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Approach 1: Evaluation of TACs and effort limitations in the North Sea Roundfish, focusing on mixed fisheries aspects

Clara Ulrich, Katell Hamon, J. Rasmus Nielsen (DTU-Aqua), Ayoe Hoff, Hans Frost (FOI)

Introduction

This approach focuses mostly on the fleet-based effects of stock-based management procedures (TAC) in a mixed-fisheries context. The main issue dealt with here is how to account for technical interactions and fishermen behaviour in the Management Strategies Evaluation. The various stocks must not be considered separately, but rather together in an integrated manner, since the catches of each stock are also dependent of the effort exerted on other stocks, and thus, of the management objectives for those. In particular, the current TAC system is meant to create discards of valuable fish, because it is not always possible to avoid catching a stock whose quota is exhausted, while targeting another one.

As such, most effort has been put on (i) description of the international fleets and metiers, and their technical interactions, (ii) development of the Fcube approach for the analysis of mixed-fisheries management, (iii), linkages with economic modelling of the dynamic of the fleets and (iv) evaluation of some current single-species Harvest Control Rules (HCR) in the mixed-fisheries context.

Two major works have been developed in parallel, and are presented below. In one side, a complex full feed-back and stochastic MSE including some major sources of uncertainty has been developed, including cod-haddock interactions only. The dynamics of all stocks are calculated simultaneously at each time step of the MSE, depending on a level of effort common for all stocks. This model was built alongside the guidelines and software developement from WP3, and participated in turn largely to the improvement of FLR in terms of fleet-based objects and methods.
On the other side, a specific approach has been developed for investigating the consistency of the various single-species management objectives in the short-term, taking into account simple hypotheses about fleet behaviour. This was done within EFIMAS as well as ICES SGMixMan and EU FP6 AFRAME. This approach, called Fcube (“Fleets and Fisheries Forecast”) was run for the main demersal species in the North Sea (cod, hadock, whiting, saithe, plaice, sole and Nephrops).
Ultimately, both works will be merged in order to get a complete approach testing alternative single-species management objectives for the whole North Sea demersal fisheries, including economic aspects as well as both short-term and long-term considerations. This will done within the final year of FP6 AFRAME project.

Description of the fishery, stocks and management system

The main demersal roundfish stocks in the North Sea are cod, haddock, saithe and whiting. Major technical interactions also occur with Nephrops, plaice and sole. Species are caught as target or bycatch by a number of fleets from a number of countries using a number of gears. The main management system is based on single-species TACs, supplemented by a variety of technical measures and, since 2003, by days at sea limitations for a number of large mesh towed gears as part of the cod recovery plan.

Description of the base case and scenario evaluations

In the MSE work, The base case simulates the current system including a large number of national fleets using various gears over time, with management objectives based on the most recent single-species HCR and using XSA-based stock assessment with discards and with perfect knowledge (no observation error). Scenarios deal with stochastic projections of the current base case, with two different hypotheses about fishermen behaviour.
In the Fcube work, the scenarios analyses the potential effects of the 2007 TAC, based on alternative hypotheses about fishermen behaviour, including a scenario of days-at-sea restriction.

MSE : Data and parameters

Stock data

Catch-at-age, indices of abundance and biological data for cod and haddock are taken from The North Sea- Demersal Working Group (ICES, 2006).

Fleet data

Main focus is on the inclusion of both the fleet level (i.e. physical group of vessels) and the fishery/metier level (activity of a vessel during one fishing trip). Fleet and fisheries data for catch and effort were provided by national labs, and were crossed with the age disaggregated data provided to STECF sub-groups SGRST meetings on fishing effort management in 2006 (STECF-SGRST 06-01, 06-04 and 06-05) available for 2003-2005 and covering most EU activities in the North Sea. Data were also crossed with available economic data from the AER database. Final fleets and fisheries were defined based on a number of procedures and trials detailled here (REMEMBER TO INCLUDE UPDATED FLEET WD!! ). In this application, 14 national fleets from Belgium, Denmark, England, Netherlands, Norway and Scotland were included. These fleets represent the largest part of the effort of the countries covered, and of cod and haddock catches. In addition, one “OTH” fleet gather all data from other activities catching the relevant species, in order to cover the full range of fishing mortality. Each fleet can engage in several fisheries (“metiers”) each year. Metiers are defined as gear type * mesh size category, following STECF specifications. 38 metiers are so defined, with 1 to 5 different metiers defined for each fleet.

