Development of tools for logbook and VMS data analysis

Original Studies for carrying out the common fisheries policy Open call for tenders No MARE/2008/10 Lot 2 Development of tools for logbook and VMS d...
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Original

Studies for carrying out the common fisheries policy Open call for tenders No MARE/2008/10 Lot 2

Development of tools for logbook and VMS data analysis

1* Wageningen Institute for Marine Resources and IMARES Ecosystem Studies 2 CEFAS Centre for Environment, Fisheries & Aquaculture Science 3 IFREMER Institut Français pour la Recherche et l’Exploitation de la Mer 4 DTUTechnical University of Denmark, AQUA National Institute of Aquatic Resources 5 FRS Fisheries Research Service 6 SFI Sea Fisheries Institute 7 MI Marine Institute * Coordinator

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The Netherlands England (UK) France Denmark Scotland (UK) Poland Ireland

Table of Contents 1

INTRODUCTION ..........................................................................................................................4 1.1 1.2 1.3

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CONTEXT...................................................................................................................................4 BACKGROUND AND GENERAL APPROACH ANALYSIS OF LOGBOOKS DATA .................................4 BACKGROUND AND GENERAL APPROACH ANALYSIS OF VMS DATA ..........................................6

METHODOLOGY .........................................................................................................................7 2.1 ANALYSIS LOGBOOKS DATA .....................................................................................................7 2.1.1 Terminology......................................................................................................................7 2.1.2 State of the art methodologies ..........................................................................................9 2.2 ANALYSIS VMS DATA .............................................................................................................12 2.2.1 State of the art methodologies ........................................................................................13 2.3 ISSUES THAT NEED TO BE CONSIDERED ....................................................................................14 2.4 REFERENCES ...........................................................................................................................15

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PROJECT STRUCTURE............................................................................................................17

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DESCRIPTION WORK PACKAGES .......................................................................................20

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PROJECT PLANNING ...............................................................................................................34

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PROJECT RESOURCES ............................................................................................................35

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DESCRIPTION OF THE CONSORTIUM................................................................................36 7.1

SUBCONTRACTOR ....................................................................................................................47

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1 Introduction 1.1

Context

The Common Fisheries Policy (CFP) requires the progressive implementation of an ecosystem-based approach to fisheries management (EBFM). This will include the integration of environmental protection requirements into the CFP, including measures to ‘limit the environmental impact of the CFP’. To that order the collection of data that describe the impact of fishing by the European Member states is required to fulfil their obligations defined, amongst others under the current Data Collection Regulation1. Currently, two main sources of data that describe the impact of fishing activities on the ecosystem can be distinguished: logbooks and VMS (Vessel Monitoring through Satellite). As part of the revision of the CFP and implementation of EBFM, the need to move toward a fleet and area-based management (Council Regulation N° 199/20082) as well as the development of indicators of fishing impact were identified. Both topics require additional and more detailed information on the fishing activities of the fleets such as can be obtained from the logbook and VMS data.

1.2 Background and general approach analysis of logbooks data In order to operationalize fleet-based management more detailed information is needed on the fishing activities of the fleets as foreseen in the forthcoming Data Collection Regulation (DCR)3. However, in order for these data to be useful for advice it is extremely important to assure that each member state uses consistent procedures for allocating trips to métiers. The collection and the analyses of logbook data will then have to be adapted and will have to consider the métier (groupings of fishing operations of similar exploitation pattern) and fleet segments (groupings of fishing vessels) as recommended by the experts456 (see Annex). This, in turn necessitates the development of common agreed methods and tools in order to be able to categorise the information provided by the logbooks into métier. Taking into account the information at the basic level of the fishing operation ensures that the outcome of this project will be compatible with any further development in the precise reporting of catches, such as electronic logbooks.

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Council Regulation (EC) No 1543/2000 of 29 June 2000 establishing a Community framework for the collection and management of the data needed to conduct the common fisheries policy 2 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32008R0199:EN: NOT 3 Proposal for a Council Regulation concerning the establishment of a Community framework for the collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy (COM (2007)196 final 4 Commission Staff Working Paper: Report of the Ad Hoc Meeting of the independents experts on the Fleet9Fishery based sampling, Nantes, France, 23927 May 2005, 34p. 5 Commission Staff Working Paper: Report of the training Workshop on Fleet9based Approach, Nantes, France, 13917 March 2006, 31p. 6 Commission Staff Working Paper: Report of the Ad Hoc Meeting of the independents experts on the Fleet9Fishery based sampling, Nantes, France, 12916 June 2006, 98p.

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Logbook data provide information on the quantities of the main species caught and kept on board; the related date; location (ICES statistical rectangle); and type of gear used. The new DCR will follow a fleet-based approach, which implies that fishing operations and fishing trips showing similar exploitation patterns can be grouped by métier for the purposes of the new regulation, allowing more accurate estimates of fishing mortalities. The information currently available in the logbooks does not contain the information on (assemblage of) targeted species as required by the definition of the métier (see section 3.2). The targeted species, which is a key information, must then be derived from all the other variables reported using a precise methodology. The objective of this study is to develop a tool allowing the classification of every trip into métiers under the new DCR fleet based approach on a regional level. This tool will be the property of the European Commission and is to be used freely by Member states when implementing their obligations regarding the DCR. To this end the study shall: 1. Develop procedures based on statistical methods to allocate trips described in logbooks to métiers. 2. Test the above procedures to assure that the allocations of trips to métiers are consistent among Member states. 3. Develop follow-up methods to assure that the allocation procedures are consistent along time. 4. Describe a generic method to automatically run the above procedures, suitable to be implemented by each Member state. The algorithms and methods will be designed in view of filling the métier/fleet matrices (Annex 1) as developed by EU experts and finally proposed by STECF/SGRN, in November 2006, for implementation in the next DCR. An important issue raised during the Training workshop (Nantes, 2006) concerns the discrimination between métiers targeting one group of species and métiers targeting a mixture of species. As in a certain number of cases, the choice is given to report both the single and the mixed species, a special attention will be given to set up clear guidelines and allocation rules. The double dimension of the DCR matrix will be addressed, as methods for allocating vessels to fleet segments will be considered. Ultimately, the general philosophy put forward by STECF/SGRN (November 2006) will be used as a guideline to the project: • all fishing practices are meant to be registered in the matrix “the new DCR should address all removals from fish and shellfish stocks, regardless who or what is at their origin”, • a fleet/métier approach can only be if on a given area and on a given time period, all fishing activities are reported, “the collection of fishery-related data should comprise all fishing activities that cause such removals” • the elementary unit to define the métier is the fishing operation, “grouping fishing operations into strata with identical features” • the reporting of information in the matrix is done with common rules at a regional level “the métiers attempt to harmonise the stratification of fishing operations at the regional level” It is expected this project will be the most effective for addressing the last two bullet points.

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1.3 Background and general approach analysis of VMS data When implementing an Ecosystem Approach to Fisheries Management (EAFM), indicators are required to describe the pressures affecting the ecosystem, the state of the ecosystem, and the response of managers (Jennings 2005). Such indicators can be used to support management decision making, track progress towards meeting management objectives and to aid communication with non-specialist audiences (Garcia et al., 2000; Rice, 2000; Rochet and Trenkel, 2003). Many indicators have been proposed (e.g. (Rice, 2000; Link, 2002; Link et al., 2002; Rochet and Trenkel, 2003), but few (if any) of those that track changes in the “state” of the marine environment or of different ecosystem components (e.g. fish, benthos, habitat) can support management directly (Rice, 2000). This is largely because the precise causes of any changes in “state” may be poorly understood, making it difficult to identify appropriate management action. To implement an EAFM successfully therefore, it is not only necessary to have a suite of indicators that accurately and comprehensively portray the “state” of various ecosystem components, but it is also critical that we have indicators that describe changes in the level of different manageable anthropogenic activities, and which indicate the impact of each activity on the various ecosystem components. Only by adequately covering both aspects will the mechanistic links between ”cause” and “effect” be well enough understood so as to provide the advice required (Daan, 2005). In order to link the state of ecosystem components such as habitat, benthos or the demersal fish community to fishing, the following pressure indicators were suggested (EC 2008/MARE/020): • Distribution of fishing activities: Indicator of the spatial extent of fishing activity. It would be reported in conjunction with the indicator for ‘Aggregation of fishing activity’. • Aggregation of fishing activities: Indicator of the extent to which fishing activity is aggregated. It would be reported in conjunction with the indicator for ‘Distribution of fishing activity’. • Areas not impacted by mobile bottom gears: Indicator of the area of seabed that has not been impacted by mobile bottom fishing gears in the last year. It responds to changes in the distribution of bottom fishing activity resulting from catch controls, effort controls or technical measures (including MPA established in support of conservation legislation) and to the development of any other human activities that displace fishing activity (e.g. wind farms). In order to quantify these indicators there is a need for high-resolution spatial data of fishing activity such as provided by VMS. With these data the study shall: 1. Develop and test methods and produce protocols on how to present these indicators using GIS and how to link VMS databases to Logbooks. 2. Model the dependence of recording rate on the precision of the suggested indicators

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2 Methodology 2.1

Analysis Logbooks data

The métier matrix developed in the meetings on Fleet and Fishery based sampling in Nantes (2005, 2006), amended by the different Regional Coordination Meetings (RCM) and finally proposed by the STECF/SGRN (2006) will be the standard matrix to be filled, using an algorithm for allocation of trips into the defined métiers See Annex I for a complete overview of the regional matrices. The frame of the matrix includes 6 nested levels of aggregation: 1. activity 2. gear class 3. gear groups 4. EU level (gear type) 5. fishing activity at regional level (e.g. group of target species). 6. the selectivity feature of the gear (mesh size class and presence/absence of a selective device with its technical characteristics) The ultimate aim is to determine the level 5, so that métiers can subsequently be easily aggregated if necessary. The Level 6 will be considered each time the availability of the related information will permit their inclusion in the analysis. The call specifically requests a standardized approach métiers matrix on a regional level. The consortium aims at developing a general approach applicable across several regions through the involvement of a large number of Member States. The regions covered will be RCM North Sea and Eastern Arctic (represented by England, Scotland, Denmark, France and The Netherlands), RCM North East Atlantic (represented by France, Scotland and Ireland) and RCM Baltic Sea (represented by Denmark and Poland). The nations will adopt a common methodology to their national catch and effort data available. Although it will be aimed at maximum consistency across regions, it is not excluded that specific methodological points may differ from one region to another, under the condition that such points can be applied by all partners of the consortium representing the region. The final product will be a set of allocation rules by region, so that each trip recorded in national databases belongs to one and only one fishing activity. Strong links with works and data needs identified in STECF-SGRN, STECF-SGRST and ICES will ensure future application of the methods developed in this study. The proposed approach should also allow enough flexibility to be used into eventual further improvement in the availability of information at the fishing operation level and in the description of the gear (presence/absence and characterisation of a selective device).

