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Case study 3: Approach 1: Evaluation of the relative performance of different mgmt strategies

Introduction - evaluation outline

The objective of approach 1 is to evaluate relative performance of potential management strategies with regard to management objectives, while taking into account the costs of the assessment and management system. The key question is: “which assessment-management combinations achieve most successfully a pre-defined set of overall management objectives with reasonable management and assessment costs?”.

Management objectives

• To gradually increase the production of wild Baltic salmon to attain at least 50% of the natural production capacity of each river with current or potential natural production of salmon
• Alternatively: to gradually increase the spawner or smolt production in each river to reach the optimum abundance levels at MSY

Management measures

• TAC based management
• Delayed opening of the coastal fishery

Data sampling and stock assessment

• Simulated smolt abundance data
• Simulated spawner abundance data

Harvest control rules

So far harvest control rules evaluated are model-free, that is the rules depend on observations directly, such as survey data.
• Similar as for North Atlantic salmon stocks where HCR are set based on the conservation limits
• Delayed opening of the coastal fishery, and other effort based management scenarios
• Similar as for the North Sea where the TAC of this year is dependent on last years TAC (Kell et al. 2006)

Assumptions:

• Assume no driftnet fishery in the future
• Assume constant effort in the river fishery

Performance criteria

• Probability to reach 50% of the smolt production capacity for each stock
• Risk of stock collapse for the weakest stock
• Probability to reach reference points based on MSY
• Profitability of the fishing activity

Scenarios

The base case OM includes a biological model that mirrors the biological model used in the ICES stock assessment working group (WGBAST), and functions for observation, assessment, advice, management, economic, and implementation including compliance.

The OM takes into account biological, economic and socio-economic uncertainties by running the operating model for a set of different scenarios. The following table provides an overview of the base case scenerio and the alternative scenerios considered.

Component Base case Alternative
Stock-recruit function Beverton-Holt Ricker
Natural mortality Normal Severe
Price Constant Elasticity
Costs Constant Increasing
Compliance Perfect Bad
Fishers behaviour Profit related CPUE related

Data and parameters

Stock data

The operating model has been populated using probabilistic estimates of life history parameters and abundance estimates as obtained from the Bayesian MCMC models used by the ICES working group for the assessment of Atlantic salmon in the Baltic Sea.

Modelling

FLR (Operating Model)

In order to evaluate the management plan an operating model using two FLStock objects are set up: one FLStock object for wild salmon, called WsalmBiol and one FLStock object for reared salmon, called RsalmBiol.

Even though wild and reared salmon are constituents of the salmon Salmo salar species, the different stock objects are needed in order to allow the storage of the different life history parameters for wild and reared salmon. The FLStock objects consist of FLQuants of 6 dimensions, defined as follows:

Dimension of the FLQuants for WsalmBiol:

Dimension Description Values
Quant Age at a certain life history stage 1-5
Year Year 1992-2037
Unit Stock assessment unit 1-4
Season Life history stage * 1-2
Area Stocks within an assessment unit 1-8
Simulation MCMC iteration 1-1000

* The life history stages include: salmon at sea (1) and salmon migrating back to the river (2). In the case of hatchery-reared salmon, the returning reared salmon escaping the fishery will not be able to reproduce, because the spawning grounds in the rivers where they have been released can no longer be reached, e.g. due to the damming of the river, so it is not necessary to keep account of the exact origin, i.e. the river where the hatchery-reared salmon are released. Therefore, the FLStock object for reared salmon, RsalmBiol, does not utilise the Area dimension which indicates the stock from which the reared salmon originate.

Dimension of the FLQuants for RsalmBiol:

Dimension Description Values
Quant Age at a certain life history stage 1-5
Year Year 1992-2037
Unit Stock assessment unit 1-4
Season Life history stage 1-2
Area Stocks within an assessment unit All
Simulation MCMC iteration 1-1000

In addition to the FLStock objects, 4 FLFleet objects are defined: Two for the historic part, when there were still 6 major salmon fisheries operating in the Baltic Sea area, and two for the future part of the operating model, when there will only be three major salmon fisheries operating in the Baltic Sea. This is due to the EU ban on driftnets starting in 2007, and due to the gradual disappearance of the coastal gillnet fishery. Even though one fleet catches both wild and hatchery-reared salmon, two different FLFleet objects are used for both stocks in order to avoid making the FLFleet objects too large.

Dimension of the FLQuants for historic FLFleet objects, WsalmEcon and RsalmEcon:

^Dimension ^Description ^Values ^

Quant Age All
Year Year 1992-2007
Unit ICES unit (a) 1-3
Season Fishery (b) 1-6
Area Country ( c) 1-4
Simulation MCMC iteration All

(a) The ICES units include: ICES units 22-29, i.e. the Baltic Main Basin (1), ICES unit 30, i.e. the Bothnian Sea(2) and ICES unit 31, i.e. the Bothnian Bay. (b) The fisheries include: Offshore Driftnet (ODN) fishery (1), Offshore Longline (OLL) fishery (2), Coastal Driftnet (CDN) fishery (3), Coastal Trapnet (CTN) fishery (4), Coastal Gillnet (CGN) fishery (5) and River Fishery (RF) (6). ( c) The countries include: Finland (1), Sweden (2), Denmark (3) and other fishing nations (4).

