ICES Journal of Marine Science, 62: 569e576 (2005)
doi:10.1016/j.icesjms.2004.12.015
Translating ecosystem indicators into decision criteria
Jason S. Link
Link, J. S. 2005. Translating ecosystem indicators into decision criteria. e ICES Journal of
Marine Science, 62: 569e576.
Defining and attaining suitable management goals probably represent the most difficult part
of ecosystem-based fisheries management. To achieve those goals we ultimately need to
define ecosystem overfishing in a way that is analogous to the concept used in singlespecies management. Ecosystem-based control rules can then be formulated when various
ecosystem indicators are evaluated with respect to fishing-induced changes. However, these
multi-attribute control rules will be less straightforward than those applied typically in
single-species management, and may represent a gradient rather than binary decision
criteria. Some ecosystem-based decision criteria are suggested, based on indicators
empirically derived from the Georges Bank, Gulf of Maine ecosystem. Further development
in the translation of ecosystem indicators into decision criteria is one of the major areas for
progress in fisheries science and management.
Published by Elsevier Ltd on behalf of International Council for the Exploration of the Sea.
Keywords: control rules, decision theoretic frameworks, ecological processes, ecosystem
approaches, ecosystem management, ecosystem overfishing, fisheries.
Received 1 April 2004; accepted 8 September 2004.
J. S. Link: National Marine Fisheries Service, Northeast Fisheries Science Center, 166
Water Street, Woods Hole, MA 02543, USA. Correspondence to J. S. Link: tel: C1 508
4952340; fax: C1 508 4952258; e-mail: [email protected].
Introduction
Ecosystem considerations in a marine scientific and
management context have been extant for more than
a century (e.g. Baird, 1873). More contemporarily,
ecosystem approaches to managing living marine resources
are somewhat behind those in other types of ecosystems
(Christensen et al., 1996; Yaffee et al., 1996; Thomas,
2000), but they are clearly gaining importance. The concept
of ecosystem-based management (EBFM) as applied to
marine fisheries was crystallized in Larkin’s (1996) paper,
with the chief observation that it provides a holistic
approach to natural resource management. Since then,
there has been clear recognition that EBFM for marine
systems is gaining momentum (Link, 2002a; Garcia et al.,
2003).
In the broader conceptual context of EBFM, debates and
discussions are moving from the definition stage (what is it,
what does it mean, why would one want to do it, etc.?)
towards the implementation stage (how does one do it?).
One of the main obstacles to further implementation is
a clear definition of ‘‘ecosystem overfishing’’ criteria akin
to overfishing definitions for individual stocks (Murawski,
2000). In essence, the question now being asked is, ‘‘how is
EBFM to be implemented’’?
The objectives here are to explore the broader framework
for ecosystem-based decision criteria, to examine a suite of
1054-3139/$30.00
vetted indicators (sensu Rice, 2000, 2003; Rochet and
Trenkel, 2003) in this context, and to make some tangible
suggestions about how to proceed in order to begin
implementing EBFM.
Decision theoretic framework
and indicators
I submit that most decision theoretic frameworks consist of
three main components, the first of which is to establish
goals. What goods and services are wanted from or out of
an ecosystem for society; what are the policy objectives;
etc.? Central to EBFM will be the rectification of
competing interests in the allocation of harvestable biomass
in an ecosystem. Critical to scientists tasked with this
mission will be the recognition of which ecosystem
configurations are feasible. The second component is to
assess the system. Where are we relative to where we want
to be? Novel methodologies and means of integration, in
addition to a broader interdisciplinary approach, are
required to assess marine ecosystems. The third component
is the definition of appropriate decision criteria (reference
points, directions or surfaces, thresholds, limits, targets,
etc., matched to control rules, action triggers, etc.) to
evaluate what steps need to be taken to achieve the goals.
That is, how do we get to where we want to be?
Published by Elsevier Ltd on behalf of International Council for the Exploration of the Sea.
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J. S. Link
The role of indicators is central to this simple, three-step
decision theoretic framework (Sainsbury and Sumaila,
2001), because they permit assessment of the status of
a system and because they form the basis, both empirical
and theoretical, for the development of reference values.