2.1.1 Terminology In this proposal, standard definitions proposed by EU Workshop on Fleet-Fishery based sampling Nantes, 23-27 May 2005, are used. These basic concepts are presently globally agreed by the EU fisheries scientific community. It started by an ICES study

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group (SGDFF7) that met in 2003 and 2004, and followed by the EU Workshop on Fleet-Fishery based sampling Nantes, 23-27 May 2005, which made a proposal for the EU fleet segmentation matrix. The proposed frame is a matrix with various levels of desegregation in two dimensions, namely the fleet segments and the métiers at regional, national or local levels. This proposal has been presented and discussed in the Regional Coordination Meetings (RCM), worked out in a training workshop (Nantes, March 2006), finalised in a third Nantes workshop (June 2006) and finally included in a general framework by the STECF/SGRN in November 2006 to the attention of the European Commission for elaborating the next Data Collection Regulation. This is a strong contribution in order to link biological and economical approaches and organise sampling data collection. Box 1. Definitions used in this proposal, based on definitions in EC a) Active vessels: vessels that have been engaged in any fishing operation (more than 0 days) during the year. If not, the vessel is considered inactive. b) Days at sea: a day present within an area shall be any continuous period of 24 hours (or part thereof) during which a vessel is present within the area and absent from port. c) Fleet segment: a group of vessels with the same length class (LOA) and predominant fishing gear during the year, according to the Appendix III. Vessels may have different fishing activities during the reference period, but they may be classified in only one fleet. d) Fishing days: each day is attributed to the area where the first fishing operation took place within that day. However, for passive gears, if no operation took place from the vessel within a day while at least one (passive) gear remained at sea, that day will be associated to the area where the last setting of a fishing gear was carried out on that fishing trip. e) Metier: a group of fishing operations targeting a similar (assemblage of) species, using similar gear, during the same period of the year and/or within the same area and are characterised by a similar exploitation pattern. f) Population of vessels: the population is all vessels in the Community Fishing Fleet Register as defined in Commission Regulation (EC) No 26/2004of 30 December 2003 on the Community fishing fleet register1. g) Selected species: species of relevance for management purpose and for which a request is expressed by a international scientific bodies or regional fisheries management organisations. h) Soaking time: time spent at sea calculated from the point where each individual unit of gear has been set, until the time when the same unit starts to be removed. I 7

ICES SGDFF: Study Group on the Development of Fishery9based Forecasts.

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Box 2. Definitions used in this proposal, based on definitions in ICES (2003) a) Landings profile: Categorical definition of the landings data by trip, in terms of species composition by weight, by value or a combination of the two, and often referred to as the (group of) dominant or target species. If discards data are available, such profile should be referred to as catch profile.

2.1.2 State of the art methodologies ICES (2003) acknowledged that two approaches prevail in literature and in the experience available in the fisheries institutes. The first one is a quantitative analysis of logbook data, using multivariate procedures. The other approach is an ad hoc trial and error process, based on qualitative a priori knowledge of the fisheries in order to identify suitable allocation threshold.

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Quantitative approaches

In the first approach, a number of published works exist using various multivariate techniques for fisheries identification. Biseau and Gondeaux (1988) described the use of a Principle Component Analysis (PCA) on two types of variables: gear used or time spent during each month in each area, and target species or proportion of each of the major species observed in each month in the landings of each vessel. By using both the species composition and the gear usage, Biseau and Gondeaux define fisheries by both its input and output, When trying to define groundfish assemblages in Oregon and Washington waters, Rogers and Pickitch (1992) used two opposite types of hierarchical clustering techniques and Detrended Correspondence Analysis (DCA). The definition of the species assemblages (= landing profile) was applied to haul-by-haul catch composition data based on weights. DCA is used to represent catch and species relationships in a low-dimensional space. DCA derives a series of ordination axes, each consisting of a set of species scores and a corresponding set of catch scores, which are weighted averages of the species scores. DCA is related to a unimodal response model of the latent environmental variable (Jongman, Ter Braak et al. 1995). The clustering of assemblages is done using two different hierarchical clustering techniques: agglomerative and divisive. The agglomerative technique was based on the Bray-Curtis dissimilarity index with group average fusion criteria. The hierarchical divisive clustering technique used was two-way indicator species analysis. The actual number of assemblages was estimated by visual inspection of catches plotted on the DCA axes, for both clustering techniques. Moreover, the consistency of clusters was checked by plotting the results of both techniques in one set of DCA plots. The assemblages were subsequently contrasted to pre-defined strategies, which are in turn based on gear type and areas. Lewy and Vinther (1994) used a hierarchical agglomerative cluster analysis when identifying Danish North Sea trawl fisheries. The species composition of the landings was expressed as the fraction of the monetary value of one species to the total monetary value on a trip-to-trip basis. The similarity matrix used for clustering is based on the squared Euclidean distance, and clusters were joined based on the

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centroid method. The estimation of the number of landing profiles was done by visual inspection of four criteria: pseudo F statistic, pseudo t statistic, cubic clustering criteria and the correlation coefficient. The landing profiles are subsequently contrasted against the vessel gross tonnage, fishing zone season and fishing effort. He et al. (1997) give another description of clustering catch compositions. A large number of catch compositions of longline sets were first clustered to an arbitrary number of 2500 clusters using a non-hierarchical cluster analysis (K-mean method). These clusters were subsequently clustered using a Hierarchical Agglomerative Clustering (HAC) analysis applying Wards minimum variance criterion. The number of clusters was selected based on the amount of sets in the smallest cluster. The landing profiles were subsequently contrasted against the spatial and temporal distribution of fishing effort. Pelletier and Ferraris (2000) used a multivariate approach in two steps to identify fisheries in two different case studies. In the Senegal case study, the first step involves a PCA trip by trip landing composition expressed as fraction of weight per species to the total landing weight. Subsequently, these fractions were log-transformed to symmetries distributions. A non-normalised PCA was performed, from which all factorial axis were retained. HAC was applied to the factorial coordinates, based on Wards minimum variance criterion. Subsequently, the association of obtained landing profiles and different variables of fishing tactics was examined. For this purpose, a Multiple Correspondence Analysis (MCA) was applied to landing profile, fishing location, gear and month. Subsequently, a HAC algorithm was applied to the factorial coordinates of the first sixteen axes, which accounted for approximately 60 % of the cumulated variation. The Celtic Sea case study is somewhat different to the Senegalese case study with respect to the definition of the landing profiles, and uses Two Way Correspondence Analysis (TWCA) rather than MCA to link landing profile to effort. A similar combination of PCA, HAC and MCA was also used by Ulrich and Andersen (2004) for the identification of Danish fisheries. PCA was applied on landings value and HAC used Ward criterion for identification of landings profile by trip, MCA and HAC methods were thus used for the final identification of fisheries, linking landings profile with mesh size within each combination of gear type and management area. The fishery observatory (SIH) of IFREMER used also a similar approach combining PCA and HAC at the vessel level to arrange vessels in fleets. Vessels were characterized by the gear-landings profiles and fishing effort (number of months, number of days, number of trips, etc.) during a reference period (e.g. one year) (Berthou et al., 2003). Recently, June 2007, the study project IBERMIX ((FISH/2004/03-33), titled “Identification and segmentation of mixed-species fisheries operating in the Atlantic Iberian Peninsula waters” was concluded. This study was carried out by the three institutes involved in the fisheries operating in the Atlantic Iberian waters: AZTI (Spain), IEO (Spain), and IPIMAR (Portugal). The main objective was the Identification of fleets/fisheries/métiers in the Atlantic Iberian waters (ICES div. VIIIc and IXa). The data source for the fleet segmentation of the Spanish fleets were the 2003-2005 logbooks and for the Portuguese fleets was the daily commercial landings in value for the years 2003-2005. The matrices were

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analyzed separately by year, using a non-hierarchical cluster analysis to classify catch/landings profiles. For the Spanish fleets, the catch profile clusters were obtained using the CLARA algorithm, while for the technical features of the fleets a PAM algorithm was carried out. For the Portuguese trawl fleet, the clusters were obtained using the PAM algorithm, while for the purse-seine fleet the CLARA algorithm was used due to the size of the data set. In both cases the analyses were made by taking Euclidean distances for the dissimilarity matrix. For the Portuguese multi-gear fleet, due to the complexity of this fleet and particularly to the use of different types of gears with no information available, two methodological approaches were undertaken. First a non-hierarchical cluster method, the PAM method was applied by year to the total matrix of daily landings using its variant CLARA. The second approach used the fishing license information as independent variables to fit a multivariate regression tree (Breiman et al., 1984; De'ath, 2002) by year with the species/groups of species in value as dependent variables. With this method a link between gear and species/groups of species could be established. The different multivariate analysis were made by S-plus© and R softwares.

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Qualitative approaches

The second approach is more qualitative and is based on a priori knowledge of the fisheries. A trial and error process is conducted in order to derive suitable discriminating thresholds allocating each fishing trip to one and only one fishery (based either on landing weights, landing values or mesh size) (e.g. Biseau, 1998; Ulrich et al., 2001). Often these approaches are not published, but might be used extensively within the institutes e.g. for designing sampling programs (ICES, 2003). Since 2000, the fishery observatory of Ifremer (SIH) conducts, on a monthly basis, a routine census of the fishing activities for all the vessels belonging to the national fleet register. This census, supported by head-to-head inquiries with the fishers, provides the information on targeted species, as defined in the FAO glossary. Although this information is only available at a month level, it may be used either to generate an annual overview of the métiers practised or to calibrate and validate the algorithm used. A similar ad hoc approach based on quantitative multivariate analyses (described in previous section) and expert a priori knowledge lead in the development of algorithms (decisions rules) to classify vessel in fleets. The Atlantic French fleet has thus been split into 13 fleets and 33 Sub-fleets (e.g. "Trawlers-non exclusive" and then "Trawlers-Dredgers") (Berthou et al., 2003). The main assumption underlying this approach is that the technical characteristics of a vessel limit possible uses in different types of fishing. Some vessels might be used for several fisheries, whereas other vessels might only be used one type of fishery. E.g. vessels that are equipped for trawling, might also be used for seining with slight modifications, whereas vessels equipped for seining would need large modifications (larger engine, other equipment) in order to be able to go trawling. This method favours the gears combination8 implemented by the vessels. 8

The threshold value for “exclusivity” is 100% meaning there is no recording of use of any other gear. Then, a vessel which spends 99% of its time Trawling and 1% with Net is “Non Exclusive Trawl” and more detailed a "trawler9netter".

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Finally, other methods have been in discussion at the training Workshop on Fleetbased Approach in Nantes,13-17 March 2006. These methods differed according to the priority of the potential use of gear (gears combination in use) or to the actual fishing behaviour in terms of time spent at sea using a specific gear (intensity of use of the different gears). For example, the possibility of using a more flexible criterion through percentage of activity of the vessels attributed to each gear has been investigated. Information on the percentage of each gear use during the year (based on total number of trips at sea, number of months or days by gear for example) is needed to achieve this typology. The possibility to apply hierarchical tree after a threshold value has also be investigated. Consequently, a vessel spending more than X% of its time using a specific gear is allocated to this specific segment, if not the hierarchical tree is to be applied. In conclusion, ICES (2003) proposed a three-step framework generally applicable to the identification of fisheries: (1) identification of the different landings profile from landings data, (2) analysis of the relationships between the features of each trip (effort data) and their outcome in terms of landings profile, and (3) aggregation of the results of step 2 to define fisheries that are considered sensible in relation to field knowledge and qualitative expertise. Such a framework was used in a number of subsequent EU-funded projects dealing with fleets and métiers. In particular, the FP6 project TECTAC (Marchal, 2005) made some significant progress towards international consistency in fisheries and métiers identification. All institutes involved, representing several North Sea and Celtic Sea countries, agreed on a common database format for logbooks data (the EFLALO format) as well as for other types of data (e.g. TACENQ format for data from onboard observers). This common data format made it possible to apply consistent methods across nations without requiring actual exchange of national logbooks data, as only generic SAS codes were written and exchanged. But although a number of generic methods were proposed and tested, one unique multivariate method for métier definition could not be agreed on (see below). The TECTAC procedure of common data format and code exchange proved however to be very useful and efficient, and subsequent FP6 projects such as CAFÉ and AFRAME have adopted a similar approach re-using the EFLALO format.

