IIFET 2004 Japan Proceedings
BIOECONOMIC EVALUATIONS OF ALTERNATIVE MANAGEMENT STRATEGIES FOR THE NEW
ZEALAND OTAGO AND SOUTHLAND STOCK OF RED ROCK LOBSTER (JASUS EDWARDSII).
Philippe Lallemand, New Zealand Seafood Industry Council Ltd, LallemandP@seafood.co.nz
Dan Holland, New Zealand Seafood Industry Council Ltd, HollandD@seafood.co.nz
Nokome Bentley, Trophia, New Zealand, nbentley@trophia.com
ABSTRACT
The Otago (CRA 7) and Southland (CRA 8) regions are managed with distinct individual transferable quotas (ITQs)
as part of the New Zealand's quota management system (QMS). However, for assessment purposes, they are
considered as a single stock known as the NSS (North-South Islands - South) rock lobster stock. Currently, a single
harvest control rule based on catch per unit of effort (CPUE) in CRA 8 triggers proportional changes in the total
allowable commercial catches (TACCs) for both CRA7 and CRA8 while each area keeps distinct TACCs, seasons
and size limits. The current management procedure was designed to reach a CPUE based rebuilding target by 2014.
We conduct a bioeconomic analysis to contrast the current management system to alternative strategies. These
include amalgamation of the two areas into a single quota management area and separate management of the two
areas with in-season adjustment of the CRA 7 TACC. We also examined alternative CPUE targets and an alternative
procedure for adjusting the TACC once the rebuilding target has been achieved. This analysis combines a spatial,
sex and length-structured simulation model of the fishery with an economic module converting catches and effort
into revenues and costs. We compare performance of alternative management strategies using a variety of
performance indicators including yields and present value of cumulative annual profits. We identify trade-offs
between average level and variability of TACCs, and between total revenues and harvests costs. This paper
describes the model used, the results of the analysis, and how alternative management strategies were selected.
Keywords: lobster, bioeconomic, ITQs, management strategies evaluation, New Zealand
INTRODUCTION
The NSS (Otago and Southland) rock lobster (Jasus edwardsii) stock has been managed with individual
transferable quotas (ITQs) as part of New Zealand’s quota management system (QMS) since 1990. The stock is
divided into two quota management areas (QMAs), the Otago-CRA7 QMA and the Southland-CRA8 QMA (Figure
1). The two QMAs have separate total allowable commercial catches (TACC), seasons and size limits. However,
they are treated as a single stock for assessment purposes, primarily because of irregular migrations of immature
animals from CRA7 to CRA8 (Street 1973, Street 1995, Kendrick and Bentley 2003). In the 2002/ 2003 fishing
year, catches from the CRA7 and CRA8 areas were around 89 tonnes and 567 tonnes respectively with a combined
ex-vessel value in excess of NZ$20 million. Like most lobster fisheries in New Zealand the great majority of the
catch is exported live, primarily to markets in Asia.
Rebuilding the NSS stock to safer and more productive levels has been a long-standing management goal. Upon
introduction into the QMS, TACCs for the two QMAs were set well below average catch levels in prior years and
were subsequently reduced further. In the mid 1990s, the status of the stock status was still thought to be poor, and it
was determined that an increased stock size could likely support higher sustainable catches (Breen & Kendrick
1998, Starr & Bentley 2002).
In the mid 1990s, the National Rock Lobster Management Group (NRLMG), which advises the New Zealand
Minister of Fisheries on rock lobster management issues, began to explore decision rules for management. Decision
rules use an agreed indicator or set of indicators from the fishery, and specify what action will be taken, dependent
on the indicators. Under this approach, also called the “management procedure approach” (e.g., Butterworth & Punt
1999), prior agreement is obtained among managers and stakeholders about the indicator data, the decision rule and
the period for which the rule will be used. The advantages of this approach over the traditional pattern of regular or
periodic stock assessments, each followed by a decision process, are (loosely based on Geromont et al. 1999) such
that harvest decision rules can be developed that are robust to uncertainty, the process leads to explicit definition of
management objectives, all participants in the fishery can become involved in the choice of rule, a long-term view is
forced, management procedures move away from regular assessments, freeing resources for other research, and the
process is more understandable to fishers than the traditional approach.
