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How useful is the ratio of fish density outside versus inside no-take marine reserves as a metric for fishery management control rules?


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A management strategy evaluation ( based on five species in the California, USA, nearshore fishery) of control rules that reduce relative fishing effort as a function of the ratio of fish density outside versus inside no-take marine reserves ( as a measure of depletion) showed that although the control rules allowed effort to increase at first, in the long term, they were effective at maintaining spawning stock biomass and yield for all simulated species, including depleted ones. Scenarios with fish movement, illegal fishing in the reserve, or post-dispersal density dependence in recruitment required higher density ratio targets, such as 60% of mature fish or 80% of all fish, to avoid stock depletion. The effort allowed by multispecies density-ratio control rules depended on the relative weight given to more or less depleted species. High variability in recruitment or in monitoring data caused the allowable effort to fluctuate. Density-ratio control rules have the advantages that they require no historical data, they can be used at local spatial scales, and they adjust to changing environmental conditions.
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How useful is the ratio of fish density outside
versus inside no-take marine reserves as a metric
for fishery management control rules?
Elizabeth A. Babcock and Alec D. MacCall
Abstract: A management strategy evaluation (based on five species in the California, USA, nearshore fishery) of control
rules that reduce relative fishing effort as a function of the ratio of fish density outside versus inside no-take marine re-
serves (as a measure of depletion) showed that although the control rules allowed effort to increase at first, in the long
term, they were effective at maintaining spawning stock biomass and yield for all simulated species, including depleted
ones. Scenarios with fish movement, illegal fishing in the reserve, or post-dispersal density dependence in recruitment re-
quired higher density ratio targets, such as 60% of mature fish or 80% of all fish, to avoid stock depletion. The effort al-
lowed by multispecies density-ratio control rules depended on the relative weight given to more or less depleted species.
High variability in recruitment or in monitoring data caused the allowable effort to fluctuate. Density-ratio control rules
have the advantages that they require no historical data, they can be used at local spatial scales, and they adjust to chang-
ing environmental conditions.
: Nous avons e
la strate
gie de gestion applique
cinq espe
ces dans la pe
che commerciale pre
s de la co
de la Californie, E
.-U., concernant les re
gles de contro
le qui re
duisent l’effort relatif de pe
che en fonction du rapport entre
la densite
des poissons a
rieur et a
rieur de re
serves marines a
che interdite (comme mesure d’e
puisement); il
t que, bien que les re
gles de contro
le permettent un accroissement de l’effort de pe
che au de
but, elles sont efficaces
long terme pour maintenir la biomasse du stock reproducteur et le rendement chez toutes les espe
ces qui ont e
d’une simulation, me
me celles qui souffrent d’e
puisement. Les sce
narios qui comprennent des de
placements des poissons,
de la pe
che ille
gale dans la re
serve ou une densite
pendance apre
s la dispersion ne
cessitent des cibles avec des rapports
de densite
plus e
s, par exemple, 60 % pour les poissons matures ou 80 % pour l’ensemble des poissons, afin d’e
puisement des stocks. L’effort permis d’apre
s les re
gles de contro
le des rapports de densite
s applique
es a
plusieurs espe
ces de
pend du poids relatif donne
aux espe
ces plus ou moins e
es. Une forte variabilite
dans le recrutement ou dans
les donne
es de surveillance entraı
ne une fluctuation dans l’effort permis. Les re
gles de contro
le base
es sur les rapports de
s ont l’avantage de ne ne
cessiter aucune donne
e historique, de pouvoir s’appliquer aux e
chelles spatiales locales et
de s’ajuster aux conditions environnementales changeantes.
[Traduit par la Re
Fishery management attempts to achieve a relatively large
sustainable yield while limiting the risk of severe population
declines due to inadvertent overf ishing. This is often accom-
plished through the use of control rules (e.g., Restrepo et al.
1998) that specify the level of fishing mortality (F) allowed
for a given level of biomass (B) (e.g., Fig. 1a). Target fish-
ing mortality rates and biomass levels, as well as current F
and B levels relative to the targets are generally estimated
with a stock assessment based on historical catch or catch-
at-age and biomass trend data. Such data are not often avail-
able for small-scale fisheries, recreational fisheries, and
bycatch species. For data-poor fisheries, particularly those
lacking historical catch data, decades may be required to
gather sufficient data to parameterize a stock assessment
model. In North America, Australia, New Zealand, and Eu-
rope, the fraction of major harvested fish stocks that are not
assessed ranges from 30% to >70% (Beddington et al.
2007), and the fracti on assessed is even lower in the biolog-
ically diverse multispecies fisheries of the tropics. Thus,
there is a need for methods to set appropriate fishery man-
agement policies for data-poor fisheries for which stock as-
sessment is not currently feasible (Kaufman et al. 2004;
Beddington et al. 2007).
No-take marine reserves and other forms of marine pro-
tected areas have been established in coastal waters through-
out the world as tools to accomplish conservat ion and
management objectives including protecting habitat, con-
serving biodiversity, increasing spawning stock biomass of
harvested fish populations within reserves, and, in some
cases, increasing fisheri es yields around the reserve (e.g.,
Received 21 October 2009. Accepted 2 November 2010.
Published on the NRC Research Press Web site at
on 4 February 2011.
Paper handled by Associate Editor Carl Walters.
E.A. Babcock.
Division of Marine Biology and Fisheries,
Rosenstiel School of Marine and Atmospheric Science,
University of Miami, 4600 Rickenbacker Causeway, Miami,
FL 33149, USA.
A.D. MacCall. National Marine Fisheries Service, Southwest
Fisheries Science Center, 110 Shaffer Road, Santa Cruz,
CA 95060, USA.
Corresponding author (e-mail:
Can. J. Fish. Aquat. Sci. 68: 343–359 (2011) doi:10.1139/F10-146 Published by NRC Research Press
Lubchenco et al. 2003). Reserves are also sometimes in-
tended as reference sites’ to allow scientists to compare
fished and unfished conditions (Bohnsack 1999).
For fish species that are harvested in areas around no-take
marine reserves, ratios of fish density outside versus inside
the reserves may be a useful indicator of the impact of fish-
ing. Specifically, the ratio of fish density outside to fish den-
sity inside reserves could be used as a proxy for biomass
depletion (B/B
) in control rules, thus eliminating the
need for a stock assessment to estimate depletion. In the ab-
sence of an assessment to estimate F/F
, the y axis of the
control rule could specify an allowable level of nominal
fishing effort (an absolute response) or a level of nominal
effort relative to effort in the previous year (a relativ e re-
sponse). For example, a reserve-based control rule might
specify that if the density ratio is above the target reference
point of 60%, the fishery may continue with no additional
restrictions, even if effort has a tendency to increase; below
a limit density ratio of 20%, the fishery would be closed. In
between, effort would be reduced progressively, for example
by reducing the length of the fishing season (Fig . 1b). Den-
sity ratios could be calculated based on monitoring data, ei-
ther from visual transects, or from cooperative surveys using
recreational or commercial fishing gear fished both outside
and inside the reserve, releasing all fish caught in the re-
serve. By using typical fishing gears, the species selectivities
in the surveys are well-aligned with the corresponding selec-
tivities and species impacts in the managed fisheries.
Management using density-ratio control rules around ma-
rine reserves would requi re much less data than would man-
agement based on stock assessment. Historical catch, effort,
or abundance trend data are not needed, nor is it necessary
to document current catches (although it still may be desir-
able). Also, because the method is based on spatially ex-
plicit monitoring data, the density ratios can be calculated
at a more local spatial scale than typical stock assessments,
so that the density ratio approach may be more consistent
with the spatial scale of the fisheries and the fish popula-
tions (Palumbi 2004). However, the method may not be ef-
fective for fisheries management if some of its assumptions
are violated. For a density-ratio control rule to be effective,
the density inside the reserve must tend toward unfished
conditions, and the estimated densities outside the reserve
must be representative of the fished portion of the stock.
Movement of adult fish across the edge of the reserves
could lead to an edge effect that might introduce bias in the
data used to calculate density ratios, which in turn could al-
low too much fishing effort to develop. The redistribution of
fishing effort around the reserves to take advantage of spill-
over would have implications for fisheries yields and for the
design of monitoring programs to be used to calculate den-
sity ratios. Of course, these problems with reserve bounda-
ries also pose difficulties for conventional assessments
(Field et al. 2006).
This paper presents a management strategy evaluation
(MSE; e.g., Punt et al. 2008) of control rules based on den-
sity ratios. We simulate fisheries for a group of five hypo-
thetical species with differing life history and fishery
characteristics, based on the assessed species in the Califor-
nia nearshore fishery, and also vary the simulated movement
rates, the spatial scale and timing of density dependence in
recruitment and other aspects of their biology. We apply
several kinds of density-ratio control rules to determine
which rule would cause a simulated fish population to re-
main above a target biomass while allowing high levels of
fishery yield, for a plausible range of fish life histories. We
also evaluate the implications of using the method for a
multispecies fishery, for imperfect control of fishing effort
and for increased stochasticity in recruitment and in the
monitoring program.
Materials and methods
The MSE simulation framework (Fig. 2) included a bio-
logical model, a sampling model, a management model, and
Fig. 1. Control rules where (a) allowable fishing mortality rate (F) is a function of biomass (B) or (b) allowable fishing effort relative to
effort in the previous year is a function of the ratio of fish densities outside versus inside a no-take marine reserve.
Fig. 2. Management strategy evaluation framework.
344 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
a fisheries model. The biological model specified the popu-
lation dynamics of the fish in each spatial area, using an
age-structured model based roughly on the stock assess-
ments for five assessed species of nearshore groundfish
from central California. In each year, the sampling model si-
mulated the collection of monitoring data inside and outside
the marine reserves, roughly equivalent to a scuba monitor-
ing program. The simulated data were then used to calculate
the ratio of fish densities outside reserves to inside reserves.
The management model was a control rule that specified the
amount of fishing effort allowed in each year based on the
density ratio calculated from the previous year’s monitoring
data. The simulated fishery then occurred, including imper-
fect control of fishing effort in some scenarios, and the num-
ber of fish removed by the fishery fed back into the
biological model.
Biological model
The spatial structure of the model consisted of five se-
quential areas along a coast, of which the center, containing
20% of the coastline, was eventually designated as a pro-
tected area. Movement and larval drift occurr ed between ad-
jacent areas only, so that the two areas adjacent to the
reserve were most likely to benefit from spillover from the
reserve, compared to the two areas farthest from the reserve.
