9.3. Fishery Dynamics and Regulatory Policy

To conclude this review of public sector applications we return to the topic of fisheries from Chapter One. The dynamics of fisheries are reflected in collapsing fish stocks, idle fishing fleets and impoverished fishing communities. The economic and social costs of these dynamics are enormous. It has been estimated that on a global scale, the loss of economic rents (profits) due to mismanagement of fisheries may easily amount to 50 per cent or more of the global landed value of the fish catch of some 100 billion US dollars annually.^[] If present trends continue then the industry's future is bleak. A report by a team of scientists and economists estimates that by the year 2048, fish stocks in all the world's main fishing regions will be close to extinction (Worm et al., 2006). Already one-third of fisheries have biologically collapsed and stocks could take decades to recover, even with a complete moratorium on fishing. It is a sobering thought that in less than 50 years there may be no commercial sea-fishing industry and no wild fish to eat on the table. The paradox of the fishing industry is that fishermen do not appear to act in their own best interests. They overexploit a renewable resource to the point of destruction, yet their livelihoods depend on a sustainable catch. It need not be that way.

^[] Global landings from ocean fisheries have in recent years been in the neighbourhood of 84 million metric tons. Average landed value may be close to USD 1.20 per kg. Various empirical studies of fisheries around the world typically suggest loss of potential profits of some 50 per cent of the value of landings.

9.3.1. Fisheries Management

Fisheries management is fundamentally a control and regulation problem, ensuring that collective fishing effort is well-balanced with available fish stocks and fish regeneration. Without any regulation fisheries tend towards bloated fishing fleets, excess effort, fish stocks that are too small, low profitability and low personal incomes. At worst, fisheries collapse entirely. But why? An information feedback view suggests that fishermen do not receive a clear feedback signal from the fishery to tell them when to stop investing in ships and gear. This signal should be strong and credible at the point in time when collective effort (and therefore the catch) is approaching the highest regeneration rate the fishery can support (the so-called maximum sustainable yield). Regulatory policy should therefore be aimed at generating a 'correct' feedback signal to curtail overinvestment and to ensure, with appropriate sanctions, that fishermen take notice and restrict the catch size.

In the following sections, we augment the simple fisheries model from Chapter 1 to illustrate the origins of the fisheries management problem and the feedback principles that lie behind effective regulation. The analysis involves a sequence of models that illustrate the challenges in coordinating fish stocks with the fleet size (and fishing effort). We begin with a simple model of fish population dynamics for a single species and demonstrate that sustainable catch increases with fleet size until a critical tipping point is reached. We then add a behavioural model of investment in ships to show the tendency for overexploitation in unregulated fisheries. Finally, we add a new module to represent the monitoring, control and surveillance of fish stocks as the basis for regulation.

Economists have also studied the overexploitation of fisheries. They explain the paradox of bloated fishing fleets in terms of the 'common property problem' (Arnason, 2005, 2006; Hardin, 1968). Ocean fish stocks have traditionally been arranged as common property resources, meaning that anyone with nets and a boat is able to harvest the resources. Under this arrangement it can be shown there are financial incentives to expand the fishing fleet (and effort) until costs equal revenues. At this special equilibrium point there are no profits left in the industry and the fish stock is depleted well below the biological optimum (and often dangerously close to collapse). Although this economic analysis does not investigate the dynamics of fish population and fleet expansion (focusing instead on feasible equilibria), it nevertheless sheds light on the decision making of fishermen that leads them, collectively, to excess fishing effort. Regulatory policy is then seen as the design of fishing restrictions, quotas, property rights or taxes to inhibit investment and limit the catch. With appropriate incentives and sanctions an ideal equilibrium is envisaged in which the catch is sustainable and fishery profit is maximised.

Improving the management of fisheries is a big task but it can yield huge social and economic benefits. Ragnar Arnason (2007), an economist from the University of Iceland and expert in fisheries management, notes that:

While mismanagement characterises the global fishery as a whole, it is important to realise that there are fisheries, sometimes quite sizeable fisheries, that do not adhere to this general pattern and are both biologically sustainable and highly profitable. These fisheries, which comprise such diverse marine conditions as those of New Zealand, Falkland Islands and Iceland, are in no way different from the other fisheries which exhibit declining stocks and negative profits. The only thing they have in common is good management. Generally, this management is based on high quality and well enforced property rights

Here we take a similar view that good fisheries management is vital to their long-term success, but we approach the topic dynamically from an information feedback perspective (Moxnes, 1998).

9.3.2. A Simple Harvested Fishery – Balancing Catch and Fish Regeneration

Cast your mind back to the very first simulator in the book. There we examined the dynamics of a simple natural fishery, free from human intervention – just fish in the sea with no fishermen or ships. The result, over a period of 40 years, was smooth S-shaped growth, as shown in Figure 9.16. A small initial population of 200 fish (scaled for numerical convenience) grows exponentially for 18 years. Then over the next decade, as the fish stock approaches its maximum sustainable value of 4 000, growth is halted and the fishery settles into a long-term equilibrium.