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IIFET 2004 Japan Proceedings
165°E
166°E
167°E
168°E
169°E
170°E
171°E
172°E
Tasman
200m
42°S
New Zealand
42°S
CRA10
CRA1
CRA9
CRA2
43°S
43°S
CRA3
CRA9
West Coast
CRA8
CRA4
CRA5
Haast
CRA7
Canterbury
44°S
CRA6
CRA8
44°S
CRA5
45°S
45°S
Legend
Otago
Fjordland
Sub-Area 1 (920,921)
Southland
46°S
Sub-Area 2 (922,923,924,925)
46°S
Otago
Sub-Area 3a (928,929)
1000m
0
20
m
CRA7
Sub-Area 3b (926,927)
47°S
47°S
Stewart Island,
Foveaux Strait,
Bluff
48°S
165°E
166°E
167°E
168°E
169°E
170°E
1:4,500,000
Map Compilation: July 2004
Map Projection: Mercator
Figure 1
48°S
1,100
171°E
550
0
172°E
© New Zealand Seafood Industry Council, Ltd 2004
1,100 Kilometers
CRA7 and CRA8 Quota Management Areas (Otago and Southland)
In 1997, a management procedure was adopted with a decision ruled that was based on standardised catch per
unit of effort (CPUE) from the commercial fishery (CRA7 and CRA8 combined) as an index of abundance, and
acted on the TACCs for both QMAs in concert (Starr et al. 1997). Standardization of CPUE accounts for changes in
the spatial and temporal distribution of effort. The rule operated to cause reductions in the TACC in 1999 and again
in 2001. The NRLMG explored refinements of management procedures for this stock and incorporated new
information from a stock assessment in 2001 to evaluate the decision rule. This led to implementation of a new
revised management procedure in 2003. The new procedure set a lower CPUE target for rebuilding and utilises a
revised decision rule that was found to adhere to the rebuilding trajectory with less variation in the TACC.
Currently, a single harvest decision rule based on CPUE in Southland triggers changes in the TACCs for both areas.
The current decision rule was designed to achieve a CPUE based rebuilding target by 2014 (Figure 2).
2.5
2.4
2.3
2.2
2.1
2.1
2.0
1.9
Standardized CPUE
kg/ potlift
1.9
1.8
1.7
1.7
1.6
1.5
1.4
Target CPUE
trajectory of 1.7
1.3
1.2
Target rebuild CPUE
trajectory
1.1
Target CPUE
trajectory of 2.1
1.0
∂CPUE
= 0.0686 kg ppl
∂t
0.9
Observed
standardised CPUE
0.8
2024-2025
2023-2024
2022-2023
2021-2022
2020-2021
2019-2020
2018-2019
2017-2018
2016-2017
2015-2016
2014-2015
2013-2014
2012-2013
2011-2012
2010-2011
2009-2010
2008-2009
2007-2008
2006-2007
2005-2006
2004-2005
2003-2004
2002-2003
2001-2002
2000-2001
1999-2000
1998-1999
1997-1998
0.7
Fishing year
Figure 2
Target biomass trajectory for CRA8i.
The NSS stock appears to be rebuilding, and managers and stakeholders were interested in exploring further
refinements to the management procedure to make it more suitable for maintenance of the fishery once it is rebuilt.
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IIFET 2004 Japan Proceedings
Furthermore, the current decision rule’s use of only Southland CPUE as an indicator was considered as a temporary
measure subject to future review. A simulation model was developed to assess both the biological and economic
effects of alternative management procedures relative to the current one. Possible changes to the management
procedure include alternative CPUE targets and an alternate decision rule for adjusting the TACC over time that is
expected to be more effective at maintaining CPUE (and presumably the vulnerable biomass) at the target level. The
simulations also explore alternative stock management structures. We compare the current management structure
with alternative management structures, with decision rules based on different CPUE schemes to adjust TACCs in
both areas. We consider separate management of the two QMAs allowing the TACC in CRA7 to be adjusted inseason in response to CPUE during the season. Finally we consider amalgamation of the two quota management
areas into a single area with consistent size limits and seasons in both areas.
The modelling is meant to provide stakeholders and managers with information that will enable them to
consider tradeoffs between multiple management objectives that may be conflicting. In 2000, a workshop of
stakeholders and the NRLMG agreed upon five primary management objectives (Bentley et al. 2003): maximize
catch, maintain high abundance, minimize frequency of catch adjustments, minimize risk of low biomass levels and
maximize the rate of rebuilding. The performance indicators associated with each management objective provides a
way of assessing trade-offs and choosing the optimum management strategy. However, there are clearly conflicts
and trade-offs between these objectives. For example some management strategies may achieve lower average
yields but maintain higher average CPUE which means lower costs and potentially larger average sized, higher
priced lobsters. Some strategies may provide better long term performance at the expense of lower catches or profits
in the short run.
Stakeholders often find it difficult to put explicit, quantitative weights on multiple performance indicators
(Bentley et al. 2003). This study provides a partial solution to this problem by using an economic model that
implicitly weights some of the important performance indicators. The simulations done in this study include an
economic submodel that calculates revenues, costs and net revenues. Different temporal patterns in the realization of
cost and benefits can be evaluated by discounting long term gains relative to those that accrue more quickly. We can
thus monetize some trade-offs and rank management strategies according to objective criteria such as the net present
value of the stream of total net revenues. Trade-offs between higher average catches or net revenues against higher
variation in catches or higher risk of stock depletions are more difficult to evaluate objectively with a single metric.
Therefore we still provide a variety of performance indicators relating to these objectives that stakeholders can use
along with projections of profitability in their decision making process. The economic results of the model also help
to fulfil increasing requirements for socio-economic analysis to support fishery management decisions in New
Zealand (e.g., Rock Lobster Management Decisions for the 2001-02 Fishing Year, Hon Pet Hodgson, New Zealand
Minister of Fisheries).
