Download managing fisheries effects on marine food webs

BULLETIN OF MARINE SCIENCE, 74(3): 583–605, 2004
MOTE SYMPOSIUM INVITED PAPER
MANAGING FISHERIES EFFECTS ON MARINE FOOD
WEBS IN ANTARCTICA: TRADE-OFFS AMONG HARVEST
STRATEGIES, MONITORING, AND ASSESSMENT IN
ACHIEVING CONSERVATION OBJECTIVES
Andrew J. Constable
ABSTRACT
Harvesting of marine living resources in the Southern Ocean is managed by the
Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR),
which is widely known for its ecosystem approach to managing fisheries and inclusion
of food-web maintenance in the conservation objectives. CCAMLR has developed a
precautionary approach to account for predators when setting catch limits for krill, the
most important prey species in the Antarctic marine ecosystem. It has also established
an ecosystem monitoring program to help detect effects of prey harvesting on predators. CCAMLRʼs approach is intended to permit both recovery of overexploited species
and ecologically sustainable development of fisheries without causing damage to the
Antarctic marine ecosystem. The trade-offs are between the harvest strategies (catches)
and the ability to obtain sufficient information and knowledge for management of fisheries in a way that achieves the conservation objectives. This paper presents general
principles of an ecosystem-based approach to fisheries using the CCAMLR approaches
as examples. It discusses the precautionary and ecosystem approaches of CCAMLR,
the types of objectives being considered for predators, and the general approaches and
monitoring programs that could achieve ecosystem/food web conservation objectives.
Like many large-scale marine ecosystems, that of the Antarctic has a checkered history of overexploitation of marine species, but unlike other systems, the Southern Ocean
does not suffer multiple other human impacts such as pollution from urban and coastal
sources and land-use practices. The politics and decision making are therefore potentially primarily oriented toward management of the fisheries. As a consequence, this region provides an opportunity for determining the simplest requirements for ecologically
sustainable management practices for fisheries and, if the attempt is successful, could be
used as a guide for establishing such practices elsewhere in the world.
Here, I discuss the ecosystem-based approaches being developed for Antarctic fisheries on the basis of the objectives of the Convention on the Conservation of Antarctic Marine Living Resources. These approaches are intended to permit both recovery
of overexploited species and ecologically sustainable development of fisheries without
causing damage to the Antarctic marine ecosystem. I proceed on the premise that ecosystem-based management encompasses the principles listed in Table 1.
The trade-offs in the management of fisheries in this region are between the catches
and harvest strategies of the fisheries and the ability to obtain sufficient information and
knowledge for management of the fisheries in a way that achieves the conservation objectives. Uncertainty in knowledge must be adequately taken into account in order that
fisheries do not develop faster than does the ability to manage them.
THE CONVENTION AND ITS CONSERVATION OBJECTIVES
Harvesting of marine living resources in the Southern Ocean is managed by the Commission for the Conservation of Antarctic Marine Living Resources under the Convention
Bulletin of Marine Science
© 2004 Rosenstiel School of Marine and Atmospheric Science
of the University of Miami
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Table 1. Summary of characteristics of the ecosystem approach to managing fisheries.
The ecosystem approach
Manages people/activities
Considers ecosystem effects
Permits action despite incomplete
knowledge
Is robust in the face of uncertainties
Makes explicit trade-offs of ecological,
social, and economic risks
Requires greater precaution than single
species (use) management
Keeps cost commensurate with value
In contrast, it
Does not manage ecosystems
Is not ecosystem engineering
Does not delay action until ecosystems are
completely understood
Is not based on a single comprehensive predictive
model that can be used to make decisions
Does not necessarily make the assumption
that improved detail/complexity will result in
diminished risk in all areas
Should not lead managers to be less precautionary
than does the precautionary approach
Does not regard cost as immaterial
on the Conservation of Antarctic Marine Living Resources. I will refer to the convention
and the commission that administers it collectively as CCAMLR. The Antarctic Treaty
Consultative Parties negotiated the convention to ensure the conservation of the marine
ecosystem in advance of the expanding krill fishery; it came into force in 1982.
CCAMLR was a major advance in the conservation and management of marine species in the Southern Ocean; not least it extended the coverage of the Antarctic Treaty
System (the Antarctic Treaty covers the area south of 60°S) to encompass the whole of
the Southern Ocean south of the Antarctic Convergence (Antarctic Polar Front; Everson,
1977). CCAMLR was considered, and remains by comparison to other international
instruments, an innovative convention (Hofman, 1984), the first to take an ecosystem approach to managing fisheries and be recognized for developing a precautionary approach
to setting catch limits (Constable et al., 2000).
Its first primary objective is to ensure that stocks are maintained close to levels that
ensure the greatest potential recruitment (birth and survival) of young fish to the population. This objective was based on conventional fisheries assessment practices and is used
in part to guide the setting of catch limits.
The second primary objective is conservation of the marine ecosystem. In this respect,
the direct effects of fishing on by-catch species, including, among others, benthos, fishes,
and seabirds, are important. The convention also requires the “maintenance of ecological relationships between harvested, dependent and related populations,” implying that
the direct and indirect effects of fishing should not substantially alter the function of the
marine ecosystem. It also includes an objective intended to allow the recovery of depleted species, such as seals and whales, and to avoid irreversible changes, i.e., changes
that cannot be reversed within two to three decades.
Finally, the convention includes rational use of marine living resources in the region
within the context of conservation.
THE ECOSYSTEM APPROACH TO MANAGING FISHERIES
CCAMLR is widely known as the only international convention currently applying an
ecosystem approach to managing fisheries. It owes this status largely to the development
of stock assessments that endeavor to account for the needs of predators (the precautionary approach of CCAMLR) as well as the establishment of the CCAMLR Ecosystem
Monitoring Program (CEMP, described by Agnew, 1997).
