Linking effort and fishing mortality in a mixed fisheries model: Comparing linear versus non-linear assumptions

Abstract

a b s t r a c tSince the implementation of the Common Fisheries Policy of the European Union in 1983, the management of EU fisheries has been enormously challenging. The abundance of many fish stocks has declined because too much fishing capacity has been utilised on healthy fish stocks. Today, this decline in fish stocks has led to overcapacity in many fisheries, leading to incentives for overfishing. Recent research has shown that the allocation of effort among fleets can play an important role in mitigating overfishing when the targeting covers a range of species (multi-species-i.e., so-called mixed fisheries), while simultaneously optimising the overall economic performance of the fleets. The so-called FcubEcon model, in particular, has elucidated both the biologically and economically optimal method for allocating catches-and thus effort-between fishing fleets, while ensuring that the quotas are not exceeded. Until now, the FcubEcon modelling approach has assumed that there is a simplified linear relationship between effort and fishing mortality. The present study introduces an extension of the FcubEcon model, the so-called SOMER model, that allows this relationship to be non-linear by linking the biological catch equation with the economic production function. Furthermore, the SOMER model relaxes the assumption of the joint production of fishing metiers, unique and separately defined sub-fleets, to allow for more detail regarding the flexibility to target specific groups of species. Thereby, the SOMER model enables the managers to assess the alternative management scenarios more accurately than the existing models.

Integrated ecological–economic fisheries models—Evaluation, review and challenges for implementation

“…15 SIMFISH Bartelings, Hamon, Berkenhagen, & Buisman, 2015Kempf et al, 2016. 16 FISHRENT IFRO Frost, Andersen, & Hoff, 2011, 2013Lassen, Anker Pedersen, Frost, & Hoff, 2013;Thøgersen et al, 2012;Salz et al, 2010. 17 FISHRENT TI Salz et al, 2011Simons, Bartelings, et al, 2014;Simons, Döring, & Temming, 2015a;Temming, 2015b.…”

Evaluating impacts of fish stock enhancement and biodiversity conservation actions on the livelihoods of small‐scale fishers on the Beijiang River, China

“…The aim of this paper is to use applied economic modeling to evaluate the probable impacts of fish stock enhancement and pollution control on fish catches and livelihoods and highlight opportunities to enhance fish stock recovery plans and actions. Economic modeling has been applied widely in fisheries management science to: estimate the result of changes in fishing effort (Chae & Pascoe, ; Thogersen, Hoff, & Frost, ); assess the performance of fishing regulation and its effects on fisheries (Edwards, Hillary, Hoshino, Pearce, & Agnew, ; Holland & Herrera, ; Johnston, Arlinghaus, & Dieckmann, ; Marchal, Little, & Thebaud, ); analyze the economic effects of marine protected areas (Merino, Maynou, & Boncoeur, ); and evaluate the potential performance of stock enhancement programmes (Ye, Loneragan, Die, Watson, & Harch, ). Application of economic modeling to evaluate impacts of restocking and conservation measures on small‐scale fishers on inland waters constitutes a notable innovation, with the potential to contribute to achieving Aichi Target 6 by 2020.…”

Recent experience with effort management in Europe: Implications for mixed fisheries

“…Indeed, recent experience shows that reductions in effort need not lead to the expected decrease in fishing mortality (ICES, 2013d;Kraak et al, 2013), equally implying that reductions in F can occur without reductions in effort (via, for instance, avoidance behaviours). Any deviations from the assumption that F is proportional to fishing effort will have implications for the management of fleets and realised management outcomes for the stocks Thógersen et al, 2012). Studies have shown that the relationship between fishing mortality and effort can be complex (e.g., Rijnsdorp et al, 2006;Thógersen et al, 2012;Ulrich et al, 2012) and influenced by such factors as the heterogeneous distribution of stocks and fleets (e.g., Rose and Kulka, 1999), targeting behaviour of fleets (Quirijns et al, 2008), interactions between vessels (Poos and Rijnsdorp, 2007), non-random behaviour of fishermen (Walters and Martell, 2004;Tidd, 2013) due to, for instance, changes in fuel costs or market value of the targeted stocks, and gear improvements (Arreguín-Sánchez, 1996).…”