The unit of effort is kWdays. Catch at age data were provided for most metiers. When SOP didn’t match to total landings provided for the same metier, a simple raising procedure was used. The difference between the catch at age such obtained and the total catch at age provided in ICES data was allocated to the OTH fleet.

These fleets were later updated and expanded in 2007 for the Fcube work (see below), but time limitations prevented the full update of the MSE work within the time frame of the EFIMAS project. This will be achieved by the end of 2008 within the FP6 AFRAME project.

MSE : Modelling

FLR

R version is R2.6.0. FLR packages used are FLCore 1.99-8, FLXSA 1.99-6, FLAssess 1.99-6 , and FLEcon 0.2, UPLOAD VERSIONS HERE

Data inputs are stored in the FLCore classes FLStocks, FLIndices and FLFleets. Additional S3 classes (matrices and lists) were created for storing remaining data. One major point of interest and discussion was how to handle large numbers of fleets and metiers. Based on the work done in 2006 and the experience gained in that particular case study, the FLFleet object in FLCore was completely redesigned during 2007 to account for the metier level.

The objects are linked through a comprehensive R code combining an Operating Model and some Management Procedures. Additional key functions were developed, including flexible harvest control rules as well as the AHF and Fcube models of fishermen behaviour in terms of capacity and activity respectively (although no simulations were run on these models so far). Given the large number of complex fleet objects to be used, another key challenge in this case study was the necessity to optimise the code and to reduce computing speed while keeping the program understandable and readable for others. Various configuration trials were tested before deciding upon the final technical choices that would give the best compromise.

Due to reduced number of years with detailed fleet data, and due to the implementation of days at sea limitations since 2003 which changed significantly the fishing patterns of the fleet, the model covers only 2003-2005 as historical data and run then forward up to 2015. Total catch at age data for both stocks over the period 1994-2002 (8 years) are though “added” when running the XSA assessment step, in order to have a sufficient number of years for performing the assessment.

Conditioning of Operating Model (OM)

Simplest approach on conditioning on ICES WG data was used, and a number of sources of uncertainty and errors were included. Results are based on 100 runs. Stochastic errors are drawn once and applied identically across the various scenarios to allow full consistency and comparability between alternative hypotheses tested.

Biological Parameters

No length-based model is included, and thus weight at age data from ICES were used. Projections including stochastic weight at age, with a mean at 2005 smoothed data and lognormal error with sd equal to sd(log(real_wts) – log(smoothed_wts)).

Stock Recruitment Relationships

A number of SRR were fitted using the as.FLSR function in FLCore, on the whole WG time serie up to 2003. The final SRR chosen was Ricker for both cod and haddock. Recruitment in 2005 is the average 2001-2004, and from 2006 onwards it is predicted with the SRR function, including autocorrelation.

Fisheries & Fleets

In base case runs, the annual level of effort is either set at the maximum or minimum across the Fmult by species corresponding to the TAC, and bounded by a maximum variation of effort of 50% by year. No model of effort allocation is implemented yet, and the percentage of effort spent in each metier is thus kept constant.

Catchability by metier in 2003-2005 is defined as partial Fbar divided by effort, and selectivity at age by metier in landings and discards is calculated as partial F at age divided by partial Fbar. Projections use the average over 2003-2005.

Quota share for each stock and each fleet are calculated as landings share of the previous year, and the catches made by each fleet exceeding this quota share are considered as overquota.

Economics

The inclusion of an economic model was made through the implementation of the AHF model into the general model. This model estimates the dynamic change in fleet capacity (entry/exit) given previous years profits, which are evaluated from standard economic indicators (landings revenues, variable and fixed costs) and exogenous price levels scaled according to supply. Large scale decommissioning plans are not considered in the present model. The equations used to evaluate the dynamic fleet capacity change in the AHF model are described shortly in Appendix 4 of ECONOWS (2008). A more detailed description of the AHF model in the form it is used in the present context is found in Hoff and Frost (2006). All used economic equations, classes and models are implemted in FLR in the FLEcon package. As such this includes (i) the class AHFind for collecting all relevant economic data to run the AHF model, and (ii) the function AHFinvest performing the evaluation of dynamic capacity change given yearly profits. The development of FLEcon has been performed under the EFIMAS project.

