Status of Fish Stocks in Europe (2018)

Infographics shows the status of the fish stocks in the EU waters as available in 2018.JRC.D.2-Water and Marine Resource

Evaluation and design of fisheries management plans: detecting the impact of management measures on fisheries dynamics using distance correlation.

The development and implementation of fisheries management plans can be expensive and time consuming. It is therefore essential to be able to determine if a plan has been effective in achieving its objectives. When the objectives of a management plan have been achieved (for example F, has been reduced to below some threshold level) it is important to determine if it was as a direct result of elements of the plan (for example, TAC restricting fishing mortality) or because of an external factor that was not included or considered by the plan (for example, fuel price rises causing a reduction in fishing effort). In the former case, we want to be able to understand which aspects of a management plan were effective so they can be considered in the design for future plans. In the latter case, there is the possibility of falsely attributing success to aspects of a plan that had no impact, thereby needlessly including them in the design of future plans. These issues can become more complicated in mixed fisheries where multiple gear types catch multiple stocks because interactions between the different biological and economic elements are not straightforward.JRC.G.3-Maritime affair

Analysis of success of achieving fishing mortality levels for the Northwest Mediterranean Multi-annual plan.

The evaluation of multi-annual plans to manage demersal fisheries performed by STECF (2016) is revisited to calculate an additional indicator of fishing mortality that better captures the effect of using ranges. The results presented are more aligned with the recent multi-annual plans objectives of providing flexible tactics. The analysis is still not a mixed fisheries analysis, it is an alternative approach to the one used by STECF (2016) which increases the degree of comparability across the different options, in particular across single point target approaches and value-range approaches.JRC.D.2-Water and Marine Resource

Compilation and quality check of the ICES stock assessment data

For the analysis of the CFP indicators for NE Atlantic stocks, stock assessment data had to be obtained from ICES. These data included time-series of stock size, fishing pressure and reference points for each stock. This document describes the compilation process of this dataset, including the data quality checks and corrections that were carried out.Embedded R code is executed to generate the polished ICES dataset used in the analysis for the CFP indicators report.JRC.D.2-Water and Marine Resource

Review of Progress in JRC Bioeconomic Modelling

1. Much of the bioeconomic modelling work performed at the JRC has been in relation to the evaluation of proposed multi-annual management plans for fisheries. These plans increasingly require the calculation and consideration of a range of economic indicators. A key element of this work is the parameterisation of the fisheries bioeconomic models. These models require a mix of economic, fisheries and biological data. The required data may be available in different data calls making it necessary to integrate data from different data sets. This integration can be performed using the common variables between the data sets, known as transversal variables. However, the different data sets can report the data at different aggregation levels. This makes linking the data sets challenging, particularly as the evaluation of a management plan requires the economic costs and revenues to be at the same scale as the plan. Modelling approaches have been developed to overcome the different levels of aggregation 2. The methods developed at the JRC for linking the data and parameterising the models were applied to STECF evaluations of multi-annual management plans in the North Sea and the Western Mediterranean. When the data is of a suitable quality the method works well even when the fisheries are complicated and involve multiple species being fished by multiple gears, for example with the North Sea evaluation. However, when the data is poor, for example with the Western Mediterranean evaluation, it is not possible to perform this type of analysis. This problem will remain unless the data collection process is improved. 3. Even when data is not missing there is the concern that it is being recorded differently by the member states, i.e. even within the same data set the data is not consistent. For example, Member States can interpret fishing effort differently. To help ensure consistency between data sets two workshops have been held on transversal variables. One of the outcomes of these workshops is a JRC led package for R, fecR, that will allow the transparent and repeatable calculation of two different types of fishing effort. 4. This report presents the experiences from two STECF EWG where the JRC modelling approaches has been used and the new R package for effort calculation.JRC.D.2-Water and Marine Resource

Report Global Trends in Fisheries Governance Improving sustainability

The new Common Fisheries Policy (CFP) of the European Union was adopted on 11 December 2013. Not only does it reform the fisheries policy governing the European waters, but for the first time in its thirty-year history, international aspects of fisheries management are included in the Basic Regulation. Until now these aspects have been covered by non-legally binding Council Conclusions. The conference Global Trends in Fisheries Governance – Improving Sustainability was organized by the Swedish Agency for Marine and Water Management, in Rosenbad Conference Centre, Stockholm, 29–30 January 2014, with the aim of analysing the external dimension of the new CFP, and increasing the understanding and interpretation of the policy and its implementation at all different management levels for improved sustainability. The Conference explored possible tools, options, responsibilities and challenges for the implementation of the external dimension of the new CFP. It was funded by the Swedish Ministry of Rural Affairs. It focused on the European Union’s bilateral relations with third countries, and the EU as a member of regional fisheries management bodies and other relevant international organizations in light of the reformed CFP.JRC.G.3-Maritime affair