METHODOLOGY
We combine a spatial, size- and sex-structured biological model with an economic sub-model that transforms
projections of catches and CPUE into revenues and costs. The structure and parameterization of the biological model
is based on the 2000 NSS stock assessment (Bentley et al 2001) and addresses the uncertainty associated with the
assessment by sampling parameters from the joint posterior distribution of the fully Bayesian assessment and
allowing for stochastic variation in both parameters and processes. The economic model is based on price and cost
data provided directly by fish processors and vessel operators respectively. We utilise this model to project
biological and economic outcomes for a variety of alternative management strategies. The models produce
information on a variety of indicators relating to the productivity, profitability and risks of the management
strategies.
BIOLOGICAL OPERATING MODEL
The structure of the size- and sex-based biological operating model is based on the assessment model used for
the 2000 stock assessment (Bentley et al. 2001). Three separate lobster populations and fisheries are simulated,
representing Otago, Stewart Island and Foveaux Strait, and Fiordland. Although the latter two areas are both part of
the CRA8 management area, we modelled Stewart Island and Foveaux Strait separately from Fiordland because the
two areas have possibly different population dynamics and relationships with Otago. The model incorporated the
minimum legal size (MLS), selectivity- and maturity-at size characteristics of each of the three areas.
The model operates on a semi-annual time step with an Autumn-Winter (AW) season followed by a SpringSummer (SS) season. A unidirectional migration pattern is modelled with lobster moving from Otago to Steward
Island or Fiordland and from Stewart Island to Fiordland. Migration occurs at the start of the SS season. Lobsters
are thought to migrate from Otago into Southland just before the onset of maturity (Booth 1997).
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IIFET 2004 Japan Proceedings
STOCK-RECRUITMENT
Total egg production in each area during each time period was modelled using an exponential fecundity-at-size
relation and numbers of mature females.
For each run in the sets of evaluations, the model randomly chose, with equal frequency, between two
alternative egg dispersal schemes. In the first scheme, larval production from each area is made proportional to egg
production in each area, and larvae are distributed among areas.
Because of the predominately westward flow of currents in the area, we assume that all Otago eggs produce
Otago larvae, and that Fiordland eggs produce larvae in all three areas. In the second scheme, the model assumes
that the source of larvae is external to the stock and thus the number of larvae is unrelated to local egg production.
In this scenario total larval supply has a mean equal to that for the unfished stock and is distributed 20% to Otago,
30% to Stewart Island and 50% to Fiordland.
A density-dependent stock-recruit function was applied before larvae recruit to the model. Density-dependent
post-settlement survival was modelled using a Beverton-Holt stock recruitment relationship. Stochastic variation in
settlement was simulated by applying a stochastic multiplier to the total numbers of larvae transported to each area.
A multiplier of one implies the mean rate of larval survival. Multipliers were generated independently for each of
the three areas from a lognormal distribution with a mean of 1 and a standard deviation sigma.
GROWTH, MORTALITY AND CATCH
The model simulates the numbers of lobsters in each of 31, 2mm tail-width size increment classes for each sex.
Females are separated into immature and mature categories so that the effect of the prohibition on the taking of
overvigorous females on exploitation rates and vulnerable biomasses can be replicated. Natural mortality and fishing
mortality occur at each time step. Natural mortality is assumed to be constant and independent of age or size.
Fishing mortality is applied using a size selectivity curve and different vulnerabilities for each sex during each
season.
The implementation allows the proportion of the annual TACC that is taken in each season (AW and SS) to be
changed from a starting seasonal split. The initial split was calculated separately for CRA7 and CRA8 based on the
average of the split over the years 1999-00 to 2001-02. The model calculates the breakdown of catches for each of 9
commercial weight grades (Table 1) based on the relative abundance of lobster of each grade.
Grade
Weight
Range (kg)
Grade
Weight
Range (kg)
Grade
Weight
Range (kg)
AAA¹
0-0.3
A
0.5-0.6
D
1-1.5
AAMinus
0.3-0.4
B
0.6-0.8
E
1.5-2
AAPlus
0.4-0.5
C
0.8-1
Z
>2
¹ not applicable for CRA8; grade below the legal size for that region
Table 1
Rock Lobster Commercial Weight Grades
The seasonal and grade-wise breakdown of catch is important since prices for different grades differ
substantially and prices tend to be considerably higher in the AW season than in the SS season. The model requires
specification of the proportion of the CRA8 TACC taken in the ‘Stewart Island’ (statistical areas 922, 923, 924, 925)
and Fiordland (926, 927, 928) areas. This is fixed at the average of the proportion of the catch taken in over the
years 1999-00 to 2001-02.
VULNERABLE BIOMASS AND CPUE
Although the model has a semi-annual time step, the CPUE used in operating the decision rule is an annual
CPUE, so the relative vulnerabilities for each sex category during each season are combined using the proportion of
catch taken during each season (autumn-winter, AW, and spring-summer, SS) in CRA8. All mature females are
assumed to be ovigerous in the Autumn-winter and are not considered part of the vulnerable biomass. For CRA7 the
length at maximum selectivity was assumed to be the CRA7 MLS. The minimum legal size limit in CRA7 is 127
mm tail length (TL). We converted this to tail width (TW) using coefficients for a linear relationship between tail
length and tail width for CRA7.
