<table><caption>**Table 1.** List of stocks included in the simulation, FAO code used in the analysis, common name, ICES category and distribution of the stock across ICES divisions.</caption><thead><tr><th>Code</th><th>Names</th><th>ICES category</th><th>Distribution (ICES divisions)</th></tr></thead><tbody><tr><td>ANK</td><td>White anglerfish</td><td>1</td><td>78abd</td></tr><tr><td>BSS</td><td>Seabass</td><td>1</td><td>8ab</td></tr><tr><td>HKE</td><td>Hake</td><td>1</td><td>3a46-8abd<br>2a4a5b6a7a-ce-</td></tr><tr><td>HOM</td><td>Horse Mackerel</td><td>1</td><td>k8</td></tr><tr><td>MAC</td><td>Mackerel</td><td>1</td><td>NEA</td></tr><tr><td>MEG</td><td>Megrim</td><td>1</td><td>78abd</td></tr><tr><td>MON</td><td>Monkfish</td><td>1</td><td>78abd</td></tr><tr><td>SOL</td><td>Sole</td><td>1</td><td>8ab</td></tr><tr><td>WHB</td><td>Blue whiting</td><td>1</td><td>1-91214</td></tr><tr><td>RJC</td><td>Raja clavata</td><td>3</td><td>8</td></tr><tr><td>RJN</td><td>Raja naevus</td><td>3</td><td>678abd</td></tr><tr><td>SDV</td><td>Smooth-hounds</td><td>3</td><td>NEA</td></tr><tr><td>NEP</td><td>Nephrops</td><td>1</td><td>8ab FU23-24</td></tr><tr><td>RJU</td><td>Raja undulata</td><td>3</td><td>8ab</td></tr><tr><td>WHG</td><td>Whiting</td><td>3</td><td>89a</td></tr><tr><td>POL</td><td>Pollack</td><td>3</td><td>89a</td></tr></tbody></table>

## 2.2 Fleets

The demersal Spanish and French fleets operating in Bay of Biscay were included in the simulation. The segmentation of the fleets and the metiers was based on the data provided to the WKMIXFISH and are listed in Table 2. Most of the stocks included in the simulation cover an area wider that the Bay of Biscay (Table 1) and to account for the catch outside the area one ghost fleet was included for each of the stocks to account for this catch.
<table><caption>Table 2. List of Spanish and French fleets and metiers included in the simulation</caption><thead><tr><th>COUNTRY</th><th>FLEET</th><th>Metier</th><th>Target stock</th><th>Mess size</th></tr></thead><tbody><tr><td rowspan="12">SP</td><td>ES_GNS&lt;10</td><td>GNS_DEF_100-119_0_0_all</td><td>DEF</td><td>100-119</td></tr><tr><td rowspan="2">ES_GNS_10&lt;24m</td><td>GNS_DEF_&gt;=100_0_0</td><td>DEF</td><td>&gt;=100</td></tr><tr><td>GNS_DEF_100-119_0_0_all</td><td>DEF</td><td>100-119</td></tr><tr><td>SP_GNS_2440</td><td>GNS_DEF</td><td>DEF</td><td>all</td></tr><tr><td>ES_GTR&lt;10m</td><td>ES_GTR</td><td>DEF</td><td>all</td></tr><tr><td>ES_GTR_10&lt;24m</td><td>ES_GTR</td><td>DEF</td><td>all</td></tr><tr><td>ES_LLS&lt;10m</td><td>ES_LLS</td><td>DEF</td><td>all</td></tr><tr><td>ES_LLS_10&lt;24m</td><td>ES_LLS</td><td>DEF</td><td>all</td></tr><tr><td>ES_LLS_24&lt;40m</td><td>ES_LLS</td><td>DEF</td><td>all</td></tr><tr><td>ES_MIS_all</td><td>ES_MIS</td><td>MIS</td><td>all</td></tr><tr><td>ES_OTB_&gt;=40m</td><td>OTB_DEF</td><td>DEF</td><td>all</td></tr><tr><td rowspan="3">ES_OTB_24&lt;40m</td><td>OTB_MPD</td><td>MPD</td><td>all</td></tr><tr><td>OTB_DEF</td><td>DEF</td><td>all</td></tr><tr><td>OTB_MPD</td><td>MPD</td><td>all</td></tr><tr><td>ES_PTB_24&lt;40m</td><td>OTB_MCF_&gt;=70_0_0</td><td>MCF</td><td>&gt;=70</td></tr><tr><td rowspan="12">FR</td><td rowspan="2">SP_OTB_40XX</td><td>ES_PTB</td><td>DEF</td><td>all</td></tr><tr><td>OTB_MPD</td><td>MPD</td><td>all</td></tr><tr><td>SP_PTB</td><td>MIS</td><td>all</td></tr><tr><td>OTB_DEF</td><td>DEF</td><td>all</td></tr><tr><td>SP_GNS_2440</td><td>OTB_MCF_&lt;=70_0_0</td><td>MCF</td><td>&gt;=70</td></tr><tr><td>SP_LLS_1040</td><td>OTB_MPD</td><td>MPD</td><td>all</td></tr><tr><td>FR_GNS_XX10</td><td>GNS_DEF_100-119_0_0_all</td><td>DEF</td><td>100-119</td></tr><tr><td rowspan="2">FR_GNS_1024</td><td>GNS_DEF_60-79_0_0</td><td>DEF</td><td>60-79</td></tr><tr><td>GNS_DEF_all_0_0_all</td><td>DEF</td><td>all</td></tr><tr><td>FR_OTB_40XX</td><td>GNS_DEF_60-79_0_0</td><td>DEF</td><td>60-79</td></tr><tr><td rowspan="2">FR_GTR_10-24m</td><td>GNS_DEF_all_0_0_all</td><td>DEF</td><td>all</td></tr><tr><td>GTR_DEF_100-119_0_0_all</td><td>DEF</td><td>100-119</td></tr><tr><td>FR_LLS_24&lt;40m</td><td>GTR_DEF_40-59_0_0</td><td>DEF</td><td>40-59</td></tr><tr><td rowspan="2">FR_LHM_all</td><td>GTR_DEF_all_0_0_all</td><td>DEF</td><td>all</td></tr><tr><td>LHM_DEF</td><td>DEF</td><td>all</td></tr><tr><td>FR_LLS_24&lt;40m</td><td>LLS_DEF</td><td>DEF</td><td>all</td></tr><tr><td>FR_MIS_all</td><td>LLS_DEF</td><td>DEF</td><td>all</td></tr><tr><td>FR_MIS_all</td><td>FR_MIS</td><td>MIL</td><td>all</td></tr><tr><td rowspan="3">FR_OTB_&lt;10m</td><td>OTB_CRU</td><td>CRU</td><td></td></tr><tr><td>OTB_CRU_&gt;=70_0_0</td><td>CRU</td><td>&gt;=70_0_0</td></tr><tr><td>OTB_CRU_all_0_0_all</td><td>CRU</td><td>all</td></tr><tr><td>FR_LHM_all</td><td>OTB_DEF_&lt;16_0_0_all</td><td>DEF</td><td>&lt;16</td></tr><tr><td>FR_LLS_24&lt;40m</td><td>OTB_DEF_&gt;=70_0_0</td><td>DEF</td><td>&gt;=70</td></tr><tr><td>FR_LLS_24&lt;40m</td><td>OTB_DEF_16-31_0_0</td><td>DEF</td><td>16-31</td></tr><tr><td>FR_MIS_all</td><td>OTB_DEF_32-69_0_0</td><td>DEF</td><td>32-69</td></tr><tr><td rowspan="7">FR_OTB_10&lt;24m</td><td>OTB_CRU</td><td>CRU</td><td></td></tr><tr><td>OTB_CRU_&gt;=70_0_0</td><td>CRU</td><td>&gt;=70</td></tr><tr><td>OTB_CRU_all_0_0_all</td><td>CRU</td><td>all</td></tr><tr><td>OTB_DEF_&lt;16_0_0_all</td><td>DEF</td><td>&lt;16</td></tr><tr><td>OTB_DEF_&gt;=70_0_0</td><td>DEF</td><td>&gt;=70</td></tr><tr><td>OTB_DEF_16-31_0_0</td><td>DEF</td><td>16-31</td></tr><tr><td>OTB_DEF_32-69_0_0</td><td>DEF</td><td>32-69</td></tr><tr><td>FR_OTB_24&lt;40m</td><td>OTB_DEF_&lt;16_0_0_all</td><td>DEF</td><td>&lt;16</td></tr><tr><td>FR_OTM_&lt;10</td><td>OTB_DEF_&gt;=70_0_0</td><td>DEF</td><td>&gt;=70</td></tr><tr><td>FR_OTM_10&lt;24m</td><td>OTM_DEF</td><td>DEF</td><td>all</td></tr><tr><td rowspan="3">FR_OTM_24&lt;40m</td><td>OTM_DEF</td><td>DEF</td><td>all</td></tr><tr><td>OTM_DEF_32-69_0_0_all</td><td>DEF</td><td>32-69</td></tr><tr><td>OTM_DEF_70-99_0_0_all</td><td>DEF</td><td>70-99</td></tr><tr><td>FR_OTB_24&lt;40m</td><td>OTM_DEF_70-99_0_0_all</td><td>DEF</td><td>70-99</td></tr><tr><td>FR_OTT_10&lt;24m</td><td>OTT_DEF</td><td>DEF</td><td>all</td></tr><tr><td>FR_OTT_24&lt;40m</td><td>OTT_DEF</td><td>DEF</td><td>all</td></tr><tr><td>FR_SSC_10&lt;40m</td><td>SSC_DEF_70-99_0_0_all</td><td>DEF</td><td>70-99</td></tr></tbody></table>
The economic data was obtained from different sources. Below each data source is described:

* Landings: Data from ICES MIXFISH that use Intercatch and Accessions data (ICES, 2022). These data are reported by national institutions. Intercatch data that includes the vessel size, effort by fleet and number of vessels has been also used.

* Price: The price of Spanish fleet has been estimated from First Sale Notes, from 2016 to 2021 at metier level. In the case of French fleet, the First Sale Notes were not available, thus, the price has been estimated from the STECF data (STECF 22 06 - EU Fleet Economic and Transversal data_fleet segment), from 2017 to 2020 and at case study level.

* Number of vessels: In the case of Spanish fleet, the number of vessels was estimated from the LoogBooks data, selecting those vessels that operate with the target gear in the target area and catching target species. Vast majority of the target vessels were Basque, then, in this case study only the Basque fleets were considered as representative of the whole fleet. In the case of French fleet, the number of vessels was obtained at metier level from the ICES Accesions data. The problem was that the same vessel can operate in one or more metiers. Thus, the sum of all the number of vessels by metier related to one fleet segment could result in an overestimation of the number of vessels. Thus, the maximum number of vessels by metier has been taken as the number of vessels of a given fleet segment, but this figure can underestimation.

* Effort: The effort comes from ICES MIXFISH, that comes from 'Intercatch' and the 'Accessions' (ICES, 2022).

* Costs: All the costs (variable costs, fixed costs, labour costs and capital cost) were estimated using STECF data. ('STECF 22 06 - EU Fleet Economic and Transversal data_fleet segment.xlsx', 'FS data' sheet). From this data, the fleet, supra region, geozm indicator and fishing_tech were selected, and costs were estimated at metier and fleet level. The estimation was not straightforward since the definition of fleet or metier from ICES MIXFISH does not correspond to the definition of AER data in terms of length of the vessel or fishing gear. For those fleets and metiers that had a direct correspondence, this data was taken directly from AER; in those cases when the gear was not specifically defined in AER data, the general gear was that includes the target gear was selected; when the vessel length was more disaggregated in AER data, the weighted average was computed. The following variables were conditioned using this data:

At fleet level:

○ Crew share (% of the gross value): Personnel costs / Gross value of landings

○ Fixed costs (by vessel): (Repair & maintenance costs + Other non-variable costs) / Number of vessels

○ Capital value: Value of physical capital

○ Fixed salaries (per crew member): 0, the whole salary is assumed variable.

○ Maximum effort: Maximum days at sea

○ Employees (by vessel): Engaged crew / Number of vessels

○ Depreciation (by vessel): Consumption of fixed capital / Number of vessels

○ Vessels (of the fleet): Number of vessels (values for simulations taken from ARVI data)

○ New vessel, investment share, w1, w2 (for capital dynamics) (Not applicable in the current case studies)

At fleet and metier:

○ Fuel cost (per unit of effort): Energy costs / Fishing days

○ Other variable cost (per unit of effort): Other variable costs / Fishing days

There are several problems associated to this data:

* The fleet segments are not the same in Intercatch and in the STECF data. Thus, correspondences between fleets and metiers need to be found, and both data bases do not always match perfectly.

* In the cost data base, the spatial dimension it is not defined at ices area level, only at supra-region area.
* The fleet is defined at country level, not at regional level, thus, the representativeness of the fleet in the national level is uncertain.

## 2.3 Scenarios

Four scenarios were run which differed in the dynamic of the fishery in response to management and how the advice is generated.

* **Statu quo:** In this scenario the effort and its distribution among metiers was kept constant in the projection and equal to the last three data years. The aim of having this scenario is twofold, on one hand is a control scenario that allows to identify problems in the conditioning of the model and on the other hand it provides scenarios against which to compare the rest of the scenarios.

* **min:** In this scenario the fleet fully complies with the landing obligation and they stop fishing when the first of the quota is consumed. There is not adaptability mechanism in the catchability or the effort share, so it could create a significant loss in fishing opportunities.

* **pre:** The effort share along metiers is given and equal to the mean of the most recent data and the total effort is calculated based on the catch quotas and the previous year effort. First, the effort corresponding to each of the catch quotas is calculated and then among those efforts the one that is more similar to the previous one is selected. Thus, the fleet dynamics have some inertia to the past but constrained by the quotas.

* **min.ms:** the fleet dynamics are the same as in the 'min' scenario but the advice is generated with a multistock HCR (Garcia, Dolder et al. 2019) that operationalizes the fishing mortality ranges based on the single stock advice and the maximization of fishing opportunities.

## 3. Indicators, targets and limits

The biomass and fishing mortality reference points used in the harvest control rules where those used by ICES in the generation of annual catch advice.

Performance statistics or indicators used to analyze the performance of the system were the same proposed within Task 6.4 to be used in general to summarize the results and no additional indicators were defined.

# 4. Main results.

## 4.1 Stock level

### SSB

The SSB for the main stocks in the area are shown in Figure 1. The SSB of all the stocks was well above the limit and trigger reference points in the projection period. For sole and seabass the biomass on the 'min' scenarios and the 'pre' and 'fix' scenarios was very different, with higher biomass in the 'min' scenarios. The SSB in the 'min' scenarios for all the stock was higher than the biomass in the 'pre' scenarios but the relative level of SSB depended on the stock. For sole and seabass in the 'min.ms' scenarios the biomass increased significantly. However, for the other stocks and scenarios the SSB level maintained similar to the historical ones.

### Fishing mortality

The fishing mortality in the status quo scenario did not correspond, in general, with the highest fishing mortality scenario. The fishing mortality in the last data year 2021 was below Fmsy for most of the stocks, thus the statu quo effort led to a fishing mortality below the Ftarget. The exceptions were Sole and the pelagic stocks. For the pelagic stocks the statu quo was the scenario with the highest fishing mortality and the other three scenarios resulted basically in the same fishing mortality which was around the target. For the rest of the stocks except Monkfish the scenario that provided the highest fishing mortality was the 'pre' scenario. For Monkfish the highest fishing mortality was obtained with the min.ms scenario as the harvest control rule increased the advice fishing mortality for this stocks when trying to harmonize the catch advice. In most of the cases the fishing mortality was within the ranges, but for anglerfish,
seabass and sole the min.ms scenario resulted in a fishing mortality below the range, and also the status quo scenario and the min scenario in the case of anglerfish and seabass.

## Average age

The scenarios had a significant impact in the average age and differences of one year between scenarios were common. The lower the fishing mortality in the scenario the higher the mean age in the population. In the medium term there were big changes in the mean age, positive in some cases, and negative in others, but then it was quite stable. However, it did not fully stabilised. The trend in the medium term was related with the trend in the SSB, if the SSB increased so did the mean age, and on the contrary it decreased if SSB decreased.

## 4.2 Fleet level

### Effort

Effort time series for the four scenarios is shown in Figure 1 for Spanish fleets and in Figure 2 for French fleets. For most of the Spanish fleets the status quo effort scenario was the scenario with the highest effort level, except for Longliners and Gillneters on the size range 24-40m. For these two fleets the effort in the 'pre' scenario and the status quo scenario was similar or even lower in the status quo scenario. The biggest difference between the 'min' and the 'pre' scenario was in these two fleets too, which means that the catch quotas in these fleets were less aligned than in the rest of the fleets. The min scenario was the more variable scenario for all the fleets. The 'min' and 'min.ms' scenario produced similar values but the effort in the min.ms scenario was somewhat lower and more stable.

In the French fleets the status quo and the 'pre' scenario were similar and in several cases the 'pre' scenario was higher. As for Spanish fleet the min scenario was very variable. In general, the effort in the min scenario was significantly lower than in the pre scenario. The effort in the min.ms was always below the effort in the min scenario and in all the cases had an increasing trend.

The uncertainty in the min scenario was very low or almost null because in all the iterations the limiting species was a category 3 species which did not include uncertainty in the projection.

### SHI, SAR and p(SSB<Blim)

SHI was always 0 and SAR was positive only in statu quo scenario in the first time period because of the high fishing mortality for mackerel. In that scenario the fishing mortality was also high for the other two pelagic species but apparently their contribution to the value was not high enough to have a real impact in the SAR indicator.

The stocks simulated with a dynamic model in the projection where all well above the Biomass target in all the scenarios except Horse mackerel at the beginning of the simulation and Black anglerfish. Horse mackerel SSB was below Blim at the beginning of the simulation with 100% probability. In all the scenarios except the 'sq' scenario the probability decreased rapidly and by 2030 the probability was close to 0. However, although vert low, remained positive afterwards. In the case of status quo scenario the probability decrease but maintained above 25% in the whole projection. In must be taken into account that the contribution of Demersal fleet in BoB to the total catch of horse mackerel is very low. Thus, the dynamic of this fleet has very limited impact of its dynamics and the performance of the stock is directly linked to the dynamic of the 'ghost' fleet that accounts for the rest of the catch and it is assumed to fish exactly the advised catch, except in the status quo scenario where the effort of this fleet is maintained constant.

For black anglerfish the probability of being below Blim was positive in some years in all the scenarios but the status quo scenario. But the probability was lower than 2% in all the cases. Black anglerfish was the only stocks for which the status quo scenario resulted in the higher biomass. This is an artefact of how the fishing activity outside Bays of Biscay is modelled and the current exploitation status of the stock. Currently the stock is exploited below Fmsy, thus in the scenarios different to status quo scenario the fishing mortality is increased in general, specially in the fleet outside Bay of Biscay that is force to catch exactly the corresponding quota.

As prices were constant along the whole simulation the revenue followed exactly the same trends as landings (Figure 8). The same happened for wages which were obtained as constant proportion of the revenues. Overall the biggest
landings were obtained in the status quo scenario and they increased slightly over time. The pre scenario showed a similar trend in terms of landings but the landings were somewhat lower. In the min sceanrio the landings were 30% lower than in the status quo scenario, they increased in the second time period and decreased again in the last period. The min.ms scenario produced the lowest landings, which increased over the years and in the last time period they were similar to the landings in the min scenario.

The unwanted catches (discards) showed similar trend as landings but there were slight differences, the discards in the medium and long term in 'pre' and status quo scenarios were similar, and the difference between min and min.ms scenarios increased in the long term (Figure 8).

The overall gross profit was negative in the whole simulation period (Figure 8). In the min scenario the gross profit did not improve in the long term, but they did in the rest of the scenarios. The most negative results were obtained in the 'pre' scenario, followed by 'status quo' scenario and the less negative were the min scenarios.

The sing of the gross value added (GVA) depended on scenario and the time period. Under the min scenario GVA was always positive and the highest value was obtained in the medium term. In the pre sceanrio the GVA was always negative but it improve significantly over time. In the min and status quo scenario the GVA was negative in the first year of the simulation but became positive afterwards. In the long term the min.ms scenario provided the best results.

The ratio of revenue between vessels smaller than 24 metres and bigger was similar along time and between scenarios. Around 70% of the revenue came from vessels smaller than 24m. In the min scenario the percentage was the highest and in the status quo scenario the lowest.

## Bycatch

The bycatch was proportional to effort but the bycatch rate depended on the gear and the stock, hence the trend was different for each of the stocks. Pre and status quo scenarios on the one hand and min and min.ms in the other showed similar trend over time. While the highest bycatch for Dolphins was observed in the status quo scenario, for the rest of the stocks the highest was observed in the pre scenario. The lowest bycatch level ocurred in the min.ms scenario.

## Catches per Km2

The area covered by the fleets was constant over time and the same in all the scenarios, hence, the catches per km2 followed the same as the sum of landings and unwanted catches.

# 5. Discussion and conclusions

For demersal species any of the scenarios resulted in biomasses above Blim with high probabiliy. For Horse Mackerel that started the simulation below Blim, the probability depended on the scenario, whike the status quo scenario resulted in a probability higher than 25%, in the pre scenario the probability was almost null.

In most of the cases the exploitation of the stocks was within the fishing mortality ranges what ensures that the long term yield is not lower than 95% of the maximum sustainable yield. For anglerfish the fishing mortality in min.ms and status quo and for sole in the min.ms scenario was below the lower bound of the range, which implies a loss in fishing opportunities.

As some of the stocks were exploited below Fmsy at the beginning of the simulation the status quo scenario was not always the scenario that resulted in the highest exploitation.

The limiting stocks were usually the stocks not dynamically simulated in the projection which resulted in low uncertainty in effort time series because no uncertainty was introduced in those stocks.
The pelagic stocks are important for the fleets in the Bay of Biscay but as the contribution of the demersal fleets in the Bay of Biscay to the overall exploitation of these stocks is very low, the management in this area had very limited impact in their dynamics.

The gross profit for many of the fleets was negative, which resulted in an overall negative gross profit. Some of the vessels considered in the simulation move to other areas along the year and not all the bycatch species in the Bay of Biscay were introduced which could be the main reason for having negative results. Moreover, the economic data comes from the STECF and the fleet segments used here and in the STECF data base do not fully much which could have an impact too.