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CEMP was initiated in 1986 to detect significant changes to the ecosystem, particularly in predators of krill, and to signal when such changes were the consequences of fishing. The CEMP is intended, in this way, to inform the commission when fishing affects
species dependent on the target species. CEMP was deliberately restricted to monitoring
a few selected predators and is established in only a few areas, and field work and data
acquisition are carried out voluntarily by members of CCAMLR. The manner in which
the data from CEMP will be used to form policy has yet to be resolved (Constable et al.,
2000), although advances toward resolution have been made in recent years (de la Mare
and Constable, 2000; Constable, 2002).
Despite the apparent simplicity of the Antarctic marine ecosystem, the effects of a
krill fishery on krill predators may be difficult to detect because of spatial and temporal
variability in the dynamics of this ecosystem (Murphy et al., 1988; Constable and Nicol,
2002; Constable et al., 2003), potential shifts in predator diet depending on availability
of prey (see, e.g., Agnew et al., 1998), and spatial differences in migration (see Constable
et al., 2003, for review). Quantitative predictions of indirect effects of krill harvesting
are currently difficult to formulate because only a few models address these issues (see
Constable, 2002, for review). In the absence of such models, an important issue is how
to manage for potential effects on predators of fishing for prey when little information is
available on predicting how predators will respond to different levels of prey harvest.
An additional complexity for managers is determining a “reference image” of the Antarctic marine ecosystem that can be used in formulating operational objectives, given
that most of the information on which our ecosystem view is based has been gathered
since 1980. Little is known, therefore, about how the system was structured before the
decimation of seals over the last 200 yrs and of the great whales over the last 100 yrs, the
substantial overharvesting of a number of fish species in the late 1960s and 1970s (Kock,
1992), and the reduction of sea-ice extent in the mid-20th century (de la Mare, 1997).
Nevertheless, CCAMLR is in the process of formulating a full ecosystem-based
management procedure for the krill fishery (Fig. 1; de la Mare, 1996, 1998; Constable,
2002). The development of an effective management procedure requires (i) operational
objectives, (ii) methods for assessing the status of the system after the monitoring of
indicators, and (iii) the decision rules for setting harvest controls based on the relative difference between the assessment and the objectives (see Fig. 1; de la Mare, 1986,
1987, 1996) while accounting for uncertainties. Developing a management procedure
within CCAMLR is best illustrated in the development of the precautionary approach
in CCAMLR (see Kock, 2000, for a full review) and methods used by the International
Whaling Commission (see de la Mare, 1986, 1996; Cooke, 1999).
The prospective development and evaluation of a management procedure by means of
computer simulations provide the opportunity to evaluate explicitly the ecological and
conservation implications of different harvest strategies and the potential problems that
might arise in making decisions given the uncertainties in knowledge and assessments.
To date, the development of management procedures has taken a single-species approach
but has considered predators in the decision rules when the target species is considered
an important prey species, such as Antarctic krill (see below). Little account has been
taken of the responses of predators or other elements of the ecosystem to proposed harvest strategies. In particular, operational objectives for food webs and ecosystems in
managed fisheries have not been well formulated, so implementing a suitably precautionary ecosystem approach to managing fisheries is difficult.
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Figure 1. The fishery control system with the ecosystem components included. Changes in catch
limits or harvest controls are predetermined in the decision rules of the management procedure.
Those rules compare the outcomes of the assessment with the operational objectives to determine
what actions are required to ensure the objectives are met.
OPERATIONAL OBJECTIVES FOR PREDATORS OF FISHED SPECIES
Species that directly interact (as predators or prey) with the target and by-catch species
of fisheries (here collectively termed “fished species”; Fig. 2) are those most likely to
exhibit an indirect response to fishing, particularly those with the strongest interactions
with the fished species (Paine, 1980; Yodzis, 1994, 2000, 2001).
The effects of fishing on dependent or related species are only important if fishing
might alter the strengths of interactions among species, i.e., might change the magnitude
and/or direction of effects between species. For example, theory suggests that predators
of fished species would be competing with the fishery only if they are feeding substantially from the fished population and the fished population is insufficient to meet the
needs of predators and support the fishery at the same time. In this case, competition
would be evident if the productivity of the predators is reduced as a result of fishing,
although before attribution of such evidence to competition, changes in the food web
not necessarily caused by fishing could perhaps be considered. Reduced net productivity
may be evident in a decrease of the biomass of the predator population (reduced growth
or weight loss in individuals), reduced recruitment or increased mortality, and/or migration from the area. In addition, wider effects on the ecosystem might result if predators
switch from preying on the fished species to preying on other species in the system.
Complexities in the model arise if the life cycles of some dependent species include
critical stages (such as breeding season for some land-based predators of krill) or if dependent species shift their foraging areas, for example seasonally.
Constable (2001) discussed how conservation objectives can be derived for predators
of fished species. Operational objectives based on reference points for “ecologically related” species (assemblages) but not directly affected by the fishing operation have been
much more difficult to formulate than reference points for target species. Most attention
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Figure 2. Schematic showing the primary food-web relationships between a fished species and
other key species in the system (solid lines and plain symbols). The effects of the fishery on this
ecosystem are shown with dashed lines and italicized symbols. Arrows indicate the direction of
effect. Type of effect is indicated by the letters: C– is a negative competitive effect on the species
to which the arrow is pointing, P– is a negative predatory effect, +? is a potential positive effect
by the fishery on an inferior competitor of the fished species, TC+? is a potential positive effect
arising from an apparent trophic cascade as a result of the fishery, and RC– is potential resource
competition between a fishery and predators of the fished species (following the schema of Fairweather, 1990, for biological interactions; after Constable, 2001).
has been given to multispecies assemblages in which all species are exploited in some
way (e.g., by May et al., 1979; Beddington and May 1982).
Three types of objectives can be formulated: (i) maintaining biodiversity of the ecosystem, (ii) minimizing competition between fisheries and predators of target species,
and (iii) maintaining productivity of predators of fished species.
MAINTAINING BIODIVERSITY
For ecological assemblages, attention seems to be focused on the maintenance of biodiversity and the potential consequences of biodiversity declines to the overall ecological
function of those assemblages. In this case, field research is concentrated on identifying
what gross changes in ecosystems occur as a result of human activities and theoretical
models on understanding the implications of those changes. In most cases, studies focus
on the extreme undesirable cases of change and the remedial action required to restore
at least the main structural components the system, i.e., the focus is on conservation and
restoration rather than prevention. These works identify species that require specific
conservation measures because they are threatened with extinction. This approach may
provide the last form of protection for a species, but in the ecosystem approach to fishing
the very need for restorative actions would signal a failure in the management of those
fisheries.
