ICES WKH TAC REPORT 2015ices.dk/sites/pub/Publication Reports/Expert Group Report/acom/201… ·...

ICES WKHERTAC REPORT 2015 ICES ADVISORY COMMITTEE

ICES CM 2015/ACOM:47

Report of the Workshop to evaluate the TAC calculation for herring in IIIa

and management plan for herring in the North Sea (WKHerTAC)

13–16 January 2015

Copenhagen, Denmark

International Council for the Exploration of the Sea Conseil International pour l’Exploration de la Mer

H. C. Andersens Boulevard 44–46 DK-1553 Copenhagen V Denmark Telephone (+45) 33 38 67 00 Telefax (+45) 33 93 42 15 www.ices.dk [email protected]

Recommended format for purposes of citation:

ICES. 2015. Report of the Workshop to evaluate the TAC calculation for herring in IIIa and management plan for herring in the North Sea (WKHerTAC), 13–16 January 2015, Copenhagen, Denmark. ICES CM 2015/ACOM:47. 141 pp.

For permission to reproduce material from this publication, please apply to the Gen-eral Secretary.

The document is a report of an Expert Group under the auspices of the International Council for the Exploration of the Sea and does not necessarily represent the views of the Council.

© 2015 International Council for the Exploration of the Sea

ICES WKHerTAC REPORT 2015 | i

Contents

Executive summary ................................................................................................................ 2

1 Terms of reference ......................................................................................................... 4

2 Agenda, participation and prior course of WKHerTAC ......................................... 6

3 Brief introduction of the management rule for WBSS ........................................... 7

4 Brief introduction of the LTMP for NSAS and previous evaluations of this .................................................................................................................................... 9

5 Evaluation of the WBSS Management rule and the NSAS LTMP ..................... 11

5.1 Evaluation procedure ......................................................................................... 11 5.1.1 Model description .................................................................................. 11

5.2 Harvest Control Rule options examined ......................................................... 20

5.3 Choice of performance indicators .................................................................... 23 5.4 Results .................................................................................................................. 26

5.5 Conclusions ......................................................................................................... 31 5.5.1 WBSS........................................................................................................ 31 5.5.2 NSAS........................................................................................................ 31

6 Conclusions and comments on herring management in the North Sea and IIIa........................................................................................................................... 32

7 References ..................................................................................................................... 35

Annex 1: List of participants and WebEx minutes ........................................... 36

Annex 2: Full list of results of scenarios tested ................................................ 44

Annex 3: R-code for conditioning of MSEs performed in WKHerTAC .................................................................................................................. 84

Annex 4: Technical minutes from RGHERMA .............................................. 134

2 | ICES WKHerTAC REPORT 2015

Executive summary

WKHerTAC evaluated the current (2008) LTMP for NSAS and the proposed TAC allo-cation strategy for herring in the IIIa following two joint requests from the European Commission (EC) and Norway. The evaluations of both requests were based on a sto-chastic medium-term simulation model. The model simulated the biological herring population and the behaviour of the fishing fleets and surveys, while the stock assess-ment estimates the stock status. Finally, the management advice and implementation are based on the adjusted management plan scenarios. In turn, management feeds back into the biological population and the fishery the year after. The simulations were run with 1000 Monte Carlo realisations (MCR) to obtain a broad range of possible outcomes given the variability in the input data. Stochasticity (randomness) was added to varia-bles and parameters to ensure that biological variation, and the uncertainty in the his-toric perception of the stock was thus reflected.

WKHerTAC considered that there were ambiguous points within the request and its background for herring in Area IIIa. The suggested management rule for IIIa could not be interpreted unambiguously. The results showed to be sensitive to different interpre-tations, which makes it vital that all the specific points in the management rule must be outlined very clearly. Consequently, ICES had to make a number of assumptions to make the provisions of the special request operable in a management strategy evalua-tion.

The two stocks were modelled in parallel to account for the known mixing in the fish-eries. The fraction of each stock in the catch in IIIa was treated as a random variable with a fixed mean independent of the abundance of the stocks. Recruitment was based on the recent low productivity regime for both stocks as estimated from 2003–2013 for NSAS and 2005–2013 for WBSS. The assessments were simulated by introducing vari-ation in the population numbers and the exploitation pattern. Annual TACs by fleet were computed on the basis of short-term forecasts. The TAC setting procedures and allocation of catch potential to each of the five fleets considered followed from the man-agement plan/rule and potential transfers from one area to another.

In total 15 HCR options were examined; six evaluating the long-term management strategy (LTMS) for herring in the North Sea and nine evaluating the consequences of the IIIa TAC setting procedure. For the LTMS the six scenarios evaluated a range of Btrigger values (1.0–1.5 million tonnes). All scenarios tested assumed a 15% constraint on TAC IAV, a 10% constraint on F (limiting departure from target F) and an interannual quota flexibility of ±10%. For the IIIa TAC setting procedure, the basis of the scenarios tested mainly differed in the fraction of IIIa TAC transferred to the North Sea (0–50%).

All scenarios tested were considered to be precautionary for NSAS and WKHerTAC concluded that if the Btrigger for NSAS is to be revised, values at or above 1.0 million tonnes would be considered precautionary. For WBSS, WKHerTAC concluded that the IIIa management rule as it is currently stated is not precautionary. To make the man-agement rule precautionary, several possible solutions could be investigated. The only option that has been tested is the % transfer which is an annually negotiated manage-ment decision. Other modifications to the rule such as reducing or capping the NSAS share of the C-fleet TAC would be likely to work in the same direction but were not evaluated. The evaluations have shown that the rule is not precautionary, throughout the entire simulation period, unless there is a transfer of at least around 10%. Addition-ally, the advice for the IIIa management rule is based on the Btrigger in the current NSAS LTMP of 1.5 million tonnes and if this value decreases (which would be precautionary

ICES WKHerTAC REPORT 2015 | 3

for NSAS) then a higher than 10% transfer, or other measures, would be required to ensure that the IIIa management rule is precautionary. A lower Btrigger in the NSAS LTMP would lead to a higher TAC for the A-fleet, which in turn would result in a higher C-fleet TAC. Depending on the % transfer of TAC from the C-Fleet to the North Sea (or other modifications to the rule), it will most likely have implications for the utilisation of WBSS at or below FMSY.


1 Terms of reference

2014/2/ACOM: The Workshop to evaluate the TAC calculation for herring in IIIa and management plan for herring in the North Sea (WKHerTAC), chaired by Lotte Worsøe Clausen, Denmark, will meet at the ICES Secretariat, 13–16 January 2015 to:

a ) Evaluate the outcome of implementing the TAC calculation strategy* on the stock of Western Baltic Spring-spawning herring for the next five years, with particular reference to: i ) the probability of the fishing mortality being at or below FMSY year-on-

year; ii ) future yields on a five year basis; and iii ) the probability of the spawning biomass falling below Blim and Btrigger;

Assuming that:

• 50% of the ICES MSY advised catch for WBSS will be allocated to SD 22–24. • The flexibility provision whereby up to 50% of the IIIa TAC can be fished in

the North Sea will apply, and that all of the catch that could be taken in the North Sea under this provision will actually be taken in the North Sea.

• the WBSS TAC will be fixed according to the ICES MSY approach (linear reduction of F) when the stock is below MSY-Btrigger.

• the +/-15% TAC constraint applies only to the TAC for the mixed NSAS/WBSS in IIIa, not to the WBSS TAC in SD 22–24.

b ) Draft advice on whether the aforementioned strategy is consistent with ICES precautionary approach in the next five years.

c ) Evaluate if the NSAS plan would be precautionary, assuming an interannual quota flexibility of +/- 10% in all simulations (see details in the request 140910a_Herring).

d ) Evaluate if the Btrigger value of 1500 kilo tonnes is optimal or if adjustments to it should be considered.

* Considering the method of calculating the TAC for herring in the Skagerrak and Kat-tegat (C fleet) is set as a sum of two components:

a ) A fixed percentage of the TAC for NSAS in the North Sea (A fleet)1 that re-sults from the application of the EU-Norway management plan; and

b ) A fixed percentage of the ICES MSY advice for the WBSS total catch. i ) These percentages are fixed at 5.7% and 41% respectively, based on the

average catch composition of NSAS and WBSS by the C fleet. The TAC is therefore given by the following formula:

TAC Skagerrak and Kattegat = (TAC_NSAS * 5.7%) + (WBSS ICES MSY advice * 41%)

1 “A fleet” was mistakenly added by ICES to the ToRs, and this text should be disre-garded as indicated by the strikethrough text.


If the TAC resulting from the application of this formula results in a TAC that is less than 85% or greater than 115% of the TAC in the previous year, the TAC in IIIa will be fixed at a level that is respectively 85% or 115% of the TAC in the previous year.

WKHerTAC will report by 23 January, 2015 for the attention of the Advisory Commit-tee.


2 Agenda, participation and prior course of WKHerTAC

The workshop was set up by ICES to help ACOM answer a request from the EU and Norway as described in the ToRs of the workshop. There were 13 participants at the workshop (Annex 2); however a larger group attended the two preceding WebEx’s. These were held prior to the workshop as preparation for the meeting in order to give feedback on suggested settings for the management plan evaluation and decide on the performance indicators. The minutes from these two WebEx’s can be found in Annex 2.

Based on the outcomes from the WebEx, all simulations and evaluations of the Refer-ence Points were run prior to the workshop, facilitating a full and thorough discussion of results, and decisions, before the advice was formulated. The agenda for the work-shop followed the outline of the report and the list of participants can be found in An-nex 2.


3 Brief introduction of the management rule for WBSS

Western Baltic spring-spawning herring (WBSS) is a relatively small stock, but it is managed by means of a highly complex governance scheme. It spawns in the western Baltic Sea, where it is exploited by several EU fishing fleets. It then migrates into the Kattegat, Skagerrak and eastern North Sea areas, where it mixes with North Sea au-tumn spawning herring (NSAS), following an extremely variable age and season-de-pendent pattern . There it is exploited by EU and non-EU fleets. Every year, TACs are set for the two management areas and quota allocations between fleets and areas are negotiated. This poses demanding scientific challenges and requires complicated po-litical processes of resource allocation among fishing fleets.

To resolve the complex governance scheme, the Parties decided, during Fisheries Con-sultations between the European Union and Norway on the Regulation of Fisheries in Skagerrak and Kattegat for 2013, to establish a Working Group on management measures for herring in ICES Division IIIa (Annex V in EU-Norway 2013a) . The Work-ing Group consisted of scientists and managers and recommended in June 2013 (EU-Norway 2013b) a basic method for setting the TAC for herring in the Skagerrak and Kattegat as a sum of two components:

a ) A fixed (X) % of the agreed TAC for NSAS in the North Sea; b ) A fixed (Y) % of the ICES advice for the WBSS total catch.

The basis for the NSAS component is the joint decision by the EU and Norway on the TAC for herring in the North Sea which is derived from the LTMP. The proportion used (X%) was derived as the C-fleet share of the overall catch of NSAS.

The basis for the WBSS component is the advice from ICES based on the MSY approach. This requires that ICES continues to provide a MSY based advice.

The method can be described by this formula:

TAC Skagerrak and Kattegat = (TAC NSAS x X) + TAC WBSS MSY advice x Y)

During the negotiations between EU and Norway in London, March 2014 (EU-Norway 2014a), it was settled in the agreed record that a management strategy for the TAC share of herring in the Skagerrak and Kattegat should be based on 41% of the ICES MSY advice for WBSS plus 3.5% of the management plan TAC for NSAS. In addition the delegations agreed on a +/- 15% stability clause (TAC constraint).

In the summer of 2014, it became clear that the Bergen report (EU-Norway 2013b) had not taken into account the part of the IIIA fleet C TAC taken in the North Sea when calculating the 3.5 %. This transfer option has been a part of the Agreed Records for several years already, and is described in point 11.2 §3 of the Agreed Records between the European Union and Norway for 2015 (EU-Norway 2014a): “The Delegations agreed that for the quotas established for herring in the Skagerrak and Kattegat areas, Norway would be able to fish 50% of its quota in the North Sea in the Norwegian Economic zone and the European Union would be able to fish 50% of its quota in European Union waters of ICES area IV.” New calculations were carried out (unpublished) that resulted in a mean of 5.7% for the period 2006-2012 based on the calculation: (NSAS catches in (fleet C + transfer C to A) /(NSAS catches in fleet A + C). In agreement with both EU and Norway, the value of 3.5% was therefore replaced by the value 5.7% in the request.

According to text in the EU-Norway request for the evaluation of the management strategy for herring in IIIa, which describes the management rules involving the WBSS


and NSAS herring, WKHerTAC came to the understanding that the two components of the intended management strategy are:

• the advised TAC for WBSS, which is based on the implementation of the ICES MSY approach (no TAC variation constraint but reduction of F below MSY Btrigger);

• the advised TAC for NSAS (*), which is based on the implementation of the LTMP.

The TACs are then allocated to the different fleets (and areas) as follow:

• TAC F-fleet (22–24): 50% of the WBSS advised TAC;

• TAC C-fleet (IIIa): 41% of the WBSS advised TAC + 5.7% of the NSAS advised TAC (incl. fleets A, B,) from the year before (**). This TAC is subject to a 15% interannual variation constraint;

• TAC D-Fleet (IIIa): assuming a constant outtake in the D-fleet as observed during the past five years;

• TAC A-fleet (IV): as set by the LTMP, with the additional up to 50% optional transfer of the C-fleet TAC (as defined above) from the IIIa to the North Sea;

• TAC B-fleet (IV): as set by the LTMP.

(*) Despite having been through several rounds of clarification of the requests with the clients, the exact implementation of the management rule for WBSS was subject to thor-ough discussions in the workshop. Specifically, it was unclear which part of the NSAS TAC should be taken into account to set 5.7% of the C-fleet catch opportunity (quote from the request on Herring in Area IIIa: ‘TAC for North Sea Autumn Spawners that results from the application of the management plan’). The report from the Working Group on management measures for herring in the Skagerrak and Kattegat, which met in Bergen on 19–12 June 2013, was consulted but did not resolve the issue. The NSAS TAC can be interpreted as consisting of the advised NSAS TAC for: 1) fleet A, 2) fleets A & B, 3) A & B & C & D, or 4) fleets A & C combined. The WKHerTAC group per-formed all simulations following option 2 but explored some scenarios applying option 3, however these are not presented or discussed in this report.

(**) Assuming a 5.7% proportion of the NSAS TAC (incl. fleets A, B, C, D, as suggested by stakeholders during the WKHerTAC meeting,) in setting the fleet C catch oppor-tunity was deemed impossible owing to circularity in the estimation of the advised TAC for the A-fleet and C-fleet (see Figure 6.1). The use of the NSAS advised TAC from the year before was identified as a potential solution to overcome the problem and still provide an evaluation of the management rule over the two stocks. As there is a strong correlation between TACs from one year to the next, the error introduced through this simplification was assumed sufficiently small to approve of this approach.


4 Brief introduction of the LTMP for NSAS and previous evaluations of this

The current management plan is the result of a process that began in the mid-1990s. Any consideration of the plan needs to be made within the context of this process and the ongoing developments in the ICES advice. Thus this section puts the new request into the context of the last 18 years of development and the recent approaches used by ICES.

The origin of the present management plan stemmed from negotiations between the EU and Norway in 1997. The background for this development was the imminent stock collapse that was recognised in 1996 and led, following the advice from ICES, to a dras-tic reduction in the catches in the middle of 1996. The key elements in this plan were a fishing mortality set separately for adult and juvenile herring (at 0.25 and 0.12 respec-tively) and a trigger biomass (1.3 million tonnes) below which the fishing mortalities should be reduced. The target fishing mortalities were decided based on extensive sim-ulations (Patterson et al., 1997) to find the level of sustainable exploitation of adults and juveniles that resulted in a low risk of bringing SSB below 800 000 tonnes, which was the MBAL at the time (Minimum Biological Acceptable Levels). The trigger biomass (1.3 Mt) was decided mainly on political grounds, but it was also thought to give some protection against falling below the MBAL.

When the rule was decided, the SSB was well below 1.3 million tonnes. The rule did not specify mortalities for that situation, but in practice the TACs set corresponded to an adult F of about 0.2. The industrial fishery on juvenile herring and sprat became heavily regulated and controlled, resulting in a fishing mortality around 0.05, well be-low the agreed level.

When ICES introduced precautionary reference points in its advisory practice, the MBAL level was adopted as Blim and the trigger biomass of 1.3 million tonnes as BPA. The target fishing mortalities in the harvest rule were adopted as FPA.

The harvest rule was amended in 2004. The amendments included specific rules to ap-ply when SSB is below 1.3 million tonnes and a constraint on TAC change from year to year.

ICES examined the performance of this revised harvest control rule in 2005 and con-sidered the target F to be consistent with the precautionary approach (ACFM, 2005). However, ICES considered that the strict application of the TAC change limit of 15% (rule number 5) may not be consistent with the precautionary approach. Assuming that paragraph 6 (reducing the TAC more than 15%) would be invoked when TAC con-straints would lead to SSB falling below BPA, the HCR (harvest control rule) was con-sidered to be in accordance with the precautionary approach.

Previous evaluations of the rule were done assuming recruitment at the historical av-erage. Since 2001, the recruitment has been at about half the long-term average. There are no indications that this is just a temporary change in stock dynamics. Hence, ICES advised that management should adapt to a regime with reduced recruitment, and noted that the performance of the existing rule at the time was at best marginal in this situation, as it may break down if the assessment and/or implementation and compli-ance were sufficiently biased.


The current plan was agreed in 2008 (EU-Norway 2008) and has a trigger biomass of 1.5 million tonnes, thus reducing the target fishing mortality of the human consump-tion fishery when the SSB is between 0.8 and 1.5 million tonnes. The target fishing mor-tality on the juveniles is reduced to 0.05 and 0.04 when below 0.8 million tonnes SSB. The 15% interannual variability (IAV) on TAC is viewed as precautionary, as long as paragraph 6 also remains in the plan. The current plan (from 1 January 2009 to 31 De-cember 2011) has thus been the basis for advice for North Sea autumn spawning her-ring.

The EU–Norway agreement called for a review of the current plan until the end of 2011. This re-examination was carried out during WKHELP in 2012 (ICES 2012), implement-ing the changes made in the benchmark assessment performed in WKPELA 2012. The benchmark assessment has led to revisions of the perception of the stock and reconsid-ered FMSY as well as a target-F. The harvest control rules for the stock were evaluated against variations in biology, testing for robustness under varying starting conditions in population size and changes in the North Sea Ecosystem.

However, further negotiations were pending. The bilateral EU/Norway consultations 2014 were finalized during spring 2014 (EU-Norway 2014b) that added a stability clause on fishing mortality to the plan. This is the plan that has now been evaluated by WKHerTAC.

In summary, the text table below presents an overview of the advice, LTMPs and TACs for WBSS and NSAS herring.

TYPE NAME AREA FLEETS CATCH1) COMMENT

Advice ICES WBSS advice IIIa, 22–24

A, C, D, F

100% WBSS WBSS advice is calculated first. Catches of NSAS herring in IIIa are fixed in the NSAS advice.

ICES NSAS advice IV, VIId, IIIa

A, B, C, D

100% NSAS Applies to the North Sea but takes into account NSAS catches in IIIa.

LTMP North Sea herring management plan

IV + VIId A, B ~99% of NSAS

The ICES advice for NS herring takes into account fleets C + D.

WBSS management arrangement

IIIa, 22–24 (flex to IVa)

F, C, D (flex from IV)

In IIIa 75% WBSS + 25% NSAS, In 22-24 100% WBSS.

C fleet TAC is taken as 41% of advice MSY catch + 5.7% of some element of the North Sea herring advised catch (but unclear which fleets are included).

TAC HER/4AB IVa, b A 99% NSAS

HER/4CXB7D IVc, VIId A 100% NSAS

HER/2A47DX IV, VIId B 99% NSAS

HER/03A IIIa C 59% WBSS + 41% NSAS

HER/03A-BC IIIa D 44% WBSS + 56% NSAS

HER/3B23.; HER/3C22.; HER/3D24

22–24 F 100% WBSS

1) stock compositions as estimated for 2013.


5 Evaluation of the WBSS Management rule and the NSAS LTMP

5.1 Evaluation procedure

The software used in WKHerTAC is essentially the one developed by WKHELP; please refer to the report from WKHELP (ICES 2012) for a thorough description. Guidelines from the Workshop on Guidelines for Management Strategy Evaluation WKGMSE 2013 (ICES 2013) have been taken into account in the evaluation by WKHERTAC.

5.1.1 Model description

The Management Strategy Evaluation (MSE) considers four components. The biologi-cal stock units of herring in the North Sea and Western Baltic [1], the five fisheries tar-geting the stock unit(s) [2], the fisheries-independent surveys [3], the stock assessment procedure to obtain a perceived status of the stock unit(s) and is used to set manage-ment targets [4]. The framework includes feedback loops, where over time, the result of setting management targets affect the stock unit(s) the year after, and thereby also affect the fisheries and management. In order to reflect the uncertainties related to stock dynamics, fisheries dynamics and management implementation, the simulations are run with 1000 replicates, each representing a different but likely version of the true dynamics of the stock unit(s) and fisheries. The combination of all replicates together indicate the range in outcomes and risk for a given stock and management structure assumption. ICES assessment results from the North Sea Autumn Spawners (NSAS) and Western Baltic Spring Spawners (WBSS) are used to condition the model for the years 1991–2013. Simulations were run until 2034 (i.e. 20 years into the future).

[1] Biological Operating Model

In the North Sea and Western Baltic, roughly speaking, two stocks of herring exist: autumn spawning herring in the North Sea and spring-spawning herring in the West-ern Baltic. During the larval phase, the North Sea herring migrate into the Skagerrak-Kattegat area where they are susceptible to pelagic trawlers targeting herring under the IIIa area TAC. During the summer feeding migration, the faster growing individu-als of the Western Baltic herring migrate into the eastern North Sea where they are susceptible to the pelagic trawlers catching herring under on the North Sea area TAC. This mixing of herring into different management areas has an effect on the survival of both herring stock units which needs to be accounted for in management.

Historical dynamics

The output of stock assessment models, carried out at ICES (ICES, 2014) were used to populate age-structured (ages 0–8) population models for both units. Within the sim-ulations, the state–space stock assessment model used to yearly assess North Sea her-ring and Western Baltic herring is not embedded but rather mimicked. This is because including the assessment model in the simulations would result in practical problems with the amount of time available to do the evaluations. We perform Monte-Carlo sim-ulations to represent the stochasticity / uncertainty of the biological stock units and its behaviour. Over the years 1947–2013 for NSAS and 1991–2013 for WBSS we consider that in the 2014 assessment, the estimated numbers-at-age and fishing mortality-at-age are not without error. Hence, to generate an appropriate starting condition for each of the 1000 MCR, new numbers-at-age, fishing mortality-at-age and total catches are drawn from a multivariate normal distribution using the variance/covariance matrix which is estimated in the 2014 assessment. In the same run, a new set of survey catcha-bilities are generated. These new values allow us to recalculate survey indices and


catches-at-age in a coherent manner. Additionally, survey and catch residuals are cal-culated and randomly applied to the new catch-at-age and index-at-age time-series to represent observation error. For each of these new sets of catch and survey time-series a new assessment is run, where the assessment result serves as the starting condition of the simulation.

Mixing

Here, mixing refers to fish moving from one management area to another and thereby becoming susceptible to a different fishery. There are two ways to account for mixing in the simulation framework. 1) Assuming that fish that move to another management area join the dominant stock unit in that area and should therefore add up to the bio-mass of the dominant stock unit. This process is also known as straying or entrainment, less likely to occur for herring with different spawning behaviour. 2) Assuming that the availability of the fisheries to catch the stock units changes. In this instance, the C and D fleet would gain access to the NSAS fish as it enters Area IIIa. Vice versa, the A and B fleet would gain access to the WBSS fish if it enters Area IV. In this simulation, we assume process 2 to apply, which implies that there is no need to simulate any actual movement of fish. The difference between option 1 and 2 is that under option 2, the fish that were not caught by either fleet C or D can return to the North Sea whereas this is not possible under option 1. The proportion of the NSAS that is caught in IIIa and WBSS that is caught in IV is described under [2] Fisheries.

Operating model dynamics

The biological operating model consists of the age-structured population dynamics of the North Sea Autumn Spawning herring stock as assessed in 2014 (ICES, 2014) and Western Baltic Spring-spawning herring as assessed in 2014. The simulation was initi-ated in 2014 and each year onwards recruits are added to the simulated population. The number of recruits of the next year is produced in spring for WBSS and autumn for NSAS each year by the spawning–stock biomass (SSB). Under the scenarios it is assumed that recruitment survival is poor for NSAS and WBSS, similar to the years 2003–2013 and 2005–2013 respectively. The approach is seen as precautionary given that it is based on a period of low productivity for both stocks. If future recruitment departs largely from the level assumed in the present simulations, a new evaluation of harvest rules will be required. It is assumed that recruitment is lognormally dis-tributed where mean and variance of this distribution follow from fitting a lognormal to the recruits in 2003/2005–2013 as estimated by the most recent stock assessment. The number of recruits added to the stock each year is drawn from this lognormal distri-bution (Figure 5.1).


Figure 5.1. Distribution of simulated recruitment for NSAS herring (left) and WBSSS herring (right) compared to the geometric mean recruitment from the observed recruitment in the recent years (2003/2005–2013 for NSAS and WBSS respectively).

The biological numbers-at-age are formed by the different cohorts, each affected by fishing mortality and natural mortality. In the simulations these cohorts are followed from ages 0 to 8 (plus group). Natural mortality, that historically varies year by year for NSAS and is age-varying but time invariant for WBSS, is assumed to be similar to the 2013 stock assessment time-series and hence no uncertainty estimate is applied here (Figure 5.2).


Figure 5.2. Distribution of simulated natural mortality for NSAS herring (left) and WBSSS herring (right) compared to the natural mortality used in the stock assessments.

The same assumption applies for maturity (Figure 5.3) and weight-at-age (Figure 5.4). In the projected period, however, all these processes are simulated with variation.

Figure 5.3. Distribution of simulated maturity for NSAS herring (left) and WBSSS herring (right) compared to the maturity used in the stock assessments.


Figure 5.4. Distribution of simulated weight-at-age for NSAS herring (left) and WBSSS herring (right) compared to the weight at used in the stock assessments.

To maintain a certain level of autocorrelation, previously observed natural mortality vectors (all ages at once) are sampled in blocks up to nine years for NSAS (2003–2012 period, similar to poor-recruitment survival regime) and glued together until the entire projection period is filled, hereby, the selected blocks of years can be reversed in order too. As natural mortality is time-invariant for WBSS, we can simply assume a constant natural mortality in future. Additionally, to maintain a degree of correlation between maturity-at-age, natural mortality-at-age and weight-at-age (both in the stock and in the fishery), year ranges are shared among these processes. Catches and survivors in the forecasted years of the stocks are calculated using the (natural and fishing) mortal-ity rates.