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IIFET 2004 Japan Proceedings
STARTING CONDITIONS
We assume that the population in 2000 is in equilibrium with the exploitation rate, estimated from the most
recent NSS assessment (Bentley et al. 2001). Those authors estimated a range of 41-59% exploitation rate. The
starting population is initialized, given the particular movement and settlement dynamics, and the exploitation rate
and an arbitrary fixed virgin recruitment. It is assumed that virgin recruitment is distributed as 20% to Otago, 30% to
Stewart Island and 50% to Fiordland. When the population reaches equilibrium with the exploitation rate, a
catchability coefficient was calculated as a simple ratio between the equilibrium vulnerable biomass and the
observed CPUE in 1999.
ECONOMIC MODEL
An economic submodel uses catch and catch per unit of effort from the biological submodel to calculate
revenues and costs. We calculate total revenues and total costs separately for each of four fleets for each year of the
simulation. We model one fleet (Otago) for CRA7 and three fleets (Stewart Island, Fiordland and Haast) for CRA8.
Although the biological model produces only a combined catch and CPUE for Fiordland and Haast, we model the
fleets separately since their characteristics are quite different. Haast, like Otago, has mostly smaller vessels that
make single-day trips while vessels in Fiordland tend to be larger and stay out for long periods with catches being
delivered to processors periodically by helicopter. Although, in this paper, we focus primarily on combined net
revenues for the CRA7 and CRA8 fleets, modelling fleet specific revenues and costs provides useful information for
different groups of stakeholders and enables us to explore the economic consequences of shifts of catch between
areas.
REVENUES
Processors provided monthly average prices for each management area and commercial grade during the
2001/2002 season. Since the simulations predict only seasonal rather than monthly catches we calculated a weighted
average seasonal price for each grade.
Prices remain constant over time in the simulations. While this is unrealistic we do not have sufficient
information to model prices. Changes in prices are primarily a function of exchange rates and conditions in Asian
markets to which most of the lobster are exported. Prices are not expected to be affected by the level of landings
since exports from CRA7 and CRA8 make up only a small percentage of the total volume of the markets they are
sold into.
The annual revenue is calculated for each of four fleets based on catch projections from the biological
simulations. The annual gross revenue for fleet f in period t is calculated as:
9
(1)
2
R ft = ∑∑ p fgs c fgst
g =1 s =1
where,
pfgs is a fleet, grade and season specific price
cfst is the fleet, grade, and season specific catch weight
s indexes 2 seasons (AW and SS).
g indexes nine weighted based grades
EFFORT AND FLEET SIZE
In order to calculate the total cost for each fleet, we need to determine the total level of effort for each fleet. The
biological model projects a combined catch for the Fiordland and Haast fleets which the economic submodel
allocates between the two fleets according to the relative proportion of catch taken in the two areas in the 2001/2002
fishing year to get the total weight of catch, cfst, for each fleet-area, f, season, s, and year, t.
The biological model also projects a corresponding catch per unit effort, CPUEast, for each area-season-year.
This must then be calibrated to account for differentials between observed average CPUE for the fleet in question
relative to the observed CPUE of the vessels used in developing the cost model. The calibrated CPUE for each fleet,
CPUEfst, is calculated as:
(2)
CPUE fst =
CPUEast
αf
where αf is the ratio of catch per unit effort of the representative vessel used to model costs relative to average
catch per unit effort for the area in the 2001/2002 fishing year (the year for which cost data were collected). The
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IIFET 2004 Japan Proceedings
calibration of the individual vessels is done for the year rather than each season since catch and effort data by season
were not available.
CPUE
a 2001
(3) α f = CPUE
f 2002
s:
From this we can calculate total annual effort, in terms of potlifts, for each fleet, Eft and combining the 2 seasons
(4)
2
c fst
s =1
CPUEsft
E ft = ∑
We determine the number of vessels in each fleet, Nf , by assuming the annual level of effort per boat, ef ,
remains constant and the fleet size expands or contracts as the TACC and CPUE change. Since CPUE tends to
increase proportionately more than the TACC, the fleet size tends to decrease. The only effect this assumption has
on cost calculations is on fixed costs which are proportionate to fleet size. Variable costs are proportional to total
effort and are unaffected by assumptions about fleet size. If, contrary to our assumption, the fleet size remained
constant and individual effort decreased we might tend to underestimate fixed costs. However, in that case, it is
likely that these vessels would shift effort into other fisheries which could defray some of their fixed costs. The
number of vessels is calculated as:
(5)
N ft =
E ft
ef
HARVEST COSTS
Since we assume a constant level of nominal fishing effort in terms of potlifts and days fished and adjust fleet
size to account for changes in total effort, we can calculate a combined variable and fixed cost for each vessel in
each fleet, tcf , that remains constant over time. We can then calculate total costs for each fleet as:
(6) TC ft = N ft × tc f
Total vessel cost for each of the four fleets incorporates variable costs for our assumed level of annual lobster
fishing activity for that fleet along with fixed operating costs and an opportunity cost of capital equal to 10% off the
market value of the fishing vessel and other capital assets.
(7)
tc f = ( total cos t per potlift × annual potlifts per boat ) + ( crew wages × hours fished ) +
fixed operating cos t + capital cos t
Costs other than labour are derived from a survey of vessel operators in both CRA7 and CRA8. The survey was
voluntary and was completed for ten vessels in CRA8 and five vessels in CRA7. This is approximately 20% of the
fleet in each area. The results showed significant differences in cost structure and efficiency across these vessels.