2.2

Analysis VMS data

Indicators can be valuable tools for tracking change, identifying problems and monitoring implementation of policies and results. They are increasingly used to assess the efficacy of EU policies, including the extent to which environmental concerns are integrated into sectoral policies. A robust set of informative indicators will help policy- and decision-makers to evaluate the performance of management measures, as well as ensure accountability to the public through regular information. A suite of both state and pressure indicators were put forward as part of the Community framework for the collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy (EC 2008/MARE/020)

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VMS is the considered the best source of data to quantify the pressure indicators that were suggested in this document: • Distribution of fishing activities • Aggregation of fishing activities • Areas not impacted by mobile bottom gears In order to achieve this the following specific tasks need to be developed: 1. Algorithms for filtering the data in order to distinguish fishing from other activities and to generate the vessel trips with the main features (date and harbour of departure and arrival) 2. Evaluation of the effect of the recording rate on the suitability of the VMS data and performance of the developed methodology to quantify these indicators. 3. Techniques to link VMS to logbooks so that different métiers can be discriminated, 4. GIS tools 5. Identification of specific pressure indicators and development of algorithms for translating the position registrations into the most accurate spatial distribution patterns at the relevant spatial resolutions

2.2.1 State of the art methodologies The ecological impact of a fishery is determined by the spatial distribution of the fishing activities which can be described by indicators such as above. The calculation of the indicator values from the spatial distribution of the fishing activities can be based on estimates in different units and spatial and temporal resolutions, using different algorithms and methods. Methods include point densities (Piet et. al. 2007), modified point density to account for uncertainty (Fock, 2008), joining points with straight lines (Stelzenmuller, 2008, Eastwood et al 2007), ellipses to account for uncertainty (Mills et al 2007). In most analyses on the ecological impact of a fishery the implicit assumption appears to be that both should result in a similar spatial distribution of the fishing activities and hence estimate of fishing impact . However, Piet & Quirijns (in press) have shown that depending on the spatial scale this may not be the case because as the spatial scale becomes smaller, more position registrations are necessary to provide an accurate spatial distribution. This probably can be resolved to a large extent by basing this distribution on the trawl tracks which has the additional advantage that these can be used to assess if a specific area was actually fished in a certain time period. This is important in the vicinity of MPAs or when real-time closures are used for management and should be reflected in the suggested third indicator, “Areas not impacted by mobile bottom gears”. The recording rate may affect both ways of estimating the spatial distribution. If based on an aggregation of the position registrations an increase of the recording rate will result in more position registration data thereby allowing accurate descriptions of the spatial distribution at smaller spatial scales. If based on the trawl tracks it will improve the accuracy of the trawl track. A more accurate calculation of the trawl track could for example result in smaller buffer zones around an MPA.

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Interpolation of vessel tracks can solve the problem that occurs when only position registration point data are used. The most straightforward method for vessel track interpolation is Straight Line (SL) interpolation, which connects two data points by a straight line (Deng, 2005) (Laurans et al., 2007). But Deng et al. (2005) stated that SL interpolation is still likely to underestimate the length of the trawl track, especially at larger time interval between position registrations. Mills et all. (2006) used a different method to reconstruct vessel tracks. This method is based on an ellipse around two position registrations. The size of the ellipse is determined by the maximum distance a vessel could have travelled between two position registrations. An novel technique with great potential to interpolate the vessel track between two position registrations is the Hermite spline interpolation. Brunel and Hintzen (2007) used the Hermite Spline (HS) method to interpolate the vessel track between two position registrations, taking vessel heading and speed into account. 2.3

Issues that need to be considered

A number of issues have been repeatedly encountered during the previous initiatives towards international métiers definitions cited above, and have hampered the processes to reach final agreements. • First, extensive discussions have already taken place about the definition of target species and landings profile. It is clear that landings profile may not reflect actual fishers’ intention when deciding to fish in one area with one type of fishing gear, as a part of uncertainty about trips outcomes always remains. Furthermore, the general absence of discards information by trip contribute to blur these linkages, as discarding behaviour may be influenced by a number of external factors such as market price or regulations, which may differ depending on country, season and area. So the landings data reflect only imperfectly what was initially targeted. In addition to that, previous works have used either landings data in weight or in value as a proxy for the target, leading to significant difference in results for highvalue species such as Nephrops or monkfish. • Second, the inspection of landings profile show that clear distinction between two (groups of) target species is not easy, because trips distribution in terms of species percentage often show a smooth transition between clusters, and the thresholds are often not unequivocal. For example, if it is easy to distinguish trips with a clear preponderance of Nephrops or a clear preponderance of demersal fish, there will also always be a number of trips with similar proportions of both types of species, which could be equally attributed to either group or alternatively constitute a “mixed” group in itself. So as noted by ICES 2003, as part of somehow subjective “expert knowledge” is often necessary to decide upon the final aggregation. • Third, discriminating thresholds for allocating trips to métiers are highly variable, both in time and space, as species assemblage varies according to stocks distribution and dynamics. This means that thresholds identified for one country are not necessarily valid for the neighbouring one, and analyses performed on one particular year are only a snapshot picture of a variable dynamic. Studies dealing with long-term dynamics in fisheries have either chosen to fix thresholds applicable to a whole time series (e.g. Ulrich and Andersen, 2004) or on the contrary to perform same analyses on each individual year (e.g. Holley and Marchal, 2004), both strategies giving a different view of the historical dynamic.

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Finally, consequent robustness trials in multivariate methods have never been fully performed on that topic, but trial runs performed during some of the studies cited above have shown a clear sensitivity of clustering results to the multivariate method used. There is a huge amount of choices to be made when using multivariate methods, with regards to method (e.g. PCA+HAC, cluster etc), data transformation (e.g. percentage, weighted percentage, etc), calculation method (e.g. centroid, Ward etc), decision criteria to decide upon the final number of clusters (e.g. percentage of variance explained, correlation coefficient etc), and, not the least, statistical software (e.g. SAS, Splus, SPAD.N etc). So identifying thresholds robust to this model uncertainty is a real challenge.

All these issues will have to be carefully examined and discussed during the early phases of the project, in WP1, in order to agree on common grounds.

2.4

References

Breiman, L.; Friedman,J. H., Olshen, R.A. and Stone, C.J. 1984. Classification and Regression Trees. Monterey, California: Wadsworth and Brooks/Cole. Daan, N. 2005. An afterthought: ecosystem metrics and pressure indicators. ICES Journal of Marine Science, 62: 612-613. De'ath, G. 2002. Multivariate Regression Trees: A New Technique for Modelling Species – Environment Relationships. Ecology 83(4):1103-1117. Deng, R., Dichmont, C., Milton, D., Haywood, M., Vance, D., Hall, N. 2005. Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia’s northern prawn fishery. . Canadian Journal of Fisheries and Aquatic Science, 62: 611-622. Eastwood, P.D., Mills, C.M., Aldridge, J.N., Houghton, C.A., Rogers, S.I., 2007. Human activities in UK offshore waters: an assessment of direct, physical pressure on the seabed. ICES Journal of Marine Science 64, 453-463. Fock, H. 2008. Fisheries in the context of marine spatial planning: Defining principal areas for fisheries in the German EEZ. Marine Policy 32 (4), pp. 728-739 Garcia, S. M., Staples, D. J. and Chesson, J. 2000. The FAO guidelines for the development and use of indicators of sustainable development of marine capture fisheries and an Australian example of their application. Ocean and Coastal Management, 43: 537-556. ICES, 2003. Study Group on the Development of Fishery-based Forecasts. ICES CM 2003/ACFM:08 Holley J.F. and P Marchal, 2004. Fishing strategy development under changing conditions: examples from the French offshore fleet fishing in the North Atlantic. ICES J. mar. Sci. 2004 61: 1410-1431. Kaufman, L. and Rousseeuw, P.J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley& Sons, Inc., 342p, New York. Link, J. S. 2002. What does ecosystem-based fisheries management mean? Fisheries, 27: 18-21. Link, J. S., Brodziak, J. K. T., Edwards, S. F., Overholtz, W. J., Mountain, D., Jossi, J. W., Smith, T. D. and Fogarty, M. J. 2002. Marine ecosystem assassment in a fisheries management context. Canadian Journal of Fisheries and Aquatic Science, 59: 1429-1440.

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Mills, C.M., Townsend, S.E., Jennings, S., Eastwood, P.D., Houghton, C.A., 2007. Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES Journal of Marine Science 64, 248-255. Piet, G. J. and Jennings, S. 2005. Response of potential fish community indicators to fishing. ICES Journal of Marine Science, 62: 214-225. Piet, G. J., Quirijns, F. J., Robinson, L. and Greenstreet, S. P. R. 2007. Potential pressure indicators for fishing, and their data requirements. ICES J. Mar. Sci., 64: 110-121. Rice, J. C. 2000. Evaluating fishery impacts using metrics of community structure. ICES Journal of Marine Science, 57: 682-688. Rijnsdorp, A. D., Buys, A. M., Storbeck, F. and Visser, E. G. 1998. Micro-scale distribution of beam trawl effort in the southern North Sea between 1993 and 1996 in relation to the trawling frequency of the sea bed and the impact on benthic organisms. Ices Journal of Marine Science, 55(3): 403-419. Rochet, M. J. and Trenkel, V. M. 2003. Which community indicators can measure the impact of fishing? A review and proposals. Canadian Journal of Fisheries and Aquatic Sciences, 60(1): 86-99. Stelzenmüller, V., Rogers, S.I., Mills, C.M., 2008. Spatio-temporal patterns of fishing pressure on UK marine landscapes, and their implications for spatial planning and management. ICES Journal of Marine Science 65, 1081-1091.

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3 Project structure The project is set up such that in each of the work-packages tools and/or protocols are developed that contribute to the process that starts with two sources of data: logbooks and VMS and should result in a classification of trips into métiers based on the species composition in the logbooks as well as the development and estimation of pressure indicators in support of an ecosystem approach to fisheries management. The aim is to apply all the tools that will be developed in this process on as many data sets across different European waters. The tools together with protocols describing their use will become available in the public domain. The project structure below should explain how the different work-packages fit into this process.

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In WP1 the various data sets of logbooks and VMS will be compiled separately into common formats and stored into one logbook and one VMS database. In WP2 a tool will be developed that can classify trips into métiers based on the species composition in the logbooks. In WP3 a tool will be developed that can distinguish fishing from other activities using VMS data In WP4 a tool will be developed that combines matching logbook and VMS recordings with the aim of checking the consistency of the two data9sources (and feeding back into WP1) but also to allow each source to benefit from the additional information provided by the other source. For example this should allow VMS trips to be allocated to métiers or to allocate catches to

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the actual fishing positions at a higher spatial resolution than ICES rectangle. The results of the consistency checks should flow back to WP1. In WP5 the effect of the VMS recording rate will be evaluated and to that end a tool that reconstructs the fishing track from the VMS positions will be developed. In WP6 a suite of pressure indicators in support of an ecosystem approach to fisheries management will be developed and estimated based on logbook, VMS data and the reconstructed tracks coming from WP5. Finally in WP7 the tools en knowledge generated in the previous work9packages will be integrated into one consistent framework and this will be applied to all the datasets across Europe that are brought together by the different partners as part of this project. The output of this work9package will be disseminated to show how the different tools combined in an integrated framework can deliver all the information necessary to estimate the fishing impact per métier from the collected logbooks and VMS data.

Finally the overall coordination should assure the communication between different WPs through e.g. the organisation of a number of project meetings and will be responsible for the communication with DGMARE and specifically the delivery of the project output.

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4 Description work packages 1 Month 1 Work package number Start date or starting event: Compilation of national data sets into one database Work package title 1 2 3 4 5 6 Participant id 1.1 1.1 1.1 0.4 0.4 Person-months per participant: 2.9

7 0.4

Objectives This WP deals with the overall task of providing national disaggregated data (both logbooks and VMS) into standardised international format, for use in the analyses performed in following WPs. The main objectives are thus: • To select the requirement for disaggregated logbooks data for the subsequent WP2 analyses, as well as the requirement for disaggregated VMS data for the subsequent WP3 analyses • To provide a standardised exchange format for national logbooks data • To facilitate the compilation of a multi-national database at the regional scale to be used in WP2 • To provide a standardised exchange format for national VMS data, consistent and compatible with the logbook format. Description of work This WP deals with the collection of raw disaggregated data based on logbooks and VMS. In order to insure maximum consistency and integration between these data sources and facilitate their use both are included in this work-package. However, these two types of data differ in their nature, amount and coverage, and thus they must first be considered separately for formatting and standardisation. Then they can be subsequently merged and summarised, which will be done in WP4. The results of this exercise, however, will feed back into WP1. A key aspect for the successful achievement of the work is that the decision upon final formats and requirements are agreed in common by the consortium, but the actual work of converting national data into these agreed formats and making them available to the general project is left under the responsibility of the individual national institutes. The work plan is divided in two tasks : Task 1.1 Standardisation of Logbooks data This task aims at standardising the disaggregated (trip by trip) logbook information in order to conduct the statistical analyses for the identification and definition of métiers as performed in WP2. As specified in the terms of reference, one main objective of this project is to allocate fishing trips (logbook data) to specific métiers. Logbook data are generally available from each member state for a long period of time (e.g. 10-20 years) and for at least all vessels exceeding 10 meters. Catch and effort data are available per ICES rectangle. The quality of the data differs across case studies, thus there is requirement for checks with reference to quality requirements associated with the Data Collection Regulation.