The evolution in mean age depended on the stock biomass at the beginning of the projection, if the biomass level was high the mean age decreased in the projection and the other way around.

As many of the processes in the projection where linearly realted with effort, the trend in the indicators was explained by the trends in effort.

The contribution of the vessels smaller than 24m to the revenue was around 70%.

The multistock HCR (min.ms scenario) (Garcia, Dolder et al. 2019) did not have the expected results and the limitation in effort was higher than in the min scenario. The HCR produced a reduction in the fishing mortality of all the stocks but monkfish. This happened because overall monkfish was the most restrictive stock but not for the demersal fleets in the Bay of Biscay. This was related with the fact that only seabass and sole stocks belonged exclclusively to the bay of biscay. The exploitation of other demersal stocks is higher outside the Bays of Biscay, hence the performance of the system is highly driven by the ghost fleets introduced to account for all the catch outside the Bay os Biscay. Including Celtic Sea demersal fishery within the case study would solve the problem but would highly increase the complexity.

**6. References**

Cobb, C. W. and P. H. Douglas (1928). "A Theory of Production." *American Economic Reviews* **18**: 139-165.

Garcia, D., S. Sánchez, R. Prellezo, A. Urtizberea and M. Andrés (2017). "FLBEIA : A simulation model to conduct Bio-Economic evaluation of fisheries management strategies." *SoftwareX* **6**: 141-147.

Garcia, D., P. J. Dolder, A. Iriondo, C. Moore, R. Prellezo and A. Urtizberea (2019). "A multi-stock harvest control rule based on "pretty good yield" ranges to support mgarixed-fisheries management." *ICES Journal of Marine Science*.

Kraak, S. B. M., F. C. Buisman, M. Dickey-Collas, Poos J.J., M. A. Pastoors, J. G. P. Smit and N. Daan (2004). How can we manage mixed fisheries? A simulation study of the effect of management choices on the sustainability and economic performance of a mixed fishery. . *ICES Doc. CM 2004/FF, 11*.

Pella, J. J. and P. K. Tomlinson (1969). "A generalized stock-production model." *Bulletin of the Inter-American Tropical Tuna Comission* **13**: 421-458.

Punt, A. E., D. S. Butterworth, C. L. de Moor, J. A. A. De Oliveira and M. Haddon (2016). "Management strategy evaluation: best practices." *Fish and Fisheries* **17**(2): 303-334.

Refsgaard, J. C., J. P. van der Sluijs, A. L. HÃjberg and P. A. Vanrolleghem (2007). "Uncertainty in the environmental modelling process - A framework and guidance." *Environmental Modelling and Software* **22**(11): 1543-1556.

Salz, P., E. Buisman, K. Soma, H. Frost, P. Accacia and R. Prellezo (2011). FISHRENT: Bio-economic simulation and optimization model for fisheries. L. r. 2011-024.
7. Figures

<figure><img src="image_135.png" /><figcaption>Figure 1. Spawning stock biomass time series for demersal stocks in the Bay of Biscay. The lines represent median values and the shaded areas correspond to the 90% confidence interval.</figcaption></figure>
<figure><img src="image_136.png" /><figcaption>Figure 2. Ratio between fishing mortality and Fmsy time series. For demersal species the horizontal red dashed lines correspond with Fupp and Flow and for all the stocks the horizontal black line with Fmsy.</figcaption></figure>
<figure><img src="image_137.png" /><figcaption>Figure 3. Mean age time series over time by scenario.</figcaption></figure>
<figure><img src="image_138.png" /><figcaption>Figure 4. Effort time series for the Spanish fleets. The fleets are listed in Table X. The area corresponds with the 90% confidence interval.</figcaption></figure>
<figure><img src="image_139.png" /><figcaption>Figure 5. Effort time series for the Spanish fleets. The fleets are listed in Table X. The area corresponds with the 90% confidence interval.</figcaption></figure>
<figure><img src="image_140.png" /><figcaption>Figure 6. p(SSB<Blim) time series for the stocks for which this probability is positive in some year.</figcaption></figure>

<figure><img src="image_141.png" /><figcaption>Figure 7. Average SAR indicator for over stocks and years in each time period.</figcaption></figure>
<figure><img src="image_142.png" /><figcaption>Figure 8. Average economic and fishing activity indicators over given time periods, landings, revenue, unwanted catch, gross profit, gross value added, proportion of revenues coming from vessels smaller than 24m.</figcaption></figure>
<figure><img src="image_143.png" /><figcaption>Figure 9. Average catch of bycatch stocks over given time periods for the scenarios run.</figcaption></figure>

<figure><img src="image_144.png" /></figure>
<figure><img src="image_145.png" /><figcaption>Figure 10. Average effort by gear and scenario in given time periods.</figcaption></figure>

<figure><img src="image_146.png" /><figcaption>Figure 11. Overall catches by km2 in given time periods for the scenarios run</figcaption></figure>
## 8.6 Annex 6: Description of analyses carried out with model FLBEIA for the Basque inshore pelagic fishery in Subarea 8 (Bay of Biscay).

Sonia Sánchez-Maroño, Marga Andrés and Dorleta García.

### 1. Description of the model used

FLBEIA has been used to simulate the impact of management strategies for the inshore pelagic fishery in the Bay of Biscay. FLBEIA follows the MSE approach (Punt *et al.*, 2016) and as such the simulation is divided into two blocks, the Operating Model (OM) and the Management Procedure (MP) (Figure 7). The OM is the part of the model that simulates the true dynamics of the fishery system (the real population). Biological populations and fleets are its essential elements, and they interact through fishing effort and catch (Figure 1). The MP describes the management process, and it is divided into three modules, the observation model (the link between the OM and the MP), the assessment procedure and the management advice. The observation model together with the assessment model generate the perceived population based on which the management advice is calculated. The advice is given in terms of catch and it can also be combined with technical management measures such as gear restrictions, temporal closures or capacity limitations.

The stochasticity in the model is introduced using Monte Carlo simulation (Refsgaard *et al.*, 2007). Uncertainty can be introduced in all the variables used to describe the system. Each input variable can be conditioned using a single value or a vector, in this last case, each model replicate is conditioned taking a single value from this vector each time.

<figure><img src="image_147.png" /><figcaption>**Figure 7:** Scheme of FLBEIA model.</figcaption></figure>

#### Biological operating model

The stocks can be age structured or aggregated in biomass. Three models are available to describe stock dynamics. In the first model the stock abundance in the projection is given as input data and is maintained unchanged in the simulation. Implicitly, it assumes that population growth is independent of fleets' catch. This model can be useful for example when nothing is known about the dynamics of a certain stock, but its incorporation into the model is justified due to the economic relevance of the stock for a particular fleet. A second model projects age structured populations
one season ahead using a stock-recruitment model for incoming recruitment and an exponential survival model for the existing age classes. Finally, populations aggregated in biomass are projected using a Pella–Tomlinson growth model (Pella & Tomlinson, 1969). In the three models, the catch is assumed to take place in the middle of the season, in accordance with the fleet dynamics models implemented.

### Fleets operating model

Fleets OM is divided into four processes: effort allocation, catch production, price formation and capital dynamics. The models used to describe these processes can differ from fleet to fleet.

**Effort model:** It describes fleet's short-term dynamics or tactical behaviour. Each season it models how much effort is exerted and how it is distributed along métiers. In the *status quo* scenario presented in this deliverable the distribution of the effort along métiers is given as input data, whereas in the maximum profit scenario, the effort allocation is the one that gives the highest possible profits. In the *status quo* scenario the total effort and its distribution among métiers is fixed and equal to the mean of last three years. Whereas in the maximum profit scenario the total effort depends on the catch quotas of the stocks and their market prices.

**Catch model:** It describes the relationship between catch and effort. In this deliverable the Cobb-Douglas production model (Cobb and Douglas, 1928) which is widely used in economy to describe production in industry has been used.

**Price formation:** It describes how price changes as a function of other factors, for example landings. Price varies at fleet and stock level. There are two models available to describe its dynamic. The constant model and a model where the price depends on the ratio between current landings and landings in a baseline time period (Kraak et al., 2004).

**Capital model:** It describes the long-term dynamics of the fleet or strategic behaviour; the investment or disinvestment of fishermen in new vessels or technological improvements. In FLBEIA the capital dynamics can be modelled through changes in fleet's capacity or changes in fleet's catchability (technological improvements). Fleet's capacity can be modelled using a constant model or the model described by Salz et al. (2011).

## 2. Description of model parameterisation and scenarios

The conditioning of the model was based on the ICES single stock assessments and mixed fisheries data for the Basque pelagic inshore fleets operating in Bay of Biscay. Moreover, economic data was obtained from the STECF annual economic report, sales data, logbooks and regional data.

The simulation started in 2022 and the stocks and fleets were projected into the future until 2050. The simulation had 500 model replicates (iterations). In each iteration the uncertainty was introduced in the stock recruitment relationship and biological parameters which was propagated in the rest of model variables in the simulation.

### 2.1 Stocks

In the biological OM, this case study includes 10 stocks (Table 3). All except one were explicitly incorporated in the model using an age structured model, where survival was simulated using the exponential survival model and recruitment using a stock-recruitment model. Biological parameters and those in the stock recruitment model were estimated based on the latest available assessment of the stock and given the same assumptions made by the assessment teams (in ICES or ICCAT). To condition the stocks assessed within ICES (anchovy, hake, horse mackerel, mackerel and sardines), all in Category 1, the 2022 assessment output was used (ICES, 2022a,b,c). For the tuna species (albacore and bluefin tuna), assessed within ICCAT, as several assessments are carried out, one of the alternative assessments was selected to condition the operating model based on expert knowledge (ICCAT, 2020; Rouyer et al., 2022).
There were other species included all together in the category OTH (other species), just to cover all the catches by stock and the revenues of the modelled fleets. In this case the population was assumed constant and big enough to avoid conditioning the operation of the fleet in the model.

<table><caption>**Table 3:** Stocks explicitly included in the inshore case study.</caption><thead><tr><th>Common name</th><th>Species</th><th>FAO</th><th>COD E</th><th>ICES stock</th><th>Distribution</th></tr></thead><tbody><tr><td>Anchovy</td><td>Engraulis encrasicolus</td><td>ANE</td><td>ANE</td><td>ane.27.8</td><td>Subarea 8 (Bay of Biscay)</td></tr><tr><td>Mackerel</td><td>Scomber scombrus</td><td>MA C</td><td>MA C</td><td>mac.27.nea</td><td>Subareas 1–8 and 14, and in Division 9.a (Northeast Atlantic and adjacent waters)</td></tr><tr><td>Horse mackerel</td><td>Trachurus trachurus</td><td>HO M</td><td>HO M</td><td>hom.27.2a4a5 b6a7a-ce-k8</td><td>Subarea 8 and divisions 2.a, 4.a, 5.b, 6.a, 7.a–c, and 7.e–k (Northeast Atlantic)</td></tr><tr><td>Northern hake</td><td>Merluccius merluccius</td><td>HKE</td><td>NHK E</td><td>hke.27.3a46-8abd</td><td>Subareas 4, 6, and 7, and in divisions 3.a, 8.a–b, and 8.d, Northern stock (Greater North Sea, Celtic Seas, and the northern Bay of Biscay)</td></tr><tr><td>Southern hake</td><td>Merluccius merluccius</td><td>HKE</td><td>SHK E</td><td>hke.27.8c9a</td><td>Divisions 8.c and 9.a, Southern stock (Cantabrian Sea and Atlantic Iberian waters)</td></tr><tr><td>Bay of Biscay sardine</td><td>Sardina pilchardus</td><td>PIL</td><td>BPIL</td><td>pil.27.8abd</td><td>Divisions 8.a-b and 8.d (Bay of Biscay)</td></tr><tr><td>Iberian sardine</td><td>Sardina pilchardus</td><td>PIL</td><td>IPIL</td><td>pil.27.8c9a</td><td>Divisions 8c and 9a (Cantabrian Sea and Atlantic Iberian Waters)</td></tr><tr><td>Bluefin tuna</td><td>Thunnus alalunga</td><td>BFT</td><td>BFT</td><td>ICCAT</td><td>East Atlantic bluefin tuna</td></tr><tr><td>Albacore</td><td>Thunnus thynnus</td><td>ALB</td><td>ALB</td><td>ICCAT</td><td>Northeast Atlantic albacore</td></tr></tbody></table>

## 2.2 Fleets

This modelled fishery was based on those vessels operating in the Bay of Biscay targeting pelagic species. There are two countries involved in this case study: Spain and France. In Spain, the number of vessels is approximately 153, and the number of vessels in France is currently around 12 pairs of trawlers (24 vessels), that target pelagic species such as anchovy. Due to data availability, in this case study the economic component was defined only for the Basque fleets, which was composed by 67 vessels, for which detailed data was available.

The Basque pelagic fishery is characterized by a multi-fleet and multi-species fisheries (Murillas & Andrés, 2016) and presents clear seasonal effort dynamics in the catch profile. The main target species in this case study are mackerel (Scomber scombrus), anchovy (Engraulis encrasicolus), albacore (Thunnus alalunga) and bluefin tuna (Thunnus thynnus) (Andrés & Prellezo, 2012; Villamor et al., 2008). In recent years sardine (Sardina pilchardus) is also targeted by this fleet.

In this case study, two main fleets were included, the first one is the purse seiners fleet (PS) and the second one hand line (LHP) and trolling fleet (LTL). The Basque purse seiner fleet was divided into several homogeneous fleet segments according to the fishing profile and fishing gear (Andrés & Prellezo, 2012): (i) pure purse seine (BC_PPS); (ii) purse seine and pole and line live bait tuna fishery (BC_PSB); (iii) purse seine and pole and line live bait tuna fishery with high catchability of BFT (BC_PSBB); and (iv) purse seine and trolling (BC_PST). The second fleet, using handline (LHL) and
trolling (LHP), was considered as an unique fleet segment (BC_HLT). Métiers of each fleet segment were defined considering the specific fishing gear and fishing profile of catches in each season of the year. Although catch profile may vary from one year to another, the sequential pattern remains approximately stable over the years (Andrés & Prellezo, 2012). In each season, the fleet targets one main species together with other secondary species with a low importance in terms of catches. In the case of purse seine fleet, the year starts with the mackerel season (February-April) in which the fleet also catches sardine and other species. The main target species of second season of the year (March-June) is anchovy with the horse mackerel, sardine and other species as secondary species. Next, comes the tuna season (June-October), targeting albacore (ALB) and bluefin tuna (BFT). At the end of the year (November), there could be a small anchovy (ANE) season. In parallel, in recent years there were a PIL season at the end of the year (October-November). In the case of handline and trolling fleet, the seasonality is marked by semesters, with the mackerel (MAC) season in the 1st semester and the ALB season in the 2nd one. Therefore, métiers were defined based on the fishing gear, main target species and the season of the year. Overall, the Basque inshore fleet was defined with 5 fleet segments that in turn were structured into a total of 41 métiers.

<table><caption>Table 4: Fleets, métiers and main target species by métier of the inshore case study</caption><thead><tr><th>FLEET</th><th>FLEET DESCRIPTION</th><th>MÉTIER</th><th>MÉTIER TEMPORAL DESCRIPTION</th><th>MAIN TARGET SPECIES</th></tr></thead><tbody><tr><td rowspan="6">BC_PPS</td><td rowspan="6">Basque fleet pure purse seiner</td><td>PPS_ANE 1</td><td>Semester 1</td><td>ANE</td></tr><tr><td>PPS_ANE 2</td><td>Semester 2</td><td>ANE</td></tr><tr><td>PPS_HO M</td><td>Year</td><td>HOM</td></tr><tr><td>PPS_MAC</td><td>Year</td><td>MAC</td></tr><tr><td>PPS_OTH</td><td>Year</td><td>OTH</td></tr><tr><td>PPS_PIL1</td><td>Semester 1</td><td>BPIL</td></tr><tr><td rowspan="10">BC_PSB</td><td rowspan="10">Basque fleet purse seiner and live bait</td><td>PPS_PIL2</td><td>Semester 2</td><td>BPIL</td></tr><tr><td>PSB_ANE 1</td><td>Semester 1</td><td>ANE</td></tr><tr><td>PSB_ANE 2</td><td>Semester 2</td><td>ANE</td></tr><tr><td>PSB_ALB</td><td>Year</td><td>ALB</td></tr><tr><td>PSB_BFT</td><td>Year</td><td>BFT</td></tr><tr><td>PSB_HO M</td><td>Year</td><td>HOM</td></tr><tr><td>PSB_MA C</td><td>Year</td><td>MAC</td></tr><tr><td>PSB_OTH 1</td><td>Semester 1</td><td>OTH</td></tr><tr><td>PSB_OTH 2</td><td>Semester 2</td><td>OTH</td></tr><tr><td>PSB_PIL1</td><td>Semester 1</td><td>BPIL</td></tr><tr><td>PSB_PIL2</td><td>Semester 2</td><td>BPIL</td></tr><tr><td rowspan="2">BC_PSB B</td><td rowspan="2">Basque fleet purse seiner and live bait with high catchability of BFT</td><td>PSBB_AN E1</td><td>Semester 1</td><td>ANE</td></tr><tr><td>PSBB_AN E2</td><td>Semester 2</td><td>ANE</td></tr></tbody></table>
<table><tbody><tr><td></td><td>PSBB_ALB</td><td>Year</td><td>ALB</td></tr><tr><td></td><td>PSBB_BFT</td><td>Year</td><td>BFT</td></tr><tr><td></td><td>PSBB_HOM</td><td>Year</td><td>HOM</td></tr><tr><td></td><td>PSBB_AC</td><td>Year</td><td>MAC</td></tr><tr><td></td><td>PSBB_OT1</td><td>Semester 1</td><td>OTH</td></tr><tr><td></td><td>PSBB_OT2</td><td>Semester 2</td><td>OTH</td></tr><tr><td></td><td>PSBB_PIL1</td><td>Semester 1</td><td>BPIL</td></tr><tr><td></td><td>PSBB_PIL2</td><td>Semester 2</td><td>BPIL</td></tr><tr><td rowspan="7">BC_PST</td><td>PST_ANE1</td><td>Semester 1</td><td>ANE</td></tr><tr><td>PST_ANE2</td><td>Semester 2</td><td>ANE</td></tr><tr><td>PST_ALB</td><td>Year</td><td>ALB</td></tr><tr><td>PST_HOM</td><td>Year</td><td>HOM</td></tr><tr><td>PST_MAC</td><td>Year</td><td>MAC</td></tr><tr><td>PST_OTH</td><td>Year</td><td>OTH</td></tr><tr><td>PST_PIL1</td><td>Semester 1</td><td>BPIL</td></tr><tr><td rowspan="6">BC_HLT</td><td>PST_PIL2</td><td>Semester 2</td><td>BPIL</td></tr><tr><td>HLT_ALB</td><td>Year</td><td>ALB</td></tr><tr><td>HLT_HOM</td><td>Year</td><td>HOM</td></tr><tr><td>HLT_MA</td><td>Year</td><td>MAC</td></tr><tr><td>HLT_HKE</td><td>Year</td><td>SHKE</td></tr><tr><td>HLT_OTH1</td><td>Semester 1</td><td>OTH</td></tr><tr><td></td><td>HLT_OTH2</td><td>Semester 2</td><td>OTH</td></tr></tbody></table>

The fleet segmentation was done in such manner that the seasonality of the fleet could be captured using a suitable effort dynamics model.

In this case study only, the Basque fleet was modelled, due to data availability constraints, and was considered as representative of the whole Spanish fleet. The economic data was obtained from different sources, depending on the availability of the data. These data could be required at fleet-segment lever or at métier level.

### Fleet level data

- **Number of vessels:** The number of vessels by fleet segment was estimated from Logbooks data, identifying the vessels that use the specific fishing gears targeting pelagic species in the area of interest.

- **Effort:** The effort, measured in number of operating days, has been estimated at métier level, allocating the adequate métier to each fishing trip, and computing the number of days between the starting and ending day of the fishing trip. The data source where the Logbooks.
- **Fixed costs:** All the costs were extracted from regional statistics (https://www.euskadi.eus/pesca-cuentas-economicas/web01-a2estadi/es/), where data was available at fleet level. Data at vessel level was also available, but just until 2009. As fixed costs were at fleet segment level, and the updated data was available only at fleet level, we took updated data of regional statistics at fleet level and, considering the relative differences of the fleet segments in relation to the fleet as a whole, data at fleet segment level was estimated.

- **Capital value:** Information was compiled from regional statistics.

## Métier level data

- **Landings:** It was assumed that catches were equal to landings. The landings by métier and year were estimated from logbook data. In this first phase, the catches were estimated without taking into account the commercial category or age and consequently, same age composition as in the assessment was assumed.

- **Price:** The average price was estimated at métier level, with information for 2016 to 2021 from the First Sale Notes. The price by size or age of the fish was not considered. The price of 'OTH' species was estimated as the sum of landing value of all non-target species divided by the total catches of those species.

- **Fuel costs:** Fuel costs were estimated as fuel costs by unit of effort at métier level. From Basurko et. al. (2013), we estimated the number of litres required to catch one tone of target species by a given fishing gear. From these data, together with the catches from logbooks, we estimated the total litres of fuel consumed per métier and year, and by multiplying this value by the fuel price we got the estimation of fuel costs at métier level.

- **Other variable costs:** Variable costs were calculated from regional statistics (available at fleet level). Then, to split these variable costs by métier, we considered the number of days allocated to each métier. The total variable costs were split among métiers according to the number of days multiplied by the number of hours operating for each métier. In those years that the data was not available (2009 onwards), we maintained the costs share.

- **Crew share:** The crew share, defined as the percentage of gross value assigned to crew as salary, comes from expert knowledge.

## 2.3 Scenarios

Two scenarios were run which differed in the dynamic of the fishery in response to management and how the advice is generated.

* **Status quo (sq):** In this scenario the effort and its distribution among métiers was kept constant in the projection and equal to the mean of the last three data years (2019-2021). The aim of having this scenario is twofold, on the one hand is a control scenario that allows to identify problems in the conditioning of the model and on the other hand it provides a scenario against which to compare the rest of the scenarios.

