| Summary: | This study is a new approach on collecting, handling and examining wildlife behavior data across mammal species in order to provide new and unique conclusions from efficient data collection schemes. Sophisticated dominance hierarchy patterns and the ability of individual recognition are well described in many large mammals such as monkeys and cetaceans through the effort of detailed long-term studies. Their implications are well known as important topics regarding management strategies, especially for endangered species. However worldwide, for other large mammals, e.g. bears, detailed long-term wildlife behavior studies are virtually not available. This is due to the inaccessibility and inefficient observation abilities for many animal species in the wild, especially long-term studies. Up to now, it is believed that long-term studies are necessary to describe the existence of social structures like dominance hierarchies and individual perception abilities reliably and to present results in a sophisticated ‘significant’ manner. To accomplish more detailed behavior investigations on species where we lack such long-term data, here a new approach to this discipline ‘behavior modeling’ is presented, concentrating on the use of marginal opportunistic samples. This statistical approach has never been conducted to behavior analysis so far. Marginal behavior data for six species were investigated and c SUMMARY. I ZUSAMMENFASSUNG . III 1 INTRODUCTION. 1 1.1 Social Structure - Why it Matters. 1 1.2 Social Structure and Dominance Hierarchy in Higher Mammals . 4 1.2.1 Overview . 4 1.2.2 Howling Monkeys (Alouatta palliata). 5 1.2.3 Humpback Whales (Megaptera novaeangliae). 7 1.2.4 Brown Bears (Ursus arctos) . 9 1.2.5 Polar Bears (Ursus maritimus) . 11 1.2.6 Spotted Seals (Phoca largha). 13 1.2.7 Muskoxen (Ovibos moschatus) . 15 1.3 Review of Using Opportunistic and Marginal Data . 16 1.4 Data Mining in Behavior Sciences . 18 1.4.1 Overview . 18 1.4.2 Why TreeNet. 20 1.5 Meta-analysis – Overview. 22 1.6 Overall Logic for ...
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