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MINIMIZING COMPETITION
Two approaches to minimizing competition between fisheries and predators have been
discussed in CCAMLR (reviewed by Constable, 2002). The first is to reduce the potential for interference with foraging of land-based predators by reducing overlap of
predator foraging areas and fishing grounds. Although this step may reduce interference
competition, it will not reduce consumption competition unless the predators feed on
one population and the fishery exploits a different one. The second approach is to limit
fishing to a level that does not alter the mean fitness of predators (Boyd, 2002) and will
work only if maximum fitness of predators occurs well below the maximum availability
of krill.
The second approach provides an operational objective in general but may be harder to
put into practice. For example, it will work only if the system is in equilibrium. As fitness
will be density-dependent, the objective will have to be coupled with target levels for the
magnitudes of the predator populations. Similarly, the field application of this approach
would produce some years in which the total allowable catch is reduced to zero.
MAINTAINING PRODUCTIVITY OF PREDATORS
Three types of krill-fishery management procedures based on maintaining the productivity of predators that arises from the consumption of krill have been presented to the
CCAMLR Scientific Committee. These are (1) maintenance of the median escapement
from the fishery of the krill spawning stock at 75% of the median preexploitation level,
(2) maintenance of abundance of predators at or above 50% of that prior to harvesting of
the prey (Butterworth and Thomson, 1995; Thomson et al., 2000), and (3) maintenance
of median annual predator productivity attributed to consumption of harvested species
at or above 80% of its preexploitation level (Constable, 2001).
These objectives recognize that fisheries affect marine food webs by removing production from the system. Objective 1 is a “bottom-up” target intended to provide for sufficient escapement of the fished species to provide for the requirements of the food web.
In its current formulation, it provides a predictive approach to setting harvest controls.
It is the current precautionary approach for krill. Objective 2 is a refined version of the
first; it still draws on the precautionary approach but applies to individual predator species. Objective 3 is a “top-down” target intended to ensure that the productivity across
all predators is maintained, but it does not specify how the individual species might
respond. It is based on the direct linkage between fishing and predators of the fished
species but recognizes that it may be difficult to manage the outcomes for individual
predators, i.e., to develop a target status for the food web based on productivity arising from fished species (Constable, 2002). These objectives were developed with the
acknowledgement that they are likely to need refining once the relationships between
predators and prey become better understood and the requirements for maintaining food
web function are better specified.
THE PRECAUTIONARY APPROACH OF CCAMLR
The precautionary approach of CCAMLR was developed in recognition of the uncertainties surrounding estimates of stock size, its relationship to preexploitation stock
size, and the dependence of predictions of future status on the accuracy and precision of
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Figure 3. Illustration of the uncertainties associated with stock assessments based on the dynamics of krill populations within plausible ranges of different population parameters (see Constable
et al., 2003). (A) Two plausible stock trajectories with similar starting points but different rates of
natural mortality (lines with cross marks, M = 0.55; plain lines, M = 0.59) and different recruitment time series (lower half of panel); trajectories in upper part of panel are for spawning biomass
(dashed lines) and total biomass (solid lines); the diamond and circle show the respective estimates of total biomass used to estimate yield, and the diagonal and square crosses are the respective median preexploitation spawning biomasses, which are the reference levels. (B) 50 plausible
trajectories. (C) Box plots showing the probability distributions for stock status over 20 yrs.
the estimates of population demographic parameters and estimates of mortality arising
from fishing. Figure 3 illustrates how these uncertainties are compounded to give many
plausible scenarios of the time series of stock status. In the final analysis, the status of
the stock in a given year, relative to preexploitation levels, cannot be indicated as a point
estimate as in a survey but must be specified as a probability distribution that integrates
across all the uncertainties.
The precautionary approach, based on Article II of the convention, includes: (i) a reference point, agreed to be the median preexploitation spawning biomass, (ii) a target
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status, currently agreed to be the median spawning biomass at least one generation time
after the start of fishing, and (iii) a threshold status, currently agreed to be 20% of the
preexploitation median spawning biomass.
All decisions are now in reference to the reference point, and the status of the stock
is expected to vary naturally over time. The abundance of the stock in the year prior to
exploitation is an inappropriate reference point because it does not take into account the
status of the stock in that year relative to the median level.
The ratio of the target status to the preexploitation median is set at 0.5 for species that
are not considered to be substantial prey species in the food web. For important prey species, such as krill, this ratio is set at 0.75 to help ensure sufficient prey escape the fishery
for consumption by predators. This target forms the basis of the “predator criterion” of
the decision rule.
If the spawning biomass of the fished species falls below the threshold status, then recruitment of new individuals to the stock will potentially be reduced substantially. This
threshold forms the basis of the “recruitment criterion” of the decision rule.
Decision rules have been formulated around these levels and take account of the probabilistic nature of the assessment process. Rational use has been interpreted to mean a
constant long-term annual yield, so the aim of the assessment is to determine the longterm annual yield that will have a high likelihood of bringing the population to the target
status and only a low likelihood of driving the population below the threshold status.
DEVELOPING OPERATIONAL OBJECTIVES FOR FOOD WEBS
A simplified food web based on the fished species at South Georgia (CCAMLR Statistical Subarea 48.3) is shown in Figure 4. In the case of the precautionary approach for
krill, the predator criterion determines how much krill might need to escape the fishery
to maintain predators such as seals, penguins, and whales.
Maintaining the ecological relationships in the Antarctic ecosystem implies that the
ecosystem should, by and large, be able to absorb the consequences of CCAMLR-regulated fishing without major changes in the strengths of natural interactions shown in
Figure 3. Constable (2001) argues that a number of points are pertinent to determining a
target status of the ecosystem. First, removal of fished species results in the removal of
production from the system and therefore reduces the potential for production of higherorder predators and may therefore reduce populations of predators in the longer term.
