# SEAwise Report on consistency of existing targets and limits for indicators in an ecosystem context

Alexander Kempf, Marc Taylor, Bernhard Kühn, Elliot Brown, Vanessa Trijoulet, Morten Vinther, Anna Rindorf, Raphael Girardin, Marie Savina-Rolland, Sigrid Lehuta, et al.

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## To cite this version:

Alexander Kempf, Marc Taylor, Bernhard Kühn, Elliot Brown, Vanessa Trijoulet, et al. SEAwise Report on consistency of existing targets and limits for indicators in an ecosystem context. Institut Agro - Rennes Angers; Technical University of Denmark. 2023, pp.245 P. hal-04495754

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WP 6

Deliverable 6.7

Lead Beneficiary: Ti-SF

Topic: Shaping ecosystem-based fisheries management

Grant Agreement No: 101000318

Dissemination level: PU

Date: 31.03.2023

Please cite this work as: Kempf, A., Taylor, M., Kühn, B., Brown, E., Trijoulet, V., Vinther, M., Girardin, R., Savina-Rolland, M., Lehuta, S., Halouani, G., Robert, M., Woillez, M., Mahevas, S., Travers, M., Andres, M., Garcia, D., Ibaibarriaga, L., Sánchez-Maroño, S., Astarloa Diaz, A., Batts, L., Reid, D., Bitetto, I., Spedicato, M.-T., Romagnoni, G., Giannoulaki, M., Sgardeli, V., Tsoukali, S., Liontakis, A., Vassilopoulou, C., Millar, S., Depestele, J., van Hoey, G., Piet, G., Hamon, K., Kraan, M., Smout. S., Ransijn, J., Thorpe, R., Lynam, C., Bluemel, J., Voss, R., Ojaveer, H., Hommik, K., Melia, P., Gascuel, D., Potier, M., Ustups, D., Plikss, M., Heath, M., Poos, J.-J., Binch, L., and Rindorf, A. (2023): SEAwise Report on consistency of existing targets and limits for indicators in an ecosystem context. Technical University of Denmark. https://doi.org/10.11583/DTU.25152662

This work is licensed under a [CCBY 4.0 license](https://creativecommons.org/licenses/by/4.0/).

Contact: Anna Rindorf, [https://orcid.org/0000-0002-4290-3983](https://orcid.org/0000-0002-4290-3983), email: ar@aqua.dtu.dk

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Executive summary

SEAwise deliverable 6.7 is the first of two deliverables in task 6.4. It investigates the consistency of existing targets and limits from the Common Fisheries Policy (CFP) and the Marine Strategy Framework Directive (MSFD). Trade-offs between different objectives (ecological, economic, social), targets and limits are highlighted. A wide range of model types (from bio-economic to full ecosystem models) has been applied to various case study areas across the North East Atlantic and Mediterranean. Although model predictions are by nature uncertain, this study provides important information on likely inconsistencies between existing targets and limits and trade-offs expected under ecosystem-based fisheries management (EBFM). The scenarios investigated include the current range of management applied in terms of the Maximum Sustainable Yield (MSY) concept (i.e. strict MSY approach vs. Pretty Good Yield (PGY) approach allowing sustainable deviations from single species FMSY point estimates). The landing obligation is a key aspect of current fisheries management and was fully considered, in particular for mixed demersal fisheries.

Maintaining current fishing effort without further management measures was the least sustainable option in nearly all cases studies. This approach led to increased risk of stocks falling below critical biomass limits. Although the fishing effort adaptions needed is highly case specific, this indicates that further management measures are likely to be needed to ensure a sustainable exploitation of all stocks.

Scenarios applying a strict MSY approach in combination with the landing obligation (i.e. FMSY as upper limit with fisheries ending when the first stock reaches FMSY) in most case studies led to the lowest fishing effort. This had positive effects on MSFD related indicators such as bycatch of Protected, Endangered and Threatened (PET) species, benthic impact and the Large Fish Indicator as well as global indicators such as CO2 emission or ecosystem-based indicators like catch per km². However, this scenario often led to the lowest catches from mixed demersal fisheries due to strong choke effects because fleets had to stop when their first quota was exhausted. This reduces social indicators such as food security, employment and wages. In terms of economic performance, the gains and loses were highly case specific. Scenarios applying the Pretty Good Yield concept and allowing sustainable deviations from the FMSY point estimate when stocks are in a healthy state often outperformed the scenarios applying FMSY as strict upper limit. Such scenarios, applying a more flexible interpretation of the MSY concept, led to reduced fishing effort compared to the status quo effort, but relaxed choke situations in mixed demersal fisheries to some extent leading to higher gross profits and in some case studies also to higher catches. Hence, they may constitute a compromise between the need to attain social as well as ecological objectives. Whether the associated effort levels lead to conflicts with MSFD objectives must be analysed when more internationally agreed thresholds become available for e.g., bycatch of PET species or benthic impact.

The majority of case studies exceeded suggested thresholds for the global ecosystem indicators catch per km² or primary production even under scenarios with high effort reductions. This can be explained to some extent by the fact that these indices are mainly driven by pelagic and industrial fisheries not always part of the models applied. Nevertheless, it indicates potential conflicts with such more holistic ecosystem indicators in their current form.

Additional trade-offs in terms of yield were identified within the food web if e.g., demersal piscivorous predators feed on small pelagic fish and both groups are fished. Further, in case studies where small-scale fisheries (SSF) play an important role (e.g., Eastern Ionian Sea) additional trade-offs became apparent as different scenarios led to different ratios between revenues from small scale fisheries and revenues from large-scale fisheries. This adds another level of complexity when such aspects need to be taken more into account in future fisheries management under EBFM.

The modelling assumed current selectivities and catchabilities will be maintained in the future. Especially trade-offs arising from fleets having to stop fishing when their first quota is exhausted or when e.g., a threshold for bycatch of PET species is reached may be resolved by improving selectivities via technical measures (e.g., closed areas or innovative gears) in the future. Deliverable 6.8 in month 36 will test such scenarios. Furthermore, the list of
indicators and their targets and limits will be updated based on research within and outside SEAwise. Predictive capability of models will be enhanced by incorporating improved biological and economic sub-models in relation to environmental change. Climate change scenarios will be run and new harvest control rules (HCRs), proposed by SEAwise, will be tested. Finally, consistent targets and limits will be proposed for implementing EBFM.

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Example of model estimates of the impact of different management scenarios for demersal fisheries in the Bay of Biscay on ecological and social indicators.
# Contents

<table><tr><td>1.</td><td>SEAwise background</td><td>7</td></tr><tr><td>1.1</td><td>The role of this deliverable</td><td>8</td></tr><tr><td>1.2</td><td>Contributors</td><td>8</td></tr><tr><td>1.3</td><td>Acronyms and abbreviations</td><td>9</td></tr><tr><td>2.</td><td>Existing indicators, targets and limits impacting fisheries management</td><td>10</td></tr><tr><td>3.</td><td>Methodology</td><td>12</td></tr><tr><td>3.1</td><td>General approach</td><td>12</td></tr><tr><td>3.2</td><td>Available Models</td><td>13</td></tr><tr><td>3.3</td><td>Scenarios</td><td>14</td></tr><tr><td>3.3.1</td><td>North East Atlantic case studies (North western waters, Bay of Biscay and Iberian waters, North Sea, Baltic)</td><td>14</td></tr><tr><td>3.3.2</td><td>Mediterranean case studies (Central and Eastern Mediterranean)</td><td>15</td></tr><tr><td>3.4</td><td>Indicators</td><td>16</td></tr><tr><td>4.</td><td>Results</td><td>19</td></tr><tr><td>4.1</td><td>North Sea</td><td>19</td></tr><tr><td>4.1.1</td><td>FLBEIA North Sea (27.4), Skagerrak (27.3.a.20) and Eastern English Channel (27.7.d)</td><td>19</td></tr><tr><td>4.1.2</td><td>Osmose in the English Channel</td><td>24</td></tr><tr><td>4.1.3</td><td>StrathE2E - North Sea</td><td>25</td></tr><tr><td>4.1.4</td><td>Stochastic Multi Species model SMS for the North Sea</td><td>28</td></tr><tr><td>4.2</td><td>Western Waters</td><td>31</td></tr><tr><td>4.2.1</td><td>FLBEIA for the Bay of Biscay (mixed demersal fisheries)</td><td>31</td></tr><tr><td>4.2.2</td><td>FLBEIA for the Bay of Biscay (pelagic).</td><td>34</td></tr><tr><td>4.2.3</td><td>StrathE2E - Celtic Sea</td><td>37</td></tr><tr><td>4.3</td><td>Baltic</td><td>40</td></tr><tr><td>4.3.1</td><td>BEE-FISH western Baltic</td><td>40</td></tr><tr><td>4.3.2</td><td>BEE-FISH central Baltic</td><td>41</td></tr><tr><td>4.4</td><td>Mediterranean</td><td>42</td></tr><tr><td>4.4.1</td><td>FLBEIA Eastern Ionian Sea (GFCM Geographical Sub Area 20)</td><td>42</td></tr><tr><td>4.4.2</td><td>BEMTOOL and Ecopath with Ecosim in the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas 17-18-19)</td><td>46</td></tr><tr><td>5.</td><td>Conclusions</td><td>49</td></tr><tr><td>6.</td><td>Further work</td><td>50</td></tr><tr><td>7.</td><td>References</td><td>52</td></tr><tr><td>8.</td><td>Appendix</td><td>53</td></tr><tr><td>8.1</td><td>Annex 1: Description of analyses carried out with the FLBEIA model for the North Sea.</td><td>53</td></tr></table>
<table><tr><td>8.2</td><td>Annex 2: Description of analyses carried out with OSMOSE for the Eastern English Channel (EEC)</td><td>67</td></tr><tr><td>8.3</td><td>Annex 3: Seawise D6.7 - report on StrathE2E modelling for the North Sea and Celtic Sea</td><td>72</td></tr><tr><td>8.4</td><td>Annex 4: Evaluating FLBEIA scenarios for North Sea demersal stocks in a multispecies (SMS) context</td><td>102</td></tr><tr><td>8.5</td><td>Annex 5: Description of analyses carried out with FLBEIA for the demersal mixed fisheries in the Bay of Biscay</td><td>121</td></tr><tr><td>8.6</td><td>Annex 6: Description of analyses carried out with model FLBEIA for the Basque inshore pelagic fishery in Subarea 8 (Bay of Biscay)</td><td>139</td></tr><tr><td>8.7</td><td>Annex 7: Description of analyses carried out with model BEE-Fish for areas Western Baltic Sea (WBS) & Central Baltic Sea (CBS)</td><td>156</td></tr><tr><td>8.8</td><td>Annex 8: FLBEIA in Eastern Ionian Sea GSA 20</td><td>164</td></tr><tr><td>8.9</td><td>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)</td><td>192</td></tr><tr><td>8.10</td><td>Annex 10: Potential bycatch thresholds</td><td>207</td></tr><tr><td>9.</td><td>Document Information</td><td>244</td></tr></table>

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# 1. SEAwise background

The SEAwise project works to deliver a fully operational tool that will allow fishers, managers, and policy makers to easily apply Ecosystem Based Fisheries Management (EBFM) in their own fisheries. With the input from advice users, SEAwise identifies and addresses core challenges facing EBFM, creating tools and advice for collaborative management aimed at achieving long-terms goals under environmental change and increasing competition for space. SEAwise operates through four key stages, drawing upon existing management structures and centered on stakeholder input, to create a comprehensive overview of all fisheries interactions in the European Atlantic and Mediterranean. Working with stakeholders, SEAwise acts to:

* Build a network of experts - from fishers to advisory bodies, decision makers and scientists - to identify widely-accepted key priorities and co-design innovative approaches to EBFM.

* Assemble a new knowledge base, drawing upon existing knowledge and new insights from stakeholders and science, to create a comprehensive overview of the social, economic, and ecological interactions of fisheries in the European Atlantic and Mediterranean.

* Develop predictive models, underpinned by the new knowledge base, that allow users to evaluate the potential trade-offs of management decisions, and forecast their long-term impacts on the ecosystem.

* Provide practical, ready-for-uptake advice that is resilient to the changing landscapes of environmental change and competition for marine space.

The project links the first ecosystem-scale impact assessment of maritime activities with the welfare of the fished stocks these ecosystems support, enabling a full-circle view of ecosystem effects on fishing productivity in the European Atlantic and Mediterranean. Drawing these links will pave the way for a whole-ecosystem management approach that places fisheries at the heart of ecosystem welfare. In four cross-cutting case studies, each centered on the link between social and economic objectives, target stocks and management at regional scale SEAwise provides:

* Estimates of impacts of management measures and climate change on fisheries, fish and shellfish stocks living close to the bottom, wildlife bycatch, fisheries-related litter and conflicts in the use of marine space in the Mediterranean Sea,

* Integrated EBFM advice on fisheries in the North Sea, and their influence on sensitive species and habitats in the context of ocean warming and offshore renewable energy,

* Estimates of effects of environmental change on recruitment, fish growth, maturity and production in the Western Waters,

* Key priorities for integrating changes in productivity, spatial distribution, and fishers' decision-making in the Baltic Sea to create effective EBFM prediction models.

Each of the four case studies will be directly informed by expert local knowledge and open discussion, allowing the work to remain adaptive to change and responsive to the needs of advice users.
## 1.1 The role of this deliverable

Task 6.4 combines target and limit reference points from improved management evaluation models from Tasks 6.2 and 6.3 and models of ecological impact from Tasks 3.5, 4.2, 4.3, 4.4 and 4.5. This allows SEAwise to propose management strategies that align Common Fisheries Policy (CFP) and Marine Strategy Framework Directive (MSFD) objectives (Descriptors 1, 3, 4, 6 and 10) as well as global objectives such as carbon emission reduction, providing the basis for defining reference levels for good environmental status (GES) compatible with Multi Annual Plans (MAPs), and vice versa.

SEAwise deliverable 6.7 is the first out of two deliverables in task 6.4. It establishes a baseline for the consistency of existing targets and limits from the CFP and the MSFD. Trade-offs between different objectives (ecological, economic, social), targets and limits are highlighted and processes behind inconsistencies are explained. The report focuses on current management measures and multispecies models available in the project after 1.5 years. Deliverable 6.7 tries to incorporate early project results from WP4 tasks where bycatch, benthic impact, food webs and sources of marine litter are analysed to provide MSFD related targets and limits. Where possible, it uses the same parameterisation for stock dynamics as defined in Task 3.5, where single species reference points are estimated.

Deliverable 6.8 in month 36 will further develop the list of indicators (e.g. also including social indicators), targets and limits based on research within SEAwise. Models will be improved by incorporating additional important processes (e.g., improved economic sub-models (Task 2.2), environmentally mediated stock recruitment relationships (Task 3.2), density dependent processes (Task 3.3) and enhanced predictive models of fish survival (Task 3.4). Climate change scenarios will be evaluated and new harvest control rules (HCRs), proposed by SEAwise, will be tested. Finally, consistent targets and limits will be proposed for the implementation of EBFM.

## 1.2 Contributors

<table><thead><tr><td>Names</td><td>Institute</td><td>Scoping and discussions</td><td>Modelling of scenarios</td><td>Report writing and editing</td></tr></thead><tbody><tr><td>Alexander Kempf</td><td>TI-SF</td><td>X</td><td></td><td>X</td></tr><tr><td>Marc Taylor</td><td>TI-SF</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Bernhard Kühn</td><td>TI-SF</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Elliot Brown</td><td>DTU-Aqua</td><td>X</td><td></td><td></td></tr><tr><td>Vanessa Trijoulet</td><td>DTU-Aqua</td><td>X</td><td></td><td></td></tr><tr><td>Morten Vinther</td><td>DTU-Aqua</td><td>X</td><td>X</td><td></td></tr><tr><td>Anna Rindorf</td><td>DTU-Aqua</td><td>X</td><td></td><td></td></tr><tr><td>Raphaël Girardin</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Marie Savina-Rolland</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Sigrid Lehuta</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Ghassen Halouani</td><td>IFREMER</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Marianne Robert</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Mathieu Woillez</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Stephanie Mahevas</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Morgane Travers</td><td>IFREMER</td><td>X</td><td></td><td></td></tr><tr><td>Marga Andres</td><td>AZTI</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Dorleta Garcia</td><td>AZTI</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>
<table><tr><td>Leire Ibaibarriaga</td><td>AZTI</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Sonia Sánchez-Maroño</td><td>AZTI</td><td></td><td>X</td><td>X</td></tr><tr><td>Amaia Astarloa Diaz</td><td>AZTI</td><td>X</td><td></td><td></td></tr><tr><td>Luke Batts</td><td>MI</td><td>X</td><td></td><td></td></tr><tr><td>David Reid</td><td>MI</td><td>X</td><td></td><td></td></tr><tr><td>Isabella Bitetto</td><td>COISPA</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Maria Teresa Spedicato</td><td>COISPA</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Giovanni Romagnoni</td><td>COISPA</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Marianna Giannoulaki</td><td>HCMR</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Vasiliki Sgardeli</td><td>HCMR</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Stavroula Tsoukali</td><td>HCMR</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Angelos Liontakis</td><td>HCMR</td><td>X</td><td></td><td></td></tr><tr><td>Celia Vassilopoulou</td><td>HCMR</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Sarah Miller</td><td>ICES</td><td>X</td><td></td><td></td></tr><tr><td>Jochen Depestele</td><td>EV-ILVO</td><td>X</td><td></td><td></td></tr><tr><td>Gert van Hoey</td><td>EV-ILVO</td><td>X</td><td></td><td></td></tr><tr><td>Gerjan Piet</td><td>WR</td><td>X</td><td></td><td></td></tr><tr><td>Katell Hamon</td><td>WR</td><td>X</td><td></td><td></td></tr><tr><td>Marloes Kraan</td><td>WR</td><td>X</td><td></td><td></td></tr><tr><td>Sophie Smout</td><td>USTAN</td><td>X</td><td></td><td></td></tr><tr><td>Janneke Ransijn</td><td>USTAN</td><td>X</td><td></td><td></td></tr><tr><td>Robert Thorpe</td><td>CEFAS</td><td>X</td><td></td><td></td></tr><tr><td>Chris Lynam</td><td>CEFAS</td><td>X</td><td></td><td></td></tr><tr><td>Joanna Bluemel</td><td>CEFAS</td><td>X</td><td></td><td></td></tr><tr><td>Rudi Voss</td><td>CAU</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Henn Ojaveer</td><td>UTARTU</td><td>X</td><td></td><td></td></tr><tr><td>Kristiina Hommik</td><td>UTARTU</td><td>X</td><td></td><td></td></tr><tr><td>Paco Melia</td><td>POLIMI</td><td>X</td><td></td><td></td></tr><tr><td>Didier Gascuel</td><td>Agrocampus</td><td>X</td><td></td><td></td></tr><tr><td>Mikaela Potier</td><td>Agrocampus</td><td>X</td><td></td><td></td></tr><tr><td>Didzis Ustups</td><td>BIOR</td><td>X</td><td></td><td></td></tr><tr><td>Maris Plikss</td><td>BIOR</td><td>X</td><td></td><td></td></tr><tr><td>Mike Heath</td><td>STRAT</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Jan Jaap Poos</td><td>WU</td><td>X</td><td></td><td></td></tr><tr><td>Logan Binch</td><td>WU</td><td>X</td><td></td><td></td></tr></table>

## 1.3 Acronyms and abbreviations

Blim: Limit Biomass. Below Blim, recruitment gets impaired

CFP: Common Fisheries Policy

EBFM: Ecosystem Based Fisheries Management

EwE: Ecopath with Ecosim

F: Fishing Mortality
FLBEIA: Bio-Economic Impact Assessment using FLR

GSA: GFCM Geographical Sub Area

GES: Good Environmental Status

GFCM: General Fisheries Commission for the Mediterranean

HCR: Harvest Control Rule

HELCOM: The Baltic Marine Environment Protection Commission (also known as the Helsinki Commission)

ICES: International Council for the Exploration of the Sea

MAP: Multi Annual Plan

MSFD: Marine Strategy Framework Directive

MSY: Maximum Sustainable Yield

PETs: Protected Endangered and Threatened Species

PGY: Pretty Good Yield

OSMOSE: Object-oriented Simulator of Marine eCOSSystEmS

OSPAR: The Convention for the Protection of the Marine Environment of the North-East Atlantic (name derived from the Oslo and Paris Commissions)

SAR: Stocks at Risk

SHI: Sustainable Harvest Indicator

SMS: Stochastic Multi Species model

SSB: Spawning Stock Biomass

STECF: Scientific Technical and Economic Committee for Fisheries

StrathE2E: Strathclyde End to End ecosystem model

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## 2. Existing indicators, targets and limits impacting fisheries management

The Common Fisheries Policy (CFP; European Commission 2013) and the Marine Strategy Framework Directive (MSFD, European Commission 2008, 2017, 2022) are the two most important overarching policy instruments impacting fisheries management directly. To implement the CFP and MSFD with their various ecological, economic and social objectives, indicators have been developed over time to measure the achievement of these objectives. While CFP indicators for ecological objectives as well as their targets and limits are in many cases internationally agreed (e.g., reference points to implement the maximum sustainable yield (MSY) concept), the situation is different for economic and social objectives of the CFP. While many indicators exist (e.g., gross profit, employment, labour condition etc.), hardly any targets and limits have been agreed so far. The MSFD indicators have been proposed and there is now guidance on which indicators to use to monitor good environmental status (GES) (European Commission 2022). However, agreed MSFD-related targets and limits for the various indicators under the different descriptors are only available for a subset of examples in the different case study regions. Scientific (i.e. ICES and
STECF) and regionally oriented (i.e., OSPAR and HELCOM) bodies have conducted first analyses where they propose candidate thresholds for some of the indicators (e.g., bycatch limits for some marine mammal populations or the Large Fish Indicator (LFI)).