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A review will first summarise the technical details of the national logbook data sets available among the various partners, including year range, format and language used, units of effort, availability of value data, availability and reliability of data for vessels less than 10m etc. Meanwhile, this WP will get inputs from WP2 about the data required to perform the statistical analyses on métiers identification. This will thus lead to a common requirement level for trip-based logbooks data. A common exchange format applicable to the disaggregated international data was defined and used during the EU projects TECTAC (“TEChnological and TACtical adaptations of important EU fleets”) and CAFÉ (“Capacity, Fishing Mortality and Effort”). This format, called “EFLALO” (Standing for “EFfort and LAndings from LOgbooks”) was used to set up national logbooks in a similar SAS-based standard, keeping information at the trip level. This has proven to be a useful and efficient way to work with such large databases, and it is expected that this format is used again in the current project. Data should only consist of the landings of the flag member state, to avoid possible discrepancies in reporting between countries. EFLALO includes information on fishing effort and landings, in weight and value for a selection of species, by vessel (greater than 10 m), ICES square and fishing trip. Landings and effort data relevant to small vessels (less than 10 m) and short fishing trips (less than one day) are sometimes available from independent enquiry and may also be included in EFLALO. However, for a number of countries, information on vessels less than 10 m is not available. In addition, multivariate techniques based on landing profiles (as those to be performed in WP2) have in the past included both catch by weight and catch by value. Typically value data only exist where logbook data have been validated against sales-slips or the actual value of the landing are recorded in the logbooks, which is not the case in all member states. It is therefore necessary to get agreement on the minimum common denominator for running consistent analyses. Due to confidentiality issues a multi-national database could not be set up under previous research projects. This prevented running consistent analyses at the regional level, and the results were only obtained at the national level. The current project intends to propose métiers definitions at the regional level, which imply that data should be analysed together at the multi-national level. Therefore this WP will also, in close collaboration with WP2, drive the temporary combination of national data (or a representative subset of them) into a multi-national database during a WP2 workshop, under a set of pre-agreed anonymity rules. Task 1.2 Standardisation of VMS data The aims of this task are similar to those of task 1.1, but now dealing with VMS data. VMS data are of more recent origin than logbooks data, and even more recent is their availability for scientific purposes. In consequence, there has not been a unified way to compile and use them at the European level yet. But some expertise is already available within the institutes involved. During this task, we will then first review the technical details of the VMS data available in the consortium. We will also get inputs from WP3 about the data required to perform the analyses considered. All this will then be used to propose a standardised format of VMS data. In consideration of the merging work between both data sources to be undertaken in WP4, particular emphasis will be put to define a VMS format directly compatible with the logbook format. Deliverables D1.1 D1.2 D1.3

An agreed format for logbooks data to be used by each partner An agreed format for VMS data to be used by each partner A database structure that can hold these two data sources

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2 Month 2 Work package number Start date or starting event: Distinction of metiers from disaggregated logbooks data. Work package title 1 2 3 4 5 6 Participant id 1.3 1.3 1.8 0.0 0.0 Person-months per participant: 4.4

7 0.0

Objectives This WP will perform the required statistical analyses of logbooks data at the fishing area level (multi-national) for identifying the métiers definitions and allocating trips to these métiers. This workpackage will propose a final robust method for this that can be further disseminated. The objectives in this WP will be to • Evaluate the data quality and remove erroneous data • identify a set of appropriate multivariate statistical methods and appropriate evaluation criteria, • develop the corresponding code to apply these methods on the international data collected in WP1, and run the analyses within this work-package on national data sets already available, • Evaluate the sensitivity of results to the multivariate method used to the different national data sets. Propose a final robust method for métiers identification that is consistent across national sets.. • Translate the results obtained with this final selected method into robust fixed allocation rules linking each fishing trip to one and only one “métier” DCR categories at level 5.

Description of work Before performing statistical analyses, exploratory analyses will be carried out to remove erroneous data and to evaluate data quality. Results of this should feed back into WP1. This WP will perform a number of statistical analyses on the multi-national database created in WP1. In order to provide a method for métiers definition consistent across countries and robust to the technical details of the method chosen, it is important to perform alternative analyses and evaluate their robustness and reliability. Based on the state of the art described above (paragraph 2.1.2), we will select a limited number of multivariate analyses to be tested and compared. This number will likely be constrained by the limited time frame of this project, thus the selected methods will only reflect a panel of the numerous approaches previously used. We will also identify a set of criteria allowing the comparison and evaluation of the various methods based on simple statistical and graphical outputs. Both methods and evaluation criteria will be coded into generic codes compatible with the input format used in WP1. The methods will be applied at the regional scale on the data sets already present within the workpackage. It is expected that most results will be obtained during a methodological workshop where the national data will be temporarily combined, in order to run the analyses on data at the regional scale rather than at the national scale. To ensure a good evaluation of the robustness of the methods, longer time series are preferred.

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The outcomes of the various methods will be compared according to the set of criteria, and the final method for metiers definition will be selected. Definition must be robust to a number of sources of variability such as the method applied or the length of the time-series. The last task in this WP will be to translate the final results obtained through multivariate analyses into simple allocation rules linking each trip to a DCR métier using cut-off points (thresholds of percentage of various species in the landings profile), which will form the basis of the future allocation of any trip within the management area into a métier category. This work-package will deliver the methodological tools to distinguish the métiers and allocate trips to these métiers and the protocols that describe how to perform this exercise. The final application of the work on all data sets available within the consortium as collated within WP1 will de done in WP7. This should deliver the final definition for the métiers.

Deliverables The deliverables are intended to match to some extent the objectives. These deliverables, however, are not intended to be separate reports but rather recognizable sections in one final report for this work package D2.1 A list of criteria to evaluate the reviewed statistical methods with respect to the project objectives. D2.2 Table with the criteria to be filled for each statistical method. D2.3 Description of selected multivariate statistical methods; required data and format; assumptions. D2.4 Evaluation of dataset quality in relation to the methods to be used, for each regional case study. D2.5 At regional scale, synthesis of the outcomes of the different methods applied. D2.6 Table defining for each métier, its definition in term of robust percentage thresholds at regional scale. D2.7

Protocols how to use the methodological tools developed in order to distinguish métiers and allocate trips to them

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3 Work package number Start date or starting event: Distinction of fishing from other activities Work package title 1 2 3 4 5 Participant id 1.1 1.1 1.1 0.0 Person-months per participant: 3.1

Month 2 6 0.0

7 0.0

Objectives 1. Development of tool that distinguishes fishing from other activities

Description of work The work in this work package consists of the following tasks: 1. Review and evaluation of existing methods that establish the main features of a vessel trip and distinguish spatial distribution of fishing from other activities 2. Application of the most promising methods to available regional datasets and evaluation of the results obtained. 3. Depending on the outcome of the evaluation further development of methodology 4. Final decision on the best methodology to establish the main activites of a vessel trip and distinguish spatial distribution of fishing from other activities An explorative analysis of the VMS data has to be developed to characterize the fishing periods of the vessel. The aim is to define some reference profiles (speed, cap) for different vessel populations. Several parameters have to be considered and tested to identify these populations thanks to the gear, the vessel size, the métier and the area. For each vessel, the analyse of the raw data would allow to qualify its reference population and take into account the parameters defining its fishing period. The pertinence for routine monthly treatment to create a very precise algorithm has to be evaluated.

Deliverables The following deliverables match the tasks in the “description of work” section. These deliverables are not intended to be separate reports but rather recognizable sections in one final report for this work package D3.1

Review of and evaluation of existing methods that establish the main activities of a vessel trip and distinguish spatial distribution of fishing from other activities D3.2 Application of the most promising methods to available regional datasets and evaluation of the results obtained. D3.3 Protocol for the application of the methods that should establish the main activities of a vessel trip and distinguish spatial distribution of fishing from other activities.

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4 Work package number Start date or starting event: Link VMS to logbooks Work package title 1 2 3 4 5 Participant id 1.0 1.0 1.0 0.0 Person-months per participant: 3.8

Month 2 6 0.0

7 0.0

Objectives To develop protocols and methods to link data provided by the logbooks and VMS systems. The objectives of this work package will be to: 1. Evaluate the consistency between effort information reported from logbooks and from VMS data respectively, 2. Merge both source of information on a trip by trip basis, in order to collate logbooks based catch information and métier definition (from WPs 1 and 2) and VMS-based effort information by haul (from WPs 1 and 3), 3. Aggregate the data obtained into the exchange format defined in WP1 (Task 1.3), and upload them into the data warehouse, 4. Provide summary outputs and spatio-temporal visualisation of the data obtained.

Description of work This work package is aimed at linking VMS and logbook data. Validation and statistical analysis of the different approaches constitutes parts of this work-package. The link between VMS and logbooks data will provide corroborated and more precise fishing trips, with relevant information collected from the VMS and from the logbooks. The standardized outputs of VMS tools, made compatible with logbooks during WP1, provide a solid foundation to compare and link VMS data to the logbooks data. This will require the establishment of a common tool and system associated to standardized databases that can show geographical and temporal distribution of the trip-by-trip catches and effort data in the logbooks based on the VMS data analysis and the logbook information which will be on métier basis. Task 4.1. Evaluation of the consistency between both data sources This will require as input results from WP1, task 1.2 and WP3 which has distinguished the fishing activities (fishing time (effort) per trip) based upon the raw VMS data, and the merged métier data including catch from WP1 task 1.1 and WP2 where one trip is assigned a single métier. Each type of data will include ICES rectangle. A comparison of the fishing activities in both data sources will be performed to assign a quality flag to each fishing activity (i.e. on trip basis). The initial suggestions for the quality flags are: 1. Matching, when time and area of VMS and métier data match 2. Misreported area, when time but not area of the VMS and métier data match 3. Misreported time, when area but not time of the VMS and métier data match 4. No logbook match, when a fishing activity has been distinguished from the VMS data but no corresponding fishing activity can be found in the logbook métier data

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5. No VMS match, when a fishing activity has been reported within the métier data (logbooks) but no corresponding fishing activity has been distinguished from the VMS data. This module should report back which flags have been assigned to individual fishing activities and any fishing activity flagged with 4 or 5 would not be used in further processing. Task 4.2.Merging of data source This module will perform the merging of inputs from both data sources. A VMS single trip includes a number of hauls (and fishing hours) that have been performed within an area. An area will be defined as ICES rectangles broken down into individual regions (e.g. 5 by 5 nautical miles). This will be merged with the corresponding catches in the same rectangle from the same trip (logbook data). Areas and times (fishing activities) from the VMS trip data will be used. These procedures can be made more precise for the countries where logbook information is available at the day or haul level rather than at the trip level. Uncertainties will be identified and methods developed to estimate those: • around a fishing activity (effort), if the vessel speed it slightly outside the bounds to be delivered from WP3 • around effort in relation to the ping rate of the VMS data to be delivered from WP4 • around catch area distribution (assignment/allocation of catch to effort) based on trip effort to be delivered fromWP5 Based on the variance around the uncertainty values a method to calculate the combined uncertainty value will be developed. When available haul-by-haul logbook information can be used for validating and evaluating the performance of the distinguishing fishing activities from the VMS data. Haul-by-haul logbook data can be used to validate the error rate around the uncertainty calculations. Such data are available by several of the partners within the consortium. Task 4.3.Definition of standardized exchange format for aggregated data The aim of this task is to produce sets of aggregated merge data in a standardised format, which will be used in subsequent work-packages for e.g. spatial visualisation. As for task 4.2, extensive experience in this already exists within the consortium. Some tools already exist for compiling, validating, analysing and summarising logbooks data at the international level. In particular, the data warehouse FishFrame (http://www.fishframe.org/) has developed some high quality interface for this and offers a variety of advantages and functionalities. It is located on a regional web server, and includes login system for users as well as a security system which allows an individual user to have access only to the data this user is permitted to. This data warehouse is being increasingly used within a number of DCR and ICES initiatives, and form the basis of a proposal under the companion tender MARE/2008/10 Lot 1. It is thus the intention that this task will use the FishFrame exchange format for aggregating data. The first task will be to define the aggregation level at which data should be uploaded to FishFrame. FishFrame currently consists of biological and logbooks data which at the highest level can be supplied aggregated on ICES rectangle, month and métier. This data can then be processed up to e.g. a stock assessment, i.e. linked to biological data under FishFrame.