Fishing mortality may be caused by a variety of fisheries, each associated with different selection patterns and catch targets. The fishing mortality encountered by a stock unit therefore depends on the sum of the fishing mortalities from each fishery.

[2] Fleet characteristics and the fishery

The five fleets (A: North Sea human consumption, B: North Sea industrial, C: IIIa hu-man consumption, D: IIIa industrial, F: 22–24 fishery) target the herring stock units (Figure 5.5). Each of these fleets catch fish at different ages following a certain selection-at-age pattern and may have access to either only the NSAS, the WBSS or both units. The five fleets together target 100% of the stock units.


Figure 5.5. Distribution of simulated selectivity for fleets fishing for NSAS herring (left) and WBSSS herring (right) compared to the selectivity estimated in the stock assessments.

The future selection patterns are assumed to follow an age-correlated random walk where each step follows a normal distribution with mean 0 and variance estimated based on the covariance of log-transformed F-at-age change (from year y to year y+1) over the years 1997–2013 for NSAS and 1991–2013 for WBSS. To prevent extreme changes, steps outside the 95% CI of the distribution were excluded. The selection pat-tern that is obtained from the assessment results, representing all fleets together, is used for this purpose.

Selection by fleet is thereafter derived by multiplying the combined selection pattern with the catch numbers-at-age proportion of each fleet (average 2011–2013, a period assumed to represent accurately the current exploitation by the fisheries). Catches weights-at-age (for the combined fishery but split by fleet afterwards) are varied in an identical manner as described for maturity- and natural mortality-at-age above.

The long-term management plan or rule model suggests quota (TAC) that meet the management targets. The fleet operating model estimates the annual effort applied by the fleets, given the allocated quotas. To this end, the fishing mortality (F) that corre-sponds to the TAC or bycatch ceiling for the five fleets is estimated. The fleets conse-quently generate fishing mortality, calculated by age group as the product of fishing effort, catchability (q), and selectivity-at-age. Fishing mortality affects the numbers-at-age in the biological operating model. Using the Baranov catch equation we can calcu-late the ‘true’ catches.

The TAC setting procedures and allocation of catch potential to each of the fleets fol-lows from the management plan / rule and potential transfers from catch potential from one fleet to another. In this case, the advised catch for WBSS is split among fleet F and fleet C & D where the F-fleet receives 50% of the advised catch, the C-fleet receives 41% and the D fleet is associated with a fixed catch of 6659 t per year. In addition to the 41% of the WBSS advised catch, the total allowed catch for the C-fleet is topped with 5.7% of the total predicted NSAS catch. Finally, the C-fleet catch may be reduced when up


to 50% of the catch is relocated to the North Sea where it is caught by the A-fleet. The advised catch for NSAS is split among fleet A and B according to the management plan. In the A, C and D fleet, however, the catches do not consist of one herring species only, but contain a mixture of both NSAS and WBSS. As the MSE evaluated the precaution-arity of the stocks to certain management rules, the mixed nature of the catches have to be accounted for in the simulations.

Over the past 13 years, on average, 36% of the C-fleet catch consists of NSAS and 64% of WBSS. On average, 60% of the D-fleet catch consist of NSAS and 40% of WBSS. On average, 1% if the A-fleet catch consists of WBSS and 99% of NSAS. These mixing per-centages are mimicked in the simulations where we assume that the C-fleet takes a similar fraction of WBSS and NSAS. The fractions are simulated assuming a multivar-iate truncated [0,1] normal distribution with means 0.36 and 0.6 for the C and D fleet with variances of 0.024 and 0.008 respectively. The WBSS fraction in the A-fleet was simulated assuming a normal truncated [0,1] distribution with mean 0.01 and variance 8e-5.

The observed ratio of the two stocks by age, quarter and year in all catches in IIIa is used as input in the evaluations of the NSAS LTMP and the Management Rule for TAC setting in IIIa for fleet C using the average proportion with a variation around it (please refer to Section 4.1 for a detailed method description). To support the choice of a mean ratio based on the time-series, the data were analysed for any trends over time (Figure 5.6). The main mixing occurs in the younger age groups and analysing these, no trends in the mixing over time or in relation to the SSB of NSAS or WBSS was evident (Figure 5.7). The most recent research indicates that the mixture of stocks in the Western Baltic and IIIa area is driven by growth of the individual fish and not the size of the originat-ing populations (or stocks) (Clausen et al., 2015). Thus the assumption of the mixing described above for the performed simulations is valid.


a)

b)

Figure 5.6. (a) The ratio of NSAS in catches taken in IIIaN over time for age groups 0, 1, 2 and (b) the ratio of the WBSS SSB to NSAS SSB over time.

Figure 5.7. The ratio of NSAS in catches taken in IIIaN over time for age groups 0, 1, 2 and the ratio of the WBSS SSB to NSAS SSB.

Owing to the mixing of fish and the transfers of catches from the C-fleet to the A-fleet, we keep track of catches by the fleet, TACs set per area and actual outtake per stock unit by each of the fleets. Note that throughout the report, ‘outtake’ is the amount of herring removed from the stock and ‘catch’ is the total catch of herring (i.e. both stocks).

[3] Surveys

Within the simulation framework, for NSAS four index series are designed, similar to the index series as currently used in the North Sea herring stock assessment, i.e. the


SCAI index, a larvae survey index representing SSB at spawning time; IBTS0, an index for the recruits at age 0; IBTS-Q1, an index for age 1 and HERAS, an acoustic survey for ages 1–8+. Survey catchability (q) is assumed to be fixed within a realisation. For WBSS five surveys are considered in the same ways as described for NSAS. These are the IBTS-Q1, IBTS-Q3, HERAS, N20 and GerAs surveys.

Re-calculation of the survey indices and their role in determining the starting condition of the simulation is described under [1].

As the stock assessment is mimicked in the projected period, and therefore does not include index values, survey indices are only calculated and used in the historic period.

Figure 5.8. Survey errors at age for NSAS herring (left) and WBSSS herring (right) derived from the most recent stock assessments.

[4] Assessment and forecast

The perception of the stock units’ status in the period before 2014 is generated through explicit inclusion of stock assessments in the simulation, which is based on fishery-independent (surveys) and -dependent (catch) data.

From 2014 onwards the assessments are simulated by introducing error in the “true” numbers-at-age and “true” exploitation pattern. This noise is estimated by running ten year retrospective analyses for each realisation. Within a realisation, the error is meas-ured by the log-ratio of the true numbers-at-age and retrospective numbers-at-age. The final result is an error time-series per retrospective year per realisation. These error time-series are resampled and used to generate future retrospective error for the fore-casted period. The same procedure as used to sample natural mortality- and maturity-at-age is applied to sample numbers-at-age and F-at-age error. Correlation among N-at-age and F-at-age is maintained here as well.

The stock assessment process results in fishing mortality estimates until year y-2, and survivor and SSB estimates for year y-1 (where year y is the year that the TAC applies). The assessment output data may deviate from the true stock unit characteristics as


modelled in the biological operating model because of the variability of the data sources that go into the assessment.

A short-term forecast is used within the MSE to set annual TACs as described below. The short-term forecast for NSAS is similar to the multi-fleet forecast as currently used within the North Sea herring assessment, but has dynamic feedback on NSAS catches as realized by the C & D fleet, while previously a fixed catch for the C & D fleet was assumed in similar evaluations (e.g. ICES WKHELP). Selectivity by fleet in the TAC and forecast year follow the exploitation pattern as estimated within the stock assess-ment multiplied with the proportional catch numbers by fleet. Recruitment in the fore-cast year is fixed to the geometric mean of the period 2003–the assessment year, while recruitment in the TAC year is taken from the assessment prediction. Stock weight-at-age and time of spawning is similar to the assessment year settings while maturity in the TAC and forecast year equals the average maturity estimate over the past three years and natural mortality is averaged over the most recent five years. The exploita-tion pattern by fleet is scaled up or down to ensure that the catch equals the TAC in the TAC year. In the forecast year, the management plan determines the increase or de-crease in fleet effort and proposes a TAC for the A- and B-fleet.

The forecast for the WBSS is somewhat simpler as it relies on a single-fleet assumption, and is implemented here as a copy of the procedure currently executed by ICES (ICES, 2014). Catches of WBSS in the North Sea are also taken into account.

However, the proposed TAC is calculated based on numbers, landings selectivity and fleet selectivity obtained from the assessment results which differ from the numbers, landings selectivity and fleet selectivity in the ‘true’ stocks. Hence, the fishing mortality needed to realise catch equalling the TAC is not identical with the target fishing mor-tality as set within the management plan. As there is no analytical solution to this equa-tion, an optimisation method is used (based on a combination of golden section search and successive parabolic interpolation (Brent, 1973)) to calculate ‘true’ fishing mortal-ity.

5.2 Harvest Control Rule options examined

The scenarios are given in Table 5.2 and describe the settings for each of the simula-tions, including the procedure to derive advised TACs for each of the fleets and the actual outtake by each of the fleets.

Quotas in Areas 22–24, IIIa and IV are set as stated in the request:

First the advised TACs are defined for both biological stocks based on the procedure implemented in the ICES advice:

• Advised TAC WBSS is based on the implementation of the ICES MSY ap-proach (hockey-stick harvest rule with plateau at FMSY=0.28 and breakpoint at Btrigger=110 000 t, no TAC variation constraint).

• Advised TAC NSAS is based on the implementation of the LTMP, including the banking-and-borrowing rule.


Those TACs are then allocated to areas and fleets as follows:

TAC in 22–24 (fleet F) 50% of advised TAC WBSS

TAC in IIIa (fleet C) 41% of advised TAC WBSS + 5.7% of advised TAC NSAS

Bycatch ceiling in IIIa (fleet D) constant in time, at 6659 t

TAC in IV (fleet A) as set by the LTMP for fleet A

Bycatch ceiling in IV (fleet B) as set by the LTMP for fleet B

In addition:

1 ) The resulting TAC in IIIa for the fleet C is subject to a 15% interannual vari-ation limit;

2 ) flexibility provision whereby up to 50% of the IIIa TAC for fleet C can be transferred to the IV TAC for fleet A.

The fleet D bycatch ceiling has been stable at 6659 t since 2011 regardless changes in the C-fleet TAC. This value is used in the management strategy evaluation.

This TAC setting procedure is summarised on the figure below.

The NSAS management rule is implemented in the simulations as follows: In the sim-ulations the basic rule is to first calculate the preliminary TAC for the A-fleet in accord-ance with fishing mortalities derived from the agreed HCR, using the expected SSB in the TAC year as follows:

SSB > Btrigger: F2–6 = 0.26; F0–1 = 0.05

Blim < SSB < Btrigger: F2–6 = 0.26 -((0.26-0.10)*(Btrigger-SSB)/(Btrigger-Blim)); F0–1 = 0.05

Fleet B Fleet A Fleet C Fleet D Fleet F

advice for WBSS (MSY)

41% of WBSS TAC

No WBSS TAC

allocated

50% of WBSS TAC

Subarea IV

Subarea IV

Division IIIa

Division IIIa

Division 22-24

By-catch quota

TAC TAC By-catch quota

TAC

advice for NSAS (LTMP)

LTMP TAC for fleet B

LTMP TAC for fleet A

5.7% of NSAS TAC

Not defined

Not defined

15% IAV limit

Up to 50 % transfer allowed

Assumed constant at 6659t


SSB < Blim: F2–6 = 0.10; F0–1 = 0.04

The TACs calculated in this first step are referred to as preliminary TACs in the follow-ing text. In a second step, the preliminary TACs are subject to a maximum 15% TAC constraint. Hereafter, the resulting fishing mortality is calculated assuming the prelim-inary TAC (including the 15% IAV constraint) which is not allowed to deviate by more than 10% from the target F. If the calculated F deviates by more than 10% from the target, a maximum 10% deviation is applied. On this basis, a new preliminary TAC is calculated. On top of this, a 10% banking and borrowing mechanism is applied where in the first year 10% of the TAC is banked and in the following years, the banked amount is repaid, followed by an immediate 10% borrowing of the new TAC.

Scenarios for simulations

Simulations are run using the TAC setting procedure described above, and assuming that all TACs are fully used.

The percentage transferred from IIIa fleet C TAC to IV fleet A TAC has been variable, and around 42 to 44% in the recent years. In order to cover a range of likely scenarios, different simulations are run with values varying it in steps of: 0, 10, 20, 30, 40, 45 and 50%.

The TAC set by the two management plans have little in common with the final outtake of NSAS and WBSS in each fleet due to the mixing and transfers. For this reason, for each of the fleets, where applicable, the expected outtake of NSAS and WBSS is used. To give an example:

Assume that:

• the A fleet TAC is set at 450.000 t in 2019, 460.000 t in 2020, and 480.000 t in 2021

• the B fleet bycatch ceiling is set at 20.000 t in 2020 and 22.000 in 2021 • The total WBSS advised TAC = 50.000 t in 2020 and 55.000 t in 2021 • The C fleet TAC is set at 0.41* 50.000 t +0.057 * 450.000 = 46.15 t in 2020

and 48.77 t in 2021 • The D fleet TAC is set at 6.6 t in 2020 and 6.6 t in 2021 • The F fleet TAC is set at 25.000 t in 2020 and 27.500 t in 2021

• The outtake of NSAS and WBSS in each fleet is then as follows: • A-fleet = 460.000 t + 0.5*46.15=483.075 t NSAS in 2020 and 504.385 t NSAS

in 2021 • B-fleet = 20.000 t in 2020 and 22.000 in 2021 • C-fleet = ~0.36 * 0.5 * 46.15=8.307 t NSAS & 14.768 t WBSS in 2020 and

8.778 t NSAS & 15.606 t WBSS in 2021 • D-fleet = ~0.6 * 6.6=3.960 t NSAS & 2.640 t WBSS in 2020 and 2021 • F-fleet=25.000 t WBSS in 2020 and 27.500 t WBSS in 2021

The different simulation runs (15 runs) are summarized in Table 5.1.


Table 5.1. Scenario description detailing the rule to derive advised TACs for each fleet and their actual outtake of WBSS and NSAS. Similar colours per column indicate identical settings. Where colours differ within one column, different scenario settings apply.

In addition the following overall assumptions apply.

• A fleet TAC is set according to the NS LTMP. • A fleet catch is determined by the A fleet TAC and the optional transfer of

the C-fleet TAC to the North Sea. The catch consists of 99% NSAS, 1% WBSS. • B fleet catch is set according to the NS LTMP. • C fleet TAC consists of the 41% of the WBSS advised TAC and 5.7% of the

NS A+B fleet TAC. • C fleet catch consists of that part of the C-fleet TAC that is not transferred to

the North Sea and consists of 36% NSAS and 64% WBSS. • D fleet TAC is set at a fixed value. • D fleet catch consists of 60% NSAS and 40% WBSS. • F fleet TAC and catch consists of 50% of WBSS advised TAC and consists of

WBSS only.

5.3 Choice of performance indicators

WKHerTAC identified the list of indicators below to provide a broad overview of the performance of the different options.

1. Risk

The WKHerTAC calculated the risk for the stock to fall below Blim according to risk type 3 (ICES 2013). The medium term (ten years) was chosen as the time period to calculate the risk over, starting in 2015 and ending in 2024. Risk type 3 is described as the maxi-mum percentage of iterations that fall below Blim in any given year (ICES 2013) (PA).

Run Stock of interest

Management regime WBSS

IAV on TAC A

IAV on F NSAS

B&B NSAS

Btrigger Transfer C-fleet to area IV

D fleet TAC & outtake

1 WBSS MSY 15% 10% yes 1500 50% 6659 t

2 WBSS MSY 15% 10% yes 1500 45% 6659 t

3 WBSS MSY 15% 10% yes 1500 40% 6659 t

7 WBSS MSY 15% 10% yes 1500 30% 6659 t

8 WBSS MSY 15% 10% yes 1500 20% 6659 t

9 WBSS MSY 15% 10% yes 1500 10% 6659 t

6 WBSS MSY 15% 10% yes 1500 0% 6659 t

4 WBSS MSY 15% 10% yes 1500 50% 8323 t

5 WBSS MSY 15% 10% yes 1500 50% 9988 t

10 NSAS MSY 15% 10% yes 1000 50% 6659 t

11 NSAS MSY 15% 10% yes 1100 50% 6659 t

12 NSAS MSY 15% 10% yes 1200 50% 6659 t

13 NSAS MSY 15% 10% yes 1300 50% 6659 t

14 NSAS MSY 15% 10% yes 1400 50% 6659 t

15 NSAS MSY 15% 10% yes 1500 50% 6659 t


This differs from risk type 2 which was applied during the most recent evaluation of herring management plan (WKHELP; ICES 2012). Risk type 2 is defined as the percent-age of runs that falls below Blim at least once during the medium term. Under equal conditions in the simulations risk type 2 will be higher or equal to risk type 3. ICES rec-ommends the use of risk type 3 as a criteria of precautionarity given more stability of this measure compared to risk type 2.

2. Stock performance

Stock development is indicated by SSB at spawning time in 2019, 2024 and 2034. The response of SSB for both the WBSS and NSAS stock is different under different Man-agement Rules, as the outtake of fish from both stocks depends on the Management Rule evaluated. These catches thereby induce mortality on both fish stocks. If mortality is less than FMSY, the stock has the potential to grow and if the mortality is higher, than the stock may decline, depending also on the strength of incoming recruitment each year. The median value of all 1000 iterations is taken to represent stock performance.

3. Fishing mortality

For the WBSS stock, three fishing mortality indicators are evaluated. 1) The probability of the fishing mortality being at or below FMSY year-on-year , 2) the mean fishing mor-tality of ages 3–6 over the simulation period and 3) the mean fishing mortality of ages 3–6 in 2019. The probability (nr 1) indicates what percentage of iterations is on average below FMSY while the other two indicators tell us about the general exploitation pres-sure of the fleets on the stock. For NSAS, only two indicators are calculated, similar to the numbers 2 and 3 above. Note however that for NSAS, ages 2–6 are used to calculate mean fishing mortality.

4. Yield

In total, four different fisheries operate in Area IV and IIIa, namely fleet A (human consumption in Area IV), B (industrial bycatch in Area IV), fleet C (human consump-tion in Area IIIa), fleet D (industrial bycatch in Area IIIa) and additionally fleet F oper-ating in Subdivisions 22–24 (human consumption). The mean outtake of these fleets, as averaged over the entire simulation period is given.

5. Stability in TAC

The stability in TACs is presented by three different indicators for the A-fleet in the North Sea and the C-fleet in Area IIIa. 1) The mean absolute relative change in TAC from year to year. Note that a negative change in TAC is expressed identical with a positive change of the same amount, i.e. a 15% reduction in TAC is similar as a 15% increase in TAC when we speak about relative change. 2) the average number of times (%) the TAC change was restricted upwards by the 15% IAV rule or downwards and 3) the average amount the TAC was revised with upwards and downwards.

The terminology for the performance indicators as used in the software and shown in the outputs, is explained in Table 5.2.


Table 5.2. Performance indicators.

INDICATOR EXPLANATION

Btrigger Btrigger biomass below which F decreases according to management rule

Ftarget adult Fishing mortality target for adults (2–6), NSAS Ftarget juv Fishing mortality target for juveniles (0–1), NSAS Ftarget 3–6 Fishing mortality target for adults (3–6), WBSS Stability Reference point below which where the 15% restriction on TAC variation is lifted. RiskP3 Lim Prob3 relative to Blim RiskP2 Lim Prob2 relative to Blim RiskP3 Trig Prob3 relative to Btrigger RiskP2 Trig Prob2 relative to Btrigger prob FMSY Probability of the fishing mortality being at or below FMSY year-on-year SSB 2019 SSB in 2019 SSB 2024 SSB in 2014 SSB 2034 SSB in 2034 mean SSB Mean SSB calculated over the whole time-series (2015–20134) F2019 2–6 Mean fishing mortality of ages 3–6 in 2019 F2019 2–6 Mean fishing mortality of ages 3–6 in 2019 mean F0–1 Mean fishing mortality of ages 0–1 over the whole time-series (NSAS) mean F2–6 Mean fishing mortality of ages 2–6 over the whole time-series (NSAS) mean F3–6 Mean fishing mortality of ages 3–6 over the whole time-series (WBSS) Yield A Mean catch of the A-fleet over the whole time-series (of NSAS) Yield B Mean catch of the B-fleet over the whole time-series (of NSAS) Yield C Mean catch of the C-fleet over the whole time-series (of NSAS and WBSS separately) Yield D Mean catch of the D-fleet over the whole time-series (of NSAS and WBSS separately) Yield F Mean catch of the F-fleet over the whole time-series (of WBSS) meanrel TACIAV.A The mean absolute relative change in TAC from year to year for the A fleet (in IV) meanrel TACIAV.C The mean absolute relative change in TAC from year to year for the C fleet (in IIIa) mean TACIAV.A The mean absolute relative change in TAC from year to year for the A fleet (in IV) mean TACIAV.C The mean absolute relative change in TAC from year to year for the C fleet (in IIIa) IAV restrict up The average number of times (%) the TAC change was restricted upwards by the 15% IAV rule

IAV restrict down The average number of times (%) the TAC change was restricted downwards by the 15% IAV rule

TAC up Mean increase in TAC if the TAC goes up TAC down Mean decrease in TAC if TAC goes down riskP3 any Prob3 relative to Blim for either WBSS or NSAS riskP3 both Prob3 relative to Blim for WBSS and NSAS at the same time mean Yield IV Mean yield in Area IV mean Yield IIIa Mean yield in Area IIIa mean Yield 22–24 Mean yield in Subdivisions 22–24 Yield 2019 IV Mean yield in Area IV in 2019 Yield 2019 IIIa Mean yield in Area IIIa in 2019 Yield 2019 22–24 Mean yield in Subdivisions 22–24 in 2019 meanrel TACIAV IV The mean absolute relative change in TAC from year to year for Area IV (fleet A and B) meanrel TACIAV IIIa The mean absolute relative change in TAC from year to year for Area IIIa (fleet C and D)

meanrel TACIAV 22–24 The mean absolute relative change in TAC from year to year for Subdivisions 22–24 (fleet F)


5.4 Results

In total 15 scenarios were evaluated for the NSAS stock and the WBSS stock (Table 5.1). The first nine tested alternative implementations of the WBSS management rule (Table 5.3) and the remainder tested alternative implementation of the NSAS management rule (Table 5.4). Note under the first nine scenarios we had to make an assumption that the 2014 management plan was in place for NSAS and for the latter six, we had to make an assumption that the management rule was in place for WBSS (with a simulation setting where the Fleet C TAC uses the NSAS TAC of the previous year, see Section 5.1.1).

The performance of each of the HCRs according to the above mentioned indicators is given in Annex 3. Note that as a general rule, unless specified otherwise, when an in-dicator is provided as a mean, first the mean is calculated over time for each iteration, after which the median is taken over these means. All numbers (SSB, F) refer to the true values in the simulated populations.

WBSS

The proposed management strategy for the TAC setting procedure for herring in IIIa is not precautionary in the medium and long term because the probability of being below Blim is larger than 5% when no transfer of TAC occurs from Division IIIa to Di-vision IV. For the scenarios tested where a minimum transfer of 10% is applied, the risk to fall below Blim is less than 5%. The transfer is an ad-hoc optional element of the WBSS management agreed upon on a yearly basis, and can therefore not be considered as a fixed element of the IIIa TAC-setting procedure. The transfer has a positive effect on the SSB of WBSS stabilizing around 175 thousand tonnes in the medium term assuming a 50% transfer from Division IIIa to Subarea IV. SSB stabilizes around 125 thousand tonnes in the medium term assuming a 0% transfer.

If no transfer is applied, the way the simulations are applied results in the WBSS stock being overfished with an average F3–6 of 0.33 while FMSY resides at 0.28. A transfer of 20% is necessary to fish the WBSS stock at FMSY. This is due to the contribution of the NSAS North Sea TAC to the C-fleet TAC. On average, the 5.7% element amounts to 17 000 tonnes, while the 41% element amounts to 23 000 tonnes, totalling 40 000 tonnes of catch for the C-fleet. From this 40 000 tonnes, on average, 25 000 tonnes is WBSS (and 15 000 tonnes NSAS) while the A-fleet takes 3700 tonnes and the D-fleet takes another 2500 tonnes of WBSS, summing up to 31 200 tonnes of WBSS in IIIa. This combined catch of WBSS in IIIa should not be higher than the F-fleet catches, as the F-fleet catches comply exactly with the MSY approach. However, the F-fleet catches are on average 26 000 tonnes and are never higher than 27 200 tonnes. Considering that the F-fleet TAC is set at 50% of the advised WBSS TAC, we conclude that the contribution of the 5.7% element is allowing too much herring (and specifically WBSS herring) to be caught in IIIa, resulting in overfishing. When a transfer of the C-fleet TAC is allowed to the North Sea, however, the catch opportunity of the C-fleet is lowered and the risk to overexploitation is reduced. Note here that the management rule applied to NSAS may play an important role too where higher TACs in the North Sea result in higher fishing pressure in IIIa.

The variability in the TAC of the C-fleet is similar among all scenarios and varies be-tween 6.6 and 7.2%, which results in absolute TAC changes between 2600 and 3000 tonnes per year. Given that the C-fleet TAC is quite stable the 15% constraint IAV on the C-fleet TAC was rarely invoked in the simulations.


The sensitivity to increased catches in the D-fleet shows a minor trade-off with the C-fleet where catches reduce. SSB trends show a marked decline from 167 000 tonnes to 158 000 tonnes under an outtake of 6600 to 9600 tonnes respectively. The sensitivity to an increase in the D-fleet TAC is low as there is no increased risk when simulated ap-plying a 50% transfer. The effects on NSAS are minor regarding stock trends, but under an increased TAC for the D-fleet, the B-fleet TAC has to reduce as well, from 15 400 tonnes to 13 300 tonnes under an outtake of 6600 to 9600 tonnes for the D-fleet respectively.

NSAS

All scenarios tested for NSAS are considered precautionary. The scenario assuming a Btrigger of 1.0 million tonnes results in the highest catch for the A fleet and lowest inter-annual variability. Catches range between 360 000 tonnes to 330 000 tonnes for the A-fleet. Under the Btrigger = 1.0 million tonnes scenario however, the stock is fished above target but, on average, just below FMSY. Owing to the 50% transfer of C-fleet catch op-portunities to the North Sea, where it is caught by the A-fleet, results, on average, in above target fishing mortalities under all NSAS scenarios tested (see Annex 3). On av-erage in the medium term, the total NSAS TAC is overshot by 5%; this does not raise concern because the overall risk to fall below Blim remains under 5%. Under higher Btrig-

ger values, ranging up to 1.5 million tonnes, the NSAS stock stabilizes around 1.30 mil-lion tonnes in the short to medium term; under the 1.0 million tonnes Btrigger the NSAS SSB stabilizes around 1.21 million tonnes. Thus if the Btrigger is to be adjusted, values at or above 1.0 million tonnes can be considered precautionary.

