DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS
Research and Development
CSG 15
Final Project Report
(Not to be used for LINK projects)
Two hard copies of this form should be returned to:
Research Policy and International Division, Final Reports Unit
DEFRA, Area 301
Cromwell House, Dean Stanley Street, London, SW1P 3JH.
An electronic version should be e-mailed to [email protected]
Project title
Strategy Development and Scenario Testing for Fisheries Recovery Plans:
Part A
DEFRA project code
HS0103
Contractor organisation
and location
University of Portsmouth (CEMARE)
Opinion Leader Research
Centre for Environment, Fisheries and Aquaculture Science (CEFAS)
Total DEFRA project costs
Project start date
£ £43,139.70
01/11/03
Project end date
01/06/04
Executive summary (maximum 2 sides A4)
Many of the key UK (and corresponding European) fisheries are currently overexploited, with stocks below the
precautionary biomass levels. Stock recovery plans have already been instigated in some of the major fisheries (e.g.
North Sea cod), involving restrictions on fishing activity with little consideration to the financial viability of the fishers or
regional communities. Further recovery plans are being considered in other fisheries.
The purpose of this study was to examine how these broader considerations can best be built into strategy development
process, and how the impact of fishery recovery programmes on the stocks, fishers and regional economy can be
assessed. An appropriate framework for such an assessment is the use of bioeconomic models. These include the main
biological and economic relationships in the fishery, and can be used to determine either optimal recovery strategies or to
estimate the impact of particular strategies on the stock and incomes of the fishers. However, existing bioeconomic
models have generally considered only the fishing sector and commercial fish stocks, whereas the impacts from stock
recovery plans will also affect other users of the resource (e.g. recreational fishers) and regional communities. Different
recovery programmes may also have differing environmental impacts.
Failure to take these impacts into consideration when formulating and analysing management options has also been a
problem with bioeconomic models to date, and more generally fisheries management policy initiatives. There is a range of
stakeholder groups with an interest in fisheries, and these have an influence on the management of the marine resource.
A key objective of the project was to determine what are the main factors affected by stock recovery, and how these may
be integrated into a single modelling framework that is capable of providing information required by a range of
stakeholders. Further, given the substantial uncertainty regarding many of the interactions that exist within the fishery
system, means by which this uncertainty can be factored into the analyses were also to be considered.
Although the study was to be generically applicable in nature, it was also specifically aimed at informing the South West
CoBAS model (Cost Benefit Analysis for Sustainability). This model, being developed as part of the Invest in Fish South
West (IiFSW) project, aims to develop and assess industry and community led stock recovery plans in a cost-benefit
framework. The IiFSW project aims to develop a detailed bioeconomic model of the fisheries in the English Channel and
CSG 15 (Rev. 6/02)
1
Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Celtic Sea that also encompasses the regional economy, and uses the model for assessing the impact on the stocks,
fishers and regional economy of alternative potential management plans for stock recovery.
Three workshops were held to consider these issues. The first workshop considered what stakeholders were expecting to
achieve through stock recovery, and how they may best be integrated into the decision making process. The second
workshop considered how to incorporate other sectors or user groups that were affected by stock recovery into the
modelling process. These included recreation fishers, tourism and regional economies as well as the fishing sector itself.
The final workshop considered how best to model the biological and environmental interrelationships, and how to
incorporate and communicate the considerable uncertainty associated with these relationships. A mix of scientific experts
(economists, biologists and environmental scientists) and stakeholders participated in each of the workshops. Following
the workshops, examples of applications of the ideas suggested by participants were investigated through a
comprehensive literature review.
The stakeholder workshop reinforced the importance of working with stakeholders when developing recovery
programmes as well as when developing models to assess recovery programmes. Stakeholders were keen to participate
in the development of such models, and means in which they could contribute to the model development and analysis
were discussed in all three workshops. Key area in which stakeholders could contribute include provision of information,
model verification and validation, and strategy formulation.
The second workshop considered what additional economic impacts might need to be considered. Recreational fishing
was discussed and methods for incorporating this group into a bioeconomic model were considered. Similarly, potential
tourism interactions with the fishing sector were discussed. Consideration was given to the effect the existence of a
fishing industry might have on tourism. While the general feeling was that the existence of a fishing industry may have
positive tourism benefits, there have been no studies to verify or quantify these benefits. This is potentially an area for
further research. Methods for including the local economy into the bioeconomic modelling framework were also
considered. While the use of Computable General Equilibrium (CGE) models had much to offer, it was felt by most in the
group that they required considerable simplification in order to solve. This loss of detail may be important when assessing
which groups may be most affected by recovery programmes. The use of input-output model to capture regional
interactions also has some deficiencies (in terms of simplifying assumptions), but these were considered more acceptable
than the CGE assumptions.
The third workshop considered the biological and environmental interactions required to develop models for developing
and assessing stock recovery strategies. Particular attention was also given to incorporating uncertainty, and the tradeoff
between complexity and robustness of models given uncertainty. For example, a trade-off that was considered acceptable
by the participants involved excluding predator-prey interactions when these are poorly understood. A key question raised
at the end of the meeting was: whether projects such as SW-CoBAS, that consider re-building of stocks, should consider
short-term predictions or long-term consequences? Short-term predictions require exact estimates and the estimation of
the uncertainty becomes critical to the process. When evaluating long-term consequences, the focus is on predicting
trends and estimating uncertainty becomes less important. The “common sense” approach is to choose between these
two extremes (short-term predictions versus long-term consequences) and evaluate the medium-term probabilities of
alternative options with a focus on predicting trends.
The results of these workshops, and subsequent further literature reviews, provide a blueprint for the development of
effective fisheries bioeconomic models. In many cases, it will not be feasible to incorporate all of the features identified in
this study into a functional bioeconomic model. This blueprint includes not only model design, but considerations for
model use and interaction with the stakeholders and decision makers. However, consideration of the flow on effects can
still be undertaken qualitatively and incorporated into the final decision analysis.
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Scientific report (maximum 20 sides A4)
Background and context of the study
The long run sustainability of many European fisheries is highly questionable, with many key European stocks currently
below their safe biological levels. Stock recovery plans were introduced in 2004 in a bid to conserve cod and northern
hake stocks, and have been proposed for southern hake, sole in the western Channel and Bay of Biscay, and Nephrops
off the Iberian Peninsula. These have involved restrictions on fishing activity, which will have had a direct impact on the
fishers and regional communities. These impacts have largely been ignored in the development of the recovery plans,
which instead have focused purely on improving the status of the resource.
Fisheries management objectives, however, can be achieved through a range of methods. These different strategies will
have differing impacts on the resource, the environment, and fishers and the community. A recovery plan that could result
in stock recovery but financial ruin if adhered to is likely to cause problems with compliance, reducing the effectiveness of
the plan. The end result in such a case may be no long-term benefits but substantial short term costs.
A framework that allows assessment of both the biological and economic impacts of stock recovery programmes is the
use of bioeconomic models. As the name implies, these include both a biological component (capturing the stock
dynamics) and an economic component (capturing the financial impact of policies on the fishers). While the development
of bioeconomic models is well established, they have generally not been used to assist in the development of fisheries
management plans in Europe. In other countries (e.g. Australia and New Zealand), they have been applied to aid in
management plan formulation and analysis, but only on a limited number of occasions.
The underutilisation of bioeconomic models in the past in the development of effective management plans may be
partially a function of their limited scope. Most bioeconomic models consider only the relationship between the fishing
fleet and the stock, whereas management has many wider objectives. Indeed, a recent study (Mardle et al., 2004) found
that, in the UK, regional employment was a major objective, and in most cases more important than economic
performance of the fleet itself. A wide variety of management objectives have also been observed around Europe (Mardle
et al., 2002).