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Combined VMS/ Métier data will be stored in parallel to the existing FishFrame data, and therefore does not have to conform to the same aggregation basis with regards to time and space. Minimum level of data that will be supplied to FishFrame will be decided according to the results of the data merging and the technical requirement of the warehouse. This data would then be made available to all users for the VMS/ Métier reporting features of FishFrame. Optionally it will also be possible to provide the individual data sources of WP 1 to 3 as this would enable greater flexibility in the analysis of comparisons between these data sources. Access to these data sources and reports based upon them would be restricted. Task 4.4. FishFrame reporting tools FishFrame will be able to provide a common report and data extraction module with relevant summarizations both in data tables and in Geographical Information Systems. This module will use core features of FishFrame to restrict access to certain types of data to users with the required privileges. The reports available will depend upon the data supplied to FishFrame, these should include: - Difference between logbook fishing activities and those identified by fishing activity from VMS data (effort) (WP3). - Difference between logbook fishing activities and catch distribution - Aggregated effort data Deliverables D4.1 A list of quality flags that may be identified during evaluation D4.2 A tool that will: - Perform the evaluation of fishing activities and return these with attached quality flag - Perform the merging of métier data and VMS data - Output the merged data in FishFrame VMS/ Métier exchange format at the requested aggregation level D4.3 Definition of the FishFrame VMS/ Métier exchange format and the required features in FishFrame to allow the upload and validation of this data D4.4 Reporting section of FishFrame dedicated to the visualization and analysis of VMS/ Métier data

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5 Work package number Start date or starting event: Evaluation of the effect of the recording rate Work package title 1 2 3 4 5 Participant id 1.2 1.8 0.0 0.0 Person-months per participant: 2.9

Month 2 6 0.0

7 0.0

Objectives 1. Assess the effect of recording rate on the fishing impact indicators based on VMS position registrations 2. Evaluate existing algorithm(s) that can be used to create a track from VMS position registrations. Further develop the most promising algorithm. 3. Assess the effect of recording rate on the fishing impact indicators based on tracks 4. Evaluate the results of both approaches and advise on the preferred recording rate

Description of work For an evaluation of the effect of the recording rate we have access to three datasets with a sampling interval smaller than the current interval of approximately 2 hours: - The Dutch Automated Position Registration (APR) database consisting of high resolution data based on a sample of about 10% of the Dutch beam trawl fleet that was equipped with APR equipment for the period 1993-2000 during which the position of the vessels was recorded every 6 minutes (see (Rijnsdorp et al., 1998). - The UK VMS data with 15 minute interval - The French Recopesca data data with 15 minute interval (the interval can be changed, less or more) Using these data we will evaluate what the effect of recording rate is on our perception of fishing impact according to the indicators put forward in EC 2008/MARE/020. For this we will follow two approaches: 1. Using only the actual position registration data 2. Using tracks calculated with an algorithm from the position registration data. For this the methodology to calculated tracks from position registration data will need to be developed. The aim is that this should go beyond the connection of the points with straight lines. In both cases we will vary the recording rate by multitudes of 6 minutes (6, 12, 18, 24….240) in case of the APR data and 15 minutes (15, 30 , 45….240) in case of the UKVMS until the current two-hour interval. Based on both datasets we will analyse how the spatial distribution at different recording rates and varying spatial scales changes in relation to the spatial distribution based on the highest recording rate which can be considered the most accurate reflection of the fishing activity. The results of these analyses will be evaluated against criteria to establish the best recording rate.

Deliverables The following deliverables are not intended to be separate reports but rather recognizable sections in one final report for this work package 28

D5.1 D5.2 D5.3

Description of the effect of the recording rate on our perception of the spatial distribution of a fleet An algorithm that allows the best estimation of the actual vessel track from VMS position registration data Advice on the preferred recording rate after an evaluation of the results of D5.1 against a set of criteria

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6 Month 2 Work package number Start date or starting event: Describing the spatial distribution of fishing activity Work package title 1 2 3 4 5 6 Participant id 1.0 1.0 1.0 0.3 0.0 Person-months per participant: 2.9

7 0.3

Objectives 1. To develop protocols and tools for deriving standardized indicators of the spatial distribution of fishing activity across Europe derived from VMS and logbook data.

Description of work This work-package will develop protocols and tools for describing the spatial distribution of fishing activity across Europe. Scale will be an important issue and the work-package will concentrate on large scales of time, space and number of vessels (e.g. annual patterns across European waters for entire fleets). The work to assess the best possible descriptors at such large scales will be informed by work on individual vessels over short timescales (conducted previously and in the other workpackages), but will not focus on results at this fine scale of individual vessel movements. Alternative methods for estimating the spatial distribution of fishing from VMS data will be compared and assessed. The issue is how best to use the point samples of vessel locations provided by VMS to estimate the overall spatial distribution of fishing activity. Current methods differ principally in whether they calculate point densities or whether they join points with estimated fishing tracks. There are also issues of the best temporal and spatial resolution for outputs. We will make recommendations on what we believe to be the best method for standardized application across Europe, based upon the considerable experience of the project partners. The indicators proposed in EC2008/MARE/020 (‘Community framework for the collection, management and use of data in the fisheries sector …’) will be considered as part of this process. This recommendation will concentrate on the currently available VMS data (2 hour interval), but will also interact with WP 5 to assess the implication of changing recording rate.

There may be trade-offs between the complexity of methods and the ability to apply those methods widely. Our priority will be to develop standardized methods applicable across Europe rather than complex highly parameterized approaches that work very well for a small subset of cases. We will produce software tools for calculating the recommended indicators that will work in the commonest used environments ArcGIS, R and SAS to facilitate widespread use. There are issues of confidentiality with unaggregated VMS data, we will work together using the tools developed to create aggregated anonymised data products that, where possible, will facilitate wider access to VMS data across Europe. The protocols and tools developed will go through first testing on national datasets in this workpackage before being added to the integrated framework in WP7 and further tested on datasets across Europe before being made generally available.

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Deliverables The following deliverables match the tasks in the “description of work” section. These deliverables are not intended to be separate reports but rather recognizable sections in one final report for this work package D6.1 D6.2

Development of pressure indicators based on the spatial distribution of fishing activity Tools and standardized protocols for producing these pressure indicators from VMS data

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7 Work package number Start date or starting event: Synthesis, output and dissemination Work package title 1 2 3 4 5 Participant id 1.1 1.1 1.1 0.4 Person-months per participant: 2.9

Month 13 6 0.4

7 0.4

Objectives 1. Integrate the tools developed in the previous work packages into a common framework 2. Apply this framework to datasets across Europe (Baltic Sea, North Sea, Bay of Biscay, East Atlantic) and show the possible output of this framework 3. Identify gaps in knowledge that may hamper the estimation of fishing impact on the ecosystem now or in the future 4. Make this framework including all tools and protocols available at a public website

Description of work The work in this work-package consists of the integration of the tools and protocols developed in the previous work-packages into one consistent framework that can provide all the information necessary to estimate the impact of fishing on the marine ecosystem based on two sources of data: logbooks and VMS. The different tools will then be applied in an integrated manner to the different data sets that have been collated as part of the project. This work will commence with a project meeting at the time all other WPs have finished after which much of the work will be done by correspondence. Finally there’ll be one of the major parts of the work-package: an extensive workshop involving all project partners to finalise this process and discuss the projects outcomes and develop standardized approaches. The type of outcome that can be expected is the distinction of métiers, distinction between fishing and other activities and the calculation of pressure indicators. This workshop should result in the drafting of protocols targeted at scientists and technicians to enable them to produce with the tools developed in the project the standardized data products and outputs that can be provided to policy makers and the public. We will also provide documentation targeted at these policy makers and the public to explain the meaning and use of these products. This WP will summarise the processes undertaken and results obtained during the previous WPs, and will be in charge of providing all necessary tools as free download on a public website for dissemination. It is clear that the present consortium does not cover all European maritime regions. Special emphasis should thus be put on providing generic tools than can be used equally in other areas. Since the results obtained with the present consortium are unlikely to be directly usable in other regions (for example, it is unlikely that the definitions of métiers in the Iberian waters would be similar to those in the North Sea because of different species composition), the aim is to make it possible for other regions to undertake the same analyses without further methodological development. The reports that will be made freely available to the wider audience will include: • the exchange format for disaggregated (trip-based) national logbooks data (corresponding to D1.1 in WP1)

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• • • • • • •

Some generic scripts (produced during WP2), compatible with this format which will (1) run the finally selected multivariate analysis on a regional scale and (2) translate the results into simple métier allocation rules. The exchange format for disaggregated VMS data (corresponding to D1.2 in WP1) Some generic scripts (produced during WP3) compatible with this format for estimating fishing activity from VMS data Some generic scripts for merging both sources of information and aggregate them into the FishFrame exchange format (corresponding to D5.2) Information on how to produce quality control and summary statistics using FishFrame warehouse Some generic scripts (produced during WP6) compatible with the above formats for deriving standardized indicators of the spatial distribution of fishing activity Extended documentation on the whole process described here.

Deliverables D 7.1 Workshop with participants from various EU member states holding VMS and/or logbook data D 7.2 Results from the application of the tools to the available data sets showing the e.g. different métiers per region and estimates of pressure indicators on the spatial distribution of fishing activity D 7.3 Dissemination of the tools and protocols D 7.4 Public website containing data exchange formats, scripts and full documentation for the analyses

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5 Project planning It is acknowledged that the project should be completed within 18 months after signature of the contract.

WP 1 2 3 4 5 6 7 Project meetings Meetings with DGMARE

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17

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All WPs except for WP7 will commence in the first month of the project. All partners should contribute data to the central database that will be developed as part of this WP1. The partners involved in the various tool development WPs (WP2-6) can commence with their own national data sets that are already available. Two months before the end of the project (i.e. month 16) the tool development WPs should be completed and all the data should have been checked (partly through the work in the tool development WPs) and incorporated in the central database. That is when all effort is concentrated on the integrative and synthesizing WP7 and all tools that were developed separately will be integrated in one consistent framework which will be applied to all available datasets covering many European waters that are now available through WP1. During the projects there’ll five project meetings. The 1st is the kick-off meeting in the first month, the 2nd is in month 5 before the interim report is due. The 3rd meeting will be somewhere half-way the project, the 4th after the tool development WPs have finished and the integrative WP7 really commences and the 5th and final meeting will be a workshop where all tools can be applied to all available data sets. An interim report is due after 6 months. The report will outline the results achieved, the difficulties faced or foreseen, the provisions made to overcome these difficulties and a detailed calendar of proposed activities for the remaining of the contract lifetime, including an outline of the draft final report. The draft final report will be made available within eighteen months from the signature of the contract. Two meetings at DG MARE in Brussels will take place, one to present and discuss the interim report and one for presentation/discussion of the draft final report.

