The Role of the European Union in Supporting Small-Scale Fisheries in East and Southern Africa to Strengthen the Resilience of Coastal Communities

openThe thesis explores the European Union's aim to support East and Southern Africa's coastal communities and preserve marine biodiversity through the implementation of south-south cooperation projects, with a focus on small-scale fisheries (SSF) as the core of a socio-economic matrix to secure efficient food system resilience, create equitable livelihoods, and implement shared sustainable practices. The EU is the largest market for aquatic resources globally. The thesis analyses the double standard in the EU's Common Fisheries Policy (CFP) regarding its commitment to the Sustainable Development paradigm. It aims to question how the EU can support coastal communities' food system resilience in East and Southern Africa, ensuring the equitability of their efforts and sustainability of the harvest in their targeted environments. The thesis further examines the actual capabilities of regional actors to safeguard biological resources and strengthen economic growth simultaneously, particularly in the context of climate change, and its impact on the SSF sub-sector, which is often politically marginalised and lacks representation in developing countries.The thesis explores the European Union's aim to support East and Southern Africa's coastal communities and preserve marine biodiversity through the implementation of south-south cooperation projects, with a focus on small-scale fisheries (SSF) as the core of a socio-economic matrix to secure efficient food system resilience, create equitable livelihoods, and implement shared sustainable practices. The EU is the largest market for aquatic resources globally. The thesis analyses the double standard in the EU's Common Fisheries Policy (CFP) regarding its commitment to the Sustainable Development paradigm. It aims to question how the EU can support coastal communities' food system resilience in East and Southern Africa, ensuring the equitability of their efforts and sustainability of the harvest in their targeted environments. The thesis further examines the actual capabilities of regional actors to safeguard biological resources and strengthen economic growth simultaneously, particularly in the context of climate change, and its impact on the SSF sub-sector, which is often politically marginalised and lacks representation in developing countries

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

Fishery reforms for the management of non-indigenous species

Marine ecosystems are undergoing major transformations due to the establishment and spread of Non-Indigenous Species (NIS). Some of these organisms have adverse effects, for example by reducing biodiversity and causing ecosystem shifts. Others have upsides, such as benefits to fisheries or replacing lost ecological functions and strengthening biogenic complexity. Stopping the spread of NIS is virtually impossible and so the societal challenge is how to limit the socioeconomic, health, and ecological risks, and sustainably exploit the benefits provided by these organisms. We propose a move away from the notion that NIS have only negative effects, and suggest a turn towards an Ecosystem-Based Fishery Management approach for NIS (EBFM-NIS) in the Mediterranean Sea, the world’s most invaded marine region. A structured, iterative, and adaptive framework that considers the range of costs and benefits to ecosystems, ecosystem services, and fisheries is set out to determine whether NIS stocks should be managed using sustainable or unsustainable exploitation. We propose fishery reforms such as multiannual plans, annual catch limits, technical measures for sustainable exploitation, and legitimization of unlimited fishing of selected NIS and introduction of a radical new license for NIS fishing for unsustainable exploitation. Depending on local conditions, investment strategies can be included within the EBFM-NIS framework to protect/enhance natural assets to improve ecosystem resilience against NIS, as well as fishery assets to improve the performance of NIS fisheries. Examples of the former include the enhancement of Marine Protected Areas, harvesting of invasive NIS within MPAs, and protection of overfished predators and key species. Examples of the latter include market promotion and valorisation of NIS products, development of novel NIS products, and innovative/alternative NIS fishing such as fishery related tourism (‘pescatourism’). The application of the suggested EBFM NIS would create jobs, protect and enhance ecosystem services, and help to meet the United Nations Sustainable Development Goal 14: Conserve and sustainably use the oceans, seas, and marine resources for sustainable development

Management plan for the natural resources of the EEZ of the Dutch Caribbean

On the 10th of October 2010 the governmental entity known as the Netherlands Antilles is scheduled to cease to exist. Each island will aquire a new status within the kingdom. Following the declaration of an Exclusive Fishery Zone (EFZ) in 1993, an Exclusive Economic Zone (EEZ) has been declared in the Dutch Caribbean on the tenth of June 2010. The EEZ area concerned, is a large expanse of sea which harbours exceptional biodiversity, and represents an important natural renewable resource potential. The Netherlands Antilles, Aruba and The Netherlands have, therefore, opted to draft a management plan for the EEZ. This initiative began in the year 2005 when the first conference regarding the management of the biodiversity in the EEZ was held. The consensus was that despite a fragmented Dutch Caribbean, the EEZ should always be integrally managed. In 2009 the participants of the second conference confirmed the need for common management and developed common goals, principles and a framework for the management of the Dutch Caribbean waters. Resulting from this conference a management plan was drafted, circulated to all stakeholders and discussed on the 1st of June 2010. Based on the input and feedback received, as well as subsequent correspondence, this final management plan was jointly developed

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health