Uncertainties

The bio-economic models examined in this thesis account for uncertainties underlying both ecological and economic processes. More generally, the management of fisheries is exposed to a wide range of uncertaintiesGarcia (1996); Hilborn and Peterman (1996); Francis and Shotton (1997). Among these are uncertainty in the size, composition, and spatial distribution of stocks; uncertainty in stock dynamics, especially in recruitment; and variations in economic parameters, such as costs and prices (Francis and Shotton,1997;Jensen,2008;Fulton et al.,2011).

Inter-annual recruitment variability is an important form of natural variability in fisheries (Fran- cis and Shotton, 1997). Two different sources of recruitment uncertainty can be identified: de- mographic and environmental stochasticities. These correspond to unpredictable environmental factors that affect the fecundity of some (in demographic stochasticity) and sometimes all (in en- vironmental stochasticity) individuals in a population (Lande, 1993). Recruitment uncertainty of

Nephrops, Hake and Sole were simulated in the BoB case study with an uniform distribution re-

lying on historical time series of recruitment. This is a simplified way to integrate uncertainty in recruitment estimations when refined data on recruitment processes are not available. Alternatively, when enough data are available, it is possible to simulate the environmental influence on recruit- ment through environmentally driven temporal correlation as was modelled in the NPF for tiger and blue endeavour prawn recruitments.

In addition to environmental stochasticity, fisheries are subject to economic uncertainty with strong influences from both internal and external factors. This is particularly the case for fuel price. The fuel price scenario assumed in the BoB case relied on projections from the International Energy Agency (IEA,2010;CAS, 2012), while the fuel price scenario in the NPF case was based on his- torical data regression. A common increasing trend in fuel price is assumed in both cases, however

6.1. Important common features of the bio-economic models S. Gourguet

the scenarios are different, as the case studies relate to different contexts in terms of factors such as national policies and taxes, and are not affected in the same way by worldwide influences. Market prices for fish assumed in this study were based on historical data for both fisheries. However, un- certainties were introduced in a different way, as market prices in the two fisheries are not impacted in the same way by external and internal factors. Uncertainties on annual mean market price in the BoB fishery were introduced through a random mean price by species following a Gaussian distri- bution calibrated from ex-vessel prices for the 2000-2009 period. The mean prices were assumed to be independent by species and by year, but also independent of the landings. Experts in the fishery have attempted to establish econometric relationships that take into account different drivers influ- encing prices (such as landings structure and quantity, national landings and imports). However up until now, robust models of such correlations have proved difficult to establish, therefore it seemed preferable in this analysis to simply introduce price uncertainty, with no particular assumptions as to the drivers of price variability. On the other hand, except for banana prawns, the main market for NPF prawns is Asia, and the price is largely dependent on the Yen-AU$ exchange rate and the total international supplies to the market (Punt et al., 2010). The assumed trends affecting prawn prices in the NPF have been observed in other wild prawn or shrimp fisheries around the world (Gillett, 2008). A fall of shrimp prices has been observed in Australia, and in the United States, Indonesia and Nigeria to cite some (Gillett, 2008). Some of the downward pressure on prices on captured shrimps comes from the increasing amount of farmed shrimp on the world market, espe- cially whiteleg shrimp (Litopenaeus vannameifrom China (Gillett, 2008). These trends are out of the control of local industry and management. Prawn prices were thus assumed to be independent of the NPF landings, and scenarios were based on projections from historical trends; they did not include price variability.

Results showed that the biological variability integrated in both models had an important impact on the economic outcomes. In the BoB fishery, this is especially true for the sub-fleets that are strongly dependent on theNephrops(Nephropstrawlers) and on the Hake (large various fish gill- netters). The variability in annual profits in the BoB fishery results from the combined effects of the biological and market prices uncertainties. Regarding the NPF, the variability in annual profits stems from the biological variability; in this case and when considering the large range

of possibilities for annual profits, it is clear that biological variability has a crucial role for bio- economic risk assessments. Furthermore based on the most likely economic scenarios identified in this work, it has been possible to highlight, in both case studies, the potential of future economic risks for both fisheries.

Box 5: Comparison of the BoB and NPF bio-economic models.

The table 6.1 summarizes the main features of the bio-economic models developed in this thesis for the BoB and the NPF fisheries. When components differed between chapters, only the most detailed representation in each of the two case studies is represented in the table.

Table 6.1: Features of BoB and NPF bio-economic models.

Bay of Biscay demersal fishery Northern Prawn Fishery

Common complexity

Multi-species 3 with population dynamics 3 with population dynamics, 2 without population dynamics Dynamic structure Age Size Stock- recruitment relationship

Hockey stick function (or also called Ockham Razor, i.e. dou- ble linear recruitment curve)

Ricker

Multi-fleet 4 main fleets split into 16 sub- fleets according to vessel length classes+one ‘other fleet’

2 sub-fisheries with one of the sub-fishery separated in two fishing strategies

Input control Number of vessels by sub-fleets Number of vessels and annual effort per vessel

Specific complexity

Time-step Annual Weekly

Effort process Adaptive effort allocation pro- cess

Other species Indirect economic effects of other species catches

Direct impact of fishing on by- catch

Uncertainty

Environmental Uniform distribution relying on historical time series of recruit- ment

Environmentally driven tempo- ral correlation

Economic Stochasticity on market prices Prawn prices scenario based on historical trends

Fuel price scenario based on In- ternational Energy Agency pro- jections

Fuel price scenario based on his- torical trends