Rather than use the survey data to calculate average costs and catch per unit effort, we identified “representative”
vessels for each of the four fleets and used their actual costs and catch per unit effort to model costs and calibrate
CPUE as described above. As this was a somewhat subjective decision, we undertook a sensitivity analysis of the
model using average harvest costs for each fleet.
Although labour is actually paid as a crew share, we model labour costs as a wage based on hourly wages
estimates from 2003 New Zealand labour market statistics (Statistics New Zealand 2003). Thus labour costs
represent an opportunity cost of labour rather than a cash outlay. We use a wage rather than a crewshare for a
number of reasons. Since most skippers are owner-operators, and their actual crew share is determined by the
vessels profitability. Also, shares of revenues for crew are likely to change over time if revenue per unit effort
changes.
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IIFET 2004 Japan Proceedings
NET REVENUES AND VALUE OF QUOTA
We subtract total costs from total revenues for each year and fleet to calculated net revenues. Because the
temporal distribution of benefits may differ across management strategies we calculated the net present value of net
revenues (NPVNR) as follows:
20
(8)
4
NPVNR = ∑∑
Rtf − TCtf
t =1 f =1
(1 + r )t
where r is the discount rate. We use a discount rate of 8% to calculate NPV. This is roughly equal to the real
yield on long term commercial bonds in New Zealand during 2003. The simulations run for 20 years from 20032022, so t=1 in 2003 and t=20 in 2022.
To calculate the benefits that accrue to stakeholders in the two different QMAs we calculate the value of a tonne
of quota (VTQ) for each QMA. We simply divide the NPVNR for each QMA by the number of tonnes of quota
outstanding in 2003, the first year of the simulation:
20
(9)
VTQCRA7 = ∑
t =1
20
Rt1 − TCt1
1
×
(1 + r )t
TACCCRA7,2003
4
(10) VTQCRA8 = ∑∑
t =1 f = 2
Rtf − TCtf
(1 + r )t
×
1
TACCCRA8,2003
For amalgamation, the calculation is slightly more complex since amalgamation is not assumed to occur until
2005. VTQ is the sum of NPVNR for each area from 2003-2004 and a proportional share of the combined NPVNR
for 2005-2022:
(11a)
 2 R − TCt1
  20 4 Rtf − TCtf
TACCCRA 7,2003 
1
×
×
VTQCRA 7 amalgamated =  ∑ t1
 +  ∑∑

t
t
TACCCRA 7,2003   t =3 f =1 (1 + r )
TACCCRA7 + CRA8,2003 
 t =1 (1 + r )
(11b)
 2 4 R − TCtf
  20 4 Rtf − TCtf
TACCCRA7,2003 
1
VTQCRA8 amalgamated =  ∑∑ tf
×
×
 +  ∑∑

t
t
TACCCRA8,2003   t =3 f =1 (1 + r )
TACCCRA7 +CRA8,2003 
 t =1 f = 2 (1 + r )
EVALUATION METHODS
We simulate catches, revenues and operating costs over a 20 year period beginning in 2003 for a variety of
management strategies. We consider two stock management structures for the CRA7 and CRA8 fisheries:
amalgamated and separate that we contrast with the current management. Any change to a new stock structure is
assumed to take place in 2005. This is the earliest point that a change in management would be feasible. For each
stock management strategy we consider two decision rules, the current decision rule and an alternative rule that we
called the “Bentley gradient” rule named after its author. A change from the current rule to the alternative rule takes
place only once the rebuilding target CPUE is achieved. For each stock structure and decision rule combination we
consider three CPUE targets: the current target and targets 10% higher and 10% lower.
The evaluation procedure addresses realism and uncertainty by incorporating: assessment results with respect to
the current state of the stock and likely dynamics parameter values, uncertainty with respect to parameter values and
the current state of the stock, uncertainty about interactions of the three sub-stocks in CRA8 and CRA7, error in the
use of CPUE as an index of vulnerable biomass, and the unpredictability of future recruitment. For each scenario
modelled, a parameter set was sampled from the joint posterior distribution of parameters obtained in Markov chain
- Monte Carlo simulations from the 2000 stock assessment. We used 1000 sample parameter sets to initialise the
model and simulated each management scenario for each sample. In each of the runs, stochastic process and
observation error were simulated, and a random state-of-nature was obtained with respect to the stock-recruit
dynamics and migration as described earlier. Random recruitment variation was based on the mean of the most
recent 10 recruitment deviations from the assessment. The model shuttles through all of the policy simulations (e.g.
alternative stock structures, decision rules and CPUE targets) with the same vector of parameters and random error
terms. This allows us to compare independently the relative outcomes of each management strategies for each of the
1000 “states of nature” simulated.