Second, the objectives of CCAMLR imply that biomass reductions of the magnitude
considered appropriate in managing single-species fisheries, say to 50% of preexploitation levels, are likely to be inappropriate for such predators. Third, some predators of
the fished species may need species-specific measures to assist recovery, e.g., the great
whales.
Given the predator criterion for determining catch limits of krill described above, a
simple expectation would be that abundances of predators whose sole prey is krill would
eventually be reduced by approximately 25%. This expectation would be met only if
per capita productivity of krill and maximum per capita productivity of the predators
remained unaltered at these new equilibria (see Mangel and Hofman, 1999). Only the
productivity of predators attributed to krill consumption would be reduced by 25%, however, and most predators do not rely solely on krill.
The consequences to the current abundance and overall productivity of predators and
the structure of the food web generally are contingent on a number of factors (Constable
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Figure 4. Structure of the food web around South Georgia Island in the Atlantic Ocean, including
the fisheries for krill, Patagonian toothfish, and mackerel icefish. The dark grey boxes represent
fished species, the light grey boxes predators of fished species, and the white boxes other types of
prey, including mesopelagic (Mesopel.) and bathypelagic (Bathypel.) fish species and zooplankton (Zoopl.) (after Constable, 2001).
2001), including (i) the degree to which predators are obligate foragers on krill (i.e.,
abundance may not decline if the predator switches prey); (ii) the availability of other
prey to replace the lost production of krill (i.e., if the predator switches prey, and the
new prey species has used the surplus production left available by removal of the fished
species, then no other alterations in the food web might arise); (iii) the degree to which
a predator population can absorb the effects of a reduced food supply (i.e., a reduction
in fished species might not cause a consequent reduction in reproductive success or increase in mortality because, prior to fishing, consumption exceeds the amount of food
required to maintain critical population processes); (iv) whether an exploitable surplus
of the fished species is present in the system; (v) the ability of a predator to compete for
food with other predators and the fishery; and (vi) possible nonlinearity of the relationship between prey availability and predator production. In addition, the consequences
to the food web generally will depend on the overall abundance of individual predators
and their individual roles as consumers of and competitors with other species, which
could lead to unexpected indirect feedbacks (positive and negative) to species of interest
(Yodzis, 2000). The situation is made more complex when a number of prey species are
being harvested. Combined, these factors potentially make the abundance and overall
production (in number or biomass) of species relatively insensitive indicators of the effects of fishing on the ecosystem.
Operational objectives for predators of fished species will need to encompass the general effect of lost production as well as ensuring that the lost production does not have an
unacceptably large effect on any one predator, including the potential for flow-on effects
in the food web.
What, then, would be an operational objective for the ecosystem that encompassed the
need to maintain ecological relationships and to allow for the recovery of some species?
In the general case, the assumption is that the catch limits are derived with adequate con-
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fidence that the median annual production of predators arising from fished species will
not be reduced by more than the expected reduction in median biomass of the fished species, although a higher target level may be necessary to produce the expected increased
production of recovering species. Further, is some minimum level of production arising
from fished species necessary to provide relative stability in or maintenance of ecological relationships and the food web?
Constable (2001) combines these elements into an operational objective intended to
maintain the production of predators arising from the consumption of fished species (an
index, W) at or above some limit reference point. A subsidiary objective would be to
ensure that the productivities of individual predator species are not disproportionately
affected even though the overall objective is satisfied. The expected outcome of these objectives is that the contributions of different species to the food-web structure would remain largely unaltered by fishing and would therefore maintain ecological relationships
in the system. This type of objective will result in attention to the primary interaction
between fished species and their predators (Andrewartha and Birch, 1984) rather than
examination of the consequences of secondary and other indirect interactions distant in
the food web from the fished species.
This approach recognizes the hierarchy of objectives related to the effects of fishing on
the productivity of a system and the potential for changes to the food web. It can easily be
made general for systems much more complex than the Antarctic and in which fisheries
are already present. The important part of the assessment is to divide the system into a
number of groups:
(i) Fished species: All such species have presumably been assigned appropriate target
levels or threshold reference points. They therefore form a contrived system in which
managers could manipulate the abundance of each species in a variety of ways by varying the harvest strategy for each of the taxa.
(ii) Obligate (dependent) predators of fished species: The effect of fishing on this
group can be considered as a whole, i.e., as the effect of production lost from the system,
or could be subdivided for exploration of the effects on individual species or groups of
species.
(iii) Prey of fished species and/or alternative prey of fished species: These taxa might
assume greater importance in the diet of predators and/or might increase their productivity as a result of reductions in abundance of their predators and competitors.
(iv) Predators of the nonfished prey species in the third group: The response of these
predators would be difficult to predict without good knowledge of the function of the
food web.
AN ILLUSTRATIVE FOOD-WEB MODEL
Constable (2001) developed a food-web model to illustrate this approach of evaluating
the effects of lost production on predators of fished species and how this information
could be combined to take account of the uncertainties in the dynamics of the food
web. It is described in more detail by Constable (2001) but can be summarized here as a
predator-prey-fisheries model in which the system is driven by the biomass of prey species. Each prey species is governed by a simple model of primary production; variations
in the biomass of prey species are caused by a mortality rate (natural and fishing mortality) and stochastic variation in available primary production. Each of these parameters
can be varied over time but is not influenced by the abundance of predators. A more
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593
detailed population model for Antarctic krill, in which both recruitment and mortality
are affected by variation in primary production and sea ice extent, is given by Constable
et al. (2003).
The fishery is modeled as using a constant annual catch.
Each of the predators is modeled as an age-structured population with constant rate of
natural mortality. Per capita recruitment is influenced by the annual carrying capacity,
which is determined by the abundance of prey and moderated by the degree of competition with other predators. Competition is determined by the abundance of other predators weighted by the reliance on the same prey. The relationship between predators and
prey is determined by relative effects rather than defining a functional feeding relationship. Biomass of predators is monitored by weight-at-age models.