* **Maximum profit under Landing Obligation (maxprof):** This scenario was selected as the alternative of traditional 'min' scenario. In the min scenario the fleet fully complies with the landing obligation, and they stop fishing when the first of the quotas is consumed, but this scenario is too restrictive for a seasonal and sequential fishery (therefore giving very inconsistent results). In a sequential fishery, when the quota of a given species is consumed, they start targeting other species. In fact, these fisheries can be considered a non-mixed fishery. 'maxprof' scenario is then more flexible than the 'min' approach and simulates sequential fisheries dynamics. This scenario seeks the total effort and effort allocation among métiers that maximises profit, considering the seasonal behaviour of the fleet and the availability of the stocks in the fishing grounds. For that, the total effort exerted by each fleet segment and the effort allocation among métiers is calculated by maximizing the profits but with the following constrains: (i) the total effort was constrained by the capacity of the fleet; and (ii) the catch quota by stock; and (iii) the effort share among métiers was restricted by an interval (defined as the maximum and minimum effort that the fleet can allocate to a given métier). These limits were set according to the historical behaviour of the fleet. The aim of these constraints was to reflect that, although there might be one métier that could be more profitable than another one, the fleet cannot necessarily allocate all its available effort to the more profitable one because the species might not be available all year round.

<figure><img src="image_148.png" /></figure>
In all the scenarios the effort of the fleets is constrained by the capacity of the fleet that is obtained from the number of vessels and the maximum number of days a vessel operated along the year.

### 3. Indicators, targets and limits

The socio-economic indicators used to analyse the socio-economic performance of the system were those proposed within the Task and no extra indicators were calculated.

The biomass and fishing mortality reference points used in the harvest control rules where those used by the assessment teams to generate the advice on fishing opportunities.

Regarding the ratio of landings value for the fleets below 24m, it was not possible to calculate this indicator at métier level, because in present case study the métiers were not defined based on the length of the vessels. Therefore, being not possible to estimate it directly, an approximation was calculated based on the historical percentage of the income of those vessels with less than 24m length.

### 4. Main results.

All the code and results were stored at a private GitHub repository (https://github.com/Fundacion-AZTI/seawise) and can be made available under request (at https://github.com/Fundacion-AZTI/seawise/archive/b92693dd9ea9525cb846d6bc45f21a08084821c1.zip).

#### 4.1. Stock level

##### SSB

The 'maxprof' scenario yields higher values of SSB for almost all species, the sardine stocks (IPIL and BPIL) are the exception (Figure 8). For both sardines, the catches in 'maxprof' increase in the projected period, while the fixed effort the catches are almost 0. For anchovy (ANE) and mackerel (MAC) the abundance is decreasing along the whole projected period.

The SSB of most of the stocks was well above the limit and trigger reference points in the projection period for the 'maxprof'. However, in the latest years of the projection period, the sardines, the mackerel, and the anchovy reach high biological risks (of falling below Blim). In the 'sq' scenario, the albacore (ALB), the mackerel and the southern hake are the stock with higher biological risks.
<figure><img src="image_149.png" /></figure>

**Figure 8:** SSB timeseries (in tonnes) for the modelled stocks (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-). Solid line corresponds to the median and shaded area to the 90% confidence interval. Horizontal dashed line corresponds to Blim.

## Fishing mortality

The fishing mortality in the status quo scenario generally corresponds to the highest fishing mortality scenario, except for the sardines (Figure 9). Moreover, in most of the cases, this mortality is at or above the fishing mortality target ($F_{MSY}$). In the maxprof scenario, the fishing mortality fluctuates around $F_{MSY}$, but reaching values well above the target.
<figure><img src="image_150.png" /><figcaption>**Figure 9:** F/F<sub>MSY</sub> ratio for the modelled stocks (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-) in the projection period. Horizontal line corresponds to F<sub>MSY</sub> level.</figcaption></figure>

## Average Age

Important differences are observed in the mean age among scenarios (Figure 10). In the 'maxprof' the mean age of all the stocks is bigger at the end of the projection period (between more than 2 years for the bluefin tuna (BFT) and 0.15 years for the Iberian sardine -BPIL-), with the exception of the anchovy (ANE) that has a very minor reduction in its mean age. Whereas for the status quo scenario, there are different trends between stocks. A big reduction for the hakes (up to half age) and lower for albacore and anchovy (0.13 and 0.07, respectively). And a great increase for the rest of stocks of around 1 year and up to 2 years for the albacore. Bluefin tuna and mackerel have very similar trends in both scenarios.
<figure><img src="image_151.png" /><figcaption>**Figure 10:** Expected mean age for the modelled stocks (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-) during the projection period.</figcaption></figure>

## 4.2. Fleet level

### Effort

Effort time series for the two scenarios at fleet level is shown in **Figure 11** for Basque fleets. For all the fleets, in the 'maxprof' scenario the effort was lower than in the status quo. The reduction is especially remarkable for those fleets targeting big pelagics (BC_HLT, BC_PSB, BC_PSBB and BC_PST), where the fleets drastically decrease their effort to levels well below to the historical observed efforts (at around 10% of the original values). Even in the pure purse seine fleet segment (BC_PPS), which is the only one that does not target big pelagics, effort levels are around half relative to the ones in the status quo scenario.
<figure><img src="image_152.png" /><figcaption>**Figure 11:** Effort timeseries (number of days at sea) for the Basque fleets (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-). Solid line corresponds to the median and shaded area to the 90% confidence interval.</figcaption></figure>

## Landings + Revenue

In the short term, the economic indicators are more favourable in the status quo scenario than in the 'maxprof' scenario (Figure 12). However, these economic results get worse as projection time advances and in the long term the tendency is changed (i.e., more favourable economic indicators in the 'maxprof' scenario). The higher revenues are achieved always at the expense of very high unwanted catch in the status quo scenario, which is completely avoided in the 'maxprof' scenario under the landing obligation.

Total fleet landings, revenues and wages decrease with time, but more rapidly in the status quo scenario. Gross profit turn from positive values in the short-term, to negative values in the long term, especially remarkable in the status quo scenario. Gross value added is also reduced with time reaching to values near to zero in the 'maxprof' scenario and to very big negative values in the status quo. Small vessels (less than 20 metres long) take advantage in both scenarios, but benefits are higher in the status quo scenario.
<figure><img src="image_153.png" /><figcaption>**Figure 12:** Economic indicators for several periods by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-) for all the Basque fleets (Table 4). Solid line corresponde to the median and shaded area to the 90% confidence interval.</figcaption></figure>

## Bycatch

In the Basque fleets, the bycatch issue affects exclusively to the purse seiners, which incidentally capture dolphins. The bycatch is much lower in the 'maxprof' scenario, than in the status quo one (Figure 13). Due to the sharp reduction in effort experienced in the 'maxprof' scenario.

## Effort by Gear

For all the gears used by the Basque fleet, in the 'maxprof' scenario there is sharp reduction of the effort compared to the status quo (Figure 14). The purse seine fleet is the one with lower reduction (~70%), followed by the pole lines (~87%). Whereas hand and trolling lines experiment an almost complete reduction (≥99%).
<figure><img src="image_154.png" /><figcaption>**Figure 13:** Bycatch of dolphins (number of individuals) for the Basque fleets (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-).</figcaption></figure>

<figure><img src="image_155.png" /></figure>
<figure><img src="image_156.png" /><figcaption>**Figure 14:** Effort (number of days at sea) by gear (LHM: pole lines; LHP: hand lines; LTL: trolling lines; and PS: purse seine), time period for the Basque fleets (Table 3) and scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-).</figcaption></figure>

Catches by km²

Not having analysed the spatially explicit information for the historical catches and therefore assuming a distribution
of the catches all along the ICES subarea 8 (675 745 km²), the reduction of catches for the 'maxprof' scenario, implies
also a reduction of the catches by km² (Figure 15).
<figure><img src="image_157.png" /></figure>

**Figure 15:** Catches (tonnes) by km² for the Basque fleets (Table 3) by scenario (sq: status quo -green-, and maxprof: profit maximisation under the landing obligation -blue-). Given the catch distribution all along the ICES subarea 8 (675 745 km²).

**5. Conclusions**

The scenario assuming that the fleet distribution is based on the maximisation of profits under the landing obligation, seems to be less favourable in the short-term. However, it turns economically more favourable in the long term. The maximisation of profits, leads the Basque pelagic fleet to reduce the effort of the métiers targeting big pelagic species (i.e., albacore and bluefin tuna) almost to zero. This is probable due to the increase of the variable costs in these métiers, due to the increase in the fuel costs in the last years and to the fact that these stocks are appearing more northern, so that the fleet must do larger trips to reach the fishing grounds. These factors and the assumption of fixed prices independent to the amount of landings, make these métiers not profitable anymore. However, it is highly probable that prices fluctuate based on the total landings and therefore modelling the elasticity of prices would give a more reliable perspective.

Regarding the biological risks, the maximisation of profits leads to a reduction of the fishing effort, and consequently a reduction in fishing mortality, that implies that most of the stocks are fished below FMSY. Therefore, the number of stocks with risk of falling below Blim above 5% (the maximum acceptable level by ICES) is lower than in the status quo scenario. However, specifically risks for sardines are higher, because the métiers fishing these species gain importance at the expenses of the reduction of the effort in the tuna directed métiers.

All the economic indicators deteriorate as time passes. However, the deterioration is bigger in the status quo scenario, than when the fleet is pursuing profit maximisation. This is motivated by the reduction of effort and consequently landings, that has a positive effect on the biological populations, and consequently allows higher catches at similar effort levels. Moreover, this reduction of effort, has a positive effect on the expected bycatch, which allows a reduction of the bycatch of dolphins.

Status quo scenario is not expected to be a potential scenario for the Basque inshore fleet, due to characteristics of this pelagic fleet. Because the fleet is highly selective and consequently, an adaptation of the effort by métier is
expected based on the available quotas for the different fleets. However, the translation of this effort is limited. As these fleets are seasonal and sequential (i.e., main target stock of the fleets is changing in each season), if the stock related to one season is not available, the effort allocated to this season would be really low (because for a period of time they do not have any alternative main species available to be fished). Thus, they have a limited capacity of increasing the effort allocated to one métier when another métier has a low quota.

## 6. References

Andrés, M. and Prellezo, R. 2012. Measuring the adaptability of fleet segments to a fishing ban: the case of the Bay of Biscay anchovy fishery. *Aquatic Living Resources* 25 (3): 205-214. DOI: 10.1051/alr/2012018.

Basurko, O.C., Gabiña, G., Uriondo, Z. 2013. Energy performance of fishing vessels and potential savings. *Journal of Cleaner Production* 54: 30-40. https://doi.org/10.1016/j.jclepro.2013.05.024.

Cobb, C. W., and Douglas, P. H. 1928. A Theory of Production. *American Economic Review* 18: 139-165.

ICCAT. 2020. Report of the 2020 ICCAT Atlantic Albacore stock assessment meeting. Online, 29 June – 8 July 2020. https://www.iccat.int/Documents/Meetings/Docs/2020/REPORTS/2020_ALB_ENG.pdf

ICES. 2022a. Working Group for the Bay of Biscay and the Iberian Waters Ecoregion (WGBIE). ICES Scientific Reports 4:52. http://doi.org/10.17895/ices.pub.20068988

ICES. 2022b. Working Group on Southern Horse Mackerel, Anchovy and Sardine (WGHANSA). ICES Scientific Reports 4:51. 518 pp. http://doi.org/10.17895/ices.pub.19982720

ICES. 2022c. Working Group on Widely Distributed Stocks (WGWIDE). ICES Scientific Reports 4:73. 922 pp. http://doi.org/10.17895/ices.pub.21088804

Kraak, S.B.M., Buisman, F.C., Dickey-Collas, M., Poos, J.J., Pastoors, M.A., Smit, J.G.P., Daan, N., et al. 2004. How can we manage mixed fisheries? A simulation study of the effect of management choices on the sustainability and economic performance of a mixed fishery. In Technical Reports: ICES Doc. CM 2004/FF, 11. 26 pp.

Murillas-Maza, A., and Andrés M. 2016. Providing incentives for fishermen through rights-based co-management systems. An impact-assessment on Basque fisheries. *Marine Policy* 70: 128-136. https://doi.org/10.1016/j.marpol.2016.04.027

Pella, J.J., and Tomlinson, P.K. 1969. A generalized stock production model. *Bulletin of the Inter-American Tropical Tuna Commission* 13: 419-496.

Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A. and Haddon, M. 2016. Management strategy evaluation: best practices. *Fish and Fisheries* 17: 303-334. doi:10.1111/faf.12104.

Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L., Vanrolleghem, P.A., Højberg, A.L. 2007. Uncertainty in the environmental modelling process - A framework and guidance. *Environmental Modelling and Software* 22(11): 1543-56.

Salz, P., Buisman, F.C., Soma, K., Frost, H., Accadia, P., and Prellezo, R. 2011. FISHRENT; Bio-economic simulation and optimisation model. LEI Report 2011-024. ISBN/EAN: 978-90-8615-514-9.

Villamor, B., Aranda, M., Punzón, A., Uriarte, A., and Báez, J.C. 2008. The anchovy fishery in the Bay of Biscay: description and analysis of the Spanish fleet (2000-2005). ICES CM 2008/:30.
Rouyer, T., Kimoto, A., Zarrad, R. Ortiz, M. Palma, C., Mayor, C. Lauretta, M., Rodriguez-Marin, E. and Walter, J. 2022. Data and initial model set-up for the 2022 VPA stock assessment of the Eastern Atlantic and Mediterranean bluefin tuna. *Collective Volume of Scientific Papers ICCAT* 79(3): 161-187.

<figure><img src="image_158.png" /></figure>
## 8.7 Annex 7: Description of analyses carried out with model BEE-Fish for areas Western Baltic Sea (WBS) & Central Baltic Sea (CBS).

### 1. Description of the model used

Ecological-economic modeling

We developed and applied a coupled three- (CBS) and two (WBS)-species, age-structured ecological-economic model, which is taking most recent biological, e.g. predation rates, as well as economic information into account. We investigate the effects of different management scenarios, reflecting different strategic long-term management objectives. Our model is an update of the ecological-economic multispecies model of Voss et al. (2014a, b), using new ecological as well as economic parametrization. It builds on the fisheries economic module of a single-species age-structured fishery model for Baltic cod developed by Tahvonen et al. (2018) and expands it to include interactions with herring and sprat stocks. We use the subscript $i \in \{C,S,H\}$ for the cod (C), sprat (S), and herring (H) fisheries. For all three fisheries we consider total economic surplus, i.e. the sum of consumer surplus and producer surplus (fishing profits).

### 2. Description of model parameterization and scenarios

We specify iso-elastic inverse demand functions

$$p_i(t) = \bar{p}_i H_i(t)^{-\eta_i}, \qquad (1)$$

with $\bar{p}_i > 0$, meaning that the price $p_i$ of species $i$ in year $t$ decreases with the quantity of fish $H_i$ supplied to the market (i.e., the catch from that species in the given year) with an elasticity $\eta_i > 0$. This assumes that the price is the same for all age (and thus size) groups. We further specify harvesting cost functions as

$$C_i(H_i(t), X_i(t), t) = c_i(t)X_i(t)^{-\chi_i} H_i(t) \qquad (2)$$

where we allow for a time trend in the cost parameter with $c_i(t) = c_{i0} \exp(\phi_i t)$. The variable $X_i(t)$ is the efficient biomass of species $i$ in year $t$. It is obtained as

$$X_i(t) = \sum_{\text{ages}=1}^{8} w_i(s)q_i(s,t)x_i(s,t), \qquad (3)$$

where $w_i(s)$ is the weight of an individual fish of species $i$ at age $s$, $x_i(s,t)$ is the number of fish in stock of species $i$ and age $s$ in year $t$, and $q_i(s,t)$ is the 'catchability' of that species at age $s$. Given that fishing gear is size-selective, it depends on the age of the fish, and may vary over time as gear selectivity changes, for example due to changing mesh sizes.
For estimating the economic model parameters, we use the method developed by Tahvonen et al. (2018). To this end, we consider the profit margin $\pi_i(t)$, i.e. the profit of harvesting species i as a percentage of revenues for that species. Using the specification of harvesting cost function, this gives

$$ \pi_i(t) = \frac{p_i(t)H_i(t) - c_{i0}\exp(\phi_i t)X_i(t)^{-\chi_i}H_i(t)}{p_i(t)H_i(t)} \quad (4) $$

Rearranging, taking logs, and adding an error term $\varepsilon_{it}$, we obtain the following equation that can be estimated using data on catches, efficient biomass, and prices:

$$ \ln((1 - \pi_i(t))p_i(t)) = \ln(c_{i0}) - \chi_i \ln(X_i(t)) + \phi_i t + \varepsilon_{it}. \quad (5) $$

Using the specification of an iso-elastic inverse demand function, we further get

$$ \ln(H_i(t)) = \ln\left(\frac{(1 - \pi_i(t))\bar{p}_i}{c_{i0}}\right)^{\frac{1}{\eta_i}} + \frac{\chi_i}{\eta_i}\ln(X_i(t)) - \frac{\phi_i}{\eta_i}t + \xi_{it}. \quad (6) $$

For cod, we use the estimates of Tahvonen et al. (2018), for herring and sprat, we use $\pi_i(t) = 0$, as these fisheries have been hardly profitable in the past (Quaas et al. 2012), data from ICES (2019) on landings, age-specific fishing mortalities, and stock sizes, and time series of prices (sprat: 2002, herring: 1988 to 2019) from the annual (until 2003) and monthly (since 2003) reports of the German Federal Office for Agriculture and Food.

We define the case-specific scenario of MMEY management where the objective is to maximize the intertemporal welfare, as the sum of consumer surplus (gross consumer benefit minus expenditure) and profits (revenues minus harvesting costs). The objective thus is to maximize the present value of the differences between gross consumer benefit and harvesting costs for all species:

$$ \max \sum_{\text{time } t=0}^{\infty} \sum_{\text{species } i} \left( \frac{1}{1+r} \right)^t \left( \frac{\bar{p}_i}{1-\eta_i} H_i(t)^{1-\eta_i} - c_i X_i(t)^{-\chi_i} H_i(t) \right) \quad (11) $$

We use an interest rate of r=1% per year.

The constraints to the optimization are that fishing mortalities must be non-negative $F_i(t) \ge 0$, the given initial stock numbers $x_i(s, 0)$ for all three species in all age groups, and the age-structured multi- species population dynamics, i.e.

$$ \begin{aligned} x_i(1, t+1) &= \phi_i \\ x_i(s, t+1) &= \alpha_i(s-1, ssb_c(t))(1-q_i(s)(1-\exp(-F_i(t))))x_i(s-1, t) \quad \text{for } s=2, \dots, 7 \\ x_i(8, t+1) &= \alpha_i(7, ssb_c(t))(1-q_i(s)(1-\exp(-F_i(t))))x_i(7, t) + \alpha_i(8, ssb_c(t))(1-q_i(8)(1-\exp(-F_i(t))))x_i(8, t) \end{aligned} $$
Age-specific natural survival rates are derived from natural mortalities as

$$ 
\begin{aligned}
\alpha_c(s, ssb_c(t)) &= \exp(-M_c(s)) \\
\alpha_s(s, ssb_c(t)) &= \exp(-M_{s1}(s) - M_{s2}(s)ssb_c(t)) \\
\alpha_H(s, ssb_c(t)) &= \exp(-M_{H1}(s) - M_{H2}(s)ssb_c(t))
\end{aligned}
\qquad (14) $$

They depend on cod predation for herring and sprat populations, which increases with the size of the cod stock. As indicator for stock abundance, we use spawning stock biomass

$$ ssb_i(t) = \sum_{s=1}^{8} w_i(s) \gamma_i(s) x_i(s, t), \qquad (15) $$

of species i, which depends on the weights $w_i(s)$, maturities $\gamma_i(s)$ and stock numbers $x_i(s,t)$ at age s and in year t. Estimates for predation mortalities are based on the 2019 key-run (ICES, 2019b) of a stochastic multi-species model (SMS: Lewy and Vinther, 2004). Age-specific weights $w_i(s)$ and maturities $\gamma_i(s)$ are taken from the ICES (2019a) assessment reports for the three stocks, using the values from 2018. All biological input data is reported in the Supplementary Material (Tab. S2).

Age-specific catchabilities $q_i(s)$ were estimated based on mean age-specific fishing mortalities for the years 2002 to 2019 as reported in ICES (2019a) with $q_i(amax)=1$ for the age class amax with highest mortality by normalization.

## Numerical optimization

In order to determine the optimal MMEY management, while respecting any given constraints in the management scenarios, we solved the optimization problem numerically. For this, the dynamic optimization was performed using the interior-point algorithm of the Knitro (version 12.1) optimization software with AMPL (A Modeling Language for Mathematical Programming, AMPL Optimization LLC, Albuquerque, USA).

## 3. Indicators, targets and limits

Model output included a set of ecological indicators:

* Total stock biomass

* Spawning stock biomass

* Mean age in the stock, weighted by age-specific weight

as well as fisheries indicators:

* Landings

* Effort

and socio-economic indicators:

* Value of landings
* Employment

* Producer surplus

* Consumer surplus

* Gross profit

Model outcome was compared to indicators and limit reference points developed by ICES.

## 4. Main results.

The simulations were carried out with partly strong differences in target fishing mortality (Tab. 1). The Status Quo values reflect the already successful reduction of F for the collapsed stocks (e.g. CBS_Cod, WBS_Herring) or in contrast, the not-yet successful conservation attempts (e.g. WBS_Cod).

<table><caption>Tab. 1: Values of target fishing mortality used as input in the model runs. CBS: Central Baltic Sea; WBS: Western Baltic Sea</caption><thead><tr><th>Scenario</th><th>CBS_Cod</th><th>CBS_Herring</th><th>CBS_Sprat</th><th>WBS_Cod</th><th>WBS_Herring</th></tr></thead><tbody><tr><td>F<sub>MSY</sub></td><td>0.3</td><td>0.21</td><td>0.31</td><td>0.26</td><td>0.31</td></tr><tr><td>Status Quo</td><td>0.1</td><td>0.39</td><td>0.38</td><td>0.9</td><td>0.15</td></tr><tr><td>F<sub>MSY upper</sub></td><td>0.59</td><td>0.26</td><td>0.41</td><td>0.44</td><td>0.379</td></tr></tbody></table>

For the Central Baltic Sea trade-offs between landings, revenues, and fishery profits become obvious (Tab. 2). Landings do not directly translate to revenues, or profits. This is explained by the inclusion of a demand system in the model framework, so that prices react on harvest levels. Out of the three input-F scenarios ($F_{MSY}$, Status Quo, $F_{MSY upper}$), $F_{MSY upper}$ creates the highest landings, and revenues. The $F_{MSY}$ strategy, however, generates higher profits at lower fishing mortality values. Following the Status Quo fishery scenario resulted in the worst combination of outcomes. Including a Welfare optimization scenario (case specific scenario) offers the inclusion of additional, interesting indicators, i.e. to include a consumer perspective by analysing the consumer surplus.

