Estimating the economic loss of recent North Atlantic fisheries management
Introduction
Marine fisheries are an important source of food and livelihood opportunities worldwide (Allison et al., 2009, Garcia and Rosenberg, 2010, Rice and Garcia, 2011). The exploitation state of fish stocks is hotly debated (Branch et al., 2011, Pauly et al., 2002, Worm et al., 2009), but there is a general consensus that marine fisheries food production potential is not achieved (Branch et al., 2011, FAO, 2012). North Atlantic fisheries are nowadays yielding less fish than in recent decades and despite significant improvements (Fernandes and Cook, 2013), the state of many of its stocks remains poor. Traditionally, the efficiency of biomass production has been the basis of fisheries management. Therefore, different regulations have aimed at maintaining fish stocks at levels at which they could produce their Maximum Sustainable Yield (MSY), i.e. the exploitation rate where the response of the stocks to fishing through individual growth and recruitment operates at its maximum capacity. In a deterministic sense, at this level, average fish biomass remains stable over time and the amount of fish that can be sustainably extracted is maximized (Schaefer, 1954). Classic approaches assume that these dynamics operate at a particular stock level, depending on the species’ life history and thus, should fisheries management succeed in maintaining each of them at their MSY, the maximum potential of food production from marine ecosystems would be achieved. Using Economic Exclusive Zone (EEZ) and fish species data from the Sea Around Us database, the food production potential wasted due to ineffective management was estimated, i.e., the difference between catch observations and their MSY estimated from historic catch series (Srinivasan et al., 2010). Srinivasan et al. (2010) estimated that catch losses amounted to 7–36% of the reported annual catch, resulting in a landed value loss between $6.4 billion and $36 billion.
In reality, it is ecologically impossible to simultaneously maximize sustainable yield for all species in a multiple species fishery (Link, 2009). Therefore, the productivity of marine ecosystems is expected to be lower than predicted by the sum of single stocks’ MSY (Link et al., 2012). The overall productivity and state of exploitation of marine ecosystems have been investigated previously with complex ecosystem models and indicators (Blanchard et al., 2012, Blanchard et al., 2009, Coll et al., 2008, Cury et al., 2008, Merino et al., 2012, Shin et al., 2005), and with single species models applied to entire exploited communities (Guillen et al., 2013, Link et al., 2012, Mueter and Megrey, 2006, Sparholt and Cook, 2009, Worm et al., 2009). For example, ‘surplus production models’ (SPM), have been used to produce simple representations of the key ecological processes underlying fisheries (Link et al., 2012). SPM can be used to estimate biological reference points (BRP’s) such as the biomass level and the rate of exploitation to achieve the MSY of single fish stocks or marine ecosystems.
SPM have allowed the extension of fisheries assessment into other disciplines beyond ecology. For example, the seminal paper by Gordon (1954) introduced the concept of Maximum Economic Yield (MEY), the bioeconomic reference point at which the economic profits of a fishery are maximized. This concept relies on fish stocks’ productivity described by SPM (Schaefer, 1954), the market price of fish and the costs of fishing. A derivation of this model was used to assess the economic efficiency at which the world’s fisheries are exploited (Arnason et al., 2009), from which global MEY was estimated based on world’s catch, value and costs databases. Arnason et al. (2009) highlight the vast economic consequences of inefficient fisheries management and the economic benefit of maintaining fish stocks at healthy levels. Due to the high uncertainty in the data and the simplified model used, the numeric results of Arnason et al. (2009) study were presented with caution and with wide confidence intervals. Nonetheless, the global cost of sub-optimal management was estimated to be in a range between $37 and 67 billion in 2004, with an historic accumulated loss of $2.2 trillion between 1974 and 2004. Arnason et al. (2009) did not explicitly evaluate the cost of rebuilding fish stocks, i.e., the cost of the necessary transition until stocks are recovered and more economic profit is obtained with less fishing effort. More recent research shows that the benefit of rebuilding global fisheries outweighs costs (Sumaila et al., 2012) and that investing in restoring overexploited stocks is economically sound (Crilly and Esteban, 2012). However, it is important to clarify that not all fish stocks are overexploited. For example, 43% of assessed EU stocks were considered overfished in 2012 (Fernandes and Cook, 2013, European Union, 2012). In any case, when fishing yields do not correspond to MSY this does not automatically mean a stock is overfished (Hilborn and Stokes, 2010). Hilborn and Stokes (2010) suggest that it would be reasonable to adopt a definition of being overfished as any stock size where the expected yield is 80% or less than MSY, which is the level at which reductions of fishing mortality towards MSY would produce measurable catch increases.