PREY DYNAMICS.—The change in biomass is given by a density-dependent model, in
which the biomass of the prey species and other competing prey species influence the
per-unit-biomass recruitment to the population. The biomass, Bss,y, of a prey species, s,
at the beginning of a given year, y, is
Bss , y = Bss , y−1e
− M s − Fs , y −1
+ Rs′, y
(1)
where Ms is the natural mortality rate, Fs,y−1 is the fishing mortality rate of the previous
year required to yield the prescribed catch, and R's,y is the recruitment biomass of the
species in that year. Recruitment is
Rs′, y =
Rs Bss , y−1 Es , y−1 ;
Es , y−1 > 0
0
Es , y−1 ≤ 0
;
(2)
where Rs is the maximum per-unit-biomass recruitment rate, and Es,y−1 is the densitydependent adjustment of the recruitment rate according to the status of the production
environment and the magnitude of the prey populations relative to that status. It is estimated by
nS
Es , y−1 = 1 −
∑c
s ′=1
s , s′
Bss′ , y−1
Kss′ , y−1
(3)
where cs,s' is the competition coefficients for each prey species and Kss,y−1 is equivalent to
the carrying capacity for the prey species in the given year.
The competition coefficients are used to weight the biomass of all prey species for
determination of the density-dependent adjustment to the per-unit-biomass recruitment;
e.g., the subject prey species, s = s', would have cs' = 1. Other species will vary from 0
to 1.
The state of the environment (carrying capacity) for the prey species, Kss,y−1, varies
each year. Its state is drawn at random from a log-normal distribution based on a specified mean, Kss , and variance, σ2Ks, such that
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2


σ Ks
s
Kss , y−1 = Kss exp  η −
2 

(4)
2
where η is drawn randomly from N ( 0; σ Ks
) , which is a normal distribution with zero
2
mean and variance σ Ks .
In this case, the carrying capacities of the prey species vary independently of one
another.
THE FISHERY.—The fishing mortality of a target species in a given year, Fs,y−1, depends on the population size of the target species and the magnitude of the constant annual yield, Ys. It is solved for by Newtonʼs method and the following function:
s
s
Yˆs =
Fs , y−1
M s + Fs , y−1
(
− M +F
Bs , y−1 1 − e ( s s , y−1 )
)
(5)
If the stock is too small to support the catch level, then the value of F is constrained to
5 yr−1.
PREDATOR MODEL.—Each predator, p, is characterized by fully age-structured models
with a plus class. In each year, the numbers, Np,a, at age a are advanced one year and
discounted by natural mortality, Mp,a, which is unrelated to the abundance of prey, such
that
N p ,a =
N p,a−1e
(N
M p ,a
p ,a −1
)
+ N p ,a e
M p ,a
;
a < a plus
;
a = a plus
(6)
Recruitment of age-0 individuals to the predator population is density dependent, such
that the maximum per capita reproduction of individuals, rp, is moderated by the biomass
of each of the predators, Bpp (number at age by weight at age, wp,a), statistically weighted
by the competition coefficient, Cp,p', for each predator as described above for prey, and
related to the abundance of prey available to the predator. The latter term is governed by
the abundance of prey weighted by the selectivity, psp,s, for that prey by the predator. It
can also be adjusted by food value, pvp,s, if required. The degree of density dependence
can be adjusted by means of the term, Ap.
The final recruitment is influenced by the natural mortality of new recruits in that first
year, Mp,0, and the number of mature adults in the population.
Therefore,
N p ,0 =
rp e
0
where
− M p ,0
a plus
∑
a = amature
N p,a Ep p ;
Ep p > 0
;
Ep p ≤ 0
(7)
CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS
 nP

 ∑ C p, p′ Bp p′ 
p′=1

Ep = 1 − 
Kp p




595
Ap
(8)
a plus
Bp p′ = ∑ N p′ ,a w p′ ,a
a =1
(9)
and
nS
Kp p, y = ∑ ps p,s pv p,s Bss , y
s =1
(10)
The parameter values used in this illustrative model are given in Table 2 and the results
summarized in Figure 5 for three predators with different degrees of reliance on each of
two prey species: Prey 1 fished and Prey 2 not fished. All species had density-dependent
characteristics. Predator 1 was the focus of the model and depended most heavily on the
fished prey species. The degrees of selectivity by Predator 1 on Prey 1 and 2 were 1.0 and
0.2 respectively. Predator 2 had equal selectivity on the two prey species, and Predator 3
depended mostly on Prey 2 (selectivities 0.2 and 1.0, respectively). Although the model
can be used to simulate different life-history parameters, these model parameters are
the same for all three predators. The only differences between predator species are the
diet selectivity and the degree of competition between species. Predator 1 and Predator
2 are in greater competition with each other (0.5) than either is with Predator 3 (each
0.1), indicating the greater degree of overlap between those two species and the relative
isolation of Predator 3 in the food web.
The simulation is seeded with initial values for each of the five species and run for 500
yrs before a trial begins. The trial is run for 100 yrs; fishing begins in year 50. Fishing
is characterized by a constant catch of 50 biomass units of Prey 1 each year. This approach is used because the Antarctic krill fishery is currently managed for a long-term
constant annual yield (Constable et al., 2000). The yield was selected to deplete the stock
in 20–30 yrs, which is the critical period over which fishing should not be allowed to
change the system irreversibly (Constable et al., 2000), so that various estimated parameters could be examined for their utility in precluding such an outcome.
The fishery depletes Prey 1 to zero after 20 yrs (Fig. 5). The abundance of Prey 2 is
three times that of Prey 1 in the absence of fishing, and its mean then increases over 10
yrs after the fishery begins as a result of the reduction in its competitor. As expected,
the trajectory of Predator 3 is relatively insensitive to changes in the diet of Predator 1;
it changes in direct response to changes in Prey 2. The time series of abundances for
Predator 2 appears mostly influenced by that of Prey 2, although the decline of Prey 1
has slightly greater effect than it did on the trajectory of Predator 3. In the absence of
information about Predator 3, however, Predator 2 could be construed to be unaffected
by fishing.