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IIFET 2004 Japan Proceedings
STOCK MANAGEMENT STRUCTURE
Under the current stock structure, TACCs in both areas are adjusted with a decision rule that uses CPUE from
CRA8 only. However, separate TACCs are maintained and the CRA7 area maintains its size concession allowing it
to take smaller lobsters but also its shorter season (five months running from 21 June to 19 November for CRA7 as
compared to a 12 month season for CRA8 beginning in April). The season length for CRA7 is an important
assumption in the model. Historically, catch has often fallen well short of the TACC. We assume catch is limited by
the minimum of the TACC or a catch consistent with an exploitation rate equal to the average over the period 1990
and 2000 (Bentley et al 2003). Essentially, this assumes that fishing effort is capped at the average level of prior
years and that catchability is assumed to remain unchanged. This current stock structure is only temporary.
We then evaluate two alternative stock management structures in comparison to the current stock structure and
decision rule.
Separate Stock Management:
For separate stock management strategies the TACC for CRA8 is adjusted in the same manner as for current
management using CPUE in CRA8. However a constant exploitation rate is maintained for CRA7. Under a CER
strategy, the TACC would be set each year based on a target exploitation rate. We use CPUE as an index of
vulnerable biomass and seek to maintain the current exploitation rate (CER) based on that index. A scalar is derived
based on the recent annual ratios between catch and CPUE. The TACC in each year could then be set by using the
scalar with the predicted CPUE. This procedure for setting TACC would maintain a constant exploitation rate
consistent with that in the last eleven years without the need to estimate vulnerable biomass in each year. The scalar
calculated is 229.3 (Bentley et al 2003).
Amalgamation:
Under the amalgamation strategies, a combined TACC is set for the CRA7 and CRA8 areas. The decision rules
used to adjust the TACC are based on CPUE in CRA8 just as they are for current management. The size concession
in the CRA7 area is removed as is the seasonal constraint. The larger minimum size reduces the available lobster in
the CRA7 areas considerably and reduces CPUE to unprofitable levels. Consequently we assume that all catch shifts
from the CRA7 to the CRA8 after amalgamation. In reality it is possible that CPUE might remain high in parts of
CRA7 and fishing might continue there after amalgamation. However, we can not predict or model this since we do
not have data on spatial heterogeneity of the CRA7 stock.
DECISION RULES
The current rule was designed for rebuilding the stock but is unlikely to perform well at maintaining the stock at
a particular level once it has been rebuilt because it acts by calculating a multiplier that determines the new catch
from the existing catch. An alternative maintenance decision rule was chosen from amongst those tested by Breen et
al (2003). Of the rules examined in that study which were based on maintaining a target CPUE, the “Bentley
gradient rule” generally performed the best over a suite of performance criteria including average and minimum
catch, and catch and biomass stability.
This rule is implemented when the CPUE rebuilding target is first reached. Consequently, the year of transition
to the new decision rule varies across the 1000 trials. The current decision rule is still used for the rebuilding phase.
The Bentley gradient rule adjusts TACC based on a ratio between the CPUE index and the value of the index
the last time that the quota was changed. In this way the rule is similar to a constant exploitation rate rule in that it
tries to keep catches proportional to vulnerable biomass. In addition, to maintain the target, the rule changes the
response according to the distance of CPUE from the target.
RESULTS
In addition to current management, we evaluated 9 strategies including each combination of alternative stock
structures, CPUE targets and decision rules. In general, the Bentley gradient rule tends to respond more quickly but
less dramatically than the current decision rule to differences between actual and target CPUE. Although average
catches tend to be slightly lower with the Bentley gradient rule, it performs better than the current decision rule in
terms of stock stability and net profitability. Consequently, we focus our discussion of results on management
strategies that use the Bentley gradient rule. We also focus primarily on results with the current seasonal distribution
of catch.
In most trials, regardless of the policy choice, CPUE tends to overshoot the target, drop down below it again
and then rises again toward the target. Consequently TACCs tend to increase, then decrease, then increase again,
though the changes in the TACC tend to lag behind changes in CPUE due to the mechanics of the decision rule. In
most cases the process of a second rebuilding after the overshoot and drop of CPUE is not complete by the end of
the 20 year period modelled. However, we maintain our focus on results for that time period in deference to the
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IIFET 2004 Japan Proceedings
interests of stakeholders who maintain that results more than 20 years out are not very meaningful in a practical
sense. In the long-run there may be changes in management structure, markets and certainly in the information
available for decision making. It is unlikely that the management procedure being modelled would outlive the period
modelled without significant changes.
We compare the performance of different management strategies using multiple indicators including average
catch, the relative variability of catch over time and the net present value of net revenues (NPVNR). Due to the wide
variation in outcomes given different states of nature it is probably unwise to focus too much attention on the
absolute outcomes. Thus, in addition to the average of the performance indicators, we consider the distribution of
outcomes that occur over the 1000 replications, each of which represent a possible state of nature. We present the
range of outcomes within which 90% of outcomes fall, with the 5th percentile representing the 50th highest value of a
particular performance indicator and the 95th percentile equal to the 950th highest outcome.