At the beginning of each year, the following equations
were used to describe the population dynamics of the target
ð1Þ N
¼ R
at the age of recruitment a
¼ N
þ MÞ' at intermediate ages
¼ N
þ MÞ' þ N
þ MÞ' at the plus group age ða
where N
is the number of fish and F
is the fishing
mortality rate at age a in area s in year t, and M is the nat-
ural mortality rate. The maximum nominal age a
is a
‘plus group’ accumulator subject to annual natural and
fishing mortality rates. For scenarios in which fish were al-
lowed to migrate between adjacent areas, movement was in-
stantaneous at the beginning of the year, immediately after
the numbers at the beginning of the year were calculated:
ð2Þ N
¼ð1 $ pÞN
þ pN
in the first area ðs ¼ 1Þ
¼ð1 $ 2pÞN
þ pN
þ pN
in intermediate areas
¼ð1 $ pÞN
þ pN
in the last areaðs
where p is the probability of a fish migrating from one area
to the adjacent one.
Recruitment follows the Beverton–Holt form (Dorn 2002),
for example when recruitment occurs within one spatial area
ð3Þ R
ð1 $ hÞþðh $ 0:2ÞB
$ s
where R
is number of recruits in area s in year t; B
spawning stock biomass calculated as the sum across all
ages of the product of numbers, weight, and fraction mature
at age; h is the steepness parameter; R
is unfished recruit-
ment; B
is unfished spawning stock biomass; s
is the stan-
dard deviation in recruitment; and 3
is an error term, which
may include autocorrelation:
ð4Þ 3
¼ r
þ h
1 þ r
where h is the is a normally distributed random variable
with mean 0 and standard deviation s
, and r
is the auto-
correlation in recruitment (Punt et al. 2008). The spatial dis-
tribution of recruitment can follow any of five models
(Ralston and O’Farrell 2008; Table 1), which allowed den-
sity dependence to occur either regionally or in the local
areas, either before or after larval dispersal.
Fishing mortality rates for each area, species and time
were calculated as follows:
ð5Þ F
¼ q
where q
is the catchability for each species (sp), calculated
ð6Þ q
and A is the number of areas, F
is the fully selected fish-
ing mortality rate on the whole population before the reserve
was implemented, and E
is the nominal effort in each area,
before the reserve was implemented (assumed to be the
same in all areas). The selectivity for each species (sp) at
age a is the sum of the selectivity at age for each fleet
) ( the fraction of fisheries catches in each fleet (c
ð7Þ S
The selectivity in each fleet was either a logistic function
of length at age (L
ð8Þ S
1 þ exp½$aðL
$ L
or double logistic in which case the left (ascending) side of
the curve followed eq. 8, while the right side declined from
1 to F
following a logistic curve:
Babcock and MacCall 345
Published by NRC Research Press
ð9Þ S
¼ 1 $
ð1 $ F
1 þ exp½$bðL
$ L
Length at age followed the von Bertalanffy growth func-
tion defined in terms of two reference ages (a
and a
; Me-
thot 2000):
ð10Þ L ¼ L
$ L
Þexp ½$Kða $ a
¼ L
$ L
1 $ exp ½$Kða
$ a
Weight at length was calculated as
ð11Þ w ¼ aL
Maturity at age was a logistic function.
Sampling model
For model runs assuming perfect information, the density
ratio was calculated as the average number of fish per area
in the fished areas outside the marine reserve, divided by the
average number of fish per area inside the reserve. For runs
with uncertainty and variability, the density ratio was calcu-
lated from a sampling program designed to mimic the Part-
nership for Interdisciplinary Studies of Coastal Oceans
(PISCO) visual transect surveys (PISCO 2008). A fixed
number of transects were sampled in each spatial area in
each year, and the number of fish seen in each transect fol-
lowed a delta lognormal distribution. For each transect,
whether each species was seen at least once was determined
with a random Bernoulli draw, with probability
ð12Þ p
¼ d
where N
is the number of fish above the age of recruit-
ment (or the age at maturity if using control rule 5) in area s
at time t and the constant of proportionality d
was calcu-
lated as
ð13Þ d
where r
is the observed proportion of positive transects in
the PISCO data in 2000–2006 and B
is the observed
depletion in 2003, according to the assessments.
For transects in which a species was observed, the count
of that species was drawn from a lognormal distribution:
ð14Þ C
¼ g
where h
is a normal variable with mean zero and standard
deviation derived from PISCO transects as
ð15Þ s
1 þðs
x and s
are the mean and standard deviation of each
species in PISCO transects for which the species was seen,
and g
is calculated as
ð16Þ g
The density ratio D
for each species was calculated as
the average number of individual fish seen per transect out-
side the reserve divided by the average number seen per
transect inside the reserve, counting zero observat ions:
ð17Þ D
where C is the number of fish seen and n is the number of
transects either in fished areas or in the marine reserve. In
practice, density ratios should be normalized so that the ratio
is 1.0 before the implementation of the reserve. In this mod-
eling exercise, the spatial areas were identical before reserve
implementation, so it was not necessary to divide by the pre-
reserve density ratio.
Multispecies density ratios were calculated with either (1)
the arithmetic mean of the values for each species, (2) the
geometric mean, (3) the harmonic mean, or (4) the mini-
mum value across the species.
Management and fisheries models
In each simulation, we ran the model for 50 years with a
constant level of fishing effort to generate a fished popula-
tion with the level of depletion required for each species
(see next section for specifics). The marine reserve was
Table 1. Recruitment models considered.
Description Formula
I Density dependence occurs independently in each spatial area
II Density dependence occurs at the regional level and recruits are distributed
evenly across areas, or larvae are distributed evenly followed by local
density dependence based on the local number of larvae
III Density dependence occurs within spatial areas, but recruits are spread evenly
across areas
ð1 $ hÞþðh $ 0:2ÞB
IV The pooled larvae from all areas are distributed evenly to each area, and then
density dependence occurs based on the number of spawners in each area
V Recruitment is independent in each area, but a fraction of the recruits in each
area drift to the adjacent areas before settling
R ¼ R
( d
Note: A is the number of areas; d is the transition matrix for larval drift; a
is age at recruitment; R
is recruitment in time t in area s; and B
spawning stock biomass. When density dependence occurs within a spatial area (s), the corresponding unfished recruitment is
¼ R
=A and unfished
biomass is
¼ B
346 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
Table 2. Control rules used in the simulations.
Control rule
Control rule
Target density
Effort increase
Fished areas
sampled Fish sampled
1 Effort NA 0.1 NA NA NA
2 DR 0.6 0.1 3 All All
3 DR 0.6 0.1 1 All All
4 DR 0.6 0.1 1 Far from reserve All
5 DR 0.6 0.1 1 All Mature
6 DR 0.8 0.1 1 All All
7 Effort NA 0 NA NA NA
8 DR 0.6 0 1 All All
Note: DR indicates density-ratio control rules.
Table 3. Input parameters for the simulation model and their source.
Description Parameter Values for each species
Stock name Black RF Blue RF Gopher RF Cabezon Vermilion RF
Primary reference Ralston and Dick
Key et al.
Key et al.
Cope and Punt
Maximum age a
35 44 24 17 65
Natural mortality M 0.14 0.1 0.2 0.275 0.1
Age at recruitment a
1.68 ( 10
3.41 ( 10
1.32 ( 10
1.24 (10
1.74 (10
Weight at length parameters
(male and female)
3 2.874 3.077 3.113 2.995
1.68 ( 10
2.93 ( 10
1.32 ( 10
1.99 ( 10
1.74 (10
3 2.889 3.077 2.997 2.995
Growth parameters
(male and female)
32.21 17.9 22.2 41.3 28.68
15 25 15 30 30
47.95 37.5 31.2 61.9 53.738
0.2022 0.147 0.186 0.18 0.1932
31.88 15.7 22.2 38.6 28.68
15 25 15 30 30
45.39 31.2 31.2 46.8 53.738
0.1979 0.295 0.186 0.28 0.1932
Length at 50% maturity L
39.53 26 17.7 25.702 38
Slope of maturity curve k
0.4103 0.7 4.3 0.743 0.5
Larval drift proportion* d 0.1 0.1 0.1 0.1 0.1
Unfished recruitment* R
100 000 100 000 100 000 100 000 100 000
Steepness h 0.65 0.58 0.65 0.7 0.65
Unfished recruits per spawner 4 1.1 0.91 0.23 0.71 2.9
Recruitment standard deviation s
0.5 0.5 0.5 1 0.7
Recruitment autocorrelation r
Adult movement proportion* p 0.1 0.1 0.1 0.1 0.1
Depletion B
0.488 0.238 1 0.4 0.59
Fishing mortality to cause observed
0.2 0.3 0.02 1.1 0.025
Number of fisheries 3 4 3 6 4
Proportion of positive transects in the
monitoring data (PISCO)
r 0.77 0.48 0.29 0.27 0.70
Mean number of fish seen in positive
transects (PISCO)
x 15.42 12.46 1.51 3.27 166.02
Standard deviation of positive
16.97 25.49 0.75 3.32 238.72
Note: Parameter values were taken from the stock assessment documents specified in the table except where noted as from PISCO data, or set arbitrarily
(indicated with an asterisk, *).
Babcock and MacCall 347
Published by NRC Research Press
then implemented and the model was run for 50 more years.
The density-ratio control rules were implemented, beginning
from the same year that the reserve was established. After
reserve implementation, the effort displaced from the re-
serves (=20% of the total) was allocated either equally to
each open area, or was redistributed according to the ideal-
free distribution (e.g., Gillis et al. 1993). In the ideal-free
case, effort was allocated proportionally to the fraction of
the total fishable biomass in each area. In the multispecies
case, effort was allocated proportional to the total biomass
in each area.