<table><caption>Tab. 2: Central Baltic Sea: Comparison of model outcomes for different fishing mortality scenarios. "Welfare" refers to a case-specific scenario, in which total welfare is optimized.</caption><thead><tr><th></th><th>Landings</th><th>Revenues</th><th>Profits</th></tr></thead><tbody><tr><td>F<sub>MSY</sub></td><td>0</td><td>0</td><td>++</td></tr><tr><td>Status Quo</td><td>-</td><td>-</td><td>0</td></tr><tr><td>F<sub>MSY upper</sub></td><td>+</td><td>++</td><td>+</td></tr></tbody></table>
<table><tr><td>Welfare</td><td>++</td><td>++</td><td>-</td></tr></table>

Surprisingly, optimization for the objective of welfare leads to lower stock sizes, and lower profits for the fishery as in the other scenarios. This can be explained by the relatively higher importance of consumer surplus in the optimization objective. The welfare-optimization will therefore need to be further restricted by side conditions, i.e. non-negative profits and/or minimum stock sizes, as has already been shown for the Central Baltic Sea (Voss et al., 2022). A reduction in consumer surplus under constrained optimization, as done in Voss et al. (2022) did not proportionally translate to changes in fishery profits. In most cases, the fisheries gained profits, but not always. Management faces several levels of trade-offs: a consumer to producer surplus trade-off, and a consumer surplus to ecological goals trade-off (as implementing minimum stock sizes reduces consumer surplus). Even within the fishery, trade-offs between the species were identified. However, we also find a win-win constellation, as accepting $B_{lim}$ levels in the management increases ecological performance as well as total profitability of the fisheries. Generally, management which applies lower F-values, will lead to a faster recovery of the depleted stock in the Baltic.

In the Western Baltic, trade-off between landings (i.e. food security objective), revenues, and fishery profits are obvious, too. Depending on the variable of choice, a different management strategy would be favorable. Only a Status Quo management will in no case lead to the most desirable outcome. Again, the inclusion of a welfare optimization scenario will lead to a substantially different outcome. Further work on this objective is planned, including inserting side conditions like non-negative profits and minimum stock sizes, to refine trade-off analysis and offer management-ready alternatives.

<table><caption>Tab. 3: Western Baltic Sea: Comparison of model outcomes for different fishing mortality scenarios. “Welfare” refers to a case-specific scenario, in which total welfare is optimized.</caption><thead><tr><th></th><th>Landings</th><th>Revenues</th><th>Profits</th></tr></thead><tbody><tr><td>$F_{MSY}$</td><td>++</td><td>0</td><td>0</td></tr><tr><td>Status Quo</td><td>-</td><td>-</td><td>+</td></tr><tr><td>$F_{MSY\_upper}$</td><td>0</td><td>+</td><td>++</td></tr><tr><td>Welfare</td><td>+</td><td>++</td><td>-</td></tr></tbody></table>
## 5. Conclusions

We used updated ecological and economic data to investigate fisheries management outcomes for the Baltic Sea. Ecological-economic multispecies models have been shown to be useful to study trade-offs between different management objectives (Voss et al., 2014a, b). Only by including the economic dimension, a balanced resource use, their equitable distribution and conservation, i.e. the “triple bottom line in fisheries management” (Halpern et al., 2013) can be addressed. The identification of trade-offs as well as potential synergies among multiple ecosystem goods and services is a central issue in ecosystem-based management (EBM; McLeod and Leslie, 2009) and it is urgently needed to progress Baltic fisheries management into this direction.

Management which is based on a multi-annual plan (EU, 2016), offers some flexibility, as fishing mortality ranges are defined, which are centered around $F_{MSY}$ estimates. For cod, however, $F_{MSY}$ is currently not defined and the basis for the scientific advice is the precautionary approach (ICES, 2021). Earlier results (Voss et al., 2022) suggest that fisheries management in the central Baltic, in a multispecies context and respecting economic objectives, should head for Maximum Multispecies Economic Yield (MMEY) instead of using MMSY to define objectives. MMEY seems closer to the ideas of EBM, as the economic dimension is explicitly included in formulating the management objectives. While both concepts (MMSY and MMEY) needed to include side conditions in order to reach the triple bottom line of fisheries management, MMEY only had to be complemented by one side condition, i.e. setting a minimum stock size for cod. Changing the minimum stock size of cod in our simulations from $B_{lim}$ to the more precautionary $B_{PA}$ (Precautionary Approach) did not change the results qualitatively.

### Synergies and trade-offs

So far, fishery management often fails to adequately address issues of socio-economic equity (Lam and Pitcher, 2012). This is a problem, as management that neglects the fair distribution of benefits that ecosystems provide causes low acceptance and compliance (Lam and Pitcher, 2012; Lam and Calcari, 2012). Our model framework offers the opportunity for a more holistic analysis by including producer as well as consumer benefits. By doing so, Voss et al. (2022) discovered a win-win situation between producer surplus (the fishery) and respecting minimum stock size limits. Environmentalists, concerned about the conservation status of cod, and the fishing industry, concerned about conservation of a viable fishery and its cultural heritage, both benefit from rebuilding the cod stock. This result is caused either by increasing cod prices due to lower supply if quotas are set more restrictive, or by decreasing costs for the cod fishery due to the stock effect (which implies that unit operating costs will be sensitive to the size of the exploited fish stock; Hannesson, 2007) or a combination of both. The consumer only pays for the stock conservation, if prices increase. Which effect is dominant can, however, not be resolved by our model.
## References

EU (2016). Regulation (EU) 2016/1139 of the European Parliament and of the Council of 6 July 2016 establishing a multiannual plan for the stocks of cod, herring and sprat in the Baltic Sea and the fisheries exploiting those stocks, amending Council Regulation (EC) No 2187/2005 and repealing Council Regulation (EC) No 1098/2007. Official Journal of the European Union, L 191, 15.7.2016. http://data.europa.eu/eli/reg/2016/1139/oj

Halpern, B.S., Klein, C.J., Brown, C.J., Beger, M., Grantham, H.S., et al. (2013). Achieving the triple bottom line in the face of inherent trade-offs among social equity, economic return and conservation. Proc Natl Acad Sci USA 110(15): 6229-6234.

Hannesson, R. (2007). A Note on the “Stock Effect”. Marine Resource Economics 22: 69-75.

ICES (2021). Cod (*Gadus morhua*) in subdivisions 24–32, eastern Baltic stock (eastern Baltic Sea). In Report of the ICES Advisory Committee, 2021. ICES Advice 2021, cod.27.24-32.

ICES (2019a). Baltic Fisheries Assessment Working Group (WGBFAS). ICES Scientific Reports. 1:20. 653 pp. http://doi.org/10.17895/ices.pub.5949

ICES (2019b). Working Group on Multispecies Assessment Methods (WGSAM). ICES Scientific Reports. 1:91. 320 pp.

Lam, M.E., Calcari, M.E. (2012). The privilege to fish. Ecol Soc 17(4):19-20.

Lam, M.E., Pitcher, T.J. (2012). The ethical dimensions of fisheries. Cur Op Env Sust 4(3): 364-373.

Lewy, P., Vinther, M. (2004). Modelling stochastic age-length-structured multi-species stock dynamics. ICES CM 2004/FF 20, 43 pp.

McLeod, K.L., Leslie, H.M. (2009). Ecosystem-based management for the oceans. Washington (D.C.): Island Press. 392 p.

Quaas M.F., Froese, R., Herwartz, H., Requate, T., Schmidt, J.O., Voss, R. (2012). Fishing Industry Borrows from Natural Capital at High Shadow Interest Rates. Ecological Economics (82):45–52.

Tahvonen, O., Quaas, M. F., Voss, R. (2018). Harvesting selectivity and stochastic recruitment in economic models of age-structured fisheries. *Journal of Environmental Economics and Management*, 92: 659–676.

Voss, R., Quaas, M.F., Schmidt, J.O., Hoffmann, J. (2014a). Regional trade-offs from multi-species maximum sustainable yield (MMSY) management options. Mar Ecol Prog Ser 498:1-12. https://doi.org/10.3354/meps10639
Voss, R., Quaas, M.F., Schmidt, J.O., Tahvonen, O., Lindegren, M., Möllmann, C. (2014b). Assessing Social – Ecological Trade-Offs to Advance Ecosystem-Based Fisheries Management. PLoS ONE 9(9): e107811. https://doi.org/10.1371/journal.pone.0107811

<figure><img src="image_159.png" /></figure>
## 8.8 Annex 8: FLBEIA in Eastern Ionian Sea GSA 20

**authors:** Vasiliki Sgardeli, Marianna Giannoulaki, Stavroula Tsoukali, Aggelos Liontakis, Celia Vassilopoulou

**contact:** vsgard@hcmr.gr, marianna@hcmr.gr

### Introduction

In the Eastern Ionian Sea operate both small pelagic fishery including purse seine fleet (PS) and demersal fishery. The latter consists of two main fleet categories: the otter bottom trawl fleet (OTB) and the small scale fleet (SSF) comprising multi-licence coastal vessels exploiting a variety of gears (mainly longlines and gillnets, but also pots, traps and dredges). The fishery can be further categorized in 22 fleet segments according to main gear and vessel length (STECF, 2021). In 2020, ~30 PS, 26 OTB vessels and ~3000 SSF vessels were active in the eastern Ionian Sea waters (DCF, 2021). The demersal fishery catches a varied mix of species (up to 100 commercial species) with European hake (*Merluccius merluccius*), Red mullet (*Mullus barbatus*), Striped red mullet (*Mullus surmuletus*) and Deep water rose shrimp (*Parapenaeus longirostris*) being the most important stocks, comprising up to 25% of total catch and total landings value (DCF, 2021). Other important stocks are Picarel (*Spicara smaris*), Bogue (Boops boops), Octopus (*Octopus vulgaris*), Common pandora (*Pagellus erythrinus*), and Common cuttlefish (*Sepia officinalis*) (Anonymous, 2021). From these stocks, only hake and red mullet are regularly assessed in STECF and GFCM groups (STECF, 2020; FAO, 2022), while assessments exist for the other 5 stocks in the Annual fleet report of 2021 (Anonymous, 2021). The rest of the commercial species of the fishery remain un-assessed. From the assessed stocks, hake, red mullet and common pandora are considered overexploited in the eastern Ionian Sea, while the rest are sustainably exploited (Anonymous, 2021; FAO, 2022; STECF, 2020).

The fishery is managed by input control rules, restricting the fleets' effort in days at sea, and a fixed number of fishing licenses. The OTB fleet is allowed to operate 8 months during the year between October and May (except 24-31 December and 24-31 May), while it is not allowed to fish less than 3 miles from the coast or at depths less than 50m and in any case not less than 1.5 miles from the coast, according to the Management plan for the Greek bottom trawls following the EU regulation 1967/2006 (Anonymous, 2013). The SSF fleet is allowed to fish all year long, however, the actual days at sea of the fleet are much less (~140 days) for reasons having to do with seasonal fish abundance (e.g. fish targeted by longlines are not abundance throughout the year), weather conditions, but also the fact that for many vessel owners fishing is a secondary occupation. A special restriction applying to the SSF fleet is the ban of the targeted fishing of hake (i.e. more than 20% of the catch consisting of hake individuals) during February (Anonymous, 2013). Both fleets are subject to spatial restrictions (closed areas). According to Petza et al. (2017), 37% of national waters are subject to spatial restrictions and 27,8% are subject to spatiotemporal restrictions and similar percentages apply to the eastern Ionian Sea. Technical measures apply for both fleets specifying minimum catching sizes and gear configurations. The demersal fisheries in the Mediterranean (including Greece) have been exempted from the discard ban (EU regulation 2015/812) and a minimum discard rate of 5% per year is allowed for the OTB fleet, while 3% and 1% discard rates are allowed for nets and longlines respectively (EU regulation 2020/4).

A particularity of the Greek demersal fishery is the large number of SSF vessels (~10000 active vessels), which are operated by local fishers and their families as a means of living. The SSF fleet may not be always profitable but can still provide sufficient income to support the fishing families (Liontakis et al., 2020). By contrast, the OTB fleet is profitable adding annually around 50 million euros to the country's GDP. The fishing fleet has been in decline since mid-1990, with withdrawal of 3,333 vessels (17.57%) in the period 2003-2020, which corresponds to a capacity reduction (GT) of 31,27% (Anonymous, 2021).

Main problems of management are the ineffective monitoring and surveillance (in part due to the large number of vessels). This causes the coastal fishery to be largely unregulated, while a big issue is the Illegal, Unreported and Unregulated (IUU) fishing. Beneficial management rules have been the ban of the OTB fleet from fishing within 1.5 miles from coast according
to EU regulation (EC 1967/2006), which has helped the mullets (red mullet and striped red mullet) populations to increase. In addition, the introduction of the 40mm square mesh size for the OTB fleet (EC 1967/2006) has had very positive effects on the hake stock (Mytilineou et al., 2021).

## Description of the model used

The bioeconomic future of the eastern Ionian Sea demersal fishery under the current management regime was projected using the FLBEIA bio-economic simulation software (Garcia et al., 2017). The model is conditioned using catch and biomass data since 1994, where available. The projections start in 2021 (first projection year) and go up to 2050. The initialization is done using the average of each parameter in the historical period 2018-2020.

The model considers two demersal fleets (large scale fleet: LSF and small scale fleet: SSF) and three main stocks, i.e. European hake (HKE), red mullet (MUT) and deep water rose shrimp (DPS). In addition, stripped red mullet (MUR) is included as a separate stock, while all other commercial stocks caught by the demersal fleets (~70) are considered as one stock (OTR) with pooled biomass and catches. To correctly project the biomass dynamics of this OTR stock, an auxiliary fleet targeting only OTR had to be defined (AUX), which represents the effort and catches of Purse Seines and Beach Seines. Table I shows the fleet structure and catch shares of species by fleet/métier combination. The small-scale fleet (SSF) has two métiers, nets (NET) and long-lines (LLS), while the large-scale fleet (LSF) has one metier, otter bottom trawl (OTB). The fishing effort, is expressed in days_at_sea*GT for the LSF, and days_at_sea for the SSF.

<table><caption>**Table I.** Catch shares by fleet and stock for the Eastern Ionian Sea demersal fishery (average values in the period 2013-2020, excluding 2015 and 2017). Stocks: HKE - European hake, MUT - red mullet, MUR - striped red mullet, DPS: deep water rose shrimp and OTR - the rest of the stocks combined. Fleets: LSF - otter bottom trawls, SSF: small scale fishery with two métiers (NET: nets, LLS: long-lines) and an auxiliary fleet (AUX) catching only the OTR stock.</caption><thead><tr><th>catch shares</th><th>OTB</th><th>SSF:NET</th><th>SSF:LLS</th><th>AUX</th></tr></thead><tbody><tr><th>HKE</th><td>0.265</td><td>0.677</td><td>0.058</td><td>-</td></tr><tr><th>MUT</th><td>0.242</td><td>0.758</td><td>-</td><td>-</td></tr><tr><th>MUR</th><td>0.092</td><td>0.908</td><td>-</td><td>-</td></tr><tr><th>DPS</th><td>1</td><td>-</td><td>-</td><td>-</td></tr><tr><th>OTR</th><td>0.127</td><td>0.386</td><td>0.062</td><td>0.425</td></tr></tbody></table>

## Description of model parameterisation

### Stock dynamics

HKE and MUT are modelled as age-structured stocks, utilizing a4a (Jardim et al., 2015) stock assessment from GFCM (FAO, 2022). DPS, MUR and OTR are modelled as biomass dynamic stocks, assessed with the time-varying productivity extension of the SPiCT model (Mildenberger et al., 2020). From the three stocks only DPS shows significant increase in its productivity in the reference period (1994-2020). Assessment results for BD stocks are included in Appendix A.

### Stock Recruitment (SR) relationship

For age structured stocks (MUT and HKE) a Ricker SR relationship has been fitted to the SSB and recruitment estimates of the a4a stock assessments. The fit was repeated for 300 randomly selected iterations. Results of these fits are included in Appendix B.
Uncertainty

Uncertainty of stock assessment parameters and SR relationships was taken into account for all modelled stocks in a Monte Carlo setting. In this, 300 different initializations were defined. From those, a subset of 292 iterations were valid (for the rest the SR fits did not converge) and were finally used.

Fishery sub-models

The fishery submodel considers a constant effort regime. The effort changes per scenario are applied once, in 2021, and the projection evolves with no further changes in effort or advice.

Economic sub-models

The economic sub-models consider fixed prices (which are size dependent for the structured stocks), a fixed capital model and a CobbDouglas production function.

Scenarios tested, Biological indicators, targets and limits

Scenarios tested

The main scenarios agreed for the Mediterranean case study were applied to the eastern Ionian fishery projections. In particular, the F01, Fup and Flw scenarios were applied in reference to the HKE stock, which is the most overexploited stock in the fishery. Reference points of the main stocks of the fishery are presented in Table II. Although the initial envision was to project scenarios only for the LSF (i.e OTB), which is currently under a management plan, it was finally decided that the scenarios will apply to both fleets. As is evident from Table I, for the majority of landings of HKE come from the SSF (~70%). For this reason, even the full closure of the LSF would not suffice to achieve the F01 (or even Fup) objective for this stock. Two scenarios combining all three stocks' objectives were applied, the PGY and Fcomb scenarios. The PGY scenario is defined as the intersection of PGY ranges of the three stocks (see Figure 1). For DPS, the PGY range corresponds to the fishing mortalities that achieve 80% of MSY, while for MUT and HKE the 'PGY ranges' are assumed equal to the Fup-Flw ranges (not strictly following the definition of PGY). Finally, the Fmsy combined scenario (Fcomb) defines a combined target Fishing mortality that is the average of the target fishing mortalities of the three stocks, weighted by each stock's share of the catch, i.e.,

$$F_{comb} = \frac{\text{lands}_{HKE} \times F_{01\_HKE} + \text{lands}_{MUT} \times F_{01\_MUT} + \text{lands}_{DPS} \times F_{msy\_DPS}}{\text{lands}_{HKE} + \text{lands}_{MUT} + \text{lands}_{DPS}}$$

For the Fcomb scenario, it was decided to have management consequences only for the LSF fleet, to have a balance between LSF and SSF in the set of scenarios investigated (see Figure 1). The final set of scenarios adapted to the eastern Ionian Sea (GSA20) are the following:

a) **SQ**: Status quo (same effort as in the period 2018-2020);

b) **F01**: Effort reduction to achieve the F0.1 (used as FMSY proxy) for HKE;

c) **Fup**: Effort reduction to achieve Fup for HKE;

d) **Flw**: Effort reduction to achieve Flw for HKE;

e) **Fcomb**: Effort reduction to achieve a combined FMSY on all main stocks (HKE, DPS, MUT);

f) **PGY**: Effort change to achieve Pretty Good Yield (PGY) from all main stocks (HKE, DPS, MUT);
<table><caption>**Table II.** Current fishing mortality and reference points for the three main stocks of the eastern Ionian Sea (GSA20) demersal fishery in 2020. For age structured stocks, HKE and MUT, reference points were estimated using FLRef package in R (Winker, 2022; R core team, 2022) and assuming a Ricker SR relationship (i.e the mean Ricker curve fitted). F01 for MUR and DPS were defined on the Yield-F curve as the points where the slope is 10% of that in the origin. MSY and biomass are in tons.</caption><thead><tr><th>Ref point</th><th>DPS</th><th>HKE</th><th>MUT</th><th>MUR</th></tr></thead><tbody><tr><td>F01</td><td>0.490</td><td>0.201</td><td>0.285</td><td>0.475</td></tr><tr><td>Fup</td><td>0.669</td><td>0.283</td><td>0.399</td><td>0.648</td></tr><tr><td>Flw</td><td>0.327</td><td>0.138</td><td>0.194</td><td>0.316</td></tr><tr><td>Fmsy</td><td>0.540</td><td>0.565</td><td>0.379</td><td>0.21</td></tr><tr><td>MSY</td><td>329.000</td><td>958.861</td><td>406.042</td><td>240</td></tr><tr><td>Bmsy</td><td>607.000</td><td>3796.300</td><td>1122.303</td><td>458</td></tr><tr><td>Blim</td><td>NA</td><td>2521.714</td><td>985.453</td><td>NA</td></tr><tr><td>SSBmsy</td><td>NA</td><td>2521.714</td><td>985.453</td><td>NA</td></tr><tr><td>Fcur</td><td>0.190</td><td>0.380</td><td>0.320</td><td>0.11</td></tr><tr><td>F/Fref</td><td>0.350</td><td>1.861</td><td>1.100</td><td>0.53</td></tr></tbody></table>

<figure><img src="image_160.png" /></figure>
**Figure 1.** Effort multipliers by fleet for the six scenarios defined for the eastern Ionian Sea (GSA 20) bioeconomic projections. The coloured areas correspond to fishing mortality ranges of the three main stocks; Fup to Flw range for MUT and HKE and PGY (fishing mortality corresponding to 80%MSY) for DPS. The effort multiplier set for the PGY scenario is defined on the intersection of the three species ranges.

**Table III.** Effort multipliers by scenario and fleet used in the bioeconomic projections of the eastern Ionian sea demersal fishery. LSF: large scale fleet, SSF: small scale fleet.

<table><thead><tr><th>scenario</th><th>LSF</th><th>SSF</th></tr></thead><tbody><tr><td>Status quo</td><td>1</td><td>1</td></tr><tr><td>F01</td><td>0.529</td><td>0.529</td></tr><tr><td>Fup</td><td>0.745</td><td>0.745</td></tr><tr><td>Flw</td><td>0.363</td><td>0.363</td></tr><tr><td>Fcomb</td><td>0.472</td><td>1</td></tr><tr><td>PGY</td><td>1.6</td><td>0.4</td></tr></tbody></table>

## Indicators, targets and limits

The aim in Deliverable 6.7 was to test the consistency of a wide range indicators and their associated targets and limits (where available). Therefore, a set of indicators has been agreed based on the indicators used to measure the performance of current management. As types of indicators ecological and socio-economic indicators in relation to CFP objectives as well as MSFD related indicators and global indicators were included to cover a broad range of indicators.