Now imagine a harvested fishery. Ships arrive in the previously pristine sea and cast their nets. The total catch depends both on the number of ships and their productivity (how many fish each ship catches in a typical trip). Common sense suggests that if you add a few more ships to a small fleet then the catch will increase. Equally, there must come a time when there are too many ships competing for a limited number of fish. We can use simulation to investigate the relationship between catch and fish population as fleet size varies.

A simple model of a harvested fishery is shown in Figure 9.17 involving a fish stock, an inflow of new fish per year and a harvest rate. The corresponding equations appear below the diagram. Net regeneration is a non-linear function of fish density as indicated by the graph. The fish stock is depleted by a harvest rate, equal to the catch and proportional to the number of ships at sea.

Consider a scenario spanning 40 years in which the fleet size starts at zero and then grows in stepwise bursts to reach a maximum of 30 ships. The productivity of these ships is identical. Bear in mind this is a scale model which can be calibrated to fit a realistic fishery without changing the pertinent dynamics. Each ship can catch 25 fish per year and for clarity we assume there is no stochastic variation in productivity. At the start there are 4 000 fish. The fishery is full and the population is in equilibrium. Then, in year four, 10 ships sail into the pristine fishery and set about harvesting for the next 12 years. The simulated result is shown in Figure 9.18. The catch (line 3) rises from zero to 250 fish per year. As a result, the fish stock (line 1) begins to fall. Then something dynamically interesting happens. The fishery is less heavily populated, and, hence, fish regenerate faster (as defined by the non-linear regeneration graph). As the years pass the number of new fish added to the population each year approaches ever closer to the harvest rate (and the catch) and so, by the end of year 15, the fish stock (line 1) settles into a sustainable harvested equilibrium.

This pattern of bountiful adjustment is repeated as 10 more ships are added in year 16. The catch (line 3) once again increases, this time reaching a value of 500 fish per year, just below the maximum sustainable yield. Gradually the regeneration rate rises to equal the catch bringing the fishery into a new and higher harvested equilibrium with a population of about 2700 fish. However, in year 28, when a further 10 ships are added to the fleet, the extra expansion pushes the catch beyond a tipping point that causes a rapid decline in the regeneration rate and the fish stock. The tipping point occurs at the peak of the non-linear relationship between net regeneration and fish density as depicted in Figure 9.16.^[] Before the peak, any reduction in fish density boosts regeneration. After the peak, however, any further reduction in fish density inhibits regeneration. The effect is clearly visible in the behaviour of new fish per year (line 2) which, shortly before year 30 and after more than two decades of growth, suddenly falls sharply to a value far below the catch (line 3). Even though the fishing fleet is later reduced to only 10 ships in year 34 (a fleet size that had previously yielded a sustainable catch), it is too late to reverse the fishery's decline and the fish stock collapses completely. Here in this dramatic switch of dynamic behaviour, from a robust sustainable catch to a fragile and declining catch, lies the key to the fisheries paradox.

^[] A thorough explanation of tipping points and the dynamics of 'quantity-induced' crises is to be found in Rudolph and Repenning (2002).

9.3.3. A Harvested Fishery with Endogenous Investment – Coping with a Tipping Point

Investment in ships is a collective decision-making process (or policy) representing, in aggregate, the judgements of those people most closely involved (fishermen in this case) and the information sources on which their decisions are based. Such decision-making processes are behavioural in the sense that they capture the broad intention of investment without necessarily assuming decision makers have perfect information or perfect foresight. As we saw in Chapter 7 (the market growth model) typical investment policy has three main parts: a goal for the intended capacity, monitoring of current capacity, and corrective action to bring capacity in line with the goal. This process of 'asset stock adjustment' applies equally well to investment in fishing fleets.

Figure 9.19 shows the investment policy for fleet adjustment in the fisheries model. Notice that connections between variables are depicted as dotted lines denoting flows of information. The connections are not 'hardwired' as they were for the natural fishery. They are discretionary and reflect the information available and deemed most relevant to investment. The desired fleet size (the goal) depends on the number of ships at sea and the propensity for growth. Specifically the desired fleet size is equal to ships at sea multiplied by a growth factor denoted as (1+ propensity for growth). We assume that the normal propensity for growth is 0.1, so the desired fleet size is 10 per cent larger than the current fleet size. In other words, fishermen normally and collectively want a bigger fleet than they now have, an attribute of human nature – bigger is better, growth is inherently attractive. This is an important behavioural assumption and recognises that fishermen lack the information (or even the inclination) to agree an optimal fleet size. They just want more and better ships. As we will see later the propensity for growth also depends on conditions in the fishery, a poor catch will dampen enthusiasm for a larger fleet, despite an underlying bias toward growth.

The rest of the asset stock adjustment formulation is standard and straightforward, just like the formulations for inventory control and for employee hiring in Chapter 5. The gap in fleet size is the difference between the desired fleet size and ships at sea. If there is a large positive gap then conditions for investment are favourable. The purchase or sale of ships closes the gap overan assumed timespan of one year, which is the time taken to adjust the fleet (including ordering, construction and delivery).

