BULLETIN OF MARINE SCIENCE, 78(3): 655–668, 2006
MOTE SYMPOSIUM INVITED PAPER
PACIFIC ROCKFISH MANAGEMENT: ARE WE CIRCLING
THE WAGONS AROUND THE WRONG PARADIGM?
Steven A. Berkeley
ABSTRACT
West Coast rockfishes are managed with traditional fishing-mortality and spawning stock biomass-based control rules, the objectives of which are to maintain a
specific biomass of mature females regardless of their size or age. The implicit assumption is that larvae produced by all females are equivalent in their probability of
survival, but recent research on black rockfish indicates that larvae of older mothers
are far more likely to survive than those of younger females. Using a simple deterministic equilibrium model that incorporates the influence of maternal age on
larval survival, I compared population age structure, fishery yield, effective larval
output, and recruitment for four different management strategies: status quo, slot
limit, marine reserves, and reduced fishing mortality. Results of these simulations
indicate that a 35% reduction in fishing mortality would achieve increases in effective larval output and yield comparable to a 20% marine reserve option. If recruitment is proportional to effective larval output, a 20% marine reserve would increase
yield at equilibrium by 9% relative to the status quo. These results suggest that managing for age structure can increase both resilience and yield.
Until recently, fisheries management has focused largely on maximizing the yield
of target species and given little consideration to the impacts of fishing on ecosystems, by-catch, habitat, or threatened species. This narrow view is rapidly broadening
in the U.S., however, and particular acceleration followed release of the 2004 report
of the U.S. Commission on Ocean Policy (http://www.oceancommission.gov). Rather
than simply focusing on single-species yield, fisheries managers increasingly must
choose among alternative actions to achieve the best compromise among competing
objectives (Sainsbury et al., 2000). For example, the management strategy evaluation
(MSE) approach has been suggested as a way of objectively evaluating the performance of different management alternatives relative to various performance criteria
(Punt et al., 2001). It involves evaluating the entire management system relative to
prescribed objectives according to quantitative performance measures. Although by
no means as comprehensive as an MSE, the present paper provides a small step in
that direction by first proposing new management objectives based on a rapidly expanding understanding of rockfish life history and then evaluating the effectiveness
of alternative management approaches in achieving these objectives.
Rockfishes comprise a diverse and economically valuable assemblage of species on
the continental shelf and slope along the west coast of the U.S. They are managed under the Pacific Fishery Management Council’s (PFMC) Groundfish Fishery Management Plan (http://www.pcouncil.org/groundfish/gffmp/fmpthru17.pdf), which lists
63 species in the genus Sebastes. As of May 2005, 12 species of Sebastes had been
formally assessed, seven of which were declared overfished because their current
spawning-stock-biomass levels are below the overfished threshold of B25% (25% of the
unfished level; Table 1). Most Pacific rockfishes mature relatively late and are generally long-lived; the maximum age of some species reaches well over 100 yrs (Cailliet
Bulletin of Marine Science
© 2006 Rosenstiel School of Marine and Atmospheric Science
of the University of Miami
655
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BULLETIN OF MARINE SCIENCE, VOL. 78, NO. 3, 2006
Table 1. Maximum age, age at 50% maturity (from Love et al., 2002), 2003 spawning biomass
as percent of B0 (virgin spawning biomass), and estimated recovery time (yr) at F = 0 of rockfish
species declared overfished (from stock assessments and rebuilding plans available at http://www.
pcouncil.org). Stock is considered recovered when spawning biomass reaches B40%. Black rockfish
is not overfished but is included because it is the model species used in simulations.
Species
Maximum Age at 50% Spawning biomass Recovery time (yr)
age
maturity
(as % of B0)
with no fishing
Black,a Sebastes melanops
50
7
49
NA
Bocaccio, S. paucispinis
50
?.
7
18
Canary, S. pinniger
84
8
8
54
Cowcod, S. levis
55
11
7
59
Darkblotched, S. crameri
105
8
14–17
8
Pacific Ocean perch, S. alutus
100
8
25
15
Widow, S. entomelas
60
6
22
23
Yelloweye, S. ruberrimus
118
19
24
21
Not overfished
a
et al., 2001; Love et al., 2002). Of the overfished species, all have maximum ages of at
least 50 yrs (Table 1).