The results furthermore show a trade-off between A and B fleet catches. Under the 1.5 million tonnes Btrigger scenario, 600 tonnes more of NSAS can be caught by the B fleet compared to the 1.0 million tonnes Btrigger scenario. The NSAS catches by the C-fleet play a role here too, where there is a trade-off between B-fleet or C-fleet catches.

The text table below indicates the trade-offs between different indicators through the colouring of the cells where dark green is considered a more positive result. All sce-narios are precautionary because the RiskP3 to Blim is below 5% and fishing mortality is at or below FMSY.

Units of biomass: 1000 tonnes.

The lowest Btrigger of 1 000 000 tonnes is most positive for the yield of the A- and C- fleets and IAV of the A-fleet but results in the lowest catch for the B fleet. The diverging yields for the A- and B- fleet can be explained through the interaction with the C-fleet. Whenever the A-fleet catches increase, the C-fleet catches may increase as well. As the C-fleet ‘competes’ with the B-fleet for juvenile fish, the B-fleet catches have to reduce.

Scenario Btrigger RiskP3 Lim RiskP3 Trig mean SSB mean F2-6 Yield A Yield C Yield B mean rel TAC-A IAV

10 1000 1% 22% 1258 0.27 358.9 7.8 14.8 13%11 1100 0% 36% 1267 0.27 355.3 7.8 14.9 14%12 1200 0% 49% 1280 0.26 350.6 7.8 15.0 15%13 1300 0% 62% 1293 0.25 344.4 7.7 15.1 16%14 1400 0% 74% 1315 0.24 337.7 7.7 15.3 16%15 1500 0% 83% 1337 0.23 331.1 7.6 15.4 16%


The highest Btrigger of 1 500 000 tonnes is most positive for the SSB and the yield of the B fleet but results in a fishing mortality that is below FMSY though still on target while under the Btrigger values of 1 000 000 tonnes and 1 100 000 tonnes the NSAS stock is fished above target but at FMSY.

WKHerTAC recognise that not all possible scenarios have been tested; the scenarios tested are generally limited to those considered optimal for addressing the requests to ICES. However, as described below the group has discussed other potential scenarios and where possible their outcome.

The results of the scenarios testing other possible trigger values in the NSAS LTMP are likely to trigger consideration of a lower Btrigger in the near future. This will have a po-tential impact on the management rule for WBSS. All the scenarios testing the manage-ment rule for setting a herring TAC for human consumption in Division IIIa uses the a NSAS Management plan with a Btrigger of 1.5 million tonnes and IAV variation limita-tion on F of 10% as a basis. These scenarios show that a transfer of at least 10% of the catches from Division IIIa to Subarea IV is required for the management rule for the C-fleet in IIIa to be precautionary. Any decrease in the Btrigger value for the NSAS would increase the TAC for the C-fleet and thus require a higher transfer for the management rule to be precautionary. In WKHerTAC, all the scenarios with a lower Btrigger assumed a transfer rate of 50%.

In the scenarios tested, it is assumed that 5.7% of only the A and B fleet NSAS TAC are taken into consideration to set the TAC for the C-fleet in IIIa, and not the sum of the A, B, C and D fleet catches. The results already show that 5.7% of the A+B-fleet is too high to result in a precautionary management rule for WBSS. If the A- and D-fleet would be taken into account, the risk to fall below Blim for WBSS will only be amplified.


Table 5.3. Results WBSS scenarios (1–9) evaluated during WKHERTAC.

Western Baltic Spring Spawning herringScenario Transfer C

fleet to area IV

D fleet TAC in IIIa

(NSAS+WBSS)

Btrigger Ftarget 3-6

Stability RiskP3 Lim RiskP2 Lim RiskP3 Trig

RiskP2 Trig

prob Fmsy SSB 2019 SSB 2024 SSB 2034

1 50% 6659 t 110 0.28 - 0% 1% 6% 14% 91% 162.2 176.0 181.52 45% 6659 t 110 0.28 - 1% 2% 6% 15% 87% 158.3 170.2 175.43 40% 6659 t 110 0.28 - 1% 2% 7% 16% 82% 154.4 164.4 169.37 30% 6659 t 110 0.28 - 1% 2% 8% 24% 69% 146.8 153.3 157.68 20% 6659 t 110 0.28 - 1% 5% 10% 35% 54% 139.6 142.6 145.89 10% 6659 t 110 0.28 - 3% 11% 20% 49% 40% 132.6 132.2 135.06 0% 6659 t 110 0.28 - 7% 20% 34% 64% 28% 125.6 123.2 125.44 50% 8323 t 110 0.28 - 1% 2% 6% 14% 89% 159.2 170.1 174.95 50% 9988 t 110 0.28 - 1% 2% 6% 16% 87% 156.3 164.7 168.2

WBSS (continued)Scenario mean SSB F2019

3-6mean

F3-6Yield A Yield F Yield C Yield D meanrel

TACIAV.Cmean

TACIAV.CIAV

restrict upIAV

restrict down

TAC up TAC down

1 167.8 0.24 0.23 3.8 27.2 13.4 2.6 0.06606 2.828 1 1 2.702 -2.962 163.1 0.25 0.238 3.8 26.9 14.6 2.6 0.06656 2.832 1 1 2.699 -2.9683 158.5 0.26 0.245 3.8 26.6 15.7 2.6 0.06704 2.834 1 1 2.694 -2.9797 149.6 0.27 0.261 3.8 25.9 17.9 2.6 0.06805 2.848 1 1 2.684 -3.0038 141.0 0.29 0.278 3.7 25.1 20.1 2.6 0.06933 2.858 1 1 2.674 -3.0369 133.1 0.31 0.295 3.7 24.4 22.2 2.6 0.07123 2.874 1 1 2.669 -3.0816 125.8 0.33 0.312 3.7 23.6 24.3 2.6 0.07271 2.896 1 2 2.674 -3.144 163.2 0.25 0.234 3.8 26.9 13.3 3.2 0.06672 2.833 1 1 2.695 -2.9745 158.7 0.25 0.238 3.8 26.5 13.1 3.8 0.06746 2.839 1 1 2.694 -2.984

North Sea autumn spawning herringScenario Btrigger F target adult F target

juvStability RiskP3 Lim RiskP2 Lim RiskP3

TrigRiskP2

TrigSSB 2019 SSB 2024 SSB 2034 mean SSB F2019

2-61 1500 0.26 0.05 Blim 0% 0% 83% 100% 1337 1276 1271 1374 0.222 1500 0.26 0.05 Blim 0% 0% 82% 100% 1340 1279 1274 1376 0.223 1500 0.26 0.05 Blim 0% 0% 81% 100% 1343 1282 1278 1380 0.227 1500 0.26 0.05 Blim 0% 0% 81% 100% 1349 1289 1285 1386 0.228 1500 0.26 0.05 Blim 0% 0% 80% 100% 1355 1295 1292 1392 0.229 1500 0.26 0.05 Blim 0% 0% 79% 100% 1362 1301 1298 1397 0.216 1500 0.26 0.05 Blim 0% 0% 79% 100% 1369 1306 1304 1403 0.214 1500 0.26 0.05 Blim 0% 0% 83% 100% 1337 1276 1271 1374 0.225 1500 0.26 0.05 Blim 0% 0% 83% 100% 1337 1276 1271 1374 0.22

NSAS continuedScenario mean

F0-1mean

F2-6Yield A Yield B Yield C Yield D meanrel

TACIAV.Amean

TACIAV.AIAV

restrict upIAV

restrict down

TAC up TAC down

1 0.05 0.23 331.1 15.4 7.6 4.0 16% 51.6 0 0 46.8 -56.82 0.05 0.23 329.8 15.3 8.3 4.0 16% 51.6 0 0 46.8 -56.83 0.05 0.23 328.3 15.2 8.9 4.0 16% 51.6 0 0 46.8 -56.87 0.05 0.23 325.4 14.9 10.2 4.0 16% 51.7 0 0 46.9 -56.78 0.05 0.22 322.6 14.7 11.4 4.0 16% 51.7 0 0 46.9 -56.69 0.05 0.22 319.8 14.5 12.6 4.0 16% 51.7 0 0 47.0 -56.56 0.05 0.22 317.2 14.2 13.7 4.0 16% 51.7 0 0 47.1 -56.44 0.05 0.23 330.6 14.4 7.6 4.9 16% 51.6 0 0 46.8 -56.85 0.05 0.23 330.8 13.3 7.5 5.9 16% 51.6 0 0 46.8 -56.8

WBSS + NSASScenario riskP3 any riskP3 both mean

Yield IVmean

Yield IIIamean

Yield 22-24

Yield 2019 IV

Yield 2019 IIIa

Yield 2019 22-24

meanrel TACIAV IV

meanrel TACIAV

IIIa

meanrel TACIAV 22-

241 1% 0% 350.3 27.5 27.2 320.7 27.4 28.1 15% 9% 6%2 1% 0% 348.7 29.3 26.9 319.5 29.4 27.9 15% 9% 6%3 1% 0% 347.1 31.1 26.6 318.0 31.4 27.6 15% 10% 6%7 2% 0% 344.0 34.6 25.9 314.8 35.3 27.2 15% 10% 6%8 2% 0% 340.7 38.0 25.1 312.2 39.1 26.7 15% 11% 7%9 3% 0% 337.7 41.3 24.4 308.8 42.7 26.1 15% 11% 7%6 7% 0% 334.6 44.5 23.6 306.0 46.3 25.6 15% 12% 8%4 1% 0% 349.0 28.9 26.9 319.3 29.0 27.8 15% 9% 6%5 1% 0% 347.7 30.3 26.5 317.9 30.5 27.6 15% 10% 6%


Table 5.3. Results NSAS scenarios (10–15) evaluated during WKHERTAC.

North Sea autumn spawning herringScenario Btrigger

(scenarios)F adult F juv Stability RiskP3 Lim RiskP2 Lim RiskP3

TrigRiskP2

TrigSSB 2019 SSB 2024 SSB 2034 mean SSB F2019

2-610 1000 0.26 0.05 Blim 1% 2% 22% 52% 1258 1173 1158 1288 0.2711 1100 0.26 0.05 Blim 0% 1% 36% 77% 1267 1184 1178 1298 0.2612 1200 0.26 0.05 Blim 0% 0% 49% 91% 1280 1205 1195 1313 0.2613 1300 0.26 0.05 Blim 0% 0% 62% 97% 1293 1223 1219 1334 0.2514 1400 0.26 0.05 Blim 0% 0% 74% 100% 1315 1248 1247 1354 0.2315 1500 0.26 0.05 Blim 0% 0% 83% 100% 1337 1276 1271 1374 0.22

NSAS continuedScenario mean F0-1 mean

F2-6Yield A Yield B Yield C Yield D meanrel

TACIAV.Amean

TACIAV.AIAV

restrict upIAV restrict

downTAC up TAC down

10 0.05 0.27 358.9 14.8 7.8 4.0 13% 46.6 0 0 43.9 -50.311 0.05 0.27 355.3 14.9 7.8 4.0 14% 48.9 0 0 45.6 -52.812 0.05 0.26 350.6 15.0 7.8 4.0 15% 50.9 0 0 47.3 -55.013 0.05 0.25 344.4 15.1 7.7 4.0 16% 52.1 0 0 48.0 -56.514 0.05 0.24 337.7 15.3 7.7 4.0 16% 52.4 0 0 47.9 -56.915 0.05 0.23 331.1 15.4 7.6 4.0 16% 51.6 0 0 46.8 -56.8

Western Baltic Spring Spawning herringScenario Btrigger Ftarget Stability RiskP3 Lim RiskP2 Lim RiskP3

TrigRiskP2

Trigprob Fmsy SSB 2019 SSB 2024 SSB 2034 mean SSB F2019

3-610 110 0.28 - 0% 1% 6% 14% 89% 160.8 172.0 177.3 165.0 0.2511 110 0.28 - 0% 1% 6% 14% 89% 160.9 172.5 178.0 165.4 0.2412 110 0.28 - 0% 1% 6% 14% 89% 161.1 173.3 179.0 165.9 0.2413 110 0.28 - 0% 1% 6% 14% 90% 161.5 174.2 179.8 166.5 0.2414 110 0.28 - 0% 1% 6% 14% 90% 161.8 175.1 180.7 167.1 0.2415 110 0.28 - 0% 1% 6% 14% 91% 162.2 176.0 181.5 167.8 0.24

Scenario mean F3-6 Yield A Yield F Yield C Yield D meanrel TACIAV.C

mean TACIAV.C

IAV restrict up

IAV restrict down

TAC up TAC down

10 0.24 4.2 27.1 13.8 2.6 6% 2.62 1 1 2.6 -2.711 0.23 4.1 27.1 13.7 2.6 6% 2.717 1 1 2.7 -2.812 0.23 4.1 27.1 13.7 2.6 6% 2.787 1 1 2.7 -2.913 0.23 4.0 27.2 13.6 2.6 7% 2.847 1 1 2.8 -3.014 0.23 3.9 27.2 13.5 2.6 7% 2.856 1 1 2.8 -3.015 0.23 3.8 27.2 13.4 2.6 7% 2.828 1 1 2.7 -3.0

WBSS + NSASScenario riskP3 any riskP3

bothmean

Yield IVmean

Yield IIIamean

Yield 22-24

Yield 2019 IV

Yield 2019 IIIa

Yield 2019 22-24

meanrel TACIAV IV

meanrel TACIAV IIIa

meanrel TACIAV 22-

2410 2% 0% 377.3 28.1 27.1 361.5 28.4 28.0 12% 9% 6%11 1% 0% 374.1 28.0 27.1 359.4 28.3 28.0 13% 9% 6%12 1% 0% 369.0 27.9 27.1 356.3 28.1 28.0 14% 9% 6%13 1% 0% 362.9 27.8 27.2 345.0 27.9 28.0 15% 9% 6%14 1% 0% 356.7 27.6 27.2 333.4 27.7 28.0 15% 9% 6%15 1% 0% 350.3 27.5 27.2 320.7 27.4 28.1 15% 9% 6%


5.5 Conclusions

5.5.1 WBSS

The management rule for WBSS as it is outlined in the request (i.e. without transfer) is not precautionary. The 5.7% share of NSAS TAC (regardless of it being based on A, A+C, A+B, or A+B+C+D outtake of NSAS) added to the IIIa human consumption TAC, may result in an overexploitation of the WBSS herring. However, if a transfer of more than 10% is applied the risk of being below Blim would be less than 5%. Other modifi-cations to the rule such as reducing or capping the NSAS share of the C-fleet TAC would be likely to work in the same direction.

Applying the management rule for WBSS as it is outlined in the request (i.e. without transfer) results in high probabilities of being above FMSY (72% probability). Assuming a full transfer of 50% of C-fleet TAC to the North Sea results in a probability between 9–15%, thus the transfer of IIIa TAC to the North Sea makes the WBSS likely to be underutilised in relation to the MSY set for WBSS.

The future yields for the WBSS stock in the C-fleet when applying the proposed man-agement rule will increase in the short term (to 57 252 t in 2019) when no transfer is done, and stabilise from 2020 onwards at a slightly lower level (from 52 751 t in 2024 to 53 427 t in 2034).

5.5.2 NSAS

All scenarios tested assumed a 15% constraint on TAC IAV, a 10% constraint on F (lim-iting departure from target F) and Banking and borrowing. All scenarios tested were precautionary for NSAS.

An evaluation of the impact of varying the Btrigger between 1.0 and 1.5 million tonnes was carried out. The scenario assuming a Btrigger of 1.0 million tonnes results in the high-est catch for the A-fleet and lowest interannual variability. Additionally, the stock is fished above the F management target but at FMSY.

In these scenarios, evaluated under a 50% transfer of C-fleet TAC to the North Sea, the total NSAS TAC is overshot by 5% on average in the medium term; however, this does not raise concern because the overall risk of being below Blim remains under 5%. SSB stabilises above BPA for all Btrigger values considered. Thus if the Btrigger is to be adjusted, values at or above 1.0 million tonnes would be precautionary.

The choice of Btrigger also has an impact on the B-fleet catch. The higher the Btrigger the higher the B-fleet allowance. The difference between the yields at the highest and low-est Btrigger settings is 4%.


6 Conclusions and comments on herring management in the North Sea and IIIa

The NSAS LMTP is precautionary. The plan includes additional components: 10% banking and borrowing, a 10% constraint on F (limiting departure from target F). The plan is precautionary across all values of Btrigger tested (from 1.0 to 1.5 million tonnes).

The IIIa management rule as it is currently stated is not precautionary. To make the management rule precautionary, several possible solutions could be investigated. The only option that has been tested is the % transfer which is an annually negotiated man-agement decision. Other modifications to the rule such as reducing or capping the NSAS share of the C-fleet TAC would be likely to work in the same direction but were not evaluated. The evaluations have shown that the rule is not precautionary, through-out the entire simulation period, unless there is a transfer of around 10%. Additionally, the advice for the IIIa management rule is based on the Btrigger in the current NSAS LTMP of 1.5 million tonnes and if this value decreases (which would be precautionary for NSAS) then a higher than 10% transfer, or other measures, would be required to ensure that the IIIa management rule is precautionary. A lower Btrigger would lead to a higher TAC for the A-fleet which in turn would result in a higher C-fleet TAC.

The Management rule considered under ToRs a–c in the WKHerTAC outlined to com-pute the C-fleet TAC consists of a fraction of the WBSS TAC advice (0.41) and a fraction of the NSAS TAC advice (0.057). This would be a sensible approach to determining fleet C-fleet TAC if herring in IIIa fluctuated as a result of fluctuations in both or either stock. However, preliminary analysis of the NSAS/WBSS split in IIIa does not suggest that the split varies linearly in relation to the NSAS abundance, however a more in depth analysis of this interaction resulting variable presence of NSAS in IIIa needs to be carried out (see Section 4.1.1). Consistent with that, the current simulation frame-work assumes that the fraction of NSAS in the C fleet varies randomly, independently of NSAS abundance. As a consequence, the implementation of the rule in the simula-tions without a transfer of around 10%, was not precautionary for the WBSS (Prob3 above 5 % for WBSS).

WKHerTAC considered that there were ambiguous points within the request and its background for herring in Area IIIa. The suggested management rule for IIIa could not be interpreted unambiguously. The results showed to be sensitive to different interpre-tations, which makes it vital that all the specific points in the management rule must be outlined very clearly. The text below aims to clarify any ambiguity by defining the choices made during the WK.

The WKHerTAC considered the formulation “with a human consumption TAC for herring in the Skagerrak and Kattegat based on 41% of the ICES MSY advice for West-ern Baltic Spring Spawners plus 5.7% of the TAC for North Sea Autumn Spawners that results from the application of the management plan”. We discussed the definition of the term “TAC for North Sea Autumn Spawners”, as this could be interpreted in dif-ferent ways as there is no TAC for NSAS. The rationale followed was that in the 2013 Bergen meeting report which defined “TAC for North Sea Autumn Spawners” as the combined advised TACs of the A- and B-fleets. We recognise that managers have al-ready used another during the EU Norway negotiations the A-fleet alone for setting the 2015 C-fleet TAC.

The group considered that managers would prefer the latest available stock predictions in the application of the proposed management rule, however, this introduces a circu-larity in the TAC setting procedure which may have a built-in ambiguity. Although


the rule appears relatively straightforward, the actual calculation of the fleet-wise TACs is not fully specified and is computationally complex. There is a concern that it may not always be mathematically solvable so that the scientific advice cannot provide the inputs required for the rule. A management rule would need to consider the circu-larity of the TAC-setting; the advised TAC for the NSAS stock includes predicted catches of NSAS in the C- and the D-fleets in Division IIIa. At the same time the pro-posed TAC setting rule for the C-fleet uses a fixed percentage of the NSAS TAC. The preconditions for a new management rule would have to have a unique solution to the TAC advice for all fleets. This could be partly accomplished by setting a procedure that would allow computing the fleet TACs in succession.

Some of the major implications of circularity and unambiguity of the C-fleet TAC rule is illustrated in Figure 6.1.

Figure 6.1. Schematic illustration of the circularity and unambiguity of the C-fleet TAC rule.

Illustrations of the circularity (steps 1–3) in the iterated calculations of the C-fleet TAC rule:

• Rule starting conditions are calculated as 41% of WBSSMSYad-

vice*(1+NSAS:WBSS) C-fleet TAC1 1 ) C-fleet TACi resulting catches are split according to stock composition:

WBSS in C-fleet + NSAS in C-fleet 2 ) NSAS in C-fleet + NSAS in D-fleet are fixed catch options for NSAS in B-

fleet and A-fleet (given F0-1 and F2-6 in LTMP) 3 ) 5.7 % of NSAS in A,B,C and D-fleets + 41% of WBSSMSYadvice C-fleet TACi+1

* Further, the application of a fraction of the TACNSAS in the TAC rule may lead to catches of WBSS herring that are not in accordance with FMSY.

This circularity may not have any single numerical solution. For practical reasons the simulations were therefore based on the advised NSAS TAC for the year before the


TAC setting of the C-fleet. For example the C-fleet TAC for 2014 is based on the advised (and assumed implemented) NSAS TAC 2013 and the advised MSY TAC for WBSS in 2014. In the simulations the NSAS advised TAC for 2014 includes the predicted catches of NSAS in the C-fleet and the D-fleet.

A singular numerical solution could be obtained by basing the percentage of the TAC for North Sea Autumn Spawners solely on the estimated A-fleet TAC from the LTMP. This basis would further reduce the complexity of the iteration process.

Alternative management rules could be considered which could potentially simplify the management of North Sea and Western Baltic herring. Inspiration can be found in the current advice setting procedure in the HAWG (where the advised fleet TACs in IIIa reflect the historic mix of both stocks), projects like JAKFISH (where the TAC for IIIa can be set independently of the TAC in IV), etc.

It is strongly recommended that any group developing management rules should com-prise all parties and include stakeholders, scientists and managers.


7 References

Clausen, L.A.W, Stæhr, K.J.S., Rindorf, A., and Mosegaard, H. 2015. Effect of spatial differences in growth on distribution of seasonally co-occurring herring Clupea harengus stocks. Journal of Fish Biology (2015) 86, 228–247.

EU-Norway. 2008. Agreed record of conclusions of fisheries consultations between Norway and the European Community for 2009, Oslo 10 December 2008.

EU-Norway. 2013a. Agreed record of fisheries consultations between the European Union and Norway on the regulation of fisheries in Skagerrak and Kattegat for 2013, Clonakilty, 18 January 2013.

EU-Norway. 2013b. Report from the Working Group on Management Measures for Herring in ICES Division IIIa (Skagerrak and Kattegat) Bergen, 19–20 June 2013.

EU-Norway. 2014a. Agreed record of fisheries consultations between the European Union and Norway on the regulation of fisheries in Skagerrak and Kattegat for 2014, London, 12 March 2014.

EU-Norway. 2014b. Agreed record of fisheries consultations between the European Union and Norway for 2014, London, 12 March 2014.

ICES. 2012. Report of the Workshop for revision of the North Sea herring Long Term Manage-ment Plan (WKHELP). Ĳmuiden, 3–4 September 2012 and Copenhagen, 1–2 October 2013. ICES CM 2012/ACOM:72.

ICES. 2013. Report of the Workshop on Guidelines for Management Strategy Evaluations (WKGMSE), 23–23 January 2013, Copenhagen. ICES CM 2013/ACOM:39


Annex 1: List of participants and WebEx minutes

NAME ADDRESS PHONE/FAX E-MAIL Valerio Bartolino Invited Expert

Swedish University of Agricultural Sciences Institute of Marine Research Turistgatan 5 453 30 Lysekil Sweden

Phone +46 7612 68049 Fax +46

[email protected]

Anne Cooper International Council for the Exploration of the Sea H. C. Andersens Boulevard 44–46 1553 Copenhagen V Denmark

Phone +45 Fax +45

[email protected]

Tomas Gröhsler Invited Expert

Thünen Institute Institute for Baltic Sea Fisheries Alter Hafen Süd 2 18069 Rostock Germany

Phone +49 381 811 6104 Fax +49 381 811 6199

[email protected]

Jan Horbowy Reviewer

National Marine Fisheries Research Institute ul. Kollataja 1 81-332 Gdynia Poland

Phone +48 587-356-267 Fax +48 587-356-110

[email protected]

Emma Hatfield European Commission DG Maritime Affairs and Fisheries Rue Joseph II, 79 1000 Brussels Belgium

Phone +32 2 29 80156

[email protected]

Niels Hintzen Invited Expert

Wageningen Imares PO Box 68 1970 AB Ĳmuiden Netherlands

Phone +31 317 487 090 Fax +31

[email protected]

Cecilie Kvamme Invited Expert

Institute of Marine Research PO Box 1870 Nordnes 5817 Bergen Norway

Phone +47 454 49 350

[email protected]

Henrik Mosegaard Invited Expert

DTU Aqua - National Institute of Aquatic Resources Marine Living Resources Charlottenlund Slot Jægersborg Allé 1 2920 Charlottenlund Denmark

Phone +45 35 88 34 61 Fax +45 35 88 34 61

[email protected]


NAME ADDRESS PHONE/FAX E-MAIL

Christian Olesen

Danish Pelagic Producers’ Organisation Willemoesvej 2 9850 Hirtshals Denmark

Phone +45 9894 4239 / +45 40203239

[email protected]

Martin Pastoors Pelagic Freezer-Trawler Association PO Box 72 2280 AB Rijswijk Netherlands

Phone +31 631901027

[email protected]

Beatriz Roel Invited Expert

Centre for Environment, Fisheries and Aquaculture Science (Cefas) Lowestoft Laboratory Pakefield Road NR33 0HT Lowestoft Suffolk United Kingdom

Phone +44 1 502 52 4358 Fax +44 1502 524 511

[email protected]

Dankert Skagen Reviewer

Fjellveien 96 5019 Bergen Norway

Phone +47 93257452 Fax +47 55 238687

[email protected]

Claus Reedtz Sparrevohn

Danish Pelagic Producers’ Organisation Willemoesvej 2 9850 Hirtshals Denmark

Phone +45 Fax +45

[email protected]

Lotte Worsøe Clausen Chair

DTU Aqua - National Institute of Aquatic Resources Section for Fisheries Advice Charlottenlund Slot Jægersborg Alle 1 2920 Charlottenlund Denmark

Phone +45 21362804 Fax +45 33963333

[email protected]


Minutes from first WebEx on WKHerTAC; Thursday 20/11/2014

Welcome, presentation round and approval of agenda; the following attended the WebEx:

Christian Olesen, Danish PO

Claus Sparrevohn, Danish PO

Dankert Skagen, ‘himself’ as external reviewer

Frank Minck, Marine Ingredients, Denmark

Martin Pastoors, Dutch Pelagic Freezer association

Jan Birger Jørgensen, Norwegian Fishermen’s Association

Sally Clink, BSAC

Verena Ohms, PELAC

Cecilie Kvamme, IMR Norway

Henrik Mosegaard, DTU Aqua Denmark

Lotte Worsøe Clausen, DTU Aqua Denmark

Norbert Rohlf, v-TI Germany

Tomas Gröhsler, v-TI Germany

Valerio Bartolino, SLU Sweden

David Miller, IMARES, the Netherlands

Anne Cooper, ICES

Helle Gjeding Jørgensen, ICES

The group went through the ToRs for WKHerTAC, adjusting these and the related sce-narios to the following outline.