The challenge is to establish ecosystem control rules that
prescribe particular management actions if the indicatorbased thresholds are exceeded.
In classical fisheries models, reference points to define
overfishing limits are generally based on (spawning) stock
biomass and fishing mortality (Hilborn and Walters, 1992;
Quinn and Deriso, 1999; Restrepo, 1999). If the reference
points are exceeded, control rules are triggered to reduce
fishing. Conversely, classical toxicological models use
a probit or logistical decay of organism survivorship
plotted against chemical concentrations, with an inflection
point termed LD50, the concentration at which 50% of the
organisms die (Suter, 1993). Such reference points can be
applied in ecological risk assessment to determine if
a concentration of a specific chemical exceeds the acceptable threshold. If so, particular control rules may be
triggered to mitigate the concentration of the chemical or its
effects (Calabrese and Baldwin, 1993; Suter, 1993).
The point of these model-based examples is that,
although the reference values are mathematically linked
to a particular process, their choice is ultimately arbitrary.
That is, other than having these values map nicely to
a particular point of change in the functional relationship
between two parameters, one might have chosen a different
level for equally good reasons. Moreover, most reference
points rely heavily upon empiricism (experimental or
observational) that links a rate or state variable to a function
of something that nominally can be managed, be it fishing
effort or chemical discharge.
By analogy, two challenges remain for the translation of
ecosystem indicators into EBFM decision criteria. First, we
need to identify key indicators and evaluate whether, or
how much (perhaps as a percentage of variance explained;
sensu Link et al., 2002), they are a function of fishing effort.
This implies an understanding of causality beyond simple
correlation. Second, we need to integrate, or evaluate
simultaneously, multiple indicators that represent all the
germane processes in an ecosystem. This means that some
form of multivariate methodology may be required (Collie
and Gislason, 2001; Brodziak and Link, 2002; Caddy,
2002; Link et al., 2002; Collie et al., 2003; Rice, 2003), and
that multiple indicators need to be evaluated.
Translation of indicators into
decision criteria
Table 1 lists a selection of what I view as suitable
indicators, along with associated reference values or
thresholds, that can serve as an initial step towards the
translation of ecosystem indicators into EBFM decision
criteria. The thresholds below were chosen as being
representative of major ecosystem processes, and also
because the effects of fishing on the indicators (or
technically the processes and biota that these indicators
represent) are generally known. Many of the thresholds
presented will appear as arbitrary as 20% of virgin biomass
P
Table 1. Ecosystem indicators translated into warning thresholds and limit reference points for EBFM (B: biomass; subscripts , TL3,
benth, plank: all surveyed species in the system, all species at trophic level 3, all benthivores, all planktivores, respectively; PP: primary
production; BPCons: the sum of biomass consumed by all species in the ecosystem; Smin: minimum number of species; L=Smax : maximum
mean number of interactions per species; Cmax: maximum number of cycles observed; Nscav-med: median abundance of scavengers;
Vjelly-med: median biovolume of gelatinous zooplankton; Amax: maximum area of living, hard coral; N/A: not applicable).
Indicator
Description
l
b
Bflatfish
Bpelagic
Mean length, all species
Slope size spectrum, all species
B of all flatfish species
B of all pelagic species
BTL4C
Bpisc
LS
L=S
Bremov
B of all species at trophic level 4 and above
B of all piscivores
Landings of target species
Mean number of interactions per species
Fishery removals of all species (landings,
bycatch, discards, etc.)
