.

The effect of each scenario on the biomass of each trophic group was measured

as the relative change between the biomass at the end of the simulation (Be)

respect to the biomass at the beginning of the simulation (Bb). We considered

significant impacts on group biomass as those higher or lower than 10% respect

Bb. For significant impacts, the Magnitude of Change (MC) was measured in

percentage since MC represents an attribute of system stability (Mackinson et al.,

1997). Simulations representing each scenario (1 to 3) were run for ten years

(1992-2002). Moreover, the biomass predicted by ECOSIM for the main fishery

resources (anchovy, common sardine, Chilean hake, and horse mackerel) under

the more realistic scenario (i.e. scenario 3) were compared with the observed

biomass of the same groups in last 10 years, obtained from Arancibia et al.,

(2003), SubPesca (2004a, b, c).

The effects of three scenarios in the ecosystem’s structure were analyzed through

the ratio of planktivorous fish biomass vs. zooplanktivorous fish biomass (PS/ZP).

This index allows an evaluation of changes in the system considering only

biological variations, oceanographic variation, fishing effort, or a combination

thereof. This index has been used at different spatial and temporal scales, in a

wide variety of ecosystems around the globe, permitting an evaluation of the state

of health of the ecosystems being exploited by fishing (Caddy and Garibaldi, 2000).

The PS/ZP ratio (Caddy and Garibaldi, 2000) was quantified in the present study

between the estimated biomass of the piscivorous fish (e.g. Chilean hake) and the

estimated biomass of the zooplanktivorous fish (e.g. horse mackerel). All

tendencies in each scenario were analyzed by Spearman correlations (rs).


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RESULTS

Physical forcing on phytoplankton biomass

The analysis of the annual ΔSST time series shows a warm period (positive ΔSST)

in the study area from 1992 to 1998, excepting 1996. Followed by cold period from

1999 to 2002 (Fig. 2a). The most elevated ΔSST (+0.77 °C) was observed in 1997,

which is consistent with the largest ENSO of the last century (McPhaden, 1999)

whereas the lowest negative ΔSST (-0.41 °C) appeared in 2000.

Observed Chl a values for the study area (SeaWiFS) and estimated Chl a values

(empirical model; Equation 1) showed a highly significant correlation (r2= 0.94;

P<0.01; Fig. 2b). The maximum Chl a values were observed in cold periods (2000

and 2001), whereas the lowest values occurred in warm periods, especially the

1997- 98 El Niño, which is consistent with our assumption.

Simulations Scenario 1: Physical forcing

When positive ΔSST occurs, a decrease is seen in the relative phytoplankton

biomass (year 6 of the simulation, Fig. 3a). This decrease occurred in conjunction

with the 1997-98 El Niño event and resulted in up to a 66% decrease in the initial

phytoplankton biomass (1992) (Table 3). Negative ΔSST, however, have the

opposite effect on the phytoplankton biomass (year 9 of the simulation), increasing

it by up to 50%.

The relative phytoplankton biomass induced by changes in ∆SST is transmitted to

higher trophic levels in the form of oscillations, which intensity and strength

decreases as trophic increases (Table 3). Time lags are also observed between

the oscillations of the phytoplankton and those of higher trophic groups; the lags

increase as does the trophic level (Table 1). For example, the response was

delayed one year for horse mackerel (TL=3.9), two years for Chilean hake

(TL=3.5), and three years for sea lions (TL=4.23).


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Scenario 2: Fishing forcing Not all the functional trophic groups showed oscillations in this scenario (Fig. 3b),

since the analysis involves target species that have different fishing mortality rates

in the studied period (Fig. 3b). For example, the relative horse mackerel and

Chilean hake biomass displayed oscillating tendencies. Small pelagic fishes

(common sardine and anchovy) exhibited decreasing tendencies in their relative

biomass throughout the entire simulation. Sea lions showed a decreasing trend in

their relative biomass up to 16% with respect to the initial biomass (Table 3). Mild,

slightly negative oscillations (< 10%) and significant increases (> 20%; Table 3) are

seen in the relative phytoplankton biomass.