The total allowed fishing effort, after the marine reserve
was implemented, depended on the eight control rules that
were considered (Table 2), numbered from the least to the
most precautionary. In addition to density-ratio control rules,
a scenario with constant effort (control rule 7), and one with
increasing effort (control rule 1) were included. In the case
of the density-ratio contro l rules, allowable effort each year,
as a fraction of the effort in the previous year, was calcu-
lated based on the density in the previous year (Fig. 1b). Be-
ginning the first year of the marine reserve, total allowable
effort decreased linearly from the starting effort if the meas-
ured density ratio fell below the target density ratio, and was
reduced to zero if the density ratio fell below the limit (20%
in all control rules). In the case that effort was reduced to
zero, the previous year’s effort’ for the purpose of calcu-
lating the allowable effort in the following year was set
equal to 10% of the effort on the year the reserve was im-
plemented; this allowed effort to increase again after it had
gone to zero, despite the fact that the control rule defines al-
lowable effort with respect to the previous year’s effort. In
some control rules the effort was allowed to increase by
10% every year that the density ratio remained above the
target density ratio, to mimic a fishery that was expanding
with no controls other than the use of a density-ratio control
rule, which only controlled effort if the density ratio
dropped below the target density ratio. The target density ra-
tio was either 60% of all fish, 80% of all fish, or 60% of
mature fish, and the density ratio was calculated either using
data from the previous year or from the three previous years,
and from either all fishing areas, or just the areas farthest
from the reserve (Table 2).
Data-rich assessed species on the US West Coast are gen-
erally managed using a fishing mortality rate target of F
which is the fishing mortality rate that would result in a
spawning potential ratio of 40% of the unfished level (Clark
2002). As a point of comparison, we ran simulations for all
five species for 150 years with constant fishing mortality
rates between 0 and 1.0 and no marine reserves, to calculate
and the associated spawning stock biomass and yield.
We also calculated the biomass and yield associated with
and F
, as well as with a constant F policy that
would result in an equilibrium spawning stock biomass of
40% of the unfished level (a B
Fig. 3. Selectivities for (a) black rockfish, (b) blue rockfish, (c) gopher rockfish, (d) cabezon, and (e ) vermilion rockfish, inferred from
figures in Ralston and Dick (2003), Key et al. (2007), Key et al. (2005), Cope and Punt (2005), and MacCall (2005). Unbroken lines are
commercial fisheries, dashed lines are recreational fisheries, and dotted lines are commercial passenger fishing vessels.
348 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
Input data for the five speci es
The input parameters (Table 3) were loosely based on the
biology and fisheries for black rockfish ( Sebastes melanops,
Ralston and Dick 2003), blue rockfish (Sebastes mystinus,
Key et al. 2007), gopher rockfish (Sebastes carnatus, Key
et al. 2005), vermilion rockfish (Sebastes miniatus, MacCall
2005), and cabezon (Scorpaenichthys marmoratus, Cope and
Punt 2005). These species were chosen because assessmen ts
were available, and they show an interesting range of life
histories, fisheries, and levels of depletion. Most of the input
parameters were taken directly from the assessments, except
that the selectivities (Fig. 3) were estimated from figures in
most cases. For the stock recruitment relationship, we used
the steepness (h) value from the assessments, but set R
the same number for all five species. This was done because
the spatial extent of the assessments was different, so that it
was difficult to determine appropriate levels of relative
abundance. We defined the stock–recruit relationship in
terms of female spawning stock biomass, not total spawning
stock biomass (SSB) or egg production, for consistency
across species. The fraction of fish that would migrate from
one area to the next was set arbitrarily at either 0 or 10%
per year across each reserve boundary. For model runs that
included a monitoring program, the number of transects in
each area in each year was set arbitrarily to 50 in the reserve
and 25 in each outside area.
Scenarios for management strategy evaluation
Perfect information
To screen a number of factors that could influence the
performance of a density-ratio control rule, we first ran the
model for each individual species with no stochasticity in
the biological processes, with perfect information about den-
sity ratios, and with perfect control of fishing effort (i.e., no
implementation error). The factors that varied between
model runs were (1) control rule (one of the 8 control rules
defined in Table 2); (2) effort distribution (even or ideal-
free); (3) adult movement (none, or 10% diffusion); and (4)
recruitment model (5 options defined in Table 1).
There were 160 combinations of these factors fo r each
species for the single-species runs.
For the multispecies cases, we used control rules 3 and 6,
for all combinations of fleet distribution, recruitment model,
and movement model. Effort was allocated according to the
multispecies density ratio, which was calculated by geomet-
ric mean, arithmetic mean, harmonic mean, or the minimum
value, for a total of 160 multispecies runs.
The ratio of spawning stock biomass at the end of each
decade after reserve establishment to spawning stock bio-
mass when reserves were established, and the average yield
across each decade after the reserve was established were
calculated as performance indicators.
Fig. 4. Effect of type of density-ratio control rule (numbered by increasing precaution) on spawning stock biomass at the end of each decade
post reserve (bars, grouped by control rule and ordered by decade within groups), and average fishery yield across each decade (points), for
(a) black rockfish, (b) blue rockfish, (c) cabezon, and (d) vermilion rockfish. The unbroken horizontal line indicates the spawning stock
biomass at 40% of the unfished level, and the broken horizontal line is the yield associated with an F
policy. Relative biomass (right side
of each plot) and yield (left side of each plot) are also shown for F
, B
, and F
strategies. Biomass and yield are plotted relative to
the values in the year the reserve was established, and model results are averaged across all 20 life history and fleet dynamics models.
Babcock and MacCall 349
Published by NRC Research Press
Implementation error and stochasticity
Four implementation error scenarios were modeled: (A)
no implementation erro r, (B) effort inflation, (C) illegal fish-
ing in the reserve, and (D) both effort inflation and illegal
fishing. In scenarios B and D, the amount of effort in the
fished areas was always 5% more than the control rule al-
lowed, and managers were not aware of this. Thus, the al-
lowable effort specified by the control rule was always
based on the ‘legal’ effort in the previous year, not the in-
flated true effort. In scenar ios C and D, the amount of illegal
fishing in the reserve increased with decreasing density ratio
up to 10% of total effort when the density ratio (D) was
very low.
ð18Þ E
¼ 0:1ð1 $ DÞ
These scenarios were run for all individual species control
rules with perfect information and no stochasticity, for six
runs per species with and without movement, using control
rules 3, 5, and 6. Recruitment was local (model I) and legal
effort was evenly distributed.
To evaluate the influence of both natural variability and
uncertainty in the calculation of density ratios we also ran
the model with variability for each individual species. For
these scenarios, we specified no implementation error, re-
cruitment model I, uniform effort distribution and move-
ment. The following factors varied: (1) monitoring program
sample size, either as specified above or multiplied by 5; (2)
log standard deviation of recruitment either 0.2 or 0.5; (3)
serial correlation of recruitment either none or 0.5; and (4)
control rule 3 or 6.
For each set of input parameters, we ran 100 simulations,
and calculated the probability that spawning stock biomass
was above 40% of the unfished spawning stock biomass
20 years after the reserve was established, as well as the
probability that the yield in the 20th year was higher than
the yield the year before the reserve was established.
Description of species
Of the five species of nearshore fishes used in this simu-
lation exercise, blue rockfish was the most depleted when
the marine reserve was implemented, with a simulated
spawning stock biomass at 23.8% of unfished levels
(Table 3). Gopher rockfish, on the other hand, was at the
unfished level when the reserve was implemented; we as-
sumed that there had been a low level of fishing mortality
for gopher rockfish before implementation of the reserve.
Because of this assumption, gopher rockfish biomass never
declined significantly with any of the control rules. We
present no results for gopher rockfish, but we kept it in the
simulations because it was interesting to have one lightly
fished species in the multispecies simulations. The other
species were at intermediate levels of depletion (Table 3).
The species varied in productivity, growth, and longevity,
and in the selectivity patterns for their fisheries (Fig. 3).
Fig. 5. Black rockfish dynamics with a target density ratio of 60% (a and b, control rule 3) versus 80% (c and d, control rule 6), including
spawning stock biomass in the reserve (bold unbroken lines, left panels) and fished areas (narrow unbroken lines, left panels), total yield
(broken line, left panels), total effort (broken lines, right panels), and density ratio (bold unbroken lines, right panels), all relative to the
levels when the reserve was established, for model runs with no movement, even distribution of fishing effort, and recruitment model II.
350 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
Perfect information scenarios
For the scenarios with perfect information and no varia-
bility, the eight control rules demonstrated the expected
tradeoff between high short term yield and the maintenance
of biomass that would allow continuing yield in the long
term (Fig. 4). Control rules 2, 3, and 4 (target density ratio
of 60% and density ratio based on all post recruit fish) al-
lowed effort to increase in the first 20 years, causing bio-
mass to decline and yields to be lower in the last decade.
These control rules perfo rmed only slightly better than an
unregulated fishery with increasing effort (rule 1), and al-
lowed populations to decline to below 40% of the unfished
spawning stock biomass for all four species. Control rules 5
(target density 80%) and 6 (density ratio computed on ma-
ture fish) were better able to maintain biomass and long
term yield, and control rule 5 successfully rebuilt blue rock-
fish to 40% of unfished spawning stock biomass (Fig. 4b).
The control rule based on absolute effort (rule 8) had the
same effect as fixing effort at the current level (rule 7) for
all species except blue rockfish (Fig. 4b), for which the den-
sity ratio control rule (8) increased spawning stock biomass
because the density ratio control rule reduced effort below
the current level.
Comparing the yields and spawning stock biomass levels
in the last year of the simulation with those that would be
expected under a constant F
policy (horizontal lines in
Fig. 4), control rule 3 resulted in a biomass of 41%–61% of
that under F
, and a yield of 68%–79% of the yield under
. In contrast, control rule 5 achieved a spawning stock
biomass of 103%–114% of that under F
, and a yield of
80%–89% of the F
Control rules 5 and 6 performed better than control rules
2, 3, and 4, but all of these rules allowed an increase in ef-
fort in the first few years after the reserve was established,
before densities could build up in the marine reserve. They
also caused effort, and consequently, biomass, to oscillate
(e.g., Fig. 5) throughout the simulation as effort increased
until the density ratio fell below the target level, which then
reduced effort until the biomass had recovered. The fluctua-
tion in effort was less extreme and appeared to damp out
over time for the control rule with a higher target density ra-
tio (rule 6, Figs. 5c and 5d), and the one based on densities
of mature fish (rule 5). Basing the allowable effort on a den-
sity ratio calculated from the previous three years, rather
than just the previous year did not reduce the fluctuati on in
effort (rule 2).
The most important biological factor influencing the ef-
fectiveness of the density-ratio control rules was whether
post-recruit fish could move between spatial areas (Fig. 6).