Stakeholder groups also have an influence on the management of the marine resource. Failure to take these groups into
consideration when formulating and analysing management options has also been a problem with bioeconomic models to
date, and more generally fisheries management policy initiatives.
Further, existing models – both biological and bioeconomic – are fraught with uncertainty in many of the key parameters.
Use of these models for effective management formulation and analysis requires these uncertainties to be both identified
and appropriately dealt with.
Terms of reference of the project
The terms of reference for the project were to lead, organise, facilitate and report on a series of workshops/focus groups
aimed at fully exploring the methodology and approaches of developing alternative strategies for fisheries recovery, within
the overall context of the CoBAS project (see Box 1).
The issues to be considered in the workshops included:
-
Do we sufficiently understand the process of stock recovery and will stocks recover if fishing mortality reduces?
Are bioeconomic models sufficiently robust to produce workable scenarios and how do we deal with the
uncertainty of the science?
What are the key ecosystem issues that need to be evaluated when developing alternative strategies? What are
the appropriate means for evaluating these?
What alternative marine based opportunities are there for employment e.g. eco-tourism?
Who are the important stakeholders and what is the means for judging this? When developing and evaluating
alternative strategies, how will stakeholders deal with uncertainty and risk?
How can Member States’ own interests be accommodated and how can Member States engagement be
encouraged?
On what basis can we compare alternative strategies and select ‘the best’?
The output from the workshop/focus groups was a series of short reports, aimed specifically at helping CoBAS in its startup phase and strategically establishing the broader issues and principles surrounding strategy scenario planning for
fisheries.
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Box 1: South West CoBAS1
The South West CoBAS study (Cost Benefit Analysis for Sustainability) aims to develop and assess industry and
community led stock recovery plans in a cost-benefit framework. South West CoBAS aims to develop a detailed
bioeconomic model of the fisheries in the English Channel and Celtic Sea that also encompasses the regional economy,
and uses the model for assessing the impact on the stocks, fishers and regional economy of alternative potential
management plans for stock recovery.
The CoBAS project is an experiment in stakeholder ownership of fisheries management plans. Critical to the project will
be the development of a bioeconomic model that is scientifically robust, easy to use and allows a wide range of factors to
be taken into consideration (e.g. impact on the community, fisher incomes etc). If successful, the approach may be
applied to other European fisheries. For this reason, the workshops will consider issues that are not only related to the
SW region, but also relevant in a broader UK and European context.
Objective of this report
The main objective of this report is to present the main findings of the three workshops, as well as an overview of how
these findings could affect the development and application of the modelling approach used in the CoBAS project. More
detailed discussion of the key points are provided in the separate workshop reports.
Structure of the workshops
The study was undertaken through a series of three workshops, each relating to a specific set of issues, and a synthesis
workshop where the findings were presented and general feedback obtained.
The three main workshops were involved in addressing the issues associated with:
1) How to involve stakeholders and policy makers in the model development process to increase acceptance of the
results and ensure implementation of the identified optimal policies (Stakeholder Workshop, 3 December 2003,
London)
2) How to incorporate economic uncertainty and broader economic considerations into bioeconomic models (e.g.
regional economies, alternative industries) (Economics workshop, 10-11 December 2003, Portsmouth)
3) How to adequately model the biological resource in the light of considerable uncertainty (Biological workshop, 1516 January 2004, Lowestoft)
The synthesis workshop was held at DEFRA on 5 March 2004. The main objective of the synthesis workshop was to
present the findings of the three main workshops, and identify any gaps from the policy makers’ perspective. Discussion
at the final workshop was a valuable input into the focus and content of this final report.
The three main workshops
The three main workshops were limited to a small number of participants (around 20) who were invited on the basis of
their expertise in an area relevant to the workshop. Participants were invited from the UK and, where appropriate the rest
of Europe and other countries. In total, 57 individuals participated over the three meetings. A brief description of the
expertise and experience of the participants is given in Table 1.
Table 1. Participants’ expertise and experience
Scientific experts in the field of:
Fisheries economics
Fisheries science
Fisheries management
Environmental issues
Tourism
Sociology
1
Active stakeholders
Commercial fishermen
Producer Organisation and Industry representatives
Conservation organisations
Recreational fishers organisations
Retail representatives
The CoBAS study has more recently become known in the public arena as the Invest in Fish: South West project (IiFSW).
CSG 15 (1/00)
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
A cross section of the participants was present at all three workshops, although in varying proportions (i.e. there were
more economists at the economics workshop than at the other two workshops, while more stakeholders attended the
stakeholder workshop than the scientific or economics workshop). The distribution of participants across broad
stakeholder groups across the three workshops is illustrated in Figure 1. Additional experts provided by the host
institutions were also present at each workshop. These have not been included in Figure 1, which represents participation
of ‘external’ experts and representatives only.
Retail/processors
7%
Recreational fishing
5%
0%
Fisheries economics
16%
Commercial fishing
12%
Sociology/ political
science
7%
Fisheries science
26%
Tourism
2%
Fisheries
administration
11%
Environment and
conservation
14%
Figure 1. Distribution of stakeholder participants across all three workshops
(excluding participation of ‘host’ institutions)
All three workshops followed the same format. Briefing notes outlining the topics for discussion and background to the
study were sent to participants several weeks before the workshop. Following a plenary session where the aims and
objectives of the workshops were presented, the participants were split into two small discussion groups to consider the
range of example questions posed in the briefing notes. The participants were told that they need not limit their discussion
to the example questions, but had freedom to consider other aspects that seemed relevant to the objectives of the
workshop.
The workshops were organised into four sessions, each involving between an hour and 1.5 hours of small group
discussion. At the end of each pair of session, the outcomes of the small group discussions were presented to the group
as a whole, and additional discussions were held.
An advantage of the small group discussion format was that different groups tended to focus on different aspects,
depending on the mix of expertise in the group. This resulted in greater detail of consideration given to different points
than would have been achieved in a larger group discussion.
The future of fisheries bioeconomic modelling
A key feature of DEFRA’s Horizon Scanning series of studies is their emphasis on being forward looking rather than
retrospective. This particular project was specifically aimed at informing a particular modelling initiative. As a result, the
general outcomes of the discussion provide an indication of the future requirements from bioeconomic models, and how
modellers may meet these requirements. Hence, this project is fundamentally different in many regards to the usual
Horizon Scanning project.
Bioeconomic models have two main roles. Firstly, and foremost, they are tools to examine consequences of management
changes in fisheries. They play a secondary role, however, in being a means by which scientists, policy makers and
stakeholders can cooperate in policy formulation and assessment. Assumptions underlying the model need to be agreed
by each party for the model results to be acceptable. The assumptions are also transparent and explicitly stated
throughout within a mathematical formulation (specified as a series of equations) of the model.
CSG 15 (1/00)
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
In this section, consideration is given to what needs to be in a bioeconomic model in order to make it more useful for
policy development and analysis. Foremost, the model must be able to take into consideration the full range of objectives
of fisheries management. This requires the incorporation of many sectors that interact with the marine ecosystem which
are not currently included in traditional bioeconomic models. Second, the model must be readily acceptable and useable
by a range of stakeholders. The role of stakeholders in the development of such a model is extremely important if it is to
be an effective tool for policy formulation. Thirdly, considerable variation exists in both the marine and economic
environments that affect the ability to both describe the current relationships within the system with certainty, and forecast
future states of the systems. These uncertainties are carried through to the relationships and parameters within the
model. To be effective, the model must be able to adequately deal with these uncertainties, and be able to provide useful
policy advice despite the uncertainties.