The economic model of capacity is used in the MSE as a process-based model for simulating the maximum effort a fleet segment can physically exert in a period, and thus acts as a upper bound for changes in effort from year to year. This method of combining TAC and effort limitations using the AHF model was first tried out in a simple bio-economic application for the Cod fishery in the North Sea, which is described in Hoff and Frost (2008), where an outline of the relevant equations for this procedure is also presented.

The economic cost and earnings data used has been drawn from the AER (Annual Economic Report) database collected until 2005 under the EU funded project ‘Economic Assessment of European Fisheries’ (Q5CA-2001-01502) under the 5’th Framework Programme, and from 2006 under the Data Collection Regulation (DCR, Commission Regulation (EC) no 1639/2001, OJ L222, 17.8.2001, p. 53). The data are distributed on vessel type (main gear) and length. Economic parameters (eg. price of capacity, price flexibility etc.) used in the models will be estimated from historical economic time-series.

It should be observed that economic data has not been available for all the fleet segments included in the model. Thus capacity change has only been evaluated for a selected number of fleet segments, while the capacity (and thus influence on the biological model) has been considered constant for the remaining fleet segments.

The development of the AHF economic dynamic capacity change model is in large parts based on experience with several other bio-economic models. The relation between the AHF model and other models is thus discussed in Economics_CS2_Midterm Report_Jan2007.

Reference Points

Usual PA reference points from ICES, as well as Fmsy taken from ACFM summary sheets. The limit reference points from ICES are no longer used in the latest HCR from EU.

Observation Error Model

In the base case, observation error is assumed in the biomass indices used for tuning in the assessment procedure. A lognormal error with sd equal to the one observed in the IBTS Q1 survey used in stock assessment is applied to the OM abundance. No stochastic obervation error is assumed in catches. However, in the scenarios where catches may exceed the TAC due to technical interactions (“max” scenario), only the catches corresponding to the TAC are included in the assessment, leading to potential large discrepancies between the simulated “official” landings and “true” landings.

Management Procedure (MP)

The “perceived” stock (MP stock) corresponds to the assessment based on FLXSA with constant settings and low shrinkage. TAC is based on some complex HCR reflecting the most recent EU-Norway agreements and proposals of the Commission, i.e.:

  • Short-term target is to have stock in safe biological limits (SSB> Bpa and F<Fpa)
  • Long-term target F is Fmsy
  • TAC should not vary by more than 15% up and down from year to year
  • if SSB is assessed below Bpa, HCR should insure that it doesn’t decrease furthermore. In the same way, if F is assessed above Fpa, it should increase.

HCR are modelled using modified versions of forward functions in FLAssess

Data and scripts

MSE : Results

The figures below show the results from scenarios assuming that fleets will go either to the maximum of the effort corresponding to the single-stock management objectives (Fmult.fl=MAX(Fmult.sp),left) or to the minimum (Fmult.fl=MIN(Fmult.sp),right), other settings being kept equal. In the MAX case, this assumes that fishermen go on fishing until the last TAC is exhausted and discard the overquota catches of the other stock, and in the MIN case, that they stop fishing when the first TAC is exhausted and do not take up the whole TAC of the other stock.

Figures 1 and 2 show SSB, recruitment, Fbar and catches for cod (left) and haddock (right) over the period 2003-2010. Black lines represent the “truth”, and green the perceived system. Full lines correspond to the median values, and dotted to 25 and 75% quantiles. Figure 1. represents scenario Minimum and Figure 2. scenario Maximum.