For the overfished blue rockfish, the population increased
without movement for all control rules; with movement, the
population was stable under control rules 5 and 6 but de-
clined under control rules 2, 3, and 4 (Fig. 6b). Specifically,
for blue rockfish under control rule 3 without movement
Fig. 6. Effect of whether or not fish move between areas (with control rules 2–4, or control rules 5 and 6) on the average across life history,
fleet dynamics and density-ratio control rules, of spawning stock biomass at the end of each decade post reserve (bars, grouped by scenario
and ordered by decade within groups), and average fishery yield across each decade (points) for (a) black rockfish, (b) blue rockfish, (c)
cabezon, and (d) vermilion rockfish. The unbroken horizontal line is spawning stock biomass at 40% of the unfished level, and the broken
horizontal line is the yield associated with an F
Babcock and MacCall 351
Published by NRC Research Press
(Figs. 7a and 7b), the density ratio dropped off as effort in-
creased and then started to oscillate, but the spawning stock
biomass trend was positive even in the fished areas. With
movement (Figs. 7c and 7d), biomass did not build up as
much inside the reserve. Therefore, the density ratio fell off
only gradually, even as the total population was declining,
and the control rule did not initiate reductions in effort until
20 years after the reserve was established. For black rock-
fish, cabezon, and vermilion rockfish, which were not overf-
ished, the movement scenarios cause the population to
decline, especially using the less precautionary control rules,
while the population was stable without movement
(Figs. 6a,6c, and 6d). The yields were higher with move-
ment for the first few decades, but declined later as the bio-
mass declined, owing to overfishing.
The spatial distribution of recruitment had ver y little in-
fluence on the performance indicators in the first decade,
but became important over time as spawning stock biomass
accumulated in the marine reserve (Fig. 8). Strictly local re-
cruitment (model I) allowed the greatest increase in bio-
mass, particularly for the overfished blue rockfish; this
recruitment scenario also showed the lowest yields in the
first few decades but higher yields in the long term. The re-
cruitment model with post-dispersal density dependence
(model IV) had the highest yields at first, but both biomass
and yield decreased with time. Many control rules per-
formed badly with post-dispersal density dependence (model
IV) because, when biomass built up in the marine reserves,
this caused recruitment, paradoxically, to be lower in re-
serves. The control rule based on the density ratio of mature
fish only (rule 6, Figs. 8c and 8d) controlled effort very
well, even with post-dispersal density dependence.
Whether the fishing fleet was assumed to be evenly dis-
tributed, or able to move to areas with higher catch-per-
unit-effort (CPUE) did not influence the results substan-
tially. Model runs with spillover of adults from the reserve
and ideal-free effort distribution resulted in slightly more ef-
fort in the areas nearer the reserve, but the difference in bio-
mass and yield between the ideal-free and evenly distributed
scenarios was very small (fractions of a percent, not shown).
For the multispecies runs, the results depended on
whether the density ratios were combined by arithmetic,
geometric, or harmonic mean, or the minimum value across
the five species (Fig. 9a for black rockfish and Fig. 9b for
blue rockfish). For every species except blue rockfish
(Fig. 9b), using the minimum density ratio caused the bio-
mass at the end of the simulation to be higher than it would
have been under single-species management focused on that
species (e.g., Fig. 9a). For a typical multispecies model run
(Fig. 9c), the density ratio for gopher rockfish stayed near
1.0, while the density ratio for blue rockfish dropped off
sharply. The arithmetic mean remained higher than the geo-
metric mean, which was higher than the harmonic mean.
The contro l rule based on blue rockfish (or the minimum
value) began to reduce effort after 20 years in this scenario
(Fig. 9d), followed by the control rule for black rockfish and
Fig. 7. Blue rockfish dynamics without movement (a and b) and with movement (c and d), including spawning stock biomass in the reserve
(bold unbroken lines, left panels) and fished areas (narrow unbroken lines, left panels), total yield (broken line, left panels), total effort
(broken lines, right panels) and density ratio (bold unbroken lines, right panels), all relative to the levels when the reserve was established,
for model runs with control rule 3, even distribution of fishing effort, and recruitment model I.
352 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
cabezon, then the harmonic mean and geometric mean, then
the arithmetic mean, then finally vermilion rockfish. The go-
pher rockfish control rule had no effect in 50 years.
Implementation error and stochasticity
For the simulations with imperfect control of effort, effort
being 5% higher than the control rule specified each year
did not change the results (Fig. 10, scenario A compared
with B, and C compared with D). For both black rockfish
(Fig. 10a) and blue rockfish (Fig. 10b), the runs with effort
inflation were identical to those without. The density ratios
control rules were all able to compensate for the increase in
effort in every year, because they continued to reduce allow-
able effort until the density ratio recovered above the target
level. On the other hand, illegal fishing in the reserve re-
duced the biomass achieved by the control rules (Fig. 10,
compare scenarios A and B with scenarios C and D). In a
typical model run for black rockfish, with no movement
and control rule 3, illegal fishing in the reserve caused bio-
mass in the reserve to fluctuate (Figs. 10c and 10d), which
was not the case for the same scenario without implementa-
tion error (Figs. 5a and 5b). This caused yield to fluctuate
much more than it did without illegal fishing and reduced
the final spawning stock biomass.
For the simulations with variability in the recruitment and
the density ratio estimates, (Fig. 11) lower sample sizes in
the monitoring program increased the probability that the bi-
omass would be above the target value of 40% of unfished
SSB in 20 years (Fig. 11a) for both the more risky and the
more precautionary control rule. The effect on yield was
more variable (Fig. 11b). Increasing the variance in recruit-
ment similarly increased spawning stock biomass (Fig. 11c).
Autocorrelation in recruitment had little effect on the results
(Figs. 11e and 11f). The increasing biomass associated with
higher variability appeared to be caused by the fact that
higher variation in the density ratio (Fig. 12) meant that
each simulated density ratio trajectory occasionally fell be-
low the density ratio target, thus requiring a reduction in ef-
A control rule based on density ratio pooled across the
previous three years (control rule 2) reduced the variability
in the density ratio so that the effort, yield and biomass tra-
jectories were more similar to what would be seen with a
higher sample size in the monitoring program.
Selection of control rules
The biomass and yield achieved with each density-ratio
control rule varied, but control rule 5 (target density ratio is
60% of mature fish) and control rule 6 (target density ratio
is 80% of all fish) both caused the spawning stock biomass
to equilibrate around 40% of the unfished level, eventually,
for most life history scenarios, achieving yields around
75%–90% of the yield that would be achieved under an
Fig. 8. Effect of recruitment model, with a density ratio target of 80% of all fish (rule 6), for (a) black rockfish and (b) blue rockfish, or a
target density ratio of 60% of mature fish (rule 5), for (c) black rockfish, and (d) blue rockfish, on spawning stock biomass at the end of
each decade post reserve (bars, grouped by recruitment model and ordered by decade within groups), and average fishery yield across each
decade (points). The unbroken horizontal line is spawning stock biomass at 40% of the unfished level, and the broken horizontal line is the
yield associated with an F
Babcock and MacCall 353
Published by NRC Research Press
policy. A related modeling study that compared cumu-
lative catches over time between optimal density-ratio con-
trol rules and optimal constant F harvest strategies, found
that the density-ratio control rules consistently achieved
around 70% of the cumulative catch achieved by the optimal
constant F strategies (McGilliard et al. 2010). In practice, it
is difficult to estimate optimal fishing mortality rates, even
for assessed species (Clark 2002), and the density-ratio con-
trol rule has the advantage that it does not require estimates
of either reference points or stock status.
Interestingly , the status of the population when the reserve
was established did not greatly influence the biomass that
was achieved at the end of the simulation under the density-
ratio control ru les. For the species that were not overfished,
the control rules allowed a greater increase in effort during
the first few decades than that allowed for the overfished
species, but the final SSB, relative to unfished levels, was
similar for all four species (not counting gopher rockfish,
which was never fished down in our simulations). There
was more variation between life history scenarios than be-
tween species with different levels of initial depletion. In
other words, if one of the more precautionary density-ratio
control rules was used, the population would eventually end
up near or above 40% of unfished SSB, irrespective of its
initial level of depletion. If this result holds for a wider vari-
ety of the fish life history types, movement patterns, and
fisheries, then the density-ratio control rule could be very
useful for low-data fisheries management.
On the other hand, the initial status of the population was
relevant when multispecies control rules were used. Because
blue rockfish started out depleted, it could develop low den-
sity ratios quickly as the population rebuilt in the reserve
and (for some scenarios) declined outside the reserve. For
vermilion rockfish, which started out at 60% of unfished
levels, the density ratio did not drop off until much later, be-
cause there was less scope for increase in the reserve. Thus,
when a multispecies density-ratio control rule was used, the
year that effort restrictions started to be imposed depended
on the relative weight given to species at differing levels of
starting depletion. This is a general problem when fish com-
plexes are managed based on an average indicator of status,
and density ratios do not perform better or worse in this re-
spect than assessment-based indicators.
Density-ratio control rules 2 through 6 had the unfortu-
nate effect that they allowed effort to increase immediately
after the reserve and the control rule were implemented,
and did not require effort reduction until the reserve had
been in place long enough for differences in fish density to
build up between fished and unfished areas (see also McGil-
liard et al. 2010). Some initial increase in effort was appro-
priate for the populations that were not overfished, but not
for the overfished blue rockfish. Additional restrictions in
Fig. 9. Multispecies density ratios compared with single-species density-ratio control rules: (a) Difference between multispecies and single-
species values for the increase in spawning stock biomass (SSB) after 50 years for black rockfish; (b) the same for blue rockfish; (c) density
ratios for a multispecies model run with perfect information, movement between areas, local recruitment and control rule 3, multispecies
density ratio calculated as the geometric mean of the values for the five species, with the other density ratio means shown for comparison;
and (d) density ratios for the five species in the same model run.
354 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
effort may be advisable for the first few years of the exis-
tence of a marine reserve, unt il there has been time for den-
sity to build up in the reserve. The more precautionary
density ratio targets (rules 5 and 6) became effective sooner
than the less precautionary ones, and also reduced the fluc-
tuation in allowable effort in the first few decades of man-
agement under the density-ratio control rule.
Fish biology
Of the variations in life history that we examined, the
amount of fish movement, not surprisingly, had the most im-
pact on the effectiveness of the density-ratio control rules.