In many cases, the issues described above are not adequately (if at all) addressed by models. However, identification of
potential areas that need to be addressed is a first step in the development of models that may be more effective for
policy analysis.
New areas for inclusion in bioeconomic model
As noted above, Mardle et al. (2002, 2004) found that regional employment considerations play a major role in the
formulation and implementation of fisheries policy throughout Europe, and in the UK in particular. In addition,
environmental impacts from fishing are also significant factors affecting decision making.
The traditional bioeconomic model has tended to exclude these components. Most bioeconomic models have focused on
the interactions between the fleets and the stocks (Figure 2). In the case of simulation models, fishing effort is given (i.e.
an exogenous variable, at least in the first year of the simulation) and the impact on the stock and fleet level profitability
are estimated given these effort levels. In optimisation models, fishing effort is estimated (i.e. endogenous) such that a
particular objective is achieved (e.g. maximise profits or sustainable yield).
costs
stocks
effort
catch
fleets
revenue
profits
Figure 2. Simple representation of traditional bioeconomic model
The economics workshop considered what additional features in bioeconomic models may be required by policy makers
and stakeholders for developing effective management strategies. Three broad additional areas were identified as
important for estimating the economic consequences of management change: incorporation of regional economic impacts
from management change, interactions with other users of the marine resource (particularly recreational fishing) and
tourism impacts (which overlaps the other two main areas). In addition, the stakeholder and biology workshop identified
features that would be important on the resource side. These included inclusion of environmental damage, ecological
interactions and spatial structures. These key interactions are illustrated in Figure 3.
CSG 15 (1/00)
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
Ecological
impacts, climate
change etc
Environmental damage
DEFRA
project code
HS0103
Economic
value
costs
stocks
effort
catch
fleets
revenue
profits
Recreational
fishing
Employment
Tourism
Regional
economy
Figure 3. Simple representation of expanded bioeconomic model
Methods for incorporating the additional components
The workshops considered alternative methods for incorporating these additional components. These are summarised
below, along with examples of models that have attempted to incorporate some of these features.
Incorporating the broader community impacts of management change
The economics workshop considered two approaches that could be used to examine the impacts of management change
on the broader community. These were the incorporation of input-output models and the use of computable general
equilibrium (CGE) models (see Box 2). In addition, the usefulness of multi-objective modelling was also briefly discussed.
Box 2. Input-output and CGE models
Input-output models are linear models of the interactions between different sectors of the economy, and are often used for
regional economic analysis. The models are based on a transactions table that includes the inputs into each sector as
columns and the outputs as rows. Intermediate inputs are those that are inputs to one sector but outputs from another
sector within the region. From the transactions table, the sector multipliers can be derived. These provide a measure of
the flow on effects of a change on one sector on the rest of the community. The magnitude of the regional multiplier is
affected by the relative proportion of inputs that are derived from other local sectors and those that are imported into the
region. The models are static, and assume that any increase in output from a given sector results in a constant
proportional increase in the demand for inputs.
CGE models utilise a social accounting matrix that is similar in many regards to the transactions table of the input-output
model. The models, solved as optimisation models, are not static in the sense that a change in one sector will result in
changes in other sectors through the increase in input demand. The models allow for substitution of inputs, as well as
changes in prices of labour and other inputs as a result of changes in the level of production of one or more sectors. A
household sector is included to allow estimation of consumption (regional or export) and price changes in the final
products. Because of their increased complexity, the sectors are often highly aggregated.
Attempts have been made to incorporate broader considerations into fisheries bioeconomic models through multiobjective programming models. Mardle et al. (2000) and Pascoe and Mardle (2003) developed multi-objective models of
the North Sea and English Channel respectively that included fishing employment and relative stability considerations as
well as the traditional factors such as profitability and stock levels.
The inclusion of more detailed economic interactions between fisheries and the broader community has been relatively
limited to date. Eide and Heen (2002) used a bioeconomic model and an input-output model to assess the onshore
impacts of changes in Norwegian fisheries due to climate change. The two models, however, were not integrated – the
output of the bioeconomic model used to modify the separate input-output model coefficients. Environmental impacts
(non-commercial bycatch and habitat damage) and regional employment considerations (based on fixed multiplier effects
CSG 15 (1/00)
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
derived from a separate input-output model) have also included in a more complete version of the English Channel multiobjective model (Mardle and Pascoe 2003a). Jin et al. (2003) integrated the fisheries model with an input-output model to
examine the broader economic impact of habitat damage in the New England Region. Dalton (2004) also integrated an
input-output model with a bioeconomic model to assess the potential benefits of marine protected areas.
The use of computable general equilibrium (CGE) models has also recently been attempted to examine the broader
economic impact of changes in fisheries. Houston et al. (1997) developed a regional CGE model for examining the impact
of different harvesting strategies for an Oregon groundfish fishery. An integrated model in the form of a CGE model has
been developed of the South-West UK (Floros et al 2003). The model is very aggregated in nature, with the fishing
industry represented by three activities, and the regional economy represented also by a limited number of activities.
Finnoff and Tschirhart (2004) developed a five-sector model based on the Alaskan Pollock fishery, with the fishery as one
sector.
Both the CGE and input-output models have advantages and disadvantages when applied to fisheries problems. The
major difficulty for developing CGE models is the limited number of sectors that can be modelled due to the complexity of
the interactions. While large-scale models can be developed, these have been limited in practice due to difficulty in
obtaining the appropriate parameter values at a disaggregated level (Yang 1999).2 In contrast, input-output models can
be more readily disaggregated (i.e. include more sectors explicitly), but do not allow for changes in the input mix, prices
and costs that might arise through changes in the output level of one or more sectors.
CGE models also tend to be equilibrium models. That is, they provide the long run equilibrium level of prices, costs and
output levels arising from a change in the underlying structure of the regional economy (such as the state of the stock in
the case of fisheries models). Where dynamic simulation models are being considered, such as in the case of the CoBAS
project, then it is unlikely that prices, costs and the level of output in other sectors would change substantially from year to
year. Attempts of developing dynamic CGE models have assumed that equilibrium is achieved in each year from a
marginal change in output from the fisheries sector (e.g. Finnoff and Tschirthart 2004). The model then moves from one
long run position to the next, rather than being a ‘true’ dynamic model.
This problem associated with ‘dynamic’ CGE models has been experienced in other areas of research. Yang (1999)
developed a method for coupling a CGE model to dynamic programming model to ‘slow down’ the speed of adjustment in
the GCE model when considering changes in savings and investment over time.
Dynamic input-output models have been developed and applied to a range of studies since the late 1960s. Zhang (2001)
proposed a ‘half-way’ solution between CGE and input-output involving a non-linear dynamic input-output model. This
method was based initially on the dynamic CGE, but uses an iterative method (rather than optimisation) to derive the
regional impact over time.
A further common difficulty with both the CGE and dynamic non-linear input-output model that prevents many sectors
being included in the model is that of finding a globally optimal solution. Both approaches problems rely on non-linear
programming methods, which experience problems of local infeasibilities and local optima (with potentially several optimal
points having similar outcomes in terms of, say, profits, but very different consequence in terms of, say, fleet structure).
The development of heuristic optimisation approaches (e.g. genetic algorithms) will overcome some of these problems.
These are not efficient methods at the moment, but continuing improvements in computing power will no doubt result in
these methods becoming more useful for solving large scale non-linear models such as those required for a detailed CGE
model.
Incorporating other users of the marine resource
The workshops also considered the role of other users of the marine resource; recreational fishers in particular.
Recreational fishing was considered to have both an impact on the stock as well as having a high economic value. On a
pound per pound basis (i.e. £/lb), the value of a recreationally caught fish was believed to be as much as three times that
if caught commercially. Charter boats in some areas are believed to earn more per day than equivalent sized fishing
boats.
