• No results found

Filin_Tjelmeland_Ecosystem approach to estimation of long-term.pdf (375.7Kb)

N/A
N/A
Protected

Academic year: 2022

Share "Filin_Tjelmeland_Ecosystem approach to estimation of long-term.pdf (375.7Kb)"

Copied!
13
0
0

Laster.... (Se fulltekst nå)

Fulltekst

(1)

The 11th Russian-Norwegian Symposium Murmansk-Russia, 15-17 August 2005

Ecosystem approach to estimation of long-term yield of cod in the Barents Sea

Anatoly Filin

Polar Research Institute of Marine Fisheries and Polar Research Institute of Marine Fisheries and

Oceanography (PINRO),

Oceanography (PINRO), MurmanskMurmansk, Russia, Russia

Sigurd Tjelmeland

Institute of Marine Research (IMR), Bergen, Norway Institute of Marine Research (IMR), Bergen, Norway

(2)

The scheme of stock harvest according to the concept of precautionary approach

Репродуктивный потенциал

0 10

0 1 2 3 5 6 7 8 9 10 11 12 13

Spawning biomass

Fi sh in g m or tal it y

B

lim

B

pa

I II III

F

pa

(3)

Harvest efficiency of cod stock in the Barents Sea

Is current harvest strategy good enough to ensure maximum

sustainable yield of cod considering interannual variations in the ecosystem of the Barents Sea?

Do the existing precautionary rules for deciding on TAC of cod correspond to the principals of rational harvest of this stock, taking into account natural fluctuations of the population?

Is it possible to increase harvest efficiency of cod stock in the Barents Sea by improving the management strategy implementing ecosystem approach in harvest management without breaking the accepted precautionary principle?

(4)

Purpose of the study

The purpose of this study is to ground methodical

approaches to development of harvest strategy for cod in the Barents Sea that is based on estimates of long-term yield

taking into account the effect of ecosystem factors on stock

dynamics.

(5)

Stages of development of ecosystem based strategy for cod harvest

1. Define the management targets

2. Specify ecosystem factors for simulation

3. Develop models for stock management that take into account the effect of species interaction and ecosystem factors on stock dynamics

4. Develop stochastic ecosystem scenarios for testing the harvest strategy 5. Perform simulations of stock dynamics

6. Develop rules for stock harvest that take into account the situation in the ecosystem

7. Assess economic efficiency of the stock harvest using the developed strategies.

(6)

Setting and implementing the targets of long-term strategy for management of cod stock

Economic, social and

political background Targets for management

Maximum long-term yield

Sustainable long-term yield

overfishing underfishing

State and dynamics of stock Ecosystem factors

Harvest Species

interactions

Climate change

Management

The objective of ecosystem strategy for management of cod stock is to obtain a

M aximum long-term yield under set limitations of interannual catch

dynamics

(7)

Scheme of species interactions used in model ecosystem analysis of cod stock dynamics in the Barents Sea

Capelin Polar cod Herring

Cod

Minke whale Harp seal

(8)

Models that expose interactions of cod and other species in the Barents Sea

Models developed at IMR

Models developed at PINRO

Joint models

MULTSPEC AGGMULT

Systmod Bifrost SeaStar GADGET

MSVPA CONCOD STOCOBAR

CodCap

EcoCod

(9)

Ecosystem scenario for estimation of long-term yield of cod in the Barents Sea

Ecosystem scenario should be realistic, which means that it should expose interannual variations of ecosystem analysis.

Ecosystem scenario should be based on dynamics of temperature conditions.

Scenario of thermal conditions development should take into account year-to- year cycling of temperature dynamics in the Barents Sea.

Stochastic temperature scenario should be based on historic data that can be selected in the following way:

- random selection;

- random selection in the given interval;

- in successive order combining warm, cold and moderate periods, based on data randomly selected in the given interval.

Τhe scenario of development of thermal conditions should determine scenarios of feeding resources dynamics for cod that are also based on historic data on capelin stock biomass and other prey species for cod in the Barents Sea.

(10)

Improvement of harvest control rules for cod based on ecosystem approach

Simulations should be used to study the necessity to apply different approaches to the harvest management depending state of the

ecosystem.

Possible approaches to improvement of harvest control rules for cod stock:

- the rules of harvest intensity remain unchanged, but new values of biological reference points obtained with ecosystem based simulations are used;

- the existing scheme for calculation of TAC remains unchanged, but the values of biological reference points become variable depending on the situation in the ecosystem;

- new additional reference points and new scheme for stock management are applied.

(11)

Conclusion

Ecosystem approach to harvest management in the Barents Sea as well as precautionary approach should ensure long-term

sustainable and plausible harvest of marine biological resources.

The developed joint Russian-Norwegian 10-year research

programme provides a good background for practical advice on optimising harvest strategy for cod in the Barents Sea based on ecosystem approach already in the nearest years.

The method of stochastic scenario modelling should be used as methodical basis for the estimation of long-term harvest of cod should be.

(12)

Main fields of further work (1)

To solve theoretical problems related to implementation of ecosystem approach to harvest management of cod stock in the Barents Sea:

- harmonisation of terms;

- improvement of the concept;

- development of methodology.

To develop multispecies and ecosystem models:

- harmonization of approaches to development of the models;

- development of testing system for the models;

- development of a joint multispecies model for the Barents Sea.

(13)

Main fields of further work (2)

To improve and expand database for ecosystem modelling:

- improvement of the current system for collection of data;

- involvement of unavailable at the moment historic data into model analysis;

- revision of historic data at PINRO and IMR;

- work on computerizing of PINRO historic data in electronic format;

- solving the problem of access to primary data while working on joint models.

Referanser

RELATERTE DOKUMENTER

Thermal contrasts, ΔT, for the camouflage net systems relative to the selected background element areas are calculated as hourly average values for all time intervals that meet

Jan Oskar Engene’s eminent empirical study of patterns of European terrorism reveals that rapid economic modernisation, measured in growth in real GDP 59 , has had a notable impact

Following coverage of the area, the backscatter is segmented into a discrete number of classes by modeling the probability distribution as a hidden Markov random field (HMRF)

Only by mirroring the potential utility of force envisioned in the perpetrator‟s strategy and matching the functions of force through which they use violence against civilians, can

Marked information can be exported from all kinds of systems (single level, multi level, system high etc.), via an approved security guard that enforces the security policy and

The total area (A) affected by a 1h sonar operation under different ramp-up schemes assuming simulated animals respond at fixed avoidance thresholds (SPL avoid ; sound pressure level

Fig. Modeling is done with the composite-roughness surface scattering kernel for the same type of bottom as in Fig. There are 10 dB between the thick marks on the vertical axes.

(ed.): Ecosystem dynamics and optimal long-term harvest in the Barents Sea Fisheries. PINRO Press, Murmansk. Conditions of summer feeding and growth of the Barents Sea