With movement, biomass in the reserve never built up to
high levels and the density ratio remained high. Fish that
are good candidates for density-ratio control rules would be
those that tend not to migrate or move across distances com-
parable with the scale of the marine reserves in the area.
The species we used to parameterize the model tend not to
move long distances after recruitment. Black, blue, and ver-
milion rockfish, and cabezon were all recaptured very close
(<1 km) to their tagging location by Lea et al. (1999), while
gopher rockfish were recaptured up to 3 km from their tag-
ging location. Jorgensen et al. (2006) found that blue rock-
fish home ranges averaged just over 8000 m
, and
individuals seldom ventured more than 100 m from their
core areas, so that the authors concluded that there would
be little spillover of blue rockfish across reserve boundaries,
if the reserves were more than a kilometre across. The exist-
ing reserves in the Channel Islands range from 10 to
100 km
in area (Channel Islands National Marine Sanctuary
2007), while the Central Coast reserves average about
17 km
(Marine Life Protection Act Initiative 2007). Our
scenario of 10% of the population moving across each re-
serve boundary every year probably overestimates the im-
pact of movement for blue rockfish in this region, but other
species may move this much or more.
A more detailed understanding of fish movement and mi-
gratory behavior would allow for better prediction of the
likely effectiveness of a density-ratio control rule. For exam-
ple, the number of fish that leave the reserve to be exposed
to the fishery would depend on whether fish movement is or
is not density dependent, whether fish move to form spawn-
ing aggregations, whether there is ontogenetic movement
from one habitat or depth range to another, and whether pre-
ferred habitat types straddle the reserve boundary (Botsford
et al. 2009). These questions should be explored, although,
for the purposes of a density-ratio control rule, the only re-
quirement is that the rate of movement across the reserve
boundary must be low enough to allow biomass to accumu-
late in the reserve.
We also found that the timing and spatial scale of density
dependence in recruitment could influence the effectiveness
of density-ratio control rules. For the case of post settlement
density dependence, the high biomass in the reserve caused
Fig. 10. Effect of imperfect control of effort on spawning stock biomass at the end of each decade post reserve [bars, grouped by scenario
(A, perfect effort control; B, effort inflation; C, illegal fishing in the reserve; D, illegal fishing and effort inflation) and ordered by decade
within groups], and average fishery yield across each decade (points) averaged across 6 runs with control rules 3,5 and 6 with and without
movement, for (a) black rockfish, and (b) blue rockfish The unbroken horizontal line is the spawning stock biomass at 40% of unfished
levels, and the broken horizontal line is the yield associated with an F
policy. Details are shown for a black rockfish model run under
scenario C without movement and with control rule 3 (c and d, compare with Figs. 5a and 5b).
Babcock and MacCall 355
Published by NRC Research Press
lower recruitment, a phenomenon that could occur for spe-
cies with strict habitat requirements in which the presence
of large numbers of conspecifics negatively impacts settlers
(Ralston and O’Farrell 2008). Using a control rule based on
the density ratio of mature fish performed well with post-
dispersal density dependence, and in fact that control rule
(rule 5) performed well for all scenarios. A density-ratio
control rule based on densities of mature fish is also more
analogous to the reference points based on spawning stock
biomass that are used for management of California near-
shore species (Kaufman et al. 2004).
Our model did not include spatial patterns in benthic hab-
itat or current flow. Differences in habitat quality inside ver-
sus outside the reserve can be removed by normalizing the
density ratio to the value that was measured the year before
the reserve was implemented (assuming monitoring was
available before the reserve), but temporal changes in habi-
tat quality, or interactions between habitat and abundance
could bias the density ratio. The density ratio could also be
biased by source–sink dynamics, for example, if many of the
larval fish settling in the reserve were produced by spawners
in the fished area. Future modeling efforts using three-
dimensional, spatially explicit models may identify issues
that were not apparent in our simple one-dimensional model.
Density-ratio control rules could also be problematic in
the presence of density dependence in growth, size and age
at maturity, or size and age of sex change for hermaphro-
dites (e.g., Ga
rdmark et al. 2006). Diseases and parasites
can also be more prevalent in higher density conditions,
which could cause abundance of some species to decline in
reserves (Behrens and Lafferty 2004). Such effects could
bias the density ratio or the assumed equivalence of popula-
tion dynamics inside and outside the protected areas.
One key gap in our modeling exercise is trophic structure.
As biomass of large fish builds up in reserves, increased pre-
dation can cause a decrease in density of prey species (e.g.,
Behrens and Lafferty 2004). Large, mobile, apex predators,
such as sharks, will not be protected by a small reserve
(Chapman et al. 2005) and so may not reach their unfished
level. This can allow the large fish one trophic level below
sharks in the food web to be even more numerous and to eat
more small fish than they would in unfished conditions. Den-
sity ratio management would probably be most effective for
large meso-predators that tend to benefit from reserves, and
not necessarily for lower trophic level fish. This is would be
an interesting avenue for further research.
Fishery characteristics
The model assumed that selectivities, relative contribu-
tions of each fishing gear, and (for the multispecies runs)
relative catch ability of each species are constant over time.
In a real fishery, there would be changes over time in the
Fig. 11. Change in the probability of achieving two management objectives (left panels, spawning stock biomass greater than 40% of unf-
ished in year 20; right panels, yield higher in year 20 than in year 1), when additional variability is added to the simulations by reducing the
sample size in the monitoring data (a and b), increasing the standard deviation in recruitment (c and d) or adding autocorrelation to recruit-
ment (e and f). Each panel includes 16 single model runs (monitoring sample size is inflated by or not, s
= 0.2 or 0.5, r
= 0 or 01) for
each species (black rockfish, blue rockfish, cabezon, and vermillion rockfish). Open boxes indicate control rule 3 (target density ratio 60%),
filled boxes indicate control rule 6 (target density ratio 80%).
356 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
relative importance of different gear types. Also, both rec-
reational and commercial fisheries can change their target
species, and thus change their catch composition without
changing fishing effort. If a multispecies control rule was
used, changes in targeting could cause overfishing of more
depleted species. The single-species control rules, because
they are based on a direct estimate of fish abundance, will
respond automatically to such changes in targeting and gear
use. Under a multispecies control rule, additional regulations
including prohibited species, or time/area closures and gear
regulations to shift fishing pressure from depleted species to
nondepleted species might be necessary (Shepherd 2003).
The control rules we simulated controlled relative or ab-
solute effort. For some fisheries it may be possible to use
density-ratio control rules to set catch quotas. The catch lim-
its could be set at current catch levels for the first year, and
then subsequent catch limits could be set with a density-
ratio control rule that specifies catch relative to the previous
year’s catch as a function of the density ratio. A catch con-
trol rule would require complete and accurate catch data, so
fisheries where a large portion of the fishing mortality is
recreational, small scale fishing or discards could be prob-
lematic. Illegal fishing in the reserve can bias the density ra-
tio, so it is necessary that the reference reserves be well
enforced. A density-ratio control rule could provide an addi-
tional incentive for illegal fishing, not just because of the
presence of large and abundant fish, but also in an attempt
to reduce the density ratio so that the fishery will not be
subject to additional effort restrictions.
One advantage of density ratio management would be that
it could be done at a more local spatial scale than stock as-
sessment-based management, which tends to be applied
coast-wide. Density ratios can be calculated at spatial scales
considered appropriate to the biology of the fish (Palumbi
2004). Provided that monitoring data are available, those
coastal species with very short dispersal distances and very
localized recruitment dynamics could be managed with den-
sity ratios calculated for reserves within a spatial area on the
scale of a few kilometres, while species with longer disper-
sal distances could be managed from density ratios calcu-
lated from whole networks of marine reserves at scales
similar to that used for stock assessment.
Implications of variability
With high levels of variability in recruitment and rela-
tively low sample sizes in the monitoring program, the den-
sity ratio could vary dramatically from one year to the next,
even with a relatively stable spawning stock biomass. While
this variability had the unintended consequence of making
any control rule more precautionary (because effort was re-
duced every time the density ratio happened to fall below
the target), in practice it would not be desirable to impose
effort controls on the fishery in response to random fluctua-
tions in the density ratio. The multiple year density ratio
(rule 2) provided for more stable management with an
equivalent level of monitoring effort.
Fig. 12. Blue rockfish dynamics with high (a and b) versus low (c and d) sample size in the monitoring program, showing expected values
of spawning stock biomass in the reserve (bold line in left panels) and fished areas (narrow lines in left panels), and total yield (broken line
in left panels), and 10 simulated density ratio time series (narrow lines in right panels), for control rule 3 with migration, even effort dis-
tribution, recruitment model I, low variability in recruitment, and no autocorrelation.
Babcock and MacCall 357
Published by NRC Research Press
We did not include environmentally driven changes in re-
cruitment beyond autocorrelation in recruitment. If poor en-
vironmental conditions caused recruitment to decline
throughout the region, the density ratio would respond im-
mediately. Fishing would be allowed to continue at current
levels despite the declining population. This is a fundamen-
tal difference from typical fishery management based on
stock assessment, in which the status of the population is
calculated relative to some constant biomass target, and
fluctuations in the stock are assumed to be caused by fishing
pressure, even if they are actually due to low frequency en-
vironmental fluctuations. In stock assessment-based manage-
ment, a series of low recruitment years would usually lead
to reduced catch quotas as managers try to stop the decline
and rebuild the population to the fixed target. A density-
ratio control rule, on the other hand, would adjust to chang-
ing environmental conditions, because the management
target is relative to curren t conditions in the reserve, not to
historical conditions. Thus, the same target density ratio
would result in a higher or lower spawning stock biomass
relative to historical unfished condit ions, depending on
whether the stock is experiencing a period of high produc-
tivity or a period of low productivity. Whether biomass
reference points should be calculated relative to a static his-
torical standard (‘biomass before fishing began’’) or a dy-
namic standard (‘the biomass that would currently be
present if the fishery had never existed’’) is a management
decision, not a scientific one, and equally extends to fishery
management under a conventional data-rich stock assess-
ment approaches (Sibert et al. 2006; Field et al. 2010). The
density ratio is most useful for managing for a dynamic bio-
mass standard.