Examples of bioeconomic models that explicitly incorporate a recreational fishing sector are limited, and have involved
interactions between a limited number of gear types and species. For example, stock recovery of a commercialrecreational species (yellow perch) was modelled by Milliman et al (1992). Whereas, Campbell and Reid (2000) estimated
the cost to recreational fishers of bycatch of the estuarine prawn beam trawl fishery in southern Queensland; while
Schuhmann and Easley (2000) looked at the benefits of reallocating catch of red drum from the commercial to
recreational sector. In contrast, Rosenman (1991) estimated the impact of the recreational mackerel fishery on the
2
Yang (1999) provides some examples of large-scale CGE models.
CSG 15 (1/00)
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Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
commercial sector. A number of other studies have developed bioeconomic models of recreational fisheries separate
from the commercial sector (e.g. Schuhmann and Easley, 1998; Woodward and Griffen, 2003).
In theory, recreational fishing can be factored into bioeconomic models in much the same manner as any other fishing
activity. A given level of fishing effort can be expected to result in a certain level of catch. This requires catchability
coefficients and an appropriate unit of fishing effort to be defined. The economic value of the catch requires an estimate
of the value of each fish caught.
Several studies have recently been conducted looking at the average value of a recreational fish in the UK (e.g. the
recent Scottish study of inshore angling and the DEFRA funded study on recreational fishing).3 In the US, regular surveys
of recreational fishing are undertaken to value the activity. The survey is a rolling 5 year study of different fisheries (i.e.
each fishery is examined every 5 years with different sets of sectors examined each year). The survey provides
information on the value of different numbers and types of fish by size.
Such data could also potentially be used to examine how demand for recreational fishing changes in response to stock
levels. Several studies on the demand for recreational fishing have focused on travel costs and willingness to pay (e.g.
Shrestha et al. 2002). While these have derived implicit demand relationships for recreational fishing, they have not been
explicitly linked to stock levels or integrated into a bioeconomic framework. The closest study to this examined the impact
of two hypothetical different stock levels of willingness to pay for a recreational species (Schuhmann, 1998).
Tourism and fisheries management
Marine and coastal tourism is one of the fastest growing areas within the world's largest industry, that being tourism(Hall,
2001). Tourism interacts with fishing both directly and indirectly. Fisheries management policies, particularly the formation
of marine reserves, could potentially enhance tourism through providing increased opportunity for, and improved quality
of, recreational activities such as scuba diving and recreational fishing. These activities have an economic impact on the
local community, and may potentially be more valuable than the fishing activities that they displace (Davis and Gartside,
2001). Tourism can also potentially provide alternative employment opportunities for fishers and vessels displaced from
the fishery. The need to consider the impacts of stock recovery on tourism was raised as an important issue at both the
stakeholder and economics workshop.
At the economics workshop, whale, seal and dolphin watching tours also considered to a potential alternative activity for
fishing vessels displaced by any stock recovery plan. Increases in stocks are likely to lead to increased populations of
whales, seals and dolphins. This in turn will attract greater interest in whale/seal/dolphin watching as the chance of seeing
a marine mammal is increased.
Examples of fisheries bioeconomic analyses that include tourism are very limited, while those that do exist focus on the
interactions with marine mammals. Boncoeur (2002) developed a bioeconomic model of a marine reserve that also
provides ecotourism benefits in the form of an enhanced seal population. The model results showed that the dynamics of
the two interacting stocks (i.e. fish and seals) reduces the benefits of the no-take zone to the fishing industry, but
improves the opportunity for the development of ecotourism. As a result, the optimal size of the reserve was found to be
larger when ecotourism is taken into account along with fishing activities. Similarly, the model developed by Finnoff and
Tschirhart (2004) cited previously included recreational demand for marine mammals as well as a commercial fishing
sector in the regional model. The model was used to examine alternative stock rebuilding scenarios considering the
benefits to the commercial sector as well as the tourism industry based on observing marine mammals.
Indirect impacts of fishing on tourism are less quantifiable. At the economics workshop, it was considered possible that
small vessels (e.g. under 10m) may attract tourists as there is a lot of activity – unloading locally with small and diverse
catches. The existence of an inshore fleet in a town may therefore have a positive impact on tourism and alternative
employment opportunities in the town, particularly tourists attracted to the ‘fishing village’ concept. Related to this, tourist
may expect fish to be locally caught, and be willing to pay a premium at restaurants for local product.
Including these types of interactions into a bioeconomic framework may be difficult. Recreational demand models may be
able to capture some of these aspects, with this feeding through to the regional economic model. The impact of changes
in fleet structure could therefore be captured. To date, however, no studies exist that link tourism demand to the existence
of fishing fleets.
As mentioned above, fisheries management measures may create additional tourist benefits as positive externalities.
Bhat (2003) found that, as the result of the creation of marine reserves in Florida, an average visitor would undertake 43–
80 per cent more trips to the Florida Keys, and experience a 69 per cent increase in the use values per trip, as a result of
3
Earlier studies on recreational fishing in the UK have focused primarily on sea bass (e.g. Dunn, Potten and Whitmarsh, 1995)
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
the induced reef quality improvements. These benefits are obviously case specific, but potential positive externalities
could be measured and included in economic analysis of stock recovery measures such as the creation of marine
reserves.
Tourism can have negative as well as positive interactions with the fishing industry. Increased demand for labour in the
tourism industry will cause a shortage of labour for the fishing industry. This may be a longer-term effect rather than short
term as the skills required in the tourism industry will not be the same as those for the fishing industry. However, potential
new entrants to the fishing industry may be diverted to the tourism industry. This interaction could conceivably be
captured in a GCE model.
Ecosystem models and spatial interactions
The previous discussion has largely focused on additional features of the economic component of the bioeconomic
model. However, considerable developments can be made to the biological component also.
A key feature of all three workshops was interest in marine reserves. This requires a spatial structure in the model in
order to estimate the effects of closing one area on the stock and effort dynamics. Wilen (2000) offers several reasons
why spatially structured models are most appropriate, the foremost being that some policies (such as marine protected
areas) cannot be modelled without such structures. Several attempts have been made at developing spatially explicit
bioeconomic models, although most of these are simple models based on hypothetical or illustrative fisheries (e.g.
Sanchirico and Wilen, 1999; Collins et al, 2003), or have been based on a fairly sessile species (e.g. Smith and Wilen,
2003). Limited attempts have also been made to develop spatial bioeconomic models of multispecies fisheries (e.g. Rueda
and Defeo, 2003; Mahévas and Pelletier, 2004), although the fishing effort dynamics in these models is relatively crude.
The biology workshop considered the feasibility of developing a spatial model for the CoBAS project given the existing
available data. While some information was available, much of the fine detail required for a spatial model did not exist.
Existing models of fisher behaviour were also fairly crude, and attempts to develop more detailed fisher behaviour models
based on existing data sets have proven to be problematic. The group generally recognised the desirability of spatial
analyses, but recognised that realistic and reliable spatial models could not be developed in the short term, and would
require new data formats for data collection. As an alternative, simpler models that use a proxy for spatial aspects could
be specified that make assumptions about the proportion of the stock protected and its contribution to the spawning stock
biomass.
The biology workshop also considered the usefulness of including multi-species and ecosystem interactions. Several
existing models exist that include such interactions, such as the Ecosim model, with also allows for spatial interactions.4
These models have been used to examine a range of issues in fisheries, primarily from an ecological perspective.