A crucial formulation in the model is the propensity for growth and the factors that determine it. As mentioned above, fishermen do not know the optimal fleet size and so they prefer, more simply and pragmatically, to grow the fleet until there is compelling evidence to stop. In a real fishery, the most persuasive information is catch per ship. Fishermen know this number from each fishing trip and it is vital to their livelihood. Significantly they do not know the fish population or the fish regeneration rate – because the fish are under water. Moreover, they do not believe scientific estimates of low fish stocks unless confirmed by the catch. Such practical considerations suggest that propensity for growth is curbed by low catch rather than by objective evidence of fish stocks. As a result, investment is boundedly rational, sensing only indirectly the true balance of the fish population on which the long-term sustainability of the fishery depends.

Figure 9.20 shows one possible formulation that captures the essential limited information characteristic of fishermen's boundedly rational decision making. Propensity for growth depends on the normal propensity for growth multiplied by the curbing effect of catch per ship. This curbing effect is non-linear and captures another typical human tendency: to ignore bad news until it is really bad. If catch per ship falls from 25 fish per year to15 per year (a 40 per cent decline), propensity for growth falls from 0.1 to 0.09 (a decline of only 10 per cent). Thereafter, the effect becomes much stronger. If the catch per ship falls to 10 fish per year (less than half the normal value) then propensity for growth falls to zero and fishermen stop purchasing ships. If the catch falls still further then the propensity for growth becomes negative and fishermen sell ships because collectively they sense it is futile to retain a large and unproductive fleet.

Catch per ship is essentially a measure of ships' productivity and is modelled here as a deterministic function of fish density. The scarcer the fish, the lower the productivity, but the relationship is non-linear. For moderate to high fish density (between 0.5 and 1) catch per ship remains close to normal. The assumption is that fishermen do not really notice a difference in the catch if the sea is teeming with fish or only half-teeming with fish, because fish tend to school or cluster. Catch per ship is still 68 per cent of normal when the fish density is only 0.2, or in other words when the fish population is 20 per cent of the maximum sustainable. Thereafter, however, catch per ship falls quickly to zero as schools of fish become increasingly difficult to find and are hotly contested by rival ships.

An overview of the model with endogenous investment is shown in Figure 9.21. In the top left quadrant is the natural fishery with its non-linear reinforcing loop depicting population dynamics. In the lower right quadrant is the fishing fleet. Investment is represented as a stock adjustment process in which a balancing loop adjusts the number of ships at sea and a reinforcing loop drives the desired fleet size. Fish biology and capital investment are linked through the catch, harvest rate and propensity for growth resulting in a dynamically complex non-linear feedback structure.

9.3.4. Simulated Dynamics of a Harvested Fishery with Endogenous Investment

The model is initialised in a sustainable equilibrium with 10 ships and 3 370 fish, resulting in a catch of 250 fish per year and equivalent net regeneration of 250 fish per year. This harvest rate is below the maximum sustainable yield to allow room for growth and to investigate the dynamics of boundedly rational investment. In order to start the model in equilibrium, the normal propensity for growth is artificially held at zero during the early years of the simulation. It is as though a 'small-is-beautiful' mindset has temporarily taken hold of ship owners. Then, in year 10, propensity for growth returns to its normal value of 0.1, or 10 per cent of the current fleet size. The results are shown in Figure 9.22. The reader can recreate this chart by running the model called 'Fish and Harvesting – Endogenous Investment' in the CD folder for Chapter 9. Set to zero the slide bar representing the normal propensity for growth. Then click the 'Run' button to see the first five years of equilibrium. Click the 'run' button again to extend the equilibrium to 10 years. Now reset the normal propensity for growth to its standard value of 0.1 by clicking on the 'u' symbol in the lower left of the slide bar. Then run the simulation all the way to 40 years.

Starting in year 10 the number of ships at sea (line 4) increases steadily. For 14 years, the catch rises. Meanwhile, the catch per ship (line 5) remains steady, suggesting that continued investment is both feasible and desirable. Below the waves conditions are changing, but remember these conditions cannot be directly observed by fishermen. The regeneration rate of fish (new fish per year, line 2) rises, just as one would expect in a well-harvested fishery. The fish population falls, but that too is expected in a harvested fishery.

Signs of trouble appear underwater in year 21 when, for the first time, regeneration (new fish per year, line 2) falls. This reversal of replenishment is a signal that the fishery has passed the tipping point of the non-linear regeneration curve. The decline in the fish stock begins to accelerate. Interestingly, however, the catch (line 3) continues to rise for fully three more years, until year 24, and the catch per ship (line 5) remains close to normal. From the viewpoint of growth-oriented fishermen floating on the waves it is business as usual. The fleet continues to grow until year 26 when it reaches a size of 46 ships. By then the catch per ship (line 5) has fallen to less than one-third of normal (only 8 fish per ship per year instead of 25), which is sufficiently low to curb further investment.