For each indicator the average over the prediction years 2025-2030, 2035-2040, 2045-2050 (i.e. 5-year periods as suggested by the most recent MSFD guidelines (European Commission 2022)) were calculated. In Table IV you may find the list of indicators calculated for this case study and D6.7

<table><caption>Table IV. Set of indicators that were calculated for GSA20 fishery within D6.7.</caption><thead><tr><th>Type of indicator</th><th>Indicator</th><th>Targets or limits available</th></tr></thead><tbody><tr><td rowspan="2">CFP ecological</td><td>Proportion of stocks fished at or below F<sub>MSY</sub></td><td>Yes</td></tr><tr><td>Proportion of stocks with median SSB below MSY B<sub>trigger</sub></td><td>Yes</td></tr></tbody></table>
<table><thead><tr><th>Type of indicator</th><th>Indicator</th><th>Targets or limits available</th></tr></thead><tbody><tr><td></td><td>Proportion of stocks with >5% probability to fall below B<sub>lim</sub></td><td>Yes</td></tr><tr><td rowspan="9">CFP socio-economic</td><td>Landings (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Unwanted catch/Discards (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Revenue (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Gross profit (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Gross value added (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Employment (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Wages (average of yearly sums across fleets/metiers)</td><td>No</td></tr><tr><td>Average yearly ratio of current revenue/break even revenue (sum across fleets/metiers)</td><td>Yes</td></tr><tr><td>Ratio landings value fleets &lt;=24m/landings value fleets &gt;24m</td><td></td></tr><tr><td>MSFD related indicators (Descriptor 3)</td><td>D1C1: Bycatch or risk for PET species (here sea turtles)</td><td>Partly (for some species and regions)</td></tr></tbody></table>
<table><thead><tr><td>Type of indicator</td><td>Indicator</td><td>Targets or limits available</td></tr></thead><tbody><tr><td>indicators are already included under CFP indicators)</td><td>D6: Relative effort of OTB gear (as proxy of impact on benthic communities</td><td>No</td></tr></tbody></table>

## 4. Main results

Tables V-VII Indicators showing the proportion of stocks fished at or below Fmsy, the proportion of stocks with median SSB below SSBmsy (median B<Bsmy for biomass dynamic stocks) and proportion of stocks with >5% probability to fall below Blim, provided for each scenario. Stock specific landings and landings' value are included in Table C1 in Appendix C. Figures 2 to 4 present the changes in the biological indicators depending on the scenario applied whereas Figures 5 to 6 show the respective changes in the socio-economic indicators.

<figure><img src="image_161.png" /></figure>
<table><caption>**Table V.** Indicators showing the proportion of stocks fished at or below Fmsy, the proportion of stocks with median SSB below SSBmsy (or B<Bmsy for biomass dynamic stocks) and proportion of stocks with >5% probability to fall below Blim, provided for each scenario.</caption><thead><tr><th>Scenario</th><th>Time period</th><th>Proportion of stocks fished at or below F<sub>MSY</sub></th><th>Proportion of stocks with median SSB below MSY B<sub>trigger</sub></th><th>Proportion of stocks with >5% probability to fall below B<sub>lim</sub></th></tr></thead><tbody><tr><td>F01</td><td>2025-2030</td><td>1</td><td>0</td><td>0</td></tr><tr><td>F01</td><td>2035-2040</td><td>1</td><td>0</td><td>0</td></tr><tr><td>F01</td><td>2045-2050</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>Flw</td><td>2025-2030</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Flw</td><td>2035-2040</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Flw</td><td>2045-2050</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Fup</td><td>2025-2030</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>Fup</td><td>2035-2040</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>Fup</td><td>2045-2050</td><td>0.75</td><td>0</td><td>0</td></tr><tr><td>PGY</td><td>2025-2030</td><td>0.5</td><td>0.25</td><td>0</td></tr><tr><td>PGY</td><td>2035-2040</td><td>0.5</td><td>0.25</td><td>0</td></tr><tr><td>PGY</td><td>2045-2050</td><td>0.5</td><td>0.25</td><td>0</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>0.5</td><td>0</td><td>0</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>0.5</td><td>0</td><td>0</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>0.5</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>**Table VII.** Socioeconomic indicators, given as average values of the given time periods, for each scenario.</caption><thead><tr><th>Scenario</th><th>Time period</th><th>Landing s</th><th>Revenue landings</th><th>Discards</th><th>Gross profit</th><th>Gross value added</th><th>Employ ment (FTE)</th><th>Wages</th><th>CR/BER</th><th>Rev_SSF /LSF</th></tr></thead><tbody><tr><td>F01</td><td>2025-2030</td><td>1670.1</td><td>2241778</td><td>7.3</td><td>-4102856</td><td>1271212</td><td>1095.9</td><td>2685807</td><td>1.9</td><td>4.7</td></tr><tr><td>F01</td><td>2035-2040</td><td>1671.4</td><td>2232116</td><td>8.1</td><td>-4154144</td><td>1188904</td><td>1095.9</td><td>2670297</td><td>1.9</td><td>4.7</td></tr><tr><td>F01</td><td>2045-2050</td><td>1646.0</td><td>2136600</td><td>7.6</td><td>-4479450</td><td>723273</td><td>1095.9</td><td>2600134</td><td>1.4</td><td>4.6</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>2535.8</td><td>3118009</td><td>7.3</td><td>-9899357</td><td>-2140488</td><td>2025.5</td><td>3878207</td><td>-3.4</td><td>9.2</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>2556.8</td><td>3177980</td><td>7.3</td><td>-9683411</td><td>-1824444</td><td>2025.5</td><td>3928256</td><td>-3.2</td><td>9.3</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>2551.8</td><td>3163629</td><td>7.1</td><td>-9725543</td><td>-1885815</td><td>2025.5</td><td>3918636</td><td>-3.2</td><td>9.3</td></tr><tr><td>Flw</td><td>2025-2030</td><td>1205.6</td><td>1712266</td><td>4.9</td><td>-2763625</td><td>1623666</td><td>752.4</td><td>2192418</td><td>0.6</td><td>4.9</td></tr><tr><td>Flw</td><td>2035-2040</td><td>1188.4</td><td>1653444</td><td>5.8</td><td>-2982657</td><td>1301426</td><td>752.4</td><td>2140814</td><td>0.3</td><td>4.8</td></tr><tr><td>Flw</td><td>2045-2050</td><td>1137.6</td><td>1494914</td><td>5.0</td><td>-3503151</td><td>559781</td><td>752.4</td><td>2030239</td><td>-0.5</td><td>4.7</td></tr><tr><td>Fup</td><td>2025-2030</td><td>2232.1</td><td>2783164</td><td>10.9</td><td>-6407684</td><td>11396</td><td>1543.0</td><td>3208313</td><td>2.7</td><td>4.5</td></tr><tr><td>Fup</td><td>2035-2040</td><td>2246.6</td><td>2825720</td><td>11.2</td><td>-6252486</td><td>227335</td><td>1543.0</td><td>3238684</td><td>3.1</td><td>4.5</td></tr><tr><td>Fup</td><td>2045-2050</td><td>2237.5</td><td>2798500</td><td>10.9</td><td>-6380272</td><td>42954</td><td>1543.0</td><td>3210386</td><td>2.9</td><td>4.5</td></tr><tr><td>PGY</td><td>2025-2030</td><td>2007.7</td><td>2418949</td><td>22.2</td><td>-2349774</td><td>2266745</td><td>934.2</td><td>2090482</td><td>12.0</td><td>1.2</td></tr><tr><td>PGY</td><td>2035-2040</td><td>2007.1</td><td>2456572</td><td>23.3</td><td>-2321556</td><td>2320383</td><td>934.2</td><td>2103192</td><td>11.9</td><td>1.2</td></tr><tr><td>PGY</td><td>2045-2050</td><td>1995.0</td><td>2416104</td><td>22.5</td><td>-2457367</td><td>2144865</td><td>934.2</td><td>2083339</td><td>11.4</td><td>1.2</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>2814.9</td><td>3265435</td><td>15.6</td><td>-9643901</td><td>-2193498</td><td>2071.8</td><td>3723974</td><td>2.8</td><td>4.3</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>2855.6</td><td>3344614</td><td>15.3</td><td>-9373853</td><td>-1806977</td><td>2071.8</td><td>3782211</td><td>3.2</td><td>4.4</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>2856.7</td><td>3345854</td><td>15.2</td><td>-9376433</td><td>-1812731</td><td>2071.8</td><td>3780624</td><td>3.2</td><td>4.4</td></tr></tbody></table>
<table><caption>**Table VIII.** MSFD related indicators regarding relative effort for each metier (OTB, NET, LLS as proxies of the impact on marine communities), turtles bycatch (mean annual bycatch in individuals) and relative effort of OTB gear type as proxy of impact on the benthic communities, given as average values of the given time periods and for each scenario.</caption><thead><tr><th>Scenario</th><th>Time period</th><th>OTB (effort change, as a proportion relative to SQ)</th><th>NET (effort change, as a proportion relative to SQ)</th><th>LLS (effort change, as a proportion relative to SQ)</th><th>D1C1: Turtles Bycatch</th><th>D6: Relative effort of OTB gear (as proxy of impact on benthic communities)</th></tr></thead><tbody><tr><td>F01</td><td>2025-2030</td><td>0.529</td><td>0.529</td><td>0.529</td><td>3.174</td><td>0.529</td></tr><tr><td>F01</td><td>2035-2040</td><td>0.529</td><td>0.529</td><td>0.529</td><td>3.174</td><td>0.529</td></tr><tr><td>F01</td><td>2045-2050</td><td>0.529</td><td>0.529</td><td>0.529</td><td>3.174</td><td>0.529</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>0.472</td><td>1</td><td>1</td><td>6</td><td>0.472</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>0.472</td><td>1</td><td>1</td><td>6</td><td>0.472</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>0.472</td><td>1</td><td>1</td><td>6</td><td>0.472</td></tr><tr><td>Flw</td><td>2025-2030</td><td>0.363</td><td>0.363</td><td>0.363</td><td>2.178</td><td>0.363</td></tr><tr><td>Flw</td><td>2035-2040</td><td>0.363</td><td>0.363</td><td>0.363</td><td>2.178</td><td>0.363</td></tr><tr><td>Flw</td><td>2045-2050</td><td>0.363</td><td>0.363</td><td>0.363</td><td>2.178</td><td>0.363</td></tr><tr><td>Fup</td><td>2025-2030</td><td>0.745</td><td>0.745</td><td>0.745</td><td>4.47</td><td>0.745</td></tr><tr><td>Fup</td><td>2035-2040</td><td>0.745</td><td>0.745</td><td>0.745</td><td>4.47</td><td>0.745</td></tr><tr><td>Fup</td><td>2045-2050</td><td>0.745</td><td>0.745</td><td>0.745</td><td>4.47</td><td>0.745</td></tr><tr><td>PGY</td><td>2025-2030</td><td>1.6</td><td>0.4</td><td>0.4</td><td>2.4</td><td>1.6</td></tr><tr><td>PGY</td><td>2035-2040</td><td>1.6</td><td>0.4</td><td>0.4</td><td>2.4</td><td>1.6</td></tr><tr><td>PGY</td><td>2045-2050</td><td>1.6</td><td>0.4</td><td>0.4</td><td>2.4</td><td>1.6</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>1</td><td>1</td><td>1</td><td>6</td><td>1</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>1</td><td>1</td><td>1</td><td>6</td><td>1</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>1</td><td>1</td><td>1</td><td>6</td><td>1</td></tr></tbody></table>
<figure><img src="image_162.png" /></figure>

**Figure 2. Biological indicators** a) Spawning stock biomass (SSB), b) total biomass of the stocks under consideration (DPS, HKE, MUT, MUR, OTR) by scenario in the eastern Ionian Sea. Coloured ranges correspond to 95% intervals from 292 iterations.

<figure><img src="image_163.png" /></figure>

**Figure 3. Biological indicators** a) fishing mortality, b) FFmsy of the stocks under consideration (DPS, HKE, MUT, MUR, OTR) by scenario in the eastern Ionian Sea. Coloured ranges correspond to 95% intervals from 292 iterations.
<figure><img src="image_164.png" /><figcaption>**Figure 4. Biological indicators** a) Probability of SSB falling below SSBmsy (or B below Bmsy for BD stocks), b) probability of SSB falling below SSBlim, c) average age (weighted by biomass at age) of the stocks under consideration (DPS, HKE, MUT, MUR) by scenario in the eastern Ionian Sea.</figcaption></figure>
<figure><img src="image_165.png" /><figcaption>**Figure 5.** Economic indicators for a) Large scale fleet (OTB) and b) Small Scale Fleet (SSF) by scenario (comparative plot) for: grossProfit, gross, ProfitMargin, gross value added (gva), Current Revenue to Break-Even Revenue (CR/BER) and CR/BERunpaid (i.e., excluding unpaid labour for costs for the SSF fleet) and effort (expressed in days-at-sea*GT for OTB fleet, and days-at-sea for SSF). Coloured ranges correspond to 95% intervals from 292 iterations.</figcaption></figure>
<figure><img src="image_166.png" /><figcaption>**Figure 6. Socioeconomic indicators** Effort (days-at-sea*GT for OTB fleet, and days-at-sea for SSF), effort share, and salaries (Wages) by scenario for each metier (OTB, LLS, NET) of the eastern Ionian Sea (GSA20)</figcaption></figure>
Conclusions

From the two demersal fleet segments of the eastern Ionian fishery, only the large scale fleet (LSF) utilizing OTB gear is under a management plan, in action since 2013 (Anonymous, 2013). This sets MSY based targets for the main stocks of the fishery, namely european hake, deep water rose shrimp and mullets. The small scale fleet (SSF) is under certain spatiotemporal regulations and technical restrictions, but is not managed, although it produces ~70% of landings and ~80% of landings value. During the scenario specification in this study it became apparent that MSY sustainability targets for hake (the most overexploited stock in the fishery) could not be achieved even if the OTB gear was to be banned. Hence, scenarios were defined that apply to both fleets.

In particular, scenarios that aim to achieve F01 and Fw or Fup fishing mortality reference points for HKE were applied. These are achieved with 47%, 26% and 63% effort reduction from both fleets, accordingly. Two scenarios combining multiple species objectives were applied. A multistock Pretty Good Yield scenario (PGY), which relaxes the MSY objective by defining a range of fishing mortalities per stock (typically those corresponding to 80%MSY) and attempts to find effort allocations which fall on the intersection of the different stock ranges (Hilborn, 2010). In the eastern Ionian fishery, the PGY scenario for hake, shrimp and mullet is achieved by a doubling of the LSF effort and reducing of the SSF effort to half. In addition, an Fmsy combined scenario was defined, which sets a target fishing mortality that is the average of the target fishing mortalities of the main stocks, weighted by their contribution to the catch. Finally, the Status quo scenario projects the future of the fishery under the current situation, i.e. considering the effort is fixed to its historical average value in the period 2018-2020.

Results showed a common high uncertainty trait in all scenarios mainly driven by the uncertainty of stock assessment, especially of BD stocks. PGY is by far the most profitable scenario for the LSF in terms of catch increase, followed by Status quo and all other scenarios ranked by the effort reduction applied. This is not the case for the SSF, where differences occur depending on the species. For hake the highest catch is for Flw, F01, for striped red mullet it is the Fcomb that gives the highest catch and for red mullet the Status quo scenario.

In terms of biological indicators, differences occured depending on the species. The Flw is the scenario with the biggest SSB and total biomass regarding hake, deep water rose shrimp and red mullet and the lowest fishing mortality and FFmsy ratio. The proportion of stocks fished at or below Fmsy is higher for Flw and F01 followed by Fcomb and Fup (Figure 2). It is only in the case of FPGY that 25% of the stocks can be with median SSB below MSY Btrigger and this is practically due to the increase of the harvest of shrimp stock, which is sustainably exploited in all other scenarios (Figure 3). Moreover, for hake and red mullet, the two stocks that an age structure model was available, an increase in the average age was predicted for the Flw and the F01 scenarios (Figure 4).

From the MSFD indicators we examined the turtles bycatch (as mean annual bycatch in individuals) and the effortof OTB gear as a proxy of the impact on the benthic communities for each scenario. For turtles' bycatch the highest values were estimated for the Fcomb and Fup scenarios. The greater impact on the benthic community is expected under the PGY scenario, which is the only scenario foreseeing an increase of OTB gear effort.

In terms of economic indicators, PGY is by far the most profitable scenario for the LSF, followed by Status quo and all other scenarios ranked by the effort reduction applied. The picture is different for the SSF fleet, whereby the better performing scenarios in terms of gva, gross profit and CR/BER are the ones applying the greatest effort reduction (e.g. Flw, followed by PGY, F01 and Fup), event though these economic indicators are still negative across all scenarios. This is a result of a reduction of costs that is greater than the increase of the revenue and is an effect of including the imputed cost of unpaid labour in variable costs. Indeed, if one looks at the Income based indicators like FFI, CR/BER_income and Revenues, the Fcomb scenario is better performing (SEAWISE deliverable report D6.4). In this scenario, the effort of the SSF fleet does not change but the LSF fleet effort is reduced by half, allowing the recovery of hake biomass and increase of biomass of all stocks, leading to an overall higher harvest. In Flw and PGY scenarios the SSF activity cannot be sustained as the Income becomes negative on average.
The effort scenarios investigated, were applied considering a decrease or increase of days at sea. Alternative management could be applied that controls the number of fishing vessels. Although this might be more difficult to implement (on a social level) it should be more profitable in economic terms for the remaining vessels, and provide win-win situations for both the fleet viability/efficiency and the ecological sustainability (Sgardeli et al., 2022). In fact, the fishing fleet of both the LSF and the SSF has been in decline since the mid-90s, with an equilibrium not yet reached. As a result, the historical cathes of both fleets show declining trends. This declining trend of the fishing fleet has not been taken into account in the projections, whereby the number of vessels of both fleets has been considered constant and equal to the average of the period 2018-2020.

A main source of uncertainty originates from the SR relationship of the two age structured stocks, hake and red mullet. The SR of these stocks has been modeled with a Ricker SR relationship, fitted to the historical stock assessment estimates of Spawning Stock Biomass (ssb) and recruitment. The fitted SRs show a strong density dependence for hake, with declining recruitment above ~2500 tons of SSB. Such a result is not unrealistic and can be attributed to increased interspecific competition and in particular to the effect of predation of hake on hake juveniles (cannibalism), which becomes more intense at higher densities of recruits. Similar effect has been found in SEAWISE deliverable report on the effect of environmental variables in recruitment (SEAWISE Deliverable 3.2 – eastern Ionian Sea case study). The red mullet stock shows very weak density dependence.

To achieve the sustainable fishing of the HKE stock (i.e. scenarios F01, Fup and FLw), an effort reduction was applied to both fleets in equal proportions. In practice, there are multiple other effort re-allocations, not investigated herein, that can achieve the given target and it is a management decision, which re-allocation provides a better trade-off between ecological, economic, social or other objectives.

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# Appendix A: Stock assessment of Biomass dynamic stocks

Stock assessment of DPS GSA 20 with time varying productivity SPiCT model.

