DAMARA project : a scientific decision-support tool for the development of a management plan in the Celtic Sea.
Date: 2016-08-16
Institutions: Directorate-General for Maritime Affairs and Fisheries
Subjects: Atlantic Ocean, catch by species, decision-making, environmental impact, exploitation of the seas, fishery management, fishery research, fishing industry

DAMARA Project: 

a scientific decision-support tool for the development of 
a management plan in the Celtic Sea 

Norman Graham 
April – 2016 

Fisheries 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Directorate-General for Maritime Affairs and Fisheries 
Directorate C – Atlantic, Outermost Regions and Arctic,  
Unit C.2 - Fisheries Conservation and Control Atlantic and Outermost  
Contact: Laurent MARKOVIC 
E-mail:  MARE-C2@ec.europa.eu 
European Commission 
B-1049 Brussels 

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EUROPEAN COMMISSION 

DAMARA Project: 

a scientific decision-support tool for the development of 
a management plan in the Celtic Sea 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Contents 
1 

Introduction ................................................................................................... 5 

1.1 

Project background ................................................................................... 6 

2  Materials and Methods .................................................................................... 7 

2.1  Stakeholder-centred model development ..................................................... 7 

2.1.1  Stakeholder interactions in practice ....................................................... 8 

2.1.2  Activities for stakeholder involvement ................................................... 8 

2.2  Model Development ................................................................................. 10 

2.3  Model framework development ................................................................. 11 

2.3.1  DAMARA and FLBEIA ......................................................................... 11 

2.3.2 

The Management Procedure Model (MPM) ............................................ 13 

2.4  Development of the DAMARA model .......................................................... 14 

2.4.1  Development process in 2015 ............................................................. 14 

2.4.2  Data processing ................................................................................ 16 

2.4.3  Modelling the economics of fleets ........................................................ 28 

2.4.4  Covariables Operating Model .............................................................. 30 

2.4.5  Management procedure ..................................................................... 30 

2.5  Model scenarios ...................................................................................... 30 

2.6 

The model interface ................................................................................ 32 

2.6.1  Stock related outputs ........................................................................ 32 

2.6.2 

Fleet based indicators ........................................................................ 36 

2.6.3 

Economic based indicators ................................................................. 41 

2.7  Key assumptions and limitations ............................................................... 47 

2.8 

Evaluation of the stakeholder involvement ................................................. 50 

3  Discussion ................................................................................................... 56 

4 

5 

6 

7 

8 

9 

Conclusions ................................................................................................. 59 

Recommendations ........................................................................................ 60 

References .................................................................................................. 60 

Acknowledgements ....................................................................................... 60 

Annex 1. First Interim Report ......................................................................... 62 

Annex II Second Interim Report ..................................................................... 78 

10  Annex III DAMARA Decision Support Tool (DST) developer/user manual............ 109 

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1  Introduction 

This is the final and consolidated report of the European Commission funded research 
project "Scientific support for the development of a management plan in the Celtic Sea" 
(DAMARA1) (Contract Number MARE/2012/22 Lot 1 - SI2.658980). This project arose 
from a North Western Waters Advisory Council initiative to develop a mixed-fisheries 
management plan for the Celtic Sea and a parallel initiative by the European Association 
of Fish Producer Organisations (EAPO) to improve selectivity in the demersal fisheries in 
that area.  During these processes, the scientific advisors and stakeholder collectively 
identified a key knowledge vacuum when trying to assess the biological and economic 
impact of different types of management interventions. This hindered the identification 
of the specific details or parameters (e.g. mesh size, area closure) of any given 
management intervention and resulted in many questions, particularly from the catching 
sector as how different interventions may affect their economic performance while trying 
to achieve the objectives of reducing unwanted catches and broader mixed-fisheries 
goals. This “knowledge vacuum” was identified as a primary barrier when trying to 
identify the operational elements and make progress with both initiatives. These issues 
were further discussed with the European Commission, who agreed with the synopsis 
and agreed to provide financial support to resource the development of approaches that 
could be used to fill such knowledge vacuum and led to the development of this “decision 
support tool”.    

The funding enabled a two year project to build a bio-economic model that could be used 
to identify trade-offs between specific management interventions. The primary objective 
of the DAMARA project has been to develop a flexible modelling framework with a user 
friendly and non-technical interface that can be used by key stakeholders to assess the 
potential impacts of different management options for mixed species fisheries to achieve 
pre-determined objectives or goals. The decision support tool is designed to allow for 
comparative analysis to be drawn between different management interventions and to 
be subsequently used as a  platform or tool for stakeholders to identify and quantify the 
various biological, economic and social trade-offs that are inherent in the decision 
making process. It is not the role of the DAMARA project to specify the objectives or how 
to achieve them, that’s the role of managers and other stakeholders and determined by 
the limits of the Common Fisheries Policy (EU Regulation 1380/2013). The DAMARA tool 
simply aims to provide a bio-economic framework that can be used to compare different 
options to achieving these specific targets e.g. FMSY and to assess what the biological and 
economic implications are of choosing different approaches or paths and thus provide an 
enabling tool for stakeholders.  

Whilst the primary objective was to assist with the development of a mixed-fisheries 
plan, the project was initiated before the most recent reform of the Common Fisheries 
Policy (EU Regulation (No) 1380/2013) which introduced some new and key elements to 
the fishery landscape that were not present at the time of inception of this project. Most 
notably are the requirements, for all stocks with catch limits, to achieve FMSY within a 
pre-defined time frame (by 2020 at the latest) and the obligation to land all catches 
a.k.a the discard ban (by 2019 at the latest).  

Thanks to flexibility of the underlying model framework supporting the decision support 
tool, FLBEIA developed by Azti (Garcia, et al, 2013), the model has been conditioned to 
deal with the landing obligation where all catches are counted against quota as well as 
different time frames for the implementation of FMSY by species. Furthermore, it is also 
possible to modify the model to deal with areas other than the Celtic Sea, although this 
will require further development work.  

1 DemersAl Mixed fishery Analysis tool for Regional Advice 

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The report presented here builds on and consolidates the two previous interim reports 
(see annex I and annex II)and includes a detailed user manual. 

1.1  Project background 

Two of the core objectives of the EU Common  Fisheries  Policy (Regulation (EU) is to  
"ensure that exploitation of marine biological resources restores and maintains 
populations of harvested species above levels which can produce the maximum 
sustainable yield" and “fishing activities are environmentally sustainable in the long-term 
and are managed in a way that is consistent with achieving economic, social and 
employment benefits and pf contributing to the availability of food supplies”  While 
delivering mortality rates that are consistent with delivering maximum sustainable yield 
through the regulation of catches (TAC’s) may be relatively straight forward in the 
context of a single species fishery; in multi-gear, multi-species and multi-fleet fisheries 
using single species TAC constraints is complex and challenging.   

In common with the majority of EU demersal fisheries, those in the Celtic sea can be 
characterised as being biologically and technically diverse with discarding of juvenile and 
over quota species problematic for many demersal species. In such fisheries, it is not 
entirely possible to control which species and how much of each is caught. In fact the 
economics will drive fishers to make best use of all TACs available to them. Under the 
current management approach, where single species TACs are largely set without due 
consideration of these technical interactions, the TACs for some stocks are exceeded in 
trying to maximise the TACs of other stocks. Unless tactical and technical adaptations 
are introduced, it may not be possible to attain single-stock MSY levels and maximise 
potential yield simultaneously. Additionally, improvements in selection pattern for many 
of the key species could result in short and medium terms gains as higher selection 
patterns are consistent with increases in Fmsy targets and therefore yield (STECF, 
2012). It is therefore important that the effects of changes in exploitation pattern are 
also considered in the context of setting fishing opportunities. 

Since the start of the DAMARA project, the fisheries management landscape has changed 
considerably. The intent to achieve MSY objectives by 2015 if possible, or 2020 at the 
latest, coupled to the introduction of the landing obligation means that if the biological 
and economic objectives of the 2013 reform of the CFP are to be met, then there is a 
substantial need to develop the tools and information systems necessary for managers 
and stakeholder to identify the potential impacts and trade-offs between different 
management options and interventions. This provides a challenge to regional advisory 
groups and managers to identify and apply instruments that are consistent with the 
overall policy objectives whilst maximising the potential yield available and avoiding 
TAC/quota overshoots. Under the new management paradigm, management of TAC 
species which are currently discarded once the quota is exhausted will become 
increasingly important. Such ‘choke’ species could become the limiting factor for many 
fisheries and unless businesses adapt technically and tactically, failure to adequately 
deal with choke species will result in premature closure of fisheries with the situation 
that some quotas may be underutilised.  

To limit the uptake rate of choke species, businesses may respond through alternative 
harvesting strategies by adjusting spatial and temporal activity and/or uptake of species 
selective gears. There are a wide range of management instruments that could be used 
including technical measures, closed areas and seasons, preferential allocation of fishing 
opportunities, etc. Adjusting these will result in various changes in exploitation rate and 
pattern with a range of biological and economic consequences. However, identifying and 
quantifying the impact of various management options is a challenge. To aid with the 
development and implementation of mixed fisheries plans it is useful to model the stock 
and economic responses in a common structure to assess, compare and contrast the 

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outcomes of the various management options.  This is best undertaken through a 
decision-support tool that can guide managers when choosing a management strategy 
for the Celtic Sea.   

2  Materials and Methods 

2.1  Stakeholder-centred model development  
This section describes and evaluates the stakeholder involvement in the DAMARA 
project.  

A review of decision support systems (DSS) that have been developed in EU-funded 
projects has been carried out by IMARES (Bolman et al, in prep). The general conclusion 
from the review was that many decision-support tools have been developed as a 
scientific effort with relatively little connection to actual decision-making. This has also 
resulted in a low uptake of the decision-support tools for decision-making. The review 
identifies 6 steps to improve the development and uptake of such tool:  

  Utilisation: In which context is the DST going to be used and by whom? 
  Objective: What are the objectives and what are the criteria to achieve 

them? 

  Stakeholders: Who needs to be involved and how?  
  Scales: Which temporal and spatial scales are relevant? 
  Data: What is the input data used by the tool and what is the output 

information? 

  Evaluation: Are the objectives achieved according to the criteria? 

To ensure that the DAMARA decision-support tool (“the model”) was fit for purpose, from 
the outset the project structure was designed to have a strong connection between the 
model developers and the model users. Throughout the two years (November 2013-
November 2015), there have been frequent engagements between the modellers and 
the stakeholder group at critical periods in the process, where stakeholder input has 
largely driven the design of the model user interface; the types of scenarios that the 
model can investigate and; the output and reporting.    

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WP5 

Dissemination 

WP2 

Stakeholder 
Interaction 

WP4 

Model 
Development  

Stakeholder 
Forum (NWWAC 
CS Focus Group, 
National 
Administrations,  

DG MARE) 

WP3 

Data 
Management/ 
Analysis   

Scientific 
Steering  
GP 

Figure 2.1-1 Project structure showing the inter-relationships between specific Work Packages . 

2.1.1  Stakeholder interactions in practice 
The objective of the stakeholder interaction was the following: “Define and implement an 
engagement process that provides the opportunity for collaborative development of a 
tool for evaluation of harvest strategies for mixed fisheries. Establish a process that 
makes the information count in the management system (i.e. how it is used to support 
the development of Mixed fishery multi-annual plans). “  

2.1.2  Activities for stakeholder involvement 
The activities that have taken place in order to facilitate the stakeholder involvement 
were three stakeholder workshops, two Webex meetings with stakeholders and two 
rounds of interviews. The first round of interviews was held in preparation of the first 
stakeholder meeting, and in the final phase of the project short phone interviews were 
held with most of the people involved to evaluate their involvement in the DAMARA 
project.  

The stakeholders that were involved during the workshops were mainly industry 
stakeholders, representatives of Belgian, French, Irish and UK fisheries associations as 
well as environmental and recreational fisheries NGO stakeholders and from National 
Administrations and the European Commission. Scientists from various disciplines were 
involved in the project, fisheries experts, ecological modellers, economic modellers and 
stakeholder experts.  

For the facilitation of the stakeholder involvement in the development of the model2, 
stakeholder experts were involved. Their approach was pragmatic, preparing the 
agendas of the stakeholder meetings in consultation with the scientists and the project 
leader, in order to ensure that the necessary topics were addressed at the right moment. 

2 The words model, decision support tool and app will be used interchangeably in this section of the report. 

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In addition, the stakeholder experts made sure that the communication between 
stakeholders and scientists went two-ways and they created settings in which ideas 
could openly be discussed. During meetings an ad hoc approach was used to enable 
addressing issues that were brought to the table during the meetings, for instance, by 
moving a brainstorm session to an informal coffee corner. This specific session focussed 
on identifying scenarios for the decision support tool and all stakeholders were 
individually asked to propose their personal top three scenarios.  To stimulate direct 
communication between stakeholders and modellers during stakeholder meetings the 
latter were asked to present and discuss their work in person. This approach allowed for 
immediate identification of critical issues and better understanding of the presented work 
and the uptake and reception of this work by the stakeholders. This direct 
communication between modellers and stakeholders helped to keep model development 
on track i.e. closely linked to the perceptions and wishes of the stakeholders. 
Additionally a semi-structured interview was held with two stakeholders during one of 
the stakeholder meetings to elaborate economic issues. This informal interview was 
conducted by one of the economic modellers and a stakeholder expert and was 
instrumental in defining and identifying key economic issues for the model. 

First workshop (June 2014)3 
The starting point for this workshop was providing an overview of DAMARA and 
specifying the primary objectives and challenges of the project. This was elaborated with 
a non-technical overview of modelling progress. In order to understand and specify 
stakeholder involvement in DAMARA seven stakeholders were interviewed in preparation 
for the workshop. In these short (30-45 minute) phone interviews eight questions were 
asked:  

(1) Organisation?  
(2) Role in organisation?  
(3) Objective DAMARA?  
(4) Interest/Stake in DAMARA?  
(5) Expected outcome? 
(6) Role in DAMARA?  
(7) Available time for DAMARA?  
(8) Other critical issues for success DAMARA?  

By discussing the outcome of the interviews in the context of the primary objectives and 
challenges of DAMARA, the participants of the workshop (stakeholders and modellers) 
were able to manage the expectations and define clear roles for the stakeholders. In this 
workshop the participants also agreed on a communication strategy and a work plan for 
the remainder of 2014.  

Second workshop (November 2014) 
In this workshop a first prototype model was presented and model assumptions, 
capabilities and limitations were discussed. Emphasis was on discussing possible output 
and trying to determine if this meets the requirements i.e. what was originally 
envisioned. Therefore the deliberations focused on determining if possible outputs are 
relevant and useful and if there might be alternatives. The workshop also discussed a 
work programme for the next stage of DAMARA.  Due to unfortunate circumstances the 
workshop started later on the day then scheduled. This however enabled interviewing 
two stakeholders from industry on economic components and their expectations as 
mentioned in the approach section. The interview focussed on eliciting insights on fleet 
response to management measures e.g. closed areas. For example: How did the Belgium 

3 The reports on the first – and second stakeholder workshops first appeared in the Interim report of the 
DAMARA project in January 2015. 

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beam trawlers respond to closure of three cod areas? Just displacement of effort? Were 
there other responses? Another question that was asked was:  “What are the main 
economic drivers for you and how would you like to see the economic output presented.” 
For a summary of this interview see appendix 2.  

Third workshop (March 2015) 
During the third stakeholder meeting the input of the stakeholders was collected on the 
scenarios that were relevant for them to model and on the user interface. A brainstorm 
was held with the stakeholders about the scenarios. From this brainstorm a short list of 
scenarios was put together by the discussion facilitators. Consequently this list was 
presented and completed together with the stakeholders. During a separate session at 
this third meeting, all elements of the user interface were shown to the stakeholders and 
their advice and direction for improvement was collected. It was agreed that the next 
step would be that by the end of May the beta-version of the model would be circulated, 
so stakeholders could try it out and provide feedback on the basis of this. See Appendix 
III for the full report of the third stakeholder meeting. 

Webex meeting I (July 2015) 
In July a Webex meeting was held with stakeholders to step-by-step go through the 
model app. In this way the stakeholders could test the model in the weeks following and 
provide feedback in text boxes in the app. 

Webex meeting II (November 2015) 
In November the last meeting was planned during which the model was finalised, the 
report written and the next steps discussed. The stakeholders were invited to come but 
only a representative from the European Commission could participate in person. This 
was related to busy schedules and the location of the meeting (Lorient, France), making 
it less easy for participants to travel to the venue. Therefore a WebEx call was planned 
with the stakeholders. In preparation for the final meeting a series of short interviews 
had been held with the stakeholders and the scientists. The outcomes hereof were 
presented during the WebEx call. The interviews had as a goal to evaluate the 
stakeholder involvement and to collect the ideas of the project participants about their 
thoughts on the next step of the DAMARA project. After the presentation a short 
discussion took place on the next steps after project completion and an update was 
given on the latest model changes. Unfortunately only two stakeholders of the same 
organisation were able to attend this WebEx meeting. These stakeholders expressed 
concern that the model results could be misinterpreted and used against fisheries. It was 
agreed upon that a framework should be established for the use of the model. The 
modellers presented the model changes that had been made, the output that the model 
generates and the scenarios that were to be presented and elaborated in the final report.  
The stakeholder interaction had a marked and fundamental role in terms of project 
direction and choice in the types of scenarios for testing as well as the overall application 
design and the visualisation of results. The background and outcomes of the interviews 
to evaluate the stakeholder involvement are presented in section 2.8. 

2.2  Model Development  
The model development was undertaken by the core modeling group during several 
dedicated working meetings held throughout the project and various conferences calls. 
Over the duration of the project, several stepped developments were undertaken, 
starting with an initial demonstration prototype and then sequential development of a 
beta version, which increased the number of stocks included as well as increasing the 
complexity of the management scenarios that could be tested. In parallel, the design of 
the user interface as well as refinement and development of the model outputs was 
undertaken in close cooperation with the stakeholders. The final version now includes 9 
key stocks, an economic model that includes price elasticity and a range of management 
scenarios which have been identified through consultation with the project stakeholder 
group. While the DAMARA project has made substantial advances, there are further 

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developments required to enhance the computational performance and expand the 
current portfolio of management scenarios that can be tested and examined, and to 
expand the number of stocks further. These issues are discussed in more detail in the 
discussion (see section 0).  

It is also noteworthy that the initial project concept and contractual agreement were 
made prior to the introduction of the latest reform of the CFP (Regulation (EU) No 
1380/2013) which introduced a number of pivotal high-level management objectives 
that were absent from the previous CFP. These include (i) the introduction of the Landing 
Obligation (Article 15); (ii) that exploitation of living marine biological resources restores 
and maintains populations of harvested species above levels which can produce the 
maximum sustainable yield. i.e. FMSY targets (Article 2.2) and that fisheries should be 
managed in a way that is consistent with the objectives of achieving economic, social 
and employment benefits, and of contributing to the availability of food supplies (Article 
2.1). In addition, the latest CFP also introduced regionalization, whereby groups of 
Member States with shared interests in marine biological resources are encouraged to 
develop mixed fisheries multi-annual plans (Article 18). The content of such plans are 
prescribed in Article 10 and may also include the ability to apply FMSY ranges to mitigate 
to some extent mixed fisheries interactions.  

These newly introduced changes, in isolation or in combination, have substantially 
changed the operating landscape of European fisheries policy. This also had significant 
bearing on the direction of the DAMARA project in terms of both the types of scenarios to 
be examined and the stakeholder/decision making process. The most substantial impact 
in effect was the new CFP effectively introduced new operating boundaries e.g. FMSY as a 
maximum fishing mortality permitted within the model and the need to consider that all 
catches would be counted against quota introducing the concept of fisheries being 
choked due to quota restrictions which had not been the case previously as discarding 
was permitted under the previous legislative framework.  However, the reformed CFP 
entered in force soon after the inception of the project so that the outcome of DAMARA is 
fully compatible with the current legislative framework. 

2.3  Model framework development 

2.3.1  DAMARA and FLBEIA 
The aim of DAMARA project was to design a tool capable of assessing the impacts of 
different management options for mixed demersal fisheries in the Celtic sea. Most of the 
model developments were made in response to stakeholder exchanges and policy driven 
(new CFP context, management plan evaluations etc.).  

The prototype is built around an existing framework called FLBEIA (FL Bio-Economic 
Impact Assessment). FLBEIA was built by AZTI (Garcia et. al., 2013) to provide a ﬂexible 
and generic simulation model to conduct Bio-Economic Impact Assessments of harvest 
control rule-based management strategies (TAC setting strategies; capacity limitation; 
area and seasonal closures; technical measures) for multi-fleet, multi-stock fisheries. 
This involves assessing the consequences of a range of different management strategies 
or options to assist and determine which approach will be the most appropriate to meet 
management objectives for the fishery.  

FLBEIA is divided into two main blocks, the operating model (OM) and the management procedure model (MPM) 
(Figure 2.3-1).  
Figure 2.4-6 provides a schematic data map showing the full data and information on the 
input and output data of the model as well as the data sources. 

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Figure 2.3-1 Conceptual diagram of FLBEIA 

2.3.1.1  The Operating Model (OM) 

Within the operating model, there are sub-models or components that deal with (i) the 
stocks and (ii) the fleets that exploit them and; (iii) other aspects of the system (called 
covariables). Typically in a biological focused simulation, key stocks are selected for 
evaluation and the partial fleet activity that relates to those stocks is included. Our 
approach of modelling the economics of the fleets (and local fishing economies) requires 
that the operating model must also consider fleet activity that relates to stocks and 
areas not explicitly modelled, for example stocks outside the Celtic Sea that are 
periodically fished by vessels which operate both inside and outside the Celtic Sea. 
Therefore, there is an implicit assumption that all fleet activity (for the fleets in focus) is 
considered.  

2.3.1.2  The Stock Component  

The model was designed to cater for the wide range of scientific information available on 
fish stocks (see also section 2.3.2.1 on the observation model). It is however noted that 
the more precise the data available, the better the forecast. Ideally, for each stock there 
should be an underlying analytical assessment4 which can be used to predict the stock 
response to different levels of catches and other measures such as changes in selectivity 
(for example: cod, haddock and whiting in the Celtic Sea). However, this is not always 
the case. For some stocks such as Nephrops there is an estimate of the overall stock size 
but not the age or length structure. Here, it is still possible to assess the impact of 
different catch levels but it is not possible to assess for example changes in gear 
selectivity due to the lack of age/length information. The final stock category relates to 
stocks without any analytical assessment. This is a significant issue with respect to 
economically important species such as anglerfish as it is not possible to assess what the 
short or medium stock term impacts may be for any management scenario. Within the 
model these stocks are assumed to have a constant or a fixed population over time. 
While this means that at least there is some consideration given to the economic 
importance, it is not possible to determine how these stocks may respond over time due 
to different management interventions.  

4 
http://www.ices.dk/sites/pub/Publication%20Reports/Advice/2015/2015/General_context_of_ICES_advice_2
015.pdf 

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2.3.1.3  The Fleet Component  

The fleets operating model is where much of the economics is imparted into the model. 
It is divided into two levels: (i) fleet-segments, describing the physical structure of the 
fleet segments (i.e. based on average vessels in terms of length and gear); and (ii) 
operational units/metiers, describing the activities of vessels at a metier level (i.e. based 
on area fished, gear used and typical species mix obtained). With this combined 
information, the economics of the fleet segments can be calculated (this is further 
described in the section 3.1 Prototype Setup).  

2.3.1.4  Covariables Component  

This part of the model is intended to incorporate all the variables that are not part of the 
biological or fleet components and that may affect any of the operating model 
components or the management process. The number of covariables is, in principle, 
unlimited. Co-variables are currently included to expand the economic detail in the 
model, these include: Fuel costs, Capital costs, Crew share, Number of vessels, 
Maximum days, Employment per vessel, Variable costs, Fixed costs, Other revenues and 
Capital values. This data is linked to the dynamics of the model (e.g. capital dynamics) 
and also to provide outputs at a disaggregated level as well as for key economic 
indicators such as gross cashflow, gross value added and net profit. The DAMARA (i.e. 
FLBEIA) model can be expanded to include other variables linked to the model as 
required, for example: ecosystem variables such as predator prey (trophic) interactions 
or environmental impact on recruitment. The limitation could come from memory 
problems with R and/or the operating system used. 

2.3.1.5  Links among and within components 

The links within the OM components are not restricted by the general settings of the 
simulation model. If the modeller decides to include these links, we will have: 

  The link within the biological component is that catch affects abundance. 
  The link within the fleets’ component is that fleets' capacity affects fishing effort. 
  The link between the biological and fleets’ components is that fishing effort and 

fish abundance affects catches which in turn affect fish abundance and the effort 
that is economically sustainable. 

2.3.2  The Management Procedure Model (MPM) 
The MPM simulates the management procedure and is the second major component of 
the FLBEIA model. This includes the collection of data for assessing the status of the 
stocks, running the actual assessments, and provision of the advice that will be used to 
manage the stocks. This Management Strategy Evaluation (MSE) allows for uncertainty 
in assessing stocks and uncertainty about the implementation of different management 
measures. A primary goal of the MSE approach is to assess the performance of different 
rules in balancing multiple and sometimes competing objectives (e.g., low risk of 
overfishing and maximising catches or profits). Simulating both the management and 
biological system, together with uncertainty, enables an evaluation of robustness of 
various management options in achieving the objectives which have been set by 
stakeholders. There are three sub-components of the MPM: (i) the observation model; 
(ii) the assessment procedure and; (iii) management advice. 

2.3.2.1  The Observation Model 

The observation model generates observed data on a stock-by-stock basis. It is a key 
component of MSE that translates the “true” underlying system (operating model) into 
noisy observations that will be used to recover estimates of the true system via the 
assessment. That is, it uses the biological operating model to project the population 
forward and generates the catch and survey data that would be observed and used in 
the assessment. Here is where many of the uncertainties can be introduced, including: 

  Measurement and sampling errors 

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  Misreporting 
  Aging errors 
  Varying catchability 

Depending on the type of assessment to be conducted (age-based, biomass dynamic, 
trends-based or none if the focus is solely on the management strategy alone) different 
data-generating functions can be used, including:  

  Perfect observations, which have no errors included. 
  Age-structured catch data with ageing and general errors included. 
  Biomass-aggregated catch data with general errors included. 
  Age-structured and biomass-aggregated abundance or CPUE indices with 

survey/CPUE-specific catchabilities. 

2.3.2.2  Assessment Procedure 

The data generated in the Observation model are passed to the assessment routines 
(e.g., commonly used assessment methods such as XSA, surplus-production), which 
integrate the catch and survey data to produce estimates of the abundance and fishing 
mortality per stock. In this sense the output of the assessment is the perceived status of 
the population. 
Within the DAMARA project, annual assessments were not conducted in the projection in 
the evaluation. The reason for this is that the focus was specifically on mixed fisheries 
issues within the Celtic Sea and not on the performance of the assessment models per 
stock. Mixed fisheries MSEs typically omit the assessment component as the focus is 
more on the mixed fisheries aspects than the quality/ability of the assessments to 
recover stock status. In addition, running an assessment for each stock and iteration 
would greatly slow down the method. Leaving the assessment aside speeds up FLBEIA 
but we recognise that this may omit an important uncertainty in the system – 
assessment model uncertainty. 

2.3.2.3  Management Advice  

Management advice is based on the application of the ICES harvest control rules (HCRs) 
that compare the status of the stock with proposed or agreed target and limit reference 
points (e.g., Bmsy, Fmsy, Blim, Flim) and adjust the advised fishing mortalities for the 
projection year accordingly. Where the stock is estimated to be above MSY Btrigger, 
catches are set in in accordance with FMSY. In circumstances where the biomass is 
estimated to be below MSY Btrigger, then the catch is set in accordance with a fishing 
mortality rate which is below FMSY the precise level of fishing mortality is determined by 
the ratio of how far the stock biomass is below MSY Btrigger. The HCRs use either stock 
status (absolute estimates of abundance estimated in the assessment or treated as 
known) or stock status indicators such as abundance indices. 

2.3.2.4  Model Outputs  

The operating model and management procedure models thus progress over a specified 
time period (e.g., 5 years) by generating the observed data under a given level of fishing 
mortality (or effort level and allocation among metiers given an assumed relationship 
between effort and fishing mortality for each fleet and stock), comparing with the HCR 
and adjusting the fishing mortality for the next projected year. 
The outputs (biological and economic) from the model under given management 
scenarios can then be graphed or tabulated for comparison and appraisal by decision-
makers. The user interface and description of the model outputs are given in section 2.6. 

2.4  Development of the DAMARA model 

2.4.1  Development process in 2015 
The starting point of the model development was the use of the existing modelling 
framework, FLBEIA as discussed in the previous section. The initial development stages 

14 

 
 
 
 
 
 
were focussed on the adaptation and the conditioning of the FLBEIA framework to 
incorporate the appropriate stock and fleet components. To begin with, the adaptation 
and conditioning of the FLBEIA framework to a level that was for the most part data 
independent; that is the data are used to prove the concept but the model itself is 
scalable to different sets of data, for example including additional sub-areas, stocks, 
different spatial scales or fleet and metiers definitions pertinent to the Celtic Seas 
fisheries. A detailed description of the initial phases of the development is contained in 
the first (annex I) and second (annex II) interim report. Here we focus on the work and 
developments to the original prototype model as described in the interim report.  

Subsequent developments have focussed on a number of key elements of the DAMARA 
model. In particular, an expansion in the number of stocks considered; expansion and 
refinement of the fleets and metiers included, and; elaboration and refinement of the 
economic and fleet dynamics and models. The DAMARA model has a spatial coverage in 
the Celtic sea (areas VIIbcefghjk) with spatial disaggregation between VIIfg and 
VIIbcehjk.  

During the latter phases of the project an important amount of work has been dedicated 
to revising the core FLBEIA code, in particular to accommodate new management 
objectives such as the introduction of the landing obligation and the accommodation of 
FMSY ranges to accommodate flexibilities to deal with mixed-fisheries TAC conflicts. 
Scenarios can now be setup using excel “scenario” files. This allows to easily compare 
different scenarios through clear definition on the different parameter settings (stock 
specific harvest control rules, fleet setting and metier settings). New harvest control 
rules have been coded. Landing obligation can now be introduced by fleet and by year. 
De minimis and inter-year flexibility functionalities are now available. They can be 
operated per fleet, per stock, per year (% de minimis catch allowed). Nevertheless, inter 
species flexibility is not yet operational.   

Important amounts of work have also focused on defining new outputs based on 
economic indicators. Related to that, fleet size adjustment, investment and 
disinvestment are possible through the definition of the Simple Capital Dynamics 
scenario. In addition, prices are size graded and include flexibilities to allow fluctuations 
in accordance with quantities landed at the stock and fleet segment level. Therefore 
there are two elements to price dynamics: changing size structures of landings (as 
different sizes command different prices); and changing landing volume (i.e. the 
quantity vs price relationship). The effects of interest rates on cost of capital (i.e. the 
cost of depreciation and interest) and fuel price changes have also been incorporated 
and have an impact on the fleet dynamics. 

Recent model development has permitted the testing of new scenarios not already 
available under FLBEIA framework. The main focus was the implementation of a landing 
obligation scenario through the limitation of discards and the uplift of the TAC to cover 
the total removals (catches) and not just landings.  

For “data-rich” stocks (where full age-based models and FMSY estimates were available 
e.g. cod and haddock):   

  FMSY could be specified as the point estimate or other values such as lower and upper bound 

 

around FMSY (so called ranges), alongside a timescale (e.g. by 2018) for achieving the F target in 
equal steps.  
It was also possible to specify so that, in combination with an overall F target, recovery of depleted 
stocks was simulated to be achieved through a linear increase in SSB up to the safeguard (set as 
Bpa/MSY Btrigger). Two recovery periods were simulated, 5 and 10 years.  

For “data-poor” stocks (where no FMSY estimates were available e.g. megrims and 
plaice): 

15 

 
 
 
 
 
 
 
 
 
 
  Harvest Control Rules were implemented based on changes in stock biomass where quotas increase or 

decrease as the stock index changes (so called “annex IV” rules), 

  Or through constant TAC strategies.   