Figure 1. Development of biological indicators (SSB, recruitment, Fbar and catches) in the minimum scenario.

ahf_oe_min_effort2007-07-31.jpg


Figure 2. Development of biological indicators (SSB, recruitment, Fbar and catches) in the maximum scenario.

ahf_oe_max_effort2007-07-31.jpg

It is clear that differences in assumed fleet behaviour gives opposite answers. Under the “Minimum” assumption (Figure 1), both stocks are within safe biological limits in 2010 with 50% probability, but at the conditions of constantly decreasing catches for both stocks (as the HCR is primarily driven by the need to rebuild cod SSB), and thus low revenues for the fleets. On the contrary under the Maximum scenario (Figure 2), the probability of having the cod stock back above Blim is lower than 25%, but the catches are maintained at a high level. Furthermore, in that scenario, the stock of haddock falls also below Bpa by the end of the simulation, when the TAC is driven by Fmsy in the HCR, indicating the Fmsy may not be a sustainable reference point and should probably be revised.

In spite of observation error in survey indices, the perceived stock is close from the simulated stock, indicating a quite robust assessment method. Indeed XSA is driven mostly by the catch at age matrix. However, in case of large overquota catches because of mismatch between single-species TAC (cod stock under the Maximum sce-nario), the perceived catch does not account for the overquota catches leading to major differences in the “true” and perceived catch-at-age matrices. The F resulting from stock assessment is therefore considerably lower than the “true” F, whereas the biomass estimate is more robust, because of the tuning with an abundance index closed to the truth.

These scenarios illustrate well the crucial issue of accounting for mixed-fisheries aspects for sustainable fisheries management. Single-species analysis of EU HCR (corresponding to cod trajectories in Figure 1 and haddock trajectories in Figure 2) may estimate reasonable chance for cod recovery and sustainability, but under alternative but plausible assumption of overquota catches the recovery is strongly jeopardised. This underlines the need to set single-species TAC at levels consistent with each other, as bycatch will always occur. It is clear that in reality the level of effort may be intermediary between the minimum and maximum assumption, leading to the necessity to couplate this MSE work with additionnal work such as the Fcube approach presented below. But using two antagonist assumptions allow us to bound the range of plausible projections.

Figure 3 and 4 shows the main economic results from the integrated bio-economic modelling. As outlined above the results are based implementation of fleet dynamics (investment/disinvestment in fleet capacity) in the model. The equations for the economic model are described in Hoff and Frost (2006,2008). Figure 3 shows fleet capacity and profit development in the minimum scenario for selected fishing fleets (the fleets for which economic data were obtainable, cf. the discussion above), and figure 4 shows the same results in the maximum scenario.

The fleet profits are genererally higher in the maximum scenario than in the minimum scenario, except for the Danish Otter trawlers between 0 and 24 m. Generally this increase in profit is expected, as the catches in the maximum scenario is higher than in the minimum scenario, but it must be remembered that fleet size and excerted effort also influences fleet profits, which is why decreases from the minimum to the maximum scenario may be observed.

The fleet capacities show more or less the same pattern, although the variation is less than for the profit. This because it is only part of the profit that is used for investment/disinvestment (cf. Hoff and Frost, 2006, 2008).

Figure 3. Total net profit and capacity of selected fleet segments in the minimum scenario

ah_min_effort_eco_results_ns_cs.jpg

Figure 4. Total net profit and capacity of selected fleet segments in the maximum scenario

ah_max_effort_eco_results_ns_cs.jpg

Fcube : A fleet-based model for providing consistent mixed-fisheries advice

Introduction

Fcube is an innovative approach developed to address some terms of the Memorandum of Understanding between ICES and the European Commission, stating that: “For each sea area ICES shall define groups of stocks within which ICES shall ensure close quantitative consistency between the advice given for each stock. This should be considered a first step in the development of fisheries-based advice. ICES will be invited to explore during the course of the agreement how advice may be further developed to advice on changes in fishing practices for defined fishing fleets.”

Fcube (standing for “Fleets and Fisheries Forecast”, Ulrich et al., in prep), builds on the explicit recognition that different fleets and different fisheries have different impacts on the different biological stocks, and that fleets can modify their activity based on internal and external stimuli (e.g. changes in resource availability, market price, or management). Initially a part of the multifleet multi-species bioeconomic simulation framework TEMAS (Ulrich et al., 2007), Fcube was further developed as a stand-alone method within ICES SGMixMan as well as EFIMAS and FP6 AFRAME. Its objective is to explore and evaluate the potential overquota catches arising from inconsistent single-species TAC, based on simple assumptions about fleets effort distribution. Its strength is the relative simplicity of data needs while being able to account for a great diversity of fleets, metiers and stocks.