Species richness (number of species)
Number of cycles
Abundance of scavengers
Volume of gelatinous zooplankton
Area of live, hard coral
S
C
Nscav
Vjelly
Acoral
Warning threshold
Limit reference point
30%
N/A
Bflatfish O 50% BP
Bpelagic O 75% BP or Bpelagic
! 20% BP
BTL4C O 25% BTL3
N/A
LP O 5% PP
10% below L=Smax
N/A
50%
10%
Bflatfish O 75% BP
Bpelagic O 85% BP or
Bpelagic ! 10% BP
BTL4C O 50% BTL3
Bpisc O Bbenth C Bplank
LP O 10% PP
N/A
Bremov O BECons
S ! Smin, for 3 years
30% below Cmax
100% above Nscav-med
100% above Vjelly-med
30% below Amax
S ! Smin, for 5 years
N/A
200% above Nscav-med
200% above Vjelly-med
50% below Amax
Translating ecosystem indicators into decision criteria
or 50% of a stock’s carrying capacity, or 50% survivorship,
but they were chosen on the particular basis identified and
represent common ecosystem processes. Factors for selecting reference points included the doubling of biomass
(100% increase), regions with the greatest slopes
(10e30%), theoretical considerations such as transfer
efficiency constraints (5e10%), minima or maxima across
time-series, points of inflection in time-series trajectories,
or changes to half the observed biomass (50% decline).
Many of the reference points are based on experience in the
Georges Bank, Gulf of Maine ecosystem (Link and
Brodziak, 2002; Link et al., 2002), where they correspond
to empirical observations after periods of intense fishing
pressure (Fogarty and Murawski, 1998; Link and Brodziak,
2002). In many respects they are effectively extensions of
previously proposed ecosystem decision criteria (Constable
et al., 2000; Murawski, 2000; Jamieson et al., 2001;
Sainsbury and Sumaila, 2001; Link et al., 2002; ICES,
2003; Rochet and Trenkel, 2003; Trenkel and Rochet,
2003; Nicholson and Jennings, 2004). For a more detailed
treatment of these particular indicators, see Brodziak and
Link (2002) and Methratta and Link (in press).
Size
Size is indicative of several key processes among biological
systems, and fishing contributes overall to smaller-sized
organisms in an ecosystem (Jennings and Kaiser, 1998;
Hall, 1999; Jennings et al., 1999; Nicholson and Jennings,
2004). Size also encapsulates not only significant biological
and ecological considerations, but also in many ways
reflects market potential.
Mean size (l, as length) of all species caught in either
fishery-independent surveys, fishery-dependent surveys,
and/or landings is a useful and simple indicator to evaluate
the overall effects of fishing on an ecosystem (Link and
Brodziak, 2002; Link et al., 2002; Rochet and Trenkel,
2003; Nicholson and Jennings, 2004; Sala et al., 2004). If
there is a decline in mean size of more than 30% between
years, then a warning or precautionary threshold (!30%)
has been exceeded and, even if the change cannot be
directly attributed to fishing, the indicator should still be
monitored more closely, with initial steps taken to mitigate
the change. The limit reference point (LRP) has been set at
a value of 50% decline, and the control rule would be to
enlarge mesh size or to take similar action to alter fishing
gear. The value of 50% was chosen because it corresponds
to an observed doubling in the time-series of l after fishing
decreased (Link and Brodziak, 2002; Methratta and Link, in
press).
The size spectrum slope (b) is also indicative of systemic
size-based processes, and notably steepens as fishing
pressure increases (Murawski and Idoine, 1992; Rice and
Gislason, 1996; Duplisea et al., 1997; Gislason and Rice,
1998; Bianchi et al., 2000; Nicholson and Jennings, 2004).
The LRP has been set at an increase in b of 10% per year,
571
because this corresponds to the maximum slope observed
after a period of intense fishing (Methratta and Link, in
press). The control rule would be to mitigate fishing effort
and mesh size such that the steepness of the slope levels off
within an acceptable rebuilding time frame, comparable to
single-species rebuilding schedules. Given the potential for
rapid changes in slope, no warning threshold was developed
for this indicator.
Aggregate biomass
As ecosystem components are differentially exploited,
species composition changes notably (Greenstreet and Hall,
1996; Fogarty and Murawski, 1998; Jennings and Kaiser,
1998; Hall, 1999; Kaiser and de Groot, 2000; Link and
Brodziak, 2002; Rochet and Trenkel, 2003; Trenkel and
Rochet, 2003). Given ecological constraints, some community configurations may not be sustainable or reflect
a shift towards more perturbed, and certainly less desirable
(economically, socially, and/or aesthetically), conditions.