Additionally, for the first time the model was run with several iterations to account for 
stochasticity in recruitment projection and provide confidence interval around forecasting 
trajectories.  

2.4.2  Data processing  
During 2015, a significant amount of work was dedicated to produce reliable model input data based on available 
data. One objective of the DAMARA project was the uses of the DST for advice and management plan evaluation; as 
such data sources come from international data base (mainly ICES and STECF), publically available and updated on 
yearly base. This process identified a number of issues in data quality, mismatches between data bases including 
differences in spatio-temporal resolution  
Figure 2.4-6 illustrates the data sources and processing steps required to produce the 
model input data. Three main data sources are needed: fisheries data that gather 
information from STECF-FDI and ICES; the biological information comes from ICES and 
economic data from the Commission's Annual Economic Report5 (AER). It is also 
important to acknowledge that code has been specifically written to draw down the 
necessary data from the core databases (ICES assessments; STECF Fishery Dependent 
Information and data from the Annual Economic Report). While this streamlines the 
process and in principle should permit a semi-automated upload procedure, a number of 
data anomalies have been identified and a considerable amount of effort was used for 
data cleaning purposes and data alignment. Until such time that data is effectively 
screened for errors etc., resource will be required when updating the core input data 
annually or in whatever time-step is considered appropriate. Furthermore any revisions 
to the structures and formatting of the external databases will require recoding of R files 
developed by the DAMARA modelling group. It is also important to note that the fleet 
and métier segmentation used in the DAMARA model (TR1; BT2 etc.) are taken from the 
definitions used in the Long Term Management Plan for Cod. Any future changes in fleet 
definitions will have significant impact on the functioning of the underlying 
FLBEIA/DAMARA model.   

Fisheries data  

Six types of input data excel files are required to process the fisheries data (fleet/metier 
level information).  When processing the data, three main points were taken into 
account:   

  To enable spatial management measures spatially resolved data were needed  
  To reduce computing time and increase reliability of the results, selection of relevant fleets and 

metiers was required 

  To enable dialogue between the fishing component and the biological component of the model, 

age-based data were required at fleet and metier level 

- 

The first processing step aims at evaluating, from STECF FDI non spatial landings (1), 
the ‘significant’ fleets and metiers through analysis of fleets contributing to >=1% 
landings of any species, and metiers contributing >= 5% to provide total landings and 
discards in tonnes by fleet, metier, stock and year (a). All followings steps are processed 
for the fleet and metier identified by this procedure.  
The STECF statistical rectangle landings (volume) information (2) by quarter is available 
since 2009 (the first year French data was submitted). It is used to calculate the 
percentage of total fleet, X metier, Y landings of species Z in rectangle A, quarter B (b). 

5 See there for 2015: https://stecf.jrc.ec.europa.eu/documents/43805/1034590/2015-07_STECF+15-07+-
+AER+2015_JRCxxx.pdf 

16 

 
 
 
 
 
 
 
 
                                           
 
This relative spatial distribution of landings is multiplied by the total landings to obtain 
spatial landings in tonnes per fleet, metier, stock year, quarter and rectangle.  
On one hand, the STECF landings and discards number at age (3) are standardized to 
ICES number at age and aggregated to fleets and metiers used in the model (c). On the 
other hand, ICES Intercatch weights at age (4) are processed to provide complete 
landings and discards weights at age by fleet, metier, stock and year (d). A quality 
check, namely SOP sum of the product, is then performed by comparing the total 
landings and discards produced by multiplying of numbers at age (c) by weights at age 
(d) with STECF nonspatial landings and discards by fleet, metier, stock and year (a). 
Where needed, SOP corrections are made. The combined results from processing step 2 
allow creating suitable input data: landings and discards in tonnes, in number at age and 
weight at age per fleet, metier, stock, year, quarter and statistical rectangle. However, 
two strong assumptions are made: i) discard rate per fleet and metier is homogeneous 
among statistical rectangle; ii) age structure of landings and discards per fleet and 
metier are homogeneous among statistical rectangle.  

The STECF non spatial effort in kW.days (5) is reformatted to provide total fishing effort 
by fleet, metier and year used in the model (e).  The STECF spatial effort in hour by 
statistical rectangle and quarter (6) is available since 2003.  However, there may be 
significant health warnings with some of the spatial effort information - it is known to be 
incomplete for small vessel (<10m).  The STECF fishing effort reports note that while 
some countries report hours fished as recorded in logbooks to produce the spatial effort 
estimates, others (including the UK) report number of days absent from port X 24 to 
estimate hours fished. So there is clearly a bias (~x3)6 towards some countries and/or 
missing data for others. However, as the DAMARA model only uses spatial effort as a 
relative reference, to apportion the kW.days absent from port data, this potential bias is 
not an issue. Furthermore, as Member States provide effort data consistently from year 
to year, as while it would bias the representation of that countries fleets, it would not 
produce misleading outputs unless the method of calculating effort had changed from 
year to year. Following the same conceptual method than for landings, a relative 
distribution of effort by fleet, metier, stock, year, quarter and rectangle (f) can be 
calculate from file 6 and spatial fishing effort in kW.days  per fleet, metier, year quarter 
and rectangle estimated.  

It is worth noting that the fleet objects could be conditioned on the spatial and temporal 
scale specified (i.e. quarterly) and that the ‘catch model’ could operate on a quarterly 
time-step.  Nevertheless, the ‘assessment model’ would continue to operate on an 
annual time-step. As such, the model was run on a yearly base.  

Biological data  

The processing steps are quite straightforward for the biological component (See  
Figure 2.4-6). For each stock, assessment outputs are collected from ICES (historical 
time series of landings numbers, discard numbers, weights at age, natural mortality, 
maturity ogive, annual F and SSB estimates). All data are then compiled together in a 
relevant format (FLBiols). One processing step provides the other necessary file needed 
to complete the biological component of the model (e.g population dynamics). For each 
aged based stock, SSB and recruitment are used to fit stock-recruitment functions based 
on stock annex advice. This relation is used in the projection procedure to forecast the 
recruitment (SRs). For biomass dynamics stocks, a surplus production model is fitted on 
available data and K,r and p estimated parameter (carrying capacity, growth rate and 
coefficient of asymmetry) are stored in a relevant format (BDs).  For fixed population 

6 This as an example/ guide estimate of the difference between fishing effort calculated by the number of days 
absent x 24 and actual fishing time based on an analysis of UK vessels.  The x3 would equate to approximately 
8 hours fishing in a day (see Engelhard et al 2015 for further elaboration) 

17 

 
 
 
 
 
 
 
                                           
 
stocks, a geometric mean assumption is used to forecast the recruitment. ICES advice 
sheet are used to update the Biological reference points (BRPs). In 2015, references 
points were updated for several stocks. The latest available information coming from 
WKMSYREF4 (ICES, 2015) was used. 

The sum of the landings numbers across fleets and discards numbers across fleets 
should be equal to the landings numbers and discard numbers in the stock object.  If 
this is not the case (e.g. for hake where the stock boundaries extend outside the model 
area), then the difference in the catches in the stock object and the fleets objects should 
be allocated to an "others" fleet, which will be set to a constant proportion of catch in the 
forward projections.  This assumption should be strongly challenged when a significant 
proportion of the catch is outside the model area for example hake and anglerfish. 
Selection pattern at age for landings and discards are computed using the proportion of 
fish under and above Minimum Conservation Reference Size (MCRS). Mean weight at 
MCRS are calculated for each stock using Weight-Length relationship and compared to 
mean weight at age of the catch (Figure 2.4-1). 

Figure 2.4-1.Left - Mean catch weight at age for whiting. Horizontal solid line indicates mean weight of 27cm fish 
(27cm being the minimum conservation reference size). Right - Age structure of the discards in number. Discards 
selection pattern will be 1 for age 0 and 1 and 0 for age 2 and above. Under the landing obligation Age 2 “discards” 
fish will be considered as landings and age 0 and 1 will be considered as discards that need to be landed.  

Economic data  

The economic data inputs required in the model are consistent with AER data provided 
by Member States at the fleet level by year. This includes fuel costs, other variable 
costs, fixed costs, crew share, and capital costs. The calculation process to estimate fleet 
operating profit (or gross cashflow), gross value added and net profit are also consistent 
with the AER approach. The AER data is reported at FAO area 27 and as a result is 
presented at a higher level than the case study area in DAMARA. To enable STECF case 
study area data to be evaluated consistently with fleet performance the following 
approach has been undertaken. 

The DAMARA model is calibrated to kW.days and takes effort data by ICES sub-area and 
main gear from the STECF database. The AER database contains effort data collected at 
three levels: by fleet segment, by gear and area, and by FAO levels. Effort in kW.fishing 
days is typically provided at the fleet segment level. Although the effort data presented 
in the STECF database and in the AER derive from the same original sources there are 
some differences in total effort indicated by fleet.  

18 

 
 
 
 
 
 
 
 
For the consistency in the costs calculation, per kW.day costs are calculated using the 
STECF effort data from the same source as that used in the biological component of 
DAMARA. Total effort per DCF segment in FAO area 27 is used to disaggregate variable 
and energy costs to the kW.day, which is re-aggregated using STECF effort for the 
segments activity in VIIb-k. 

Likewise, the number of vessels specified in the STECF effort database active in VIIb-k is 
used to calibrate the fixed costs on a per vessel basis. To disaggregate the AER 
economic fixed cost data to the vessel level it was required to compose a composite total 
fleet size for the fleet segments due to inconsistencies between the AER total fleet sizes, 
the STECF total fleet sizes and the STECF fleet sizes active in VIIb-k. Inconsistencies for 
some fleet segments showed a total fleet size compared to a fleet size active in VIIb-k at 
variance with total effort in kW.days and effort in VIIb-k.  

These inconsistencies are clear when average effort per vessel is compared for the same 
fleet segment at the overall level and at the VIIb-k level, the latter level showing 
average effort levels far in excess of the former. This could be explained by large 
numbers of vessels active outside VIIb-k applying low levels of effort thereby bringing 
down average vessel effort levels at the macro scale. Therefore, using this total fleet size 
to disaggregate the AER fixed costs data could lead to below average vessel costs for the 
model fleet segments and hence would lead to mismatched fixed and variable costs 
when re-aggregated for the model. 

To adjust this inconsistency a composite total fleet size was created using the average 
effort per vessel in VIIb-k (using STECF effort in VIIb-k and STECF vessel numbers 
active in VIIb-k) and adding on the average effort per vessel outside VIIb-k (using the 
difference between STECF total effort and STECF VIIb-k effort divided by STECF total 
vessel numbers) thereby creating an average total effort per vessel. This variable is then 
divided by the STECF total effort to provide a composite total fleet size. This method 
takes into account lower average effort for the vessels outside VIIb-k without depressing 
the fixed costs to an unrealistic vessel average. 

The use of this composite total fleet size allows the fixed costs to be disaggregated in a 
form that allows re-aggregation at the level of the focus area that aligns reasonably with 
the variable costs and revenue generated in the model. A consequence of this method is 
that the output of the model at a vessel level may show some fleets having much higher 
or lower average costs and revenues than others; however at the fleet level or the 
kW.day level the fleets will be acceptably comparable.  

A recommendation from this exercise is that more consistency between effort and fleet 
sizes from different sources such as the AER and the STECF effort databases should be 
made in order to resolve the inconsistency identified here. 

In this section the effort and fleet size for one fleet in the model are shown to describe 
the problem of mismatches between both capacity indicators. The total effort is the sum 
of effort in region Cel1, annex IIA (cod recovery zone), annex IIB (Southern Hake and 
Nephrops recovery zone)  and Bay of Biscay (BoB) while the focus area is Cel1, ICES 
divisions VIIbcefghjk (in figure labelled VIIbk for brevity).  

19 

 
 
 
 
 
 
 
 
 
 
Figure 2.4-2. Effort in all areas and in VIIbk for IRL_DTS_O10M fleet from STECF database 

The STECF database shows that for this fleet the effort, in kW.fishing days, applied in 
the focus area (VIIbk) is ~90% of total effort for the fleet. 

Figure 2.4-3. Number of vessels in all areas and in VIIbk for IRL_DTS_O10M fleet from STECF database 

However, the STECF database shows the number of vessels for this fleet in VIIbk as 
~50% of the total. 

Figure 2.4-4. Number of vessel in all areas from AER with number of vessels in VIIbk from STECF database 

Using the AER total vessel numbers compared against the STECF vessels in VIIb-k the 
comparison shows ~75% of vessel numbers active in VIIb-k. Note, this total vessel 
number compared to VIIb-k vessel numbers reflects more adequately the comparison of 
effort for this fleet however there is still a substantial difference and the difference is 
significant for other fleets. 

20 

 
 
 
 
 
 
 
  
 
 
 
 
Figure 2.4-5. Number of vessels in all areas (composite) and number of vessel in VIIb-k from STECF database 

Finally, using the composite total vessel numbers the comparison aligns reasonably with 
the effort in Figure 2.4-2 

21 

 
 
 
 
  
 
22 

 
 
 
 
Figure 2.4-6 Schematic Datamap of how the various data sources from the ICES assessment, STECF Fisheries Dependent Information (FDI) database and the STECF Annual 
Economic Report (AER) data were used to condition the model. 

23 

 
 
 
 
2.4.2.1  Stock Component  

Initially, the prototype model was limited to three species: cod, haddock and whiting all 
of which have age-structured analytical assessments. For these stocks, the biological 
operating model was created based on 2013 ICES Assessment working Group on the 
Celtic Seas (WGCSE). Stock-recruitment functions for use in the biological operating 
model were fitted to the assessment-derived spawning stock biomass and recruitment 
estimates by species. However, not all commercially exploited stocks benefit from full 
aged-based analytical assessments and some are very economically important e.g. 
monkfish and megrim, are classified as “data limited”, meaning that it is not possible to 
forecast the potential impacts of various management scenarios. It was recognised at an 
early stage that the model must also consider other stocks with lower levels of 
assessment. To this end, the modelling team also included anglerfish (monk) and 
Nephrops to test the model under different levels of data quality.  

For Nephrops, a simple biomass dynamic surplus production model is fitted to the 
absolute biomass data available for Nephrops on the Smalls (WGCSE 2013). This allows 
for dynamic changes in stock biomass as a result of different management interventions 
to be evaluated. However, for anglerfish a fixed-population was included, which means 
that the stock does not exhibit a response to any specific management interventions, but 
it does allow for future catches. Clearly this is unrealistic in practice, but in order to 
develop the prototype model, this assumption is necessary. This is however the most 
expedient method of including these economically valuable stocks in the model. 
During 2015, four additional stocks were included, namely Plaice (VIIfg), Sole (VIIfg), 
Northern Hake and megrim and during this time the model was updated with the most 
recently available assessment data. There is currently no analytical assessment for 
Megrim and it was introduced in the model as a fixed population and therefore 
management option was defined as fixed advice although it is noted that ICES is 
currently trying to develop an assessment for this important stock. The DAMARA model 
should be updated when such an assessment becomes available, this is also true for 
other stocks such as anglerfish. STECF data for hake are incomplete and lack age-
disaggregated data. To overcome this, ICES assessment data in length were converted 
back to ages allowing modelling hake as an age structure population in the model.  
The full list of stocks currently included is given in Error! Reference source not 
ound.. 

Stock 

Anglerfish 

Cod 

Population 
dynamics 
Fixed 
population* 
Age-structured  Age-dependent  Segmented-

Production 
(stochasitc) 
- 

Natural 
mortality 
- 

regression 

Haddock 
Nephrops 
(FU22) 
Northern Hake  Age-structured  Age-dependent  Segmented-

Age-structured  Age-dependent  Ricker 
Biomass 
dynamic 

Logistic 

- 

Northern 
Megrim 
Plaice (VIIfg) 

Age-structured 

Fixed 

Age-structured 

Fixed 

Sole (VIIfg) 

Age-structured 

Fixed  

Whiting 

Age-structured 

Fixed (age-
dependent in 
update) 

regression 
Segmented-
regression 
Segmented-
regression 
Segmented-
regression 
Segmented-
regression 

Table 2.4-1. List of stocks included in the current DAMARA model (January 2016) 

24 

Management 

Fixed Advice 

Dynamic HCR 

Dynamic HCR 
Fixed Advice 

Dynamic HCR 

ICES DLS 
approach** 
ICES DLS 
approach** 
Dynamic HCR 

Dynamic HCR 

 
 
 
 
 
 
*Population abundance set high enough to allow for future catches, useful where stock is 
of economic importance but no assessment is available. 
**Both data limited stocks (ICES Category 3) where TACs are adjusted based on the 
relative change of stock indicators.  

2.4.2.2  Fleet component  

The fleet and metier definitions were based on readily available and accessible 
information from STECF and agreed with the stakeholders.  The data on fleet activity 
(catch and effort) was generated by aggregating country and vessel length 
disaggregated metier-based (i.e. activity) data used for evaluation of the cod 
management plan in the North sea  into consistent fleet segments assuming that there is 
no switching of gear type (e.g. otter trawlers do not deploy beam trawls, netters do not 
use trawl etc.).   

The activity of the metiers in terms of mesh-size used (e.g. TR1 or TR2) was then split 
into two spatial units, one covering VIIfg which is the area to be covered by original 
Multi-Annual Plan proposed by the NWWAC and referred to as the “management area”. 
Activity relevant to  the broader area covering the area VIIb-k, referred to as “focus 
area” or the wider Celtic Sea is further included. This nested approach is necessary given 
that many of the species fished in VIIfg are part of wider Celtic sea stocks and therefore 
any management intervention restricted to VIIfg (or indeed a smaller area e.g. single or 
groups of statistical rectangle) will have implications on a wider geographic (stock) scale. 
Furthermore, when assessing the economic implications of interventions in VIIfg, it is 
also necessary to consider the revenues and costs associated with activity outside the 
management area. It is also necessary to account for activity outside the Celtic Sea 
(VIIb-k) as some vessels operate outside the Celtic Sea e.g. West of Scotland and Irish 
Sea and several stocks contained in the model, particularly hake are widely distributed 
and as such substantial catches and associated revenue are generated from outside 
VIIb-k. This has been dealt with via an additional “elsewhere” component e.g. outside 
VIIb-k within the model.   

Disaggregating by mesh size and area provides metier-based catches which have the 
characteristics required (i.e. different selectivity patterns, different areas fished) to allow 
the range of effort, quota, spatial and selectivity management measures to be applied 
within the prototype. The results of the fleet and métier definitions component was a set 
of fleet units based on country, gear type and vessel length (e.g. IRL_OTB_>10m) that 
operate in a number of mesh and spatially separated metiers (e.g. TR1_VIIfg, 
TR2_VIIbc,e-k . The data was taken “as was” and excluded if it failed to load (e.g., fleet 
segments without data).  In the final fleets object, there are 22 fleets, fishing in up to 
five métiers each and up to three stocks per metier. 

In terms of the number of fleets to be included in the model, there is a trade-off 
between the number of fleets and model computational complexity. It is therefore 
necessary to reduce the dimensions (number of fleets) in the model in order to maintain 
a realistic run time. From a pragmatic perspective, it was decided that the model should 
only contain those fleets where the landings of any of the stocks contained in the model 
were greater to or equal to 2% of the overall landings.  So as not to ignore the landings 
from fleets that fell below this threshold, the total landings of each of the stocks are 
binned into an “OTHER_Fleets”, with the métier defined as “OTHER_Metiers”. The choice 
of 2% as threshold maintains the vast majority of landings while maintaining a tractable 
number of fleets (16; Figure 2.4-7) and fleet-métier combinations (22; Table 2.4-27) to 
explicitly model the fishing system. . Thresholds below 2% would quickly result in an 

7 Note, this is prior to the inclusion of any spatial definition in métier, when included, this results in [fleet-
métier * No areas] management units, depending on the granularity of spatial definition. 

25 

 
 
 
 
 
 
 
                                           
 
exponential growth in the number of fleets, and consequently management units, that 
would require inclusion.  

metier  Country 

fleet 

Country 

fleet 

BEL 

BEL 

GBR 

GBR 

GBR 

GBR 

GBR 

GBR 

GBR 

ESP 

ESP 

DTS 

TBB 

DFN 

DTS 

DTS 

DTS 

DTS 

HOK 

TBB 

DTS 

DTS 

Vessel 
Length 
O10M 

O10M 

TR2 

BT2 

O10M  GN1 

O10M 

U10M 

O10M 

U10M 

O10M 

O10M 

O10M 

O10M 

TR1 

TR1 

TR2 

TR2 

LL1 

BT2 

TR1 

TR2 

ESP 

FRA 

FRA 

FRA 

FRA 

FRA 

IRL 

IRL 

IRL 

IRL 

IRL 

HOK 

DFN 

DTS 

DTS 

HOK 

PG 

DFN 

DTS 

DTS 

OTH 

TBB 

Vessel 
Length 
O10M 

metier 

LL1 

O10M  GN1 

O10M 

O10M 

O10M 

TR1 

TR2 

LL1 

O10M  GT1 

O10M  GN1 

O10M 

O10M 

TR1 

TR2 

O10M 

NONE 

O10M 

BT2 

Table 2.4-2 . Fleets and metiers contained in the DAMARA model. 

Figure 2.4-7. Relationship between number of fleets and landings thresholds. 

In addition to the above list of métiers, there are circumstances where the inclusion of 
additional métier may be required. In particular, if there is requirement to evaluate a 
closed area for example. A highly resolved spatial model could be represented with a 
metier for each fleet in each statistical rectangle. This would, however, slow the model 
considerably and potentially increase sensitivity of the modelling process in cases where 
a small number of observations occur in a given rectangle. A solution is to spatially 

26 

 
 
 
 
 
 
 
separate those areas/rectangles being considered for specific management measures 
(e.g., closure, combination of closure and gear measures) before defining the spatial 
units (thereby having a “closed unit” and “non-closed unit” in the model. 
It should be noted however, that in order to investigate potential area closures, it is first 
necessary to identify which areas are being considered and also what fleets fall within 
the scope of the closure. This requires reconditioning the model and it therefore not 
possible to simply interrogate via the model interface without the specific scenario being 
explicitly modelled in the first instance. Scenarios are included which allows for the 
simulation of an area closure and a change in selectivity. The closure can be simulated 
by specifying area, gear area or a list of these. For example, VIIfg or TR1_VIIfg (or 
TR1_VIIfg, TR2_VIIfg) or none. In dealing with the effort that would be displaced as a 
consequence of a closed area, the model currently redistributes the effort proportionally 
between the remaining métiers for example, if the closure affects only TR1 and TR2 
vessels, then the effort is redistributed to the TR1 and TR2 fleets operating outside 
closed area. The model could also incorporate a user-driven effort reallocation if 
required, so for example if stakeholders with detailed knowledge of the fishery anticipate 
that a closed area may result in 80% of the effort being redirected into a specific area, 
then this can be included as a scenario.   

Changes in selectivity through for example technical measures can be simulated by 
specifying the change and which fleets it applies to. An example of the introduction of a 
square mesh panel to TR1 and TR2 vessels can be simulated.  

The inclusion of both hake and megrim in the list of stocks also required a pragmatic 
solution as both of these are widely distributed with most of the catches taken outside 
the Celtic sea, but given the transient nature of the fleets operating in the Celtic sea, 
these catches may be of significant economic importance and therefore need to be 
included to ensure that the economic indicators reflect this reality. To accommodate this, 
“HKE_OTH” and “MEG_OTH” fleets were defined to accommodate the catches taken 
outside the management area. Furthermore, given the desire to expand the geographic 
scope of the model, accommodation of stocks that are more widely distributed will be an 
important and necessary feature of future model development.  

2.4.2.3  Catch model  

The catch model describes how effort exerted per metier translates into catch. To do so 
an age-structured catch model was set up using catchability calculated based on the 
Cobb-Douglas production function: Catchability calculated using the Cobb-Douglas 
production function, so that: 

catch.qa,fl,m,s = Ca,fl,m,s/(Ba,s*Efl,m) 

 
  Where Ca,fl,m,s is Catch of age a, for fleet fl, métier m and stock s, and B is 

biomass and E is effort. 

  Cobb-Douglas alpha and beta parameters (elasticity parameters) were set at 1 

and 1 respectively for every métier/stock (based on expert knowledge). 

2.4.2.4  Effort model  

Two effort (kW.days) models, which describe the short-term dynamics of the fleets, were 
chosen for the prototype for illustration: 

  Fixed effort, which averages the effort components across the years 2009-2012. 
  Simple Mixed Fisheries Behaviour based on an Fcube approach of Ulrich et al, 

(2011). This enables the effort in a mixed fisheries context to be defined based 
on the effort required to obtain the minimum effort species, maximum effort 
species or other mixed fisheries scenarios. The minimum situation was chosen for 
the prototype to reflect a landings obligation scenario. 

27 

 
 
 
 
 
 
 
2.4.2.5   

2.4.3  Modelling the economics of fleets 
The main publication of fleet economics in Europe is the Annual Economic Report (AER). 
The AER dataset is part of the DCF data collected annually. It is compiled on a yearly 
basis and is designed to report the costs and earnings of the main fleet segments across 
Europe. It is therefore the main source of publicly available fleet economic data for the 
European Union fleet. 

AER data is submitted by Member States and in most cases is collated at a fleet level 
from a sample of data collected through fishing company accounts surveys. The cost 
data reported in the AER is aggregated to a Member State by a main DCF fishing gear 
classification and vessel length category by year. In the latest 2015 report, data from 
2008-2013 is presented with provisional data for 2014. Landings volumes and landings 
values are presented by ICES sub-area, however other economic variables are presented 
for FAO areas 27, 37 and ‘Others’ primarily. Therefore, cost data covers fleets operating 
over a wide area. As the DCF fishing gear and vessel length categorisations define the 
segment, the values contained within the AER dataset show total values of income and 
costs for segments that may exploit multiple regional seas (e.g. North Sea and Celtic 
Sea) and ICES divisions within a FAO area. 
Given the economics data covers fishing activity including and beyond the Celtic Sea 
case study, an approach is developed to represent the costs and earnings of fleets 
relevant to the DAMARA model. 

2.4.3.1  Structure of fleet cost data 

Fleet cost data can be divided into three main categories: costs incurred at the fleet level 
annually (e.g. fixed costs, capital costs), costs incurred per kW.day fished (e.g. fuel 
costs and other variable costs) and crew costs which are generally modelled as a 
percentage of revenue achieved by a fleet. In the AER, the economic cost data required 
for DAMARA is represented in the following variables: 

Fixed costs (i.e. per vessel costs) 

  Repair costs 
  Other non-variable costs 
  Annual depreciation  
  Depreciated replacement value – used to calculate other investment costs in the 

vessel 

Variable costs (i.e. per kW.day costs) 

  Energy costs 
  Other variable costs 

Other costs 

  Crew costs (i.e. as % of revenue) 

Once the data is created for input, variable costs are calculated in the DAMARA model 
from kW.fishing days multiplied by the variable cost categories. In order to calculate 
fixed costs, the numbers of vessels are multiplied by the fixed cost categories. In this 
way, fixed costs, variable and crew costs are defined to reflect the operating position of 
the fleet segments modelled in the DAMARA case study of the Celtic Sea. 

2.4.3.2  Calibrating total landings value by fleet in the case study area 

There are three components to revenues calculated for fleets in the DAMARA model. 
Revenue from species caught in the case study area that are explicitly modelled, species 
caught in the case study area that are not explicitly modelled and species that are 
caught outside the case study area. 

28 

 
 
 
 
 
 
 
 
 
Revenue from species explicitly modelled in DAMARA can be calculated using estimated 
landing volumes of those species multiplied by average price (see section 2.4.3.5). 
Revenue from other species in the case study area is modelled using an estimation of 
revenue per kW.day fished in the case study area. It is assumed that all landings in the 
case study area are made by the vessels modelled (i.e. for this Celtic Sea case study the 
STECF vessel numbers operating in VIIb-k in the selected fleets). Revenue from fishing 
outside the case study area is calculated from an average total revenue per vessel 
outside and is provided as an input to the model. This approach estimates outside 
revenue from vessels in the case study area by calibrating their total revenue to the 
average vessel level using the composite total fleet indicator8. This approach provides an 
estimation of total revenue for the relevant fleet segments in the model that aligns (i.e. 
does not surpass) to the total revenue estimates per fleet segment provided in the AER. 

2.4.3.3  Fleet profit functions 

Fleet profit is calculated at two levels: 

  Gross cashflow (i.e. operating profit) = total landings value – total fixed costs – 

total variable costs – total crew cost 

  Net profit = Gross cashflow – depreciation costs 

2.4.3.4  Missing data 

The AER database does not provide a complete set of data for all fleets and for all years. 
For DAMARA a complete set of input data is required. Where data is missing then an 
average value of the three years proceeding or preceding is used depending on 
availability.  

2.4.3.5  Price analysis 

A key input data to enable calculation of revenue from stocks modelled explicitly in 
DAMARA is average price. This is calculated for each fleet based on total landings value 
divided by total landings volume from species in the case study area (i.e. Celtic Sea) 
sourced from the AER. 

There are two basic elements to the price dynamics: firstly, changing prices by size 
grade and secondly changing prices through changes in landings volume of those 
species. 

For size grade prices, there is a first step required in the input data that provides a 
mapping between age (a primary stock index in DAMARA) and size grade (i.e. individual 
length of fish) of the stocks. Price by size grade has been calculated from national sales 
notes data for the main fleets in the model. Using these estimates of average price per 
size grade for the stocks modelled explicitly, an average price adjustment factor is 
calculated for each stock and input to the model. This adjustment factor allows the 
average overall price per stock for each fleet from the AER to be adjusted by age/size so 
that the total landing value of the fleets will depend on the size of the fish that will be 
landed. Hence, particularly in the case of the Landing Obligation, as landings by age vary 
then revenue gained will also vary and the value attributed to low fish sizes previously 
discarded will be lower than average. 

For changing prices through changes in landings volume for the stocks modelled 
explicitly, flexibility (or elasticity) parameters are provided based on changes in average 
prices by fleet and year in the AER database. 

8 Variable costs associated with this extra revenue from outside the focus area are accounted for by 
subtracting an average variable cost per total revenue from this additional income source. 

29 

 
 
 
 
 
 
 
 
                                           
 
The DAMARA model uses the standard FLBEIA price flexibility function to enable dynamic 
prices. 

2.4.4  Covariables Operating Model 

2.4.5  Management procedure  
The observation model, which generates the observed data by stock, was set up as 
perfect observations without any errors. That is the status of the populations is known 
perfectly. This was viewed as the simplest possible entry point for the prototype. The 
assessment procedure was chosen in that no assessments are run in the projections with 
the population status being known exactly and management advice procedure follows 
the ICES approach, with harvest control rules (ICES-HCR) implemented within the model 
for stocks with reference points: cod, haddock, whiting, sole and plaice, and hake, with 
fixed advice for monkfish, megrim and Nephrops. Stock-recruitment projection in the 
management procedure component is based on the geometric mean of the last three 
year’s observed recruitment. 

2.5  Model scenarios 
In an ideal world, it would be best to provide the user with a free choice in terms of the 
types of management interventions (scenarios) that the DAMARA model could 
investigate and report. However, given the vast array of possible input and output 
controls available to fisheries managers, it is not computationally possible to have a 
model that can be freely interrogated in such a way. As a pragmatic approach to 
overcome this, the DAMARA model is built up using a library of pre-run scenarios, the 
outputs of which are stored and available for the user to interrogate. The user can then 
select the scenario (or multiple scenarios) of interest and interact with the DAMARA app 
to select a wide range of biological, fleet and economic indicators that provide 
information on the potential impacts of the selected scenarios. This is discussed and 
demonstrated in more detail in section 2.6. 

In practice, if stakeholders wish to assess the impacts of management scenarios that are 
not contained within the existing library, then the DAMARA model will require 
conditioning and possible addition or modification to the existing code (FLBEIA) to 
undertake this. For some scenarios, this may be relatively straightforward, for example 
the impact of changing an area closure to include more fleets or statistical rectangles can 
be undertaken without too much additional coding. However, if more complex scenarios 
are to be considered, the re-coding “overhead” may be much more significant. Based on 
previous discussions with the stakeholders (see section 2.1), a number of scenarios 
based on the types of management interventions that could be considered in future have 
been identified. These are currently available and integrated into the existing model and 
the outcomes of these can be viewed on the DAMARA app which can be sourced here 
(https://shiny.marine.ie/damaratest/ user name: damarauser password: 
Orang3roughy).  