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6 Project resources Table 6.1 Manpower input by Work Package and institute and total budget (€) per institute WP 1 2 3 4 5 6 7

Compilation of national data sets into one database Distinction of métiers Distinction of fishing from other activities Link VMS to logbooks Evaluation of the effect of the recording rate Describing the spatial distribution of fishing activity Synthesis output and dissemination Coordination Total Man-months Total budget (€)

IMARES

CEFAS

IFREMER

DTU/AQUA

FRS

SFI

MI

1.1

1.1

1.1

2.9

0.4

0.4

0.4

1.3 1.1 1.0

1.3 1.1 1.0

4.4 3.1 1.0

1.8 1.1 3.8

0.0 0.0 0.0

0.0 0.0 0.0

0.0 0.0 0.0

2.9

1.2

1.8

0.0

0.0

0.0

0.0

1.0

2.9

1.0

1.0

0.3

0.0

0.3

2.9 4.2 15.6 197334

1.1

1.1

1.1

0.4

0.4

0.4

9.7 137472

13.5 164993

11.6 141208

1.1 22722

0.7 13550

1.1 22722

The total requested budget is €700.000,-. This includes both personnel costs as well as travel and subsistence for the project meetings and meetings with DGMARE.

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7 Description of the consortium Partner 1: Wageningen IMARES (Institute for Marine Resources and Ecosystem Studies) Wageningen UR and TNO have joined forces by housing their applied marine research in a newly established institute, IMARES. In Wageningen IMARES, Alterra Texel, and the Netherlands Institute for Fisheries Research (known by its acronym RIVO) have been joined. They are part of the EU network MARBEF. Wageningen IMARES is an independent unit of Wageningen University and research Centre (Wageningen UR). A staff or more than 120 individuals is involved in research at three locations. Wageningen IMARES provides knowledge that is essential for the sustainable use and management of the sea and saline coastal areas. The focus is on strategic and applied ecological research relating to economic developments that respect the sea’s ecology and ecological values. The field of work comprises a wide variety of issues, e.g.: sustainable fisheries, effects of windmill parks, oil and gas production, sand extraction, intrusion of salty water in land areas used for agriculture or the production of drinking water, restoration of brackish transition areas, coastal defence, large-scale land reclamation for harbour industries, development of Marine Protected Areas in response to the Bird and Habitat Directives, implementation of EU marine strategy and the Water Framework Directive, environmental risk assessment and development of new forms of aquaculture. The research is focused on changes introduced by human intervention as opposed to natural changes, and their effects on ecosystem dynamics, assessment of (profitable) functionality and multifunctional use from an ecological perspective, protection of marine ecosystems, and the development of management systems, and provision of advice based on policy scenario’s for the marine environment, particularly fisheries (modelling). Key personnel: Dr. Gerjan Piet has been working for more than 15 years on the development of an ecosystem approach, indicator-based management and the analysis of VMS or logbooks and is the author of more than 30 publications in peer-reviewed journals. He has participated in and coordinated several EU-funded projects that involve database development, indicators or the analysis of VMS or logbooks in relation to fisheries management (e.g. DATRAS, INDECO, INDENT, CEDER, IMAGE). He is a member of several ICES working groups, including the Working Group on the Ecosystem Effects of Fishing and has chaired other working groups. He has been actively involved in the STECF working groups on the DCR and has chaired some of the subgroups. Ir. Niels Hintzen is a mathematical modeler with extensive experience in the analysis of VMS data and modeling of fishing tracks. Ir. Floor Quirijns has been working as a fisheries scientist for six years. At IMARES she is responsible for the collaborative research projects with the fishing industry (self sampling program of discards, F-project); and analyses of data on landings and effort (including VMS). She has participated in several EU projects on fisheries data (e.g. TecTac, CEDER) and she has been actively involved in ICES study groups and workshops on cooperative research and better use of commercial catch and effort data (SGFI, WKUFS, WKSC). 36

Sieto Verver has been working for 9 years on logbook databases and the analysis of logbook data for stock-assessment purposes. He is mainly involved in EU-DCR related projects and is project leader of the Dutch Market Sampling project. He participated in several ICES assessment working groups, is current member of PGCCDBS and RCM North Sea and he has chaired the ICES Workshop on concurrent sampling in 2008. Piet, G. J. and Jennings, S. 2005. Response of potential fish community indicators to fishing. ICES Journal of Marine Science, 62: 214-225. Piet, G. J., Quirijns, F. J., Robinson, L. and Greenstreet, S. P. R. 2007. Potential pressure indicators for fishing, and their data requirements. ICES J. Mar. Sci., 64: 110-121. Piet G.J. and F.J. Quirijns. In press. The importance of scale for fishing impact estimations. Canadian Journal of Fisheries and Aquatic Sciences Partner 2: Cefas (Centre for Environment, Fisheries & Aquaculture Science) as an Executive Agency of the Department for Environment, Food and Rural Affairs (Defra) is a multidisciplinary scientific research and consultancy centre specialising in fisheries science and management and marine monitoring and assessment. The organisation employs over 550 and provides its services to a large number of UK and international public and private sector clients, including Defra, the Environment Agency, and the European Commission. Cefas enjoys an outstanding reputation within the world community of fisheries science and management and has been at the forefront of fisheries management. Cefas plays a leading role within the International Council for the Exploration of the Seas (ICES) and other international organisations (e.g. EIFAC, NASCO, NAFO, ICCAT, IWC). This work, directed towards the collection and analysis of appropriate data on fish and fisheries in their broadest sense, is aimed at providing coherent management advice to the Ministers of the member states so that the Common Fisheries Policy can be implemented and improved. The Fisheries Resource Management (FRM) Science Group provides advice on the status and management of UK and European fisheries. This is based on monitoring, assessment and modelling of fish and shellfish stocks, studying their response to exploitation, and conducting research on the environments in which they live and species with which they interact. Key personnel: Dr Andy South is an expert in the use of spatial data in providing advice for policy. He is leading a team developing methods for extracting, analysing and displaying spatial data for answering policy relevant questions, concentrating on bringing together data on fish abundance, fishing effort and regulated areas. He has a background in ecology and modelling approaches for predicting the spatial distribution of animals at a range of scales, and in developing user friendly software tools for visualising data. He has contributed to EU projects CEDER & INCOFISH, is a member of the ICES working group on data and information management, and contributed to the 2006 UNESCO workshop on marine spatial planning. Dr Stuart Reeves has over twenty years experience as a fisheries scientist. He is currently the leader of the Fishery Dynamics team at Cefas, and is working on approaches to using information on fishing fleets and their activity in fishery

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management advice. He is also the chair of the ICES Study Group on Mixed Fishery management. Dr Janette Lee is a GIS analyst and software developer. She has many years experience in the design and development of software tools for a broad range of environmental applications. Most recently Janette has been involved in the creation of tools for the extraction and processing of VMS data and the generation of fishing pressure surfaces from those data. Dr Vanessa Stelzenmüller holds a Ph. Degree in Marine Environmental Science from the University of Oldenburg, Germany. As a marine scientist she works at CEFAS on national and international projects dealing with human pressures on the marine environment including the assessment spatio-temporal variability of fishing pressure, the assessment of sensitivity of marine resources and the development of concepts and tools for management and marine spatial planning. Her expertise in the fields of marine resource management, spatial modelling, GIS applications and spatial marine ecology are expressed in numerous peer-reviewed publications. Partner 3 : IFREMER (Institut Français pour la Recherche et l’Exploitation de la Mer) Is the National institute of marine research, the French public institute for marine research. Ifremer contributes, through studies and expert assessments, to knowledge about the ocean and its resources, monitoring of marine and coastal zones and the sustainable development of maritime activities. To these ends, it designs and operates observational, experimental and monitoring tools and facilities. Ifremer manages the ocean research fleet for the French scientific community. Key personnel: Dr Patrick Berthou has been actively involved since 1977 in a number of projects relating to fisheries data network, fish biology, fish stock assessment and fisheries management. Dr Berthou is a senior fisheries research scientist at Ifremer, and between 1999 and 2004 co-ordinated the Fisheries Observatory, a project monitoring resources, harvesting and economic data. He is currently leader of one of Ifremer two major programmes, SIDEPECHE: Fisheries observatory, technology of observation, economy and assessment of the fisheries resources and their uses. Within this programme he has created a project, RECOPESCA: a network of fishing effort and environmental parameter measurements aboard fishing vessels, participating on a voluntary basis. In parallel, he is involved in the development of the analysis of VMS data. Dr Stephanie Mahévas is a senior scientist. She is a mathematician specialized in probabilistic and statistics modelling (Phd in 1997). She has been working on Markov models to practical problems of large dimensions. Since 1998, she has been employed at Ifremer and has worked on fishery dynamics models and statistical data analysis using generalized linear models and mixed models. She has developed statistical methods to estimate parameters linking the fishing effort to fishing mortality. She has co-developed ISIS-Fish a simulation model for assessing the impact of spatial management measures on resources and fishing activity. She has also works on statistical simulation design to perform sensitivity analyses and to analyse simulations outputs. She is responsible f the data quality project of the fishery observatory of IFREMER (SIH). She was involved in the EU-funded project “Technological developments and tactical adaptions of important EU fleets” (TECTAC, Q5RS-2002-01291) and is currently involved in the EU-funded project

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“Capacity, F and Effort” (CAFE, FP6-2005-022644), the EU-funded project “Understanding the mechanisms of stock recovery” (UNCOVER, FP6-2004-SSP-4), and the EU-funded project “ A framework for fleet and area based fisheries management” (AFRAME, FP6-2007: 044168). Dr Paul Marchal has a background in fish stock assessment and mixed fisheries modeling. Over the past 15 years he has worked in fisheries research labs in England (CEFAS), Denmark (DIFRES), France (IFREMER) and most recently New Zealand (SeaFIC). Paul chaired the ICES (International Council for the Exploration of the Sea) working group for the assessment of deep-sea stocks and fisheries (2006-2007). He has also coordinated and/or contributed to several European projects pertaining to the impact of management regulations on fleet behaviour and the pressure they exert on commercial fish stocks (FER: 1999-2001, TECTAC: 2002-2005, CAFÉ: 2006-2009). Paul is currently undertaking a bio-economic evaluation of the New Zealand’s Individual Transferable Quotas system (TRANZEF project: 2007-2009). Dr Emilie Leblond has worked as a fisheries engineer at Ifremer since 2001. She is involved in the Fisheries Observatory managing the network of surveyors. She has experience in producing standardised synopses on fleet activities and characteristics and is involved in processes of qualification and extrapolation of fisheries statistics data (fishing effort and catches). In addition, she is project leader of RECOPESCA, a network of fishing effort and environmental parameter measurements aboard fishing vessels, participating on a voluntary basis. Dr Martial Laurans has been actively involved since 1998 in a number of projects relating to fish biology, fish stock assessment and fisheries management. He has been actively involved in EU-funded research projects (SIAP, POORFISH) around the analysis and modelling of the linkages between management, stock assessment and fishing mortality. He is also a member ICES Working Group of crab. He is currently implied in two major projects from Ifremer, the RECOPESCA Project and the analysis of VMS data. Sébastien Demanèche is a statistician, graduate of the E.N.S.A.I. (National School of Statistics and Data Analysis) in 2000. Since 2001, he has specialized in fishery statistics, especially in participating to the development of the IFREMER (French Research Institute for Exploitation of the Sea) Halieutic Information System (S.I.H.). He has been involved in the elaboration of statistical methodologies (compilation, analyse and production of statistical information, validation of data and procedures for data re-evaluation) on the basis of the IFREMER S.I.H. database (dealing with calendar of activity, landings, administrative identifications, economic and technical data concerning the whole french fishing vessels) in response to scientific issues peculiar to the IFREMER institute and/or in support to experts' scientific advice. He has been implicated in the development of the economic part of the S.I.H. and in its extension to the French overseas territories (Reunion, French West Indies, Guyana). He is also in charge of the elaboration of a sampling on-site strategy to estimate catches and fishing effort of small-scale fleets without sufficient declarative data (Reunion, French West Indies, Guyana, Mediterranean). He has been implicated in the establishment of the new EU Data Collection Regulation (DCR) related to the professional fisheries sector (implementation of a new approach fleets*métiers). He has also been involved, for the statistician part, in the EU-funded project "Small Scale Coastal Fisheries in Europe (N° FISH/2005/10)" coordinated by IFREMER.