Bioeconomic analyses have generally been limited (see, for example, Sumaila and Vasconcellos, 2000). The usefulness
of these models for policy analysis has been questioned (Christensen and Walters, 2004), as the added complexity
introduced into the model increases substantially the uncertainty of the model results. Also, the fishing effort dynamics are
relatively crude despite the detail on the biological and ecological interactions. The biological workshop concluded that such
detailed models were perhaps not useful for the basis of assessing stock recovery options given the current state of
knowledge on the interactions between the species, although interest in their further development should be maintained.
Spatial and ecosystem based models are going to become increasingly useful as more information is collected on the
fisheries, and demand for micromanagement of the fisheries increases. Many future fisheries management problems will
require detailed spatial models for their analysis.
Bringing the stakeholders into the equation
All three workshops considered the potential role that stakeholders could play in the development and use of the
bioeconomic models for policy development. This included data provision, model validation, and most importantly,
strategy formulation.
There is an increasing worldwide trend in the involvement of stakeholders in the development of fisheries management
policies and plans. Stakeholder involvement ranges from participation in management advisory committees (such as in
Australia and the USA), to full co-management (e.g. some fisheries in Norway and Uganda) and community based
management (e.g. inshore fisheries in many African and south-east Asian countries) (FAO, 2004). The importance of
stakeholder involvement in fisheries policy development has recently been highlighted at the European level. The creation
4
A good review of the Ecosim/Ecopath models is presented by Christensen and Walters (2004).
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
of regional advisory councils (RACs) is a key proposal under the reform of the CFP. The objective of creating RACs is to
develop a framework to enable the fishing sector to work with scientists in collating reliable data, and to look at how best
specific fisheries can be managed. The RACs are also to act as the link between the stakeholders at the local level and
the Commission and the Member States concerned.
The role of the RACs is to be advisory only. However, in order to provide this advice, stakeholders need to be familiar
with, and use, the modelling and other tools used in the analysis of fisheries. This is in order to both assist with the
provision of information necessary for model development, but also to appreciate the limits of the models for recovery
plan formulation based on the modelling tools.
Role in strategy formulation
The stakeholder workshop concluded that the main role of stakeholders is in strategy formulation, although all workshops
considered the role stakeholders can play in providing information necessary for model development. While not directly
linked to model development, the formulation of stakeholder-led recovery programmes requires stakeholders to
understand the limits of the models as well as modellers to understand the likely scenarios and strategies that the
stakeholders wish to consider in order to provide a useful decision making aid.
The workshop participants noted that industry-led stakeholder recovery plans have several desirable features. First, they
build on existing stock recovery plans making them more bottom up. As a result, they provide an effective solution to the
problem of non-compliance endemic in existing stock recovery plans by encouraging all round participation. Second, they
are regionally based, and therefore sensitive to particular needs and situations. As a result they are more flexible in their
approach. This flexibility is further enhanced through ensuring that all stakeholder groups work together in the strategy
development.
The workshops considered the range of groups that may be involved in this task, and agreed that it extended beyond the
fishing industry to include environmental groups, recreational groups and regional economic advisory groups. However,
these groups have different objectives that need to be accommodated, making the identification of an optimal strategy
complex. For example, fisher’s interest in stock recovery lies mainly in their desire for sustainable fish stocks that
guarantee their livelihoods and the future of their communities. In contrast, those representing environmental groups want
to see greater priority given to the protection of the bio-ecosystem of the sea in any stock recovery model, while anglers
want their activities to be taken more seriously, not merely by what and where they fish but also in terms of their economic
contribution to the industry. 5
While all of these objectives are valid, and therefore need to be considered in the development of recovery strategies,
they do not necessarily have to be given equal weight. The stakeholder workshop concluded that the commercial fishing
sector has the most to gain or lose through fisheries management, so should have greater influence. This was not
universally accepted, however, with the anglers suggesting that their interests may be more significant given the
economic importance of the recreational fishing industry.
Given this, the stakeholder workshop suggested a two-level working group for strategy development. The first core group
consisted of fishermen (the regional catching sector), fisheries scientists, national fisheries managers and possibly
regional administrators (i.e. local councils). The aim of the core group would be to develop the initial strategy before
consulting with the second level group, which would consist of a wider group of stakeholders. This second group would
include a number of other stakeholder groups, including (but not exclusively) anglers, retailers and environmental groups.
The role of the second group would be to ensure that their concerns were addressed by the strategy and suggest
modifications where their objectives were not met. The two-level approach ensures that the commercial sectors concerns
are addressed first, but that other stakeholders concerns are also accommodated. The workshop concluded that such an
approach would be both open and transparent.
Stakeholder involvement in strategy analysis
Stakeholder involvement potentially extends beyond recovery plan development, with stakeholders playing a role – either
directly or indirectly – in the evaluation of the alternative strategies. Stakeholders can have an indirect role through
providing information required for the analyses as well as verifying and validating models and model assumptions.
Stakeholders can also play a direct role in the strategy evaluation through participating in model simulation activities as
well as providing a qualitative evaluation of the alternative strategies based on their own knowledge and understanding of
the processes.
5
Mardle, Wattage and Pascoe (2003) found that not only did the importance of the different stakeholder groups vary, but their perception of their own
importance compared to other stakeholders also varied. For example, environmental groups felt that not only were environmental factors the most
important consideration in developing management plans, but also that their views were more important than other stakeholders.
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title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Providing information
Data used in developing fisheries bioeconomic models is collected from stakeholders in a number of ways. Formal data
collection programmes, such as logbook programmes, are currently used in the EU and many other regions of the world.
Economic surveys are also undertaken periodically. Industry confidence in these data, however, is limited, as
misreporting is considered commonplace.
In the absence of reliable data, stakeholders can provide direct input into bioeconomic models through highlighting areas
of major uncertainty and providing information on expected outcomes based on their own experiences. Examples of
models constructed solely on the base of stakeholder provided information exist. For example, Scholz et al. (2004)
developed a model for assessing marine protected areas based on stakeholder knowledge. The results of the analyses
derived from the fishers knowledge corresponded closely to those derived from more objective approaches.
A number of techniques exist for obtaining expert knowledge for use in model development. These have had only limited
application in fisheries model development. These range from formal techniques such as the Delphi technique to less
formal rapid appraisal methods.
Stakeholder participation in the modelling process is also believed to have potentially longer-term benefits in terms of
improved data quality. From the biology workshop, it was felt that giving increased ownership to stakeholders in terms of
assisting in stock assessments and policy analyses may result in greater compliance with data collection, resulting in
improved data from with to work. Additionally, dialog with stakeholders may result in more efficient and meaningful data
collection systems being developed.
Model verification and validation
A major element of model development is verification and validation. Verification is the process by which the assumptions
underlying the model and the subsequent mathematical relationships within the model are agreed to best represent the
existing state of knowledge. Validation is the process by which the model results are compared with either real world
results (where available) or are tested for consistency against a priori expectations where real world impacts are not
known.
Stakeholders can play a valuable role in both these processes. First, stakeholders can ensure that the underlying
assumptions of the model are valid. Where simplifying assumptions have to be made, stakeholders can suggest the likely
implications of these on the model outputs. Second, stakeholders can comment on the realism of the model results. If
these differ substantially from a priori expectations, then they may indicate either specification error (the wrong functions)
or coding error (mistakes in the equations).
Model validation is a two way process that can benefit the stakeholders as well as the model development. If the results
do differ from the stakeholder a priori expectation but no fault can be found with the individual assumptions, specification
or coding, then the stakeholders may need to re-assess their own perceptions of how the system being modelled
operates. This feedback to the stakeholders during the model development phase can also benefit the strategy
development. If recovery plans are being developed on the basis of false perceptions, then identifying these may result in
more robust strategy formulation.