Note that this currently hosts the full beta version. As of writing there are some small 
additions yet to be made to the model. Once complete, the model will be migrated to a 
permanent site. Updated web address and login details will be forwarded to relevant 
stakeholders as soon as this happens. 

1 
2 
3  Closure_TR1_30E4 
4 
SMP_selectivity 
5  MaxProfit 

Fmsy2016(baseline)  6 
7 
Fmsy2020 
8 
9 
10 

FullEconomics 
Fmsy_upper 
Fmsy_lower 
TR2_no_cod 
Full_Econ_MaxProfit 

Table 2.5-1 List of the currently available scenarios in the DAMARA scenario library 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
The scenarios shown in  

Table 2.5-1 List of the currently available scenarios in the DAMARA scenario library have 
been selected in consultation with stakeholders and also by the project consortium. The 
model outputs from these scenarios are now contained in the DAMARA library and can be 
interrogated through the DAMARA app (see above for log in details).  

As noted previously, the 2013 reform of the Common Fisheries Policy (Regulation (EU) 
No 1380/2013) has introduced a number of key management changes, in particular the 
requirement to fish stocks at FMSY by 2015 or 2020 at the latest and the introduction of 
the landing obligation. In the conditioning of the DAMARA model, scenario 1 is effectively 
the baseline case and therefore provides the outcomes of the implementation of the 
landing obligation and FMSY by 2016 for all stocks. For computational expediency and lack 
of time, an assumption that all stocks will be subject to the Landing Obligation from 
2017 onwards has been made. This is a knife-edged approximation to a gradual phasing 
in of the Landing Obligation between 2016 and 2019. It is noted that in practice the 
Landing Obligation will be phased in across species between 2016 and 2019, but without 
prior knowledge of how the phasing in will be done in practice i.e. which species will 
apply to which fleets or fisheries in which years, using a knife edged approximation 
midway through the phasing in period is a pragmatic solution. It may be the case that 
stakeholders may wish to explore species specific options, but this will require some 
degree of re-coding of the FLBEIA model engine to implement a new effort model.  While 
technically feasible, this was beyond the scope of the current project and could form part 
of a future development to support specific management needs to evaluate phasing 
options. 

Summary of the scenarios currently available:  

1.  FMSY2016(baseline)  

As noted above, this assumes all stocks will be fished at FMSY by 2016 with the Landing 
Obligation implemented for all TAC species simultaneously at the start of 2017.  

2.  FMSY2020 

Assumes all stocks will be fished at FMSY by 2020 with the Landing Obligation 
implemented for all TAC species simultaneously at the start of 2017.  The move towards 
Fmsy has been implemented in equal steps (i.e. for 2016, 5 years from the deadline, 
TAC is set according to 4/5 current F and 1/5 Fmsy, and so on, while F does not increase 
in any year while it remains above Fmsy). 

3.  Closure_TR1_30E4 

Assumes all stocks will be fished at FMSY by 2016 with the Landing Obligation 
implemented for all TAC species simultaneously at the start of 2017 plus the inclusion of 
an area closure (ICES statistical rectangle 20E4) that affects only the TR1 métiers. Note 
that effort displacement is dealt with by redistributing the effort from TR1 métiers in 
30E4 to the other métiers fished by DTS fleets operating outside the closure.   

4.  SMP_selectivity 

Assumes all stocks will be fished at FMSY by 2016 with the Landing Obligation 
implemented for all TAC species simultaneously at the start of 2017 plus the inclusion of 
an additional selectivity device, a 120mm square mesh panel in all TR métiers9.   

9 Note that while it is now mandatory to use a 120mm square mesh panel and should now be considered as 
the baseline scenario, the model and data used for the current DAMARA set up (2016) was undertaken prior to 

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
5.  MaxProfit 

The MaxProfit scenario optimises the fishing decision of a fleet, optimally allocating the 
fishing effort of a fleet to the different metiers. The process maximises the profit under a 
TAC constraint calculating the total effort and the effort share over metiers that 
maximises the profits of the fleet. This scenario assumes a fixed fleet at “current” vessel 
numbers. 

6.  FullEconomics 

The FullEconomics scenario incorporates simple capital dynamics functionality as well as 
price dynamics. Therefore vessel numbers change according to profit levels and prices 
change according to landing volumes. It also sets the interest rate to be 5% and allows 
for fuel price increases of 5%. It should be noted that this scenario is run separately 
from other scenarios for demonstration purposes.  

7.  Fmsy_upper 

Assumes all stocks will be fished at the upper FMSY range by 2016 with the Landing 
Obligation implemented for all TAC species simultaneously at the start of 2017. 

8.  Fmsy_lower 

Assumes all stocks will be fished at the lower FMSY range by 2016 with the Landing 
Obligation implemented for all TAC species simultaneously at the start of 2017. 

9.  TR2_no_cod 

Assumes all stocks will be fished at FMSY by 2016 with the Landing Obligation 
implemented for all TAC species simultaneously at the start of 2017 plus removing all 
catches of cod from the TR2 fleet to simulate the inclusion of a cod-avoiding strategy. 

10.  Full_Econ_MaxProfit 

The Full_Econ_MaxProfit scenario is a combination of scenarios 5 and 6 but differs to 
these scenarios as the optimal solution takes account of a changing (optimal) fleet size 
according to the simple capital dynamics relationship specified in FLBEIA. 

2.6  The model interface 

The DAMARA project can be loosely grouped into two main theatres of work, one which 
has focussed on the development of the model “engine” and the running of the scenarios 
which is described extensively above and the second which has focussed on how the 
results and types of information that are of interest to the stakeholders can be conveyed 
and interacted with.  

In this section of the report the aim is to go through a number of screen shots from the 
model-app and walk the end user through each of these, explaining what each of the 
outputs means and how to interpret them in a non-technical way.  

2.6.1  Stock related outputs  
Here we describe the impacts that different management options may have on the future 
trends of the stocks. The app allows different management scenarios to be contrasted 
directly.  

The app uses as its baseline the assumption that all stocks will be at FMSY by 2016 and 
that the landing obligation will be implemented for all stocks in 2017. It is acknowledged 
that this is not the case in practice, as the landing obligation will be phased in across a 
range of demersal TAC species between 2016 and 2019. The choice to have all stocks 

the introduction of the legislation requiring the mandatory use of the 20mm square mesh panel (EU 
Implementing Reg (No) 2015/741)  

32 

 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                   
 
covered by 2017 is done purely for computational reasons and while it would be possible 
to recondition the model to deal with specific phasing in options, this will require a 
considerable amount of recoding of the model which was not possible within the existing 
project framework. 
Figure 2.6-1 Stock specific output  

  Shows the predicted change, including uncertainty bounds, in catch; landings; 

discards; fishing mortality (F); spawning stock biomass (SSB); and recruitment 
(REC). 

  Catches are predicted to decline markedly until 2017, and then to recover but to 

lower levels than seen previously. 

  Lack of recovery in catches is principally due to mixed species choke issues 

meaning that the stock is fished well below FMSY levels.  

  Unless technical and tactical adaptations can be introduced to limit choke issues, 

the stock will be underexploited  

  Discards are shown to decrease to very low levels following the introduction of 
the landing obligation in 2017. Note that discards are still evident but post 
Landing Obligation these are in fact fish below MCRS and must be retained but 
cannot be sold for human consumption.  

33 

 
 
 
 
 
 
Figure 2.6-2 FMSY in 2016 or FMSY in 2020 

  Shows the comparison between baseline scenario (FMSY in 2016) and achieving 

FMSY by 2020 on catch, fishing mortality and SSB 

  Shows that delaying FMSY until 2020 will reduce the impact in terms of 

reductions in catches in the short term 

  Because fishing mortality is not reduced as quickly, there is predicted to be a 

marginal reduction in catch and SSB beyond 2020. 

  Note that it is possible to select more than two scenarios of comparison and all 

stock-level indicators. 

34 

 
 
 
 
 
 
Figure 2.6-3 Comparison between species  

  Compares predictions in stock indicators between stocks. 
  Both cod and haddock catches expected to decline substantially following the 

introduction of the landing obligation and FMSY in 2016. 

  Landings of cod expected to recover to previous levels but haddock catches 

expected to remain below previous levels due to choke issues unless technical 
and/or tactical measures are introduced to mitigate choke effects. 

  Both stocks expected to be fished below FMSY and therefore underexploited. 

35 

 
 
 
 
 
 
 
2.6.2  Fleet based indicators 
Here we show the predicted impacts that different management options may have on 
specific fleet segments. This is particularly useful for the identification of fleets which are 
most impacted by management choices and could also be used to identify and inform 
possible fleet specific behavioural adaptations for example, through effort displacement 
in response to an area closure. 

Figure 2.6-4 Single fleet analysis 

  Shows the predicted impact on discards and landings associated with the 

introduction of the landing obligation in 2017 for haddock and the impact of 
achieving FMSY in 2016. 

  Landings are predicted to decline sharply in response and slowly rebuild but not 

to levels seen previously due to choke issues.  

36 

 
 
 
 
 
 
 
Figure 2.6-5 Comparison of management scenarios on a single fleet 

  Shows the predicted impact on discards and landings by delaying the 

achievement of FMSY until 2020. 

  The substantial reduction in discards and fishing at FMSY either in 2016 or 2020 is 

predicted to result in a substantive increase in landings. 

  Delaying the introduction of FMSY to 2020 is predicted to have small negative 

impact on landings beyond 2020 but this is highly uncertain. 

37 

 
 
 
 
 
 
 
Figure 2.6-6 Changes in landings of different species on a single fleet 

  Compares the impacts of delaying FMSY to 2020 for three stocks on one fleet. 
  Landings of all three species expected to decline sharply following the 

introduction of the landing obligation. 

  Landings of cod and whiting are predicted to return to previous levels but this is 

not the case for haddock 

  Delaying the introduction of FMSY to 2020 lessens the short term impact on 

landings however the largest impact is the introduction of the landing obligation 
in 2017. 

38 

 
 
 
 
 
 
 
Figure 2.6-7 Changes in landings and quota allocation between countries and between fleets with FMSY in 2020 

  Compares the impacts of delaying FMSY to 2020 for Celtic Sea cod and how this 

impacts on landings and share of TAC between countries and fleets. 

  Landings of cod expected to decline sharply following the introduction of the 

landing obligation, particularly for France 

  Delay in implementation of FMSY to 2020 lessens the impact on catches  
  Landings of cod predicted to return to previous by 2020  

39 

 
 
 
 
 
 
 
Figure 2.6-8 Impact of an area closure on a single metier  

  Shows the predicted impact on landings associated with an area closure affecting 

one fleet – ICES statistical rectangle 30E4 closed to FRA TR1 vessels 

  Catches from the closed metier (FRA_TR1_30E4) simply redistributed to the 
remaining “open” metiers belonging to the same fleet (FRA DTS) based on 
relative catches between them 

  DAMARA model can also incorporate stakeholders input into likely redistribution of 

effort as result of an area closure or could also incorporate full behavioural 
models  

40 

 
 
 
 
 
 
2.6.3  Economic based indicators 

Figure 2.6-9 Capacity & Fixed Costs  

  Capacity is effort-based, measured in kilowatt-days (000’s) and fixed costs are 
value-based, measured in €000’s. These are shown for the Irish demersal trawl 
and seine fleet (DTS) over 10m in vessel length 

  Fixed costs and capacity are both proportional to the number of vessels in each 

year. Average fixed costs per vessel are based on the average across the 
preceding three years of the baseline year, i.e. in the model presented 2011 to 
2013. 

  The constant trend in both indicators shows a stable fleet size over the simulation 

period which is due to the lack of capital dynamics  in these scenarios 

  No differences between Fmsy2016 and Fmsy2020 can be seen due to this lack of 

 

capital dynamics 
In a scenario that assumes constant fleet size (i.e. no exit/entry dynamics) and 
so of interest is the economic impact of the scenario were the fleet to remain its 
current size. In a scenario that estimates changing number of vessels, for 
example through profit maximisation or simple capital dynamics then both fixed 
costs and capacity vary proportionally.  

41 

 
 
 
 
 
 
 
Figure 2.6-10 Effort & Fuel Costs 

  Effort, measured in kilowatt-days (000’s) and fuel costs, measured in €000’s, are 
shown for the French demersal trawl and seine fleet (DTS) over 10m in vessel 
length 

  Fuel costs are linked to effort applied by the fleet and so both indicators show 

similar trends 

  Effort is indicated to decrease from 2016 to 2018 in both Fmsy2016 and 

Fmsy2020 scenarios and then stabilises. Due to the goal of meeting MSY in 2020 
in the Fmsy2020 scenario, the curve of effort and fuel costs is less steep than in 
the Fmsy2016 scenario where MSY is achieved in 2016. 
In the short term, effort and fuel costs are ~25% higher for Fmsy2020 than 
Fmsy2016 
In the long term fuel costs and effort show no difference between both scenarios  

 

 

42 

 
 
 
 
 
 
 
Figure 2.6-11 Revenue in Focus Area & Elsewhere 

  Revenue in the focus area (VIIb-k) and revenue elsewhere (€000’s) are shown 
here for the United Kingdom fleet of demersal trawlers and seiners over 10m in 
vessel length 

  Revenue in the focus area is calculated from the average price of landings by 

stock modelled plus an estimate of revenue per effort-day fished from other 
stocks 

  Revenue in the focus area shows a significant reduction with the onset of the 
landings obligation, by ~33% in both Fmsy2020 and Fmsy2016 scenarios, as 
effort decreases 

  There is only a slight improvement in revenue in the focus area when Fmsy is 
pushed out to 2020 as effort is maintained at a slightly higher level (see effort 
output) 

  Revenue elsewhere is fixed per vessel based on previous annual landing value, 

hence no dynamics. It can be seen that this fleet segment is not heavily 
dependent on the focus area and most of its revenue comes from other areas 
such as the Irish Sea, English Channel or elsewhere. 

43 

 
 
 
 
 
 
Figure 2.6-12 Total Costs & Profits 

  Total costs and profits are shown in €000s for the Belgian beam trawl fleet (TBB) 

over 10m in vessel length 

  Total costs per fleet includes the sum of crew, fuel, fixed, variable and capital 

(i.e. depreciation) costs and profit per fleet is the total profit calculated from total 
revenue minus total costs 

  The onset of the LO shows a significant drop in total costs, by ~25%. In the 

Fmsy2020 and Fmsy2016 scenarios, number of vessels remains constant so this 
is driven primarily by the reduction in effort and related variable costs (i.e. fuel 
costs and other variable costs) 

  Profits of the fleet however drop by ~50%, resulting in significant net losses. This 
is due to the static nature of the fleet in the Fmsy2020 and Fmsy2016 scenarios 
as revenue drops significantly but fixed and capital costs remain at unchanged 
fleet size 

  Profitability is only restored by 2021 in both scenarios of Fmsy2016 and 

Fmsy2020. This is due to the improved stock status by this time. 

44 

 
 
 
 
 
 
 
 
Figure 2.6-13 Total Revenue & Breakeven Revenue 

  Breakeven revenue and total revenue are here shown in €000s for the Irish 

demersal trawler and seine fleet over 10m in vessel length 

  Total revenue of this fleet is not impacted significantly by the LO with the 
Fmsy2020 resulting in momentary peaks in revenue in 2017 and 2019 

  The Irish DTS over 10m fleet has some activity outside the focus area (e.g. VIIa) 

that is assumed to be maintained into the future at a constant level.   
  Break even revenue (BER) provides an indication of how much revenue is 

required to achieve positive profits for the fleet. If BER is higher than revenue 
then the fleet is likely to be unprofitable. This comparison therefore provides an 
estimate of profitability into the future of simulation 

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2.6-14 Total Revenue & Gross Value Added 

  Gross value added and total revenue are shown in €000’s for the Irish beam 

trawler fleet over 10m in vessel length 

  Gross value added is a measure of the value of “goods and services” produced 
and represents the returns to the factors of production, namely, labour, capital 
and resource. It is estimated by summing crew costs, capital costs (depreciation 
and interest) and operating profit. GVA follows a similar trend to total revenue. 

  For Fmsy2016, both indicators fall with the introduction of the LO by ~25%  
  For Fmsy2020 the indictors only register a slight decrease of ~12.5% 
  Both indicators recover by 2021 and increase from there 

46 

 
 
 
 
 
 
2.7  Key assumptions and limitations  

Data sources used for prototype model development were based solely on available 
regularly collected data and analyses (e.g. STECF catch and effort data; ICES 
assessment outputs; economic data from AER). During the initial prototype stage, no 
attempt has been made within the protocol to quality-control the data input other than 
that already conducted for the STECF database. Subsequently however, as the model 
developed, the input data have been subject to increased scrutiny. A number of errors in 
the external data used for the model have been identified throughout this process, and 
indeed resolving these data problems has used a considerable amount of resources, in 
fact one dedicated modellers meeting was used solely for this purpose. This in effect 
means that any annual data refreshments beyond the scope of the DAMARA project, will 
require resources for data screening purposes and it is therefore not simply a case of 
having an automated uploading routine. While data uploads have been streamlined 
through the provision of specific R code, it is important to note that the code is written to 
specifically deal with the current structures in the external STECF and ICES databases 
and therefore any planned revisions to these will have implications for any future data 
uploads.  

Furthermore, it is also important to remember that the model currently uses the fleet 
definitions as specified in the 2008 long term management plan for cod (Regulation (EC) 
1342/2008) e.g. TR1, TR2 etc. While it is acknowledged that these gear/mesh groupings 
appear to have permeated beyond the cod plan and are now included in for example 
technical regulations for the Celtic Sea as well as the regional discard plans, there are 
limitations and considerations in using these. While the current grouping of for example 
TR1 and TR2 may adequately segregate demersal gadoid fisheries from Nephrops 
directed fisheries in the North Sea, in the Celtic Sea TR2 gear may be used to target 
both Nephrops and in a mixed gadoid fishery and it is not possible to segregate out 
these activities with the existing level of aggregation. Highly-resolved French landings 
and effort data, provided by the French administration, were analysed in the frame of 
the DAMARA project (Mateo et al. in prep). Effort maps by fishing gears (Figure 2.7-1) 
illustrate a clear spatial segregation of effort between single (OTB) and twin trawls 
(OTT). These results, in line with the analyses of spatial pattern in the retained catches 
(Principal component analysis followed by hierarchical clustering) indicate that these two 
gears are not characterised by the same catches assemblages, and therefore should not 
really be grouped in the same fleet and metier (Figure 2.7-2). As such, using 
management unit definitions such as TR1, that group both OTT and OTB (in this case), 
does not provide sufficient resolution to disentangle vessels targeting Nephrops with a 
100mm mesh size from those targeting finfish with the same mesh band. Unfortunately 
this is unavoidable given the current resolution of the spatially resolved data available 
from existing sources. Further refinement would require data to be provided at DCF 
Level 6 which includes information on the target species assemblage i.e. TR1_CRU and 
TR1_DEM which denotes fishing activity targeting crustaceans (CRU e.g. Nephrops) from 
that focussing on demersal finfish species (DEM).  

Running the model on a very small spatial scale could be time consuming and subject to 
strong assumptions. An interesting alternative is to analyse fine scale logbook and VMS 
data at an international level. Indeed, the spatial structures identified in term of landings 
assemblage and effort patterns can be used to define relevant fleet and métier for mixed 
fisheries models. It is worth noting that any future decisions to introduce alternative fleet 
or metier units will have significant implications for the DAMARA model as currently 
structured. Additionally, the results of such analysis can also be used to calculate landing 
per unit of effort (lpue), a metrics often used to identifying area with high catch rates. 
Such expertise on the fisheries could be useful in defining relevant scenarios.  

47 

 
 
 
 
 
 
Figure 2.7-1. Effort maps by fishing gear based on fine scale spatial data (French data average 2010-2012). 

Figure 2.7-2. Spatial pattern in the retained catches of French demersal fisheries in the Celtic sea (Average 2010-
2012). Each identified cluster of homogeneous landings assemblage is plotted with a specific colour.  

The discard information data are “non-spatial”, therefore the model is not be capable of 
assessing closing areas where the perception is that discards rates are higher than in 
other areas. Despite the fact that discards data are fragmented and often cover very 
little percentage of fishing trip, attempt has been done to investigate spatial distribution 
of discards. Analysis of national observed at sea program highlighted some potential 
discards hot spots, that could vary in time (season) and space as the combined effect of 
environment, population dynamics, effort and gear used. French observer’s at sea data 
has been analysed using nested grids methods in order to produce maps of discards in 
the Celtic sea. The analysis has focused on the three main gadoids species (cod, 
haddock and whiting) in 2012. An illustration for one of the most covered metier 
(OTT_DEF_100_119) is given in Figure 2.7-3. The maps support the fact that discards 
rate and quantity per km² are heterogeneous in space and that comparing years and 
species maps could help in identifying discards hotspots. Here again, analysis of dataset 
gathering raw data from observer at sea program coming for the various countries 
operating in this area could significantly increase the quantity of data and provide 
usefully results that can be used to modulate the strong assumption of homogeneous 
discards rates and define scenarios.    

48 

OTBOTTGN/GTDRBSennesOTM 
 
 
 
 
 
 
Figure 2.7-3. Nested grids based of discards estimates from French observers at sea data in 2012 for OTT_DEF_100-
119 metier. Legends indicate kg/km² of discards (raised by cell using landings volume). From left to right: haddock, 
cod and whiting.  

The ‘selection pattern-at-age’ (i.e. landings and discards) is also ‘non-spatial’, therefore 
the model would not be capable of assessing closed areas where the perception is there 
are a higher number of juveniles/small fish than other areas. Here again, detailed 
analysis of both combined observer at sea program and sales note by grade could help in 
modulated age structure of catches in the input data. Such work could be considered in 
future but would require a dedicated regional project. 

The development of bio-economical models for mixed fisheries management purposes 
requires large datasets to properly describe the fishery from each country playing a 
substantial role in the fishery.  Those datasets include catch composition, discards, and 
possibly catch-at-age for all species, fleets and metiers. Economic information is also 
needed in order to assess how fishers will respond to mixed-fishery management 
measures. One of the major constraints in the implementation of such approaches is the 
lack of detailed international data at a sufficient resolution, especially the lack of 
sufficiently disaggregated catch and effort data by metier. Input and output stock 
assessment data from ICES and STECF data call are combined to build the biological 
components of the models, to define the split of effort among fleet and the catchability 
of each metier. The major challenge lies in properly allocating catch data for each 
species to fleet and effort information to each metier as the level of aggregation is 
variable between data sources and countries which tend to result in a long process of 
aggregation/disaggregation of the available datasets.  

From the economic perspectives it is not an easy task to link AER and STECF/ICES data 
because fleet segment definitions are distinct. The AER aggregates the economic and 
technical indicators by DCF fleet segment according to FAO area. This aggregation 
complicates analysis of sea regions within FAO areas as can be seen in this project. 
Within FAO area 27 important sea regions such as the North Sea, the Celtic Sea, the 
Irish Sea, North-western Waters and the English Channel exist. This problem has been 
noted in AER working groups when regional analyses are attempted and in many years 
incomplete regional analyses are carried out due to lack of data from certain Member 
States. Due to this lack of regional disaggregation of the economic and technical data 
unconventional methods have had to be utilised here to disaggregate certain economic 
indicators. Moreover, it is well known that price vary between season and fish grade, 
information that are not provided in the AER and therefore require additional data and 
assumption.  It is noteworthy that access and data sharing won’t solve all the problems 
identified in this project but could definitely improve some of the very strong assumption 
that need to be done when using actual STECF, ICES and AER data. Underlying data 
analyses would require important quantity of work that needed to be considered. In the 
dynamics of scientists and stakeholders interactions, it would be interesting to discuss 
the results during dedicated meeting to validate the outputs and define model structures 
and scenarios.  

49 

 
 
 
 
 
 
 
 
The model simulates the EU regulations (LO, MSY etc.) only in the 'Focus Area' therefore 
in terms of the economic impact on the fleets the economic profile they have in other 
areas ('Elsewhere') does not change as it does in the 'Focus Area'. This results in an 
economic impact in the 'Focus Area' but no corresponding impact outside it. Hence, the 
fleets with more activity outside the 'Focus Area' show an overall economic impact much 
less than they will in reality as the regulations will impact them in all sea-regions, for 
example, due to chokes in 'Elsewhere' areas not being modelled). 

The model developed in this project is generic enough to accommodate higher 
resolution. Nevertheless, increasing the spatial and temporal resolution of the model 
would indeed increases the time needed to run the model. These issues could be 
overpassed if preferment computational servers can be accessed. Alternatively, the 
model can still be run at statistical rectangle level and fine scale international data of 
landing, discards, effort and economic data be used to help in understanding the 
fisheries, defining relevant fisheries units, design pertinent scenarios (effort reduction, 
time are closures, …). In principle, it should be possible to compile quarterly information 
(catch-numbers-at-age, catch- and stock- weights at age) as this information are 
provided by member states through ICES data call. However, the assessments are run 
annually as such the assessment outputs are on a yearly basis. ICES stock assessment 
data are non-spatial (e.g at the stock level) and ICES input data (e.g INTERCATCH) 
cannot be used because the way area is documented varies across countries and years. 
Additionally, ICES Intercatch data base is recent and only contains data since 2013, 
which preclude its use for model calibration. Given the above limitations, the model 
would be able to assess spatial/temporal closures where the catch rate of a particular 
species may be higher in one area than another and therefore the objective would be to 
bring catch rates across multiple stocks more in line with quota availability.  In order to 
inform candidate rectangles for closure, we would provide VMS level data on catch 
compositions/species clusters to identify suitable closure areas for fleets/metiers (see 
example above).  The model runs would then be based on aggregating identified closure 
areas so that there is a "closed" and an "open" area within the model. Similarly, discard 
atlas mapping can be used to improve assumption on spatial allocation of discard data 
(see examples above).  

2.8  Evaluation of the stakeholder involvement  
In order to evaluate the stakeholder involvement, twenty people that had been involved 
in the DAMARA project were approached for a phone interview about their experiences 
and their opinions about the project. The list of twenty people consisted of all the 
stakeholders that had been involved at some stage in the project, all the scientists, the 
project leader, the project commissioner (EC) and the secretary of the NWWAC who had 
been involved in testing and commenting on the beta version of the model. 
This resulted in sixteen phone interviews (of ca. 30 min) held with stakeholders and 
scientists that had been involved in the development of the decision support tool. Six 
scientists were interviewed, eight stakeholders (one from an NGO, one from a 
government administration and six from industry), the project leader and the project 
commissioner from the European Commission were interviewed. The questions that were 
asked to the respondents are displayed in Table 2.8-1.  

The impossibility to plan an interview due to full schedules was one reason for the fact 
that four people approached were not interviewed, non-response was the other reason. 
The people that were not interviewed were two industry stakeholders, the NWWAC 
secretary and a recreational angling stakeholder. The stakeholder experts believe that 
not interviewing these people did not result in a significant bias in the interview results 
as the NWWAC secretary did not attend any of the stakeholder sessions10 and the 

10 The NWWAC secretary position was vacant during most of the DAMARA project and the new secretary was 
newly appointed towards the end of the project.  

50 

 
 
 
 
 
                                           
 
recreational angling stakeholder only attended one, and also because one of these 
people was from the same organisation as one of the stakeholders that was interviewed. 

Table 2.8-1 Interview questions 
Stakeholders 

1.  Since when are you involved in the DAMARA project and how did you get involved? 
2.  How did you experience the project in general? 
3.  Do you feel your input has been incorporated? If not, is it clear to you why not? 
4.  Did this project meet your expectations? Did your expectations change throughout the project? 
5.  What do you think of the end result? Do you think it should be used in decision making? 
6.  What do you think should be the next step after this project? What role do you see in this for yourself? 
7.  Do you have anything to add? 

Scientists 

1.  How do you look back at the process of developing this decision support tool? 
2.  Did you do anything different during the development of this model, in comparison to your other 

modelling projects, due to the input of the stakeholders? 

3.  Do you think the DAMARA project will influence your next modelling project? 
4. 
If we could do the whole project all over again, what would you do different? 
5.  What do you think should be the next step after this project? What role do you see in this for yourself? 
6.  Do you have anything to add? 

Stakeholder interaction 

All the scientists and all the stakeholders interviewed were very positive about the 
cooperation. One scientist commented that this way of cooperating was an example to 
be followed in similar situations. Not all the scientists had joined stakeholder meetings. 
The reason for this being that the budget was limited and strategic choices had to be 
made in order to optimally use available resources. At every stakeholder meeting 
enough stakeholders were present for useful discussion between stakeholders and 
scientists. Due to budget constraints some scientists mainly attended modellers 
meetings and were therefore not present at most stakeholder meetings. Despite not 
attending the stakeholder meetings these scientists felt the stakeholder input that they 
could work with was informative and helped them to develop a relevant and valued 
model.  

None of the stakeholders attended every stakeholder meeting. One NGO stakeholder 
could not remember getting a second invitation or thought she had other obligations at 
that moment. Some industry stakeholders could not make it to all the meetings but they 
felt represented by the other industry stakeholders that were present. Several 
explanations were given for the constructive cooperation between scientists and 
stakeholders: 

  The project was the result of a need identified by stakeholders themselves; they 
felt the need to be able to review management scenarios. This ethos was present 
throughout the project; 

  The stakeholders were involved from the beginning and during the entire project; 
  The right people were involved. The right group of stakeholders was participating 

for the Celtic Sea and the scientists involved were highly regarded by the 
stakeholders.  

  The project leader’s familiarity with the sector was mentioned as helpful for 

having an open dialogue; 

  The assistance of stakeholder experts focussing on the stakeholder process has 

been beneficial; 

  The stakeholders felt that the scientists were genuinely interested in their input. 

One stakeholder commented: ‘If I would have to grade this project I would give it 
an A+’; 

  Both among stakeholders and among scientists there is a feeling that they 

mutually helped each other in developing the project. 

51 

 
 
 
  
 
 
 
 
The scientists appreciated the cooperation amongst each other. They valued the fact that 
scientists with different disciplinary backgrounds were involved (ecologists, economists 
and stakeholder experts) and that the different scientists brought in different skills and 
knowledge. 
Some points for improvement with regards to the stakeholder involvement were brought 
forward: 

  Three of the respondents said that more frequent communication between the 
meetings could have taken place with the stakeholders. Different reasons were 
mentioned for this. According to one stakeholder and the project leader more 
feedback could have been provided on the stakeholder input to keep them up to 
date, and one scientist would have liked to see a structured communication 
channel in order to have the possibility to ask questions to the stakeholders in 
between the stakeholder meetings in order to be able to clarify their input; 
  A couple of respondents mentioned that time was and will remain an issue for 

projects like this, because the people involved have many competing demands. 
Sitting together was regarded valuable, only talking through WebEx would not be 
the solution. Different preferences were brought forward for how to improve 
efficient use of time. One stakeholder suggested that project meetings would be 
planned attached to other meetings where largely the same group is present, 
such as an NWWAC meeting. Another stakeholder mentioned a lack of focussed 
time. For him it would be useful to have longer sessions in one block to have 
some time to get up to speed and be more focussed, so for instance two 
meetings of four days instead of four meetings of two days. 

Stakeholder input 

All the stakeholders that were interviewed felt that whenever it was possible, their input 
was incorporated in the model. If anything that they brought up could not be modelled, 
the stakeholders felt that this was always discussed openly and well justified by the 
scientists. None of the stakeholders could recall any of their input that was not 
incorporated, which indicated for them that they had no grievances about any issue not 
having been heard. One stakeholder added that not only the input that he gave during 
the sessions was incorporated but also his comments during informal conversations with 
the project leader at other occasions. Another stakeholder felt his input was taken 
seriously because the project leader knew the situation of his members well. 