Current rockfish management is based on maintaining the spawning output of the
stock at or above 40% of the level that would exist without fishing (B40%). Managers
achieve this goal by using an F50% harvest policy as the proxy for Fmsy. An F50% harvest
rate is used as a precautionary measure to ensure that rockfish stocks do not drop
below B40%. Should they do so, then the fishing mortality rate is decreased linearly
from F50% to F = 0 at the minimum abundance threshold of B10% (called the “40-10
adjustment”). Allowable biological catch (ABC) is determined by multiplication of
the appropriate fishing mortality rate by the exploitable biomass. Once the ABC is
determined, the PFMC sets the optimum yield (OY), which cannot exceed the ABC.
The OY is allocated among different fisheries and gear types, often with some form
of temporal landing limits, but no attempt is made to regulate directly the size of
fish that may be caught either recreationally or commercially (although trawl nets
are subject to minimum mesh size restrictions, which indirectly affect the minimum
size). One of the critical assumptions underlying this management strategy is that all
larvae have an equal probability of survival regardless of their parents’ age.
Recent evidence suggests, however, that maternal age can have a substantial influence on larval survival. Indeed, Berkeley et al. (2004a), in a series of laboratory
rearing experiments, showed that the larvae of older female black rockfish greatly
outperformed those of younger females, growing three times faster and surviving
starvation twice as long. Studies of Atlantic cod (Solemdal et al., 1993; Marteinsdottir and Steinarsson, 1998; Trippel, 1998; Vallin and Nissling, 2000) and Atlantic
striped bass (Monteleone and Houde, 1990; Secor, 2000a,b) found similar maternal
age effects on larval growth and survival, suggesting that these results are not limited
to black rockfish. Because fishing, even at current target levels, results in substantial
population age truncation (Murawski et al., 2001; Berkeley et al., 2004b), the current
management approach may not be the most effective way of maintaining high yields,
if older fish contribute disproportionately to recruitment. Although population age
structure is increasingly being recognized as an important consideration for management (Trippel et al., 1997; Scott et al., 1999; Vallin and Nissling, 2000), few investigators have attempted to quantify or model maternal age effects on larval survival
BERKELEY: PACIFIC ROCKFISH MANAGEMENT
657
or recruitment. One such modeling effort (Scott et al., 1999) incorporated maternal
effects on larval survival into a modeled population of Icelandic cod and quantified
the extent to which fishing-induced age truncation reduces potential recruitment. In
another study, Murawski et al. (2001) demonstrated that incorporating maternal age
effects on larval viability in Georges Bank cod reduced the maximum fishing mortality rate for stock persistence from F = 1.40 to F = 0.88.
If older fish produce more competent larvae, they presumably contribute disproportionately to recruitment, and the management approach that results in the highest proportion of old fish for a given spawning biomass would be biologically most
desirable. On the other hand, a trade-off is likely between age composition and yield.
Any management option that increased the proportion of older fish without a concomitant loss of yield would clearly be biologically preferable to the status quo. To
explore the possibility of finding such an alternative approach, I used a simple deterministic equilibrium model that incorporates the influence of maternal age on larval survival to compare first-order effects on population age structure, fishery yield,
effective larval output, and recruitment for four different management strategies:
status quo, slot limit, marine reserves, and reduced fishing mortality.
I used black rockfish (Sebastes melanops Girard) as a model species to illustrate
how alternative management approaches affect age composition, which in turn affects recruitment through age effects on larval growth rate and survival. Black rockfish is a widely distributed nearshore species, found from southern California to the
Aleutian Islands (Love et al., 2002). It is moderately long-lived, reaching a maximum
age of 50 and a maximum size of 690 mm and 5 kg (Love et al., 2002). Like all species
in the genus Sebastes, it is a moderately fecund, primitive live bearer. Black rockfish
have a gestation period of approximately 37 d (Boehlert and Yoklavich, 1984) and
extrude one brood of approximately 350,000–1,000,000 larvae per year (Bobko and
Berkeley, 2004). Some females reach maturity by age 4; age at 50% maturity is approximately 7 (Bobko and Berkeley, 2004). Estimates of natural mortality range from
0.14 to 0.18 yr−1 (Ralston and Dick, 2003). Although the model is constructed around
the specific biological parameters of black rockfish, the principles illustrated by these
simulations are more general, and the conclusions are certainly not restricted to this
one species.