In this simple model, recruitment of new individuals is the only population parameter
to vary each year as a result of changes in prey abundance, because all other parameters
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BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004
Table 2. Parameter values used in the illustrative food-web model described in the text (after
Constable, 2001).
Prey characteristics
Annual carrying capacity of primary
production
Natural mortality (yr−1)
Maximum per-unit-biomass
recruitment
Competition with the other prey
species
Mean
CV
Predator characteristics
Maximum per capita recruitment
(age 0)
Degree of density dependence in
recruitment
Age at maturity (yrs)
Plus class
Natural mortality (yr−1)
Body growth
[Length = L∞(1−exp(−K.age))]
Length-to-weight conversion
(a.Lengthb)
Competition coefficients
Selectivity of prey (Diet 1 given here)
Food value of prey species
Prey 1
10,000
Prey 2
10,000
0.45
0.3
0.4
0.3
0.3
0.5
0.3
0.3
Predator 1
Predator 2
Predator 3
0.1
0.1
0.1
2.4
2.4
2.4
8
10
0.07
0.03
1
8
10
0.07
0.03
1
8
10
0.07
0.03
1
K
a
0.8
0.1
0.8
0.1
0.8
0.1
b
with Predator 1
with Predator 2
with Predator 3
Prey 1
Prey 2
Prey 1
Prey 2
3
1
0.5
0.1
1.0
0.2
1
1
3
0.5
1
0.1
0.5
0.5
1
1
3
0.1
0.1
1
0.2
1.0
1
1
Recruits
Adults
L∞
are equal. Recruitment is therefore used as an index of annual production in this simulation. Per capita recruitment for each trial is shown in Figure 5C. Notably, Predators 2
and 3, and successive trials, differ little in per capita recruitment, indicating the relative
insensitivity of this parameter, in this simulation, in species for which diet is mixed.
In Figure 5D, for the illustrative model, W is compared to total production, both integrated over all predators.
The results of this analysis indicated that a management procedure based on the per
capita recruitment of Predator 1 may be equivalent to one based on W when Predator 1
depends mostly on the fished prey species. In this case, the time series of these parameters obviously declines after 10 yrs and before elimination of the fished prey, but a small
change in the diet of Predator 1 that reduced its dependence on the fished prey could
render per capita recruitment of Predator 1 relatively insensitive to the effects of fishing
on Prey 1 until after its disappearance (Constable, 2001). The prey-switching scenario
may be a realistic one and raises concerns about the utility of per capita recruitment as an
index of food-web status. In addition, a population decline could result in an increase to
preexploitation levels in per capita recruitment in the longer term, because of the interaction among numbers of predators, prey abundance, and per capita recruitment.
Figure 5B,D shows that predators as a group may exhibit little or no overall change as
a result of substantial overharvesting of prey species and that substantial declines in one
or more predators as a result of prey harvesting may only be detected after irreversible
CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS
597
Figure 5. Trajectories of characteristics of a simulated food web of three predator species and two
prey species over 100 yrs. (A) Biomass of Prey species 1 (solid line) and 2 (dashed line). Prey 1
is the fished species; 50 biomass units of it are removed each year if possible after Year 50. (B)
Relative biomass of three predators with different feeding-selection patterns on Prey species 1
and Prey species 2 respectively: (Predator 1 (solid line), 1.0 and 0.2; Predator 2 (dashed line), 0.5
and 0.5; Predator 3 (dotted line), 0.2 and 1.0. (C) Per capita recruitment for each predator (lines
as in panel B) and (D) productivity integrated for all predators with total production (dashed line)
and production arising from consumption of the fished species, W (solid line) (summarized from
Constable, 2001).
change has occurred. Constable (2001) argues that parameters intended to reveal the effects on predators must be appropriately statistically weighted by the relative importance
of fished species in predatorsʼ diets. This weighting is likely to yield more sensitive signals of the effects of fishing than are currently being used in monitoring programs.
The initial evaluation presented here uses a one-way food-web model, in which the
predators being monitored do not affect the mortality rate of the prey. This work must
be extended to a full food-web model with two-way relationships, including functional
feeding relationships between predators and prey. Nevertheless, the results illustrate the
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potential complexities involved in monitoring only a subset of the predator-prey system.
MONITORING FOR EFFECTS OF FISHING IN LARGE-SCALE, DYNAMIC SYSTEMS
The scales of interactions of different species with each other, with the environment,
and with fisheries are an important issue in design of monitoring and assessment programs (see, e.g., Murphy et al., 1988). For fisheries, managers could monitor predators to
determine whether harvesting affects their populations and to determine when harvest
controls must be modified to conform to conservation objectives for predators. Such
monitoring should take account of (i) the spatial and temporal relationships between
predators and harvested species and (ii) the metapopulation structure and dynamics of
the predators. The first will indicate how local consumption of harvested species on
foraging grounds might be affected by harvesting. The second will reveal how any effect
at the local scale might be manifested in the whole predator metapopulation, depending on the linkages between the local populations of that predator. In other words, the
monitoring must reveal how a change in local consumption of harvested species leads to
a change in one or more characteristics of the local population of predators (e.g., a landbased colony) at that time and then how such local effects might lead over time to an
impact on the whole population through changes in survival, reproductive performance,
or the movement of the predators (as offspring, immatures, or adults) among areas of local concentration. Importantly, it must determine what can be used to predict the timing
and magnitude of population-wide and regional effects.
The predators most accessible to monitoring are land-based species during their breeding season. An important question is whether accessible colonies of these predators are
representative of the region, i.e., whether a change in one predator species at a given
location at a given time represents the changes experienced by all species of predators at
that time and whether that change is representative or indicative of the overall impact of
the fishery over time on the populations of predators.
Fishing is now commonly considered a large-scale experiment (Beddington and de la
Mare, 1985; Ludwig et al., 1993; Mangel et al., 1996; Walters 1986). The principles for
designing a monitoring program that tests adequately for the effects of an environmental perturbation, such as fishing, are well described in the literature (e.g., by Downes
et al., 2002; Mangel et al., 1996). Two parts of the system can be manipulated for this
purpose—the location and timing of harvesting activities and the location and timing
of monitoring. If the former cannot be controlled, then the monitoring program must
have appropriate coverage across the whole of the harvesting unit and across all times
to provide representative information on the effects of fishing. Otherwise, the effects of
fishing may remain undetected.