Fcube has been coded as an FLR method, and has been integrated within the FLEcon package.

The results presented below are those obtained during the ICES SGMixMan meeting in january 2008, and further details can be obtained in the WG report.

The model

Data are structured by fleet segment Fl (e.g. group of vessels), metier Mt (type of activity practiced by the fleet; a fleet can engage in several metiers over a year) and stock St. Catchability estimates by fleet and metier q, as well as effort distribution by fleet and metier Effshare in the TAC year Y is user-input, e.g. as historical average or use of alternative models. Then average catchability by fleet is estimated as

q(Fl,St,Y)=∑Mt(q(Fl,Mt,St,Y)*Effshare(Fl,Mt,Y))

The fishing mortality corresponding to the single-stock TAC (Ftarg) is converted into “Stock dependent fleet effort”. The “stock-dependent fleet effort” is the estimated effort a fleet should develop in order to catch its quota share for a particular stock. The total target fishing mortality Ftarget(St) is first divided across fleet segments (partial fishing mortalities) through assumptions about quota share (e.g. historical landing share or alternative model) These partial fishing mortalities are subsequently used for estimating the stock-dependent fleet effort:

F(Fl,St,Y)=Ftarg(St,Y)*QuotaShare(Fl,St)
E(Fl,St,Y)=F(Fl,St,Y)/q(Fl,St,Y)

It is unlikely that the effort corresponding to each single-species TAC is the same across species, and the resulting effort is therefore a choice. The user can explore the outcomes of a number of assumptions or rules about fleets own behaviour (e.g. going on fishing after some quotas are exhausted) or management scenarios (e.g. all fisheries are stopped when the quota of a particular stock is reached).

E(Fl,Y)=rule(E(Fl,St1,Y),E(Fl,St2,Y),E(Fl,St3,Y)…)

Final effort by fleet is then used to recalculate the actual fishing mortality by stock and corresponding catches. Difference between catches and TAC is interpreted as overquota.

Brief presentation to ECONOWS Jan 2008

North Sea application

As for the MSE work, data have been compiled by crossing various sources of information ( INCLUDE DATA WD ), but the data used in 2007 and 2008 were updated in comparison to those used in the MSE application. The final data used in the North Sea Fcube run contained 18 national fleets from 6 countries, from 2003 to 2006. These fleets engage in one to seven different fisheries, resulting in 64 metiers (combination of country*fleet*fishery) catching cod, haddock, whiting, saithe, plaice, sole and Nephrops in various proportions. Only the four Nephrops Functional Units having independent biomass estimates (FU6 to FU9) are intergrated (ICES WGNSSK, 2006). Visual inspection of data showed that effort distribution and landings share was fairly stable over the most recent years, while catchability estimates was noisier, although the time-series were too short to detect any trends.

The runs presented here aimed at exploring the potential outcomes of the true single-species TAC regulating the demersal fisheries in 2007 under a number of hypotheses. The TACs in place in 2007 differ from the single-species scientific advice provided by ICES, therefore the corresponding levels of landings fishing mortality in 2007 (expressed as Fbar) were recalculated using additional FLR functions and subsequently used as target F in Fcube inputs.

A number of alternative scenarios were run :

