Work Package 6‎ > ‎Task 6.1‎ > ‎

Extended description of Task 6.1

The Rio Declaration and the later implementation of precautionary approach in fisheries management, has emphasised the need of developing proper harvest control rules (HCR) based on useful indicator in order to measure state of the ecosystem and possible vulnerabilities. 

Most all commercially important fisheries within the north European region are now managed through different HCR regimes which calculate annual total allowable catch (TAC) on the basis of predefined indicators. Most often only two indicators are chosen; fishing mortality rate and estimated spawning biomass of the stock. In some cases the set of indicators is extended to also include other biological or social indicators. The special case of the North-East Arctic Cod stock includes a prognostic element in the HCR regime in order to facilitate a wish of reducing the probability of significant annual fluctuations in proposed TAC-values.

Automated decision rules are well known from other optimal control problems. A new industrial standard of approximate reasoning by the use of fuzzy logic algorithms has developed fast since the idea first was presented in the 1960ies (Zadeh, 1973). The flat structure of a set of indicators has shown to be efficient in optimise predefined objective functions in complex systems which could not be easily controlled by conventional methods (e.g. optimal control theory). HCR in fisheries have many of the properties of systems previously proved to be efficiently controlled by fuzzy logic. 

Increased uncertainty related to global warming issues, also including the possibility of significant ecosystem changes, makes HCR particularly interesting as a management tool. The importance of choosing relevant indicators is urgent and not so far little has been done in this field. Development of proper indicator sets and corresponding rules, as well as expressing objective functions which take into consideration global warming consequences are needed. A particularly challenging area is the possibility of adding adaptive management rules, aiming to gain knowledge and automatically over time improve the set of rules applied. 

In the Arctic animal populations behaviour and human activities reflect adaptation to significant fluctuations between and within years. The later HCR development in the above mentioned cod fishery aims to reduce the hence following fluctuations in fisheries. Economically this may be less beneficial, as proved in several studies (Hannesson, 1975; Eide 2007) where pulse fisheries, taking advantage on natural environmental fluctuations, are shown to produce more economic rent than stable catches. The ecosystem may also be less vulnerable by choosing pulse fishing strategies, as it may put a lower fishing pressure on low stock levels.
The new prognostic element in HCR rules demands better and more accurate ecosystem models in a period of global climatic changes with uncertainties related to ecosystem capability of adapting to environmental changes and increased probability of significant shifts in ecosystem structure. In seems natural to seek for more robust indicators also in a situation of increased fluctuations. Such an investigation needs not necessarily to be based on high resolution deterministic ecosystem models, as the HCR properties are the target of the investigation. A stochastic ecosystem model based on cellular automata principles (Wolfram, 2002) will represent real systems, where the probability spaces are defined through the findings of other ATP workpackages. Cellular automata ecosystem modelling includes various types of spatial distribution of harvest activities, which may facilitate studies of the impact of changes in spatial distribution of species and economic activities.

The HCR concept also allows for meta-rules, which are rules on how HCR are changed as functions of new knowledge gained over time. Such dynamic HCR-rules may be studied through scenario-modelling as described by Hagen et al. (1998). The fleet model EconMult (Eide and Flaaten, 1998) is suitable as object of HCR management in such a scenario model, covering natural fluctuations and possible impact of global warming, including abrupt changes.

ACIA. (2004). Impacts of a warming arctic: Arctic climate impact assessment. Cambridge University Press, Cambridge, UK

Eide, Arne (2007). Economic impacts of global warming: The case of the Barents Sea fisheries. Natural Resource Modeling, 20(2): 199-221.

Eide, Arne and Ola Flaaten (1998). Bioeconomic Multispecies Models of the Barents Sea Fisheries. In Rødseth, Tor (ed.): Models for Multispecies Management, Physica-Verlag, Heidelberg, New York, pp.141-172.

Hagen, Gro, E. Hatlebakk and Tore Schweder (1998). Scenario Barents Sea: A Tool for Evaluating Fisheries Management Regimes. In Rødseth, Tor (ed.): Models for Multispecies Management, Physica-Verlag, Heidelberg, New York.

Hannesson, Rögnvaldur  (1975). Fishery dynamics: a North Atlantic cod fishery. Canadian Journal of Economics 8:151-173.

Wolfram, Stephen (2002). A New Kind of Science. Wolfram Media Inc. ISBN 1579550088.

Zadeh, Lotfi A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics 3:28–44.