Relative performance of the alternative management strategies is arguably more relevant to fishery managers
and stakeholders than the numerical outcomes. Since all randomization is held constant across management
strategies for each of the 1000 replications, we can compare the relative performance of the management strategies
for each potential state of nature. For each scenario we calculate the ratio of a particular performance indicator (e.g.
average catch or NPVNR) for each replication relative to that indicator for that replication for the current
management strategy. The average of this ratio suggests the degree to which alternative strategies outperform
current management on average. By considering the full distribution of the ratio across the 1000 different “states of
nature” we can estimate the probability the scenario would be expected to produce higher or lower average catch
than current management given the full range of “states of nature” modelled with the 1000 replications. We present
the average of the ratio and the range of the 5th and 95th percentile. If, for example, the 5th percentile of the ratio of
average catch is greater than 1.0, this means that over 95% of the trials of this strategy had a higher average catch
than current management.
AVERAGE CATCH AND VARIATION IN TACCS
We first consider the average annual catches over the 20 year period projected by the model for the different
strategies. There are relatively small differences between average annual catches across management strategies
(Figure 3)
900,000
5.0%
4.5%
4.0%
3.5%
%
Average Catch (kg)
800,000
3.0%
Current
Management
700,000
2.5%
Current
Management
TACC
(2001-02)
(657,000)
2.0%
1.5%
600,000
1.0%
0.5%
0.0%
500,000
Separated
Separated
Amalgamated
Target CPUE= 1.7 kg ppl
Figure 3
Target CPUE= 1.9 kg ppl
Amalgamated
Stock Management Types
Stock Management Types
Target CPUE= 2.1 kg ppl
Target CPUE= 1.7 kg ppl
Average annual catch for the combined
CRA7 and CRA8 Fisheries between
2003-2022.ii
Figure 4
Target CPUE= 1.9 kg ppl
Target CPUE= 2.1 kg ppl
Frequency of runs for which CRA7 and
CRA8 TACCs fell below half the current
TACC.iii
The strategies shown result in average annual catch from 95-100% of the current management scenario average
catch of 685 tonnes. However, for a given policy scenario, a wide range of average catch levels are possible
depending on the state of nature (i.e. the random parameterization of the simulation for a particular policy scenario,
particularly variations in recruitment).
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IIFET 2004 Japan Proceedings
For certain states of nature, all of these management strategies could result in a higher average catch than
current management, but that current management will, on average, provide a higher average catch than any of the
strategies that use the Bentley gradient rule. Thus, the high variability in average catch across management strategies
is caused not only by random variation in recruitment, growth, movement, etc., but in how the different management
strategies respond to those different states of nature.
The variability of TACCs and the probability that they fall below some arbitrary benchmark also varies across
management strategies. We consider the percentage of replications where the combined CRA7 and CRA8 TACC
fell below 50% of the current combined TACC at some point during the 20 year period modelled (Figure 4). This is
an unlikely occurrence with any of the management strategies modelled, occurring in less than 5% of the
replications for all management strategies. In general the likelihood of occurrence is slightly greater for more
conservative strategies with higher CPUE targets. For a given CPUE target, the frequency of the TACC falling
below the benchmark is slightly higher for strategies with amalgamation than for separate management. For example
for the current CPUE target, the TACC falls below the benchmark in 3.7% of the replications with amalgamation,
2.7% with current management and 0.8% with separate management. The lower rate of occurrence for separate
management is due to the ability of the fishery to maintain a high exploitation rate on the younger animals in the
CRA7 area even when the biomass of legal size animals in CRA8 is low.
PROFITABILITY
The NPVNR over the 21 year period modelled is a primary performance indicator that provides a direct means
to quantify trade-offs between higher catches or revenues and lower harvest costs. We continue to focus on
strategies with the Bentley gradient decision rule, and we present both absolute results and results in terms of the
ratios of NPV of revenues relative to current management on a replication-by-replication basis. The CPUE target
has a very minor effect on the NPVNR for the combined CRA7 and CRA8 areas (Figure 5).
260
115%
240
110%
105%
200
%
million NZ$
220
Current
Management
100%
180
Current
Management
95%
160
90%
140
85%
120
100
80%
Separated
Amalgamated
Joint
Target CPUE= 1.7 kg ppl
Figure 5
Target CPUE= 1.9 kg ppl
Separated
Stock Management Types
Stock Management Types
Target CPUE= 2.1 kg ppl
Target CPUE= 1.7 kg ppl
NPVNR for the combined CRA7 and
CRA8 Fisheries between 2003-2022.iv
Figure 6
Target CPUE= 1.9 kg ppl
Target CPUE= 2.1 kg ppl
Average of the ratio of combined CRA7
and CRA8 NPVNR relative to current
management.v
While average catches are, in general, higher for strategies with lower CPUE targets, the positive effect of
higher catches is offset by higher harvest costs that result from lower CPUE. There are relatively small differences
in the NPVNR across stock structures. Amalgamation yields a slightly higher discounted profit stream on average
than separate management. As was the case for average catch, there is considerable variation in the NPVNR across
replications for a given policy scenario.
The ratios of outcomes taken on a replication by replication basis suggest somewhat greater differences between
management strategies (Figure 6). However, the three amalgamation strategies still provide a lower NPVNR than
current management for more than 5% of the replications. The separated stock structure with the Bentley gradient
rule also yields a higher NPVNR than current management for most replications, but again yields lower NPV than
current management in more than 5% of replications.