SEAwise collected information on existing targets and limits of CFP and MSFD indicators for the different case study regions. Simulations were carried out with models available to SEAwise task 6.4 after the first 1.5 years. Scenarios were agreed for the North East Atlantic and the Mediterranean case studies, simulating the currently available range of management options (e.g., FMSY ranges or effort levels to reach certain objectives in the Mediterranean) with varying degree of implementation (i.e. of the landing obligation). Model output was used to calculate as many CFP and MSFD related indicators as possible to analyse whether existing targets and limits are consistent with each other or whether trade-offs must be expected because of technical or biological interactions or inconsistent objectives (e.g., ecological vs. socio-economic objectives; MSY vs. GES).

<figure><img src="image_9.png" /></figure>
# 3. Methodology

## 3.1 General approach

Deliverable 6.7 is based on existing indicators, targets and limits identified in SEAwise Task 6.5 (Performance of existing management plans and measures) and WP4 tasks (Figure 3.1). Scenarios were run under current environmental conditions and harvest control rules (see the description under 3.3 for more details on scenarios). Based on model outputs, as many indicators as possible were calculated (including estimates not internally forecasted by the simulation models which were based on e.g., relationships with predicted effort by metier). Where targets and limits were available, the model predictions were compared to the respective values to identify which targets and limits were respected in each scenario and which not. Where targets and limits have yet to be agreed, scenarios were ranked based on the desired direction of indicator development.

<figure><img src="image_10.png" /><figcaption>Figure 3.1. General approach to test the consistency of existing targets and limits for indicators in an ecosystem context.</figcaption></figure>

Task 6.4 received input from various tasks (Figure 3.2). The following input has been received:

* Task 3.5: Parameterisation of single species stock dynamic models. Current single species reference points (CFP: MSY-related indicators; MSFD: Descriptor 3)

* Task 4.2: Catch per unit of effort (CPUE) for sensitive species by gear type and region based on ICES WGBYC. Bycatch thresholds for examples per case study (MSFD Descriptor 1)

* Task 4.3: Approach to exchange effort by metier has been discussed for Deliverable 6.8.

* Task 4.4: List of food web indicators (MSFD Descriptor 4)

* Task 4.5: Information about relationships between effort and observed litter (MSFD descriptor 10)

* Task 6.2: Improved ecological models (mainly relevant for month 36)

* Task 6.3: Improved socio-economic models

* Task 6.5: First set of management strategies as well as CFP and MSFD indicators to measure performance in relation to CFP and MSFD objectives
<figure><img src="image_11.png" /><figcaption>Figure 3.2: Flow of data and information to and from task 6.4</figcaption></figure>

## 3.2 Available Models

For each case study region, a suite of models for a range of stocks and fisheries was available (Table 3.1). For deliverable 6.8 in month 36, further models developed in SEAwise will become available (e.g., Osmose for the North Sea, FLBEIA and Ecopath with Ecosim for the Celtic Sea, Isis-Fish model for the Bay of Biscay, SMS for Baltic Riga herring). For further details on models and their parameterisation see Annexes.

<table><caption>Table 3.1. Models available for SEAwise deliverable 6.7</caption><thead><tr><th>Case study</th><th>Model name</th><th>Model type</th><th>Spatial extent</th><th>Species/stocks included</th><th>Fleets/metiers/fishe-ries included</th></tr></thead><tbody><tr><td rowspan="4">North Sea</td><td>Bio-Economic Impact Assessment using FLR (FLBEIA)</td><td>Bio-economic mixed fisheries simulation model</td><td>ICES areas 4, 7d, 3a20 and 6a for stocks extending to 6a (cod, saithe, haddock, anglerfish)</td><td>Cod, whiting, haddock, saithe, plaice (4; 7d), sole (4; 7d), turbot, witch, anglerfish, lemon sole, ling, brill, dab, Nephrops FUs</td><td>Demersal Fleets and Metiers (42 fleets and 132 metiers)</td></tr><tr><td>Object-oriented Simulator of Marine ecosystEmS (Osmose)</td><td>Multispecies and individual based model</td><td>Ices area 7d</td><td>Mackerel, horse mackerel, sardine, herring, poor cod, North Sea cod, whiting, pouting, striped red mullet, dragonet, lesser spotted dogfish, sole, plaice, squids</td><td>None (F by species/stock)</td></tr><tr><td>Strathclyde End to End ecosystem model (StrathE2E)</td><td>End to end model</td><td>North Sea</td><td>End-to-end, nutrients, plankton, benthos, fish, birds, pinnipeds, cetaceans but all as function groups not species</td><td>Pelagic and demersal fisheries</td></tr><tr><td>Stochastic Mult Species model (SMS)</td><td>Multispecies stock assessment model</td><td>ICES area 4</td><td>Cod, haddock, saithe, whiting, hake, plaice, sole, herring, sprat, sandeel, Norway pout, mackerel, horse mackerel, seals, harbour</td><td>None (F by species/stock)</td></tr></tbody></table>
<table><tbody><tr><td rowspan="3">Western Waters</td><td>FLBEIA</td><td>Bio-economic mixed fisheries simulation model</td><td>ICES area 8</td><td>Hake, megrim, monkfish, horse mackerel, mackerel, sole, blue whiting, thornback ray, black-bellied angler, Nephrops, seabass, starry smooth-hound, cuckoo ray, undulate ray</td><td>Demersal fleets and metiers</td></tr><tr><td>FLBEIA</td><td>Bio-economic mixed fisheries simulation model</td><td>ICES area 8</td><td>Anchovy, North East Atlantic mackerel, western horse mackerel, northern hake, sardine in Bay of Biscay, Iberian sardine, bluefin tuna, albacore</td><td>Pelagic fleets and metiers</td></tr><tr><td>StrathE2E</td><td>End to end model</td><td>Celtic Sea</td><td>End-to-end, nutrients, plankton, benthos, fish, birds, pinnipeds, cetaceans but all as function groups not species</td><td>Pelagic and demersal fisheries</td></tr><tr><td>Baltic</td><td>BEE-FISH</td><td>Bio-economic simulation model</td><td>ICES Subdivisions 25-32, Central Baltic, ICES SD 22-24, Western Baltic</td><td>Cod, herring, sprat</td><td>Trawl fisheries</td></tr><tr><td rowspan="2">Mediterranean</td><td>FLBEIA</td><td>Bio-economic mixed fisheries simulation model</td><td>GFCM GSA20</td><td>European hake, red mullet, deep water rose shrimp, striped red mullet, others (all other commercial stocks caught by the fleets to be treated as one biomass dynamic stock)</td><td>Demersal fleets (OTB, SSF)</td></tr><tr><td>BEMTOOL</td><td>Bio-economic simulation model</td><td>GFCM GSAs 17, 18 and 19 (Southern Adriatic and Western Ionian Sea)</td><td>European hake (GSAs 17-18 combined; GSA 19), red mullet (GSAs 17-18 combined; GSA 19), deep water rose shrimp (GSAs 17-18-19 combined) giant red shrimp (GSAs 18-19 combined), Norway lobster (GSA 17-18 combined).</td><td>Demersal Fleets and Metiers: specifically, 1) Mixed demersal trawlers 2) Mixed deep waters trawlers; 3) Small scale; 4) Longliners. In total: 16 fleet segments and related metier.</td></tr></tbody></table>

## 3.3 Scenarios

The parameterisation of the biological part was the same as used for Task 3.5 to forecast stock developments in a base scenario (i.e. no environmental changes) if possible. Where this was not possible, the parameterisation (i.e. stock recruitment relationship(s), biological parameters as M, weight at age etc.) were chosen as close as possible to the latest benchmark decisions for a given stock. Uncertainty was included at least for the biological parameters (i.e. recruitment) whenever possible. In addition, assessment and/or advice error in a Management Strategy Evaluation (MSE) shortcut approach was added if feasible.

### 3.3.1 North East Atlantic case studies (North western waters, Bay of Biscay and Iberian waters, North Sea, Baltic)

The ICES MSY approach is the most important management strategy applied to provide advice on fishing opportunities in the North East Atlantic. In addition to the ICES MSY approach, sustainable FMSY ranges delivering at least 95% of the maximum yield (Pretty Good Yield (PGY) concept) from multi annual plans (MAPs) for the North Sea,
Baltic and Western Waters are considered. As further important management measure, the landing obligations exist for all EU, UK and Norwegian waters of the North East Atlantic.

Next to a baseline scenario applying current effort/F levels, in total 3-4 scenarios were run for task 6.4 to mimic current management approaches:

a) A "status quo" effort/F scenario. The effort/F is set to the average of the last three years OR to the value of the most recent year if trends are obvious.

b) A "min" scenario. The ICES FMSY harvest control rule is applied with FMSY as target for each stock. The fleets/metiers stop when the first quota is exhausted. The scenario implies a strict implementation of the landing obligation.

c) A "pretty good yield" scenario: Same as the min scenario, but using the fishing mortality ranges to provide more flexibility to the catch advice setting. In some cases FMSY was replaced by fishing at the "FMSY upper" level (FMSY upper indicates the upper limit of sustainable fishing mortalities delivering at least 95% of the maximum yield) is allowed when stocks are in good status; i.e. above MSY Btrigger at the beginning of the advice year. In addition, a buffer to year-to-year advice variability could be introduced, such that TACs are limited to max. +/- 20 percent from one year to the next, but limited to TACs associated with FMSY upper or lower. The scenario is somewhat more flexible in the use of the upper FMSY range, possibly releasing some choking behaviour when most-limiting stocks are in good status. In other cases, FMSY was replaced by the fishing mortality produced by a multi-stock HCR that tried to balance the single stock fishing mortality targets with the aim of maximizing fishing opportunities within the fishing mortality ranges.

d) An additional case-specific scenario that mimics the current situation in the region regarding fleet dynamics, uptake of quotas or likelihood of certain species/stocks becoming choke species under the current level of implementation and control of the landing obligation.

### 3.3.2 Mediterranean case studies (Central and Eastern Mediterranean)

For the *Central Mediterranean Case study* (GSA17-18-19), the GFCM MAP for demersal stocks in the Adriatic (Recommendation GFCM/43/2019/5), establishing maximum capacity and effort limits for both bottom and beam trawlers, was used as baseline. The MAP is aimed at achieving the MSY target in 2026 for all key stocks through a fishing effort regime.

For the *Eastern Mediterranean Case study* (GSA 20) there is no MAP, but there is a national management plan for trawlers using bottom otter trawls (OTB) which has been in effect since 2013 and several management measures for Small Scale Fisheries (SSF). The management measures in this national plan are generally based on MSY targets in line with the EU-MAP objectives.

For the Mediterranean Case study the following scenarios were explored for task 6.4:

a) Status quo (same effort as in the last historic year used to parameterise the model or average last three years);

b) Effort reduction to achieve the F0.1 (used as FMSY proxy) of the most overexploited stock in 2026;

c) Effort reduction to achieve a combined FMSY (or PGY) on all the target stocks;

d) FMSY range (low and upper, 2 scenarios) of the most overexploited stock. One of these additional scenarios could be overlapping with scenario 3, in which case it could not be necessary to run both scenarios of this point.
If the type of model (e.g., ecosystem models) was not able to run the base scenarios, simple F multipliers were applied to status quo effort or fishing mortality to demonstrate the direction of change in indicators with increasing or decreasing fishing pressure. For more details on scenarios implemented for each case study and model, see Annexes.

## 3.4 Indicators

The set of indicators was agreed based on the indicators used to measure the performance of current management in task 6.5 (Table 3.2). Ecological and socio-economic indicators in relation to CFP and MSFD objectives as well as global ecosystem indicators were included in the analysis to cover a broad range of indicators. For each indicator, the averages over the prediction years 2025-2030, 2035-2040, 2045-2050 (i.e. 6-year periods as suggested by the most recent MSFD guidelines (European Commission 2022)) were calculated. Not all indicators could be calculated for all models (for more details see Annexes) but the strength of each model was utilised to cover as many indicators as possible in each case study region.

<table><caption>Table 3.2. Set of indicators to be considered for each case study and model. D[x] followed by C[y] indicates the respective MSFD descriptor and criterion.</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>Also relevant for MSFD D3C1</td></tr><tr><td>Proportion of stocks with median SSB below MSY B<sub>trigger</sub></td><td>Yes</td><td>Also relevant for MSFD D3C2</td></tr><tr><td>Proportion of stocks with >5% probability to fall below B<sub>lim</sub></td><td>Yes</td><td>Also relevant for MSFD D3C2</td></tr><tr><td>Proportion of fleets with Sustainable Harvest Indicator (SHI) above 1</td><td>Yes</td><td>Details can be found in balance indicator guidelines (COM 2014, 545 final)</td></tr><tr><td>Proportion of fleets with number of stocks at risk (SAR) > 0</td><td>Yes</td><td>Details can be found in balance indicator guidelines (COM 2014, 545 final)</td></tr><tr><td rowspan="5">CFP socio-economic</td><td>Landings (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr><tr><td>Unwanted catch/discards (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr><tr><td>Revenue (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr><tr><td>Gross profit (average of yearly sums across fleets/metiers)</td><td>No</td><td>Gross profit (GP) = Income from landings + other income – crew costs – unpaid labour – energy costs – repair and maintenance costs – other variable costs – non-variable costs<sup>1</sup></td></tr><tr><td>Gross value added (average of yearly sums across fleets/metiers)</td><td>No</td><td>Gross value added (GVA) = Income from landings + other income – energy costs – repair costs – other variable costs – non-variable costs<sup>1</sup></td></tr><tr><td></td><td>Employment (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr></tbody></table>
<table><tr><td></td><td>Wages (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr><tr><td></td><td>Average yearly ratio of current revenue/break even revenue (sum across fleets/metiers) Ratio landings value fleets &lt;=24m/landings value fleets &gt;24m</td><td>Yes</td><td>CR/BER = revenue / break-even revenue = Income from landings + other income / BER<sup>1</sup> BER = (Fixed costs + opportunity costs of capital +depreciation) / (1-(crew costs + unpaid labour + energy costs + repair and maintenance costs + other variable costs)/Revenue)<sup>1</sup></td></tr><tr><td></td><td>Accident rates</td><td>No</td><td></td></tr><tr><td rowspan="7">MSFD related indicators (Descriptor 3 indicators are already included under CFP indicators)</td><td>D1C1: Bycatch or risk for PET species</td><td>Partly (for some species and regions). For details see Annex 10.</td><td>Biological Extraction of, or mortality/injury to, wild species (by commercial and recreational fishing and other activities)<sup>2</sup></td></tr><tr><td>D4C1: Biodiversity within trophic guilds<sup>3</sup></td><td>No</td><td>Simpson or Shannon indices could be used to measure biodiversity<sup>2</sup></td></tr><tr><td>D4C2: Balance between trophic guilds<sup>3</sup></td><td>No</td><td>Biomass per guild is preferable over abundance<sup>2</sup></td></tr><tr><td>D4C2: Biomass of forage fish</td><td>No</td><td>Indicator to detect potential food limitation for higher trophic level species</td></tr><tr><td>D4C3: Size structure within guilds<sup>3</sup></td><td>No</td><td>Examples of size-based indicators applied for fish include the large fish indicator (LFI), typical/median//95th percentile of length, and mean maximum length (MML). The LFI relies on the estimation of the size of a 'large fish' to be estimated separately by guild and region, and the indicator is therefore not directly comparable between regions but may provide useful results within a region. Mean maximum length (MML) integrates aspects of species diversity and size structure (mean possible length in the guild) and has the advantage that it can be estimated without information on size distribution of individuals in the guild.<sup>2</sup></td></tr><tr><td>D4C4: Average recruitment success within guilds<sup>2</sup></td><td>No</td><td>R/SSB may be used as proxy for recruitment success.<sup>2</sup></td></tr><tr><td>D6: Effort by demersal gear type</td><td>No</td><td>Provides indications for pressure indicators D6C1 (physical loss of seabed) and D6C2 (physical disturbance to the seabed). Impacts on benthic habitats (State) will be analysed together with SEAwise task 4.3 following deliverable 6.7.</td></tr><tr><td rowspan="3">Global indicators</td><td>D10: Amount of marine litter</td><td>No</td><td>According to the MSFD Descriptor 10 Marine litter should not cause harm to the coastal and marine environment. Litter in the environment (D10C1) includes litter on the coastline, on the surface and on the seabed. For seafloor litter, the assessment is based on annual surveys. A trend analysis detects the direction of development of the parameter (number or mass/km<sup>2</sup>)<sup>2</sup>. In SEAwise, this is analysed using several litter categories that allow to evaluate also risks of entanglement, ingestion and transport.</td></tr><tr><td>Carbon emission from fisheries (average of yearly sums across fleets/metiers)</td><td>No</td><td></td></tr><tr><td>Ratio of fisheries catches to Primary production (Fogarty ratio in Link and Watson 2019)<sup>4</sup></td><td>No</td><td>On the basis of simple trophic transfer calculations, the Fogarty ratio of catches relative to PP ranges from 0.1 to 3‰. This again is suggestive of reasonable limits to catch potential and hence a possible threshold. Thus, given</td></tr></table>
<table><tr><td></td><td></td><td>worldwide catches of 0.1 to 0.42 Gt year-1 would imply an expected ratio of catch to PP of 0.22 to 0.92‰, suggesting a possible „Not to Exceed“ threshold ~1. Even an extreme estimate of catches near 1 Gt year-1 would result in a value of 2.2‰ (~2.5‰).<sup>4</sup></td></tr><tr><td>Catches per km<sup>2</sup> per year (Ryther index in Link and Watson 2019)<sup>4</sup></td><td>No</td><td>Given that the surface area of the world's ocean is approximately 363 M km<sup>2</sup>, one can estimate the areal values of catch, which can be reasonably expected. Link and Watson call this the Ryther index. Ryther's original work not only related landings to PP but also provided a global thinking and evaluation of fisheries catch. Thus, world wide catches of 0.1 to 0.42 Gt year-1 would result in a yield of 0.27 to 1.14 t km-2 year-1, suggestive of a possible „Not ot Exceed“ threshold ~1. Even extreme estimates of global total catch around 1 Gt year-1 would result in a value of 2.7 t km-2 year-1 (~3 t km-2 year-1).<sup>4</sup></td></tr><tr><td>Ratio of fisheries catches to Chorophyl a (Friedland ratio in Link and Watson 2019)<sup>4</sup></td><td>No</td><td>Acknowledging that estimates of PP are not always available but that satellite imagery able to produce estimates of chlorophyll a may be more so, Link and Watson propose a proxy index. Coupling catch statistics with chlorophyll a estimates, and acknowledging all the important nuances of chlorophyll a and different pathways of production, they propose a unitless ratio of catch: Chlorophyll a to evaluate relative fishery productivity in those instances where PP estimates are not readily available. They call this the Friedland ratio index. Using logic similar to the Fogarty ratio, an empirical „Not to Exceed“ threshold ~1 emerges.<sup>4</sup></td></tr></table>

<footnote n="1">¹Scientific, Technical and Economic Committee for Fisheries (STECF): The 2021 Annual Economic Report on the EU Fishing Fleet (STECF 21-08) Annex. EUR 28359 EN. Publications Office of the European Union, Luxembourg, 2021. ISBN 978-92-76-43549-5, doi:10.2760/549599, JRC 126139.</footnote>

<footnote n="2">²European Commission 2022. MSFD CIS Guidance Document No. 19, Article 8 MSFD, May 2022.</footnote>

<footnote n="3">³Trophic guilds according to European Commission (2022): Apex fish predators, Apex marine mammal predators, Sub-apex demersal predators, Sub-apex pelagic predators, Planktivorous fish and invertebrates, Benthic feeding invertebrates, Benthic filter feeding invertebrates, Secondary producers, Benthic primary producers, Pelagic primary producers</footnote>

<footnote n="4">⁴Link, J.S. and Watson, R.A. 2019. Global ecosystem overfishing: Clear delineation within real limits to production. Sci. Adv. 5. eaav0474</footnote>
# 4. Results

For the different case studies and models a sub-set of indicators were selected to demonstrate main trade-offs between indicators and to compare model results to existing targets and limits. More indicators and description of models, methods and results can be found in the respective Annexes.

## 4.1 North Sea

### 4.1.1 FLBEIA North Sea (27.4), Skagerrak (27.3.a.20) and Eastern English Channel (27.7.d)

The bio-economic model FLBEIA for the North Sea, Skagerrak and Eastern English Channel was applied to analyse the effect of different harvest strategies in relation to the MSY concept and implementation level of the landing obligation. Further details on the model, methods and results can be found in Annex 1.