Methods
For all simulations, I assumed a natural mortality rate of 0.16 yr−1 and knife-edged maturity
at age 7 (i.e., no fish are mature at age 6 and 100% are mature at age 7). Selectivity patterns
vary markedly by area and fishery. In these simulations, I assumed selectivity is asymptotic
and knife-edged at age 5, the approximate age-at-50% selectivity for the Oregon sport fishery
(Ralston and Dick, 2003). I further assumed that spawning biomass is proportional to larval output, a conservative assumption because, in black rockfish, relative fecundity increases
with age (Bobko and Berkeley, 2004). To begin the simulations, I started with a cohort of 1000
age 5 fish having growth, mortality, and maturation characteristics similar to those of black
rockfish. To construct the age distribution of an unfished population at equilibrium (on the
assumption of constant recruitment), I assumed the cohort was reduced at a constant rate as
follows:
N t = N t −1e−( M + F )
(1)
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where Nt = cohort size in year t; Nt−1 = cohort size at beginning of previous year; M = instantaneous natural mortality rate ( = 0.16/yr); F = instantaneous fishing mortality rate, which is
0 in the unfished population; and t = age in years.
The cohort was reduced according to this function until fewer than five fish (0.5% of the
initial cohort) remained. This is the equilibrium age structure of the unfished population.
The adult biomass was determined by multiplication of the number-at-age for all mature age
classes (i.e., ≥ age 7) by the mean weight-at-age (from Bobko and Berkeley, 2004) and summing
over all age classes as follows:
n
SSB = ∑ Bt = N t ( wt )
t=7
(2)
where t = age (yr), bt = biomass at age t, Nt = number at age t, n = maximum age in population,
and wt = mean weight at age t. The resulting biomass is B 0 , the unfished spawning biomass at
equilibrium on the assumption of constant recruitment.
Effective larval output is the spawning biomass adjusted for maternal age specific larval
survival. For this analysis I assumed larval survival was directly proportional to larval growth
rate. Although empirical data on bluefish and Atlantic cod indicate that a doubling of larval
growth rate can increase survival by a factor of 5–10 (Meekan and Fortier, 1996; Hare and
Cowan, 1997), my assumption of direct proportionality was more conservative.
Larval growth rate increased with maternal age according to the relationship
(
G = −0.13 + 0.20 1 − e−0.26 t
) (3)
where G = Larval growth rate (length) and t = maternal age (figure 1a of Berkeley et al.,
2004a).
To model the effect of each management option on effective larval output, I set relative
larval survival at the asymptote of Eq. 3 (age 27) equal to 1.0. Relative larval survival for each
age class in the population was then derived directly from the following relationship, shown
graphically in Figure 1:
(
RLSt = −1.58 + 2.58 1 − e−0.247 t
) (4)
where RLSt = relative larval survival at age t and t = maternal age.
Effective larval output (ELO) is the product of age-specific spawning biomass (Bt ) and relative larval survival (RLSt ), summed over all age classes:
n
ELO = ∑ ( Bt )( RLSt )
t=7
(5)
Thus, for the same spawning biomass, the management option having the greatest proportion of older age classes will have the highest effective larval output. These simulations assumed constant recruitment within and among management options (i.e., regardless of ELO,
I began each simulation with a cohort of 1000 recruits).
Because recruitment was likely to be related to ELO, in a second series of simulations, I
scaled recruitment to effective larval output (determined as above) instead of holding recruitment constant. For ELO to be directly related to recruitment, year-class strength must
be density independent and determined during the larval and early pelagic juvenile stages.