By now, the hidden fish stock (line 1) has fallen to only 300, less than one-tenth of its initial value. With so few fish in the sea, the regeneration rate is precariously low at only 30 new fish per year, well below the catch of around 300 fish per year. Fishermen are now well aware of the underwater crisis and respond accordingly by selling ships. The fleet size (ships at sea, line 4) falls from a peak of 47 ships in year 26 to 39 ships in year 30. But it is too little action too late. The boundedly rational investment policy fails to reduce the fleet quickly enough to halt the decline of the fish stock. By year 30 there are only four fish left in the sea and regeneration has fallen practically to zero. The fishery has collapsed and is left with a huge excess of relatively new ships owned by fishermen reluctant to sell and still dependent on the fishery for their livelihood. The feedback structure of an unregulated fishery leads to boom and bust in the catch.

9.3.5. Control and Regulation – Policy Design for Sustainable Fisheries

The purpose of regulatory policy is to persuade fishermen to reduce their fishing effort when the population offish is deemed to be too low. But how? This question is explored in Figure 9.23, which shows the policy structure behind fishing effort. The fishing fleet is disaggregated to show both ships at sea and ships in harbour. A corresponding distinction is drawn between investment policy (whether to purchase ships) and deployment policy (whether to go fishing or to deliberately idle some ships in harbour). The concentric circles around these policies represent information filters, indicating that fishermen, as normal boundedly-rational decision makers, simplify complex (and often conflicting) information about the state of the fishery. Like everyone else, they act on the basis of evidence from their own experience, paying most attention to signals that suit their local interests (For a reminder on information filters and bounded rationality, review Chapter 7.) We therefore continue to assume that investment decision making is myopic, driven by the normal propensity for growth and the catch per ship as described above. Even when there is scientific information available about the fishery (shown in the grey region), it is not easy to inject this evidence into commercial decision making. Incidentally, if you think this is an unreasonable assumption then consider the difficult task for climate scientists in convincing us to travel less, or to turn down our thermostats, if we are to halt global warming. Like fisheries, global warming is a problem of managing the commons. Like fishermen, we are reluctant to take scientific advice because the need to take immediate action is not compelling and the required changes in behaviour threaten our lifestyle.

Regulation acts principally through deployment policy by requiring fishermen to reduce their fishing effort and/or to be selective in what they catch. In practice, there are a variety of different approaches to regulation. For example, there are biological restrictions such as mesh size regulations, total allowable catch, area closures and nursery ground protection. Alternatively, there are economic restrictions on days at sea, fishing time and transferable quotas. Here we will focus attention on economic restrictions. In Figure 9.23, deployment policy takes information, supplied to regulators by marine scientists, about fish density and optimal fish density. The regulators use this information to determine, on average, how many ships from the total fleet should be at sea and how many should be kept in harbour. This policy can be interpreted either as a limit on days at sea or a daily limit on fishing time.

Will fishermen, however, pay any attention to these restrictions and the scientific information on which they are based? There is no particular reason why they should unless violations are noticed and punished. Effective fisheries management requires credible surveillance of ships' activities and strict enforcement of the rules backed by a judicial system capable of issuing sanctions to violators (Dudley, 2003). Only then will scientific information about the state of the fisheries penetrate the filters of behavioural decision making in Figure 9.23 and lead to a more appropriate deployment of ships. Under schemes such as limited fishing days and closed areas it is necessary to monitor the fishing vessels' actual days at sea and their location when out fishing. The labour and equipment needed for such surveillance at sea is very expensive. In fact, regulatory economists have estimated that the management costs of fisheries can be as much as 25 per cent of the value of landings. In short, a great deal of administrative effort lies behind successful fisheries management to ensure that valid scientific information is brought to bear, both forcefully and fairly, on fishing activity.

9.3.6. Formulation of Deployment Policy

Figure 9.24 extends the previous model of a harvested fishery to include the deployment of ships. The original formulations for fish population, fish regeneration, ships at sea and investment policy are shown on the left of the diagram. The new formulations for deployment and for ships in harbour are shown on the right. Deployment policy is subdivided in two stages: there is a recommended fleet size (shown as the shaded region on top) and there is surveillance of ships and enforcement (the shaded region below).

The equations for deployment policy are shown in Figure 9.25. The recommended fleet size is defined as the product of total ships and the sustainability index. The idea is to restrict the number of ships at sea if marine scientists think fish stocks are too low. Of course, nobody knows for sure the actual number of fish in the sea. There are only estimates of fish density. Imagine that marine biologists monitor the fish population to arrive at an estimated fish density. In practice, this is likely to be a time-consuming process of compiling sonar data collected by biologists during missions at sea. It is modelled with a smoothing function (SMTH1) where the scientific measurement of density is captured in the ratio of fish stock to maximum fishery size, and the time to estimate fish density is half a year. Armed with this sample evidence biologists then need to establish if the density is high enough to ensure a sustainable catch. If not, then they will recommend limits on fishing. The benchmark for comparison is the assumed optimal fish density, which is set at 0.6, corresponding to the peak of the regeneration curve in Figure 9.17. The sustainability index depends, non-linearly, on the ratio of the estimated fish density to the optimal fish density. When the ratio is in the range 1 to 1.2 (or more) the index takes a neutral value of one and there are no restrictions on the active fleet size. As the density ratio dips below 1, the index falls; gradually at first, but then very swiftly. For example, if the density ratio is 0.8 (meaning that the estimated fish density is 80 per cent of the optimal) then the index takes a value of 0.76 (meaning that the recommended fleet size is 76 per cent of the total ships). If the density ratio falls to 0.5 however, (meaning that the estimated fish density is only half the optimal), then the index takes a value of 0.03 (meaning that all but three per cent of the fleet is supposed to be idled). This aggressive cutback acknowledges the fragility of the fishery around the tipping point.