Ultimately the power of the program for management purposes will be dictated by
the spatial and temporal relationships between the predator populations, the harvested
prey population, the harvesting activities, and the monitoring activities, as well as the
procedures that have been put in place for translation of the observed changes into management action.
CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS
599
SPATIAL CONFIGURATION OF MONITORING
Regular monitoring of harvested species is conducted primarily at local scales (foraging areas) rather than over the range of a whole population and, at best, only annually. Similarly, regular monitoring of predators is primarily limited to the age groups of
populations found breeding on land and is restricted to the breeding season.
Local availability of a harvested prey species will be influenced by its population
abundance and by the factors that influence the spatial patchiness of the population (e.g.,
for krill, Murphy et al., 1988; Miller and Hampton 1989; Hofmann et al., 1998). As a
result, the density of a harvested species in a specified predator foraging area may or may
not be directly correlated with population abundance.
For predators, each colony is a single sampling replicate influenced by the local abundance of harvested prey species. Only monitoring of many replicate colonies randomly
distributed throughout the range of the harvested prey population will provide representative estimates of the overall status of predators relative to the overall status of prey. The
number of colonies needed will be dictated by the degree of homogeneity of prey density
and the variability in measured parameters. Ideally, the sources of variation should be
evaluated before full implementation of the monitoring program so that the number of
monitoring sites chosen is sufficient for management purposes.
Given the expense and difficulty of repeated sampling of the whole population of the
harvested prey species, as well as the sampling of many predator colonies, satisfying the
usual demands for representative sampling may not be possible. The resulting loss of
power in the spatial design will have to be compensated for in the way the information is
used in assessments encapsulated within a management procedure (see, e.g., Mapstone,
1995).
MONITORING OVER TIME
The main issue in design of a time series of sampling events is the magnitude of
interannual variation that might arise (see Murphy et al., 1988, for review), including
variation in abundance and/or productivity of harvested prey species; their availability
on foraging grounds; feeding behavior of the predators, including prey switching (see,
e.g., Agnew et al., 1998); and predator productivity (Constable, 2001). In addition, variation in the fishery might arise through varying dependence on particular fishing grounds
coupled with varying relationships between fishing grounds and the predator foraging
areas.
Consequently, changes in predator performance from one year to the next may simply
reflect this natural variation. A baseline period must therefore be established before, or
at least early in, fishery development (de la Mare and Constable, 2000) so that changes in
predator performance can be compared to this baseline as the fishery develops, revealing
whether fishing for prey affects predators. The magnitude of change in predator performance that will trigger modification of harvest controls must also be determined.
Careful attention to monitoring for trends in abundance of harvested species, such as
long-term trends or cycles in abundance, may also be needed.
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MONITORING TO DETECT EFFECTS OF FISHING
A number of factors influence replicate observations of a predator parameter (indicator) at a given location (Constable, 2002; Table 3). For the purposes of detecting the
effects of fishing, the value of the indicator is expected to correspond directly to the
amount of the harvested species consumed by the predator. This value will be influenced
by the degree to which the indicator is affected by such consumption (correlation with
amount consumed) and the precision of the field method used to estimate the parameter.
This correlation may also depend on the total quantity of food consumed and the composition of the diet, i.e., the quantity of other prey consumed.
The amount consumed will be affected by the amount of harvested species available,
the functional feeding relationship between the predator and availability of harvested
prey species, and physical factors that might restrict foraging activities, such as the presence of sea ice in polar regions (see Constable et al., 2003).
The local availability of a harvested prey species in a foraging area will be influenced
by its regional abundance and by the factors that determine its patchiness, dynamics, and
productivity, particularly in relation to the foraging area of interest.
The regional abundance of a harvested species will be influenced by the general dynamics of the population and the influence of interannual variation in the physical environment, productivity, and natural mortality (Constable et al., 2003).
Fishing for prey can affect a monitored predator in two ways. Fishing that takes place
outside the foraging area affects the regional abundance of the harvested species (Butterworth and Thomson 1995; Thomson et al., 2000), producing a population-wide or
“global” effect on prey (Ea = a proportional change in overall density, Table 3). Fishing
that occurs in the foraging area of the monitored predator is likely to produce a local effect, reduction of the availability of the harvested prey species (Ef = proportional change
in density, Table 3). This latter reduction would be expected to cause an observed change
in the indicator in that season, as opposed to changes arising from the more distant largescale effects on the whole population of the harvested species, which might take some
time to become manifest in the foraging area.
These two effects—large-scale (Ea) and small-scale (Ef) proportional reductions in
harvested species density—will potentially produce different signals in the monitoring
program. If the fishery is random throughout the range of the harvested speciesʼ population, then Ea and Ef would differ little or not at all, and any predator colony could be
monitored, but if harvesting is nonrandom, then clearly, Ef will be greater in areas being
harvested than in other areas that experience only the global effect of Ea. In this case, it
would be better to monitor predators in the areas in which fishing is occurring, because
the effects in harvested areas will potentially be greater than those in nonharvested areas
and more easily detected.
Nonrandom harvesting is likely to provide more opportunities for discriminating between the effects of fishing and the effects of changes in other factors in the environment, because metapopulations of harvested species might cover large geographic areas.
A randomly distributed fishery throughout the range of a krill population will affect all
predator-monitoring sites equally and provide no means of studying the relative effects
of fishing and the environment.
This framework for local and population-wide effects provides a foundation for designing a monitoring program. If no areas or predators are to be disproportionately affected
601
CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS
Table 3. General factors that potentially influence the characteristics of the population of the
fished prey species, the consumption of that species by predators, and the consequent values of
predator parameters (indicators) being monitored. The factors influencing these characteristics
are combined into functions at the bottom of the table (after Constable, 2002).
