Decision support tool for fishers incorporating information from tasks 4.1, 4.2 and information on unwanted catches derived from scientific data.
Creators: name: Reid, Dave
name: Fauconet, Laurence
Publisher: name: Zenodo
Year: 2018

description: The main aim of this deliverable was to provide fishermen with the detailed knowledge that can come from using scientific data to illustrate the spatial and temporal distributions of the fish, the catches, and the discards. We set out to use the many data and models now available to scientists to potentially assist fishers in making their strategic choices, including fine-scale real-time mapping of catches and activity data, discards hotspots, juveniles surveys etc. The aim was for Decision Support Tools (DST) to be provided to e.g. assess the role of “choke” species at the local scale. The role of the scientist here is as an advisor to the fishers, on where and when they might fish to reduce “choke” problems, and avoid unwanted catches. No single approach was possible across all the case studies, and indeed was probably not desirable, as each case study had its own specific challenges. These arose from a combination of how the fish were distributed i.e. in discreet areas, or widely spread, and on the nature of the LO challenge, e.g. avoidance of particular species or size classes, and the limitations in fishing imposed by geography and other legislation drivers. Many of the analyses presented here have also been developed into bespoke Decision Support Tools, mainly involving web based apps to help fishermen understand and use the results and particularly maps of catch and discard distributions. DST can take many forms. At their simplest, these can represent maps of where fish are found (from surveys), and caught and discarded (from observers). However, more detailed analyses can be used to analyse spatial patterns and their variation, how discards and catches of numbers of species co-occur in space or time, or not. The information can also be represented in an interactive form using web based apps. But the DST process can also be simply the provision of important understandings about discarding and its drivers, e.g. quota management rules, or about the interaction of economic profitability with discarding – is it economically better to NOT discard? We present examples of all these types of Decision Support information in the report. These cover case studies from the Baltic, through western waters to the Mediterranean. They cover many different metiers and fleets, from single to multi-species, using a wide variety of gears. Perhaps the key message is that we can identify many different approaches that could help reduce or even eliminate discarding, but they all tend to be specific to local conditions. It should be possible to export the approaches to other fisheries, but in broad terms rather than the specifics. Essentially, the causes of discarding are common, but the solutions tend to be local and specific. Some of the elements brought together here have been submitted or published in the peer reviewed press, while others represent recently completed work. <strong>Chapters 1 &amp; 2</strong> report on an analysis approach developed by IFREMER and that has been applied to combined discard observer data from <strong>IFREMER &amp; MI, for the Celtic Sea case study</strong>. In <strong>Chapter 1</strong>, the analysis was designed to highlight where different species were being discarded either together or in isolation. The initial approach is statistical, and looks for clusters that can be discriminated from each other. In Chapter 1, we looked at the results for the combined data from Ireland and France, and then at what the differences were between emergent clusters for the two countries. We also compared the spatial patterns of discard clusters to landings clusters. This asks the question can we predict discards on the basis of the spatial structure of the landings. While some discard clusters corresponded well to landings clusters this was more unusual than cases where no obvious common pattern was found. So high landings of a species may not predicate high discards of that species. We also looked more closely at the Irish cluster patterns alone in <strong>Chapter 2</strong> to find out if this would provide useful information for Irish fishermen, working under specific quota arrangements and often with unique metiers. In this case we also looked at clustering of discards for fish that were either above or below MCRS. The second step was to look for discard clusters that were also clustered spatially. Many of the clusters could be seen scattered across the Celtic Sea, but some such as &gt;MCRS megrim discards were aggregated in the south, while &gt; MCRS hake discards were concentrated in the Nephrops fishery areas. Hake discards &lt; MCRS were in similar locations to the adults, but &lt;MCRS megrim discards were concentrated in one small part of the adult discard area. In <strong>chapter 3</strong> by <strong>IFREMER for the English Channel case study</strong>, the analysis makes use of landings and VMS data to help fishermen define the spatial distribution of the species or sizes of fish that they would wish to avoid, or indeed to concentrate upon. The work is focused on the development of a web based app that fishermen can use interactively to choose their precise area of operation. This may be to avoid catching &lt;MCRS fish, or to avoid excessive catches of a “choke species”. This type of app has been developed as a Decision Support Tool (DST) in a number of the case studies reported in this Deliverable. Fishermen can then define a maximum or minimum proportion of a given species in the landings that he is willing to accept depending on the objective i.e. avoiding or targeting the species. The time scale over which the maps are presented can also be controlled, either for a year, quarter or month. A likely side-effect of introducing the landing obligation of the 2013 Common Fisheries Policy into mixed fisheries is the occurrence of the “choke species” problem. When discarding no longer is an option, leasing quota or changing fishing practices remain important tools to avoid choke species. In <strong>Chapter 4 (DTU-Aqua in the North Sea case study)</strong>, the scale and tactics linked to using avoidance behaviour to reduce choke species is investigated by analysing the fishing behaviour of a single demersal trawler in the North Sea. Analysis combined qualitative information collected through interviews with the