The scenarios chosen were designed as baseline runs, excluding any additional effects of climate change, species interactions or economic developments that will be explored in future work within Seawise. We explored four baseline scenarios:

One "Status quo" effort scenario, where the fleets can fish with the effort of the last data year (2021) without choking effects and three landing obligation scenarios with different interpretations of the MSY concept. The first landing obligation scenario is a classic "Fmsy-Min" scenario, applying the ICES harvest control rule with FMSY as target fishing mortality. Fleets stop fishing when their first quota is exhausted. A second Pretty good Yield (PGY) landing obligation scenario ("PGY-Min") allows harvesting up to the upper sustainable FMSY range, if the stock is above MSY Btrigger. Additionally, we considered a 20% limit to year-to-year TAC changes, as stability of income and harvest has a high value among fishermen. A last landing obligation scenario, which is case study specific to the North Sea ("Case Study") relaxes the degree of choking stocks by excluding witch as a choking stock as witch is currently managed under a combined TAC with lemon sole, making choking effects less likely. Additionally, we only allowed more southerly distributed fleets fishing with Beam Trawls or TR2 gears (mesh sizes between 80mm and 100mm, typically used in the fisheries for sole and plaice) to be choked by sole and plaice, whereas the other fleets have a reduced number of potential choking stocks. Furthermore, we looked at the effects of an additional 27% implementation error on the TAC limits of cod when TACs are set below 35000 tonnes. TAC overshoots for North Sea cod were observed in the last years according to ICES advice indicating a lack of control of the landing obligation in reality.

**Main results:**

Scenarios with an implemented landing obligation outperformed the Status quo effort scenario in all aspects (ecological and individual economic) apart from the total amount of catches (food security). The strictest implementation of the MSY concept and of the landing obligation (Fmsy-Min scenario), however, led to losses in economic and social indicators compared to the more flexible scenarios (PGY-Min and Case specific). In relation to MSFD related and global indicators, a strict implementation of the landing obligation and interpretation of the MSY concept (Fmsy-Min) was the best option.

Under the Status quo scenario, several of the stocks were harvested above FMSY, leading to an increased risk of stocks falling below Blim (Figure 4.1.1). The scenario with a strict implementation of the landing obligation and MSY approach (Fmsy-Min) allowed for more sustainable harvesting and no stocks had an increased risk of falling below Blim. Relaxing the choking situation in the PGY-Min and Case Study scenario, led to an increased proportion of stocks
being fished above FMSY (but below FMSY upper indicating the upper limit of sustainable fishing mortalities delivering at least 95% of the maximum yield). None of the stocks had a risk above 5% to fall below Blim in these scenarios. Therefore, the Status quo was the scenario clearly exceeding existing targets and limits, while for PGY-min and the case specific scenario it depends on the interpretation of the MSY concept and whether sustainable FMSY ranges are allowed.

Looking at the Gross profit across fleets for the different time periods showed clear economic gains in scenarios with an implementation of the landing obligation (Figure 4.1.1). The Status quo effort scenario generated the lowest gross profit indicating benefits of reducing fishing effort in the future. Relaxing the choking situation in the PGY and Case study specific scenario generated the highest Gross profit over all periods, with the Case study specific scenario performing best. The Fmsy-Min scenario performed better than the Status quo effort scenario but worse compared to the other landing obligation scenarios. The same ranking could be also observed for catches and wages between the landing obligation scenarios. However, in terms of catch the status quo effort scenario outperformed the other scenarios at the cost of more depleted stocks and lower economic performance.

As the bycatch of harbour porpoise and seals was assumed proportional to the effort of specific gears in the model, the scenarios with highest bycatch was the scenario with the highest effort being the Status quo scenario, followed by the Case Study specific, PGY-min and the FMSY-min scenario (Figure 4.1.2). Bycatch levels in the landing obligation scenarios could be reduced by 30-53 % for harbour porpoise and 27-49 % for seals relative to the Status quo scenario, through the reduction in fishing effort. For harbour porpoise the highest bycatch from the Status Quo scenario of 1547 individuals is slightly below the estimated threshold of 1622 individuals (see Annex 10 for details on thresholds). For seals, the highest bycatch of 2146 individuals in the Status Quo scenario is also lower than the PBR threshold set by OSPAR of 7617 individuals (see Annex 10 for details on thresholds). However, these predictions of bycatch are highly uncertain as both the current level of bycatch is very poorly known and a simple linear relationship between effort and bycatch numbers may not be true in reality. Therefore, these values can only be seen as indicative of the likely direction and ball park magnitude of change.

The biomass ratio of apex fish predators (AFP) to sub-demersal predators (SDP) revealed the highest proportion of AFP (in our case cod) for the Fmsy-Min scenario, relative to all the benthic gadoids and flatfish in the model (Figure 4.1.2). The other scenarios group themselves again in the pattern from lowest to highest fishing mortality with a higher amount of AFP in scenarios with lower fishing pressure.

The proportion of large fish in the three landing obligation scenarios with values between 0.27 – 0.29 was higher than the Status quo scenario with values around 0.2, reflecting that lower exploitation levels in the landing obligation scenarios lead to a higher proportion of older age classes and a shift in age-distribution (Figure 4.1.2). The differences between Fmsy-Min having the highest LFI compared to the two relaxed choking scenarios (Fmsy-Upper and Case Study) could be explained by the lower exploitation, leading to a high biomass of stocks with large fish sizes (cod, saithe and plaice). In general, stock recovery and accompanying shift in age class distribution under the landing obligation scenarios could help in reaching a target LFI for the North Sea of LFI > 0.3 currently discussed as potential threshold.

The potential highest benthic impact followed the effort of the demersal gears and was highest in the Status quo scenario, followed by the Case study specific scenario, PGY-Min and Fmsy-Min scenarios. Only for Beam trawls, effort levels of the PGY-min scenario increase over the simulation period, reaching higher levels than the Case study specific scenario by 2035 – 2040, due to higher exploitation levels of the flatfish plaice, sole and witch, which are caught predominantly with Beam trawls.

Carbon emissions by the fishery are highest under the Status quo effort scenario, followed by the Case study specific scenario, PGY-Min and Fmsy-Min, reflecting the general effort pattern of the scenarios (Figure 4.1.3). The Fogarty ratio (catch per net primary production) and Ryther index (catches per surface area) for the North Sea simulations exceed the threshold of 1 and are even at their upper limit (2.2 for Fogarty) or exceed it (2.7-3 for Ryther), indicating
severe ecosystem overfishing for all scenarios. However, changes in the demersal fisheries does not have a large impact on these indicators as the main catches come from pelagic and industrial species that had to be assumed constant for this analysis.

<figure><img src="image_12.png" /><figcaption>Figure 4.1.1. CFP related indicators by scenario and time-period predicted from the FLBEIA model for the North Sea, Skagerrak and Eastern English Channel</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure 4.1.2. MSFD related indicators by scenario and time-period predicted from the FLBEIA model for the North Sea, Skagerrak and Eastern English Channel</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure 4.1.3. Global indicators by scenario and time-period predicted from the FLBEIA model for the North Sea, Skagerrak and Eastern English Channel.</figcaption></figure>

<figure><img src="image_15.png" /></figure>
4.1.2 Osmose in the English Channel

An Osmose model for the English Channel was used to run straightforward scenarios including status quo fishing mortalities and status quo multiplied with a factor of 0.8 and 0.6 (Figure 4.4). The focus was on MSFD and food web related indicators. More details can be found in Annex 2. The predicted biomass of Apex predators decreased with increasing fishing pressure, while e.g., the biomass of forage fish was less clearly related to fishing pressure indicating benefits of released predation pressure in the model when fishing harder on predators. Also, the response of sub-demersal predators was non-linear, while benthic risk was clearly highest in the status-quo scenario.

<figure><img src="image_16.png" /><figcaption>Figure 4.1.4. MSFD related indicators by scenario and time period predicted with the Osmose model for the Eastern English Channel.</figcaption></figure>
4.1.3 StrathE2E – North Sea

The end-to-end ecosystem model StrathE2E was used to analyse the impact of different levels of fishing pressure on the North Sea ecosystem. This section focuses on ecological and ecosystem wide indicators. Other aspects including e.g., catch levels and revenues can be found in Annex 3.

Biomasses of fish and top predators decreased with increasing effort multiplier (Figure 4.1.5). Released from predation, the biomass of carnivorous zooplankton increased. Small cascading trophic effects were present at the phytoplankton and zooplankton levels. Migratory fish appeared resilient to increasing fishing effort in the model. This was because this guild was not a permanent resident in the model domain. A seasonal immigration flux of migratory fish into the model was part of the boundary conditions for the model, which was independent of the effort multiplier scenarios – therefore the global biomass of the migratory fish stock (archetype: mackerel) was not affected by harvesting within the model domain. The assumption is that harvesting within the North Sea model represents a minor component of the total annual removals from the global stock in the northeast Atlantic. Nevertheless, the net migration flux of migratory fish (annual immigration less annual emigration) was dependent on the fishing effort, since their biomass was harvested inside the model.

The scaling of fishing effort had a large effect on the biomasses of the top predators in the model (birds, pinnipeds and cetaceans), which were severely depleted relative to an unfished state, even in the baseline model (Figure 4.1.5). This was partly due to direct by-catch by certain gears, and partly as a bottom-up trophic effect of depletion of their food supply. The ratio of biomasses of top predators to fish declined with increasing fishing. By-catch quantities, and in the case of cetaceans the directed landings quantity, varied in response to changing abundances in the sea, and the changing mortality rate due to fishing gears. However, the predictions of bycatch are highly uncertain as both the current level of bycatch is very poorly known and a simple linear relationship between effort and bycatch numbers may not be true in reality. Therefore, these values can only be seen as indicative of the likely direction and ball park magnitude of change. Further, the controlling effect of food supply on several of these species has not been confirmed by data analysis (Engelhard et al. 2014).

Net primary production decreased with fishing effort (Figure 4.1.6). In StrathE2E, phytoplankton dynamics are integrated into the model food web and so primary production is subject to top-down cascading trophic effects arising from the removal of higher trophic levels from the system. The Fogarty index included this dynamic aspect of the primary production. Levels of the Fogarty index and the Ryther index in even the baseline model both exceeded the thresholds suggested by Link and Watson (2019) as representing optimal harvesting of the ecosystem, and were clearly in the realm of ecosystem over-exploitation according to Link and Watson (Figure 4.1.6).
<figure><img src="image_17.png" /><figcaption>Figure 4.1.5. Steady state annual average biomasses (thousands of tonnes) for each guild in the North Sea relative to effort multiplier scenarios.</figcaption></figure>
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Figure 4.1.6. Upper panel: Annual net primary production (mMolesN.m⁻².y⁻¹) simulated by the North Sea model in relation to effort multiplier scenarios. Middle panel: Fogarty index (landings or catch divided by net primary production) relative to effort multiplier scenarios. Lower panel: Ryther index (catch or landings tonnes.km⁻².y⁻¹) relative to effort multiplier scenarios. Green shaded areas in the Fogarty index and Ryther index panels are regarded as optimal ranges (Link & Watson 2019; Beet & Gaichas 2022). The orange shaded areas are regarded as representing ecosystem overfishing. Vertical grey line at effort multiplier = 1 in each panel represents the baseline 2003-2013 model.
4.1.4 Stochastic Multi Species model SMS for the North Sea

The multi species model SMS (ICES, 2021) was run for the North Sea to analyse food web interactions between demersal fish predators and small pelagics (herring, sprat, Norway pout and sandeel). The fishing mortality trajectories for the main demersal predators (cod, haddock, whiting, saithe) predicted from the North Sea FLEBIA scenario runs (Status quo, Fmsy-Min, PGY-Min, Case study) were directly implemented in SMS. This allowed demonstration of the resulting impact of different realized fishing mortalities for demersal predators on small pelagics and industrial species when simulating dynamic predation mortalities. In all scenarios, the herring stock was fished according to the ICES MSY approach, while the industrial species sprat, sandeel and Norway pout were managed by an escapement strategy. For further details see Annex 4.

Trophic cascades in SSB developments were obvious (Figure 4.1.7). The higher the fishing mortalities for demersal fish in the different scenarios, the lower the predicted SSB of the top predatory fish saithe and cod. However, sub-apex predators like haddock and whiting increased in SSB despite higher fishing mortalities due to lower predation from cod and saithe. The resulting impact on small pelagics was highly dependent on the main predators feeding on them. Herring and Norway pout benefitted from higher average fishing mortalities on gadoids (indicating that the reduction of predation from cod and saithe was more important than the increase in predation from haddock and whiting), while sprat and sandeel showed the opposite pattern. Yield (Figure 4.1.8) for the small pelagics and industrial species followed the same patterns as presented for SSB. Within the range of scenario F for the demersal species, yield of forage fish varied by more than 25%. Annual yield of herring increased by 150 kt (~33%), while yield of sandeel decreased by 75 kt (~15%) due to a higher demersal F.

<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /><figcaption>Figure 4.1.7. Spawning stock biomass in relation to average fishing mortality applied for the demersal predators cod, haddock, whiting and saithe. A loess-smoother is added for easier interpretation.</figcaption></figure>
<figure><img src="image_23.png" /><figcaption>Figure 4.1.8. Yield in relation to average fising mortality applied for the demersal predators cod, haddock, whiting and saithe. A loess-smoother is added for easier interpretation.</figcaption></figure>
## 4.2 Western Waters

### 4.2.1 FLBEIA for the Bay of Biscay (mixed demersal fisheries)

The bio-economic model FLBEIA was used to simulate dynamics in the mixed demersal fisheries of the Bay of Biscay. Details on the model, methods and more results can be found in Annex 5.

The following four scenarios were tested:

**Status 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 a scenario against which to compare the rest of the scenarios.

**Fmsy-Min:** In this scenario, the fleet fully complies with the landing obligation and stop fishing when the first of the quota is consumed. There is no adaptability mechanism in the target fishing mortality ($F_{MSY}$ for all stocks), the catchability or the effort share, therefore it could create a significant loss in fishing opportunities.

**PGY-Min:** The fleet dynamics are the same as in the 'min' scenario but the advice is generated with a multi-stock HCR that operationalizes the $F_{MSY}$ fishing mortality ranges in Pretty Good Yield (PGY) scenario. The multi-stock HCR is based on the single stock advice and the maximization of fishing opportunities.

**Case specific:** The effort share along metiers is given as input data and is equal to the mean of the most recent data. 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 being constrained by the quotas.

**Main results:**

For demersal species any of the scenarios resulted in biomasses above $B_{lim}$ with high probability in the mid-to long-term (Figure 4.2.1). For Horse Mackerel, which started the simulation below $B_{lim}$, the probability was dependent on the scenario. The status quo scenario resulted in a probability higher than 25% to fall below $B_{lim}$, whereas in the case specific and the other scenarios, the probability was almost null. Therefore, only the Status quo scenario was incompatible with existing biomass limits agreed within ICES.

The Fmsy-Min and PGY-Min scenarios resulted in 100% of the stocks fished at or below $F_{MSY}$, while the Status quo and case specific scenario did not reach 100% (Figure 4.2.1). In most of the cases, the exploitation of the stocks was within the fishing mortality ranges, which ensures that the long-term yield is not lower than 95% of the maximum sustainable yield. For anglerfish, the fishing mortality in $F_{MSY}$-min and Status quo and for sole in the $F_{MSY}$-min scenario was below the lower bound of the range, which implies a loss in fishing opportunities. This indicates a trade-off coming from technical interactions in mixed fisheries when fishing fleets must stop if the first quota is exhausted (choke species problem).

The gross profit was lowest for the status quo and case specific scenario and Fmsy-Min and PGY-Min showed a better performance. Gross profit for many of the fleets was negative, which resulted in an overall negative gross profit in all scenarios (Figure 4.2.1). 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 database do not fully match, which could also have an impact. For catch, as indicator of food security and
wages, as a social indicator, the pattern was exactly the opposite (Status quo best and Min scenarios worst), showing
a clear trade-off between indicators.

<figure><img src="image_24.png" /><figcaption>Figure 4.2.1. CFP related indicators by scenario and time-period predicted from the FLBEIA model for the demersal mixed fisheries in the Bay of Biscay</figcaption></figure>

With regard to MSFD related indicators, many of the processes in the projection were linearly related with effort (Figure 4.2.2). The bycatch was assumed proportional to effort, but the bycatch rate depended on the gear and the stock, hence the trend was different for each of the stocks. The case specific and status quo scenarios on the one hand and Fmsy-Min and PGY-Min on the other showed similar trends over time. While the highest bycatch for Dolphins was observed in the Status quo scenario, for the rest of the PET species, the highest bycatch was observed in the case specific scenario. Much lower bycatch was predicted for the Fmsy-Min and PGY-Min scenarios. When comparing to bycatch thresholds discussed in literature for Balearic shearwater (Genovart et al. (2016) calculates a threshold of 101 individuals; see Annex 10), this threshold gets exceeded in all scenarios. This happens without the fisheries in the Mediterranean indicating a serious mismatch between fisheries and environmental objectives. For common dolphin different thresholds are available (985 individuals in ICES 2021 and 4927 in ICES 2020; see also Annex 10) dependent on the method to derive acceptable bycatch levels. Especially the predictions for the Status
quo scenario would exceed the more restrictive threshold, while especially the FMSY-min and PGY-min scenarios would be below. However, the predictions of bycatch are highly uncertain as both the current level of bycatch is very poorly known and a simple linear relationship between effort and bycatch numbers may not be true. Therefore, these values can only be seen as indicative of the likely direction and ball park magnitude of change.

The Ryther index calculated for the demersal mixed fisheries is below one in the Fmsy-Min and PGY-Min scenarios and around one in the Status quo and case specific scenarios (Figure 4.2.3). However, when combining the estimates with the ones observed for the pelagic fisheries, the status quo and case specific scenario would be above one indicating at least slight ecosystem overfishing according to Watson and Link (2019). In addition, not all catches from the region are represented in the model.

<figure><img src="image_25.png" /><figcaption>Figure 4.2.2. MSFD related indicators by scenario and time-period predicted from the FLBEIA model for the demersal mixed fisheries in the Bay of Biscay</figcaption></figure>
<figure><img src="image_26.png" /></figure>

Figure 4.2.3. Global indicator by scenario and time-period predicted from the FLBEIA model for the demersal mixed fisheries in the Bay of Biscay

## 4.2.2 FLBEIA for the Bay of Biscay (pelagic).

The bio-economic model FLBEIA has been parameterised for the pelagic fleets fishing in the Bay of Biscay. For details on the model, methods and detailed results can be found in Annex 6. These fleets are characterized by catching several species along the year but at fishing operation level the catch is composed almost 100% by a single stock. The target stock depends on the season with some overlap between species in specific months.

Two scenarios were run to compare different fishing strategies. A Status quo effort where the effort is and its distribution along metiers is equal to the last three year mean and a scenario where the total effort and distribution are those that maximise the profit ("maxprof") of the pelagic fisheries. In this case the landing obligation scenario does not make a difference because the discards in this fishery are null or very low.

The "maxprof" scenario had a positive effect on the proportion of stocks fished at or below FMSY and also the number of stocks with more than 5% risk to fall below Blim was lower (Figure 4.2.4). However, both scenarios exceeded existing reference points in the long-term.

The maximisation of profit led to higher gross profit (also still, negative in the long-term). This came at the cost of much lower catches and wages indicating a trade-off between the economic objectives and food security as well as social benefits (Figure 4.2.4). Small vessels benefited from the status quo effort scenarios, while the large vessels (being more effective) benefited from the profit maximisation.

The maximisation of profit led to substantially less fishing effort having a positive effect on the bycatch of dolphins (Figure 4.2.5). However, the predictions of bycatch are highly uncertain as both the current level of bycatch is very poorly known and a simple linear relationship between effort and bycatch numbers may not be true. Therefore, these values can only be seen as indicative of the likely direction and ball park magnitude of change. CO2 emissions and the Ryther index as indicator for ecosystem overfishing were considerably lower under the "maxprof" scenario (Figure 4.2.6).
<figure><img src="image_27.png" /><figcaption>Figure 4.2.4. CFP related indicators by scenario and time-period predicted from the FLBEIA model for the pelagic fisheries in the Bay of Biscay.</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>Figure 4.2.5. MSFD related indicators by scenario and time-period predicted from the FLBEIA model for the pelagic fisheries in the Bay of Biscay.</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>4.2.6. Global indicators by scenario and time-period predicted from the FLBEIA model for the pelagic fisheries in the Bay of Biscay fisheries.</figcaption></figure>
### 4.2.3 StrathE2E – Celtic Sea

The end-to-end ecosystem model StrathE2E was used to analyse the impact of different levels of fishing pressure on the North Sea ecosystem. This section focuses on ecological and ecosystem wide indicators. Other aspects including e.g., catch levels and revenues and further details can be found in Annex 3.

As in the North Sea model, biomasses of fish and top predators decreased with increasing effort multiplier (Figure 4.2.7). Released from predation, the biomass of carnivorous zooplankton increased. Small cascading trophic effects were present at the phytoplankton and zooplankton levels. Migratory fish – which were sustained by a constant annual boundary immigration regardless of fishing effort as in the North Sea model – formed the major part of landings and revenue at high fishing effort multipliers. The resident planktivorous and demersal fish in the model were depleted and extirpated at the highest fishing efforts.