Surveillance of ships and enforcement together capture the pressures on fishermen to act on scientific advice. However, compliance may not be timely or complete and this inevitable foot-dragging is recognised in the equations. The number of ships moved to harbour is driven by the difference between ships at sea and recommended ships at sea. Hence, if there are more ships at sea than recommended, the surplus is supposed to be idled. Some fishermen may cheat and ignore the recommendation. The scope for cheating is captured in the effectiveness of the fisheries management regime, a number that multiplies the surplus fleet. The parameter is set at 1 in the base case model, meaning that fishermen are completely honest. However, the parameter can be varied on a scale from zero to 1 to explore the implications of weak management regimes in which only a fraction of surplus ships are idled. Even when the regime is presumed to be strong, redeployment does not happen instantly. In a given week or month, only a fraction of the surplus ships are brought into harbour, according to the time to achieve compliance. This parameter is set at 0.5 years in the base case. For example, if there are 10 surplus ships (ships at sea – recommended ships at sea = 10) and no cheating (effectiveness of fisheries management regime = 1), then ships are moved to harbour at an initial rate of 20 ships per year (10/0.5), which is roughly 2 ships per month.

9.3.7. Stock and Flow Equations for Ships at Sea, Ships in Harbour and Scrap Rate

The remaining equations in Figure 9.25 define the stock and flow network for ships. Ships at sea are represented as a stock that accumulates the difference between the purchase or sale of ships and ships moved to harbour. Initially, there are 10 ships at sea. Ships in harbour are represented as a stock that accumulates the difference between ships moved to harbour and the scrap rate of ships. Initially, there are no ships in harbour because, in the beginning, fish are abundant and the fishery is underexploited. The scrap rate of ships is formulated as the ratio of ships in harbour to the lifetime of idle ships. We implicitly assume that older ships are idled first and can be kept seaworthy for years. The lifetime of idle ships is set at five years in the base case.

9.3.8. Simulated Dynamics of a Regulated Fishery – the Base Case

To run the base case open the model called 'Fish and Harvesting – Policy Design' in the CD folder for Chapter 9. The opening screen is shown in Figure 9.26. Notice the four slide bars at the bottom of the diagram that together determine the regulatory regime. Consider the base case settings. The assumed optimal fish density is 0.6, which is equal to the biological optimum. This is the benchmark against which fish density is compared when setting the recommended fleet size. The effectiveness of the fisheries management regime is 1, which means there is no cheating on restrictions to the number of ships allowed at sea (or the equivalent days at sea). The time to achieve compliance is half a year. Finally, the lifetime of idle ships is set at five years. To inspect the model press the button labelled 'To Model'. To return to the opening screen press the tab labelled 'Interface' on the extreme left of the page.

The time chart is deliberately truncated to show just the first five simulated years. Study the trajectories and then imagine how the future will unfold. To begin with the regulatory regime is dormant because the fishery is underexploited.

Growth-oriented investment leads to a steady rise in ships at sea (line 4) and the catch (line 3). Meanwhile, the fish stock (line 1) remains steady and plentiful. Click on the page tab in the lower left corner of the chart to see three more time charts. These charts provide more information than can be displayed here in the book. Page 2 shows the deployment of ships, recommended ships at sea and the sustainability index. Page 3 shows the purchase of ships, ships moved to harbour and the scrap rate. Page 4 shows fish density, the assumed optimal fish density and the sustainability index.

To simulate the model, press the 'Reset' button on the opening screen and then press 'Run'. The first five years are replayed. Press the 'Run' button again to see the next five years, and so on to year 40. The results are shown in Figure 9.27. With regulation the fishery is sustainable, as shown in the top chart. Surprisingly there is considerable volatility in the catch (line 3), the fish stock (line 1) and ships at sea (line 4). In the early years of the simulation, the catch grows in exactly the same way as it would in an unregulated fishery (compare with Figure 9.22, during the growth phase). By year 12, the catch reaches 800 fish per year and there are 33 ships at sea. Meanwhile, the onset of regulation can be traced in the bottom chart. In year 11, biologists notice that fish density (line 1) falls below the assumed optimal fish density (line 2). This is the tipping point. But it takes time for the regulatory machinery to work. In year 12, the sustainability index (line 3) begins to fall sharply leading to a decline in recommended ships at sea (line 4). By year 13, the regulators recommend 30 ships at sea, by year 14 they recommend 15 ships, and by year 15 only 5 ships, which is far below the peak fleet of 33 ships in year 12. Fishermen reluctantly move their ships to harbour. The process of idling ships is not instantaneous, but it is swift (reflecting the assumption that the time to achieve compliance is only half a year). By year 14, there are 25 ships at sea and 12 ships in harbour (see page 2 of the time chart to confirm this deployment). By year 16 there are just 6 ships at sea and 25 in harbour. Note also that two idled ships have been scrapped.