Scenario 3: Interplay between physical and fishing forcings Under this scenario, the biomass of fishery resources (e.g. small pelagic fishes,

Chilean hake, horse mackerel) and top predators oscillated (Fig. 3c). The

phytoplankton biomass has irregular cycles that are milder than those in the first

scenario (Fig. 3a). Horse mackerel shows wider oscillations, followed by the small

pelagic fishes, Chilean hake, and sea lions. The largest magnitudes of change

were observed in horse mackerel (Table 3).

In combination, the effects of ΔSST and F are strengthened or weakened

depending on the functional group and trophic level. In intermediate to high trophic

levels (sea lions, Chilean hake, and horse mackerel) biomass changes are greater

than those in the first and second scenarios. On the other hand, in intermediate to

low trophic levels (i.e., small pelagic fishes and phytoplankton) biomass changes

are smaller than those in the first scenario, but higher than those in the second

scenario (Fig. 4).

Phytoplankton and small pelagic fishes has greater magnitudes of change with

ΔSST and decrease when considering the combination of ΔSST and F. Unlike

these first two cases, the combination of ΔSST and F strengthen the MC for

Chilean hake, horse mackerel, and sea lions (Table 3).


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The biomass estimated by EwE for the main fishery resources (under scenario 3)

showed dissimilar tendencies with respect to those observed; the latter are

determined by hydroacoustics or swept area. Small pelagic fish biomass (Figs. 5a,

b) oscillated, tending to decrease toward the end of the period. The oscillations in

the EwE estimates were milder. Nevertheless, the EwE estimates of common

sardine biomass did not agree with the drop in observed biomass. The tendency of

the observed horse mackerel biomass to decrease was only reflected by EwE for

the first years (Fig. 5c). Chilean hake biomass, however, increased in both

observations and EwE, although EwE was more conservative (Fig. 5d).

Changes on ecosystem structure The CChME’s trophic structure, analyzed with PS/ZP in Scenario 1, tended to

decrease significantly (rs= -0.75; P<0.01) reaching a minimum in 1998 (Fig. 6b).

Under the influence of F, PS/ZP tended to decrease insignificantly (rs= -0.26;

P>0.05), also lessening at the end of the period. However, when the forcings were

combined (ΔSST and F), a marked oscillation was seen in the PS/SZ tendency,

with a significant negative slope (rs= -0.60; P<0.05), especially as of 1997. This

indicates a probable change in the CChME’s structure.

DISCUSSION

Surprisingly, the empirical model developed by Watters et al. (2003), for the

Equatorial Pacific Ocean, allows to satisfactorily predict the Chl a concentrations in

the study are using ∆SST, which is demonstrated by the highly significant

correlation between Chl a values obtained from the model and those from the

SeaWiFS at a medium spatial scale. This is in accordance with Carr (2002) who

informs the same correlation but in a bigger spatial scale, i.e., Humboldt Current

System. Consequently, the bottom-up changes induced by ∆SST on the

phytoplankton, and maybe on other groups, in CChME could be similar to those

modelled here.


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The effects of the environmental forcing function applied produced both direct and

indirect effects on the trophic web of CChME, as inferred by Watters et al. (2003)

for the Equatorial Pacific Ocean. Oscillations on phytoplankton biomass were

trespassed to higher trophic levels through cascade effects indicating that

predation and environment play important structuring roles in marine ecosystems

(Herendeen, 2004).

Specifically, the results of this work indicate that such forcing could have

repercussions on fishery resources (small pelagics). This is not surprising, since it

has been informed that both, anchovy and common sardine are affected

significantly by environmental variability, exhibiting high variability in recruitment

(Cubillos and Arcos, 2002, Arcos et al., 2004), biomass and landings (Yáñez et al.,

1992; SubPesca, 2004a). The explanation can be found in the strong trophic link

between small pelagics and phytoplankton, characterized by fast responses of

considerable magnitude (Fig. 3a) in this upwelling ecosystem of short trophic

chains (Neira and Arancibia, 2004). This situation is not seen in ecosystems with

longer trophic chains and several components, where a weaker coupling between

lower trophic level is found (Micheli, 1999; Watters et al., 2003).