There is a risk of depleting the stock relative to the dy-
namic biomass standard if, for any reason other than envi-
ronmental variability, the fish biomass declines in the
reserve. Fish movement across the reserve boundary, illegal
fishing in the reserve, a density-mediated disease outbreak,
or unanticipated trophic interactions could all cause the
density-ratio control rule to fail because densities in the re-
serve remain below the level that would have been seen if
there was no fishing in the region. Therefore, a precaution-
ary management system based on density ratios should also
examine the trend in density of fish in the reserve to identify
whether one of these risky scenarios is occurring. If the
monitoring program included length data (which it should),
indicators such as the average length of fish inside and out-
side the reserve, and fraction of fish above the age at matur-
ity inside and outside the reserve, could also be used to
determine whether densities inside the reserve are increasing
as expected. For example, one could develop a decision tree
management system (e.g., Prince et al. 2008; Wilson et al.
2010) in which, if density is stable or increasing in the re-
serve, a density-ratio control rule is used, but if density is
decreasing in the reserve more stringent management meas-
ures are specified.
Based on our simulations, a target density ratio of 60% of
mature fish or 80% of all fish would perform well for a
wide range of fish life history characteristics. Density ratios
calculated across multiple years are useful for dampening
out the variability in the density ratio for a given level of
monitoring effort. For multispecies fisheries, if a few species
are thought to be at risk relative to the others, then it may be
necessary to impose independent management measures for
the at-risk stocks, and apply density-ratio control rules for
the more productive species that are the primary basis of
the fishery. Once a density-ratio control rule is established,
it is important to monitor indicators, such as a sudden de-
crease in density inside the reserve, or dramatic change in
the relative densities of the species in the fishery, which
might be an early warning that a density-ratio control rule
is not adequate.
Thanks to Carey McGilliard, John Field, Rod Fujita, Mei-
sha Key, Jono Wilson, Burr Heneman, and the rest of the
density ratio working group and the monitoring data applica-
tions project for input on the methodology. Thanks to Jenn
Caselle and PISCO for providing the PISCO subtidal moni-
toring data. Thanks to the Commonweal Ocean Policy Pro-
gram for funding E. Babcock’s work on this project, and
the Marine Protected Area Science Integration project for
funding the working group meetings.
Beddington, J.R., Agnew, D.J., and Clark, C.W. 2007. Current pro-
blems in the management of marine fisheries. Science, 316(5832):
1713–1716. doi:10.1126/science.1137362. PMID:17588923.
Behrens, M.D., and Lafferty, K.D. 2004. Effects of marine reserves
and urchin disease on southern Californian rocky reef commu-
nities. Mar. Ecol. Prog. Ser. 279: 129–139. doi:10.3354/
Bohnsack, J.A. 1999. Incorporating no-take marine reserves into
precautionary management and stock assessment. In Proceedings
of the 5th National NMFS Stock Assessment Workshop. 24–26
February 1998, Key Largo, Florida. Edited by V.R. Restrepo.
NOAA Tech. Memo. NMFS-F/SPO-40. pp. 8–16. Available
Botsford, L.W., Brumbaugh, D.R., Grimes, C., Kellner, J.B., Lar-
gier, J., O’Farrell, M.R., Ralston, S., Soulanille, E., and Wespes-
tad, V. 2009. Connectivity, sustainability, and yield: bridging the
gap between conventional fisheries management and marine
protected areas. Rev. Fish Biol. Fish. 19(1): 69–95. doi:10.
Channel Islands National Marine Sanctuary. 2007. Final Environ-
mental Impact Statement for the establishment of marine re-
serves and marine conservation areas. National Oceanic and
Atmospheric Administration. National Marine Sanctuary Pro-
gram. Avail ab le fr om cha nn e li sla nd s.noa a .go v /m ar in er es /pd fs/
Chapman, D.D., Pikitch, E.K., Babcock, E.A., and Shivji, M.S.
2005. Marine reserve design and evaluation using automated
acoustic telemetry: a case-study involving coral reef-associated
sharks in the Mesoamerican Caribbean. Mar. Technol. Soc. J.
39(1): 42–55. doi:10.4031/002533205787521640.
Clark, W.G. 2002. F
revisited 10 years later. N. Am. J. Fish.
Manage. 22(1): 251–257. doi:10.1577/1548-8675(2002)
Cope, J.M., and Punt, A.E. 2005. Status of Cabezon (Scorpae-
nichthys marmoratus) in California waters as assessed in 2005.
Pacific Fishery Management Council, Portland, Oregon. Avail-
able from
358 Can. J. Fish. Aquat. Sci. Vol. 68, 2011
Published by NRC Research Press
Dorn, M.W. 2002. Advice on West Coast rockfish harvest rates
from Bayesian meta-analysis of stock–recruit relationships. N.
Am. J. Fish. Manage. 22(1): 280–300. doi:10.1577/1548-
Field, J.C., Punt, A.E., Methot, R.D., and Thomson, C.J. 2006.
Does MPA mean ‘Major Problem for Assessments’? Consider-
ing the consequences of place-based management systems. Fish
Fish. 7: 284–302.
Field, J.C., MacCall, A.D., Bradley, R., and Sydeman, W.J. 2010.
Estimating the impacts of fishing on dependent predators: a
case study in the California Current. Ecol. Appl.
100621213203003 . [In press.] doi:10.1890/09-0428.1.
rdmark, A., Jonzen, N., and Mangel, M. 2006. Density-depen-
dent body growth reduces the potential of marine reserves to en-
hance yields. J. Appl. Ecol. 43(1): 61–69. doi:10.1111/j.1365-
Gillis, D.M., Peterman, R.M., and Tyler, A.V. 1993. Movement dy-
namics in a fishery: application of the ideal free distribution to
spatial allocation of effort. Can. J. Fish. Aquat. Sci. 50(2): 323–
333. doi:10.1139/f93-038.
Jorgensen, S.J., Kaplan, D.M., Klimley, A.P., Morgan, S.G.,
O’Farrell, M.R., and Botsford, L.W. 2006. Limited movement
in blue rockfish Sebastes mystinus: internal structure of home
range. Mar. Ecol. Prog. Ser. 327: 157–170. doi:10.3354/
Kaufman, L., Henneman, B., Barnes, J.T., and Fujita, R. 2004.
Transition from low to high data richness: an experiment in eco-
system based fishery management in California. Bull. Mar. Sci.
74: 693–708.
Key, M., MacCall, A.D., Bishop, T., and Leos, B. 2005. Stock as-
sessment of the gopher rockfish (Sebastes carnatus). Pacific
Fishery Management Council, Portland, Oregon. Available from
Key, M., MacCall, A.D., Field, J., Aseltine-Neilson, D., and Lynn,
K. 2007. The 2007 assessment of blue rockfish (Sebastes mysti-
nus) in California. Pacific Fishery Management Council, Port-
land, Ore. Available from
Lea, R. N., McAllister, R. D., and VenTresca, D. A. 1999. Biologi-
cal aspects of nearshore rockfishes of the genus Sebastes from
central California With notes on ecologically related sport
fishes. State of California, Department of Fish and Game Fish
Bulletin 177.
Lubchenco, J., Palumbi, S.R., Gaines, S.D., and Andelman, S.
2003. Plugging a hole in the ocean: the emerging science of
marine reserves. Ecol. Appl. 13(1): S3–S7. doi:10.1890/1051-
MacCall, A.D. 2005. Assessment of vermilion rockfish in southern
and northern California. Pacific Fishery Management Council,
Portland , Ore. Av ail able fromwww.pc ounci /wp-c onten t/
Marine Life Protection Act Initiative. 2007. [Online] Guide to the
central California marine protected areas. Available from www.
McGilliard, C.R., Hilborn, R.Q., MacCall, A., Punt, A.E., and
Field, J.C. 2010. Can information from marine protected areas
be used to inform control-rule-based management of small-scale,
data-poor stocks? ICES J. Mar. Sci.. doi:10.1093/icesjms/fsq151.
Methot, R.D. 2000. Technical description of the Stock Synthesis
assessment program. NOAA Tech. Memo. NMFS-NWFSC-43.
Avail a bl e from www. nwfs c .no a
Palumbi, S. 2004. Marine reserves and ocean neighborhoods: the
spatial scale of marine populations and their management.
Annu. Rev. Environ. Resour. 29(1): 31–68. doi:10.1146/
PISCO (Partnership for Interdisciplinary Studies of Coastal
Oceans). 2008. [Online] PISCO subtidal database. Available
Prince, J.D., Peeters, H., Gorfine, H., and Day, R.W. 2008. The no-
vel use of harvest policies and rapid visual assessment to man-
age spatially complex abalone resources (Genus Haliotis). Fish.
Res. 94(3): 330–338. doi:10.1016/j.fishres.2008.07.016.
Punt, A.E., Dorn, M.W., and Haltuch, M.A. 2008. Evaluation of
threshold management strategies for groundfish off the U.S.
West Coast. Fish. Res. 94: 251–266. doi:10.1016/j.fishres.2007.
Ralston, S., and Dick, E.J. 2003. The status of black rockfish (Se-
bastes melanops) off Oregon and Northern California in 2003.
NMFS Santa Cruz Laboratory Contribution Number 655. Pacific
Fishery Management Council, Portland, Ore. Available from
Ralston, S., and O’Farrell, M.R. 2008. Spatial variation in fishing
intensity and its effect on yield. Can. J. Fish. Aquat. Sci. 65
588–599. doi:10.1139/F07-174.
Restrepo, V.R., Thompson, G.G., Mace, P.M., Gabriel, W.L., Low,
L.L., MacCall, A.D., Methot, R.D., Powers, J.E., Taylor, B.L.,
Wade, P.R., and Witzig, J.F. 1998. Technical Guidance on the
use of precautionary approaches to implementing National Stan-
dard 1 of the Magnuson–Stevens Fishery Conservation and
Management Act. NOAA Tech. Memo. NMFS-F/SPO-31.
Available from
Shepherd, J.G. 2003. Fishing effort control: could it work under the
common fisheries policy? Fish. Res. 63(2): 149–153. doi:10.
Sibert, J., Hampton, J., Kleiber, P., and Maunder, M. 2006. Bio-
mass, size, and trophic status of top predators in the Pacific
Ocean. Science, 314(5806): 1773–1776. doi:10.1126/science.
1135347. PMID:17170304.
Wilson, J.R., Prince, J.D., and Lenihan, H.S. 2010. A management
strategy for sedentary nearshore species that uses marine pro-
tected areas as a reference.. Mar. Coastal Fish: Dyn. Man. Ecos.
Sci. 2: 14–27. doi:10.1577/C08-026.1.