Preference elicitation for choosing alternative strategies
Different recovery strategies will result in differing impacts on a range of differing variables. For example, one strategy
may increase the benefits to the recreational sector while reducing the benefits to the commercial sector. Where these
changes can be valued in a common currency (e.g. benefits can be measured in economic terms), then the trade-offs are
explicit.
A difficulty lies in the case where the impacts cannot be valued readily in common terms. For example, increased
commercial fishing may result in increased damage to sensitive environments. While it is theoretically possible to
measure the economic impact on resources that do not have a direct use value, this is a complex process and beyond the
scope of most bioeconomic modelling analyses.
Trade-offs between such non-commensurate impacts can be evaluated through multi-objective programming techniques.
However, the importance associated with each impact needs to be determined in order to provide advice on the overall
best strategy. These importance weights need to be elicited from the stakeholders concerned (see Mardle and Pascoe
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Strategy Development and Scenario Testing for Fisheries
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DEFRA
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(2003b) and Mardle et al. (2004) for details and examples of such elicitation procedures). As noted previously, however,
the importance of each objective is likely to vary between the different stakeholders.
Preference elicitation techniques can be used directly to determine preferred recovery strategies. Each stakeholder has
an implicit subjective “model” (e.g. expectations derived from past experiences) of how the strategy will affect the different
components of the system. Preference elicitation techniques can be used to determine the strength of preference of one
system over another of the different stakeholders. Examples where this has been applied can be seen in Leung et al.
(1998) and Soma (2003).
While such approaches allow for preferences to be identified, these will be based on stakeholders’ perceptions and misperceptions of the current and future state of the system. They do, however, provide a good starting point for facilitating
discussions between different stakeholders. While they should not replace a more objective analysis
Communicating with stakeholders
Difficulty in communication between scientists, economists and stakeholders was raised in all three workshops. Many
stakeholders do not understand, and therefore mistrust, the model and methods used for stock assessment and policy
assessment.
The existing management advisory system relies on scientific rigour being demonstrated to other scientists and scientific
organisations (e.g. ICES). The methods used are complex, and stakeholders do not usually participate in the stock
assessment process. As a result, there has been little need to explain these methods in a way that non-participating
scientists can readily understand.
Increased stakeholder involvement in policy formulation and decision making will require these methods to be more
accessible to stakeholders. This may require education of stakeholders in the main techniques used.
Similarly, existing bioeconomic models are also generally user-unfriendly. In most cases, these models are developed for
use only by the modeller, so a simple user interface is not normally developed. Use of the models requires knowledge not
only of the model structure (e.g. in order to find where to change parameter values), but also the modelling software (in
order to modify the model and to run the simulations).
Development of user-friendly interfaces has been undertaken for a number of other natural resource models with the
particular aim of facilitating stakeholder involvement (e.g. see Argent and Grayson, 2003). Ideally, the model design
should be sufficiently flexible to enable stakeholders to run a wide range of alternative scenarios without the need to
understand the modelling language. The use of graphic user interfaces – both for input to and output from the model – is
one way in which stakeholders can readily run different simulations and see the results. Increased stakeholder use of
models will require increased emphasis on model appearance and usability as well as robustness and scientific rigour in
model construction.
It is unlikely that any model will be able to be constructed that allows stakeholders to examine all feasible scenarios
without the assistance of a modeller. Further, as discussed in the next section, models contain uncertainties and
simplifications that may affect the interpretation of some of the model results. As a result, it is still best practice for
stakeholders to examine the different scenarios with the aid of a model developer, or someone familiar with the model
structure and underlying assumptions to ensure that simulations are correctly implemented and that results are not
misinterpreted.
Dealing with complexity and uncertainty
A major concern for stakeholders identified at all three workshops is the uncertainty inherent in the modelling process.
This uncertainty arises through poor data on which the models are based as well as lack of knowledge about the forms of
the underlying relationships, as well as naturally occurring stochastic variation in biological and environmental processes.
Types of uncertainty
Rosenberg and Restrepo (1994) identified five main types of uncertainty that affect fisheries bioeconomic modelling.
Measurement error arises through errors in the data used in the analyses. These may arise through problems such as
mis-reporting of catches, inaccurate recording (e.g. poor age estimates from otoliths), unrepresentative survey sampling,
etc. These errors result in incorrect parameter estimation, potentially leading to incorrect model results.
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
A second form of error, process error, occurs as a result of natural variability in the biological and economic system. For
example, growth, recruitment, natural mortality, migration are all affected by changes in environmental conditions, and are
therefore unable to be forecast with precision, as future fluctuations in environmental conditions are unknown. Similarly,
prices and costs (especially fuel costs) are affected by factors outside of the fishing industry. This results in model
estimates relating to any one time period most likely being inaccurate, but trends over time are likely to be reasonably
reliable as environmental fluctuations and economic conditions average out over time.
A third area of uncertainty relates to parameter estimation (estimation error). While estimation error these can arise
through measurement error, it can also arise as a result of errors in the estimation process. These errors may include
incorrect assumptions about the relationships between the variables within the system being estimated, with different
assumptions resulting in different parameter estimates.
Modelling error involves inappropriate specification of the model, For example, the true dynamics of the system may differ
to those included in the model as a result of imperfect knowledge of the true system. As a result, the model outcomes
from a given simulation may diverge from what would actually happen in the fishery.
Finally, implementation error occurs when the industry responds differently to a given scenario than is assumed in the
model. For example, a mesh size restriction may be found to generate a given level of benefits in the model, but if this is
not fully implemented by the industry then these benefits will not be achieved.
Modelling uncertainty
Trade-off between model complexity and uncertainty
Studies have tended to indicate that there is a parabolic form to the relationship between model complexity and
performance (Figure 4., Costanza and Sklar 1985, Håkanson 1995). Too much complexity leads to too much uncertainty
and problems with interpretation of the model’s dynamics and predictions, while too little detail results in models that
cannot produce realistic behaviours. Thus, there may be an ‘optimum’ level of model complexity and this may be
substantially below the maximum possible. Indeed, complexity introduced for the sake of completeness may be
counterproductive if the resulting model is actually of poor quality. The key challenge facing modellers is therefore in
striking a balance between complexity and uncertainty.
For a model to have utility for management purposes it must have acceptable predictive ability, given the variable quality
and quantity of information about system properties that may be contained in the available data (Linhart and Zucchini
1986).
Models that are highly complex require many parameters, estimates of which will contain their own uncertainty. The
compounding effect of this will be that the resulting model estimates will have very wide error distributions (i.e. confidence
interval), reducing their usefulness for decision-making. On the other hand, models that are overly simple will have very
few parameters to estimate, but may not be able to describe the system very well, e.g., the model predictions of key time
series variables will, over certain parts of the historical time series, deviate considerably from the observations. The
parameter estimates in overly simplified models will tend to be overly precise and reflect too much apparent certainty in
the estimated quantities.
Model performance
‘Optimum’ complexity ?
Low level of
realism
fewer
parameters
Model complexity
too many
assumptions?
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High data
requirements
many
parameters
increased
uncertainty ?
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
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Figure 4. The trade-off between model complexity and model performance (after Costanza and Sklar 1985)
It might also be the case that although detailed data are available and complex operating models of the fishery can be
constructed, only relatively simple models, (e.g., non-equilibrium spatially aggregated biomass dynamics models), provide
accurate estimates of stock size and suggest beneficial management procedures for the future. Even with highly detailed
data available, it might be that simulation evaluation exercises will reveal that relatively simple models and associated
‘harvest control rules’ (e.g. minimum landing size or area closures) are still the most appropriate for stock assessment
and fisheries management.