Bird and pinniped guilds in the baseline 2003-2013 Celtic Sea model were more severely depleted relative to an unfished state than in the equivalent North Sea baseline. All top predator guilds were extirpated by even modest increases in effort compared to the North Sea. Direct effects of fishing on the top-predators were entirely due to bycatch, there being no hunting for cetaceans in the Celtic Sea.

Net primary production decreased with fishing effort (Figure 4.2.8), as in the North Sea. Overall levels of both the Fogarty and Ryther indices were lower than in the North Sea, but still exceeded the thresholds suggested by Link and Watson (2019) as representing optimal harvesting of the ecosystem, even in the baseline 2003-2013 fishing effort scenario. Higher effort scenarios were clearly in the realm of ecosystem over-exploitation.

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /><figcaption>Figure 4.2.7. Steady state annual average biomasses (thousands of tonnes) for each guild in the North Sea relative to effort multiplier scenarios.</figcaption></figure>
<figure><img src="image_32.png" /></figure>

Figure 4.2.8. Upper panel: Annual net primary production (mMolesN.m⁻².y⁻¹) simulated by the Celtic Sea model in relation to effort multiplier scenarios. Middle panel: Fogarty index (landings or catch divided by net primary production) relative to effort multiplier scenarios. Lower panel: Ryther index (catch or landings tonnes.km⁻²y⁻¹) relative to effort multiplier scenarios. Green shaded areas in the Fogarty index and Ryther index panels are regarded as optimal ranges (Link & Watson 2019). The orange shaded areas are regarded as representing ecosystem overfishing. Vertical grey line at effort multiplier = 1 in each panel represents the baseline 2003-2013 model.
## 4.3 Baltic

Model runs with the bio-economic model BEE-FISH were simulated for the Western Baltic (ICES Sub-divisions 22-24) and the Central Baltic (ICES Sub-divisions 25-32) separately. The simulations were carried out with partly strong differences in target fishing mortality (Status-quo, Fmsy, Fmsy-upper). A case specific "welfare" scenario was run additionally. In this case-specific scenario, the objective was to maximize the intertemporal welfare, as the sum of consumer surplus (gross consumer benefit minus expenditure) and profits (revenues minus harvesting costs). For details see Annex 7.

### 4.3.1 BEE-FISH western Baltic

In the Western Baltic, trade-offs between catch (i.e. food security objective), stock status in relation to existing reference points, and fishery profits became obvious (Figure 4.3.1). Depending on the focus, different management strategies would be favorable. A Status-quo management will in no case lead to the most desirable outcome and puts several stocks at risk to fall below the limit biomass $B_{lim}$. In addition, the proportion of stocks fished at or below $F_{MSY}$ is only 50% under Status-quo management. Setting $F_{MSY}$ as target (Fmsy-Min) created the highest catches without exceeding targets and limits for stock status in the mid- to long-term. The Pretty Good Yield (PGY) scenario allowing fishing in the sustainable range above $F_{MSY}$ (i.e. up to Fmsy-upper) created highest profits without exceeding biomass limits. The inclusion of a welfare optimization scenario led to lowest gross profits among the scenarios. 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. More details can be found in Annex 7.

<figure><img src="image_33.png" /><figcaption>Figure 4.3.1. CFP indicators per scenario and time period predicted by the BEE-FISH model for the Western Baltic</figcaption></figure>
### 4.3.2 BEE-FISH central Baltic

Similar to the Western Baltic, also the Central Baltic Sea case study showed trade-offs between landings, stock status, and fishery profits (Figure 4.3.2). Landings did 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 (Fmsy, Status Quo, Fmsy-upper (PGY)), Fmsy-upper created the highest landings, but was also most problematic in terms of exceeding biomass limits and fishing above FMSY. The FMSY strategy, generated higher profits at lower fishing mortality values and led to a recovery of stocks leading for all stocks to less than 5% probability to fall below Blim after 2030. The Status Quo fishery scenario resulted in the worst combination of outcomes. More details can be found in Annex 7.

<figure><img src="image_34.png" /><figcaption>Figure 4.3.2. CFP indicators per scenario and time period predicted by the BEE-FISH model for the Central Baltic</figcaption></figure>
## 4.4 Mediterranean

### 4.4.1 FLBEIA Eastern Ionian Sea (GFCM Geographical Sub Area 20)

The bio-economic mixed fisheries simulation model FLBEIA was run to compare scenario outcomes for different implementations of the MSY concept and associated effort management. Further details can be found in Annex 8.

From the two demersal fleet segments of the eastern Ionian fishery, only the large-scale fleet (LSF) utilizing Otter Trawls (OTB) is under a management plan (in effect since 2013). This sets MSY-based targets for the main stocks of the fishery, namely European hake (HKE), deep water rose shrimp (DPS) and mullets (MUT). 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, 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 $F_{01}$ (as proxy for $F_{MSY}$) or $F_{MSY}$ lower (Flw) or $F_{MSY}$ upper (Fup) as target fishing mortality for HKE were applied. These are achieved with 47%, 26% and 63% effort reduction from both fleets, respectively. Two scenarios combining multiple species objectives were also applied. A multi-stock Pretty Good Yield scenario (PGY), which relaxes the MSY objective by defining a range of fishing mortalities per stock corresponding to 80% MSY, was also explored. In the eastern Ionian fishery, the PGY scenario for hake, shrimp and mullet was achieved by doubling the LSF effort and reducing the SSF effort by half (Figure 4.4.1) indicating a trade-off between the LSF and SSF fleets. In addition, an $F_{MSY}$ combined scenario (Fcomb) 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 projected 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 trade-off between the amount of catch and social indicators one side and the profitability of the fleets and fishing effort applied as proxy for the strength of impacts on the ecosystem on the other. 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 was not the case for the SSF, where differences depended on the species. Combining both fleets, PGY was the most profitable scenario (Figure 4.4.2). The better performing scenarios in terms of Gross profit were the ones applying the greatest effort reduction (e.g. Flw, followed by PGY, F01 and Fup; Figure 4.4.3), even though economic indicators were still negative across all scenarios (Figure 4.4.2). This was a result of a reduction of costs that were greater than the increase of the revenue and was an effect of including the imputed cost of unpaid labour in variable costs.

For hake, the highest catch was predicted for Flw and F01. For striped red mullet, it was the Fcomb scenario that gave the highest catch and for red mullet the Status quo scenario. Overall, the highest total catches (i.e. in terms of food security) were achieved in the status-quo scenario followed by the Fcomp scenario (Figure 4.4.2). Wages were also highest in the Fcomb and Status quo scenarios associated with overall higher fishing effort compared to other scenarios. The ratio of revenues coming from the small vessels compared to the revenues coming from larger vessels was also highest for the Fcomb scenario as no reduction of fishing effort in the SSF was predicted.

In terms of biological indicators, Flw was the scenario with the biggest SSB and total biomass for hake, deep water rose shrimp and red mullet and the lowest fishing mortality in terms of $F/F_{MSY}$ ratio (Figure 4.4.2). The proportion of stocks fished at or below $F_{msy}$ was higher for Flw and F01 followed by Fcomb and Fup (Figure 4.4.2). In the PGY scenario, 25% of the stocks were below MSY Btrigger with their median SSB, which was due to the increase of the harvest of the shrimp stock, which was sustainably exploited in all other scenarios. None of the scenarios had a greater than 5% risk of falling below Blim (Figure 4.4.2).
Among the MSFD indicators, the highest turtle bycatch (mean annual bycatch in individuals) were estimated for the Fcomb and Status quo scenarios (Figure 4.4.3). The greater impact on the benthic community as derived by the effort of OTB gear as a proxy was greater under the PGY scenario, which was the only scenario foreseeing an increase of OTB gear effort. This indicates potential trade-offs between CFP socio-economic and MSFD indicators as e.g., wages were highest in the Fcomb scenario, catches were highest in the Status quo effort scenario and gross profit was highest in the PGY scenario.

<figure><img src="image_35.png" /></figure>

Figure 4.4.1. 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 red mullet (MUT) and hake (HKE) and PGY (fishing mortality corresponding to 80%MSY) for deep water rose shrimp (DPS). The effort multiplier set for the PGY scenario is defined on the intersection of the three species ranges.
<figure><img src="image_36.png" /><figcaption>Figure 4.4.2. CFP related indicators per scenario and time period predicted by the FLBEIA model for the Eastern Ionian Sea (GSA20)</figcaption></figure>
<figure><img src="image_37.png" /><figcaption>Figure 4.4.3. MSFD related indicators per scenario and time period predicted by the FLBEIA model for the Eastern Ionian Sea (GSA20).</figcaption></figure>
### 4.4.2 BEMTOOL and Ecopath with Ecosim in the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas 17-18-19)

In this case study, the bio-economic model BEMTOOL and the ecosystem model Ecopath with Ecosim (EwE) were applied consistently to estimate not only CFP but also MSFD relevant indicators. Details can be found in Annex 9.

The following scenarios were analysed:

**Status quo:** effort equal to fishing opportunities of 2023 for GFCM Geographical Sub Area (GSAs) 17-18, while for GSA 19 same effort as in 2021;

**Fmsy_DPS:** Effort reduction to achieve the $F_{0.1}$ (used as $F_{MSY}$ 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 $F_{0.1}$ of Deep Water Shrimp (DPS) in GSAs 17-18-19.

**Fcomb_(PGY):** Effort reduction to achieve a $F_{MSY}$ 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% for trawlers in GSAs 17-18-19 towards a combined reference point estimated weighing the $F_{0.1}$ of the above mentioned stocks by their total catch.

BEMTOOL was used to forecast several ecological and socio-economic CFP indicators (Figure 4.4.4; more indicators can be found in Annex 9). Overall, reducing effort resulted in improvement of ecological, economic and social indicators especially in the long-term in the more restrictive scenarios FMSY_DPS and Fcomb_PGY compared to the Status quo scenario. A stronger reduction of effort (i.e. 69% in the FMSY_DPS scenario)) would not produce greater advantage to the system compared to the 58% reduction in the Fcomp_PGY scenario, as some stocks would remain underutilized, while the challenge for the economic-social systems would be very impacting in the FMSY_DPS scenario, especially for trawlers and in the short term.

Regarding the ecological indicators, the proportion of stocks fished at or below $F_{MSY}$ is 0.2 in the Status quo scenario in all time periods of the simulation and 0.8 for the FMSY and the Fcomb-PGY scenarios in the first period of the simulation and then stabilized at 1. In the Status quo scenario the proportion of stocks with a higher than 5% probability to fall below $B_{lim}$ was 0.2, while in other scenarios the proportion was zero. Therefore, only the status quo scenario led to problems with existing reference points for the stocks simulated in the model.

While higher catches were predicted for the Status quo scenario in the first time period (2025-2030) analysed, in the longer-term the other two scenarios outperformed the Status quo scenario with the Fcomb-PGY scenario having the highest overall catches. However, on an individual fleet level, the ranking of scenarios may be different (see Annex 9). The same overall ranking of the scenarios was occurred for gross profit. The ranking of scenarios for wages and the ratio of revenues from vessels <=24m to vessels above 24m (<=18m and >18 used in the model) was more variable, however, the Status quo scenario was always the least preferred one (Figure 4.4.4).
<figure><img src="image_38.png" /><figcaption>Figure 4.4.4. CFP related indicators per scenario and time period predicted by the BEMTOOL model for the Adriatic and western Ionian Seas (GFCM Geographical Sub Areas 17-18-19).</figcaption></figure>

MSFD food web indicators were calculated by EwE at guild level, according to scenarios of fishing effort reduction (i.e. the same effort reductions predicted by BEMTOOL for the three scenarios) and by time periods. Overall, benefits of effort reductions occurred, but also trade-offs within the food web.

A 69% or 58% reduction of fishing effort produced positive effects on piscivores in terms of biodiversity, when compared to Status quo scenario (Figure 4.4.5); the biodiversity of benthivorous fish showed an initial increase with lower fishing pressure, and then it was quite stable (see Table 8.7 in Annex 9). Conversely, biodiversity of planktivorous and that of top predators was very similar across fishing scenarios.

The biomass of both piscivorous and benthivorous fish increased under reduced fishing effort, reflecting the reduction of mortality on these groups when bottom trawling was reduced (Figure 4.4.6). The biomass of planktivorous fish declined in the model, plausibly because of the increasing predation from the piscivorous fish. Similarly, the biomass of top predators declined, possibly as a result of competition from piscivorous fish. The biomass increased faster than the biodiversity index (see Annex 9), and it also increased for benthivorous, in contrast to the biodiversity index. This confirms that biomass did not capture all information: plausibly, in the benthivorous
fish the biomass increased only for few groups which dominate the guild, leading to a lower biodiversity. The change in biomass, however, were only substantial for the piscivorous fish: for all other groups the differences between scenarios were too small to be distinguished visually in plots and only discernible when looking at Table 8.8 of Annex 9.

For the Maximum Mean Length (MML) indicator, the effect of the effort scenarios across trophic guilds was clearly visible (Figure 4.2.5): the reduction of effort led to a higher MML which stabilized around a value above 40 cm (often used as potential threshold associated with large fish indicators) in both effort reduction scenarios. Conversely, the status quo scenario showed a stabilization below 40 cm. When considering the individual guilds (see Table 8.9 in Annex 9), the most important change observed with a large increase in MML under effort reduction took 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).

<figure><img src="image_39.png" /><figcaption>Figure 4.4.5. Diversity of piscivorous fish (left) and mean maximum length (MML) across guilds under three effort scenarios.</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>Figure 4.4.6. Biomass in tons of piscivorous (left) and benthivorous (right) fish under the three effort reduction scenarios.</figcaption></figure>
# 5. Conclusions

A wide range of model types (from bio-economic to full ecosystem models) has been applied to various case studies across the North East Atlantic and Mediterranean to test the consistency of different types of indicators relevant to the CFP and MSFD and to identify main trade-offs.

Model predictions are by nature uncertain and can also be biased to some extent because of structural uncertainties and assumptions behind modelling approaches and data availability and details on sources of uncertainty and model assumptions can be found in the Annexes. Thus, the results should be interpreted in the context of the models used and assumptions made. Additionally, it should be kept in mind that these baseline runs are simplified scenarios not considering effects of climate change or future fuel and fish price developments that will likely change yield and profits of fishermen as well as ecosystem impacts. In some cases important limitations have been identified that will be improved in future model implementations. Moreover, some of the reference points used were estimated from other models than those applied in SEAwise and they might not be consistent with the implemented operating models. Uncertainties in the modelling approaches were mainly restricted to uncertainties from the biological system (i.e. from the stock recruitment relationships). Additional sources of uncertainties (e.g. catchability, fleet dynamics or implementation error) in full MSE loops may increase the chance to fall below $B_{lim}$ for some of the stocks investigated. In spite of these uncertainties, this modelling study provides important information on trade-offs that have to be expected under ecosystem-based fisheries management (EBFM) when bringing in additional targets and limits in a more ecosystem context when moving forward from the current single stock MSY approach for commercially important fish stocks.

The scenarios investigated the current range of management strategies applied in terms of fishing opportunities aligned with the maximum sustainable yield concept (i.e. strict MSY approach vs. Pretty Good Yield (PGY) approach allowing sustainable deviations from e.g., single species FMSY point estimates). The landing obligation as further important corner stone of current fisheries management was also simulated in the context of mixed demersal fisheries in the North East Atlantic. Across case studies the following main conclusions can be drawn:

* The current fishing effort (without further management via e.g., TACs and the landing obligation) were the least sustainable option in nearly all cases studies (apart from the Eastern Baltic Sea), leading to increased risk of stocks falling below $B_{lim}$. Although the degree of fishing effort adaptions needed is highly case specific, this indicates that further reductions in fishing effort are likely to be beneficial to ensure a sustainable exploitation of all stocks.

* Scenarios applying a strict MSY approach (i.e. $F_{MSY}$ point estimates as upper limit) in combination with the landing obligation (FMSY-min scenarios) led in most case studies to the lowest fishing effort applied. This had positive effects on MSFD related indicators (e.g., bycatch of PET species, benthic impact, Large fish indicator) as well as global indicators as CO2-emission or ecosystem indicators like the Ryther or Fogarty indices suggested by Watson and Link (2019). However, this scenario often led to much lower catches (compared to e.g., Status quo) due to strong choke effects because fleets had to stop fishing when their first quota was exhausted. This reduced food security, but also employment and wages as social indicators. In terms of economic performance, the gains and losses were highly case specific, but often scenarios applying the Pretty Good Yield concept and allowing sustainable deviations from the $F_{MSY}$ point estimate when stocks were in a healthy state outperformed the scenarios with the $F_{MSY}$ point estimate as strict limit in terms of gross profit (e.g., Mediterranean case studies, western Baltic Sea, Bay of Biscay and North Sea).

* The scenarios applying a more flexible interpretation of the MSY concept (PGY or average MSY across stocks in the Mediterranean) may constitute a compromise as they also led to reduced fishing effort compared to the status quo effort in all scenarios, but relaxed choke situations in mixed demersal fisheries to some extent
leading to higher gross profits and in some case studies also to higher catch. Whether the associated effort levels lead to conflicts with MSFD objectives should be analysed when internationally agreed thresholds become available for e.g., bycatch of PET species or benthic impact.

* Bycatch thresholds currently discussed were tested e.g., for the North Sea and Bay of Biscay case studies. For the North Sea conflicts with bycatch limits were estimated to be unlikely although bycatch rates were highly uncertain (e.g., bycatch of harbour porpoise would more than double if an outlier is used in the calculations, see ICES 2021). In the Bay of Biscay all scenarios exceeded the threshold discussed for Balearic shearwater indicating at least a potential mismatch between fisheries and environmental objectives. For dolphins, the sustainability of bycatch levels was unclear, because different methods lead to very different acceptable bycatch thresholds. However, these predictions of bycatch are highly uncertain as both the current level of bycatch is very poorly known and a simple linear relationship between effort and bycatch numbers may not be realistic. Therefore, the conclusions can only be seen as indicative of the likely direction and ball-park magnitude of change. Overall, this highlights that further work is required to come to certain conclusions regarding actual bycatch in absolute numbers and acceptable bycatch limits for PETs. The comparison between scenarios in relative terms, instead of absolute values, is probably more accurate.

* All models assumed current selectivities and catchabilities to be sustained in the future. However, fishers can to some extent to regulate the catchabilities to mitigate choking effects. Further, trade-offs coming from choke species problems or e.g. bycatches of PET species may be resolved by improving selectivities via technical measures (e.g., closed areas or innovative gears). Adaptability of fishers and alternative selectivities will be tested in deliverable 6.8 in month 36.

* The majority of case studies exceeded the suggested thresholds for the Fogarty/Ryther indices even under their scenarios with highest effort reduction, indicating ecosystem overfishing. This can be explained to some extent by the fact that these indices are mainly driven by pelagic and industrial fisheries not always part of the models (e.g., FLBEIA for the North Sea). The values for these indicators are also highly model dependent indicating substantial structural uncertainties and may not be fully comparable across ecosystems.

* Especially in case studies where small-scale fisheries (SSF) play an important role (e.g., Eastern Ionian Sea, pelagic fisheries in the Bay of Biscay) different scenarios led to additional trade-offs as seen in different ratios between revenues from SSF and large-scale fisheries. This adds another level of complexity to be considered in fisheries management.

* Food web interactions can also lead to trade-offs between species and yield that can be taken from them (e.g., Demersal piscivorous predators vs. small pelagic fish; Osmose in the English Channel, SMS for the North Sea, StrathE2E for the North Sea and Celtic Sea). With improved models from task 6.2 this topic will be further investigated in month 36.

## 6. Further work

Deliverable 6.7 sets the baseline for further work in SEAwise on indicators, targets and limits. Additional social indicators will be derived from work in SEAwise WP2. The MSFD related indicators will be also re-evaluated for the final deliverable 6.8 in month 36 based on final results and improved models from SEAwise WP4 tasks. For example, Swept Area Ratio (SAR) maps delivered by Task 5.3 in month 18 will be used to distribute effort by fleets and metiers into space. SAR will be scaled according to the predicted effort from the model scenarios in task 6.4. Finally, based on SAR by c-square, the Relative Benthic Status (RBS) used in task 4.3 can be calculated for each scenario and
compared to thresholds that have to be agreed soon according to the EU Action Plan 2023. More internationally agreed bycatch limits will likely become available for PET species according to this plan. The update of the indicator list and knowledge becoming available from further work in SEAwise will allow for an improved assessment of scenario outcomes in month 36. The resulting impact of scenarios with shifts in fishing effort due to spatial management options will be evaluated using information on changes in catchabilities and selectivities coming from SEAwise WP 5 and the impact on indicators will be evaluated.

Deliverable 6.8 in month 36 will enhance results from deliverable 6.7 by incorporating climate change effects via improved task 6.2 models for the simulation of stock dynamics (Figure 6.1). Although the general approach remains similar to the one of deliverable 6.7, the aim in month 36 will be to test harvest control rules developed within SEAwise that help to harmonize goals mentioned in the CFP and MSFD. The analyses will make targets and limits consistent with each other where this is possible, and highlight trade-offs where this is not the case. Models will also be improved in their socio-economic parts via SEAwise task 6.3 and e.g., scenarios of price developments will be investigated together with climate change impacts.

