Whistle communication in mammal-eating killer whales (Orcinus orca): further evidence for acoustic divergence between ecotypes

Public signaling plays an important role in territorial and sexual displays in animals; however, in certain situations, it is advantageous to keep signaling private to prevent eavesdropping by unintended receivers. In the northeastern Pacific, two populations of killer whales (Orcinus orca), fish-eating “resident” killer whales and mammal-eating “transient” killer whales, share the same habitat. Previous studies have shown that residents use whistles as private signals during close-range communication, where they probably serve to coordinate behavioral interactions. Here, we investigated the whistling behavior of mammal-eating killer whales, and, based on divergent social structures and social behaviors between residents and transients, we predicted to find differences in both whistle usage and whistle parameters. Our results show that, like resident killer whales, transients produce both variable and stereotyped whistles. However, clear differences in whistle parameters between ecotypes show that the whistle repertoire of mammal-eating killer whales is clearly distinct from and less complex than that of fish-eating killer whales. Furthermore, mammal-eating killer whales only produce whistles during “milling after kill” and “surface-active” behaviors, but are almost completely silent during all other activities. Nonetheless, whistles of transient killer whales may still serve a role similar to that of resident killer whales. Mammal-eating killer whales seem to be under strong selection to keep their communication private from potential prey (whose hearing ranges overlap with that of killer whales), and they appear to accomplish this mainly by restricting vocal activity rather than by changes in whistle parameters

On the efficiency of artificial neural networks for plastic analysis of planar frames in comparison with genetic algorithms and ant colony systems

WOS: 000412313900008The investigation of plastic behavior and determining the collapse load factors are the important ingredients of every kinematical method that is employed for plastic analysis and design of frames. The determination of collapse load factors depends on many effective parameters such as the length of bays, height of stories, types of loads and plastic moments of individual members. As the number of bays and stories increases, the parameters that have to be considered make the analysis a complex and tedious task. In such a situation, the role of algorithms that can help to compute an approximate collapse load factor in a reasonable time span becomes more and more crucial. Due to their interesting properties, heuristic algorithms are good candidates for this purpose. They have found many applications in computing the collapse load factors of low-rise frames. In this work, artificial neural networks, genetic algorithms and ant colony systems are used to obtain the collapse load factors of two-dimensional frames. The latter two algorithms have already been employed in the analysis of frames, and hence, they provide a good basis for comparing the results of a newly developed algorithm. The structure of genetic algorithm, in the form presented here, is the same as previous works; however, some minor amendments have been applied to ant colony systems. The performance of each algorithm is studied through numerical examples. The focus is mainly on the behavior of artificial neural networks in the determination of collapse load factors of two-dimensional frames compared with other two algorithms. The investigation of results shows that a careful selection of the structure of artificial neural networks can lead to an efficient algorithm that predicts the load factors with higher accuracy. The structure should be selected with the aim to reduce the error of the network for a given frame. Such an algorithm is especially useful in designing and analyzing frames whose geometry is known a priori