vessel owner and skipper, along with quantitative analysis on fisheries data. From the interviews, saithe and cod were identified as potential choke species and subsequent analysis focused on these two species. The analysis of catch and quota composition showed that cod would choke the fishery early if no catch-quota balancing options were available, resulting in a 87% reduction in revenue, while saithe could choke the fishery later, resulting in a 43% reduction in revenue. Avoidance behaviour was difficult to detect from fisheries data, which was explained by avoidance primarily taking place through very fine-scale tactical choices rather than large displacements. Catch composition showed that saithe is distributed more patchily than cod, with most hauls containing small amounts of saithe and a few hauls containing large amounts. This chapter shows the choke species problem seen from the perspective of an individual fisher and highlights the amount of real-time tactical decisions and trade-offs that need to be made when operating in mixed-fisheries. It also illustrates how solutions and mitigation can be very local and specific, and this will probably more often be the case than we can identify global or generic mitigation. Generally fisheries science and management advice use both scientific (e.g. observers and surveys) and commercially derived data (e.g. landings and VMS) to estimate the distribution and abundance of marine species. In <strong>Chapter 5,</strong> <strong>(DTU-Aqua in the North Sea case study),</strong> the emphasis is on a new type of commercial data with high resolution and coverage that has not previously used for scientific purposes. While currently used datasets include the total weight by species on per hauls basis, the new data, primarily developed for traceability purposes, also include the commercial size class for the species landed, recorded directly by fishers on a haul-by-haul basis. Thus, this dataset has the potential to provide knowledge on landed fish with as high spatio-temporal resolution as when coupling logbooks and sales slips but with the addition of detailed knowledge on the size distribution. This chapter describes the coverage and completeness of the dataset, and explores the reliability of the data available. It is concluded that the main limitation here is the small number of fishing vessels covered by the dataset, but that the data from those vessels are accurate, detailed and reliable. There is therefore, clear scope to develop this type of data across more vessels. This type of data could provide knowledge on detailed spatial patterns of fishing effort and commercial species distributions as well as serve as a reference fleet. Because these vessels provide direct observations at the haul level it could also be used for analysis at a vessel or métier level, for instance on catchability, spatial selectivity, seasonal patterns or to compare and verify outcomes of spatial fishery models. The uses for survey and observer data in helping fishermen to determine where and when to fish is illustrated with an example from the <strong>Balearics case study by IEO</strong> in <strong>Chapter 6</strong>. The surveys were used to model the spatial patterns of species abundance for the main commercial species. The observer data were then used to provide a temporal dimension to this, and looked at both the catch make up, and the discarding practices. The results from this were a series of maps of species distribution, above and below MCRS, species overlaps, fishing grounds, discard hotspots etc. This was then all brought together in an on-line tool where fishermen could access: maps, from both surveys and observer data; biological data such as length distributions, maturity etc., by species and fishing ground; and discarding patterns. This type of DST is now being developed in other case study areas e.g. English Channel and the Celtic Sea. <strong>Chapter 7 by MI in the Celtic Sea case study,</strong> explores a different aspect of discarding behaviour that could help understand and reduce drivers for discarding. In most countries quotas are assigned on an annual basis, and it has long been speculated that discarding would tend to increase towards the end of the year as quota runs out. In Ireland, quota is assigned on a monthly basis. This allowed us to examine what happens with discarding as quota is exhausted 12 times a year, as opposed to once, as in most jurisdictions. The study focused on the mixed demersal fishery, and on the key species of cod, haddock and whiting. Cod and haddock represent strong choke problems. The “Challenge” trials reported in D4.2 showed that the boats studied were choking on cod and haddock with 4 days in the month. The findings showed that there was little evidence of an increase in cod or haddock discarding as the month progressed, presumably as they had no available quota over most of each month. However, whiting, which had a much less restrictive TAC, did show an increase towards the end of the month. What this suggests is that increased flexibility in when a TAC can be used, might help reduce the overall discarding bulk in some fisheries, and particularly whiting in the Celtic Sea. In <strong>Chapter 8 (MI, IFREMER &amp; CEFAS in the Celtic Sea case study),</strong> we present the results from a detailed analysis of observer data from Ireland, France and the UK used as an ensemble of the catches taken in the Celtic Sea. The analysis focuses on mapping hot spots of CPUE, and catch proportion for three key species; cod, haddock and whiting, and over and under MCRS. The analysis can be extended to any species, both commercial and non-commercial. The maps are based on consistent observations of particular catch rates, so only those locations where one would consistently (over 5 years) see high or low levels for these categories. The data are then interpolated to provide regional coverage. The maps are then drawn together into a web based app, where the fishermen can choose the species (or size class) of interest, and then map CPUE or catch proportion at the selected level of intensity. They can also map a number of species or sizes together on one map, and change the levels to show, to help choose likely places to avoid or find particular species or sizes. The app represents a DST for fishermen. We plan to incorporate additional species, as well as discarding hotspots. The app is a prototype, and we plan to develop the approach working with individual fishermen to best fit it to their needs, and engage