Small-scale fisheries are learning contexts of importance for generating, transferring, and updating ecological knowledge of natural environments through everyday work practices. The rich knowledge fishers have of local ecosystems is the result of the intimate relationship fishing communities have had with their natural environments across generations (see e.g. Urquhart and Acott 2013). This relationship develops strong emotional bonds to the physical and social place. For fishing communities and fishers – who depend directly on local ecosystems to maintain their livelihoods – fishing environments are natural places for living, working and defining themselves. Previous research on fishers’ ecological knowledge has mainly been descriptive, i.e., has focused on aspects such as reproduction, nutrition and spatial-temporal distribution and population dynamics, from a traditional view of knowledge that only recognises scientific knowledge as the true knowledge. By doing this, fishers’ ecological knowledge has been investigated separately from the learning contexts in which it is generated, ignoring the influence of social, cultural and historical aspects that characterise fishing communities, and the complex relationships between fishers and the natural environments they live and work in. This thesis investigates ecological knowledge among small-scale fishers living and working in the ecosystems of Lake Vättern and the Blekinge Archipelago (Baltic Sea) in Sweden and explores how ecological knowledge is generated with particular regard to the influences of work and nature on fishers’ knowledge of ecosystems. The aim of this thesis is to contribute to the knowledge and understanding of informal learning processes of ecosystem complexity among small-scale fishers. This knowledge further contributes to the research field of ecological knowledge and sustainable use and management of natural resources. It addresses the particular research questions of what ecological knowledge fishers generate, and how its generation is influenced by their fishing work practices and relationships to nature. The thesis consists of three articles. Article I focuses on the need to address the significant lack of theoretical and methodological frameworks for the investigation of the cognitive aspects involved in the generation of ecological knowledge. Article II deals with the need to develop theoretical, methodological and empirical frameworks that avoid romanticising and idealising users’ ecological knowledge in local (LEK), indigenous (IEK) and traditional (TEK) ecological knowledge research, by rethinking it as being generated through work practices. Article III addresses the lack of studies that explicitly explore theories linking complex relations and knowledge that humans form within and of ecosystems. It also addressed the lack of attention from environmental education researchers to theory and empirical studies of ‘sense of place’ research, with a particular focus on environmental learning. Research into the question of what ecological knowledge fishers generate shows differences in their ways of knowing ecosystem complexity. These differences are explained in terms of the influences of the species being fished, and the sociocultural contexts distinguishing fishers’ connection to the fishing profession (i.e., familial tradition or entrepreneurship) (Article I), but also by the fishing strategies used (Article II). Results answering the research question of how work practices influence fishers’ knowledge of ecosystem complexity show a way of rethinking their ecological knowledge as generated in a continuous process of work (Article II), thus, far from romantic views of knowledge. Results answering the research question of how fishers’ relationships to nature influence their knowledge of ecosystem complexity demonstrate the complex interconnections between psychological processes such as identity construction, proximity maintenance and attachment to natural environments (Article III). Finally, more similarities than differences between fishers’ knowledge were found, despite the variation in cases chosen, with regards to landscape, target species, regulations systems and management strategies, fishing environments scales, as well as cultural and social contexts. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p><p> </p> / Ecological knowledge and sustainable resource management: The role of knowledge acquisition in enhancing the adaptive capacity of co-management arrangements

The oceans around the world have been heavily impacted by overfishing due to very intensive commercial fishing in recent times. A large number of fish stocks have already been fully exploited. Vessel Monitoring System has been put in place to regulate fishing vessels and enforce sustainable fisheries management. Data coming from such systems can be used for the detection of illegal, unregulated, and unreported fishing. In this thesis, we present various machine learning models for the classification of fishing trip trajectories. To train these models, we develop a trajectory segmentation algorithm to create trip trajectories out of raw data and design a graphical user interface for labeling the trip trajectories into fishing and non-fishing. We also examine the impact of the temporal resolution of the data. In conclusion, the CNN-Transformer network performed the best on the binary classification of two different fishing vessel types. During the project, we realized that segmentation of real trajectory data into trips poses many problems and presents the biggest obstacle. The experiment on the varying temporal resolution of the data showed that having a higher temporal resolution gives better modeling results but only to a certain point. / Haven runt om i världen har drabbats hårt av överfiske på grund av ett mycket intensivt kommersiellt fiske på senare tid. Ett stort antal fiskbestånd har redan utnyttjats fullt ut. Fartygsövervakningssystem har införts för att reglera fiskefartyg och upprätthålla hållbar fiskeförvaltning. Data som kommer från sådana system kan användas för att upptäcka olagligt, oreglerat och orapporterat fiske. I detta examensarbete presenterar vi olika maskininlärningsmodeller för klassificering av fisketursbanor. För att träna dessa modeller utvecklar vi en segmenteringsalgoritm för att skapa turbanor av rådata och designa ett grafiskt användargränssnitt för att märka resbanorna till fiske och icke-fiske. Vi undersöker också effekten av den tidsmässiga upplösningen av datan. Sammanfattningsvis presterade CNN-Transformer-nätverket bäst i den binära klassificeringen av två olika fiskefartygstyper. Under projektet insåg vi att segmentering av verkliga bandata till resor utgör många problem och utgör det största hindret. Experimentet på den varierande tidsupplösningen av data visade att en högre tidsupplösning ger bättre modelleringsresultat men bara till en viss punkt.