Generating the CFP indicators sampling frame for FAO area 27 (Northeast Atlantic)

For the analysis of the CFP indicators it is necessary to generate the sampling frame. The sampling frame is the collection of species in EU Fisheries Managment Zones (FMZ) for which the CFP is at least partially responsible for their management. This document describes the generation of the sampling frame. Embedded R code is executed to generate the sampling frame.JRC.D.2-Water and Marine Resource

P491-BioMod Deliverable 4913 Management Strategy Evaluation Error Model

In terms of analysing and simulating fisheries management systems, implementation error can be considered to be the difference between management decisions (for example, the TAC or effort limits set for the following year) and what the fishing fleets actually do (for example, the actual catch taken or fishing effort applied that year). It is thought that implementation error can have a strong impact on the potential success of a management plan, but the precise level of impact and the effect of implementation error combined with other sources of uncertainty requires further study. This document describes the application of a simple Management Strategy Evaluation simulation to investigate the potential impacts of management plan implementation error on the sustainability of a stock. The simulation is based on the cod stock in the Eastern English Channel and Southern Celtic Seas. The results are illustrative only and are not meant to provide advice for the stock. It was found that including implementation error in the projections (i.e. the realised catch being greater than the desired TAC) not only led to the stock being more exploited (lower SSB, higher fishing mortality) but also increased the uncertainty in the stock status. This will be of particular interest when a risk-based approach to fisheries management is being considered. The implementation of a management plan is strongly in influenced by a range of economic factors that drive the dynamics of the fishing fleet. The simulations here do not make a full economic analysis and focus on the sustainabilty of the stock rather than the economic viability of the fishing fleet. Future work will include more economic modelling to improve the implementation error model. Note that this report was not prepared using MS Word. It was prepared using Latex / KnitR and R. This allows the computer code that was used to generate the results to be embedded in the report and executed during the report compilation, including the plotting of figures. This is preferable for scientific report writing as it ensures that the results presented here are ‘live’. Consequently, the following report may not strictly adhere to the JRC template.JRC.G.3-Maritime affair

Deep Sea – Close Kin: A Genetic Approach for Improved Fisheries Management

Deep-sea fish stocks consist of species that live at depths of greater than 400 metres. While being important for EU fisheries, this natural renewable resource is particularly vulnerable to over-fishing, as many deep sea species are slow-growing and commonly of low fecundity. Generally little is known about the biology of deep sea species, and there prevails a substantial lack of scientific data on deep-sea stocks. This constitutes a major impediment to management strategies underpinning sustainable and profitable deep sea fisheries. Europe’s deep-sea fisheries began in the 1970’s and were entirely unregulated. The fleet grew as rewards were high, but many species were rapidly depleted. It was only in 2003 that a management plan was brought into action. While some measures to better protect commercially exploited deep sea fish have been adopted, such as the limitation of fishing effort or total allowable catches, these have been insufficient to allow stocks to recover and there is a general consensus that most deep-water stocks remain below safe biological limits for exploitation. In a recent communication to the Council and the European Parliament, the European Commission has emphasized the need to improve our knowledge on deep sea fish species to move away from the current prevailing unsustainable exploitation. Ideally, this would be the development of a robust and practical approach to estimate the abundance of deep sea species to support stock assessments and reduce the uncertainty about the state and rebuilding rates of commercially exploited deep sea stocks. The current rapid technology development and concurrent steep drop in costs of large-scale genotyping offers major opportunities for fisheries management. This report explores whether the concept of genetic close-kin abundance estimation, recently applied to establish biomass estimates of Southern Bluefin Tuna, can be applied to fisheries management of deep sea fish species.JRC.G.3-Maritime affair

Common Fisheries Policy Monitoring - Protocol for computing indicators

Common Fisheries Policy Monitoring - Protocol for computing indicators This document presents the protocol to compute indicators for monitoring the Common Fisheries Policy. A set of indicators both design-based and model-based are described mathematically. The list of stocks that should form the dataset on which the indicators are computed is also described as well as a set of rules to update the stocks' lists when needed. The protocol was presented and approved by the STECF's 2015 winter plenary (STECF-PLEN-15-03).JRC.G.3-Maritime affair