Some evidence indicates that this is the case for rockfish (Ralston and Howard, 1995). Instead
BERKELEY: PACIFIC ROCKFISH MANAGEMENT
659
of beginning with 1000 recruits, I began the simulation for each management option with
recruitment scaled to the following ratio:
 ELO 
R=
 × 1000
 ELOB40% 
(6)
where R = number of age 5 recruits, ELO = effective larval output, and ELOB = effective
40%
larval output at B40% (status quo).
Thus, for example, if a management option resulted in effective larval output 10% greater
than that in the status quo option (i.e., R = 1.10 × 1000), the model was rerun starting with
1100 age 5 recruits for that management option. All other methods remained as before. In
these simulations, to compare marine reserves with the reduced fishing mortality options, I
adjusted fishing mortality so that the effective larval output was the same as in the respective
marine reserve option.
Status Quo.—To model the status quo, I used the current PFMC management target level
of B40% to establish a hypothetical status quo population. To determine the fishing mortality
rate and resulting age composition associated with this alternative, I started with a cohort of
1000 age 5 fish having a natural mortality rate (M) of 0.16 and solved for the fishing mortality
rate (F) that resulted in a spawning biomass at equilibrium (i.e., when the cohort was reduced
to 0.5% of the starting cohort) that was 40% of the unfished population, using Eqs. 1 and 2.
Catch (numbers) was determined by multiplication of the rate of exploitation by the population size at the beginning of each year (the Baranov catch equation; Ricker, 1975) for each
age group in the population:
 FA 
Ct = N t 
 Z 
where
(7)
FA
= rate of exploitation
Z
(8)
and where Ct = catch in numbers at age t, Nt = population size at beginning of year t, F = instantaneous fishing mortality rate, A = annual mortality rate = 1 − e−z, Z = instantaneous rate
of total mortality. Yield in weight (Y) is determined by multiplication of the catch-at-age (Ct )
by the mean weight-at-age (wt ) and summing over all age classes in the population:
n
Y = ∑ Ct wt
t =5
(9)
Slot Limit.—A slot limit prescribes a minimum and a maximum size for retention. Slot
limits will only be effective for species that can be released alive with little mortality as a result
of capture. As release mortality approaches 100%, the resulting age composition approaches
that of the status quo option, but with a loss of yield. All rockfishes (e.g., Sebastes) have an air
bladder, and few species can be brought to the surface without suffering severe barotrauma.
Although release mortality varies with depth of capture and among species, our field observations suggest that most species suffer at least 25% release mortality, and for deeper-water species, the figure probably approaches 100%. Although no data are available, release mortality
for any species of rockfish seems unlikely to be < 25% and is probably closer to 50% even for
the most hardy shallow-water species. I therefore evaluated this management alternative for
release mortalities of 25% and 50%, which probably bracket the minimum release mortality
for any species of shallow-water rockfish. As before, I began the slot limit simulation with a
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cohort of 1000 age 5 fish. For simplicity, I set the minimum as the size-at-age 5 and the maximum as the size at age 11. Age 5 is the age at entry to the fishery used in all simulations, and
age 12 is the approximate inflection point in Eq. 4 (Fig. 1). After age 12, larval growth rate
increases little with maternal age. The cohort is therefore reduced by both fishing and natural
mortality (F + M) from age 5 to 11, as per Eq. 1, whereas fish ≥ age 12 are reduced through
natural mortality and release mortality. Release mortality was modeled as either 25% or 50%
of the exploitation rate, calculated from Eq. 8. The starting cohort was again reduced until
five fish remained and the resulting number in each age group was converted to biomass according to Eq. 2. I determined the appropriate fishing mortality rate iteratively, starting with
an estimated F, calculating the resulting population age structure for this F, and using this
rate to determine the exploitation rate of fish ≥ age 12 (from Eq. 8), which was either 25% or
50% of the exploitation rate of fish inside the slot limit. I used this age 12+ exploitation rate
to calculate the equivalent F for age 12+ fish from Eq. 8. This F was then entered into Eq. 1 for
fish age 12+. The resulting age composition was converted to population biomass according to
Eq. 2 and the process repeated iteratively until I found the fishing mortality rate that reduced
the spawning biomass to 40% of B0. Yield was calculated in the same manner as in the status
quo option except yield is zero for fish age 12+, some of which are subject to fishing mortality
but cannot be retained as catch. The calculations were repeated for recruitment scaled to effective larval output with Eq. 6.