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Berthou et al. (2003). Typologies des flottes de pêche : Méthodes Ifremer-SIH. Rapport interne Ifremer DRV/SIH/n°4/082003. Marchal, P. [ed.], 2006. Technological Developments and tactical adaptations of important EU fleets (TECTAC, n° QLK5-2001-01291). Final report, 651 pp (main report), 369 pp (annexes). ICES(2003), Report of the study group on the development of fishery-based forecasts, ICES CM 2003/ACFM:08 Partner 4: DTU-AQUA (Technical University of Denmark, National Institute of Aquatic Resources) Effectively from January 2008, the Danish Institute for Fisheries Research (DIFRES) has changed its name to National Institute of Aquatic Resources – in short DTU Aqua. Since the 1st of January 2007 the National Institute of Aquatic Resources has merged with the Technical University of Denmark, the Danish Institute for Food and Veterinary Research, Risø National Laboratory, the Danish National Space Centre and the Danish Transport Research Institute. The name of the new organisation is the Technical University of Denmark. The Technical University of Denmark (DTU) is a modern self-governed university that operates at a high international level in a wide array of research areas within science and technology, i.e. biotechnology, microbiology, chemical engineering, information and communications technology, material science, food science, fisheries research, transport and traffic research, micro- and nanotechnology, energy research (both conventional and sustainable energy), medico technology, and space technology. The University's research and teaching is provided by 22 departments and research centres. DTU has a very wide range of advanced research infrastructures and laboratories, and acts in a number of areas as national and international reference laboratory. The University embraces most of the engineering disciplines, and educates engineers to Bachelor, Masters and PhD level. In addition, the University offers a comprehensive continuing education programme, with a number of courses taught in English. The University has a staff of approx. 2000 scientists, 690 PhD students, 6000 Bachelor and Masters Students, 400 foreign students a year on English-taught courses and Master programmes, and an annual budget of 3,2 billion DKK. DTU-Aqua carries out research, investigations and provides advice concerning sustainable exploitation of live marine and fresh water resources. The institute deals with chain considerations from water to table. This includes interaction between the aquatic environment, productivity and variation in fish stocks, methods for fish stock assessment, development of methods for sustainable fisheries management, and stock enhancement. Moreover, processing and improvement of fish products as well as quality assurance in the fish industry are important parts of the research areas of the institution. DTU-Aqua has approximately 300 employees and an annual budget of around 25 million EURO. In relation to marine fisheries DTU-Aqua has 5 sections dealing with (1) Ecosystem modelling (2) Management Systems (Fisheries and Ecosystem Based Management) (3) Population Dynamics and Stock assessment, (4) Fisheries Technology and (5) Data collection, Monitoring and Data Management. The institute has expertise in Data Collection, Databases, Research Surveys, Fish Stock Assessment, Fisheries Biology, Fisheries Dynamics, Marine Ecosystems, Fisheries Technology, Mathematical Modelling, Bio-Economics, Fisheries Management Evaluation Tools, Ecosystem Based Management Methods, Development of Software

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and provision of Management Advice through ICES and NAFO, the EU commission and other fisheries commissions, and the Danish Ministry of Food, Agriculture and Fisheries. The contributions to the project will be delivered by the staff in the Management Section, the Data Management Section and the IT/T Section. . Key personnel: J. Rasmus Nielsen, Senior Scientist, is head of section and research co-ordinator of Management System Section within DTU-Aqua. He is author of more than 30 scientific peer reviewed publications. His main research areas has been fish population dynamics, fisheries dynamics, stock and fisheries assessment, fisheries management research including bio-economic fisheries management evaluation tools, fisheries hydro-acoustics, and research survey design. Important working areas have been research in fish stock and fisheries distribution patterns and methods to determine that, research in fisheries management tools on fleet basis, stock assessment, research based fisheries management advice. He has coordinated several national and international scientific research projects among other some very large EU Projects (EU FP6 EFIMAS; EU ISDBITS; EU FP5 EASE (co-coordinator)), and participant and task coordinator of several other international research projects. He has a long advisory experience and has since 1997 been responsible for the Norway pout assessment in the North Sea under ICES WGNSSK and national member of the ICES Fisheries Management Committee. He has for more than a year been scientific advisor in Vietnam under the joint Danish Government (DANIDA) & Vietnam Government Development Aid & Research Project. He has extensive university teaching experience and has been super-visor for Ph.D. and Master of science students (DTU, KU, ENSAR, RIMP). Clara Ulrich Rescan, Senior Scientist at the Section For Fisheries Advice, with MSc in Agronomy and Ph.D (2000) at the fishery laboratory of ENSAR (Rennes, France). She has been involved in several projects relative to management strategies evaluation, mixed-fisheries, fleets dynamics and fish stock assessment. She has participated in several large EU-funded projects and as international work package coordinator in a number of them (EU FP6 EFIMAS, EU FP6 COMMIT, EU FP6 AFRAME, EU FP7 JAKFISH). Besides, she is member of several ICES and STECF working groups, and is appointed on the reserve list of experts for the STECF members. She participates also to supervision of master and PhD students. Brian James Cowan is a computer scientist and head of the Software Development and GIS section at DTU-Aqua. He has experience in developing and coordinating the development of a wide variety of software application for the fisheries industry. He has been involved in the development of applications covering areas such as fisheries stock assessment (InterCatch & FishFrame), the modelling of the spoilage and growth of bacteria in seafood product (Seafood Spoilage & Safety Predictor - SSSP) and information systems for research vessels and pelagic trawler. Francois Bastardie is a Research Scientist recently hired at DTU-Aqua to develop bioeconomic management evaluation models. He has got a Ph.D in ecological sciences in 2004. He developed advanced modelling skills during his post-doctoral position at the IFREMER institute (France) in particular in fishery modelling, in Rprogramming including the FLR framework and in statistical analysis of fishery data. He has participated in the EFIMAS, PROTECT, CEVIS and IMAGE EU-funded projects developing Management Strategy Evaluation tools for fisheries management. He is a member of the ICES WGBFAS working group.

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Henrik Jensen is a senior research scientist. In recent years he’s primary work has been on sandeel biology and fishery and on industrial fisheries in the North Sea. He was EU coordinator for "Modelling the population dynamics of sandeel (Ammodytes marinus) populations in the North Sea on a spatial resolved level (DG XIV no. 98/025). Participant in many EU-financed projects of which can be mentioned: “Population structure in the lesser sandeel (Ammodytes marinus) and its implications for fishery-predator interactions” (DG XIV no. 94/071) and “A comparison of distribution of seabirds and prey fish stocks in the North Sea and adjacent areas” (DG XIV no. 92/3501). Coordinator and participant in the DTU AQUA research projects: "Analysis of biological key parameters, population structure and population dynamics of the lesser sandeel (Ammodytes marinus) in the North Sea, based on detailed information about the sandeel fishery" (2003-2006) and “Development and performance test of method for establishing an area based recruitment index for North Sea sandeels” (2007-2008). In these last mentioned projects there was a substantial work on the estimation of fishing effort for the Danish vessels fishing sandeels in the North Sea. For this purpose a method was established which allowed estimation of fishing effort for individual fishing grounds, using VMS data and detailed knowledge about sandeels and sandeel fishery. Henrik Jensen is a member of the ICES Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak and the ICES Working Group on Ecosystem Effects on Fishing Activities, and member of the STECF Ad Hoc Working group on sandeel fisheries from 2003-2008. Marie Storr Poulsen (M.Sc.) is head of the monitoring section within DTU-Aqua. Education: 2001: M. Sc. in biology at the university of Copenhagen – Denmark. Employment: 2002-2006 Greenland Institute of Natural Resources with primary working area on cod stock assessment, cruise leader, coordination of market sampling and participating in development of the Greenland inshore logbook. Aug. 2006- 2008 as a research assistant at DIFRES. Main research area; stock assessment and cod biology in the Baltic. Important workings areas include develop of commercial reference fleet and use of VMS data, developing surveys with commercial ships and in cooperation with fishermen. Membership in international working groups; ICES – NWWG (2003-2006), Joint Scientific Committee working group (EU-2004) and ICES- WGNSSK (2006) and responsible for the Western Baltic cod assessment in WGBIFS(2007-). Danish project coordinator in LOT 3 (Danish effort system in Kattegat). Participant as cruise leader on a commercial vessel in REX II (2006), and the scientific vessel DANA (since 2006 -) in the Baltic. Henrik Degel is a Fishery Biologist. He has been employed at DTU Aqua since 1983 except for few years employment in Greenland Fisheries Research Institute and the UN organization FAO where he was working in Asia occupied by sampling of fisheries data around the Bay of Bengal as part of the project: BOBP. He graduated from university in 1989 and has worked with scientific surveys, Fish Stock Assessment, Discards and coordination of sampling of fisheries data in Denmark. Henrik Degel has been member of several STECF sub-groups and expert groups and many ICES WGs. At present, he is chair for the WGBIFS (Baltic International Fish Survey) and member for of WGBFAS (Baltic Fish Assessment WG) where he is data responsible for all cod stocks in the region. Henrik Degel has been the Coordinator for 3 EU-studies ((1994/058, 1996/08, 1998/024). Lotte Worsøe Clausen, is a Research Assistant at DIFRES. She has more than 8 years experience within the area of quality assurance and control of ageing techniques along with validation of these methods. This involves development of validation techniques such as the use of analysis of macro-and micro structures of otoliths,

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otolith weight and microchemistry, use of experimental data, and so forth. She is an expert in herring otoliths, specifically the interpretation of the otolith microstructure and its application in separation of spawning stocks. She is responsible for the quality assurance and control of ageing techniques at DIFRES. Membership of international working groups: Member of TACADAR (QLRT-2001-01891), IBACS (QLRT-2001-01610), and CODYSSEY (QLRT-2001-00813) Co-chair of the Study Group on Ageing Issues of Baltic Cod (SGABC). Herring Assessment Working Group for the Area South of 62º N since 2000. Responsible for the assessment of sprat in the North Sea and Sub.div. IIIa. Involved in the splitting of North Sea Autumn spawners and Baltic Spring spawners. Other relevant activities: Supervising bachelor and master students in the laboratory. Involved in the planning of sampling and laboratory activities in the Monitoring section of Danish Institute for Fisheries Research, dept. of Marine Fisheries Nielsen, J.R.*, Sparre, P.J.*, Hovgaard, H.*, Frost, H.*, and Tserpes, G.* 2006. Effort and Capacity Based Fisheries Management. Chapter 7: p. 163-216. In: Motos, L. and Wilson, D. (editors). 2006. The Knowledge Base for Fisheries Management. Developments in Aquaculture and Fisheries Sciences Series, 36. Elsevier. *Authorship equal. Tserpes, G.*, Peristeraki, P.*, and Nielsen, J.R.* 2006. Ecological Side-Effects of Fishing from the Fisheries Management Perspective. Chapter 10: p. 267294. In: Motos, L. and Wilson, D. (editors). 2006. The Knowledge Base for Fisheries Management. Developments in Aquaculture and Fisheries Sciences Series, 36. Elsevier. *Authorship equal. Lewy, P.*, J. R. Nielsen*, and H. Hovgård*. 2004. Survey gear calibration independent of spatial fish distribution. Can. J. Fish. Aquat. Sci.: 61 (4): 636-647. *Authorship equal. Marchal, P., J.R. Nielsen, H. Hovgård and H. Lassen. 2001. Time changes in fishing power in Danish cod fisheries of the Baltic Sea. ICES Journal of Marine Science 58: 298-310. Ulrich C, B.S. Andersen, P.J. Sparre, J. R Nielsen, 2007. TEMAS : Fleet-based bioeconomic simulation software to evaluate management strategies accounting for fleet behaviour. ICES J. Mar. Sci. 64: 647-651. Ulrich C., Andersen B.S., 2004. Dynamics of fisheries, and the flexibility of vessel activity in Denmark between 1989 and 2001. ICES J. Mar. Sci. 61, 308-322. Ulrich C., Pascoe S., Sparre P.J., De Wilde J.W., Marchal P., 2002. Influence of technical development on bio-economics in the North Sea flatfish fishery regulated by catches- or by effort quotas. Can. J. Fish. Aquat. Sci. 59 (5), 829-843. Ulrich C., Gascuel D., Dunn M.R., Le Gallic B., Dintheer C. 2001. Estimation of technical interactions due to the competition for resource in a mixed-species fishery, and the typology of fleets and métiers in the English Channel. Aquat. Living Resour. 14, 267-281. Cowan,B.J.*; Skov,O.*; Geitner,K*. 2007. Visualising in real-time. Geo-connexion vol. 6. *Authorship equal. Sandbeck,P.; Cowan,B.J.; Jansen,T. 2004 Use of XML technology in the Baltic Sea fisheries database. IOC workshop report, no. 188 Sandbeck P., Rolev A. M. and Jensen H. 2005. Mapping of the fishing grounds of the lesser sandeels Ammodytes marinus in the North Sea. Third International Symposium