The following characteristics of the model were specifically mentioned by the scientists to 
have been driven by the stakeholders: 

  The scenarios; 
  The amount of economic output that the model can generate. Without 

stakeholder input the model would probably have a more biological focus. 
Specifically mentioned in this regard is the development of a price model that is 
more realistic than the assumptions about price that were in the model before; 

  The user interface; 
  Fleet capacity indicators. 

Expectations  

To the question whether the stakeholders’ expectations of the project had been met, 
most of them said they had been met up till now, but all of them are waiting anxiously 
for the model to become operational. One stakeholder commented that it had exceeded 
his expectations; he had not expected this quality of work in the time that was available 
for the project.  

52 

 
 
 
 
 
 
 
 
 
 
 
 
End result 

Although the model was not completely finished at the moment of interviewing, most of 
the stakeholders were satisfied with what they knew about how the end result would 
look. According to two stakeholders they should have had such a model already before 
the reform of the Common Fisheries Policy in 2013, but in the current context of 
developing multi-annual plans in the NWWAC, the model is also valuable and timely. One 
stakeholder expressed concerns about the data quality, especially socio-economic data, 
this, according to him, was something to become clear when the model is in use.  
Most scientists were satisfied with what they had achieved, and the quality of it, in the 
short time period of two years. Two scientists mentioned that it was good that they 
started with the FLEIBIA model, giving a head start to the project.  Two scientists 
commented that, with the knowledge on hindsight, they would have liked to have a data 
call or small project group working on improving the data for DAMARA.   
Next steps 
To the question what should be the next step when the DAMARA-project ends, the 
stakeholders responded that the model should be taken in use, ‘this was not an 
academic exercise for me’ as one stakeholder formulated it. A stakeholder working at a 
government administration advised to present the model at the relevant platforms. And 
a stakeholder from industry advised to give trainings to the users of the model. A 
government stakeholder reckoned that the model could also prove to be useful as a 
process tool rather than only for its outcomes; by using the model decision making can 
also become more transparent. Several stakeholders expressed interest to roll out the 
model to other seas, providing it works well. While the stakeholders had the practical 
focus that the model should get used as soon as possible, the scientists mostly 
responded that the model should be improved in a next phase.  

The stakeholders brought up various questions that need to be discussed and issues that 
need to be solved when the model will be taken into use:  

  First, the necessity to find resources to be able to continue updating and 

improving the model, in order to be able to develop new scenarios when they 
are brought up by stakeholders. In addition it would be good if the model 
could be adjusted to provide more detailed output than is currently the case.  

  Another important question that came up, is who will be the users of the 

model and how it will be used. Without this question being explicitly asked in 
the interviews, it appeared that different assumptions were held about the 
possible users of the model and the institutions where it should be used. Thus 
even though it is unanimously agreed upon that the model should be made 
operational, there is no agreed upon concrete idea where and how exactly it 
should be used. The following users were mentioned: 

  Two stakeholders mentioned the potential of the model to make it accessible 
to ordinary people and fishermen, to help them determine what management 
scenarios they would like to see;  

  Two other stakeholders see managers, stakeholders and scientists as the 

future users; 

  An industry stakeholder regards the model too practical to be used for NGOs, 

who, according to him, work from general principles, which means that they 
cannot offer much when the discussion is about technical measures;  
  The NGO stakeholder however sees the possibility that the model could be 
used in different ways by different stakeholders or in similar ways to meet 
different objectives; 

  One stakeholder thinks that the model should be taken into use by scientists 

at ICES and STECF, with stakeholders staying close to it and having a voice in 
the data that are to be fed into the model; 

  Several stakeholders said that the model should be used in the NWWAC for 

helping to develop multi-annual plans; 

53 

 
 
 
 
 
 
  Another stakeholder envisioned a broad adoption of the model, by the 

European Commission, the European Parliament and ICES. 

A few concerns and preconditions were expressed in relation to the use of the model: 

  The NGO stakeholder stressed the importance that, if the model will be used, it 
should be as realistic as possible and its limitations and uncertainties should be 
made explicit in an accessible way, so it is clear for everyone what the model can 
do and what it cannot do; 

  Two industry stakeholders expressed concern that the results of the model would 

be used against fisheries. 

  One industry stakeholder is concerned about the relevance of the model in the 

radically changed context of the landing obligation, as, according to him models 
are based on assumptions which are backward looking, and which may no longer 
apply in the new context; 

  Two stakeholders, finally, expressed concern about the data quality. One of them 
expects that, once the model is in use, it will become clear what data are lacking, 
and whether it is possible to provide those data. 

When asked about a next step of the project, most scientists answered steps to extend 
and improve the model, of which the following suggestions for improvement were made: 

  Three scientists mentioned the improvement of behavioural aspects of the model, 

for instance by adding random utility modelling, and more information about the 
allocation of capacity when a metier closes; 

  One scientist suggested that policies should be better included in the model; 
  All scientists said that the data should be improved and refined, to be able to 

have more detailed output, specifically with regards to the economic data that are 
at a higher level of aggregation than the biological data; 

  Rolling out the model to different areas was, as mentioned earlier, a next step 

that was expressed by both scientists and stakeholders; 

  Having time to interpret the output of the scenarios, to properly understand 

them, was something that a scientist brought forward as a desirable next step; 
  Another ideal next step mentioned by the same scientist, would be to make the 
model faster, so the stakeholders can pick options for scenarios and run the 
model with the different scenario’s themselves; 

  The NGO representative suggested that also non-commercial species could be 

included in the model and possibly other users of the marine environment than 
fishermen; 

  An industry stakeholder finally suggested that the model could be grounded, by 

comparing the model outcomes with real world outcomes. 

When asked what role the respondents see for themselves in possible next steps of the 
model, the scientists expressed interest to be involved in a next phase. Some 
stakeholders are communicating about the model in relevant meetings and already give 
presentations about it, other stakeholders are waiting for the project management to 
take the initiative for a next phase of the project. One stakeholder suggested to plan a 
review session with the stakeholders after a certain period that the model has been in 
use. 

Lessons learned by the participants 

The stakeholders were asked whether the involvement in the project had provided them 
with a new perspective on fisheries science. All stakeholders replied that they have a lot 
of experience with working with scientists, none of them answered the question 
confirmative, but on a procedural level some stakeholders had gained new insights. Two 
stakeholders replied that the project had given him a better idea how fisheries science 

54 

 
 
 
 
 
 
 
 
 
 
can be used and what modelling can do, ‘I guess it improved my expectations of what 
science can do’, according to one of them. The second stakeholder, due to the project, 
felt assured that scientists and fishermen can work together as this project generated 
trust. And the third felt assured that there are some brilliant people working in fisheries 
model development.  

The scientists were asked whether they thought this project would influence their future 
modelling work, most scientist answered confirmative: 

  Most scientists enjoyed the interaction with stakeholders and some of them could 
imagine including stakeholders in a similar meaningful way in future projects. One 
of them warned for stakeholder fatigue, as many projects currently require the 
participation of stakeholders. In this project the interests of the stakeholders to 
participate was clear, and it is important to think for every new project what the 
interest of the stakeholders is in order to have them participate in a meaningful 
way; 

  Most scientists indicated that they had also learned a lot from each other and 
from the modelling work. One scientist said that the project had provided him 
with a greater awareness of the importance of economic aspects in fisheries 
behaviour and that he had come to view mixed fisheries more as a system than 
as a number of stocks. 

55 

 
 
 
 
 
 
 
 
3  Discussion  

The DAMARA project has delivered a flexible and adaptable modelling framework and the 
test scenarios and how the model disseminates the outcomes have all been driven by 
stakeholder needs. This means that the DAMARA project has delivered a model 
framework that can be readily understood and interrogated by those with the 
responsibility to shape fisheries policy in the Celtic Sea. This was a key objective of the 
project.  

The outcomes from the DAMARA project clearly show that to enable stakeholders to base 
decisions on an objective basis, and acknowledging that management questions are 
continuously evolving, then support for further development of the FLBEIA/DAMARA 
programme is necessary.  The FLEBIA framework is highly flexible and capable to assess 
impacts of a wide range of management interventions that can be adapted and 
developed. There is a clear legislative need to promote and support (e.g. EMFF 
Regulation (EU) No 508/2014) the decision making processes with the analytical tools 
necessary for stakeholders to assess trade-offs and the likelihoods of achieving high-
level management objectives such as attaining FMSY and economic stability 
simultaneously.  

Most stakeholders involved in the development of the decision support tool were industry 
stakeholders. Although the interests of the different national fleets may not always align, 
industry stakeholders largely have a similar focus: representing the interests of their 
members. It remains to be seen whether this industry bias will have consequences for 
the use of the model and for the legitimacy of the model in the eyes of other types of 
stakeholders.  

What possibly contributed to the success of the cooperation between stakeholders and 
scientists and the cooperation among stakeholders and scientists is the fact that many of 
the people involved knew each other from other occasions. In addition the key position 
of the project leader and his reputation may have generated extra goodwill and trust. 
These personal relationships and connections form the context of this project. Possibly in 
other situations, where participants are less familiar with each other or where the group 
is more diverse, more time is needed to get to know each other in order to generate 
trust.  

Looking at the objective of involving stakeholders in the DAMARA project, it can be 
divided in two parts: 

1. Define and implement an engagement process that provides the opportunity for 

collaborative development of a tool for evaluation of harvest strategies for mixed 
fisheries.  

2. Establish a process that makes the information count in the management system 

(i.e. how it is used to support the development of Mixed fishery multi-annual plan). 

It can be concluded that the first part has been achieved. From the interview results it 
has become clear that scientists and stakeholders feel that developing the model was a 
cooperative effort. Both stakeholders and scientists are very positive about the process 
and what has been achieved.  
The second part of the objective is still in a very early phase. It became clear from the 
interviews that making the model operational is very relevant for the stakeholders but 
that there is not yet a concrete, shared idea of how this process to make the information 
count in the management system will work in practice. The project time was mainly used 
(and needed) for developing the tool and less for embedding the tool in the management 
system. A common framework has been built that can be used to assess the biological 
and economic implications of various management options. Now that the first version of 
the model is finished, and it is clear what it can and what it cannot do, it may become 

56 

 
 
 
 
 
 
 
 
 
 
clearer in which management arenas the model can be used. The next step would thus 
be to have management discussions on the basis of the model output. One particular use 
would be to support the formation of joint recommendations for multi-annual plans from 
regional groups of Member States with input from the Advisory Councils. Essentially, 
having a decision support tool is not a goal, but it is a means to facilitate discussions 
regarding management options. 

The core FLBEIA engine used in the DAMARA model offers a framework that is flexible 
and adaptable to assess impacts of a wide range of different management choices or 
scenarios and all of the key useable input and output controls e.g. TAC, spatial/temporal 
closure; gear modifications; effort and capacity can be readily evaluated. Risks of 
achieving or avoiding specific targets or limits are available in the current model and 
uncertainties can also be propagated. In addition the framework can also be updated to 
include full Management Strategy Evaluation approaches with full feedback. The 
approach benefits from having stocks assessments as an integral part of the model and 
it has the ability for a much wider scope in terms of species and areas and could also 
incorporate more complex multi-species models as these become available. The DAMARA 
project has built on the existing FLBEIA framework. Without the existence of FLBEIA and 
support of the FLBEIA developers (AZTI) as well as the wider FLR project, the provision 
of a Decision Support Tool for the Celtic Sea mixed demersal fisheries would not be as 
advanced as it currently stands. This starting point enabled the DAMARA project group to 
advance this work further and in particular the conditioning and the extension of the 
model for the Celtic Sea and to dedicate a considerable amount of time in developing the 
user interface.  

The primary objective of the DAMARA project was to develop a model that could be used 
to interrogate specific management scenarios and to present the outcomes in an 
understandable and non-technical format. The development of the DAMARA app has 
been a key achievement in the project and has provided a user friendly and 
understandable interface to what is essentially a complex multi-fleet, multi-stock bio-
economic model, which is a significant achievement. The ongoing interplay between the 
stakeholders and the modellers was key to this development and while biological impacts 
of TACs are generally well dealt within the current scientific system (e.g. ICES advice 
sheets), up until now, no readily available and interpretable outputs describing the fleet 
and economic impacts have been available. Of course, these are of major interest to 
both the fisheries stakeholders and managers. The further development of price models 
which included size-graded prices and price elasticity associated with supply is also a key 
feature and one that was expressly asked for by the stakeholders, this adds significant 
realism and meaningfulness to the economic forecasts.  

While the DAMARA model does provide a significant advancement in delivering a useful 
and useable decision support tool, there are a number of limitations that must be borne 
in mind. Given the complexity of the system that is being modelled – stocks, fleet and 
economic interactions - the computational demands are significant. In practice this has 
meant a multi stage process. Firstly the scenarios that are to be investigated need to be 
identified by the stakeholders. While standardised scenario input files have been 
developed to facilitate this, the actual running of new scenarios may entail some degree 
of modification to the underlying FLBEIA code and/or the input data. The experience 
gained over recent months when accommodating policy changes such as the landing 
obligation has shown that the resources required to do this can be substantial. Given the 
immense number of plausible management scenarios, it is simply not possible to deliver 
a model that can be interrogated without relying on the outputs from pre-defined runs.  
The DAMARA model is now available to act as a decision support tool for the 
development of a Multi-Annual Plan for the Celtic Sea. Thus far, a limited number (10) of 
scenarios have been assessed and these can be readily interrogated via the online 
DAMARA app. While there are obvious model developments, such as the introduction of 
quarterly time steps to accommodate possible management measures based on a 

57 

 
 
 
 
 
 
temporal scale of less than one year, further and ongoing engagement between 
stakeholders and modellers is required. This is an essential component as this will 
identify the specificities of the scenarios that the DAMARA model will be used to assess. 
This means that there is a need to provide resources on an ongoing basis to support the 
engagement between stakeholders and modellers and to update and modify the 
underlying code of the FLBEIA engine. How this is achieved requires further discussion. 
It is noted that the recently adopted European Maritime and Fisheries Fund (EMFF 
Regulation (EU) No 508/2014) specifically supports cooperation between stakeholders to 
promote cooperation between fishermen and scientists (Article 28) “data collection and 
management activities, studies, pilot projects, dissemination of knowledge and research 
results, seminars and best practices”;  While article 37 “Support for the design and 
implementation of conservation measures and regional cooperation” which specifically 
supports “stakeholder participation and cooperation between Member States in designing 
and implementing conservation measures and regionalisation”.  Furthermore, Article 
86(f) permits the use of EMFF funds for “cooperation activities between Member States 
in the field of data collection, including those between the various regional stakeholders, 
and including the setting–up and running of regionalised databases for the storage, 
management and use of data which will benefit regional cooperation and improve data 
collection and management activities as well as improving scientific expertise in support 
of fisheries management.”. Clearly, the EMFF envisages and supports the need for 
stakeholder cooperation and for the development of tools to promote and support 
sustainable fisheries management. However, the challenge remains how these potential 
resources are used to support the DAMARA model beyond the end of the current project.  

There are therefore a number of outstanding issues that need to be considered or 
resolved. As it stands the model is capable of assessing a number of input and output 
controls and is relatively easy to use and the outputs are understandable and easily 
interpreted. However, it remains unclear in what type of forums the model will be used 
and whether there are multiple forums in which the DAMARA model would be used on for 
what purpose e.g. Advisory Councils, Regional Groups of Member States or the European 
Commission. The original concept was to support a NWWAC initiative to describe the 
elements of a multi annual plan for demersal fisheries in the inner Celtic Sea (VIIfg), 
since then the reform of the CFP has introduced both regionalised decision making and 
the desire to implement multi-annual plans. The DAMARA model has been specifically 
designed to help stakeholders fulfil these objectives but the remaining question is how 
DAMARA can be enabled to facilitate this process and in which forum. These are open 
questions, but dissemination of the DAMARA project through for example a multi-
stakeholder forum (Advisory Council) may help to provide further clarification. 

58 

 
 
 
 
 
 
4  Conclusions  
The DAMARA consortium has delivered a useful and workable modelling framework that 
can be used by non-technical stakeholders to assess the potential biological and 
economic consequences of different management interventions in the Celtic Sea. 
The success of the project is largely due to the strong multi-disciplinary nature of the 
project team and the modelling work and interface to be led by the end users from the 
outset. 

The DAMARA project benefited immensely from the existing FLBEIA framework which 
permitted the consortium to dedicate more time to developing the user interface. 
The model can be considered to be fit for purpose and should now be used as the basis 
to investigate potential management interventions that could be included in the 
development of a mixed-fisheries management plan for the Celtic Sea.  

Due to the complexities of the system being modelled, it is necessary that pre-defined 
management scenarios are identified and the model code is conditioned to consider 
these. This will require interactions between regional managers, stakeholders and 
modellers with the detailed expertise of the FLBEIA code. Therefore there is a clear need 
to support the DAMARA model beyond the scope of the existing project if the model is to 
be further developed and in particular be used to assess the biological and economic 
consequences of scenarios that have not been explicitly modelled to date. How this 
continued support is achieved needs further dialogue between all the relevant 
stakeholders, the current modelling team and national, regional and European 
administrations.  

The DAMARA model is dependent on external databases. Any changes in database 
structure and naming conventions will require recoding of the extraction module 
developed under the DAMARA project. Critically, if there are any changes made to the 
fleet and métier definitions currently used, then this will require substantial recoding and 
development work of the underlying FLBEIA code.   

The model framework is suited for use in other areas e.g. Irish Sea and West of 
Scotland, but the application into other areas will require further development work in 
order to deal with the relevant stocks and fleets operating in these areas and the 
involvement of the relevant stakeholders in these areas. Resources will therefore be 
required to facilitate the expansion into other areas.   

59 

 
 
 
 
 
 
 
 
 
 
 
5  Recommendations  

A workshop should be convened where key stakeholders including national 
administrators, advisory councils, professional bodies such as EAPO and the European 
Commission. The seminar should be tasked with three key objectives:  

(i) 
(ii) 

(iii) 

Demonstrate the capabilities of the DAMARA model;  
Discuss how and in which fora the model can be used to support the 
development of multi-annual plans or other management questions and;  
Identify how the DAMARA model can be supported beyond the lifetime of the 
current project. 

6  References 

Garcia, D., Urtizberea, A., Diez, G., Gil, J. and Marchal. P. 2013. Bio-economic 
management strategy evaluation of deepwater stocks using the FLBEIA model. Aquat. 
Living Resour. 26, 365–379 

Ulrich C., Reeves S.A., Vermard Y., Holmes S.J., Vanhee W., 2011. Reconciling single-
species TACs in the North Sea demersal fisheries using the Fcube mixed-fisheries advice 
framework. ICES J. Mar. Sci. 68, 1535–1547. 

Engelhard, G.H.,  Lynam, C/P., , García-Carreras, B., Dolder, P.J. and Mackinson, S. 
2015. Effort reduction and the large fish indicator: spatial trends reveal positive impacts 
of recent European fleet reduction schemes. Environmental Conservation, 42, pp 227-
236. doi:10.1017/S0376892915000077 

7  Acknowledgements  

The DAMARA project team would like to acknowledge and sincerely thank the following 
individuals and organisations for their continued support and significant input to the 
development of the DAMARA model. Without their contributions and collaborations, the 
delivery of the DAMARA model would not have been possible.  

Fishing Sector Representatives 
Barrie Deas, National Federation of Fishermens Organisations, England, Wales and 
Northern Ireland 
Eibhlín O'Sullivan, Irish South and West Fish Producers' Organisation, Ireland 
Emiel Brouckaert, Rederscentrale, Belgium 
European Association of Producer Organisations  
Francis O’Donnell, Irish Fish Producers Organisation, Ireland 
Jacques Pichon, Association Nationale des Organisations de Producteurs, France 
Paul Trebilcock, Cornish Fish Producers Organisation, England 
Sean O’Donoghue, Killybegs Fishermen’s Organisation, Ireland 

North Western Waters Advisory Council 
Alexandre Rodríguez, (now LDAC) 
Barbara Schoute 

NGO Representatives  
John Crudden, European Anglers Association, Europe 
Liane Veitch, Client Earth, Europe  

European Commission 
Laurent Markovic  

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
Christian Tritten 
Fuensanta Candela (Ret) 

Administration representatives 
Colm O'Suilleabhain, Department of Agriculture, Food and Marine, Ireland 

61 

 
 
 
 
 
 
 
8  Annex 1. First Interim Report

62 

 
 
 
Introduction 
This short report aims to outline the progress to date associated with the DEMARA 
project and to provide an outline of the proposed work plan to end June 2014 as 
specified in article I.4.1 of the contractual agreement between the European Commission 
and Marine Institute. 
Synopsis of Activity November 2013 – February 2014 
To date there have been 5 work items of note: 

Initial “kick off meeting” held with EC 9th December 2013 (Brussels) 

1.  Contract signed 13th November 2013 (project start date) 
2. 
3.  Clarification of points relating to economic data via teleconference 17th January 2014 
4.  First coordination meeting held 23-24 January, 2014 
5.  Selectivity modelling meeting Aberdeen 18-19 March, 2014  

Summary of Initial kick-Off meeting (Brussels 9th December 2013) 
Much of the meeting focus was given to the type of tool to be developed, the geographic 
range, granularity and defining the end-users. Several key issues were discussed a) 
whether the model was restricted to ICES areas VIIf,g; b) who the likely end-
users were and how that may influence the model design ; c) current and future 
data requirements and; d) making the model understandable and user-friendly.  
Since its inception, the intention has always been to develop a modelling tool that is 
flexible and modular so that it could be applied in any area, the key limiting issue would 
be the scale and resolution of the available data. If fine scale resolution is required, then 
the available data must be at an appropriate level of granularity e.g. VMS-linked catch 
data would be required for fine scale spatial approaches, similarly if the economic 
consequences of a given management intervention at a metier level is required, then 
there is a need to have economic data at a metier rather than a fleet segment level, this 
can conceivably be achieved by “smearing” fleet aggregated data across metiers with the 
appropriate vessel and gear characteristics together with some adjustments made for 
differences in catch composition (value). Discussions within the group are currently 
underway to identify the range of options available to undertake this, but in principle this 
could be achieved through analysis of the different catch and associated revenue profiles 
within a fleet segment and using catch and economic return as a means of 
disaggregating fleet segments into metiers of a desired management scale. Otherwise, 
metier-specific data would need to be collated. However, it is not the intention of the 
project to undertake extensive data collection to satisfy the desire to have fine-scale 
information, but rather to build a model that can deal with different levels of granularity, 
the resolution of the output of the model would by default be driven by the granularity of 
the available data. For example, if catch and effort data is only available at a statistical 
rectangle level, then any corresponding spatial analysis could only be undertaken at that 
spatial scale. Conversely, if catch and effort data are linked and derived from VMS data, 
then management scenarios could be modelled at a much finer resolution. If end-users 
would like to explore more fine scale scenarios in economic terms without the 
assumptions made where fleet data is de-segregated into metier-based aggregations, 
then this would require the collection of additional economic data at a metier (or 
appropriate management unit) level. 
It was noted that it would be of interest to managers to assess what the potential 
responses by fleets or metiers may be to different management options. This would 
require the building of a separate behavioural model. However, this was not expressly 
specified in the proposal, but the consortium felt that this type of approach could be 
integrated into the modular modelling framework if such a behavioural model became 
available. However, it is the intention to explore possible responses of 
fleets/management units to particular measures through the stakeholder consultation 
process. 

65 

 
 
 
 
 
 
 
In terms of spatial coverage, it was reiterated that the consortium had focussed on 
VIIf,g in the project proposal to avail of and support the NWW AC initiative to develop a 
mixed fisheries management plan for that area. The need for a decision support tool to 
develop multi-annual, mixed fisheries plans was raised by the NWW AC with the 
Commission as a limiting factor in the continued development of the NWW AC Celtic Sea 
management plan initiative, which had been developed by the NWW AC through close 
collaboration with scientists from the surrounding national laboratories. It was stressed 
during the ‘kick-off’ meeting that it is not the intention to build a model that is only 
applicable for that area or indeed for that specific management plan, but rather to 
support this initiative to identify the types of demands that may be asked of the model 
and how that could be visualised and therefore build a tool that would be applicable 
elsewhere. In essence, the VIIf,g NWW AC initiative provides a solid platform to design 
and build a tool that is based on ‘real life’ demands, and results in a tool which is “fit for 
purpose” as far as the end-users are concerned. This does not constrain the geographic 
scope of the model but rather supports the model development and the design of the 
user interface.  
Failure to recognise the needs of the end user, their skill set and overall model 
maintenance requirements has resulted in the ongoing use of decision tools (beyond 
project stage) being quite limited. The importance of producing clear and understandable 
outputs as well as a clear manual on how to use the decision support tool was stressed 
by the Commission and in particular the ability to readily update the model with the 
most recently available data. The consortium agreed that developing such a “user-
friendly” system with clear and understandable outputs was a priority and the group was 
informed that progress had been made through the GEPETO project using so called 
“Shiny” (R-project) user interfaces where  used can readily select different options using 
tags or sliders to visualise data sets or model outputs.  
Several examples are shown below based on work done under the auspices of the 
GEPETO project where users can select different data sets and are intended to show 
users how different indicators associated with the Celtic Sea management plan are being 
met e.g. number of stocks relative to Fmsy; discard rates and to also show where the 
primary sources of fishing mortality are occurring by fleet and area. This application, 
while not yet publically available through the GEPETO project, will be used as part of the 
DAMARA project to show how different management strategies might affect the target 
trajectories. It is intended to progress this work further within the DAMARA project and 
to develop these types of outputs which will visually describe the potential impacts of 
different management interventions. The primary data sources for these are the SETCF 
catch and effort database which is publically available and stored in a standard format, 
the DAMARA model would simply link to the public interface, while the stock specific data 
is generated using data from ICES. Depending on ongoing developments with the 
regional database projects, these input files could be drawn down from there. It is the 
intention of the DAMARA project team to consider data availability and sources and in 
particular streamline the process for updating the input files each year. If separate data 
sources are required e.g. logbook-linked VMS data, then the specific procedures and 
data formats will be specified and will link to other initiatives in this field e.g. the ICES 
Study Group on VMS (SGVMSS) https://code.google.com/p/vmstools/  
It could be anticipated that additional data sets maybe required for running the DAMARA 
model in the future i.e. post-project completion. It is the intention of the DAMARA group 
to identify these and to transmit to national laboratories these possible needs so that 
they may be considered under future national DC MAP (i.e. the future revised DCF 
Regulation) programmes so that provisions are made for this possible eventuality. How 
and where this data will be housed into the future will require discussions with the 
project client.  

66 

 
 
 
The upper panel shows landings by member state and country is displayed for the Celtic 
Sea (from the STECF effort data base), different countries, species, gear types and gears 
can be selected. The second image shows how the outputs from several different stock 
assessments can be used to visualise the trends in fishing mortality relative to Fmsy, 

67 

 
 
 
 
while the third panel shows the catch of hake (England, TR2, by year) broken into 
landings and discards.  
Another important question raised during the ‘kick-off’ meeting was: who are the end-
users going to be? This can have significant bearing on the model design and the user 
interface. This will influence both the model design and the design of the user interface.  
It was acknowledged that the project has been commissioned by the EC and ultimately, 
the design will need to satisfy their needs, but it was further noted that the inception 
and genesis of the decision support tool was largely driven by the NWW AC initiative and 
cooperative dialogue with the Commission to further develop a Celtic Seas mixed-
fisheries management plan. The NWW AC noted that there was a need to develop a 
formal basis and model structure to evaluate the various trade-offs that would be 
required to fulfil this. As noted in the agreed project proposal: 
“Focussing the development of the decision support tool as an aid to the 
operationalisation of the NWW AC mixed-fisheries management plan for VII f,g will allow 
the use of an existing stakeholder structure and a well advanced management plan 
proposal, to shape the design and features of the decision model. The NWW AC has 
strongly endorsed this proposal and supports the development of the DST through is 
Celtic Sea focus group” 
During the proposal phase, the reform of the CFP through the introduction of 
regionalisation signalled a significant paradigm shift in by allowing Member States with a 
direct management interest in a particular region, to submit joint recommendations for 
multi-annual plans which would then be evaluated by the European Commission. 
Furthermore, Member States wishing to submit such plans must consult the relevant 
Advisory Council (article 18.2). This means that the Decision Support Tool could be used 
by several groups to this effect and may provide a useful platform for MS and Advisory 
Councils to cooperate and identify the potential management options for achieving the 
overarching goals of the CFP (e.g. Articles 1 and 2) and also provide the Commission 
with appropriate means to evaluate the joint recommendations.  It is noted however, 
that the NWW AC mixed-fishery plan initiative is presently the most advanced initiative 
for a mixed-fishery management plan for that area. 

68 

 
 
 
 
 
Summary of 1st Coordination Meeting Dublin 23-24th January, 2014 
A one and a half day coordination meeting was held in Dublin which was used to review 
the project tasks; disseminate the outcomes of the initial kick-off meeting;  identify 
linkages with the GEPETO project; identify data needs; summarise available and 
pertinent models and; to formulate a work plan for the following 6 months i.e. until the 
next coordination meeting. 
The stakeholder engagement process received substantial attention during the kick-off 
meeting and this was discussed at length between the project partners and the 
Commission representative. It was recognized that stakeholder engagement is 
particularly challenging in a situation where the institutional setup in Europe has 
changed dramatically through the introduction of regionalisation and as noted above, 
there are now several stakeholders with a potential interest in the development of the 
Decision Support Tool, namely: 1) European Commission (who commissioned the study), 
2) North Western Waters Group (member states) and 3) North Western Waters AC.  
Summary of Presentations 

1.  Norman Graham (project coordinator) gave a summary of the project. This included a detailed 

overview of the project proposal, the need for a Decision Support Tool to aid the development of 
mixed-fisheries management plans and what work items need to be developed and delivered in the 
following 6 months. The key areas to be addressed during the coordination meeting were summarised 
and identified as (i) develop a platform for stakeholder interactions; (ii) data sourcing, delivery and 
housing; (iii) review and consideration of available mixed-fisheries modelling approaches; (iv) 
planning schedule for modelling and stakeholder workshops; (v) timetable for the development of a 
dummy proto-type model for dialogue with stakeholders and (vi) to provide a planned timetable for the 
delivery of these objectives.  

2.  Colm Lordan (Marine Institute, Ireland) explained the progress in the GEPETO Interreg study that will 
be finished in August 2014. He coordinated the Celtic sea case study in GEPETO looking at mixed 
demersal fisheries. Thus far, the GEPETO project has compiled information on landings (and catches) 
of all species in the area. GEPETO has attempted to assist the NWW AC from November 2011 
onwards in developing a mixed fisheries management plan using SMART indicators. High level 
objectives have been set, however the next step in the process (identification of actual measures) has 
not been advanced. Scientists were asked to suggest possible management measures and discuss trade-
offs, but this process has not progressed largely due to difficulties in maintaining stakeholder dialogue 
and a lack of an objective framework in which to evaluate management options. 

3.  Coilin Minto (GMIT) presented a review of existing mixed fisheries modelling approaches (see table 

below). Focussed on MTAC, FCube and Effort allocation models. It is proposed that this review should 
form part of the interim report to further explain the overall model development. 

69 

 
 
 
Model 

Regulation 

Type 

DCR data only 

Spatial 

Selectivity 

Language 

Comment 

Areas Used 

AHF 

Effort/TAC  

Simulation  

BIRDMOD 

Effort  

Simulation  

BEMMFISH  

Effort  

Simulation  

Y  

Y  

N  

EcoCoRP  

Effort  

Simulation  

Y +MSVPA  

FLR/FLBEIA 

Effort/TAC  

General  

ISIS-Fish 

Effort/TAC/MPA  

Simulation  

MEFISTO 

Effort  

Simulation  

TEMAS  

Effort/TAC/MPA  

Simulation  

Y  

Y  

Y  

Y  

Summary of mixed-fisheries models

N  

N  

N  

N  

Y  

Y  

N  

Y  

N  

Y  

Y  

Y  

Y  

Y  

Y  

Y  

R  

R  

Sensitive  

Atlantic/Med.  

Case-specific  

Med.  

Java  

Max 4 spp, 3 fleets  

Med.  