Marine Reserves.—For these simulations, I assumed marine reserves were no-take and
100% effective. In other words, a 20% marine reserve provided total protection to 20% of the
adult population. The analysis tacitly assumed that adult fish did not move out of the reserve.
Larvae, on the other hand, from both inside and outside the reserve, were assumed to contribute to recruitment both inside and outside the reserve through larval dispersal. The reserve
size of 20% was arbitrary but is commonly cited as a minimum target for marine reserve
protection (Reef Fisheries Plan Development Team, 1990; U.S. Coral Reef Task Force, 2000;
Bohnsack et al., 2003) and is sufficiently large to illustrate how marine reserves are likely to
perform regarding age composition. In model calculations fish inside marine reserves can
be included in the exploitable biomass or excluded from it. In the “included” marine reserve
simulation, I calculated a fishing mortality rate that resulted in B 40% for the entire population. Because 20% of the stock is unavailable to the fishery, fishing mortality on the 80% of
the stock outside the reserve was higher than that in the status quo, such that the stock-wide
spawning biomass was reduced to B 40%. The age composition and spawning biomass inside
the reserve were calculated according to Eqs. 1 and 2, where F = 0, and the resulting number
and biomass for each age class were multiplied by 0.2 to represent the population inside the
reserve. For the population outside the reserve, I used Eq. 1 to calculate the age composition
and Eq. 2 to calculate the spawning biomass, which I then multiplied by 0.8 (the proportion
of stock outside the reserve). The result was added to the spawning biomass inside the reserve and F adjusted until the sum of the spawning biomasses inside and outside the reserve
equaled B40%. Catch and yield were calculated from Eqs. 7 and 9.
For the “excluded” marine reserve simulation, fish inside the reserve were excluded from
the fishable stock. Fishing mortality was set at the same level as in the status quo option, but
applied only to the 80% of the stock outside the reserve. By definition, yield is 80% of the yield
under the status quo (B40%) alternative. As in the “included” option, 20% of the total population has the same age composition as the unfished population.
For both marine reserve options, the simulations were run on the assumption of a constant
recruitment of 1000 age 5 fish (200 of which are in the reserve and 800 of which are outside
the reserve) and repeated for recruitment scaled to reflect effective larval output relative to
the status quo option according to Eq. 6.
Reduced Fishing Mortality.—I explored the effect of reducing fishing mortality rate
stock-wide to mimic the effects of the two marine reserve options, which increase the proportion of age 12+ spawning biomass compared to the status quo. In the first simulation, I
calculated the fishing mortality rates that, if applied stock-wide, would result in the same
BERKELEY: PACIFIC ROCKFISH MANAGEMENT
661
Figure 1. Index of effective larval output. Effective larval output is the ratio of larval growth rate
at age to maximum observed larval growth rate (from Berkeley et al., 2004a).
proportion of spawning biomass ≥ age 12 as in each of the two marine reserve alternatives, on
the assumption of constant recruitment. Again, Eq. 1 was used to determine age composition
and Eq. 2 spawning biomass. I solved for the fishing mortality rates that resulted in the same
age 12+ spawning biomass as realized in the two marine reserve simulations. I then used Eq.
7 to determine catch and Eq. 9 to determine yield for each of these fishing mortality rates.
In the second simulation, I adjusted recruitment to account for effective larval output by
solving Eqs. 1 and 2 for the fishing mortality rates that produced the same effective larval
outputs as the two marine reserve alternatives. Recruitments were determined from Eq. 6 and
the simulations rerun to produce spawning biomass and yields relative to the status quo.
Results
Age Structure.—When recruitment was held constant, status quo management
reduced the number of spawning age classes from 32 in the virgin stock to 17 and
reduced the percentage of the spawning population ≥ age 12 from 50% to 26% (Table
2). The status quo strategy reduced the spawning biomass of fish ≥ age 12 by almost
80%, while reducing total spawning biomass by 60% (by definition), illustrating that
fishing affects older age classes disproportionately. The slot limit with 25% release
mortality performed somewhat better in protecting older age classes, allowing 22
age classes to persist in the spawning population and increasing the percentage of age
12+ biomass to 32%. Increasing the release mortality to 50% reduced the effectiveness of the slot limit, allowing only 20 spawning age classes and an age 12+ spawning
biomass of 30%, only slightly greater than those under the status quo.