  • “max” : underlying assumption is that the fleets continue fishing until their last quota is exhausted. The difference between the estimated landings and the actual TAC for the other stocks is considered as over-quota catches.
  • “min” : underlying assumption is the opposite, the fleets stop fishing as soon as their first quota is exhausted, and do not take the whole of their quota for the other stocks.
  • “val” : underlying assumption is that the global effort of each fleet is influenced by the monetary value each fleet can get from its quota share across stocks. The value of the quota share (quota share * mean price by fleet and stock) is thus used as a weighting factor of the estimated effort necessary to catch each quota share. The final level of effort is set at the level of this weighted mean.
  • “status quo_E” : underlying assumption is that the global effort of the fleets is not affected by the single-stock quotas, and fleets effort in 2007 is set at the 2006 level
  • “DAS_reduction” : this scenario mimics the imposed reduction in Days-at-sea limitation that occurred between 2006 and 2007. This reduction was differential across gears and mesh sizes, as Nephrops trawling (<90 mm) was reduced by 10%, demersal trawling between 90 and 120mm as well as beam trawling were reduced by 8%, and large mesh size demersal trawling (>120mm) was reduced by 7%. In this scenario, the effort of each metier within each fleet was reduced accordingly compared to its 2006 level.
  • “cod” : this scenario is run specifically to investigate what would be necessary to avoid overquota catches of cod. The underlying assumption is that fleets set their effort at the level corresponding to their cod quota share, regardless of other stocks.

For each scenario, landings F by stock is then recalculated as the sum of partial F by fleet and metier, i.e. effort by fleet * effort share by metier * mean catchability * landings selectivity. This fishing mortality is thus used to re-run single-stock forecast using the same methods and settings as in the base case, and the difference between estimated landings and base case landings is referred to as overquota landings.

Results

The 2007 TAC showed contrasted results in term of corresponding variation in fishing mortality (Fmult) compared to 2004-2006 average:

COD HAD PLE POK SOL WHG NEP
0.40 1.02 0.95 1.00 1.08 2.73 1.12

Cod TAC appears as very restrictive, as a relatively strong 2005 year-class would allow high catches of age-2 cod in 2007 according to the single-stock forecast. In contrast the whiting TAC is much higher than recent landings, and appears not to be restrictive at all, implying that high levels of fishing mortality would be required in order to take the TAC.
The various scenarios showed highly contrasting results (Figure 3) :

Figure 3 (Left). Estimated 2007 landings by stock for the various Fcube scenarios. Horizontal lines correspond to base case TAC for cod, haddock, plaice, saithe and sol respectively (whiting TAC being close to cod TAC level).
Figure 4 (Right). Fcube estimates of the level of effort by fleet necessary to take up the fleet’s quota share by stock (1=COD, 2=HAD, 3=NEP6, 4=NEP7, 5=NEP8, 6=NEP9, 7=PLE, 8=POK, 9=SOL, 10=WHG). Plots are not all at the same scale.

Table 1. Catches under each scenarium per species with indication of relative deviation from 2007 TAC.

Maximum and minimum scenarios represent the two extreme bounds of the range of possibilities, and because of the co-occurrence of a very restrictive cod TAC and a very unrestrictive whiting TAC, the maximum is estimated four or five times higher than the min. Thus the max doesn’t appear a plausible scenario here, especially given the low monetary value of whiting.

On the other hand, the scenarios of no or limited reduction of effort provide results closer to previously observed patterns. Indeed, under these scenarios, there is a fairly close match between the TAC in place and the estimated landings for haddock, plaice, saithe and sole (less than 15% difference). Only cod show large overquota catches, (of the order of magnitude of the TAC itself), while whiting TAC is taken at 50%, and Nephrops at 70-75% only.

An interesting result is the consistency observed between the “val” (“value”) scenario and scenarios of status quo effort. In the “val” scenario, fleets are assumed to set their effort as a weighted mean of the relative value of their quota shares, i.e. they would focus more on catching up the quota share for the species giving the maximum value, while under- or over-catching the quota share for the species giving less value. This scenario is a simple and rough proxy for economy-driven fleets behaviour, but is indeed the one giving the results closest to the observed historical patterns for most fleets.