Next to this, additional models will become available to complete the matrix of models available and indicators that can be calculated within each case study region. SEAwise PIs are working for example on: a model for the Gulf of Riga herring, a FLBEIA bio-economic model for the Celtic Sea, an Isis-Fish model for the Bay of Biscay and an OSMOSE model in the North Sea. The OSMOSE model in the North Sea it is a spatially explicit (1/9 ICES Statistical Rectangle), individual-based ecosystem model that simulates the interactions between individuals via food web dynamic processes (predation and resource competition). A novel fleet model will be developed to represent the dynamic and strategic choices fishers make, as well as their socioeconomic priorities with respect to fishing in the North Sea. Also the construction of an Ecopath with Ecosim (EwE) ecosystem model is currently in progress for the Celtic Sea area, i.e. ICES subdivisions 7e, 7f, 7g, 7h and 7j2. The ecosystem is represented in 53 functional groups (either multi-species or single-species groups). 40 out of 53 functional groups will be targeted by fisheries which are represented by 44 fishing fleets. The effects of environment will be also modelled through temperature time series (sea surface and bottom temperature) coupled with species response functions to temperature variations. These two models will possibly allow an explicit evaluation of some MSFD indicators that cannot be directly evaluated using mixed-fisheries models. Furthermore, OSMOSE will provide means for testing spatial management measures.

<figure><img src="image_41.png" /><figcaption>Figure 6.1. Schematic workflow for deliverable 6.8 in month 36.</figcaption></figure>
## 7. References

Engelhard, G. H., Peck, M. A., Rindorf, A., C. Smout, S., van Deurs, M., Raab, K., ... & Dickey-Collas, M. (2014). Forage fish, their fisheries, and their predators: who drives whom?. ICES Journal of Marine Science, 71(1), 90-104.

EU Action Plan 2023. COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS. EU Action Plan: Protecting and restoring marine ecosystems for sustainable and resilient fisheries. COM(2023) 102 final

European Commission 2008. Marine Strategy Framework Directive. Directive 2008/56/EC amended as in Commission Directive (EU) 2017/845 of 17 May 2017.

European Commission 2013. Regulation (EU) on the Common Fishery Policy. Regulation No 1380/2013. Official Journal of the European Union, L354: 1-40.

European Commission 2017. Criteria and methodological standards on good environmental status of marine waters and specifications and standardised methods for monitoring and assessment, and repealing Decision 2010/477/EU. COMMISSION DECISION (EU) 2017/848

European Commission 2022. MSFD CIS Guidance Document No. 19, Article 8 MSFD, May 2022.

Genovart M, Arcos JM, Álvarez D, McMinn M, Meier R, B. Wynn R, Guilford T, Oro D, Österblom H. 2016. Demography of the critically endangered Balearic shearwater: the impact of fisheries and time to extinction. Journal of Applied Ecology 53:1158-1168

ICES (2020) Workshop on fisheries Emergency Measures to minimize Bycatch of short-beaked common dolphins in the Bay of Biscay and harbour porpoise in the Baltic Sea (WKEMBYC). In: Reports. IS (ed), Book 2:43

ICES (2021) Workshop on estimation of MOrtality of Marine MAmmals due to Bycatch (WKMOMA). In: Reports IS (ed)

Link, J.S. and Watson, R.A. 2019. Global ecosystem overfishing: Clear delineation within real limits to production. Sci. Adv. 5. eaav0474.

STECF 2021: The 2021 Annual Economic Report on the EU Fishing Fleet (STECF 21-08) Annex. EUR 28359 EN. Publications Office of the European Union, Luxembourg, 2021. ISBN 978-92-76-43549-5, doi:10.2760/549599, JRC 126139.
# 8. Appendix

## 8.1 Annex 1: Description of analyses carried out with the FLBEIA model for the North Sea.

Bernhard Kühn, Marc Taylor, Alexander Kempf

### 1. Description of the FLBEIA model of the North Sea mixed demersal fishery

The mixed fisheries model of the North Sea is defined using the procedure of WGMIXFISH-Advice (ICES, 2021, 2022). The modelling framework is FLBEIA (Garcia et al., 2017), and includes 42 fleets (137 total métiers) and 20 stocks for the North Sea mixed fisheries. Stock dynamics are either age-based (COD-NS, HAD, PLE-EC, PLE-NS, POK, SOL-NS, SOL-EC, TUR, WHG-NS, WIT), or fixed (NEP6, NEP7, NEP8, NEP9, NEP5, NEP10, NEP32, NEP33, NEP34, NEPOTH-NS) and differ whether they are actively managed via a TAC advice or, in some cases, considered as bycatch stocks. Stocks included in the FLBEIA North Sea mixed fisheries model are listed in Table 1.

<table><caption>Table 1: Stocks included in the North Sea mixed fishery model, Scientific name – binominal nomenclature showing genus and species, stock code – ICES stock code, FAO – species code used by the FAO, ICES data category – determines the type of data and assessment available for the stock (1 – data rich with quantitative assessment, 2 – qualitative assessment, 3 – stocks, for which survey-based indices and assessment are available, 4 – stocks, for which only commercial catch data is available, 5 – data poor stocks, where only landings data are available, 6 – neglectable stocks caught primarily as bycatch), Model abbreviation – the stock code used in the model)</caption><thead><tr><th>Scientific name</th><th>Stock code</th><th>FAO</th><th>Common name</th><th>ICES data category</th><th>Model abbreviation</th></tr></thead><tbody><tr><td>Gadus morhua</td><td>cod.27.47d20</td><td>COD</td><td>cod</td><td>1</td><td>COD-NS</td></tr><tr><td>Melanogrammus aeglefinus</td><td>had.27.46a20</td><td>HAD</td><td>haddock</td><td>1</td><td>HAD</td></tr><tr><td>Pollachius virens</td><td>pok.27.3a46</td><td>POK</td><td>saithe</td><td>1</td><td>POK</td></tr><tr><td rowspan="2">Solea solea</td><td>sol.27.4</td><td rowspan="2">SOL</td><td rowspan="2">sole</td><td rowspan="2">1</td><td>SOL-NS</td></tr><tr><td>sol.27.7d</td><td>SOL-EC</td></tr><tr><td>Pleuronectes platessa</td><td>ple.27.420, ple.27.7d</td><td>PLE</td><td>plaice</td><td>1</td><td>PLE-NS<br>PLE-EC</td></tr><tr><td>Merlangius merlangus</td><td>whg.27.47d</td><td>WHG</td><td>whiting</td><td>1</td><td>WHG-NS</td></tr><tr><td rowspan="10">Nephrops norvegicus</td><td>nep.fu.5,</td><td rowspan="10">NEP</td><td rowspan="10">Norway lobster</td><td rowspan="10">Cat. 1 for FUs 6-9,<br>Cat. 4 for other FUs</td><td>NEP5</td></tr><tr><td>nep.fu.6,</td><td>NEP6</td></tr><tr><td>nep.fu.7,</td><td>NEP7</td></tr><tr><td>nep.fu.8</td><td>NEP8</td></tr><tr><td>nep.fu.9</td><td>NEP9</td></tr><tr><td>nep.fu.10</td><td>NEP10</td></tr><tr><td>nep.fu.32</td><td>NEP32</td></tr><tr><td>nep.fu.33</td><td>NEP33</td></tr><tr><td>nep.fu.34,</td><td>NEP34</td></tr><tr><td>nep-IVnotFU</td><td>NEPOTH-NS</td></tr><tr><td>Scophthalmus maximus</td><td>tur.27.4</td><td>TUR</td><td>turbot</td><td>1</td><td>TUR</td></tr><tr><td>Glyptocephalus cynoglossus</td><td>wit.27.3a47d</td><td>WIT</td><td>witch flounder</td><td>1</td><td>WIT</td></tr></tbody></table>

The model is conditioned with historical data up to 2021, and forecasts future conditions thereafter. Stock dynamics are based on the assessments conducted in 2022. Fleets and métiers are parameterized based on the work conducted during WGMIXFISH for this model, which is specifically valuable in defining fleets as it contains additional information on vessel length - an important attribute of the fishery segments in terms of their economic characteristics. Fleets are defined based on their country of origin (Belgium – BE, Denmark – DK, England – EN, France – FR, Germany – GE, Netherlands – NL, Norway – NO, Scotland – SC, Sweden – SW, Other – OTH), main gear
employed (e.g. Static, Pelagic, Danish seine, Otter trawl, Beam trawl), and vessel length (<10 m, 10-24 m, 24-40m and >40 m). Within each fleet, further segmentation of métiers is based on main fishing operations in terms of variations in gear (i.e. mesh size) and geographic area (i.e. ICES areas 3a, 4, 6a, and 7d). Each métier is further parameterized in terms of catchability of each of the stocks. Based on these catchabilities, catches are predicted under changing effort and stock sizes.

## 2. Description of model parameterisation

The North Sea FLBEIA model includes assumptions about future catchability, effort, capacity and quota shares. Future catchabilities were based on historic data and kept constant as last year values for the simulations. The effort model mainly determines how fleets derive to their catch, by simulating the tactical behaviour of the fleets in each time step related to the stock abundance, management restrictions and effort-shares among metiers. The effort per fleet and effort share among its métiers determined at each time step is based on the FLBEIA-internal ‘simple mixed fisheries behaviour’ (SMFB), taking a fixed effort share per métier as input and calculating realised effort based on stock abundance and management restrictions (‘min’ fleet control for landing obligation scenarios, ‘fixed’ for status-quo effort). A detailed description of this model can be found in the Technical manual for FLBEIA (García *et al.*, 2017).

Briefly, the effort of each fleet is restricted by the quotas for a list of restrictive stocks (COD-NS, HAD, POK, WHG-NS, PLE-NS, PLE-EC, SOL-NS, SOL-EC, TUR, WIT). The effort to catch the given quota for a stock is calculated via the Cobb-Douglas production function, dependent if the stock is age-based or biomass-based. The fleet control determines how an overall effort for a fleet is calculated, given the individual efforts for each of its stocks caught. In the scenario with a landing obligation, a “min” fleet control was used, that limits the effort to the minimum among possible efforts and to stay below/equal to the capacity for this fleet. Subsequently the catch is calculated with the derived effort level and compared to the quotas for each of the stocks. This process is reiterated until the derived effort level matches the quota restrictions as close as possible. Any deviations between actual catch and quota for a given season is proportionally added/removed to the shares of remaining seasons under the constraint that the annual quota shares remain the same (not relevant for our model, since we do only consider annual quotas).

As capital model, we assumed ‘fixed capital’ where future capacity is unchanged during the simulation. Quota shares among countries down to métier in the model is set to the average catches of the last three historical data years, reflecting a situation where quota swapping already has taken place as this was not explicitly modelled in the North Sea FLBEIA model.

The cost model does not affect fleet behaviour, but simply upscales/downscales variable costs based on the costs per unit of effort, crew share per unit of landings and capital costs per unit of capital. Fixed costs are taken directly from the input data. Similarly we assumed a constant price model, assuming constant price development based on the input from WGMIXFISH.

Economic variables for fleets and métiers were defined using data available from the Scientific, Technical and Economic Committee for Fisheries (STECF) from the recent Annual Economic Report (AER) (STECF 22-06) (STECF, 2022). This data release includes economic information (e.g. costs, revenue) for different fishing segments over the period of 2008-2020. We used average values from the last 3 data years (2018-2020) to condition economic parameters in the FLBEIA model for the most recent data year (2021). Due to the differences in the level of fleet segmentation between the STECF data and the FLBEIA model (ICES WGMIXFISH fleet definition), fleets could only be matched to the lowest level possible, not considering further métier segmentation regarding gear, mesh size and finer spatial scale operations within the North Sea, as the data only specifies aggregated information over the larger FAO Area 27. Also, as landing (monetary) values were only reported as aggregate, the STECF data might contain species not considered in our model. To overcome this mismatch, the FLBEIA model was conditioned with the relative costs to revenue ratio to match the level of profitability reported for fleet segments in the STECF data. With
this concession, the final results of the economic outcomes may be more appropriately interpreted in relative terms
between scenarios rather than absolute terms. To split the related costs in the FLBEIA model into fixed costs,
variable costs and crew share the following STECF data categories were used:

**Fixed costs** – Calculated as the sum of "Consumption of fixed capital" and "Other non-variable costs". "Consumption of fixed capital" was referred to as "Annual depreciation costs" in previous versions of the STECF data. These costs are defined at the fleet level in the FLBEIA model, and are constant over time (i.e. we do not assume any changes in the fleet size).

**Variable costs** – Calculated as the sum of "Fuel costs", "Value of unpaid labour", "Repair and maintenance costs" and "Other variable costs". These costs are defined at the métier level in the FLBEIA model, and are a function of changes in fleet effort over time multiplied by the effort share of a given métier. Fuel costs are of particular interest in the future scenarios and were examined for their consistency to particular fishing operations. A large degree of variability is observed, which is seen to be in part determined by the type of fishing operation, e.g. use of active vs. passive gears. Further variability is likely due to vessel size, and thus efficiency.

**Crew share costs** – A large part of salaries paid to fishers is in the form of a proportion of the landings value. These rates are not provided within the STECF data, but were assumed to make up the bulk of the "Personnel costs" category, which is technically defined as the "Total remuneration, in cash or in kind, payable by an employer to an employee (regular and temporary employees as well as home-workers)". Thus, the crew share was calculated as the ratio of Personnel costs / landings value, which was quite stable over time among fleets and lends support for this assumption. Furthermore, these crew shares were roughly on the scale often reported (Guillen et al., 2017). Crew share costs are defined at the fleet level in the FLBEIA model and are a function of the changes in landings value (i.e. revenue).

## Revenue and scaling of costs in FLBEIA

Revenue by fleet is based on the total landings value, which is provided at the fleet/métier level for each stock, but does not differentiate prices for different sizes/ages (i.e. €/kg) of the landings. Fish prices for 2021 were taken directly from the WGMIXFISH data call. Using the ratios *fixed costs / revenue* and *variable costs / revenue* derived from the STECF data, the fixed and variable cost of the FLBEIA fleets and métiers could be estimated based on their revenue. The last data year (2021) thus represents these STECF cost / revenue ratios exactly, but will change during the forecasts to reflect changes in catches and fishing effort.

# 3. CFP/MSFD and Global Indicators

## Indicators

We calculated the CFP, MSFD and Global indicators as described in the template for task 6.4.

## Scenarios

The scenarios were designed as baseline runs, excluding any additional effects of climate change, species interactions or economic developments that will be explored in future work within Seawise. We explored four baseline scenarios: a status quo effort scenario, where the fleets can fish with the effort of the last data year (2021), without choking effects and three landing obligation scenarios (“Min fleets control”) with various implementation levels. The first landing obligation scenario is a classic “Min” scenario, applying the ICES harvest control rule with $F_{MSY}$ as Ftarget. A second landing obligation scenario (“PGY-Min”) allows harvesting up to the upper $F_{MSY}$ range, if the stock is above Btrigger. Additionally we considered a 20% limit to year-to-year TAC changes, as stability of income and harvest has a high value among fishermen. This rule is also only applicable if the stock is above Btrigger. A last landing obligation scenario, which is case study specific to the North Sea (“Case study”) now relaxes the degree of
choking stocks by excluding Witch as a choking stock and only allowing more southerly distributed fleets (EN, BE, NL and FR) fishing with Beam Trawls or TR2 gears to be choked by sole and plaice, whereas the other fleets have a reduced number of potential choking stocks. Additionally we looked at the effects of an additional 27% implementation error on the TAC limits of cod, as there was a TAC overshoot on the North Sea cod stock in the last years (2020, 2021).

<table><caption>Table 2: Description of the FLBEIA scenarios run in task 6.3 and 6.4 as baseline runs</caption><thead><tr><td>Scenario</td><td>Abbreviation</td><td>Harvest control rule</td><td>Additional changes</td><td>Stock restrictions</td><td>Description</td></tr></thead><tbody><tr><td>Baseline Status Quo Effort</td><td>Status Quo effort</td><td>None</td><td>None</td><td>None</td><td>Simulating that fleets fish with the effort of the last data year with no restrictions on choking species</td></tr><tr><td>Baseline F<sub>MSY</sub> Min</td><td>FMSY-Min</td><td>ICES-HCR F<sub>MSY</sub></td><td>None</td><td>COD-NS, PLE-NS, PLE-EC, SOL-NS, SOL-EC, WHG-NS, POK, WIT, TUR, NEP6, NEP7, NEP8, NEP9</td><td>Simulating a perfect implementation of the landing obligation with F<sub>MSY</sub> as F<sub>target</sub>, where fleets need to stop fishing if their first quota is exhausted</td></tr><tr><td>Baseline F<sub>MSY</sub>Upper Min + TAC buffer</td><td>PGY-Min</td><td>ICES-HCR F<sub>MSY</sub>Upper</td><td>Limit year-to-year TAC changes to ± 20% if the stock is above Btrigger</td><td>COD-NS, PLE-NS, PLE-EC, SOL-NS, SOL-EC, WHG-NS, POK, WIT, TUR, NEP6, NEP7, NEP8, NEP9</td><td>Simulating a relaxation of the effects from the landing obligation, allowing fleets to fish at F<sub>MSY</sub>Upper if stocks are above Btrigger, with the additional constraint of limiting advised year-to-year TAC-changes by 20%</td></tr><tr><td>Baseline Case Study specific</td><td>Case study</td><td>ICES-HCR F<sub>MSY</sub></td><td>27% TAC implementation error for cod, if the TAC is set below 35000t</td><td>COD-NS, WHG-NS, POK, TUR, NEP6, NEP7, NEP8, NEP9 for all fleets; Beam trawlers and TR2 of EN, BE, FR and NL fleets being additionally choked by PLE-NS, PLE-EC, SOL-NS and SOL-EC</td><td>Simulating a scenario that reflects the current situation, with an enforced landing obligation("Min scenario"), but a more realistic choking situation with only beam trawlers and TR2 gears belonging to the English, Dutch, French or Belgian fleets being choked by sole and plaice and witch being not limiting anymore. Additionally there is an assumed 27% implementation error on the cod TAC-advice, as there was a TAC overshoot</td></tr></tbody></table>
# 4. Main results.

## Stock dynamics

Comparing the different, baseline management scenarios shows a clear pattern of higher biomass in the scenarios with lower F and vice versa (Figure 1). Fishing with the effort of the recent past (Status Quo effort), without considering any choking effects leads to the lowest biomass for all stocks, with increased risk of falling below Btrigger for COD-NS, HAD-NS, PLE-EC, POK, SOL-EC, SOL-NS, TUR and WIT and even below Blim for PLE-EC, SOL-NS and SOL-EC. The catches in this scenario are the highest for the majority of stocks, however cannot prevail at such a high level especially for those stocks dropping below Blim (SOL, PLE). Contrary in the FMSY-Min scenario, fleets are choked early by Witch (WIT-NS) and fished below FMSY (except for Witch) (Figure 2). Therefore, stocks are rebuilding to the largest biomass levels across scenarios due to lower catches. The other two scenarios (PGY-Min, Case study specific), are in between these two extrema, leading to a later choking than in the FMSY-Min scenario either through a higher Ftarget (FMSYUpper) if stocks are above Btrigger or a general modification of restrictive stocks (Case study specific). Still, the majority of stocks (except for Witch) are fished below their single species FMSY values. Witch was the main choking stock in the FMSY-Min scenario, followed by North Sea sole and PLE-EC. A similar pattern can be found for the PGY-Min scenario with SOL-EC being the dominant choking stock, followed by witch at least in the 2025 – 2030 period. However, this changes again, with witch as the dominant choking stock in the later periods. Lifting some of the choke restrictions in the Case study specific scenario, with no choking on witch and only certain fleets being choked by flatfish, allowed for a majority of fleets, who catch only few flatfish to have a higher quota utilisation of the gadoid stocks, which lead to saithe being the dominant choking species now.

<figure><img src="image_42.png" /></figure>
<figure><img src="image_43.png" /><figcaption>Figure 1: B/Blim, F/Fmsy and Catch for the age-based stocks per scenario (colour) in the FLBEIA North Sea model, median trajectories with uncertainty (5-95% quantiles) are shown. Reference points (Blim and Fmsy) and the first projection year are shown as dashed lines. The dotted line denotes Btrigger.</figcaption></figure>
<figure><img src="image_44.png" /><figcaption>Figure 2: Probability of choking (standardised) per stock (colour) across all fleets per scenario (x-axis)</figcaption></figure>

## CFP Indicators

### Ecological indicators

Percentage of stocks < FMSY/ risk of falling below Blim/ SHI and SAR

Under the Status Quo scenario the majority of stocks was harvested above FMSY, leading to an increased risk of stocks falling below Blim, a circa 42% of fleets having a Sustainable harvest Indicator (SHI) above 1 and the risk indicator SAR (number of fleets with stocks at risk) increasing to approx. 30% (Figure 3). The scenarios with a landing obligation (Min-Scenario) allowed for more sustainable harvesting, with a SHI and SAR indicator of 0, as no stocks had an increased risk of falling below Blim. Still, relaxing the choking situation in the PGY-Min and Case study specific scenario, lead to an increased proportion of stocks being fished above FMSY, compared to the FMSY-Min scenario, where all stocks were fished below FMSY.
<figure><img src="image_45.png" /><figcaption>Figure 3: Ecological CFP Indicators of the North Sea FLBEIA model per scenario and time period</figcaption></figure>

## Socio-economic indicators

### Catch/Landings/Discards

Catch differences between scenarios were dependent on fishing pressure, with highest catches under Status quo and lowest catches under FSMY-Min (Figure 4). However, discarding under the Status quo scenario was highest leading to landings in the Status quo scenario that were approximately equal (2025-2030) to the PGY-min scenario or slightly below (2035-2050). Overall landings were highest for the Case study specific scenario, followed by PGY-Min, Status quo and lowest for FMSY-Min.