Dimension of the FLQuants for future FLFleet objects, WsalmEcon and RsalmEcon:

Dimension Description Values
Quant Age All
Year Year 2008-2037
Unit ICES unit 1-3
Season Fishery * 1-3
Area Country 1-4
Simulation MCMC iteration 1-1000

* The fisheries include: Offshore Longline (OLL) fishery (1), Coastal Trapnet (CTN) fishery (2) and River Fishery (RF) (3). As such, the FLFleet objects are incompatible with the FLStock objects, since the statistical ICES Units do not correspond with the assessment units as defined by the similarlities between different stocks. Especially in the case of the fishing effort it is important to convert the fishing effort of different nations in different ICES areas into the total fishing effort of different fleets on stocks of different assessment units Therefore, additional FLQuant objects are designed.

The following table indicates how the different stocks are distributed across the different assessment units and which fisheries are exploiting these stocks.

Salmon stock Assessment unit Fisheries affecting the stock
1 Tornionojoki Unit 1 ODN, OLL, CDN, CTN[Unit 1], CGN[Unit 1], RF
2 Simojoki Unit 1 ODN, OLL, CDN, CTN[Unit 1], CGN[Unit 1], RF
3 Kalixälven Unit 1 ODN, OLL, CDN, CTN[Unit 1], CGN[Unit 1], RF
4 Råneälven Unit 1 ODN, OLL, CDN, CTN[Unit 1], CGN[Unit 1], RF
5 Piteälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
6 Åbyälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
7 Byskeälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
8 Rickleån Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
9 Sävarån Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
10 Ume/Vindelälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
11 Öreälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
12 Lögdeälven Unit 2 ODN, OLL, CDN, CTN[Unit 2], CGN[Unit 2], RF
13 Ljungan Unit 3 ODN, OLL, CDN, CTN[Unit 3], CGN[Unit 3], RF
14 Mörrumsån Unit 4 ODN, OLL, RF
15 Emån Unit 4 ODN, OLL, RF

Conditioning of Operating Model

The operating model has been populated using probabilistic estimates of life history parameters and abundance estimates as obtained from the Bayesian MCMC models used by the ICES working group for the assessment of Atlantic salmon in the Baltic Sea. The ICES stock assessment model relies on a wide variety of data from the fisheries (catch and effort data) and from the different salmon stocks (tagging data, parr density data, smolt trapping data, fish ladder data and yolk-sac-fry mortality data). A total of 1000 samples have been taken from the joint posterior pdf, thereby accounting for the correlation between the resulting life history parameters and abundance estimates.

Model assumptions have been described in P. Levontin - Equations for the Operating Model and Management Procedure in the Baltic Salmon Case Study. Conditioning of the OM to the set of sub-models is describes in the figure below.

Conditioning Of OM

Figure. Conditioning of the operating model

Historical Estimates of Time Series

Simulations will be initiated based on the WGBAST 2006 assessments. The starting year for simulations is 1992, projections for the historic part (until 2007) rely on estimated recruitment time series. From 2008 onwards, the management procedure will be simulated for up to 30 years into the future.

Robustness trials are conducted at each of the three phases of the operating model’s development. First, model’s predictions for the historic period are compared with time series from the latest WGBAST report. In the second phase, full simulations will be run according to robustness trial scenarios specified in the table below. The purpose of these simulations is to assess the sensitivity of management strategies to different sources of uncertainty, such as structural uncertainty, parameter uncertainty and other hypotheses represented as scenarios. Finally, the output from the simulations will be explored with the help of Bayesian Belief Network model that would enable synthesis of information across all simulations.

Scenarios
Base case12345678910111213141516
Biological Stock B&H x x xxxxxxx x x x x x x
recruitRicker x x
Repeat Semelparity x x xxxxxxxx x x x x x x
spawnerIteroparity x
M74 Constant x xxx xxxxxx x x x x x x
occurrenceFluctuating x
Economic Price Constant x xxxx x xxx x x x x
Elasticity x x x x
Cost Constant x xxxxx xxx x x x x x
Increasing xx x
River Constant x xxxxxxx xx x x x x x x
benefitsIncreasing x
Fleet behaviour Reporting Current x xxxxxxxxxx x x x x
Improved x x
Compliance Perfect x xxxxxxxxxx x x x x
Bad x x
Fishermen Profit realted x xxxxxxxxx x x x
behaviourCatch related x x x x
Dioxine Partial ban x xxxxxxxx x x x x x x x
Offshore ban x

Table. Specification of robustness trials)

Stock Recruitment Relationships

The ICES assessment model has been run assuming either a Beverton-Holt or a Ricker stock-recruit function.