To maintain overall spawning biomass at B40% under the “included” marine reserve
option, fishing mortality outside the marine reserve would have to increase from F
= 0.140 under the status quo to F = 0.238, an increase of more than 70%. This option
outperforms either of the previous options in increasing the number of age classes
to the full complement (32) inside the reserve and increasing the proportion of age
12+ biomass in the entire population to 33%. The “excluded” marine reserve option
has, by definition, zero fishing mortality inside the reserve and F = 0.140 (the status
32
17
22
20
32c
32c
18
21
# of spawning
age classes
100
40
40
40
40
52
45
54
% of B0
Age-12+ biomass/ Age-12+ biomass/total
Yield
Effective larval
unfished age-12+ spawning biomassb (%) (% of B40%) output (% of B40%)
a
biomass (%)
100
50.4
0
20.7
26.1
100
100
25.5
32.2
92
102
23.8
30.0
89
102
26.3
33.1
97
103
36.5
35.4
80
136
26.3
29.1
92
115
36.5
33.9
78
141
b
a
Age-12+ spawning biomass as a percentage of the age-12+ spawning biomass in an unfished population.
Age-12+ spawning biomass as a percentage of the total spawning biomass in the population under each management option.
c
Inside reserve.
B0
Status quo (B40%, F = 0.1402)
Slot limit with 25% release mortality (F = 0.1650)
Slot limit with 50% release mortality (F = 0.185)
20% marine reserve, “included” (F outside = 0.2384)
20% marine reserve, “excluded” (F outside = 0.1402)
F reduced to 0.1164 (= 20% reserve, “included”)
F reduced to 0.0856 (= 20% reserve, “excluded”)
Management option
Table 2. Effects of alternative management strategies on number of spawning age classes, spawning biomass, relative proportion of older fish (age 12+) in the
population, fishery yield, and effective larval output at equilibrium. F = fishing mortality rate. B40% = 40% of unfished spawning biomass. The biomass within
“included” marine reserves is included in calculations involving fishable biomass; that within “excluded” reserves is not. The fishing mortality rates in the last
two rows have been adjusted to produce the percentage age-12+ spawning biomass as the two marine reserve options.
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663
quo fishing mortality rate) outside the reserve. Therefore, 80% of the overall stock is
reduced to B40%. In this option, the stock inside the reserve has the full complement
of age classes and 35% of the spawning biomass is age 12+.
When fishing mortality rate was reduced such that the age 12+ spawning biomass
matched that of the “included” marine reserve option, fishing mortality would have
to be reduced 17% from status quo levels, to F = 0.116. The resulting overall spawning biomass would be B45%. Even under this reduced fishing mortality rate, only 18
spawning age classes persisted, fewer than in either slot-limit option. To match the
age 12+ biomass of the “excluded” marine reserve option, fishing mortality would
have to be reduced by 39%, to F = 0.086, resulting in an overall spawning biomass of
B54%. This option increased the number of spawning age classes to 21.
Yield.—Fishery yield is a major consideration when management alternatives are
evaluated. With recruitment held constant, the slot-limit options reduced yield (i.e.,
yield per recruit) by 8% and 11%, respectively, relative to the status quo, while maintaining the spawning biomass at B40% (Table 2). The marine reserve options reduced
yield per recruit by 3% and 20%, respectively. The two reduced-fishing-mortality options reduced it 8% and 22% to achieve the same age 12+ biomasses as the two marine
reserve options. In both mortality reduction options, the overall spawning biomass
was higher than in the equivalent marine reserve option, but note that all simulations were on a per recruit basis (i.e., I started with the same recruitment) despite the
considerable differences in spawning biomass.