The highly effective regulatory regime cuts the catch dramatically. By the third quarter of year 14, the catch is 280 fish per year, down by almost two-thirds from its peak of 800 in year 12, and equal to the regeneration rate of new fish per year.

Thereafter, the fish stock is gradually replenished as further reductions in fishing effort and the catch take effect. By year 18, the fish density is restored to 0.5, which is slightly below optimal. The sustainability index recovers, thereby allowing a relaxation of regulatory restrictions, and the number of recommended ships at sea rises to 20. Fishermen re-activate their idled ships and the catch rises, reaching a new peak of 588 fish per year in year 21. From its original peak in year 12, the regulated fishery has been through a complete cycle of decline and recovery spanning almost a decade. Although a collapse of the fishery has been averted, the economic fortunes of the fishing community have varied widely in this period with a six-fold difference between the lowest catch and the highest. Moreover, further change is in store because, by year 21, the catch once again exceeds the regeneration rate of new fish per year, causing fish density to fall and invoking another round of regulation. In the remaining years of the simulation, the fishery settles into a pattern of fluctuating catch and fleet size, a pattern which is clearly sustainable but with a fish density (line 1, bottom chart) that is well below the assumed optimal fish density (line 2). This enduring biological discrepancy suggests the productivity of the fishery is too low and that fleet size regulation (even when implemented effectively) fails to maintain adequate fish stocks.

9.3.9. Policy Design – A Higher Benchmark for Fish Density

To improve fishery productivity it is necessary for regulators to set a benchmark for fish density that is higher than the biological optimum (Roughgarden & Smith, 1996). At first glance, this suggestion may seem counterproductive since crowding of fish inhibits their reproduction. In practice, however, a conservative benchmark is essential to counteract the growth bias of fishermen and the inevitable administrative delays in regulation.^[]

^[] Clover (2004, Chapter 7) exposes the fatal practical flaw, in fisheries management, of rigidly applying the scientific concept of 'maximum sustainable yield' (MSY). 'The pursuit of maximum sustainable yield encouraged fishermen to drive down the original population to a lower level – taking up to half of the total spawning stock every year – in the belief that this would boost the productivity of the population ... To decide what level of catches approached the magic figure of MSY there was little room for error. Scientists needed accurate figures for fishing mortality (i.e. fishermen must not cheat by misreporting catches or discards) and natural mortality. There were also dangers of missing or misinterpreting environmental forces or unforeseen predator effects.' The result was overfishing. 'A near-definitive demolition of MSY as a concept was written in 1977 by the Canadian biologist Peter Larkin. His short poem on the topic is better known:

Here lies the concept MSY

It advocated yields too high

And didn't spell out how to slice the pie

We bury it with best of wishes

Especially on behalf of fishes.'

Figure 9.28 shows the effect on fishery performance of increasing the assumed optimal fish density from 0.6 (the theoretical biological optimum for the model's harvested fishery in equilibrium) to 0.8 (a pragmatic benchmark for the same fishery). To run this simulation, press the 'Reset' button, move the slider for assumed optimal fish density to 0.8, and then press 'Run'. Everything about the fishery is stabilised – the fish stock, the catch, ships at sea, fish density and the sustainability index. Moreover, in the medium to long term, the catch is higher than in the base case. This beneficial transformation happens because overfishing is nipped in the bud. As before, the fleet size starts at 10 ships and grows. However, by year 10, the number of ships at sea (line 4, top chart) already begins to level out. This cautious deployment stems from an early recognition by regulators that fish density (line 1, bottom chart) is below the new assumed optimal (line 2). By year 12 there are only 24 ships at sea (line 4, top chart) and the catch (line 3) is 590 fish per year. Compare these figures with a fleet of 33 ships and a catch of 800 fish per year at the same time in the base case. Because the catch is reduced the fish stock falls only gently and the regeneration rate (new fish per year, line 2) continues to rise until, in the second quarter of year 14, it equals and slightly surpasses the catch. At this point the fishery is in a stable equilibrium. By coincidence the fish density (line 1, bottom chart) settles at the biological optimum density, which is below the regulators' benchmark or assumed optimal. It is precisely this discrepancy, however, that creates and maintains the regulatory pressures on fishermen to restrict fishing effort and to keep more ships in harbour. The outcome is a win–win compromise for regulators and fishermen alike.