Examining groups of species precludes spurious conclusions from changes in just one species and confirms
patterns if seen across a range of similar taxa. Aggregate
biomass estimates may be used with different Boolean
thresholds as well.
Excessive biomass of flatfish (Bflatfish) can be indicative
of a heavily fished ecosystem (Pauly, 1979; Kaiser and
Ramsay, 1997; Hall, 1999). If Bflatfish exceeds 50% of the
biomass of all fish species (BP) in any given year then
a warning threshold has been exceeded, prompting
mitigating actions on fishing. The LRP is set at
Bflatfish O 75% BP, because this corresponds to the
observed maximum after a period of heavy fishing (Link
and Brodziak, 2002; Methratta and Link, in press), and the
control rule would be to alleviate fishing for the total
system.
Similarly, high biomass of pelagic species (Bpelagic) can
be indicative of a change in ecosystem state as a function of
fishing down foodwebs, targeting other species and subsequent predatory release, differential fishing pressure, or
a shift in energy flux (Jennings and Kaiser, 1998; Pauly
et al., 1998; Hall, 1999; Overholtz et al., 2000; Jennings
et al., 2001, 2002; Link and Brodziak, 2002). Conversely,
not enough Bpelagic may mean insufficient forage base for
other targeted or protected species. Therefore, the reference
values for this indicator are two-tailed. If Bpelagic O 75%
BP, or if Bpelagic ! 20% BP in any given year, then
a warning threshold has been exceeded. The LRPs are set at
Bpelagic O 85% BP, and at Bpelagic ! 10% BP, because
these values correspond approximately to the minimum and
maximum observed in the time-series (Link and Brodziak,
2002; Methratta and Link, in press), and the control rule
would be to alleviate fishing for the entire system or for
pelagics, respectively.
In addition, we need to account for energy flows within
an ecosystem and for some of the constraints imposed by
572
J. S. Link
trophic transfer. Some ecosystem states, even if desirable,
may not be sustainable in the long term given ecotrophic
transfer efficiencies (Pauly and Christensen, 1995; Jennings
and Kaiser, 1998; Pauly et al., 1998). If the biomass at
trophic level 4 and above (BTL4C) is O25% of the biomass
at trophic level 3 (BTL3), then a warning boundary has been
exceeded. The LRP is set at BTL4C O 50% BTL3, based
upon theoretical constraints and modelling results (Link,
2003), and the control rule would be to alleviate fishing on
TL3 species. The potentially controversial control rule to
increase fishing effort on TL4 is not feasible given the other
aggregate biomass and size-based indicators listed above.
Similarly, if the biomass of all piscivores in the system
(Bpisc) exceeds the total of biomass for both benthivores
and planktivores (Bbenth and Bplank, respectively), then an
LRP has been exceeded, and the advice would be to
alleviate fishing on benthivores and/or planktivores. Again,
the control rule to increase fishing on piscivores is not
feasible given the indicators discussed above. These values
were chosen based upon theoretical constraints and
empirical observations (Link and Brodziak, 2002), as well
as modelling results (Link, 2003). No warning threshold
was developed for this indicator given the prominence
many countries place on large piscivores.
Finally, ecosystems can only produce a fixed amount of
biomass per unit time. Removing more than what is
produced is symptomatic of excessive exploitation, even at
the systemic level. Even assuming that all fish feed at TL3
and have an ecotrophic transfer efficiency of 10e30%, and
that there is some recycling of material within an
ecosystem, the limits on how much fish can be produced
is still a small fraction of primary and secondary production
(Elton, 1927; Pauly and Christensen, 1995). Thus, if the
landings of all targeted species (LP) are O5% of primary
production (PP) in a given year integrated on an areal basis,
then a warning boundary has been exceeded. Based upon
theoretical constraints, the LRP is set at LP O 10% PP, and
the control rule would be to reduce total removals from the
system.