ToR a)

Evaluate the outcome of implementing the TAC calculation strategy on the stock of Western Baltic Spring-spawning herring for the next five years, with particular refer-ence to (refer to request 140910b_Herring):

i ) the probability of the fishing mortality being at or below FMSY year-on-year; ii ) future yields on a five year basis; and iii ) the probability of the spawning biomass falling below Blim and Btrigger.

The baseline HCR to be applied is the following:

The TAC for Area IV is set based on the NSAS assessment (SAM) and forecast applying the current agreed management plan. 15% change limits apply following the plan.

The TAC for Area 22–24 is set as 50% of the TAC based on the assessment of the WBSS herring (SAM) applying the ICES MSY approach (i.e. reduction of F below MSYBtrigger). No change limits apply.

The TAC for IIIa is to be determined by taking a fixed proportion of each of these TACs.


• The proportion from WBSS remains fixed at 41%. • The proportion from NSAS remains fixed at 5.7%.

Assuming that:

1 ) The flexibility provision whereby up to 50% of the IIIa TAC can be fished in the North Sea will apply, and that all of the catch that could be taken in the North Sea under this provision will actually be taken in the North Sea. The flexibility will be set to 40, 45 and 50% respectively.

2 ) The +/-15% TAC constraint applies only to the TAC for the mixed NSAS/WBSS in IIIa, not to the WBSS TAC in SD 22–24.

3 ) All fish caught in 22–24 are WBSS herring.

Possible scenarios are then:

• Variation of the proportion of the NSAS stock caught in IIIa: • Uniform distribution, with a range corresponding to what has been ob-

served in the entire time-series of splitting data; • Extreme possibilities, highest and lowest observed of the entire time-

series of splitting data +/-3.5%. • Variation of the allocation of WBSS TAC from IIIa to North Sea; varying it

in steps from slightly below the observed current utilisation of 42–44% up to the maximum allowed 50% for the Fleet C TAC (40, 45 and 50%).

• Variation in TAC uptake. It will be assumed that Fleets A+C use 100% but fleets B+D have historically not always used their TAC (bycatch ceiling) fully. Additional scenarios regarding the impact of the landings obligation on the sprat fishery will thus be: 1 scenario with full uptake by these fleets. If this is un-precautionary, then scenarios using the historic actual uptake by B and D (applying any reasonable covariate e.g. year-class strength or sprat TAC) will be explored.

ToR b)

Draft advice on whether the aforementioned strategy is consistent with ICES precau-tionary approach in the next five, ten and 20 years. In the light of the drastic change in management of the pelagic fishery through the enforcement of the landing obligation starting 2015, it was decided to put more emphasis on the short-term results (five years), but still presenting the requested ten and 20 years results too.

Evaluation performance statistics are then:

• Percentage of Monte Carlo simulations with SSB below Blim (PA; using ‘Risk3’) for both stocks;

• SSB in 2020 in relation to Blim and Btrigger for both stocks; here a clear definition of what ‘medium term’ is meant to be in the request is needed. Lotte will check with the request authors;

• Mean yield (total, and by fleet) in the three management areas; • Mean % absolute change in yield (total, and the targeting fleets A+C) in the

three management areas. The probability of the fishing mortality being at or below FMSY year-on-year for both stocks.


ToR c)

Evaluate if the proposed NSAS plan would be precautionary, assuming an interannual quota flexibility of +/- 10% in all simulations (F target is slightly higher compared to the current; see details in the request 140910a_Herring).

Basic HCR: as in 2014 Management Plan (EU-Norway agreement March 2014).

Evaluation criteria:

1 ) Risk: maximum annual risk (‘Risk 3’) computed during a period of the projections.

2 ) Stock performance: • SSB in 2020 (and 2030, 2040 same time frame as for WBSS) (median of

all iterations); • Mean SSB over the simulation period.

3 ) Fishing mortality: • Mean fishing mortality of year classes 2–6 over the simulation period; • Mean fishing mortality of year classes 0–1 over the simulation period; • Fishing mortality of year classes 2–6 in 202? (median of all iterations).

4 ) Yield: • Mean catch of A-fleet over the simulation period; • Mean catch of B-fleet over the simulation period.

5 ) Stability in TAC: • Mean % absolute TAC change between consecutive years over the

simulation period [abs(TAC year2–TAC year1)]/TAC year1 * 100; • Mean absolute TAC change in tons between consecutive years over the

simulation period [abs(TAC year2–TAC year1)]; • Number of times the TAC change was restricted by the 15% IAV rule; • Mean percentage TAC change for all A-fleet TAC increases; • Mean percentage TAC change for all A-fleet TAC decreases.

A table (like the one produced in WKHELP) would be provided where the perfor-mance of alternative harvest rules will be evaluated according to the above mentioned indicators. Note that as a general rule, unless specified otherwise, when an indicator is provided as a mean, first the mean is calculated over time for each iteration, after which the median is taken over these means. All numbers (SSB, F) refer to the true values in the simulated populations.

ToR d)

Evaluate whether the Btrigger value of 1 500 000 tonnes is optimal, or whether consider-ation should be given to adjusting it.

The term ‘optimal’ is subject to interpretation; the group discussed this and will re-check with the authors of the request, but assume that it means stable and high yield for now.

The approach used by WKHELP for evaluation of Btrigger will be adopted. The F-values are fixed in the request, thus explorations will be performed on the impact of varying Btrigger on interannual variability and P(B<Blim). The evaluation will be performed in


steps from 1 500 000 to 1 000 000; the resolution of steps will depend on processing time and initial results; i.e. find out what level has a slightly greater than 5% P(B<Blim), and then examine the SSB range above this in 100 kt steps or less.

The group must decide on which recruitment regime the evaluations will be performed with; currently a low recruitment regime is assumed by HAWG.

AOB

Information sharing: All information will be shared using the SharePoint site for the group (https://community.ices.dk/ExpertGroups/wkherTAC/_lay-outs/15/start.aspx#/); ICES has arranged for all participants to have access to this site. Use the ‘Alert’ function on the site to get notified when new files or discussions have been added there.

David will send out an initial e-mail once the proposed evaluation plan and initial MSE code has been uploaded to the SharePoint. A core-group of Valerio Bartolino, Dankert Skagen, Beatriz Roel, Niels Hintzen and himself to discuss coding, operating model conditioning, etc.; however the whole group will be kept informed through the Share-Point.

Martin Pastoors pointed out that WKHerTAC would benefit from the experience and process taken by the very recent work done on the mackerel management plan, thus he will assure that the group is informed about the development there.

Next meeting

A WebEx will be held on December 15th at 10 am (Cph. time) where preliminary re-sults will be presented and further explorations defined. At this WebEx, the report out-line will be available as well as assigned lead-authors for the various sections.

https://community.ices.dk/ExpertGroups/wkherTAC/_layouts/15/start.aspx%23/

https://community.ices.dk/ExpertGroups/wkherTAC/_layouts/15/start.aspx%23/


Minutes from second WebEx on WKHerTAC; Monday 15/12/2014

The following attended the WebEx:

Christian Olesen, Danish PO

Claus Sparrevohn, Danish PO

Dankert Skagen, ‘himself’ as external reviewer

Frank Minck, Marine Ingredients, Denmark

Martin Pastoors, Dutch Pelagic Freezer association

Sally Clink, BSAC

Verena Ohms, PELAC

Cecilie Kvamme, IMR Norway

Lotte Worsøe Clausen, DTU Aqua Denmark

Norbert Rohlf, v-TI Germany

Tomas Gröhsler, v-TI Germany

Valerio Bartolino, SLU Sweden

David Miller, IMARES, the Netherlands

Beatriz Roel, Cefas, UK

Anne Cooper, ICES

Helle Gjeding Jørgensen, ICES

Due to a delay in running the simulations there were no initial results to discuss as planned. Instead the WebEx was used to finalise some settings for the simulations and performance statistic and to discuss the outline of the report. A new WebEx to look at initial results will be scheduled for the morning of Tuesday 6 January 2015 (10:00am CPH time).

‘Risk’/Probability definition

There was some discussion over which definition of risk should be used and how many iterations were necessary to avoid any potential bias in the results. The group unani-mously agreed that ‘Risk 3’ (the maximum annual probability of SSB<Blim) would be used. In addition it was decided to do an evaluation of the effect of the number of iterations run on the probability estimated. This will be done by examining the esti-mate probability for a range of different iterations for a representative scenario. Unless this evaluation suggests otherwise, a minimum of 1000 iterations will be done for each scenario.

Report outline

Lotte presented an outline for the report that was accepted by the group. The outline, along with assigned names for each section, is uploaded to the SharePoint site.


Alternative plots

Martin showed some of the plots being used for the mackerel MSE work. It was agreed that these were indeed useful representations of the outcomes. It was decided to pro-ceed with the current format of tables, but to include these new graphs if possible.

Operating model conditioning

David went through a short presentation on the key elements for conditioning the op-erating models of the two stocks: future recruitment, maturity, M, W@A and selectiv-ity.

The plan is to proceed with the same conditioning used for NSAS in WKHELP (2012), and where possible to apply the same procedures to the WBSS. There was some con-cern over the fact that the method of resampling recruitment values from a lognormal distribution fit to the recent low productivity? period did not capture the possibility of recruitment reducing at low SSB. However, since the evaluations are short/medium term in length and do not include high F scenarios, it is unlikely that the populations would reduce to a level where this becomes an issue. If any of them did, the resultant strategy would be excluded in any event since the SSB at which recruitment begins to decrease is well below Blim. The report should include a full description of this issue, and why it is considered pragmatic rather than problematic.

For WBSS, the ‘low recruitment period’ will be considered as 2005–2013. The M and maturity ogive are constant over time, so these fixed values will be used in the projec-tions. The W@A will be resampled in blocks (as for NSAS) from the period 2000–2013. Selectivity will be projected using a random walk with correlated F at age, as is done for the NSAS. Some consideration will be needed in the report over what the possible impacts of the landings obligation will be on selectivity, and what implications this could have for the results.

Next meeting

A WebEx will be held on January 6th at 10 am (CPH time) where preliminary results will be presented and further explorations defined.


Annex 2: Full list of results of scenarios tested

Comments on the simulated trajectories by stock and fleet (Annex 3, Figures 1–30)

Median annual catch, adult fishing mortality and SSB simulated trajectories, including their confidence intervals, are shown in Figures 1 to 29 (odd numbers) for both stocks, from 2013 to 2034. Figures 2 to 30 (even numbers) show the expected mean catches and advice by fleet, together with the uptake by stock for the same period. Each type of figure corresponds to a particular scenario modelled.

SSB, F, catch and uptake by stock in 2013 correspond to the initial conditions in the simulations which are based on the historic assessments. Values in 2014 result from projecting the initial conditions and take into account the advice for 2014. From there afterwards values result from the simulated stock developments and management of the fisheries.

Examination of the figures by stock suggest that both the stock and the catches stabilize shortly after implementation of a Management Rule (MR), generally in about five years. If we first focus on NSAS and on its main area of distribution, the drop in catches in Area IV from 2014 to 2015 is a result of the assumptions that are made to test the Banking & Borrowing option. We chose the worst case scenario and that includes 10% Banking in the first year of the simulations. Likewise with the steep rise in catches from 2015 to 2016. This comes from the assumption in worst case scenario that the 10% banked in year 1 will be caught and that on top of that, another 10% will be borrowed. The same effect is evident in the fleet A catch. As a result of that, it is in the initial period that F2–6 for NSAS is above FMSY in all scenarios. Reduction of Btrigger from 1.2 to 1.0 million result on F2–6 stabilizing around or above the FMSY target (Figures 19, 21 and 23). Otherwise, the F2–6 stabilises below FMSY. It is in the Btrigger = 1.0 million tonnes (Figure 19) that the SSB 5th percentile touches Blim in the projections pointing at a higher risk in those years.

For the WBSS, median catches stabilise at about 50 thousand tonnes. Median F3–6 is above FMSY throughout the simulated period in the 0 and 10% transfer scenarios (Fig-ures 11 and 17) and at FMSY or just below unless the transfer is at or above 40% (Figures 1 to 5; 13 and 15). Median SSB is above Blim at all times for all scenarios considered. Examination of the SSB plot in Figure 11, where the 5th percentile is generally below Blim, suggest that the WBSS MR without a transfer would not be precautionary.

The catch by fleet figures show that the median catch for fleet A is always above the advice except for scenarios 6 and 9 (Figures 12 and 18) where they practically coincide. This is because there was little or no transfer (10% or less) from the C-fleet TAC. Under all scenarios, except the 0% transfer (Figure 12), the C-fleet catch lies below the advised catch, the difference between uptake and advice being larger for maximum transfer 50%. Note that the TAC advice stabilizes at a lower level in the 0% transfer scenario compared to non-zero transfer scenarios. The scenarios with increased TAC for the D-fleet (Figures 8 and 10) result in a reduction in the TAC and uptake for fleet B which fluctuates at a slightly lower level.

Comparison between the impact of NSAS LTMP and IIIa Management Rule (MR) Scenarios on NSAS and WBSS throughout the simulation period (Figures 31 and 32)

The scenarios evaluating the NSAS LTMP have a major effect on the NSAS but, only a small effect on the catch, fishing mortality and SSB of WBSS. WBSS remains within precautionary levels and the stock is exploited below MSY (Figure 30). However, all


the scenarios assume a 50% transfer from the C-fleet to the North Sea. A small reduc-tion in the NSAS catch is apparent when the IIIa MR scenarios where the transfer is decreased, are implemented (Figure 32).

Evaluation of the NSAS LTMP and the IIIa MR in the short, medium and long term (Boxplots, Fig-ures 33–38)

Boxplots comparing median, 1st and 3rd quartiles and confidence intervals for the catch, fishing mortality and SSB between stocks and for the management scenarios (la-belled runs in the figures) evaluated are shown in Figures 33 to 38 of this Annex. The comparison is carried out in the short, medium and long term (in years 2019, 2024 and 2034 respectively). The first set of figures (Figures 33–35) focuses on the impact on the stocks dynamics of implementing the NSAS LTMP, while Figures 36–38 focuses on the IIIa MR.

The impact on F of reducing the NSAS LTMP Btrigger beyond 1.1 million results in a median above FMSY in the short term and medium term (Figures 33 and 34). In the long term, all Btrigger values tested result in a median F2–6 at or below FMSY. SSB box plots are generally above Blim for all periods while catches seem to be marginally favoured by a lower Btrigger. There is little or no impact of these options, which assume 50% transfer from C-fleet to the North Sea, on the WBSS. Worth noticing is that there is a non-zero probability of the F3–6 being above FMSY, particularly in the short term. WBSS SSB box-plots are always well above Blim.

The first seven MR in Figures 36 to 38 consist of a gradual decrease of the transfer from 50% to 0% in run 6. This results in an increasing fraction of the simulations falling be-low Blim towards the medium term, with a slight improvement in the long term. The impact on F3–6, with the majority of the simulations resulting in a value above FMSY for a transfers 10% or less, is more noticeable in the short term. Reduction in the transfer result in a slight decrease in NSAS F2–6 as the transfer is reduced. The last two plots in Figures 36 to 38 illustrate the impact of increasing the uptake of the D-fleet given 50% transfer. The results are largely insensitive to these changes as there would be trade-off between the catch of the C- and D-fleets. The WBSS catch decreased slightly com-pared with run 1 more so in the short term possibly because of the lower percentage composition of WBSS relative to NSAS in the catch of the D-fleet.

As a general comment, the impact of the different scenarios tested become apparent soon in the simulated trajectories (short or medium term) as the stocks tend to stabilize thereafter.


Figure 1. IIIa management rule – scenario 1 (50% transfer, D-fleet = 6659 t). Mean projections (con-tinuous line) with 95% CI (dotted line) of the main stock metrics for the time period 2013–2034 for scenario 1. A) Herring catch for the North Sea (black) and Area IIIA + SD 22–24 (red); B) fishing mortality for the NSAS (black) and WBSS (red); C) SSB of the NSAS (black) and WBSS (red).


Figure 2. IIIa management rule – scenario 1 (50% transfer, D-fleet = 6659 t). Fleet-specific (A–F) pro-jection (continuous line) of the median catch for the time period 2013–2034 in the scenario 1. Ad-vised catch (dashed line) and expected outtake (dotted line) for the NSAS (black) and the WBSS (red).


Figure 10. IIIa management rule – scenario 5 (50% transfer, D-fleet = 9988 t). Fleet-specific (A–F) projection (continuous line) of the median catch for the time period 2013–2034 in the scenario 5. Advised catch (dashed line) and expected outtake (dotted line) for the NSAS (black) and the WBSS (red).


Figure 12. IIIa management rule – scenario 6 (0% transfer, D-fleet = 6659 t). Fleet-specific (A-F) pro-jection (continuous line) of the median catch for the time period 2013–2034 in the scenario 6. Ad-vised catch (dashed line) and expected outtake (dotted line) for the NSAS (black) and the WBSS (red).


Figure 19. NSAS LTMP – scenario 10 (Btrigger = 1.0 mill t). Median projections (continuous line) with 95% CI (dotted line) of the main stock metrics for the time period 2013–2034 for scenario 10. A) Herring catch for the North Sea (black) and Area IIIA + SD 22–24 (red); B) fishing mortality for the NSAS (black) and WBSS (red); C) SSB of the NSAS (black) and WBSS (red).


Figure 20. NSAS LTMP – scenario 10 (Btrigger = 1.0 mill t). Fleet-specific (A–F) projection (continuous line) of the median catch for the time period 2013–2034 in the scenario 10. Advised catch (dashed line) and expected outtake (dotted line) for the NSAS (black) and the WBSS (red).


Figure 31. NSAS LTMP. Median projections of the fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right), in the time period 2013–2034, for the scenarios eval-uating the NSAS LTMP (run 10–15).


Figure 32. IIIa management rule. Median projections of the fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right), in the time period 2013–2034, for the sce-narios evaluating the IIIa management rule (run 1–9).


Figure 33. NSAS LTMP – short term. Boxplot of the projected fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2019 (short term) for the scenarios eval-uating the NSAS LTMP (run 10–15). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Figure 34. NSAS LTMP – medium term. Boxplot of the projected fisheries catch (A,B), fishing mor-tality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2024 (medium term) for the sce-narios evaluating the NSAS LTMP (run 10–15). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Figure 35. NSAS LTMP – long term. Boxplot of the projected fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2034 (long term) for the scenarios eval-uating the NSAS LTMP (run 10–15). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Figure 36. IIIa management rule – short term. Boxplot of the projected fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2019 (short term) for the sce-narios evaluating the IIIa management rule (run 1–9). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Figure 37. IIIa management rule – medium term. Boxplot of the projected fisheries catch (A,B), fish-ing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2024 (medium term) for the scenarios evaluating the IIIa management rule (run 1–9). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Figure 38. IIIa management rule – long term. Boxplot of the projected fisheries catch (A,B), fishing mortality (C,D) and SSB (E,F) of the NSAS (left) and WBSS (right) in 2034 (long term) for the sce-narios evaluating the IIIa management rule (run 1–9). Dashed red line is FMSY in C–D and Blim in E–F for the two stocks. Boxplots represent the median, 1st and 3rd quartile, and 95% CI.


Annex 3: R-code for conditioning of MSEs performed in WKHerTAC

Conditioning of NSAS #-------------------------------------------------------------------------------

# WKHELP

#

# Author: Niels Hintzen

# IMARES, The Netherland

#

# Performs an MSE of North Sea Herring under different TAC scenario's

#

# Date: 05-Jun-2012

#

# Build for R2.13.2, 32bits

#-------------------------------------------------------------------------------

rm(list=ls())

library(FLSAM)

library(MASS)

library(msm)

#path <- "D:/Work/Herring MSE/NSAS/"

path <- "W:/IMARES/Data/ICES-WG/WKHerTAC/NSAS/"

inPath <- paste(path,"Data/",sep="")

codePath <- paste(path,"R/",sep="")

outPath <- paste(path,"Results/",sep="")

if(substr(R.Version()$os,1,3)== "lin"){

path <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",path)

inPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",inPath)

codePath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",codePath)

outPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",outPath)

}

#- Load objects

load(file=paste(outPath,"NSH.RData", sep=""))

load(file=paste(outPath,"NSHctrl.RData", sep=""))

load(file=paste(outPath,"NSHsam.RData", sep=""))

load(file=paste(outPath,"NSHtun.RData", sep=""))

#- Settings


histMinYr <- 1947

histMaxYr <- 2013

futureMaxYr <- histMaxYr + 27

histPeriod <- ac(histMinYr:histMaxYr)

projPeriod <- ac((histMaxYr+1):futureMaxYr)

recrPeriod <- ac(2003:2013)

selPeriod <- ac(2003:2013)

fecYears <- ac(2003:2013)

nyrs <- futureMaxYr-dims(NSH)$maxyear

nits <- 1000

settings <- list(histMinYr=histMinYr,histMaxYr=histMaxYr,futureMaxYr=futureMaxYr,

histPeriod=histPeriod,projPeriod=projPeriod,recrPeriod=recrPeriod,

nyrs=nyrs,nits=nits,fecYears=fecYears)

source(paste(codePath,"functions.r",sep=""))

#-------------------------------------------------------------------------------

# 0): Create stock object & use vcov for new realisations

#-------------------------------------------------------------------------------

stocks <- monteCarloStock(NSH,NSH.sam,nits,run.dir=outPath)

stocks <- window(stocks,start=histMinYr,end=futureMaxYr)

[email protected] <- [email protected] * stocks@harvest / (stocks@harvest + stocks@m) * (1 - exp(-stocks@harvest - stocks@m))

[email protected][,ac(1978:1979)] <- NA

[email protected] <- [email protected]

[email protected][,projPeriod] <- [email protected][,ac(histMaxYr)]


#- Sample from selected year range to fill future time period of m, weight, fec and land-ings.wt

# Retain a certain degree of autocorrelation and take fec, weight, m and landings

# weight from the same years to retain correlation between fec, wt, landings.wt

# and m (if there is any with m...)

# Take blocks of years instead of randomly years glued together. In some instances,

# reverse the blocks of year for variation

#- Take blocks of years, sample blocks from fecYears and add up till length of time-series

yrs <- projPeriod

saveBlcks <- matrix(NA,nrow=nits,ncol=length(yrs))

full <- F

rw <- 1


# replaced the version below because of occassional errors

while(full==F){

tmpx <- 0

while(tmpx<1) {

set.seed(as.integer(Sys.time()))

sam <- sample(1:length(fecYears),nits*1000,replace=T)

samM <- matrix(sam,nrow=nits,ncol=1000)

idx <- which(t(apply(samM,1,cumsum)) == length(yrs),arr.ind=T)

tmpx <- length(idx)

}

if((rw+nrow(idx) - 1) > nrow(saveBlcks)){ stprow <- nrow(saveBlcks); full <- T} else { stprow <- rw+nrow(idx) -1}

for(iRow in 1:(stprow-rw+1))

if ((rw+iRow-1) <= nrow(saveBlcks) && iRow>0) saveBlcks[(rw+iRow-1),1:idx[iRow,2]] <- samM[idx[iRow,1],1:idx[iRow,2]]

rw <- stprow + 1

}

#- Take the sampled blocks and assign years to it

saveBlcksYrs <- ar-ray(NA,dim=c(nits,length(yrs),2),dimnames=list(nits=1:nits,blocks=1:length(yrs),strt-stp=c("start","stop")))

for(iCol in 1:ncol(saveBlcksYrs)){

#-saveBlcks - 1 because fecYears[1] is the first possible year

#-saveBlcks + 2 because rev(fecYears)[1] is the last possible year and correction for as.integer

#-If blck is reversed is decided afterwards, hence the tight bounds in runif

strstp <- as.integer(runif(nits,an(fecYears[1])+saveBlcks[,iCol]-1,an(rev(fecYe-ars)[1])-saveBlcks[,iCol]+2))

rv <- sample(c(T,F),nits,replace=T)

strt <- ifelse(rv==F,strstp,strstp-saveBlcks[,iCol]+1)

stp <- strt + saveBlcks[,iCol] - 1

saveBlcksYrs[,iCol,"start"] <- ifelse(rv==F,strt,stp)

saveBlcksYrs[,iCol,"stop"] <- ifelse(rv==F,stp,strt)

#-Correct for those records where only one option in year is possible

idx <- which((an(fecYears[1])+saveBlcks[,iCol]-1) == an(rev(fecYears)[1])-saveBlcks[,iCol]+2)

saveBlcksYrs[idx,iCol,"start"] <- (an(fecYears[1])+saveBlcks[,iCol]-1)[idx]

saveBlcksYrs[idx,iCol,"stop"] <- ifelse((saveBlcksYrs[idx,iCol,"start"] + saveBlcks[idx,iCol] - 1) > an(rev(fecYears)[1]),

(saveBlcksYrs[idx,iCol,"start"] - saveBlcks[idx,iCol] + 1),

(saveBlcksYrs[idx,iCol,"start"] + saveBlcks[idx,iCol] - 1))

#-add start-stop for those blocks equal to length(fecYears)


idx <- which(saveBlcks[,iCol] == length(fecYears))

if(length(idx) > 0){

saveBlcksYrs[idx,iCol,"start"] <- ifelse(rv[idx]==F,an(fecYears[1]),an(rev(fecYe-ars)[1]))

saveBlcksYrs[idx,iCol,"stop"] <- ifelse(rv[idx]==F,an(rev(fecYears)[1]),an(fecYe-ars[1]))

}

#-If block is large, sampled size might exceed bounds, so take only possible options

if(length(idx) > 0) idx <- which(is.na(strstp)==T & is.na(saveBlcks[,iCol])==F)[which(!which(is.na(strstp)==T &

is.na(saveBlcks[,iCol])==F) %in% idx)]

if(length(idx) == 0)idx <- which(is.na(strstp)==T & is.na(saveBlcks[,iCol])==F)


#-Calculate options that are possible

opts <- mapply(function(strt,stp){

return(fecYears[which(!fecYears %in% strt:stp)])},

strt=an(rev(fecYears)[1])-saveBlcks[idx,iCol]+2,

stp =an(fecYears[1]) +saveBlcks[idx,iCol]-2,SIMPLIFY=F)