Effective Larval Output.—Because the ultimate objective of management is
not to protect spawning biomass per se, but to ensure sufficient larval output, I compared effective larval output for the four management options. Because the spawning
output of older fish is weighted more heavily than that of younger fish, management
options that result in a greater proportion of older age classes have a greater effective
larval output for a given spawning biomass. As measured by effective larval output,
the slot limits performed slightly better than the status quo, increasing effective larval output by approximately 2% for both release mortality alternatives (Table 2). The
“included” marine reserve did somewhat better, increasing effective larval output by
3%. The “excluded” marine reserve increased effective larval output substantially, to
136% of that at B40%, but both mortality reduction options outperformed the marine
reserve alternatives in this regard (ELO = 115% and 141% of B 40%, respectively), a consequence of their higher total spawning biomasses (albeit, with greater reductions in
yield per recruit).
Adjusted Recruitment.—When recruitment was scaled to effective larval output, the differences in yield among management options diverged (Table 3). The fishing mortality rates shown in Table 3 for the two mortality reduction options have
been recalculated so that their effective larval outputs (and hence recruitments) are
the same as those of the corresponding marine reserve options. Note that the fishing
mortality rates shown in Table 3 are somewhat higher than those in Table 2, which
were calculated to produce the same age 12+ biomass as in the two marine reserve
options.
With recruitments adjusted for effective larval output, the slot-limit options resulted in 6%–10% lower yield than the status quo, a slight improvement compared to
the 8%–11% reductions without adjustment (Table 3). On the other hand, the marine
reserve and reduced-fishing-mortality options resulted in small to substantial increases in recruitment and either the same or moderately increased yield compared
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Table 3. Effects of alternative management strategies on recruitment and yield on the assumption
that increased effective larval output results in an equivalent increase in recruitment. F = fishing
mortality rate. B40% = 40% of unfished spawning biomass. The biomass within “included” marine
reserves is included in calculations involving fishable biomass; that within “excluded” reserves is
not. The fishing mortality rates in the last two rows are not the same as those shown in Table 2.
Here the fishing mortality rates have been adjusted to produce the same effective larval outputs as
the two marine reserve options.
Management option
Status quo (B40%, F = 0.1402)
Slot limit with 25% release mortality
Slot limit with 50% release mortality
20% marine reserve, “included”
20% marine reserve, “excluded”
F reduced to 0.135 (= 20% reserve, “included”)
F reduced to 0.0914 (= 20% reserve, “excluded”)
Recruitment
(% of B40%)
100
102
102
103
136
103
136
Yield
(% of B40%)
100
94
90
99
109
101
110
to the status quo. The two best options, by all measures, are the “excluded” marine
reserve and the corresponding reduced fishing mortality (Table 3). In both, recruitment is 36% higher than in the status quo because of the higher proportion of older
fish in the population, and yield is 9%–10% higher because of higher recruitment.
One major difference, though, is that the marine reserve option entails a 20% reduction in fishing mortality rate from the status quo, whereas mortality reduction
entails a 35% reduction. By the criteria established in the introduction, the marine
reserve and mortality reduction options are all preferable to the status quo, although
the “included” ones are only marginally so.
Discussion
The prevailing management paradigm for West Coast groundfish asserts that
sustainability is solely a function of spawning biomass, and overfishing is therefore
defined in terms of a minimum spawning biomass (B40%) and maximum fishing mortality rate (set to ensure that spawning biomass is maintained above the minimum
level). The target level of spawning biomass is used as a proxy for maximum sustainable yield (MSY), but as the model developed herein demonstrates, managing
solely for spawning biomass will maximize neither yield nor population resilience
and is thus inappropriate as an MSY proxy. When maternal effects are accounted
for, increasing the proportion of older fish in the population, even for the same total spawning biomass, can clearly increase larval output, recruitment, and yield. We
may therefore be circling the wagons around the wrong management paradigm.