The North Atlantic basin is a dynamic environment for physical and biological processes (Beaugrand et al., 2002, Marshall et al., 2001, Parsons and Lear, 2001) and is home to some of the largest populations of commercially exploited stocks (Trenkel et al., 2014). With this at the background and due to the importance of North Atlantic global climate, BASIN (Wiebe et al., 2009) is a joint EU/North American research initiative with the goal of elucidating the mechanisms uderlying observed changes in the North Atlantic ecosystems and their services, and Euro-BASIN is a programme to implement this vision funded by the European Commission 7th Framework Programme (St. John et al. 2014). In the context of Euro-BASIN, this article aims to reflect the economic relevance of fisheries within the North Atlantic basin using some of the methods described above to estimate the economic cost of ineffective fisheries management, defining ‘ineffective’ as a deviation from maximum economic rent (Arnason et al., 2009). To do so, we tested alternative aggregations of fisheries production and economic indicators and parameterized a simple bioeconomic model. The scope and scale of this study is vast and complex and requires simplifications. The ecological complexity, regional differences and dynamics of individual fish stocks in the North Atlantic are simplified in an aggregated single stock of fish, which is exploited by an aggregated single fishery. While this approach has significant ecological difficulties, aggregated fisheries production functions are not new, and have been used to assess the economic efficiency of global fisheries as a single exploited unit (Arnason et al., 2009), at ecosystem level (Crilly and Esteban, 2012, Link et al., 2012, Sparholt and Cook, 2009) and at species-EEZ level (Srinivasan et al., 2010). The implications of this approach and justification for the use of an aggregated model will be discussed in detail throughout the manuscript. Furthermore, we explore the possible impact of parameter uncertainties and the assumptions made to obtain our numeric results. Finally, we discussed the use of multidisciplinary approaches in analyzing marine resources at the basin scale. These results provide background context to the work conducted in Euro-BASIN in the Bio-economic modeling (WP7) and Living resources (WP5) workpackages.
Section snippets
The data
- –
Biological parameters: Catch data from ICES FishStatPlus database (www.ices.dk), FAO Fishery Statistics (www.fao.org) and Sea Around Us catch database (www.seaaroundus.org) were used to estimate the biological parameters of the surplus production model of the North Atlantic (NA) fisheries from 1950 to 2010. The data used comprise 59 ICES stocks, 18 species and 2 habitats exploited in the North Atlantic for the ICES area (see Table 1). These data were used to explore how alternative levels of
Results
The total MSY for all the ICES stocks combined was estimated to be between 6.68 and 9.75 million tonnes, depending on the level of catch aggregation from which the estimates were calculated (Table 1 and Fig. 1).
Biological parameters were estimated for each of the ICES stocks and were then aggregated into species, habitat and total ICES areas. The total MSY estimate for the ICES fisheries decreases exponentially with the level of aggregation, with MSY estimates 30% lower when using ICES area
Discussion
We have provided an assessment of the economic losses due to the choices taken in the management of North Atlantic fisheries. We have used methods previously implemented in the assessment of the economic losses of global fisheries (Arnason et al., 2009). Such a focus on the North Atlantic, in the context of the Euro_BASIN project, is motivated by the fact that its fisheries have a long history and economic importance, with significant catch-independent and dependent data sets, which are managed
Acknowledgments
This research was supported by European Union seventh framework programme through the project EURO-BASIN (264933). We than Dr. Froese for making publicly available the MSY estimation algorithm used in this study. We also thank Francesc Maynou, Hilario Murua, Gerry Scott for the valuable comments in the preparation of this manuscript.
References (75)
- et al.
Rebuilding EU fish stocks and fisheries, a process under way?
Marine Policy
(2013) - et al.
A comparison of recent changes in distribution of capelin (Mallossus villotus) in the Barents Sea, around Iceland and in the Northwest Atlantic
Progress in Oceanography
(2013) - et al.
Linking global and local scales: designing dynamic assessment and management processes
Global Environmental Change
(2000) - et al.
Ecosystem oceanography for global change fisheries
Trends in Ecology and Evolution
(2008) - et al.
Reversal of fish stock decline in the Northeast Atlantic
Current Biology
(2013) - et al.
Fish recruitment prediction, using robust supervised classification methods
Ecological Modelling
(2010) - et al.
Towards the implementation of an integrated ecosystem fleet-based management of European fisheries
Marine Policy
(2012) - et al.
Estimating MSY and MEY in multi-species and multi-fleet fisheries, consequences and limits: an application to the Bay of Biscay mixed fishery
Marine Policy
(2013) - et al.
Modelling multi-species interactions in the Barents Sea ecosystem with special emphasis on minke whales and their interactions with cod, herring and capelin
Deep-Sea Research Part II: Topical Studies in Oceanography
(2009) - et al.
Impacts of global environmental change and aquaculture expansion on marine ecosystems
Global Environmental Change
(2010)