Category
Large-scale, prey population
Predator foraging grounds
Harvesting
Interannual variation
Indicator
Characteristic
Observation of a predator
parameter (indicator)
Consumption of prey by predator
Local abundance of prey
Total abundance of prey
Factor
Abundance of target species
Large-scale dynamics (production,
mortality)
Spatial patchiness (affected by behavior,
oceanography, ice, mortality, production)
Abundance of target species
Local population dynamics (immigration,
emigration, production, mortality)
Effect of ice, oceanography, and other
physical factors on availability
Functional feeding relationship
Large-scale effects on abundance in a given
year
Small-scale effects on availability in a given
year
Large-scale dynamics in abundance
Small-scale availability
Trends in productivity
Trends in natural mortality
Changes in behavior of fishery
Trends in fish catch
Consumption of target species
Correlation between indicator and
consumption of target prey species
Bias
Measurement precision
Function
I(C,c,B,ε)
Symbol
AL
D
S
Af
Ld
Lp
f
Ea
Ef
Ta
TL
TP
TM
Th
TF
C
c
B
ε
C[f(Af,Ef),Ld,Lp]
Af[AL,S,TL,Th]
AL[D,Ea,Ta,TP,TM,TF]
by fishing once the fishery is fully established, then Ea and Ef should be equal in the fully
developed fishery, or at least, Ef for an area or predator should never exceed Ea.
In the absence of information about scale of fishing effects (Ea and/or Ef), effects must
be determined at the local scale during the early stages of the fishery and then the indicators of these effects must be used in a wider monitoring program as the fishery develops.
Only by doing so early can managers ensure that Ef does not exceed the magnitude of Ea
that will arise in the fully developed fishery. This approach will require the cooperation
of fishers, who should concentrate their effort in only a few areas to establish the nature
of the effects of fishing and how these can best be managed.
Effects of environmental variation can be distinguished from those of fishing only if
monitoring results from fished areas are contrasted with those from nearby reference
locations that are monitored but not fished. Such differentiation will only be available in
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the early phases of the fishery, while Ea is substantially less than Ef. If such differentiation is not needed, then reference locations may not be required. The different scales of
interaction for different predators must at least be considered.
CONCLUDING REMARKS
A number of trade-offs must be considered in the establishment of fishery harvest
strategies that will achieve conservation objectives. These trade-offs inevitably involve
the returned value of the fishery, the cost and extent of field monitoring, the degree of
confidence (uncertainty) in the resulting assessments, and the ecological risks of incorrectly interpreting monitoring results. In addition, the monitoring, assessment, and management procedures must remain commensurate with the value of the fishery.
CCAMLR has yet to extend its precautionary approach to include assessments of
predator reliance on fished species in determining appropriate catch limits or other controls on harvesting activities. The ecosystem approach to managing fisheries therefore
remains to be fully developed in CCAMLR, but a work program is underway to develop
such a management procedure for krill fisheries over the next few years (see Constable,
2002, for review).
The greatest opportunity for testing the effects of fishing in marine ecosystems is
during the early phases of the fishery, when local effects of fishing are not confounded
by the large-scale regional effects of a fully developed fishery on the fished population.
An experimental approach with spatially explicit variation in catch limits will help test
the assumptions underpinning the candidate management procedures (Beddington and
de la Mare, 1985; Butterworth 1986; Constable 2002; Constable and Nicol, 2002). Such
testing will help to ensure the objectives can be met in the longer term on the basis of the
available monitoring programs.
Evaluating potential management procedures in advance of the full development of
a fishery also involves building simulation models of the system (Fig. 1) to determine
how well the operational objectives for the ecosystem can be met given a combination of
decision rules, monitoring programs, and assessment methods (Smith, 1993; de la Mare
1987, 1988, 1996, 1998; Cooke, 1999; Mangel, 2000) and should enable a number of
questions to be addressed.
First, what combinations of monitoring, assessments, and decision rules meet the required performance standards for different plausible formulations of the Antarctic ecosystem? Here, performance is judged by how well the objectives are met in the model
ecosystem. It is important to determine early whether a monitoring program and the assessment methods are able to provide solid foundations for the management procedure.
Notably, the governing feature of the management procedure is not the precision with
which an indicator can be measured but how well the indicator can be used to make
robust decisions about harvest controls (de la Mare, 1996).
Second, could the different parts of the management system be simplified and work
just as effectively? A simpler monitoring program at specific locations in the Antarctic
could save considerable expense and facilitate better areal coverage.
Third, how could the performance of the management system be improved by changes
in its decision rules?
The advantage of such evaluations is that the initial management system can be built
on the simplest of decision rules and then progressively modified to improve performance according to the performance criteria. In some instances, the decision rules need
CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS
603
not be based directly on the performance criteria to be potentially able to achieve the
desired effects. Importantly, management procedures must be robust to all the uncertainties in our understanding of food-web structure and other elements in the assessment and
decision-making process.
The greatest challenge to the ecosystem approach to managing fisheries is to find
agreement on the operational objectives for food webs, including the recovery of species.
Marine ecosystems clearly fluctuate naturally, and few species vary in entirely the same
way. Recognition is growing that risk-averse management procedures are preferable,
taking much better account of the uncertainties in our knowledge as well as the uncertain behaviors of marine systems. CCAMLR has shown how a phased approach in the
early stages of the fishery can help ensure that the fishery does not grow faster than our
capacity to manage it. This approach allows for the acquisition of essential information
on the basis of which all the objectives for conservation and rational use can be evaluated
before conflict arises between the objectives for conservation and rational use.
ACKNOWLEDGMENTS
Many thanks are due to W. K. de la Mare, S. Nicol, I. Ball, C. Davies, I. Everson, D. J. Agnew,
and G. Parkes for many stimulating and challenging discussions over the years about ecosystembased management. I also thank A. Williamson for her great assistance in the preparation of
the materials presented here and F. C. Coleman and A. B. Thistle for their patient and thorough
editing of the manuscript. Thanks are due also to the organizers of the symposium for inviting
me to prepare this paper. Finally, I would like to thank my colleagues in the CCAMLR and at the
Australian Antarctic Division for the many conversations and meetings that contributed to the
substance of this manuscript.
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