Abstract: The supposition that prey animals respond to a predator with an intensity that matches the risk posed by the predator is known as the threat-sensitive predator avoidance hypothesis. Many studies have provided support for this hypothesis; yet, few studies have attempted to determine how such abilities are acquired by prey species. In this study, we investigated whether fathead minnows (Pimephales promelas) could learn to recognize an unknown predator (northern pike, Esox lucius) in such a way that they could match the intensity of their antipredator response with the threat posed by the predator. We exposed pike-naive minnows to conspecific alarm cues paired with either a high or low concentration of pike odor. The following day, both groups were tested for a response to either high or low concentration of pike odor alone. We found that minnows conditioned with alarm cues paired with a given concentration of pike odor subsequently responded with a higher intensity to higher concentrations of pike odor, and with a lower intensity to lower concentrations of pike odor. These results demonstrate that during a single conditioning trial, minnows learn the identity of the predator in a threat-sensitive manner. Minnows use predator odor concentrations that they experience in subsequent interactions to adjust the intensity of their antipredator behavior. © Springer-Verlag 2006.

Abstract: When agonistic interventions are nepotistic, individuals are expected to side more often with kin but less often against kin in comparison with non-kin. As yet, however, few mammal studies have been in a position to test the validity of this assertion with respect to paternal relatedness. We therefore used molecular genetic kinship testing to assess whether adult female rhesus macaques (Macaca mulatta) from the free-ranging colony of Cayo Santiago (Puerto Rico) bias their interventions in ongoing dyadic aggressive interactions towards maternal and paternal half-sisters compared with unrelated females. It turned out that females supported maternal half-sisters significantly more often than paternal half-sisters or non-kin regardless of the costs associated with such interventions. Similarly, females targeted maternal half-sisters significantly less often than non-kin when this was associated with high costs. Unrelated females provided significantly higher mean rates of both high- and low-cost support to each other than did paternal half-sisters. However, females targeted paternal half-sisters significantly less often than non-kin when targeting was at low cost, suggesting that females refrain from intervening against paternal half-sisters. Our data confirm the general view that coalition formation in female mammals is a function of both the level of maternal relatedness and of the costs of intervention. The patterns of coalition formation among paternal kin were found to be more complex, and may also differ across species, but clear evidence for paternal kin discrimination was observed in female rhesus as predicted by kin selection theory.

Abstract: Abstract  Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.

Abstract: Abstract  SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.

Abstract: Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a behavior space spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research.