Babcock and MacCall 359
Published by NRC Research Press
... As such trigger systems are especially useful in fisheries that experience shifts in fisher behavior that may be unrelated to the status of the stock -e.g., new and expanding fisheries, opportunistic fisheries that switch targeting behaviors, and multispecies fisheries. A wide variety of singleindicator trigger systems have been proposed and evaluated, with guidance that is also germane to multi-indicator alternatives De Oliveira and Butterworth, 2004;Pomarede et al., 2010;Babcock and MacCall, 2011;Little et al., 2011;McGilliard et al., 2011;Cook, 2013;Carruthers et al., 2014;Geromont and Butterworth, 2015). Dowling et al. (2008) provide examples of multi-indicator frameworks for Australian Commonwealth fisheries. ...
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As the world population grows, fisheries practitioners will be under increased pressure to address global challenges in data-limited fisheries management. With a focus on addressing localized and case-specific management needs, we provide a practical guide to the design and development of multi-indicator frameworks for fishery management. In a data-limited context, indicators are observations or estimates of the state of the fishery resource that are typically proxies for variables of interest, rather than quantities such as stock biomass estimated from data-rich stock assessments. Indicator frameworks structure the integration and interpretation of indicators to guide tactical fishery decision-making, often when the application of more formal analytical assessments is not feasible, yet where indicators in combination provide insight into stock status. With a focus on multi-indicator frameworks, we describe a pragmatic approach for their development via a set of organizational steps, considering a wide spectrum of types and severity of information limitations. We highlight where multi-indicator frameworks can be insightful and informative in relation to single indicator approaches but also point to potential pitfalls, with emphasis on critical evaluation and detection of performance flaws during the design phase using methods such as management strategy evaluation.
... These examples of effective adaptive management relied on data-limited stock assessment and management protocols, which are far simpler to apply and require less data, time, and money than conventional methods (Table 2). Available performance information suggests that such tools, properly applied, can effectively prevent overfishing and generate desirable levels of sustainable yield, when key assumptions are met (Babcock and MacCall, 2011;Carruthers et al., 2014;Fulton et al., 2016). A critical remaining step for sustainable fisheries management in Belize will be to add effective finfish management and standardized data collection to the MAA program, enabling the implementation of the Adaptive Management Framework, based on best-available data-limited approaches. ...
Fisheries are critically important for nutrition, food security, livelihoods, and culture of hundreds of millions of people globally. As climate impacts on ocean ecosystems increase, policy-makers are asking critical questions about how to implement reforms at local and national levels to reach goals around improving performance of management systems, sustainability, equity, and resilience to climate change. These goals can be achieved by enhancing the structure, function, and biodiversity of marine ecosystems as climate change proceeds, together with adaptive, sustainable management. However, resource, technical, and governance capacities vary widely across management systems. These capacities will determine, in part, the best policy approaches to build resilience and overcome systemic challenges to equity and sustainability to stressors such as climate change. To illuminate how fisheries resilience can be improved within the constraints imposed by these capacity limits, we present case studies from Myanmar, Belize, Peru, and Iceland, which offer a spectrum of capacity conditions to explore social–ecological resilience challenges and solutions. Using a set of nine social–ecological resilience criteria, we examine each system’s attributes that may confer or undermine resilience and explore interactions between them. We use this assessment to identify policy approaches that can help build resilience in each particular context.
... To date, investigations of the dynamics of fished populations inside MPAs and the associated fishery yield outside MPAs have focused on longer-term, steady-state outcomes (McGilliard et al., 2011;White et al., 2011;Botsford et al., 2019), with a few notable exceptions analysing short-term responses of yield outside the MPAs (e.g. Babcock and MacCall, 2011). For example, Kerwath et al. (2013) noted an increase in fishery catch-per-unit-effort near a South African MPA and proposed an informal, verbal population model to explain the timescales associated with that pattern. ...
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Adaptive management of marine protected areas (MPAs) to determine whether they are meeting their intended goals requires predicting how soon those goals will be realized. Such predictions have been made for increases in fish abundance and biomass inside MPAs. However, projecting increases in fishery yield (“fishery spillover”) is more complex because it involves both how the fishery is managed and uncertainty in larval connectivity. We developed a two-patch, age-structured population model, based on a renewal equation approach, to project the initial timing of increase in fishery yield from larvae exported from a no-take MPA. Our results link our understanding of the predicted timing of increases in biomass (and thus reproduction) in MPAs with the time-lags associated with new recruits entering the fishery. We show that the time-lag between biomass peaking within the MPA and the increased fishery yield outside the MPA reaching its maximum depends, in a predictable way, on the age-dependent patterns of growth, natural mortality, and fishing mortality. We apply this analysis to 16 fishery species from the US Pacific coast; this difference ranged from 7 to 18 years. This model provides broadly applicable guidance for this important emerging aspect of fisheries management.
... Proxy for biomass depletion (e.g., density ratio) could be combined with the PSA risk ranks to prioritize species for precautionary management and further analysis (Fujita et al., 2014). Ratios of fish density outside versus inside of no-take reserve areas may serve as an important indicator to know the impact of fishing to a particular stock or stock complexes (Babcock and MacCall, 2011). The species complex, e.g., highly vulnerable and or overexploited, could be managed via indicator species-high valued fish or endangered or keystone species-in an adaptive management framework (McDonald et al., 2017) to set the harvest control rules. ...
Productivity susceptibility analysis (PSA) is being widely used as a semi-quantitative risk assessment tool in data and capacity limited situations. This tool rapidly and cost-effectively assists in identifying the potential risk of a fishing type regarding its bycatch stocks. The Hilsa (Tenualosa ilisha) is an iconic flagship species and geographical indication product of Bangladesh. We performed a PSA to evaluate the relative risk to bycatch stocks in gillnet fishing (gillnet shares > 95 % of Hilsa catch) along with target stock, Hilsa. Of the 130 identified species, Hilsa and 74 bycatch stocks were subjected to a PSA depending on data availability and the magnitude of capture. We validated our vulnerability results by comparing them with two other empirically derived assessment outcomes, the IUCN Red List and the exploitation rate. We also compared PSA scores with the catch trend of stocks from fisher's subjective recognition. Hilsa was found to be moderately vulnerable to gillnet fishing. The majority of the bycatch were found to be highly susceptible to fishing, with 17 bycatch species found to be in the high-risk category. Five species classified as high-risk group were known to be threatened species listed in the national IUCN Red List. Our finding revealed 82 % accordance level between the exploitation rate and PSA-derived vulnerability scores. It implies that the exploitation rate associated with overfishing corresponds to the vulnerability scores. Moreover, with few exceptions, we found that species with vulnerability score over 1.8 was at depleted stock state. Our result also revealed that around 55 % of inland bycatch and 42 % of marine bycatch is associated with overfishing (vulnerability score > 1.8). Data quality analysis indicated that the majority of bycatch species received the low data quality scores. It emphasizes the need for improved data collection on species-specific life-history traits. However, the baseline information of our current study could assist the fishery managers to formulate a better management plan for the sustainability of Hilsa and the bycatch stocks.
... Research on reserves as reference areas for harvest decision-making is largely conceptual as few applications have been documented. Using management strategy evaluation (MSE), Babcock & MacCall [45] and McGilliard et al. [46] showed that the density of individual fished species inside and outside of reserves can reliably be employed to set catch levels and/or provide triggers to close a season using simple control rules associated with the density ratio. McGilliard et al. [46] optimized the control rule that would be most robust in meeting fishery management objectives. ...
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Overfishing and other anthropogenic impacts to ocean ecosystems have motivated widespread implementation of no-take marine reserves to protect biodiversity and fished resources. Fully protected marine reserves now comprise approximately 2.5% of the ocean and calls for enhanced protections abound. The benefits to marine biodiversity within reserve borders are clear, yet the contributions beyond borders remain the subject of continued scholarship. In this article, six co-benefits of marine reserves for fisheries are explored. Broadly, the six co-benefits fall within two categories 1) use of reserves as tools to adaptively learn, promote resiliency, and manage marine resources, especially in the face of a changing climate and 2) use of reserves to provide credit against uncertainty in management and sustainability criteria. Broader understanding and consideration of the benefits of marine reserves can further policy discussions and deepen conversations regarding social, ecological, and economic tradeoffs of implementing marine reserves.
... Effectively managed no-take and especially no-entry MPAs are valuable as reference areas for elucidating the effects of global and regional-scale pressures on marine ecosystems-for example, climate change (Carr et al., 2017) and diffuse pollution-as the effects of these pressures on the protected ecosystem are not confounded by the effects of local human activities. For MPAs where management measures effectively protect populations of fished species confined within MPA boundaries, reference monitoring can be used to minimize the 'shifting baseline' phenomenon in defining healthy stocks (Pauly, 1995), and thus help evaluate fisheries management measures outside MPAs (Babcock & MacColl, 2011;Dunham, 2018;Schroeter et al., 2001). Yet, these benefits cannot be realized without human pressure monitoring ensuring effective protection. ...
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• The global extent of marine protected areas (MPAs) has increased rapidly in the last decade, and monitoring and evaluation are now required for effective and adaptive management of these areas. • We classify monitoring in MPAs into four categories and identify a critically important, but undervalued category: human pressure monitoring that targets human activities and their impacts. • Human pressure monitoring is fundamental for interpreting the results of ecological performance monitoring and for evaluating MPA management effectiveness. The consequences of ecological performance monitoring that show unsuccessful MPA performance while falsely assuming successful mitigation of human pressures could jeopardize MPA performance analysis and adaptive management, and thus be worse than not monitoring at all. • Human pressure monitoring enables using MPAs as reference areas where the effects of global or regional pressures (e.g. climate change) can be disentangled from the effects of local human activities, as well as to minimize the shifting baseline phenomenon in defining healthy stocks. These benefits cannot be realized without active human pressure monitoring integrated into an adaptive management cycle that ensures effective MPA protection. • In the absence of human pressure monitoring, all ecological monitoring within MPAs falls in the ambient monitoring category: monitoring that is not intended to measure conservation outcomes. • We discuss the implications for monitoring programme design and provide a structure for decision‐makers on how to prioritize monitoring activities within MPAs that place greater emphasis on improving MPAs as biodiversity conservation tools over proving MPA performance.