them in trying out the approach. The main thrust for <strong>Chapter 9</strong> by <strong>IFREMER in the Celtic Sea case study</strong>, is to find an appropriate way to map discard observer data to help fishermen choose where and when to fish, to reduce the capture of unwanted fish. Such spatio-temporal reallocations of fishing effort are part of adaptation strategies that could help mitigate the impact of the landing obligation. If the primary objective is to reduce discards while maintaining commercial catches, maps of landings and discards can provide fishers with insights on appropriate fishing grounds and/or periods. When using on-board observer programme data to explore spatial and/or temporal patterns of landings and discards, one common problem is the non-random spatial distribution of the data. To overcome this issue, a non-parametric mapping method based on nested grids was developed using the French on-board observer data. The method relies on an iterative process of cell division where the size of the cells varies according to the number of observations. Landings and discards are then estimated in each grid cell. Two contrasting fishing métiers, trawlers and netters, are examined to illustrate the advantages and issues of the nested grid method, and discuss how it can be applied to any fishing métier. Spatial reallocation strategies could be found for the trawling métier, but not for the netting one. Accurate effort data are required to verify that the on-board observed data spatial coverage is sufficient to produce meaningful maps. A potential application of this study is to create an atlas of landings and discards for each métier observed by on-board observer programmes. The objective of the study presented in <strong>Chapter 10</strong> (<strong>IFREMER in the English Channel case study)</strong> was to analyse, at fine scale, the annual, seasonal and spatial distributions of several species in the Eastern English Channel (EEC). Data from scientific surveys are not available for all times of the year, but do provide consistent yearly and spatially resolved abundance indices. On-board commercial data do cover the whole year, but generally provide a biased perception of stock abundance. The combination of scientific and commercial catches per unit of effort (CPUEs), standardized using a delta-generalized linear model, allowed us to infer spatial and monthly dynamics of fish distributions in the EEC, which could be compared with previous knowledge on their life cycles. Considering the scientific survey as a repository, the degree of reliability of commercial CPUEs was assessed with survey-based distribution using the Local Index of Collocation. Large scale information was in agreement with literature, especially for cuttlefish. Fine scale consistency between survey and commercial data was significant for half of the 19 tested species (e.g. whiting, cod). For the other species (e.g. plaice, thornback ray), the results were inconclusive, mainly owing to poor commercial data coverage and/or to particular aspects of the species biology. The approach allowed a more representative mapping of the spatial temporal abundance distribution pattern of a number of species throughout the year. The information can then be used in both targeting, and avoidance in the context of the LO. <strong>Chapter 11 (DTU-Aqua, North Sea case study)</strong> focuses on the use of Remote Electronic Monitoring (REM) with CCTV which is often considered as a potential tool to ensure compliance with fishing regulations. Since 2008 several trials have been conducted in the European Union on the use of REM with CCTV, often in the context of the landing obligation. One of the largest and longest running European trials was the 2010 to 2016 Cod Catch Quota Management trial (CCQM) in Denmark. This paper reviews the methods and experiences gained from this trial, with focus on the last two years where criteria for video audits were expanded and major technical developments took place. The cost-effectiveness and potential of REM for compliance, management and scientific purposes is discussed. The present study demonstrates that REM is capable of high precision detection of non-compliance with a discard ban and that developments in the transmission of REM data allowed for a smoother and more reliable Monitoring, Control and Surveillance (MRS) system. Although further developments are needed, especially within the field of automated image analysis, we conclude that REM is one of the few feasible tools where fisheries information and compliance can be ensured under a landing obligation. In <strong>Chapter 12 (UAZ-IMAR</strong><strong>, Azores case study)</strong> species Distribution Models of 15 species of deep-water sharks and rays were developed based on survey data for the Azores case study. Deep-sea sharks, even if only occasionally taken as bycatch of the deep-water longline fisheries in the Azores, could rapidly choke the fisheries of this Portuguese outermost region, as many of those species are currently under a TAC 0. Maps predicting occurence by species and combined occurence of all species at the range of the whole Azores EEZ were developed to help fishers identify areas they should avoid to limit the risks of catching those species. Composite maps combining the distribution of the main shark species caught by the bottom longliners on one side, and by the deep-water drifting longliners on the other side were created to better highlight for those two specific groups of fishers the main areas to be avoided. This information was completed by fine-scale information on shark spatial and vertical movements derived from acoustic telemetry data from 2 species: kitefin shark <em>Dalatias licha</em>, and bluntnose sixgill shark <em>Hexanchus griseus</em>. Telemetry data help identify potential essential habitats for those species. Our study highlights that areas to avoid fishing and limits in fishing depths at some time of the day could be promising mitigation measures for some species of deep-water elasmobranchs but not for all. <strong>Chapter 13 (NAYS, Eastern Mediterranean case study)</strong> examines the scientists story in the context of the operation of the LO in the Mediterranean, and where no TACs are operated. This is quite a different situation to most of the other case studies as discarding is not driven by quota restrictions at all, and no choke species effect is in play. The study concludes that the LO and the effort based management scheme in the Mediterranean are incompatible.