9.3.10. Dynamics of a Weakly Regulated Fishery

So far we have optimistically assumed that all fishermen will abide by the regulatory rules. However, what if the regulatory regime is perceived to be weak and fishermen find ways to evade surveillance or believe that violations will not be punished? To explore this scenario first press the 'Reset' button. Notice that the assumed optimal fish density returns to its original value of 0.6, as in the base case. Now reduce the effectiveness of the fisheries management regime from its default value of 1 to 0.5. This change means that, whenever the fleet size is larger than recommended, fishermen intend to move only half the surplus ships to harbour. Then, to further weaken regulation, increase the time to achieve compliance from 0.5 years to 1 year. This change means that it takes regulators a year to implement restrictions on fleet size, even for those fishermen who intend to comply. Press the 'Run' button to create the time charts shown in Figure 9.29. As before ships at sea (line 4, top chart) and the catch (line 3) grow from the outset. At the start of year 11, the fish density (line1, bottom chart) falls below the assumed optimal density (line 2). However, due to measurement delays by biologists, administrative inertia of regulators and evasion of rules by fishermen the number of ships at sea (line 4, top chart) continues to grow until midway through year 13. Two years of overfishing causes a sharp decline in the fish stock (line 1, top chart). As a result, by year 15 the sustainability index falls to zero, leading regulators to call for a total ban on fishing by setting recommended ships at sea to zero (line 4, bottom chart). However, weak regulation means that the ban is widely ignored. By the end of year 15 there are still around 18 ships at sea, and even two years after the start of the ban, at the end of year 16, 10 ships remain surreptitiously at sea. By this time the fish stock is severely depleted and the catch is down to 170 fish per year, just 20 per cent of its peak value.

It is not until midway through year 18 that the catch (line 3, top chart) finally falls below the regeneration rate (new fish per year, line 2) and the fish stock (line 1) begins to edge upwards. From such a low base it takes the fishery a whole decade to recover. By year 27 the fish density (line 1, bottom chart) is nearing the assumed optimal density and conditions are, at long last, suitable to allow fleet expansion. Ships at sea begin to increase, along with the catch. By the end of the simulation, in year 40, the fleet size and catch are right back to where they started in year 0 and the stage is set for another cycle of boom and bust. The weakly regulated fishery survives, but only just. Moreover, its output (average catch) is severely depressed by comparison with either the stable and conservatively managed fishery in Figure 9.28 or the cyclical base case fishery in Figure 9.27. Because of interlocking feedback loops and non-linearities the productivity of fisheries is sensitive to the regulatory regime and output deteriorates quickly if surveillance and/or sanctions are perceived to be weak.

Incidentally, marine scientists have noted that richly diverse fisheries, supporting many different species, are more robust and less prone to collapse than fisheries with only a few dominant species. One theory is that interlocking species are better able to self-regulate their fecundity or collective fertility. We can test this proposition in the simulator by flattening the hump in the curve for net regeneration. Press the 'Reset' button. Then double-click on the graphical input device labelled 'net regeneration' and the characteristic hump-shaped graph will appear. The maximum net regeneration is 550 fish per year at a fish density of 0.6. Re-draw the graph so it is flatter in the region to the left of the maximum point. However, for logical consistency, be sure the line still passes through the (0,0) point. Click 'OK' to return to the main interface and move the sliders back to the settings for a weakly regulated fishery (effectiveness of fisheries management regime = 0.5 and time to achieve compliance = 1 year). Then press 'Run'. The resulting time charts are similar to the base case (Figure 9.27) and exhibit cyclicality in the catch, fish stock and ships at sea. The implication is that stable fecundity improves fisheries management to such an extent that even weak regulation works. Flattening the hump in fecundity simplifies the fishery's feedback structure (by effectively removing a non-linearity) and makes it easier for regulators to maintain a sustainable balance of ships, fishing effort and fish.

9.3.11. Policy Design – Lower Exit Barriers Through Quicker Scrapping of Idle Ships

The hardship caused to fishing communities by collapsing fish stocks has led many governments to introduce social support schemes. For example, Canada's Atlantic Fisheries Adjustment Package paid minimum income support of $400 per month to fishers and plant workers who had lost their jobs in the 1990s. The total cost of the package was $4 billion over 10 years. Such schemes have been criticised as a pointless waste of money because they prolong excess capacity. The argument is that subsidised fishermen and their vessels remain too long in the industry.

An alternative approach is to pay fisherman to scrap their idle ships and to leave the industry permanently. The rationale is to lower the exit barriers from the industry and to remove excess capacity more quickly, once and for all. We can test this policy in the simulator. Move the slider for the lifetime of idle ships from its default value of five years to two years to represent an increase in the scrap rate of surplus ships. This change optimistically assumes that a suitable financial incentive can be devised to boost the scrap rate. Given a successful incentive, then what outcome would you expect? Obviously there will be fewer ships, but will there be an improvement in the performance of the fishery? Take a few moments to reflect. Then press the 'Run' button to create the time charts shown in Figure 9.30.