Trophodynamics
Indicators of foodweb structure reflect how the total energy
in an ecosystem is processed, the strength and breadth of
predatoreprey interactions, potential competitive interactions, and similar ecological processes. Fishing changes the
foodweb structure of ecosystems (Jennings and Kaiser,
1998; Pauly et al., 1998; Jennings et al., 1999; Overholtz
et al., 2000).
The mean number of interactions per species (L=S)
reflects how connected a foodweb is and, potentially, how
stable a foodweb may be (Link, 2002b). Changes to this
indicator reflect notable differences in the structure and
dynamics of a foodweb. A decline in L=S of 10% below the
maximum observed across the time-series represents
a warning threshold. Because there is currently no
mechanism ultimately to influence the value of this
indicator, it should be used only as a tool to invoke further
precautionary action. This value corresponds to the
maximum difference observed in the time-series (Link
and Brodziak, 2002; Methratta and Link, in press).
The amount of target species removed also reflects how
energy is distributed in a foodweb, particularly relative to
how much of the same species is consumed within the
system (Overholtz et al., 2000). The indicator of total fish
biomass (all species) consumed by all species within the
foodweb (BPCons) relative to total biomass (all species)
removals from fisheries (landings, bycatch, discards, etc.;
Bremov), should provide a useful assessment of how much
energy is being used within the system relative to what is
being taken out. The LRP is set at Bremov O BPCons, and
the control rule would be to lower the total removals from
the system. This value was chosen on the basis of
theoretical considerations and minima in the time-series
(Link and Brodziak, 2002; Methratta and Link, in press).
No warning threshold was developed for this indicator
given the severity of the implications of removing food
before it reaches other targeted species.
Diversity
Diversity is often espoused as an ecosystem goal, but is
difficult to translate into an operational format given the
broad range of diversity indicators and the multiple
community configurations that can produce similar values
for any given diversity indicator (Rice, 2000, 2003). Many
countries, however, have some form of legislation to
prevent the human-induced extinction of species. Diminishing the number of species can be a potential side effect of
fishing (Rice and Gislason, 1996; Gislason and Rice, 1998;
Jennings and Kaiser, 1998; Hall, 1999). If the total number
of all species caught in fishery-independent surveys (S)
declines below the minimum number of total species (Smin)
observed for the time-series up to that point, and persists for
a period of 3 years, then a warning threshold has been
exceeded. Because there is currently no mechanism
ultimately to influence the value of this indicator, it should
principally be used only as a precautionary tool. However,
given the severity of species extinction, an LRP has been
set at S ! Smin for a period of 5 years or more, and other
precautionary measures (e.g. reserves, closed areas) should
be enacted. The values were chosen based upon theoretical
considerations and empirical observations, although we
have not observed any species extinctions (Link and
Brodziak, 2002; Methratta and Link, in press).
Network properties
The cybernetic indicators of ecosystems may be more
useful than their current limited use might suggest. How
energy is used, cycled, and recycled within a system
Translating ecosystem indicators into decision criteria
represents a suite of fundamental ecosystem processes and
structures (Jorgensen and Muller, 2000; Fath et al., 2001).
The number of cycles (C) represents how often species A
eats species B, and vice versa (Link, 2002b). This indicator
reflects the connectedness and interdependence of species,
as well as the degree of energy recycling within an
ecosystem. If a decline in C is O30% below the maximum
observed across the time-series (Cmax), then a warning
threshold has been exceeded. Again, because there is
currently no mechanism ultimately to influence the level of
this indicator, it can be used only as a tool to invoke further
precaution. This value was chosen on the basis of minima
in the time-series (Link and Brodziak, 2002; Methratta and
Link, in press).
Indicator species
Individual species may also be indicative of detrimental
changes in ecosystems. Akin to the canaries used
historically in mining operations, changes in the populations of these species reflect significant changes to
ecosystem functioning. Many of these non-target species
are important for evaluating incidental effects of fishing
(Jennings and Kaiser, 1998; Hall, 1999; Kaiser and de
Groot, 2000; Pope et al., 2000; Gislason, 2001).