#-Sample from those options and define start and stop points

res <- an(unlist(lapply(opts,sample,1)))

saveBlcksYrs[idx,iCol,"start"] <- res

saveBlcksYrs[idx,iCol,"stop"] <- ifelse((res + saveBlcks[idx,iCol] - 1) > an(rev(fecYe-ars)[1]),

res - saveBlcks[idx,iCol] + 1,

res + saveBlcks[idx,iCol] - 1)

}

}

#-Create year strings and bind them together

yrStrngsIter<- apply(saveBlcksYrs,1,function(x){map-ply(seq,from=na.omit(x[,1]),to=na.omit(x[,2]))})

# if each sampled block was the same length, it creates a matrix instead of a list, gives an error below.

# so added fix to change any matrices into lists first

for (i in 1:nits) {

if (!is.list(yrStrngsIter[[i]])) {

tmp <- list()

for (cC in 1:ncol(yrStrngsIter[[i]])) tmp[[cC]]<- yrStrngsIter[[i]][,cC]

yrStrngsIter[[i]] <- tmp

rm(tmp)

}

}


yrStrngs <- lapply(yrStrngsIter,function(x){do.call(c,x)})

yrStrngsC <- do.call(c,yrStrngs)

stocks@mat [,projPeriod][] <- array(iter(stocks@mat,1) [,ac(yr-StrngsC)],dim=dim(stocks@mat [,projPeriod]))

[email protected] [,projPeriod][] <- array(iter([email protected],1)[,ac(yr-StrngsC)],dim=dim([email protected][,projPeriod]))

stocks@m [,projPeriod][] <- array(iter(stocks@m,1) [,ac(yr-StrngsC)],dim=dim(stocks@m [,projPeriod]))

#-------------------------------------------------------------------------------

# 1): Create survey object & use vcov for new realisations + error on realisations

#-------------------------------------------------------------------------------

surveys <- lapply(NSH.tun,propagate,iter=nits)

for(iSurv in names(surveys)) surveys[[iSurv]] <- window(sur-veys[[iSurv]],start=range(surveys[[iSurv]])["minyear"],end=futureMaxYr)

dmns <- dimnames(surveys[["HERAS"]]@index)

dmns$year <- dimnames(surveys[["SCAI"]]@index)$year

dmns$unit <- names(surveys)

surv <- FLQuant(NA,dimnames=dmns)

#- Get redrawn survey Qs and Ks

load(file=paste(outPath,"random.param.RData",sep=""))

#- Get the index of each parameter in the random.param object

Qidx <- unlist(apply(NSH.ctrl@catchabilities,1,function(x)c(na.omit(x))))

#- Create objects for surveyQ and surveyK's

surveyQ <- FLQuants("SCAI"= FLQuant(NA,dimnames=dimnames(sur-veys[["SCAI"]]@index)),

"IBTS0"= FLQuant(NA,dimnames=dimnames(sur-veys[["IBTS0"]]@index)),

"IBTS-Q1"=FLQuant(NA,dimnames=dimnames(surveys[["IBTS-Q1"]]@index)),

"HERAS"= FLQuant(NA,dimnames=dimnames(surveys[["HERAS"]]@index)))

surveyK <- surveyQ

for(iSurv in names(surveys)) surveyK[[iSurv]][] <- 1

#- Fill the Qs by survey

for(iYr in dimnames(surveyQ[["SCAI"]])$year)


surveyQ[["SCAI"]][,iYr] <- exp(random.param[,which(colnames(random.param) == "logScaleSSB")])

for(iYr in dimnames(surveyQ[["IBTS0"]])$year)

surveyQ[["IBTS0"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("IBTS0",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["IBTS-Q1"]])$year)

surveyQ[["IBTS-Q1"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("IBTS-Q1",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["HERAS"]])$year)

surveyQ[["HERAS"]][,iYr] <- t(exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("HERAS",names(Qidx))]]]))

#- Index var no longer used but filled anyway

for(iSurv in names(surveys)) surveys[[iSurv]]@index.var[,projPeriod][] <- sur-veys[[iSurv]]@index.var[,ac(histMaxYr)]

#- Vary selectivity of survey

for(iSurv in names(surveys)){

# iSurv <- names(surveys)[4]

cat("Vary selection of:",iSurv,"\n")

#- Get residuals

Resids <- subset(residuals(NSH.sam),fleet==iSurv)

iResids <- FLQuant(1,dimnames=c(dimnames(NSH.tun[[iSurv]]@index)[1:5],iter="1"))

#- Substract residuals (log scale)

for(i in 1:nrow(Resids)){

if(iSurv == "SCAI") iResids[1,ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

if(iSurv != "SCAI") iResids[ac(Resids$age[i]),ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

}

#- Take blocks of residuals, sample blocks from 1-10 and add up till length of time-series

yrs <- range(surveys[[iSurv]])["minyear"]:futureMaxYr


full <- F

rw <- 1

cat("Sampling iters:",iSurv,"\n")

while(full==F){

tmpx <- 0

while(tmpx<1) {


sam <- sample(1:10,nits*1000,replace=T)




tmpx <- length(idx)

}




rw <- stprow + 1

}


saveBlcksYrs <- ar-ray(NA,dim=c(nits,length(yrs),2),dimnames=list(nits=1:nits,yrs=yrs,strt-stp=c("start","stop")))


strstp <- as.integer(runif(nits,range(surveys[[iSurv]])["minyear"]+saveBlcks[,iCol]-1,range(NSH.tun[[iSurv]])["maxyear"]-saveBlcks[,iCol]+2))






}

cat("Populating surv:",iSurv,"\n")

for(iTer in 1:nits){

blk <- which(is.na(saveBlcksYrs[iTer,,1])==F)

idx <- ac(unlist(mapply(seq,from=saveBlcksYrs[iTer,blk,"start"],to=saveBlck-sYrs[iTer,blk,"stop"])))

#- Fill survey pattern with random draws of historic years

if(iSurv == "SCAI") iter(surv[1, ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "IBTS0") iter(surv[1, ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "IBTS-Q1")iter(surv[1, ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "HERAS") iter(surv[ac(1:8),ac(yrs),iSurv],iTer) <- iResids[,idx]

}

}

#- Recalculate survey index values (also historic)

SCAIfactor <- c(exp(yearMeans(

log(win-dow(quantSums(stock.n(NSH)* exp(-NSH@harvest*harvest.spwn(NSH)-m(NSH)*m.spwn(NSH)) * stock.wt(NSH) *mat(NSH)),1972,histMaxYr) *


subset(catchabili-ties(NSH.sam),fleet=="SCAI")$value) -

subset(residu-als(NSH.sam),fleet=="SCAI")$log.mdl)))

surveys[["SCAI"]]@index[,ac(range(surveys[["SCAI"]])["minyear"]:histMaxYr)] <- sweep(quantSums(stock.n(stocks) * exp(-stocks@harvest*harvest.spwn(stocks)-m(stocks)*m.spwn(stocks)) * stock.wt(stocks) *

mat(stocks))[,ac(range(surveys[["SCAI"]])["minyear"]:histMaxYr)],

c(2,6), sur-veyK[["SCAI"]][,ac(range(surveys[["SCAI"]])["minyear"]:histMaxYr)],"^") *

surveyQ[["SCAI"]][, ac(range(surveys[["SCAI"]]) ["minyear"]:histMaxYr)] * 1/SCAIfactor * surv[1, ac(range(surveys[["SCAI"]]) ["minyear"]:histMaxYr),"SCAI"]

surveys[["IBTS0"]]@index[,ac(range(surveys[["IBTS0"]])["minyear"]:(histMaxYr+1))] <- sweep((stock.n(stocks)["0",] * exp(-stocks@harvest["0",] * mean(range(sur-veys[["IBTS0"]]) [c("startf","endf")]) -

m(stocks)["0",] * mean(range(surveys[["IBTS0"]]) [c("startf","endf")])))[,ac(range(sur-veys[["IBTS0"]])["minyear"]:(histMaxYr+1))],

c(2,6), sur-veyK[["IBTS0"]][,ac(range(surveys[["IBTS0"]])["minyear"]:(histMaxYr+1))],"^") *

surveyQ[["IBTS0"]][, ac(range(surveys[["IBTS0"]]) ["minyear"]:(histMaxYr+1))] * surv[1, ac(range(surveys[["IBTS0"]]) ["minyear"]:(histMaxYr+1)),"IBTS0"]

surveys[["IBTS-Q1"]]@index[,ac(range(surveys[["IBTS-Q1"]])["min-year"]:(histMaxYr+1))] <- sweep((stock.n(stocks) * exp(-stocks@harvest * mean(range(surveys[["IBTS-Q1"]])[c("startf","endf")]) -

m(stocks) * mean(range(surveys[["IBTS-Q1"]]) [c("startf","endf")])))[ac(1),ac(range(sur-veys[["IBTS-Q1"]])["minyear"]:(histMaxYr+1))],

c(1,2,6),sur-veyK[["IBTS-Q1"]][,ac(range(surveys[["IBTS-Q1"]])["minyear"]:(histMaxYr+1))],"^") *

surveyQ[["IBTS-Q1"]][, ac(range(surveys[["IBTS-Q1"]])["minyear"]:(histMaxYr+1))] * surv[ac(1), ac(range(surveys[["IBTS-Q1"]]) ["minyear"]:(histMaxYr+1)),"IBTS-Q1"]

surveys[["HERAS"]]@index[,ac(range(surveys[["HERAS"]])["minyear"]:histMaxYr)] <- sweep(setPlusGroup(stock.n(stocks) * exp(-stocks@harvest * mean(range(sur-veys[["HERAS"]]) [c("startf","endf")]) -

m(stocks) * mean(range(surveys[["HERAS"]]) [c("startf","endf")])),8)[ac(1:8),ac(range(sur-veys[["HERAS"]]) ["minyear"]:histMaxYr)],

c(1,2,6),sur-veyK[["HERAS"]][,ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr)],"^") *

surveyQ[["HERAS"]][, ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr)] * surv[ac(1:8),ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr),"HERAS"]

surveys[["HERAS"]]@index[ac(1),ac(range(surveys[["HERAS"]])["minyear"]:1996)] <- -1


#-------------------------------------------------------------------------------

#- 2): Use stock object + error on realisations

#-------------------------------------------------------------------------------

dmns <- dimnames([email protected])

dmns$year <- dmns$year[1]:futureMaxYr

dmns$iter <- 1:nits

ctch <- FLQuant(NA,dimnames=dmns)


yrs <- range(NSH)["minyear"]:futureMaxYr


full <- F

rw <- 1

cat("Sampling iters:","catch.n","\n")

while(full==F){

tmpx <- 0

while(tmpx<1) {





tmpx <- length(idx)

}




rw <- stprow + 1

}




strstp <- as.integer(runif(nits,range(NSH)["minyear"]+saveBlcks[,iCol]-1,range(NSH)["maxyear"]-saveBlcks[,iCol]+2))







}

#- Substract and calculate residuals (non-standardized)

Resids <- subset(residuals(NSH.sam),fleet=="catch")

iResids <- FLQuant(NA,dimnames=c(dimnames([email protected])[1:5],iter="1"))

for(i in 1:nrow(Resids))

iResids[ac(Resids$age[i]),ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

#- Fill the object with the residuals





iter(ctch[, ac(yrs),],iTer) <- iResids[,idx]

}

#- Because residuals are not estimated everywhere, some are NA, replace with 1

[email protected][which(is.na(ctch))] <- 1

#- Add error to catch.n observations

[email protected] <- [email protected] * ctch


save.image(file.path(outPath,"catchSurveys.RData"))

#-------------------------------------------------------------------------------

#- 3): Perform starting point assessment + retrospective to measure assessment

# error

#-------------------------------------------------------------------------------

dms <- dims(stocks)


resN <- ar-ray(NA,dim=c(dms$age,dms$year,1,1,nyrs,nits),dimnames=list(age=dmns$age,year=dmns$year,unit="unique",season="all",area=projPeriod,iter=1:nits))

resF <- ar-ray(NA,dim=c(dms$age,dms$year,1,1,nyrs,nits),dimnames=list(age=dmns$age,year=dmns$year,unit="unique",season="all",area=projPeriod,iter=1:nits))

#===============================================================================


# DO THIS ON NEMO

#===============================================================================

#- Prepare objects for retro, no hess estimation

iter_retro <- FLSAMs()

base.assess <- NSH.sam

base.assess@control@nohess <- T

ctrl <- NSH.ctrl

ctrl@nohess <- T

#- Start max number of cores (12 - 1)

cl <- startCluster(11)

exportCluster(cl,lst=list("stocks","base.assess","surveys","ctrl","ret-roLB","retro","histMaxYr","NSH.tun"))

#- Call retros

start.time <- Sys.time()

retros <- clusterApplyLB(cl,1:nits,fun=retroLB)

end.time <- Sys.time()

stopCluster(cl)

#===============================================================================

# END DO THIS ON NEMO

#===============================================================================

#- Take blocks of 20 years, sample blocks from retro years and add up till length of time-series

# So from 1957 - 2013 (see explanation below

#DDDMMMM change 11 to 27...

saveBlcksYrs <- array(NA,dim=c(nits,2),dimnames=list(nits=1:nits,strt-stp=c("start","stop")))

strstp <- as.integer(runif(nits,1957+20-1,histMaxYr-20+2))


strt <- ifelse(rv==F,strstp,strstp-20+1)

stp <- strt + 20 - 1

saveBlcksYrs[,"start"] <- ifelse(rv==F,strt,stp)

saveBlcksYrs[,"stop"] <- ifelse(rv==F,stp,strt)

saveBlcksYrs <- as.data.frame(saveBlcksYrs)

saveBlcksYrs <- cbind(saveBlcksYrs,retroYr = as.integer(runif(nits,1,11)))

saveBlcksYrs <- cbind(saveBlcksYrs,mult=rbinom(nits,1,0.5))

saveBlcksYrs$mult[which(saveBlcksYrs$mult==0)] <- -1


retros[[65]] <- retros[[sample(c(1:nits)[-c(65,708,883)],1)]]



iter_retro <- as(retros,"FLSAMs")

for(iRun in 1:nits){

print(iRun)

iter_ns <- lapply(iter_retro[[iRun]],function(x){return(win-dow(slot(x,"stock.n"),start=histMinYr,end=histMaxYr))})

iter_fs <- lapply(iter_retro[[iRun]],function(x){return(window(slot(x,"har-vest"),start=histMinYr,end=histMaxYr))})

iter_nsFLQ <- FLQuant(array(un-list(iter_ns),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

iter_fsFLQ <- FLQuant(array(un-list(iter_fs),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

for(i in 2:11){

iter_nsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

iter_fsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

}

#- Drop histMaxYr assessment because it is treated as 'the truth' so no error there

iter_errorN <- exp(sweep(log(iter_nsFLQ[,,,,,2:11]),1:5,log([email protected][,histPeriod,,,,iRun]),"-"))

iter_errorF <- exp(sweep(log(iter_fsFLQ[,,,,,2:11]),1:5,log(stocks@har-vest[,histPeriod,,,,iRun]),"-"))

#- Align error terms to what they are in relation to terminal year in their own retro aspect

# So the 2001 assessment has terminal year 2010 but is lined up as histMaxYr now to make calcs easier

for(i in 1:10){

iter_errorN[,(1+i):67,,,,i] <- iter_errorN[,1:(67-i),,,,i]

iter_errorF[,(1+i):67,,,,i] <- iter_errorF[,1:(67-i),,,,i]

}

#- Now fill resN and resF: resN only needs 20 years of data, for 11 years ahead

# So sample 11 times blocks of length 20 years from a yearspan of 1957:histMaxYr and

# allow these errors to be mirrored (multiply with -1) to assure retrospective bias

# can go in two ways.

for(i in 1:27){

refyr <- histMaxYr-1+i

resN[,ac((refyr-19):(refyr)),,,i,iRun] <- exp(saveBlcksYrs[iRun,"mult"] * log(iter_er-rorN[,ac(saveBlcksYrs[iRun,"start"]:saveBlcksYrs[iRun,"stop"]),,,,saveBlck-sYrs[iRun,"retroYr"]]))


resF[,ac((refyr-19):(refyr)),,,i,iRun] <- exp(saveBlcksYrs[iRun,"mult"] * log(iter_er-rorF[,ac(saveBlcksYrs[iRun,"start"]:saveBlcksYrs[iRun,"stop"]),,,,saveBlck-sYrs[iRun,"retroYr"]]))

}

}

resN[is.na(resN)][] <- 1

resF[is.na(resF)][] <- 1

resN <- FLQuant(resN,dimnames=list(age=dmns$age,year=dmns$year,unit="unique",sea-son="all",area=projPeriod,iter=1:nits))

resF <- FLQuant(resF,dimnames=list(age=dmns$age,year=dmns$year,unit="unique",sea-son="all",area=projPeriod,iter=1:nits))

save(resN, file=paste(outPath,"resNFinal.RData",sep=""))

save(resF, file=paste(outPath,"resFFinal.RData",sep=""))

save(iter_retro,file=paste(outPath,"retros.RData" ,sep=""))

#-------------------------------------------------------------------------------

# 1): Create biological population object

#-------------------------------------------------------------------------------

stocks2 <- stocks


stocks2@harvest[,1:68,,,,iTer] <- iter_retro[[iTer]][[ac(histMaxYr)]]@harvest

[email protected][,1:68,,,,iTer] <- iter_retro[[iTer]][[ac(histMaxYr)]]@stock.n

}

save(stocks,stocks2,file=file.path(outPath,"stocks_stocks2.RData"))

stocks <- stocks2

biol <- as.FLBiol(stocks)

#- Random draw from lognormal distribution for new recruitment, estimate lognormal parameters first

recrAge <- dimnames(rec(stocks))$age

pars <- optim(par=c(17.1,0.20),fn=optimRecDis-tri,recs=sort(c(rec(NSH[,ac(recrPeriod)]))),

method="Nelder-Mead")$par

biol@n[1,projPeriod] <- rtlnorm(length(pro-jPeriod)*nits,mean=pars[1],sd=pars[2],lower=0.01*min(biol@n[recrAge,],na.rm=T))

#-------------------------------------------------------------------------------

# 2): Create fisheries object

#-------------------------------------------------------------------------------

dmns <- dimnames(m(biol))


dmns$unit <- c("A","B","C","D")

fishery <- FLCatch(price=FLQuant(NA,dimnames=dmns))

name(fishery) <- "catches"

desc(fishery) <- "North Sea Herring"

fishery@range <- range(biol)

#-------------------------------------------------------------------------------

#- Get the proportional contribution of each fishery to the landings and weight

#-------------------------------------------------------------------------------

#-------------------------------------------------------------------------------

#- Partial Ns per fleet and plusgroup setting

#-------------------------------------------------------------------------------

partialN <- read.csv(paste(inPath,"partial_ns.csv",sep=""),header=T)

dimnames(partialN)[[1]] <- ac(0:9) #Biological sampling is up to age 9

Ns <- partialN

partialN[partialN==0] <- NA

pg <- range(stocks)["plusgroup"]

pgplus <- dimnames(partialN)[[1]][which(dimnames(partialN)[[1]] > pg)]

partialN[ac(pg),] <- apply(Ns[ac(c(pg:pgplus)),],2,sum,na.rm=T); partialN <- par-tialN[ac(dimnames(partialN)[[1]][1]:pg),]

idx <- lapply(as.list((histMaxYr-2):histMaxYr),func-tion(x){grep(x,colnames(partialN))})

dmns$year <- ac(histMaxYr+1); dmns$iter <- 1;

propN <- FLQuant(NA,dimnames=dmns); propWt <- FLQuant(NA,dimnames=dmns)

N <- array(unlist(lapply(idx,function(x){sweep(partialN[,x],1,row-Sums(partialN[,x],na.rm=T),"/")})),

dim=c(length(dimnames(partialN)[[1]]),length(dmns$unit),3))

N[is.na(N)] <- 0

#check: apply(N,c(1,3),sum,na.rm=T) #must equal 1

propN[] <- apply(N,1:2,mean,na.rm=T)

#-------------------------------------------------------------------------------

#- Partial Wts per fleet and plusgroup setting

#-------------------------------------------------------------------------------

partialWt <- read.csv(paste(inPath,"partial_ws.csv",sep=""),header=T)

dimnames(partialWt)[[1]]<- ac(0:9) #Biological sampling is up to age 9

partialWt[partialWt==0] <- NA

#- Define the average (weighted) weight-at-age and calculate the deviation of each fleet from that average weight

# We assume that the combination of the A,B,C and D fleet together make up the average weight at age in the fishery

#- Plusgroup correction


res <- partialWt * Ns

res[ac(pg),] <- colSums(res[ac(c(pg:pgplus)),],na.rm=T); res <- res[ac(dimnames(res)[[1]][1]:pg),]

res <- res / partialN

partialWt <- res

Wt <- array(unlist(lapply(idx,function(x){sweep(partialWt[,x],1,(row-Sums(partialWt[,x]*partialN[,x],na.rm=T)/rowSums(partialN[,x],na.rm=T)),"/")})),

dim=dim(N))

#check: apply(Wt * N,c(1,3),sum,na.rm=T) #must equal 1

propWt[] <- apply(Wt,1:2,mean,na.rm=T)

#- Put all proportions equal to NA to zero, so that they don't get any weight

[email protected][is.na(propN)==T][] <- 0; [email protected][is.na(propWt)==T][] <- 0

#-Take single fleet weights and numbers and multiply by the proportions

for(iFsh in dimnames([email protected])$unit){

[email protected][, ac(histMinYr:histMaxYr),iFsh] <- sweep([email protected],1,propN[,,iFsh],"*")*[email protected] / sweep([email protected],1,propWt[,,iFsh],"*")

[email protected][, ac(histMinYr:histMaxYr),iFsh] <- sweep([email protected],1,propWt[,,iFsh],"*")

[email protected][, ac(histMinYr:histMaxYr),iFsh] <- 0


}

[email protected]@.Data[is.infinite([email protected])==T] <- 0

[email protected]@.Data[is.na([email protected])==T] <- 0

fishery@landings[, ac(histMinYr:histMaxYr)] <- computeLandings(fish-ery[,ac(histMinYr:histMaxYr)])

fishery@discards[, ac(histMinYr:histMaxYr)] <- computeDiscards(fish-ery[,ac(histMinYr:histMaxYr)])

#check: computeLandings(NSH) / window(unitSums(fishery@land-ings),1947,2013) #must equal 1

#-Calculate deterministic landing.sel

for(iFsh in dimnames([email protected])$unit)

landings.sel(fishery)[,ac(histMinYr:histMaxYr),iFsh] <- FLQuant(sweep(sweep(har-vest(stocks[,ac(histMinYr:histMaxYr)]),c(1),propN[,,iFsh],"*"),2:6,

fbar(stocks[,ac(histMinYr:histMaxYr)]),"/"),

dimnames=dimnames(stocks[,ac(histMinYr:histMaxYr)]@stock.n))

catch.q( fishery)[] <- 1

discards.sel(fishery)[] <- 0

[email protected][] <- 0



#-------------------------------------------------------------------------------

#- Vary selectivity of fleet (add random walk to last year selection pattern)

#-------------------------------------------------------------------------------

dmns <- dimnames(NSH@harvest)

dmns$year <- projPeriod

dmns$iter <- 1:nits

ages <- dimnames([email protected])$age

#- Create random walk over Fs (as multiplier from last years selection pattern)

covmat1 <- cov(apply(log(NSH@har-vest[,ac(2003:2013),drop=T]),1,diff))

covmat1[ac(3:8),ac(3:8)] <- covmat1[ac(3:8),ac(0:2)] <- covmat1[ac(0:2),ac(3:8)] <- 0

covmat2 <- cov(apply(log(NSH@har-vest[,ac(1997:2013),drop=T]),1,diff))

covmat2[ac(0:2),ac(0:2)] <- 0; covmat <- covmat1 + covmat2

wF <- FLQuant(array(t(mvr-norm(nits*nyrs,rep(0,length(ages)),cov(apply(log(NSH@har-vest[,selPeriod,drop=T]),1,diff)))),

dim=c(length(ages),nyrs,1,1,1,nits)),

dimnames=dmns)

wF <- FLQuant(array(t(mvr-norm(nits*nyrs,rep(0,length(ages)),covmat)),


dimnames=dmns)

qtil <- quantile(c(wF),probs=c(0.05,0.95))

qtilold <- quantile(c(wF[ac(4:8),]),probs=c(0.25,0.75))

[email protected][which(wF<qtil[1] | wF>qtil[2])][] <- 0

wF[ac(4:8),]@.Data[which(wF[ac(4:8),]<qtilold[1] | wF[ac(4:8),]>qtilold[2])][] <- 0

rwF <- FLQuant(aperm(ap-ply(wF,c(1,3:6),cumsum),c(2,1,3:6)),dimnames=dmns)

rwF <- sweep(rwF,c(1,3:5),apply(log(NSH@har-vest[,selPeriod]),1,mean),"+")

fbarages <- ac(range(NSH)["minfbar"]:range(NSH)["maxf-bar"])

landsel <- sweep(exp(rwF),c(2:6),apply(exp(rwF)[fbar-ages,],2:6,mean),"/")

for(iFsh in 1:dims(fishery)$unit)

landings.sel(fishery)[,projPeriod,iFsh] <- FLQuant(sweep(land-sel,c(1),propN[,,iFsh],"*"),

dimnames=dimnames(stocks[,ac(projPeriod)]@stock.n))

plot(landings.sel(fishery[,projPeriod,1]))


#-------------------------------------------------------------------------------

#- Vary landing weights (sample from observed landing weights but with some sort

# of autorcorrelation to biol)

#-------------------------------------------------------------------------------

projFishLandwt <- array(iter([email protected],1)[,ac(yr-StrngsC)],dim=dim([email protected][,projPeriod,1]))


[email protected][,projPeriod,iFsh] <- sweep(projFish-Landwt,1,propWt[,,iFsh],"*")

#-------------------------------------------------------------------------------

# 4): Save the objects

#-------------------------------------------------------------------------------

save(biol ,file=paste(outPath,"biol.RData", sep=""))

save(pars ,file=paste(outPath,"recPars.RData", sep=""))

save(fishery ,file=paste(outPath,"fishery.RData", sep=""))

save(propN ,file=paste(outPath,"propN.RData", sep=""))

save(propWt ,file=paste(outPath,"propWt.RData", sep=""))

save(ctch ,file=paste(outPath,"ctch.RData", sep=""))

save(landsel ,file=paste(outPath,"landsel.RData", sep=""))

save(surveys ,file=paste(outPath,"surveys.RData", sep=""))

save(surv ,file=paste(outPath,"surv.RData", sep=""))

save(surveyQ ,file=paste(outPath,"surveyQ.RData", sep=""))

save(surveyK ,file=paste(outPath,"surveyK.RData", sep=""))

save(stocks ,file=paste(outPath,"stocks.RData", sep=""))

save(settings ,file=paste(outPath,"settings.RData", sep=""))

save.image( file=paste(outPath,"setup07012015.RData", sep=""))