VENSIM  

Case-specific  

Nth Sea  

R  

Java  

General toolbox  

Many  

Good spatial  

Many  

Compiled  

Area-specifc  

Med.  

Visual Basic  

Not maintained  

Baltic/Nth Sea  

71 

 
 
 
 
 
4.  Dorleta Garcia (AZTI) presented FLBEIA, an FLR (Fisheries Library for R) tool to conduct bio-

economic impact assessment of fisheries management strategies. This modelling approach was explored 
in greater detail in sub-group (see below) and will form the core starting point for the model 
development team.  

5.  Simon Mardle (FISHOR) presented bio-economic modelling in 2006 through the Invest in Fish South 

West project, which was aimed at the Channel area. The approach contains many of the features that 
would be desired for the DAMARA approach, but mostly focussed on UK fisheries. There was strong 
stakeholder interaction in the project and Simon Mardle has subsequently provided detailed summaries 
of the consultation process and identified the types of management measures which were identified as 
part of the Invest in Fish Project. The experience gained from this process with be extremely valuable to 
the DAMARA project and will be considered in detail by the partners dealing with stakeholder 
interactions.   

6.  Will LeQuesne (CEFAS) presented a simple catch and CPUE analysis method based on the STECF 

catch and effort data (by rectangle). He emphasized that our tool should work with an accepted database 
(even if the data is problematic sometime). We should not try to solve the data issue in this 
project. Short term prediction could just as well be done with recent CPUE. Trade-offs need to be made 
between sophistication and transparency.  

The consortium spent some time considering each of the presentations and which 
elements are relevant to the DAMARA project. It was then decided to deal with two key 
aspects by sub-group, i) stakeholder engagement and ii) model development. 
The following sections summarise the findings of these two sub-groups  

1.  Stakeholder engagement 

A stakeholder engagement subgroup worked on the key-message from the project, the 
overall scheme of inputs and outputs and a timeline for actions. It was concluded that 
inputs and outputs should be presented in an open way at the stakeholder workshop and 
not try to lead in any particular direction. The group identified a number of possible input 
controls that could be investigated and also discussed the type of outputs that would be 
required.     

1. 

Inputs (levers): effort, catch, spatial (limited access areas; closed areas, real time closures), technical 
(gear) measures, time (season).  

2.  Limits (MSY considerations [Fmsy; Bmsy Trigger etc]; minimisation of unwanted catch) 
3.  Outputs: biological, economic and social (employment, number of vessels) and how to present these 

outputs 

4.  Focus on long term transitions and short term effects.  
5.  Reflect on how results-based management would link with inputs and outputs.  

In order to steer the design of the decision support tool, it was envisaged that a small 
dedicated focus group would be set up comprising of members of the NWW AC Celtic 
Seas focus group, representatives from DG MARE and regional managers (Director 
Generals) would consider the types of inputs (management options) and outputs (type 
and visualisation) that could be explored.  
Following discussions at the first coordination meeting it was felt that this would be too 
ambitious due to a number of external issues and in particular the need for MS to 
develop joint recommendations for the formulation of discard plans. The introduction of 
the landings obligation from January 1st 2015 and the desire by member states to 
develop their own regional discard plans has resulted in a considerable uplift in meetings 
and work load for both national administrations (Director Generals) and their supporting 
scientific laboratories. France, Ireland, UK, The Netherlands and Spain are heavily 
committed to the formulation of a discard plans for both pelagic and demersal species in 
the Celtic Seas which is been conducted by the Director Generals Working Group on 
Western Waters (DGWWG). Presently there are monthly meetings of the Director General 
groups as well as monthly meetings of DG technical groups. The coordinator of DAMARA 
and the Work Package Leader on Stakeholder Engagement are both heavily involved in 
this process. In addition, it is noted that there is a complicated institutional arrangement 
between Member States, RACs and EC (largely driven by the development of discard 
plans) and it was therefore concluded that a separate focus group with all stakeholders 

72 

 
 
 
 
would not be feasible at this stage. Notwithstanding, discussions with national 
administrations have been encouraged and at least one administrator (Irish Department 
of Agriculture, Food and the Marine) has confirmed participation in future stakeholder 
meetings (1st meeting scheduled for June 11). Project partners have been encouraged to 
engage with national administrations to provide a focal point for the DAMARA project.  
It is noted that the Stakeholder Work Package leader (Martin Pastoors) will leave his 
current position with IMARES at the end of April. The role of WP leader will be transferred 
to Marloes Kraan (see http://www.marloeskraan.eu/). In terms of consultation with the 
NWW AC, the DAMARA project will presented at the forthcoming NWW AC Focus Group 
meeting on Celtic Seas (March 26). During this, it is intended to present the outcomes of 
the kick-off and coordination meetings, show the developments to date from the GEPETO 
project and to propose a road map for the continued interaction between the NWW AC 
and DAMARA. Agreement has been made with the NWW AC that a small group (2-3) of 
key stakeholders from the NWW AC Celtic Seas Focus Group which will be mandated by 
the NWW AC to act as the permanent focal points for the DAMARA project. Using such an 
approach it is hoped that specific and dedicated meetings could be readily arranged, 
minimising the need for complex coordination or the tendency to run the NWW AC 
stakeholder meetings on the back of existing NWW AC business meetings. 
http://www.nwwrac.org/admin/publication/upload/AGENDA_NWWRAC 
AC_GEPETO_Mtg_CELTIC_SEA_DUBLIN_26March2014_EN.pdf 
Clearly, there is also a need to consult with member states on the types of outputs (and 
design) that could be presented from the model. Given that the project coordinator is 
involved in the technical working group of DGNWWG which is currently developing the 
draft recommendations for discard plans for the western Waters region, it is intended to 
use the DGNWWG forum to inform Member States on progress in the project and to use 
these meetings to canvas opinions from the regional managers as to the types of 
questions and management options that they may want to be investigated.  

2.  Outcomes of modelling planning and initial work plan 

The DAMARA project requires the development of a multi-fleet, mixed-fishery bio-
economic decision support tool for the Celtic Sea. The best option identified at the start 
up meeting is the application and development of the FLBEIA bio-economic modelling 
package in R. This will require in depth evaluation by the modelling team to ensure it 
meets the needs of the project. It is agreed that a prototype using a small case study 
within the project’s case study area will be used to do this. This will be completed as the 
first task of the group. A plan for full-scale module development will result. It is also 
worth noting that one of the project partners (BIM) is also in the process of developing a 
NWW AC initiative to develop a mixed fisheries management plan for the Irish Sea with 
the intention to apply the FBEIA modelling framework for this development.  
(http://www.nwwrac.org/admin/publication/upload/MINUTES_WG4_NWWRAC_PARIS_07
1113_EN.pdf)  
FLBEIA (FL Bio-Economic Impact Assessment) is an R package build on top of FLR 
libraries.  The purpose of the package is to provide a ﬂexible and generic simulation 
model to conduct Bio-Economic Impact Assessments of harvest control rule based 
management strategies under a Management Strategy Evaluation (MSE) framework. As 
such, the model is divided into two main blocks, the operating model (OM) and the 
management procedure model (MPM). In turn, these two blocks are divided in 3 
components.   

73 

 
 
 
Conceptual diagram of FLBEIA 

The OM is formed by the biological, the ﬂeet and the covariables components and the 
MPM by the observation, the assessment and the management advice components. The 
model is multistock, multiﬂeet and seasonal and uncertainty is introduced by means of 
Montecarlo simulation.  The algorithm has been coded in a modular way to ease its 
checking and to make it ﬂexible. The package  provides  functions  that  describe  the  
dynamics  of  the  different model components,  under certain assumptions. In each 
specific model implementation the user chooses which of the functions are used. 
Furthermore, if in a specific case study or scenario, for some of the components, the 
functions provided within FLBEIA do not fulﬁl the requirements, the user can code the 
functions that adequately describe the dynamics of those components and use existing 
ones for the rest of the components. As the user can construct its own model, selecting 
existing sub-models and constructing new ones, we can deﬁne it as a framework more 
than as a model. The package is still under development but most of its functionalities 
are already available. At the moment there are no functions to model trophic interactions 
but it is something planned in the short term. Main limitations of the model are that the 
stocks must be age-structured or aggregated in biomass (length structure is not allowed) 
and that spatial dimension is not considered explicitly.  Spatial characteristics could be 
modelled assigning stocks and/or ﬂeets/metiers to speciﬁc areas. 
http://www.azti.es/wp-content/uploads/2013/10/Manual_FLBEIA_v1.2-18Abr2013.pdf 
http://www.azti.es/downloads/downloads/flbeia/#tab-description 
The prototype model should, if possible include stocks and fleets with range of 
contrasting characteristics to deliberately try and expose challenges associated with 
applying the model in different settings.  E.g. stocks with full analytical assessment and 
population model available from ICES vs data-limited stocks. Fleets from different MS to 
expose challenges associated with fleet economic data. 
Other issues will require further consultation with key stakeholders, but as a starting 
point the following assumptions will be made: 
Fleet definition: fleets in prototype model to be based on finest scale resolution of 
fleets available consistent with STECF effort, landings and economics data (economic 
data will require disaggregating across metiers).  
Spatial scale: for the prototype demonstration, the spatial scale will be based on data 
from STECF dataset (VIIe-k). Choice and implications of scale will be discussed during 
stakeholder presentation of the demonstration model and with the broader project team. 
Defining spatial resolution at this scale will enable the identification of spatially defined 
metiers. 
Temporal scale: The prototype will use a yearly time-step. Issues that require a lower 
level of temporal resolution will be considered during the prototype development for 
potential inclusion in the decision-support tool e.g. for assessment of temporal closures. 
Species coverage: The prototype will include a limited number of quota stocks with 
contrasting issues (e.g. fully assessed, non-assessed).   

74 

 
 
 
Data sources: prototype model development to be solely based on available regularly 
collected data and analyses (e.g. STECF catch and effort data; ICES assessment outputs; 
economic data from AER) 

75 

 
 
 
 
3.  Work Plan February  July 2014 
  Week ending February 7th: DAMARA FLBEIA Dropbox repository set up with data, code and 

 

 
 

documentation folders to enable reproducibility and collaborator access from the outset. (STATUS – 
Completed)  
21st February: Define agreed case study for proto-type model application (STATUS – completed). 
18-19th March – Selectivity modellers meeting to explore options for meta-analysis to develop 
parameterised selectivity model (STATUS – completed). 
26th March: Develop understanding of main capabilities to inform realistic expectations during initial 
AC stakeholder discussions, NWW AC, Dublin Castle, Ireland. 
26th March: Agree stakeholder group; identify roles and responsibilities and identify meeting schedule.  
7-11th April Modelling workshop, AZTI. By the end of the workshop a comprehensive list of data 
requirements for establishing the prototype model should be prepared alongside preliminary plan for 
implementing management controls in the model and generating a dynamic response from the fleets in 
reaction to management changes. 
6th May: Review of selectivity meta-analysis for comparison with the North Sea selectivity model 
 
  End May: Prototype model based on selected case study operational and preliminary outputs prepared 

 
 

for stakeholder feedback. 
June 11, Dublin: Stakeholder meeting to discuss prototype model and user interface. 

 
  1st July, Edinburgh Coordination meeting (provisional date).  

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DAMARA Project – 1st Coordination Meeting 
23 January (14:00) – 24 January (16:00) 
Marine Institute,  
80 Harcourt St,  
Dublin 2 

Agenda 

  Welcome (NG) 
  Meeting Objectives (NG) 
  DAMARA – project “refresher” (NG) 
  GEPETO and DAMARA – data/information interface (CL) 
  Model users – discussion on design parameters/id possible mgt needs/options (MP/CT/all) 
  Group discussion – potential data gaps and solutions – task allocation 
  Summary discussion/presentations of existing “bio-eco-tech” models (NG; SM; DG; CM(?); 

WQ/PD; JC) – to include Fcube; FLBEIA; NsSEL; FishRent 

  Modelling direction - all 
  Task planning and allocation (Jan-June 2014) 
  Working Group Meetings 
  AOB  

77 

 
 
 
 
 
9  Annex II Second Interim Report

78 

 
 
  
 
1  Introduction 

Following the decision to use the FLBEIA modelling framework as the basis to produce a 
decision support tool to aid the development of a mixed fisheries management plan for 
the Celtic Sea, the majority of the work during 2014 has focussed on the development of 
a “proof of concept” prototype model. This was undertaken largely by the core modelling 
group during a series of dedicated working meeting held throughout 2014 (Data meeting 
- CEFAS, Lowestoft 19-20 March 2014; Model working meeting - AZTI, Bilbao, 8-10 
April2014; Model working meeting - CEFAS, Lowestoft, 28-30 July 2014). This was 
coordinated through various conference and skype calls throughout the period.  
Further development work was undertaken during the coordination meeting (BIM, Dublin, 
25-26 November 2014) where the prototype model was demonstrated to the 
stakeholders. This report describes the model development and stakeholder interactions.  
The primary objective of the DAMARA project is to develop a framework that can be used 
by stakeholders to assess the potential impacts of different management options for 
mixed species fisheries to achieve pre-determined objectives or goals. These goals could 
include fishing at levels consistent with achieving maximum sustainable yield in ways 
that are economically sustainable or obtaining sustainable catches in the shortest time 
possible. It is not the role of the DAMARA project to specify these goals or how to achieve 
them, that’s the role of managers and other stakeholders and determined by the limits of 
the Common Fisheries Policy (EU Regulation 1380/2013). The DAMARA project aims to 
provide a framework that can be used to compare different options to achieving these 
specific targets e.g. Fmsy and to assess what the biological and economic implications are 
of choosing different approaches or paths and thus provide an enabling tool for 
stakeholders.  
The purpose of this section is to provide a non-technical overview of the modelling 
framework and prototype application to date to facilitate discussion and feedback.  
Specifically, here we describe: 

  Principals of the modelling approach; 

o  Processes; 

  FLBEIA prototype for the Celtic Sea (work conducted to date); 

o  Setup 
o  Model output 

  Key assumptions, uncertainties, and limitations; 
  Proposed sensitivities to the assumptions, uncertainties and limitations; 
  Stakeholder consultation process 
  Proposed work plan for 2015. 

It further describes the data used to develop the prototype model by way of example, 
though it should be noted that it is not expected at this stage to reflect the final data 
used in the operational model, but as a mechanism to demonstrate the types of 
management scenarios that can be evaluated and outputs from the model which are 
demonstrated though a prototype model. The stages foreseen in the forthcoming months 
will be to build on the prototype described here guided by stakeholder engagement as to 
the types of scenarios that will be asked of the full model and the types of outputs.  
There are several stages involved in building a framework. The first stage is to build a 
model framework or “engine” that is capable of answering how the biological system 
(“the stocks”) will respond to different levels of effort and activities by the fleets involved 
(“the fisheries”) and how the management and advisory system operates (“the 
management”). The DAMARA team has built a prototype model which has been used to 
show the proof of concept and to describe the types of things the model can (and can’t) 
do as well as describe the assumptions and approaches taken. 

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2  Principles of the modelling approach (non-technical summary) 

The prototype is built around an existing framework called FLBEIA (FL Bio-Economic 
Impact Assessment). FLBEIA was built by AZTI (Garcia et. al., 2013) to provide a ﬂexible 
and generic simulation model to conduct Bio-Economic Impact Assessments of harvest 
control rule based management strategies (TAC setting strategies; capacity limitation; 
area and seasonal closures; technical measures) for multi-fleet, multi-stock fisheries. 
This involves assessing the consequences of a range of different management strategies 
or options to assist and determine which approach will be the most appropriate to meet 
the objectives of the fishery.  
FLBEIA is divided into two main blocks, the operating model (OM) and the management 
procedure model (MPM) (Figure 2.3-1).   

Figure 
Figure 2-1 Conceptual diagram of FLBEIA 

2.1  The Operating Model (OM) 
Within the operating model, there are sub-models or components that deal with (i) the 
stocks and (ii) the fleets that exploit them and; (iii) other aspects of the system (called 
covariables). Typically in a biological focused simulation, key stocks are selected for 
evaluation and the partial fleet activity that relates to those stocks is included. Our 
approach of modelling the economics of the fleets (and local fishing economies) requires 
that the operating model must also consider fleet activity that relates to stocks and areas 
not explicitly modelled, for example stocks outside the Celtic Sea that are periodically 
fished by vessels which operate both inside and outside the Celtic Sea. Therefore, there 
is an implicit assumption that all fleet activity (for the fleets in focus) is considered.  

2.1.1  The Stock Component  
Ideally, for each stock there should be an underlying analytical assessment which can be 
used to predict the stock response to different levels of catches and other measures such 
as changes in selectivity (for example: cod, haddock and whiting in the Celtic Sea). 
However, this is not always the case. For some stocks such as Nephrops where there is 
an estimate of the overall stock size but not the age or length structure. Here, it is still 
possible to assess the impact of different catch levels but it is not possible to assess for 
example changes in gear selectivity due to the lack of age/length information. The final 
stock category relates to stocks without any analytical assessment. This is a significant 
issue with respect to economically important species such as anglerfish as it is not 
possible to assess what the short or medium stock term impacts may be for any 
management scenario. Within the model these stocks are assumed to have a constant or 
a fixed population over time. While this means that at least there is some consideration 

80 

 
 
 
 
 
 
given to the economic importance, it is not possible to determine how these stocks may 
respond over time due to different management interventions.  

2.1.2  The Fleet Component  
The fleets operating model is where much of the economics is imparted into the model. It 
is divided into two levels: (i) fleet-segments, describing the physical structure of the fleet 
segments (i.e. based on average vessels in terms of length and gear); and (ii) 
operational units/metiers, describing the activities of vessels at a metier level (i.e. based 
on area fished, gear used and typical species mix obtained). With this combined 
information, the economics of the fleet segments can be calculated (this is further 
described in the section3.1 Prototype Setup ).  

2.1.3  Covariates Component  
This part of the model is intended to incorporate all the variables that are not part of the 
biological or fleet components and that may affect any of the operating model 
components or the management process. The number of covariates is, in principle, 
unlimited. The limitation could come from memory problems with R and/or the operating 
system used. 

2.1.4  Links among and within components 
The links within the OM components are not restricted by the general settings of the 
simulation model. If the modeller decides to include these links, we will have: 

  The link within the biological component is that catch affects abundance. 
  The link within the fleets’ component is that fleets' capacity affects fishing effort. 
  The link between the biological and fleets’ components is that fishing effort and fish abundance affects 

catches. 

2.2  The Management Procedure Model (MPM) 
The MPM simulates the management procedure and is the second major component of 
the FLBEIA model. This includes the collection of data for assessing the status of the 
stocks, running the actual assessments, and provision of the advice that will be used to 
manage the stocks. This Management Strategy Evaluation (MSE) allows for uncertainty in 
assessing stocks and uncertainly about the implementation of different management 
measures. A primary goal of the MSE approach is to assess the performance of different 
rules in balancing multiple and sometimes competing objectives (e.g., low risk of 
overfishing and maximising catches or profits). Simulating both the management and 
biological system, together with uncertainty, enables an evaluation of robustness of 
various management options in achieving the objectives which have been set by 
stakeholders. There are three sub-components of the MPM: (i) the observation model; 
(ii) the assessment procedure and; (iii) management advice. 

2.2.1  The Observation Model 
The observation model generates observed data on a stock-by-stock basis. It is a key 
component of MSE that translates the “true” underlying system (operating model) into 
noisy observations that will be used to recover estimates of the true system via the 
assessment. That is, it uses the biological operating model to project the population 
forward and generates the catch and survey data that would be observed and used in the 
assessment. Here is where many of the uncertainties can be introduced, including: 

  Measurement and sampling errors 
  Misreporting 
  Aging errors 
  Varying catchability 

Depending on the type of assessment to be conducted (age-based, biomass dynamic, 
trends based or none if the focus is solely on the management strategy alone) different 
data-generating functions can be used, including:  
  Perfect observations, which have no errors included. 

81 

 
 
 
 
 
  Age-structured catch data with ageing and general errors included. 
  Biomass-aggregated catch data with general errors included. 
  Age-structured and biomass-aggregated abundance or CPUE indices with survey/CPUE-specific 

catchabilities. 

2.2.2  Assessment Procedure 
The data generated in the Observation model are passed to the assessment routines 
(e.g., commonly used assessment methods such as XSA, surplus-production), which 
integrate the catch and survey data to produce estimates of the abundance and fishing 
mortality per stock. In this sense the output of the assessment is the perceived status of 
the population. 
If the goal of the procedure is to check the performance of the assessment method under 
various combinations of errors above, the assessment should be run but if the goal is to 
assess the performance of the management strategy not conditional on the assessments 
being accurate then the assessment component can be omitted and the stock status 
indicators passed to the management procedure directly from the operating model 
(though errors can be introduced here). Doing so speeds up FLBEIA but may omit an 
important uncertainty in the system – assessment model uncertainty. Mixed fisheries 
MSE will typically omit the assessment component as the focus is more on the mixed 
fisheries aspects than the quality/ability of the assessments to recover stock status. 

2.2.3  Management Advice  
Management advice is based on harvest control rules (HCRs) that compare the status of 
the stock with proposed or agreed target and limit reference points (e.g., Bmsy, Fmsy, 
Blim, Flim) and adjust the advised fishing mortalities for the projection year accordingly. 
The HCRs use either stock status (absolute estimates of abundance estimated in the 
assessment or treated as known) or stock status indicators such as abundance indices. 

2.2.4  Model Outputs  
The operating model and management procedure models thus progress over a specified 
time period (e.g., 5 years) by generating the observed data under a given level of fishing 
mortality (or effort level and allocation among metiers given an assumed relationship 
between effort and fishing mortality for each fleet and stock), assessment, comparing 
with the HCR and adjusting the fishing mortality for the next projected year. 
The outputs (biological and economic) from the model under given management 
scenarios can then be graphed or tabulated for comparison and appraisal by decision 
makers. Examples of possible outputs are shown at the end of the following section. 

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3  FLBEIA prototype for the Celtic Sea 

This section describes the development of the prototype application of FLBEIA to the 
Celtic Sea areas VIIbcefghjk with spatial disaggregation between VIIfg  and VIIbce,hjk. It 
is important to highlight that the purpose of the prototype is to demonstrate the model 
capabilities and the types of outputs that can be provided, rather than demonstrating 
application to a realistic characterisation of the Celtic Sea fisheries per se.  Application is 
in active development and all sections are open to comment and critique en route to 
further developments within reasonable expectations. 

3.1  Prototype setup 
For the initial working prototype, data was prepared from STECF databases for ICES sub-
areas and initially limited to VIIe-k for cod, haddock and whiting although other species 
e.g., plaice, sole will be included in later. The aim of this stage is to develop the modeling 
framework to a level that is for the most part data independent; that is the data are used 
to prove the concept but the model itself is scalable to different sets of data, for example 
including additional sub-areas, different spatial scales or fleet and metiers definitions. 
The three species included here have age-structured analytical assessments available, 
but it was recognised that the prototype must also consider other stocks with lower levels 
of assessment. To this end, the modelling team suggest including anglerfish (monk) and 
Nephrops to test the model under different levels of data quality.  

3.2  Prototype Operating Model  

3.2.1  Stock Component  
For cod in VIIe-k; haddock in VIIb-k and whiting in VIIe-k the biological operating model 
was created based on 2013 ICES Working Group on the Celtic Seas (WGCSE) 
assessments. Stock-recruitment functions for use in the biological operating model were 
fit to the assessment-derived spawning stock biomass and recruitment estimates by 
species.  
For stocks without a full analytical assessment (based on a population dynamics model 
that can be projected) (e.g., monkfish, Nephrops), the first and most basic approach 
could be to assume a fixed population that is unaffected by catch. Clearly this is 
unrealistic in practice, but in order to develop the prototype model, this assumption is 
necessary. This would be the most expedient method of including these economically 
valuable stocks in the prototype. As and when ICES (or others) develop analytical 
assessments for these stocks these can simply be incorporated. It is not however, the 
intention of the DAMARA project to develop these. To illustrate the various levels of 
assessment, the prototype included a simple biomass dynamic surplus production model 
fit to the absolute biomass data available for Nephrops on the Smalls (WGCSE 2013). 
Anglerfish was included as a fixed population. 

3.2.2  Fleet Component 

An essential first step in establishing the fleet operating models is to define the fleet and 
metier segments where:  

 

fleet, defined by: 

country (including if available region within country) 

o 
o  main gear used 
o 

length (or size) group 

  metier, i.e. combination of fleet, area fished and expected catch composition, defined by: 

o 

area (e.g. ICES sub-area, or perhaps ICES rectangle) 

mesh This is a task that requires consideration of both the scale of management (i.e. 
what would be a manageable number of “units” on which to base individual management 
decisions) and the scale of information (i.e. at what level of disaggregation would 
managers like to understand the impact of the management measures).  The two 
concepts are not mutually exclusive as, for example, you could manage collections of 
units and still consider the impact on individual units so approaching the problem from 

83 

 
 
 
both perspectives is important to design the appropriate segmentation that captures 
what is needed by stakeholders. 
It is noted that all activity must be accounted for (including all areas and all species, 
even if external to the model), otherwise economic viability of the fleets cannot be 
measured. One issue that a small case study area must overcome is that only a few 
fleets will rely totally (or even mostly) on it for their fishing. 
For simplicity, the prototype fleet and metier definitions were based on readily available 
and accessible information from STECF.  The data on fleet activity (catch and effort) was 
generated by aggregating country and vessel length disaggregated metier-based (i.e. 
activity) data used for evaluation of the cod plan into consistent fleet segments assuming 
that there is no switching of gear type (e.g. otter trawlers do not deploy beam trawls, 
netters do not use trawl etc.).    
Given that the economic module of FLBEIA is founded on a similar structure to the EIAA 
model and FishRent it is naturally designed around the data collected for the AER. These 
were then matched to economic information from the AER to provide costs and earnings 
information for each of the fleets, where available.   
For the prototype, the main source of economic data is the AER. This enables a complete 
review of the quality of that data to be assessed for the full model. The process for 
review which is ongoing considers two assessments: 

1.  For Ireland and the UK, we are able to go back to the original economic survey data and build-up a 

tailored view of the economic performance of the fleet segments for those vessels primarily fishing in 
ICES sub-areas VIIf&g. With this a direct comparison with the AER can be made as it is the same data 
used to collate economic performance for both views (i.e. different fleet segments). This is possible as 
partners in the project have direct access to the raw economic data for Ireland and the UK. For France, 
the University of Western Brittany have been contacted to ask for assistance in firstly reviewing the 
AER data for the French fleets operating in VIIf&g and secondly to help in identifying potential ways 
of improving the data (ideally from publically available sources). Economic data for Belgium and Spain 
will be reviewed by following step 2 for the full model. 

2.  For Ireland, UK, France and Belgium the data collated in step 1 will be presented to industry 

representatives on the stakeholder panel of the project for review. Their assessment will provide two 
outcomes for the validation: 

a.  To confirm the levels of activity of the fleets operating in the case study area (i.e. VIIf&g), and 
b.  To confirm the economic performance of the fleets modelled. 

It is worth noting the process that the majority of Member States use to collect economic 
data under the Data Collection Framework for the AER. It is for the most part surveyed 
on a yearly basis using representative samples of the national fleets and requested 
directly from end of year vessel company accounts. This mean that the raw data 
collected for individual vessels is of high quality. In the case of the UK this can be used to 
create views of the economic performance of different fleet segments.  
The economic data used in the prototype model is presented in Table 3-3. Note that 
variable costs, and fuel costs, in the prototype are specified by metier but have not been 
calibrated to indicate differences in trip distances, they will be in the full model. 
The activity of the fleets segments in terms of mesh-size used (e.g. TR1 or TR2) was 
then split into two spatial units (VIIfg, VIIbce-k) to provide metier-based catches which 
have the characteristics required (i.e. different selectivity patterns, different areas fished) 
to allow the range of effort, quota, spatial and selectivity management measures to be 
applied within the prototype.   
The approach of defining a metier as the combination of a fleet and an area fished strikes 
a balance between model flexibility and complexity. A highly resolved spatial model could 
be represented with a metier for each fleet in each statistical rectangle. This would, 
however, slow the model considerably and potentially increase sensitivity of the 
modelling process in cases where a small number of observations occur in a given 
rectangle. A solution is to spatially separate those areas/rectangles being considered for 
specific management measures (e.g., closure, combination of closure and gear 
measures) before defining the spatial units (thereby having a “closed unit” and “non-
closed unit” in the model. 
The results of the fleet and métier definitions component was a set of fleet units based 
on country, gear type and vessel length (e.g. IRL_OTB_>10m) that operate in a number 
of mesh and spatially separated metiers (e.g. TR1_VIIfg, TR2_VIIbc,e-k). 

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It should be noted that no effort was expended on ensuring data quality at this stage, as 
this was not considered a priority for the prototype.  Instead, the data was taken “as 
was” and excluded if it failed to load (e.g., fleet segments without data).  Clearly this is 
an important consideration in the final conclusion of the fleet and metier segmentation to 
be used in the model. 
In addition to the component sections below, the following steps were followed to 
generate the fleet component: 

  Landings and discards numbers-at-age for o10m vessels were used to apportion volumes for u10m 
vessels according to the same age-pattern, and then the combined dataset was standardized to ICES 
assessed data on total catch-at-age (to ensure consistency with assessment removals). 

  Fishing effort and capacity (number of vessels:  note this is in same units as effort) was taken from the 

STECF database according to the same métier definitions as for catch-at-age. 

  Landings and discards weights at age were taken from the relevant stock assessment data. 
  Historic landings selectivity and discards selectivity were calculated at-age according to relative 

proportion of the total catch-at-age of that stock by that métier. 

There were many fleets and métiers that were excluded from the final FLFleetExts object 
either due to: i) effort but no catch of the 3 stocks; ii) catch but no effort (i.e. poor 
data); or iii) no catch-at-age data. No attempt was made to address these data issues in 
the prototype due to time constraints, and the relevant fleet or métier was excluded.  In 
the final fleets object, there were 22 fleets, fishing in up to five métiers each and up to 
three stocks per metier. 

3.2.3  Catch model 
The catch model describes how effort exerted per metier translates into catch. To do so 
an age-structured catch model was set up using: 

  Catchability calculated using the Cobbs-Douglas production function, so that: 

catch.qa,fl,m,s = Ca,fl,m,s/(Ba,s*Efl,m) 

o 
o  Where C,a,fl,m,s is Catch of age a, for fleet fl, métier m and stock s, and B is biomass and E is 

effort. 

o  Cobbs-Douglas alpha and beta parameters were set at 1 and 1 respectively for every 

métier/stock (based on expert knowledge). 

3.2.4  Effort model 
Two effort (kW days) models, which describe the short-term dynamics of the fleets, were 
chosen for the prototype for illustration: 

  Fixed effort, which averages the effort components across the years 2009-2012. 
  Simple Mixed Fisheries Behaviour. This enables the effort in a mixed fisheries context to be defined 
based on the effort required to obtained the minimum effort species, maximum effort species or other 
mixed fisheries scenarios. The minimum situation was chosen for the prototype to reflect a landings 
obligation scenario. 

3.2.5  Economic Model 
The inputs and outputs for the model have been defined and developed as well as the 
calculation steps required to simulate their changing levels over the modelled period. 
A summary of the input data feeding into the model for the economics module, all by 
fleet and metier are: 
  Landings volume  
  Landings value  
  Discards volume  
  Effort, days at sea  
  Prices  
  Fuel prices  
  Costs (by fleet only): crew, fuel, variable, fixed, capital, and investment. 