### Revenues/Gross profit/Gross value added/Wages

Looking at the total revenues, Gross profit, and Gross Value added (Figure 4) across fleets for the different time periods shows clear economic gains in favour of the baseline scenarios with an implementation of the landing obligation. Although revenues where lowest for the FMSY-Min scenario from 2025 – 2030, it changed throughout the simulation as stocks could recover, leading to higher revenues in the later periods (2035 – 2050) than the Status
Quo effort scenario. Trying to relax the choking situation in the PGY-Min and Case study specific scenario could generate the highest revenues from landings over all periods, with the Case study specific scenario performing best. Gross Profit and Gross value added show a similar pattern with the Status Quo effort scenario performing the worst over all time periods considered.

## Wages

The Wages as a social indicator showed the same pattern as revenues (Figure 4), with wages being minimal for the FMSY-Min scenario in the beginning of the simulations due to the stark drop in fishing effort. However, wages could recover above the level of the Status quo scenario which remained relatively constant throughout the simulation period. Highest wages could be achieved in the two Min-scenarios, where the choking situation was relaxed (PGY-Min and Case Study specific).

## Revenue to Break even revenue (BER)

The indicator "Revenue to BER" is a measure for how good are fleets able to cover next year's costs with the current revenues made. The general pattern of the landing obligation scenarios outperforming the Status Quo effort scenario also holds here (Figure 4). Among the scenarios with a landing obligation the PGY-Min scenario is slightly better than the other two, likely related to the fact that trade-off between exploitation levels, stock sizes and needed effort to fill the quota is best for this scenario.

## Revenue ratio of small to large fleets

The revenue ratio of small (<24m) to large fleets (>24m) was highest for the Status quo scenario and at a similar level for all other scenario in the order PGY-Min, Case Study and FMSY-Min (from large to small) (Figure 4). The pattern is possibly as with lower stock sizes under the Status quo effort scenario, large fleets having a lower CPUE than small fleets.

<figure><img src="image_46.png" /></figure>
NS-FLBEIA

<figure><img src="image_47.png" /><figcaption>Figure 4: Socio-economic CFP Indicators of the North Sea FLBEIA model per scenario and time period</figcaption></figure>

MSFD Indicators

Bycatch of harbour porpoise and seals

As the bycatch of harbour porpoise and seals was directly linked to the effort of the specific gears in the model, it reflects the main effort pattern between the different scenarios, with highest bycatch in the scenario with the highest effort being the Status quo scenario, followed by Case Study specific, PGY-Min and the FMSY-Min scenario (Figure 5). Bycatch levels in the landing obligation scenarios could be reduced by 30-53 % for harbour porpoise and 27-49 % in seals relative to the Status quo scenario, simply through higher stock biomass of target species and an
accompanying reduction in effort. Bycatch levels were in general lower than the historical bycatch levels, the implementation was based on (year 2020), due to lower effort levels. For harbour porpoise the highest bycatch from the Status Quo scenario of 1547 individuals is slightly below the estimated threshold of 1622 individuals by PBR. For seals, the highest bycatch of 2146 individuals in the Status Quo scenario is also lower than the PBR threshold set by OSPAR of 7617 individuals.

### Biomass ratios

The biomass ratio (Figure 5) of apex fish predators (AFP) to sub-demersal predators (SDP) revealed the highest proportion of AFP (in our case cod) for the FMSY-Min scenario, relative to all the benthic gadoids and flatfish in the model. The other scenarios group themselves again in the pattern from lowest to highest fishing mortality with a higher amount of AFP in scenarios with lower fishing pressure. The ratio of sub-demersal predators to sub-pelagic predators (only saithe) is highest for scenarios with high fishing pressure (Status quo) and lowest for scenarios with lower fishing pressure (FMSY-Min). This holds also for the ratio of Apex fish predators (AFP) to sub-pelagic predators.

### Large fish indicator (LFI)

The proportion of large fish (LFI) in the landing obligation scenarios with values between 0.27 – 0.29 was higher than the Status quo scenario with values around 0.2, reflecting that lower exploitation levels in the landing obligation scenarios lead to a higher proportion of older age classes and a shift in age-distribution (Figure 5). The differences between FMSY-Min having the highest LFI compared to the two relaxed choking scenarios (Fmsy-Upper and Case Study) could be explained by the lower exploitation, leading to a high biomass of stocks with large fish sizes (cod and plaice). Differences between exploitation levels of the flatfish plaice, sole and witch, which contribute relative little to the LFI due to their smaller sizes also determine the LFI in the model. A higher exploitation level of these leads to a larger proportion of stocks, which are generally larger in size (like cod and saithe). In general, we could see that the stock recovery and accompanying shift in age class distribution under the landing obligation scenarios could help in reaching the target LFI for the North Sea of LFI > 0.3.

### Recruitment success

R/SSB as a measure of recruitment success was in general not a suitable ecosystem indicator (Figure 5), at least not for the current scenarios, as no guild (Apex fish predators, Sub-demersal predators, sub-pelagic predators) seemed to be dominated by impaired recruitment, even though some stocks (e.g. SOL-EC) had impaired recruitment under the Status Quo effort scenario. Therefore differences in the indicators are only due to their differences in SSB levels, with high R/SSB for scenarios with higher fishing mortality (Status quo & Case Study), due to their lower SSB levels. This makes an interpretation in terms of recruitment success questionable.

### Effort of demersal gears

Effort of the demersal gears Danish Seines/Seines and Otter trawls showed similar pattern with highest effort and potential highest benthic impact of the Status quo scenario, followed by the Case study specific scenario, PGY-Min and Fmsy-Min (Figure 5). For Beam trawls, effort levels of the PGY-Min scenario increase over the simulation period, reaching higher levels than the Case study specific scenario by 2035 – 2040, due to higher exploitation levels of the flatfish plaice, sole and with, which are caught predominantly with Beam trawls.
NS-FLBEIA

<figure><img src="image_48.png" /><figcaption>D4C2 - Biomass ratio of
apex fish predators /
sub-demersal predators</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>D4C2 - Biomass ratio of
sub-demersal predators /
sub-pelagic predators</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>D4C4 - R/SSB apex fish predators</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>D4C4 - R/SSB
sub-pelagic predators</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>D6 - Effort of Danish seines
or seines</figcaption></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /><figcaption>D4C2 - Biomass ratio of
apex fish predators /
sub-pelagic predators</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>D4C3 - Large Fish Indicator</figcaption></figure>

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /><figcaption>D4C4 - R/SSB
sub-demersal predators</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>D6 - Effort of beam trawls</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>D6 - Effort of otter trawls</figcaption></figure>

<figure><img src="image_60.png" /></figure>

Scenario

<figure><img src="image_61.png" /></figure>

CaseStudy

<figure><img src="image_62.png" /></figure>

Fmsy-Min

<figure><img src="image_63.png" /></figure>

PGY-Min

<figure><img src="image_64.png" /></figure>

Status quo

<caption>Figure 5: MSFD Indicators of the North Sea FLBEIA model per scenario and time period</caption>
## Global Indicators

### CO2 emissions

Carbon emissions by the fishery are highest under the Status quo effort scenario, followed by the Case study specific scenario, PGY-Min and Fmsy-Min, reflecting the general effort pattern of the scenarios (Figure 6).

### Fogarty Ratio/Ryther index

The Fogarty ratio (catch per primary production) and Ryther index (catches per surface area) for the North Sea simulations exceed the threshold of 1 and are even at their upper limit (2.2 for Fogarty) or exceed it (2.7-3 for Ryther), indicating severe ecosystem overfishing for all scenarios (Figure 6).

### NS-FLBEIA

<figure><img src="image_65.png" /><figcaption>Figure 6: Global Indicators of the North Sea FLBEIA model per scenario and time period</figcaption></figure>

## 5. Conclusions

Comparing the different scenario in terms of ecological, economic, social and global indicators revealed various trade-off between those different objectives. The Status quo effort scenario was suboptimal in all indicators, showing considerable overharvesting above FMSY for some stocks, an increased risk of falling below Blim and economic losses in terms of revenue, gross profit and a decreased ability to invest as the Break-even revenue ratio indicates. Indicators of ecosystem health like the LFI showed shifts in the age-distribution of the stocks. Additionally the scenario was characterised by an increased impact on bottom habitats through demersal gears and the highest carbon emissions among scenarios. Although bycatch levels remained below thresholds (harbour porpoise and seals) in the North Sea, the Status quo scenario had the highest bycatch levels, possibly underestimating the real impact also on other species.

The reduction in effort under all landing obligation scenarios allowed for sustainable harvesting, even though some conditions were relaxed to match economic objectives. Scenarios with greater flexibility could economically profit from increased yield, by simultaneously balancing ecological and ecosystem objectives. However, global indicators like the Fogarty/Ryther indices pointed towards a potential underestimation of ecosystem impacts by the other indicators considered, as current catch levels seem to be largely above thresholds. Additionally, it should be kept in mind that these baseline runs are simplified scenarios not taking into account effects of climate change, species interactions or future fuel and fish price developments that will likely change yield and profits of fishermen as well as ecosystem impacts.
## References

Garcia, D., Sánchez, S., Prellezo, R., Urtizberea, A., and Andrés, M. 2017. FLBEIA: A simulation model to conduct Bio-Economic evaluation of fisheries management strategies. SoftwareX, 6: 141–147.
https://linkinghub.elsevier.com/retrieve/pii/S2352711017300171.

García, D., Prellezo, R., Sánchez, S., Andrés, M., Urtizberea, A., and Carmona, I. 2017. Technical manual for FLBEIA a R package to conduct Bio-Economic Impact assessments using FLR (version 1.15). https://flbeia.azti.es/wp-content/uploads/2017/07/FLBEIA_manual.pdf.

Guillen, J., Boncoeur, J., Carvalho, N., Frangoudes, K., Guyader, O., Macher, C., and Maynou, F. 2017. Remuneration systems used in the fishing sector and their consequences on crew wages and labor rent creation. Maritime Studies, 16: 1–36.
Maritime Studies. http://dx.doi.org/10.1186/s40152-017-0056-6.

ICES. 2021. Inter-Benchmark Process to evaluate a change in operating model for mixed fishery consid- erations in the Celtic Sea and North Sea (IBPMIXFISH). ICES Scientific Reports, 3: 69pp.

ICES. 2022. Working Group on Mixed Fisheries Advice (WGMIXFISH-ADVICE; outputs from 2021 meeting). ICES Scientific Reports, 4: 215pp.

STECF. 2022. The 2022 annual economic report on the EU fishing fleet (STECF 22-06). 448 pp.

<figure><img src="image_66.png" /></figure>
## 8.2 Annex 2: Description of analyses carried out with OSMOSE for the Eastern English Channel (EEC)

### 1. Description of the model used

An end-to-end model was implemented in the Eastern English Channel to represent the ecosystem functioning by coupling two existing sub-models, the multispecies individual-based model OSMOSE, representing the dynamics of exploited species and the biogeochemical model ECO-MARS3D which provide plankton prey fields. OSMOSE_EEC is a spatially explicit individual-based model. It represents the life cycle of 14 high trophic level species from egg to adult stages, grouped into schools and defined by their size, weight, age, taxonomy and spatial position. The two sub-models were linked through trophic interactions to characterize the food web structure of the Eastern English Channel ecosystem from plankton up to top predators for the period 2000 - 2009. The trophic interactions depend on opportunistic predation based on prey size selection and spatio-temporal co-occurrence between predators and their preys over space and time. There are no fleets in the model. The fishing pressure is modelled using a species-specific fishing mortality rate.

### 2. Description of model parameterization and scenarios

#### Model parameterization

The basic units of OSMOSE are fish schools, which are composed of individuals that belong to the same species, and which have the same age, size, food requirements and, at a given time step, the same geographical coordinates. From the school states, biomass and abundance can be tracked at the population or community levels along with the size, age, and spatial dimensions.

The OSMOSE_EEC model was calibrated for the period 2000 – 2009 and it operates on a time step of 15 days. The spatial resolution of the grid is 0.1° x 0.1°. Different groups are represented in OSMOSE_EEC:

- 2 groups (dinoflagellates and diatoms) considered as forcing variable (LTL biotic resources)

- 3 groups (microzooplankton, mesozooplankton, macrozooplankton) considered as forcing variable (LTL biotic resources)

- 5 benthic invertebrate groups depending on size considered as forcing variables

- 1 explicit squid group (high trophic level species)

- 13 explicit species (high trophic level species)

The fishing activity is described in the model through a global fishing mortality rate by species only. The fishing management is not taken into account in the model.

The main biological processes occurring in each time step are movement, mortality (predation and other sources of mortality), growth and reproduction.

- Movement

At each time-step, schools are moved following a random walk method within their distribution area set up as a presence/absence map.

- Mortality
Within each time step, the total mortality of a given school is comprised of predation mortality caused by other schools, starvation mortality, fishing mortality, and diverse other natural mortality rate. The four different mortalities are computed so as to represent quasi simultaneous processes, and we consider that there is competition and stochasticity in the predation process. Within each time step, OSMOSE considers each pair of school/source of mortality in turn in a random order. To ensure that the random order of the mortality sources and of the schools does not bias the resulting instantaneous mortality rates applied and effectively correspond to the mortality rates specified in input, all the mortality events are iterated within a time step over a fixed number of sub-time step.

The main assumption in OSMOSE is that predation is an opportunistic process, which depends on:

* the overlap between predators and potential prey items in the horizontal dimension

* size adequacy between the predators and the potential prey (determined by predator/prey size ratios)

* the accessibility of prey items to predators, which depends on their vertical distribution (this being determined by means of accessibility coefficients). Thus, in OSMOSE, the food web structure emerges from local predation and competition interactions.

In OSMOSE_EEC the fishing mortality is species-specific. It was parameterized by providing an annual fishing rate by species (F). The number of dead fishes in a school is computed as follows:

$$N_{fished} = N \times (1 - \exp^{-F})$$

### Growth

Individuals of a given school are assumed to grow in size and weight at a given time only when the amount of food they ingested fulfill maintenance requirements (i.e., only when their predation efficiency at that time is greater than the predation efficiency ensuring body maintenance of school). The growth of individuals is calculated using a Von Bertalanffy model.

### Reproduction

For a given species, the number of eggs released in the system depends on:

* the spawning stock biomass

* the proportion of females

* the relative fecundity of females (the number of eggs emitted per gram of mature female)

* the seasonality of spawning

### Scenarios

Three scenarios were simulated:

* A statu-quo scenario which corresponds the reference model. In this scenario, the fishing mortality corresponds to the average situation of the ecosystem for the period 2000 – 2009.

* A scenario where F of demersal species was reduced by 20% to represent likely management towards GES (i.e. protecting seabed habitats).

* A scenario where F of demersal species was reduced by 40% to represent likely management towards GES (i.e. protecting seabed habitats).
All the scenarios are run for 100 years with 15 replicates to take into account the stochasticity of the model.
All the indicators were averaged over the 3 periods: 2025 – 2030, 2035 – 2040, and 2045 – 2050.

### 3. Indicators, targets and limits

List of indicators

<table><thead><tr><th>Indicators</th><th>Target groups</th></tr></thead><tbody><tr><td>Catch</td><td>Species level</td></tr><tr><td rowspan="6">Biomass</td><td>Species level</td></tr><tr><td>Forage fish</td></tr><tr><td>Sub apex demersal predators</td></tr><tr><td>Planktivorous</td></tr><tr><td>Sub apex pelagic predators</td></tr><tr><td>Apex predator</td></tr><tr><td rowspan="4">H index</td><td>Planktivorous</td></tr><tr><td>Apex predator</td></tr><tr><td>Sub apex demersal predators</td></tr><tr><td>Sub apex pelagic predators</td></tr><tr><td rowspan="4">Large Fish Index 30</td><td>Apex predator</td></tr><tr><td>Planktivorous</td></tr><tr><td>Sub apex demersal predators</td></tr><tr><td>Sub apex pelagic predators</td></tr><tr><td>Community Risk*</td><td>Benthic communities</td></tr></tbody></table>

<footnote>* Community Risk is a score that gives more weight to risk of collapse of a species than to widespread risk of depletion of many species.</footnote>
Composition of target groups

<table><thead><tr><th>Species</th><th>Forage fish</th><th>Sub apex demersal predators</th><th>Planktivorous</th><th>Sub apex pelagic predators</th><th>Apex predator</th></tr></thead><tbody><tr><td>Lesser Spotted Dogfish</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>Red Mullet</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Mackerel</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>Herring</td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>Sardine</td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>Squids</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>Pouting</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Whiting</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>Poor Cod</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Cod</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>Dragonet</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Sole</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Plaice</td><td></td><td>X</td><td></td><td></td><td></td></tr><tr><td>Horse Mackerel</td><td></td><td></td><td></td><td>X</td><td></td></tr></tbody></table>

Indicators targets from Task 4.4 analysis**

<table><thead><tr><th></th><th>Min.</th><th>1st Qu.</th><th>Median</th><th>Mean</th><th>3rd Qu.</th><th>Max.</th></tr></thead><tbody><tr><td>ind_target_Demersal_biomass</td><td>122926.12</td><td>137049.67</td><td>138785.75</td><td>139821.12</td><td>143100.50</td><td>152398.51</td></tr><tr><td>ind_target_Benthivorous/Planktivorous</td><td>0.16</td><td>0.17</td><td>0.18</td><td>0.18</td><td>0.19</td><td>0.19</td></tr><tr><td>ind_target_Benthivorous_biomass</td><td>14592.67</td><td>15029.92</td><td>15512.61</td><td>15757.06</td><td>16630.54</td><td>17240.65</td></tr><tr><td>ind_target_LFI50</td><td>0.46</td><td>0.51</td><td>0.55</td><td>0.54</td><td>0.56</td><td>0.61</td></tr><tr><td>ind_target_Mean_Maximum_Length</td><td>51.75</td><td>53.36</td><td>54.27</td><td>54.29</td><td>55.40</td><td>56.66</td></tr><tr><td>ind_target_total_biomass</td><td>264098.87</td><td>282100.02</td><td>286878.98</td><td>289207.55</td><td>301031.70</td><td>307926.99</td></tr><tr><td>ind_target_Typical_Length_Indicator</td><td>56.04</td><td>57.34</td><td>58.83</td><td>58.69</td><td>59.73</td><td>60.74</td></tr></tbody></table>
Indicators limits from Task 4.4 analysis**

<table><thead><tr><td>Community risk index</td><td>Threshold</td><td>Index</td></tr></thead><tbody><tr><td>0.05</td><td>138991.347</td><td>Biomass demersal</td></tr><tr><td>0.1</td><td>130294.827</td><td>Biomass demersal</td></tr><tr><td>0.2</td><td>112901.788</td><td>Biomass demersal</td></tr><tr><td>0.05</td><td>0.18302151</td><td>Biomass ratio Benthivorous/Planktivorous</td></tr><tr><td>0.1</td><td>0.1713971</td><td>Biomass ratio Benthivorous/Planktivorous</td></tr><tr><td>0.2</td><td>0.14814828</td><td>Biomass ratio Benthivorous/Planktivorous</td></tr><tr><td>0.05</td><td>16009.5954</td><td>Biomass Benthivorous</td></tr><tr><td>0.1</td><td>15250.2087</td><td>Biomass Benthivorous</td></tr><tr><td>0.2</td><td>13731.4354</td><td>Biomass Benthivorous</td></tr><tr><td>0.05</td><td>0.63525018</td><td>LFI50</td></tr><tr><td>0.1</td><td>0.55751622</td><td>LFI50</td></tr><tr><td>0.2</td><td>0.40204829</td><td>LFI50</td></tr><tr><td>0.05</td><td>56.2109701</td><td>Mean Maximum Length</td></tr><tr><td>0.1</td><td>53.623335</td><td>Mean Maximum Length</td></tr><tr><td>0.2</td><td>48.448065</td><td>Mean Maximum Length</td></tr><tr><td>0.05</td><td>295036.069</td><td>Total biomass</td></tr><tr><td>0.1</td><td>284601.6</td><td>Total biomass</td></tr><tr><td>0.2</td><td>263732.662</td><td>Total biomass</td></tr><tr><td>0.05</td><td>62.0859021</td><td>Typical Length Indicator demersal</td></tr><tr><td>0.1</td><td>59.3269852</td><td>Typical Length Indicator demersal</td></tr><tr><td>0.2</td><td>53.8091514</td><td>Typical Length Indicator demersal</td></tr></tbody></table>

<footnote>** The trophic guilds used to compute targets and limits of the indicators are different between WP4.4 and WP6.4.</footnote>
## 8.3 Annex 3: Seawise D6.7 - report on StrathE2E modelling for the North Sea and Celtic Sea

Michael Heath, University of Strathclyde

23 Feb 2023

StrathE2E is a shelf-sea ecosystem model which combines a coarsely spatially explicit end-to-end ecology model, and a fishing model (Heath et al. 2020). The ecology model is a set of coupled, time-dependent ordinary differential equations representing the (daily) rates of change in mass due to flows of nutrient through a network of food web guilds spanning dissolved material, detritus and microbes, through plankton, benthos and fish to megafauna (Table 1). The flows represent predation, food assimilation, metabolism, excretion, reproduction, passive advection, active migrations, and fishery captures. The spatial resolution is coarse, in keeping with the guild granularity of the food web - two horizontally well-mixed zones ("inshore" and "offshore"), linked by advection and migration. Each zone is further subdivided into seabed habitats and water column layers. Habitats are seabed biogeochemical compartments representing spatial variability in the processing of detritus and nutrient recycling, and the sensitivity of these to disturbance e.g. by fishing abrasion. External environmental drivers of the ecology model are annual cycles of time-varying physical and chemical data (temperatures, hydrodynamics, sea surface light, turbidity, inorganic nutrient inputs) which are inherently climate-sensitive.