Conditioning of WBSS #-------------------------------------------------------------------------------

# WKHELP

#

# Author: Niels Hintzen

# IMARES, The Netherland

#

# Performs an MSE of North Sea Herring under different TAC scenario's

#

# Date: 05-Jun-2012

#

# Build for R2.13.2, 32bits

#-------------------------------------------------------------------------------

rm(list=ls())


library(FLSAM)

library(MASS)

library(msm)

#path <- "D:/Work/Herring MSE/WBSS/"

path <- "W:/IMARES/Data/ICES-WG/WKHerTAC/WBSS/"

inPath <- paste(path,"Data/",sep="")

codePath <- paste(path,"R/",sep="")

outPath <- paste(path,"Results/",sep="")

if(substr(R.Version()$os,1,3)== "lin"){

path <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",path)

inPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",inPath)

codePath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",codePath)

outPath <- sub("W:/IMARES/Data/ICES-WG/","/media/w/",outPath)

}

#- Load objects

load(file=paste(outPath,"WBSS.RData", sep=""))

load(file=paste(outPath,"WBSSctrl.RData", sep=""))

load(file=paste(outPath,"WBSSsam.RData", sep=""))

load(file=paste(outPath,"WBSStun.RData", sep=""))

if (1==2)

{

# just to check that the assessment output are as in HAWG 2014.

plot(WBSS)

obsvar.plot<-function (sam)

{

obv <- params(sam)

obv <- obv[obv$name=="logSdLogObs",]

obv$fleet<-c(rep("catch",3),rep("HERAS",4),rep("GerAs",3),rep("N20",1),rep("IBTS Q1",1),rep("IBTS Q3",2))

obv$age<-c("0","1-4","5-8","1","2","3-6","7-8","0-3","4-5","6-8","0","1-4","1-2","3-4")

obv$str <- paste(obv$fleet, ifelse(is.na(obv$age), "", obv$age))

obv$value<-exp(obv$value)

obv <- obv[order(obv$value), ]

bp <- barplot(obv$value, ylab = "Observation Variance", main = "Observation vari-ances by data source",

col = factor(obv$fleet))

axis(1, at = bp, labels = obv$str, las = 3, lty = 0, mgp = c(0,

0, 0))

legend("topleft", levels(factor(obv$fleet)), pch = 15, col = 1:nlevels(obv$fleet),


pt.cex = 1.5)

}

obsvar.plot(WBSS.sam)

}

#- Settings

histMinYr <- 1991

histMaxYr <- 2013

futureMaxYr <- histMaxYr + 27



recrPeriod <- ac(2005:2013) # conditioning following decisions of

selPeriod <- ac(1991:2013) # the group

fecYears <- ac(2000:2013) #

nyrs <- futureMaxYr-dims(WBSS)$maxyear

nits <- 1000

settings <- list(histMinYr=histMinYr,histMaxYr=histMaxYr,futureMaxYr=futureMaxYr,

histPeriod=histPeriod,projPeriod=projPeriod,recrPeriod=recrPeriod,

nyrs=nyrs,nits=nits,fecYears=fecYears)

source(paste(codePath,"functions.r",sep=""))

#-------------------------------------------------------------------------------

# 0): Create stock object & use vcov for new realisations

#-------------------------------------------------------------------------------

stocks <- monteCarloStock(WBSS,WBSS.sam,nits,run.dir=outPath)

stocks <- window(stocks,start=histMinYr,end=futureMaxYr)

[email protected] <- [email protected] * stocks@harvest / (stocks@harvest + stocks@m) * (1 - exp(-stocks@harvest - stocks@m))




#- Sample from selected year range to fill future time period of m, weight, fec and land-ings.wt

# Retain a certain degree of autocorrelation and take fec, weight, m and landings

# weight from the same years to retain correlation between fec, wt, landings.wt

# and m (if there is any with m...)

# Take blocks of years instead of randomly years glued together. In some instances,

# reverse the blocks of year for variation


#- Take blocks of years, sample blocks from fecYears and add up till length of time-series

yrs <- projPeriod


full <- F

rw <- 1

# replaced the version below because of occassional errors

while(full==F){

tmpx <- 0

while(tmpx<1) {


sam <- sample(1:length(fecYears),nits*1000,replace=T)



tmpx <- length(idx)

}




rw <- stprow + 1

}


saveBlcksYrs <- ar-ray(NA,dim=c(nits,length(yrs),2),dimnames=list(nits=1:nits,blocks=1:length(yrs),strt-stp=c("start","stop")))


#-saveBlcks - 1 because fecYears[1] is the first possible year

#-saveBlcks + 2 because rev(fecYears)[1] is the last possible year and correction for as.integer

#-If blck is reversed is decided afterwards, hence the tight bounds in runif

strstp <- as.integer(runif(nits,an(fecYears[1])+saveBlcks[,iCol]-1,an(rev(fecYe-ars)[1])-saveBlcks[,iCol]+2))






#-Correct for those records where only one option in year is possible

idx <- which((an(fecYears[1])+saveBlcks[,iCol]-1) == an(rev(fecYears)[1])-saveBlcks[,iCol]+2)


saveBlcksYrs[idx,iCol,"start"] <- (an(fecYears[1])+saveBlcks[,iCol]-1)[idx]

saveBlcksYrs[idx,iCol,"stop"] <- ifelse((saveBlcksYrs[idx,iCol,"start"] + saveBlcks[idx,iCol] - 1) > an(rev(fecYears)[1]),

(saveBlcksYrs[idx,iCol,"start"] - saveBlcks[idx,iCol] + 1),

(saveBlcksYrs[idx,iCol,"start"] + saveBlcks[idx,iCol] - 1))

#-add start-stop for those blocks equal to length(fecYears)

idx <- which(saveBlcks[,iCol] == length(fecYears))


saveBlcksYrs[idx,iCol,"start"] <- ifelse(rv[idx]==F,an(fecYears[1]),an(rev(fecYe-ars)[1]))

saveBlcksYrs[idx,iCol,"stop"] <- ifelse(rv[idx]==F,an(rev(fecYears)[1]),an(fecYe-ars[1]))

}

#-If block is large, sampled size might exceed bounds, so take only possible options

if(length(idx) > 0) idx <- which(is.na(strstp)==T & is.na(saveBlcks[,iCol])==F)[which(!which(is.na(strstp)==T &

is.na(saveBlcks[,iCol])==F) %in% idx)]

if(length(idx) == 0)idx <- which(is.na(strstp)==T & is.na(saveBlcks[,iCol])==F)


#-Calculate options that are possible

opts <- mapply(function(strt,stp){

return(fecYears[which(!fecYears %in% strt:stp)])},

strt=an(rev(fecYears)[1])-saveBlcks[idx,iCol]+2,

stp =an(fecYears[1]) +saveBlcks[idx,iCol]-2,SIMPLIFY=F)

#-Sample from those options and define start and stop points

res <- an(unlist(lapply(opts,sample,1)))

saveBlcksYrs[idx,iCol,"start"] <- res

saveBlcksYrs[idx,iCol,"stop"] <- ifelse((res + saveBlcks[idx,iCol] - 1) > an(rev(fecYe-ars)[1]),

res - saveBlcks[idx,iCol] + 1,

res + saveBlcks[idx,iCol] - 1)

}

}

#-Create year strings and bind them together

yrStrngsIter<- apply(saveBlcksYrs,1,function(x){map-ply(seq,from=na.omit(x[,1]),to=na.omit(x[,2]))})

# if each sampled block was the same length, it creates a matrix instead of a list, gives an error below.

# so added fix to change any matrices into lists first

for (i in 1:nits) {

if (!is.list(yrStrngsIter[[i]])) {


tmp <- list()

for (cC in 1:ncol(yrStrngsIter[[i]])) tmp[[cC]]<- yrStrngsIter[[i]][,cC]

yrStrngsIter[[i]] <- tmp

rm(tmp)

}

}

yrStrngs <- lapply(yrStrngsIter,function(x){do.call(c,x)})

yrStrngsC <- do.call(c,yrStrngs)

stocks@mat [,projPeriod][] <- array(iter(stocks@mat,1) [,ac(yr-StrngsC)],dim=dim(stocks@mat [,projPeriod]))

[email protected] [,projPeriod][] <- array(iter([email protected],1)[,ac(yr-StrngsC)],dim=dim([email protected][,projPeriod]))

stocks@m [,projPeriod][] <- array(iter(stocks@m,1) [,ac(yr-StrngsC)],dim=dim(stocks@m [,projPeriod]))

#-------------------------------------------------------------------------------

# 1): Create survey object & use vcov for new realisations + error on realisations

#-------------------------------------------------------------------------------

surveys <- lapply(WBSS.tun,propagate,iter=nits)

for(iSurv in names(surveys)) surveys[[iSurv]] <- window(sur-veys[[iSurv]],start=range(surveys[[iSurv]])["minyear"],end=futureMaxYr)

dmns <- dimnames(surveys[["GerAS"]]@index)

#dmns$year <- dimnames(surveys[["SCAI"]]@index)$year

dmns$unit <- names(surveys)

surv <- FLQuant(NA,dimnames=dmns)

#- Get redrawn survey Qs and Ks

load(file=paste(outPath,"random.param.RData",sep=""))

#- Get the index of each parameter in the random.param object

Qidx <- unlist(apply(WBSS.ctrl@catchabilities,1,function(x)c(na.omit(x))))

#- Create objects for surveyQ and surveyK's

surveyQ <- FLQuants("HERAS" = FLQuant(NA,dimnames=dimnames(sur-veys[["HERAS"]]@index)),

"GerAS" = FLQuant(NA,dimnames=dimnames(surveys[["Ge-rAS"]]@index)),

"N20" = FLQuant(NA,dimnames=dimnames(sur-veys[["N20"]]@index)),

"IBTS Q1" = FLQuant(NA,dimnames=dimnames(surveys[["IBTS Q1"]]@index)),

"IBTS Q3" = FLQuant(NA,dimnames=dimnames(surveys[["IBTS Q3"]]@index)))


surveyK <- surveyQ

for(iSurv in names(surveys)) surveyK[[iSurv]][] <- 1

#- Fill the Qs by survey

for(iYr in dimnames(surveyQ[["GerAS"]])$year)

surveyQ[["GerAS"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("GerAS",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["N20"]])$year)

surveyQ[["N20"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("N20",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["IBTS Q1"]])$year)

surveyQ[["IBTS Q1"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("IBTS Q1",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["IBTS Q3"]])$year)

surveyQ[["IBTS Q3"]][,iYr] <- exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("IBTS Q3",names(Qidx))]]])

for(iYr in dimnames(surveyQ[["HERAS"]])$year)

surveyQ[["HERAS"]][,iYr] <- t(exp(random.param[,which(colnames(random.param) %in% "logFpar")[Qidx[grep("HERAS",names(Qidx))]]]))

#- Index var no longer used but filled anyway

for(iSurv in names(surveys)) surveys[[iSurv]]@index.var[,projPeriod][] <- sur-veys[[iSurv]]@index.var[,ac(histMaxYr)]

#- Vary selectivity of survey

for(iSurv in names(surveys)){

# iSurv <- names(surveys)[4]

cat("Vary selection of:",iSurv,"\n")

#- Get residuals

Resids <- subset(residuals(WBSS.sam),fleet==iSurv)

iResids <- FLQuant(1,dimnames=c(dimnames(WBSS.tun[[iSurv]]@index)[1:5],iter="1"))

#- Substract residuals (log scale)

for(i in 1:nrow(Resids)){

if(iSurv == "SCAI") iResids[1,ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

if(iSurv != "SCAI") iResids[ac(Resids$age[i]),ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

}



yrs <- range(surveys[[iSurv]])["minyear"]:futureMaxYr


full <- F

rw <- 1

cat("Sampling iters:",iSurv,"\n")

while(full==F){

tmpx <- 0

while(tmpx<1) {





tmpx <- length(idx)

}




rw <- stprow + 1

}




strstp <- as.integer(runif(nits,range(surveys[[iSurv]])["minyear"]+saveBlcks[,iCol]-1,range(WBSS.tun[[iSurv]])["maxyear"]-saveBlcks[,iCol]+2))






}

cat("Populating surv:",iSurv,"\n")





if(iSurv == "GerAS") iter(surv[ac(0:8), ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "N20") iter(surv[ac(0), ac(yrs),iSurv],iTer) <- iResids[,idx]


if(iSurv == "IBTS Q1") iter(surv[ac(1:4), ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "IBTS Q3") iter(surv[ac(1:4), ac(yrs),iSurv],iTer) <- iResids[,idx]

if(iSurv == "HERAS") iter(surv[ac(1:8), ac(yrs),iSurv],iTer) <- iResids[,idx]

}

}

#- Recalculate survey index values (also historic)

surveys[["IBTS Q1"]]@index[,ac(range(surveys[["IBTS Q1"]])["minyear"]:(histMaxYr))] <- sweep((stock.n(stocks) * exp(-stocks@harvest * mean(range(surveys[["IBTS Q1"]])[c("startf","endf")]) -

m(stocks) * mean(range(surveys[["IBTS Q1"]]) [c("startf","endf")])))[ac(1:4),ac(range(sur-veys[["IBTS Q1"]])["minyear"]:(histMaxYr))],

c(1,2,6),sur-veyK[["IBTS Q1"]][,ac(range(surveys[["IBTS Q1"]])["minyear"]:(histMaxYr))],"^") *

surveyQ[["IBTS Q1"]][, ac(range(surveys[["IBTS Q1"]])["minyear"]:(histMaxYr))] * surv[ac(1:4), ac(range(surveys[["IBTS Q1"]]) ["minyear"]:(histMaxYr)),"IBTS Q1"]

surveys[["IBTS Q3"]]@index[,ac(range(surveys[["IBTS Q3"]])["minyear"]:(histMaxYr))] <- sweep((stock.n(stocks) * exp(-stocks@harvest * mean(range(surveys[["IBTS Q3"]])[c("startf","endf")]) -

m(stocks) * mean(range(surveys[["IBTS Q3"]]) [c("startf","endf")])))[ac(1:4),ac(range(sur-veys[["IBTS Q3"]])["minyear"]:(histMaxYr))],

c(1,2,6),sur-veyK[["IBTS Q3"]][,ac(range(surveys[["IBTS Q3"]])["minyear"]:(histMaxYr))],"^") *

surveyQ[["IBTS Q3"]][, ac(range(surveys[["IBTS Q3"]])["minyear"]:(histMaxYr))] * surv[ac(1:4), ac(range(surveys[["IBTS Q3"]]) ["minyear"]:(histMaxYr)),"IBTS Q3"]

surveys[["HERAS"]]@index[,ac(range(surveys[["HERAS"]])["minyear"]:histMaxYr)] <- sweep(setPlusGroup(stock.n(stocks) * exp(-stocks@harvest * mean(range(sur-veys[["HERAS"]]) [c("startf","endf")]) -

m(stocks) * mean(range(surveys[["HERAS"]]) [c("startf","endf")])),8)[ac(1:8),ac(range(sur-veys[["HERAS"]]) ["minyear"]:histMaxYr)],

c(1,2,6),sur-veyK[["HERAS"]][,ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr)],"^") *

surveyQ[["HERAS"]][, ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr)] * surv[ac(1:8),ac(range(surveys[["HERAS"]]) ["minyear"]:histMaxYr),"HERAS"]

surveys[["HERAS"]]@index[ac(1),"1991"] <- -1

surveys[["HERAS"]]@index[,"1999"] <- -1


surveys[["N20"]]@index[,ac(range(surveys[["N20"]])["minyear"]:(histMaxYr))] <- sweep((stock.n(stocks) * exp(-stocks@harvest * mean(range(sur-veys[["N20"]])[c("startf","endf")]) -

m(stocks) * mean(range(surveys[["N20"]]) [c("startf","endf")])))[ac(0),ac(range(sur-veys[["N20"]])["minyear"]:(histMaxYr))],

c(1,2,6),sur-veyK[["N20"]][,ac(range(surveys[["N20"]])["minyear"]:(histMaxYr))],"^") *

surveyQ[["N20"]][, ac(range(surveys[["N20"]])["minyear"]:(histMaxYr))] * surv[ac(0), ac(range(surveys[["N20"]]) ["minyear"]:(histMaxYr)),"N20"]

surveys[["GerAS"]]@index[,ac(range(surveys[["GerAS"]])["minyear"]:histMaxYr)] <- sweep(setPlusGroup(stock.n(stocks) * exp(-stocks@harvest * mean(range(sur-veys[["GerAS"]]) [c("startf","endf")]) -

m(stocks) * mean(range(surveys[["GerAS"]]) [c("startf","endf")])),8)[ac(0:8),ac(range(sur-veys[["GerAS"]]) ["minyear"]:histMaxYr)],

c(1,2,6),sur-veyK[["GerAS"]][,ac(range(surveys[["GerAS"]]) ["minyear"]:histMaxYr)],"^") *

surveyQ[["GerAS"]][, ac(range(surveys[["GerAS"]]) ["minyear"]:histMaxYr)] * surv[ac(0:8),ac(range(surveys[["GerAS"]]) ["minyear"]:histMaxYr),"GerAS"]

surveys[["GerAS"]]@index[,ac(c(1991:1993,2001))] <- -1

#-------------------------------------------------------------------------------

#- 2): Use stock object + error on realisations

#-------------------------------------------------------------------------------


dmns$year <- dmns$year[1]:futureMaxYr

dmns$iter <- 1:nits

ctch <- FLQuant(NA,dimnames=dmns)


yrs <- range(WBSS)["minyear"]:futureMaxYr


full <- F

rw <- 1

cat("Sampling iters:","catch.n","\n")

while(full==F){

tmpx <- 0

while(tmpx<1) {






tmpx <- length(idx)

}




rw <- stprow + 1

}




strstp <- as.integer(runif(nits,range(WBSS)["minyear"]+saveBlcks[,iCol]-1,range(WBSS)["maxyear"]-saveBlcks[,iCol]+2))






}

#- Substract and calculate residuals (non-standardized)

Resids <- subset(residuals(WBSS.sam),fleet=="catch")

iResids <- FLQuant(NA,dimnames=c(dimnames([email protected])[1:5],iter="1"))

for(i in 1:nrow(Resids))

iResids[ac(Resids$age[i]),ac(Resids$year[i]),] <- exp(Resids$log.obs[i] - Resids$log.mdl[i])

#- Fill the object with the residuals





iter(ctch[, ac(yrs),],iTer) <- iResids[,idx]

}

#- Because residuals are not estimated everywhere, some are NA, replace with 1


[email protected][which(is.na(ctch))] <- 1

#- Add error to catch.n observations

[email protected] <- [email protected] * ctch


save.image(file.path(outPath,"catchSurveys.RData"))

#-------------------------------------------------------------------------------

#- 3): Perform starting point assessment + retrospective to measure assessment

# error

#-------------------------------------------------------------------------------

load(file.path(outPath,"catchSurveys.RData"))

dms <- dims(window(stocks,start=histMinYr,end=futureMaxYr+2))

dmns <- dimnames(window(stocks,start=histMinYr,end=futureMaxYr+2)@stock.n)

resN <- ar-ray(NA,dim=c(dms$age,dms$year,1,1,nyrs,nits),dimnames=list(age=dmns$age,year=dmns$year,unit="unique",season="all",area=projPeriod,iter=1:nits))

resF <- ar-ray(NA,dim=c(dms$age,dms$year,1,1,nyrs,nits),dimnames=list(age=dmns$age,year=dmns$year,unit="unique",season="all",area=projPeriod,iter=1:nits))

#===============================================================================

# DO THIS ON NEMO

#===============================================================================

#- Prepare objects for retro, no hess estimation

iter_retro <- FLSAMs()

base.assess <- WBSS.sam

base.assess@control@nohess <- T

ctrl <- WBSS.ctrl

ctrl@nohess <- T

#- Start max number of cores (12 - 1)

cl <- startCluster(25)

exportCluster(cl,lst=list("stocks","base.assess","surveys","ctrl","ret-roLB","retro","histMaxYr","WBSS.tun"))

#- Call retros


retros <- clusterApplyLB(cl,1:nits,fun=retroLB)

#retros <- clusterApplyLB(cl,1:nits,fun=retroLB)

end.time <- Sys.time()


stopCluster(cl)

#===============================================================================

# END DO THIS ON NEMO

#===============================================================================

# load the retros

load(file.path(outPath,"retrosWBSS.RData"))

#- Take blocks of nyr years, sample blocks from retro years and add up till length of time-series

# So from 1957 - 2013 (see explanation below

#DDDMMMM change 11 to 27...

nyr <- 6

saveBlcksYrs <- array(NA,dim=c(nits,2),dimnames=list(nits=1:nits,strt-stp=c("start","stop")))

strstp <- as.integer(runif(nits,1991+nyr-1,histMaxYr-nyr+2))


strt <- ifelse(rv==F,strstp,strstp-nyr+1)

stp <- strt + nyr - 1

saveBlcksYrs[,"start"] <- ifelse(rv==F,strt,stp)

saveBlcksYrs[,"stop"] <- ifelse(rv==F,stp,strt)

saveBlcksYrs <- as.data.frame(saveBlcksYrs)

saveBlcksYrs <- cbind(saveBlcksYrs,retroYr = as.integer(runif(nits,1,11)))

saveBlcksYrs <- cbind(saveBlcksYrs,mult=rbinom(nits,1,0.5))

saveBlcksYrs$mult[which(saveBlcksYrs$mult==0)] <- -1

cl<- lapply(retros,FUN=class)

cll<- lapply(cl,FUN=function(x) {x=="logical"})

good <- which(cll==F)

bad <- which(cll==T) # the retro didn't work 27 iterations

badrepl <- sample(good,27)

for (iR in 1:27) retros[[bad[iR]]]<-retros[[badrepl[iR]]]

#for(iRun in 1:nits) # replace the failed retros by good ones from other iterations


#{

#print(iRun)

#iter_retro[[iRun]] <- retros[[iRun]]

#}

#

#for(iRun in 1:nits){

# print(iRun)

# iter_ns <- lapply(iter_retro[[iRun]],function(x){return(win-dow(slot(x,"stock.n"),start=histMinYr,end=histMaxYr))})

# iter_fs <- lapply(iter_retro[[iRun]],function(x){return(window(slot(x,"har-vest"),start=histMinYr,end=histMaxYr))})

# iter_nsFLQ <- FLQuant(array(un-list(iter_ns),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

# iter_fsFLQ <- FLQuant(array(un-list(iter_fs),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

# for(i in 2:11){

# iter_nsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

# iter_fsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

# }

#

# until iter_retro is ready, run this

for(iRun in 1:nits){

print(iRun)

iter_ns <- lapply(retros[[iRun]],function(x){return(win-dow(slot(x,"stock.n"),start=histMinYr,end=histMaxYr))})

iter_fs <- lapply(retros[[iRun]],function(x){return(win-dow(slot(x,"harvest"),start=histMinYr,end=histMaxYr))})

iter_nsFLQ <- FLQuant(array(un-list(iter_ns),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

iter_fsFLQ <- FLQuant(array(un-list(iter_fs),dim=c(dms$age,length(histPeriod),1,1,1,nyrs)),dimnames=list(age=dms$min:dms$max,year=histPeriod,unit="unique",season="all",area="unique",iter=1:nyrs))

for(i in 2:11){

iter_nsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

iter_fsFLQ[,ac(histMaxYr-i+2),,,,i] <- NA

}

#- Drop histMaxYr assessment because it is treated as 'the truth' so no error there

iter_errorN <- exp(sweep(log(iter_nsFLQ[,,,,,2:11]),1:5,log([email protected][,histPeriod,,,,iRun]),"-"))

iter_errorF <- exp(sweep(log(iter_fsFLQ[,,,,,2:11]),1:5,log(stocks@har-vest[,histPeriod,,,,iRun]),"-"))


#- Align error terms to what they are in relation to terminal year in their own retro aspect

# So the 2001 assessment has terminal year 2010 but is lined up as histMaxYr now to make calcs easier

for(i in 1:10){

iter_errorN[,(1+i):23,,,,i] <- iter_errorN[,1:(23-i),,,,i]

iter_errorF[,(1+i):23,,,,i] <- iter_errorF[,1:(23-i),,,,i]

}

#- Now fill resN and resF: resN only needs 6 years of data, for 27 years ahead

# So sample 27 times blocks of length 6 years from a yearspan of 1991:2013 and

# allow these errors to be mirrored (multiply with -1) to assure retrospective bias

# can go in two ways.

for(i in 1:27){

refyr<-histMaxYr-1+i

resN[,ac((refyr-5):(refyr)),,,i,iRun] <- exp(saveBlcksYrs[iRun,"mult"] * log(iter_er-rorN[,ac(saveBlcksYrs[iRun,"start"]:saveBlcksYrs[iRun,"stop"]),,,,saveBlck-sYrs[iRun,"retroYr"]]))

resF[,ac((refyr-5):(refyr)),,,i,iRun] <- exp(saveBlcksYrs[iRun,"mult"] * log(iter_er-rorF[,ac(saveBlcksYrs[iRun,"start"]:saveBlcksYrs[iRun,"stop"]),,,,saveBlck-sYrs[iRun,"retroYr"]]))

}

}

resN[is.na(resN)][] <- 1

resF[is.na(resF)][] <- 1

resN <- FLQuant(resN,dimnames=list(age=dmns$age,year=dmns$year,unit="unique",sea-son="all",area=projPeriod,iter=1:nits))

resF <- FLQuant(resF,dimnames=list(age=dmns$age,year=dmns$year,unit="unique",sea-son="all",area=projPeriod,iter=1:nits))

resN<-resN[,ac(histMinYr:futureMaxYr)]

resF<-resF[,ac(histMinYr:futureMaxYr)]

save(resN, file=paste(outPath,"resNFinal.RData",sep=""))

save(resF, file=paste(outPath,"resFFinal.RData",sep=""))

save(iter_retro,file=paste(outPath,"retros.RData" ,sep=""))

#-------------------------------------------------------------------------------

# 1): Create biological population object

#-------------------------------------------------------------------------------

stocks2 <- stocks


print(iTer)

stocks2@harvest[,1:22,,,,iTer] <- retros[[iTer]][[ac(histMaxYr)]]@harvest

[email protected][,1:22,,,,iTer] <- retros[[iTer]][[ac(histMaxYr)]]@stock.n


}

save(stocks,stocks2,file=file.path(outPath,"stocks_stocks2.RData"))

stocks <- stocks2

biol <- as.FLBiol(stocks)

#- Random draw from lognormal distribution for new recruitment, estimate lognormal parameters first

recrAge <- dimnames(rec(stocks))$age

pars <- optim(par=c(17.1,0.20),fn=optimRecDis-tri,recs=sort(c(rec(WBSS[,ac(recrPeriod)]))),

method="Nelder-Mead")$par

biol@n[1,projPeriod] <- rtlnorm(length(pro-jPeriod)*nits,mean=pars[1],sd=pars[2],lower=0.01*min(biol@n[recrAge,],na.rm=T))

#-------------------------------------------------------------------------------

# 2): Create fisheries object

#-------------------------------------------------------------------------------

# need to update all this when the partialN and partialW are available

dmns <- dimnames(m(stocks))

dmns$unit <- c("A","C","D","F")

#Fleet A (IV) : directed fishery for herring in the North Sea

#Fleet C (IIIa): directed fishery for herring in which trawlers (with 32 mm minimum mesh size) and purse-seiners participate.