## DPS GSA 20

<figure><img src="image_167.png" /><figcaption>**Figure A1.** Residual diagnostics of DPS GSA20 SPiCT time varying productivity assessment.</figcaption></figure>

<table><caption>**Table A1.** Average estimates of SPiCT time varying productivity fit for DPS GSA 20 stock.</caption><thead><tr><th>K</th><th>r</th><th>MSY</th><th>B</th><th>F</th><th>Bmsy</th><th>Fmsy</th><th>BBmsy</th><th>FFmsy</th></tr></thead><tbody><tr><td>1222</td><td>1.089</td><td>329</td><td>966.56</td><td>0.19</td><td>607</td><td>0.54</td><td>1.59</td><td>0.35</td></tr></tbody></table>
<figure><img src="image_168.png" /><figcaption>**Figure A2.** Fit of DPS GSA20 SPiCT time varying productivity assessment.</figcaption></figure>

<figure><img src="image_169.png" /></figure>
MUR GSA 20

<figure><img src="image_170.png" /><figcaption>**Figure A3.** Residual diagnostics of MUR GSA20 SPiCT time varying productivity assessment.</figcaption></figure>

<table><caption>**Table A2.** Average estimates of SPiCT time varying productivity fit for MUR GSA 20 stock.</caption><thead><tr><th>K</th><th>r</th><th>MSY</th><th>B</th><th>F</th><th>Bmsy</th><th>Fmsy</th><th>BBmsy</th><th>FFmsy</th></tr></thead><tbody><tr><td>924</td><td>1.055</td><td>240</td><td>821.22</td><td>0.11</td><td>458</td><td>0.53</td><td>1.79</td><td>0.21</td></tr></tbody></table>
<figure><img src="image_171.png" /></figure>

spict_v1.3.4@81154d

<figure><img src="image_172.png" /><figcaption>**Figure A4.** Fit of MUR GSA20 SPiCT time varying productivity assessment.</figcaption></figure>
<figure><img src="image_173.png" /><figcaption>**Figure A5.** Residual diagnostics of OTR GSA20 SPiCT time varying productivity assessment.</figcaption></figure>

<table><caption>**Table A3.** Average estimates of SPiCT time varying productivity fit for OTR GSA 20 stock.</caption><tr><td>K</td><td>46513</td></tr><tr><td>r</td><td>1.091</td></tr><tr><td>MSY</td><td>12606</td></tr><tr><td>B</td><td>40415.43</td></tr><tr><td>F</td><td>0.15</td></tr><tr><td>Bmsy</td><td>23163</td></tr><tr><td>Fmsy</td><td>0.54</td></tr><tr><td>BBmsy</td><td>1.74</td></tr><tr><td>FFmsy</td><td>0.27</td></tr></table>
<figure><img src="image_174.png" /><figcaption>Figure A6. Fit of MUR GSA20 SPiCT time varying productivity assessment.</figcaption></figure>
<figure><img src="image_175.png" /><figcaption>Figure B1. Fit of Ricker stock recruitment relationship to recruitment and ssb estimates for HKE stock in GSA20.</figcaption></figure>

<figure><img src="image_176.png" /><figcaption>Figure B2. Fit of Ricker stock recruitment relationship to recruitment and SSB estimates for MUT stock in GSA20.</figcaption></figure>
## Appendix C. Revenues by stock

<table><caption>**Table C1.** Average Revenues and Landings (t) estimated for the indicated time periods, by scenario, fleet segment and stock, for the eastern Ionian sea demersal fishery. LSF: large scale fleet, SSF: small scale fleet.</caption><thead><tr><th>Scenario</th><th>Time period</th><th>Fleet segment</th><th>Stock</th><th>Revenues</th><th>Landings</th></tr></thead><tbody><tr><td>F01</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>692540</td><td>141.6</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>999611</td><td>111.6</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>60050</td><td>5.2</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>409868</td><td>55.9</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>2598542</td><td>467.4</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>3642735</td><td>561.5</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>799959</td><td>51.3</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>2746524</td><td>202.0</td></tr><tr><td>F01</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>5777540</td><td>1650.7</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>693941</td><td>141.9</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>989010</td><td>109.9</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>60050</td><td>5.2</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>435445</td><td>57.9</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>2598544</td><td>467.4</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>3471284</td><td>524.2</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>799959</td><td>51.3</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>2982310</td><td>211.9</td></tr><tr><td>F01</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>5777543</td><td>1650.7</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>693943</td><td>141.9</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>895054</td><td>99.7</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>60050</td><td>5.2</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>435507</td><td>57.9</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>2598544</td><td>467.4</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>3101882</td><td>471.2</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>799959</td><td>51.3</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>2982189</td><td>212.0</td></tr><tr><td>F01</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>5777543</td><td>1650.7</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>625198</td><td>127.9</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>715019</td><td>84.8</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>53680</td><td>4.6</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>259551</td><td>39.2</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>2310650</td><td>415.6</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>4801003</td><td>775.8</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>1514466</td><td>97.1</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>3647770</td><td>296.6</td></tr><tr><td>Fcomb</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>10880212</td><td>3108.6</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>626402</td><td>128.1</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>716897</td><td>82.7</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>53680</td><td>4.6</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>282203</td><td>41.4</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>2310650</td><td>415.6</td></tr></tbody></table>
<table><thead><tr><th>Scenario</th><th>Time period</th><th>Fleet segment</th><th>Stock</th><th>Revenues</th><th>Landings</th></tr></thead><tbody><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>4888194</td><td>774.1</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>1514466</td><td>97.1</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>3980811</td><td>316.6</td></tr><tr><td>Fcomb</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>10880213</td><td>3108.6</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>626403</td><td>128.1</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>705573</td><td>81.4</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>53680</td><td>4.6</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>282426</td><td>41.4</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>2310650</td><td>415.6</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>4828953</td><td>766.3</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>1514466</td><td>97.1</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>3984408</td><td>316.7</td></tr><tr><td>Fcomb</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>10880213</td><td>3108.6</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>488654</td><td>99.9</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>785844</td><td>86.4</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>41165</td><td>3.5</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>326743</td><td>42.6</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>1783760</td><td>320.8</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>3020367</td><td>456.5</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>548381</td><td>35.2</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>2211725</td><td>155.4</td></tr><tr><td>Flw</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>3965972</td><td>1133.1</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>489439</td><td>100.1</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>750147</td><td>83.3</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>41165</td><td>3.5</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>344935</td><td>43.5</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>1783761</td><td>320.8</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>2677765</td><td>403.1</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>548381</td><td>35.2</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>2389537</td><td>161.8</td></tr><tr><td>Flw</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>3965974</td><td>1133.1</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>489440</td><td>100.1</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>591009</td><td>65.8</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>41165</td><td>3.5</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>345182</td><td>43.5</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>1783761</td><td>320.8</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>2065635</td><td>310.3</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>548381</td><td>35.2</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>2391311</td><td>161.8</td></tr><tr><td>Flw</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>3965974</td><td>1133.1</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>918770</td><td>187.9</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>1201067</td><td>139.5</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>84662</td><td>7.3</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>468243</td><td>68.0</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>3654084</td><td>657.2</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>4052115</td><td>636.6</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>1127831</td><td>72.3</td></tr><tr><td>Fup</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>3115550</td><td>244.2</td></tr></tbody></table>
<table><thead><tr><th>Scenario</th><th>Time period</th><th>Fleet segment</th><th>Stock</th><th>Revenues</th><th>Landings</th></tr></thead><tbody><tr><td>Fup</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>8124407</td><td>2321.3</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>919815</td><td>188.1</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>1212619</td><td>136.5</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>84662</td><td>7.3</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>508450</td><td>71.6</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>3654086</td><td>657.2</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>4060990</td><td>629.6</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>1127831</td><td>72.3</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>3413574</td><td>259.5</td></tr><tr><td>Fup</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>8124413</td><td>2321.3</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>919823</td><td>188.1</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>1176302</td><td>132.2</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>84662</td><td>7.3</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>508436</td><td>71.6</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>3654086</td><td>657.2</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>3961861</td><td>610.2</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>1127831</td><td>72.3</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>3413565</td><td>259.5</td></tr><tr><td>Fup</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>8124413</td><td>2321.3</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>1368085</td><td>279.8</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>2601441</td><td>296.3</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>181616</td><td>15.6</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>1076540</td><td>152.5</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>7855664</td><td>1412.9</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>2265899</td><td>350.3</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>604850</td><td>38.8</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>1794652</td><td>137.5</td></tr><tr><td>PGY</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>4366526</td><td>1247.6</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>1345319</td><td>275.1</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>2642956</td><td>296.0</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>181616</td><td>15.6</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>1165455</td><td>160.1</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>7855670</td><td>1412.9</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>2271452</td><td>344.8</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>604850</td><td>38.8</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>1969087</td><td>145.6</td></tr><tr><td>PGY</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>4366530</td><td>1247.6</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>1345026</td><td>275.1</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>2526480</td><td>282.5</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>181616</td><td>15.6</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>1165443</td><td>160.0</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>7855670</td><td>1412.9</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>2164371</td><td>331.3</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>604850</td><td>38.8</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>1968993</td><td>145.6</td></tr><tr><td>PGY</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>4366530</td><td>1247.6</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_LSF</td><td>DPS</td><td>1086370</td><td>222.2</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_LSF</td><td>HKE</td><td>1383050</td><td>165.3</td></tr></tbody></table>
<table><thead><tr><th>Scenario</th><th>Time period</th><th>Fleet segment</th><th>Stock</th><th>Revenues</th><th>Landings</th></tr></thead><tbody><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUR</td><td>113447</td><td>9.8</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_LSF</td><td>MUT</td><td>483115</td><td>75.6</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_LSF</td><td>OTR</td><td>4875027</td><td>876.8</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_SSF</td><td>HKE</td><td>4148776</td><td>677.4</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUR</td><td>1511287</td><td>96.9</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_SSF</td><td>MUT</td><td>3192082</td><td>268.9</td></tr><tr><td>Status quo</td><td>2025-2030</td><td>GSA20_SSF</td><td>OTR</td><td>10839024</td><td>3096.9</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_LSF</td><td>DPS</td><td>1069931</td><td>218.8</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_LSF</td><td>HKE</td><td>1403387</td><td>164.5</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUR</td><td>113447</td><td>9.8</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_LSF</td><td>MUT</td><td>525586</td><td>80.3</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_LSF</td><td>OTR</td><td>4875010</td><td>876.8</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_SSF</td><td>HKE</td><td>4320176</td><td>694.6</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUR</td><td>1511283</td><td>96.9</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_SSF</td><td>MUT</td><td>3491757</td><td>285.6</td></tr><tr><td>Status quo</td><td>2035-2040</td><td>GSA20_SSF</td><td>OTR</td><td>10838988</td><td>3096.9</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_LSF</td><td>DPS</td><td>1069395</td><td>218.7</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_LSF</td><td>HKE</td><td>1401775</td><td>163.0</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUR</td><td>113447</td><td>9.8</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_LSF</td><td>MUT</td><td>526016</td><td>80.4</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_LSF</td><td>OTR</td><td>4875010</td><td>876.8</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_SSF</td><td>HKE</td><td>4325191</td><td>690.9</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUR</td><td>1511283</td><td>96.9</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_SSF</td><td>MUT</td><td>3494748</td><td>285.8</td></tr><tr><td>Status quo</td><td>2045-2050</td><td>GSA20_SSF</td><td>OTR</td><td>10838988</td><td>3096.9</td></tr></tbody></table>
## 8.9 ANNEX 9. Description of analyses carried out with modelling for indicators, targets and limits in the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas 17-18-19).

Authors: Isabella Bitetto¹, Giovanni Romagnoni¹, Maria Teresa Spedicato¹

¹Fondazione COISPA ETS, Bari, Italy

### 8.1 Description of the model used

The analysis was conducted in the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas 17-18-19) using 1) using BEMTOOL bioeconomic model for the estimation of the indicators related to the stock status and fisheries, including economic and social indicators, and 2) Ecopath with Ecosim model (EwE) for the ecosystem indicators related to the Descriptor 4 of the Marine Strategy Framework Directive (MSFD).

#### 8.1.1 BEMTOOL

BEMTOOL is an integrated bioeconomic modelling tool that follows a multi-fleet and multiple species approach, simulating the effects of management scenarios on stocks and fisheries (e.g. STECF, 2019; 2020; 2021a; Russo, Bitetto et al., 2017; Rossetto et al., 2015). Such effects in mixed fisheries are measured by a suite of indicators with associated uncertainty.

In this case study, BEMTOOL is implemented in the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas - GSAs 17, 18 and 19) to inform and support the modelling of indicators, targets and limits, linked to the management of key stocks: European hake (HKE), red mullet (MUT) and deep water rose shrimp (DPS). These stocks are among the main target of the fisheries and of the Multi Annual Management Plan in the Adriatic region (GFCM Recommendations GFCM/43/2019/5; GFCM/44/2021/1). In the GSA19 a MAP for demersal stocks is not yet in place. However, considering the possible connectivity of the populations in the whole area (Spedicato et al. 2022) also HKE and MUT in GSA19 were included in this analysis.

In details the stocks included are:

* European hake in GSAs 17-18 (HKE17-18);

* European hake in GSA 19 (HKE19);

* Red mullet in GSAs 17-18 (MUT17-18);

* Red mullet in GSA 19 (MUT19);

* Deep-water rose shrimp in GSAs 17-18-19 (DPS17-18-19).

The relevant fisheries are subject to an effort regime to progressively achieve the MSY ($F_{MSY}$) target in 2026 for all the key stocks. There are, thus, fishing opportunities already established for 2023.

In the simulation and forecast scenarios 22 fleets are considered. These include both active and passive demersal gears operated by fleet segments that rely on, and influence some or all of the stocks above mentioned. These fleets encompass all small and medium scale fisheries that will allow to investigate potential differences in the dynamic of the indicators induced by the simulated scenarios.

The following scenarios were explored for task 6.4 until 2050:
**S0_Status quo:** effort equal to fishing opportunities of 2023 for GSAs 17-18, while for GSA 19 same effort as in 2021;

**S1_Fmsy_DPS:** Effort reduction to achieve the F₀.₁ (used as FMSY proxy) of the most overexploited stock in 2026: this corresponds to an effort reduction of 69% on trawlers in GSAs 17-18-19, toward the F₀.₁ of DPS 17-18-19.

**S2_Fcomb_(PGY):** Effort reduction to achieve a FMSY combined (here considered as a proxy of PGY) on all the target stocks (HKE 17-18, MUT 17-18 and DPS 17-18-19): this corresponds to an effort reduction of 58% on trawlers in GSAs 17-18-19 toward a combined reference point estimated weighing the F₀.₁ of the above mentioned stocks by their total catch.

A similar reduction would have been required by using as reference Flow of European hake.

### 8.1.2 Ecopath with Ecosim (EwE)

The model includes single species for the most important commercial ones, i.e. European hake, red mullet, anchovy, sardine, Bluefin tuna, swordfish, blue and red shrimp, giant red shrimp, deepwater rose shrimp, mantis shrimp, Norway lobster, cuttlefish, and functional groups for other species with lower or no commercial importance. Several groups are split by depth range into shelf and slope groups, to account for the differences in the catch and bycatch in the fisheries targeting deepwater shrimps. None of these groups is resolved at age stanzas levels.

The most important fleets included are the bottom trawlers, divided by area and LOA; other fleets included in the model are the pelagic fisheries for small pelagic fish, longlines (demersal and pelagics aggregated), and polyvalent gears, which include setnets and other gears typical of small scale fisheries. Finally, the pelagic purse seine for tuna and dredges for clams are included. The fleets of eastern GSA18 (Albania and Montenegro) are aggregated by country with no resolution on the gears.

Ewe model was parameterized based on year 2008 and fitted to time series from 2008 to 2020, the model was limited so far to the South Adriatic and Western Ionian Sea (GSA 18 and 19).

## 8.2 Description of model parameterisation

### 8.2.1 BEMTOOL

The stock dynamics was modelled consistently with the most recent stock assessments (STECF-EWG 22-16 and STECF-EWG 21-15) and associated Reference Points aligned with the objectives of Maximum Sustainable Yield (MSY). From stock assessment outputs, *FLStock* objects have been created to be used by the *FLCore R* package (Kell et al. 2007), including catch data and estimated stock parameters up to 2021.

To project the scenarios, stock-recruitment relationships of the stocks were estimated using Eqsim (Table 8.1). An example is reported in Figure 8.1.

<table><caption>Table 8.1. Summary of the estimated SRR parameters by stock.</caption><thead><tr><td>Stock</td><td>SRR type</td><td>coefficient a</td><td>coefficient b</td></tr></thead><tbody><tr><td>HKE1718</td><td>Segmented regression</td><td>330.50</td><td>1300</td></tr><tr><td>HKE19</td><td>Bevholt</td><td>169.63</td><td>2.50E-03</td></tr><tr><td>MUT1718</td><td>Bevholt</td><td>1083131</td><td>1000</td></tr><tr><td>MUT19</td><td>Ricker</td><td>154.3937</td><td>6.33E-04</td></tr><tr><td>DPS171819</td><td>Segmented regression</td><td>1510.992</td><td>1500</td></tr></tbody></table>
<figure><img src="image_177.png" /><figcaption>Figure 8.1 Stock recruitment relationships for HKE17-18 (segmented regression) (left upper panel) MUT17-18 (Beverton and Holt) (right panel).</figcaption></figure>

Process uncertainty is considered in the projections, using the Eqsim SRR coefficients and Monte Carlo realizations combined with a multiplicative error on the estimated recruitments.

DCF data were used to parameterize the different components of BEMTOOL model:

* ✓ FDI time series 2013-2021 as regards landings, discards and fishing effort (https://stecf.jrc.ec.europa.eu/dd/fdi);

* ✓ Annual Economic Report (AER) as regards landings and fishing effort (https://stecf.jrc.ec.europa.eu/dd/fleet) for the years not covered by the FDI in the Mediterranean, i.e. 2008-2012;

* ✓ Data Call launched in SEAwise to gather data from MED&BS Data Call as regards biological parameters, catches and discards by length and age, as well as economic data at GSA level by Member State.

Considering possibility of including the effects of changes in the exploitation pattern in future applications of the model the following configuration was used:

$$F_f(a) = (Z_{\text{inp}} - \text{mean}(M)) * Sel_f(a) * f_{\text{act},f} * p_f$$

where F is fishing mortality, Z total mortality, M natural mortality, fact,f in the forecast is the ratio between the product of the number of fishing days, the number of vessels and the average GT (or Kw) of the fleet segment f for each month of forecast to the product of the number of fishing days, the number of vessels and the average GT (or Kw) of the fleet segment f in the last year of the simulation. This quantity is considered as reference for the application of change in fishing effort. Sel_f(a) is the fleet selectivity at a given length/age; p_f is the monthly ratio between the fleet segment catch to the total catch in the simulation (in the forecast it is fixed as an average of the last (n) years).

For HKE17-18 and HKE19, the model was parameterized using the maximum total mortality at age Zmax associated with a dome-shaped fleet selectivity (asymmetric normal), tuned according to the F at age of the assessment, starting from the selectivity parameters in Sala and Lucchetti (2011) (SL50%= 14.2, SR= 3.6 cm).

For MUT 17-18 and MUT19, the model was parameterized using Zmax associated with a dome-shaped fleet selectivity (asymmetric normal), tuned according to the F at age of the assessment, starting from the parameters in Sala and Lucchetti (2011) (SL50%= 0.7, SR= 4.6 cm).

For DPS17-18-19 the value of Zmean in the age range 0-3 was used, tuned according to the F at age of the assessment, starting from the parameters in the ImpleMed project (2021).

Discard was modelled through a reverse ogive model for the trawl fleets.

Reference points for all the stocks, as obtained during the stock assessment process at STECF and GFCM, are reported in Table 8.2. Following STECF decision in the absence of full MSY evaluations, and/or biomass reference points F₀.₁
forms a good proxy for MSY. Thus, for all stocks with analytical assessments F0.1 is provided based on the stock conditions over the last three years. MSY advice in terms of F and catch for 2023 are based on this approach (STECF 2021b; 2023). F0.1 is usually estimated using the FLBRP available in FLR.

$F_{MSY}$ ranges, Fupper and Flower, are estimated from the following empirical relationships provided by STECF EWG 15-06 (STECF, 2015):

$$ \text{Flow} = 0.00296635 + 0.66021447 \times \text{F0.1} $$

$$ \text{Fupp} = 0.007801555 + 1.349401721 \times \text{F0.1} $$

However, as these ranges cannot be tested according to the ICES procedure, Fupper is not considered for advice, while Flow can be used, given that it is a precautionary RP.

<table><caption>Table 8.2 Stock assessment results used to parameterize BEMTOOL model.</caption><thead><tr><th>Stock</th><th>Ref. year</th><th>F current</th><th>F<sub>MSY</sub> (F<sub>0.1</sub>)</th><th>F/ F<sub>MSY</sub></th><th>Fupp</th><th>Flow</th><th>SSB current</th><th>Blim</th><th>Bpa</th><th>Source</th><th>Assessment method</th></tr></thead><tbody><tr><td>HKE17-18</td><td>2021</td><td>0.390</td><td>0.232</td><td>1.681</td><td>0.250</td><td>0.120</td><td>3054</td><td>1344</td><td>1881</td><td>STECF-EWG 22-16</td><td>SS3</td></tr><tr><td>HKE19</td><td>2021</td><td>0.335</td><td>0.211</td><td>1.588</td><td>0.292</td><td>0.142</td><td>1527</td><td>NA</td><td>NA</td><td>STECF-EWG 22-16</td><td>a4a</td></tr><tr><td>MUT17-18</td><td>2020</td><td>0.370</td><td>0.360</td><td>1.028</td><td>0.490</td><td>0.230</td><td>8815</td><td>NA</td><td>NA</td><td>STECF-EWG 21-15</td><td>a4a</td></tr><tr><td>MUT19</td><td>2021</td><td>0.533</td><td>0.380</td><td>1.403</td><td>0.540</td><td>0.270</td><td>611</td><td>NA</td><td>NA</td><td>STECF-EWG 22-16</td><td>XSA</td></tr><tr><td>DPS17-18-19</td><td>2021</td><td>2.410</td><td>0.750</td><td>3.213</td><td>1.010</td><td>0.500</td><td>2199</td><td>NA</td><td>NA</td><td>STECF-EWG 22-16</td><td>a4a</td></tr></tbody></table>

The scenarios are defined reducing accordingly the effort (total days as vessels*average day per vessel) by fleet segment. Only the fishing effort of trawl fleets was reduced by decreasing the fishing days until 2026, while the number of vessels has not been modified.

Price dynamic was modelled as a function of the variation of landing (modified from Salz et al., 2011), through an elasticity coefficient.

The variable costs (fuel and other) have been assumed to vary proportionally to the annual fishing days, while fixed and the maintenance costs depending on the annual GT on the basis of the historical data. The capital costs depend on the annual GT (Saltz et al., 2011; Frost et al., 2013).

In 2019 the AER revisited and updated the calculation method used for depreciation costs and capital value. The opportunity cost was based on capital value and inflation and interest rate, through the formula:

$$ \text{Opportunity cost} = \text{capital value} \times \frac{(1+\text{interest rate})}{(1+\text{inflation rate})} - 1 $$

Annual values of interest rate and inflation rate were obtained from sdw.ecb.europa.eu/ and https://it.inflation.eu/, respectively. Total capital costs were calculated as the sum of opportunity and depreciation cost.

The labour costs have been assumed in line with the crew share system on the difference revenues minus variable costs, and the depreciation costs depending on the annual GT.

Before launching the scenarios, the model was first parameterized in the hindcasting mode for testing and the assessed fishing mortality, spawning stock biomass and observed catches were compared with the simulated ones.
Figure 8.2 reports two examples of hindcasting of the catches, F and SSB related to the stocks of HKE17-18 and MUT17-18.

<figure><img src="image_178.png" /><figcaption>Figure 8.2. Examples of hindcasting replicating the stock assessments for HKE17-18 and MUT17-18. Catches, F and SSB with confidence intervals.</figcaption></figure>

The scenarios have been projected until 2050.

### 8.2.2 Ecopath with Ecosim (EwE)

The stocks included in the model are modelled as biomass pools, based on the biomass in the surveys and catches reported in the AER data. The model is fitted to time series of catches and biomass, and driven by the effort annual time series at fleet level, obtained from AER. The dynamics of the main species are generally captured satisfactorily, with general increasing and decreasing trends observed in the data well replicated by the model, while annual fluctuations are not always perfectly captured, as expected by this modelling approach.

Also in Ewe the scenarios S0, S1 and S2 were tested until 2050.

### 8.3 Indicators, targets and limits

The CFP ecological and socio-economic indicators were estimated in BEMTOOL, while the MSFD related indicators (except Descriptor 3 indicators) in EwE. Table 8.3 summarizes the estimated indicator and if a target or a limit reference point is available.