Management Procedure

Data sampling and stock assessment

- Simulated smolt abundance data
- Simulated spawner abundance data

Harvest control rule

So far harvest control rules evaluated are model-free, that is the rules depend on observations directly, such as survey data.
• Similar as for North Atlantic salmon stocks where HCR are set based on the conservation limits
• Delayed opening of the coastal fishery, and other effort based management scenarios
• Similar as for the North Sea where the TAC of this year is dependent on last years TAC (Kell et al. 2006)

Results

The work in progress regarding approach 1 has been presented at the ICES ASC in 2005 (Levontin and McAllister, 2005) and at the ICES symposium on management strategies in Galway, Ireland, in 2006 (Levontin et al., 2006).

The main product in approach 1 is the complete and functional population dynamic OM for Baltic salmon in FLR (FLCore-version 2.0-2), constructed together with COMMIT-project. The biological OM was applied by the Baltic Salmon and Sea-trout working group (WGBAST) in formulating their advice for 2007. Long-term stock projections for different Baltic salmon stocks were run for possible future levels of effort and fixed target levels of catch. – The projections are described in section 5.4 in the WG-report (WGBAST 2007).

Levontin, P., McAllister, M. (2005). Evaluating management options for Baltic salmon (Salmo Salar) using bio-economic operating models in a generic simulation framework. Proceedings of the ICES Annual Science Conference, Aberdeen. ICES CM 2005/W:00.
Abstract: The population of wild Baltic salmon has undergone in the last century a severe decline, due to dam building, overfishing and pollution. The international effort to reverse this decline can claim success in some of the main Baltic rivers where stocks are seen to be recovering, yet some wild stocks remain highly depleted. From a policy perspective there is a desire to identify a management strategy that would have a high probability of safeguarding the stocks while minimizing economic and social hardships of regulation. The difficulty in designing experiments in the real world makes simulation an attractive realm in which to evaluate alternative management regimes. We utilize operating models that are designed to simulate a complex bio-economic reality. We consider both structural and parameter model uncertainty by using Bayesian methods that allow us to incorporate information from a variety of sources - both data and expert knowledge. We evaluate management procedures for Baltic salmon in a generic simulation framework based on the statistical programming language R. Within the scope of the planned simulations, it is possible to explore many factors and scenarios. For example, the ecological effects of changes in the predator population (seals) and in the availability of food (sprat). The simulation-evaluation of harvest control rules is linked to the analysis of socio - economic changes associated with each regulation regime. The goal is to identify those stock rebuilding programs that are most robust to various sources of uncertainty and that are more likely to secure commitment from fishermen.

Levontin, P., Kulmala, S., Lindroos, M., Michielsens, C. and McAllister, M. (2006). Evaluating fisheries management options for Atlantic salmon stocks (Salmo salar) in the Baltic Sea. Proceedings of the ICES symposium on management strategies: case studies of innovation, Galway. ICES SFMS 2006.
Abstract: In this paper, different management strategies are formulated and tested in simulations. The goal is to identify those that perform better under a range of uncertainties. Several aspects of a simulation framework for fishery management system evaluation are explored with an example of Baltic salmon fishery. The aim is to examine simplifications of biological hypothesis, assessment procedure and decision rules in fisheries management evaluations. Models are always simplifications of reality, so it is crucial to know how much information is lost or distorted through simplification. In this study, we aim to expose the trade offs between complexity of the operating model and the loss or distortion of desired information, such as the ranking of management regimes and understanding of risks associated with their implementation. The second part of the paper focuses on the information needs of a successful management strategy. Assessment, monitoring and implementation of management decisions is resource consuming; replacing complicated annual assessment as a basis for complex decision making system with a more simple management procedure could benefit all stake-holders. Using simulation approach, we seek to identify those management procedures that don’t require extensive knowledge of the fisheries system.

Dissemination and References

Levontin, P., McAllister, M. (2005). Evaluating management options for Baltic salmon (Salmo Salar) using bio-economic operating models in a generic simulation framework. Proceedings of the ICES Annual Science Conference, Aberdeen. ICES CM 2005/W:00.
Levontin, P., Kulmala, S., Lindroos, M., Michielsens, C. and McAllister, M. (2006). Evaluating fisheries management options for Atlantic salmon stocks (Salmo salar) in the Baltic Sea. Proceedings of the ICES symposium on management strategies: case studies of innovation, Galway. ICES SFMS 2006.
Levontin, P., Kulmala, S., Lindroos, M., Michielsens, C. and McAllister, M. (2007). Evaluating fisheries management options for Atlantic salmon stocks (Salmo salar) in the Baltic Sea. Proceedings of the ICES symposium on management strategies: case studies of innovation, Galway. ICES Journal of Mariene Science 64: 000-000.

Links to Other Work and Acknowledgements

All the work has been carried out in close connection with the EU-funded COMMIT project.

 
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