... Schroeter et al. (2001) demonstrated the application of reserves in evaluating the fishery status of the warty sea cucumber (Parastichopus parvimensis) in the northern Channel Islands. Similarly, Babcock and MacCall (2011) explored the application of reserves for stock assessments for a suite of nearshore California fishes. ...
Full-text available
Most fisheries in the world do not collect the relatively rich data streams necessary for formal stock assessment. Expanding and improving the current suite of data limited stock evaluation methods will be critical for ensuring that more of the world’s fisheries are managed on the basis of scientific stock status evaluations. Here I offer perspectives on three emerging ways to help achieve this goal: (1) making use of data streams that are not usually used for stock evaluation; (2) extracting more information relevant to fisheries management from stock evaluations by scaling the use of adaptive management frameworks; and (3) using human-centered design processes to deploy appropriate technologies to dramatically increase the amount of data that even resource-limited fisheries can collect, analyze, and apply to management. Because climate change is already altering fish stock distribution and abundance patterns and these changes will likely become more profound over time, I also offer a perspective on the need for data limited approaches for anticipating these changes in order to facilitate fishery planning and adaptive management.
Coral reefs in the Arabian Seas exist in and are resilient to a harsh environment with extremes of temperature and salinity. Temperatures range from 16¯ C in the winter to 37¯ C in the summer and salinity may reach 40 ‰. These coral assemblages and their associated biota and fisheries are under threat from a wide variety of impacts, including global climate change and associated ocean warming, coral disease, heavy tourism pressure, sedimentation and physical habitat destruction from intense, widespread coastal development, overfishing, industrial pollution, heated, hypersaline brine effluent from desalination, and shipping. Coral reef management is primarily accomplished through the implementation of MPAs, with unknown success due to the lack of MPA management effectiveness assessments. Fisheries are the most important renewable resource in the Arabian seas and the second most important natural resource after oil and gas, but reef fisheries management in the region is poorly developed and needs to move toward a precautionary, ecosystem-based management approach. There has been increasing interest in coral reef research in the Arabian Seas, primarily to understand the resilience of corals to global environmental change. Recent advances in GIS and remote sensing provide useful tools for managing marine ecosystems.
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The natural, pre-human, abundance of most large predators is unknown due to the lack of historical data and the poor understanding of the natural factors that control their populations. We assessed the relationship between the biomass of predatory reef fishes and several anthropogenic and environmental variables to (1) predict among site variability in predator abundance in response to both human impacts and natural factors, and (2) estimate historical baselines of fish predator biomass in the absence of humans. We hypothesized that predatory fish abundance declines with human influence but is also strongly associated with natural environmental variability. We assessed the biomass structure of reef fishes at 39 sites over three years across the greater Caribbean. Using generalized linear mixed effect models, we examined the relationships between the biomass of predatory reef fishes and a comprehensive set of 29 anthropogenic, physical, spatial, biotic, and management-related covariates. We used the best explanatory models to predict the biomass of fish predators in the absence of humans. Predatory reef fish biomass was higher in marine reserves but strongly negatively related to human impacts, especially coastal development. Over 50% of the variability in predator biomass, however, was also explained by non-human factors including reef complexity, ocean productivity, and prey abundance. Comparing site-specific predicted values to field observations suggests predatory reef fish biomass has declined by 80-95% in most sites, even within most marine reserves. Bottom-up forces are critical (yet often overlooked) drivers of reef fish communities across gradients of human exploitation. This suggests that we could underestimate historical biomass at sites that provide ideal conditions for predators or greatly overestimate that of seemingly predator-depleted sites that may have never supported large predator populations due to suboptimal environmental conditions. We highlight areas that are natural “hot spots” of predator biomass that can be targeted for strategic protection and restoration.
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McGilliard, C. R., Hilborn, R. MacCall, A., Punt, A. E., and Field, J. C. 2011. Can information from marine protected areas be used to inform control-rule-based management of small-scale, data-poor stocks? – ICES Journal of Marine Science, 68: 201–211. Many small-scale, nearshore fisheries lack the historical catch and survey information needed for conventional stock-assessment-based management. The potential use of the ratio of the density of fish outside a marine protected area to that inside it each year (the density ratio, DR) in a control rule is evaluated to determine the direction and magnitude of change in fishing effort in the next year. Management strategy evaluation was used to evaluate the performance of this DR control rule (DRCR) for a range of movement rates of larvae and adults and other biological scenarios, and the parameters of the control rule that maximized cumulative catch (over 95 years) for each scenario were found. The cumulative catch under the optimal DRCR was 90% of the cumulative catch from an optimal constant effort rule (CER). A small range of parameter values for the DRCR produced 75% or more of the cumulative catch produced from optimal CERs for a variety of assumptions about biology and initial stock status. The optimal DRCR was most sensitive to the movement patterns of larvae and adults and survey variability.
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Fishing mortality is rarely, if ever, evenly distributed over space, yet this is a common assumption of many fisheries models. To evaluate the effect of spatial heterogeneity in fishing mortality on yield, we constructed age-structured models that allowed for differing levels of fishing in three regions within the boundaries of a stock and explored alternative assumptions about the life stage in which density-dependent compensation operates. If the fishing mortality rate (F) is not excessive (i.e., F <= F-MSY defined for the spatially homogeneous case; MSY, maximum sustainable yield), simulations demonstrated that minor to moderate spatial variation in fishing intensity does not impact sustainable yield. However, if fishing mortality is excessive (F > F-MSY), spatial variation in fishing intensity often improves yield and can actually produce yields in excess of MSY when compensation occurs after dispersal, and the density-dependent recruitment rate is a function of the local density of adults. The yield premium generated in these simulations by postdispersal density dependence is due to a low level of compensatory mortality in heavily fished areas coupled with dispersal of propagules into these areas from lightly fished adjacent regions.
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Home range has been estimated for a limited number of marine fishes; however, the use of space and timing of activities within the home range has rarely been studied. In addition, understanding movement patterns of exploited fish species has been identified as a crucial science gap, impeding informed marine reserve design. We used a radio-acoustic positioning telemetry (VRAP) system to monitor detailed movements of 10 blue rockfish Sebastes mystinus around shallow rock pinnacles and stands of bull kelp Nereocystis leutkeana in central California in September 2002, The mean home range was 8783 m(2) +/- 1137 SE; however, activity was highly concentrated in 1 to 3 core areas within each home range. Mean core areas measured 1350 m(2) 286 SE, but accounted for similar to 83 % of activity. All core areas were centered over rock pinnacles where rockfish were highly aggregated. Individuals exhibited high site fidelity and made only brief radial excursions away from these centers or moved directly from one pinnacle to the next along defined corridors. Patterns of diel activity and nocturnal sheltering corresponded closely with nautical twilight. Cores overlapped, but estimated locations of nocturnal shelters differed significantly among individuals. Movement patterns were correlated with wind velocity, upwelling index, water temperature and habitat structure.
Over the past two decades, populations of rockfish Sebastes spp. off the U.S. West Coast have declined sharply, leading to heightened concern about the sustainability of current harvest policies for these populations. In this paper, I develop a hierarchical Bayesian model to jointly estimate the stock−recruit relationships of rockfish stocks in the northeastern Pacific Ocean. Stock−recruit curves for individual stocks are linked using a prior distribution for the “steepness” parameter of the Beverton–Holt stock−recruit curve, defined as the expected recruitment at 20% of unfished biomass relative to unfished recruitment. The choice of a spawning biomass per recruit (SPR) harvest rate is considered a problem in decision theory, in which different options are evaluated in the presence of uncertainty in the stock−recruit relationship. Markov chain Monte Carlo sampling is used to obtain the marginal distributions of variables of interest to management, such as the yield at a given SPR rate. A wide range of expected yield curves were obtained for different rockfish stocks. The stocks of Pacific ocean perch S. alutus in the Gulf of Alaska and the Aleutian Islands are apparently the most resilient, with maximum sustainable yield (MSY) harvest rates greater than F 30% (the fishing mortality rate that reduces SPR to 30% of its unfished value) for all model configurations. In contrast, the MSY harvest rate for the West Coast stock of Pacific ocean perch was lower than F 70%. The SPR rates at MSY for other stocks were clustered between F 40% and F 60% and depended on both the stock−recruit model (Beverton–Holt or Ricker) and the model for recruitment variability (lognormal or gamma). Meta-analysis results should be interpreted cautiously due to autocorrelation in the model residuals for several stocks and the potential confounding effect of decadal variation in ecosystem productivity. An F 40% harvest rate, the current default harvest rate for rockfish, exceeded the estimated F MSY rate for all West Coast rockfish stocks with the exception of black rockfish S. melanops. A harvest rate of F 50% is suggested as a risk-neutral F MSY proxy for rockfish. A more risk-averse alternative would be to apply an SPR harvest rate in the F 55%−F 60% range.
2 EXECUTIVE SUMMARY Stock This is the first assessment of blue rockfish (Sebastes mystinus) on the West coast of the US. This assessment determines the status of the California stock from the Oregon border to Point Conception where blue rockfish are most commonly found, using data through 2006. This assessment treats these fish as a single stock. Blue rockfish are also harvested in Oregon and Washington, but black rockfish are more sought after in those waters. In southern California waters, the disappearance of that stock is believed to be related to environmental conditions, such as the lack of kelp in the warmer waters since the 1990s. The variability in growth over time and between areas along the coast of California were evident while assessing this stock, but sufficient data did not allow the complex modeling needed to appropriately assess blue rockfish. Genetic evidence has also suggested two species of blue rockfish in California, so this status report is in effect an assessment of a blue rockfish "complex" instead of a single species.
This paper reviews the original derivation of the F35% (later F40%) harvest strategy, which consists of fishing at a rate that reduces spawning biomass per recruit to 35% (or 40%) of the unfished value, and investigates its applicability to long-lived stocks with low resiliency, such as some of the Pacific Coast rockfishes Sebastes spp. The life history parameters are unimportant (at least in deterministic calculations), but the possibility of extremely low levels of resiliency—well below the bounds of the original analysis—does render the strategy unworkable in the sense that there is no harvest rate that will obtain a large fraction of the maximum sustainable yield (MSY) across the entire range of possibilities. At low but still workable levels of resiliency, the F40% strategy results in undesirably low levels of biomass and recruitment by present-day standards. That can be cured by adopting a higher target for spawning biomass per recruit, though at some cost in yield. A purely biomass-based strategy and a modified FMSY strategy are discussed as alternatives for cases where adequate historical data are available.