The simulation of the trajectory of horse mackerel and Chilean hake biomass

indicate that these fishery resources can be sensitive to environmental variations

(ΔSST). Their observed biomass (hydroacoustics or swept area) and the EwE

estimates do not show similar tendencies, which is probably due to other regulatory

mechanisms. For example, horse mackerel biomass estimated with EwE (Fig. 5)

tends to increase, probably due to interspecific interactions such as the increased

euphausiid biomass. Euphausiids are the main prey of horse mackerel in the

CChME (Arancibia and Aedo, in press). The greatest Chl a values registered in

year 2000 (La Niña) would have allowed increased euphausiids biomass. Neira et

al. (2004) show that the trophic structure of the CChME changes in year 1998, with

euphausiids as the dominant prey in diet of both horse mackerel and Chilean hake.


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This is consistent with observations in other upwelling areas (Barber and Chávez,

1983, 1986; Fiedler et al., 1992; Chávez et al., 2002). Daneri et al. (2000) reports

that the CChME has one of the highest levels of primary production of the world’s

oceans. The high phytoplankton biomass provides the ideal medium for the

development of the other components of a bottom-up controlled ecosystem.

The combined forcing (∆SST and F; scenario 3; Fig. 3c) hide the impact of fishing

(i.e. horse mackerel), which is decreasing the observed biomass of the fishery

resources. Daskalov (2002) showed that the combination of overexploitation of

some fishery resources in the Black Sea (fishing forcing) and eutrophication

(environmental forcing) had generated important changes in trophic structure, and

that these changes were passed along through the trophic levels in a cascade

effect.

On the other hand, the abundance of the horse mackerel stock in the CChME has

declined due to overexploitation (Arancibia et al., 1998; Cubillos et al., 1999;

SubPesca, 1999; Quiñones et al., 2003) and meso-scale oceanographic changes

like ENSO (Arcos et al., 2001). Both the estimated and observed (hydroacoustics)

Chilean hake biomass tended to increase. This is probably because the

abundance and/or availability of demersal species increases after an El Niño event

(Escribano et al., 2004), as found in Perú (Barber and Chávez, 1986) and South

Africa (Shannon et al., 1988), where ecosystemic components respond to local,

regional, and even large-scale environmental changes (Watters et al., 2003). In the

present study, the simulated values were more conservative than the observed

values (SubPesca, 2004b).

Nonetheless, Arancibia (unpublished) indicates that the Chilean hake biomass,

estimated annually with hydroacoustics in central Chile, has been consistently

overestimated (2000 to 2004). This author proposes that Chilean hake biomass

was 50% less than officially reported by SubPesca in 2004 (SubPesca, 2004b).


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The changes induced by F as top-down forcing on the biomass of fishery

resources generated cascade effects of lesser magnitudes in lower tropic levels

(phytoplankton) and of greater magnitudes in higher tropic levels (horse mackerel)

(Fig. 3b).

Therefore, fishing promotes changes in the biomass of target species but can also

indirectly affect phytoplanktonic biomass due to indirect trophic effects and the

ecosystem’s structure, as reported by Daskalov (2002) and Harvey et al. (2003) for

the Black Sea and the Baltic Sea, respectively.

The relative biomass of the CChME’s small pelagics tended to decrease, whereas

that of horse mackerel and Chilean hake oscillated. This could be due to the

control type and trophic interaction manifested in the systems, generating complex

indirect responses in predator-prey interactions, as reported by Daskalov (2002)

and Herendeen (2004). But, likely the biomass of Chilean hake has been over-

estimated; meaning that that it's fishing mortality has been under-estimated.

Other factors to consider are generation times, lags in the response of each

species to the forcings, resilience (sensu Mackinson et al., 1997), and life strategy

(r, K) (sensu Micheli, 1999). The response times are fastest in short-lived

organisms (phytoplankton, small pelagics) and slower in longer-living organisms

(Chilean hake, sea lions, horse mackerel). All these factors can act together to

weaken or strengthen coupling, a characteristic of marine environments (Micheli,

1999; Harvey et al., 2003; Watters et al., 2003; Herendeen, 2004).

The comparative analysis of the magnitude of change in relative biomass of fishery

resources (anchovy, common sardine, horse mackerel, Chilean hake),

phytoplankton and the top predator (sea lion), indicates that environmental forcing

(ΔSST) is more important than fishing mortality (F). Arancibia & Neira (2005)

shown that effects due to ENSO are lower than fishing effort effects on the

ecosystem structure. Probably, these authors did not consider the importance of

ΔSST as physical forcing on phytoplankton, which can also change highest trophic


16

levels, i.e. sea lion. This is supplied by the presence of short trophic webs and the

strong connection between small pelagics and phytoplankton (Neira et al., 2004).