Finally, the “cod” scenario investigates which TAC reduction for the other stocks would be necessary to avoid overquota catches of cod. Because of the large effort reduction required by the very restrictive TAC and the incoming of the larger 2005 year-class at age 2 in 2007, dramatic TAC reductions would be necessary in the other fisheries, in the order of 50% for haddock and plaice, 60% for saithe and sole, 70% for Nephrops and 80% for whiting. For all fleets, it is clear that the effort necessary to take up their own quota share of cod is much lower that what is necessary for taking up the quota of the other species (Figure 4). This scenario is indeed equivalent to the “min” scenario. In the current situation, it is thus expected that large overquota catches of cod have taken indeed taken place in 2007. The ICES WGNSSK uses the B-Adapt method to assess North Sea cod stock. B-Adapt estimates an amount of “missing catches”, that were removed from the stock but not recorded in the inputs landing and discards. These unallocated removals are interpreted as the likely overquota catches due to restrictive TACs and mixed-fisheries effects. The estimated amount of these removals varies across years, but was estimated around 17 to 20 000 tonnes over 2004-2006. The results obtained from Fcube runs are very consistent with these findings, since we estimate overquota catches being around 25 000 tonnes in 2007 in the three scenarios “val”, “statusquo_E” and “DAS_reduction”. In that sense, Fcube provides a valuable process-based modelling of the dynamics of overquota discarding, which is supported by the findings of independent analyses as done here with B-Adapt.

Conclusion

The ICES SGMixMan was very positive about that work. The results were found to be very consistent with qualitative and quantitative observations. They could thus be interpreted in the light of current knowledge on the state of the stocks and the expected effects of main management measures. There has long been evidence and claims from the industry about the negative effects of restrictive cod TACs, and this could be reproduced quantitatively here with a fairly simple model.
The SG thus considered that such mixed-fisheries forecast were consistent and robust while not being too data-demanding, and thus could potentially be used in the future to deliver timely mixed-fisheries advice.
This work was then further presented to ICES AMAWGC in february 2008, which also came with positive feedbacks, and discussed the potential application and use of it in an advisory context.

Data and scripts

Dissemination

ECONOWS (2008). ECONOWS: Report from the Economic Workshops of EFIMAS.

Hamon, K., Ulrich, C., Hoff, A. and Kell, L. 2007. Evaluation of management strategies for the mixed North Sea roundfish fisheries with the FLR framework Presentation to MODSIM07 Conference, 10-13 december 2007, Christchurch, New Zealand with Hamon et al. peer-review publication in conference proceedings.

Hoff, A., and Frost, H. (2006). Economic response to harvest and effort control in fishery. Report no. 185. Institute of Food and Ressource Economics. Copenhagen.

Hoff, A., Frost, H. (2008). Modelling economic response to harvest and effort control in the North Sea cod fishery. Aquat. Living Resour., 21 (forthcoming).

ICES SGMixMan 2006, 2007, 2008

MSE :

Presentation to AFH (French Association of Fisheries Science) Conference, 19-21 June 2007, La Rochelle, France

Presentation to MODSIM07 Conference, 10-13 december 2007, Christchurch, New Zealand
with Hamon et al. peer-review publication in conference proceedings

Presentation to ICES SGMixMan, January 2008

Presentation in relation to ICES SGMixMan, Jan 2008

Presentation to EFIMAS Conference, March 2008

Mixed Fisheries MSE in ICES WGMIXMAN, EFIMAS Conf., Bruxelles March 2008

Presentation to EU PROFET Conference, Copenhagen, June 2008\\EU FP6 PROFET Conference, Cph. June 2008

Fcube :

Presentation to ICES SFMS, ICES Symposium on Fisheries Management Strategies, 27-30 June 2006, Galway, Ireland.

Reeves and Ulrich, 2007 paper presented to ICES ASC, 17-21 September 2007, Helsinki, Finland.

Presentation to ICES AMAWGC, February 2008

Links to Other Works

This work is strongly linked to the EU FP6 AFRAME project (2007-2009), and will be followed up during the second year of that project. In particular, the MSE work will be further developed in order to include the Fcube method and expand the range of stocks, so as to provide a complete tool for testing alterntive scenarios for consistent single-species advice in a mixed-fisheries context.

Besides, this approach participates directly to the general ongoing move within ICES, STECF and EU-DCR towards implementation of more fleet-based advice, and is developed in full consistency with these with regards to data needs and definitions.

References

Acknowledgements

EFIMAS Contribution to the work

This work has been conducted under the EFIMAS Project, the AFRAME Project, ICES SGMixMan and ICES WGNSSK.

Clara Ulrich 2008/03/06 11:37J. Rasmus Nielsen 2008/06/10 14:46

 
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