Fish behaviour analysis is a fundamental research area in marine ecology as it is helpful for detecting environmental changes by observing unusual fish patterns or new fish behaviours. The traditional way of analysing fish behaviour is by visual inspection using human observers, which is very time consuming and also limits the amount of data that can be processed. Therefore, there is a need for automatic algorithms to identify fish behaviours by using computer vision and machine learning techniques. The aim of this thesis is to help marine biologists with their work. We focus on behaviour understanding and analysis of detected and tracked fish with unusual behaviour detection approaches. Normal fish trajectories exhibit frequently observed behaviours while unusual trajectories are outliers or rare trajectories. This thesis proposes 3 approaches to detecting unusual trajectories: i) a filtering mechanism for normal fish trajectories, ii) an unusual fish trajectory classification method using clustered and labelled data and iii) an unusual fish trajectory classification approach using a clustering based hierarchical decomposition. The rule based trajectory filtering mechanism is proposed to remove normal fish trajectories which potentially helps to increase the accuracy of the unusual fish behaviour detection system. The aim is to reject normal fish trajectories as much as possible while not rejecting unusual fish trajectories. The results show that this method successfully filters out normal trajectories with a low false negative rate. This method is useful to assist building a ground truth data set from a very large fish trajectory repository, especially when the amount of normal fish trajectories greatly dominates the unusual fish trajectories. Moreover, it successfully distinguishes true fish trajectories from false fish trajectories which result from errors by the fish detection and tracking algorithms. A key contribution of this thesis is the proposed flat classifier, which uses an outlier detection method based on cluster cardinalities and a distance function to detect unusual fish trajectories. Clustered and labelled data are used to select feature sets which perform best on a training set. To describe fish trajectories 10 groups of trajectory descriptions are proposed which were not previously used for fish behaviour analysis. The proposed flat classifier improved the performance of unusual fish detection compared to the filtering approach. The performance of the flat classifier is further improved by integrating it into a hierarchical decomposition. This hierarchical decomposition method selects more specific features for different trajectory clusters which is useful considering the trajectory variety. Significantly improved results were obtained using this hierarchical decomposition in comparison to the flat classifier. This hierarchical framework is also applied to classification of more general imbalanced data sets which is a key current topic in machine learning. The experiments showed that the proposed hierarchical decomposition method is significantly better than the state of art classification methods, other outlier detection methods and unusual trajectory detection methods. Furthermore, it is successful at classifying imbalanced data sets even though the majority and minority classes contain varieties, and classes overlap which is frequently seen in real-world applications. Finally, we explored the benefits of active learning in the context of the hierarchical decomposition method, where active learning query strategies choose the most informative training data. A substantial performance gain is possible by using less labelled training data compared to learning from larger labelled data sets. Additionally, active learning with feature selection is investigated. The results show that feature selection has a positive effect on the performance of active learning. However, we show that random selection can be as effective as popular active learning query strategies in combination with active learning and feature selection, especially for imbalanced set classification.

In many cases in the past fish bones recovered during archaeological excavations at Upper Palaeolithic sites were often assumed to result from human activity without any consideration for alternate accumulation processes. Many of these assemblages had not been analysed in a scientifically rigorous manner, with some receiving no consideration at all. A review of current evidence and results of new analyses indicate that salmonids (salmon and trout) are the most frequently recorded fish at the European Palaeolithic cave sites. Two potential accumulation agents for fish remains were explored: brown bears (Ursus arctos) and eagle owls (Bubo bubo). Controlled feeding experiments integrated with ecological studies indicate that salmonid remains survive the digestive systems of both species and result in distinctive patterning in assemblage characteristics. Post-depositional taphonomic processes, such as trampling, also produce distinct taphonomic signatures and are an agent of differential inter-species preservation. A thorough consideration of depositional and post-depositional processes of archaeological assemblages in central Italy (Grotta di Pozzo, Maritza, La Punta and Ortucchio) and Spain (El Juyo, Altamira, Salitre, Castillo and Rascaño) shows that the fish remains from these sites result from human activity. The overrepresentation of cranial elements at the Italian sites suggest that fish were processed by removing the head to perhaps smoke or dry before transportation to other locations for consumption. This research lead to improved methods of analysis, and thus enhanced understanding of the role of fishing and fish consumption in Upper Palaeolithic hunter-gatherer societies.

Στην παρούσα έρευνα αναπτύχθηκε μια γρήγορη μεθοδολογία για την ποσοτικοποίηση των αλιευτικών ιχνών από ηχογραφίες ηχοβολιστή πλευρικής σάρωσης. Πεδίο έρευνας ήταν δυο γειτονικές περιοχές, ενός αλιευτικού πεδίου μηχανοτρατών πυθμένα με υδραετούς («πόρτες») στον Πατραϊκό κόλπο. Οι υψηλής ανάλυσης ηχογραφίες αποκάλυψαν αλιευτικά ίχνη στον πυθμένα από τη σύρση των «πορτών» του αλιευτικού εξοπλισμού και υβώματα βιογενούς προέλευσης (biogenic mounds). Η περιοχή έρευνας ταξινομήθηκε σε επιμέρους περιοχές με βάση τους ακουστικούς τύπους που προέκυψαν από την ερμηνεία των ηχογραφιών. Η στατιστική ανάλυση των αλιευτικών ιχνών περιέλαβε τον υπολογισμό: 1) πυκνοτήτων ανά μονάδα επιφανείας, 2) μέσων διευθύνσεων ανά μονάδα επιφανείας, 3) συνολικής μέσης διεύθυνσης, και 4) συνολικού μήκους. Χρησιμοποιήθηκαν τα λογισμικά ArcMap® και Matlab®. Τα αποτελέσματα αποκαλύπτουν την ύπαρξη έντονης αλιευτικής δραστηριότητας στη περιοχή έρευνας, η οποία φαίνεται να ελέγχεται από την βυθομετρία και την παρουσία των βιογενών υβωμάτων. / This study suggests a promising, reliable and rapid methodology for the quantification of trawl marks from sidescan sonar records. Two adjacent sites were surveyed, from an otter trawl fishing ground in the Gulf of Patras. The high resolution sidescan sonar records revealled trawl marks made by the doors of the otter trawls as well as biogenic mounds. The study area was classified on the basis of the acoustic types resulted from the interpretation of the sidescan records. The statistical analysis of the trawl marks included the calculation of: 1) densities per surface block 2) mean directions per surface block, 3) total mean direction and 4) total length. ArcMap® and Matlab® softwares were used. The results reveal the existence of intensive fishing activity, which seems to be controled by the bathymetry and the presence of biogenic mounds.

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Der Bericht fasst Statistiken zur sächsischen Fischerzeugung und weiteres Zahlenmaterial zur sächsischen Fischerei für das Jahr 2013 zusammen. Die Broschüre dient der Information über die Aktivitäten der Berufs- und Angelfischerei im Freistaat Sachsen.

No description available.

No description available.

No description available.

Der Bericht fasst Statistiken zur sächsischen Fischerzeugung und weiteres Zahlenmaterial zur sächsischen Fischerei für das Jahr 2013 zusammen. Die Broschüre dient der Information über die Aktivitäten der Berufs- und Angelfischerei im Freistaat Sachsen.