Landings and discards volumes as well as effort in days required are provided by the 
production module in the model. A separate price module has been developed to 
accommodate price dynamics in changing levels of landings (see below) to indicate 

85 

 
 
 
landings value. Initial ideas regarding the use of discards volume have also been 
considered to assist in the development of scenarios where some catch may not be fully 
appreciated by vessels (e.g. that for fishmeal) which could affect their profitability 
considerably.  
It is worth noting that calibration of fleet effort in days fished from an economic point of 
view requires specific consideration in the model. For example, the biological module 
does not necessarily require this directly as its fishing mortality to a stock that is being 
tracked and modelled. Of course the level of analysis is important as fishing effort 
towards a stock is not necessarily represented in a directly comparable way to the fishing 
effort exerted by a fleet during a year. For the economic and biological module to link 
these different views need to be reconciled and combined. Data preparation and 
calibration have been considered in this way for the prototype and will be developed 
further for the full model. 
Related to this is the activity of fleets outside the case study area which is also an 
important aspect of fleet profitability. The development of data for the prototype has 
considered the implementation of this, for example as fixed external revenue and 
changing external revenue. Of course the dynamics of fleets that only spend a small 
proportion of their average time in the case study area are less impacted by measures 
only focused in that area. Therefore, data and economic methods have been developed 
for implementation in the full model.  
For the economics to work, fleets need to represent the (total) activities of vessels as 
that’s where the viability of fleets is measured. The outputs provided directly from the 
economic module, at various levels of aggregation, are by fleet: 

  Total revenue (case study area and externally) 
  Total costs (crew, fuel, variable and fixed) 
  Total effort 
  Gross cashflow (or ‘operating profit’) 
  Gross value added 
  Net profit 
  Break even revenue 

The prototype model has been developed to include economic data at the levels 
described above and tested under the prototype environment. The use and development 
of these components in FLBEIA is ongoing. 
Dynamics of economic variables in the model are being developed under several 
components: 

  Price dynamics 

o  Prices – market changes, driven externally by larger markets in Europe etc. 
o  Fuel prices – again externally driven 

  Fleet dynamics 

o  Fleet investment (new vessels, new gear, etc.) 
o  Fleet exit/entry (from the modelled area) 

  Activity dynamics 

o  Fleet metier choice (within the modelled area) 
o  Changing Quota / catch composition – could include assumptions of buying/leasing quota to 

better match changing catch composition (even though that’s very difficult!) 

Price dynamics 
Price models have been investigated and several approaches have been recommended. 
Also, offline price analysis has been undertaken to ensure that publically available data is 
representative of market prices seen by the fleets modelled in the Celtic Sea. 
FLBEIA contains 2 price models to consider the future projection of prices in the fishery 
modelled. These include: 

 

 

constant (or fixed) prices – where the base year prices are given as input data and are unchanged within 
the simulation, 
elastic prices – base year prices are adjusted by a price elasticity parameter in a given year based on the 
change in the level of landings to the base year. 

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The price model function is dimensioned by fleet and stock and updates the price-at-age 
at stock, metier and fleet level in each time step and iteration. An example of price 
model specification in FLBEIA is: 
>fleets.ctrl[['FL1']][['ST1']]$price.dyn <- 'fixedPrice' 
Although price is stored at metier and stock level in FLFleetsExt, this function assumes 
that price is common to all metiers within a fleet and it is calculated at fleet level. 
Note that to write new functions for price it must be taken into account that the input 
arguments must be found among fleets.om function arguments and that the output must 
be a list with an updated FLFleetsExt object. 
The following sub-sections consider the various price model options for DAMARA using 
the case study ICES sub-areas VIIf and VIIg.  
It’s recommended that all three approaches plus that of constant prices be implemented 
in the DAMARA model. It is also recommended that different price models are specifiable 
for each stock modelled and tested accordingly. 
Price elasticity model 1 (in FLBEIA) 
Price flexibility assumptions indicate the percentage change in price given a 1% change 
in landings – typically 0.3% - where an increase in landings will indicate a decrease in 
price and vice-versa.  
The following is the specification for FLBEIA – however the landings base year (0) should 
be the denominator: 

There are two issues to consider for FLBEIA with regard to this formulation for ICES sub-
areas VIIf and VIIg: 

1.  The landings from VIIf and VIIg alone form only a small proportion of European landings for species 

and therefore may not drive prices. 

2.  Calculating the price flexibility value will likely be larger for many fleets and stocks due to small 

amounts of data as well as point 1 above. 

Price elasticity model 2 
Price elasticity of supply is a measure used in economics to show the responsiveness, or 
elasticity, of the quantity supplied of a good or service to a change in its price. The 
elasticity is represented in numerical form, and is defined as:  
the %age change in the quantity supplied divided by the %age change in price 
If the coefficient is less than one, supply is inelastic, and if the coefficient is greater than 
one, supply is elastic. 
Price forecasting models 
AR model: an autoregressive (AR) model is one where the current value of a variable 
(i.e. price) depends on the values that the variable took in previous periods plus an error 
term.  
MA model: a moving average (MA) process is one in which the systematic component is 
an average of the previous m time periods.  
ARMA model: an ARMA model combines AR and MA models, so that the current value 
depends linearly on its own previous values as above. 
A simple AR model based on the previous period (with monthly dummy variables) has 
been investigated for selected UK and Irish fleets and species. Results for UK TR2 fleets 
Nephrops is shown in Figure 3-1 below: 

87 

 
 
 
Figure 3-1. Example of price formation for a fleet and species over time with forecasting models imposed. 
UK Nephrops prices were taken as an example of fluctuating prices to gauge the effect of 
using forecasting models. Forecasting from the beginning of 2012 (grey line) follows the 
trend of the actual prices (blue line) very well. Forecasting from the beginning of 2012 is 
less close however the peaks still appear to be consistent suggesting that prices in late 
2011 did not follow previous trends. This is a particularly powerful approach in the short-
medium term11. 
Fleet dynamics 
Fleet dynamics considers two key elements as indicated above, namely: investment in 
vessels (e.g. new vessel, new gear etc.) and vessel exit/entry. The former is restricted in 
the sense that new licenses are suddenly available, vessels would be replaced if anything 
but may increase in capacity, and new gear would generally improve the efficiency of the 
fishing. The latter is somewhat related to choice of fishing location, however it also 
relates to vessels exiting or entering different fleet segments. Furthermore, these two 
elements can work together. 
The number of vessels over time changes in a fleet and is an important relationship to 
model. Vessels are only allocated to a fleet based on “similar” fishing activity that is 
typically based on gear used, size of vessel and area fished. An output showing changes 
to fleet size versus total vessels in all fleets is presented in Figure 3-2 from STECF-DCF 
data. Where total vessels (dotted line) is relatively stable the number of vessels in each 
fleet has changed considerably in the under 10m, 12-18m and 18-24m demersal trawl 
fleets presented.  

Figure 3-2. Number of vessels attributed to UK fleets in STECF-DCF database based on main gear. 
The capital model in FLBEIA is intended to simulate the strategic behaviour of the fleets 
with regard to investment and disinvestment dynamics. At a case study level this is more 

11 Gordon, D. 2010 Price Modelling in the Canadian Fish Supply Chain with Forecasts and Simulations of the Ex-
vessel Price of Fish. FAO workshop on Supply Chain Analysis Tokyo, Japan, December 2010. 

88 

 - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,5002007_12007_32007_52007_72007_92007_112008_12008_32008_52008_72008_92008_112009_12009_32009_52009_72009_92009_112010_12010_32010_52010_72010_92010_112011_12011_32011_52011_72011_92011_112012_12012_32012_52012_72012_92012_112013_1Price in tonnesForecast of UK TR2 Nephrops price using simple AR modelPriceCONDITION_2012CONDITION_20100100020003000400050006000700005010015020025030035040020082009201020112012Total vessels in UK (dotted line)Total vessels by fleet (solid lines)GBR_DTS_VL0010GBR_DTS_VL1012GBR_DTS_VL1218GBR_DTS_VL1824GBR_DTS_VL2440GBR_DTS_VL40XXTOTAL VESSELS 
 
 
 
                                           
 
relevant at a national level than at the case study level in DAMARA. However, the capital 
model can affect fleet capacity and catchability. For example, catchability could be 
modified through investment in technological improvement and capacity could be 
modified as a result of an increase (investment) or decrease (disinvestment) in the 
number of vessels. Changes in fleet capacities could produce a variation in quota share 
among fleets, if for example vessels do not operate on a system of individual quotas. It is 
important that the model is not developed with ‘generalistic’ dynamics that have no basis 
for validation.  
FLBEIA currently has a fixedCapital approach and a simpleCapitalDynamics (SCD) 
approach. The SCD is based on modifying the number of vessels according to changes in 
Revenue measured against break-even.  
It is recommended that both approaches are used but with some modification to the SCD 
approach to ensure that changes in fleet capacity reflect those expected either through 
econometric analysis or stakeholder validation. This can result in desired modelling 
outcomes with appropriate parameterisation.  
Activity dynamics 
Catch composition and allocation of quota to fleets could change under certain scenarios. 
For example, under the landing obligation and may be due to changing gear, area fished 
as well as changes in buying/selling quota. Assumptions on each of these will be 
considered offline and the results fed into the model for analysis. Production module 
parameters as well as economic module parameters can both be considered for update if 
a scenario requires it.  
Stakeholder input, as undertaken in DAMARA, into defining options available to vessels is 
critical in providing initial calibration of the model to the case study. 
Fixed prices are used in the prototype ( 

Figure 3-3). Price is not currently disaggregated by fleet. 

Figure 3-3. Price information used for the prototype. The average price across the 4 years is used. 
Additional value information by fleet demonstrates the importance of different species to 
different fleets operating in the region (Figure 3-4). 

89 

0.000.501.001.502.002.503.003.502008200920102011Price (EUR per KG)CODHADOTHERWHG 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3-4 Species composition by value in sub-areas VIIf&g (by species and by fleet). 

3.2.6  Covariables Operating Model 

Additional important economic covariates were included in the covariables operating 
model, including: 
  FuelCost 
  CapitalCost 
  Salaries   
  Number of Vessels 
  Max Days 
  Employment per vessel 
  Variable costs 
  Fixed costs 

3.2.7  Prototype Management Procedure Model 
The management procedure model of the prototype is currently quite simplified in its 
setup. 
Observation Model 
The observation model, which generates the observed data by stock is currently setup as 
perfect observations without any errors. That is the status of the populations is known 
perfectly. This was viewed as the simplest possible entry point for the prototype. 
Assessment Procedure 
Again, the simplest possible setup for the prototype was chosen in that no assessments 
are run in the projections with the population status being known exactly. 
Management advice 
ICES harvest control rules were used per stock (Table 3-1) 

90 

0500000010000000150000002000000025000000Total VALUE (EUR)CREMNZCODWHGSCEHADANFSOLNEPOTHER050000001000000015000000200000002500000030000000OTHERNEPSOLANFHADSCEWHGCODMNZCRETotal VALUE (EUR)GBR_FPO_VL1218IRL_TBB_VL2440IRL_DRB_VL1824IRL_DTS_VL1218GBR_FPO_VL0010FRA_DTS_VL2440IRL_DTS_VL2440BEL_TBB_VL2440IRL_DTS_VL1824FRA_DTS_VL1824 
 
 
 
Table 3-1. Harvet control rule reference points used per stock. Reference points extracted from the ICES stock 
assessments.  

Stock  Fmsy  Btrigger 

Blim 

(tonnes) 

(tonnes)  

COD 

0.4 

10,300 

7,300 

HAD 

0.33  7,500 

3,000 

WHG  0.36  21,000 

15,000 

Stock-recruitment projection in the management procedure component is based on the 
geometric mean of the last three year’s recruitment. 

3.2.8  Prototype Model Scenarios  
To test the prototype and to demonstrate the types of outputs that the model is capable 
of, a number of basic scenarios were explored. These were presented to the stakeholder 
group during the DAMARA coordination meeting November 2014. 
The scenarios were deliberately chosen to obtain maximum contrast and used solely for 
demonstration purposes only. Based on previous discussions with the stakeholders, the 
scenarios were based on the types of management interventions that could be 
considered in future. The impacts of these scenarios on fishing mortality, spawning stock 
biomass and catches are forecasted into the future (to 2017) together with the predicted 
impacts on a number of economic indicators.  
The scenarios include a situation where there is no change in fishing effort (1); the 
impact of a combined area/metier closure (2); where fishing activity ceases when the 
first TAC is taken (3) which would be consistent with the forthcoming landing obligation; 
a combination of a minimum TAC and area/metier closure (4) and finally, the impact of a 
change in gear selectivity (5) based on increasing the mesh size in a square mesh panel 
from 100 to 120mm. Selectivity changes were imparted by proportionally altering the 
catchabilities by stock (cod, haddock, whiting) and metier (all fleet TR1 and TR2) for 
whiting, haddock and cod. Baseline and modified selectivity profiles were based on the 
meta-analytical model from the North Sea (Fryer et al., 2014). Further consideration of 
selectivity scenarios is provided in the Prototype Limitations and Comments section below 
(see section 3.3.4). In summary, the prototype scenarios consisted of: 

1.  Assuming no change in fishing effort  

2.  Area closure (metier closure) TR1_VIIfg 

3.  Minimum TAC scenario (“choke”) 

4.  Minimum TAC scenario + closure 

5.  Change in gear selectivity  

3.2.9  Prototype code repository 
The prototype code is currently housed in a Dropbox repository. The structure of the 
repository is: 
Fleet conditioning 
1.  Aggregate stat rectangle level data (Fleet/Metier generation code) 
2.  Generate FLFleet objects and covariates 
3.  Condition fleets for forecast years 

Stock conditioning 
4.  Condition stocks for forecast years 

Management setup 
5. 

Implement effort closures and/or selectivity adjustments 

91 

 
 
 
6.  Advice/Management set-up (HCRs, quota shares) 

Uncertainty setup 
7.  Model set-up (observation errors, assessment models) 

3.2.10 Prototype Model Output 
Plotting routines were developed to present stakeholders with a range of visualization 
options for investigating each scenario run. The output is specifically designed to assist 
with the engagement of stakeholders at different levels of the management process. The 
output can be viewed hierarchically as: 

1)  Stock-level results – what the scenarios mean for the forecasted stock in terms of  fishing mortality, 

spawning stock biomass, landings (Figure 3-5); Kobe plots by representing each stock within the mixed 
fishery by scenario (Figure 3-6). 

2)  Fleet-level results – what the scenarios mean by fleet, in terms of (for example): 

 Effort allocation (Figure 3-7). 

a. 
b.  Economic implications/indicators (Figure 3-8)  

3)  Metier-level results, e.g., economic performance of a given metier. 

In such a manner, the output moves from more traditional output (stock-level) to, 
importantly, what the economic consequences of the scenarios are at the fleet and 
metier level thus closing the bioeconomic loop.  Practically, the output will also be 
provided in tabular format, such that scenario output can be investigated further. 
Figure 3-5 below shows the predicted outomes of the five scenarios on fishing mortality, 
landings and spawning stock biomass.  The model predictions (2013 to 2017) are 
highlighted in grey. Note theat the model is set up using the assessment  outputs from 
the 2013 ICES WGCSE working goup meaning that the outputs relate to the 2012 fishing 
year. The figure show that the choke scenarios (3 & 4) have the largest impact  with 
substantial reductions in fishing mortality and landings compared to the status quo effort 
scenario (1). The area/metier closure (2) has an intermediate impact, while inclreasing 
the mesh size in the square mesh panel (5) has the least impact.   
Further stages of model development will result in a graphical user interface (developed 
under the GEPETO project) where the user chooses the output to be visualized/tabulated 
(Figure 3-9). This app will present the results of model runs stored in a database of 
model/scenario runs. Composite summaries will also be included (e.g., Figure 3-10). It is 
intended to also include the predicted outcomes (e.g. Figure 3-5) of the various 
management strategies in future versions of the visualisation tools to allow stakeholders 
to visualise the outcomes of different management interventions.  

92 

 
 
 
Figure 3-5. Forecasted (grey area) impacts of the 5 scenarios on fishing mortality (upper panel); landings (middle 
panel) and spawning stock biomass (lower panel). 
The outputs can also be presented as a series of Kobe plots which show the predicted 
trajectory of the stock relative to F and SSB targets/limits, this provides a quick visual 
contrast and can be useful for direct comparisons between different scenarios.  

93 

 
 
 
 
Figure 3-6. Kobe plot showing the forecasted trajectory of two different scenarios (1 and 3) relative to Fmsy and Bmsy 
targets.  
The model can also estimate the required fishing effort for a given fleet required to meet 
specific catch objectives. In the example presented below (Figure 3-7) it is clear that 
when minimum TAC (choke) scenarios are considered, the level of effort required reduces 
substantially. This of course assumes that there is no change in technical or tactical 
behaviour by the fleet in response to the management intervention.  Note that scenarios 
2 and 5 (area/metier closure and enhanced selectivity measures) imply that there would 
be no change in fishing effort given that the fleets would continue to fish beyond the 
minimum TAC (choke) scenarios and therefore the predicted effort levels do not differ 
between scenarios 1, 2 and 5.  

Figure 3-7. Predicated effort required to meet the catch objectives associated with the different scenarios.  
The economic impact of the minimum TAC (choke scenario) on the economic 
performance of two fleets is shown in Figure 3-8. This shows that the minimum TAC 
(choke) scenario has a substantial negative short term impact before the situation 
stabilises again on the back of resultant increases in SSB (see Figure 3-5).    

Figure 3-8. Predicted impacts on a range of economic indicators associated with two metiers under minimum TAC 
scenario.  

94 

 - 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 45,000,000200320042005200620072008200920102011201220132014201520162017Scenario CHOKE: GBR_DTS_o10mRevenueGross marginTotal CostsGVANet profitBE-Revenue - 10,000,000 20,000,000 30,000,000 40,000,000 50,000,000 60,000,000 70,000,000 80,000,000200320042005200620072008200920102011201220132014201520162017Scenario CHOKE: IRL_DTS_o10mRevenueGross marginTotal CostsGVANet profitBE-Revenue 
 
 
 
 
 
 
Figure 3-9. Proposed format of user interface/"App", where combinations of scenarios, fleets can be chosen to 
visualize/tabulate by stakeholders. Upper panel shows trends in relative fishing mortality (F/Fmsy) while the lower 
panel shows the trends in relative biomass (B/MSY B trigger). From InterReg GEPETO project 
http://idoshiny02.cloudapp.net:3838/statusapp/ 

95 

 
 
 
 
 
 
Figure 3-10. Proposed format of user interface/"App", where combinations of scenarios, fleets can be chosen to 
visualize/tabulate by stakeholders. 
See http://idoshiny02.cloudapp.net:3838/statusapp/ for the prototype visualisation tool. 
It is also possible to present both biological and economic information simultaneously, 
the rose plot shown in Figure 3-11 presents one possible way to show the impact of 
individual scenarios. This shows the situation at a given time where the circle represents 
the current fishing mortality and spawning stock biomass of a number of stocks (lower 
segments) and fleets (upper segments). The black dots represent specific biological 
objectives (Fmsy and MSY B trigger) for the stocks. The relative difference between the 
current status and coloured segments show the potential impact of a given scenario on 
the number of vessels required by fleet segment for the realisation of specific catch 
targets (upper left); how this would affect their profits (upper right) and the overall 
impact of the scenario on fishing mortality (lower left) and spawning stock biomass 
(lower right). The current number of vessels engaged in the fishery is shown in the 
centre. These could be combined to provide a time series or to contrast different 
scenarios.  
(Note that this output is taken from a simulation run on the Iberian Sea and is only 
shown for illustrative purposes). 

Fleets 

Stocks 

Figure 3-11. Composite example summary from Iberian Sea examples. Top row is Fcube effort dynamics, bottom row 
is profit-maximization effort dynamics; left-column is single species HCRs, right column is a multispecies HCR. Solid 
circles represent the status quo with points representing the MSY reference points.  

The outputs described above are intended for illustrative purposes only and intended to 
demonstrate the types of outputs that are possible and to prime discussion with 

96 

 
 
 
 
 
 
 
 
stakeholders. Ultimately the layout and information contained in the outputs will be 
driven by the needs of the stakeholder group. Initial discussions during the November 
stakeholder meeting suggested that in a number of respects, the outputs described 
above contained too much information and a more rationalised user interaction will be 
required. During the stakeholder consultation meeting, visualisation tools that have been 
developed under the auspices of the GEPETO project were also shown. It was concluded 
that the ability to toggle on and off different fields (e.g. specific species, fleets, 
management options and indicators) would allow for a better and clearer user 
interaction. The next phase of the project will focus on expanding the prototype model 
and working in close collaboration with stakeholder explore and decide on the types of 
user interfaces that would be required by the end users. 

3.3  Key assumptions, uncertainties, limitations and sensitivities thereon 

3.3.1  Data limitations 
Data sources used for prototype model development were based solely on available 
regularly collected data and analyses (e.g. STECF catch and effort data; ICES assessment 
outputs; economic data from AER). During the prototype stage, no attempt has been 
made within the protocol to quality-control the data input other than that already 
conducted for the STECF database.  

AREA 

3.3.1.1  Lasting data sources and quality 
Data limitations of prototype: it is spatially coarse (VIIfg or VIIb-k -dfg) and not seasonal 
because the aggregated STECF data are annual. It was noted that FLBEIA could work on 
data from a step before this to make model quarterly. Please note that several questions 
arose from the modeling group for the stakeholder group to clarify spatial and temporal 
levels, both from what is achievable with data available and what is required by 
stakeholders. In Table 3-2 the TACs for species captured in Western Waters are shown 
for illustrative purposes to highlight the complexity of the spatial aspect of the model.  
TAC SPECIES 
Greater silver smelt  EC and international waters of III and IV 
Greater silver smelt  EC and international waters of V, VI and VII 
Boarfish 
Herring 
Herring 
Herring 
Herring 
Cod 
Cod 
Cod 
Cod 
Cod 
Megrims 
Megrims 
Monkfish 
Monkfish 
Haddock 
Haddock 
Haddock 
Haddock 
Whiting 
Whiting 
Whiting 
Hake 
Blue whiting 
Blue Ling  

(all waters) 
Vb and VIb; EC waters of VIaN (1)  
VIIbc ; VIaS (1) 
VIIa (1) 
VIIg(1), VIIh(1), VIIj(1) and VIIk (1) 
Norwegian waters of I and II 
VIb Vb XII and XIV 
VIa Vb 
VIIa 
VIIb-c,e-k, VIII, IX and X; EC waters of CECAF 34.1.1  
VI; EC waters of Vb; international waters of XII and XIV 
VII 
VI; EC waters of Vb; international waters of XII and XIV 
VII 
EC waters of VIb; international waters of XII and XIV 
EC waters of Vb and VIa  
VIIb-k, VIII, IX and X; EC waters of CECAF 34.1.1  
VIIa 
VI; EC waters of Vb; international waters of XII and XIV 
VIIa 
VIIb, VIIc, VIId, VIIe, VIIf, VIIg, VIIh and VIIk 
VI and VII; EC waters of Vb; international waters of XII and XIV 
EC Int Wat I, II, III, IV, V, VI, VII, VIIIa, VIIIb, VIIId, VIIIe, XII and XIV 
Vib, VI, VII 
VI, VII, VIII, IX, X, XII, XIV  

Ling 

Norway lobster 
Norway lobster 
Plaice 
Plaice 
Plaice 
Plaice 
Plaice 
Pollack 
Pollack 
Saithe 

VI; EC waters of Vb  
VII 
VI; EC waters of Vb ; international waters of XII and XIV 
VIIa 
VIIb and VIIc 
VIIf and VIIg 
VIIh, VIIj and VIIk 
VI; EC waters of Vb; international waters of XII and XIV 
VII 
VI; EC waters of Vb; EC and international waters of XII and XIV 

97 

 
 
Saithe 

Skates/Rays 

Mackerel 
Common sole 
Common sole 
Common sole 
Common sole 
Common sole 
Horse mackerel 

VII, VIII, IX and X; EC waters of CECAF 34.1.1  
VIa, VIb, VIIa-c VIIe-k 

VI, VII, VIIIa, VIIIb, VIIId and VIIIe; EC Vb; nonEC IIa; Int Wat  XII and XIV 
VI; EC waters of Vb ; international waters of XII and XIV 
VIIa 
VIIb and VIIc 
VIIf and VIIg 
VIIh, VIIj, and VIIk 
EC IIa, IVa, VI, VIIa-c, VIIe-k, VIIIa,b,d,e Int Waters of Vb, XII, XIV 

Table 3-2. List of TAC managed species in Western Waters and adjoining areas.  
For the initial areas of ICES subdivisions VIIfg there are specific TACs for only Common 
Sole and Plaice. The most important species in this fishery (i.e. cod, haddock, whiting, 
monkfish and Nephrops) have TACs that cover the entire Celtic Sea and Western Waters 
(ICES subdivisions VIIb-k). 

3.3.2  Economic data limitations and solutions 
The economic data used in the prototype model are presented in Table 3-3. Note that 
variable costs, and fuel costs, in the prototype are specified by metier but have not been 
calibrated to indicate differences in trip distances, they will be included in the full model. 
There are then two key limitations/issues with the AER data: 

  The fleet segmentation used to represent the data, and 
  The quality and completeness of the data 

AER and fleet segmentation 
The definition of fleets in the AER cost data appears relatively high as areas fished by 
fleets are wide. For economic data, ideally fleets should be defined at a level that 
contains vessels with similar activity, for example using the same main gear, being of the 
same length and fishing in the same area. The AER cost data aims to do this, however, 
the majority of fleets contain activity across the whole of the North East Atlantic which 
means that inevitably the fleet segments contain vessels with different activity even 
though they use the same main gear. For example, costs for the GBR_DTS fleet are 
reported for all UK demersal trawlers operating in the Celtic Sea, North Sea, English 
Channel and West of Scotland. With the two step process described above, we’ve been 
working to developing procedures to overcome these data issues and represent the cost 
data of groups of vessels in focus, i.e. in the DAMARA case in ICES areas VIIf&g. 
AER and quality 
As already mentioned, the basic quality of the AER economic data is very good as it’s 
derived from vessel company accounts in most Member States. However, the issue is 
really around the level of aggregation and as indicated above the averaging process of 
presenting fleets at the NE Atlantic level can appear not to represent fleets at lower 
levels. 
The project team have been using the approaches above to find robust approaches to 
improving the views of this economic data for specific cases studies that are transferable.  

98 

 
 
 
 
Table 3-3. Economic data developed from AER and STECF effort databases used in the prototype model. 

€/Day 
Fuel 
Cost 

% over 
revenue 
Crew 
Cost 

€/Day 
Variable Cost 
- Fuel cost 

€/vessel 
and year 

Fixed Cost 

Fleet 

Number of 
vessels 
Number of 
vessels 

FTE per 
vessel 
Employment 

Max Kwdays 
per vessel 

per vessel  Max KWdays 

€/vesse
l 
Capital 
Cost 

 67,638  
107,83
3  

 -  

 -  

 -  

Days 
Max 
days 

 55  

 16  

 69  

 225  

 225  

 816  

 100,767  

 151  

 59,199  

 -  

 -  

 -  

 -  

 -  

 -  

 48  

 16,443  

 59,759  

 165  

 15  

 27  

 37  

 4,338  

 18,757  

 141  

 56,942  

 57,691  

 225  

 6,229  

 23,438  

 103  

 44  

 14,074  

 40,732  

 67  

 7  

 12  

 18  

 5,575  

 2,403  

 115  

 827  

 3,228  

 166  

 109  

 19,508  

 29,806  

 112  

 -  

 -  

 -  

 3  

 355  

 63,194  

 14,370  

 85  

 137  

 52,501  

 32,950  

 180  

 7  

 6,823  

 6,798  

 48  

 674  

 51,231  

 17,693  

 21  

 1  

 -  

 -  

 1,026  

 4,880  

 -  

 -  

 -  

 -  

 24  

 19  

 3  

 4,831  

 20,723  

 60  

 122  

 1  

 1,856  

 580  

 76  

 126  

 6  

 37  

 3  

 22  

 83  

 47  

 33  

 161  

 16  

 30  

 44  

 17  

 6  

 1  

 12  

 46  

 73  

 7  

 283  

 6  

 1  

 43  

 -  

 21  

 60  

 17  

 6  

 12  

 8  

 1  

 205  

23% 

 50  

 95,343  

 43,795  

 171  

0% 

40% 

26% 

0% 

0% 

21% 

24% 

3% 

 -  

 -  

 1,656  

 234,772  

 72  

 76,990  

 -  

 -  

 -  

 -  

 229  

 118,145  

 375  

 104,213  

 -  
155,04
3  
132,68
9  

 -  

 -  
165,47
4  
144,83
5  

 74  

 2,608  

 1,177  

 -  

 10  

 13  

 6  

 107  

 40  

 40  

 1  

34% 

32% 

44% 

30% 

28% 

45% 

48% 

33% 

48% 

46% 

46% 

51% 

53% 

40% 

0% 

28% 

28% 

24% 

27% 

40% 

25% 

0% 

0% 

BEL_DTS_o10m 

 1,321  

BEL_TBB_o10m 

 369  

ESP_DFN_o10m 

ESP_DTS_o10m 

ESP_HOK_o10m 
FRA_DFN_o10
m 
FRA_DFN_u10
m 

FRA_DTS_o10m 

FRA_DTS_u10m 
FRA_HOK_o10
m 
FRA_HOK_u10
m 

FRA_PG_o10m 

FRA_PG_u10m 

 -  

 -  

 -  

 48  

 14  

 60  

 82  

 58  

 6  

 14  

 18  

FRA_TBB_o10m 

 314  

FRA_TBB_u10m 
GBR_DFN_o10
m 
GBR_DFN_u10
m 

 -  

 347  

 4  

GBR_DTS_o10m 

 246  

GBR_DTS_u10m 
GBR_HOK_o10
m 
GBR_HOK_u10
m 

GBR_PG_o10m 

GBR_PG_u10m 
GBR_TBB_o10
m 
GBR_TBB_u10
m 

 17  

 1,463  

 2  

 -  

 -  

 -  

IRL_DFN_o10m 

 2,386  

IRL_DTS_o10m 

 140  

IRL_HOK_o10m 

IRL_PG_o10m 

 -  

 -  

IRL_TBB_o10m 

 615  

NLD_DTS_o10m 

NLD_DTS_u10m 

 942  

 266  

 5  

 5  

 -  

 -  

 -  

 4  

 1  

 6  

 1  

 3  

 1  

 2  

 0  

 3  

 -  

 6  

 1  

 5  

 3  

 92,913  

 105,597  

 10,176  

 204,379  

 36,946  

 58,402  

 12,937  

 119,810  

 10,422  

 30,220  

 6,332  

 25,548  

 16,461  

 26,387  

 379  

 67,088  

 7,663  

 84,291  

 6,903  

 12  

 6,197  

 2  

 -  

 -  

 6  

 -  

 2,109  

 23,447  

 700  

 104,090  

 -  

 41  

 28,313  

 8  

 -  

 -  

 163,336  

 4,573  

 15,320  

 10  

 108,502  

 8  

 1  

 23,181  

 36  

In the first stage prototype, market prices have been approximated from landings 
volumes and values reported against fleet segments. Some initial price analysis indicates 
that landings from sub-areas VIIf&g in the Celtic Sea are too small to drive market 
prices, however that there have been some significant changes in prices for the three 
species considered over recent years. FLBEIA has the potential to include different price 
models to consider such issues. The prototype will in the first instance be built assuming 
constant prices, however parameters will be collated from recent published analysis as 

99 

 
 
 
 
 
 
well as calculated from available data to enable effects of changing prices and price 
flexibility to be investigated via sensitivity runs.  
Further, an assessment of the capability of FLBEIA to the economic effects of changing 
fuel prices will be considered in the prototype. This is dependent on fuel prices being 
separated from other variable costs, but also on the possibility of modeling fuel (e.g. 
Davie et al, 2014) use in a coarsely spatial model such as the prototype. In the first 
instance, however, fuel prices will be considered constant. 
In the prototype, emphasis is on the modular structure of the model such that if/when 
higher temporal resolution is required/possible, definitions can be expanded according to 
data availability. 

3.3.3  Management strategies/scenarios 
The prototype included 5 scenarios to demonstrate the flexibility of the FLBEIA approach 
in addressing the requirements of the stakeholders. To further develop the scenarios, we 
propose a scenario matrix/decision tree approach whereby given settings of a reasonable 
number of management actions are proposed by the stakeholders, e.g.: 
-  Effort-capacity restrictions: effort reductions for specific fleets and metiers; 
- 
-  Area closure: statistical rectangles and time period of closure.  