The StrathE2E fishing model integrates properties of a set of up to 12 fishing gears (Table 2; annual averaged activity rate, distribution of activity across habitats, selectivity for guilds, catching power, discard and seabed abrasion rates), to generate guild-level fishing mortality and discard rates, and habitat-level abrasion rates. These are injected annually as parameters into the ecology model and assumed to remain constant over each upcoming year of simulation.

The StrathE2E model is available as an R-package ([https://www.marineresourcemodelling.maths.strath.ac.uk/strathe2e/index.html](https://www.marineresourcemodelling.maths.strath.ac.uk/strathe2e/index.html)), and as a web-app ([https://outreach.mathstat.strath.ac.uk/apps/StrathE2EApp/](https://outreach.mathstat.strath.ac.uk/apps/StrathE2EApp/)). Here, we used version 4.0.0 of the R-package to implement models for the North Sea and the Celtic Sea during the period 2003-2013. The North Sea model is provided as a working example within the R-package. The Celtic Sea implementation is available from [https://www.marineresourcemodelling.maths.strath.ac.uk/strathe2e/articles/Implementations.html](https://www.marineresourcemodelling.maths.strath.ac.uk/strathe2e/articles/Implementations.html). Maps of the geographic domain, inshore and offshore zones and seabed sediment habitats are shown in Figure 1 and Table 3.

Both models relied on physical environmental driving data from a 7km resolution NEMO-ERSEM model ([https://www.uk-ssb.org/science_components/work_package_4/](https://www.uk-ssb.org/science_components/work_package_4/)). Other driving data were obtained from ICES and BODC data archives (ocean nutrient data), EMEP ([https://www.emep.int/](https://www.emep.int/); atmospheric nutrient inputs), and fishing fleet data (activity, selectivity and power, discard and seabed abrasion rates) from ICES and STECF ([https://www.ices.dk/data/dataset-collections/Pages/Fish-catch-and-stock-assessment.aspx](https://www.ices.dk/data/dataset-collections/Pages/Fish-catch-and-stock-assessment.aspx) and [https://stecf.jrc.ec.europa.eu/dd/fdi](https://stecf.jrc.ec.europa.eu/dd/fdi)). Full documentation on the paramteterisation of the models is available from [https://www.marineresourcemodelling.maths.strath.ac.uk/resources/StrathE2E2/documents/4.0.0/StrathE2E2_North_Sea_model.pdf](https://www.marineresourcemodelling.maths.strath.ac.uk/resources/StrathE2E2/documents/4.0.0/StrathE2E2_North_Sea_model.pdf). In summary, the model ecology parameters were optimised so as to maximise the likelihood of a database of ecosystem state measurements for the North Sea given physical and
chemical driving data for corresponding time periods. The same basic ecology parameters were then used
in the Celtic Sea model, which was validated against an equivalent set of observational data (Figure 2 & 3).

<figure><img src="image_67.png" /><figcaption>Figure 1. Maps of the North Sea (upper) and Celtic Sea (lower) implementations of StrathE2E. In the left-hand panels, blue shades indicate the inshore zone of each model, orange shades the offshore zone. Percentage cover of rock is shown in the right-hand panels. Sediment classes are indicated by coloir shads in the left hand panels (D1/S1 muddy sediments; D2/S2 sandy sediments, D3,S3 gravelly sediments.</figcaption></figure>
<table><caption>Table 1. Ecological guilds or classes of dead and living material included in the StrathE2E model. Detritus and bacteria were represented as a composite guild.</caption><thead><tr><th>Type of guild or class</th><th>StrathE2E</th></tr></thead><tbody><tr><td>Dissolved inorganic nutrients</td><td><ul><li>Nitrate in water column, sediment porewaters.</li><li>Ammonia in water column, sediment porewaters</li></ul></td></tr><tr><td>Dead organic material and bacteria</td><td><ul><li>Suspended detritus and bacteria</li><li>Labile sediment detritus and bacteria</li><li>Refractory sediment detritus</li><li>Macrophyte debris</li><li>Corpses</li><li>Fishery discards</li></ul></td></tr><tr><td>Primary producers</td><td><ul><li>Phytoplankton</li><li>Macrophytes</li></ul></td></tr><tr><td>Zooplankton</td><td><ul><li>Omnivorous zooplankton</li><li>Carnivorous zooplankton</li><li>Larvae of planktivorous fish</li><li>Larvae of demersal fish</li><li>Larvae of suspension and deposit feeding benthos</li><li>Larvae of carnivore and scavenge feeding benthos</li></ul></td></tr><tr><td>Benthos</td><td><ul><li>Suspension and deposit feeders</li><li>Carnivore and scavenge feeders</li></ul></td></tr><tr><td>Fish</td><td><ul><li>Planktivorous</li><li>Migratory</li><li>Demersal (benthic-piscivorous)</li></ul></td></tr><tr><td>Upper (apex) trophic levels</td><td><ul><li>Seabirds</li><li>Pinnipeds</li><li>Cetaceans</li></ul></td></tr></tbody></table>

<table><caption>Table 2. Fishing gears represented in the North Sea and Celtic Sea fishing models. Each gear is defined by its power per guild in the ecology model (expresses the harvest ratio (equivalent to fishing mortality) per guild generated per unit of activity), seabed abrasion rate, discard rate per guild, and at-sea processing rate per guild (which generates offal return s to the sea. The driving variable associated with each gear is its activity rate (time spent fishing per unit sea surface area per day over each seabed habitat in the ecology model).</caption><thead><tr><th>Gear</th><th>Model region</th></tr></thead><tbody><tr><td>Pelagic trawls and seines</td><td>North Sea and Celtic Sea</td></tr><tr><td>Sandeel and sprat trawls (Otter trawls 30-70mm and TR3)</td><td>North Sea and Celtic Sea</td></tr><tr><td>Longlines targeting mackeral</td><td>North Sea and Celtic Sea</td></tr><tr><td>Beam trawls (BT1 and BT2)</td><td>North Sea and Celtic Sea</td></tr><tr><td>Demersal seine</td><td>North Sea and Celtic Sea</td></tr><tr><td>Demersal otter trawl (TR1)</td><td>North Sea and Celtic Sea</td></tr><tr><td>Gillnets and longlines targeting demersal fish</td><td>North Sea and Celtic Sea</td></tr><tr><td>Beam trawls targeting shrimp</td><td>North Sea and Celtic Sea</td></tr><tr><td>Nephrops trawls (TR2)</td><td>North Sea and Celtic Sea</td></tr><tr><td>Pots and creels</td><td>North Sea and Celtic Sea</td></tr><tr><td>Mollusc dredges</td><td>North Sea and Celtic Sea</td></tr><tr><td>Whale hunting harpoon vessels</td><td>North Sea only</td></tr><tr><td>Kelp harvesting vessels</td><td>Celtic Sea only</td></tr></tbody></table>

<caption>Table 3. Areas of each seabed sediment habitat in the North Sea and Celtic Sea models as proportions of the whole model domain.</caption>
<table><thead><tr><th>Spatial zone</th><th>Sediment category</th><th>North Sea area proportion</th><th>Celtic Sea area proportion</th></tr></thead><tbody><tr><td>Inshore</td><td>Rock</td><td>0.0030</td><td>0.0110</td></tr><tr><td>Inshore</td><td>Mud</td><td>0.0110</td><td>0.0024</td></tr><tr><td>Inshore</td><td>Sand</td><td>0.1878</td><td>0.0343</td></tr><tr><td>Inshore</td><td>Gravel</td><td>0.0478</td><td>0.0440</td></tr><tr><td>Offshore</td><td>Rock</td><td>0.0057</td><td>0.0380</td></tr><tr><td>Offshore</td><td>Mud</td><td>0.2665</td><td>0.0469</td></tr><tr><td>Offshore</td><td>Sand</td><td>0.4595</td><td>0.6009</td></tr><tr><td>Offshore</td><td>Gravel</td><td>0.0187</td><td>0.2225</td></tr><tr><td>Whole model area</td><td>All sediments</td><td>1.0000</td><td>1.0000</td></tr></tbody></table>

<figure><img src="image_68.png" /><figcaption>Figure 2. Comparison of model outputs and observed data for the North Sea during 2003-2013, using the optimised ecology model parameters.</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>Figure 3. Comparison of model outputs and observed data for the Celtic Sea during 2003-2013, using the optimised ecology model parameters.</figcaption></figure>

Annual averaged fishing gear activity rates across the seabed habitats in the North Sea and Celtic Sea models (2003-2013) are shown in Figures 4. These were inputs to the fleet model, which returned harvest ratios (fishing mortalities) for each guild to the ecology model, along with seabed swept area ratios and discard rates (Figures 5 - 7).
<figure><img src="image_70.png" /><figcaption>Figure 4. Activity density (seconds m⁻² d⁻¹) by each gear over each seabed sediment habitat of the 2003-20123 North Sea (upper panel) and Celtic Sea (lower panel) models.</figcaption></figure>
<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /><figcaption>Figure 5. Harvest ratios (d⁻¹) by each gear on each guild in the 2003-2013 North Sea (upper panel) and Celtic Sea (lower panel) models.</figcaption></figure>
<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /><figcaption>Figure 6. Seabed abrasion rates (area proportions d⁻¹) by each gear in each seabed sediment habitat of the 2003-2013 North Sea (upper panel) and Celtic Sea (lower panel) models.</figcaption></figure>
<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /><figcaption>Figure 7. Discard rates (proportion by weight of catch by each gear on each guild in the 2003-2013 North Sea (upper panel) and Celtic Sea (lower panel) models.</figcaption></figure>
Integrated annual harvest ratios of each guild by all gears combined in the 2003-2013 models are shown in Table 4. These integrate both landings and discarded by-catch of, for example, birds, pinnipeds and cetaceans. Similarly, the annual swept area ratios (proportion of seabed area swept per year) integrated across gears for each sediment habitat and for the whole model areas are shown in Table 5.

<table><caption>Table 4. Annual integrated harvest ratios on each guild due to all gears in the 2003-2013 North Sea and Celtic Sea models</caption><thead><tr><td>Guild</td><td>North Sea</td><td>Celtic Sea</td></tr></thead><tbody><tr><td>Planktivorous fish</td><td>0.2135</td><td>0.0870</td></tr><tr><td>Demersal fish</td><td>0.0823</td><td>0.1267</td></tr><tr><td>Migratory fish</td><td>0.3623</td><td>0.3810</td></tr><tr><td>Benthos suspension/deposit feeders</td><td>0.0827</td><td>0.0239</td></tr><tr><td>Benthos carnivore/scavenge feeders</td><td>0.1172</td><td>0.0378</td></tr><tr><td>Carnivorous zooplankton (squids)</td><td>0.1968</td><td>1.1332</td></tr><tr><td>Birds</td><td>0.0006</td><td>0.0008</td></tr><tr><td>Pinnipeds</td><td>0.0059</td><td>0.0279</td></tr><tr><td>Cetaceans</td><td>0.1142</td><td>0.0467</td></tr><tr><td>Macrophytes</td><td>0</td><td>0.0004</td></tr></tbody></table>

<table><caption>Table 5. Annual integrated swept area ratios for each sediment habitat due to all gears in the 2003-2013 North Sea and Celtic Sea models</caption><thead><tr><td>Spatial zone</td><td>Sediment category</td><td>North Sea</td><td>Celtic Sea</td></tr></thead><tbody><tr><td>Inshore</td><td>Rock</td><td>0.1787</td><td>0.0503</td></tr><tr><td>Inshore</td><td>Mud</td><td>5.6319</td><td>3.0205</td></tr><tr><td>Inshore</td><td>Sand</td><td>1.0520</td><td>0.0872</td></tr><tr><td>Inshore</td><td>Gravel</td><td>0.6392</td><td>0.0815</td></tr><tr><td>Offshore</td><td>Rock</td><td>0.2311</td><td>0.3082</td></tr><tr><td>Offshore</td><td>Mud</td><td>1.9297</td><td>2.2346</td></tr><tr><td>Offshore</td><td>Sand</td><td>0.2841</td><td>0.2817</td></tr><tr><td>Offshore</td><td>Gravel</td><td>0.4817</td><td>0.7459</td></tr><tr><td>Whole model area</td><td>All sediments</td><td>0.9458</td><td>0.4662</td></tr></tbody></table>

## Model scenario runs

Each model was run with the baseline (2003-2013) activity rates of all gears scaled by a vector of multipliers: (0, 0.2, 0.4, 0.7, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 6.0, 8.0) for 100 years.

The StrathE2E model outputs data on daily and annual averaged biomasses of all the guilds in the food web. In addition, the annual integrated fluxes between all nodes in the network, along with all imports and exports from the model system, are saved as a flow matrix. The flow matrix is formatted for input to the R-package NetIndices, which generates a range of Graph Theory network metrics (Soetaert & Kones 2014).

The currency of the StrathE2E model is micro-moles of nitrogen m⁻². These units were converted to Tonnes wet weight (WW) in the whole model region assuming domain areas of 485,605 km² for the North Sea, and 217,131 km² for the Celtic Sea. Conversions for mMN to gWW are shown in Table 6.

<table><caption>Table 6. Conversion rates between molar weights of nitrogen and wet weight for guilds in the ecology model.</caption><thead><tr><td>Guild</td><td>mMN . gWW⁻¹</td></tr></thead><tbody><tr><td>Macrophytes</td><td>2.070</td></tr></tbody></table>
<table><tr><td>Phytoplankton</td><td>1.258</td></tr><tr><td>Omnivorous zooplankton</td><td>1.258</td></tr><tr><td>Carnivorous zooplankton</td><td>1.258</td></tr><tr><td>Benthos suspension/deposit feeders larvae</td><td>1.258</td></tr><tr><td>Benthos carnivore/scavenge feeders larvae</td><td>1.258</td></tr><tr><td>Benthos suspension/deposit feeders</td><td>0.503</td></tr><tr><td>Benthos carnivore/scavenge feeders</td><td>1.006</td></tr><tr><td>Planktivorous fish lavae</td><td>1.258</td></tr><tr><td>Planktivorous fish</td><td>2.038</td></tr><tr><td>Migratory fish</td><td>2.314</td></tr><tr><td>Demersal fish larvae</td><td>1.258</td></tr><tr><td>Demersal fish</td><td>1.296</td></tr><tr><td>Birds</td><td>2.516</td></tr><tr><td>Pinnipeds</td><td>2.516</td></tr><tr><td>Cetaceans</td><td>2.516</td></tr></table>

Landed weights of guilds in the model were converted to revenue (euro, deflated to 2013) using data on ex-vessel prices per guild for the North Sea and Celtic Sea (Table 7). For fish and invertebrates these were assembled from landed weight and value per species in the supporting datasets of the Scientific, Technical and Economic Committee for Fisheries (STECF) 2019 Annual Economic Reports (AER) on the EU Fishing Fleets (STECF 19-06; Carvalho et al. 2019). This particular report was selected from the annual series of such reports as it was the last to include data from the UK following its exit from the European Union. Species-level data were aggregated to guilds using a database of species-guild associations built in support of the model. Comprehensive coverage of nations and species was available for the period 2008 – 2018, and these data were averaged for use in the model.

Data on ex-vessel prices per fresh body weight of cetaceans (Minke whales) hunted in the North Sea were assembled from a forensic assembly and analysis of Norwegian whale hunting data from 1993-2021 (Heath, unpublished).

Data on French ex-vessel prices for harvested kelp in the Celtic Sea were obtained from BIM 2020

<table><caption>Table 7. Ex-vessel prices of landings for each of the guilds in the model (euro, deflated to 2013).</caption><thead><tr><td>Guild</td><td>Price per tonne (euro, deflated to 2013)</td></tr></thead><tbody><tr><td>Macrfophytes (kelp)</td><td>45.00</td></tr><tr><td>Pelagic invertebrates</td><td>3785.67</td></tr><tr><td>Benthos suspension/deposit feeders</td><td>581.24</td></tr><tr><td>Benthos carnivore/scavenge feeders</td><td>3452.63</td></tr><tr><td>Demersal fish</td><td>2344.72</td></tr><tr><td>Planktivorous fish</td><td>313.26</td></tr><tr><td>Migratory fish</td><td>1012.09</td></tr><tr><td>Pinnipeds</td><td>0.00</td></tr><tr><td>Birds</td><td>0.00</td></tr><tr><td>Cetaceans</td><td>1061.66</td></tr></tbody></table>

Annual average guild biomasses, landings and discards in the final year of each 100 year scenario run were used as the basis for deriving indices and revenues

Derived indices were:

* Ratio of the annual average biomass of apex predators (birds, pinnipeds and cetaceans) to fish;

* Annual trophic levels (calculated by NetIndices) aggregated to the whole ecosystem using biomass weighting;

* Fogarty index (Beet & Gaichas 2022, Link & Watson. 2019)

* Ryther index (Beet & Gaichas 2022, Link & Watson 2019)

The formula for calculating trophic level, is contained in the NetIndices documentation (Soetaert & Kones 2014).
The Fogarty index is defined as ratio of total catches to total primary productivity in an ecosystem (Link & Watson, 2019). The units are parts per thousand, assuming the same currency for catch and primary production (in our case mMolesN.m⁻².y⁻¹. Annual net primary production is an output from the StrathE2E model, derived as the gross nitrate and ammonia uptake less metabolic losses.

The Ryther index is defined as total catch per unit sea surface area in the ecosystem (Link & Watson, 2019). The units are mt km⁻² y⁻¹

# RESULTS

## North Sea

Guild-level biomasses, catch and landed weights, and revenues in the North Sea model in relation to the effort multiplier scenarios are shown in Figures 8 – 11. Biomasses of fish and top predators decreased with increasing effort multiplier. Released from predation, the biomass of carnivorous zooplankton increased. Small cascading trophic effects were present at the phytoplankton and zooplankton levels.

Migratory fish appeared resilient to increasing fishing effort in the model. This was because this guild was not a permanent resident in the model domain. An seasonal immigration flux of migratory fish into the model was part of the boundary conditions for the model, and this was independent of the effort multiplier scenarios – ie the global biomass of the migratory fish stock (archetype: mackerel) was not affected by harvesting within the model domain. The assumption is that harvesting within the North Sea model represents a minor component of the total annual removals from the global stock in the northeast Atlantic. Nevertheless, the net migration flux of migratory fish (annual immigration less annual emigration) was dependent on the fishing effort since biomass was harvested inside the model.

The model was able to sustain increasing total landings and revenue with increasing fishing effort even out to 8x the baseline rates. However, this was sustained largely by the annual immigration of migratory fish. The resident planktivorous and demersal fish in the model were depleted and extirpated at the highest fishing efforts.

The scaling of fishing effort had a large effect on the biomasses of the top predators in the model (birds, pinnipeds and cetaceans), which were severely depleted relative to an un-fished state, even in the baseline model (Figures 12 – 14). This was partly due to direct by-catch by certain gears, and partly as a bottom-up trophic effect of depletion of their food supply. The ratio of biomasses of top predators to fish declined with increasing fishing. By-catch quantities, and in the case of cetaceans the directed landings quantity, varied in response to changing abundances in the sea, and the changing mortality rate due to fishing gears.

Mean trophic levels of the entire food web, the upper part of the web (fish and top predators, and the predators themselves, along with the components of the catch (landings and discards), all declined with increasing fishing effort (Figure 15). This reflected the progressive loss of high trophic level guilds from the food web.