Acute increases in scavenger population abundance after
fishing activities are reasonably well documented, and there
is also some evidence that fishing activities induce chronic
increases in scavenger populations (Ramsay et al., 1998;
Hall, 1999; Demestre et al., 2000; Fonds and Groenewold,
2000; Greenstreet and Rogers, 2000; Link and Almeida,
2002). Increases in taxa such as crabs, sculpins, starfish,
and similar organisms are indicative of notable fishing
activity in an ecosystem. If the population abundances of all
major scavengers (Nscav) increases by 100% (i.e. population
doubling) above the median observed across the time-series
(Nscav-med), then a warning threshold has been exceeded.
Note that use of the median can lead to an annually shifting
baseline, but a doubling of biomass would be notably
detected above any minor shifts to a long-term median. An
LRP is set at 200% above Nscav-med, and the control rule
would be to alleviate all fishing effort in an ecosystem. The
threshold value is based on an observed doubling of
biomass in the time-series (Link and Almeida, 2002; Link
and Brodziak, 2002; Methratta and Link, in press).
Gelatinous zooplankton blooms are typically associated
with overfishing, climate change, or eutrophication (Zaitsev,
1992; Mills, 2001; Shiganova et al., 2001; Brodeur et al.,
2002; Gucu, 2002), with all these perturbations notably
altering ecosystem functioning and structure so that large
blooms can dampen fish populations for long periods of
time (Shiganova and Bulgakova, 2000; Purcell and Arai,
2001; Brodeur et al., 2002). If the abundance or biovolume
of gelatinous zooplankton (Vjelly) increases by 100% above
the median observed across the time-series (Vjelly-med), then
a warning boundary has been crossed. The key here would
573
be to evaluate indicators of total fishing effort, or proxies
thereof, against indicators of eutrophication or climate
change. An LRP is set at 200% relative to Vjelly-med (i.e.
doubling; Methratta and Link, in press), and the control rule
would be to alleviate the perturbation by whichever of the
alternate causal processes appears to be the most important.
Coral is a unique, sensitive, biotic habitat that integrates
hundreds, if not thousands, of years of ecosystem
functioning. Many factors can cause declines in coral
biomass (Barber et al., 2001; Knowlton, 2001; Fossa et al.,
2002; Hall-Spencer et al., 2002; Szmant, 2002), of which
fishing is only one. Similar to gelatinous zooplankton,
ancillary indicators need to be examined to evaluate the
causality of any observed declines in coral biomass. If
a decline in living, hard coral biomass expressed in units of
area (Acoral) is O30% below the maximum observed across
the time-series (Amax), then a warning threshold has been
exceeded. Based on theoretical considerations, an LRP is
set at a decline of 50%, and the control rule would be to
mitigate the cause of the coral decline.
Conclusions
When visiting a medical doctor, pulse rate and body weight
are not the only indicators used to make a diagnosis. The
human body is complex, and multiple indicators are
required before a physician can claim whether a patient is
healthy or unhealthy. Such a physician will typically
require further indicators before making a diagnosis pointing to a potential cause of illness. Similarly, it is time to
move away from a simple binary decision theoretic
framework when thinking about fisheries management
(especially EBFM), and recognize the truly multivariate
nature of the issue. We should also cease trying to find just
one or two diagnostic indicators for ecosystem overfishing.
Further work is required to validate and refine the
thresholds proposed here, but as proposed they could serve
as useful starters from a precautionary perspective.
Although we should begin to apply or develop appropriate
models for predicting values of the various indicators as
a function of fishing, waiting to implement them in
a decision criteria format until they are fully vetted and
validated in ecological and fisheries theory is imprudent,
given the precautionary approach to act conservatively with
the best available information (Sainsbury and Sumaila,
2001). Granted, fully deterministic multispecies, aggregate
biomass, and system level models as functions of various
perturbations, merit continued development (Hollowed
et al., 2000; Whipple et al., 2000). However, many
examples of environmental decision-making are based
upon empirical thresholds and standards (e.g. Varma
et al., 2000; Dale and Beyeler, 2001; EPA, 2001; Hall,
2001; Kurtz et al., 2001; Schiller et al., 2001; Suter, 2001).