Selectivity scenarios, pertaining to particular fleets and metiers; 

The combination of these provides a scenario to be investigated. All scenarios will form 
the setup to be run to provide the database of results for visualization. 

3.3.4  Selectivity modelling 
For accurate modelling of selectivity changes, it will be necessary to compare extant 
selectivity trial data for the Celtic Sea with the output from the North Sea model used in 
the prototype (Anon, 2002). This model has been further elaborated to include seasonal 
affects (Fryer et al., 2014) and will be used for future parameterisation of the selectivity 
model (Figure 3-12). The influence of square mesh panel in whitefish can be investigated 
in this manner. The appropriateness of the Fryer et al. (2014) model for selectivity 
scenarios in the Celtic Sea will be tested during 2015 through comparison of selectivity 
estimates obtained from appropriate experiments conducted in the Celtic Sea with the 
predicted model outputs. It will be necessary to consider other selectivity measures of 
relevance to a mixed fisheries management plan in the Celtic Sea and whether sufficient 
data exists to parameterize further scenarios.  

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Figure 3-12The effect of panel mesh size and position on gear L50 (left) and SR (right) for a typical Scottish trawler 
targeting whitefish in December (top), September (middle) and June (bottom). From Fryer et al (2014). 

3.3.5  Spatial scale  
For the prototype demonstration, the spatial scale was based on data from STECF 
dataset (VIIe-k). Choice and implications of scale will be discussed during stakeholder 
presentation of the demonstration model and with the broader project team. Defining 
spatial resolution at this scale will enable the identification of spatially defined metiers. 
Whether ICES stat rectangle or VMS based area definitions are most appropriate in the 
model is an important question. A comparison of catch compositions from Irish, French 
and UK data is underway as part of the DAMARA project to extend the work of Gerritsen 
et al. (2012). Ultimately, a compromise between the level of spatial detail required by 
the stakeholders and model run-time will dictate the spatial resolution. Closely 
investigating the VMS-linked data will enable a comparison of potential area definitions 
among member state fleets to see whether such area definitions are consistent or change 
by country. It was agreed to undertake analysis using French-Irish-British data to assess 

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overlaps, and consistency both between countries and comparing the VMS derived data 
on catch composition with STECF stat rectangle data. It is envisaged that spatial closures 
will occur at the statistical rectangle but the designation of rectangles constituting 
contiguous regions (e.g., predominantly haddock or whiting) would benefit from the 
proposed in-depth, cross-member-state VMS analysis. 

3.3.6  Temporal scale 
The prototype uses a yearly time-step. Issues that require a lower level of temporal 
resolution will be considered during the prototype development for potential inclusion in 
the decision-support tool e.g. for assessment of temporal closures. The temporal scale 
aimed for will be quarterly. This is of particular importance from a choke species 
perspective. 

3.3.7  Discard estimates 
Treatment of stocks without discard estimates: For some stocks discards are known to 
occur but cannot be quantified or are not included in the assessment because of a lack of 
time series of sufficient quality.  How important is it to include the available discard 
information (as opposed to assuming that discards are zero in the fisheries)? Only 
haddock has discards in prototype as other species do not have readily available discard 
assessments undertaken for the area. The updated whiting assessment will also include 
discards.  Inclusion of discards in the forecasts within FLBEIA without their inclusion in 
the assessments will result in forecasts diverging from the single species forecasts. 

4  Stakeholder interaction and feedback 
This section describes the progress that has been made till date regarding the 
stakeholder interaction in the DAMARA project and it outlines the next steps in this 
process. 
The objective of the stakeholder interaction is to:  “Define and implement an 
engagement process that provides the opportunity for collaborative development of a 
tool for evaluation of harvest strategies for mixed fisheries. Establish a process that 
makes the information count in the management system (i.e. how it is used to support 
the development of Mixed fishery multi-annual plan). “ 
To reach these objectives six tasks were identified: 

1.  Identification of key stakeholders to form the Celtic Sea Focus Group in collaboration with 

NWWRAC and national and regional administrations. 

2.  Formation of a well prepared scoping 3 day meeting with representatives of all involved parties to 

focus on agreements on what is expected of each participant, when input and participation is required, 
how we will work together and what the specific products are that will be produced (e.g. written advise, 
workshop reports, etc.). 

3.  Undertake a second workshop to focus on the contents of the decision-support tool. Specifically to 
i) Define expectations of a tool; ii) define information needs and identify sources, iii) identify linkages 
to ICES WG Mixfish and STECF, iv) identify stakeholder input required to economic data. 
4.  Undertake a stakeholder workshop to review technical specification to ensure that the DST is 

developing as intended and to define how to evolve as required 

5.  Undertake a stakeholder workshop to review the prototype tool and example application, define 
how to evolve it as required, and to define objectives for mixed fisheries and the possible harvest 
strategies to achieve them. 

6.  Undertake a stakeholder workshop for the joint evaluation and interpretation of decision-support 
tool. Run the decision-support tool and discuss and agree on interpretation and decide on inputs to 
multi-annual plan and define technical elements of plan based on the model and define who will do 
what. 

In the first part of DAMARA several of these tasks were addressed. The first step was  the identification of key 
stakeholders to form the Celtic Sea Focus Group in collaboration with NWWAC and national and regional 
administrations.  Based on this step selected stakeholder were in invited to take part in the DAMARA 
stakeholder process. To ensure effective involvement of the stakeholders four workshops were proposed.  

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 

 FIRST scoping workshop (June 2014) to focus on work agreement and discuss 
the main ingredients of the decision-support tool. 

  SECOND workshop (November 2014) to present and discuss a first prototype 

model. Emphasis was on: (1) model assumptions, capabilities and limitations; (2) 
Does the model do what's required?; and (3) Are the outputs relevant and useful 
– are there alternatives? 

  Third workshop (scheduled for March 2015) to review the second prototype 

model  and discuss and agree on the user interface.   

  Fourth and final workshop  (summer 2015) to evaluate and interpret the final 
decision-support tool. Emphasis in the this meeting will also be on formulating 
recommendations how to proceed after DAMARA.   

First workshop (June 2014) 
The starting point for this workshop was providing an overview of DAMARA and 
specifying the primary objectives and challenges. This was elaborated with a non-
technical overview of modelling progress. In order to understand  and specify stakeholder 
involvement in DAMARA seven stakeholders were interviewed in preparation of the 
workshop. In these short (30-45 minute) phone interviews eight questions were asked: 
(1) Organisation?; (2) Role in organisation?; (3) Objective DAMARA?; (4) Interest/Stake 
in DAMARA?; (5) Expected outcome?; (6) Role in DAMARA?; (7)Available time for 
DAMARA?; and (8) Other critical issues for success DAMARA? By discussing the outcome 
of the interviews in the context of the context of the primary objectives and challenges of 
DAMARA the participants of the workshop (stakeholders and modellers) were able to 
manage the expectations and define clear roles for the stakeholders. In the workshop we 
also agreed on a communication strategy and a work plan for the remainder of 2014. For 
a summary of the first workshop see appendix 1.    
 Second workshop (November 2014) 
In this workshop a first prototype model was presented and model assumptions, 
capabilities and limitations were discussed. Emphasis was on discussing possible output 
and trying to determine if this meets the requirements i.e. what was originally 
envisioned. Therefore the deliberations focused on determining if possible outputs are 
relevant and useful  and if there might be alternatives? The workshop also discussed a 
work programme for the next stage of DAMARA.  Due to unfortunate circumstances the 
workshop started later on the day then scheduled. This later start enabled interviewing 
two stakeholders from industry on economic components and their expectations.  The 
interview focussed on eliciting insights on fleet response to management measures e.g. 
closed areas. For example: How did the Belgium beam trawlers respond to closure of 
three cod areas? Just displacement of effort? Were there other responses? Another 
question that was asked was:  “What are the main economic drivers for you and how 
would you like to see the economic output presented.” For a summary of this interview 
see appendix 2.  

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5  Proposed work plan Next Steps 

In the forthcoming months, the project team will build on the prototype model to 
produce a beta version which will include all the key stocks. For this purpose, a further 
modelling meeting is scheduled for end February 2015. While the prototype model has 
demonstrated proof of concept, it is necessary that the next phase of the project 
focusses on ensuring that model is fit for purpose in that it is; i) capable of providing 
predictions based on a range of plausible “real life” scenarios and; ii) that the 
dissemination of the outputs is in a form that is understandable and useful for 
stakeholders. A stakeholder meeting is scheduled for the end of March 2015 to progress 
these elements further.  As noted in section 3.3.3, for computational reasons it is 
necessary to build a catalogue of plausible management scenarios. It is envisaged that 
the range of scenarios will be determined through further telephone interviews and 
during subsequent stakeholder face-to-face meetings. Furthermore, the DAMARA 
consortium has also been in contact with the EC Joint Research Centre who are currently 
developing modelling approaches to develop Impact Assessments for forthcoming Multi-
Annual Management Plans. It is anticipated that linkages between the DAMARA project 
and the JRC will be developed through 2015.   

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6  References  
Fryer, R.J., O'Neill, F.G. and Edridge, A. (2014). A meta-analysis of haddock size-
selection data. Fish and Fisheries. DOI: 10.1111/faf.12107 
Gerritsen, H.D., Lordan, C., Minto, C. and Kraak, S.B.M. (2012). Spatial patterns in the 
retained catch composition of Irish demersal otter trawlers: High-resolution fisheries data 
as a management tool. Fisheries Research, 129-130: 127-136. 
Davie, S., Minto, C., Officer, R., Lordan, C., and Jackson, E. (2014). Modelling fuel 
consumption of fishing vessels for predictive use. ICES Journal of Marine Science, doi: 
10.1093/icesjms/fsu084. 

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Appendices  

Appendix I. Report first DAMARA workshop  

Date: 11 June 2014 
Place: BIM, Dublin 
Marloes Kraan and David Goldsborough 

Agreements 

1.  Goal of Damara is to build a decision support tool, not to develop a management plan.  
2.  Member states [MS] will be informed on the process (one way) and will be interviewed by the 

stakeholder WP (David Goldsborough and Marloes Kraan) on their wishes in relation to Damara 
(two way). This is seen as necessary for the uptake of the tool once it has been built. MS will not be 
participating in (in order to prevent institutional un-clarity or strive in the project) nor steering the 
project. Rather the project is led by the SH (=NWWAC), with the commission as observer (as the 
client of the project). 

3.  Timeline [Alex will set up a doodle to put dates to the SH meetings] 

a.  Final GEPETO meeting in July in Vigo 
b.  Before the end of July SH give direction to modellers. David, Marloes and Norman will 

guide that process of communication.[see action points 6 and 7] 

c.  End of July - Meeting modellers in Lowesoft 
d.  October - 2nd SH meeting to try and critically evaluate the first prototype model. [type: 

lunch to lunch meeting] 

e.  Meeting modellers 
f.  December / January – 3rd SH meeting 

4.  Communication 

a.  Dropbox/share point or some sharing place for documents to give comments on. If action 

is required from SH to comment, emails will be sent to alert them.  

b.  Meeting documents will be shared under the NWWAC website. 
c.  After crucial steps in the project, 2 pages non-technical feedback/summary docs will be 

distributed. 

Action points 

1.  All parties [NWWAC representatives to ask their MS] need to look for good economic data of the 

fleets.  

2.  Modellers will look for a market model that can feed into the DAMARA model – to have good 

informed price info. [Simon] 

3.  Modellers are asked to give clarity on the assumptions used to build the model. 
4.  Modellers are asked to do some simple uncertainty analysis. [such as know the fill-ins] 
5.  Modellers are asked to do some simple sensitivity analysis.  
6.  NWWAC is asked to think about how the tool (model) can help them best to develop a management 

plan, and inform the modellers before the July meeting. 

a.  The NWWAC has written a paper in march 2011 – good start; good idea to add new 

developments to that (such as the reformed CFP – discard ban) and add detail to high level 
objectives. 

b.  Think about the following ‘area’s’ of influencing, for the prototype:  

i.  Spatial scale 
ii.  Temporal scale 
iii.  Capacity 
iv.  Effort 
v.  selectivity 

7.  Modellers will let the NWWAC know how they came to 22 fleets. NWWAC is asked to comment on 

the current ‘set up’ of 22 fleets. 

8.  All data available on trials (of square mesh panels, selectivity trials) good to bring that in. 
9.  Norman will check and see which ideas there are to get VMS data in. 
10.  Rewrite the report of the first modellers meeting fit for SH (see 4c above). [Coílín and Norman] 

106 

 
 
 
 
11.  Simon will discuss with Sean options to model downstream economic effects of measures [ for instance 

change selectivity, effect on supply chain (next to effect on stock and fleet). 

12.  Look into the indicators of the MSFD. [Norman] 

Feedback to modellers from this meeting 
Action point 6b  
The NWWAC SH discussed some possible changes over time that they would like to see 
back in the prototype model: 

i. 

ii. 
iii. 

iv. 
v. 

Spatial scale: closed area’s -> Norman: most critical is think about redistribution of effort -> that should 
come from the SH. You (NWW AC) can go to your members and ask them what will happen 
Temporal scale: RTC’s  -> Coílín: probably too difficult to model; area’s too small and short time. 
Capacity: nr. of vessels -> flagged issues here; technical overcapacity (EU ‘thing’, but with pelagic fleet 
not a ‘real’ problem); fleets may move (example Belgium: fishes in more area’s than Celtic Sea); 
economic performance needs to be modelled. Norman will look into this (balanced capacity). 
Effort: could be interesting but relation between effort and F is not linear.  
Selectivity: square mesh panels 

Other issues mentioned: 

-  We’d like that the model has a ‘flag-system’- for instance if MSFD requirements are met or not given 

certain measures taken. 

-  Where is the F coming from – problem identification (colour matrix) 
-  Discard ban: what will the effects be of the discard ban; how will it play out if all catches have to be 
landed? An issue here is that modelling might be more difficult as ‘normal’ behaviour is likely to 
change.  

-  Management plan: once the NWW AC has a better picture of the contours of the Mixed fisheries plan – 

the possible changes for the prototype model will become more clear.  

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Appendix II Discussion about fish prices with Industry (Dublin, November 
2014) 

Prices 
  Prices are not linked to Celtic sea – they’re driven externally 
  Assuming a general trend is realistic 
  Fishermen ideally want stability in prices 
  Cod in the North Sea is perhaps a driver for cod price as well as other fish landed at port 
  Sole swaps – price of gadoids in Brittany the same 
  2014 has been a bad year for prices – fish are being stored - difficult to know what’s driving it! 
  There is always some unpredictability in the market 
  Fishermen do know of EUMOFA, but they don’t use it – for example to find higher prices in auctions. For 

big vessels it is interesting – but there appears to be a lack of need at that level.  

Market 
  The French supply chain demands landing in Brittany so little option to land in UK 
  Belgian boats land in South Wales 
  Auction power on production – they’d like contracts for stability 
  Hake, monk, megrim at auction – fishermen complain about prices – predictable price would mean more 

fishing 

Activity 
  Most French vessels have 2 days steaming and then fish for 1 week 
  They aim to catch enough volume to fill a truck – 1 Belgian beamer, 2 French <20m vessels 
  2 type of French vessels – those that stay in Celtic sea: nephrops VIIfg and gadoids VIIfg and VIIe 
  Cod plan for Belgians is a problem – days at sea 200 for individual vessels – 35 vessels – 270 days is 

required 

  Vessels need all areas to fish 

Behaviour 
  Minimum turnover per day a sea is typically used, and for some average revenue per day 
  Prices won’t change strategy per trip – they do consider response time (trial and error) and look at market 
  Belgian beamers target sole and monk primarily 

Displacement 
  Vessels would mostly move to neighbouring metiers if displaced (to make use of same quota) 
  Any upset (e.g. landing obligation) would result in fishing closer to home – reaction is to stay closer to port 
  Fuel prices are not really a factor (e.g. beam trawls going to area VIII rather than VII) 

Landing obligation 
  The aim of vessels is to avoid catches of non-target species – it’s a first step, but with that can continue! 

Fishermen however don’t see it as feasible.  
It’s suggested that survival exemption could be applied for most of the flatfish 

 
  Haddock, plaice, boarfish in the Celtic Sea are particularly difficult – but any species can be choke. 

Level of analysis 
  Metier 
  Length of vessel not an issue (an aggregated view of the fleet is fine) 

Data 
 
  Also a good view of costs 

Industry has a very good view of vessel activity (numbers and time) 

To get best data into the model would be to use AER and then review by stakeho 

108 

 
 
 
 
10  Annex III DAMARA Decision Support Tool (DST) developer/user 

manual 

109 

 
 
 
DAMARA Decision Support Tool (DST) 
developer/user manual 

DAMARA project team 

January 21, 2016 

Contents 

1 Purpose of this document 

2 Data 

3 Data processing 

4 Model conditioning scripts 

5 Running the model 

6 User interface 

1 

Purpose of this document 

110 

110 

111 

2 

7 

14 

The purpose of this document is to provide a technical user manual aimed at scientists 
(e.g. through JRC or STECF) to enable running of additional scenarios, alternate 
conditioning, further application (e.g. other areas) and future development. It is not 
directed towards stakeholders or end-users, for which there is separate guidance built 
into front-end of the DST (the ‘Shiny app’). It also details some of the conditioning steps 
that has been undertaken for the application to the Celtic Sea case study, though not 
exhaustively. For further detail, please refer to the final project report. 
The guidance is structured as follows: 

•  Data  and  processing  scripts:  structure  of  the  data,  and  the  steps  in  processing 

the data for conditioning the model 

•  Model  conditioning  scripts:  structure  of  the  code  for  elements  of  conditioning, 

and running the DST 

•  Running  the  model  and  parameter  setting:  structure  of  the  folders  which  are 
used,  what  they  need  to  contain  and  how  the  code  reads  from  the  folders  to  run 
the scenarios 

•  Result  indicator  summary  files:  indicators  that  are  produced  as  outputs  from 

running the DST (and which are read into the User interface) 

•  User  interface:  the  ‘Shiny’  R  app  that  is  used  to  present  the  results  of  the 

scenarios for managers and stakeholders 

2 

Data 

The DST was developed with readily available data accessible to scientists working within 
Europe. This principally included information from the annually collected STECF Fisheries 
Dependent Information (FDI) database (https://datacollection.jrc.ec.europa.eu/ 
dd/effort), STECF Annual Economic Report (AER) data (https://datacollection.jrc. 

110 

 
 
ec.europa.eu/dd/fleet) and stock assessment outputs from ICES (https://www.ices.dk). 
While the STECF data is publically accessible and downloadable from the website, the 
ICES data was collated from the Celtic Seas assessment working group assessment 
outputs via direct contact with relevant assessment scientists (WGCSE; 
http://www.ices.dk/community/ groups/Pages/WGCSE.aspx). 
The following ‘raw’ datasets were used to set-up the model: 

2.1. STECF FDI non-spatial landings and discards by species, country, m´etier, year 

This dataset includes total landings (tonnes) by species or species group (e.g. 
anglerfishes are under a single code), by country, gear type and mesh size (e.g. 
TR1) and year according to area groupings as required for analyses undertaken by 
the STECF FDI group. These are generally aggregations of ICES divisions and 
subdivisions, such as 7BCEFGHJK denoting ICES areas VIIb,c,e,f,g,h,j,k. 

2.2 STECF FDI landings (tonnes) by species, country, m´etier, ICES statistical rectangle, 
year and quarter 

This dataset is similar to [1] above, but covers landings only disaggregated by 
ICES statistical rectangle and year-quarter. 

2.3 STECF FDI landings and discards numbers-at-age by species, country, m´etier, year 

This dataset provides landing and discard numbers at age by year (in thousands). 

3.4 STECF FDI fishing effort (measured in killowats per day) by country, m´etier, year 

This dataset contains the total fishing effort (KW day−1). 

3.5 STECF FDI fishing effort by country, m´etier, ICES statistical rectangle, year and 
quarter. 

This dataset contains the fishing effort deployed (hour−1) per ICES statistical 
rectangle and year-quarter. 

3.6 ICES stock assessment outputs 

This data is in the form of a collection of ‘.RData’ files storing the population 
variables for each of the stocks in FLR FLStock format. Each stock object is given 
the variable name ‘stock’ with the name slot denoting the stock/species (e.g. 
COD). Within the FLStock object is contained all of the assessment outputs from 
the most recent ICES WGCSE assessment including population numbers at age, 
fishing mortality estimates at age, weights at age in the catch, landings and 
discards etc... 

3.7 STECF AER data by DCF fleet segment 

This dataset contains multiple variables used for conditioning the economic inputs 
to the model. This includes: data on landings (tonnes) and landings value (€), 
variable costs (€ kw−1day−1, broken down by fuel costs, crew costs etc...), fixed 
costs (€ vessel−1, broken down by depreciation costs, investment costs etc...), 
employment (FTE vessel−1), and revenue from other species (€ kw−1day−1). 

3 

Data processing 

This section describes the data pre-processing that was undertaken. 

111 

 
 
Figure 1 summarises the processing steps which were required to generate the input 
variables for the biological and the fleet components of the model. Because the data 
were of variable quality and completeness, there was a necessary compromise between 
making use of what was available and developing the model at the resolution (spatial, 
age-disaggregated) required. The processing step attempts to make best use of the 
different datasets, to provide landings and discards at-age for the fleets and m´etier 
included in the model. Further details on data quality etc.. are described in the final 
project report. 
In summary, the following processing steps take place using the R scripts developed in 

the project (all scripts are housed on github, 

https://github.com/mintoc/DAMARA/tree/master/data_ processing: 

3.1 Evaluating Threshold for fleets and metiers.R 

This script seeks to help identify an appropriate aggregation of the catch and effort 
data in the STECF database. This is in order to define a number of fleet and 
m´etier reflecting the important dynamics in the fisheries, without resulting in an 
computationally overly burdensome large number of fleet and m´etier. 

112 

 
113 

 
 
Firstly, fleets are generated based on aggregating common gear types into a single 
fleet definition consistent with the DCF/AER data. For example, catches and effort 

by French TR1 and TR2 gears for vessels >10m may be assigned to the FRA DTS 
O10M fleet. 
Secondly, an aggregating process is undertaken to reduce the number of fleets 
incorporated in the final model. Ultimately it is a subjective decision as to what are 
the ‘improtant‘ fleets to be modelled, but as can be seen in the example in Figure 
2, aggregating smaller fleets into an ‘others‘ fleet significantly reducing the number 

of fleets in the model, in this case where fleets catch <2% of any single stock 
being modelling, the fleet is aggregated into the other fleets - this reduces the 

number of fleets from >60 to 17 (16 modelled fleets, and an additional ‘others’ 
fleet to capture the residual catch. A similar process can be undertaken for the 
smaller m´etier within a fleet. 
The final output is a list of fleet and m´etier (‘List of Fleets and metiers.csv’) that 
can be used consistently throughout the data processing. A separate script (‘00 
Cleaning fleets and metiers.R’) then provides a basis for applying the structure to 
the other datasets. 

Figure 2: Example of trade-off between the number of fleets and the coverage of fleet-
based modelling of catch by the fisheries 

3.2 Comparison ICES STECF catch at age for correction factors.R 

As there are known to be inconsistencies between the STECF and ICES catch data, 
and we sought to ensure that the best quality data were input to the model, this 
script compares the sum of products (SOP) of the STECF landings and discards at 
age with the ICES assessment landings and discards at age. Further, it then 
generates a ‘correction factor’ to be applied to the STECF fleet and m´etier based 
data, to ensure consistency with the assessment. In this sense, the STECF catch-
at-age is only used as a relative reference to apportion catches to the fleet and 
m´etier. 

3. 3 Landings Discards N  at age.R 

This script does three things: 

•  It  processes  the  STECF  catch-at-age  data  to  ensure  it  is  the  same  fleet  and 

m´etier structure as identified for the model. 

•  It aggregated the data across some ages where the ICES assessment contains 

a plusgroup, but the STECF data details catches separately 

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•  It applies the ICES/STECF correction factor to normalise the STECF data to the 

total ICES assessment estimates of landing and discard numbers-at-age 
Finally, it produces the input file ‘File1 Landings Discards numbers atage perFleet   
nonspatial nontemporal.csv’. The file contains the following structure: 

Variables include: 

variable 
landings.n 

description 
landings numbers at 
age 

units 
thousands 

discards.n  discards numbers at 

thousands 

age 

The exception in the DAMARA implementation was for Hake and Megrim, where no 
reliable data from STECF was available. In this circumstance, m´etier based data 
was obtained directly from the stock coordinators and mapped to the fleet and 
m´etier used in the model. Thiis was a separate input file (HKEMEG.RData) 

3.4 Landings Discards t.R 

This code aggregates the landings (t) and discards (t) according to the fleet and 
m´etier definitions required for each stock included in the model. It produces the 
input file ‘File2 Landings and discards tonnage.csv’. The file contains the following 
structure: 

Variables include: 

variable  description  units 
landings 
landings 
discards  discards 

tonnes 
tonnes 

The exception in the DAMARA implementation was for Hake and Megrim, where no 
reliable data from STECF was available. In this circumstance, m´etier based data 
was obtained directly from the stock coordinators and mapped to the fleet and 
m´etier used in the model. Thiis was a separate input file (HKEMEG.RData) 

3.5 Relative Landings by rectangle and quarter.R 

This code aggregates the spatial landings according to the fleet and m´etier 
definitions required, and produces a file which has the proportion of landings by 
each fleet and m´etier in each ICES statistical rectangle and quarter. It produces 
the input file File3 Proportion spatial and temporal landings.csv. The file contains 
the following structure: 

3.6 Relative Effort by rectangle and quarter.R 

115 

 
 
 
 
This code operates similarly to 03, in that it aggregates the spatial effort by fleet 
and m´etier and then produces an input file with the relative effort distribution 
across ICES statistical rectangles and quarter. The input file produced is ‘File4 
Proportion spatial and temporal effort.csv’. The file contains the following structure: 

3.7 KWdays Fishing effort.R 

This code produces an overall input of fishing effort (kw−1day−1), at the fleet and 
m´etier level per year. The final input file produced is File5 KWDays Effort.csv. The 
file contains the following structure: 

3.8 Processing economics.R 

This code tidies up some of the pre-processed AER data to produce two input files: 

•  File6 Economic  Data.csv 

Pre-processing the economic data was carried out in Excel and involves 
disaggregating AER data for the DCF segments active in the ‘Focus Area’ and 
for those fleets included in the model based on the fleets that landed equal to 
or over 2% of the overall landings. Operational cost variables from the AER 
were divided by the total effort (kW Days) of the fleets in Annexes BoB, Cel1, 
IIA and IIB from the STECF effort database to provide operational costs per 
kWDay. The AER operational costs of ‘Other variable costs’ and ‘Energy costs’ 
were disaggregated in this manner. Landed revenue from other species not 
included in the biological model from the ‘Focus Area’ by fleet was also 
disaggregated in this manner. Fixed and capital costs were disaggregated on a 
vessel basis through the use of the composite fleet size variable of the 
included fleets. Fixed costs refer to the sum of AER variables ‘Other non-
variable costs’ and ‘Repair costs’. Depreciation costs refer to the AER variable 
‘Annual depreciation’ and capital costs were estimated as depreciation costs 
plus 10% of the capital value of the vessel (AER variable ‘Depreciated 
replacement value’). Other Revenue was calculated on a per vessel level 
covering the revenue attained from outside the ‘Focus Area’. Employment, 
engine power, total effort and effort in the ‘Focus Area’ were also calculated on 
a vessel level. Finally, crew share was calculated as a percentage of total 
revenue dividing AER variables ‘Crew wage costs’ by ‘Landings value’. 
This file contains all the fleet and m´etier level economic input variables. The 
file contains the following structure: 

Variables include: 

116 

 
 
 
 
variable 
KWDays/vessel 

description 
measure of fishing effort 

AvgKW/vessel 

measure of vessel power 

units 
kw 
day−1vessel−1 
kw power 
vessel−1 

KWDaysInB-KIncFG/vessel 

fishing effort in the Focus Area  kw 

MaxCapacity/vessel 

MaxCapacity/fleet 

Maximum effort deployable 
per vessel 
Maximum effort deployable 
per fleet 
total fixed cost per vessel 
crewshare of revenue 
depreciation cost per vessel 
Capital value per vessel 
Cost of capital per vessel 
Number of jobs per vessel 
variable costs per unit effort 
Fuel cost per unit effort 
Number of vessels in fleet 

fixedCost/vessel 
crewShare 
depreciationCost/vessel 
capitalValue/vessel 
capitalCosts/vessel 
employment/vessel 
variableCost/kwDay 
fuelCost/KwDay 
#Vessels 
OtherRevenueinBKIncFG/KwDay  Revenue from other species in 

OtherRevenue/vessel 

OtherAreaVariableCost/vessel 

fcost total 
vcost total 

•  File7 Price Data.csv 

focus area 
Revenue from fishing outside 
focus area 
Variable costs outside focus 
area 
Total fixed costs for the fleet 
Total variable costs in the 
focus area for a fleet 

day−1vessel−1 
kw vessel−1 

kw fleet−1 

€ vessel−1 
No unit 
€ vessel−1 
€ vessel−1 
€ vessel−1 
FTE vessel−1 
€ kw−1 day−1 
€ kw−1 day−1 
No unit 
€ kw−1 day−1 

€ vessel−1 

€ vessel−1 

€ fleet−1 
€ fleet−1 

This file contains the fleet and m´etier species stock level inputs on landed 
price (€ tonne−1) and price elasticity parameters. Price elasticities were 
estimated outside of the model and determine the response of fish prices to 
landings. An example of the data used to estimate the price elasticies is found 
in Figure 3, with more detail on the conditioning in section 4. 
Variables include: 

variable 
Price 
Price elasticity 

description 
Landed price of species 
Price elasticity parameter 

units 
€ tonne−1 
No unit 

117 

 
Figure 3: Example of estimated price elasticity for haddock for each market grade. Data 
used was external to the DAMARA model 

3.9. Additional data inputs 

There were additional data inputs to the model coming from sources other than 
STECF. These were: 

•  ICES assessment data 

These were in the format of an FLR FLStock object per stock. Variables 
contained included: 

variable 
catch 
catch.n 
catch.wt 
discards 
discards.n 
discards.wt 
landings 
landings.n 
landings.wt 
stock 
stock.n 
stock.wt 
m 
mat 
harvest 
harvest.spwn 

m.spwn 

description 
catches 
catch numbers-at-age 
catch weight-at-age 
discards 
discard number-at-age 
discard weight-at-age 
landings 
landings numbers-at-age 
landings weight-at-age 
stock size 
stock numbers-at-age 
stock weight-at-age 
natural mortality 
maturity 
fishing mortality 
proportion harvest before 
spawning 
proportion natural mortality 
before spawning 

•  Stock-Recruit fits 

units 
tonnes 
thousands or millions age−1 
kg or g age−1 
tonnes 
thousands or millions age−1 
kg or g age−1 
tonnes 
thousands or millions age−1 
kg or g age−1 
tonnes 
thousands or millions age−1 
kg or g age−1 
- 
- 
f or hr 
- 

- 

For each of the stocks with population dynamics, a stock-recruit relationship is 
fit using the standard FLR procedure by calling as.FLSR() on the stock object 

 
 
 
 
and estimating the parameters using fmle(). These are then saved as a list of 
SR fits9 
(sr.fits.RData). 

•  Biomass-Dynamic fit for Nephrops in FU22 

To illustrate the capabilities of the biomass dynamics component of the model, 
a surplus production assessment model was fit for Nephrops in FU 22. The 
model estimated the intrinsic rate of population growth and carrying capacity 
of the stock using ICES landings and two indices of abundance from ICES 
(2013)12 the underwater TV survey as an absolute index and the Irish otter 
trawl Nephrops directed fleet as a relative index of abundance. A state space 
surplus production model was implemented in AD-Model Builder. It is 
important to stress that the purpose of this exercise was not to develop an 
assessment model for FU22 but to illustrate the capabilities of FLBEIA to 
incorporate biomass dynamic stocks. 
•  Grade/age-specific fish price factors 

Sales notes data from Ireland, the UK and France were analysed to estimate 
average prices by size grade for the model species. Annex II of Council 
Regulation 2406/96113specifies the size categories of fish stocks by weight per 
fish and by number of fish per weight. This analysis resulted in an adjustment 
factor which determined the variation of the age-specific price from the mean 
price for the stock and fleet. The process broadly was to match the weight of 
different grades of fish to the mean weight at age for the species to allocate 
different grade prices to ages. Figure 4 demonstrates how this was applied, 
with each age being associated a price that deviated from the mean according 
to its relationship to the weight at each grade and age. The overall value of 
landings for each stock, fleet and m´etier was then normalised to the total 
value of landings for the fleet, m´etier and stock to ensure consistency with 
the overall landed value. 

