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Cornell, United States

Parra G.J.,Flinders University | Parra G.J.,South Australian Research And Development Institute | Corkeron P.J.,Bioacoustics Research Program | Arnold P.,Museum of Tropical Queensland
Animal Behaviour | Year: 2011

Dolphins live in complex social systems with a wide variety of grouping and association patterns. Understanding the spatiotemporal variation of these associations (fission-fusion dynamics) is necessary to investigate the underlying factors and mechanisms shaping mammalian social systems in aquatic environments. We used boat-based surveys, photoidentification, focal observations, association analyses and social network techniques to quantify variation in the grouping patterns and fission-fusion dynamics of small, sympatric populations of Australian snubfin dolphins, Orcaella heinsohni, and Indo-Pacific humpback dolphins, Sousa chinensis, off the northeast coast of Queensland. Schools of snubfin dolphins were larger and more stable, irrespective of behavioural activity, than those of humpback dolphins. While associations of both species showed nonrandom patterns and structure, the social network of snubfin dolphins was characterized by numerous strong associations, whereas the strength of the humpback dolphin's social network did not differ from random. Modelling of temporal patterns of association indicated long-lasting associations were an important feature of snubfin dolphins' fission-fusion dynamics. In contrast, associations among humpback dolphins over time were best described by short-term relationships. The contrasting grouping and fission-fusion dynamics of snubfin and humpback dolphins appear to be a response to different feeding habits and prey availability. Future studies involving molecular techniques and direct quantification of food availability and predation risk will help elucidate the suite of interacting ecological, social and evolutionary factors shaping their social structures. © 2011 The Association for the Study of Animal Behaviour. Source

Keen S.,Bioacoustics Research Program | Ross J.C.,Cornell Laboratory of Ornithology | Griffiths E.T.,Bioacoustics Research Program | Lanzone M.,Powdermill Avian Research Center | Farnsworth A.,Cornell Laboratory of Ornithology
Ecological Informatics | Year: 2014

Numerous methods are available for analysis of avian vocalizations, but few research efforts have compared recent methods for calculating and evaluating similarity among calls, particularly those collected in the field. This manuscript compares a suite of methodologies for analyzing flight calls of New World warblers, investigating the effectiveness of four techniques for calculating call similarity: (1) spectrographic cross-correlation, (2) dynamic time warping, (3) Euclidean distance between spectrogram-based feature measurements, and (4) random forest distance between spectrogram-based feature measurements. We tested these methods on flight calls, which are short, structurally simple vocalizations typically used during nocturnal migration, as these signals may contain important ecological or demographic information. Using the four techniques listed above, we classified flight calls from three datasets, one collected from captive birds and two collected from wild birds in the field. Each dataset contained an equal number of calls from four warbler species commonly recorded during acoustic monitoring: American Redstart, Chestnut-sided Warbler, Hooded Warbler, and Ovenbird. Using captive recordings to train the classification models, we created four similarity-based classifiers which were then tested on the captive and field datasets. We show that these classification methods are limited in their ability to successfully classify the calls of these warbler species, and that classification accuracy was lower on field recordings than captive recordings for each of the tested methods. Of the four methods we compared, the random forest technique had the highest classification accuracy, enabling correct classification of 67.6% of field recordings. To compare the performance of the automated techniques to manual classification, the most common method used in flight call research, human experts were also asked to classify calls from each dataset. The experts correctly classified approximately 90% of field recordings, indicating that although the automated techniques are faster, they remain less accurate than manual classification. However, because of the challenges inherent to these data, such as the structural similarity among the flight calls of focal species and the presence of environmental noise in the field recordings, some of the tested automated classification techniques may be acceptable for real-world applications. We believe that this comparison of broadly applicable methodologies provides information that will prove to be useful for analysis, detection and classification of short duration signals. Based on our results, we recommend that a combination of feature measurements and random forest classification can be used to assign flight calls to species, while human experts oversee the process. © 2014 Elsevier B.V. Source

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