Further, other disciplines and approaches, such as management strategy evaluation, operations research, decision
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J. S. Link
support systems, decision analysis, and other decisionmaking theory (Clemen, 1996; Peterman and Anderson,
1999; Sainsbury et al., 2000; Varma et al., 2000; Eisenack
and Kropp, 2001) should help to delineate ecosystem
decision criteria better, and subsequently improve EBFM.
Applying the proposed thresholds to other ecosystems
will be an intriguing exercise: how applicable are these
reference values to other ecosystems with very different
characteristics (e.g. Sherman et al., 1993)? Comparative
ecosystem studies are required to elucidate this issue, and
are very worthwhile doing. However, the point is that the
specific threshold values proposed may be less important
than actually evaluating these types of indicators that reflect
system-wide responses to ecosystem perturbations. Many
of the indicators may simultaneously reflect systemic
overfishing, and thus be exceeded concurrently. Using all
indicators in concert will be helpful. However, use of each
one separately as an LRP will be much more precautionary
than waiting until all of them, or some fraction thereof, are
exceeded before control rules are enacted. Linking control
rules to each indicator should help to mitigate rigorously
the global marine fisheries crisis, which has been expressed
in multiple fashions (Pauly and Christensen, 1995; Jennings
and Kaiser, 1998; Pauly et al. 1998; Hutchings, 2000;
Garcia and Leiva Moreno, 2003; Myers and Worm, 2003).
Many of the indicators described actually form a gradient
from acceptable to concern to problem, when viewed as
below warning threshold, above warning threshold but
within limit, and beyond limit. Many decision criteria
frameworks allow for more than two categories (Clemen,
1996; Peterman and Anderson, 1999; ICES, 2003), and I
submit that EBFM may also need to have a more nuanced
set of categories for delineating ecosystem overfishing.
With few exceptions, the proposed control rules for the
indicators described above prescribe reducing fishing
mortality in some form. This is in agreement with other
reports and studies (Hall, 1999; NMFS, 1999; NRC, 1999),
suggesting that appropriate EBFM begins with effective,
precautionary, single-species fisheries management. In
many respects this remains true. Yet that is not all that
EBFM entails: allocation of biomass among competing user
sectors is an important aspect. For instance, specifying that
flatfish biomass should not exceed a threshold is one thing;
specifying which species mix of those flatfish may be
exploited by which fleet is another. Therein lies the
challenge, and opportunity, for EBFM.
In addition to evaluating trade-offs in the allocation
among harvestable biomass, the other major challenge
remains the translation of ecosystem indicators into
decision criteria. There are several approaches that hold
promise to this end (Constable et al., 2000; Collie and
Gislason, 2001; Jamieson et al., 2001; Sainsbury and
Sumaila, 2001; Link et al., 2002; Collie et al., 2003; Rochet
and Trenkel, 2003; Trenkel and Rochet, 2003, Nicholson
and Jennings 2004). Hopefully, the approach proposed here
also helps to further the discussion of how EBFM can be
implemented. Clearly, however, translation of ecosystem
indicators into decision criteria is one of the major areas for
progress in fisheries science and management.
Acknowledgements
I thank many of my colleagues at the NEFSC, particularly
Tim Smith, William Overholtz, Jon Brodziak, Steve
Murawski, Richard Merrick, Elizabeth Methratta, Steve
Edwards, Mike Fogarty, Buck Stockhausen, Steve Cadrin,
Chris Legault, Deb Palka, Jay O’Reilly, Jack Green, and
Thomas Noji, for prior discussions that led to the
formulation of many of the ideas presented. I thank
Philippe Cury and Villy Christensen for inviting me to
give an associated talk at the Ecosystem Indicators
Symposium, and Buck Stockhausen, Elizabeth Methratta,
Steve Edwards, Chris Legault, Coleen Moloney, Hein Rune
Skjoldal, and Niels Daan for constructive and helpful
comments on earlier versions of the manuscript.
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