#Fleet D (IIIa): All fisheries in which trawlers (with mesh sizes less than 32 mm) and small purse-seiners, fishing for sprat along the Swedish coast and in the Swedish fjords, participate. For most of the landings taken by this fleet, herring is landed as bycatch. Danish and Swedish bycatches of herring from the sprat fishery and the Norway pout and blue whiting fisheries are listed under Fleet D.

#Fleet F (22-24): Landings from Subdivisions 22–24. Most of the catches are taken in a directed fishery for herring and some as bycatch in a directed sprat fishery.

#

fishery <- FLCatch(price=FLQuant(NA,dimnames=dmns))

name(fishery) <- "catches"

desc(fishery) <- "Western Baltic Herring"

fishery@range <- range(stocks)

#-------------------------------------------------------------------------------

#- Get the proportional contribution of each fishery to the landings and weight

#-------------------------------------------------------------------------------


#-------------------------------------------------------------------------------

#- Partial Ns per fleet and plusgroup setting

#-------------------------------------------------------------------------------

partialN <- read.csv(paste(inPath,"partialN.csv",sep=""),header=T)

dimnames(partialN)[[1]] <- ac(0:8) #Biological sampling is up to age 8

Ns <- partialN

partialN[partialN==0] <- NA

idx <- lapply(as.list(2008:2013),function(x){grep(x,colnames(partialN))})

dmns$year <- ac(histMaxYr+1); dmns$iter <- 1;

propN <- FLQuant(NA,dimnames=dmns); propWt <- FLQuant(NA,dimnames=dmns)

N <- array(unlist(lapply(idx,function(x){sweep(partialN[,x],1,row-Sums(partialN[,x],na.rm=T),"/")})),

dim=c(length(dimnames(par-tialN)[[1]]),length(dmns$unit),4))

N[is.na(N)] <- 0

#check: apply(N,c(1,3),sum,na.rm=T) #must equal 1

propN[] <- apply(N,1:2,mean,na.rm=T)

#-------------------------------------------------------------------------------

#- Partial Wts per fleet and plusgroup setting

#-------------------------------------------------------------------------------

partialWt <- read.csv(paste(inPath,"partialW.csv",sep=""),header=T)

dimnames(partialWt)[[1]]<- ac(0:8) #Biological sampling is up to age 8

partialWt[partialWt==0] <- NA

# Define the average (weighted) weight-at-age and calculate the deviation of each fleet from that average weight

# We assume that the combination of the C, D and F fleets together make up the average weight at age in the fishery

Wt <- array(unlist(lapply(idx,function(x){sweep(partialWt[,x],1,(row-Sums(partialWt[,x]*partialN[,x],na.rm=T)/rowSums(partialN[,x],na.rm=T)),"/")})),

dim=dim(N))

#check: apply(Wt * N,c(1,3),sum,na.rm=T) #must equal 1

propWt[] <- apply(Wt,1:2,mean,na.rm=T)

#- Put all proportions equal to NA to zero, so that they don't get any weight

[email protected][is.na(propN)==T][] <- 0; [email protected][is.na(propWt)==T][] <- 0

#-Take single fleet weights and numbers and multiply by the proportions

for(iFsh in dimnames([email protected])$unit){

[email protected][, ac(histMinYr:histMaxYr),iFsh] <- sweep([email protected],1,propN[,,iFsh],"*")*[email protected] / sweep([email protected],1,propWt[,,iFsh],"*")


[email protected][, ac(histMinYr:histMaxYr),iFsh] <- sweep([email protected],1,propWt[,,iFsh],"*")



}

[email protected]@.Data[is.infinite([email protected])==T] <- 0

[email protected]@.Data[is.na([email protected])==T] <- 0

fishery@landings[, ac(histMinYr:histMaxYr)] <- computeLandings(fish-ery[,ac(histMinYr:histMaxYr)])

fishery@discards[, ac(histMinYr:histMaxYr)] <- computeDiscards(fish-ery[,ac(histMinYr:histMaxYr)])

#check: computeLandings(WBSS) / window(unitSums(fishery@land-ings),1947,2011) #must equal 1

#-Calculate deterministic landing.sel


landings.sel(fishery)[,ac(histMinYr:histMaxYr),iFsh] <- FLQuant(sweep(sweep(har-vest(stocks[,ac(histMinYr:histMaxYr)]),c(1),propN[,,iFsh],"*"),2:6,

fbar(stocks[,ac(histMinYr:histMaxYr)]),"/"),

dimnames=dimnames(stocks[,ac(histMinYr:histMaxYr)]@stock.n))

catch.q( fishery)[] <- 1

discards.sel(fishery)[] <- 0



#-------------------------------------------------------------------------------

#- Vary selectivity of fleet (add random walk to last year selection pattern)

#-------------------------------------------------------------------------------

dmns <- dimnames(WBSS@harvest)

dmns$year <- projPeriod

dmns$iter <- 1:nits

ages <- dimnames([email protected])$age

#- Create random walk over Fs (as multiplier from last years selection pattern)

wF <- FLQuant(array(t(mvr-norm(nits*nyrs,rep(0,length(ages)),cov(apply(log(WBSS@har-vest[,selPeriod,drop=T]),1,diff)))),


dimnames=dmns)

qtil <- quantile(c(wF),probs=c(0.025,0.975))

[email protected][which(wF<qtil[1] | wF>qtil[2])][] <- 0


rwF <- FLQuant(aperm(ap-ply(wF,c(1,3:6),cumsum),c(2,1,3:6)),dimnames=dmns)

rwF <- sweep(rwF,c(1,3:5),apply(log(WBSS@har-vest[,selPeriod]),1,mean),"+")

fbarages <- ac(range(WBSS)["minfbar"]:range(WBSS)["maxfbar"])

landsel <- sweep(exp(rwF),c(2:6),ap-ply(exp(rwF)[fbarages,],2:6,mean),"/")

for(iFsh in 1:dims(fishery)$unit)

landings.sel(fishery)[,projPeriod,iFsh] <- FLQuant(sweep(land-sel,c(1),propN[,,iFsh],"*"),

dimnames=dimnames(stocks[,ac(projPeriod)]@stock.n))

#-------------------------------------------------------------------------------

#- Vary landing weights (sample from observed landing weights but with some sort

# of autorcorrelation to biol)

#-------------------------------------------------------------------------------

projFishLandwt <- array(iter([email protected],1)[,ac(yr-StrngsC)],dim=dim([email protected][,projPeriod,1]))


[email protected][,projPeriod,iFsh] <- sweep(projFish-Landwt,1,propWt[,,iFsh],"*")

[email protected][,projPeriod] <- projFishLandwt


#-------------------------------------------------------------------------------

# 4): Save the objects

#-------------------------------------------------------------------------------

save(biol ,file=paste(outPath,"biol.RData", sep=""))

save(pars ,file=paste(outPath,"recPars.RData", sep=""))

save(fishery ,file=paste(outPath,"fishery.RData", sep=""))

save(propN ,file=paste(outPath,"propN.RData", sep=""))

save(propWt ,file=paste(outPath,"propWt.RData", sep=""))

save(ctch ,file=paste(outPath,"ctch.RData", sep=""))

save(landsel ,file=paste(outPath,"landsel.RData", sep=""))

save(surveys ,file=paste(outPath,"surveys.RData", sep=""))

save(surv ,file=paste(outPath,"surv.RData", sep=""))

save(surveyQ ,file=paste(outPath,"surveyQ.RData", sep=""))

save(surveyK ,file=paste(outPath,"surveyK.RData", sep=""))

save(stocks ,file=paste(outPath,"stocks.RData", sep=""))

save(settings ,file=paste(outPath,"settings.RData", sep=""))


save.image( file=paste(outPath,"setup7_1_2015.RData", sep=""))


MSE #-------------------------------------------------------------------------------

#

# Code for HERTAC to simulate different scenarios of population

# structure and see if it can be managed sustainably

#

# Code by: Niels Hintzen

#

# To be used with R3.0.x on both Windows as Linux

#-------------------------------------------------------------------------------

rm(list=ls())

library(FLCore)

library(FLFleet)

library(minpack.lm)

library(MASS)

#- Set paths

codePath <- "I:/WKHerTAC/NSAS/R/"

dataPath <- "I:/WKHerTAC/NSAS/Data/"

outPath <- "I:/WKHerTAC/NSAS/Results/"

NSASPath <- "I:/WKHerTAC/NSAS/Results/"

WBSSPath <- "I:/WKHerTAC/WBSS/Results/"

combPathCode <- "I:/WKHerTAC/NSASWBSS/R/"

combPathResults <- "I:/WKHerTAC/NSASWBSS/Results/"

combPathData <- "I:/WKHerTAC/NSASWBSS/Data/"

FLMETApath <- "I:/WKHerTAC/FLMeta/"

#- Paths for Nemo

if((substr(R.Version()$os,1,3)== "lin" & length(dir("/media"))>0) | (substr(R.Ver-sion()$os,1,3)=="min" & length(dir("/media"))>0)){

codePath <- sub("I:/","/media/w/",codePath)

dataPath <- sub("I:/","/media/w/",dataPath)

outPath <- sub("I:/","/media/w/",outPath)

NSASPath <- sub("I:/","/media/w/",NSASPath)

WBSSPath <- sub("I:/","/media/w/",WBSSPath)

combPathCode <- sub("I:/","/media/w/",combPathCode)

combPathResults<- sub("I:/","/media/w/",combPathResults)

combPathData <- sub("I:/","/media/w/",combPathData)

FLMETApath <- sub("I:/","/media/w/",FLMETApath)

}

if((substr(R.Version()$os,1,3)== "lin" & length(dir("~/wur"))>0) | (substr(R.Ver-sion()$os,1,3)=="min" & length(dir("~/wur"))>0)){


codePath <- sub("I:/","~/wur/DATA/",codePath)

dataPath <- sub("I:/","~/wur/DATA/",dataPath)

outPath <- sub("I:/","~/wur/DATA/",outPath)

NSASPath <- sub("I:/","~/wur/DATA/",NSASPath)

WBSSPath <- sub("I:/","~/wur/DATA/",WBSSPath)

combPathCode <- sub("I:/","~/wur/DATA/",combPathCode)

combPathResults<- sub("I:/","~/wur/DATA/",combPathResults)

combPathData <- sub("I:/","~/wur/DATA/",combPathData)

FLMETApath <- sub("I:/","~/wur/DATA/",FLMETApath)

}

load(file.path(combPathResults,"startingConditions.RData"))

#- Set paths

codePath <- "I:/WKHerTAC/NSAS/R/"

dataPath <- "I:/WKHerTAC/NSAS/Data/"

outPath <- "I:/WKHerTAC/NSAS/Results/"

NSASPath <- "I:/WKHerTAC/NSAS/Results/"

WBSSPath <- "I:/WKHerTAC/WBSS/Results/"

combPathCode <- "I:/WKHerTAC/NSASWBSS/R/"

combPathResults <- "I:/WKHerTAC/NSASWBSS/Results/"

combPathData <- "I:/WKHerTAC/NSASWBSS/Data/"

FLMETApath <- "I:/WKHerTAC/FLMeta/"

if((substr(R.Version()$os,1,3)== "lin" & length(dir("~/wur"))>0) | (substr(R.Ver-sion()$os,1,3)=="min" & length(dir("~/wur"))>0)){

codePath <- sub("I:/","~/wur/DATA/",codePath)

dataPath <- sub("I:/","~/wur/DATA/",dataPath)

outPath <- sub("I:/","~/wur/DATA/",outPath)

NSASPath <- sub("I:/","~/wur/DATA/",NSASPath)

WBSSPath <- sub("I:/","~/wur/DATA/",WBSSPath)

combPathCode <- sub("I:/","~/wur/DATA/",combPathCode)

combPathResults<- sub("I:/","~/wur/DATA/",combPathResults)

combPathData <- sub("I:/","~/wur/DATA/",combPathData)

FLMETApath <- sub("I:/","~/wur/DATA/",FLMETApath)

}

#- Source the FLMeta package code

source(file.path(FLMETApath,"FLMETA.r"))

source(file.path(combPathCode,"functions.r"))

source(paste(combPathCode,"04_forecastScenariosNSAS.r", sep="")) #NSAS

source(paste(combPathCode,"04_forecastScenariosWBSS.r", sep="")) #WBSS

#- Settings

histMinYr <- 1991

histMaxYr <- 2013


nyrs <- 27

futureMaxYr <- histMaxYr + nyrs



nits <- 1000

settings <- list(histMinYr=histMinYr,histMaxYr=histMaxYr,futureMaxYr=future-MaxYr,

histPeriod=histPeriod,projPeriod=projPeriod,nyrs=nyrs,nits=nits)

datum <- date()

#--------------------------------------------------------------

#- 0) Meta match case scenario: 2 biological unit, 5 fisheries, 2 stock assessments

#

# 5 fishing fleets

# 2 populations, one in North Sea, one in Western Baltic

#--------------------------------------------------------------

#- Load data from north-stock assessments (WP4north)

biolsL <- list();

load(file=file.path(NSASPath,"biol.RData")); biolsL[["NS"]] <- biol

load(file=file.path(WBSSPath,"biol.RData")); biolsL[["WB"]] <- biol

biols <- biolsL; rm(biolsL)

stocksL <- list();

load(file=file.path(NSASPath,"stocks.RData")); stocksL[["NS"]] <- stocks

load(file=file.path(WBSSPath,"stocks.RData")); stocksL[["WB"]] <- stocks

stocks <- stocksL; rm(stocksL)

fisheriesL <- list();

load(file=file.path(NSASPath,"fishery.RData")); fisheriesL[["NS"]] <- fishery

load(file=file.path(WBSSPath,"fishery.RData")); fisheriesL[["WB"]] <- fishery

fisheries <- fisheriesL; rm(fisheriesL)

devNs <- list();

load(file=file.path(NSASPath,"resNFinal.RData")); devNs[["NS"]] <- resN

load(file=file.path(WBSSPath,"resNFinal.RData")); devNs[["WB"]] <- resN

devFs <- list();

load(file=file.path(NSASPath,"resFFinal.RData")); devFs[["NS"]] <- resF

load(file=file.path(WBSSPath,"resNFinal.RData")); devFs[["WB"]] <- resF

propNs <- list();

load(file=file.path(NSASPath,"propN.RData")); propNs[["NS"]] <- propN

load(file=file.path(WBSSPath,"propN.RData")); propNs[["WB"]] <- propN

catchResids <- list();

load(file=file.path(NSASPath,"ctch.RData")); catchResids[["NS"]] <- ctch

load(file=file.path(WBSSPath,"ctch.RData")); catchResids[["WB"]] <- ctch


#- Fill the fisheries object with the right data and merge the C & D fleet together

fisheries[[1]] <- window(fisheries[[1]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

fisheries[[2]] <- window(fisheries[[2]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

dmns <- dimnames(fisheries[[1]]@landings.n)

dmns$unit <- c("NS","WB")

dmns$area <- c("A","B","C","D","F")

fishery <- FLCatch(landings.n=FLQuant(NA,dimnames=dmns))

[email protected][,,"NS",,"A"] <- fisheries[["NS"]]@landings.wt[,,"A"]

[email protected][,,"NS",,"B"] <- fisheries[["NS"]]@landings.wt[,,"B"]

[email protected][,,"NS",,"C"] <- fisheries[["NS"]]@landings.wt[,,"C"]

[email protected][,,"NS",,"D"] <- fisheries[["NS"]]@landings.wt[,,"D"]

[email protected][,,"WB",,"A"] <- fisheries[["WB"]]@landings.wt[,,"A"]

[email protected][,,"WB",,"C"] <- fisheries[["WB"]]@landings.wt[,,"C"]

[email protected][,,"WB",,"D"] <- fisheries[["WB"]]@landings.wt[,,"D"]

[email protected][,,"WB",,"F"] <- fisheries[["WB"]]@landings.wt[,,"F"]

units(fishery)[1:10] <- as.list(c("tonnes","thousands","kg",NA,"tonnes","thou-sands","kg",NA,NA,"euro"))

range(fishery)["plusgroup"] <- range(fishery)["max"]

#--------------------------------------------------------------

#- Setup biol (2 populations)

#--------------------------------------------------------------

#- First NSAS

NSASorig <- window(biols[[1]][,,,,,1:nits],start=histMinYr,end=future-MaxYr)

biol <- NSASorig

dmns <- dimnames(biol@n); dmns$unit <- "NS"

biol <- setDimnames(biol,dmns)

bunits <- c("NS","WB")

biol <- expand(biol,unit=bunits)

#- Then add the south

WBSSorig <- window(biols[["WB"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

biols[["WB"]] <- WBSSorig

dmns <- dimnames(biols[["WB"]]@m)

dmns$unit <- "WB"

biols[["WB"]] <- setDimnames(biols[["WB"]],dmns)

for(iSlot in slotNames(biol)[1:5])

slot(biol,iSlot)[,,"WB"] <- slot(window(biols[[2]],start=histMinYr,end=future-MaxYr),iSlot)

units(biol)[1:5] <- as.list(c("thousands",NA,"kg",NA,NA))


biol <- window(biol,end=futureMaxYr)

dmns <- dimnames(biol@n); dmns$area <- "unique"

biol <- setDimnames(biol,dmns)

#--------------------------------------------------------------

#- Stocks setup & catch residuals & devN & devF

#--------------------------------------------------------------

stocks[["NS"]] <- window(stocks[["NS"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

stocks[["WB"]] <- window(stocks[["WB"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

dmns <- dimnames(stocks[[1]]@stock.n)


stock <- FLStock(stock.n=FLQuant(NA,dimnames=dmns))

stock[,,"NS"] <- stocks[["NS"]]

stock[,,"WB"] <- stocks[["WB"]]

catchResids[["NS"]] <- window(catchRe-sids[["NS"]][,,,,,1:nits],start=histMinYr,end=futureMaxYr)

catchResids[["WB"]] <- window(catchRe-sids[["WB"]][,,,,,1:nits],start=histMinYr,end=futureMaxYr)

dmns <- dimnames(catchResids[[1]])


catchResid <- FLQuant(NA,dimnames=dmns)

catchResid[,,"NS"] <- catchResids[["NS"]]

catchResid[,,"WB"] <- catchResids[["WB"]]

devNs[["NS"]] <- window(devNs[["NS"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

devNs[["WB"]] <- window(devNs[["WB"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

dmns <- dimnames(devNs[[1]])


devN <- FLQuant(NA,dimnames=dmns)

devN[,,"NS"] <- devNs[["NS"]]

devN[,,"WB"] <- devNs[["WB"]]

devFs[["NS"]] <- window(devFs[["NS"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

devFs[["WB"]] <- window(devFs[["WB"]][,,,,,1:nits],start=histMinYr,end=fu-tureMaxYr)

dmns <- dimnames(devFs[[1]])


devF <- FLQuant(NA,dimnames=dmns)

devF[,,"NS"] <- devFs[["NS"]]

devF[,,"WB"] <- devFs[["WB"]]


#--------------------------------------------------------------

#- Combine catches into one object

#--------------------------------------------------------------

#- Sync catches between fishery and original object

[email protected][,ac(histPeriod),"NS",,"A"] <- fisheries[["NS"]]@land-ings.n[,ac(histPeriod),"A"]

[email protected][,ac(histPeriod),"NS",,"B"] <- fisheries[["NS"]]@land-ings.n[,ac(histPeriod),"B"]

[email protected][,ac(histPeriod),"NS",,"C"] <- fisheries[["NS"]]@land-ings.n[,ac(histPeriod),"C"]

[email protected][,ac(histPeriod),"NS",,"D"] <- fisheries[["NS"]]@land-ings.n[,ac(histPeriod),"D"]

[email protected][,ac(histPeriod),"WB",,"A"] <- fisheries[["WB"]]@land-ings.n[,ac(histPeriod),"A"]

[email protected][,ac(histPeriod),"WB",,"C"] <- fisheries[["WB"]]@land-ings.n[,ac(histPeriod),"C"]

[email protected][,ac(histPeriod),"WB",,"D"] <- fisheries[["WB"]]@land-ings.n[,ac(histPeriod),"D"]

[email protected][,ac(histPeriod),"WB",,"F"] <- fisheries[["WB"]]@land-ings.n[,ac(histPeriod),"F"]


for(iUnit in c("NS","WB")){

for(iFleet in c("A","B","C","D","F")){

if(iFleet %in% dimnames(propNs[[iUnit]])$unit){

[email protected][,ac(histPeriod),iUnit,,iFleet] <- sweep(harvest(stocks[[iU-nit]])[,ac(histMinYr:histMaxYr)],c(1,3:5),propNs[[iUnit]][,,iFleet],"*")

}

}

}

[email protected][,ac(projPeriod),"NS",,"A"] <- fisheries[["NS"]]@land-ings.sel[,ac(projPeriod),"A"]

[email protected][,ac(projPeriod),"NS",,"B"] <- fisheries[["NS"]]@land-ings.sel[,ac(projPeriod),"B"]

[email protected][,ac(projPeriod),"NS",,"C"] <- fisheries[["NS"]]@land-ings.sel[,ac(projPeriod),"C"]

[email protected][,ac(projPeriod),"NS",,"D"] <- fisheries[["NS"]]@land-ings.sel[,ac(projPeriod),"D"]

[email protected][,ac(projPeriod),"WB",,"A"] <- fisheries[["WB"]]@land-ings.sel[,ac(projPeriod),"A"]

[email protected][,ac(projPeriod),"WB",,"C"] <- fisheries[["WB"]]@land-ings.sel[,ac(projPeriod),"C"]

[email protected][,ac(projPeriod),"WB",,"D"] <- fisheries[["WB"]]@land-ings.sel[,ac(projPeriod),"D"]

[email protected][,ac(projPeriod),"WB",,"F"] <- fisheries[["WB"]]@land-ings.sel[,ac(projPeriod),"F"]


#- Set all units correct

units(fishery)[1:10] <- as.list(c("tonnes","thousands","kg",NA,"tonnes","thou-sands","kg",NA,NA,"euro"))

units(biol)[c(1,3)] <- as.list(c("thousands","kg"))

#- Source recruitment scenario functions

source(file.path(combPathCode,"functions.r"))

#- Setup proportion of NSAS and WBSS in C and D fleet catches

prop <- FLQuant(NA,dimnames=list(age="all",year=pro-jPeriod,unit=c("NSAS","WBSS"),season="all",area=c("A","B","C","D","F"),iter=1:nits))

propNSASinIIIaC <- c(0.507462687,0.309090909,0.432675045,0.443708609,0.413357401,0.277511962,0.393285372,0.285714286,0.147826087,0.342857143,0.379310345,0.349775785,0.415492958)

propNSASinIIIaD <- c(0.8,0.5,0.677419355,0.490909091,0.638297872,0.365591398,0.596491228,0.627118644,0.340909091,0.782608696,0.692307692,0.814814815,0.551724138)

propWBSSinIVA <- c(0.01986755,0.021212121,0.006397076,0.013231457,0.011345219,0.022083919,0.002889414,0.000423,0.025,0.004831,0.001432,0.0051,0.001)

props <- mvrnorm(length(projPeriod)*nits*2,c(mean(propNSASi-nIIIaC),mean(propNSASinIIIaD)),cov(cbind(propNSASinIIIaC,propNSASinIIIaD)))

idx <- which(props[,1] > 0 & props[,1] < 1 & props[,2] > 0 & props[,2] < 1)

props <- props[idx,]

idx <- sample(1:nrow(props),length(projPeriod)*nits,replace=F)

prop[,,"NSAS",,"C"] <- props[idx,1]

prop[,,"NSAS",,"D"] <- props[idx,2]

prop[,,"WBSS",,"C"] <- 1 - prop[,,"NSAS",,"C"]

prop[,,"WBSS",,"D"] <- 1 - prop[,,"NSAS",,"D"]

props <- rnorm(length(projPeriod)*nits*10,mean=mean(propWBS-SinIVA),sd=sd(propWBSSinIVA))

idx <- which(props>0 & props < 1)

prop[,,"WBSS",,"A"] <- props[sample(idx,length(projPeriod)*nits,replace=F)]

#- Save starting conditions

save.image(file.path(combPathResults,"startingConditions.RData"))

load(file.path(combPathResults,"startingConditions.RData"))

#------------------------------------------------------------------------------#

# 1) Define the HCR & historic TACs

#------------------------------------------------------------------------------#

source(paste(combPathCode,"07_scenarioDescriptionNSAS.r", sep="")) #NSAS

source(paste(combPathCode,"07_scenarioDescriptionWBSS.r", sep="")) #NSAS

#- WBSS runs

mpPoints <- list("NS"=FIAVBB$opt6,"WB"=MSY$opt1); run <- "WBSS1"












mpPoints <- list("NS"=FIAVBB$opt6,"WB"=MSY$opt1); run <- "WBSS1AB"









#- NSAS runs

mpPoints <- list("NS"=FIAVBB$opt1,"WB"=MSY$opt1); run <- "NSAS1"







mpPoints <- list("NS"=FIAVBB$opt1,"WB"=MSY$opt1); run <- "NSAS1AB"






maxEff <- 1000

TAC <- FLQuant(NA,dimnames=list(age="all",year=histMinYr:(fu-tureMaxYr+3),unit=c("NS","WB"),season="all",area=c("A","B","C","D","F"),iter=1:nits))

TAC[,ac(2001:2014),"NS",,"A"] <- c(265,265,400,460,535,455,341,201,171,164,200,405,478,470) *1000

TAC[,ac(2001:2014),"NS",,"B"] <- c(36,36,52,38,50,42,32,19,16,14,16,18,14,13) *1000

TAC[,ac(2001:2013),"NS",,"C"] <- c(34,17,24.1,13.4,22.9,11.6,16.4,9.2,5.1,12.0,6.6,7.8,11.8)*1000

TAC[,ac(2001:2013),"NS",,"D"] <- c(12,9,8.4,10.8,9.0,3.4,3.4,3.7,1.5,1.8,1.8,4.4,1.6) *1000