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IIFET 2004 Japan Proceedings
DISTRIBUTIONAL IMPACTS
The relative effects on catch and average net revenues of different management strategies are quite different for
quota owners in the CRA7 and CRA8 areas. The CPUE target has relatively little effect on the benefits that are
expected to accrue to quota owners from the two QMAs, but the stock structure has significant and contrary impacts
for the two groups. Under the amalgamation strategies there is, by assumption, no longer any catch taken from the
old CRA7 QMA. However, the CRA7 quota is used in what was the old CRA8 QMA.
The value of a tonne of quota (VTQ) for CRA7 is considerably increased under amalgamation relative to
current or separate management, but there is little gain in quota value for current CRA8 quota owners. This is
expected since the current price of a tonne of CRA8 quota is considerably higher than a tonne of CRA7 quota due to
higher average prices and profit margins in CRA8. CPUE is also higher in CRA8, but higher costs per unit of
nominal effort tend to offset the benefits of higher catch rates somewhat. Separate management also increases the
VTQ for CRA7 though less than amalgamation.
CONCLUSION
These simulations are not meant to identify an optimal management alternative, but rather to provide
stakeholders with the information they need to choose among alternatives. Management strategy evaluation using
fishery simulation modelling offers a means to identify management strategies that are cost effective, robust to
uncertainty, and consistent with the objectives of stakeholders and managers. Adding an economic component that
objectively quantifies trade-offs between higher revenues and lower costs or differences in the temporal stream of
benefits can enhance the ability of managers and stakeholders to make sound decisions. However, stakeholders may
have diverse and conflicting objectives, and a mean prediction of profitability is only one of multiple indicators
relevant to evaluating performance of alternative strategies.
The simulations showed relatively small differences in performance (both biological and economic) across
management strategies. Variation in results related to uncertainty in model parameters is greater than the average
differences between the management strategies. However, comparing performance for each simulation suggests that
the relative performance of different management strategies varies with the state of nature. If uncertainty in model
parameters can be reduced it may be possible to identify superior management strategies more clearly.
While differences in performance are small, it is notable that the economic performance indicators (i.e.,
NPVNR) provide contrary rankings of management strategies than those based on average catch levels. This is due
to differences in the size composition of catch and prices across areas, and the effects of lower costs that are
achieved by maintaining higher CPUE. In the case of amalgamation, catch is shifted from the CRA7 area which had
smaller margins between revenue and cost per kilo of lobster to the CRA8 area where margins are higher. The
relative difference between the biological and economic indicators would be even greater if the average prices of the
lobsters decreased which has in fact occurred since the price data was assembled. Lower prices squeeze profit
margins and increase the importance of maintaining lower harvest costs.
This analysis highlights the importance of considering distributional effects. While the simulations suggest that
the net value of the combined CRA7 and CRA8 fishery would be increased by amalgamating the two areas, it also
appears that the gains from this policy would be favourable to CRA7 quota owners while CRA8 quota owners may
in fact be only slightly better off than they would with separate management. There may also be important negative
effects on non quota owners as a result of shifting catch from CRA7 to CRA8. In particular, part-time lobster fishers
in CRA7 that relied on leasing of quota would probably no longer be able to continue fishing lobster. They may lose
a significant portion of their income they may find difficult to replace. Differences in overall catch and profitability
across management strategies are not large and are subject to a good deal of uncertainty. Consequently,
distributional issues may well prove more important than overall net benefits in the decision making process.
The New Zealand government is encouraging increased involvement of fishery stakeholders in management of
their fisheries by empowering them to develop fishery plans which can include procedures for determining TACCs
among other things. This has increased the interest of commercial stakeholders in developing the means to predict
the likely economic and biological results of alternative management strategies, which requires not only new models
but new data collection. This work was done at the request and expense of the fishing industry. The economic data
used for this model are not collected by the government, and this type of bioeconomic analysis has not been part of
management decision making for New Zealand fisheries in the past. However, we expect that increased use of
fishery plans will provide sufficient incentives for commercial stakeholders to provide the funding and confidential
economic data necessary to undertake this type of analysis.
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ENDNOTES
i
The target biomass trajectory for CRA8 was generated by plotting a straight line from the observed starting value
in the 1997–98 fishing year to the mean 1979–80 to 1981–82 CPUE which is to be achieved in 2014-15. Observed
CPUE is generated from the standardised CPUE analysis for CRA8 multiplied by the geometric mean of the
arithmetic (sum of annual catch divided by sum of potlifts) CPUE indices.
ii
The diamonds, circles or squares indicate the average annual catch for each stock strategy when using different
CPUE targets. Vertical lines the 5th and 95th percentile of average annual catch. The horizontal line shows the
average catch for the current management strategy.
iii
The diamonds, circles or squares indicate the percentage of simulated TACC falling below 50% of the current
combined TACC for each stock strategy when using different CPUE targets
iv
The diamonds, circles or squares indicate the average NPVNR for each stock strategy when using different CPUE
targets. Vertical lines indicate the 5th and 95th percentile of NPVNR. The horizontal line shows the average NPVNR
for the current management strategy.
v
The diamonds, circles or squares indicate the average of the ratio for each stock strategy when using different
CPUE targets. Vertical lines indicate the 5th and 95th percentile of the ratio
12