In BEMTOOL the ecological and socio-economic indicators were estimated following the methods applied in the stock assessment working groups (e.g. STECF 2022a; and GFCM) and in the Annual Economic Report (AER, e.g. STECF 2022b).
<table><caption>Table 8.3 Set of indicators considered in the Case Study</caption><thead><tr><th>Type of indicator</th><th>Indicator</th><th>Targets or limits available</th><th>Comments</th></tr></thead><tbody><tr><td rowspan="5">CFP ecological</td><td>Proportion of stocks fished at or below F<sub>MSY</sub></td><td>Yes</td><td>Yes</td></tr><tr><td>Proportion of stocks with median SSB below MSY B<sub>trigger</sub></td><td>Yes</td><td>Not estimated at this stage</td></tr><tr><td>Proportion of stocks with >5% probability to fall below B<sub>lim</sub></td><td>Yes</td><td>Estimated for few stocks</td></tr><tr><td>Proportion of fleets with Sustainable Harvest Indicator (SHI) above 1</td><td>Yes</td><td>Not estimated at this stage</td></tr><tr><td>Proportion of fleets with number of stocks at risk (SAR) > 0</td><td>Yes</td><td>Not estimated at this stage</td></tr><tr><td rowspan="10">CFP socio-economic</td><td>Landings (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Unwanted catch/Discards (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes for the target stocks</td></tr><tr><td>Revenue (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Gross profit (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Gross value added (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Employment (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Wages (average of yearly sums across fleets/metiers)</td><td>No</td><td>Yes</td></tr><tr><td>Average yearly ratio of current revenue/break even revenue (sum across fleets/metiers)</td><td>Yes</td><td>Yes</td></tr><tr><td>Ratio landings value fleets &lt;=24m/landings value fleets &gt;24m</td><td>No</td><td>Yes, however we used the following ratio landings value fleets &lt;=18m/landings value fleets &gt;18m because oft he aggregation level we used for the selected fleets, as the segments 24-40 LOA was poorly represented</td></tr><tr><td>Accident rates</td><td>No</td><td>No</td></tr><tr><td>MSFD related indicators (Descriptor 3)</td><td>D1C1: Bycatch or risk for PET species</td><td>Partly (for some</td><td>No</td></tr></tbody></table>
<table><thead><tr><th>indicators are already included under CFP indicators)</th><th></th><th>species and regions)</th><th></th></tr></thead><tbody><tr><td></td><td>D1C3: Biomass of forage fish</td><td>Yes</td><td>No</td></tr><tr><td></td><td>D4C1: Biodiversity within trophic guilds³</td><td>No</td><td>Yes, Shannon indices per the guild</td></tr><tr><td></td><td>D4C2: Ratio between trophic guilds³</td><td>No</td><td>Yes, we used biomass per guild</td></tr><tr><td></td><td>D4C3: Large fish indicator across guilds³</td><td>No</td><td>Yes we used the mean maximum length (MML) and Large Species Indicator (LSI) across guilds</td></tr><tr><td></td><td>D4C4: Average recruitment success within guilds¹</td><td>No</td><td>No</td></tr><tr><td></td><td>D6: Effort (translated into swept area ratio)? by demersal gear type (at least Otter Trawls, Beam Trawls, Seines where possible)</td><td>No</td><td>Yes as relative effort of trawling gears</td></tr><tr><td></td><td>D10: Amount of marine litter</td><td>No</td><td>Developed a trend analysis on litter numbers and mass and spatial mapping of main marine litter categories. Relationships with effort not clear.</td></tr><tr><td rowspan="4">Global indicators</td><td>Carbon emission from fisheries (average of yearly sums across fleets/metiers)</td><td>No</td><td>Not developed at this stage</td></tr><tr><td>Ratio of fisheries catches to Primary production (Fogarty ratio in Link and Watson 2019)</td><td>No</td><td>Not developed at this stage</td></tr><tr><td>Catches per km2 per year (Ryther index in Link and Watson 2019)</td><td>No</td><td>Not developed at this stage</td></tr><tr><td>Ratio of fisheries catches to Cholophyl a (Friedland ratio in Link and Watson 2019)</td><td>No</td><td>Not developed at this stage</td></tr></tbody></table>

In **Ecopath with Ecosim (EwE)** the indicators are provided at guild level, using the guild resolution divided into top predators, piscivores, benthivores and planktivores. According to the guild repartitions, only fish are considered in the piscivores, benthivores and planktivores groups, while the top predator guild includes fish (e.g. pelagic sharks, tuna, swordfish) as well as seabirds, marine mammals, and sea turtles.

The indicators used are: Shannon index of biodiversity (MSFD D4C1); Biomass (MSFD D4C2) and Mean Maximum Length (MML; MSFD D4C3). This is based on the maximum reported length of the most abundant species in each functional group. Reported length from survey or catch data specific to the study area were used, where available, rather than absolute maximum size which can be considerably larger for the same species in other systems. The Large Species Indicator (LSI) was also calculated, and provided for comparison. The LSI was also based on the maximum reported length of the most abundant species in each functional group. A threshold for what is considered a "large" fish is set at 40 cm, dividing the community into categories of size ("large" and "small"). The representative species per functional group and their maximum size and size category are reported in Table 8.4. The MML was provided across guild (as per MSFD definition) but also calculated at guild level.
<table><caption>Table 8.4. Functional groups of fish (excluding top predators), with their most representative species, their guild, maximum reported size in the study area (used for MML), and size category based on the 40 cm threshold (used for LSI).</caption><thead><tr><th>functional group</th><th>Representative species</th><th>Guild</th><th>Max reported size (cm)</th><th>Size category</th><th>Source</th></tr></thead><tbody><tr><td>Rays and Skates</td><td>Raja clavata</td><td>benthivore</td><td>91.5</td><td>Large</td><td>DCF Biological sampling</td></tr><tr><td>Demersal sharks</td><td>Galeus melastomus</td><td>benthivore</td><td>59.5</td><td>Large</td><td>MEDITS</td></tr><tr><td>Medium pelagic fish</td><td>Sarda sarda</td><td>piscivore</td><td>58</td><td>Large</td><td>Fishbase</td></tr><tr><td>Demersal piscivorous fish Slope</td><td>Conger conger</td><td>piscivore</td><td>265</td><td>Large</td><td>Fishbase</td></tr><tr><td>Demersal piscivorous fish Shelf</td><td>Lepidopus caudatus</td><td>piscivore</td><td>178</td><td>Large</td><td>Fishbase</td></tr><tr><td>Anglerfish</td><td>Lophius piscatorius</td><td>piscivore</td><td>136.5</td><td>Large</td><td>MEDITS</td></tr><tr><td>Bogue and picarels</td><td>Boops boops</td><td>planktivore</td><td>29.5</td><td>Small</td><td>MEDITS</td></tr><tr><td>mesopelagics</td><td></td><td>planktivore</td><td>10</td><td>Small</td><td>guestimate</td></tr><tr><td>Jellyfish feeding pelagics</td><td>Centrolophus niger</td><td>planktivore</td><td>94</td><td>Large</td><td>Fishbase</td></tr><tr><td>Demersal fish Slope</td><td>Helicolenus dactylopterus</td><td>benthivore</td><td>34</td><td>Small</td><td>MEDITS</td></tr><tr><td>Demersal fish Shelf</td><td>Micromesistius poutassou</td><td>benthivore</td><td>47.5</td><td>Large</td><td>MEDITS</td></tr><tr><td>Other small pelagics</td><td>Liza ramada</td><td>planktivore</td><td>57</td><td>Large</td><td>CAMPBIOL</td></tr><tr><td>Anchovy</td><td>Engraulis encrasicolus</td><td>planktivore</td><td>19.5</td><td>Small</td><td>MEDITS</td></tr><tr><td>Sardine</td><td>Sardina pilchardus</td><td>planktivore</td><td>22.5</td><td>Small</td><td>MEDITS</td></tr><tr><td>Mackerel</td><td>Scomber scombrus</td><td>piscivore</td><td>39</td><td>Small</td><td>CAMPBIOL</td></tr><tr><td>Horse mackerel</td><td>Trachurus trachurus</td><td>planktivore</td><td>46</td><td>Large</td><td>CAMPBIOL</td></tr><tr><td>Red mullet</td><td>Mullus barbatus</td><td>benthivore</td><td>33.5</td><td>Small</td><td>MEDITS</td></tr><tr><td>Hake</td><td>Merluccius merluccius</td><td>piscivore</td><td>100</td><td>Large</td><td>CAMPBIOL</td></tr></tbody></table>

## 8.4 Main results

### 8.4.1 BEMTOOL

Table 8.5 and 8.6 report the CFP indicators, ecological and socio economic respectively. Regarding the ecological indicators, the proportion of stocks fished at or below FMSY is 0.2 and it increases to 0.8 in the first period of the simulation and then stabilized at 1. It is worth noting that the fishing mortality of the most part of the stocks was already decreasing in the last period of the hindcasting phase (Figure 8.3) that contributes to the recovery of the stock since the initial phase of the simulation. In scenarios 1 and 2 F decreases below the FMSY for all the stock except DPS17-18-19 that attained the FMSY.

Figure 8.4 clearly presents the increase of the SSB in S1 and S2 while in S0 the risk of the decrease of the DPS SSB is high.
<table><caption>Table 8.5 CFP indicators (eco)</caption><thead><tr><th>Scenario</th><th>Time period (provide average values over the time period)</th><th>Proportion of stocks fished at or below FMSY</th><th>Proportion of stocks with median SSB below MSY Btrigger</th><th>Proportion of stocks with >5% probability to fall below Blim</th></tr></thead><tbody><tr><td>S0_Status quo</td><td>2025-2030</td><td>0.2</td><td>NA</td><td>0.2</td></tr><tr><td>S0_Status quo</td><td>2035-2040</td><td>0.2</td><td>NA</td><td>0.2</td></tr><tr><td>S0_Status quo</td><td>2045-2050</td><td>0.2</td><td>NA</td><td>0.2</td></tr><tr><td>S1_Fmsy_DPS</td><td>2025-2030</td><td>0.8</td><td>NA</td><td>0</td></tr><tr><td>S1_Fmsy_DPS</td><td>2035-2040</td><td>1</td><td>NA</td><td>0</td></tr><tr><td>S1_Fmsy_DPS</td><td>2045-2050</td><td>1</td><td>NA</td><td>0</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2025-2030</td><td>0.8</td><td>NA</td><td>0</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2035-2040</td><td>1</td><td>NA</td><td>0</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2045-2050</td><td>1</td><td>NA</td><td>0</td></tr></tbody></table>

<table><caption>Table 8.6 CFP indicators (socio-economic)</caption><thead><tr><th>Scenario</th><th>Time period (provide average values over the time period)</th><th>Landings (average of yearly sums across fleets/metiers) all stocks</th><th>Revenue landings (average of yearly sums across fleets/metiers)</th><th>Unwanted catch/Discards (average of yearly sums across fleets/metiers) target stocks</th><th>Gross profit (average of yearly sums across fleets/metiers)</th><th>Gross value added (average of yearly sums across fleets/metiers)</th></tr></thead><tbody><tr><td>S0_Status quo</td><td>2025-2030</td><td>46200</td><td>265681005</td><td>564.5</td><td>62351368</td><td>130949876</td></tr><tr><td>S0_Status quo</td><td>2035-2040</td><td>46589</td><td>267266042</td><td>532.4</td><td>62880177</td><td>132534914</td></tr><tr><td>S0_Status quo</td><td>2045-2050</td><td>45595</td><td>261080061</td><td>519.4</td><td>59127261</td><td>126348933</td></tr><tr><td>S1_Fmsy_DPS</td><td>2025-2030</td><td>41389</td><td>236791393</td><td>245.1</td><td>69327947</td><td>154191455</td></tr><tr><td>S1_Fmsy_DPS</td><td>2035-2040</td><td>50366</td><td>275765263</td><td>226.7</td><td>89968081</td><td>196616511</td></tr><tr><td>S1_Fmsy_DPS</td><td>2045-2050</td><td>50522</td><td>276395642</td><td>227.6</td><td>90254234</td><td>197246890</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2025-2030</td><td>44226</td><td>252846151</td><td>317.9</td><td>75047338</td><td>162511885</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2035-2040</td><td>51896</td><td>286130289</td><td>304.3</td><td>92363419</td><td>197961198</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2045-2050</td><td>51526</td><td>284774565</td><td>302.0</td><td>91627248</td><td>196605474</td></tr></tbody></table>

<table><caption>Table 8.6 continuation CFP indicators (socio-economic)</caption><thead><tr><th>Scenario</th><th>Time period (provide average values over the time period)</th><th>Employment (average of yearly sums across fleets/metiers)</th><th>Wages (average of yearly sums across fleets/metiers)</th><th>Average yearly ratio of current revenue/break even revenue (sum across fleets/metiers)</th><th>Ratio landings value fleets &lt;=24m/landings value fleets &gt;24m (used &lt;=18 and &gt;18)</th></tr></thead><tbody><tr><td>S0_Status quo</td><td>2025-2030</td><td>10019</td><td>68598508</td><td>0.4202</td><td>0.359</td></tr><tr><td>S0_Status quo</td><td>2035-2040</td><td>10019</td><td>69654737</td><td>0.4232</td><td>0.365</td></tr><tr><td>S0_Status quo</td><td>2045-2050</td><td>10019</td><td>67221672</td><td>0.4025</td><td>0.353</td></tr><tr><td>S1_Fmsy_DPS</td><td>2025-2030</td><td>10019</td><td>84863508</td><td>0.4587</td><td>0.510</td></tr><tr><td>S1_Fmsy_DPS</td><td>2035-2040</td><td>10019</td><td>106648430</td><td>0.5723</td><td>0.551</td></tr><tr><td>S1_Fmsy_DPS</td><td>2045-2050</td><td>10019</td><td>106992656</td><td>0.5740</td><td>0.435</td></tr></tbody></table>
<table><tr><td>S2_Fcomb_(PGY)</td><td>2025-2030</td><td>10019</td><td>87464546</td><td>0.4900</td><td>0.434</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2035-2040</td><td>10019</td><td>105597779</td><td>0.5858</td><td>0.465</td></tr><tr><td>S2_Fcomb_(PGY)</td><td>2045-2050</td><td>10019</td><td>104978227</td><td>0.5817</td><td>0.463</td></tr></table>

<figure><img src="image_179.png" /><figcaption>Figure 8.3 Trajectories of fishing mortality for the three scenarios and the five stocks tested.</figcaption></figure>

<figure><img src="image_180.png" /><figcaption>Figure 8.4 Trajectories of the SSB for the three scenarios and the five stocks tested.</figcaption></figure>

Figure 8.5 illustrates some examples of the trajectories of the total landings and GVA for the three scenarios. Landings of three trawl fleet segments (LOA1840) after a decrease in the short terms tend to recover reaching a level similar to the status quo situation. Small scale fleets (e.g. PGP and HOK) that are not reduced take an advantage of the stock recovery as their landings remarkably increase. The total landings reflect mirror the compensation of the effects of the reduction of the fishing effort of trawlers in the production system. The indicator GVA reflects the landing dynamics of this mixed fishery.
<figure><img src="image_181.png" /></figure>

**Figure 8.5** Examples of the trajectories of the total landings and GVA for the three scenarios: landings of three trawl fleet segments (LOA1840) (upper panel), of the two small scale fleets (PGP and HOK) and total landings (central panel); GVA of a trawl segment (LOA 1840), of a small scale fleet segment (PGP0012) and all the fleet.

### 8.4.2 Ecopath with Ecosim (EwE)

All the MSFD indicators were calculated at guild level, according to scenarios of fishing effort reduction and by time periods. The indicators show that, under a 69% or 58% reduction of fishing effort produced positive effects on piscivores in terms of biodiversity, when compared to status quo scenario (Figure 8.6); the biodiversity of benthivorous fish shows an initial increase with lower fishing pressure, and then it is quite stable (Table 8.7). Conversely, biodiversity of planktivorous and that of top predators is very similar across fishing scenarios.

The biomass of both piscivorous and benthivorous fish increases under scenarios 1 and 2, reflecting the reduction of mortality on these groups when bottom trawling is reduced (Figure 8.7). The biomass of planktivorous fish declines, plausibly because of the increasing predation from the piscivorous fish. Similarly, the biomass of top predators decline, possibly as a result of competition from piscivorous fish. The biomass increases faster than the biodiversity index, and it also increases for benthivorous, in contrast to the biodiversity index. This confirms that biomass does not capture all information: plausibly, in the benthivorous fish the biomass increases only for few groups which dominate the guild, leading to a lower biodiversity. The change in biomass, however, are only substantial for the piscivorous fish: for all other groups the differences between scenarios are too contained for visually appreciating in the plots (e.g. Figure 8.7), and only discernible when looking at Table 8.8.

For the MML indicator, the effect of the effort scenarios across trophic guilds is clearly visible (Figure 8.6): the reduction of effort leads to a higher MML which stabilizes around a value above 40 cm in both scenarios. Conversely, the status quo scenario shows a stabilization below 40 cm. When considering the individual guilds (Table 8.9), the most important change observed with a large increase in MML under effort reduction takes place for the piscivorous fish, with benthivorous and planktivorous showing minor changes. The small reduction of MML in benthivorous fish is attributed again to the predation of piscivorous fish. The change in piscivorous fish is attributed to a rapid growth in the average size of this group, possibly a result of the avoided decline of specific groups (e.g. piscivorous demersal slope fish).
The Large Species Indicator shows a similar pattern as MML: LSI increases for piscivorous fish. Compared to MML, however, there is also a slight increase in the benthivorous fish LSI (Table 8.10), while no change is observed in planktivorous fish. The increase in benthivorous fish shown by the LSI but not by MML under effort reduction may point at how the two indicators include size, with the rough "large/small" categorization not necessarily reflect well the structure changes in the community. The MML patterns are closer to what we expect to find, and observe in the other indicators.

<table><caption>Table 8.7. Shannon's H index of biodiversity calculated by guilds for projected time periods under scenarios of effort reductions.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Shannon's H</th></tr><tr><th>Years</th><th>Top predators</th><th>piscivores</th><th>benthivores</th><th>planktivores</th></tr></thead><tbody><tr><td rowspan="3">Scenario 0</td><td>2025-2030</td><td>0.575</td><td>0.689</td><td>0.57</td><td>2.024</td></tr><tr><td>2035-2040</td><td>0.851</td><td>0.851</td><td>0.647</td><td>2.13</td></tr><tr><td>2045-2050</td><td>0.834</td><td>0.836</td><td>0.64</td><td>2.143</td></tr><tr><td rowspan="3">Scenario 1</td><td>2025-2030</td><td>0.575</td><td>0.689</td><td>0.57</td><td>2.024</td></tr><tr><td>2035-2040</td><td>0.849</td><td>0.912</td><td>0.655</td><td>2.125</td></tr><tr><td>2045-2050</td><td>0.832</td><td>0.908</td><td>0.638</td><td>2.133</td></tr><tr><td rowspan="3">Scenario 2</td><td>2025-2030</td><td>0.575</td><td>0.689</td><td>0.57</td><td>2.024</td></tr><tr><td>2035-2040</td><td>0.849</td><td>0.902</td><td>0.654</td><td>2.125</td></tr><tr><td>2045-2050</td><td>0.832</td><td>0.897</td><td>0.638</td><td>2.134</td></tr></tbody></table>

<table><caption>Table 8.8. Biomass calculated by guilds for projected time periods under scenarios of effort reductions.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Biomass</th></tr><tr><th>Years</th><th>Top predators</th><th>piscivores</th><th>benthivores</th><th>planktivores</th></tr></thead><tbody><tr><td rowspan="3">Scenario 0</td><td>2025-2030</td><td>38742</td><td>39718</td><td>26420</td><td>171955</td></tr><tr><td>2035-2040</td><td>38126</td><td>39893</td><td>26684</td><td>172129</td></tr><tr><td>2045-2050</td><td>38085</td><td>39877</td><td>26732</td><td>172077</td></tr><tr><td rowspan="3">Scenario 1</td><td>2025-2030</td><td>38576</td><td>43728</td><td>26956</td><td>170497</td></tr><tr><td>2035-2040</td><td>38002</td><td>43556</td><td>26806</td><td>170538</td></tr><tr><td>2045-2050</td><td>37968</td><td>43484</td><td>26832</td><td>170466</td></tr><tr><td rowspan="3">Scenario 2</td><td>2025-2030</td><td>38576</td><td>43101</td><td>26846</td><td>170695</td></tr><tr><td>2035-2040</td><td>37971</td><td>42956</td><td>26728</td><td>170753</td></tr><tr><td>2045-2050</td><td>37934</td><td>42882</td><td>26750</td><td>170686</td></tr></tbody></table>

<table><caption>Table 8.9. Mean Maximum Length calculated across guilds (all species) and by guilds for projected time periods under scenarios of effort reductions.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Mean MaMean Maximum Length</th></tr><tr><th>Years</th><th>All species</th><th>piscivores</th><th>benthivores</th><th>planktivores</th></tr></thead><tbody><tr><td rowspan="3">Scenario 0</td><td>2025-2030</td><td>39.56</td><td>81.42</td><td>47.27</td><td>31.49</td></tr><tr><td>2035-2040</td><td>39.45</td><td>80.51</td><td>47.54</td><td>31.46</td></tr><tr><td>2045-2050</td><td>39.43</td><td>80.38</td><td>47.56</td><td>31.46</td></tr><tr><td>Scenario 1</td><td>2025-2030</td><td>41.11</td><td>86.71</td><td>47.14</td><td>31.37</td></tr></tbody></table>
<table><caption>Table 8.10. Large Species Indicator calculated across guilds (all species) and by guilds for projected time periods under scenarios of effort reductions.</caption><tr><td rowspan="2"></td><td>2035-2040</td><td>41.13</td><td>87.33</td><td>47.09</td><td>31.32</td></tr><tr><td>2045-2050</td><td>41.11</td><td>87.32</td><td>47.1</td><td>31.31</td></tr><tr><td rowspan="2">Scenario 2</td><td>2025-2030</td><td>40.86</td><td>85.88</td><td>47.17</td><td>31.39</td></tr><tr><td>2035-2040</td><td>40.87</td><td>86.45</td><td>47.16</td><td>31.33</td></tr><tr><td></td><td>2045-2050</td><td>40.86</td><td>86.44</td><td>47.17</td><td>31.33</td></tr></table>

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Years</th><th colspan="4">Large Species Indicator</th></tr><tr><th>All species</th><th>piscivores</th><th>benthivores</th><th>planktivores</th></tr></thead><tbody><tr><td rowspan="3">Scenario 0</td><td>2025-2030</td><td>0.4</td><td>0.77</td><td>0.49</td><td>0.33</td></tr><tr><td>2035-2040</td><td>0.4</td><td>0.76</td><td>0.49</td><td>0.33</td></tr><tr><td>2045-2050</td><td>0.4</td><td>0.76</td><td>0.49</td><td>0.33</td></tr><tr><td rowspan="3">Scenario 1</td><td>2025-2030</td><td>0.41</td><td>0.8</td><td>0.53</td><td>0.33</td></tr><tr><td>2035-2040</td><td>0.41</td><td>0.8</td><td>0.53</td><td>0.32</td></tr><tr><td>2045-2050</td><td>0.41</td><td>0.8</td><td>0.53</td><td>0.32</td></tr><tr><td rowspan="3">Scenario 2</td><td>2025-2030</td><td>0.41</td><td>0.79</td><td>0.52</td><td>0.33</td></tr><tr><td>2035-2040</td><td>0.41</td><td>0.8</td><td>0.52</td><td>0.32</td></tr><tr><td>2045-2050</td><td>0.41</td><td>0.8</td><td>0.52</td><td>0.32</td></tr></tbody></table>

<figure><img src="image_182.png" /><figcaption>Figure 8.6. Diversity of piscivorous fish (left) and mean maximum length across guild under three effort scenarios.</figcaption></figure>

<figure><img src="image_183.png" /><figcaption>Figure 8.7. Biomass in tons of piscivorous (left) and benthivorous (right) fish under three effort scenarios.</figcaption></figure>
## 8.5. Conclusions

We observed in the scenarios the concurrence of CFP ecological and socio-economic indicators capturing the effects of reduction of fishing effort in the biological and economic-social systems.