TAC[,ac(2006:2014),"WB",,"C"] <- c(102,69,51.7,37.7,33.9,30,45,55,46.75) *1000

TAC[,ac(2014), "WB",,"D"] <- 1.130

TAC[,ac(2006:2014),"WB",,"F"] <- c(47.5,49.5,45,27.2,22.7,15.8,20.9,25.8,19.754) *1000

outtakeTAC <- FLQuant(0,dimnames=list(age="all",year=pro-jPeriod,unit=c("NSAS","WBSS"),season="all",area=c("A","B","C","D","F"),iter=1:nits))

outtakeTAC[,ac(2014),"NSAS",,"A"] <- 490622

outtakeTAC[,ac(2014),"NSAS",,"B"] <- 7398

outtakeTAC[,ac(2014),"NSAS",,"C"] <- 9777

outtakeTAC[,ac(2014),"NSAS",,"D"] <- 2493

outtakeTAC[,ac(2014),"WBSS",,"A"] <- 452

outtakeTAC[,ac(2014),"WBSS",,"C"] <- 15936

outtakeTAC[,ac(2014),"WBSS",,"D"] <- 1130

outtakeTAC[,ac(2014),"WBSS",,"F"] <- 19754

TACusage <- FLQuant(array(1,dim=c(1,length(histMinYr:future-MaxYr)+3,2,1,5,nits)),dimnames=dimnames(TAC[,ac(histMinYr:(futureMaxYr+3))]))

HCRTAC <- TAC; HCRTAC[] <- NA; SSB <- HCRTAC[,,,,1]; HCRSSB <- SSB


#------------------------------------------------------------------------------#

# 2) Start running the MSE

#------------------------------------------------------------------------------#


for (iYr in an(projPeriod)[-length(projPeriod)]){

cat(iYr,"\n")

cat(paste("\n Time running",round(dif-ftime(Sys.time(),start.time,unit="mins"),0),"minutes \n"))

#----------------------------------------

# define year names

#----------------------------------------

TaY <- ac(iYr-1) # terminal year in the assessment

ImY <- ac(iYr) # intermediate year in the short-term forecast, ie, current year

FcY <- ac(iYr+1) # year for which the advice is given, ie, forecast two years ahead the last year in the assessment

FuY <- c(ImY,FcY) # combination of future years

#----------------------------------------

# update the biol number-at-age in ImY

#----------------------------------------

#- Define mortality rates for iYr-1 to calculate survivors to iYr

m <- m(biol)[,ac(iYr-1),,]

z <- areaSums(landings.sel(fishery)[,ac(iYr-1),,,,]) + m

#- Update biological model to iYr

#- Survivors

survivors <- n(biol)[,ac(iYr-1)] * exp(-z)

n(biol)[ac((range(biol,"min")+1):range(biol,"max")),ac(iYr),,] <- survivors[-dim(sur-vivors)[1],,,,,]@.Data

#- Plusgroup

if (!is.na(range(biol,"plusgroup"))){

n(biol)[ac(range(biol,"max")),ac(iYr),] <- n(biol)[ac(range(biol,"max")),ac(iYr),] + survivors[ac(range(biol,"max"))]

}

cat("\n Finished biology \n")


#----------------------------------------


# update the fishery

#----------------------------------------

#- Update fishery to year iYr-1

landings.n(fishery)[,ac(iYr-1)] <- sweep(sweep(landings.sel(fishery)[,ac(iYr-1),,,,],c(1:4,6),z,"/"),c(1:4,6),n(biol)[,ac(iYr-1)]*(1-exp(-z)),"*")

#----------------------------------------

# update the assessment

#----------------------------------------

#- Create stock object for assessment

yrmin1 <- iYr -1

TaY <- yrmin1 #Terminal assessment year

ImY <- TaY+1 #Intermediate Year

FcY <- TaY+2 #Forecast year

idxyrmin1 <- which(dimnames(biol@n)$year == yrmin1)

tmp_biol <- biol[,1:idxyrmin1] #Same but faster as window(biol,histMinYr,yrmin1)

tmp_fishery <- fishery[,1:idxyrmin1]#Same but faster as window(fish-ery,histMinYr,yrmin1)

tmp_stocks <- stock[,1:idxyrmin1] #Same but faster as window(stocks,histMinYr,yr-min1)

#- Update stocks to year iYr -1

tmp_stocks <- updateStocks(tmp_stocks,tmp_fishery,yrmin1,tmp_biol,catchResid)

#- Overwrite results from update to stock again (but not for 2011, as that result is already known)

if(iYr > an(projPeriod[1]))

stock <- tmp2stocks(stock,tmp_stocks,TaY)

cat("\n Finished update \n")


#-Do the assessment

stock[,ac(histMinYr:TaY)]@stock.n <- biol@n[,ac(histMinYr:TaY)] * devN[,ac(histMinYr:TaY),,,ac(iYr),]

stock[,ac(histMinYr:TaY)]@harvest <- areaSums(landings.sel(fish-ery)[,ac(histMinYr:TaY)]) * devF[,ac(histMinYr:TaY),,,ac(iYr),]

stock@stock[,ac(histMinYr:TaY)] <- computeStock(stock[,ac(histMinYr:TaY)])

units(stock)[1:17] <- as.list(c(rep(c("tonnes","thousands","kg"),4),

rep("NA",2),"f",rep("NA",2)))


survivors[ac(0),] <- biol@n[ac(0),ac(iYr)] * devN[ac(0),ac(iYr),,,ac(iYr),]

#----------------------------------------

# prepare the forecast

#----------------------------------------

#Set plusgroup at 7 (which is true plusgroup - recruitment)

survivors[-1,] <- FLQuant(setPlusGroup(stock[,ac(TaY)]@stock.n * exp(-stock[,ac(TaY)]@harvest-stock[,ac(TaY)]@m),7)@.Data,

dimnames=list(age=dimnames([email protected])$age[-1],year=ac(TaY),unit=dimnames([email protected])$unit,

season=dimnames([email protected])$sea-son,area=dimnames([email protected])$area,iter=1:nits))

cat("\n Finished stock assessment \n")


#----------------------------------------

# run the forecast & define TACs

#----------------------------------------

#- Project 1-fleet setup for WBSS

projWBSS <- projectWBSS(stock[,1:idxyrmin1,"WB"],survi-vors[,,"WB"],tmp_fish-ery[,,"WB",,c("A","C","D","F")],iYr,areaSums(TAC[,,"NS",,c("A","C","D","F")]),mpPoints[["WB"]]$scen,NULL,histMaxYr,mpPoints[["WB"]],areaSums(out-takeTAC[,ac(ImY),"WBSS",,c("A","C","D","F")]))

#- Split TAC of WBSS

TAC[, ac(FcY),"WB",,c("F")] <- projWBSS[["TAC"]] * 0.5

if(run=="WBSS10") TAC[, ac(FcY),"WB",,c("C")] <- projWBSS[["TAC"]] * 0.41 + areaSums(TAC[,ac(ImY),"NS",,c("A")]) * 0.057

if(run=="WBSS11") TAC[, ac(FcY),"WB",,c("C")] <- projWBSS[["TAC"]] * 0.41 + areaSums(TAC[,ac(ImY),"NS",,c("A","B")]) * 0.057

#if(!run %in% c("WBSS10","WBSS11")) TAC[, ac(FcY),"WB",,c("C")] <- pro-jWBSS[["TAC"]] * 0.41 + areaSums(TAC[,ac(ImY),"NS",,c("A","B","C","D")]) * 0.057

if(substr(run,6,7)=="AB"){

TAC[, ac(FcY),"WB",,c("C")] <- projWBSS[["TAC"]] * 0.41 + areaSums(TAC[,ac(ImY),"NS",,c("A","B")]) * 0.057

} else {

TAC[, ac(FcY),"WB",,c("C")] <- projWBSS[["TAC"]] * 0.41 + areaSums(TAC[,ac(ImY),"NS",,c("A","B","C","D")]) * 0.057

}

#- Stabilizer of 15%

bidx <- which(TAC[,ac(FcY),"WB",,c("C")] > 1.15*TAC[,ac(ImY),"WB",,c("C")]) #bigger than 1.15x ImY TAC


sidx <- which(TAC[,ac(FcY),"WB",,c("C")] < 0.85*TAC[,ac(ImY),"WB",,c("C")]) #smaller than 0.85x ImY TAC

if(length(bidx)>0) TAC[,ac(FcY),"WB",,c("C"),bidx]<- 1.15*TAC[,ac(ImY),"WB",,c("C"),bidx]

if(length(sidx)>0) TAC[,ac(FcY),"WB",,c("C"),sidx]<- 0.85*TAC[,ac(ImY),"WB",,c("C"),sidx]

TAC[, ac(FcY),"WB",,c("D")] <- 6659 * mpPoints[["WB"]]$multD

HCRTAC[,ac(FcY),"WB",,c("F")] <- projWBSS[["HCRTAC"]]

HCRSSB[,ac(FcY),"WB",,] <- pro-jWBSS[["SSB"]][["HCRSSB"]][,ac(FcY)]

SSB[, ac(FcY),"WB",,] <- projWBSS[["SSB"]][["SSB"]][,ac(FcY)]

#- Calculate actual outtakes due to all sorts of mixing and transfers

outtakeTAC[,ac(FcY),"WBSS"] <- TAC[,ac(FcY),"WB"]

outtakeTAC[,ac(FcY),"WBSS",,c("C")] <- TAC[,ac(FcY),"WB",,c("C")] * (1-mpPoints[["WB"]]$C2NS) #allowing up to 50% to be taken in the NS

#- Project 4-fleet setup for NSAS

outtakeTAC[,ac(FcY),"NSAS",,c("C")] <- out-takeTAC[,ac(FcY),"WBSS",,c("C")] * prop[,ac(FcY),"NSAS",,"C"] #outtake of NSAS in C-fleet is total catch of C-fleet times proportion that is NSAS

outtakeTAC[,ac(FcY),"NSAS",,c("D")] <- out-takeTAC[,ac(FcY),"WBSS",,c("D")] * prop[,ac(FcY),"NSAS",,"D"]

outtakeTAC[,ac(FcY),"WBSS",,c("C")] <- out-takeTAC[,ac(FcY),"WBSS",,c("C")] - outtakeTAC[,ac(FcY),"NSAS",,c("C")]

outtakeTAC[,ac(FcY),"WBSS",,c("D")] <- out-takeTAC[,ac(FcY),"WBSS",,c("D")] - outtakeTAC[,ac(FcY),"NSAS",,c("D")]

TAC[,ac(FcY),"NS",,c("C")] <- outtakeTAC[,ac(FcY),"NSAS",,"C"]

TAC[,ac(FcY),"NS",,c("D")] <- outtakeTAC[,ac(FcY),"NSAS",,"D"]

projNSAS <- projectNSH( stock[,1:idxyrmin1,"NS"],survi-vors[,,"NS"],tmp_fishery[,,"NS",,c("A","B","C","D")],iYr, TAC[,,"NS",,c("A","B","C","D")], mpPoints[["NS"]]$scen,NULL,histMaxYr,mpPoints[["NS"]], out-takeTAC[,ac(ImY),"NSAS",,c("A","B","C","D")])

#- Collect information on TACs

TAC[, ac(FcY),"NS",,c("A","B","C","D")] <- projNSAS[["TAC"]]

HCRTAC[,ac(FcY),"NS",,c("A","B","C","D")] <- projNSAS[["HCRTAC"]]

HCRSSB[,ac(FcY),"NS",,] <- pro-jNSAS[["SSB"]][["HCRSSB"]][,ac(FcY)]

SSB[, ac(FcY),"NS",,] <- projNSAS[["SSB"]][["SSB"]][,ac(FcY)]

outtakeTAC[,ac(FcY),"NSAS"] <- TAC[,ac(FcY),"NS"]

outtakeTAC[,ac(FcY),"NSAS",,"A"] <- TAC[,ac(FcY),"NS",,"A"] + TAC[,ac(FcY),"WB",,c("C")] * mpPoints[["WB"]]$C2NS

outtakeTAC[,ac(FcY),"WBSS",,"A"] <- outtakeTAC[,ac(FcY),"NSAS",,"A"] * prop[,ac(FcY),"WBSS",,"A"]

outtakeTAC[,ac(FcY),"NSAS",,"A"] <- outtakeTAC[,ac(FcY),"NSAS",,"A"] - outtakeTAC[,ac(FcY),"WBSS",,"A"]


cat("\n Finished forecast \n")


#-Calculate effort accordingly (assuming constant catchability)

[email protected][,ac(ImY),"NS",,c("A","B","C","D")] <- sweep(landings.sel(fish-ery[,ac(ImY),"NS",,c("A","B","C","D")]),c(5:6),

pmin(f31tF(out-takeTAC[,ac(ImY),"NSAS",,c("A","B","C","D")] *

TA-Cusage[,ac(ImY),"NS",,c("A","B","C","D")],

biol[,ac(ImY),"NS"],ImY,fish-ery[,ac(ImY),"NS",,c("A","B","C","D")]),maxEff),"*")

[email protected][,ac(ImY),"WB",,c("A","C","D","F")] <- sweep(landings.sel(fish-ery[,ac(ImY),"WB",,c("A","C","D","F")]),c(5:6),

pmin(f31tF(out-takeTAC[,ac(ImY),"WBSS",,c("A","C","D","F")] *

TA-Cusage[,ac(ImY),"WB",,c("A","C","D","F")],

biol[,ac(ImY),"WB"],ImY,fish-ery[,ac(ImY),"WB",,c("A","C","D","F")]),maxEff),"*")

cat("\n Finished effort calc \n")


}

save.image(file=paste(combPathResults,run,".RData",sep=""))


Annex 4: Technical minutes from RGHERMA

• Report from the Review Group for Herring and Mackerel Management Plan Requests (RGHERMA)

• 13–16 January 2015 at ICES, Copenhagen, Denmark • Participants: Chris Zimmerman (Chair), Dankert Skagen and Jan Horbowy

(Reviewers) and Anne Cooper (Secretariat) • EG: WKHerTAC

The Review Group considered the report from a workshop (WKHERTAC) set up to address the following joint requests from EU and Norway to evaluate a procedure for TAC calculation for herring in IIIa and the management plan for herring in the North Sea:

Joint EU-Norway request on Herring in Area IIIa

ICES is requested to evaluate a proposed management strategy for the TAC setting procedure for herring in IIIa that is part of the Agreed Records of the EU-NOR bilat-eral consultations in 2014.

The management strategy aims at ensuring a long-term sustainable exploitation of this resource. It covers all catches of herring in the directed fishery in the area (C-fleet).

The European Union and Norway request ICES to evaluate the outcome of imple-menting the strategy on the stock of Western Baltic Spring-spawning herring, with particular reference to:

1 ) the probability of the fishing mortality being at or below FMSY year-on-year;

2 ) future yields on a five, ten and 20 year basis; 3 ) the probability of the spawning biomass falling below Blim and Btrigger in the

medium term.

The Parties will assume the costs of this advice in accordance as follows: EU: 87%, Norway: 13%.

Background

The management strategy is given the agreed record of EU-Norway consultations on the Skagerrak, states the following:

"The Delegations noted that the Working Group on management measures for herring in the Skagerrak and Kattegat met in Bergen on 19 and 20 June 2013. This Working Group provided a number of options for a methodology to set a TAC for herring in this area that is consistent with maximum sustainable yield.

The Delegations agreed to a management strategy corresponding to option B as defined in the report of the Working Group, with a human consumption TAC for herring in the Skagerrak and Kattegat based on 41% of the ICES MSY advice for Western Baltic Spring Spawners plus 5.7% of the TAC for North Sea Autumn Spawners that results from the application of the management plan. The Parties will meet in 2014 in order to review the parameters in the management strategy.


However, in the interests of stability, the Delegations agreed that the application of this man-agement strategy should be associated with a TAC constraint of +/- 15%."

Joint EU-Norway request to Evaluate the proposed Long-Term Management Plan for Herring in the North Sea

The Parties have agreed to revise the existing long-term management plan for her-ring in the North Sea as follows:

4 ) Every effort shall be made to maintain a minimum level of Spawning–Stock Biomass (SSB) greater than 800 000 tonnes (Blim).

5 ) Where the SSB is estimated to be above 1.5 million tonnes the Parties agree to set quotas for the directed fishery and for bycatches in other fisheries, reflecting a fishing mortality rate of no more than 0.26 for 2 ringers and older and no more than 0.05 for 0–1 ringers.

6 ) Where the SSB is estimated to be below 1.5 million tonnes but above 800 000 tonnes, the Parties agree to set quotas for the direct fishery and for bycatches in other fisheries, reflecting a fishing mortality rate on 2 ringers and older equal to:

0.26-(0.16*(1 500 000-SSB)/700 000) for 2 ringers and older, and no more than 0.05 for 0–1 ringers

7 ) Where the SSB is estimated to be below 800 000 tonnes the Parties agree to set quotas for the directed fishery and for bycatches in other fisheries, re-flecting a fishing mortality rate of less than 0.1 for 2 ringers and older and of less than 0.04 for 0–1 ringers.

8 ) Where the rules in paragraphs 2 and 3 would lead to a TAC which devi-ates by more than 15 % from the TAC of the preceding year the parties shall fix a TAC that is no more than 15% greater or 15% less than the TAC of the preceding year. However, if the resulting fishing mortality rate would be more than 10% higher or more than 10% lower than that indi-cated by the rules in paragraphs 2 and 3, the TAC shall be fixed at a level corresponding to a fishing mortality that is respectively 10% higher or 10% lower than that indicated by the rules of paragraphs 2 and 3.

9 ) Notwithstanding paragraph 5 the Parties may, where considered appro-priate, reduce the TAC to a level that corresponds to a fishing mortality more than 10 % lower than that indicated by the rules of paragraphs 2 and 3.

10 ) Bycatches of herring may only be landed in ports where adequate sam-pling schemes to effectively monitor the landings have been set up. All catches landed shall be deducted from the respective quotas set, and the fisheries shall be stopped immediately in the event that the quotas are ex-hausted.

11 ) The allocation of the TAC for the directed fishery for herring shall be 29% to Norway and 71% to the EU. The bycatch quota for herring shall be al-located to the EU.

12 ) A review of this arrangement shall take place no later than 31 December 2017.


ICES is requested to evaluate whether such a plan would be precautionary. It is also requested to evaluate whether the Btrigger value of 1 500 000 tonnes is optimal, or whether consideration should be given to adjusting it. When performing its evalua-tions, ICES is requested to assume that an interannual quota flexibility of +/- 10% will apply.

The Parties will assume the costs of this advice in accordance as follows: EU: 71%, Norway: 29%.

General comments

The reviewers greatly appreciate the efforts by the Workshop members to produce a good report. The group worked under severe time pressure due to several unexpected obstacles. These included both changes in manpower at a late stage and at short notice, and the realization at a late stage that the rule for the WBSS stock would not be in accordance with the precautionary approach. The group managed to handle these problems very well, thanks to great efforts by the members.

The reviewers followed the process throughout, and proposed adjustments and ap-proved solutions underway.

The two requests are closely linked and were handled together where practical.

Comments per section

Section 3 is a brief introduction to the management plan for WBSS, and gives a good and useful introduction to the background and the ambiguity problems with interpret-ing the request.

Section 4 is a similar description of the management plan for the NSAS, which is also very useful to understand the background for the request and the complexity of the rules that are being evaluated.

Section 5 describes the simulation method and the results. This is the main part of the report.

The evaluations of the two plans are closely linked. The same software was used for both stocks, and the projections were run in parallel with interactions between the TAC decisions for the two stocks. For the evaluation of the WBSS, the proposed manage-ment plan for North Sea herring (2014) was assumed.

The description of the method is extensive with all necessary detail included, and the R-code is included as Annex 4. The following is a brief summary of the method, ac-cording to the reviewer's perception, with some comments by the reviewers.

Briefly, the method was a stochastic forward projection model where future assess-ment error is imitated by random multipliers and removals by the fishery are according to the management plans. The stochastic elements include recruitment which was lognormally distributed with parameters based on the recent low recruitment regime, with no specific stock recruit relation. The distribution of drawn recruitments follows the distribution of historical recruitments very closely. It may be noted that this distri-bution is relatively narrow. Weights, maturities and natural mortalities, were drawn in blocks from historical values, to ensure consistency between these variables. As in pre-vious evaluations (WKHELP 2012), initial values for the projection were obtained by parametric bootstrap (1000 iterations) with the SAM model. There is documentation in the report that the simulated random variables behave as expected, in accordance with


the historical experience. The projections were made with 1000 realizations which was considered both by the group and the reviewers to be adequate to get sufficient preci-sion in the estimates of risk.

The decisions on removals are based on managers’ perception of the stock status, pro-jecting forward the perceived stock numbers-at-age. No full assessments are done within the loop to obtain the perceived stock status, rather it is derived by adding noise to the true population values. This noise is basically derived from retrospective devia-tions in the historical period. The reason for this short-cut is that the current assessment method (SAM) is far too time consuming to be included in a stochastic simulation loop. This approach was accepted by the reviewers at an early stage, and is in line with pre-vious practice for these stocks.

In the simulations, another shortcut was made. The TACs for the A and B fleets to some extent depend on the TAC for the C-fleet. The current practice by the HAWG is to as-sume a catch for the C-fleet. The proposed rule makes the C-fleet TAC conditional on the A and B fleet TACs. This leads to a circular process (described in Section 3). An algorithm to solve that has not yet been designed and tested, and doing so was out of reach for the WKHERTAC due to time constraints. In the simulations, this problem was avoided by using the North Sea TACs from the year before to calculate the C-fleet TAC. This is a weakness of the simulations. Apparently, the risk to Blim for WBSS de-pends strongly on the magnitude of the part of the C-fleet TAC that is derived from the North Sea TAC. Since the use of previous years TAC introduces a delay, it may amplify the uncertainty in the effects of the C-fleet catch, but as there is a strong corre-lation between TACs from one year to the next, the error introduced through this sim-plification probably is small and it should not introduce systematic bias. Therefore, this shortcut should not alter the overall conclusions to a significant extent.

The algorithm for splitting the decided C-fleet TAC on the two stocks uses a random ratio that is independent of the abundance of each of the stocks. This is justified by considering how this ratio has been historically in relation to the abundances of the stocks. This analysis could probably be extended, but gives clear evidence that the modelling approach is in line with the history.

The method is adequately described and extensively documented. The description is not quite easy to read, which is not surprising given the complexity that has to be ac-counted for. A clearer description of how the NSAS part of the C-fleet TAC was ob-tained (using the previous year’s TACs to avoid circularity) would have been useful, as this is a weak point in the simulations. It is mentioned in other sections, but missing here. The selection patterns shown in Figure 5.5 have some strange outliers, and it is not quite clear how they are handled and how they can influence the final selection. Likewise, the explanation of how the C-fleet TAC influences the A-fleet TAC is not clear although it is stated that the procedure by HAWG is followed. Finally, it would have been helpful if the preparation of initial values were better separated from the forwards projection part in the description.

The scenarios tested and the performance measures are well described, and cover what is needed to advice on the requests. The tabular and graphical overviews of the deci-sion processes in the plans and of the simulated options are very useful. Risks were evaluated as 'Type 3': The highest annual risk in the simulation period (here 2015–2024), which is now ICES standard practice. The risk type 2 (number of iterations where SSB<Blim at least once in the period) is shown for comparison. This was the criterion used in previous evaluations.


The results cover what is needed and are well presented. Important issues are high-lighted and adequately explained. The choice of performance indicators seems ade-quate. Detailed results and explanations to the results are annexed to the report (Annex 3).

The method appears sound, the key elements have been accepted in previous reviews and the present reviewers see no reason for not accepting this approach.

Section 6 contains conclusions and some suggestions for improvements of the plan. It summarizes the main points and concerns very well.

WKHerTAC has some suggestions for improvements. The collection of suggestions could surely be expanded, but further exploration could not be done at the WKHerTAC meeting.

There is some concern expressed that the circularity in setting TACs might not be math-ematically solvable. The presentation of this problem varies somewhat between sec-tions in the report. However, so far any designing, programming and testing of an algorithm for interplay between the TACs for the A, B and C fleets has not been done, and this question is probably best left open for the time being. Decoupling the TAC-setting process in IIIa from that in the North Sea would simplify the process and make it more transparent, and may be one useful way forward.

In addition to what is said in the conclusions section, it may be noted that if the state of the NSAS improves, it will make the TAC for WBSS larger, and if that happens with-out increased amounts of NSAS in IIIa and without a corresponding increase in the WBSS stock, the burden on the WBSS becomes intolerable. Simulations were done as-suming recruitments of both stocks at the current low level. That is entirely adequate, but it conceals the effect on the WBSS stock of better recruitment in the North Sea.

Reviewers’ conclusions and recommendations

Despite some shortcomings that have been noticed above, the evaluation is satisfactory for providing advice. All issues in the Terms of Reference have been properly ad-dressed, the report is clear and well written and contains all relevant information.

The rule as it is proposed for the WBSS is not in accordance with the precautionary approach, since some transfer of quotas from IIIa to the North Sea is necessary to keep the probability of reaching Blim for WBSS sufficiently low, and such transfers are not part of the rule. Some possible ways of improving the rules are suggested, but as the simulations were duly restricted to what was needed to address the present request, they are not sufficient for advising on fully specified alternatives.

A key issue was that the amount of NSAS in IIIa does not seem to depend on the mag-nitude of the NSAS stock. This is a critical point when designing management rules for this mixing area. There has been done substantial work on stock compositions in IIIa, which should allow further in-depth considerations on this issue.

From a reviewer’s perspective, the complexity of the rules has reached a level where the specification almost unavoidably becomes ambiguous and unexpected side effects easily can occur. Thus, future revisions should aim at simplifying the rules rather than introducing new elements to amend weak points.

For the NSAS, the reviewers agree that the options examined were all in accordance with the precautionary approach. That includes reducing the trigger biomass to 1.0 mill tonnes, banking and borrowing , as well as transferring up to 50% of the C-fleet TAC to the North Sea.


Appendix 2

Checklist for review process

General aspects

• Has the WG answered those ToRs relevant to providing advice? Yes • Is the assessment according to the stock annex description? Not applicable • Is general ecosystem information provided and is it used in the individual

stock sections. Indirectly to some extent. • Has the group carried out evaluations of management plans? Yes

• Has the group collected and analysed mixed fisheries data? Yes

For stocks where management plans or recovery plans have been agreed

• Has the management plan been evaluated in earlier reports? Only previous versions

• If the management plans has been evaluated during this WG: • Is the evaluation credible and understandable Yes

• Are the basic assumptions, the data and the methods (software) appro-priate and available? Yes

Not relevant

For update assessments

• Have the data been used as specified in the stock annex? • Has the assessment, recruitment and forecast model been applied as speci-

fied in the stock annex? • Is there any major reason to deviate from the standard procedure for this

stock?

• Does the update assessment give a valid basis for advice? If not, suggested what other basis should be sought for the advice?

For overview sections

• Are the main conclusions in accordance with the WG report?

• Verify that tables and figures been updated and are correct (except for the advice table).

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