Net primary production decreased with fishing effort (Figure 16). In StrathE2E, phytoplankton dynamics are integrated into the model food web and so primary production is subject to top-down cascading trophic effects arising from the removal of higher trophic levels from the system. The Fogarty index included this dynamic aspect of the primary production. Levels of the Fogarty index and the Ryther index in even the baseline model both exceeded the thresholds suggested by Link and Watson (2019) as representing optimal harvesting of the ecosystem, and were clearly in the realm of ecosystem over-exploitation.
<figure><img src="image_77.png" /><figcaption>Figure 8. Steady state annual average biomasses (thousands of tonnes) for each guild in the North Sea relative to effort multiplier scenarios.</figcaption></figure>
<figure><img src="image_78.png" /><figcaption>Figure 9. Steady state catch and landing weights (thousands of tonnes) for each guild in the North Sea relative to effort multiplier scenarios</figcaption></figure>
<figure><img src="image_79.png" /><figcaption>Figure 10. Steady state revenues (millions of euros deflated to 2013) for each guild in the North Sea relative to effort multiplier scenarios</figcaption></figure>
<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /><figcaption>Figure 11. Steady state total catch weight (thousands of tonnes) and revenues (millions of euros deflated to 2013) broken down by guild in the North Sea relative to effort multiplier scenarios</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>Figure 12. Steady state ratios of annual average biomasses of apex predators (birds, pinnipeds and cetaceans) to the biomass of fish in the North Sea model, relative to effort multiplier scenarios</figcaption></figure>

<figure><img src="image_83.png" /></figure>
<figure><img src="image_84.png" /><figcaption>Figure 14. Steady state annual catches and landings of the apex predators in the North Sea model, relative to the effort multiplier scenarios</figcaption></figure>
<figure><img src="image_85.png" /><figcaption>Figure 15. Community trophic level of combined guilds in the model as a function of the multiplier applied to all gears in the North Sea. Upper panel: trophic levels in the sea. Lower panel: trophic levels in the catch</figcaption></figure>
<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

Figure 16. Upper panel: Annual net primary production (mMolesN.m⁻².y⁻¹) simulated by the North Sea model in relation to effort multiplier scenarios. Middle panel: Fogarty index (landings or catch divided by net primary production) relative to effort multiplier scenarios. Lower panel: Ryther index (catch or landings tonnes.km⁻².y⁻¹) relative to effort multiplier scenarios. Green shaded areas in the Fogarty index and Ryther index panels are regarded as optimal ranges (Link & Watson 2019; Beet & Gaichas 2022). The orange shaded areas are regarded as representing ecosystem overfishing. Vertical grey line at effort multiplier = 1 in each panel represents the baseline 2003-2013 model.
Celtic Sea

Guild-level biomasses, catch and landed weights, and revenues in the Celtic Sea model in relation to the effort multiplier scenarios are shown in Figures 17 – 20. As in the North Sea model, biomasses of fish and top predators decreased with increasing effort multiplier. Released from predation, the biomass of carnivorous zooplankton increased. Small cascading trophic effects were present at the phytoplankton and zooplankton levels.

Migratory fish – which were sustained by a constant annual boundary immigration regardless of fishing effort as in the North Sea model – formed the major part of landings and revenue at high fishing effort multipliers. The resident planktivorous and demersal fish in the model were depleted and extirpated at the highest fishing efforts.

Bird and pinniped guilds in the baseline 2003-2013 Celtic Sea model were more severely depleted relative to an unfished state than in the equivalent North Sea baseline (Figures 21 – 23). All top predator guilds were extirpated by even modest increase in effort compared to the North Sea.

Direct effects of fishing on the top-predators were entirely due to bycatch, there being no hunting for cetaceans in the
Celtic Sea.

As in the North Sea, mean trophic levels of the entire food web, the upper part of the web (fish and top predators, and the predators themselves, along with the components of the catch (landings and discards), all declined with increasing fishing effort (Figure 24). This reflected the progressive loss of high trophic level guilds from the food web. IN addition, the model indicated that trophic levels were overall lower in the Celtic Sea than in the North Sea across all effort scaling scenarios, suggesting a less efficient transfer of energy up the food web.

Net primary production decreased with fishing effort (Figure 25), a sin the North Sea. Overall levels of both the Fogarty and Ryther indices were lower than in the North Sea, but still exceeded the thresholds suggested by Link and Watson (2019) as representing optimal harvesting of the ecosystem, even in the baseline 2003-2013 fishing effort scenario. Higher effort scenarios were clearly in the realm of ecosystem over-exploitation.

<figure><img src="image_89.png" /></figure>
<figure><img src="image_90.png" /><figcaption>Figure 17. Steady state annual average biomasses (thousands of tonnes) for each guild in the Celtic Sea relative to effort multiplier scenarios.</figcaption></figure>
<figure><img src="image_91.png" /><figcaption>Figure 18. Steady state catch and landing weights (thousands of tonnes) for each guild in the Celtic Sea relative to effort multiplier scenarios</figcaption></figure>
<figure><img src="image_92.png" /><figcaption>Figure 19. Steady state revenues (millions of euros deflated to 2013) for each guild in the Celtic Sea relative to effort multiplier scenarios</figcaption></figure>
<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /><figcaption>Figure 20. Steady state total catch weight (thousands of tonnes) and revenues (millions of euros deflated to 2013) broken down by guild in the Celtic Sea relative to effort multiplier scenarios</figcaption></figure>

<figure><img src="image_95.png" /></figure>
Figure 21. Steady state ratios of annual average biomasses of apex predators (birds, pinnipeds and cetaceans) to the biomass of fish in the Celtic Sea model, relative to effort multiplier scenarios

<figure><img src="image_96.png" /></figure>

Figure 22. Steady state annual average biomasses of the apex predators in the Celtic Sea model, relative to effort multiplier scenarios
<figure><img src="image_97.png" /><figcaption>Figure 23. Steady state annual catches and landings of the apex predators in the Celtic Sea model, relative to the effort multiplier scenarios</figcaption></figure>
<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /><figcaption>Figure 24. Community trophic level of combined guilds in the model as a function of the multiplier applied to all gears in the Celtic Sea. Upper panel: trophic levels in the sea. Lower panel: trophic levels in the catch</figcaption></figure>
<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

Figure 25. Upper panel: Annual net primary production (mMolesN.m⁻².y⁻¹) simulated by the Celtic Sea model in relation to effort multiplier scenarios. Middle panel: Fogarty index (landings or catch divided by net primary production) relative to effort multiplier scenarios. Lower panel: Ryther index (catch or landings tonnes.km⁻².y⁻¹) relative to effort multiplier scenarios. Green shaded areas in the Fogarty index and Ryther index panels are regarded as optimal ranges (Link & Watson 2019; Beet & Gaichas 2022). The orange shaded areas are regarded as representing ecosystem overfishing. Vertical grey line at effort multiplier = 1 in each panel represents the baseline 2003-2013 model.
CONCLUSIONS

Both the North Sea and Celtic Sea StrathE2E models illustrate the trade-offs between economic yield from fisheries and conservation of the ecosystem. Sustained by the seasonal invasion of these shelf ecosystems by portions of the wider northeast Atlantic stocks of migratory fish (especially Atlantic mackerel), the models indicate that it would be technically possible to generate higher economic yields than in the baseline 2003-2013 cases. However this assumes that harvesting within each ecosystem does not significantly impact the wider ocean-scale stock, and the penalty would be extirpation of the rest of the fish and top-predator guilds in the food web.

The simulated Fogarty and Ryther indices both suggest that even the baseline 2003-2013 systems are subject to ecosystem exploitation. Top predator depletion and mean trophic levels suggest that the baseline Celtic Sea is in a more heavily exploited state than the North Sea.

References:

Beet, A. & Gaichas, S. 2022. Ecosystem Overfishing Indicators. MAFMC SSC Ecosystem working group, 28 April 2022. https://noaa-edab.github.io/presentations/20220428_SSCEcoWG_Gaichas.html#1

BIM 2020. Scoping a seaweed biorefinery concept for Ireland. Report for Bord Iascaigh Mhara. CyberColloids Ltd. 72pp. https://bim.ie/wp-content/uploads/2021/02/BIM-Scoping-a-seaweed-biorefinery-concept-for-Ireland.pdf

Carvalho, N., Keatinge, M. & Guillen Garcia, J. (editors) (2019). Scientific, Technical and Economic Committee for Fisheries (STECF): The 2019 Annual Economic Report on the EU Fishing Fleet (STECF 19-06), EUR 28359 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-09517-0, doi:10.2760/911768, JRC117567.

Heath, M.R., Speirs, D.C., Thurlbeck, I., & Wilson, R.J. (2021). StrathE2E: an R package for modelling the dynamics of marine food webs and fisheries. Methods in Ecology and Evolution, 12, 280-287. https://doi.org/10.1111/2041-210X.13510

Link, J.S. & Watson, R.A. 2019. Global ecosystem overfishing: Clear delineation within real limits to production. Science Advances 5 (6), eaav0474. DOI: 10.1126/sciadv.aav0474

Soetaert, K. & Kones, J.K. (2014). Package NetIndices, network indices and food web descriptors in R. 10 pp. http://cran.nexr.com/web/packages/NetIndices/vignettes/NetIndices.pdf
## 8.4 Annex 4: Evaluating FLBEIA scenarios for North Sea demersal stocks in a multispecies (SMS) context.

Morten Vinther, DTU Aqua, March 2023

### Summary

Scenarios for North Sea stocks with Fishing mortality (F) for demersal species estimated externally from FLBEIA runs, and F on pelagic and short-lived estimated dynamically within the SMS model showed that the actual F chosen for demersal species affect the stock size and yield of prey species like herring, sandeel, sprat and Norway pout. Yield of these species may differ by more than 25% as an effect of F and stock sizes on the demersal species. Due to predation and cannibalism estimated within the SMS scenarios, the yield of demersal species will also differ from the yield estimated by FLBEIA scenario.

### Introduction

A series of forecast scenarios for the North Sea demersal stocks (cod, whiting, haddock, saithe, plaice and sole) was made using the FLBEIA model (see section 8.1, Annex 1). These scenarios were made assuming a constant natural mortality (M), i.e. assuming that predation mortality is independent of the stock sizes of predators and preys. The SMS model, with the most recent review by ICES WGSAM (ICES, 2021c) of the hindcast mode, can make similar scenarios, however with dynamic estimation of M, as function of the stock size of predator and prey sizes. The purpose of such scenarios would be to analyse the effect on pelagic and industrial species given input F values (from FLBEIA) for the demersal species.

### Data and methods

For a useful comparison of scenario F values from one model FLBEIA, in another model SMS, requires as a minimum that the historical data (assessments) are similar for the two models. FLBEIA scenarios are based on the most recent ICES assessment, while the SMS key-run (ICES, 2021c) was based on available data in the autumn 2020. Several stocks have however been benchmarked by ICES since 2020. For some stocks, input data have changed considerably, while reference points have just been changed for other stocks. The following changes were made for the SMS (catch data used by SMS were left unchanged due to workload for updating such):

**Cod:** Benchmarked in 2021. Survey data and reference points were changed. Natural mortality of ages 3+ were updated with *ad hoc* values since 2011 by adding 0.16 per year for M1. This is approximately the same as done by ICES WGNSSK to model a potential natal migration of "North Sea cod" back to area 6a.

**Whiting:** Interbenchmarked in 2021 and revised in 2022. Updated survey data and reference points in SMS.

**Haddock:** Benchmarked in 2022. Change of survey data and reference points.

**Saithe:** Interbenchmark in 2019. Update of reference points.

**Mackerel:** SMS uses the estimated stock numbers from the ICES assessment as input. No changes made to SMS even though the ICES assessment result in 2022 are considerably different from the results for 2020.

**Herring:** Inter-benchmarked in 2021. Reference points were updated in SMS.

**Plaice:** Benchmarked in 2022. Update of survey data, natural mortality and reference points

**Sole:** Benchmarked in 2022. Update of survey data and reference points.
## Assumptions for F in 2020.

The FLBEIA scenarios include F values from 2021, while the terminal year for SMS is 2019. F in 2020 for used in SMS scenarios was derived from the relative change in F as estimated by the ICES assessment between 2019 and 2020, and the F in 2019 as estimated from SMS.

<table><caption>Table 1. Average F as estimated by SMS for 2019, F for 2019 and 2020 as estimated by ICES in the most recent stock assessment, and F used for SMS in 2020.</caption><thead><tr><th>Stock</th><th>SMS 2019</th><th>ICES 2019</th><th>ICES 2020</th><th>ICES change</th><th>SMS 2020</th></tr></thead><tbody><tr><td>Cod</td><td>0.54</td><td>0.49</td><td>0.36</td><td>73%</td><td>0.40</td></tr><tr><td>Whiting</td><td>0.187</td><td>0.21</td><td>0.19</td><td>90%</td><td>0.17</td></tr><tr><td>Haddock</td><td>0.33</td><td>0.33</td><td>0.28</td><td>85%</td><td>0.28</td></tr><tr><td>Saithe</td><td>0.31</td><td>0.49</td><td>0.44</td><td>90%</td><td>0.28</td></tr><tr><td>Mackerel</td><td>0.187</td><td>0.18</td><td>0.23</td><td>128%</td><td>0.24</td></tr><tr><td>Herring</td><td>0.21</td><td>0.196</td><td>0.198</td><td>101%</td><td>0.21</td></tr><tr><td>N.sandeel</td><td>0.134</td><td>no data</td><td>no data</td><td></td><td>0.13</td></tr><tr><td>S.sandeel</td><td>0.06</td><td>no data</td><td>no data</td><td></td><td>0.06</td></tr><tr><td>Nor.pout</td><td>0.54</td><td>0.26</td><td>0.26</td><td>100%</td><td>0.54</td></tr><tr><td>Sprat</td><td>0.38</td><td>1.22</td><td>1.83</td><td>150%</td><td>0.57</td></tr><tr><td>Plaice</td><td>0.32</td><td>0.109</td><td>0.095</td><td>87%</td><td>0.28</td></tr><tr><td>Sole</td><td>0.3</td><td>0.47</td><td>0.33</td><td>70%</td><td>0.21</td></tr></tbody></table>

## Scenario F values

For the demersal stocks, the FLBEIA model has produced a list of annual F per year, stock and scenario. These are applied directly by SMS even though the FLBEIA scenarios assume constant M, while M is estimated and variable between years in SMS scenarios.

Scenario F values for herring and mackerel are calculated by year within SMS from the ICES Advisory Rule (AR). The short lived species sandeel, sprat and Norway pout are managed by the "escapement strategy" where TAC is set such that a minimum biomass is left (escaped) after the fishery has taken place. This approach is combined with the use of an $F_{cap}$ (and upper limit for F). The escapement strategy is not implemented in SMS. Instead, a HCR is applied, which estimates forecast F from the total biomass (TSB) in the beginning of the TAC year. F becomes zero for TSB below trigger1 and set at $F_{cap}$ for TSB above trigger2. With TSB between trigger1 and trigger2, F is reduced linearly from $F_{cap}$.

<table><caption>Table 2. Parameters for HCR for short lived species</caption><thead><tr><th>Stock</th><th>Ftarget (Fcap)</th><th>Trigger 1 (1000 t)</th><th>Trigger 2 (1000 t)</th></tr></thead><tbody><tr><td>N.sandeel</td><td>0.30</td><td>940</td><td>1060</td></tr><tr><td>S.sandeel</td><td>0.60</td><td>390</td><td>800</td></tr><tr><td>Nor.pout</td><td>0.70</td><td>200</td><td>350</td></tr><tr><td>Sprat</td><td>0.69</td><td>175</td><td>400</td></tr></tbody></table>

For comparison, an extra scenario, "ICES-AR" was added. This scenario uses the ICES advice rule as HCR for all species except the short-lived. In contrast to the FLBEIA scenarios, F-values in the ICES-AR scenario are estimated dynamically from the HCR and model SSB in the scenario year.
## Results

Updating the SMS input data to follow the presently applied ICES data had in general a minor effect on the results. Major changes are seen for cod (Figure 1), whiting (Figure 2) and plaice (Figure 3). The plaice assessment has changed considerably, but as plaice is not considered as a predator or prey in SMS, the changes will only affect the plaice assessment.

Figure 4 shows the applied stock recruitment relations fitted in the SMS hindcast and used for scenarios.

Figures 5-16 shows the SMS hindcast and scenario results. There is a large initial increase in SSB for both cod (Figure 5) and whiting (Figure 6) as a result of the decrease in scenario F. SSB stabilizes afterwards at a lower value due to predation mortality and the SSB-recruitment relations applied.

Haddock (Figure 7) maintains a higher scenario SSB, due to the lower F, probably due to the lower cannibalisms, compared to cod and whiting.

Saithe (Figure 8) is not considered as a prey in SMS, and scenario results for saithe will only differ due to the chosen F.

All scenarios for mackerel (Figure 9) are identical. Mackerel is not considered as a prey in SMS, such that the scenario results are independent of other species. The same F values, derived from ICES AR, are used in all scenarios because FLBEIA scenarios do not include mackerel.

The same HCR (ICES AR) is applied for all scenarios for herring (Figure 10). The difference in output between scenarios is an indirect effect caused by the different stock sizes of predators and preys derived from the varying F values used by the FLBEIA scenarios. Yield of herring varies between 20-25% due to the chosen F for demersal species by the FLBEIA scenarios

The same picture is seen for Northern sandeel (Figure 11) and Southern sandeel (Figure 12), where yield of sandeel depends on the chosen F for demersal species. Yield of Northern Sandeel seems more sensitive to F on demersal species, than for Southern sandeel.

In relative terms, yield of Norway pout (Figure 13) seems very sensitive to F on demersal species, Scenario yield is however relatively small compared to the hindcast value, which might be an effect of a too restrictive proxy for the escapement strategy.

As a short-lived prey species, yield of sprat (Figure 14) is also sensitive to F and stock sizes of demersal species.

Plaice (Figure 15) and Sole (Figure 16) are not considered as a predator or prey in SMS, so the F and stock dynamic of these two species will not affect the other species.
<figure><img src="image_103.png" /><figcaption>Figure 1: Cod. Comparison of the result from the SMS 2020 key-run and the SMS configuration where SMS input data are updated with the most recent input data from the ICES assessment.</figcaption></figure>
<figure><img src="image_104.png" /><figcaption>Figure 2: Whiting. Comparison of the result from the SMS 2020 key-run and the SMS configuration where SMS input data are updated with the most recent input data from the ICES assessment.</figcaption></figure>
<figure><img src="image_105.png" /><figcaption>Figure 3: Plaice. Comparison of the result from the SMS 2020 key-run and the SMS configuration where SMS input data are updated with the most recent input data from the ICES assessment.</figcaption></figure>
<figure><img src="image_106.png" /><figcaption>Figure 4: SSB-recruitment relations, showing, median and ± 1 and ± 2 std. The mean values (dotted line) are also shown. Years shown in red are not applied in the fitting</figcaption></figure>
<figure><img src="image_107.png" /><figcaption>Figure 5: Cod. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /></figure>

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /><figcaption>Figure 6: Whiting. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_114.png" /><figcaption>Figure 7: Haddock. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /><figcaption>Figure 8: Saithe. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_121.png" /><figcaption>Figure 9: Mackerel. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>

<figure><img src="image_125.png" /></figure>

<figure><img src="image_126.png" /></figure>

<figure><img src="image_127.png" /></figure>

Figure 10: Herring. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.
<figure><img src="image_128.png" /><figcaption>Figure 11: Northern sandeel. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_129.png" /><figcaption>Figure 12: Southern sandeel. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_130.png" /><figcaption>Figure 13: Norway pout. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_131.png" /><figcaption>Figure 14: Sprat. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_132.png" /><figcaption>Figure 15: Plaice. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators.</figcaption></figure>
<figure><img src="image_133.png" /><figcaption>Figure 16: Sole. SMS results from scenarios with given input F values (2020-2060) for demersal species estimated within FLBEIA scenarios and from the ICES Advisory Rule. "Eaten biomass" is the biomass of the given species eaten by the all SMS predators</figcaption></figure>
## 8.5 Annex 5: Description of analyses carried out with FLBEIA for the demersal mixed fisheries in the Bay of Biscay.

Dorleta Garcia, Marga Andrés, Sonia Sanchez and Leire Ibaibarriaga

### 1. Description of the model used

FLBEIA (Garcia, Sanchez, et al. 2017) has been used to simulate the impact of management strategies for the mixed demersal fishery in the Bay of Biscay. FLBEIA follows the MSE approach (Punt, Butterworth et al. 2016) and as such the simulation is divided into two blocks, the Operating Model (OM) and the Management Procedure (MP) (Figure 1). 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, van der Sluijs 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 taken a single value from this vector each time.

<figure><img src="image_134.png" /><figcaption>Figure 1: Conceptual diagram of the 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 and 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, Buisman 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, Buisman 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 fleets operating in Bay of Biscay. Moreover, economic data was obtained from the STECF annual economic report, sales data 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

16 stocks were included in the biological operating model which are listed in Table 1. Stocks in ICES category 1, except nephrops, were included in the simulation using an age structured assessment model. The survival from one year class to the next was simulated using the classical exponential survival model and recruitment was simulated using an stock recruitment relationship. The parameters for the stock recruitment model and the biological parameters used in the projection were those used by ICES benchmarks in the calculation of reference points. The last assessment from 2022 was used to condition all the stocks, except SDV and WHG that are in category 3 and were assessed for the last time in 2021.