If you were expecting improved stability of the catch and fish stock then you will be surprised. The fishery remains strongly cyclical with periods of boom and bust. Why? Note that the first 16 years of the simulation are almost identical to the base case in Figure 9.27. A decline of fish density triggers regulatory restrictions leading to a sharp reduction of ships at sea and the catch, but the recovery phase of the fishery is much different. In the interval between years 16 and 22, the number of ships at sea (line 4) grows much more slowly than in the base case. In fact, there is a distinct flattening of the fleet trajectory in year 18, caused by accelerated scrapping of idle ships – exactly as intended by the fast exit policy. Moreover, with fewer ships at sea the fish stock (line 1) rebuilds to a higher level than in the base case, reaching a peak of almost 3000 in year 22 by comparison with a peak of only 2 000 in year 18 of the base case.

Then a surprise is sprung. The abundance of fish leads to a relaxation of regulatory restrictions. Although old ships have left the industry permanently, fishermen have not. When times are good they return and buy new ships. So the cycle of fleet expansion and overshoot begins again, driven by the assumed natural propensity for growth. Ironically, because the fish stock recovers to a higher level than in the base case there is scope for greater fleet expansion, leading to a bigger catch followed by a sharper decline. Essentially lower exit barriers elongate and amplify the harvesting cycle in a regulated fishery. Whenever fish are plentiful there will always be ships and fishermen to harvest them.

A closer look at ship deployment and the scrap rate shows why cyclicality persists despite lowers exit barriers. Figure 9.31 is a simulation made under the same condition as before in which the lifetime of idle ships is reduced to two years instead of five. However, the horizontal time axis of the two charts is magnified to show years 10 through 30, and different variables are plotted. These variables can be viewed in the simulator itself by clicking on the page tab in the bottom left of the chart and advancing to pages two and three. Also the time axis can be modified by double clicking on any chart to open a window called 'define graph'. At the bottom of the window are two boxes, labelled 'from' and 'to', that set the length of the display. By inserting the values 10 and 30 (then clicking 'OK') the graph is redrawn to show the trajectories in the truncated interval from year 10 to year 30, rather than the full interval from year 0 to year 40.

The bottom chart takes us deep inside the fishermens' world as they decide to purchase ships, move them to harbour or scrap them. The top chart shows the effect of these decisions on the deployment of ships and the size of the fleet. During years 10 to 14 fishermen invest steadily in new ships as the fishery enters the final years of its long growth phase. The purchase or sale of ships (line 1, bottom chart) remains almost constant at about three ships per year. A positive value for this variable indicates purchases and a negative value indicates sales. In the third quarter of year 11 there is a rise in ships moved to harbour (line 2) as regulatory restrictions are applied. The restrictions are minor to begin with but quickly gather pace, reaching a peak of 22 ships per year in year 14. This rapid rate of idling of the fleet leads to a dramatic fall in ships at sea (line 1, top chart) and a corresponding rise in ships in harbour (line 2, top chart). Notice that during the early years of this redeployment fishermen continue to purchase new ships. This persistent investment, in the face of regulatory pressure to reduce fishing effort, reflects the growth bias of fishermen and their reluctance to believe bad news about the declining fish stock. Of particular interest is the scrap rate of ships (line 3, bottom chart) which rises to a peak of 10 ships per year in year 15 and then gradually declines back to zero by year 20. This surge in scrapping is a direct consequence of the short lifetime of idle ships. Capacity leaves the industry as intended and the total ships in the fishery, at sea and in harbour, (line 3, top chart) falls from a peak of 37 in year 14 to 13 in year 18, a reduction of almost two-thirds in only four years. If regulators were to achieve such a drastic reduction in fleet capacity their policy of lowering exit barriers would surely be deemed a success. With a much reduced fleet the sustainability index (line 5, top chart) returns to a healthy value of one by year 20, and remains high for eight years. But the replenished fishery attracts new vessels. Over the same eight year interval, from year 20 to year 28, the purchase or sale of ships (line 1, bottom chart) remains positive. All the new ships go to sea, and none are scrapped or moved to harbour. By year 28 the active fleet size is back to its previous peak of 33 ships at sea and the stage is set for another round of regulatory restrictions and scrapping of idle ships.

9.3.12. Sustainability, Regulation and Self-Restraint

The model shows that regulation of fishing effort works and that catastrophic collapse of fish stocks can be avoided by establishing scientifically credible benchmarks for fish density and by putting in place surveillance and judicial systems to enforce the benchmarks. Even so, the result is not necessarily a stable catch. A regulated fishery may be sustainable but strongly cyclical (as in Figures 9.27 and 9.29), with wide fluctuations in the fish stock and the catch as well as large variations in economic output and the standard of living of the fishing community. Fisheries are highly non-linear, multi-loop feedback systems and managing them can be tricky. Setting a conservative benchmark for fish density (as in Figure 9.28) stabilises the fishery and raises overall productivity – assuming the benchmark can be enforced.

Other policy options can be imagined. The reader is invited to run more experiments with the fisheries policy simulator by adjusting the sliders that represent policy variables. Regulatory policy is obviously important, but so too are the biological attributes of the fishery and the aspirations of fishing communities. Regulation is never perfect. Nevertheless, even gentle regulation can work well in a robust and ecologically diverse fishery – as we discovered earlier by flattening the net regeneration curve. Gentle regulation also works if people moderate their growth expectations, a change that can be investigated in the simulator by reducing the propensity for growth.