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DEEPFISHMAN Using bioeconomic modeling for evaluation of management measures – an example Institute of Economic Studies.

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Presentation on theme: "DEEPFISHMAN Using bioeconomic modeling for evaluation of management measures – an example Institute of Economic Studies."— Presentation transcript:

1 DEEPFISHMAN Using bioeconomic modeling for evaluation of management measures – an example Institute of Economic Studies

2 Construct a general bio-economic model applicable to each case study for which there exist appropriate data. Data requirements 1. Stocks 2. Harvest 3. Costs 4. Revenue

3 The model should be 1. Dynamic 2. Stochastic 3. Capable of incorporating various management regimes The model should be as simple as possible (one cohort) to facilitate computations. Probably Matlab-based.

4 Case study: Application of a stochastic, multi- species model for comparative evaluation of fisheries policies in Denmark, Iceland and Norway Sustainable utilisation of marine resources in the presence of volatile environment, both in the ecological, physical and economic sense. Based on a feedback model developed by Sandal and Steinshamn (1997a, 1997b, 2001a).

5 Main characteristics: Feedback; optimal control (harvest) is a direct function of the state variable (stock) Deterministic and stochastic version Non-linear input functions Multi-species; cod and capelin, herring and cod Aggregate model Goal: Find the time path of harvest that maximises the present value of profits

6 Functions Logistic growth functions Linear profit functions Linear inverse demand function Non-linear cost function Interaction between species (cod and herring/capelin)

7 Functions (cont.) Revenue functions Cost functions

8 Development of fishable stock (4-14 years old)

9 Recruitment of three year old individuals. Millions.

10 Harvest control rule Introduced in 1995  25% of fishable stock (average of estimate at beginning of year and prediction for next year)  155 thousand ton minimum Change in 2000  minimum abolished  30 thousand ton limit on TAC-changes from year to year Change in 2006  Average of 25% of estimated stocks at the beginning of the year and last year catches  30 thousand ton limit abolished

11 Harvest control rule Change in 2006  Average of 25% of estimated stocks at the beginning of the year and last year catches  30 thousand ton limit abolished Change in 2007  TAC set at 130 thousand tons for the fishing year 2007/2008  Average of 20% of estimated stocks, but a 130 thousand ton minimum

12 Catch- share 25% 12.5% Stock t Catch t-1 Harvest control rule and catch share. H t = 0.5*(0.25*X t + H t-1 )

13 Harvest control rule A harvest control rule can only be effective if  Stocks are correctly estimated  Actual and projected (TAC) catches are the same

14 Catches and TAC

15 Catches above TAC each fishing year. Percentages of TAC.

16 Feedback models  Developed by Sandal and Steinshamn (1997, 2001), see also Arnason et al. (2004).  Single species and two-species models, with or without stochasticity  Biological growth function Allow for interaction between species in two- species models. Aggregated biomass model.  Profit function Demand and cost functions estimated separately

17 Results  Steady state cod stock (thousand tons) with harvesting Single-species1.230 Multi-species1.445 Current estimates 650  Evaluations of fishery policies Stock evaluation; a value smaller than unity represents over- exploitation Single-species0.53 Multi-species0.43 Harvest evaluation; a value greater than unity represents over- exploitation Single-species11.8 Multi-species16.2

18 Actual and optimal harvest. Single-species deterministic (σ=0) and stochastic models (σ>0).

19 Two-species conclusions  No harvesting of cod until cod stock is in excess of 500 thousand tons  Size of capelin has little effect on this conclusion  Minimum biomass before harvesting increases slightly with the biomass of capelin, possibly because the intrinsic growth rate of cod increases as the biomass of capelin increases, making it more beneficial to conserve cod.  Higher cod stocks; harvest is generally slightly lower the bigger the stock of capelin. However, this effect is reversed at low levels of capelin, probably to save the capelin.

20 Comparison between actual and optimal harvest.

21 Conclusions  Linear harvest control rules are not desirable; over-fishing when stocks are low, under-exploitation when stocks are high  Single-species models not very sensitive to introduction of stochasticity when stocks are low. Larger role when stocks are large.  Single-species and multi-species model yield similar harvest results.  Multi-species models yield more conservative harvest policies  The Icelandic cod stock has been overexploited


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