However, the effect of both forcings on small pelagics leads to a slightly reduced

change in relative biomass than the values derived from environmental forcing

alone (Fig. 4).

Horse mackerel, a medium-sized pelagic fish, clearly exhibits larger changes in its

relative biomass range when both forcing are operating than when only one is

operating. This is due to horse mackerel is more sensitive to fishing pressure

because it is in a higher trophic level, as seen indirectly by horse mackerel

landings in central-southern Chile. In the mid-1990s, landings were at 3.6-4.0

million tons, decreasing drastically until reaching 1.1 million tons in 1999, a rate

that is currently maintained. It should also be noted that the average size of the

specimens has also decreased (SubPesca, 2004c).

It is readily apparent that the CChME’s structure has been changing, particularly in

recent years, without discounting the fishing influence. Arancibia and Neira (2005)

show that the process of fishing down the food webs clearly exists (sensu Pauly et

al., 1998), which agrees with the PS/ZP ratio (sensu Caddy & Garibaldi, 2000; Fig.

6) in any of the three scenarios detailed here: environmental forcing, fishing

forcing, or both simultaneously. This shift has occurred between 1999 and 2000.

In brief, this study showed that combination of fisheries and physical forcing can

cause important alterations in the trophic structure in the CChME. These findings

may provide insights for ecosystem management, suggesting that this type of

studies could provide better understanding of fisheries dynamics in a holistic

context.


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Acknowledgements A.C. Milessi is grateful to the German Service for Academic Exchange (DAAD) and

Agencia de Cooperación Internacional de Chile (AGCI), through which a grant

(A/01/17601) for post-graduate studies at the Oceanography Department,

University of Concepción was kindly provided. This paper is part of the PhD thesis

of A.C.M.


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Tables

Table 1. Trophic groups and trophic levels of the CChME, modified from Neira et

al. (2004) (j-juveniles; a-adults).


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Table 2. Mortality by fishing (F, year-1) for the main CChME fishery resources, from

Arancibia and Neira (2003) and Arancibia et al. (2003).


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Table 3. Magnitudes of change (%) in the biomass of the main CChME groups in

the three simulation scenarios. Max: maximum magnitudes of change; Min:

minimum magnitudes of change.


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Figure Captions

Fig. 1. Study area in the central Chile marine ecosystem (CChME). The stars (∗)

indicate positions used for estimating sea surface temperature anomalies (∆SST).

Fig. 2. a) Sea surface temperature anomalies (∆SST) in the CChME, 1992-2002;

b) observed Chl a values (mg/m-3) from satellite images (SeaWiFS) and estimated

values from an empirical model (equation 1).

Fig. 3. Ten-year simulations, using ECOSIM, of the biomass of the main groups

making up the CChME. a) Scenario using ∆SST as a forcing function; b) scenario

considering fishing mortalities (F) from 1992-2002; and c) scenario including both

variables (∆SST and F). In order to simplify the interpretation, only the following are

shown: phytoplankton (TL= 1), common sardine (TL= 2.2), anchovy (TL= 2.2),

Chilean hake (TL= 3.5), horse mackerel (TL= 3.9), and sea lion (TL= 4.23).

Fig. 4. Relative values expressed as a percentage of change in the biomass of the

main trophic groups of the CChME’s marine ecosystem in the three simulated

scenarios ( -scenario 1; -scenario 2; -scenario 3).

Fig. 5. Relative biomass estimated by EwE (dotted line) and observations (solid

line) for the main CChME fishery resources, 1992-2002: (a) anchovy, (b) common

sardine, (c) horse mackerel, (d) Chilean hake.

Fig. 6. The PS/ZP ratio for the central Chile marine ecosystem (CChME) under

three simulated scenarios. (Symbols: -scenario 1; ○-scenario 2; ●-scenario 3).


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Figure 1.


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Figure 2.


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Figure 3.


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Figure 4.


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Figure 5.


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Figure 6.

Documents

Simulating the effects of El Niño Southern Oscillation and ... Doccuments/2005/M/M0405.pdf · roles of the oceanographic variability and fishing pressure on the ecosystem are not