•  Selectivity conditioning under the landings obligation 

Because under the landings obligation the selection pattern (that is, the 
proportion of each age landed and discarded) will differ from historic 
observation the parameterisation under the LO was based on a separate 
analysis. Here, selection pattern at age for landings and discards were 
computed using the proportion of fish under and above MLS (Minimum 
Landing Size). Mean weight at MLS are calculated for each stock using Weight-
Length relationship and compared to mean weight at age of the catch. This 
then determined a ‘LO selectivity func.csv’ input file. 

•  Gear-based selectivity changes 

To illustrate the effects of a change in selectivity, an example selectivity 
change of the introduction of a 120mm square mesh panel for the TR1 and 
TR2 in the Celtic Sea was implemented. The process was: 

–  Obtain current and SMP selectivity parameters  (Length at 50% selectivity 

and selectivity range) for cod, haddock and whiting from Fryer et al

14. 

12 ICES. 2013 . Report of the Working Group for Celtic Seas Ecoregion (WGCSE) , 8– 17 May 2013 , Copenhagen, 

Denmark. ICES CM 2013/ACOM:12:1 

13 COUNCIL REGULATION (EC) No 2406/96 of 26 November 1996 laying down common marketing standards for certain 

fishery products 

14 Fryer, R. J., O’Neill, F.G. and Edridge, A. (2014), A meta-analysis of haddock size-selection data. Fish and Fisheries. doi: 

10.1111/faf/1210. 

 
 
 
 
 
 
                                           
 
–  Using  average  length-at-age  by  species,  calculate  percentage  change  in 

selectivity at age, which we term catchability multipliers. 

–  Alter  the  catchability  for  all  TR1  and  TR2  m´etiers  by  multiplying  their 

historical catchability-at-age by the catchability multipliers by species. 

Figure 4: Plot overlaying mean weights per market grade [purple = grade 5, blue= grade 
4, green = grade 3, yellow = grade 2, red =- grade 1] with the mean weight-at-age for 
each of the age-structured stocks in the model 

Note that this approach does not account for growth within a year, which may 
be important but is used to illustrate the capabilities of the model to include 
selectivity changes. 

The processing steps above use the STECF landings- and discards-at-age data by 
m´etier and year to allocate the absolute estimates of landings and discards at age 
coming from the relevant stock assessments to the m´etier, which is then further 
disaggregated spatially using the spatial landings information by statistical rectangle and 
quarter from STECF as a relative reference. The assumptions inherent in this process 
(e.g. common spatial structure in catch-at-age) are not insignificant, and where possible 
attempts should be made to improve the data quality in the model. However, the final 
output is catch-at-age that is consistent with recent ICES assessments while 
characterising the catch (including discarding and selection patterns) of the different 
fleets and m´etier in the model. A similar process is undertaken to apportion the fishing 
effort data across ICES statistical rectangles and quarters. 
The biological parameters were compiled from the respective stock assessments and 
processed into FLR FLStock objects. FLStock objects are not an input to FLBEIA (with 
FLBiols used to capture the biological data) but the DAMARA data processing steps used 
some of the additional information contained in the FLStock objects to parameterise 
missing information elsewhere (e.g. landings- and discards-weights-at-age). 

4 

Model conditioning scripts 

 
 
 
 
 
 
 
 
4.1 

Folder structure 

All the code, data and functions required to run the model are stored on github 

https:// github.com/mintoc/DAMARA. To run the model, the following folder 
structure is required: 

•  code  >All  scripts  for  running  model  [need  as  minimum  the  mastercode2.r  and 

scenarios.r]  –  scenario  inputs  >All  set-up  files  [This  folder  must  contain  the 
Biological Reference Points (BRPs) used in any Harvest Control Rules (HCRs), a file 
titled  ”scenario  inputs’.csv’,  which  covers  the  individual  model  run  settings  and 
individual scenario fleet and stock files settings files (more detail in Section 5)] 

•  data >All data input files 

–  fleets >All the fleet inputs 

–  object  inputs  >Some  of  the  pre-processed  object  inputs  [sr  fits  and  NEP  BD 

must be in here] 

–  model inputs >[empty] For storing the model inputs processed from the data 

–  stocks >All FLStock objects [only] 

–  temp data >[empty] For storing some temporary generated objects 

•  results >[empty] For storing the results of the scenarios 

•  plots >[empty] For storing any plots 

4.2 

R package versions 

The FLBEIA code operating in the DAMARA project can be installed from github through 
the following R commands: library(devtools) install github(”pdolder/FLBEIA@damara”) 
Specific packages and the version numbers that have been used are: 
reshape2 1.4.1 

FLFleet 2.5.20140531 

FLBEIA 1.06  — 

— 

—

Rdonlp2 3.0.0 — FLAssess 2.5.20130716 — FLash 2.5.2 — FLCore 2.5.20151202 

— MASS 7.3-44 — lattice 0.20-33 — devtools 1.9.1 — dplyr 0.4.1 — nloptr 1.0.4 

These may themselves call for various dependencies. The packages called during a run 
are automatically output to a Package versions.csv file in the ‘code’ folder. 

4.3 

Conditioning scripts 

This section describes the R scripts that have been developed to condition the model and 
their function. All scripts are housed on github 

(https://github.com/mintoc/DAMARA/tree/ master/model_code) and scripts [2 - 7] can 
be sourced directly from github. 

•  01 MasterCode2.r: This script does two things; 

Firstly allows you to set the common settings such as the working directory, 
number of years for the simulation and number of iterations for any stochastic 
processes or errors modelled (here implemented for stochastic recruitment). 
Second, it loads the parameter settings from the ’scenarios input.csv’ for each 
scenario in a loop, and calls the following scripts [2-7] to create the model input 

 
 
 
 
 
 
 
objects to FLBEIA. Rather than creating each component of the model for each 
scenario, where common settings are used and the input object has already been 
created this will skipped - reducing the build time in different combinations of 
settings. 
At the end of the code all conditioning should have been completed, but more 
specifically the main.ctrl object is created for the scenarios with the projection 
years set for the simulations. The code which calls the FLBEIA function on the 
conditioned objects can then be called through the ‘08 scenarios.r’ script. 

•  02 Generate  FLFleetExts.r 

This script currently takes the input files in the ‘fleets’ folder to build the fleet and 

covars input objects. There are two additional elements that can be affected 

within the script: – The catchability, catch.q, is set for each metier/stock based 

on the Cobb-Douglas production function, q = Bβ

C

·Eα. Where q is the catchability for 

the stock by a metier, C is the Catch of the stock by the metier, B is the biomass of 

the stock and E is the effort by the metier. Cobb-Douglas α and β parameters are 
set [currently default at 1 and 1, n.b. these are hard-coded at line 235-236]. 

–  The spatial structure of the model is defined so that any closure scenarios 
can be implemented. The base case is to retain only separation between VIIfg 
and VIIbce,hjk - to capture the more limited distribution of the sole and plaice 
stocks  in  the  Celtic  Sea  case  study.  However,  you  can  also  separate  (and 
therefore  affect  a  closure  on)  any  combination  of  ICES  statistical  rectangles. 
This  is  done  by  changing  lines  [84  -  91]  and  allows  for  the  closure  of  any 
m´etier  fishing in  a  particular  ICES  rectangle  or  group  of  rectangles,  through 
building  the  fleet  object  at  the  appropriate  scale.  The  decision  was  taken  to 
keep the spatial scale as aggregated as possible to reduce computing time for 
model runs. 

At the end of running the code, there should be a fleets.RData, covars.RData and 
biols.RData in the ‘object inputs’ folder. 

•  03 Condition  FLFleet Exts.r 

This script conditions the FLFleetExt object for the simulation years. This includes: 

–  Number of years historic catchability, selectivity etc. used to parameterise the 
projection  years  [currently  n=3].  A  special  case 
the 
implementation of the landings obligation, where the selectivity (split between 
landed and discarded catch-at-age is defined based on ‘LO selectivity func.csv’ 

is  made 

for 

input, which defines ogives for catches >minimum conservation reference size 

(MCRS) and <MCRS based on analyses within the project. 

–  Specify  the  effort  model,  effort  restriction  and  catch  restriction  (all  picked  up 

from the scenario specification file). 

–  Specify  the  catch  model  for  each  fleet/stock  combination.  Currently  hard-
coded as considered this won’t  change from  one  scenario to the next  (i.e. an 
age-structured  population  in  one  scenario  will  remain  so  in  all  other 
scenarios). 

–  Condition the fleets.ctrl object, specifically in relation to the different Landings 

Obligation options available (de minimis, inter-year flexibility etc..) 

 
 
 
 
 
 
–  Specify  the  capital  model  to  be  used  for  each  fleet.  Currently  set  as  same 

across all fleets, picked up from the scenario specification file(s). 

–  Specify  the  price  model  to  be  used  for  each  stock.  Currently  set  as  same 

across all stocks, picked up from the scenario specification file(s). 

–  Condition the covariates: Again, currently set to the average of the past three 

years. 
–  Specify 

the 

covariate  process  model.  Currently  only 

fixedCovar 

available/specified, this value is picked up from the scenario specification file. 

–  Expands  the  FLFleetExt  object  to  the  number  of  projection  years  specified  in 

the initial file [02 MasterCode.r]. 

At the end of running the code, there should be a conditioned and expanded fleets 
object, saved in the temp inputs folder, i.e. /temp input/fleets.RData. In addition, 
there should be (one or more) fleets.ctrl, covars and covars.ctrl objects in the 
‘model inputs’ folder named to reflect the parameter settings which have defined 
them. 

4.4 

04 Condition  stocks.r 

This script expands and conditions the biols object for the projection years and sets 
up the parameter arrays for the Stock-Recruitment forecasts. 
The biological parameters (natural mortality, growth, maturity etc..) are 
conditioned based on the past 3 years average. 
The stock-recruit model fits are picked up from the sr fits.RData’ object in the 
‘object inputs’ folder and are set up for the operating model (OM) projection years 
according to the model needs. Here, multiplicative uncertainty around any of the 
biological processes (e.g. wt-at-age, m, s-r relationships) can be added to the 
projections. It is also possible to add uncertainty in the fixed population stocks and 
the biomass-dynamic stocks to deal with the lack of population models available. 
However, at present only recruitment uncertainty is included through a log-normal 
error from the stock-recruit fit is used to parameterise recruitment draws in the 
simulations. 

The growth models are also set within this script (i.e. fixedPopulation, ASPG 

(for agestructured stocks), BDPG (for biomass population growth). This is 
currently hard-coded as not considered to change from scenario to scenario. 
At the end of running the code, the biols and biols.ctrl objects should be created in 
the ‘model inputs’ folder. 

4.5 

05 Setup assessment  and Obs controls.r 

This script is to allow observation models (OMs) to be set-up for any or all of 

the stocks in the model. This is currently hard-coded so that ’NoAssessment’ is 
undertaken. Including assessment models would allow for consideration of the 
impact of measurement error, implementation error etc. but was deemed 
unnecessary for the desired output. 
It is also possible to add observation errors to the model, for example, to deal with 
uncertainty around catch under a landings obligation or assess the impact of mis- 

 
 
 
 
 
 
 
 
or underreporting. Currently, ’perfectObs’ (perfect observation) is hard-coded, 
but it may be that we could consider uncertainty around future landings and 
selectivity resulting from policy changes as part of the DST. 
Here is also where stock indices can be set-up for the simulations; in the Celtic Sea 
case study we have included indices for Plaice and megrim, for the former because 
of the lack of absolute assessment estimates of stock size and fishing mortality and 
the latter because of the lack of biological reference points. By including indices, we 
have been able to implement the data-poor AnnexIV harvest control rules defined 
in FLBEIA. 

4.6 

06 Setup management rules.r 

This script sets up the management rules to be implemented including; 

–  The  number  of  years  (most  recent)  over  which  the  quota  allocations  will  be 

based in the projection years [default to n=3]. 

–  The biological reference points are input to the model. These are read from the 

file ‘BRPs’ in the scenario inputs folder. 

–  The Harvest Control Rules are defined for each stock, these are picked up from 
the  scenario  specification  file.  Several  options  are  available  as  detailed  in 
Section  5  The  intermediate  year  assumption  is  set  for  the  management 
procedure  model  (MPM),  currently  hard-coded  as  the  geometric  mean 
recruitment, based on past ten years. 

4.7 

07 Introduce  management measures.r 

This script is where the closed area and gear selectivity changes are introduced. 
This modifies the FLFleetExt object, so that: 

–  When  an  area  closure  is  introduced,  any  metier  with  the  area  included  in  its 
name has its effort share set to zero. The remaining metiers have their effort 
increased proportionally so the total effort share in the remaining metiers is 1. 
Other  approaches,  such  as  stakeholder  input  reallocations  could  be  coded 
here,  though  they  are  likely  to  be  very  case  specific,  so  no  general  coding  of 
this approach has been included. 

–  Gear  selectivity  changes  are  enacted  here,  in  the  same  way  as  the  area 
closures  are  introduced.  In  essence,  the  relative  change  in  catchability  for  a 
gear change is enacted by modifying the catch.q slot in the FLFleets object for 
the projection years. 

4.8 

08 Scenarios.r / 08 Secenarios  parrallel.r 

If the various objects have been successfully conditioned this code calls the main 
FLBEIA function on them, producing the model output as a .RData file and a 
number of summary indicator files. These indicator files are detailed in Section 5.3. 
There are two versions of this code: 

One version that runs each scenario sequentially in a loop; this is for running on 
single core machines. This version will not duplicate model runs, so here it is 
possible to continue to add scenarios to run additional simulations. 

 
 
 
 
 
 
 
 
 
Another version runs in parallel, taking advantage of the ‘parallel’ package. Here, 
several simulations can be run on a more powerful muti-core laptop or cluster. 

Note that memory requirements on several iterations (>50 iterations) are 
significant. 

5 

Running the model 

Different scenarios can be run by updating three principle configuration files which are in 
.csv format. These are: 

5.1 Biological Reference Points: BRPs.csv  

Input for each stock (where available). E.g. 

5.2  Main configuration file: scenario inputs.csv 

This configuration file is intended to specify the settings for the scenario runs, as 
required by the user. Each should be labelled with a unique ID (can be a number, or 
text) and contain the settings allowable in FLBEIA. The following settings are currently 
available for the DST (see FLBEIA manual for more details). They are shown in their 
input format, with options for implementation detailed below in the text. 

•  Flag 

– Run ID: Unique Run ID, based on numeric or text. 

•  Fleet settings 

–  effort.models.input: Type of effort dynamics model to be used in the scenario. 
Can be either fixedEffort or SMFB (simple mixed fisheries behaviour), SMFB 

lo  (simple  mixed  fisheries  behaviour  with  landings  obligation)  or  MaxProfit 

lo  (Profit  maximisation  with  the  landings  obligation).  Note,  for  fixedEffort  or 
SMFB this is set consistently across all fleets; SMFB lo and MaxProfit lo can be 
set by fleet, though require additional fleet settings input file (detailed below). 

 
 
 
 
 
 
 
 
–  effort.restr.input:  The  restriction  which  limits  a  fleets  activity.  Under 

fixedEffort this should be NULL, under SMFB this should be either min, max, 

previous or a named stock code. Under SMFB lo or MaxProfit lo this is set 
in  the  fleet  settings  input  file  and  can  be  controlled  by  fleet  and  before/after 

the  LO  implementation.  For  example,  to  be  based  on  previous  prior  to  the 

LO and min after the LO. It should always be set to min for the LO due to the 
nature of the restriction on the fishery. 

–  restriction.input:  When  under  SMFB,  SMFB  lo  or  MaxProfit  lo  fleets  should  be 

limited  by  landings  or  catch  -  though  under  the  LO  years,  this  should  be 

‘catch’ only. Under fixedEffort this should be NULL. 

–  capital.models.input: The options are fixedCapital [based on mean of last 3 

years  values],  SCD  (simple  capital  dynamics  based  on  only  in  revenue  and 
profits  within  the  FocusArea  and  species),  or  the  DAMARA  implementation 

SCD damara which takes account of revenue and costs for the vessels from 
both within and outsiide of the Focus Area. 

–  price.models.input: Options include fixedPrice [mean of last 3 years values] 

or  elasticPrice,  where  elasticity  parameters  must  be  defined  to  determine 
the relationship between price and landings volume. 

•  Covariates settings 

–  covars.process.models.input:  Currently  only  fixedCovar  available/specified 

[mean of last 3 years values]. 

•  Harvest  Control  Rules  -  these  depend  on  the  type  of  information  available  to 
manage the stock. There are three  categories implemented (in order  of increasing 
data requirements): 

–  HCRs for fixed populations with dynamics: As no Biological Reference Points or 
population  dynamics  are  available,  the  advice  option  is  for  a  constant 

fixedAdvice based on a fixedPopulation. This has been implemented for 
anglerfishes and Nephrops in Functional Unit 22. 

 
 
 
 
 
 
 
–  HCRs  for  stocks  with  population  dynamics:  For  stocks  with  population 
dynamics but without reference points, or where only an index of abundance is 
available  its  possible  to  implement  data-poor  methods.  In  this  case,  the 

AnnexIV  rules  can  be  implemented.  For  the  DAMARA  model,  this  has  been 
applied to Plaice VIIfg and Megrim. 

–  HCRs for stocks with population dynamics: For populations with dynamics and 
reference  points,  it  is  possible  to  define  any  harvest  control  rule  desired. 
Several  variants  are  implemented  for  the  DAMARA  model.  These  include  the 

IcesHCR  which  results  in  TACs  based  on  Fmsy,  with  a  sloping  rule  where  f 
reduces below Bpa; MAPHCR where the HCR is based on those developed for 
the  Multi-Annual  Management  Plans  where  the  target  is  Fmsy  or  some  other 
target  (e.g.  Flower  or  Fupper  for  the  lower  and  upper  ranges  of  the  Fmsy 
estimates) with a biomass safeguard rule where TACs are set to rebuild stocks 

within  n  years  when  biomass  drops  below  Bpa.  Defined  TAC  constraints  can 
also be implemented. The final option is a HCR defined under DAMARA based 
on  the  MAP  HCR  but  allowing  the  additional  input  to  implement  the  Fmsy 
target  in  a  given  year,  e.g.  2017,  2018  etc...This  is  called  CFPMSYHCR  and 
sets TACs consistent with Fmsy or rebuilding from the target year, but before 
sets  the  TAC  to  achieve  an  f  consistent  with  equal  steps  towards  the  target 
(e.g. in 2015 if the target is Fmsy in 2016 the TAC would be set based on an f 
of 1/2*Fsq and 1/2*Fmsy; providing this does not increase f). 

•  Management control settings 

–  area.close.input: Here a closure can be simulated by specifying the area, gear 
area or a list of these. For example, VIIfg, or TR1 VIIfg or (TR1 VIIfg,TR2 

VIIfg) or none. 

–  selectivity.input: Here a selectivity change can be simulated by specifying the 

change  and  which  fleets  to  which  it  applies.  Currently  only  ”SMP”  can  be 

used  to  simulate  the  introduction  of  a  SMP  to  TR1  and  TR2  vessels  or  ”TR2 

nocod”  for  a  hypothetical  scenario  which  would  result  from  some  gear 

change  that  resulted  in  no  catches  of  cod  by  TR2  gears.  none  can  also  be 
specified. 

 
 
 
 
 
 
 
 
Each scenario using the lo requires a separate input file for the stocks. This file (named 
in the format sc *scenarioName* stocks.csv) determines for those stocks with a dynamic 
HCR: 

•  Recovery.year: The years within which the stock must be recovered to >Bpa . 
•  St.Adv.Catch:  The  year  advice  becomes  based  on  catch,  rather  than  landings 

(under the LO, this should be consistent with the fleet input). 

•  Cup:  Any  upper  TAC  constant,  e.g.  1.15  for  a  limit  of  +15%,  or  Inf  if  no  upper 

TAC constraint. 

•  Clo:  Any lower  TAC  constant,  e.g.  0.85  for  a  limit  of  -15%,  or  0  if  no  lower  TAC 

constraint. 

•  Clim.yrs: The years that the TAC constraint applies, e.g. 2014:2016 for apply in 

2014, 2015 and 2016 but no other years. 

•  Ftarg: The target fishing mortality, e.g. Fmsy, Fup or some other value. 

•  Yrtg: The target year by which the Ftarg must be reached, e.g. 2016 or 2020 

Similarly to the stocks input file, under the lo effort dynamics you must specify a number 
of additional input file for the fleet settings. This file (named in the format sc 
*scenarioName* fleets.csv) determines the individual fleet settings as follows: 

•  catch.yr:  The  year  when  fleets  start  to  become  limited  by  their  catch,  rather  than 

landings 

(implemented before this year), e.g. 2016. The catch years should be the same as 
the stock setting. 

•  LO.yr:  The  year  the  landings  obligation  is  implemented,  and  the  different  LO 

implementation measures are available, e.g. 2016. 

•  effort.restr.preLO:  The  effort  model  in  place  before  the  landings  obligation,  e.g. 

previous, stock, max or any of the available effort restrictions under the SMFB 
setting. 

•  effort.restr.postLO:  The  effort  model  in  place  when  the  landings  obligation  comes 

into force, generally this should be min to restrict fleets to the first quota caught. 
•  De.min.yr: The year from which the de minimis exemption applies. This can either 

be a logical F, or FALSE so that the de minimis does not apply, or a given year. 
•  De.min.val:  The  percentage  of  the  quota  that  can  be  discarded  under  the  de 

minimis, for example 0.05 for 5%. 

•  yrTransfer.yr: The  year from which inter-year  transferability of quota applies. This 

can either be a logical F, or FALSE so that inter-year transfers are not allowed, or 
a given year. 

 
 
 
 
 
 
 
•  yrTransfer.val:  The  percentage  of  the  quota  that  can  be  overfished  and  deducted 

from next year’s quota, for example 0.1 for 10%. 

•  Inter.sp.flex.yr: The year from which inter-species flexibility (i.e. where one species 

can be landed against another quota) applies. This is currently not operational. 

•  yrTransfer.val:  The  percentage  of  the  species  quota  which  can  be  landed  against 

another species. This is currently not operational. 

This structured to allow multiple different scenarios to be run efficiently. Thus you can 
build and run a list of user-generated scenarios in three simple steps: 

1.  The  scenario  specific  setting  can  be  input  in  the  scenario  settings.csv  file  with 
unique run ID values (and the corresponding fleet and stock input files generated). 

2.  The  number  of  years  over  which  the  projections  should  run  are  input  and  various 

parameter  settings generated using the  ’01 MasterCode2.r’ script. The objects 
generated are then placed into the data model inputs folder. 

3.  The  scenario  are  then  run  by  loading  the  model  inputs  created  in  the  data  model 

inputs folder, and calling the FLBEIA function using the 08 Scenarios.r or the 
08 Scenarios parrallel.r script. 

The code runs in such a way that if a unique combination of parameter settings for the 
fleets, stocks or management measures has already been created it will not duplicate 
this step, but skip to the next step of the model parameterisation. 
The code then will produce a number of result indicator files. 

5.3 

Results indicator files 

The code calling to the FLBEIA function (scenarios*.r) also produces a number of 
indicator outputs which can be used to assess the performance of the management 
measures from a biological and economic perspective. The file summarise different 
quantiles from the iterations, including q05, q25, q50, q75 and a95. The indicators are 
contained in the following files: 

•  Biological results  summary.csv 

•  BiologicalRelative results summary.csv 

•  BiologicalRisk results  summary.csv 

•  Fleets results summary.csv 

•  Economic results summary damara.csv 

•  EconomicRisk results summary damara.csv 

•  Metier catch results summary.csv 

•  Metier effort results summary.csv 

•  partial F  results summary.csv 

•  ChokeSpecies results  summary.csv 

•  quota uptake results summary.csv 

•  TAC results summary.csv 

The indicators (per year) include: 

 
 
 
 
 
 
indicator 
biomass 
catch 
discards 
f 
landings 
rec 
ssb 
catch.iyv 

land.iyv 

ssb.iyv 

Fnow.Fpa 

Fnow.Flim 

Fnow.Fmsy 

Bnow.Bpa 
Bnow.Blim 

indicator 

Bnow.Btrig 

Btrig.Prob 

Bpa.Prob 

Blim.Prob 

Fpa.Prob 

Flim.Prob 

Fmsy.Prob 

description 
stock biomass 
total catch 
total discards 
fishing mortality 
total landings 
recruitment 
spawning stock biomass 
inter-annual variability in 
catch 
inter-annual variability in 
landings 
inter-annual variability in 
ssb 

Fishing mortality relative 
to Fpa 
Fishing mortality relative 
to Flim 
Fishing mortality relative 
to Fmsy 
ssb relative to Bpa 
ssb relative to Blim 

unit 
tonnes 
tonnes 
tonnes 
- 
tonnes 
000 
tonnes 
- 

- 

- 

- 

- 

- 

- 
- 

type 
biological 
biological 
biological 
biological 
biological 
biological 
biological 
biological 

level 
stock 
stock 
stock 
stock 
stock 
stock 
stock 
stock 

biological 

stock 

biological 

stock 

biological 

stock 

biological 

stock 

biological 

stock 

biological 
biological 

stock 
stock 

description 

unit 

type 

level 

- 

- 

- 

- 

- 

- 

- 

ssb relative to 
Bmsytrigger 

Probability B <Bmsy 
trigger (number itera- 
tions/total iteration) 

Probability B <Bpa 
(number iterations/total 
iteration) 

Probability 

B  <Blim 

(number

iterations/total 

iteration) 

Probability F <Fpa 
(number iterations/total 
iteration) 

Probability F <Flim 
(number iterations/total 
iteration) 

Probability 

F  <Fmsy 

(number

iterations/total 

iteration) 

biological 

stock 

biological 
risk 

stock 

biological 
risk 

stock 

biological 
risk 

stock 

biological 
risk 

stock 

biological 
risk 

stock 

biological 
risk 

stock 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
landings 

total landings 

tonnes 

fleet 

discards 

total discards 

tonnes 

fleet 

price 

price per tonne 

€ tonne−1 

fleet 

tacshare 

fleet quota 

tonnes 

fleet 

capacity 

fleet capacity 

000 

KW- 

fleet 

and 

and 

and 

and 

stock 
fleet 
stock 
fleet 
stock 
fleet 
stock 
fleet 

fleet 

costs4 

effort 

profits4 

DAS FocusArea 

DAS Elsewhere 

days 
€ 000 

partial costs (Focus 
area) 
fishing effort (Focus 
area) 
partial profits (Focus 
area) 
days at sea in focus area  Days 

days 
€ 000 

000 

days at sea outside 
focus area 

Days 

fleet 

KW- 

fleet 

fleet 

fleet 

fleet 

revenueFocusArea  Revenue inside focus 

€ 000 

fleet 

area 

revenueElsewhere  Revenue outside focus 

€ 000 

fleet 

totalRevenue 

crewCosts 

fuelCosts 

variableCosts 

fixedCosts 

totalCosts 

area 
Revenue inside and 
outside focus area 
crewcost (share of 
revenue) 
cost of fuel 

other variable costs 
(except fuel) 
total fixed costs 

total (fixed and variable) 
costs 

€ 000 

fleet 

€ 000 

fleet 

€ 000 

€ 000 

€ 000 

€ 000 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

fleet 

4 Note: These indicators are superseded within DAMARA by ’totalCosts’ and ’netProfit’ but are 

still contained within the results files 
description 
indicator 
total cost of vessel 
depreciationCosts 
depreciation 
total cost of investment 
Gross Cash Flow 
Gross Value Added 
net profit 
Break Even Revenue 
BER index, 
Revenue/BER 
Full Time Employment 
Number of vessels in 
fleet 

investmentCosts 
GCF 
GVA 
netProfit 
BER 
BERindex 

employment 
numberVessels 

Total 

unit 
€ 000 

€ 000 
€ 000 
€ 000 
€ 000 
€ 000 
€ 000 

- 
- 

type 
fleet 

fleet 
fleet 
fleet 
fleet 
fleet 
fleet 

fleet 
fleet 

level 
fleet 

fleet 
fleet 
fleet 
fleet 
fleet 
fleet 

fleet 
fleet 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
negProf 

belowBER 

Risk of falling into 
negative profit (number 
iterations/total iteration) 
Risk of falling below BER 
(number iterations/total 
iteration) 

- 

- 

fleet 

fleet risk 

fleet 

fleet risk 

landings 

total landings 

tonnes 

m´etier 

discards 

total discards 

tonnes 

m´etier 

price 

price per tonne 

€ tonne−1  m´etier 

effshare 

effort 

Fleet share of effort 
spent in m´etier 
Fleet total effort in 
m´etier 

landings pF 

partial landings

fishing mortality 

discards pF 

partial discards

Choke 

UptakeC 

UptakeL 

tac 
tac.iyv 

fishing mortality 

Risk of choke by stock 
(number of iterations / 
total iterations) 

Quota uptake relative to 
catch (catch / Quota) 
Quota uptake relative to 
landings (landings / 
Quota) 

Total Allowable Catch 
inter-year variability in 
tac 

- 

- 

- 

- 

- 

- 

- 

m´etier 

m´etier 

m´etier 

m´etier 

choke 

quota 

quota 

tonnes 
- 

tac 
tac 

stock and 
fleet, 
m´etier 
stock and 
fleet, 
m´etier 
stock and 
fleet, 
m´etier 
fleet 
metier 
fleet 
metier 

and 

and 

and 

and 

fleet 
m´etier 
fleet 
m´etier 

and 

fleet 
stock 

and 

and 

fleet 
stock 
fleet 
stock 

stock 
stock 

These result indicator files are then read directly into the Shiny R app for visualisation. 

6 

User interface 

The user interface ‘Shiny app’ picks up the scenario output files (Section 5.3) and 

displays them using the package ‘shiny’ in R (Wingston et al, 2015)15. 
The two main files for the interface are: 

•  ui.R 

This sets the front-end of the interface as a dashboard with side-panels for visualising 
the biology, fleet-level, metier-level and economic indicators as well as an overall 
summary. A ‘Dashboard’ panel is the key panel for choosing which scenario to visualise 
and whether uncertainty or percentage changes from the baseline should be visualised. 

15 Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2015). shiny: Web Application Framework 

for R. R package version 0.12.2. http://CRAN.R-project.org/package=shiny 

 
 
 
 
 
 
 
 
 
                                           
 
Options chosen there propagate throughout the interface. The main options for each 
side-panel were developed as check boxes for the user to choose what outputs they wish 
to visualise. Tool tips are overlayed to provide additional information to the user on what 
the options are. Finally, a ‘Stakeholder feedback’ panel was developed that allows the 
user to include comments and requests that are then saved with the users name and 
date to be collated by the developers. 

•  server.R 

This file takes the input list from ui.R and produces the relevant plots, which are then 
passed backed to the interface for visualisation. In addition download handlers were 
implemented to allow the user to download the results behind any plot that they 
visualise on the app. This was developed to allow stakeholders to actively engage with 
the output in a framework they are familiar with. The outputted files are comma 
separated. 
Finally for developers, note that some initial difficulties were experienced with the app 
crashing, this was due to a bug in the dplyr package 
(https://github.com/hadley/dplyr/issues/322). Should this arise again the solution is to 
install dplyr from source. 
Further details of implementation of the application can be found in the code located at 

https: //github.com/mintoc/DAMARA/tree/master/app_code. The code is currently 
private until a permanent home for the code is developed. 

 
 
 
 
 
 
 
 
 
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(http://publications.europa.eu/others/agents/index_en.htm). 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ISBN:978-92-79-57543-3 
doi: 10.2771/69952