The trade-off between complexity and uncertainty is also complicated by stakeholder belief in the models. From the
workshops, stakeholders have highly detailed knowledge about different components of the fisheries that they consider
essential to include in the analyses. Excluding these details, while potentially improving the overall robustness and
usefulness of the model, reduces the stakeholder acceptance of the results. In contrast, including the detail results in
greater uncertainty in the model results, reducing the usefulness of the model for decision-making. As the true state of the
resources can never be known (unless they are driven to extinction), there is no objective measure against which to
assess the consequences of using simple rather than detailed models.
Biological uncertainty
As part of the biological workshop, a provisional list of the uncertainties related specifically to biological modelling was
developed. Fifty, generally accepted, separate issues related to uncertainty in biological modelling for the provision of
scientific advice were identified. In order to measure the importance of these issues in current modelling practices,
participants at the workshop were asked to ‘vote’ for the most important as faced by fisheries scientists. 6
The top ten issues considered under this exercise are presented in Figure 5. Overall, the participants felt that discarding
practices, mis-reporting, poor stock recruitment relationships, lack of data for <10m vessels and changes in fish
distribution and migration due to climate variability were all very important issues that were not being taken account of
fully.
The overriding issue that was identified to add uncertainty into the modelling process is that of unreported catch. In the
exercise undertaken, unreported catch was divided into effects of misreporting and discarding. The terms have slightly
different connotations, however the effect they have on the modelling process is the same – one of underestimation of
fishing mortality. Hence, in some cases, as reliable as the (mathematical) techniques used are to model a situation, if
inaccurate data is used then advice produced may as a consequence be affected. This is a difficult issue to overcome as,
in the current management system, catch unreporting is not necessarily an illegal activity and establishing an accurate
estimate is difficult. In very recent years, initiatives such as onboard discard officers have been employed to assist in the
measurement process. As a result, some information is becoming available to include in the modelling process.
Poor stock recruitment relationships
Mis-reporting (and degradation of age matrix)
Discarding – limited knowledge of scale of problem
Changes in migration due to temperature/climate
Permanent regime shifts
Model uncertainty
Implementation error (behaviour of fleets to management)
Multispecies interactions
Lack of data from <10m boats
Retrospective bias in assessments
0
5
10
15
20
25
Number of votes
6
The voting system used is described in more detail in the biological workshop report, however in essence each participant had six votes to cast and
could give a maximum of two to any one issue.
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Figure 5. The key issues identified surrounding biological uncertainty
Another issue that was of particular concern is poor stock recruitment relationships. This issue highlights the ‘process
error’ that exists in the biological system and is directly reflected in the modelling. For the majority of stocks, the variability
in recruitment makes it a difficult parameter to predict, as it is not in most cases related to the stock size that exists. For
example, recruitment may be low when stock is high and vice-versa. Functional relationships have been proposed (e.g.
by Beverton and Holt, and Ricker), but they do not generally take account of the complexities of stock recruitment. The
typical strategy for investigating the sensitivity of models to recruitment (and hence the robustness of advice produced) is
to perform stochastic analysis such Monte Carlo simulation. This enables tolerances to be developed to present
underlying sensitivity.
Relating to process error, the issue that was considered particularly important was the effects of climatic changes,
specifically changes in migration due to temperature/climate and permanent regime shifts. In the provision of short-term
advice, the effects of this may be unnoticeable. However, if advice towards a long-term strategy is sought then this
becomes a significant issue, especially with reference to stock recovery.
Amongst those issues that received no votes were: artificial boundary issues (e.g. ICES stock areas) used to model
stocks, biological sampling (e.g. maturity, mortality) and technical interactions. These are all important components in the
modelling process.
In terms of types of uncertainty, the issues raised were categorised according to the scheme of Rosenberg and Restrepo
(1994). That is (i) measurement error through data inadequacy, (ii) process error through natural variability of the
biological system, (iii) estimation error in modelling dynamic processes, (iv) modelling error by using inappropriate
models, and (v) implementation error where changes in behaviour are not accounted for. In the list of key issues faced
(Figure 5) with uncertainty, modelling error, measurement error and process error were the main categories covered.
Implementation error was found to be a single issue related to the behaviour of fishers. However, estimation error related
to the modelling of dynamic processes was not considered to be of principal concern.
Economic uncertainty
Economic uncertainty exists in bioeconomic simulation models for similar reasons as the main sources of biological
uncertainty. The economic components are subject to uncertainty through measurement, estimation and modelling error.
The economics workshop participants considered that the main sources of economic uncertainty in bioeconomic models
were future prices of both outputs and inputs. These are largely affected by changes in the economic (and natural)
environment. Further, the potential for implementation error may also be substantial if fishers do not behave in real life as
assumed in the model in response to management changes (i.e. do not fully comply with the restrictions or policies).
Monte Carlo simulation and sensitivity analysis were considered the most appropriate means of dealing with uncertainty
related to measurement, estimation and modelling error. Monte Carlo simulation involves running the model many times
with different combinations of input and output prices. The parameter values in the model are taken randomly from the
expected distribution of the values, based on historical levels of variance. As a distribution is known, this approach more
appropriately deals with risk rather than uncertainty per se, and a probability distribution of model outcomes can be
developed.
Sensitivity analysis is more useful when parameter values are uncertain (i.e. no known probability distribution), and
involves determining the range of parameter values over which the general model results (i.e. which is the ‘best’ recovery
plan strategy) does not change. For example, one strategy may remain superior to the others over a wide range of prices
and costs; or, conversely, a strategy may be optimal only given a limited range of prices and costs. While the probability
of each outcome is unknown, the conditions under which one strategy is preferable to another can be determined,
providing useful information for managers.
Developing appropriate “future” scenarios
Uncertainty in economic parameters arises through changes in factors external to the fishing and associated industries.
For example, changes in international fuel supplies affect world oil prices and subsequently the costs of fishing. Changes
in governments, general inflation rates, consumer tastes and attitudes also affect prices of both inputs and outputs.
Similarly, many biological parameters are affected by environmental factors. Changes in distributions and growth rates of
fish species are affected by water temperature, which is affected by climate change. Future environmental conditions will
affect the ability of stocks to recover, and in some cases may have contributed to their current low levels.
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Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Forecasting future conditions contains considerable uncertainties due to (1) lack of knowledge about existing system
conditions and dynamics, (2) the potential for technical change and innovation to change the incentives facing
stakeholders in the system, (3) unpredictable environmental shifts, and (4) the complexity of human behavioural
responses to these incentives that is difficult to capture in any simple model (Robinson, 2003). An alternative to
forecasting environmental (biological and economic) conditions is to derive a range of potential scenarios.
Scenario analysis is used to consider alternative future environments (both physical as well as economic), each with
different consequences in terms of the economic and biological parameters within the models. Scenario analysis has
been used in a range of natural resource modelling analyses (see for example Wollenberg et al. 2000, Mohren 2000).
Pauly et al. (2003) considered a range of scenarios that would affect long term fisheries prospects.
Scenarios are not necessarily predictions about future events, and no probabilities are usually assigned to any given
scenario. Instead, they identify states of existence that may realistically occur, and the management strategies can be
assessed under these conditions. This allows the robustness of the strategies to be assessed under a range of differing
environmental (biological and economic) conditions, and alternative strategies (or even adaptive management plans) can
be developed if they are not robust under all conditions.
Scenario analysis may also enable stakeholders to consider strategies that would not otherwise be considered. They
introduce hypothetical possibilities that spur the imagination and force stakeholders to critical think about risks and system
relationships that exist in the fishery. According to Wollenberg et al. (2000), the development and use of scenarios help
stakeholders overcome cognitive biases to (1) undervalue strategies that are hard to imagine, (2) give more consideration
to recent conditions rather than potential future conditions, (3) underestimate uncertainties, (4) be overconfident about
their own judgement and their ability to influence events beyond their control, and (5) overestimate the probability of
desirable events.