The number of CFP ecological indicators is quite limited in the Mediterranean, where the shortness of the time series has constrained so far the implementation of more structured schemes, including biomass reference points in addition to $F_{MSY}$ proxy, as $F_{0.1}$, for more stocks. This impacts the possibility of estimating stock-recruitment relationships that enable to better capturing the stock dynamic. There are recent progresses aiming at filling such gap (STECF 2022a), given that also the length of the time series is in the meanwhile increasing. It should also be noted that during this year a benchmarking stock assessment process is taking place at GFCM level. Along the present and following months, most of the stocks here analyzed are in this process that will produce updated, but also more robust stock assessments. These can be used at a following step in SEAwise to improve and expand our results.

The situation of the stocks in the Mediterranean is considered critical though some signs of improvement are detected (FAO, 2022). Indicators of the CFP ecological system, as SSB and fishing mortality, also highlight these improvements in the stocks' trajectories of the scenarios modelled in this deliverable, especially for the Adriatic, where a MAP is in place and so a reduction of fishing opportunities already started. The scenarios of fishing effort reduction, however, are evidencing that a stronger reduction of effort (i.e. 69%) would not produce greater advantage to the system compared to a 58%, as some stocks can remain underutilized, while the challenge for the economic-social systems would be likely too impacting, especially for trawlers and in the short terms. Indeed the indicator "Ratio landings value fleets <=24m/landings value fleets >24m (used <=18 and >18)" is increasing that would demonstrate a potential advantage for the fleets not impacted by the reduction of effort opportunities.

The simulations in these scenarios have not implemented a suite of specific submodels, e.g. fishers' behaviour, fuel consumption, differentiated fish price, or impact on the employment that remain constant given that a reduction of vessels is not considered. These sub-models are under study and could be part of the process in the next working steps of SEAwise.

We have not introduced in the simulated scenarios other possible options, as an improvement of the fleet selectivity or the closure of key hot spot areas that will be further explored in SEAwise and can also elucidate the effects of such measures on target and limit reference points, as a consequence of improvement of the exploitation pattern.

In conclusion, in EwE we observe that the indicators respond according to expectation, with recovery of some indicators in the piscivorous fish groups after reduction of the fishing pressure; the benthivorous groups instead show worsening indicators, possibly a result of the recovery of piscivorous fish with consequent increase in predatory pressure. Observing the temporal changes allows to disentangle the time dynamics of the different groups, permitting a finer understanding of the consequences of fishing reduction across the trophic web (e.g. some groups responding faster than others) and offering an example of the complementarity of the indicators (e.g. biodiversity and biomass indicators showing lagged and differing patterns).

## 6. References

* FAO. 2022. General Fisheries Commission for the Mediterranean – Report of the twenty-third session of the Scientific Advisory Committee on Fisheries, FAO headquarters, Rome, Italy, 21–24 June 2022. FAO Fisheries and Aquaculture Report No. 1395. Rome. https://doi.org/10.4060/cc3109en

* Frost H., P. Andersen and A. Hoff. 2013. Management of Complex Fisheries: Lessons Learned from a Simulation Model. Canadian Journal of Agricultural Economics 61: 283–307.

* Kell, L.T., Mosqueira, I., Grosjean, P., Fromentin, J.-M., Garcia, D., Hillary, R., Jardim, E., Mardle, S., Pastoors, M.A., Poos, J.J., Scott, F., Scott, R.D., 2007. FLR: an open-source framework for the evaluation and development of management strategies. Ices Journal of Marine Science 64, 640–646. https://doi.org/10.1093/icesjms/fsm012
Implemented 2021. Improving the selectivity of trawl gears in the Mediterranean Sea to advance the sustainable exploitation pattern of trawl fisheries. IMPLEMED project. doi: 10.2926/194244

Rossetto M., Bitetto L, Spedicato M. T., Lembo G., Gambino M., Accadia P., Melià P. (2014). Multi-criteria decision-making for fisheries management: A case study of Mediterranean demersal fisheries. Marine Policy, 53, 83-93.

Russo, T., Bitetto, I., Carbonara, P., Cariucci, R., D'Andrea, L., Facchini, M.T., Lembo, G., Maiorano, P., Sion, L., Spedicato, M.T., Tursi, A. and Cataudella, S. (2017). A Holistic Approach to Fishery Management: Evidence and Insights from a Central Mediterranean Case Study (Western Ionian Sea). Front.Mar.Sci., 4:193, https://doi.org/10.3389/fmars.2017.00193.

Sala, A., Lucchetti, A., 2011. Effect of mesh size and codend circumference on selectivity in the Mediterranean demersal trawl fisheries. Fisheries Research, 110 (2), 252-258. Simmonds, J., Hjorleifsson, E., Millar, C., 2022. MSY: Estimation of Equilibrium Reference Points for Fisheries.

Salz, P., Buisman, E., Frost, H., Accadia, P., Prellezo, R., and Soma, K. 2011. FISHRENT; Bio-economic simulation and optimisation model for fisheries. LEI Report 2011-024; ISBN/EAN: 978-90-8615-514-9. 74 pp.

Scientific, Technical and Economic Committee for Fisheries (STECF) 2015 – Standardization procedures for data preparation, stock assessment methods and estimate of MSY reference points for Mediterranean stocks (STECF-15-11). 2015. Publications Office of the European Union, Luxembourg, EUR 27408 EN, JRC 97017, 104 pp.

Scientific, Technical and Economic Committee for Fisheries (STECF) 2019 – Methods for developing fishing effort regimes for demersal fisheries in Western Mediterranean-Part III (STECF19-01). Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-08330-6, doi:10.2760/249536, JRC116968.

Scientific, Technical and Economic Committee for Fisheries (STECF) 2020 – Evaluation of fishing effort regime in the Western Mediterranean – part V (STEC-20-13). EUR 28359 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-27701-9, doi:10.2760/143313, JRC122924

Scientific, Technical and Economic Committee for Fisheries (STECF) – 2021a. Evaluation of the fishing effort regime in the Western Mediterranean – part VI (STECF-21-13). Publications Office of the European Union, Luxembourg, 2021, EUR 28359 EN, ISBN 978-92-76-43488-7, doi:10.2760/121901, JRC126965

Scientific, Technical and Economic Committee for Fisheries (STECF) 2021b – Stock assessments in the Mediterranean Sea 2021 – (Adriatic and Ionian Seas) (STECF-21-15). Publications Office of the European Union, Luxembourg, 2021.

Scientific, Technical and Economic Committee for Fisheries (STECF) 2022a. Quality checking of MED & BS data and reference points (STECF-22-03). EUR 28359 EN, Publications Office of the European Union, Luxembourg, 2022

Scientific, Technical and Economic Committee for Fisheries (STECF) - The 2022b Annual Economic Report on the EU Fishing Fleet (STECF 22-06), Prellezo, R., Sabatella, E., Virtanen, J. and Guillen, J. editors, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/120462

Scientific, Technical and Economic Committee for Fisheries (STECF) – 2023. Stock Assessments: demersal stocks in Adriatic, Ionian and Aegean Seas and straits of Sicily (STECF-22-16). Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/25344, JRC132157.

Spedicato M.T., R. Cannas, K. Mahé, B. Morales, C. Tsigenopoulos, L. Zane, S. Kavadas, I. Maina, G. Scarcella, P. Sartor, V. Bandelj, T. Russo, F. Fiorentino. 2022. Study on advancing fisheries assessment and management advice in the Mediterranean by aligning biological and management units of priority. species. MED_UNITs. Luxembourg: Publications Office of the European Union, 2022. ISBN 978-92-95225-33-6. doi: 10.2926/909535
## 8.10 Annex 10: Potential bycatch thresholds

### Background

When marine megafauna are under threat and/or of conservation concern it is often useful to estimate targets or limits for removals that population can sustain in order to help meet conservation objectives (Good et al. 2020). In the related research area of fisheries assessment and management, reference points are a good example of the combined use of targets and limits to improve the status of managed populations (Hilborn & Walters 2013, Hilborn et al. 2020). For conservation of protected, endangered and threatened (PET) species, thresholds have been used to indicate the level of incidental mortality (e.g. fisheries bycatch) a population can sustain and also trigger management action in order to achieve conservation targets (Berggren et al. 2002, Dillingham & Fletcher 2008, Geijer & Read 2013, Casale & Heppell 2016, Marchowski 2022).

The majority of work on thresholds has focused on marine mammal populations (Wade 1998, Winship et al. 2009, Hammond et al. 2019, Genu et al. 2021). In cetaceans, in particular, approaches for setting threshold values for removals of protected cetacean species have been extensively discussed in Europe (ICES 2019), with a focus on three approaches in particular: fixed percentages of abundance, Potential Biological Removal (PBR) and the Catch Limit Algorithm (CLA) and/or its analogous Removal Limit algorithm (RLA).

The fixed percentage of abundance is the simplest management approach while CLA/RLA is the most data demanding approach that requires both a time series of abundance estimated and anthropogenic removals. Due to lack of available data, it is not often implemented, although it is the most appropriate method to set limits on the bycatch of harbour porpoises or common dolphins according to ICES (ICES 2013).

PBR was designed to set limits to anthropogenic mortality of small cetaceans and pinnipeds with minimal data requirements and was developed and applied, as part of an adaptive management framework, in the USA under the Marine Mammal Protection Act (Wade 1998). It defines the maximum number of animals, in addition to natural mortality, that may be removed from a stock while allowing it to reach or maintain its optimum sustainable population (at or above the level that will result in maximum productivity). Given uncertainties associated with defining this threshold in data poor conditions, PBR is regularly recalculated as population abundance is updated.

PBR is the product of a minimum population estimate (Nmin), to account for uncertainties associated with the abundance estimation process, and half of the maximum theoretical productivity rate (Rmax). The latter approximates the minimum maximum net productivity of the population (assuming that there is no density-dependence effects in population dynamics). This potential biological removal number is then further censored to account for practical uncertainties associated with the management of the population and their lethal threats. As with any other thresholds, PBR is estimated in relation to a given management objective and therefore
is set to maximise the chances to meet this management objective. The $N_{min} \frac{1}{2} R_{max}$ product is multiplied by a recovery factor (Fr) between 0.1 and 1.0 which aims to integrate information about the population trajectory and status, and the uncertainties associated with the estimation of the number of removals the population faces.

$$PBR = \frac{R_{max} N_{min} f}{2} \qquad (1)$$

For small cetaceans, the maximum productivity rate - $R_{max}$- is very difficult to estimate in practice, so a default value of 4% is used (Wade 1998). In contrast, $N_{min}$ and $Fr$ values may change depending on the conservation objectives, so they need to be estimated, typically by means of simulations (also called tuning).

Under the Marine Mammal Protection Act, the conservation objective was defined to “maintain or recover a population at/to its maximum net productivity level -typically 50% of the populations carrying capacity- with 95% probability within a 100-year period”. PBR estimates were deemed robust to uncertainties and able to reach that objective when $N_{min}$ was established as the 20th percentile of the abundance (assuming a log normal distribution), while $Fr$ was set at 0.5 as default (Wade 1998). This value is used for populations which are threatened or of unknown status and provides a safety factor to account for levels of unknown bias or estimations problems that have been observed in some population of marine mammals. Values between 0.1-0.3 are usually reserved for endangered species or population known to be in decline, while values higher than 0.5 are used for populations known to be at their optimum sustainable population level, or of unknown status but known to be increasing (Wade 1998).

This approach, originally designed for marine mammals, it has been also applied to other taxonomic groups such as seabirds, although issues with the method have been highlighted more recently (Miller et al. 2019). In seabirds, ideally a population viability analysis (PVA) should be conducted to estimate a threshold but often the data is not available (Dierschke 2022). Alternatively, a method called the BirdLife International Threshold (BLT) offers a relatively simple and data moderate quantitative method for obtaining threshold values. These values are indicative of the resilience of a population to incidental mortality from fisheries bycatch and can be useful for management.

BLT is a relatively new method proposed at a OSPAR-HELCOM workshop (OSPAR-HELCOM 2019), recommended by (BirdLife International 2019) and further endorsed in an OSPAR pilot assessment (Dierschke 2022). A recent study by Marchowski (2022) used the two different methodologies, BLT and PBR, in a review of seabird *bycatch* in the Southern Baltic. In this short report, we use a similar approach and apply both the BLT and PBR methodologies to obtain a range of thresholds for Northern gannet and Northern fulmar populations in Irish waters.

In turtles, the already mentioned PBR or the Reproductive Value Loss Limit (RVLL, generalized from the PBR management model) have been applied (Curtis & Moore 2013, Casale & Heppell 2016) while in fishes, the concepts of maximum sustainable yield (MSY) and maximum rate of fishing mortality (FMSY as a threshold for the sustainability of the stock) are well developed and tested. In many cases those management measures work well (Hilborn & Ovando 2014)
exemplifying the robustness of those models. Moreover, the MSY is conceptually comparable
with the CLA and PVA models proposed for marine mammals and birds.
## Cetaceans

There is not a clear consensus yet on an operationalised conservation objective that should be achieved in European waters. In the Workshop on Fisheries Emergency Measures to Minimize Bycatch (WKEMBYC; ICES 2020b) the conservation objective adopted by the Marine Mammal Protection Act (to maintain or recover a population at/to 50% of the population carrying capacity with 95% probability within a 100-year period) was followed. At OSPAR's Marine Mammal Expert Group, in contrast, a different objective was set, more akin to a restoration objective, (i.e., ASCOBANS objective), which established that "a population should [be able to] recover to or be maintained at 80% of carrying capacity, with probability 0.8, within a 100-year period".

As an appropriate interpretation of the European ambition into an operationalised conservation objective is still being debated, here we estimated PBR using two alternative approaches. On one hand, we used the preestablished parameters values estimated by Wade (1998) under the conservation objective of Marine Mammal Protection Act (conventional PBR), meaning that Nmin should be the 20th percentile of the abundance and Fr should be 0.5 by default, although lower values (typically for endangered species or in decline) or higher values (for populations known to be at their optimum sustainable population level, or increasing) can be applied as well.

Secondly, we applied the parameters obtained from the bias trial conducted under ASCOBANS's conservation objective (Genu et al. 2021, ICES 2021b), where Nmin was also established as the 20th percentile of the abundance, but the default value of Fr was defined between 0.1 and 0.15, depending on the case study and the degree of conservationism (modified PBR, mPBR hereafter).

## Common dolphins in Northeast Atlantic

Common dolphin is one of the most widespread and abundant cetacean species globally and it is widely distributed from tropical to cool temperate waters of both hemispheres in all major ocean basins (Murphy et al. 2019). At European level, common dolphin's assessment unit encompasses a large part of the Northeast Atlantic, i.e., OSPAR regions II, III and IV (Fig. 1), although its major abundance is concentrated in the Bay of Biscay (Hammond et al. 2017). In 2013, its conservation status for the European Marine Atlantic was assessed as 'Unfavourable-Inadequate' based on the known human pressure of fishery bycatch (Murphy et al. 2019).

<figure><img src="image_184.png" /><figcaption>**Figure 1.** Assessment unit for the common dolphin (note that common dolphins are rarely observed in the North Sea). From WKMOMA (ICES 2021b).</figcaption></figure>
For the PBR estimation, we used the latest and most comprehensive abundance estimate provided by SCANS III and ObSERVE surveys for the European Atlantic waters: 634286 individuals (95% CI 352 227–1 142 213, CV=0.307)(ICES 2020b). As Fr values, 0.5 and 0.1 were used for conventional PBR and mPBR approaches, that provided a limit of 4927 and 985 individuals respectively (Table 1).

<table><caption>**Table 1.** Bycatch thresholds estimated through the two alternative PBR approaches. Parameters needed to estimate the thresholds are also included as well as the organisms and/or the studies that apply them.</caption><thead><tr><th>Species</th><th>Method</th><th>N</th><th>Nmin</th><th>Rmax</th><th>Fr</th><th>Threshold</th><th>Organization</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="2">Common dolphin</td><td>PBR</td><td>634286</td><td>492653</td><td>0.04</td><td>0.5</td><td>4927</td><td>ICES</td><td>ICES (2020b)</td></tr><tr><td>mPBR</td><td>634286</td><td>492653</td><td>0.04</td><td>0.1</td><td>985</td><td>OSPAR</td><td>ICES (2021b)</td></tr></tbody></table>

According to the data presented in the latest Working Group on Bycatch of Protected Species for the 2017-2021 period (WGBYC; ICES 2022d), common dolphin in the Northeast Atlantic is caught, although in different rates, in midwater pair trawls (PTM), midwater otter trawls (OTM), bottom otter trawls (OTB), bottom pair trawls (PTB), gillnets (GNS) and trammel nets (GTR), purse seiners (PS) and otter twin trawls (OTT), longlines (LLS) and beam trawls (TBB) (Table 2).

Bycatch rates for these metiers were estimated using the number of animals recorded bycaught divided by days at sea observed. The number of bycaught animals and days at sea observed were aggregated by ICES Ecoregion and Métier Level 4 (rates based on less than 50 days at sea observed should be taken with caution). Confidence intervals around the bycatch rates were estimated assuming a binomial distribution (ICES 2022d).

Note that those bycatch numbers are only associated with the observed DaS. In 2017, the bycatch rates extrapolated to the fishing effort resulted in 8904 bycatch deaths (95% CI 3142–20 026) in the Bay of Biscay and 9373 (95% CI 3184–21956) when also considering the Greater North Sea and Celtic Seas Ecoregions (ICES 2020b). In 2020, the total number of animals bycaught was estimated to be 6,404 individuals (95% CI 3,051–9,414) for the entire assessment area (ICES 2021b). In both cases, the number of bycaught animals exceeded by far the limits considered to be acceptable
<table><caption>**Table 2.** Common dolphin bycatch rates (no. of animals per day) and 95% confidence intervals around the rates provided by métier level 4 over the 2017-2021 period. Days at Sea (Das) refers to observed effort while coverage (not always available) refers to the % of days observed respect to total effort.</caption><thead><tr><th>Region</th><th>Metier</th><th>DaS</th><th>Total effort in days</th><th>Coverage (%)</th><th>No. of animals</th><th>Rate</th><th>CI5-CI95</th></tr></thead><tbody><tr><td rowspan="2">Bay of Biscay - Iberian Coast<br>Celtic sea</td><td rowspan="2">PTM</td><td>1080</td><td>18988</td><td>5.6</td><td>115</td><td>0.106</td><td>0.090-0.123</td></tr><tr><td>333</td><td></td><td>--</td><td>1</td><td>0.003</td><td>0-0.009</td></tr><tr><td rowspan="3">Bay of Biscay - Iberian Coast<br>Celtic sea<br>Greater North Sea</td><td rowspan="3">OTB</td><td>1637</td><td>79099</td><td>2</td><td>2</td><td>0.001</td><td>0-0.003</td></tr><tr><td>3842</td><td></td><td>--</td><td>12</td><td>0.003</td><td>0.001-0.004</td></tr><tr><td>4194</td><td></td><td>--</td><td>5</td><td>0.001</td><td>0.0004-0.002</td></tr><tr><td rowspan="3">Bay of Biscay - Iberian Coast<br>Celtic sea<br>Greater North Sea</td><td rowspan="3">GNS</td><td>2875</td><td>64250</td><td>4.5</td><td>23</td><td>0.008</td><td>0.005-0.010</td></tr><tr><td>1285</td><td></td><td>--</td><td>8</td><td>0.006</td><td>0.003-0.010</td></tr><tr><td>8170</td><td></td><td>--</td><td>4</td><td>0.0004</td><td>0.0001-0.0009</td></tr><tr><td rowspan="3">Bay of Biscay - Iberian Coast<br>Celtic sea<br>Greater North Sea</td><td rowspan="3">GTR</td><td>1730</td><td>148398</td><td>1.1</td><td>26</td><td>0.015</td><td>0.010-0.020</td></tr><tr><td>108</td><td></td><td>--</td><td>1</td><td>0.009</td><td>0-0.027</td></tr><tr><td>641</td><td></td><td>--</td><td>3</td><td>0.004</td><td>0.001-0.009</td></tr><tr><td>Bay of Biscay - Iberian Coast</td><td>PTB</td><td>682</td><td>9515</td><td>7.1</td><td>85</td><td>0.124</td><td>0.102-0.146</td></tr><tr><td>Bay of Biscay - Iberian Coast</td><td>OTM</td><td>15</td><td>1279</td><td>1.1</td><td>2</td><td>0.129</td><td>0-0.322</td></tr><tr><td rowspan="2">Bay of Biscay - Iberian Coast<br>Greater North Sea</td><td rowspan="2">PS</td><td>914</td><td>68418</td><td>1.3</td><td>11</td><td>0.012</td><td>0.006-0.018</td></tr><tr><td>57</td><td></td><td>--</td><td>5</td><td>0.08</td><td>0.035-0.159</td></tr><tr><td>Celtic Sea</td><td>OTT</td><td>1990</td><td></td><td>--</td><td>2</td><td>0.001</td><td>0-0.002</td></tr><tr><td>Bay of Biscay - Iberian Coast</td><td>LLS</td><td>1637</td><td>79099</td><td>2.1</td><td>2</td><td>0.001</td><td>0-0.003</td></tr><tr><td>Greater North Sea</td><td>TBB</td><td>1526</td><td></td><td>--</td><td>1</td><td>0.0006</td><td>0-0.001</td></tr></tbody></table>