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on GIS/Spatial Analyses in Fishery and Aquatic Sciences. Shanghai Fisheries University, Shanghai, China, August 22-26 2005. Degel,H.; Jansen,T. FishFrame. Fisheries and stock assessment data framework Degel,H.; Jansen,T. BaltCom - Baltic Sea Commercial Catch Database: Description of web application, calculation and use Jansen,T.; Degel,H. FishFrame. Estimation of discard using FishFrame 4.2 Jansen,T.; Degel,H.; Heilmann,J.P. BaltCom Datawarehouse - Online data mining using MS Analysis Services Partner 5: FRS (Fisheries Research Service) Fisheries Research Services (FRS) is an agency of the Scottish Government and its principal advisor on fisheries management and marine monitoring and assessment. A recognised leader in marine research, FRS has an established record of accomplishment in fisheries science and in the development and application of fish stock assessment methodology. FRS staff play an important role within the International Council for the Exploration of the Seas (ICES) contributing to many ICES working groups, study groups and advisory committees. The Agency also provides experts to the Commission’s Scientific, Technical and Economic Committee for Fisheries (STECF) and STECF study groups. A principal goal of FRS is to provide sound and objective advice on the sustainable management of the sea. To achieve this FRS’ Fisheries Management Programme focuses on the monitoring and assessment of marine bioresources around Scotland and multidisciplinary research to provide the science required to understand the quality and diversity of commercial fish and shellfish stocks in the wider ecosystem context. Quality measures include the external peer-review of all completed research projects commissioned by the Scottish Government (FRS ROAMEs) , and operating principles for research that correspond to the Joint Code of Practice for Research issued by the UK’s: Biotechnology and Biological Sciences Research Council; Department for Environment, Food and Rural Affairs; Food Standards Agency; and Natural Environment Research Council. FRS has contributed to the development of fisheries-based assessments through founding membership of the ICES Study Group on the development of fishery-based forecasts and attendance at each of the three expert group/training workshops on fleetfishery based sampling. Its statistical analysis of individual fishing trip records and identification of Scottish metiers in the North Sea was presented to a meeting of Scottish fishermen and acknowledged by them to represent a true reflection of current activities. Key personnel Neil Campbell B.Sc(hons), M.Sc, Ph.D (Senior Analyst, Fisheries Data Group, FRS) Neil Campbell joined FRS Marine Laboratory in 2005 as a fish population modeller and data analyst. Prior to this, he worked at Aberdeen University for six years on two EU-funded fisheries research projects (HOMSIR, contract QLK5-1999-01438 and WESTHER, contract QLRT-2001-01-056), working on stock identification of small pelagic fishes. In FRS, his responsibilities have included application of statistical techniques to fisheries data, the development of ecological indicators for measurement of fishing pressure, the assessment of Nephrops stocks and functional modelling of the Scottish pelagic fishing fleet. He has taken part in a number of ICES stock assessment working groups on demersal species in the North Sea and western waters, as well as participating in an EC Scientific, Technical and Economic Committee on Fisheries meeting as an invited expert. In 2006 he attended the second

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‘Nantes’ meeting (Training Workshop on the Fleet Based Approach, IFREMER, Nantes, France) in the series of which the regional metier matrices were developed and difficulties assigning trips to metiers were raised. Partner 6 : SFI (Sea Fisheries Institute) The SFI founded in 1921 as the Sea Fisheries Laboratory in Hel in order to carry out research in sea hydrology and biology for the needs of fisheries, is the oldest marine research institution in Poland. The SFI belongs to a group of fisheries research institutions which is supervised by the Ministry of Agriculture and Rural Development. The principal areas of research conducted at the Institute include fishery biology, oceanography, marine ecology, fish processing technologies, and fishery economics. The main task of the SFI is to develop and provide scientific foundations for the rational use and exploitation of living marine resources. Research carried out by the institute forms the basis for establishing catch quotas and contributes to developing of the European Research Area. The Institute has been a member of EFARO, which associates main governmental institutions carrying out applied research on behalf of sea fishery and aquaculture. International cooperation within the International Council for the Exploration of the Sea (ICES) and the UE's Common Fisheries Policy (CFP) is of great importance for SFI activity. The Institute participates also in number of international research projects like BECAUSE, ELME, EFIMAS, FISBOAT, INDECO, IN EX FISH, EUROCEANS, MARIFISH and others. Key Personnel: Dr. Ryszard Grzebielec is an expert of fisheries data analyses. His areas of expertise center on fisheries in the Baltic Sea and on other areas as well. He is leader of the Polish National program for fisheries data collection (biological sampling and survey data) according to UE fisheries DCR directives. He has participated in INEXFISH project and support colleagues worked in FISBOAT and EFIMAS. Dr. Emil Kuzebski, Master of Science in Economics (management and organisation). Since 1994 has been working in Sea Fisheries Institute. Since 1999 Head of Fisheries Economics Department. In 2000 awarded diploma of Philosophy Doctor of Agriculture Science in Fisheries. In 2000 worked as an consultant for FAO, dealing with investigation of trade and distribution channels in Polish fish market. Delegate of Polish Government to the OECD Fisheries Committee, Paris 1997-1999. Expert of Polish government in enlargement negotiations. In 2002-2004 involved in Economic Assessment of European Fisheries, Concerted Action EU project. Responsible for economic part of the Polish national programme for collection of fisheries data. Acting as a STECF expert. National expert in the Institutional Twinning Programme for Implementation of the Fishery EU-Acquis communautaire in Poland involved in development of Sea Fisheries Information System (SFIS) database dealing with the aspects related to quota management, statistics, vessels register and VMS (20012003). Grzebielec R. (1993). Fishing effort and CPUE vs Fishing mortality and Fish Biomass (based on the Polish Baltic fleet in 1972-1988). Studia i materiały, Seria B, MIR, Gdynia. Kuzebski E, : Economic performance of Polish fishing fleet. Wiad. Ryb. (Fishing News) Nr 5-6 (139), p. 12-14, 2004 r.

45

Horbowy J. Kuzebski E., Ruciński M., Impact of EU structural funds on the state of fleet and fish resources in Baltic Sea., WWF, 2005. Kuzebski E., Sea catches in 2007 r. Wiad. Ryb. (Fishing News) 1-2 (161), Jan-Feb 2008 , p. 9-11. Partner 7 : MI (Marine Institute) The Marine Institute (Foras na Mara) is the national agency responsible to the Irish government for advice on and implementation of marine research, technology, development and innovation (RTDI) policy and marine research services that critically inform policy objectives, management and sustainable development strategies for marine resources. The Institute was set up under the 1991 Marine Institute Act with the following role: “to undertake, to co-ordinate, to promote and to assist in marine research and development and to provide such services related to research and development that, in the opinion of the Institute, will promote economic development and create employment and protect the marine environment”. The Marine Institute is headquartered at Rinville, near Oranmore, County Galway on the west coast of Ireland. It also operates an aquaculture and catchment management laboratory and office facility at Furnace, near Newport, Co. Mayo as well as offices and laboratories in a number of Ireland’s fishing ports. The Marine Institute oversees the operation of Ireland’s two purpose-built research vessels, the RV Celtic Voyager and RV Celtic Explorer as well as co-ordinating Sea Change - a seven-year Marine Knowledge, Research & Innovation Strategy for Ireland 2007 – 2013. The vision statement of the Marine Institute is: “A thriving maritime economy in harmony with the ecosystem and supported by the delivery of excellence in our services.” The Marine Institute has seven service areas. Fisheries Science Services (FSS) research, assess and advise on the sustainable exploitation of the marine fisheries resources in the waters around Ireland. These waters contain some of the most productive fishing grounds in the world with International fishing fleets operating here landing around 1.5 million tonnes of fish annually. FSS, working with its international partners, conduct a broad range of scientific projects. These projects provide the scientific research, assessment and advice that help underpin the management of this valuable resource. Currently, FSS consists of around 50 scientific staff under the directorship of Dr. Paul Connolly. Key personnel: Dr Colm Lordan is a team leader in the Fisheries Science Services. His responsibilities include leading and managing the Nephrops, Hake, Anglerfish Megrim and Celtic Seas Demeral, assessment team. He participates in various ICES stock assessment and advisory working groups including ACFM, WGNEPH, WGSSDS, WGNSDS, WGHMM. He has acted as chair of several of ICES working groups (WGSSDS, WKNEPTV, RGCS etc.). He is in charge of developing UWTV survey programmes for Nephrops stocks and he is overseeing elements of the DCR national programme including Nephrops and biological sampling. He is supervisor to several research and PhD students, and coordinates training courses in fish stock

46

assessment, quantitative methods, data analysis and mapping. He has had over 550 days of sea going research experience on commercial and research vessels and he has worked as chief scientist on various fishing and multi-disciplinary ecosystem surveys for over 10 years. He has also been the Marine Institutes lead scientist in a number of EC funded projects (EASE Q5CA-2002-01693, EC STUDY CONTRACT 98/096, FISH/2004/03-39). Dr. Hans Gerritsen and Sarah Davie will also be engaged in this study.

7.1

Subcontractor

EDS will be subcontracted by IFREMER for the development of specific software routines.

47

Annex I Métier/Fleet Matrix Source: report of STECF/SGRN906903. Revision of the Biological Data Requirements under the Data Collection Regulation. Brussels, 27 November 9 1 December 2006. 101 p. Table 4.1 - Métier matrix for the Baltic

Gear groups

Gear type

Target assemblage

Mesh size and other selective devices

Demersal fish Small pelagic fish Freshwater species Mixed pelagic and demersal fish Demersal fish Demersal fish Small pelagic fish Freshwater species Small pelagic fish Demersal fish Freshwater species Small pelagic fish Demersal fish Freshwater species Finfish Anadromous species Demersal fish Catadromous species Finfish Catadromous species Small pelagic fish Anadromous species Freshwater species Mixed pelagic and demersal fish Catadromous species Demersal fish Small pelagic fish Anadromous species Demersal fish Freshwater species Anadromous species

(a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a)

Bottom otter trawl [OTB] Bottom trawls

Multi-rig otter trawl [OTT] Bottom pair trawl [PTB]

Trawls

Midwater otter trawl [OTM] Pelagic trawls

Fishing activity

Pelagic pair trawl [PTM] Rods and Lines Hooks and Lines

Longlines

Hand and Pole lines [LHP] [LHM] Drifting longlines [LLD] Set longlines [LLS] Pots and Traps [FPO] Fyke nets [FYK]

Traps

Traps Stationary uncovered pound nets [FPN]

Trammel net [GTR]

Nets

Nets

Set gillnet [GNS]

Driftnet [GND]

> 40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

40

Gear groups

24-40

Gear classes

18-24

Activity

LOA classes 15-18

Level 6

12-15

Level 5

10-12

Level 4

6-10

Level 3

40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

40

Gear classes

24-40

Activity

LOA classes 18-24

Level 6

15-18

Level 5

12-15

Level 4

10-12

Level 3

6-10

Level 2

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