Results of the simple simulations described here provide valuable comparisons of
alternative management strategies, although some caveats are associated with these
conclusions. For example, the effect of marine reserves would probably be less definitive than my analysis suggests because a single-species analysis, such as I have
conducted, does not account for possible density-dependent changes in growth rate
or changes in predator-prey interactions that are likely to result from the elimination
of fishing at higher trophic levels (Graham et al., 2003; Micheli et al., 2004). In these
simulations, I have assumed that recruitment is directly proportional to effective
larval output. Clearly, at some point, as population abundance increases, densitydependent controls must come into play, limiting recruitment. Whether or not these
BERKELEY: PACIFIC ROCKFISH MANAGEMENT
665
controls would operate at the levels of increased larval output predicted here is uncertain. Nevertheless, these results indicate that, if recruitment level in rockfishes
is established in the larval and pelagic juvenile stage, as suggested by Ralston and
Howard (1995), then reducing fishing mortality, either through a simple reduction in
F or through marine reserves, would actually increase yields in the long term.
In these simulations I have not included several additional age effects further indicating that managing for age structure is critical, such as increased relative fecundity
with age (Bobko and Berkeley, 2004), the storage effect of long-lived fish (Warner and
Chesson, 1985), and the effects of maternal age on birth date (Bobko and Berkeley,
2004; Wright and Gibb, 2005) and on larval resistance to starvation (Berkeley et al.,
2004a), both of which can affect larval survival independently of larval growth rate.
The increases in resilience and yield demonstrated for marine reserves and reduced
fishing mortality rates are therefore conservative.
Although convincing evidence indicates that the larvae of older fish are more likely
to survive, year-class strength is still largely a function of stochastic environmental processes. I assert, however, that the evidence supports the conclusion that, for
any given set of environmental conditions, survival will be higher for larvae of older
fish. The experiments of Berkeley et al. (2004a) demonstrating higher growth rates
for larvae of older fish were conducted at ad libitum food levels, suggesting that the
larvae of older fish grow faster than those of younger fish even under favorable feeding conditions. If higher growth rate leads to higher larval survival, then for a given
spawning biomass, the greater the proportion of old fish, the greater the recruitment
(until density-dependent controls begin to dampen recruitment).
Of the options considered, slot limits do not appear to be useful for rockfish because older fish will continue to be caught, many of which will die as a result. Even if
release mortality could be substantially reduced, the utility of this approach would
be questionable because it would have to be based on length rather than age, and
the correlation between length and age is low in rockfish (Love et al., 2002; Bobko
and Berkeley, 2004), making designation of appropriate size limits problematic. For
species that are shown to suffer little capture mortality, this option may provide a
relatively simple way of maintaining population age structure.
Given the assumptions of these simulations, both marine reserve options would
result in increased recruitment and, perhaps more importantly, either no loss of or a
modest increase in yield over the long term. The 20% “excluded” marine reserve increased recruitment by almost 1.4 times over the current B40% management strategy
and yield by 9%. The same recruitment level and slightly higher yield (10% increase)
can be achieved by a simple reduction of F (Table 3), but the necessary reduction
would be almost 35%, which might be unacceptable in the short term. If the management goal is solely to increase recruitment or yield, then the simpler alternative is a
reduction in fishing mortality, but marine reserves have other important functions
that should enter into their consideration. For example, they protect by-catch species, maintain species diversity, protect the entire suite of age classes and therefore
increase population resilience (sensu Longhurst, 2002), preserve ecosystem function, and eliminate the physical impacts of fishing gear on habitat. Further, although
reduced fishing mortality should ultimately increase yield just as much, marine reserves would result in smaller initial reductions in yield (because smaller reductions
in F could produce the same effective larval output).
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As this analysis illustrates, several management alternatives can increase effective
larval output while maintaining or increasing yield, by increasing the proportion of
older age classes in the spawning population. Of the options analyzed here, marine
reserves appear to be the best alternative from a strictly biological standpoint, but
these simulations do not include any cost, benefit, or risk analysis. Without a comprehensive management-strategy evaluation drawing firm conclusions on the best way
to manage Pacific rockfishes is premature. Nevertheless, the conclusions certainly
suggest that managing for age structure is a worthwhile and attainable objective.
Acknowledgements
I would like to thank S. Sogard for her worthwhile and helpful suggestions on an initial
draft of this manuscript. Three anonymous reviewers provided excellent suggestions that
greatly improved this manuscript.
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Address: University of California, Santa Cruz, Long Marine Lab, 100 Shaffer Road, Santa
Cruz, California 95060.