The development of scenarios for strategy analysis is not straightforward, and methods for developing such strategies is
beyond the scope of this study.7 Details on the scenario planning process can be found in O’Brien (2004). Robinson
(2003) provides details on an alternative approach – backcasting – that also offers the potential for considering
uncertainty in strategy analysis.
Communicating uncertainty
To the public “uncertainty” can mean doubtfulness or ignorance. However, to the scientist “uncertainty” is more often
about acknowledging variability (variation), as well as recognising knowledge-gaps. Some variability can be explained,
while some can not. Uncertainty is closely associated with ‘risk’; the more you know about a process the lower the risks
associated with it and the less precautionary you need to be.
The biological workshop participants were provided with time to discuss problems associated with communicating
‘uncertainty’, both to stakeholders and to policy-makers. The general consensus was that it is very easy to
miscommunicate and misunderstand uncertainty, particularly if the issue concerned is very technical. It is all too easy to
get lost in the detail and forget about the most pertinent messages.
Due to the need in the political arena for ‘simple’ answers with apparent certainty, it may not be an easy process to
communicate underlying uncertainty. For example, in the fisheries context, Ministers often want simple choices (point
estimates) not complicated advice, which might ‘muddy the waters’ of international negotiations. In the past, stock
assessment reports have included estimates of uncertainty. However, politicians stated that they did not want complicated
advice, and thus confidence limits were removed. In 2003, ICES chose not to provide ‘point-estimates’ for some stocks
because of the inherent uncertainty. Nevertheless, the EU requires a discrete figure for practical management reasons,
and thus subsequently requested these from STECF (Scientific, Technical, and Economic Committee for Fisheries).
It was acknowledged by the participants at the biological workshop, that scientists should learn how to communicate the
inherent uncertainty in a way that the target audience will understand and acknowledge. It was suggested that there is a
widely held perception that managers/scientists may not wish to communicate uncertainty, since this might undermine
confidence in the stock assessment process. Over time, it was felt that the audience might get accustomed to hearing
about uncertainty and the way that it is evaluated. This is highly relevant for dialogue with stakeholders (in particular,
fishermen) and the need to find ways to engage in discourse about uncertainty without undermining confidence in the
science. This would facilitate a better understanding of the concept, and allow for more informed decision-making. There
is a clear need for dialogue over a time-scale which is independent of the decision-making process, i.e. not only in
December when quotas are set, and the debate becomes highly politicised.
7
A separate Horizon Scanning project is developing such scenarios that could be used in assessing fisheries recovery strategies in conjunction with
bioeconomic modelling.
CSG 15 (1/00)
17
Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
Decision making in light of uncertainty
Given the uncertainty inherent in the parameters and relationships in fisheries bioeconomic models, any model outcome
will also have a wide error distribution. However, as note above, the probabilities of some events occurring are unknown,
and hence a probability distribution cannot be assigned to the model outcomes. Determining appropriate management
responses based on such models is subsequently difficult, as their exists the potential for the ‘wrong’ strategy to be
imposed even though it appears the best strategy based on the available information. In the worst case, the
implementation of a wrong strategy may result in the extinction of one or more stocks.
In such a case where the probabilities of alternative outcomes are unknown, standard risk analyses do not apply, and it is
not relevant to estimate expected net benefits from the alternative strategies for the different scenarios with unknown
probabilities of occurrence (Palmini, 1999). However, a number of criteria exist that allows decisions to be made in the
face of such uncertainty. These include the maximin, minimax and minimax-regret approaches.
The maximin criterion considers the strategy that produces the largest minimum benefits under the alternative scenarios.
For example, if the worst outcome under the different scenarios considered for strategy A was X, and the worst outcome
for scenario B was Y, then strategy A would be preferred if X>Y (and the converse if X<Y). This effectively ensures the
best ‘worst’ outcome, and is a pessimistic and highly risk averse criterion. It does not take into consideration the potential
benefits of either strategy.
The related minimax criterion considers the best strategy as that which minimises the greatest loss. This is applicable
when the worst outcome is expected to be an actual loss, where as the maximin criterion is associated with positive
results. For example, if the worst outcomes under strategies A and B were a loss of U and V respectively, then A is
preferable to B if the loss U<V. This is again a pessimistic and risk averse criterion that ignores the potential benefits
under the alternative scenarios.
An alternative is the minimax-regret criterion. This considers the opportunity cost of not adopting the strategy, given by
the difference between the benefits from a given strategy under a particular scenario and the maximum benefits that can
be obtained under that scenario. As with the standard minimax criteria, the worst outcome under each strategy is
compared over the range of scenarios. An advantage of this approach over the other two criteria is that the potential
gains from each strategy are also considered. Minimax-regret is still a conservative criterion, but is not as extreme in its
pessimism as the maximin and minimax criteria.
These criteria have been used in several natural resource studies, including fisheries. For example, Pascoe et al (1994)
employed the minimax-regret criterion (along with a bioeconomic analysis) to determine the optimal recovery strategy for
the southern shark fishery given uncertainty as to the underlying stock structure. Defeo and Seijo (1999) considered all
three criteria for determining optimal yields in a hypothetical fishery subject to uncertainty. More recently, van den Bergh
(2004) considered the use of the minimax-regret criterion for considering strategies relating to climate change. 8
The potential impacts of adopting a ‘wrong’ strategy are determined to some extent by the management system imposed.
Rigid, inflexible systems are more likely to produce greater undesirable consequences if implemented incorrectly than
flexible systems that can adjust to new information rapidly.
Summary and Conclusions
Bioeconomic models have been applied to many fisheries and other natural resources to provide insights into how the
systems operate as well as provide advice on how best to manage these systems. Most models have been limited in their
focus, concentrating on the fishery or resource itself rather than the interactions with local economy, other resource users
and the environment. This latter deficiency is being redressed through the development of ecosystem models. However,
these can still only be developed at a very basic level due to considerable uncertainty as to the relationships that exist in
the system.
The objectives of the study were to consider what types of relationships could be incorporated into bioeconomic models to
improve their use for decision making given that impacts from fisheries management does impinge on other users of the
resource, the environment, and other sectors of the economy. Decision makers need to consider these impacts as well as
the direct fisheries impacts. Consequently, there are many stakeholders involved in the decision making process. Given
8
van den Bergh (2004) also advocated the use of qualitative rather than quantitative methods for assessing the opportunity costs in the minimax-regret
analysis in cases of substantial uncertainty. They argue that in such cases the quantitative model results are most likely no more reliable than qualitative
estimates, while in some cases quantitative results may not be feasible if the relationships in the system cannot be quantified.
CSG 15 (1/00)
18
Project
title
Strategy Development and Scenario Testing for Fisheries
Recovery Plans:
Part A
DEFRA
project code
HS0103
this, incorporating these stakeholders into the modelling and analysis process is essential, and the project also aimed at
considering methods for this. Finally, considerable uncertainty exists in many of the model parameters and relationship.
Methods for capturing these uncertainties while still providing useful management advice were also considered.
The study was undertaken through three workshops, each containing a mix of economists, biologists and stakeholders.
The objectives of these workshops were to consider the information needs of stakeholders in formulating appropriate
recovery strategies and how best these needs could be provided through quantitative analyses of available data.
The results of these workshops, and subsequent further literature reviews, provide a blueprint for the development of
effective fisheries bioeconomic models. In many cases, it will not be feasible to incorporate all of the features identified in
this study into a functional bioeconomic model. This blueprint includes not only model design, but considerations for
model use and interaction with the stakeholders and decision makers. However, consideration of the flow on effects can
still be undertaken qualitatively and incorporated into the final decision analysis.
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Part A
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project code
HS0103
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